diff --git a/backend/.gitignore b/backend/.gitignore
new file mode 100644
index 0000000..218354d
--- /dev/null
+++ b/backend/.gitignore
@@ -0,0 +1,11 @@
+__pycache__
+
+migrations
+
+.env
+.env.dev
+.env.prod
+data/
+*.conf
+.sh
+certbot/
\ No newline at end of file
diff --git a/backend/Dockerfile b/backend/Dockerfile
new file mode 100644
index 0000000..d301204
--- /dev/null
+++ b/backend/Dockerfile
@@ -0,0 +1,19 @@
+FROM python:latest
+
+RUN mkdir /backend
+WORKDIR /backend
+
+COPY . /backend/
+
+RUN apt-get update && apt-get install -y ffmpeg
+RUN pip install --upgrade pip
+RUN pip install -r requirements.txt
+
+# Set environment variables for ffmpeg and ffprobe
+ENV FFMPEG_PATH /usr/bin/ffmpeg
+ENV FFPROBE_PATH /usr/bin/ffprobe
+
+# for error message
+ENV PYTHONUNBUFFERED 1
+
+# CMD ["flask", "run", "--host=0.0.0.0", "--port=5001"]
\ No newline at end of file
diff --git a/backend/apis_v1/MzRequest.py b/backend/apis_v1/MzRequest.py
new file mode 100644
index 0000000..b1a5a03
--- /dev/null
+++ b/backend/apis_v1/MzRequest.py
@@ -0,0 +1,245 @@
+from flask import Response
+import json
+from module import crud_module
+from flask_restx import Resource, Namespace
+from werkzeug.datastructures import FileStorage
+
+####################################생성 전 정보#######################################
+
+MzRequest = Namespace(
+    name="MzRequest",
+    description="MzRequest CRUD를 작성하기 위해 사용하는 API.",
+)
+
+common_parser = MzRequest.parser()
+common_parser.add_argument('token', location='headers')
+
+post_parser = common_parser.copy()
+post_parser.add_argument('age', location='form', required=False)
+post_parser.add_argument('gender', location='form', required=False)
+post_parser.add_argument('file', type=FileStorage, location='files', required=False)
+# celery 받는거, post&get설정, crud_module에 함수 추가, db_module에 함수 추가
+
+@MzRequest.route('', methods=['POST', 'GET'])
+@MzRequest.doc(responses={200: 'Success'})
+@MzRequest.doc(responses={404: 'Failed'})
+class MzRequestClass(Resource):
+
+    @MzRequest.expect(post_parser)
+    def post(self):
+        """
+        # mz 요청 정보 저장
+        # @header : token
+        # @return : {id: "id"}
+        """
+        try:
+            result, message = crud_module.upload_mz_request()
+            return Response(
+                response = json.dumps(message),
+                status = result,
+                mimetype = "application/json"
+            )
+        except Exception as ex:
+            print("******************")
+            print(ex)
+            print("******************")
+    
+    @MzRequest.expect(common_parser)
+    def get(self):
+        """
+        # mz 요청 정보 리스트 가져오기
+        # @header : token
+        # @return : {"mz_request_list": result_list}
+        """
+        try:
+            result, message = crud_module.get_mz_request_list()
+            return Response(
+                response = json.dumps(message),
+                status = result,
+                mimetype = "application/json"
+            )
+        except Exception as ex:
+            print("******************")
+            print(ex)
+            print("******************")
+
+@MzRequest.route('/<int:mzRequestId>', methods=['GET'])
+@MzRequest.doc(responses={200: 'Success'})
+@MzRequest.doc(responses={404: 'Failed'})
+class MzRequestClass(Resource):
+  
+    @MzRequest.expect(common_parser)
+    def get(self, mzRequestId):
+        """
+        # mz 요청 정보 가져오기
+        # @header : token
+        # @return : {"mz_request": result_data}
+        """
+        try:
+            result, message = crud_module.get_mz_request(mzRequestId)
+            return Response(
+                response = json.dumps(message),
+                status = result,
+                mimetype = "application/json"
+            )
+        except Exception as ex:
+            print("******************")
+            print(ex)
+            print("******************")
+            
+
+@MzRequest.route('/<int:mzRequestId>/status/<taskId>')
+@MzRequest.doc(responses={200: 'Success'})
+@MzRequest.doc(responses={404: 'Failed'})
+class MzRequestClass(Resource):
+  
+    @MzRequest.expect(common_parser)
+    def get(self, mzRequestId, taskId):
+        """
+        # celery 상태값 가져오기
+        # @header : token
+        # @return : {"mz_request": result_data}
+        """
+        try:
+            result, message = crud_module.get_celery_task_status(mzRequestId, taskId)
+            return Response(
+                response = json.dumps(message),
+                status = result,
+                mimetype = "application/json"
+            )
+        except Exception as ex:
+            print("******************")
+            print(ex)
+            print("******************")
+
+@MzRequest.route('/<int:mzRequestId>/result/<taskId>')
+@MzRequest.doc(responses={200: 'Success'})
+@MzRequest.doc(responses={404: 'Failed'})
+class MzRequestClass(Resource):
+  
+    @MzRequest.expect(common_parser)
+    def get(self, mzRequestId, taskId):
+        """
+        # celery 결과값 가져오기
+        # @header : token
+        # @return : {"mz_request": result_data}
+        """
+        try:
+            result, message = crud_module.get_celery_result_done(mzRequestId, taskId)
+            return Response(
+                response = json.dumps(message),
+                status = result,
+                mimetype = "application/json"
+            )
+        except Exception as ex:
+            print("******************")
+            print(ex)
+            print("******************")
+
+####################################생성된 결과#######################################
+
+patch_parser = common_parser.copy()
+patch_parser.add_argument('type', location='form', required=False)
+patch_parser.add_argument('rating', location='form', required=False)
+# parser.add_argument('file', type=FileStorage, location='files', required=False)
+
+survey_parser = common_parser.copy()
+survey_parser.add_argument('user_phone', location='form', required=False)
+survey_parser.add_argument('sns_time', location='form', required=False)
+survey_parser.add_argument('image_rating_reason', location='form', required=False)
+survey_parser.add_argument('voice_to_face_rating', location='form', required=False)
+survey_parser.add_argument('dissatisfy_reason', location='form', required=False)
+survey_parser.add_argument('additional_function', location='form', required=False)
+survey_parser.add_argument('face_to_gif_rating', location='form', required=False)
+survey_parser.add_argument('more_gif', location='form', required=False)
+survey_parser.add_argument('more_gif_type', location='form', required=False)
+survey_parser.add_argument('waiting', location='form', required=False)
+survey_parser.add_argument('waiting_improvement', location='form', required=False)
+survey_parser.add_argument('recommend', location='form', required=False)
+survey_parser.add_argument('opinion', location='form', required=False)
+
+@MzRequest.route('/<int:mzRequestId>/mz-result', methods=['POST'])
+@MzRequest.doc(responses={200: 'Success'})
+@MzRequest.doc(responses={404: 'Failed'})
+class MzResultClass(Resource):
+  
+    @MzRequest.expect(common_parser)
+    def post(self, mzRequestId):
+        """
+        # mz 결과 재생성
+        # @header : token
+        # @return : {id: "id"}
+        """
+        try:
+            result, message = crud_module.regenerate_mz_result(mzRequestId)
+            return Response(
+                response = json.dumps(message),
+                status = result,
+                mimetype = "application/json"
+            )
+        except Exception as ex:
+            print("******************")
+            print(ex)
+            print("******************")
+
+@MzRequest.route('/<int:mzRequestId>/mz-result/<int:mzResultId>', methods=['GET', 'POST', 'PATCH'])
+@MzRequest.doc(responses={200: 'Success'})
+@MzRequest.doc(responses={404: 'Failed'})
+class MzResultClass(Resource):
+  
+    @MzRequest.expect(common_parser)
+    def get(self, mzRequestId, mzResultId):
+        """
+        # mz 결과 정보 가져오기
+        # @header : token
+        # @return : {"mz_result": result_data}
+        """
+        try:
+            result, message = crud_module.get_mz_result(mzRequestId, mzResultId)
+            return Response(
+                response = json.dumps(message),
+                status = result,
+                mimetype = "application/json"
+            )
+        except Exception as ex:
+            print("******************")
+            print(ex)
+            print("******************")
+    
+    @MzRequest.expect(survey_parser)
+    def post(self, mzRequestId, mzResultId):
+        """
+        # mz 설문조사 결과 정보 저장하기
+        # @header : token
+        # @return : {"mz_result_id": "id", "mz_survey_id": "id"}
+        """
+        try:
+            result, message = crud_module.upload_mz_survey(mzRequestId, mzResultId)
+            return Response(
+                response = json.dumps(message),
+                status = result,
+                mimetype = "application/json"
+            )
+        except Exception as ex:
+            print("******************")
+            print(ex)
+            print("******************")
+
+    @MzRequest.expect(patch_parser)
+    def patch(self, mzRequestId, mzResultId):
+        """
+        # mz 결과 별점 수정
+        # @header : token
+        # @return : {"message": "rating updated successfully"}
+        """
+        try:
+            result, message = crud_module.update_mz_result_rating(mzRequestId, mzResultId)
+            return Response(
+                response = json.dumps(message),
+                status = result,
+                mimetype = "application/json"
+            )
+        except Exception as ex:
+            print("******************")
+            print(ex)
+            print("******************")
diff --git a/backend/apis_v1/User.py b/backend/apis_v1/User.py
new file mode 100644
index 0000000..10da86f
--- /dev/null
+++ b/backend/apis_v1/User.py
@@ -0,0 +1,197 @@
+from flask import Response
+import json
+from module import user_module
+from flask_restx import Resource, Namespace
+
+####################################회원#######################################
+Users = Namespace(
+    name= "Users",
+    description="Users CRUD를 작성하기 위해 사용하는 API.",
+)
+
+common_parser = Users.parser()
+
+email_validation_parser = common_parser.copy()
+email_validation_parser.add_argument('email', location='form', required=False)
+
+regist_parser = common_parser.copy()
+regist_parser.add_argument('email', location='form', required=False)
+regist_parser.add_argument('password', location='form', required=False)
+regist_parser.add_argument('age', location='form', required=False, type=int)
+regist_parser.add_argument('gender', location='form', required=False)
+
+token_parser = common_parser.copy()
+token_parser.add_argument('token', location='headers')
+
+@Users.route('/email/validation', methods=['POST'])
+@Users.doc(response={200: 'SUCCESS'})
+@Users.doc(response={404: 'Failed'})
+class UserEmailValidaionClass(Resource):
+
+    @Users.expect(email_validation_parser)
+    def post(self):
+        """
+        # 아이디 중복체크
+        # @form-data : email
+        # @return : 200 or 409
+        """
+        try:
+            result, message = user_module.email_validation()
+            return Response(
+                response = json.dumps(message),
+                status = result,
+                mimetype = "application/json"
+                )
+        except Exception as ex:
+            print("******************")
+            print(ex)
+            print("******************") 
+
+@Users.route('', methods=['POST', 'GET'])
+@Users.doc(response={200: 'SUCCESS'})
+@Users.doc(response={404: 'Failed'})
+class UsersClass(Resource):
+
+    @Users.expect(regist_parser)
+    def post(self):
+        """
+        # 회원가입
+        # @form-data : email, password, age, gender
+        # @return : 200 or 404
+        """
+        try:
+            result, message = user_module.create_users()
+            return Response(
+                response = json.dumps(message), ##회원가입 성공일때는 status를 201로 받아 나머지는 다 200
+                status = result,
+                mimetype = "application/json"
+            )
+        except Exception as ex:
+            print("******************")
+            print(ex)
+            print("******************")
+    
+    @Users.expect(token_parser)
+    def get(self):
+        """
+        # 회원정보 받아오기
+        # @header : token
+        # @return : {age: "age", gender: "gender"}
+        """
+        try:
+            result, message = user_module.get_user_info()
+            return Response(
+                response = json.dumps(message),
+                status = result,
+                mimetype = "application/json"
+                )
+        except Exception as ex:
+            print("******************")
+            print(ex)
+            print("******************") 
+
+
+# @Users.route('/email')
+# @Users.expect(parser)
+# @Users.doc(responses={200: 'Success'})
+# @Users.doc(responses={404: 'Failed'})
+# class UserEmailClass(Resource):
+
+#     def post(self):
+#         """
+#         # 아이디 찾기
+#         # @form-data : name, phone
+#         # @return : {email: "email"}
+#         """
+#         try:
+#             result, message = user_module.find_email()
+#             return Response(
+#                 response = json.dumps(message),
+#                 status = result,
+#                 mimetype = "application/json"
+#             )
+#         except Exception as ex:
+#             print("******************")
+#             print(ex)
+#             print("******************")
+
+# @Users.route('/password/validation')
+# @Users.expect(parser)
+# @Users.doc(responses={200: 'Success'})
+# @Users.doc(responses={404: 'Failed'})
+# class UserPasswordValidationClass(Resource):
+  
+#     def post(self):
+#         """
+#         # 비밀번호 찾기 전 정보 검증
+#         # @form-data : email, phone
+#         # @return : message
+#         """
+#         try:
+#             result, message = user_module.password_validation()
+#             return Response(
+#                 response = json.dumps(message),
+#                 status = result,
+#                 mimetype = "application/json"
+#             )
+#         except Exception as ex:
+#             print("******************")
+#             print(ex)
+#             print("******************")
+
+# @Users.route('/password')
+# @Users.expect(parser)
+# @Users.doc(responses={200: 'Success'})
+# @Users.doc(responses={404: 'Failed'})
+# class UserUpdatePasswordClass(Resource):
+
+#     def patch(self):
+#         """
+#         # 비밀번호 변경(변경할 비밀번호 정보 받아와서 비밀번호 변경)
+#         # @form-data : email, phone, password
+#         # @return : message
+#         """
+#         try:
+#             result, message = user_module.update_password()
+#             return Response(
+#                 response = json.dumps(message),
+#                 status = result,
+#                 mimetype = "application/json"
+#             )
+#         except Exception as ex:
+#             print("******************")
+#             print(ex)
+#             print("******************")
+
+Auth = Namespace(
+    name= "Auth",
+    description="User의 Auth를 작성하기 위해 사용하는 API.",
+)
+
+parser = Auth.parser()
+parser.add_argument('email', location='form')
+parser.add_argument('password', location='form')
+
+@Auth.route('', methods=['POST'])
+@Auth.doc(response={200: 'SUCCESS'})
+@Auth.doc(response={404: 'Failed'})
+class AuthClass(Resource):
+
+    @Auth.expect(parser)
+    def post(self):
+        """
+        # 로그인
+        # @form-data : email, password 
+        # @return : {"token": "token", "user_name": "user_name"}
+        """
+        try:
+            result, message = user_module.login()
+            return Response(
+                response = json.dumps(message),
+                status = result,
+                mimetype = "application/json"
+            )
+        except Exception as ex:
+            print("******************")
+            print(ex)
+            print("******************")
diff --git a/backend/app.py b/backend/app.py
new file mode 100644
index 0000000..10b4340
--- /dev/null
+++ b/backend/app.py
@@ -0,0 +1,58 @@
+from flask import Flask, request, redirect
+from flask_cors import CORS
+from flask_restx import Api
+from db.db_connection import db_connection, db
+# from prometheus_flask_exporter import PrometheusMetrics
+from apis_v1 import User, MzRequest
+import ssl
+
+app = Flask(__name__)
+# app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024 # 용량제한
+app.config.update(DEBUG=True)
+
+CORS(app, resources={r'*': {'origins': '*'}}, supports_credentials=True)
+
+db_connection(app)
+with app.app_context():
+    db.create_all()
+# metrics = PrometheusMetrics.for_app_factory()
+# metrics.init_app(app)
+
+api = Api(
+    app,
+    version='v1',
+    title="Make Generator",
+    description="NAVER boostcamp",
+    terms_url="/",
+    contact="vivian0304@naver.com",
+    license="MIT",
+    prefix='/api/v1'
+)
+
+api.add_namespace(User.Users, '/users')
+api.add_namespace(User.Auth, '/auth')
+api.add_namespace(MzRequest.MzRequest, '/mz-request')
+
+# @app.before_request
+# def before_request():
+#     if request.url.startswith('http://'):
+#         url = request.url.replace('http://', 'https://', 1)
+#         code = 301
+#         return redirect(url, code=code)
+
+# @app.route('/users', methods=['OPTIONS'])
+# @app.route('/auth', methods=['OPTIONS'])
+# @app.route('/mz-request', methods=['OPTIONS'])
+# def preflight():
+#     response = flask.Response()
+#     response.headers['Access-Control-Allow-Origin'] = '*'
+#     response.headers['Access-Control-Allow-Headers'] = '*'
+#     response.headers['Access-Control-Allow-Methods'] = '*'
+
+    #return response
+
+if __name__ == "__main__":
+#     ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLS)
+#     ssl_context.load_cert_chain(certfile='newcert.pem', keyfile='newkey.pem')
+#     app.run(port=5050, debug=True,ssl_context=ssl_context)
+    app.run(port=5050, debug=True)
diff --git a/backend/bucket/m_config.py b/backend/bucket/m_config.py
new file mode 100644
index 0000000..087a49d
--- /dev/null
+++ b/backend/bucket/m_config.py
@@ -0,0 +1,15 @@
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+# AWS_ACCESS_KEY = os.getenv('AWS_ACCESS_KEY')
+# AWS_SECRET_ACCESS_KEY = os.getenv('AWS_SECRET_ACCESS_KEY')
+# AWS_S3_BUCKET_REGION = os.getenv('AWS_S3_BUCKET_REGION')
+# AWS_S3_BUCKET_NAME = os.getenv('AWS_S3_BUCKET_NAME')
+# AWS_S3_BUCKET_URL = os.getenv('AWS_S3_BUCKET_URL')
+
+ACCESS_KEY = os.getenv("AWS_ACCESS_KEY")
+SECRET_KEY = os.getenv("AWS_SECRET_ACCESS_KEY")
+BUCKET_NAME = os.getenv("MINIO_BUCKET")
+MINIO_API_HOST = os.getenv("MINIO_ENDPOINT")
\ No newline at end of file
diff --git a/backend/bucket/m_connection.py b/backend/bucket/m_connection.py
new file mode 100644
index 0000000..64ab6de
--- /dev/null
+++ b/backend/bucket/m_connection.py
@@ -0,0 +1,62 @@
+from minio import Minio
+from bucket.m_config import ACCESS_KEY, SECRET_KEY
+from bucket.m_config import BUCKET_NAME, MINIO_API_HOST
+
+def minio_connection():
+    try:
+        print(MINIO_API_HOST)
+        print(ACCESS_KEY)
+        print(SECRET_KEY)
+        storage = Minio(
+            MINIO_API_HOST,
+            ACCESS_KEY,
+            SECRET_KEY,     
+            secure=True,
+        )
+    except Exception as e:
+        print(e)
+        return False
+    else:
+        print("storage bucket connected!")
+        return storage
+
+def minio_put_object(storage, filename, data):
+    '''
+    minio bucket에 지정 파일 업로드
+    :param minio: 연결된 minio 객체(Minio client)
+    :param filename: 파일 위치
+    :param data: 데이터
+    :return: 성공 시 True, 실패 시 False 반환
+    '''
+    try:
+        storage.fput_object(BUCKET_NAME, filename, data, content_type="audio/wav")
+        #storage.fput_object(BUCKET_NAME, filename, data)
+        print(f"{filename} is successfully uploaded to bucket {BUCKET_NAME}.")
+    except Exception as e:
+        print(e)
+        return False
+    return True
+
+# def minio_list_object(storage, age, gender):
+#     '''
+#     minio bucket에서 해당 성별과 나이에 맞는 이미지 리스트 가져오기
+#     :param storage: 연결된 minio 객체(Minio client)
+#     :param prefix: Object name starts with prefix.
+#     :param age: 나이
+#     :param gender: 성별
+#     :return: 성공 시 list 반환, 실패 시 False 반환
+#     '''
+#     try:
+#         prefix = "output_condition"
+#         contents_list = storage.list_objects(bucket, prefix)['Contents']
+#         file_list = [content['Key'] for content in contents_list]
+#         condition_file_list = []
+#         for file in file_list:
+#             _, file_name = file.split('-')
+#             idx = file_name.rindex('.')
+#             if file_name[idx+1:] == 'jpg' and file_name[:idx] == f'{gender}_{age}':
+#                 condition_file_list.append(file)
+#     except Exception as e:
+#         print(e)
+#         return False
+#     return condition_file_list
\ No newline at end of file
diff --git a/backend/db/db_config.py b/backend/db/db_config.py
new file mode 100644
index 0000000..eae4ab4
--- /dev/null
+++ b/backend/db/db_config.py
@@ -0,0 +1,12 @@
+# HOST = 'db' #docker db
+# PORT = 27017  #defalut port
+
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+USERNAME = os.getenv('USERNAME')
+PASSWORD = os.getenv('PASSWORD')
+HOST = os.getenv('HOST')
+DATABASE = os.getenv('DATABASE')
diff --git a/backend/db/db_connection.py b/backend/db/db_connection.py
new file mode 100644
index 0000000..6181a2d
--- /dev/null
+++ b/backend/db/db_connection.py
@@ -0,0 +1,16 @@
+from flask import Flask
+from flask_sqlalchemy import SQLAlchemy
+from flask_migrate import Migrate
+from db.db_config import USERNAME, PASSWORD, HOST, DATABASE
+
+db = SQLAlchemy()
+
+def db_connection(app):
+
+    app.config['SQLALCHEMY_DATABASE_URI'] = f'mysql+pymysql://{USERNAME}:{PASSWORD}@{HOST}/{DATABASE}'
+    app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
+
+    db.init_app(app)
+    migrate = Migrate(app, db)
+    print(db)
+
diff --git a/backend/db/enum_classes.py b/backend/db/enum_classes.py
new file mode 100644
index 0000000..f9c5b9f
--- /dev/null
+++ b/backend/db/enum_classes.py
@@ -0,0 +1,23 @@
+import enum
+
+class ScopeClass(enum.Enum):
+    user = "user"
+    origin = "origin"
+    
+class StatusClass(enum.Enum):
+    success = "SUCCESS"
+    origin = "ORIGIN"
+    processed = "PROCCESSED"
+    deleted = "DELETED"
+    failure = "FAILURE"
+    pending = "PENDING"
+
+class FaceTypeClass(enum.Enum):
+    mosaic = "mosaic"
+    character = "character"
+
+class SchemaName(enum.Enum):
+    user = "user"
+    mzRequest = "mzRequest"
+    mzResult = "mzResult"
+    video = "video"
diff --git a/backend/db/schema.py b/backend/db/schema.py
new file mode 100644
index 0000000..f28ce5c
--- /dev/null
+++ b/backend/db/schema.py
@@ -0,0 +1,127 @@
+from db.db_connection import db
+from datetime import datetime
+from db.enum_classes import ScopeClass, StatusClass, FaceTypeClass
+
+class User(db.Model):
+    __tablename__ = 'users'
+    id = db.Column(db.Integer, primary_key=True)
+    email = db.Column(db.String(50), unique=True, nullable=False)
+    password = db.Column(db.String(255), nullable=False)
+    age = db.Column(db.Integer, nullable=True)
+    gender = db.Column(db.String(10), nullable=True)
+    created_at = db.Column(db.DateTime, nullable=False, default=datetime.utcnow)
+    updated_at = db.Column(db.DateTime, onupdate=datetime.utcnow)
+    deleted_at = db.Column(db.DateTime, onupdate=datetime.utcnow)
+
+    def get_email(self):
+        return self.email
+
+    def get_id(self):
+        # In SQLAlchemy, primary key field is automatically named `id`
+        return self.id
+
+    def __init__(self, email, password, age, gender, created_at):
+        self.email = email
+        self.password = password
+        self.age = age
+        self.gender = gender
+        self.created_at = created_at
+
+class MzRequest(db.Model):
+    id = db.Column(db.Integer, primary_key=True, autoincrement=True)
+    user_id = db.Column(db.Integer, db.ForeignKey('users.id'), nullable=True)
+    age = db.Column(db.Integer, nullable=True)
+    gender = db.Column(db.String(10), nullable=True)
+    voice_url = db.Column(db.String(255), nullable=True)
+    status = db.Column(db.String(10), nullable=True, default='Proceeding')
+    ata = db.Column(db.TIMESTAMP, nullable=True) # 완료시간
+    # celery_id = 
+    # celery_id = db.Column(db.String(255), nullable=True)
+    created_at = db.Column(db.TIMESTAMP, nullable=True)
+    updated_at = db.Column(db.TIMESTAMP, nullable=True)
+    deleted_at = db.Column(db.TIMESTAMP, nullable=True)
+
+    def __init__(self, user_id, age, gender, voice_url, status, ata, created_at):
+        self.user_id = user_id
+        self.age = age
+        self.gender = gender
+        self.voice_url = voice_url
+        self.status = status
+        self.ata = ata
+        self.created_at = created_at
+
+class MzResult(db.Model):
+    id = db.Column(db.Integer, primary_key=True)
+    mz_request_id = db.Column(db.Integer, db.ForeignKey('mz_request.id'), nullable=False)
+    condition_image_url = db.Column(db.String(255), nullable=True)
+    condition_gif_url = db.Column(db.String(255), nullable=True)
+    voice_image_url = db.Column(db.String(255), nullable=True)
+    voice_gif_url = db.Column(db.String(255), nullable=True)
+    condition_image_rating = db.Column(db.Integer, nullable=True)
+    #condition_gif_rating = db.Column(db.Integer, nullable=True)
+    voice_image_rating = db.Column(db.Integer, nullable=True)
+    #voice_gif_rating = db.Column(db.Integer, nullable=True)
+    condition_image_score = db.Column(db.Float, nullable=True)
+    condition_gif_score = db.Column(db.Float, nullable=True)
+    voice_image_score = db.Column(db.Float, nullable=True)
+    voice_gif_score = db.Column(db.Float, nullable=True)
+    survey = db.Column(db.Integer, default=0)
+    created_at = db.Column(db.TIMESTAMP, nullable=False)
+    updated_at = db.Column(db.TIMESTAMP, nullable=True)
+    deleted_at = db.Column(db.TIMESTAMP, nullable=True)
+
+    def __init__(self, mz_request_id, created_at):
+        self.mz_request_id = mz_request_id
+        self.created_at = created_at
+
+class MzSurvey(db.Model):
+    id = db.Column(db.Integer, primary_key=True)
+    mz_result_id = db.Column(db.Integer, db.ForeignKey('mz_result.id'), nullable=False)
+    user_phone = db.Column(db.String(50), nullable=True)
+    sns_time = db.Column(db.Integer, nullable=True)
+    image_rating_reason = db.Column(db.Text, nullable=True)
+    voice_to_face_rating = db.Column(db.Integer, nullable=True)
+    dissatisfy_reason = db.Column(db.String(10), nullable=True) # 복수 선택
+    additional_function = db.Column(db.Text, nullable=True)
+    face_to_gif_rating = db.Column(db.Integer, nullable=True)
+    more_gif = db.Column(db.Integer, nullable=True)
+    more_gif_type = db.Column(db.String(10), nullable=True) # 복수 선택
+    waiting = db.Column(db.Integer, nullable=True)
+    waiting_improvement = db.Column(db.Integer, nullable=True)
+    recommend = db.Column(db.Integer, nullable=True)
+    opinion = db.Column(db.Text, nullable=True)
+    created_at = db.Column(db.TIMESTAMP, nullable=True)
+    updated_at = db.Column(db.TIMESTAMP, nullable=True)
+    deleted_at = db.Column(db.TIMESTAMP, nullable=True)
+
+    def __init__(self, 
+                mz_result_id, 
+                user_phone,
+                sns_time, 
+                image_rating_reason,
+                voice_to_face_rating,
+                dissatisfy_reason,
+                additional_function,
+                face_to_gif_rating,
+                more_gif,
+                more_gif_type,
+                waiting,
+                waiting_improvement,
+                recommend,
+                opinion, 
+                created_at):
+        self.mz_result_id = mz_result_id
+        self.user_phone = user_phone
+        self.sns_time = sns_time 
+        self.image_rating_reason = image_rating_reason
+        self.voice_to_face_rating = voice_to_face_rating
+        self.dissatisfy_reason = dissatisfy_reason
+        self.additional_function = additional_function
+        self.face_to_gif_rating = face_to_gif_rating
+        self.more_gif = more_gif
+        self.more_gif_type = more_gif_type
+        self.waiting = waiting
+        self.waiting_improvement = waiting_improvement
+        self.recommend = recommend
+        self.opinion = opinion
+        self.created_at = created_at
\ No newline at end of file
diff --git a/backend/docker-compose.yml b/backend/docker-compose.yml
new file mode 100644
index 0000000..de59308
--- /dev/null
+++ b/backend/docker-compose.yml
@@ -0,0 +1,77 @@
+version: '3'
+services:
+  frontend:
+    container_name: frontend
+    image: "makezenerator/frontend:latest"
+    environment:
+      NODE_ENV: production
+    ports:
+     - 3000:3000
+    command: pm2-runtime start ./build/index.js --env production 
+  
+  backend:
+    restart: unless-stopped
+    container_name: backend
+    image: "makezenerator/backend:latest"
+    ports:
+      - 5050:5050
+    env_file:
+      .env.prod
+    environment:
+      FLASK_DEBUG: 1 #리로딩 설정
+    command: gunicorn -w 1 -b 0.0.0.0:5050 app:app --reload
+
+  rabbitmq:
+    container_name: rabbitmq
+    hostname: rabbit
+    image: "rabbitmq:3-management"
+    environment:
+      - RABBITMQ_DEFAULT_USER=admin
+      - RABBITMQ_DEFAULT_PASS=mypass
+    ports:
+      - "15672:15672"
+      - "5672:5672"
+
+  simple_worker:
+    container_name: simple_worker
+    image: "makezenerator/simple_worker"
+    env_file:
+      .env.prod
+    depends_on:
+      - rabbitmq
+      - backend
+
+  nginx:
+    container_name: nginx
+    image: nginx:latest
+    restart: unless-stopped
+    volumes:
+      - ./certbot/conf:/etc/letsencrypt
+      - ./certbot/www:/var/www/certbot
+      - ./conf/nginx.conf:/etc/nginx/nginx.conf
+      - ./conf/makezenerator.com.conf:/etc/nginx/conf.d/makezenerator.com.conf
+      - ./conf/api.makezenerator.com.conf:/etc/nginx/conf.d/api.makezenerator.com.conf
+    ports:
+      - 80:80
+      - 443:443
+    command: "/bin/sh -c 'while :; do sleep 6h & wait $${!}; nginx -s reload; done & nginx -g \"daemon off;\"'"
+
+  certbot:
+    container_name: certbot
+    image: certbot/certbot
+    restart: unless-stopped
+    volumes:
+      - ./certbot/conf:/etc/letsencrypt
+      - ./certbot/www:/var/www/certbot
+    entrypoint: "/bin/sh -c 'trap exit TERM; while :; do certbot renew; sleep 12h & wait $${!}; done;'"
+
+  node:
+   image: prom/node-exporter
+   container_name: node-exporter
+   ports:
+     - 9100:9100
+   networks:
+     - promnet
+networks:
+  promnet:
+    driver: bridge
\ No newline at end of file
diff --git a/backend/module/crud_module.py b/backend/module/crud_module.py
new file mode 100644
index 0000000..da3e2cc
--- /dev/null
+++ b/backend/module/crud_module.py
@@ -0,0 +1,499 @@
+from flask import request 
+from db.enum_classes import ScopeClass, StatusClass, FaceTypeClass, SchemaName
+import module
+from static import status_code
+from module import db_module, file_module
+from celery import Celery
+from db.db_config import USERNAME, PASSWORD, HOST, DATABASE
+
+# Create a Celery instance named 'celery_app'
+celery = Celery('tasks', broker='amqp://admin:mypass@rabbit:5672')
+
+# Configure Celery settings
+celery.conf.update(
+    # Configure the result backend using MySQL
+    CELERY_RESULT_BACKEND=f'db+mysql+pymysql://{USERNAME}:{PASSWORD}@{HOST}/{DATABASE}',
+    
+    # Set the task serializer to JSON
+    CELERY_TASK_SERIALIZER='json',
+    
+    # Do not ignore results (set to False)
+    CELERY_IGNORE_RESULT=False,
+)
+REQUEST_LIMIT = 10
+################### MZ REQUEST ###################
+"""
+* TODO mz request upload
+"""
+def upload_mz_request():
+    try:
+        token = request.headers.get('Token')
+        user = module.token.get_user(token)
+        if user == False:
+            return 401, {"error": status_code.token_error}
+        # check the number of requests
+        result, message = module.db_module.count_mz_request_list(user)
+        print(result, message)
+        if result == 200:
+            request_count = message['request_count']
+            if request_count >= REQUEST_LIMIT:
+                return 405, {"error": request_count}
+        else:
+            return result, message 
+
+        age = request.form.get('age')
+        if age == None or not age.isdigit():
+            return 404, {"error": f'{status_code.field_error}age'}
+        gender = request.form.get('gender')
+        if gender == None or (gender != 'man' and gender != 'woman'):
+            return 404, {"error": f'{status_code.field_error}gender'}
+        voice_url = None
+        status = request.form.get('status')
+        ata = request.form.get('ata')
+        
+        result, message = module.db_module.create_mz_request(user, age, gender, voice_url, status, ata)
+        request_id = message['mz_request_id']
+        result_id = message['mz_result_id']
+
+        if 'file' not in request.files:
+            return 404, {"error": f'{status_code.field_error}file'} 
+        filename = request.files['file'].filename
+        if filename == None or filename == '':
+            return 404, {"error": f'{status_code.field_error}file'}
+        f = request.files['file']
+        file_result, location = file_module.file_upload(request_id, result_id, SchemaName.mzRequest.value, f)
+        if file_result == False:
+            return 400, location
+
+        _, _ = module.db_module.update_mz_request_voice_url(request_id, location)
+
+        celery_task_id = celery.send_task('tasks.run_mz', kwargs= 
+                    {
+                        'request_id' : request_id,
+                        'result_id' : result_id,
+                        'age' : age,
+                        'gender' : gender,
+                        'file_url' : location
+                    })
+        
+        print("==========================")
+        print(celery_task_id)
+        print("==========================")
+        message["celery_task_id"] = str(celery_task_id)
+
+        return result, message
+    except Exception as ex:
+        print(ex)
+        return 400, {"error": str(ex)}
+
+"""
+* mz request get
+"""
+def get_mz_request(mz_request_id):
+    try:
+        token = request.headers.get("Token")
+        user_id = module.token.get_user(token)
+        if user_id == False:
+            return 401, {"error": status_code.token_error}
+        result, message = module.db_module.read_mz_request(mz_request_id, user_id)
+        return result, message
+    except Exception as ex:
+        print(ex)
+        return 400, {"error": str(ex)}
+
+"""
+* mz request list get
+"""
+def get_mz_request_list():
+    try:
+        token = request.headers.get("Token")
+        user_id = module.token.get_user(token)
+        if user_id == False:
+            return 401, {"error": status_code.token_error}
+        result, message = module.db_module.read_mz_request_list(user_id)
+        return result, message
+    except Exception as ex:
+        print(ex)
+        return 400, {"error": str(ex)}
+
+################### MZ RESULT ###################
+"""
+* TODO mz result regenerate
+"""
+def regenerate_mz_result(mz_request_id):
+    try:
+        token = request.headers.get("Token")
+        user_id = module.token.get_user(token)
+        if user_id == False:
+            return 401, {"error": status_code.token_error}
+        # 다시 생성중으로 변경 
+        result, message = module.db_module.update_mz_request_status(mz_request_id, 'Proceeding')
+        if result != 200:
+            return result, message
+
+        result, mz_result_id = module.db_module.create_mz_result(mz_request_id)
+        _, request_info = module.db_module.read_mz_request(mz_request_id, user_id)
+        if _ !=200:
+            return 400, {'error' : request_info['error']}
+        info = request_info['mz_request']
+        celery_task_id = celery.send_task('tasks.run_mz', kwargs= 
+                    {
+                        'request_id' : mz_request_id,
+                        'result_id' : mz_result_id,
+                        'age' : info['age'],
+                        'gender' : info['gender'],
+                        'file_url' : info['voice_url']
+                    })
+        
+        print("==========================")
+        print(celery_task_id)
+        print("==========================")
+
+        return result, {"mz_request_id" : str(mz_request_id), "regenerate_mz_result_id" : str(mz_result_id)}
+    except Exception as ex:
+        print(ex)
+        return 400, {"error": str(ex)}
+
+"""
+* mz result get
+"""
+def get_mz_result(mz_request_id, mz_result_id):
+    try:
+        token = request.headers.get("Token")
+        user_id = module.token.get_user(token)
+        if user_id == False:
+            return 401, {"error": status_code.token_error}
+        result, message = module.db_module.read_mz_result(mz_request_id, mz_result_id)
+        return result, message
+    except Exception as ex:
+        print(ex)
+        return 400, {"error": str(ex)}
+
+"""
+* mz result rating update
+"""
+def update_mz_result_rating(mz_request_id, mz_result_id):
+    try:
+        token = request.headers.get("Token")
+        user_id = module.token.get_user(token)
+        if user_id == False:
+            return 401, {"error": status_code.token_error}
+        
+        rating_type = request.form.get('type')
+        if rating_type != 'voice' and rating_type != 'condition':
+            return 404, {"error": f'{status_code.field_error}rating_type'}
+        rating_num = request.form.get('rating')
+        if rating_num == None or not rating_num.isdigit():
+            return 404, {"error": f'{status_code.field_error}rating_num'}
+
+        result, message = module.db_module.update_mz_result_rating(mz_request_id, mz_result_id, rating_type, rating_num)
+        return result, message
+    except Exception as ex:
+        print(ex)
+        return 400, {"error": str(ex)}
+
+################### MZ SURVEY ###################
+def upload_mz_survey(mz_request_id, mz_result_id):
+    try:
+        token = request.headers.get('Token')
+        user = module.token.get_user(token)
+        if user == False:
+            return 401, {"error": status_code.token_error}
+
+        user_phone = request.form.get('user_phone')
+        sns_time = request.form.get('sns_time')
+        image_rating_reason = request.form.get('image_rating_reason')
+        voice_to_face_rating = request.form.get('voice_to_face_rating') 
+        dissatisfy_reason = request.form.get('dissatisfy_reason')
+        additional_function = request.form.get('additional_function')
+        face_to_gif_rating = request.form.get('face_to_gif_rating')
+        more_gif = request.form.get('more_gif')
+        more_gif_type = request.form.get('more_gif_type')
+        waiting = request.form.get('waiting')
+        waiting_improvement = request.form.get('waiting_improvement')
+        recommend = request.form.get('recommend')
+        opinion = request.form.get('opinion')
+        
+        
+        result, message = module.db_module.create_mz_survey(mz_result_id,
+                                                            user_phone,
+                                                            sns_time, 
+                                                            image_rating_reason,
+                                                            voice_to_face_rating,
+                                                            dissatisfy_reason,
+                                                            additional_function,
+                                                            face_to_gif_rating,
+                                                            more_gif,
+                                                            more_gif_type,
+                                                            waiting,
+                                                            waiting_improvement,
+                                                            recommend,
+                                                            opinion)
+        _, _ = module.db_module.update_mz_result_survey(mz_request_id, mz_result_id, 1)
+        
+        return result, message
+    except Exception as ex:
+        print(ex)
+        return 400, {"error": str(ex)}
+
+# 1. 백엔드에 샐러리 ID가 있다면 샐러리 ID와 요청해야하는 request_id를 함께 넘겨주기
+# 2. 백엔드에서는 result ID로 조회
+#     1) 만약 DB 존재한다면 -> DB에서 찾은 정보 바로 return
+#     2) 존재하지 않는다면 -> status 조회 후 SUCCESS가 나오는 경우, result_db에 저장하고 return
+
+
+# """
+# * celery status
+# * Define a route for getting the status of a task
+# """
+# def get_celery_task_status(mz_request_id, task_id):
+#     try:
+#         token = request.headers.get("Token")
+#         user_id = module.token.get_user(token)
+#         if user_id == False:
+#             return 401, {"error": status_code.token_error}
+#         result, message = module.db_module.read_mz_request(mz_request_id, user_id)
+#         if result == 200:
+#             # Get the status of the task using the task ID
+#             task_status = celery.AsyncResult(task_id).status
+#             # Update status in DB 
+#             try:
+#                 result, message = module.db_module.update_mz_request_status(mz_request_id, task_status)
+#             except Exception as ex:
+#                 print(ex)
+#                 return 400, {"error": str(ex)}
+
+#             # Return the task status as a JSON response
+#             return 200, {'Task Status': task_status}
+#         else:
+#             return 400, {'Cannot find mz_request'}
+#     except Exception as ex:
+#         print(ex)
+#         return 400, {"error": str(ex)}
+
+# """
+# * celery result
+# * Define a route for getting the result of a completed task
+# """
+# def get_celery_result_done(mz_request_id, task_id):
+#     try:
+#         token = request.headers.get("Token")
+#         user_id = module.token.get_user(token)
+#         if user_id == False:
+#             return 401, {"error": status_code.token_error}
+#         result, message = module.db_module.read_mz_request(mz_request_id, user_id)
+#         if result == 200:
+#             # Get the result of the task using the task ID
+#             task_result = celery.AsyncResult(task_id).result
+            
+#             # Update image and gif in result record
+#             try:
+#                 result, message = module.db_module.update_mz_result_image_gif(task_result)
+#             except Exception as ex:
+#                 print(ex)
+#                 return 400, {"error": str(ex)}
+
+#             # Return the task result as a JSON response
+#             return 200, {'Task Result': task_result}
+#         else:
+#             return 400, {'Cannot find mz_request'}
+#     except Exception as ex:
+#         print(ex)
+#         return 400, {"error": str(ex)}
+
+# """
+# * Whitelist face image delete
+# """
+# def delete_whitelist_face_image(whitelistFaceId, _id):
+#     try:
+#         token = request.headers.get('Token')
+#         user = module.token.get_user(token)
+#         if user == False:
+#             return 401, {"error": status_code.token_error}
+#         result, message = module.db_module.delete_whitelist_face_image(whitelistFaceId, _id)
+#         return result, message
+#     except Exception as ex:
+#         print(ex)
+#         return 400, {"error": str(ex)}
+
+# ################### VIDEO ###################
+
+# """
+# * origin video upload
+# """
+# def origin_video_upload():
+#     try:
+#         token = request.headers.get('Token')
+#         user = module.token.get_user(token)
+#         if user == False:
+#             return 401, {"error": status_code.token_error}
+#         if 'file' not in request.files:
+#             return 404, {"error": f'{status_code.field_error}file'}
+#         filename = request.files['file'].filename
+#         if filename == None or filename == '':
+#             return 404, {"error": f'{status_code.field_error}file'}
+#         f = request.files['file']
+#         fileResult, location = file_module.file_upload(user, SchemaName.video.value, f)
+#         if fileResult == False:
+#             return fileResult, location
+#         result, message = module.db_module.create_video(user, location)
+#         if result == True: ###### result-> result == 200
+#             return 200, {"id" : message, "url": location}
+#         else:
+#             return 400, message
+#     except Exception as ex:    
+#         print(ex)
+#         return 400, {"error": str(ex)}
+    
+# """
+# * video delete
+# """
+# def delete_video(_id):
+#     try:
+#         token = request.headers.get('Token')
+#         user = module.token.get_user(token)
+#         if user == False:
+#             return False, {"error": status_code.token_error}
+#         result, message = db_module.delete_video(user, _id)
+#         return result, message
+#     except Exception as ex:    
+#         print(ex)
+#         return False, {"error": str(ex)}
+
+# """
+# * update video before save s3
+# """
+# def update_video_upload():
+#     try:
+#         token = request.headers.get('Token')
+#         user = module.token.get_user(token)
+#         if user == False:
+#             return False, {"error": status_code.token_error}
+        
+#         # video id가져옴
+#         videoId = request.form.get('video_id')
+#         if videoId == None or videoId == '':
+#             return False, {"error": f'{status_code.field_error}video_id'}
+
+#         # video url 찾기
+#         result, videoUrl = db_module.read_origin_video(videoId, user)
+#         if result == False:
+#             return result, videoUrl
+
+#         # faceType 가져옴
+#         faceType = request.form.get('face_type')
+#         if faceType == None or faceType == '':
+#             return False, {"error": f'{status_code.field_error}face_type'}
+        
+#         if faceType != FaceTypeClass.character.value and faceType != FaceTypeClass.mosaic.value:
+#             return False, {"error": f'{status_code.enum_class_error}face_type'}
+
+#         # blockCharacterId 선택적으로 가져옴
+#         if faceType == FaceTypeClass.character.value:
+#             blockCharacterId = request.form.get('block_character_id')
+#             if blockCharacterId == None or blockCharacterId == '':
+#                 return False, {"error": f'{status_code.field_error}block_character_id'}
+#             result, blockCharacterImg = db_module.read_block_character_url(blockCharacterId)
+#             if result == False:
+#                 return result, blockCharacterImg
+#         else:
+#             blockCharacterId = None
+#             blockCharacterImg = None
+        
+#         # TODO - whitelistFaceId 없을 경우에 대한 것 처리하기
+#         whitelistFaceId = request.form.getlist("whitelist_face_id")
+#         result, whitelistFaceImgList = db_module.read_whitelist_face_url(user, whitelistFaceId)
+#         if result == False:
+#             return result, whitelistFaceImgList
+
+#         # True or False 리턴
+#         result, message = db_module.update_video(videoId, user, faceType, whitelistFaceId, blockCharacterId) # ID를 받아와서 찾은다음에 url
+
+#         if result == True:
+#             if faceType == FaceTypeClass.mosaic.value:
+#                 task = celery.send_task('tasks.run_mosaic', kwargs= 
+#                     {
+#                         'whitelistFaceImgList' : whitelistFaceImgList, # url 리스트
+#                         'videoUrl' : videoUrl,
+#                         "user" : str(user)
+#                     })
+#                 result2, message = db_module.update_video_celery(videoId, user, task, task.status)
+
+#                 if result2 == True:
+#                     return True, {"id" : str(task.id)}
+#                 else:
+#                     return False, {"error":status_code.update_02_fail}
+#             elif faceType == FaceTypeClass.character.value:
+#                 task = celery.send_task('tasks.run_character', kwargs=
+#                     {
+#                         'whitelistFaceImgList' : whitelistFaceImgList, 
+#                         'blockCharacterImgUrl' : blockCharacterImg, 
+#                         'videoUrl' : videoUrl,
+#                         "user" : str(user)
+#                     })
+#                 result2, message = db_module.update_video_celery(videoId, user, task, task.status)
+
+#                 if result2 == True:
+#                     return True, {"id" : str(task.id)}
+#                 else:
+#                     return False, {"error":status_code.update_02_fail}
+#         else:
+#             return result, message
+#     except Exception as ex:
+#         print(ex)
+#         return False, {"error": str(ex)}
+
+# """
+# * 셀러리 id를 통해 상태 체크
+# """
+# def get_after_video_status(taskId):
+#     try:
+#         token = request.headers.get("Token")
+#         user = module.token.get_user(token)
+#         if user == False:
+#             return False, {"error": status_code.token_error}
+#         result, message = db_module.read_celery_status(user, taskId)
+#         if message == StatusClass.failure.value:
+#             status = celery.AsyncResult(taskId, app=celery)
+#             result2 = db_module.update_video_celery_failure(user, taskId) #video컬렉션의 status를 FAILURE로 업데이트
+#             if result2 == True:
+#                 return True, {"status" : StatusClass.failure.value} #셀러리의 결과과 failure이고, video 컬렉션의 status 업데이트를 성공한 경우
+#             else:
+#                 return False, {"error", status_code.update_02_fail} #셀러리의 결과과 failure이고, video 컬렉션의 status 업데이트를 실패한 경우
+#         elif result == 0:
+#             return True, {"status" : StatusClass.pending.value} #PENDING
+#         else:
+#             return True, {"status" : message} #SUCCESS
+#     except Exception as ex:
+#         print(ex)
+#         return False, {"error": str(ex)}
+    
+# """
+# * 특정 유저에 대한 비디오 결과 모두 조회하기
+# """
+# def get_multiple_after_video():
+#     try:
+#         token = request.headers.get("Token")
+#         user = module.token.get_user(token)
+#         if user == False:
+#             return False, {"error": status_code.token_error}
+#         result, message = module.db_module.read_proccessed_video(user)
+#         return result, message
+#     except Exception as ex:
+#         print(ex)
+#         return False, {"error": str(ex)}
+
+# """
+# * read celery task status
+# """
+# def read_celery_task_status(taskId):
+#     try:
+#         status = celery.AsyncResult(taskId, app=celery)
+#         if status == StatusClass.success:
+#             result = celery.AsyncResult(taskId).result
+#             return True, {"status", result}
+#         else:
+#             return False, {"status", status}
+#     except Exception as ex:
+#         print(ex)
+#         return False, {"error": str(ex)}
\ No newline at end of file
diff --git a/backend/module/db_module.py b/backend/module/db_module.py
new file mode 100644
index 0000000..b04352d
--- /dev/null
+++ b/backend/module/db_module.py
@@ -0,0 +1,570 @@
+from datetime import datetime
+from db.enum_classes import ScopeClass, StatusClass, FaceTypeClass
+from static import status_code
+from db import schema
+from db.db_connection import db
+from sqlalchemy import desc
+from pytz import timezone
+
+################### MZ REQUEST ###################
+"""
+* mz request create
+"""
+def create_mz_request(user, age, gender, voice_url, status, ata):
+    try:
+        new_mz_request = schema.MzRequest(user_id=user, 
+                                            age=age, 
+                                            gender=gender, 
+                                            voice_url=voice_url,
+                                            status=status, 
+                                            ata=ata, 
+                                            created_at=datetime.now(timezone('Asia/Seoul')))
+        db.session.add(new_mz_request)
+        db.session.commit()
+        if new_mz_request.id is not None:
+            result, mz_result_id = create_mz_result(new_mz_request.id)
+        return 200, {"mz_request_id" : str(new_mz_request.id), "mz_result_id" : str(mz_result_id)} #true->200
+    except Exception as ex:
+        db.session.rollback()
+        print(ex)
+        return 400, {"error": str(ex)} #false->400
+
+"""
+* mz request read
+"""
+def read_mz_request(id, user_id):
+    try:
+        mz_request = schema.MzRequest.query.filter_by(id = id, user_id = user_id, deleted_at=None).first()        
+        if mz_request:
+            result_data = {
+                "id": mz_request.id,
+                "age": mz_request.age,
+                "gender": mz_request.gender,
+                "voice_url": mz_request.voice_url,
+                "status": mz_request.status,
+                "ata": mz_request.ata.isoformat() if mz_request.ata else None,
+                "created_at": mz_request.created_at.isoformat(),
+                "updated_at": mz_request.updated_at.isoformat() if mz_request.updated_at else None
+            }
+            return 200, {"mz_request": result_data} #true->200
+        else:
+            return 404, {"error": "MZ request not found"}
+    except Exception as ex:
+        print(ex)
+        return 400, {"error": str(ex)}  #false->400
+"""
+* count mz request which is not failed 
+"""
+def count_mz_request_list(user_id):
+    try:
+        mz_request_list = schema.MzRequest.query.filter(schema.MzRequest.user_id==user_id,
+                                                        schema.MzRequest.deleted_at==None, 
+                                                        schema.MzRequest.status!='Failed').all()                                            
+        if mz_request_list == None:
+            request_count = 0
+        else:
+            request_count = len(mz_request_list)   
+        return 200, {'request_count' : request_count}
+    except Exception as ex:
+        print(ex)
+        return 400, {"error": str(ex)}  #false->400
+"""
+* mz request list read
+"""
+def read_mz_request_list(user_id):
+    try:
+        mz_request_list = schema.MzRequest.query.filter_by(user_id=user_id, deleted_at=None).order_by(desc(schema.MzRequest.id)).all()
+        
+        # 결과 리스트를 준비합니다.
+        result_list = []
+        
+        for mz_request in mz_request_list:
+            # 각 MzRequest에 대한 MzResult 중 가장 최신의 결과 찾기
+            latest_mz_result = schema.MzResult.query.filter_by(mz_request_id=mz_request.id).order_by(desc(schema.MzResult.id)).first()
+            
+            # MzRequest 정보와 함께 최신 MzResult의 id를 결과 리스트에 추가
+            result_data = {
+                "id": mz_request.id,
+                "user_id": mz_request.user_id,
+                "age": mz_request.age,
+                "gender": mz_request.gender,
+                "voice_url": mz_request.voice_url,
+                "status": mz_request.status,
+                "ata": mz_request.ata.isoformat() if mz_request.ata else None,
+                "latest_mz_result_id": latest_mz_result.id if latest_mz_result else None,  # 최신 MzResult의 ID 추가
+                "created_at": mz_request.created_at.isoformat() if mz_request.created_at else None,
+                "updated_at": mz_request.updated_at.isoformat() if mz_request.updated_at else None,
+            }
+            result_list.append(result_data)
+        return 200, {"mz_request_list": result_list}
+    except Exception as ex:
+        print(ex)
+        return 400, {"error": str(ex)}  #false->400
+
+"""
+* mz request voice_url update
+"""
+def update_mz_request_voice_url(mz_request_id, location):
+    try:
+        mz_request = schema.MzRequest.query.filter_by(id = mz_request_id).first()
+        if mz_request:
+            mz_request.voice_url = location
+            # mz_request.updated_at = datetime.now(timezone('Asia/Seoul'))
+            db.session.commit()
+            return 200, {"message": "status updated sucessfully"}
+        else:
+            return 404, {"error": str("Can't find mz request")}
+    except Exception as ex:
+        db.session.rollback()
+        print(ex)
+        return 400, {"error": str(ex)} #false->400
+
+
+"""
+* mz request status update
+"""
+def update_mz_request_status(mz_request_id, task_status):
+    try:
+        mz_request = schema.MzRequest.query.filter_by(id = mz_request_id).first()
+        if mz_request:
+            mz_request.status = task_status
+            if task_status == 'Success':
+                mz_request.updated_at = datetime.now(timezone('Asia/Seoul'))
+            db.session.commit()
+            return 200, {"message": "status updated sucessfully"}
+        else:
+            return 404, {"error": str("Can't find mz request")}
+    except Exception as ex:
+        db.session.rollback()
+        print(ex)
+        return 400, {"error": str(ex)} #false->400
+
+
+################### MZ RESULT ###################
+"""
+* mz result create
+"""
+def create_mz_result(mz_request_id):
+    try:
+        new_mz_result = schema.MzResult(mz_request_id, datetime.now(timezone('Asia/Seoul')))
+        db.session.add(new_mz_result)
+        db.session.commit()
+        return 200, new_mz_result.id #true->200
+    except Exception as ex:
+        db.session.rollback()
+        print(ex)
+        return 400, {"error": str(ex)} #false->400
+
+"""
+* mz result read
+"""
+def read_mz_result(mz_request_id, mz_result_id):
+    try:
+        mz_result = schema.MzResult.query.filter_by(mz_request_id = mz_request_id, id = mz_result_id, deleted_at=None).first()
+        if mz_result:
+            result_data = {
+                "id": mz_result.id,
+                "mz_request_id": mz_result.mz_request_id,
+                "condition_image_url": mz_result.condition_image_url,
+                "condition_gif_url": mz_result.condition_gif_url,
+                "voice_image_url": mz_result.voice_image_url,
+                "voice_gif_url": mz_result.voice_gif_url,
+                "condition_image_rating": mz_result.condition_image_rating,
+                "voice_image_rating": mz_result.voice_image_rating,
+                "created_at": mz_result.created_at.isoformat(),
+                "updated_at": mz_result.updated_at.isoformat() if mz_result.updated_at else None,
+                "survey" : mz_result.survey,
+            }
+            return 200, {"mz_result": result_data} #true->200
+        else:
+            return 404, {"error": "MZ result not found"}
+    except Exception as ex:
+        print(ex)
+        return 400, {"error": str(ex)} #false->400
+
+# """
+# * mz result image/gif update
+# """
+# def update_mz_result_image_gif(mz_request_id, task_result):
+#     try:
+#         mz_result = schema.MzResult.query.filter_by(mz_request_id = mz_request_id, id = task_result.result_id)
+#         if mz_result:
+#             mz_result.condition_image_url = task_result.condition_image_url
+#             mz_result.condition_gif_url = task_result.condition_gif_url
+#             mz_result.voice_image_url = task_result.voice_image_url
+#             mz_result.voice_gif_url = task_result.voice_gif_url
+#             mz_result.updated_at = datetime.now(timezone('Asia/Seoul'))
+#             db.session.commit()
+#             return 200, {"message": "image and gif updated sucessfully"}
+#         else:
+#             return 404, {"error": str("Can't find mz result")}
+#     except Exception as ex:
+#         db.session.rollback()
+#         print(ex)
+#         return 400, {"error": str(ex)} #false->400
+"""
+* mz result rating update
+"""
+def update_mz_result_rating(mz_request_id, mz_result_id, rating_type, rating_num):
+    try:
+        mz_result = schema.MzResult.query.filter_by(mz_request_id = mz_request_id, id=mz_result_id, deleted_at=None).first()
+        if mz_result:
+            if rating_type == 'voice':
+                mz_result.voice_image_rating = rating_num
+            elif rating_type == 'condition':
+                mz_result.condition_image_rating = rating_num
+            mz_result.updated_at = datetime.now(timezone('Asia/Seoul'))
+            db.session.commit()
+            return 200, {"message": "rating updated successfully"} #true->200
+        else:
+            return 404, {"error": str("Can't find mz result")}
+    except Exception as ex:
+        db.session.rollback()
+        print(ex)
+        return 400, {"error": str(ex)} #false->400
+"""
+* mz result survey update
+"""
+def update_mz_result_survey(mz_request_id, mz_result_id, survey):
+    try:
+        mz_result = schema.MzResult.query.filter_by(mz_request_id = mz_request_id, id=mz_result_id, deleted_at=None).first()
+        if mz_result:
+            mz_result.survey = survey
+            mz_result.updated_at = datetime.now(timezone('Asia/Seoul'))
+            db.session.commit()
+            return 200, {"message": "rating updated successfully"} #true->200
+        else:
+            return 404, {"error": str("Can't find mz result")}
+    except Exception as ex:
+        db.session.rollback()
+        print(ex)
+        return 400, {"error": str(ex)} #false->400
+
+################### MZ SURVEY ###################
+def create_mz_survey(result_id,
+                        user_phone,
+                        sns_time, 
+                        image_rating_reason,
+                        voice_to_face_rating,
+                        dissatisfy_reason,
+                        additional_function,
+                        face_to_gif_rating,
+                        more_gif,
+                        more_gif_type,
+                        waiting,
+                        waiting_improvement,
+                        recommend,
+                        opinion):
+    try:
+        new_mz_survey = schema.MzSurvey(mz_result_id=result_id,
+                                        user_phone=user_phone,
+                                        sns_time=sns_time,
+                                        image_rating_reason=image_rating_reason,
+                                        voice_to_face_rating=voice_to_face_rating,
+                                        dissatisfy_reason=dissatisfy_reason,
+                                        additional_function=additional_function,
+                                        face_to_gif_rating=face_to_gif_rating,
+                                        more_gif=more_gif,
+                                        more_gif_type=more_gif_type,
+                                        waiting=waiting,
+                                        waiting_improvement=waiting_improvement,
+                                        recommend=recommend,
+                                        opinion=opinion, 
+                                        created_at=datetime.now(timezone('Asia/Seoul')))
+        db.session.add(new_mz_survey)
+        db.session.commit()
+        return 200, {"mz_result_id" : str(result_id), "mz_survey_id" : str(new_mz_survey.id)} #true->200
+    except Exception as ex:
+        db.session.rollback()
+        print(ex)
+        return 400, {"error": str(ex)} #false->400
+# """
+# * WhitelistFaceImage delete
+# """
+# def delete_whitelist_face_image(whitelistFaceId, _id):
+#     try:
+#         whilelistFaceImage = schema.WhitelistFaceImage.objects(_id = ObjectId(_id), whitelist_face_id = whitelistFaceId, is_deleted=False)
+#         if whilelistFaceImage.count() == 0:
+#             return 404, {"error": f'{status_code.id_error}whitelist_face_image'} #false->404
+#         deleteWhilelistFaceImage = whilelistFaceImage.update(
+#             is_deleted=True,
+#             updated_at =datetime.now()
+#         )
+#         if deleteWhilelistFaceImage > 0:
+#             return 200, {"message": status_code.delete_01_success} #true->200
+#         else:
+#             print("Can't be deleted")
+#             return 404, {"error": status_code.delete_02_fail} #false->404
+#     except Exception as ex:
+#         print(ex)
+#         return 400, {"error": str(ex)}  #false->400
+
+# """
+# * WhitelistFaceImg corresponding to id read
+# """
+# def read_whitelist_face_url(user, whitelistFaceId): #id 리스트로 받아옴
+#     try:
+#         whitelistFaceImgList = []
+#         for x in whitelistFaceId:
+#             temp = schema.WhitelistFace.objects(user_id = ObjectId(user), _id = ObjectId(x), is_deleted = False).first()
+#             for y in schema.WhitelistFaceImage.objects(whitelist_face_id = temp._id, is_deleted=False):
+#                 whitelistFaceImgList.append(y.url) 
+#         return True, whitelistFaceImgList
+#     except Exception as ex:
+#         print(ex)
+#         return False, {"error": str(ex)}
+
+# ################################################ BLOCkCHARACTER ################################################
+# """
+# * OriginBlockCharacter create
+# """
+# def create_origin_block_character(location):
+#     try:
+#         blockCharacter = schema.BlockCharacter(location, ScopeClass.origin.value, False, datetime.now())
+#         result = schema.BlockCharacter.objects().insert(blockCharacter)
+#         return 200, {"id": str(result._id)} #true->200
+#     except Exception as ex:
+#         print(ex)
+#         return 400, {"error": str(ex)} #false->400
+
+# """
+# * OriginBlockCharacter read
+# """
+# def read_origin_block_character():
+#     try:
+#         temp = schema.BlockCharacter.objects(scope = ScopeClass.origin.value, is_deleted=False)
+#         tempJson = {}
+#         tempJson["data"] = []   
+#         for x in temp:
+#             tempJson1 = {"id" : str(x._id), "url" : x.url}
+#             tempJson["data"].append(tempJson1)
+#         return 200, tempJson #true->200
+#     except Exception as ex:
+#         print(ex)
+#         return 400, {"error": str(ex)} #false->400
+
+# """
+# * BlockCharacter create
+# """
+# def create_block_character(user, location):
+#     try:
+#         blockCharacter = schema.BlockCharacter(location, ScopeClass.user.value, False, datetime.now(), user)
+#         result = schema.BlockCharacter.objects().insert(blockCharacter)
+#         return 200, {"id": str(result._id)} #true->200
+#     except Exception as ex:
+#         print(ex)
+#         return 400, {"error": str(ex)} #false->400
+ 
+# """
+# * UserBlockCharacter read
+# """
+# def read_user_block_character(user):
+#     try:
+#         temp = schema.BlockCharacter.objects(user_id = ObjectId(user), scope = ScopeClass.user.value, is_deleted=False)
+#         tempJson = {}
+#         tempJson['data'] = []
+#         for x in temp:
+#             tempJson1 = {"id" : str(x._id), "url" : x.url}
+#             tempJson['data'].append(tempJson1)
+#         return 200, tempJson  
+#     except Exception as ex:
+#         print(ex)
+#         return 400, {"error": str(ex)}
+
+# """
+# * BlockCharacter delete
+# """
+# def delete_block_character(user, _id):
+#     try:
+#         blockCharacter = schema.BlockCharacter.objects(_id = ObjectId(_id), scope = ScopeClass.user.value, user_id = user, is_deleted=False)
+#         if blockCharacter.count() == 0:
+#             return 404, {"error": f'{status_code.id_error}block_character'} #false->404
+#         deleteBlockCharacter = blockCharacter.update(
+#             is_deleted=True,
+#             updated_at =datetime.now()
+#         )
+#         if deleteBlockCharacter > 0:
+#             return 200, {"message": status_code.delete_01_success} #true->200
+#         else:
+#             print("Can't be deleted")  
+#             return 400, {"error": status_code.delete_02_fail} #false->400
+#     except Exception as ex:
+#         print(ex)
+#         return 400, {"error": str(ex)} #false->400
+
+# """
+# * BlockCharacter corresponding to id read
+# """
+# def read_block_character_url(blockCharacterId):
+#     try:
+#         blockCharacter = schema.BlockCharacter.objects(_id = ObjectId(blockCharacterId), is_deleted=False).first()
+#         return True, blockCharacter.url
+#     except Exception as ex:
+#         print(ex)
+#         return False, {"error": str(ex)}
+    
+# ################################################ VIDEO ################################################
+# """
+# * Video create
+# """
+# def create_video(user, location):
+#     try:
+#         video = schema.Video(user, location, ScopeClass.origin.value, datetime.now())
+#         result = schema.Video.objects().insert(video)
+#         return True, str(result._id)
+#     except Exception as ex:
+#         print(ex)
+#         return False, {"error": str(ex)}
+
+# """
+# * OriginVideo read
+# """
+# def read_origin_video(_id, user):
+#     try:
+#         video = schema.Video.objects(_id = _id, user_id = user).first()
+#         if video.processed_url_id == None:
+#             return True, video.origin_url
+#         else:
+#             return False, {"error": status_code.celery_error}
+#     except Exception as ex:
+#         print(ex)
+#         return False, {"error": str(ex)}
+
+# """
+# * ProccessedVideo read
+# """
+# def read_proccessed_video(user):
+#     try:
+#         temp = schema.Video.objects(user_id = ObjectId(user), status = StatusClass.success.value)
+#         tempJson = {}
+#         tempJson['data'] = []
+#         for x in temp:
+#             # 셀러리 id
+#             temp2 = schema.Celery.objects(_id = x.processed_url_id).first()
+#             if temp2 == None:
+#                 continue
+#             tempJson1 = {"id" : str(x._id), "url" : temp2.result.replace('\"', '')}
+#             tempJson['data'].append(tempJson1)
+#         return True, tempJson
+#     except Exception as ex:
+#         print(ex)
+#         return False, {"error": str(ex)}
+
+# """
+# * Video db update
+# """
+# def update_video(_id, user, faceType, whitelistFace, blockCharacterId=""):
+#     try:
+#         if faceType != FaceTypeClass.character.value and faceType != FaceTypeClass.mosaic.value:
+#             print("Can't find face type") 
+#             return False, {"error": f'{status_code.enum_class_error}face type'}
+#         video = schema.Video.objects(_id = ObjectId(_id), user_id = user)
+#         if video.count() == 0:
+#             return False, {"error": f'{status_code.id_error}video'}
+#         if blockCharacterId == "" or blockCharacterId == None:
+#             processedVideo = video.update(
+#                 status = StatusClass.processed.value, 
+#                 face_type = faceType, 
+#                 whitelist_faces = whitelistFace, 
+#                 completed_at = datetime.now()
+#             )
+#         else:
+#             processedVideo = video.update(
+#                 status = StatusClass.processed.value, 
+#                 face_type = faceType, 
+#                 block_character_id = ObjectId(blockCharacterId), 
+#                 whitelist_faces = whitelistFace, 
+#                 completed_at = datetime.now()
+#             )
+#         if processedVideo > 0:
+#             return True, {"message": status_code.update_01_success}
+#         else:
+#             print("Can't be modified")  
+#             return False, {"error": status_code.update_02_fail}
+#     except Exception as ex:
+#         print(ex)
+#         return False, {"error": str(ex)}
+
+# """
+# * Video celeryId update
+# """
+# def update_video_celery(_id, user, task, status):
+#     try:
+#         # if status not in StatusClass:
+#         #     print("Can't find stauts") 
+#         #     return False
+#         video = schema.Video.objects(_id = ObjectId(_id), user_id = user)
+#         if video == None:
+#             return False, {"error": f'{status_code.id_error}video'}
+#         processedVideo = video.update(
+#             status = status, 
+#             processed_url_id = task.id, 
+#             completed_at = datetime.now()
+#         )
+#         if processedVideo > 0:
+#             return True, {"message" : status_code.update_01_success}
+#         else:
+#             print("Can't be modified")  
+#             return False, {"error" : status_code.update_02_fail}
+#     except Exception as ex:
+#         print(ex)
+#         return False, {"error": str(ex)}
+
+# """
+# * video delete
+# """
+# def delete_video(user, _id):
+#     try:
+#         video = schema.Video.objects(_id = ObjectId(_id), user_id = user)
+#         if video.count() == 0:
+#             return False, {"error": f'{status_code.id_error}video'}
+#         deleteVideo = video.update(
+#             status = "deleted",
+#             updated_at = datetime.now()
+#         )
+#         if deleteVideo > 0:
+#             return True, {"message" : status_code.delete_01_success}
+#         else:
+#             print("Can't be deleted")
+#             return False, {"error" : status_code.delete_02_fail}
+#     except Exception as ex:
+#         print(ex)
+#         return False, {"error": str(ex)} 
+    
+# """
+# * read celery status
+# """
+# def read_celery_status(user, taskId):
+#     try:
+#         temp = schema.Video.objects(user_id = user, processed_url_id = taskId).first()
+#         temp3 = schema.Celery.objects(_id = temp.processed_url_id).first()
+#         if temp3 != None:
+#             if temp3.status == StatusClass.success.value:
+#                 temp2 = schema.Video.objects(user_id = user, processed_url_id = taskId).update(status = StatusClass.success.value)
+#                 if temp2 > 0:
+#                     return True, temp3.status
+#                 else:
+#                     # db 업데이트 실패
+#                     return False, {"error" : status_code.update_02_fail}
+#             elif temp3.status == StatusClass.failure.value:
+#                 # failure일 경우
+#                 return True, temp3.status
+#         else:
+#             # pending일 경우 (셀러리 결과가 db에 저장되지 않음)
+#             return 0, StatusClass.pending.value
+#     except Exception as ex:
+#         print(ex)
+#         return False, {"error": str(ex)} 
+
+# """
+# * video status update when the celery status is failure
+# """
+# def update_video_celery_failure(user, taskId):
+#     video = schema.Video.objects(user_id = user, processed_url_id = taskId)
+#     if video.count() == 0:
+#         return False
+#     updateVideo = video.update(status = StatusClass.failure.value)
+#     if updateVideo > 0:
+#         return True
+#     else:
+#         return False
\ No newline at end of file
diff --git a/backend/module/file_module.py b/backend/module/file_module.py
new file mode 100644
index 0000000..3f01f45
--- /dev/null
+++ b/backend/module/file_module.py
@@ -0,0 +1,81 @@
+from bucket.m_connection import minio_connection, minio_put_object
+from bucket.m_config import BUCKET_NAME, MINIO_API_HOST
+from datetime import datetime
+from pytz import timezone
+import random 
+import os
+import subprocess
+
+"""
+* 파일 업로드
+"""
+def file_upload(request_id, result_id, collctionName, f):
+    try:
+        # 1. local에 파일 저장 - 파일 경로 때문에 저장해야함
+        f.save(f.filename)
+        # subprocess.run(['ffmpeg', '-i', f.filename,
+        #                 '-acodec', 'pcm_s16le', '-ar', '48000', '-ac', '1', f.filename, '-y'])
+        
+        # 2. 파일명 설정
+        name, ext = os.path.splitext(f.filename)
+        fileTime = datetime.now(timezone('Asia/Seoul')).strftime('%Y-%m-%d')
+        filename = f"{int(request_id):05}" + "_" + f"{int(result_id):05}" + "_voice_" + fileTime + ext
+        
+        # 3. 버킷 연결
+        storage = minio_connection()
+        
+        # 4. 버킷에 파일 저장
+        ret = minio_put_object(storage, f'{collctionName}/{filename}', f.filename)
+        location = f'https://{MINIO_API_HOST}/{BUCKET_NAME}/{collctionName}/{filename}'
+
+        # 5. local에 저장된 파일 삭제
+        os.remove(f.filename)
+            
+        # 6. 버킷에 파일 저장 성공 시 진행
+        if ret :
+            # 6-3. 성공 message return
+            if location != None:
+                return 200, location #true->200
+            else:
+                print("Can't find location")
+                return False, {"error":"Can't find location"} #false ->400 
+        
+        # 6. 버킷에 파일 저장 실패 시 진행 (ret == False 일 경우)
+        else:
+            return False, {"error":"Can't saved in minio bucket"} #false ->400
+        
+    except Exception as ex:
+        print("******************")
+        print(ex)
+        print("******************")
+        return False, {"error" : str(ex)}  #false -> 400
+
+# """
+# * condition image 및 gif 파일 읽고 랜덤 선정
+# """
+# def read_random_condition(age, gender):
+#     try:
+#         # 1. age 반올림
+#         age = round(age, -1)
+        
+#         # 2. 버킷 연결
+#         storage = minio_connection()
+        
+#         # 3. 버킷에서 리스트 가져오기 
+#         ret = minio_list_object(storage, age, gender)
+
+#         # 4. 버킷에서 리스트 가져오기 성공 시 랜덤 선정 
+#         if ret == False:
+#             return False, {"error":"Can't find list"} #false ->400 
+#         else:
+#             print(ret)
+#             choicejpg = random.choice(ret)
+#             idx = choicejpg.rindex('.')
+#             choicemp4 = choicejpg[:idx] + '.mp4'
+#             return True, {"image" : choicejpg, "gif" : choicemp4}
+        
+#     except Exception as ex:
+#         print("******************")
+#         print(ex)
+#         print("******************")
+#         return False, {"error" : str(ex)}  #false -> 400
\ No newline at end of file
diff --git a/backend/module/module_config.py b/backend/module/module_config.py
new file mode 100644
index 0000000..3a2cc43
--- /dev/null
+++ b/backend/module/module_config.py
@@ -0,0 +1,7 @@
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+SECRET_KEY = os.getenv('SECRET_KEY')
+TOKEN_EXPIRED = int(os.getenv('TOKEN_EXPIRED'))
diff --git a/backend/module/token.py b/backend/module/token.py
new file mode 100644
index 0000000..0d75f53
--- /dev/null
+++ b/backend/module/token.py
@@ -0,0 +1,101 @@
+from module.module_config import SECRET_KEY, TOKEN_EXPIRED
+from datetime import datetime, timedelta
+from db import schema
+import jwt
+# from bson import ObjectId
+from static import status_code
+
+def create_token(email):
+    try:
+        if email != None:
+            date = datetime.utcnow() + timedelta(seconds = TOKEN_EXPIRED)
+            payload = {
+                "email" : email,
+                "exp" : date
+            }
+            token = jwt.encode(payload, SECRET_KEY, algorithm = 'HS256')
+            if token == None:
+                print(status_code.user_auth_03_fail)
+                return False, {"error":status_code.user_auth_03_fail}
+            return True, token
+        else:
+           return False, {"error":status_code.user_auth_02_notmatch}
+    except Exception as ex:
+        print("***********")
+        print(ex)
+        print("***********")
+        return False, {"error": str(ex)}
+
+"""
+* 토큰 유효성 검사
+* 로그인 유효성 검사가 필요한 함수 위에 @login_required를 넣으면 됨
+*   예시)    @app.route('/action', methods = ["POST"])
+*            @login_required
+*            def sample_action():
+*                ###
+*                ###
+*                return Response()
+* 수정이 필요할 수 있음
+* 페이지를 넘길 때 유효성 검사를 하려면 어떻게 해야..
+* 토큰이 만료된 경우 처음화면으로 돌아가게 할 수 있나?
+* app.py에 적용 필요함
+"""
+def decode_token(my_token): # 매개 변수로 토큰을 받아옴
+    try:
+        # 1. payload 변수에 jwt토큰을 decode
+        payload = jwt.decode(my_token, SECRET_KEY, algorithms = "HS256")
+
+    # 3. 부적절한 토큰인 경우 예외 발생 
+    except jwt.InvalidTokenError as err:
+        print(err)
+        print("Invalid token")
+        payload = None
+        return payload
+    else:
+        return payload
+
+def login_required(f, my_token):
+    @wraps(f)
+    def decorated_function(*args, **kwagrs):
+        # 1. 토큰을 가져옴
+        # 추후에 가져오는 방법이 바뀔 수 있음
+        # 프론트에서 JWT토큰을 헤더에 넣어 'my_token' 키로 전달
+        # my_token = request.headers.get("my_token")
+
+        # 2-1. 토큰을 가져오면
+        #if my_token != None:
+        payload = decode_token(my_token)
+        # 2-2. 토큰 가져오는 것을 실패하면
+        #else:
+            #print("Can't find token")
+            #return False
+        #return f(*args, **kwagrs)
+    return decorated_function
+
+"""
+* 토큰으로부터 email 얻기
+* 수정이 필요할 수 있음
+* app.py에 적용 필요함
+"""
+def get_user(my_token):
+    try:
+        payload = jwt.decode(my_token, SECRET_KEY, algorithms = "HS256")
+        
+        if payload is not None:
+            email = payload['email']
+            user = schema.User.query.filter(schema.User.email == email, schema.User.deleted_at.is_(None)).first()
+            return user.id
+        else:
+            return False
+        
+    except jwt.InvalidTokenError as ex:
+        print("***********")
+        print(ex)
+        print("***********")
+        return False
+    except Exception as ex:
+        print("***********")
+        print(ex)
+        print("***********")
+        return False
+ 
\ No newline at end of file
diff --git a/backend/module/user_module.py b/backend/module/user_module.py
new file mode 100644
index 0000000..11dfb07
--- /dev/null
+++ b/backend/module/user_module.py
@@ -0,0 +1,223 @@
+from flask import request
+import hashlib
+from datetime import datetime
+from pytz import timezone
+from module.token import create_token
+# from module.db_module import create_whitelist_face
+from static import status_code
+from db import schema
+from db.db_connection import db
+import module
+
+"""
+* 이메일 중복체크
+"""
+def email_validation():
+    try:
+        email = request.form.get('email')
+        if email == None or email == '':
+            return 404, {"error": f'{status_code.field_error}email'}
+        user = schema.User.query.filter_by(email=email).first()
+        if not user:
+            print("This email is already exist")
+            return 409, {"error": status_code.user_email_validation_03_already}
+        else:
+           return 200, {"message": status_code.user_email_validation_01_success}
+    except Exception as ex:
+        print('*********')
+        print(ex)
+        print('*********')
+        return 400, {"error": str(ex)}
+       
+"""
+* 회원가입
+"""
+def create_users():
+    try:
+        email = request.form.get('email')
+        if email == None or email == '':
+            return 404, {"error": f'{status_code.field_error}email'}
+        password = request.form.get('password')
+        if password == None or password == '':
+            return 404, {"error": f'{status_code.field_error}password'}
+        age = request.form.get('age')
+        if age is None:
+            age = 20
+        if not age.isdigit():
+            return 404, {"error": f'{status_code.field_error}age'}
+        gender = request.form.get('gender')
+        if gender is None:
+            gender = 'man'
+        if gender != 'man' and gender != 'woman':
+            return 404, {"error": f'{status_code.field_error}gender'}
+        
+        # Check if email already exists
+        existing_user = schema.User.query.filter_by(email=email).first()
+        if existing_user:
+            return 409, {"error": status_code.user_email_validation_03_already}
+
+        pwHash = hashlib.sha256(password.encode('utf-8')).hexdigest()
+        
+        # Creating a new User instance
+        new_user = schema.User(email=email, password=pwHash, age=age, gender=gender, created_at=datetime.now(timezone('Asia/Seoul')))
+        
+        # Adding the new user to the session and committing to the database
+        db.session.add(new_user)
+        db.session.commit()
+
+        return 201, {"id": new_user.email}
+    except Exception as ex:
+        db.session.rollback()
+        print('*********')
+        print(ex)
+        print('*********')
+        return 400, {"error": str(ex)}
+    
+"""
+* 로그인
+"""
+def login():
+    try:
+        email = request.form.get('email')
+        if email == None or email == '':
+            return 404, {"error": f'{status_code.field_error}email'}
+        password = request.form.get('password')
+        if password == None or password == '':
+            return 404, {"error": f'{status_code.field_error}password'}
+
+        pwHash = hashlib.sha256(password.encode("utf-8")).hexdigest()
+        user = schema.User.query.filter_by(email=email, password=pwHash).first()
+        if not user:
+            return 404, {"error": "Can't find user"}
+        
+        result, token = create_token(user.email)
+        if result == False:
+            return 400, token
+        else:
+            return 200, {"token": token, "email": user.email}
+    except Exception as ex:
+        db.session.rollback()
+        print('*********')
+        print(ex)
+        print('*********')
+        return 400, {"error": str(ex)}
+
+
+"""
+* 유저 정보 받아오기: age, gender
+"""
+def get_user_info():
+    try:
+        token = request.headers.get('Token')
+        user_id = module.token.get_user(token)
+        if user_id == False:
+            return 401, {"error": status_code.token_error}
+        
+        user = schema.User.query.filter_by(id=user_id).first()
+        
+        return 200, {"age" : str(user.age), "gender" : str(user.gender)}
+    except Exception as ex:
+        print('*********')
+        print(ex)
+        print('*********')
+        return 400, {"error": str(ex)}
+
+"""
+* 이메일 찾기
+"""
+# def find_email():
+#     try:
+#         name = request.form.get('name')
+#         if name == None or name == '':
+#             return 404, {"error": f'{status_code.field_error}name'}
+#         phone = request.form.get('phone')
+#         if phone == None or phone == '':
+#             return 404, {"error": f'{status_code.field_error}phone'}
+
+#         user = schema.User.objects(name = name, phone = phone).first()
+#         if user == None:
+#             print("Can't find user")
+#             return 404, {"error": status_code.user_email_find_02_fail}
+#         else:
+#             return 200, {"email": user.email}
+#     except Exception as ex:
+#         print('*********')
+#         print(ex)
+#         print('*********')
+#         return 404, {"error": str(ex)}
+    
+'''
+* 비밀번호 찾기 전 확인
+'''
+# def password_validation():
+#     try:
+#         email = request.form.get('email')
+#         if email == None or email == '':
+#             return 404, {"error": f'{status_code.field_error}email'}
+#         phone = request.form.get('phone')
+#         if phone == None or phone == '':
+#             return 404, {"error": f'{status_code.field_error}phone'}
+        
+#         user = schema.User.objects(email = email, phone = phone).first()
+#         if user == None:
+#             print("Can't find user")  
+#             return 404, {"error": "Can't find user"}
+#         else:
+#             return 200, {"message": status_code.user_password_validation_01_success}
+#     except Exception as ex:
+#         print('*********')
+#         print(ex)
+#         print('*********')
+#         return 400, {"error": str(ex)}
+
+'''
+* 비밀 번호 변경
+'''
+# def update_password():
+#     try:
+#         email = request.form.get('email')
+#         if email == None or email == '':
+#             return 404, {"error": f'{status_code.field_error}email'}
+#         password = request.form.get('password')
+#         if password == None or password == '':
+#             return 404, {"error": f'{status_code.field_error}password'}
+#         phone = request.form.get('phone')
+#         if phone == None or phone == '':
+#             return 404, {"error": f'{status_code.field_error}phone'}
+        
+#         pwHash = hashlib.sha256(password.encode('utf-8')).hexdigest()
+#         dbResponse = schema.User.objects(email = email, phone = phone).update(set__password = pwHash, set__updated_at = datetime.now())
+#         if dbResponse > 0:
+#             return 200, {"message": status_code.user_password_replace_01_success}
+#         else:
+#             print("Can't be modified")
+#             return 400, {"error": status_code.user_password_replace_02_fail}
+#     except Exception as ex:
+#         print('*********')
+#         print(ex)
+#         print('*********')
+#         return 400, {"error": str(ex)}
+
+'''
+* 회원탈퇴
+'''
+# def delete_member(user_id):
+#     try:
+        
+#         # user_id는 token
+#         idReceive = get_id(user_id)
+
+#         # 2. DB에서 유저아이디 일치하는 Document -> activation_YN = N
+#         dbResponse = schema.UploadCharacter.objects(user_id = idReceive).update(set__activation_YN = "N", set__mod_date = datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
+
+#         if dbResponse.modified_count > 0:
+#             return True
+#         else:
+#             print("Can't be modified")
+#             return False
+
+#     except Exception as ex:
+#         print('*********')
+#         print(ex)
+#         print('*********')
+#         return False
diff --git a/backend/requirements.txt b/backend/requirements.txt
new file mode 100644
index 0000000..ab1208b
--- /dev/null
+++ b/backend/requirements.txt
@@ -0,0 +1,19 @@
+flask
+flask_restx
+boto3
+awscli
+Werkzeug
+PyJWT
+flask_migrate
+# celery
+pymysql
+Flask-SQLAlchemy
+SQLAlchemy
+gunicorn
+flask_cors
+# prometheus_flask_exporter
+python-dotenv
+cryptography
+celery[redis]
+minio
+pydub
\ No newline at end of file
diff --git a/backend/simple_worker/Dockerfile b/backend/simple_worker/Dockerfile
new file mode 100644
index 0000000..2b88969
--- /dev/null
+++ b/backend/simple_worker/Dockerfile
@@ -0,0 +1,18 @@
+FROM python:3.8-slim
+
+# layer caching for faster builds
+COPY requirements.txt /
+RUN apt-get update && apt-get install -y \
+    gcc \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+RUN pip install --upgrade pip
+RUN pip install -r /requirements.txt
+
+#COPY app.py /app.py
+ADD . /simple_worker
+WORKDIR /simple_worker
+
+CMD ["celery", "-A", "tasks", "worker", "-l", "info", "--pool=gevent", "--concurrency=4"]
+#-Q celery_worker
+#ENTRYPOINT ['celery','-A','test_celery', 'worker', '--loglevel=info']
\ No newline at end of file
diff --git a/backend/simple_worker/db_config.py b/backend/simple_worker/db_config.py
new file mode 100644
index 0000000..8b93ca3
--- /dev/null
+++ b/backend/simple_worker/db_config.py
@@ -0,0 +1,9 @@
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+USERNAME = os.getenv('USERNAME')
+PASSWORD = os.getenv('PASSWORD')
+HOST = os.getenv('HOST')
+DATABASE = os.getenv('DATABASE')
diff --git a/backend/simple_worker/db_connection.py b/backend/simple_worker/db_connection.py
new file mode 100644
index 0000000..4e86c61
--- /dev/null
+++ b/backend/simple_worker/db_connection.py
@@ -0,0 +1,38 @@
+import pymysql
+from db_config import USERNAME, PASSWORD, HOST, DATABASE
+from datetime import datetime
+from pytz import timezone
+
+class Database(): 
+    def __init__(self):
+        self.db = pymysql.connect(
+            host=HOST[:-5],
+            port=3306,
+            user=USERNAME,
+            passwd=PASSWORD,
+            db=DATABASE,
+            init_command='SET SESSION wait_timeout = 86400'
+        )
+        self.cursor = self.db.cursor()
+
+    def update_mz_request_status_ata(self, request_id, status_to_change):
+        self.cursor.execute('UPDATE mz_request \
+                SET status = %s, \
+                ata = %s \
+                WHERE id = %s', (status_to_change, str(datetime.now(timezone('Asia/Seoul'))), request_id))
+        self.db.commit()
+        
+
+    def update_mz_result_image_gif(self,mz_request_id, task_result):
+        condition_image_url = task_result['condition_image_url']
+        condition_gif_url = task_result['condition_gif_url']
+        voice_image_url = task_result['voice_image_url']
+        voice_gif_url = task_result['voice_gif_url']
+
+        self.cursor.execute('UPDATE mz_result \
+                SET condition_image_url = %s, \
+                condition_gif_url = %s, \
+                voice_image_url = %s, \
+                voice_gif_url = %s \
+                WHERE mz_request_id = %s', (condition_image_url, condition_gif_url, voice_image_url, voice_gif_url, mz_request_id))
+        self.db.commit()
\ No newline at end of file
diff --git a/backend/simple_worker/minio_config.py b/backend/simple_worker/minio_config.py
new file mode 100644
index 0000000..29713af
--- /dev/null
+++ b/backend/simple_worker/minio_config.py
@@ -0,0 +1,9 @@
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+ACCESS_KEY = os.getenv("AWS_ACCESS_KEY")
+SECRET_KEY = os.getenv("AWS_SECRET_ACCESS_KEY")
+BUCKET_NAME = os.getenv("MINIO_BUCKET")
+MINIO_API_HOST = os.getenv("MINIO_ENDPOINT")
\ No newline at end of file
diff --git a/backend/simple_worker/minio_connection.py b/backend/simple_worker/minio_connection.py
new file mode 100644
index 0000000..a3bf25a
--- /dev/null
+++ b/backend/simple_worker/minio_connection.py
@@ -0,0 +1,82 @@
+from minio import Minio
+#from minio.error import ResponseError
+from minio_config import ACCESS_KEY, SECRET_KEY
+from minio_config import BUCKET_NAME, MINIO_API_HOST
+import random
+
+def minio_connection():
+    try:
+        storage = Minio(
+            MINIO_API_HOST,
+            ACCESS_KEY,
+            SECRET_KEY,     
+            secure=True,
+        )
+    except Exception as e:
+        print(e)
+        return False
+    else:
+        print("storage bucket connected!")
+        return storage
+
+def minio_list_object(storage, age, gender):
+    '''
+    minio bucket에서 해당 성별과 나이에 맞는 이미지 리스트 가져오기
+    :param storage: 연결된 minio 객체(Minio client)
+    :param prefix: Object name starts with prefix.
+    :param age: 나이
+    :param gender: 성별
+    :return: 성공 시 list 반환, 실패 시 False 반환
+    '''
+    try:
+        prefix = "output_condition/"
+        obj_list = list(storage.list_objects(BUCKET_NAME, prefix))
+        file_list = [obj.object_name for obj in obj_list]
+        condition_file_list = []
+        for file in file_list:
+            _, file_name = file.split('-')
+            idx = file_name.rindex('.')
+            if file_name[idx+1:] == 'jpg' and file_name[:idx] == f'{gender}_{age}':
+                condition_file_list.append(f"https://{MINIO_API_HOST}/{BUCKET_NAME}/{file}")
+    except Exception as e:
+        print(e)
+        return False
+    return condition_file_list
+
+def read_random_condition(age, gender):
+    try:
+        # 1. age 반올림
+        age = round(int(age), -1)
+        if age == 50:
+            age = 40
+        
+        # 2. 버킷 연결
+        storage = minio_connection()
+        
+        # 3. 버킷에서 리스트 가져오기 
+        ret = minio_list_object(storage, age, gender)
+
+        # 4. 버킷에서 리스트 가져오기 성공 시 랜덤 선정 
+        if ret == False:
+            return False, {"error":"Can't find list"} #false ->400 
+        else:
+            print(ret)
+            choicejpg = random.choice(ret)
+            idx = choicejpg.rindex('.')
+            choicemp4 = choicejpg[:idx] + '.mp4'
+            return True, {"image" : choicejpg, "gif" : choicemp4}
+        
+    except Exception as ex:
+        print("******************")
+        print(ex)
+        print("******************")
+        return False, {"error" : str(ex)}  #false -> 400
+    
+# def check_object_existence(storage, bucket_name, object_name):
+#     try:
+#         # 객체 존재 여부 확인
+#         storage.stat_object(bucket_name, object_name)
+#         return True
+#     except ResponseError as err:
+#         if err.code == 'NoSuchKey':
+#             return False
\ No newline at end of file
diff --git a/backend/simple_worker/requirements.txt b/backend/simple_worker/requirements.txt
new file mode 100644
index 0000000..2d63ad4
--- /dev/null
+++ b/backend/simple_worker/requirements.txt
@@ -0,0 +1,9 @@
+celery[redis]
+pymysql
+Flask-SQLAlchemy
+SQLAlchemy
+python-dotenv
+requests
+minio
+gevent
+pytz
\ No newline at end of file
diff --git a/backend/simple_worker/tasks.py b/backend/simple_worker/tasks.py
new file mode 100644
index 0000000..ff12811
--- /dev/null
+++ b/backend/simple_worker/tasks.py
@@ -0,0 +1,94 @@
+import time
+from celery import Celery
+from celery.utils.log import get_task_logger
+import requests
+from db_config import USERNAME, PASSWORD, HOST, DATABASE
+import requests
+from minio_connection import read_random_condition
+from db_connection import Database
+import json
+
+logger = get_task_logger(__name__)
+
+# Create a Celery instance named 'celery_app'
+# celery = Celery('tasks',backend='db+mysql+pymysql://root:rootpwd@mysql-db:3306/MZ', broker='amqp://admin:mypass@rabbit:5672')
+celery = Celery('tasks',backend=f'db+mysql+pymysql://{USERNAME}:{PASSWORD}@{HOST}/{DATABASE}', broker='amqp://admin:mypass@rabbit:5672')
+db = Database()
+
+@celery.task()
+def run_mz(request_id, result_id, age, gender, file_url):
+    logger.info('Got Request - Starting work')
+    time.sleep(4)
+
+    target_server_url = 'http://175.45.193.25:3002/makevideo'
+    data = {'age' : age, 
+            'gender' : gender, 
+            'voice_url': file_url, 
+            'request_id' : request_id, 
+            'result_id' : result_id}
+    logger.info(data)
+    condition_image_url = None
+    condition_video_url = None
+    voice_image_url = None
+    voice_video_url = None
+    return_message = 200
+
+    try: 
+        # condition output 
+        result, message = read_random_condition(age, gender)
+        if result:
+            condition_image_url = str(message['image'])
+            condition_video_url = str(message['gif'])
+            logger.info(condition_image_url)
+            logger.info(condition_video_url)
+        else:
+            raise Exception('condition')
+        
+        # voice output
+        headers = {"user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.89 Safari/537.36","Content-Type":"application/json"}
+        response = requests.post(target_server_url,data=json.dumps(data),headers=headers)
+        response_data = response.json()
+        status_code = response_data.get("status_code")
+        logger.info(status_code)
+
+        if status_code == 200: # 성공 시 
+            voice_image_url = str(response_data.get("voice_image_url"))
+            voice_video_url = str(response_data.get("voice_video_url"))
+            logger.info(voice_image_url)
+            logger.info(voice_video_url)
+        elif status_code == 404: # voice_image_url 만 받아온 경우 
+            voice_image_url = str(response_data.get("voice_image_url"))
+            logger.info(voice_image_url)
+            error = response_data.get("error")
+            raise Exception(error)
+        else: # 실패 시 
+            # Update status
+            error = response_data.get("error")
+            raise Exception(error)
+
+    #except requests.RequestException as e:
+    except Exception as e:
+        logger.info(str(e))
+        return_message = f'failed with exception: {str(e)}'
+    
+    # Update result 
+    result_to_change = {
+        'condition_image_url' : condition_image_url,
+        'condition_gif_url' : condition_video_url,
+        'voice_image_url' : voice_image_url,
+        'voice_gif_url' : voice_video_url
+    }
+    logger.info(result_to_change)
+    db.update_mz_result_image_gif(request_id, result_to_change)
+
+    if None in [condition_image_url, condition_video_url, voice_image_url]:
+        status_to_change = 'Failed'
+    else:
+        status_to_change = 'Success'
+    logger.info(status_to_change)
+    db.update_mz_request_status_ata(request_id, status_to_change)
+
+    logger.info('Work Finished ')
+    # return response.status_code
+    return return_message
+
diff --git a/backend/static/status_code.py b/backend/static/status_code.py
new file mode 100644
index 0000000..2246ed3
--- /dev/null
+++ b/backend/static/status_code.py
@@ -0,0 +1,72 @@
+#######################CRUD#######################
+# create code
+create_01_success = "create success"
+create_02_fail = "create fail"
+
+# read code
+read_01_success = "read success"
+read_02_fail = "read fail"
+
+# update code
+update_01_success = "update success"
+update_02_fail = "update fail"
+
+# delete code
+delete_01_success = "delete success"
+delete_02_fail = "delete fail"
+
+#######################ERROR#######################
+# token error code
+token_error = {"code": "TK02", "message": "Invalid token or can't find user. Please check the token"}
+
+# field error code
+field_error = "Can't find field: "
+
+# enum class error code
+enum_class_error = "Can't find enum class element: "
+
+# _id error code
+id_error = "Can't find _id or invalid _id: "
+
+# celery error code
+celery_error = "Already processed"
+
+#######################AI#######################
+# push celery code
+celery_push_01_success = "celery push success"
+celery_push_02_fail = "celery push fail"
+
+# read code
+read_celery_status_01_success = "read celery status success"
+read_celery_status_02_fail = "read celery status fail"
+
+######################USER######################
+# email validation code
+user_email_validation_01_success = "user email validation success"
+user_email_validation_02_fail = "user email validation fail"
+user_email_validation_03_already = "This email is already exist"
+
+# signup code
+user_signup_01_success = "user signup success"
+user_signup_02_fail = "user signup fail"
+
+# auth code
+user_auth_01_success = "user auth success"
+user_auth_02_notmatch = "user auth notmatch"
+user_auth_03_fail = "user auth fail"
+
+# find email code
+user_email_find_01_success = "user email find success"
+user_email_find_02_fail = "user email find fail"
+
+# password validation code
+user_password_validation_01_success = "user password validation success"
+user_password_validation_02_fail = "user password validation fail"
+
+# replace password code
+user_password_replace_01_success = "user password replace success"
+user_password_replace_02_fail = "user password replace fail"
+
+# delete user code
+user_delete_01_success = "user delete success"
+user_delete_01_fail = "user delete fail"
diff --git a/data/README.md b/data/README.md
new file mode 100644
index 0000000..360c808
--- /dev/null
+++ b/data/README.md
@@ -0,0 +1,138 @@
+
+  
+
+# 🔊 Voice2Face-Data
+
+  
+
+<img  src="https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=Python&logoColor=white"> <img  src="https://img.shields.io/badge/opencv-5C3EE8?style=for-the-badge&logo=opencv&logoColor=white"> <img  src="https://img.shields.io/badge/git-F05032?style=for-the-badge&logo=git&logoColor=white"> <img  src="https://img.shields.io/badge/NCP-03C75A?style=for-the-badge&logo=Naver&logoColor=white"> <img  src="https://img.shields.io/badge/Linux-FCC624?style=for-the-badge&logo=Linux&logoColor=white"> <img  src="https://img.shields.io/badge/Selenium-43B02A?style=for-the-badge&logo=Selenium&logoColor=white"> <img  src="https://img.shields.io/badge/Numpy-013243?style=for-the-badge&logo=Numpy&logoColor=white"> <img  src="https://img.shields.io/badge/Pytorch-EE4C2C?style=for-the-badge&logo=Pytorch&logoColor=white"> <img  src="https://img.shields.io/badge/FFmpeg-007808?style=for-the-badge&logo=FFmpeg&logoColor=white">
+
+
+
+## Project Structure
+
+```
+data
+┣ audio
+┃ ┣ audio_check_dB.py
+┃ ┣ audio_dB_crop.py
+┃ ┗ audio_wav_cropping.py
+┣ crawling
+┃ ┣ crawling_detect.py
+┃ ┣ crawling_rename_video.py
+┃ ┣ crawling_select_csv.py
+┃ ┣ crawling_urlsave.py
+┃ ┗ crawling_videosave.py
+┣ image
+┃ ┣ image_clipseg2.py
+┃ ┗ image_face_frame.py
+┣ relabel
+┃ ┣ relabel_detect_getframe.py
+┃ ┣ relabel_select_csv.py
+┃ ┗ relabel_Vox_age.py
+┣ total
+┃ ┣ total_audio_video_image.py
+┃ ┗ total_origin_remove.py
+┣ video
+┃ ┣ video_clipimage.py
+┃ ┗ video_download.py
+┣ README.md
+┗ requirements.txt
+```
+## Usage
+
+  
+
+#### audio
+ - `audio_check_dB.py`: 특정 dB 값을 확인하여 사람의 음성 여부를 판별하는 스크립트입니다.
+ - `audio_dB_crop.py`: 오디오 파일에서 인간의 목소리 세그먼트를 추출하고 감지된 음성 세그먼트를 포함하는 새로운 오디오 파일을 10초로 자르는 스크립트입니다.
+ - `audio_wav_cropping.py`: JSON POINT에 맞춰 오디오를 자르는 스크립트입니다.
+
+#### crawling
+
+ - `crawling_detect.py`: 비디오 클립에서 얼굴과 오디오를 감지하고 세분화하는 스크립트입니다.
+ - `crawling_rename_video.py`: 'download' 폴더에서 비디오 이름과 CSV의 인덱스를 맞추는 스크립트입니다.
+ - `crawling_select_csv.py`: 주어진 CSV 파일에서 YouTube ID를 찾아 해당하는 파일 이름에서 정보를 추출하고, 이 정보를 새로운 CSV 파일에 저장하는 간단한 데이터 처리 작업을 수행하는 스크립트입니다.
+ - `crawling_urlsave.py`: Selenium을 사용하여 YouTube 크롤링을 수행하여 약 162개의 비디오에 대한 이름, 제목 및 URL 정보를 Youtube_search_df.csv에 저장하는 스크립트입니다.
+ - `crawling_videosave.py`: '`crawling_urlsave.py`'를 통해 얻은 URL에서 비디오를 다운로드하는 스크립트입니다. 비디오는 'download' 폴더에 저장됩니다.
+
+#### image
+
+ - `image_clipseg2.py`: CLIPSeg 모델을 사용하여 텍스트 프롬프트를 기반으로 이미지 세분화를 수행하는 스크립트입니다. 이미지를 불러와 텍스트 프롬프트로 처리하고, 식별된 객체를 기반으로 세분화된 이미지를 생성합니다.
+ - `image_face_frame.py`: 비디오에서 사람의 얼굴이 정면이고, 눈을 뜨고 있을 때 캡쳐하고 배경을 제거하는 스크립트입니다.
+
+#### relabel
+
+ - `relabel_detect_getframe.py`: 주어진 비디오에서 얼굴을 감지하고, 감지된 얼굴에 대해 성별과 연령을 추정하여 화면에 표시하고, 일정한 간격으로 프레임을 캡처하여 이미지 파일로 저장하는 기능을 수행합니다.
+ - `relabel_select_csv.py`: 데이터 경로에서 YouTube ID를 추출하고, 파일 이름에서 필요한 정보를 추출하여 새로운 CSV 파일에 저장하는 스크립트입니다.
+ - `relabel_Vox_age.py`: 이미지 폴더에서 이미지들을 읽어와 각 이미지의 나이를 예측하고, 가장 흔한 나이 그룹을 세서 출력하고, 그 결과를 CSV 파일에 저장하는 작업을 수행합니다.
+
+#### video
+
+ - `video_clipimage.py`: 주어진 이미지에서 얼굴을 감지하고, 감지된 얼굴 영역을 사각형으로 표시한 후 해당 얼굴을 256x256 크기로 조정하여 저장하는 작업을 수행합니다.
+ - `video_download.py`: 주요 기능은 주어진 YouTube 비디오 링크에서 비디오를 다운로드하고, 다운로드한 비디오를 mp4 또는 mp3 형식으로 변환하는 스크립트입니다.
+
+#### total
+-   `total_audio_video_image.py`: 오디오, 비디오 및 이미지와 관련된 작업을 총 수행하는 스크립트입니다.
+-   `total_origin_remove.py`: 데이터 경로에서 원본 파일을 제거하는 스크립트입니다.
+
+
+  
+
+## Getting Started
+
+  
+### Setting up Vitual Enviornment
+
+  
+1. Initialize and update the server
+
+```
+
+su -
+
+source .bashrc
+
+```
+
+  
+
+2. Create and Activate a virtual environment in the project directory
+
+  
+
+```
+
+conda create -n env python=3.8
+
+conda activate env
+
+```
+
+  
+
+4. To deactivate and exit the virtual environment, simply run:
+
+  
+
+```
+
+deactivate
+
+```
+
+  
+
+### Install Requirements
+
+  
+
+To Install the necessary packages liksted in `requirements.txt`, run the following command while your virtual environment is activated:
+
+```
+
+pip install -r requirements.txt
+
+```
+
+  
\ No newline at end of file
diff --git a/data/audio/audio_check_dB.py b/data/audio/audio_check_dB.py
new file mode 100644
index 0000000..a1a4bda
--- /dev/null
+++ b/data/audio/audio_check_dB.py
@@ -0,0 +1,52 @@
+import librosa
+import numpy as np
+import matplotlib.pyplot as plt
+
+'''
+You can determine the minimum, maximum, and average dB values 
+to set a threshold for identifying voice regions based on dB levels. 
+After visually inspecting the waveform and setting a threshold,
+adding 80 to it, you can conveniently apply this threshold value to `audio_crop.py`.
+'''
+
+# Load audio file
+audio_path = "voice2face-data/audio/input.wav"
+y, sr = librosa.load(audio_path, sr=None)
+
+# Calculate spectrum and check maximum and minimum dB values
+D = librosa.amplitude_to_db(librosa.stft(y), ref=np.max)
+max_db = np.max(D)
+min_db = np.min(D)
+
+# Set threshold value
+threshold_db = -60
+
+# Consider regions with dB values above the threshold as voice regions
+voice_indices = np.where(D > threshold_db)
+
+print("Threshold:", threshold_db)
+print("Maximum dB value in regions with voice:", np.max(D[voice_indices]))
+print("Minimum dB value in regions with voice:", np.min(D[voice_indices]))
+
+# Calculate average dB value in regions with voice
+average_db = np.mean(D[voice_indices])
+print("Average dB value in regions with voice:", average_db)
+
+# Plot waveform and spectrum
+plt.figure(figsize=(12, 6))
+
+# Plot waveform
+plt.subplot(2, 1, 1)
+plt.plot(y)
+plt.title("Waveform")
+plt.xlabel("Sample")
+plt.ylabel("Amplitude")
+
+# Plot spectrum
+plt.subplot(2, 1, 2)
+librosa.display.specshow(D, sr=sr, x_axis='time', y_axis='log')
+plt.colorbar(format='%+2.0f dB')
+plt.title('Log-frequency power spectrogram')
+
+plt.tight_layout()
+plt.show()
diff --git a/data/audio/audio_dB_crop.py b/data/audio/audio_dB_crop.py
new file mode 100644
index 0000000..8bb3f58
--- /dev/null
+++ b/data/audio/audio_dB_crop.py
@@ -0,0 +1,107 @@
+import os
+import librosa
+import soundfile as sf
+import matplotlib.pyplot as plt
+from pydub import AudioSegment
+
+'''
+Extracts human voice segments from an audio file and creates a new audio file with the detected voice segments 
+within a 10-second duration.
+
+Args:
+    audio_file (str): Path to the input audio file. If the file format is .m4a, it will be converted to .wav.
+    
+Returns:
+    save_file (str): Path to the saved audio file with detected voice segments.
+'''
+
+def detect_human_voice(audio_file):
+    '''
+    Detects human voice segments in an audio file.
+
+    Args:
+        audio_file (str): Path to the input audio file.
+
+    Returns:
+        voice_indices (list): List containing indices of the detected voice segments.
+    '''
+    # Read the audio file
+    y, sr = librosa.load(audio_file, sr=None)
+
+    # Detect voice activity
+    # ----- Need to Modify threshold-----#
+    voice_segments = librosa.effects.split(y, top_db=18)
+
+    # Generate indices of voice segments
+    voice_indices = []
+    for start, end in voice_segments:
+        voice_indices.extend(range(start, end))
+
+    return voice_indices
+
+def save_full_audio_with_detected_voice(audio_file, save_file):
+    '''
+    Saves the full audio file with detected voice segments.
+
+    Args:
+        audio_file (str): Path to the input audio file.
+        save_file (str): Path to save the audio file with detected voice segments.
+    '''
+    # Read the entire audio file
+    y, sr = librosa.load(audio_file, sr=None)
+
+    # Detect human voice segments and get their indices
+    voice_indices = detect_human_voice(audio_file)
+
+    # Extract human voice segments using the indices
+    combined_audio = y[voice_indices]
+
+    # Save the extracted audio segments to a file
+    sf.write(save_file, combined_audio, sr)
+
+    # Visualize and save the waveform of the original and detected voice segments
+    plt.figure(figsize=(12, 6))
+
+    # Original audio waveform
+    plt.subplot(2, 1, 1)
+    plt.plot(y)
+    plt.title("Original Audio Waveform")
+    plt.xlabel("Sample")
+    plt.ylabel("Amplitude")
+
+    # Waveform of detected voice segments
+    plt.subplot(2, 1, 2)
+    plt.plot(combined_audio)
+    plt.title("Detected Voice Waveform")
+    plt.xlabel("Sample")
+    plt.ylabel("Amplitude")
+
+    plt.tight_layout()
+    save_path = os.path.join(os.path.dirname(save_file), 'result')
+    if not os.path.exists(save_path):
+        os.makedirs(save_path)
+    save_file_path = os.path.join(save_path, os.path.basename(save_file[:-4] + "_waveform_comparison.png"))
+    plt.savefig(save_file_path)
+
+    # Save the extracted audio segments to a file
+    audio_save_file_path = os.path.join(save_path, os.path.basename(save_file))
+    sf.write(audio_save_file_path, combined_audio, sr)
+
+    plt.show()
+
+# Define paths for the original file and the file to save with detected voice segments
+# ------Need to modify path------ #
+audio_file_path = "voice2face-data/audio/input.m4a"
+save_file_path = "voice2face-data/audio/detected_voice.wav"
+
+# Check if the file extension is ".m4a" for conversion and processing
+if audio_file_path.endswith('.m4a'):
+    # Convert m4a file to wav format
+    wav_path = audio_file_path[:-4] + ".wav"
+    audio = AudioSegment.from_file(audio_file_path)
+    audio.export(wav_path, format="wav")
+    # Process the converted wav file
+    save_full_audio_with_detected_voice(wav_path, save_file_path)
+else:
+    # Process the original file without conversion
+    save_full_audio_with_detected_voice(audio_file_path, save_file_path)
diff --git a/data/audio/audio_wav_cropping.py b/data/audio/audio_wav_cropping.py
new file mode 100644
index 0000000..fc52070
--- /dev/null
+++ b/data/audio/audio_wav_cropping.py
@@ -0,0 +1,107 @@
+import os 
+import json
+from pydub import AudioSegment
+
+def process_wav_and_json(wav_file: str, json_file: str, wav_file_name: str, save_folder: str):
+    """ wav 파일을 받아 json 에서 필요한 부분을 잘라내 형태에 맞게 저장할 코드
+
+    Args:
+        wav_file (str): json 파일과 매칭되는 wav_file 경로
+        json_file (str): wav_file croping에 사용될 json_file 경로
+        wav_file_name (str): folder명 지정에 사용할 wav_file 자체 이름
+        save_folder (str): 정제된 파일을 저장할 경로
+    """
+    count = 1
+    audio = AudioSegment.from_wav(wav_file)
+
+    # 저장할 폴더 생성
+    wav_file_folder = wav_file_name.split("_")[:-2]
+    wav_file_folder = "_".join(wav_file_folder)
+    wav_file_folder = os.path.join(save_folder,'data', wav_file_folder,'audio')
+    os.makedirs(wav_file_folder, exist_ok=True)
+
+    # json 파일 열기
+    with open(json_file, 'r') as f:
+        data = json.load(f)
+        data= data[0]
+
+    # 각 시간대별로 돌아가며 동영상 croping
+    for sentence_info in data['Sentence_info']:
+        start_time = round(int(sentence_info['start_time']), 3)
+        end_time = round(int(sentence_info['end_time']), 3)
+        time = end_time - start_time
+        if time <3 or time  > 10 :
+            continue
+
+        start_time_ms = start_time * 1000
+        end_time_ms = end_time * 1000
+
+
+        segment = audio[start_time_ms:end_time_ms]
+        output_wav_file = f"{wav_file_name.split('_')[-1]}_{count:03d}.wav"
+
+        output_wav_path = os.path.join(wav_file_folder, output_wav_file)
+
+        segment.export(output_wav_path, format='wav')
+        count += 1
+
+        print(f"Saved {output_wav_path}")
+        
+
+def find_matching_json(file_name: str, json_folder: str) ->str :
+    """wav 파일과 파일명이 똑같은 json 파일을 찾아 경로 반환, 
+    만약 없다면 None 반환
+
+    Args:
+        file_name (str): wav 파일 이름 가져오기
+        json_folder (str): json이 저장된 위치 확인
+
+    Returns:
+        str: wav 파일에 해당하는 json 파일 반환
+    """
+
+    for root, dirs, files in os.walk(json_folder):
+        for file in files:
+            if file.split('.')[0] == file_name:
+                json_file = os.path.join(root, file)
+                return json_file
+    return None
+            
+def find_wav(wav_folder: str, json_folder: str, save_folder: str):
+    """ 처음 정제할 wav 파일 찾기
+        이후 json 파일을 찾아 wav를 정제한다.
+
+    Args:
+        wav_folder (str): wav 파일들이 저장된 폴더 위치 
+        json_folder (str): json 파일들이 저장된 폴더 위치
+        save_folder (str): croping 된 파일들을 저장할 위치
+    """
+
+    for root,_,files in os.walk(wav_folder):
+        for file in files:
+            if file.endswith('.wav'):
+                wav_file = os.path.join(root, file)
+                wav_file_name = wav_file.split('/')[-1].split('.')[0]
+                json_file = find_matching_json(wav_file_name, json_folder)
+
+                if json_file:
+                    process_wav_and_json(wav_file, json_file, wav_file_name, save_folder)
+                    os.remove(wav_file)
+
+
+def main(wav_folder: str, json_folder: str, save_folder: str):
+    find_wav(wav_folder, json_folder, save_folder)
+    print('End of processing')
+
+
+
+
+if __name__ == '__main__':
+    # WAV 파일이 들어있는 폴더 경로
+    wav_folder = '/home/carbox/Desktop/data/009.립리딩(입모양) 음성인식 데이터/01.데이터/2.Validation/원천데이터'   
+    # JSON 파일이 들어있는 폴더 경로 
+    json_folder = '/home/carbox/Desktop/data/009.립리딩(입모양) 음성인식 데이터/01.데이터/2.Validation/라벨링데이터'  
+    # 원천데이터를 저장할 폴더 경로
+    save_folder = '/home/carbox' 
+
+    main(wav_folder, json_folder, save_folder)
diff --git a/data/crawling/crawling_detect.py b/data/crawling/crawling_detect.py
new file mode 100644
index 0000000..917859f
--- /dev/null
+++ b/data/crawling/crawling_detect.py
@@ -0,0 +1,139 @@
+import os
+import pandas as pd
+from moviepy.editor import VideoFileClip
+import numpy as np
+import face_recognition
+import shutil
+
+'''
+Detects faces and audio in video clips and refines them.
+
+Extracts faces from the video clips and selects segments with audio to rebuild new videos.
+New videos are organized in the "processed_videos" folder.
+
+'''
+
+# Function to extract audio from video clips with detected faces
+def extract_audio_with_face(video_clip, start_time, end_time):
+    '''
+    Extracts audio from a video clip with detected faces within a specified time range.
+
+    Args:
+        video_clip (VideoFileClip): Input video clip.
+        start_time (float): Start time of the segment containing the detected faces.
+        end_time (float): End time of the segment containing the detected faces.
+
+    Returns:
+        audio (AudioClip): Extracted audio clip.
+    '''
+    audio = video_clip.audio.subclip(start_time, end_time)
+    return audio
+
+# Function to extract audio from video clips with detected faces in multiple segments
+def extract_audio_with_faces(video_clip, face_detections):
+    '''
+    Extracts audio from a video clip with detected faces in multiple segments.
+
+    Args:
+        video_clip (VideoFileClip): Input video clip.
+        face_detections (list): List of tuples containing start and end times of segments with detected faces.
+
+    Returns:
+        final_audio (ndarray): Concatenated audio array from all detected face segments.
+    '''
+    audio_clips = []
+
+    for start_time, end_time in face_detections:
+        audio_clip = extract_audio_with_face(video_clip, start_time, end_time)
+        audio_clips.append(audio_clip)
+
+    final_audio = np.concatenate([clip.to_soundarray() for clip in audio_clips])
+    return final_audio
+
+# Function to detect faces in video clips
+def detect_faces(video_clip):
+    '''
+    Detects faces in a video clip.
+
+    Args:
+        video_clip (VideoFileClip): Input video clip.
+
+    Returns:
+        face_detections (list): List of tuples containing start and end times of segments with detected faces.
+    '''
+    frames = [frame for frame in video_clip.iter_frames()]
+    frame_rate = video_clip.fps
+    frame_times = np.arange(len(frames)) / frame_rate
+    face_detections = []
+
+    for i, frame in enumerate(frames):
+        face_locations = face_recognition.face_locations(frame)
+        if face_locations:
+            start_time = frame_times[max(0, i - 1)]
+            end_time = frame_times[min(len(frames) - 1, i + 1)]
+            face_detections.append((start_time, end_time))
+
+    return face_detections
+
+# Function to create a new video from detected face segments
+def create_new_video(video_clip, face_detections, output_path):
+    '''
+    Creates a new video from detected face segments.
+
+    Args:
+        video_clip (VideoFileClip): Input video clip.
+        face_detections (list): List of tuples containing start and end times of segments with detected faces.
+        output_path (str): Path to save the new video.
+    '''
+    new_video_clip = None
+
+    for start_time, end_time in face_detections:
+        subclip = video_clip.subclip(start_time, end_time)
+        if new_video_clip is None:
+            new_video_clip = subclip
+        else:
+            new_video_clip = new_video_clip.append(subclip)
+
+    new_video_clip.write_videofile(output_path)
+
+# Read data from a CSV file
+csv_file_path = "/Users/imseohyeon/Documents/crawling/data/Youtube_search_df.csv"
+df = pd.read_csv(csv_file_path)
+
+# Paths for input and output folders
+DOWNLOAD_FOLDER = "/Users/imseohyeon/Documents/crawling/download/"
+NEW_FOLDER = "/Users/imseohyeon/Documents/crawling/processed_videos/"
+
+# Create a new folder if it doesn't exist
+if not os.path.exists(NEW_FOLDER):
+    os.makedirs(NEW_FOLDER)
+
+# Process each video to extract audio from segments with detected faces and create new videos
+for idx, row in df.iterrows():
+    video_filename = f"{idx}_video.mp4"
+    video_path = os.path.join(DOWNLOAD_FOLDER, video_filename)
+
+    if os.path.exists(video_path):
+        try:
+            video_clip = VideoFileClip(video_path)
+            face_detections = detect_faces(video_clip)
+
+            if face_detections:
+                final_audio = extract_audio_with_faces(video_clip, face_detections)
+                output_path = os.path.join(NEW_FOLDER, f"{idx}_new_video.mp4")
+                create_new_video(video_clip, face_detections, output_path)
+
+                print(f"Processing complete for {video_filename}")
+            else:
+                print(f"No faces detected in {video_filename}")
+        except Exception as e:
+            print(f"Error processing {video_filename}: {e}")
+    else:
+        print(f"File {video_filename} does not exist.")
+
+# Move processed videos to another folder
+processed_files = os.listdir(NEW_FOLDER)
+for file in processed_files:
+    shutil.move(os.path.join(NEW_FOLDER, file), DOWNLOAD_FOLDER)
+
+print("All videos processed")
diff --git a/data/crawling/crawling_rename_video.py b/data/crawling/crawling_rename_video.py
new file mode 100644
index 0000000..3c005ad
--- /dev/null
+++ b/data/crawling/crawling_rename_video.py
@@ -0,0 +1,30 @@
+import os
+import pandas as pd
+
+'''
+Match the video names in the 'download' folder with the index in the CSV.
+This facilitates the subsequent video relabeling task.
+'''
+
+# Read links from the CSV file
+csv_file_path = "/Users/imseohyeon/Documents/crawling/data/Youtube_search_df.csv"
+df = pd.read_csv(csv_file_path)
+
+# Path to the folder where downloaded videos are stored
+DOWNLOAD_FOLDER = "/Users/imseohyeon/Documents/crawling/download/"
+
+# Iterate over all files in the folder and rename them
+for filename in os.listdir(DOWNLOAD_FOLDER):
+    # Full path of the file
+    file_path = os.path.join(DOWNLOAD_FOLDER, filename)
+    # Check if the file is a .mp4 file
+    if filename.endswith(".mp4"):
+        # Extract the index value from the file name (assuming the video title is stored as the index)
+        idx = filename.split("_")[0]  # Example: "0_video.mp4" -> "0"
+        # Create a new file name
+        new_filename = f"{idx}_video.mp4"
+        # Create the new file path
+        new_file_path = os.path.join(DOWNLOAD_FOLDER, new_filename)
+        # Rename the file
+        os.rename(file_path, new_file_path)
+        print(f"File renamed: {filename} -> {new_filename}")
diff --git a/data/crawling/crawling_select_csv.py b/data/crawling/crawling_select_csv.py
new file mode 100644
index 0000000..64097a7
--- /dev/null
+++ b/data/crawling/crawling_select_csv.py
@@ -0,0 +1,74 @@
+import os
+import pandas as pd
+from argparse import ArgumentParser
+
+def parse_args():
+    """
+    Command-line arguments parser.
+
+    Returns:
+        argparse.Namespace: Parsed arguments.
+    """
+    parser = ArgumentParser()
+
+    parser.add_argument('--csv_file', type=str, default='output_test.csv',
+                        help="Path to the CSV file containing data.")
+    parser.add_argument('--data_path', type=str, default='origin/video',
+                        help="Path to the directory containing files and folders.")
+    parser.add_argument('--save_csv', type=str, default='new_output.csv',
+                        help="Path to save the new CSV file.")
+
+    args = parser.parse_args()
+    return args
+
+def list_files_and_folders(data_path):
+    """
+    List files and folders in the given directory path.
+
+    Args:
+        data_path (str): Path to the directory.
+
+    Returns:
+        list or None: List of files and folders if directory exists, otherwise None.
+    """
+    if os.path.isdir(data_path):
+        items = os.listdir(data_path)
+        return items
+    else:
+        return None
+
+def main(csv_file, data_path, save_csv):
+    """
+    Main function to process data.
+
+    Args:
+        csv_file (str): Path to the CSV file.
+        data_path (str): Path to the directory containing files and folders.
+        save_csv (str): Path to save the new CSV file.
+    """
+    # Read CSV file
+    csv_data = pd.read_csv(csv_file, header=None)
+
+    # Get list of files and folders in the data path
+    youtube_ids = list_files_and_folders(data_path)
+
+    # Process each YouTube ID
+    for youtube_id in youtube_ids:
+        # Filter rows corresponding to the YouTube ID
+        filtered_df = csv_data[csv_data[0].astype(str).str.contains(youtube_id)]
+        first_row = filtered_df.iloc[0:1]
+
+        # Extract information from file name
+        file_name = list_files_and_folders(os.path.join(data_path, youtube_id))[0]
+        file_name_list = file_name.split("_")
+        
+        # Add extracted information as new columns
+        first_row[4] = file_name_list[0]
+        first_row[5] = file_name_list[1]
+        
+        # Save to new CSV file
+        first_row.to_csv(save_csv, mode="a", index=False, header=False)
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(**args.__dict__)
diff --git a/data/crawling/crawling_urlsave.py b/data/crawling/crawling_urlsave.py
new file mode 100644
index 0000000..36d9c8d
--- /dev/null
+++ b/data/crawling/crawling_urlsave.py
@@ -0,0 +1,97 @@
+from selenium import webdriver
+from selenium.webdriver.common.by import By
+from selenium.webdriver.common.keys import Keys
+
+import requests
+from bs4 import BeautifulSoup
+import time
+import pandas as pd
+import os
+
+'''
+YouTube crawling using Selenium
+
+Saves information of approximately 162 videos including name, title, and URL to Youtube_search_df.csv.
+'''
+
+# Initialize WebDriver (executable_path not required as it's added to PATH)
+browser = webdriver.Chrome()
+
+# URL to access
+url = "https://youtube.com/"
+
+# Search keyword
+keyword = "Solo Travel"
+
+# Scroll until the specified line
+# finish_line = 40000 (about 162 videos)
+finish_line = 10000
+
+browser.maximize_window()
+browser.get(url)
+time.sleep(2)
+search = browser.find_element(By.NAME, "search_query")
+time.sleep(2)
+search.send_keys(keyword)
+search.send_keys(Keys.ENTER)
+
+# Switch to search result page for parsing
+present_url = browser.current_url
+browser.get(present_url)
+last_page_height = browser.execute_script("return document.documentElement.scrollHeight")
+
+# Scroll 100 times
+scroll_count = 0
+while scroll_count < 100:
+    # Scroll down
+    browser.execute_script("window.scrollTo(0, document.documentElement.scrollHeight);")
+    time.sleep(2.0)       # Waiting for data to load when scrolling down
+    new_page_height = browser.execute_script("return document.documentElement.scrollHeight")
+    
+    # Increase scroll count
+    scroll_count += 1
+
+html_source = browser.page_source
+soup = BeautifulSoup(html_source, 'html.parser')
+
+# Retrieve all search results up to the finish line
+# Extract all content-related sections
+elem = soup.find_all("ytd-video-renderer", class_="style-scope ytd-item-section-renderer")
+
+# Retrieve necessary information
+df = []
+for t in elem[:100]:  # Retrieve only the first 100 video information
+    title = t.find("yt-formatted-string", class_="style-scope ytd-video-renderer").get_text()
+    name = t.find("a", class_="yt-simple-endpoint style-scope yt-formatted-string").get_text()
+    content_url = t.find("a", class_="yt-simple-endpoint style-scope ytd-video-renderer")["href"]
+    df.append([name, title , 'https://www.youtube.com/'+content_url])
+
+## Save data
+# Create DataFrame
+new = pd.DataFrame(columns=['name', 'title' , 'url_link'])
+
+# Insert data
+for i in range(len(df)):
+    new.loc[i] = df[i]
+
+# Create directory to save data
+df_dir = "./data/"
+if not os.path.exists(df_dir):
+    os.makedirs(df_dir)
+
+# Save data
+new.to_csv(os.path.join(df_dir, "Youtube_search_df.csv"), index=True, encoding='utf8')  # Save with index
+
+## Save column information
+# Column description table
+col_names = ['name', 'title' ,'url_link']
+col_exp = ['Channel name', 'Video title', 'URL link']
+
+new_exp = pd.DataFrame({'col_names':col_names,
+                        'col_explanation':col_exp})
+
+# Save
+new_exp.to_csv(os.path.join(df_dir, "Youtube_col_exp.csv"), index=False, encoding='utf8')
+
+# Close the browser
+browser.close()
diff --git a/data/crawling/crawling_videosave.py b/data/crawling/crawling_videosave.py
new file mode 100644
index 0000000..8c7c422
--- /dev/null
+++ b/data/crawling/crawling_videosave.py
@@ -0,0 +1,53 @@
+import os
+import pandas as pd
+from pytube import YouTube
+import time
+
+'''
+Download videos from URLs obtained through 'crawling_urlave.py'.
+Videos are saved in the 'download' folder.
+'''
+
+# Read links from the CSV file
+csv_file_path = "/Users/imseohyeon/Documents/crawling/data/Youtube_search_df.csv"
+df = pd.read_csv(csv_file_path)
+
+# Define the download folder path
+DOWNLOAD_FOLDER = "/Users/imseohyeon/Documents/crawling/download/"
+
+# Create the download folder if it doesn't exist
+if not os.path.exists(DOWNLOAD_FOLDER):
+    os.makedirs(DOWNLOAD_FOLDER)
+
+# Iterate over each video and download
+for idx, row in df.iterrows():
+    video_url = row['url_link']
+    try:
+        # Get video information using Pytube
+        yt = YouTube(video_url)
+        length_seconds = yt.length
+
+        # Set the filename
+        filename = f"{idx}_video.mp4"
+
+        # If the video length exceeds 5 minutes, download only the first 5 minutes
+        if length_seconds > 5 * 60:
+            print(f"{yt.title} video exceeds 5 minutes. Downloading only the first 5 minutes.")
+            stream = yt.streams.filter(adaptive=True, file_extension='mp4').first()
+            if stream:
+                print(f"Downloading: {yt.title}")
+                stream.download(output_path=DOWNLOAD_FOLDER, filename=filename)
+                print(f"{yt.title} download complete")
+            else:
+                print(f"No highest quality stream available for {yt.title}.")
+        else:
+            # Download the entire video for videos less than 5 minutes long
+            stream = yt.streams.get_highest_resolution()
+            if stream:
+                print(f"Downloading: {yt.title}")
+                stream.download(output_path=DOWNLOAD_FOLDER, filename=filename)
+                print(f"{yt.title} download complete")
+            else:
+                print(f"No highest quality stream available for {yt.title}.")
+    except Exception as e:
+        print(f"Failed to download {yt.title}: {e}")
diff --git a/data/image/image_clipseg2.py b/data/image/image_clipseg2.py
new file mode 100644
index 0000000..26f9521
--- /dev/null
+++ b/data/image/image_clipseg2.py
@@ -0,0 +1,104 @@
+from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
+from PIL import Image
+import torch
+import numpy as np
+
+'''
+This script performs image segmentation using the CLIPSeg model based on provided text prompts. It loads an image, processes it with text prompts, and generates a segmented image based on the identified objects.
+
+Inputs:
+- image: The image to be segmented.
+- positive_prompts: Text prompts describing the objects to be identified, separated by commas.
+- negative_prompts: Text prompts describing the objects to be ignored, separated by commas.
+- threshold: Threshold value for segmentation, between 0 and 1.
+
+Outputs:
+- output_image: Segmented image with identified objects highlighted.
+- final_mask: Final mask representing the segmented areas.
+
+'''
+
+# load CLIPSeg model & processor
+processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
+model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
+
+# image path & load
+image_path = "/root/project/voice2face-data/file/face_detected_256x256.png"
+image = Image.open(image_path)
+
+def process_image(image, positive_prompts, negative_prompts, threshold):
+    '''
+    This function performs image segmentation based on provided text prompts and threshold.
+
+    Args:
+    - image: PIL image object.
+    - positive_prompts: Text prompts describing the objects to be identified, separated by commas.
+    - negative_prompts: Text prompts describing the objects to be ignored, separated by commas.
+    - threshold: Threshold value for segmentation, between 0 and 1.
+
+    Returns:
+    - output_image: Segmented image with identified objects highlighted.
+    - final_mask: Final mask representing the segmented areas.
+    '''
+
+    # image segmentation with img & prompt
+    def get_masks(prompts, img, threshold):
+        prompts = prompts.split(",")
+        masks = []
+        for prompt in prompts:
+            inputs = processor(
+                text=prompt.strip(), images=image, padding="max_length", return_tensors="pt"
+            )
+            with torch.no_grad():
+                outputs = model(**inputs)
+                preds = outputs.logits
+
+            pred = torch.sigmoid(preds)
+            mat = pred.cpu().numpy()
+            mask = Image.fromarray(np.uint8(mat * 255), "L")
+            mask = mask.convert("RGB")
+            mask = mask.resize(image.size)
+            mask = np.array(mask)[:, :, 0]
+
+            # normalize the mask
+            mask_min = mask.min()
+            mask_max = mask.max()
+            mask = (mask - mask_min) / (mask_max - mask_min)
+            mask = mask > threshold
+            masks.append(mask)
+        return masks
+
+    # Make mask's Positive prompts, Negative prompts
+    positive_masks = get_masks(positive_prompts, image, threshold)
+    negative_masks = get_masks(negative_prompts, image, threshold)
+
+    # Make Result mask combined masks
+    pos_mask = np.any(np.stack(positive_masks), axis=0)
+    neg_mask = np.any(np.stack(negative_masks), axis=0)
+    final_mask = pos_mask & ~neg_mask
+
+    # Result image 
+    final_mask = Image.fromarray(final_mask.astype(np.uint8) * 255, "L")
+    output_image = Image.new("RGBA", image.size, (0, 0, 0, 0))
+    output_image.paste(image, mask=final_mask)
+    return output_image, final_mask
+
+# base prompt
+positive_prompts = "face"
+negative_prompts = "background"
+threshold = 0.5
+
+# 텍스트 프롬프트 및 임계값 설정
+# positive_prompts = input("what you want to identify (comma separated): ")
+# negative_prompts = input("what you want to ignore (comma separated): ")
+# threshold = float(input("enter the threshold value (between 0 and 1): "))
+
+# process of segmentation
+output_image, final_mask = process_image(image, positive_prompts, negative_prompts, threshold)
+
+# save result img
+output_image_path = "/root/project/voice2face-data/file/segmented_image.png"
+output_image.save(output_image_path)
+
+# print success message
+print("Segmented image saved successfully at:", output_image_path)
diff --git a/data/image/image_face_frame.py b/data/image/image_face_frame.py
new file mode 100644
index 0000000..56bb442
--- /dev/null
+++ b/data/image/image_face_frame.py
@@ -0,0 +1,78 @@
+import cv2
+import os
+from rembg import remove
+
+def process_video(video_path: str, save_folder: str):
+    """Video 에서 사람의 얼굴이 정면이고, 눈을 뜨고 잇을 때 캡쳐 및 배경 제거
+
+    Args:
+        video_path (str): 타겟 비디오 파일경로
+        save_folder (str): 이미지를 저장할 위치 
+    """
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_alt2.xml')
+    eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye_tree_eyeglasses.xml')
+
+    cap = cv2.VideoCapture(video_path)
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        faces = face_cascade.detectMultiScale(gray, 1.1, 4)
+
+        for (x, y, w, h) in faces:
+            # 얼굴 영역을 30% 확장
+            expansion_rate = 0.3
+            new_width = int(w * (1 + expansion_rate))
+            new_height = int(h * (1 + expansion_rate))
+            new_x = x - int(w * expansion_rate / 2)
+            new_y = y - int(h * expansion_rate / 2)
+
+            # 확장된 영역이 프레임을 벗어나지 않도록 조정
+            new_x = max(0, new_x)
+            new_y = max(0, new_y)
+            new_width = min(new_width, frame.shape[1] - new_x)
+            new_height = min(new_height, frame.shape[0] - new_y)
+
+            roi_color = frame[new_y:new_y+new_height, new_x:new_x+new_width]
+            eyes = eye_cascade.detectMultiScale(cv2.cvtColor(roi_color, cv2.COLOR_BGR2GRAY))
+            if len(eyes) >= 2:  # 두 눈이 검출되면
+                face_img = cv2.resize(roi_color, (256, 256))
+                face_img = remove(face_img, bgcolor=(255, 255, 255, 255))
+
+                video_name = "_".join(os.path.splitext(os.path.basename(video_path))[0].split("_")[:-2])
+                origin_file_folder = os.path.join(save_folder,'data', video_name)
+                os.makedirs(origin_file_folder, exist_ok=True)
+
+                cv2.imwrite(f'{os.path.join(origin_file_folder, video_name)}.png', face_img)
+                cap.release()
+                return 
+            
+    print(os.path.basename(video_path))
+    cap.release()
+
+
+def main(video_folder: str, save_folder: str):
+    """ 비디오 폴더 경로에서 .mp4 파일 선택,
+        정제 이후 파일 제거
+
+    Args:
+        video_folder (str): 탐색할 비디오가 있는 경로
+        save_folder (str): 이미지 저장을 위한 파일
+    """
+    for root,_,files in os.walk(video_folder):
+        for file in files:
+            if file.endswith('.mp4'):
+                video_file = os.path.join(root, file)
+                process_video(video_file, save_folder)
+                os.remove(os.path.join(root,file))
+
+    
+if __name__ == '__main__':
+    video_folder = '/home/carbox/Desktop/data/009.립리딩(입모양) 음성인식 데이터/01.데이터/2.Validation/원천데이터'
+    save_folder = "/home/carbox"
+    main(video_folder, save_folder)
+
+
diff --git a/data/relabel/relabel_Vox_age.py b/data/relabel/relabel_Vox_age.py
new file mode 100644
index 0000000..842afa7
--- /dev/null
+++ b/data/relabel/relabel_Vox_age.py
@@ -0,0 +1,84 @@
+import cv2
+import argparse
+import os
+import csv
+from collections import Counter
+
+def predict_age(face):
+    '''
+    Function to predict age from a face image.
+    
+    Args:
+        face: Image to predict age from.
+
+    Returns:
+        age: Predicted age group index.
+    '''
+    blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
+    ageNet.setInput(blob)
+    agePreds = ageNet.forward()
+    age = agePreds[0].argmax()
+    return age
+
+def count_and_print_age(folder_path):
+    '''
+    Function to count and print the most common age group from images in a folder.
+    
+    Args:
+        folder_path: Path to the folder containing images.
+
+    Returns:
+        most_common_age: Index of the most common age group.
+    '''
+    age_list = []
+    try:
+        for filename in os.listdir(folder_path):
+            if filename.endswith(".jpg"):
+                image_path = os.path.join(folder_path, filename)
+                frame = cv2.imread(image_path)
+                if frame is not None:
+                    age = predict_age(frame)
+                    age_list.append(age)
+    except FileNotFoundError:
+        print(f"Folder '{folder_path}' not found.")
+        return None
+
+    if age_list:
+        most_common_age = Counter(age_list).most_common(1)[0][0]
+        print("Most common age group index:", most_common_age)
+        return most_common_age
+    else:
+        print("No images detected in the folder.")
+        return None
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--folder', required=True, help='Path to the folder containing images.')
+    parser.add_argument('--csv_source', default="/home/carbox/Desktop/git/dataset/vox1/vox1_meta.csv")
+    parser.add_argument('--output_csv', default="/home/carbox/Desktop/git/dataset/test/csv/test.csv")
+    args = parser.parse_args()
+
+    ageProto = "weights/age_deploy.prototxt"
+    ageModel = "weights/age_net.caffemodel"
+
+    MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
+    ageList = ['0', '1', '2', '3', '4', '5', '6', '7']  # Index 형태로 출력하기 위해 수정
+
+    ageNet = cv2.dnn.readNet(ageModel, ageProto)
+
+    with open (args.output_csv, 'w',  newline='') as output_csv:
+        csvwriter = csv.writer(output_csv, delimiter='\t')
+
+        with open(args.csv_source, 'r') as file:
+            for idx, line in enumerate(file):
+                if idx == 0:
+                    csvwriter.writerow(line.strip().split("\t") + ["age"])  # 헤더 부분에 age를 추가하여 리스트를 연결
+                    continue
+                line = line.strip()  # Remove leading/trailing whitespaces
+                if line:
+                    fields = line.split("\t")
+                    image_name = fields[1]
+                    age_index = count_and_print_age(os.path.join(args.folder, image_name))
+                    if age_index is not None:
+                        fields.append(str(age_index))  # age_index를 문자열로 변환하여 fields에 추가
+                        csvwriter.writerow(fields)
diff --git a/data/relabel/relabel_detect_getframe.py b/data/relabel/relabel_detect_getframe.py
new file mode 100644
index 0000000..73915b4
--- /dev/null
+++ b/data/relabel/relabel_detect_getframe.py
@@ -0,0 +1,141 @@
+import cv2
+import math
+import argparse
+import os
+
+def highlightFace(net, frame, conf_threshold=0.7):
+    '''
+    Function to detect faces in a frame using a pre-trained deep learning model.
+    
+    Args:
+        net: Pre-trained deep learning model.
+        frame: Input frame.
+        conf_threshold: Confidence threshold for face detection.
+
+    Returns:
+        frameOpencvDnn: Copy of the input frame with face rectangles drawn.
+        faceInfo: List containing information about detected faces (bbox, center, width, height).
+    '''
+    frameOpencvDnn = frame.copy()
+    frameHeight = frameOpencvDnn.shape[0]
+    frameWidth = frameOpencvDnn.shape[1]
+    blob = cv2.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, False)
+
+    net.setInput(blob)
+    detections = net.forward()
+    faceInfo = []
+    for i in range(detections.shape[2]):
+        confidence = detections[0, 0, i, 2]
+        if confidence > conf_threshold:
+            x1 = int(detections[0, 0, i, 3] * frameWidth)
+            y1 = int(detections[0, 0, i, 4] * frameHeight)
+            x2 = int(detections[0, 0, i, 5] * frameWidth)
+            y2 = int(detections[0, 0, i, 6] * frameHeight)
+            cx = (x1 + x2) // 2
+            cy = (y1 + y2) // 2
+            w = x2 - x1
+            h = y2 - y1
+            faceInfo.append({'bbox': (x1, y1, x2, y2), 'center': (cx, cy), 'width': w, 'height': h})
+            cv2.rectangle(frameOpencvDnn, (x1, y1), (x2, y2), (0, 255, 0), int(round(frameHeight / 150)), 8)
+    return frameOpencvDnn, faceInfo
+
+def save_frame(frame, output_folder, frame_count, folder_name, gender, age, center):
+    '''
+    Function to save a frame with a specific filename format.
+    
+    Args:
+        frame: Frame to be saved.
+        output_folder: Folder where frames will be saved.
+        frame_count: Frame count.
+        folder_name: Name of the folder containing the video.
+        gender: Gender of the detected face.
+        age: Age range of the detected face.
+        center: Center coordinates of the detected face bbox.
+    '''
+    if not os.path.exists(output_folder):
+        os.makedirs(output_folder)
+    subfolder_path = os.path.join(output_folder, folder_name)
+    if not os.path.exists(subfolder_path):
+        os.makedirs(subfolder_path)
+    cv2.imwrite(os.path.join(subfolder_path, f"{gender}_{age}_{center[0]}-{center[1]}.jpg"), frame)
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--folder', default='/Users/imseohyeon/Documents/gad/video')
+parser.add_argument('--output_folder', default='frame')
+parser.add_argument('--capture_interval', type=int, default=50)  # Adjusted frame interval for capturing
+
+args = parser.parse_args()
+
+faceProto = "opencv_face_detector.pbtxt"
+faceModel = "opencv_face_detector_uint8.pb"
+ageProto = "age_deploy.prototxt"
+ageModel = "age_net.caffemodel"
+genderProto = "gender_deploy.prototxt"
+genderModel = "gender_net.caffemodel"
+
+MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
+ageList = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']
+genderList = ['Male', 'Female']
+
+faceNet = cv2.dnn.readNet(faceModel, faceProto)
+ageNet = cv2.dnn.readNet(ageModel, ageProto)
+genderNet = cv2.dnn.readNet(genderModel, genderProto)
+
+for root, dirs, files in os.walk(args.folder):
+    for folder_name in dirs:
+        folder_path = os.path.join(root, folder_name)
+        for filename in os.listdir(folder_path):
+            if filename.endswith(".mp4"):
+                video_path = os.path.join(folder_path, filename)
+                break
+        else:
+            continue
+        
+        video = cv2.VideoCapture(video_path)
+        padding = 20
+        frame_count = 0
+        while True:  # Infinite loop for processing each frame
+            hasFrame, frame = video.read()
+            if not hasFrame:
+                break
+
+            resultImg, faceInfo = highlightFace(faceNet, frame)
+            if faceInfo:  # Process only if faces are detected
+                for faceData in faceInfo:
+                    bbox = faceData['bbox']
+                    center = faceData['center']
+                    width = faceData['width']
+                    height = faceData['height']
+                    
+                    face = frame[max(0, bbox[1] - padding): min(bbox[3] + padding, frame.shape[0] - 1),
+                                 max(0, bbox[0] - padding): min(bbox[2] + padding, frame.shape[1] - 1)]
+                    
+                    blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
+                    genderNet.setInput(blob)
+                    genderPreds = genderNet.forward()
+                    gender = genderList[genderPreds[0].argmax()]
+                    print(f'Gender: {gender}')
+
+                    ageNet.setInput(blob)
+                    agePreds = ageNet.forward()
+                    age = ageList[agePreds[0].argmax()]
+                    print(f'Age: {age[1:-1]} years')
+
+                    cv2.putText(resultImg, f'{gender}, {age}', (bbox[0], bbox[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv2.LINE_AA)
+                    cv2.putText(resultImg, f'Center: ({center[0]}, {center[1]})', (bbox[0], bbox[1] - 40), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv2.LINE_AA)
+                    cv2.putText(resultImg, f'Box: ({bbox[0]}, {bbox[1]}, {width}, {height})', (bbox[0], bbox[1] - 70), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv2.LINE_AA)
+                    
+                    cv2.imshow("Detecting age and gender", resultImg)
+                    
+                    # Capture and save frames at specified intervals
+                    if frame_count % args.capture_interval == 0:
+                        save_frame(frame, args.output_folder, frame_count, folder_name, gender, age, center)
+            
+            frame_count += 1
+            
+            # Press 'q' to exit
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+        
+        video.release()
+        cv2.destroyAllWindows()
diff --git a/data/relabel/relabel_select_csv.py b/data/relabel/relabel_select_csv.py
new file mode 100644
index 0000000..0ece279
--- /dev/null
+++ b/data/relabel/relabel_select_csv.py
@@ -0,0 +1,57 @@
+import os
+import pandas as pd
+from argparse import ArgumentParser
+
+def parse_args():
+    parser = ArgumentParser()
+
+    # Conventional args
+    parser.add_argument('--csv_file', type=str, default='output_test.csv')
+    parser.add_argument('--data_path', type=str, default='origin/video')
+    parser.add_argument('--save_csv', type=str, default='new_output.csv')
+
+    args = parser.parse_args()
+
+    return args
+
+
+def list_files_and_folders(data_path):
+    if os.path.isdir(data_path):
+        items = os.listdir(data_path)
+        return items
+    else:
+        return None
+
+def main(csv_file, data_path, save_csv):
+    ageList_s = [0,4,8,15,25,38,48,60]
+
+    csv_data = pd.read_csv(csv_file, header=None)
+    youtube_ids = list_files_and_folders(data_path)
+    count = 0
+    for youtube_id in youtube_ids:
+        filtered_df = csv_data[csv_data[0].astype(str).str.contains(youtube_id)]
+        first_row = filtered_df.iloc[0:1]
+        file_name = list_files_and_folders(os.path.join(data_path, youtube_id))
+        for i in file_name:
+            file_name_list = i.split("_")
+            if file_name_list[-1] =="Store":
+                continue
+            else:
+                print(youtube_id, file_name_list)
+
+                first_row[4] =  file_name_list[0]
+                
+            first_row[5] =  ageList_s.index(int(file_name_list[1]))
+
+            first_row.to_csv(save_csv, mode="a", index=False, header=False)
+
+        
+        count += 1
+        
+
+    print(count)
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(**args.__dict__)
\ No newline at end of file
diff --git a/data/requirements.txt b/data/requirements.txt
new file mode 100644
index 0000000..6a236ce
--- /dev/null
+++ b/data/requirements.txt
@@ -0,0 +1,18 @@
+opencv-python
+rembg
+librosa
+numpy
+matplotlib
+soundfile
+pydub
+pandas
+moviepy
+face_recognition
+selenium
+requests
+beautifulsoup4
+pytube
+transformers
+Pillow
+torch
+facenet-pytorch
diff --git a/data/total/total_audio_video_image.py b/data/total/total_audio_video_image.py
new file mode 100644
index 0000000..0b6384a
--- /dev/null
+++ b/data/total/total_audio_video_image.py
@@ -0,0 +1,102 @@
+import os
+import numpy as np
+import librosa
+import soundfile as sf
+import matplotlib.pyplot as plt
+from pydub import AudioSegment
+import cv2
+from facenet_pytorch import MTCNN
+import subprocess
+
+# Initialize MTCNN for face detection
+mtcnn = MTCNN()
+
+from moviepy.editor import VideoFileClip
+
+# 1. 비디오에서 음성 추출
+def extract_audio_from_video(video_file, audio_file):
+    # ffmpeg를 사용하여 비디오에서 오디오 추출
+    command = f"ffmpeg -i {video_file} -vn -acodec pcm_s16le -ar 44100 -ac 2 {audio_file}"
+    subprocess.call(command, shell=True)
+
+# 2. 사람 음성부분 추출 
+def detect_human_voice(audio_file): 
+    # 오디오 파일에서 사람 음성부분을 감지하여 해당 인덱스 반환
+    y, sr = librosa.load(audio_file, sr=None)
+    voice_segments = librosa.effects.split(y, top_db=18)
+    voice_indices = []
+    for start, end in voice_segments:
+        if end - start >= sr * 1:  # 1초 이상인 경우에만 추가
+            voice_indices.extend(range(start, end))
+    return voice_indices 
+
+# 3. 음성부분만 모아 다시 저장. + 비디오도 이 간격 맞춰 다시 저장 
+def save_detected_voice(audio_file, video_file, save_audio_file, save_video_file):
+    # 감지된 사람 음성부분을 추출하여 저장
+    y, sr = librosa.load(audio_file, sr=None)
+    voice_indices = detect_human_voice(audio_file)
+    combined_audio = y[voice_indices]
+    sf.write(save_audio_file, combined_audio, sr)
+
+    # 비디오도 해당 음성에 맞게 잘라서 저장
+    audio_clip = AudioSegment.from_wav(save_audio_file)
+    video_clip = VideoFileClip(video_file)
+    video_duration = int(video_clip.duration * 1000)  # 비디오의 길이를 정수로 변환
+    if len(audio_clip) > video_duration:
+        audio_clip = audio_clip[:video_duration]
+    else:
+        audio_clip += audio_clip[-1] * (video_duration - len(audio_clip))
+
+    audio_clip.export(save_audio_file, format="wav")
+    video_clip.write_videofile(save_video_file, codec='libx264', audio_codec='aac')
+
+# 4. 새로운 비디오에서 얼굴인식되는 부분중에 frame 추출 + frame에서도 256x256으로 얼굴 부분 bbox 맞춰 잘라 이미지 저장. 
+def extract_frames_with_faces(video_file, output_folder):
+    # 비디오 파일에서 프레임 추출하여 얼굴을 인식하고 이미지 저장
+    cap = cv2.VideoCapture(video_file)
+    frame_rate = cap.get(cv2.CAP_PROP_FPS)
+    frame_count = 0
+    success, frame = cap.read()
+    while success:
+        frame_count += 1
+        if frame_count % (10 * frame_rate) == 0:  # 10초마다 프레임 추출
+            try:
+                boxes, _ = mtcnn.detect(frame)
+                if boxes is not None:
+                    for i, box in enumerate(boxes):
+                        x, y, w, h = [int(coord) for coord in box]
+                        face_image = frame[y:y+h, x:x+w]
+                        cv2.imwrite(os.path.join(output_folder, f"frame_{frame_count}_{i}.jpg"), face_image)
+            except Exception as e:
+                print(f"Failed to detect face in frame {frame_count}: {e}")
+        success, frame = cap.read()
+    cap.release()
+
+
+# Define paths
+video_file_path = "/Users/imseohyeon/Documents/voice2face-data/code/file/testvideo.mp4"
+audio_file_path = "/Users/imseohyeon/Documents/voice2face-data/code/file/testaudio.mp3"
+detected_voice_file_path = "/Users/imseohyeon/Documents/voice2face-data/code/file/combined_voice.wav"
+output_frame_folder = "/Users/imseohyeon/Documents/voice2face-data/code/file/images"
+trimmed_video_file_path = "/Users/imseohyeon/Documents/voice2face-data/code/file/trimmed_video.mp4"
+
+# Convert audio file to WAV format
+converted_audio_file_path = os.path.splitext(audio_file_path)[0] + ".wav"
+AudioSegment.from_file(audio_file_path).export(converted_audio_file_path, format="wav")
+
+# Extract audio from video
+extract_audio_from_video(video_file_path, converted_audio_file_path)
+
+# Create necessary folders
+for folder in [output_frame_folder, os.path.dirname(detected_voice_file_path)]:
+    os.makedirs(folder, exist_ok=True)
+
+# Step 2: Save the detected human voice segment and corresponding video
+save_detected_voice(converted_audio_file_path, video_file_path, detected_voice_file_path, trimmed_video_file_path)
+
+# If no human voice segments are detected, delete the corresponding video file
+if not os.path.exists(detected_voice_file_path):
+    os.remove(trimmed_video_file_path)
+else:
+    # Step 3: Extract frames with detected faces every 10 seconds
+    extract_frames_with_faces(trimmed_video_file_path, output_frame_folder)
diff --git a/data/total/total_origin_remove.py b/data/total/total_origin_remove.py
new file mode 100644
index 0000000..68f66a0
--- /dev/null
+++ b/data/total/total_origin_remove.py
@@ -0,0 +1,60 @@
+import os
+import re
+import tarfile
+
+def extract_tar_if_needed(root_folder: str):
+    """ tar 파일을 추출하는 함수
+
+    Args:
+        root_folder (str): tar 파일이 들어있는 폴더 경로
+    """
+    for root, dirs, files in os.walk(root_folder):
+        for file in files:
+            if file.endswith('.tar'):
+                # .tar 파일과 같은 이름을 가진 폴더가 있는지 확인
+                tar_path = os.path.join(root, file)
+                folder_name = os.path.splitext(tar_path)[0]
+                if not os.path.exists(folder_name):
+                    # 동일한 이름의 폴더가 없으면 압축 해제
+                    print(f"Extracting {tar_path}")
+                    with tarfile.open(tar_path) as tar:
+                        tar.extractall(path=root)
+                    print(f"Extracted to {folder_name}")
+                    # 압축 해제 후 dirs 리스트 업데이트
+                    dirs.append(os.path.basename(folder_name))
+
+                # 추출 후 삭제
+                os.remove(os.path.join(root, file))
+                print(f"Deleted {file} ")
+
+
+
+def delete_files_except_extensions(root_folder: str, keep_extensions: list, pattern: str):
+    """ 정제에 필요한 파일을 제외한 다른 파일들 삭제 
+
+    Args:
+        root_folder (str): 타겟 폴더
+        keep_extensions (list): 유지할 확장자 wav, 혹은 끝나는 부분 (A_001.mp4) 확인
+        pattern (str): 유지할 파일명 패턴
+    """
+    for root, dirs, files in os.walk(root_folder):
+        for file in files:
+            if not any(file.endswith(ext) for ext in keep_extensions):
+                if re.search(pattern, file):
+                    continue
+                file_path = os.path.join(root, file)
+                os.remove(file_path)
+                print(f"Deleted {file}")
+
+def main(root_folder: str, keep_extensions: list, pattern: str):
+    # 먼저 .tar 파일이 있으면 압축을 해제
+    extract_tar_if_needed(root_folder)
+    # 압축 해제 후 (해당되는 경우), 지정된 확장자 외의 파일 삭제
+    delete_files_except_extensions(root_folder, keep_extensions, pattern)
+    print("End of processing")
+
+if __name__ == '__main__':
+    root_folder = '~/Desktop/GPU/009.립리딩(입모양) 음성인식 데이터/01.데이터/2.Validation/원천데이터'  # 탐색을 시작할 폴더 경로
+    keep_extensions = ['A_001.mp4', '.wav']  # 유지하고 싶은 파일 확장자 목록
+    pattern = r'.*_A_.*\.json'
+    main(root_folder, keep_extensions, pattern)
diff --git a/data/video/video_clipimage.py b/data/video/video_clipimage.py
new file mode 100644
index 0000000..e5c1bfb
--- /dev/null
+++ b/data/video/video_clipimage.py
@@ -0,0 +1,41 @@
+import cv2
+
+def detect_and_save_faces(image_path, output_path):
+    """Detects faces in the input image and saves them as 256x256 pixel images.
+    
+    Args:
+        image_path (str): Path to the input image file.
+        output_path (str): Path to save the detected face images.
+    """
+    # Load the pre-trained face cascade classifier
+    face_cascade = cv2.CascadeClassifier('code/file/haarcascade_frontalface_default.xml')
+
+    # Read the input image
+    img = cv2.imread(image_path)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+
+    # Iterate over each detected face
+    for (x, y, w, h) in faces:
+        # Draw rectangle around the face
+        cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
+        
+        # Resize the face region to 256x256 pixels
+        face_crop = cv2.resize(img[y:y+h, x:x+w], (256, 256))
+        
+        # Save the face image
+        cv2.imwrite(output_path, face_crop)
+
+    # Display the image
+    cv2.imshow('Image view', img)
+
+    # Wait for 'q' key to be pressed
+    while cv2.waitKey(0) & 0xFF != ord('q'):
+        pass
+
+    cv2.destroyAllWindows()
+
+# Example usage
+detect_and_save_faces('code/file/image.png', 'code/file/face_detected_256x256.png')
diff --git a/data/video/video_download.py b/data/video/video_download.py
new file mode 100644
index 0000000..5b156a0
--- /dev/null
+++ b/data/video/video_download.py
@@ -0,0 +1,99 @@
+from pytube import YouTube  # 유튜브 영상을 다운로드하기 위한 모듈
+import os.path              # 경로를 설정하기 위한 모듈
+import ffmpeg               # 미디어를 변환하기 위한 모듈
+from getpass import getuser  # 기본 경로를 다운로드 폴더로 지정하기 위한 모듈
+
+class Download:
+    '''
+    파일을 변환하기 위해선 ffmpeg란 프로그램을 별도로 설치해 컴퓨터 환경변수 설정을 마쳐야 함.
+    '''
+
+    def __init__(self, link):
+        '''
+        Download 클래스의 생성자입니다.
+        
+        Args:
+            link (str): 유튜브 영상의 링크.
+        '''
+        # link 인자는 GUI에서 입력된 값을 받을 때 사용
+        # 컴퓨터 이용자명을 받아서 다운로드 폴더를 기본 폴더로 지정
+        self.parent_dir = f"/Users/{getuser()}/Documents/voice2face-data/code/file"
+        self.yt = YouTube(link)
+
+    def getVideoName(self):
+        '''(GUI 버전) 비디오 이름을 내보내는 함수'''
+        '''
+        유튜브 비디오의 제목을 반환하는 함수입니다.
+        
+        Returns:
+            str: 비디오의 제목.
+        '''
+        name = self.yt.title
+        return name
+
+    def downloadMp3(self):
+        '''mp3 파일로 다운로드하는 함수'''
+        '''
+        mp3 형식으로 비디오를 다운로드하는 함수입니다.
+        
+        Returns:
+            str: 다운로드한 mp3 파일의 이름.
+        '''
+        # mp4 형태지만 영상 없이 소리만 있는 파일 다운로드
+        stream = self.yt.streams.filter(only_audio=True).first()
+        stream.download(self.parent_dir)
+
+        src = stream.default_filename   # mp4로 다운받은 영상 제목(파일명과 같음)
+        dst = "testaudio.mp3"  # 변경된 파일명
+
+        # mp4에서 mp3로 변환
+        ffmpeg.input(os.path.join(self.parent_dir, src)).output(os.path.join(self.parent_dir, dst)).run(overwrite_output=True)
+
+        # 변환되기 전 mp4 파일 삭제
+        os.remove(os.path.join(self.parent_dir, src))
+
+        return dst  # 저장한 파일명 리턴
+
+    def downloadMp4(self):
+        '''mp4 파일로 다운로드하는 함수'''
+        '''
+        mp4 형식으로 비디오를 다운로드하는 함수입니다.
+        
+        Returns:
+            str: 다운로드한 mp4 파일의 이름.
+        '''
+        audio = self.downloadMp3()   # mp3 파일 다운로드
+        video = self.yt.streams.filter(adaptive=True, file_extension='mp4').first()  # 비디오 객체 가져오기
+        print(video)
+        video.download(self.parent_dir)  # mp4 파일 다운로드
+
+        # mp4로 해상도 높은 파일을 받으면 vcodec만 존재
+        # -> 비디오에 소리를 입히려면 acodec 있는 파일 받아 FFmpeg로 병합
+        # -> downloadMp3로 mp3 파일을 받고 오디오 소스로 사용
+        inputAudio = ffmpeg.input(os.path.join(self.parent_dir, audio))
+        inputVideo = ffmpeg.input(os.path.join(self.parent_dir, video.default_filename))
+
+        # 영상에 소리 입혀 "new.mp4"파일로 내보내기
+        ffmpeg.output(inputAudio, inputVideo, os.path.join(self.parent_dir, "new.mp4"), vcodec='copy', acodec='aac').run(overwrite_output=True)
+
+        # # 변환이 끝나 더 이상 필요 없는 mp3, mp4 파일 지우기
+        # os.remove(os.path.join(self.parent_dir, video.default_filename))
+        # os.remove(os.path.join(self.parent_dir, audio))
+
+        # "new.mp4"를 영상 제목으로 바꾸기
+        os.rename(os.path.join(self.parent_dir, "new.mp4"), os.path.join(self.parent_dir, video.default_filename))
+
+        return video.default_filename   # 저장한 파일명 리턴
+
+# 위의 클래스 정의 부분을 여기에 넣어주세요.
+
+# 다운로드를 수행할 링크
+link = "https://youtu.be/2DnGKEeRB4g?si=93Cf_Mg2n53kSpGQ"
+
+# Download 클래스 인스턴스 생성
+downloader = Download(link)
+
+# mp4 파일로 다운로드
+downloaded_file = downloader.downloadMp4()
+
+print(f"다운로드가 완료되었습니다: {downloaded_file}")
diff --git a/frontend/.gitignore b/frontend/.gitignore
new file mode 100644
index 0000000..8f6c617
--- /dev/null
+++ b/frontend/.gitignore
@@ -0,0 +1,12 @@
+.DS_Store
+node_modules
+/build
+/.svelte-kit
+/package
+.env
+.env.*
+!.env.example
+.vercel
+.output
+vite.config.js.timestamp-*
+vite.config.ts.timestamp-*
diff --git a/frontend/.npmrc b/frontend/.npmrc
new file mode 100644
index 0000000..b6f27f1
--- /dev/null
+++ b/frontend/.npmrc
@@ -0,0 +1 @@
+engine-strict=true
diff --git a/frontend/Dockerfile b/frontend/Dockerfile
new file mode 100644
index 0000000..5935de9
--- /dev/null
+++ b/frontend/Dockerfile
@@ -0,0 +1,13 @@
+FROM node:18 as build
+
+WORKDIR /app
+
+COPY package.json ./
+COPY package-lock.json ./
+RUN npm install
+RUN npm install -g pm2
+
+COPY . ./
+RUN npm run build
+
+#CMD ["pm2-runtime", "start", "./build/index.js", "--env", "production"]
diff --git a/frontend/README.md b/frontend/README.md
new file mode 100644
index 0000000..497f1ad
--- /dev/null
+++ b/frontend/README.md
@@ -0,0 +1,157 @@
+# 🔊목소리로 가상 얼굴 생성 서비스 [너의 목소리가 보여]
+
+  <img  src="https://img.shields.io/badge/figma-_F24E1E?style=for-the-badge&logo=figma&logoColor=white">
+  <img src="https://img.shields.io/badge/svelte-FF3E00?style=for-the-badge&logo=svelte&logoColor=white">
+
+
+## Project Structure
+
+```
+frontend
+┣ src
+┃ ┣ components
+┃ ┃ ┣ button
+┃ ┃ ┃ ┣ basic_filled.svelte
+┃ ┃ ┃ ┣ moving_filled.svelte
+┃ ┃ ┃ ┣ request_filled.svelte
+┃ ┃ ┃ ┗ result_save.svelte
+┃ ┃ ┣ header
+┃ ┃ ┃ ┣ header_login.svelte
+┃ ┃ ┃ ┗ header_non.svelte
+┃ ┃ ┣ image
+┃ ┃ ┃ ┣ PlaceholderImage.svelte
+┃ ┃ ┃ ┗ SocialProperty1Github.svelte
+┃ ┃ ┣ modal
+┃ ┃ ┃ ┗ basic_modal.svelte
+┃ ┃ ┣ rating
+┃ ┃ ┃ ┣ Star.svelte
+┃ ┃ ┃ ┗ StarRating.svelte
+┃ ┃ ┗ survey
+┃ ┃   ┣ input_area.svelte
+┃ ┃   ┗ survey.svelte
+┃ ┣ routes
+┃ ┃ ┣ aboutus
+┃ ┃ ┃ ┗ +page.svelte
+┃ ┃ ┣ home
+┃ ┃ ┃ ┗ +page.svelte
+┃ ┃ ┣ infogather
+┃ ┃ ┃ ┣ +page.svelte
+┃ ┃ ┃ ┗ VoiceButtonDefaultVariant3.svelte
+┃ ┃ ┣ join
+┃ ┃ ┃ ┣ +page.svelte
+┃ ┃ ┃ ┣ ButtonStyleFilled.svelte
+┃ ┃ ┃ ┣ ButtonStyleOutlined.svelte
+┃ ┃ ┃ ┣ PlaceholderImage.svelte
+┃ ┃ ┃ ┣ radio.svelte
+┃ ┃ ┃ ┣ RadioBigDefault.svelte
+┃ ┃ ┃ ┗ RadioSmallS.svelte
+┃ ┃ ┣ loading
+┃ ┃ ┃ ┗ +page.svelte
+┃ ┃ ┣ login
+┃ ┃ ┃ ┣ +page.svelte
+┃ ┃ ┃ ┗ PlaceholderImage.svelte
+┃ ┃ ┣ maintenance
+┃ ┃ ┃ ┗ +page.svelte
+┃ ┃ ┣ result
+┃ ┃ ┃ ┣ +page.svelte
+┃ ┃ ┃ ┗ PlaceholderImage.svelte
+┃ ┃ ┗ resultlist
+┃ ┃ ┃ ┣ +page.svelte
+┃ ┃ ┃ ┗ ButtonStyleFilled.svelte
+┃ ┃ ┣ +layout.svelte
+┃ ┃ ┣ +page.js
+┃ ┃ ┣ +page.svelte
+┃ ┃ ┣ styles.css
+┃ ┣ app.html
+┃ ┣ app.pcss
+┃ ┗ hooks.js
+┣ static
+┣ .gitignore
+┣ .npmrc
+┣ Dockerfile
+┣ package.json
+┣ package-lock.json
+┣ postcss.config.cjs
+┣ README.md
+┣ svelte.config.js
+┣ tailwind.config.cjs
+┗ vite.config.js
+```
+## Usage
+
+  
+### `src` 
+#### `components`
+: 페이지 내부에 포함할 컴포넌트를 모듈화 하기 위해 보관해둔 폴더
+ - `button`: 화면 변경이나,  input event 등의 trigger로 사용하기 위한 버튼을 보관한 폴더
+ - `header`: 웹사이트 header 보관 폴더
+ - `image`: 웹에 사용되는 이미지 style을 지정할 폴더
+ - `modal`: 주의사항을 표시할 modal을 보관한 폴더
+ - `rating`: 별점 표시, 전송을 위한 컴포넌트를 보관한 폴더
+ - `survey`: 설문조사 항목에 대한 컴포넌트를 보관한 폴더
+
+#### `routes` 
+: 서비스에 사용된 페이지를 담아둔 폴더
+
+ - `aboutus`: 팀, 팀원 소개를 위한 페이지
+ - `home`: 로그인 후 생성요청이나, 결과 확인으로 이동할 수 있는 화면
+ - `infogather`: 사용자가 생성요청을 보낼 수 있는 페이지
+   - `VoiceButtonDefaultVariant3.svelte`: 음성 녹음 및 Blob을 생성하기 위한 모듈
+ - `join`: 회원가입을 위한 페이지
+ - `loading`: 사용자가 이미지 생성 요청 후 넘어갈 수 있는 페이지로 결과나 `home`화면으로 이동할 수 있다.
+ - `login`: 로그인 페이지
+ - `maintenance`: 서비스 점검 시 사용될 페이지
+ - `result`: 결과를 확인할 수 있는 페이지
+ - `resultlist`: 결과 목록 페이지
+ 
+#### `hooks.js`
+: 서비스 점검 시 모든 화면을 maintenance 화면으로 이동하는 event 를 발생시킬 훅을 추가하는 파일
+### `static`
+: 서비스에 사용된 이미지를 보관해둔 폴더
+
+
+## Getting Started
+This project was developed and tested on the following operating systems:
+- **Linux**: Ubuntu 20.04 LTS
+- **Windows**: Windows 11 Home
+
+### 1. Install Requirements
+
+To run this project, you'll need Node.js installed on your system. We recommend using NVM (Node Version Manager) to manage your Node.js versions.
+```
+curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.35.3/install.sh | bash
+source ~/.bashrc
+nvm install 20.11.1
+```
+
+This project uses Tailwind CSS, Flowbite, and Svelte routing for UI and navigation.
+```
+# basic package
+npm install
+npx svelte-add@latest tailwindcss
+npm install -D tailwindcss svelte-routing flowbite-svelte flowbite
+```
+### 2. Developing
+
+To start the development server, run:
+
+```bash
+npm run dev
+
+# or start the server and open the app in a new browser tab
+npm run dev -- --open
+```
+
+### 3. Building
+
+To create a production build, use:
+
+```
+npm run build
+```
+  
+## Links
+- [Wireframe](https://www.figma.com/file/MBWE1CthewJVCl0KH8xEcM/%5BV1%5D-%ED%99%94%EB%A9%B4-%EA%B5%AC%EC%84%B1?type=whiteboard&node-id=0:1&t=jPWmEIfP3RobQG6G-1)
+- [Prototyping](https://www.figma.com/file/fN6DWRmoszsytULLaZ4cni/Voice2Face-V1?type=design&node-id=1603:2&mode=design&t=jPWmEIfP3RobQG6G-1)
+- [Origin github](https://github.com/Make-Zenerator/voice2face-frontend.git)
+
diff --git a/frontend/package-lock.json b/frontend/package-lock.json
new file mode 100644
index 0000000..6ec85e6
--- /dev/null
+++ b/frontend/package-lock.json
@@ -0,0 +1,4460 @@
+{
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+        "autoprefixer": "^10.4.16",
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+        "postcss-load-config": "^5.0.2",
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+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/wrap-ansi-cjs/node_modules/ansi-styles": {
+      "version": "4.3.0",
+      "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-4.3.0.tgz",
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+      "dependencies": {
+        "color-convert": "^2.0.1"
+      },
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+      },
+      "funding": {
+        "url": "https://github.com/chalk/ansi-styles?sponsor=1"
+      }
+    },
+    "node_modules/wrap-ansi-cjs/node_modules/emoji-regex": {
+      "version": "8.0.0",
+      "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz",
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+      "dev": true
+    },
+    "node_modules/wrap-ansi-cjs/node_modules/string-width": {
+      "version": "4.2.3",
+      "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz",
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+      "dev": true,
+      "dependencies": {
+        "emoji-regex": "^8.0.0",
+        "is-fullwidth-code-point": "^3.0.0",
+        "strip-ansi": "^6.0.1"
+      },
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/wrap-ansi-cjs/node_modules/strip-ansi": {
+      "version": "6.0.1",
+      "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.1.tgz",
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+      "dependencies": {
+        "ansi-regex": "^5.0.1"
+      },
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+      }
+    },
+    "node_modules/wrappy": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
+      "integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ=="
+    },
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+      "resolved": "https://registry.npmjs.org/ws/-/ws-7.4.6.tgz",
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+        "node": ">=8.3.0"
+      },
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+        "bufferutil": "^4.0.1",
+        "utf-8-validate": "^5.0.2"
+      },
+      "peerDependenciesMeta": {
+        "bufferutil": {
+          "optional": true
+        },
+        "utf-8-validate": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/yallist": {
+      "version": "4.0.0",
+      "resolved": "https://registry.npmjs.org/yallist/-/yallist-4.0.0.tgz",
+      "integrity": "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A=="
+    },
+    "node_modules/yaml": {
+      "version": "2.4.0",
+      "resolved": "https://registry.npmjs.org/yaml/-/yaml-2.4.0.tgz",
+      "integrity": "sha512-j9iR8g+/t0lArF4V6NE/QCfT+CO7iLqrXAHZbJdo+LfjqP1vR8Fg5bSiaq6Q2lOD1AUEVrEVIgABvBFYojJVYQ==",
+      "dev": true,
+      "bin": {
+        "yaml": "bin.mjs"
+      },
+      "engines": {
+        "node": ">= 14"
+      }
+    },
+    "node_modules/yamljs": {
+      "version": "0.3.0",
+      "resolved": "https://registry.npmjs.org/yamljs/-/yamljs-0.3.0.tgz",
+      "integrity": "sha512-C/FsVVhht4iPQYXOInoxUM/1ELSf9EsgKH34FofQOp6hwCPrW4vG4w5++TED3xRUo8gD7l0P1J1dLlDYzODsTQ==",
+      "dependencies": {
+        "argparse": "^1.0.7",
+        "glob": "^7.0.5"
+      },
+      "bin": {
+        "json2yaml": "bin/json2yaml",
+        "yaml2json": "bin/yaml2json"
+      }
+    },
+    "node_modules/yamljs/node_modules/brace-expansion": {
+      "version": "1.1.11",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.11.tgz",
+      "integrity": "sha512-iCuPHDFgrHX7H2vEI/5xpz07zSHB00TpugqhmYtVmMO6518mCuRMoOYFldEBl0g187ufozdaHgWKcYFb61qGiA==",
+      "dependencies": {
+        "balanced-match": "^1.0.0",
+        "concat-map": "0.0.1"
+      }
+    },
+    "node_modules/yamljs/node_modules/glob": {
+      "version": "7.2.3",
+      "resolved": "https://registry.npmjs.org/glob/-/glob-7.2.3.tgz",
+      "integrity": "sha512-nFR0zLpU2YCaRxwoCJvL6UvCH2JFyFVIvwTLsIf21AuHlMskA1hhTdk+LlYJtOlYt9v6dvszD2BGRqBL+iQK9Q==",
+      "dependencies": {
+        "fs.realpath": "^1.0.0",
+        "inflight": "^1.0.4",
+        "inherits": "2",
+        "minimatch": "^3.1.1",
+        "once": "^1.3.0",
+        "path-is-absolute": "^1.0.0"
+      },
+      "engines": {
+        "node": "*"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/isaacs"
+      }
+    },
+    "node_modules/yamljs/node_modules/minimatch": {
+      "version": "3.1.2",
+      "resolved": "https://registry.npmjs.org/minimatch/-/minimatch-3.1.2.tgz",
+      "integrity": "sha512-J7p63hRiAjw1NDEww1W7i37+ByIrOWO5XQQAzZ3VOcL0PNybwpfmV/N05zFAzwQ9USyEcX6t3UO+K5aqBQOIHw==",
+      "dependencies": {
+        "brace-expansion": "^1.1.7"
+      },
+      "engines": {
+        "node": "*"
+      }
+    }
+  }
+}
diff --git a/frontend/package.json b/frontend/package.json
new file mode 100644
index 0000000..143ab12
--- /dev/null
+++ b/frontend/package.json
@@ -0,0 +1,30 @@
+{
+  "name": "mz-v1-front",
+  "version": "0.0.1",
+  "scripts": {
+    "dev": "vite dev",
+    "build": "vite build",
+    "preview": "vite preview",
+    "start": "vite start"
+  },
+  "devDependencies": {
+    "@fontsource/fira-mono": "^4.5.10",
+    "@neoconfetti/svelte": "^1.0.0",
+    "@sveltejs/adapter-auto": "^3.0.0",
+    "@sveltejs/adapter-node": "^5.0.1",
+    "@sveltejs/kit": "^2.5.2",
+    "@sveltejs/vite-plugin-svelte": "^3.0.0",
+    "autoprefixer": "^10.4.16",
+    "flowbite": "^2.3.0",
+    "flowbite-svelte": "^0.44.24",
+    "postcss": "^8.4.32",
+    "postcss-load-config": "^5.0.2",
+    "svelte": "^4.2.7",
+    "tailwindcss": "^3.3.6",
+    "vite": "^5.0.3"
+  },
+  "type": "module",
+  "dependencies": {
+    "pm2": "^5.3.1"
+  }
+}
diff --git a/frontend/postcss.config.cjs b/frontend/postcss.config.cjs
new file mode 100644
index 0000000..e48cff5
--- /dev/null
+++ b/frontend/postcss.config.cjs
@@ -0,0 +1,13 @@
+const tailwindcss = require("tailwindcss");
+const autoprefixer = require("autoprefixer");
+
+const config = {
+  plugins: [
+    //Some plugins, like tailwindcss/nesting, need to run before Tailwind,
+    tailwindcss(),
+    //But others, like autoprefixer, need to run after,
+    autoprefixer,
+  ],
+};
+
+module.exports = config;
diff --git a/frontend/src/app.html b/frontend/src/app.html
new file mode 100644
index 0000000..4c3707c
--- /dev/null
+++ b/frontend/src/app.html
@@ -0,0 +1,12 @@
+<!doctype html>
+<html lang="en">
+	<head>
+		<meta charset="utf-8" />
+		<meta name="viewport" content="width=device-width, initial-scale=1" />
+		<link rel="icon" type="image/ico" sizes="32x32" href="https://storage.makezenerator.com:9000/voice2face-public/site/main/favicon.ico">
+		%sveltekit.head%
+	</head>
+	<body data-sveltekit-preload-data="hover">
+		<div style="display: contents">%sveltekit.body%</div>
+	</body>
+</html>
diff --git a/frontend/src/app.pcss b/frontend/src/app.pcss
new file mode 100644
index 0000000..1a7b7cf
--- /dev/null
+++ b/frontend/src/app.pcss
@@ -0,0 +1,4 @@
+/* Write your global styles here, in PostCSS syntax */
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
diff --git a/frontend/src/components/button/basic_filled.svelte b/frontend/src/components/button/basic_filled.svelte
new file mode 100644
index 0000000..db3ad55
--- /dev/null
+++ b/frontend/src/components/button/basic_filled.svelte
@@ -0,0 +1,27 @@
+<script>
+  
+    export let styleVariant = "outlined";
+    export let style;
+    const variantsClassName = "style-variant-" + styleVariant;
+    export let name = "Do something";
+    export let type = "button";
+  </script>
+  
+  <button
+  type = {type}
+    style="{'background: var(--neutral-10, #486284);border-radius: 50px; border-style: solid; border-color: var(--neutral-10, #486284); border-width: 1px; padding: 12px 20px 12px 20px; display: flex; flex-direction: row; gap: 10px; align-items: center; justify-content: center; position: relative; overflow: hidden;' + style}"
+  >
+    <div
+      style="
+        color: var(--neutral-0, #ffffff);
+        text-align: left;
+        font-family: var(--body-small-font-family, 'DmSans-Regular', sans-serif);
+        font-size: var(--body-small-font-size, 24px);
+        line-height: var(--body-small-line-height, 24px);
+        font-weight: var(--body-small-font-weight, 400);
+        position: relative;
+      "
+    >
+      {name}
+    </div>
+  </button>
\ No newline at end of file
diff --git a/frontend/src/components/button/moving_filled.svelte b/frontend/src/components/button/moving_filled.svelte
new file mode 100644
index 0000000..aa0c81f
--- /dev/null
+++ b/frontend/src/components/button/moving_filled.svelte
@@ -0,0 +1,42 @@
+<script>
+    import { goto } from '$app/navigation';
+    export let styleVariant = "outlined";
+    const variantsClassName = "style-variant-" + styleVariant;
+    export let name = "Do something";
+    export let type = "button";
+    export let targetPath = "/";
+    export let id;
+    export let latest_id;
+    export let gender;
+
+    async function handleClick() {
+        // id와 latest_id를 sessionStorage에 저장
+        sessionStorage.setItem('id', id);
+        sessionStorage.setItem('latest_id', latest_id);
+        sessionStorage.setItem('gender', gender);
+        // targetPath로 페이지 이동
+        goto(targetPath);
+    }
+    
+  </script>
+  
+  <button
+  type = {type}
+  on:click={handleClick}
+    style="{'background: var(--neutral-10, #6b6b6b);border-radius: 50px; border-style: solid; border-color: var(--neutral-10, #486284); border-width: 1px; padding: 12px 20px 12px 20px; display: flex; flex-direction: row; gap: 10px; align-items: center; justify-content: center; position: relative; overflow: hidden;'}"
+  >
+    <div
+      style="
+        color: var(--neutral-0, #ffffff);
+        text-align: left;
+        font-family: var(--body-small-font-family, 'DmSans-Regular', sans-serif);
+        font-size: var(--body-small-font-size, 18px);
+        line-height: var(--body-small-line-height, 18px);
+        font-weight: var(--body-small-font-weight, 400);
+        position: relative;
+
+      "
+    >
+      <strong>{name}</strong>
+    </div>
+  </button>
\ No newline at end of file
diff --git a/frontend/src/components/button/request_filled.svelte b/frontend/src/components/button/request_filled.svelte
new file mode 100644
index 0000000..0318fdb
--- /dev/null
+++ b/frontend/src/components/button/request_filled.svelte
@@ -0,0 +1,61 @@
+<script>
+    import { goto } from '$app/navigation';
+    export let styleVariant = "outlined";
+    let className = "";
+    export { className as class };
+    export let style;
+    const variantsClassName = "style-variant-" + styleVariant;
+    export let name = "Do something";
+    export let type = "";
+    
+
+    async function reRequest() {
+
+      const token = sessionStorage.getItem('auth_token');
+      const result_id= sessionStorage.getItem('id');
+
+      try {
+        const response = await fetch(`https://api.makezenerator.com/api/v1/mz-request/${result_id}/mz-result`, {
+          method: 'POST',
+          headers: {
+            'Token': token,
+          },
+
+          });
+          if (response.ok) {
+            alert('제출된 정보로 재생성 요청했습니다.');
+            goto('/resultlist');
+          }
+          else{
+            const errorResponse = await response.json();
+            console.error('Failed to save rating:', errorResponse);
+            alert(`재요철 실패: ${errorResponse.message}`);
+          }
+        
+      } catch (error) {
+        console.error('Error saving rating:', error);
+        alert('재요청 중 에러가 발생했습니다.');
+
+      }
+    }
+  </script>
+  
+  <button
+  type = {type}
+    on:click={reRequest}
+    style="{'background: var(--neutral-10, #486284);border-radius: 50px; border-style: solid; border-color: var(--neutral-10, #486284); border-width: 1px; padding: 12px 20px 12px 20px; display: flex; flex-direction: row; gap: 10px; align-items: center; justify-content: center; position: relative; overflow: hidden;' + style}"
+  >
+    <div
+      style="
+        color: var(--neutral-0, #ffffff);
+        text-align: left;
+        font-family: var(--body-small-font-family, 'DmSans-Regular', sans-serif);
+        font-size: var(--body-small-font-size, 22px);
+        line-height: var(--body-small-line-height, 24px);
+        font-weight: var(--body-small-font-weight, 400);
+        position: relative;
+      "
+    >
+      {name}
+    </div>
+  </button>
\ No newline at end of file
diff --git a/frontend/src/components/button/result_save.svelte b/frontend/src/components/button/result_save.svelte
new file mode 100644
index 0000000..130237d
--- /dev/null
+++ b/frontend/src/components/button/result_save.svelte
@@ -0,0 +1,30 @@
+<script>
+    export let targetImage = "/join/placeholder-image2.svg";
+    export let fileName = "default";
+    
+    async function downloadImage() {
+    const response = await fetch(targetImage);
+    const blob = await response.blob();
+    const url = window.URL.createObjectURL(blob);
+    const a = document.createElement('a');
+    a.href = url;
+    a.download = fileName+"."+targetImage.split('.').pop();
+    document.body.appendChild(a);
+    a.click();
+    window.URL.revokeObjectURL(url);
+    document.body.removeChild(a);
+}
+</script>
+
+<img on:click={downloadImage}
+              class="tabler-download2"
+              style="
+                flex-shrink: 0;
+                width: 48px;
+                height: 48px;
+                position: relative;
+                overflow: visible;
+              "
+              src="/join/tabler-download1.svg"
+              alt="save"
+            />
\ No newline at end of file
diff --git a/frontend/src/components/header/header_login.svelte b/frontend/src/components/header/header_login.svelte
new file mode 100644
index 0000000..01ef166
--- /dev/null
+++ b/frontend/src/components/header/header_login.svelte
@@ -0,0 +1,143 @@
+<script>
+    import ButtonStyleFilled from "../../routes/join/ButtonStyleFilled.svelte";
+    import { goto } from '$app/navigation';
+
+    let className = "";
+    export { className as class };
+
+
+    function handleLogout() {
+    // 로컬 스토리지에서 'auth_token' 제거
+    sessionStorage.clear();
+    alert("로그아웃 되었습니다.");
+    goto('/');
+  }
+  </script>
+<style global>
+    a {
+   text-decoration: none;
+ }
+
+ @font-face {
+    font-family: 'yg-jalnan';
+    src: url('https://cdn.jsdelivr.net/gh/projectnoonnu/noonfonts_four@1.2/JalnanOTF00.woff') format('woff');
+    font-weight: normal;
+    font-style: normal;
+}
+</style>
+
+
+
+<link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+<link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&family=Titan+One&display=swap" rel="stylesheet">
+
+
+
+<div
+style="
+  background: var(--neutral-0, #ffffff);
+  border-style: solid;
+  border-color: var(--neutral-2, #dee5ed);
+  border-width: 0px 0px 0.5px 0px;
+  padding: 8px 98px 8px 98px;
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: space-between;
+  flex-shrink: 0;
+  width: 1440px;
+  height: 72px;
+  position: relative;
+  overflow: hidden;
+"
+>
+<div>
+<a href="/home" style="flex-shrink: 0; width: 299px; height: 55px; position: static">
+  <div
+    style="
+    color: var(--7b95b7, #6b6b6b);
+    text-align: center;
+    font-family: 'yg-jalnan';
+    line-height: 28px;
+    font-weight: 400;
+    font-size: 23px;
+    position: absolute;
+    left: 173px;
+    top: 25px;
+    "
+  >
+    <strong>Make Zenerator</strong>
+  </div>
+  <img
+    class="{'image-2 ' + className}"
+    style="
+      width: 70px;
+      position: absolute;
+      left: 100px;
+      top: 5px;
+      object-fit: cover;
+    "
+    src="./logo/logo.png"
+    alt="logo"
+  />
+</a>
+</div>
+<div
+  style="
+    display: flex;
+    flex-direction: row;
+    gap: 48px;
+    align-items: center;
+    justify-content: flex-start;
+    flex-shrink: 0;
+    position: relative;
+  "
+>
+  <a href="/aboutus"
+    style="
+      color: var(--7b95b7, #6b6b6b);
+      text-align: center;
+      font-family: var(
+        --body-small-font-family,
+        'DmSans-Regular',
+        sans-serif
+      );
+      font-size: var(--body-small-font-size, 24px);
+      line-height: var(--body-small-line-height, 24px);
+      font-weight: var(--body-small-font-weight, 400);
+      position: relative;
+    "
+  >
+    About us
+</a>
+  <a href="/"
+  on:click={handleLogout}
+    style="
+      color: var(--7b95b7, #6b6b6b);
+      text-align: center;
+      font-family: var(
+        --body-small-font-family,
+        'DmSans-Regular',
+        sans-serif
+      );
+      font-size: var(--body-small-font-size, 22px);
+      line-height: var(--body-small-line-height, 24px);
+      font-weight: var(--body-small-font-weight, 400);
+      position: relative;
+    "
+  >
+    로그아웃
+</a>
+  <ButtonStyleFilled
+    styleVariant="filled"
+    style="
+      background: var(--6b6b6b, #000000);
+      border-color: var(--7b95b7, #6b6b6b);
+      flex-shrink: 0;
+    "
+    name="생성하기"
+    targetPath="/infogather"
+  ></ButtonStyleFilled>
+</div>
+</div>
\ No newline at end of file
diff --git a/frontend/src/components/header/header_non.svelte b/frontend/src/components/header/header_non.svelte
new file mode 100644
index 0000000..116cc2e
--- /dev/null
+++ b/frontend/src/components/header/header_non.svelte
@@ -0,0 +1,132 @@
+<script>
+    import ButtonStyleFilled from "../../routes/join/ButtonStyleFilled.svelte";
+    let className = "";
+    export { className as class };
+
+    
+  </script>
+<style global>
+     a {
+    text-decoration: none;
+  }
+
+  @font-face {
+    font-family: 'yg-jalnan';
+    src: url('https://cdn.jsdelivr.net/gh/projectnoonnu/noonfonts_four@1.2/JalnanOTF00.woff') format('woff');
+    font-weight: normal;
+    font-style: normal;
+}
+</style>
+
+<link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+<link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&family=Titan+One&display=swap" rel="stylesheet">
+
+
+<div
+  style="
+    background: var(--neutral-0, #ffffff);
+    border-style: solid;
+    border-color: var(--neutral-2, #dee5ed);
+    border-width: 0px 0px 0.5px 0px;
+    padding: 8px 98px 8px 98px;
+    display: flex;
+    flex-direction: row;
+    align-items: center;
+    justify-content: space-between;
+    flex-shrink: 0;
+    width: 1440px;
+    height: 72px;
+    position: relative;
+    overflow: hidden;
+  "
+>
+<div>
+  <a href="/" style="flex-shrink: 0; width: 299px; height: 55px; position: static">
+      <div
+    style="
+    color: var(--7b95b7, #6b6b6b);
+    text-align: center;
+    font-family: 'yg-jalnan';
+    line-height: 28px;
+    font-weight: 400;
+    font-size: 23px;
+    position: absolute;
+    left: 173px;
+    top: 25px;
+    "
+  >
+    <strong>Make Zenerator</strong>
+  </div>
+  <img
+  class="{'image-2 ' + className}"
+  style="
+    width: 70px;
+    position: absolute;
+    left: 100px;
+    top: 5px;
+    object-fit: cover;
+  "
+  src="./logo/logo.png"
+  alt="logo"
+/>
+    </a>
+    </div>
+  <div
+    style="
+      display: flex;
+      flex-direction: row;
+      gap: 48px;
+      align-items: center;
+      justify-content: flex-start;
+      flex-shrink: 0;
+      position: relative;
+    "
+  >
+    <a href="/aboutus"
+    style="
+    color: var(--7b95b7, #6b6b6b);
+    text-align: center;
+    font-family: var(
+      --body-small-font-family,
+      'DmSans-Regular',
+      sans-serif
+    );
+    font-size: var(--body-small-font-size, 24px);
+    line-height: var(--body-small-line-height, 24px);
+    font-weight: var(--body-small-font-weight, 400);
+    position: relative;
+  "
+>
+      About us
+</a>
+    <a href="/login" 
+    style="
+    color: var(--7b95b7, #6b6b6b);
+    text-align: center;
+    font-family: var(
+      --body-small-font-family,
+      'DmSans-Regular',
+      sans-serif
+    );
+    font-size: var(--body-small-font-size, 22px);
+    line-height: var(--body-small-line-height, 24px);
+    font-weight: var(--body-small-font-weight, 400);
+    position: relative;
+  "
+>
+      로그인  
+</a>
+    <ButtonStyleFilled
+      styleVariant="filled"
+      style="
+        background: var(--6b6b6b, #000000);
+        border-color: var(--7b95b7, #6b6b6b);
+        flex-shrink: 0;
+      "
+      name ="회원가입"
+      targetPath="/join"
+      
+    ></ButtonStyleFilled>
+  </div>
+</div>
\ No newline at end of file
diff --git a/frontend/src/components/image/PlaceholderImage.svelte b/frontend/src/components/image/PlaceholderImage.svelte
new file mode 100644
index 0000000..58a409e
--- /dev/null
+++ b/frontend/src/components/image/PlaceholderImage.svelte
@@ -0,0 +1,11 @@
+<script>
+  let className = "";
+  export { className as class };
+  export let style;
+  export let targetPath= "/";
+</script>
+<img
+  class="{'placeholder-image ' + className}"
+  style="{'border-radius: 24px;padding: 10px; display: flex; flex-direction: row; gap: 10px; align-items: center; justify-content: center; flex-shrink: 0;width: 250px; height: 250px; position: relative; overflow: visible;' + style}"
+  src= {targetPath}
+/>
diff --git a/frontend/src/components/image/SocialProperty1Github.svelte b/frontend/src/components/image/SocialProperty1Github.svelte
new file mode 100644
index 0000000..3b55635
--- /dev/null
+++ b/frontend/src/components/image/SocialProperty1Github.svelte
@@ -0,0 +1,18 @@
+<script>
+  export let property1 = "github";
+  let className = "";
+  export { className as class };
+  export let style;
+  const variantsClassName = "property-1-" + property1;
+  export let targetPath = "";
+
+  function navigate() {
+    window.location.href = targetPath;
+  }
+</script>
+<img
+  class="{'social-property-1-github ' + className + ' ' + variantsClassName}"
+  style="{'flex-shrink: 0;width: 30px; height: 30px; position: relative; overflow: visible;' + style}"
+  src="logo/github.png"
+  on:click={navigate}
+/>
diff --git a/frontend/src/components/modal/basic_modal.svelte b/frontend/src/components/modal/basic_modal.svelte
new file mode 100644
index 0000000..42e630a
--- /dev/null
+++ b/frontend/src/components/modal/basic_modal.svelte
@@ -0,0 +1,71 @@
+<script>
+	export let showModal; // boolean
+	export let p_header = "주의사항";
+
+	let dialog; // HTMLDialogElement
+
+	$: if (dialog && showModal) dialog.showModal();
+</script>
+
+<!-- svelte-ignore a11y-click-events-have-key-events a11y-no-noninteractive-element-interactions -->
+<dialog
+	bind:this={dialog}
+	on:close={() => (showModal = false)}
+	on:click|self={() => dialog.close()}
+>
+	<!-- svelte-ignore a11y-no-static-element-interactions -->
+	<div on:click|stopPropagation style="display: flex ; flex-direction: column ; align-items: center; gap: 5px; padding:5px, 5px, 5px, 5px;">
+		<p style="font-size: 18pt; ">
+			<strong>{p_header}</strong>
+		  </p>
+		<hr />
+		<slot />
+		<hr />
+		<!-- svelte-ignore a11y-autofocus -->
+		<button autofocus on:click={() => dialog.close()}>닫기</button>
+	</div>
+</dialog>
+
+<style>
+	dialog {
+		max-width: 32em;
+		border-radius: 0.2em;
+		border: none;
+		padding: 0;
+	}
+	dialog::backdrop {
+		background: rgba(0, 0, 0, 0.3);
+	}
+	dialog > div {
+		padding: 1em;
+	}
+	dialog[open] {
+		animation: zoom 0.3s cubic-bezier(0.34, 1.56, 0.64, 1);
+	}
+	@keyframes zoom {
+		from {
+			transform: scale(0.95);
+		}
+		to {
+			transform: scale(1);
+		}
+	}
+	dialog[open]::backdrop {
+		animation: fade 0.2s ease-out;
+	}
+	@keyframes fade {
+		from {
+			opacity: 0;
+		}
+		to {
+			opacity: 1;
+		}
+	}
+	button {
+		display: block;
+	}
+
+	hr {
+		width: 100%; /* hr의 너비를 부모 요소의 전체 너비로 설정 */
+	}
+</style>
diff --git a/frontend/src/components/rating/Star.svelte b/frontend/src/components/rating/Star.svelte
new file mode 100644
index 0000000..353c2cd
--- /dev/null
+++ b/frontend/src/components/rating/Star.svelte
@@ -0,0 +1,22 @@
+<script>
+	export let title = 'star';
+	export let filled = false;
+	export let starId;
+</script>
+<style>
+	.star {
+		margin-right: 4px;
+	}
+	.starPath {
+		fill: none;
+		stroke: #999;
+	}
+	.filled {
+		fill: #FFD400;
+	}
+</style>
+
+<svg xmlns="http://www.w3.org/2000/svg" width="50" height="50" viewBox="0 0 51 48" class="star" on:mouseover on:mouseleave on:click data-starid={starId} let:class>
+	<title>{title}</title>
+	<path class:filled  data-starid={starId} class="starPath" d="m25,1 6,17h18l-14,11 5,17-15-10-15,10 5-17-14-11h18z" />
+</svg>
\ No newline at end of file
diff --git a/frontend/src/components/rating/StarRating.svelte b/frontend/src/components/rating/StarRating.svelte
new file mode 100644
index 0000000..f40c5f7
--- /dev/null
+++ b/frontend/src/components/rating/StarRating.svelte
@@ -0,0 +1,143 @@
+<script>
+
+	import Star from './Star.svelte';
+	
+	// User rating states
+	let rating = null;
+	let hoverRating = null;
+	
+	// form interaction states
+	let collectFeedback = false;
+	let feedbackCompleted = false;
+	
+	let result ='';
+	export let ABtype ;
+	export let result_id;
+	export let latest_id;
+	
+	// using curried function to initialize hover with id
+	const handleHover = (id) => () => {
+		hoverRating = id;
+	} 
+	const handleRate = (id) => (event) => {
+		if (collectFeedback && 
+				rating && 
+				rating.toString() === event.srcElement.dataset.starid
+		) {
+			collectFeedback = false;
+			return;
+		}
+		rating = id;
+		collectFeedback = true;
+		saveRating();
+	} 
+	
+	let stars = [
+		{ id: 1, title: 'One Star' },
+		{ id: 2, title: 'Two Stars' },
+		{ id: 3, title: 'Three Stars' },
+		{ id: 4, title: 'Four Stars' },
+		{ id: 5, title: 'Five Stars' },
+	]
+
+	
+    function checkRating() {
+        if (rating === null) {
+            alert('별점을 선택해주세요.');
+        } else {
+			saveRating();
+        }
+    } 
+    
+
+	async function saveRating() {
+    const token = sessionStorage.getItem('auth_token'); // sessionStorage에서 토큰 가져오기
+	const formData = new FormData();
+    formData.append('type',ABtype);
+	formData.append('rating',rating);
+	
+    try {
+        const response = await fetch(`https://api.makezenerator.com/api/v1/mz-request/${result_id}/mz-result/${latest_id}`, {
+            method: 'PATCH',
+            headers: {
+                'Token': token,
+      },
+            body: formData,
+        });
+
+        if (response.ok) {
+            const result = await response.json();
+            alert(`별점 ${rating}점이 성공적으로 저장되었습니다.`);
+        } else {
+            const errorResponse = await response.json();
+            console.error('Failed to save rating:', errorResponse);
+        }
+    } catch (error) {
+        console.error('Error saving rating:', error);
+    }
+}
+
+	
+</script>
+<style>
+	.feedback {
+		position: relative;
+	}
+	.collectFeedbackContainer {
+		width: 300px;
+		position: absolute;
+		top: 44px;
+		left: 0;
+		background: #fff;
+		border: 1px solid #666;
+		padding: 8px;
+		transition: transform .2s ease-out;
+	}
+	.collectFeedbackContainer textarea {
+		display: block;
+		width: 100%;
+		height: 120px;
+		resize: none;
+	}
+	.cancel {
+		background: none;
+		text-decoration: underline;
+		border: none;
+	}
+	.starContainer {
+		display: flex; 
+        flex-direction: row; 
+        gap: 4px;
+		transition: background .2s ease-out;
+		border-radius: 10px;
+		padding: 4px 6px 0;
+	}
+	.feedbackContainer:hover .starContainer {
+		background: #efefef;
+	}
+	.secondaryAction {
+		margin: 8px;
+		font-size: 12px;
+		display: inline-block;
+	}
+	.feedbackContainerDisabled {
+		pointer-events: none;
+	}
+	:global(button) {
+		cursor: pointer;
+	}
+</style>
+
+<div class="feedback"> 
+	<span id="feedbackContiner" class="feedbackContainer" class:feedbackContainerDisabled={feedbackCompleted}>
+		<span class="starContainer">
+		{#each stars as star (star.id)}
+			<Star 
+					filled={hoverRating ? (hoverRating >= star.id) : (rating >= star.id)} 
+					starId={star.id}
+					on:mouseover={handleHover(star.id)} 
+					on:mouseleave={() => hoverRating = null}
+					on:click={handleRate(star.id)}
+				/>
+		{/each}
+</div>
\ No newline at end of file
diff --git a/frontend/src/components/survey/input_area.svelte b/frontend/src/components/survey/input_area.svelte
new file mode 100644
index 0000000..6f0e195
--- /dev/null
+++ b/frontend/src/components/survey/input_area.svelte
@@ -0,0 +1,30 @@
+<script>
+    import { onMount } from 'svelte';
+
+    export let value='';
+
+    function autoGrow(oField) {
+        if (oField.scrollHeight > oField.clientHeight) {
+            oField.style.height = oField.scrollHeight + "px";
+        }
+    }
+
+    onMount(() => {
+        autoGrow(value); // 초기 로드 시 자동 높이 조정
+    });
+</script>
+
+<style>
+    .auto-grow-textarea {
+        width: 100%; /* 전체 폭을 차지 */
+        min-height: 50px; /* 최소 높이 */
+        overflow-y: hidden; /* 스크롤바 숨기기 */
+        padding: 8px; /* 패딩 조정 */
+        box-sizing: border-box; /* 패딩을 포함한 총 너비 유지 */
+        border: 1px solid #ccc; /* 테두리 스타일 */
+        resize: none; /* 사용자가 크기 조절 못하게 함 */
+        font-size: 15pt;
+    }
+</style>
+
+<textarea bind:value class="auto-grow-textarea" on:input={e => autoGrow(e.target)}></textarea>
diff --git a/frontend/src/components/survey/survey.svelte b/frontend/src/components/survey/survey.svelte
new file mode 100644
index 0000000..cb022a0
--- /dev/null
+++ b/frontend/src/components/survey/survey.svelte
@@ -0,0 +1,425 @@
+<script>
+    import Radio from "../../routes/join/radio.svelte";
+    import TextArea from "./input_area.svelte";
+    import {goto} from '$app/navigation';
+    export let style;
+  
+    
+    let token = null;
+
+
+    token = sessionStorage.getItem('auth_token'); 
+
+
+
+    const SNS_time =[
+        {value: 0, label: '1시간 미만'},
+        {value: 1, label:'1시간 ~ 3시간 미만'},
+        {value: 2, label: '3시간 ~ 5시간 미만'},
+        {value: 3, label: '5시간 이상'},
+    ]
+    let SNS = null;
+
+    let reason_img_rate; 
+    
+    const well_serviced = [
+        {value: 0, label: '매우 아니다'},
+        {value: 1, label: '아니다'},
+        {value: 2, label: '그렇다'},
+        {value: 3, label: '매우 그렇다'},
+        {value: 4, label: '모르겠다'},
+    ]
+    let v2f = null;
+
+    const dissatisfied_generated_image = [
+        {value: 0, label: '외국인이라 어색하다'},
+        {value: 1, label: '생성된 이미지가 깨끗하지 않다'},
+        {value: 2, label: '목소리가 잘 반영된 것인지 의문스럽다'},
+        {value: 3, label: '성별이 바뀌어 생성된 것 같다'},
+    ] //checkbox
+
+    let image_add_function;
+
+    const face_to_gif_well = [
+        {value: 0, label: '매우 아니다'},
+        {value: 1, label: '아니다'},
+        {value: 2, label: '그렇다'},
+        {value: 3, label: '매우 그렇다'},
+        {value: 4, label: '모르겠다'},
+    ]
+    let f2g= null;
+
+    const more_based_gif = [
+        {value: 1, label: '예'},
+        {value: 0, label: '아니요'},
+    ] //bool
+    let mbg = null;
+
+    const gif_based_additional_function = [
+        {value: 0, label: '드라마/영화 명장면'},
+        {value: 1, label: '예능 움짤'},
+        {value: 2, label: '뮤직비디오'},
+        {value: 3, label: '뉴스'},
+    ] // checkbox
+    
+    const waiting_or_not = [
+        {value: 0, label: '이정도면 기다릴 수 있다'},
+        {value: 1, label: '대기하지 않을 것 같다'},
+    ] //bool
+    let won = null;
+
+    const waiting_about = [
+        {value: 0, label: '1분 이내 생성 결과 확인하기'},
+        {value: 1, label: '생성된 결과를 가입한 이메일로 전달받기'},
+        {value: 2, label: '예시 영상 구경하기'},
+        {value: 3, label: '생성완료되면 카톡으로 알림받기'}
+    ]
+
+    const dissatified_service = [
+        {value: 1, label: '있다'},
+        {value: 0, label: '없다'},
+    ] //bool
+
+    let service = null;
+
+    const agree = [
+        {value: 0, label: '동의 안함'},
+        {value: 1, label:  '동의 함'},
+
+    ]
+    let agree2 = null;
+
+    let service_comments;
+    
+    let email=null;
+    export let survey_id ;
+    export let survey_latest_id;
+    
+    // 생성 얼굴 만족도
+    let selectedValues1 = [];
+    $: sortedSelectedValues1 = [...selectedValues1].sort((a, b) => a - b);
+    $: selectedString1 = sortedSelectedValues1.join(", ");
+
+    // 짤 추천
+    let selectedValues2 = [];
+    $: sortedSelectedValues2 = [...selectedValues2].sort((a, b) => a - b);
+    $: selectedString2 = sortedSelectedValues2.join(", ");
+
+    let selectedValues3 = [];
+    $: sortedSelectedValues3 = [...selectedValues3].sort((a, b) => a - b);
+    $: selectedString3 = sortedSelectedValues3.join(", ");
+
+
+    async function handleSubmit() {
+
+        if (SNS == null) {
+          alert("1번 문항에 답변이 작성되지 않았습니다.");
+          return 
+        }
+        else if (reason_img_rate == null) {
+          alert("2번 문항에 답변이 작성되지 않았습니다.");
+          return
+        }
+        else if (v2f == null) {
+          alert("3번 문항에 답변이 작성되지 않았습니다.");
+          return
+        }
+        else if (f2g == null) {
+          alert("5번 문항에 답변이 작성되지 않았습니다.");
+          return
+        }
+        else if (mbg == null) {
+          alert("6번 문항에 답변이 작성되지 않았습니다.");
+          return
+        }
+        else if (won == null) {
+          alert("7번 문항에 답변이 작성되지 않았습니다.");
+          return
+        }
+        else if (service == null) {
+          alert("8번 문항에 답변이 작성되지 않았습니다.");
+          return
+        }
+
+        const formData = new FormData;
+        formData.append('sns_time', SNS);
+        formData.append('user_phone', email);
+        formData.append('image_rating_reason', reason_img_rate); ///
+        formData.append('voice_to_face_rating', v2f);
+        formData.append('dissatisfy_reason', selectedString1);
+        formData.append('additional_function', image_add_function);
+        formData.append('face_to_gif_rating', f2g); 
+        formData.append('more_gif', mbg);
+        formData.append('more_gif_type', selectedString2); ///
+        formData.append('waiting',won);
+        formData.append('waiting_improvement', selectedString3);
+        formData.append('recommend', service);
+        formData.append('opinion', service_comments);
+
+
+    try {
+      const response = await fetch(`https://api.makezenerator.com/api/v1/mz-request/${survey_id}/mz-result/${survey_latest_id}`, {
+        method: 'POST',
+        headers: {
+            'Token':token,
+        },
+        body: formData,
+      });
+
+      if (response.ok) {
+        alert("설문에 응해주셔서 감사합니다! \n더 좋은 서비스 제공을 위해 노력하는 Make Zenerator 되겠습니다.");
+        goto('/home');
+      }
+      else {
+        alert('Error ');
+      }
+      
+    } catch (error) {
+      console.error('Failed to submit the form:', error);
+    }
+  }
+
+</script>
+
+<style>
+    .question {
+        font-size: 24px;
+        font-weight: bold;
+    }
+
+    .t_box {
+        width: 30%;
+        height:40px;
+    }
+
+
+    .survey-container {
+    display: flex;
+    flex-direction: column;
+    align-items: flex-start;
+    justify-content: center;
+    border-radius: 10px;
+    box-shadow: 0 4px 64px rgba(0, 0, 0, 0.05);
+    border: 1px solid rgba(135, 135, 135, 1);
+    background-color: #fff;
+    margin-top: 56px;
+    letter-spacing: 0.5px;
+    padding: 44px 60px;
+    gap: 20px;
+  }
+  .checkboxContainer {
+    display: flex; 
+    flex-wrap: wrap; 
+    justify-content: space-between; 
+    width: 100%;
+  }
+
+  .checkboxRow {
+    display: flex; 
+    flex-direction: row; 
+    flex-basis: 100%; 
+    justify-content: space-around;
+  }
+
+  .checkboxLabel {
+    font-size: 15pt; 
+    flex-basis: 45%; 
+    display: flex; 
+    align-items: center; 
+  }
+  </style>
+
+    
+
+
+<form on:submit|preventDefault={handleSubmit} style="{'background: var(--neutral-0, #ffffff);padding: 0px 0px 120px 0px; display: flex; flex-direction: column; gap: 120px; align-items: center; justify-content: flex-start; position: relative; width: 1200px;'+ style}">
+
+<div style="'font-size: 20pt">
+    <div class= survey-container >
+        <p class="question">1. SNS나 인터넷, 유튜브를 하루 평균 몇 시간 사용하시나요?</p>
+        <Radio options={SNS_time} fontSize={20} legend=''  bind:userSelected={SNS}/>
+    </div>
+    <div class= survey-container>
+        
+        <p class="question">2. 생성된 이미지들에 대해 해당 별점을 주신 이유가 무엇인가요? </p>
+        <TextArea bind:value={reason_img_rate} />
+        <br>
+        <p class="question"> 3. 목소리를 기반으로 생성된 얼굴이 본인의 목소리를 잘 반영한 것 같다 생각하시나요?</p>
+        <Radio options={well_serviced} fontSize={20} legend=''  bind:userSelected={v2f} />
+        <br>
+        {#if v2f in [0, 1]}
+        <p class="question">3+. 생성된 얼굴이 만족스럽지 않다면, 어떤 점이 불만족스러우신가요? (복수선택 가능)</p>
+        <div class="checkboxContainer">
+            <div class="checkboxRow">
+            {#each dissatisfied_generated_image.slice(0, Math.ceil(dissatisfied_generated_image.length / 2)) as item (item.value)}
+                <label class="checkboxLabel">
+                <input
+                    type="checkbox"
+                    bind:group={selectedValues1}
+                    value={item.value}
+                /> {item.label}
+                </label>
+        {/each}
+        </div>
+    
+    <div class="checkboxRow">
+      {#each dissatisfied_generated_image.slice(Math.ceil(dissatisfied_generated_image.length / 2)) as item (item.value)}
+        <label class="checkboxLabel">
+          <input
+            type="checkbox"
+            bind:group={selectedValues1}
+            value={item.value}
+          /> {item.label}
+        </label>
+      {/each}
+    </div>
+  </div>
+{/if}
+        <br>
+        <p class="question"> 4. 생성된 이미지를 기반하여 추가되면 좋을 듯한 기능 혹은 아이디어가 있으신가요?</p>
+        <TextArea bind:value={image_add_function} />
+        <br>
+
+    </div>
+    <div class= survey-container>
+        <p class="question">5. 생성된 얼굴이 영상에 자연스럽게 적용되었다고 생각하시나요?</p>
+        <Radio options={face_to_gif_well} fontSize={20} legend=''  bind:userSelected={f2g}/>
+        <br>
+        <p class="question"> 6. 더 다양한 영상이 추가되길 원하시나요?</p>
+        <Radio options={more_based_gif} fontSize={20} legend=''  bind:userSelected={mbg} />
+        <br>
+        {#if mbg}
+
+                <p class="question"> 6+. 합성의 기반이 될 영상을 추가한다면 어떤 종류를 원하시나요? (복수선택 가능)</p>
+                <div style="display: flex; items-align:flex-start; gap: 15px; font-size: 15pt;">
+                {#each gif_based_additional_function as item (item.value)}
+                <label>
+                    <input 
+                        type="checkbox"
+                        bind:group={selectedValues2}
+                        value={item.value}
+                    /> {item.label}
+                </label><br>
+                {/each}
+                </div>
+                <br>
+            {/if}
+            
+    </div>
+    <div class= survey-container>
+        <p class="question">7. 현재 서비스 제공 소요시간은 약 2분 입니다. 해당 시간동안 로딩 화면에서 대기하실 의향이 있으신가요?</p>
+        <Radio options={waiting_or_not} fontSize={20} legend=''  bind:userSelected={won}/> <br>
+        {#if won}
+<div class="question">7+. 대기하지 않기를 원하신다면 어떤 방향으로 서비스가 개선되기를 희망하시나요?</div>
+<div class="checkboxContainer">
+  <div class="checkboxRow">
+    {#each waiting_about.slice(0, Math.ceil(waiting_about.length / 2)) as item}
+      <label class="checkboxLabel">
+        <input
+          type="checkbox"
+          bind:group={selectedValues3}
+          value={item.value}
+        /> {item.label}
+      </label>
+    {/each}
+  </div>
+
+  <div class="checkboxRow">
+    {#each waiting_about.slice(Math.ceil(waiting_about.length / 2)) as item}
+      <label class="checkboxLabel">
+        <input
+          type="checkbox"
+          bind:group={selectedValues3}
+          value={item.value}
+        /> {item.label}
+      </label>
+    {/each}
+  </div>
+</div>
+{/if}
+    </div>
+    <div class= survey-container>
+        <p class="question">8. 이 서비스를 친구나 가족에게 추천할 의향이 있으신가요?</p>
+        <Radio options={dissatified_service} fontSize={20}  legend='' bind:userSelected={service}/><br>
+        <p class="question">9. 해당 서비스에 대한 건의사항이나 불편했던 점, 좋았던 점 등에 대해 자유롭게 작성해주세요</p>
+        <TextArea bind:value={service_comments} /> <br>
+    </div>
+    <div class= survey-container style="align-items: center;"> 
+        <div> 
+            <p class= question >개인정보 수집 이용 동의서 (선택)</p>
+            <p style="font-size: 15pt;"> - 추첨을 통한 기프티콘 제공을 위해 개인정보 수집 이용 동의를 받고 있습니다.</p>
+
+        </div>
+        <div
+        style="
+            overflow-y: auto;
+            background: #ffffff;
+            border-radius: 10px;
+            border: 1px solid #000000;
+            padding: 10px 20px;
+            display: flex;
+            flex-direction: column;
+            gap: 10px;
+            align-items: flex-start;
+            justify-content: flex-start;
+            width: 70%;
+            height: 30%;
+            max-height: 350px;
+            color: rgba(0, 0, 0, 0.87);
+            text-align: left;
+            font-family: 'DmSans-Medium', sans-serif;
+            font-size: 20px;
+            line-height: 23px;
+            font-weight: 500;
+            margin: 2px;
+        "
+        >
+        
+        <p><strong>1. 수집하는 개인정보의 항목:</strong></p>
+        <ul>
+            <li>- 이메일</li>
+        </ul>
+        <p><strong>2. 개인정보의 수집 및 이용 목적:</strong></p>
+        <ul>
+            <li>- 설문조사 응답 결과 확인 및 통보</li>
+            <li>- 응답자와의 연락 및 안내</li>
+        </ul>
+        <p><strong>3. 개인정보의 보유 및 이용 기간:</strong></p>
+        <ul>개인정보 수집 및 이용 목적 달성 후 즉시 파기합니다.</ul>
+        <p><strong>4. 동의 거부 권리:</strong></p>
+
+        <p>본인은 개인정보 제공에 대한 동의를 거부할 권리가 있습니다. <br>
+            단, 동의를 거부할 경우 설문조사 경품 추첨에 제한이 있음을 인지합니다.  </p>
+    </div>
+            <p class="question"> 본인은 위와 같이 개인정보 수집 및 이용에 동의 합니다.</p>
+            <Radio options={agree} fontSize={20} legend='' bind:userSelected={agree2} />
+        {#if agree2} 
+            <p class="question"> 이메일 입력</p>
+            <input class="t_box" style="width: 50%;"type="text" placeholder='makezenerator@gmail.com' bind:value={email} />
+        {/if}
+        <br>
+        
+
+        <button type="submit"
+    style="{'background: var(--neutral-10, #000000);border-radius: 50px; border-style: solid; border-color: var(--neutral-10, #486284); border-width: 1px; padding: 12px 20px 12px 20px; display: flex; flex-direction: row; gap: 10px; align-items: center; justify-content: center; position: relative; overflow: hidden;' + style}"
+    >
+
+    
+    <div
+     
+      style="
+        color: var(--neutral-0, #ffffff);
+        text-align: left;
+        font-family: var(--body-small-font-family, 'DmSans-Regular', sans-serif);
+        font-size: var(--body-small-font-size, 24px);
+        line-height: var(--body-small-line-height, 24px);
+        font-weight: var(--body-small-font-weight, 400);
+        position: relative;
+      "
+    >
+      제출하기
+    </div>
+    </button>
+    </div>
+</form>
+
diff --git a/frontend/src/hooks.js b/frontend/src/hooks.js
new file mode 100644
index 0000000..8316747
--- /dev/null
+++ b/frontend/src/hooks.js
@@ -0,0 +1,19 @@
+// export async function handle({ event, resolve }) {
+//   // 'maintenance' 페이지로의 요청을 제외하고 모두 리디렉션
+//   if (!event.url.pathname.startsWith('/maintenance')) {
+//     return Response.redirect(new URL('/maintenance', event.url), 302);
+//   }
+
+//   const response = await resolve(event);
+//   return response;
+// }
+
+export async function handle({ event, resolve }) {
+  // 사용자가 '/maintenance' 페이지에 접근하면 메인 페이지('/')로 리디렉션
+  if (event.url.pathname.startsWith('/maintenance')) {
+    return Response.redirect(new URL('/', event.url), 302);
+  }
+
+  const response = await resolve(event);
+  return response;
+}
\ No newline at end of file
diff --git a/frontend/src/routes/+layout.svelte b/frontend/src/routes/+layout.svelte
new file mode 100644
index 0000000..f67d7ab
--- /dev/null
+++ b/frontend/src/routes/+layout.svelte
@@ -0,0 +1 @@
+<script>import "../app.pcss";</script><slot></slot>
\ No newline at end of file
diff --git a/frontend/src/routes/+page.js b/frontend/src/routes/+page.js
new file mode 100644
index 0000000..a72419a
--- /dev/null
+++ b/frontend/src/routes/+page.js
@@ -0,0 +1,3 @@
+// since there's no dynamic data here, we can prerender
+// it so that it gets served as a static asset in production
+export const prerender = true;
diff --git a/frontend/src/routes/+page.svelte b/frontend/src/routes/+page.svelte
new file mode 100644
index 0000000..9b55b7b
--- /dev/null
+++ b/frontend/src/routes/+page.svelte
@@ -0,0 +1,190 @@
+<script>
+    import ButtonStyleFilled from "./join/ButtonStyleFilled.svelte";
+    import ButtonStyleOutlined from "./join/ButtonStyleOutlined.svelte";
+    import Header from "../components/header/header_non.svelte";
+
+  </script>
+  <div
+  style="{'background: var(--neutral-0, #ffffff);padding: 0px 0px 120px 0px; display: flex; flex-direction: column; gap: 100px; align-items: center; justify-content: flex-start;  position: relative; ' }"
+  >
+  <Header/>
+ <div>
+    <div
+      style="
+        display: flex;
+        flex-direction: column;
+        gap: 120px;
+        align-items: center;
+        justify-content: center;
+        flex-shrink: 0;
+        position: relative;
+      "
+      name="생성하기"
+    >
+      <div
+        style="
+          display: flex;
+          flex-direction: row;
+          gap: 110px;
+          align-items: center;
+          justify-content: flex-end;
+          flex-shrink: 0;
+          width: 1200px;
+          position: relative;
+        "
+      >
+        <div
+          style="
+            display: flex;
+            flex-direction: column;
+            gap: 24px;
+            align-items: flex-start;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            width: 612px;
+            position: relative;
+          "
+        >
+          <div
+            style="
+              display: flex;
+              flex-direction: column;
+              gap: 16px;
+              align-items: flex-start;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              position: relative;
+            "
+          >
+            <div
+              style="
+                display: flex;
+                flex-direction: column;
+                gap: 8px;
+                align-items: flex-start;
+                justify-content: flex-start;
+                flex-shrink: 0;
+                position: relative;
+              "
+            >
+              <div
+                style="
+                  color: #000000;
+                  text-align: left;
+                  font-family: var(
+                    --title-small-font-family,
+                    'DmSans-Bold',
+                    sans-serif
+                  );
+                  font-size: var(--title-small-font-size, 32px);
+                  line-height: var(--title-small-line-height, 40px);
+                  font-weight: var(--title-small-font-weight, 700);
+                  position: relative;
+                  width: 612px;
+                "
+              >
+                10초의 새로운 경험
+              </div>
+              <div
+                style="
+                  color: #000000;
+                  text-align: left;
+                  font-family: var(
+                    --title-large-font-family,
+                    'DmSans-Bold',
+                    sans-serif
+                  );
+                  font-size: var(--title-large-font-size, 60px);
+                  line-height: var(--title-large-line-height, 76px);
+                  font-weight: var(--title-large-font-weight, 700);
+                  position: relative;
+                  width: 612px;
+                "
+              >
+                내 목소리 다른 얼굴
+              </div>
+            </div>
+            <div
+              style="
+                color: #8c8686;
+                text-align: left;
+                font-family: var(
+                  --body-large-font-family,
+                  'DmSans-Regular',
+                  sans-serif
+                );
+                font-size: var(--body-large-font-size, 20px);
+                line-height: var(--body-large-line-height, 32px);
+                font-weight: var(--body-large-font-weight, 400);
+                position: relative;
+                width: 612px;
+              "
+            >
+              Introducing MZ team&#039;s new voice to face technology
+              <br />
+              where you can experience your own impressions and characters with
+              your voice.
+            </div>
+          </div>
+          <div
+            style="
+              display: flex;
+              flex-direction: row;
+              gap: 24px;
+              align-items: flex-start;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              position: relative;
+            "
+          >
+            <ButtonStyleFilled
+              styleVariant="filled"
+              style="background: #000000; border-color: #000000; flex-shrink: 0"
+              name="나의 다른 모습 체험하기"
+              targetPath="/login"
+            ></ButtonStyleFilled>
+            <ButtonStyleOutlined
+              style="border-color: #000000; flex-shrink: 0"
+              name="회원가입"
+              targetPath="/join"
+            ></ButtonStyleOutlined>
+
+
+          </div>
+        </div>
+        <video autoplay loop muted style="width:40% ">
+          <source src="https://storage.makezenerator.com:9000/voice2face-public/site/main/main_sample.mp4" type="video/mp4">
+          Your browser does not support the video tag.
+        </video>
+      </div>
+      <div
+        style="
+          display: flex;
+          flex-direction: column;
+          gap: 20px;
+          align-items: flex-end;
+          justify-content: center;
+          flex-shrink: 0;
+          position: relative;
+        "
+      >
+        
+
+          <img
+            class="group3"
+            style="
+              flex-shrink: 0;
+              width: 300px;
+              height: 100%;
+              position: relative;
+              overflow: visible;
+              top: -18px;
+            "
+            src="/logo/boostcampLogo.jpg"
+            alt="boostcamp"
+          />
+
+      </div>
+    </div>
+  </div>
+  </div>
\ No newline at end of file
diff --git a/frontend/src/routes/aboutus/+page.svelte b/frontend/src/routes/aboutus/+page.svelte
new file mode 100644
index 0000000..ee2b04e
--- /dev/null
+++ b/frontend/src/routes/aboutus/+page.svelte
@@ -0,0 +1,378 @@
+<script>
+    import PlaceholderImage from "../../components/image/PlaceholderImage.svelte";
+    import SocialProperty1Github from "../../components/image/SocialProperty1Github.svelte";
+    import Header from "../../components/header/header_login.svelte";
+    import HeaderNon from "../../components/header/header_non.svelte";
+    import { onMount } from 'svelte';
+    let className = "";
+    export { className as class };
+    let token;
+    let isUserLoggedIn = false;
+
+    onMount(() => {
+    token = sessionStorage.getItem('auth_token');
+    isUserLoggedIn = !!token; 
+  });
+
+  </script>
+  <div
+  style="{'background: var(--neutral-0, #ffffff);padding: 0px 0px 120px 0px; display: flex; flex-direction: column; gap: 40px; align-items: center; justify-content: flex-start; height: 2200px; position: relative; '}"
+>
+{#if isUserLoggedIn}
+<Header />
+{:else}
+<HeaderNon />
+{/if} <div></div>
+    <div
+      style="
+        color: #000000;
+        text-align: center;
+        font-family: var(--title-large-font-family, 'DmSans-Bold', sans-serif);
+        font-size: var(--title-large-font-size, 60px);
+        line-height: var(--title-large-line-height, 76px);
+        font-weight: var(--title-large-font-weight, 700);
+        position: relative;
+        width: 412px;
+        height: 62px;
+      "
+    >
+      About Us
+    </div>
+    <div
+      style="
+        color: #8c8686;
+        text-align: center;
+        font-family: var(--body-large-font-family, 'DmSans-Regular', sans-serif);
+        font-size: var(--body-large-font-size, 20px);
+        line-height: var(--body-large-line-height, 32px);
+        font-weight: var(--body-large-font-weight, 400);
+        position: relative;
+        width: 612px;
+      "
+    >
+    안녕하세요! <br>
+    사용자의 목소리를 기반으로 가상의 얼굴을 생성하는 서비스,<br>
+    Voice2Face를 개발 중인 Make Zenerator 팀입니다. <br>
+    (문의&불편사항: mz240103@gmail.com)<br>
+
+
+    </div>
+    <img src = "/logo/MZ_aboutus.jpg" alt = "MZ_members">
+    <div></div><div
+    style="
+      color: #000000;
+      text-align: center;
+      font-family: var(--title-large-font-family, 'DmSans-Bold', sans-serif);
+      font-size: var(--title-large-font-size, 50px);
+      line-height: var(--title-large-line-height, 76px);
+      font-weight: var(--title-large-font-weight, 700);
+      position: relative;
+      width: 412px;
+      height: 32px;
+    "
+  >
+    MZ Members
+  </div><div></div>
+    <div
+      style="
+        display: flex;
+        flex-direction: column;
+        gap: 54px;
+        align-items: center;
+        justify-content: flex-start;
+        flex-shrink: 0;
+        position: relative;
+      "
+    >
+      <div
+        style="
+          display: flex;
+          flex-direction: row;
+          gap: 10px;
+          align-items: flex-start;
+          justify-content: flex-start;
+          flex-shrink: 0;
+          position: relative;
+        "
+      >
+        <div
+          style="
+            display: flex;
+            flex-direction: column;
+            gap: 21px;
+            align-items: center;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            position: relative;
+          "
+        >
+        <div
+        style="
+          display: flex;
+          flex-direction: column;
+          gap: 18px;
+          align-items: flex-start;
+          justify-content: flex-start;
+          flex-shrink: 0;
+          position: relative;
+        "
+      >
+        <div
+          style="
+            color: #000000;
+            text-align: center;
+            font-family: 'DmSans-Bold', sans-serif;
+            font-size: 30px;
+            line-height: 40px;
+            font-weight: 700;
+            position: relative;
+            width: 294px;
+            height: 40px;
+          "
+        >
+          신호준
+        </div>
+      </div>
+      <SocialProperty1Github style="flex-shrink: 0" targetPath="https://github.com/internationalwe/"></SocialProperty1Github>
+          
+          <PlaceholderImage
+            style="flex-shrink: 0;"
+            targetPath="https://storage.makezenerator.com:9000/voice2face-public/site/aboutus/hojun.jpg"
+          ></PlaceholderImage>
+        </div>
+        <div
+          style="
+            display: flex;
+            flex-direction: column;
+            gap: 21px;
+            align-items: center;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            position: relative;
+          "
+        >
+          <div
+            style="
+              display: flex;
+              flex-direction: column;
+              gap: 18px;
+              align-items: flex-start;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              position: relative;
+            "
+          >
+            <div
+              style="
+                color: #000000;
+                text-align: center;
+                font-family: 'DmSans-Bold', sans-serif;
+                font-size: 30px;
+                line-height: 40px;
+                font-weight: 700;
+                position: relative;
+                width: 294px;
+                height: 40px;
+              "
+            >
+              김태양
+            </div>
+          </div>
+          <SocialProperty1Github style="flex-shrink: 0" targetPath="https://github.com/taeyang916"></SocialProperty1Github>
+          <PlaceholderImage
+            style="flex-shrink: 0;"
+            targetPath=" https://storage.makezenerator.com:9000/voice2face-public/site/aboutus/taeyang.jpg "
+          ></PlaceholderImage>
+        </div>
+        <div
+          style="
+            display: flex;
+            flex-direction: column;
+            gap: 21px;
+            align-items: center;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            position: relative;
+          "
+        >
+          <div
+            style="
+              display: flex;
+              flex-direction: column;
+              gap: 18px;
+              align-items: flex-start;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              position: relative;
+            "
+          >
+            <div
+              style="
+                color: #000000;
+                text-align: center;
+                font-family: 'DmSans-Bold', sans-serif;
+                font-size: 30px;
+                line-height: 40px;
+                font-weight: 700;
+                position: relative;
+                width: 294px;
+                height: 40px;
+              "
+            >
+              정소윤
+            </div>
+          </div>
+          <SocialProperty1Github style="flex-shrink: 0" targetPath="https://github.com/soyoonjeong"></SocialProperty1Github>
+          <PlaceholderImage
+            style="flex-shrink: 0;"
+            targetPath=" https://storage.makezenerator.com:9000/voice2face-public/site/aboutus/soyoon.jpg "
+          ></PlaceholderImage>
+        </div>
+      </div>
+      <div
+        style="
+          display: flex;
+          flex-direction: row;
+          gap: 10px;
+          align-items: flex-start;
+          justify-content: flex-start;
+          flex-shrink: 0;
+          position: relative;
+        "
+      >
+        <div
+          style="
+            display: flex;
+            flex-direction: column;
+            gap: 21px;
+            align-items: center;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            position: relative;
+          "
+        >
+          <div
+            style="
+              display: flex;
+              flex-direction: column;
+              gap: 18px;
+              align-items: flex-start;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              position: relative;
+            "
+          >
+            <div
+              style="
+                color: #000000;
+                text-align: center;
+                font-family: 'DmSans-Bold', sans-serif;
+                font-size: 30px;
+                line-height: 40px;
+                font-weight: 700;
+                position: relative;
+                width: 294px;
+                height: 40px;
+              "
+            >
+              김혜지
+            </div>
+          </div>
+          <SocialProperty1Github style="flex-shrink: 0" targetPath="https://github.com/carboxaminoo"></SocialProperty1Github>
+          <PlaceholderImage
+            style="flex-shrink: 0;"
+            targetPath=" https://storage.makezenerator.com:9000/voice2face-public/site/aboutus/hyeji.jpg "
+          ></PlaceholderImage>
+        </div>
+        <div
+          style="
+            display: flex;
+            flex-direction: column;
+            gap: 21px;
+            align-items: center;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            position: relative;
+          "
+        >
+          <div
+            style="
+              display: flex;
+              flex-direction: column;
+              gap: 18px;
+              align-items: flex-start;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              position: relative;
+            "
+          >
+            <div
+              style="
+                color: #000000;
+                text-align: center;
+                font-family: 'DmSans-Bold', sans-serif;
+                font-size: 30px;
+                line-height: 40px;
+                font-weight: 700;
+                position: relative;
+                width: 294px;
+                height: 40px;
+              "
+            >
+              임서현
+            </div>
+          </div>
+          <SocialProperty1Github style="flex-shrink: 0" targetPath="https://github.com/dlatjgus0612"></SocialProperty1Github>
+          <PlaceholderImage
+            style="flex-shrink: 0;"
+            targetPath="https://storage.makezenerator.com:9000/voice2face-public/site/aboutus/seohyun.jpg"
+          ></PlaceholderImage>
+        </div>
+        <div
+          style="
+            display: flex;
+            flex-direction: column;
+            gap: 21px;
+            align-items: center;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            position: relative;
+          "
+        >
+          <div
+            style="
+              display: flex;
+              flex-direction: column;
+              gap: 18px;
+              align-items: flex-start;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              position: relative;
+            "
+          >
+            <div
+              style="
+                color: #000000;
+                text-align: center;
+                font-family: 'DmSans-Bold', sans-serif;
+                font-size: 30px;
+                line-height: 40px;
+                font-weight: 700;
+                position: relative;
+                width: 294px;
+                height: 40px;
+              "
+            >
+              성주희
+            </div>
+          </div>
+          <SocialProperty1Github style="flex-shrink: 0" targetPath="https://github.com/jouhy"></SocialProperty1Github>
+          <PlaceholderImage
+            style="flex-shrink: 0;"
+            targetPath=" https://storage.makezenerator.com:9000/voice2face-public/site/aboutus/juhy.jpg "
+          ></PlaceholderImage>
+        </div>
+      </div>
+    </div>
+  </div>
+  
\ No newline at end of file
diff --git a/frontend/src/routes/home/+page.svelte b/frontend/src/routes/home/+page.svelte
new file mode 100644
index 0000000..e16fedf
--- /dev/null
+++ b/frontend/src/routes/home/+page.svelte
@@ -0,0 +1,203 @@
+<script>
+    import ButtonStyleFilled from "../join/ButtonStyleFilled.svelte";
+    import ButtonStyleOutlined from "../join/ButtonStyleOutlined.svelte";
+    import Header from "../../components/header/header_login.svelte";
+    import { onMount } from 'svelte';
+    import { goto } from '$app/navigation';
+
+    onMount(() => {
+    const token = sessionStorage.getItem('auth_token');
+    if (!token) {
+      alert(`세션이 만료되었습니다.\n다시 로그인 해주세요.`);
+      goto('/');
+    }
+  });
+
+  </script>
+  <div
+    style="{'background: var(--neutral-0, #ffffff);padding: 0px 0px 120px 0px; display: flex; flex-direction: column; gap: 180px; align-items: center; justify-content: flex-start; height: 845px; position: relative; ' }"
+  >
+    <Header/>
+    <div
+      style="
+        padding: 32px 0px 0px 0px;
+        display: flex;
+        flex-direction: column;
+        gap: 100px;
+        align-items: center;
+        justify-content: center;
+        flex-shrink: 0;
+        position: relative;
+      "
+    >
+      <div
+        style="
+          display: flex;
+          flex-direction: column;
+          gap: 24px;
+          align-items: center;
+          justify-content: flex-start;
+          flex-shrink: 0;
+          position: relative;
+        "
+      >
+        <div
+          style="
+            display: flex;
+            flex-direction: column;
+            gap: 16px;
+            align-items: center;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            position: relative;
+          "
+        >
+          <div
+            style="
+              display: flex;
+              flex-direction: column;
+              gap: 8px;
+              align-items: flex-start;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              position: relative;
+            "
+          >
+            <div
+              style="
+                color: var(--6b6b6b, #000000);
+                text-align: center;
+                font-family: var(
+                  --subtitle-large-font-family,
+                  'DmSans-Medium',
+                  sans-serif
+                );
+                font-size: var(--subtitle-large-font-size, 28px);
+                line-height: var(--subtitle-large-line-height, 32px);
+                font-weight: var(--subtitle-large-font-weight, 500);
+                position: relative;
+                align-self: stretch;
+                display: flex;
+                align-items: center;
+                justify-content: center;
+              "
+            >
+              <strong>무료로 경험하는</strong>
+            </div>
+            <div
+              style="
+                color: var(--6b6b6b, #000000);
+                text-align: center;
+                font-family: var(
+                  --title-large-font-family,
+                  'DmSans-Bold',
+                  sans-serif
+                );
+                font-size: var(--title-large-font-size, 60px);
+                line-height: var(--title-large-line-height, 76px);
+                font-weight: var(--title-large-font-weight, 700);
+                position: relative;
+              "
+            >
+              얼굴 생성하기
+            </div>
+          </div>
+          <div
+            style="
+              color: var(--7b95b7, #6b6b6b);
+              text-align: center;
+              font-family: var(
+                --body-large-font-family,
+                'DmSans-Regular',
+                sans-serif
+              );
+              font-size: var(--body-large-font-size, 20px);
+              line-height: var(--body-large-line-height, 32px);
+              font-weight: var(--body-large-font-weight, 400);
+              position: relative;
+              align-self: stretch;
+            "
+          >
+            목소리를 통해 나의 또 다른 얼굴을
+            <br />
+            찾아보세요
+          </div>
+        </div>
+        <div
+          style="
+            display: flex;
+            flex-direction: row;
+            gap: 24px;
+            align-items: flex-start;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            width: 196px;
+            position: relative;
+            justify-content: center;
+          "
+        >
+          <ButtonStyleFilled
+            styleVariant="filled"
+            style="
+              background: var(--6b6b6b, #000000);
+              border-color: var(--6b6b6b, #000000);
+              flex-shrink: 0;
+            "
+            name="생성하기"
+            targetPath="/infogather"
+          ></ButtonStyleFilled>
+          <ButtonStyleOutlined
+            style="
+              border-color: var(--6b6b6b, #000000);
+              flex-shrink: 0;
+            "
+            name="결과확인"
+            targetPath="/resultlist"
+          ></ButtonStyleOutlined>
+        </div>
+      </div>
+      <div
+        style="
+          display: flex;
+          flex-direction: column;
+          gap: 20px;
+          align-items: center;
+          justify-content: flex-start;
+          flex-shrink: 0;
+          position: relative;
+        "
+      >
+        <div 
+          style="
+            display: flex;
+            flex-direction: row;
+            gap: 64px;
+            align-items: center;
+            justify-content: center;
+            flex-shrink: 0;
+            position: relative;
+            margin-top: 20px;
+          "
+        >
+        
+        </div>
+        
+      </div>
+      <img
+        class="group3"
+        style="
+          flex-shrink: 0;
+          width: 300px;
+          
+          position: relative;
+          overflow: visible;
+          top: -80px;
+        "
+        src="/logo/boostcampLogo.jpg"
+        alt="boostcamp"
+      />
+    </div>
+  </div>
+
+
+
diff --git a/frontend/src/routes/infogather/+page.svelte b/frontend/src/routes/infogather/+page.svelte
new file mode 100644
index 0000000..8dc0cda
--- /dev/null
+++ b/frontend/src/routes/infogather/+page.svelte
@@ -0,0 +1,518 @@
+<script>
+  import BasicButton from "../../components/button/basic_filled.svelte";
+  import VoiceButtonDefaultVariant3 from "./VoiceButtonDefaultVariant3.svelte";
+  import { Progressbar, Button } from 'flowbite-svelte';
+  import { sineOut } from 'svelte/easing';
+  import Radio from "../join/radio.svelte";
+  import Header from "../../components/header/header_login.svelte"
+  export { className as class };
+  import { goto } from '$app/navigation';
+  import { onMount } from 'svelte';
+  import Modal from "../../components/modal/basic_modal.svelte";
+  
+
+  export let targetPath = "/loading";
+
+  let className = "";
+  let info_gender;
+
+  let info_age=null;
+  let progress = 0;
+
+  let audioUrl = '';
+  let audioBlob= null;
+  let showModal = true;
+  
+  const options = [{
+		value: 'man',
+		label: '남',
+	},  {
+		value: 'woman',
+		label: '여',
+	}]
+
+  onMount(() => {
+    const token = sessionStorage.getItem('auth_token');
+    if (!token) {
+      alert(`세션이 만료되었습니다.\n다시 로그인 해주세요.`);
+      goto('/');
+    }
+  });
+
+
+  async function requestimage(){
+    const formData = new FormData();
+    if (info_age == null) {
+      alert("나이를 입력해주세요")
+      return 
+    } 
+    formData.append('age', info_age);
+    formData.append('gender', info_gender);
+    if (audioBlob) {
+    formData.append('file', audioBlob, 'svelte_audio.wav');
+    } else {
+      alert("목소리를 입력해주세요")
+      return
+    }
+
+    
+
+    try{
+        const token = sessionStorage.getItem('auth_token');
+        const response = await fetch('https://api.makezenerator.com/api/v1/mz-request', {
+            method: 'POST',
+            headers: {
+                'Token': token,
+            },
+            body: formData,
+        });
+
+        if (response.ok) {
+            const data = await response.json();
+            alert(`성공적으로 요청했습니다.`);
+            goto(targetPath); 
+        } else if (response.status === 401){
+          alert(`세션이 만료되었습니다.\n다시 로그인 해주세요.`);
+          goto('/');
+        } else if (response.status === 400){
+          alert("데이터베이스 에러");
+        } else if (response.status === 405){
+          alert("생성횟수가 10회를 초과하여 요청실패 되었습니다.")
+        }
+        else {
+            const errorResponse = await response.json(); 
+            alert(`요청 실패: ${errorResponse.error}`);
+        }
+    } catch (error) {
+        console.error('생성 요청 중 에러 발생:', error);
+        alert('요청 중 에러가 발생했습니다.');
+    }
+}
+
+
+  let showExample = false; 
+
+    function toggleExample() {
+    showExample = !showExample; 
+  }
+</script>
+<style>
+  input[type="number"]::-webkit-inner-spin-button,
+  input[type="number"]::-webkit-outer-spin-button {
+    -webkit-appearance: none;
+    margin: 0;
+  }
+
+  input[type="number"] {
+    -moz-appearance: textfield;
+  }
+
+</style>
+
+<form on:submit|preventDefault={requestimage} style="{'background: var(--neutral-0, #ffffff);padding: 0px 0px 120px 0px; display: flex; flex-direction: column; gap: 120px; align-items: center; justify-content: flex-start; height: 900px; position: relative; '}">
+
+  <div
+  style="{'background: var(--neutral-0, #ffffff);padding: 0px 0px 120px 0px; display: flex; flex-direction: column; gap: 40px; align-items: center; justify-content: flex-start; position: relative; '}"
+>
+
+  <Header/>
+  <div
+    style="
+      display: flex;
+      flex-direction: row;
+      gap: 82px;
+      align-items: center;
+      justify-content: center;
+      flex-shrink: 0;
+      width: 1103px;
+      height: 700px;
+      position: relative;
+    "
+  >
+    <div
+      style="
+        display: flex;
+        flex-direction: row;
+        gap: 82px;
+        align-items: center;
+        justify-content: flex-start;
+        flex-shrink: 0;
+        height: 800px;
+        position: relative;
+      "
+    >
+      <div
+        style="
+          display: flex;
+          flex-direction: row;
+          gap: 82px;
+          align-items: flex-start;
+          justify-content: flex-start;
+          flex-shrink: 0;
+          height: 675px;
+          position: relative;
+        "
+      >
+        <div
+          style="
+            background: #ffffff;
+            border-radius: 10px;
+            border-style: solid;
+            border-color: #878787;
+            border-width: 0.5px;
+            padding: 47px 0px 47px 0px;
+            display: flex;
+            flex-direction: column;
+            gap: 10px;
+            align-items: center;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            width: 451px;
+            position: relative;
+            box-shadow: 0px 4px 64px 0px rgba(0, 0, 0, 0.05);
+            overflow: hidden;
+          "
+        >
+          <div
+            style="
+              display: flex;
+              flex-direction: column;
+              gap: 20px;
+              align-items: center;
+              justify-content: center;
+              flex-shrink: 0;
+              height: 83px;
+              position: relative;
+            "
+          >
+            <div
+              style="
+                display: flex;
+                flex-direction: column;
+                gap: 20px;
+                align-items: center;
+                justify-content: center;
+                flex-shrink: 0;
+                position: relative;
+              "
+            >
+              <div
+                style="
+                  color: #000000;
+                  text-align: center;
+                  font-family: 'DmSans-Bold', sans-serif;
+                  font-size: 46px;
+                  line-height: 40px;
+                  font-weight: 700;
+                  position: relative;
+                  width: 451px;
+                  height: 40px;
+                "
+              >
+                생성하기
+              </div>
+            </div>
+          </div>
+          <div
+            style="
+              display: flex;
+              flex-direction: column;
+              gap: 14px;
+              align-items: flex-start;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              position: relative;
+            "
+          > 
+            <div
+              style="
+                display: flex;
+                flex-direction: column;
+                gap: 15px;
+                align-items: flex-start;
+                justify-content: flex-start;
+                flex-shrink: 0;
+                width: 331px;
+                position: relative;
+              "
+            >
+              <div
+                style="
+                  color: #000000;
+                  text-align: left;
+                  font-family: 'DmSans-Medium', sans-serif;
+                  font-size: 19px;
+                  line-height: 40px;
+                  font-weight: 500;
+                  position: relative;
+                  width: 331px;
+                  height: 25px;
+                  display: flex;
+                  align-items: center;
+                  justify-content: flex-start;
+                "
+              >
+                성별
+              </div>
+              <div
+                style="
+                  display: flex;
+                  flex-direction: row;
+                  gap: 18px;
+                  align-items: center;
+                  justify-content: center;
+                  flex-shrink: 0;
+                  width: 337px;
+                  height: 18px;
+                  position: relative;
+                "
+              >
+              <Radio {options} fontSize={20} legend='' bind:userSelected={info_gender}/>
+              </div>
+            </div>
+            <div
+              style="
+                color: #000000;
+                text-align: left;
+                font-family: 'DmSans-Medium', sans-serif;
+                font-size: 19px;
+                line-height: 40px;
+                font-weight: 500;
+                position: relative;
+                width: 337px;
+                height: 26px;
+                display: flex;
+                align-items: center;
+                justify-content: flex-start;
+              "
+            >
+              나이
+            </div>
+            <div
+  style="
+    background: #ffffff;
+    border-radius: 10px;
+    border-style: solid;
+    border-color: #000000;
+    border-width: 1px;
+    padding: 1px 43px 1px 43px;
+    display: flex;
+    flex-direction: row;
+    gap: 10px;
+    align-items: center;
+    justify-content: center;
+    flex-shrink: 0;
+    width: 337px;
+    height: 46px;
+    position: relative;
+    overflow: hidden;
+  "
+>
+  <input
+    type="number"
+    bind:value="{info_age}"
+    min=20
+    max=50
+    style="
+      color: rgba(0, 0, 0, 0.87);
+      text-align: center;
+      font-family: 'DmSans-Medium', sans-serif;
+      font-size: 16px;
+      line-height: 40px;
+      font-weight: 500;
+      border: none;
+      width: 100%; 
+      height: 40px; 
+      background: transparent; 
+      -webkit-appearance: none; 
+      margin: 0; 
+    "
+
+    placeholder="나이 입력" 
+  />
+</div> 
+
+            <div
+              style="
+                color: #000000;
+                text-align: left;
+                font-family: 'DmSans-Medium', sans-serif;
+                font-size: 19px;
+                line-height: 40px;
+                font-weight: 500;
+                position: relative;
+                width: 337px;
+                height: 21px;
+                display: flex;
+                align-items: center;
+                justify-content: flex-start;
+              "
+            >
+              목소리
+            </div>
+            <div
+              style="
+                display: flex;
+                flex-direction: row;
+                gap: 57px;
+                align-items: center;
+                justify-content: flex-start;
+                flex-shrink: 0;
+                width: 337px;
+                position: relative;
+              "
+            >
+            <VoiceButtonDefaultVariant3 
+            on:audioRecorded={(event) => {
+              audioUrl = event.detail;
+            }} 
+            on:progressUpdated={(event) => {progress = event.detail;}}
+            on:blobUpdated={(event) => { audioBlob = event.detail; }}
+/>
+            {#if audioUrl == ''}
+            <Progressbar {progress}
+            animate
+            precision={2}
+            tweenDuration={10100}
+            easing={sineOut}
+            size="h-6" />
+          
+              {:else}
+                <audio controls src = {audioUrl}></audio>
+              {/if}
+            </div>
+            <div style="line-height: 18pt;">
+              <strong>주의사항</strong><br>
+              1. 마이크가 제대로 작동하는지 확인해주세요. <br>
+              2. 조용한 장소에서 녹음해주세요. <br>
+              3. 마이크를 누르면 <strong>1초 뒤</strong> 녹음을 시작합니다.<br>
+              4. 10초 동안 아무 말이나 해주세요. <br>
+              5. <strong>목소리 주인공</strong>의 성별과 나이를 입력해주세요. <br>
+              6. 생성 횟수는 <strong>10회</strong>로 제한합니다.
+              
+
+            </div>
+            
+              <div style="line-height: 18pt;">
+                <p><strong>예시문장</strong></p>
+                <p>안녕하세요, 저희는 엠지 팀입니다.</p>
+                <p>현재 목소리를 통해 얼굴을 만들어주는 </p>
+                <p>프로젝트를 진행하고 있습니다.</p>
+                <p>내 목소리의 또 다른 나를 만들어보세요!</p>
+              </div>
+          </div>
+        </div>
+      </div>
+    </div>
+    <div
+      style="
+        display: flex;
+        flex-direction: column;
+        gap: 24px;
+        align-items: center;
+        justify-content: flex-start;
+        flex-shrink: 0;
+        position: relative;
+      "
+    >
+      <div
+        style="
+          display: flex;
+          flex-direction: column;
+          gap: 16px;
+          align-items: center;
+          justify-content: flex-start;
+          flex-shrink: 0;
+          position: relative;
+        "
+      >
+        <div
+          style="
+            display: flex;
+            flex-direction: column;
+            gap: 8px;
+            align-items: flex-start;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            position: relative;
+          "
+        >
+          <div
+            style="
+              color: var(--6b6b6b, #000000);
+              text-align: center;
+              font-family: var(
+                --title-large-font-family,
+                'DmSans-Bold',
+                sans-serif
+              );
+              font-size: var(--title-large-font-size, 60px);
+              line-height: var(--title-large-line-height, 76px);
+              font-weight: var(--title-large-font-weight, 700);
+              position: relative;
+            "
+          >
+            얼굴 생성하기
+          </div>
+        </div>
+        <div
+          style="
+            color: var(--7b95b7, #6b6b6b);
+            text-align: center;
+            font-family: var(
+              --body-large-font-family,
+              'DmSans-Regular',
+              sans-serif
+            );
+            font-size: var(--body-large-font-size, 20px);
+            line-height: var(--body-large-line-height, 32px);
+            font-weight: var(--body-large-font-weight, 400);
+            position: relative;
+            align-self: stretch;
+          "
+        >
+          나의 성별, 나이, 목소리를 입력해주세요. <br>
+          (베타 테스트 단계로 나이는 <strong>20~50세</strong>까지 설정 가능합니다.)
+        </div>
+      </div>
+      <div
+        style="
+          display: flex;
+          flex-direction: column;
+          gap: 24px;
+          align-items: center;
+          justify-content: flex-start;
+          flex-shrink: 0;
+          position: relative;
+        "
+      >
+        <BasicButton
+          styleVariant="filled"
+          style="
+            background: var(--6b6b6b, #000000);
+            border-color: var(--6b6b6b, #000000);
+            flex-shrink: 0;
+            width: 143px;
+          "
+          name="생성하기"
+          type="submit"
+        ></BasicButton>
+      </div>
+    </div>
+  </div>
+</div>
+</form>
+
+
+<Modal bind:showModal>
+
+  <ol class="definition-list" style="font-size: 14pt; line-height: 18pt;">
+    <li>1. 마이크가 제대로 작동하는지 확인해주세요.</li>
+    <li>2. 조용한 장소에서 녹음해주세요.</li>
+    <li>3. 마이크를 누르면 <strong>1초 뒤</strong> 녹음을 시작합니다.</li>
+    <li>4. 10초 동안 아무 말이나 해주세요.</li>
+    <li>5. <strong>목소리 주인공</strong>의 성별과 나이를 입력해주세요.</li>
+    <li>6. 생성 횟수는 <strong>10회</strong>로 제한합니다.</li>
+    <li>(베타 테스트 단계로 나이는 <strong>20~50세</strong> 까지 설정 가능합니다.)</li>
+  </ol>
+</Modal>
\ No newline at end of file
diff --git a/frontend/src/routes/infogather/VoiceButtonDefaultVariant3.svelte b/frontend/src/routes/infogather/VoiceButtonDefaultVariant3.svelte
new file mode 100644
index 0000000..edc6ec3
--- /dev/null
+++ b/frontend/src/routes/infogather/VoiceButtonDefaultVariant3.svelte
@@ -0,0 +1,77 @@
+<script>
+  import { createEventDispatcher } from 'svelte';
+  
+  const dispatch = createEventDispatcher();
+  export let variant = 'variant-3';
+  let className = '';
+  export { className as class };
+  const variantsClassName = 'default-' + variant;
+  
+  let mediaRecorder;
+  let audioChunks = [];
+  let isRecordingComplete = false; 
+  let progress = 0;
+
+  async function startRecording() {
+    audioChunks = []; 
+    isRecordingComplete = false; 
+
+    try {
+      const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
+      mediaRecorder = new MediaRecorder(stream);
+      
+      mediaRecorder.ondataavailable = event => {
+        audioChunks.push(event.data);
+      };
+      
+      mediaRecorder.onstop = () => {
+        const audioBlob = new Blob(audioChunks, { type: 'audio/wav' });
+        const audioUrl = URL.createObjectURL(audioBlob);
+
+        dispatch('audioRecorded', audioUrl );
+        dispatch('blobUpdated', audioBlob);
+        isRecordingComplete = true;
+      };
+
+      setTimeout(() => {
+        mediaRecorder.start();
+        progress = 100;
+        dispatch('progressUpdated', progress);
+
+        setTimeout(() => {
+          mediaRecorder.stop();
+        }, 10100); 
+      }, 1000);
+      
+    } catch (err) {
+      console.error('오디오 입력을 시작하는데 실패했습니다:', err);
+    }
+  }
+
+  function restartRecording() {
+    audioChunks = []; 
+    isRecordingComplete = false; 
+    dispatch('audioRecorded', "");
+    dispatch('progressUpdated',0);
+  }
+</script>
+
+<div style="{'width: 100px; height: 50px; position: relative; '}">
+  {#if !isRecordingComplete}
+    <img
+      class="{'frame-6 ' + className + ' ' + variantsClassName}"
+      style="..."
+      src="/join/frame-60.svg"
+      on:click={startRecording}
+      alt="audio record"
+    />
+  {:else}
+    <img
+      class="{'frame-6 ' + className + ' ' + variantsClassName}"
+      style="..."
+      src="/join/restart.png"
+      on:click={restartRecording}
+      alt="restart"
+    />
+  {/if}
+</div>
diff --git a/frontend/src/routes/join/+page.svelte b/frontend/src/routes/join/+page.svelte
new file mode 100644
index 0000000..775778b
--- /dev/null
+++ b/frontend/src/routes/join/+page.svelte
@@ -0,0 +1,569 @@
+<script>
+  import BasicFilled from "../../components/button/basic_filled.svelte";
+  import PlaceholderImage from "./PlaceholderImage.svelte";
+  import Header from "../../components/header/header_non.svelte";
+  import Radio from "./radio.svelte"
+  import { goto } from '$app/navigation';
+  export let targetPath = "/";
+  let join_email;
+  let join_pswd;
+  let join_check;
+  let join_gender;
+  let join_age;
+  let join_agree = false;
+  let passwordMismatch = false;
+
+  const options = [{
+  value: 'man',
+  label: '남',
+},  {
+  value: 'woman',
+  label: '여',
+}]
+
+$: {
+  if (join_pswd !== join_check && join_check) {
+    passwordMismatch = true;
+  } else {
+    passwordMismatch = false;
+  }
+}
+
+
+async function handleSubmit(event) {
+    event.preventDefault();
+
+    if (!join_agree) {
+      alert("개인정보 수집 이용에 동의를 체크해주셔야 서비스를 이용하실 수 있습니다.");
+      return;
+    }
+
+    if (join_pswd !== join_check) {
+      alert("비밀번호가 일치하지 않습니다.");
+      return;
+    }
+
+    const formData = new FormData();
+    formData.append('email', join_email);
+    formData.append('password', join_pswd);
+    formData.append('age', join_age);
+    formData.append('gender', join_gender);
+    
+
+    try {
+      const response = await fetch('https://api.makezenerator.com/api/v1/users', {
+        method: 'POST',
+        body: formData,
+      });
+
+      if (response.ok) {
+        const result = await response.json();
+        alert("회원가입 완료했습니다!!");
+        goto(targetPath); // 성공 시 리디렉션
+
+      } else if (response.status === 409){
+        alert(`이메일이 중복되었습니다. 다른 이메일을 입력해주세요. `);
+      }
+        else {
+        const error = await response.json();
+        alert(`회원가입 실패: ${error.message}`);
+      }
+    } catch (error) {
+      console.error('회원가입 중 에러 발생:', error);
+      alert('회원가입 중 에러가 발생했습니다.');
+    }
+  }
+</script>
+
+<style>
+  .custom-scroll::-webkit-scrollbar {
+    display: none;
+}
+  input[type="number"]::-webkit-inner-spin-button,
+  input[type="number"]::-webkit-outer-spin-button {
+    -webkit-appearance: none;
+    margin: 0;
+  }
+
+  input[type="number"] {
+    -moz-appearance: textfield;
+  }
+
+</style>
+<form 
+  on:submit|preventDefault={handleSubmit} 
+  style="{
+  'background: var(--neutral-0, #ffffff);padding: 0px 0px 120px 0px; display: flex; flex-direction: column; gap: 100px; align-items: center; justify-content: flex-start; height: auto; position: relative; ' }">
+<div
+  style="{'background: var(--neutral-0, #ffffff);padding: 0px 0px 0px 0px; display: flex; flex-direction: column; gap: 99px; align-items: center; justify-content: flex-start; height: auto; position: relative; ' }"
+>
+ <Header/>
+  <div
+    style="
+      display: flex;
+      flex-direction: row;
+      gap: 41px;
+      align-items: center;
+      justify-content: center;
+      flex-shrink: 0;
+      position: relative;
+    "
+  ><div style=" display: flex; justify-contents: center; align-items: center; gap: 80px">
+    <div
+      style="
+        background: #ffffff;
+        border-radius: 10px;
+        border-style: solid;
+        border-color: #878787;
+        border-width: 0.5px;
+        padding: 47px 0px 80px 0px;
+        display: flex;
+        flex-direction: row;
+        align-items: flex-start;
+        justify-content: center;
+        flex-shrink: 0;
+        width: 468px;
+
+        position: relative;
+        box-shadow: 0px 4px 64px 0px rgba(0, 0, 0, 0.05);
+        overflow: hidden;
+      "
+    >
+      <div
+        style="
+          display: flex;
+          flex-direction: column;
+          gap: 43px;
+          align-items: center;
+          justify-content: flex-start;
+          flex-shrink: 0;
+          height: 822px;
+          position: relative;
+        "
+      >
+        <div
+          style="
+            color: #000000;
+            text-align: center;
+            font-family: 'DmSans-Bold', sans-serif;
+            font-size: 46px;
+            line-height: 40px;
+            font-weight: 700;
+            position: relative;
+            width: 451px;
+            height: 40px;
+          "
+        >
+          회원가입
+        </div>
+        <div
+          style="
+            display: flex;
+            flex-direction: column;
+            gap: 22px;
+            align-items: center;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            position: relative;
+          "
+        >
+          <div
+            style="
+              display: flex;
+              flex-direction: column;
+              gap: 13px;
+              align-items: center;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              position: relative;
+            "
+          >
+            <div
+              style="
+                display: flex;
+                flex-direction: column;
+                gap: 10px;
+                align-items: flex-start;
+                justify-content: flex-start;
+                flex-shrink: 0;
+                position: relative;
+              "
+            >
+              <div
+                style="
+                  color: #000000;
+                  text-align: left;
+                  font-family: 'DmSans-Medium', sans-serif;
+                  font-size: 19px;
+                  line-height: 40px;
+                  font-weight: 500;
+                  position: relative;
+                  width: 125px;
+                  height: 23px;
+                  display: flex;
+                  align-items: center;
+                  justify-content: flex-start;
+                "
+              >
+                이메일 주소
+              </div>
+              <div
+                style="
+                  background: #ffffff;
+                  border-radius: 10px;
+                  border-style: solid;
+                  border-color: #000000;
+                  border-width: 1px;
+                  padding: 1px 43px 1px 43px;
+                  display: flex;
+                  flex-direction: row;
+                  gap: 10px;
+                  align-items: center;
+                  justify-content: flex-start;
+                  flex-shrink: 0;
+                  width: 337px;
+                  position: relative;
+                  overflow: hidden;
+                "
+              >
+                <input type='email' bind:value={join_email} placeholder="이메일 입력"
+                  style="
+                    color: rgba(0, 0, 0, 0.4);
+                    text-align: center;
+                    font-family: 'DmSans-Medium', sans-serif;
+                    font-size: 16px;
+                    line-height: 30px;
+                    font-weight: 500;
+                    position: relative;
+                    width: 100%;
+                    display: flex;
+                    align-items: center;
+                    justify-content: center;
+                    border: none;
+                  "
+                />
+          
+              </div>
+              <div
+                style="
+                  color: #000000;
+                  text-align: left;
+                  font-family: 'DmSans-Medium', sans-serif;
+                  font-size: 19px;
+                  line-height: 40px;
+                  font-weight: 500;
+                  position: relative;
+                  width: 125px;
+                  height: 23px;
+                  display: flex;
+                  align-items: center;
+                  justify-content: flex-start;
+                "
+              >
+                비밀번호
+              </div>
+              <div
+                style="
+                  background: #ffffff;
+                  border-radius: 10px;
+                  border-style: solid;
+                  border-color: #000000;
+                  border-width: 1px;
+                  padding: 1px 43px 1px 43px;
+                  display: flex;
+                  flex-direction: row;
+                  gap: 10px;
+                  align-items: center;
+                  justify-content: flex-start;
+                  flex-shrink: 0;
+                  width: 337px;
+                  position: relative;
+                  overflow: hidden;
+                "
+              >
+                <input type='password' bind:value={join_pswd} placeholder="비밀번호 입력"
+                  style="
+                    color: rgba(0, 0, 0, 0.4);
+                    text-align: center;
+                    font-family: 'DmSans-Medium', sans-serif;
+                    font-size: 16px;
+                    line-height: 30px;
+                    font-weight: 500;
+                    position: relative;
+                    width: 100%;
+                    display: flex;
+                    align-items: center;
+                    justify-content: center;
+                    border: none;
+                  "
+                />
+                
+              </div>
+              <div
+                style="
+                  color: #000000;
+                  text-align: left;
+                  font-family: 'DmSans-Medium', sans-serif;
+                  font-size: 19px;
+                  line-height: 40px;
+                  font-weight: 500;
+                  position: relative;
+                  width: 125px;
+                  height: 23px;
+                  display: flex;
+                  align-items: center;
+                  justify-content: flex-start;
+                "
+              >
+                비밀번호 확인
+              </div>
+              <div
+                style="
+                  background: #ffffff;
+                  border-radius: 10px;
+                  border-style: solid;
+                  border-color: #000000;
+                  border-width: 1px;
+                  padding: 1px 43px 1px 43px;
+                  display: flex;
+                  flex-direction: row;
+                  gap: 10px;
+                  align-items: center;
+                  justify-content: flex-start;
+                  flex-shrink: 0;
+                  width: 337px;
+                  position: relative;
+                  overflow: hidden;
+                "
+              >
+                <input type='password' bind:value={join_check} placeholder="비밀번호 입력"
+                  style="
+                    color: rgba(0, 0, 0, 0.4);
+                    text-align: center;
+                    font-family: 'DmSans-Medium', sans-serif;
+                    font-size: 16px;
+                    line-height: 30px;
+                    font-weight: 500;
+                    position: relative;
+                    width: 100%;
+                    display: flex;
+                    align-items: center;
+                    justify-content: center;
+                    margin: 0;
+                    background: transparent;
+                    -webkit-appearance: none;
+                    border: none;
+                  "
+                />
+              </div>
+              <div
+                style="
+                  color: #000000;
+                  text-align: left;
+                  font-family: 'DmSans-Medium', sans-serif;
+                  font-size: 19px;
+                  line-height: 40px;
+                  font-weight: 500;
+                  position: relative;
+                  width: 125px;
+                  height: 23px;
+                  display: flex;
+                  align-items: center;
+                  justify-content: flex-start;
+                "
+              >
+                성별
+              </div>
+              <div
+                style="
+                  display: flex;
+                  flex-direction: row;
+                  gap: 18px;
+                  align-items: center;
+                  justify-content: center;
+                  flex-shrink: 0;
+                  width: 337px;
+                  position: relative;
+                  align-items: center;
+                "
+              >
+              <Radio {options} fontSize={20} legend='' bind:userSelected={join_gender}/>
+              </div>
+              <div
+                style="
+                  color: #000000;
+                  text-align: left;
+                  font-family: 'DmSans-Medium', sans-serif;
+                  font-size: 19px;
+                  line-height: 40px;
+                  font-weight: 500;
+                  position: relative;
+                  width: 125px;
+                  height: 23px;
+                  display: flex;
+                  align-items: center;
+                  justify-content: flex-start;
+                "
+              >
+                나이
+              </div>
+              <div
+                style="
+                  background: #ffffff;
+                  border-radius: 10px;
+                  border-style: solid;
+                  border-color: #000000;
+                  border-width: 1px;
+                  padding: 1px 43px 1px 43px;
+                  display: flex;
+                  flex-direction: row;
+                  gap: 10px;
+                  align-items: center;
+                  justify-content: flex-start;
+                  flex-shrink: 0;
+                  width: 337px;
+                  position: relative;
+                  overflow: hidden;
+                "
+              >
+              <input
+              type="number"
+              bind:value="{join_age}"
+              min=0
+              max=120
+              style="
+                color: rgba(0, 0, 0, 0.87);
+                text-align: center;
+                font-family: 'DmSans-Medium', sans-serif;
+                font-size: 16px;
+                line-height: 40px;
+                font-weight: 500;
+                border: none;
+                width: 100%; 
+                height: 46px; /* 입력 상자의 높이 조정 */
+                background: transparent; /* 배경색 투명 */
+                -webkit-appearance: none; /* 스타일 초기화 */
+                margin: 0; /* margin 초기화 */
+              "
+          
+              placeholder="나이 입력" 
+            />
+              </div>
+            </div>
+            <div
+              style="
+                display: flex;
+                flex-direction: column;
+                gap: 0px;
+                align-items: flex-start;
+                justify-content: flex-start;
+                flex-shrink: 0;
+                position: relative;
+              "
+            >
+              <div
+                style="
+                  color: #000000;
+                  text-align: left;
+                  font-family: 'DmSans-Medium', sans-serif;
+                  font-size: 20px;
+                  line-height: 40px;
+                  font-weight: 500;
+                  position: relative;
+                  width: 339px;
+                  height: 43px;
+                  display: flex;
+                  align-items: center;
+                  justify-content: flex-start;
+                "
+              >
+                개인정보 수집 이용 동의서
+              </div>
+              <div
+                style="
+                  display: flex;
+                  flex-direction: column;
+                  gap: 5px;
+                  align-items: flex-end;
+                  justify-content: flex-start;
+                  flex-shrink: 0;
+                  position: relative;
+                "
+              >
+              <div class="custom-scroll"
+                style="
+                  overflow-y: auto;
+                  background: #ffffff;
+                  border-radius: 10px;
+                  border: 1px solid #000000;
+                  padding: 10px 20px;
+                  display: flex;
+                  flex-direction: column;
+                  gap: 10px;
+                  align-items: flex-start;
+                  justify-content: flex-start;
+                  width: 337px;
+                  height: 158px;
+                  max-height: 200px;
+                  color: rgba(0, 0, 0, 0.87);
+                  text-align: left;
+                  font-family: 'DmSans-Medium', sans-serif;
+                  font-size: 12px;
+                  line-height: 15px;
+                  font-weight: 500;
+                  margin: 0px;
+                "
+              >
+                
+                  <p><strong>본인은 아래와 같이 개인정보의 수집 및 이용에 동의합니다.</strong></p>
+                  
+                  <p><strong>1. 수집하는 개인정보의 항목:</strong></p>
+                  <ul>
+                      <li>이메일 (Email)</li>
+                      <li>성별 (Gender)</li>
+                      <li>나이 (Age)</li>
+                      <li>목소리 (Voice)</li>
+                  </ul>
+                  
+                  <p><strong>2. 개인정보의 수집 및 이용 목적:</strong></p>
+                  <ul>
+                      <li>회원 서비스 제공을 위한 이메일 통보 및 연락</li>
+                      <li>회원의 특성에 따른 맞춤형 서비스 제공</li>
+                      <li>목소리 데이터를 활용한 얼굴 생성 기술 개발 및 서비스 향상</li>
+                      <li>수집된 데이터를 기반으로 한 모델 학습을 통한 개선된 서비스 제공</li>
+                  </ul>
+                  
+                  <p><strong>3. 개인정보의 보유 및 이용 기간:</strong></p>
+                  <p>개인정보 수집 및 이용 목적 달성 후 즉시 파기합니다. 단, 관련 법령에 따라 보존할 필요가 있는 경우에는 해당 법령의 규정에 따라 보존합니다.</p>
+                  
+                  <p><strong>4. 동의 거부 권리:</strong></p>
+                  <p>본인은 개인정보 제공에 대한 동의를 거부할 권리가 있습니다. 단, 필수 항목에 대한 동의를 거부할 경우 회원 가입이 제한될 수 있습니다.</p>
+                  
+                  <p><strong>본인은 위와 같이 개인정보 수집 및 이용에 동의합니다.</strong></p>
+                </div>
+                <label style="display: flex; align-items: center; cursor: pointer;">
+                  <input type="checkbox" bind:checked={join_agree}>
+                  <span style="margin-left: 8px;">동의함</span>
+              </div>
+            </div>
+          </div>
+          <BasicFilled
+            styleVariant="filled"
+            style="background: var(--7b95b7, #6b6b6b); flex-shrink: 0"
+            name="회원가입"
+            type="submit"
+          ></BasicFilled>
+        </div>
+      </div>
+    </div>
+    <div style="display: flex; ">
+      <PlaceholderImage
+      style="flex-shrink: 0; width: 600px;"
+      targetPath="logo/join.png"
+    ></PlaceholderImage>
+    </div>
+    
+  </div>
+  </div>
+</div>
+</form>
\ No newline at end of file
diff --git a/frontend/src/routes/join/ButtonStyleFilled.svelte b/frontend/src/routes/join/ButtonStyleFilled.svelte
new file mode 100644
index 0000000..6a69983
--- /dev/null
+++ b/frontend/src/routes/join/ButtonStyleFilled.svelte
@@ -0,0 +1,46 @@
+<script>
+  import { goto } from '$app/navigation';
+  import { page } from '$app/stores'; // SvelteKit의 페이지 정보에 접근하기 위해 import
+  import { get } from 'svelte/store'; // store 값을 직접 읽기 위해 import
+  export let styleVariant = "outlined";
+  let className = "";
+  export { className as class };
+  export let style;
+  const variantsClassName = "style-variant-" + styleVariant;
+  export let name = "Do something";
+  
+  export let targetPath = "/";
+  export let type="button";
+  
+  function handleClick() {
+    const currentPath = get(page).url.pathname; // 현재 페이지의 경로를 가져옴
+    if (currentPath === targetPath) {
+      window.location.reload(); // 현재 경로와 targetPath가 같으면 페이지 새로고침
+    } else {
+      goto(targetPath); // 다르면 targetPath로 이동
+    }
+  }
+</script>
+<link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+<link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&family=Titan+One&display=swap" rel="stylesheet">
+
+
+<button
+on:click={handleClick} type={type}
+  style="{'background: var(--neutral-10, #486284);border-radius: 50px; border-style: solid; border-color: var(--neutral-10, #486284); border-width: 1px; padding: 12px 20px 12px 20px; display: flex; flex-direction: row; gap: 10px; align-items: center; justify-content: center; position: relative; overflow: hidden;' + style}"
+>
+  <div
+    style="
+      color: var(--neutral-0, #ffffff);
+      text-align: left;
+      font-family: var(--body-small-font-family, 'DmSans-Regular', sans-serif);
+      font-size: var(--body-small-font-size, 22px);
+      line-height: var(--body-small-line-height, 24px);
+      font-weight: var(--body-small-font-weight, 400);
+      position: relative;
+    "
+  >
+    {name}
+  </div>
+</button>
\ No newline at end of file
diff --git a/frontend/src/routes/join/ButtonStyleOutlined.svelte b/frontend/src/routes/join/ButtonStyleOutlined.svelte
new file mode 100644
index 0000000..18e91f6
--- /dev/null
+++ b/frontend/src/routes/join/ButtonStyleOutlined.svelte
@@ -0,0 +1,35 @@
+<script>
+    export let styleVariant = "outlined";
+    let className = "";
+    export { className as class };
+    export let style;
+    const variantsClassName = "style-variant-" + styleVariant;
+    export let name = "Do something"
+    import { goto } from '$app/navigation';
+    export let targetPath = "/";
+    function handleClick() {
+      goto(targetPath); // 버튼 클릭 시 targetPath로 이동
+    }
+  </script>
+<link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+<link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&family=Titan+One&display=swap" rel="stylesheet">
+
+
+  <button on:click={handleClick}
+    style="{'border-radius: 50px; border-style: solid; border-color: var(--neutral-10, #ffffff); border-width: 1px; padding: 12px 20px 12px 20px; display: flex; flex-direction: row; gap: 10px; align-items: center; justify-content: center; position: relative; overflow: hidden;' + style}"
+  >
+    <div
+      style="
+        color: var(--neutral-10, #6b6b6b);
+        text-align: left;
+        font-family: var(--body-small-font-family, 'DmSans-Regular', sans-serif);
+        font-size: var(--body-small-font-size, 22px);
+        line-height: var(--body-small-line-height, 24px);
+        font-weight: var(--body-small-font-weight, 400);
+        position: relative;
+      "
+    >
+      {name}
+    </div>
+  </button>
\ No newline at end of file
diff --git a/frontend/src/routes/join/PlaceholderImage.svelte b/frontend/src/routes/join/PlaceholderImage.svelte
new file mode 100644
index 0000000..ed5aee6
--- /dev/null
+++ b/frontend/src/routes/join/PlaceholderImage.svelte
@@ -0,0 +1,12 @@
+<script>
+    let className = "";
+    export let targetPath = "/join/placeholder-image.svg"
+    export { className as class };
+    export let style;
+  </script>
+  <img
+    class="{'placeholder-image ' + className}"
+    style="{'border-radius: 24px;padding: 10px; display: flex; flex-direction: row; gap: 10px; align-items: center; justify-content: center; align-self: stretch;flex: 1;height: auto; position: relative; overflow: visible;' + style}"
+    src= {targetPath}
+    alt= "placeholder"
+  />
\ No newline at end of file
diff --git a/frontend/src/routes/join/RadioBigDefault.svelte b/frontend/src/routes/join/RadioBigDefault.svelte
new file mode 100644
index 0000000..5926924
--- /dev/null
+++ b/frontend/src/routes/join/RadioBigDefault.svelte
@@ -0,0 +1,46 @@
+<script>
+    export let unknownProp94 = "default";
+    let className = "";
+    export { className as class };
+    export let style;
+    const variantsClassName = "unknown-prop-94-" + unknownProp94;
+    export let name = "라디오 버튼";
+  </script>
+  <div
+    style="{' display: flex; flex-direction: row; gap: 14px; align-items: center; justify-content: flex-start; position: relative; ' + style}"
+  >
+    <div style="flex-shrink: 0; width: 16px; height: 16px; position: static">
+      <div
+        style="
+          background: #ffffff;
+          border-radius: 50%;
+          border-style: solid;
+          border-color: #676666;
+          border-width: 1px;
+          width: 16px;
+          height: 16px;
+          position: absolute;
+          left: 0px;
+          top: 0px;
+        "
+      ></div>
+    </div>
+    <div
+      style="
+        color: #676666;
+        text-align: left;
+        font-family: 'DmSans-Medium', sans-serif;
+        font-size: 18px;
+        line-height: 40px;
+        font-weight: 500;
+        position: relative;
+        width: 96px;
+        height: 14px;
+        display: flex;
+        align-items: center;
+        justify-content: flex-start;
+      "
+    >
+      {name}
+    </div>
+  </div>
\ No newline at end of file
diff --git a/frontend/src/routes/join/RadioSmallS.svelte b/frontend/src/routes/join/RadioSmallS.svelte
new file mode 100644
index 0000000..2d69635
--- /dev/null
+++ b/frontend/src/routes/join/RadioSmallS.svelte
@@ -0,0 +1,46 @@
+<script>
+    export let unknownProp29 = "s";
+    let className = "";
+    export { className as class };
+    export let style;
+    const variantsClassName = "unknown-prop-29-" + unknownProp29;
+    export let name="작은 라디오버튼";
+  </script>
+  <div
+    style="{' display: flex; flex-direction: row; gap: 5px; align-items: center; justify-content: flex-start; width: 62px; position: relative; ' + style}"
+  >
+    <div style="flex-shrink: 0; width: 8px; height: 8px; position: static">
+      <div
+        style="
+          background: #ffffff;
+          border-radius: 50%;
+          border-style: solid;
+          border-color: #000000;
+          border-width: 1px;
+          width: 8px;
+          height: 8px;
+          position: absolute;
+          left: 0px;
+          top: 3px;
+        "
+      ></div>
+    </div>
+    <div
+      style="
+        color: #000000;
+        text-align: left;
+        font-family: 'DmSans-Medium', sans-serif;
+        font-size: 9px;
+        line-height: 40px;
+        font-weight: 500;
+        position: relative;
+        width: 97px;
+        height: 14px;
+        display: flex;
+        align-items: center;
+        justify-content: flex-start;
+      "
+    >
+      {name}
+    </div>
+  </div>
\ No newline at end of file
diff --git a/frontend/src/routes/join/radio.svelte b/frontend/src/routes/join/radio.svelte
new file mode 100644
index 0000000..72f8f3b
--- /dev/null
+++ b/frontend/src/routes/join/radio.svelte
@@ -0,0 +1,160 @@
+<script>
+    // based on suggestions from:
+    // Sami Keijonen https://webdesign.tutsplus.com/tutorials/how-to-make-custom-accessible-checkboxes-and-radio-buttons--cms-32074
+    // and Inclusive Components by Heydon Pickering https://inclusive-components.design/toggle-button/
+  
+    export let options;
+    export let legend;
+    export let userSelected = options[0].value;
+      export let fontSize = 30;
+      export let flexDirection = 'row'
+      
+      const uniqueID = Math.floor(Math.random() * 100)
+  
+    const slugify = (str = "") =>
+      str.toLowerCase().replace(/ /g, "-").replace(/\./g, "");
+  </script>
+  
+  <div role="radiogroup" 
+                   class="group-container"
+                   aria-labelledby={`label-${uniqueID}`}
+                   style="font-size:{fontSize}px; flex-direction:{flexDirection}" 
+                   id={`group-${uniqueID}`}>
+          <div class="legend" 
+                           id={`label-${uniqueID}`}>{legend}
+                  </div>
+    {#each options as { value, label }}
+      <input
+        class="sr-only"
+        type="radio"
+        id={slugify(label) + '-' + uniqueID}
+        bind:group={userSelected}
+        value={value} />
+        <label for={slugify(label) + '-' + uniqueID}> {label} </label>
+    {/each}
+  </div>
+  
+  <style>
+
+    
+              :root {
+          --accent-color: CornflowerBlue;
+          --gray: #ccc;
+      }
+      
+     .group-container {
+        border-radius: 2px;
+        display: flex;
+        flex-direction: row;
+      }
+  
+    .legend {
+      font-weight: bold;
+    }
+    label {
+      user-select: none;
+      line-height: 1.2em;
+      margin-right: 80px;
+    }
+    .radio-button-container {
+        display: flex;
+        justify-content: center; /* 가로 방향 중앙 정렬 */
+        align-items: center; /* 세로 방향 중앙 정렬 */
+        height: 100vh; /* 전체 높이를 차지하도록 설정 (예시) */
+    }
+  
+    .sr-only {
+      position: absolute;
+      clip: rect(1px, 1px, 1px, 1px);
+      padding: 0;
+      border: 0;
+      height: 1px;
+      width: 1px;
+      overflow: hidden;
+    }
+  
+    input[type="radio"] {
+      position: absolute;
+    }
+  
+    input[type="radio"] + label {
+      display: block;
+      position: relative;
+      text-align: left;
+    }
+  
+    input[type="radio"] + label::before {
+        content: "";
+        position: relative;
+        display: inline-block;
+        margin-right: 0.5em;
+        width: 1em;
+        height: 1em;
+        background: transparent;
+        border: 1px solid var(--gray, #ccc);
+        border-radius: 50%;
+        top: 0.2em;
+    }
+  
+    input[type="radio"]:checked + label::before {
+      border: 1px solid var(--gray, #ccc);
+      border-radius: 50%;
+    }
+  
+    input[type="radio"] + label::after {
+      content: "";
+      position: absolute;
+      display: inline-block;
+      width: 0.5em;
+      height: 0.5em;
+      top: 0.45em;
+      left: 0.25em;
+      background: var(--accent-color, #282828);
+      border: 1px solid var(--accent-color, #282828);
+      border-radius: 50%;
+      transform: scale(0);
+    }
+  
+    input[type="radio"]:checked + label::after {
+      opacity: 1;
+      transform: scale(1);
+    }
+  
+    input[type="radio"]:focus + label::before {
+      box-shadow: 0 0 0 1px var(--accent-color, #282828);
+      border-radius: 50%;
+    }  
+    
+    input[type="radio"]:disabled + label {
+      color: darken(var(--gray, #ccc), 10);
+    }
+  
+    input[type="radio"]:disabled + label::before {
+      background: var(--gray, #ccc);
+    } 
+    /* gravy */
+  
+  
+    input[type="radio"] + label::before {
+        transition: background 0.3s ease-out;
+    }
+  
+    input[type="radio"]:checked + label::before {
+      transition: background 0.3s ease-in;
+    }
+  
+    input[type="radio"] + label::after {
+      transition: transform 0.2s ease-out;
+    }
+  
+    input[type="radio"]:checked + label::after {
+      transition: transform 0.2s ease-in;
+    }
+  
+    input[type="radio"]:focus + label::before {
+      box-shadow: 0 0px 8px var(--accent-color, #282828);
+      border-radius: 50%;
+    }
+  
+  </style>
+  
\ No newline at end of file
diff --git a/frontend/src/routes/loading/+page.svelte b/frontend/src/routes/loading/+page.svelte
new file mode 100644
index 0000000..811f19c
--- /dev/null
+++ b/frontend/src/routes/loading/+page.svelte
@@ -0,0 +1,148 @@
+<script>
+    import ButtonStyleOutlined from "../join/ButtonStyleOutlined.svelte";
+    import HeaderLogin from "../../components/header/header_login.svelte";
+    import PlaceholderImage from "../join/PlaceholderImage.svelte";
+    import { onMount } from 'svelte';
+    import { goto } from '$app/navigation';
+    onMount(() => {
+    const token = sessionStorage.getItem('auth_token');
+    if (!token) {
+      alert(`세션이 만료되었습니다.\n다시 로그인 해주세요.`);
+      goto('/');
+    }
+  });
+
+  </script>
+  <div
+    style="{'background: var(--neutral-0, #ffffff); display: flex; flex-direction: column; gap: 160px; align-items: center; justify-content: flex-start; height: 845px; position: relative; '}"
+  >
+   <HeaderLogin />
+    <div
+      style="
+        display: flex;
+        flex-direction: row;
+        gap: 82px;
+        align-items: center;
+        justify-content: center;
+        flex-shrink: 0;
+        width: 1103px;
+        height: 429px;
+        position: relative;
+      "
+    >
+      <div
+        style="
+          display: flex;
+          flex-direction: row;
+          gap: 82px;
+          align-items: center;
+          justify-content: flex-start;
+          flex-shrink: 0;
+          height: 423px;
+          position: relative;
+        "
+      >
+        <div
+          style="
+            display: flex;
+            flex-direction: row;
+            gap: 82px;
+            align-items: center;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            height: 401px;
+            position: relative;
+          "
+        >
+          <div
+            style="
+              background: #ffffff;
+              border-radius: 10px;
+              border-style: solid;
+              border-color: #878787;
+              border-width: 0.5px;
+              padding: 47px 0px 47px 0px;
+              display: flex;
+              flex-direction: row;
+              gap: 10px;
+              align-items: flex-start;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              width: 451px;
+              height: 395px;
+              position: relative;
+              box-shadow: 0px 4px 64px 0px rgba(0, 0, 0, 0.05);
+              overflow: hidden;
+            "
+          >
+            <div
+              style="
+                display: flex;
+                flex-direction: column;
+                gap: 20px;
+                align-items: center;
+                justify-content: center;
+                flex-shrink: 0;
+                height: 193px;
+                position: relative;
+              "
+            >
+              <div
+                style="
+                  display: flex;
+                  flex-direction: column;
+                  gap: 20px;
+                  align-items: center;
+                  justify-content: center;
+                  flex-shrink: 0;
+                  height: 294px;
+                  position: relative;
+                "
+              >
+                <div
+                  style="
+                    text-align: center;
+                    position: relative;
+                    width: 451px;
+                    height: 30px;
+                    font-size: 16pt;
+                  "
+                >
+                  <span>
+                    <span class="div-2-span2"><br /></span>
+                    <span class="div-2-span3">
+                      생성 요청했습니다.
+                      <br />
+                      예상 소요시간은 5~10분 입니다.
+                    </span>
+                  </span>
+                </div>
+              </div>
+              <ButtonStyleOutlined
+                style="
+                  border-color: var(--7b95b7, #6b6b6b);
+                  flex-shrink: 0;
+                  height: 46px;
+                "
+                name="메인으로 돌아가기"
+                targetPath="/home"
+              ></ButtonStyleOutlined>
+              <ButtonStyleOutlined
+                style="
+                  border-color: var(--7b95b7, #6b6b6b);
+                  flex-shrink: 0;
+                  height: 46px;
+                "
+                name="결과목록으로 가기"
+                targetPath="/resultlist"
+              ></ButtonStyleOutlined>
+            </div>
+          </div>
+          <PlaceholderImage
+            style="flex-shrink: 0; width: 405px; height: 405px"
+            targetPath="/logo/loading.png"
+          ></PlaceholderImage>
+        </div>
+      </div>
+    </div>
+  </div>
\ No newline at end of file
diff --git a/frontend/src/routes/login/+page.svelte b/frontend/src/routes/login/+page.svelte
new file mode 100644
index 0000000..af56f2b
--- /dev/null
+++ b/frontend/src/routes/login/+page.svelte
@@ -0,0 +1,296 @@
+<script>
+  import BasicFilled from "../../components/button/basic_filled.svelte";
+  import PlaceholderImage from "./PlaceholderImage.svelte";
+  import Header from "../../components/header/header_non.svelte";
+  import { goto } from '$app/navigation';
+
+  let login_email;
+  let login_pswd;
+  let targetPath = "/home";
+
+  async function login() {
+    const formData = new FormData();
+    formData.append('email', login_email);
+    formData.append('password', login_pswd);
+
+    try{
+        const response = await fetch('https://api.makezenerator.com/api/v1/auth', {
+            method: 'POST',
+            body: formData,
+        });
+
+        if (response.ok) {
+            const data = await response.json(); // 백엔드로부터 받은 데이터를 JSON으로 파싱
+            const { token, email } = data; // 파싱된 JSON 객체에서 token과 email을 추출
+
+            // 토큰과 이메일을 로컬 스토리지에 저장
+            sessionStorage.setItem('auth_token', token);
+
+            alert(`로그인 성공!`);
+
+
+            goto(targetPath); // 사용자를 홈 페이지로 리다이렉트
+        } else {
+            alert('로그인 실패 \n 이메일과 비밀번호를 확인해주세요');
+        }
+    } catch (error) {
+      console.error('로그인 중 에러 발생:', error);
+      alert('로그인 중 에러가 발생했습니다.');
+    }
+  }
+
+
+</script>
+<form on:submit|preventDefault={login} style="{'background: var(--neutral-0, #ffffff);padding: 0px 0px 120px 0px; display: flex; flex-direction: column; gap: 120px; align-items: center; justify-content: flex-start; height: 845px; position: relative; '}">
+<div
+  style="{'background: var(--neutral-0, #ffffff); display: flex; flex-direction: column; gap: 160px; align-items: center; justify-content: flex-start; height: 845px; position: relative; ' }"
+>
+  <Header/>
+  <div
+    style="
+      display: flex;
+      flex-direction: row;
+      gap: 82px;
+      align-items: center;
+      justify-content: center;
+      flex-shrink: 0;
+      width: 1103px;
+      height: 429px;
+      position: relative;
+    "
+  >
+    <div
+      style="
+        display: flex;
+        flex-direction: row;
+        gap: 82px;
+        align-items: center;
+        justify-content: flex-start;
+        flex-shrink: 0;
+        height: 423px;
+        position: relative;
+      "
+    >
+      <div
+        style="
+          display: flex;
+          flex-direction: row;
+          gap: 82px;
+          align-items: center;
+          justify-content: flex-start;
+          flex-shrink: 0;
+          height: 401px;
+          position: relative;
+        "
+      >
+        <div
+          style="
+            background: #ffffff;
+            border-radius: 10px;
+            border-style: solid;
+            border-color: #878787;
+            border-width: 0.5px;
+            padding: 47px 0px 47px 0px;
+            display: flex;
+            flex-direction: row;
+            gap: 10px;
+            align-items: flex-start;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            width: 451px;
+            height: 395px;
+            position: relative;
+            box-shadow: 0px 4px 64px 0px rgba(0, 0, 0, 0.05);
+            overflow: hidden;
+          "
+        >
+          <div
+            style="
+              display: flex;
+              flex-direction: column;
+              gap: 20px;
+              align-items: center;
+              justify-content: center;
+              flex-shrink: 0;
+              position: relative;
+            "
+          >
+            <div
+              style="
+                display: flex;
+                flex-direction: column;
+                gap: 20px;
+                align-items: center;
+                justify-content: center;
+                flex-shrink: 0;
+                position: relative;
+              "
+            >
+              <div
+                style="
+                  color: #000000;
+                  text-align: center;
+                  font-family: 'DmSans-Bold', sans-serif;
+                  font-size: 46px;
+                  line-height: 40px;
+                  font-weight: 700;
+                  position: relative;
+                  width: 451px;
+                  height: 40px;
+                "
+              >
+                로그인
+              </div>
+              <div
+                style="
+                  display: flex;
+                  flex-direction: column;
+                  gap: 10px;
+                  align-items: flex-start;
+                  justify-content: flex-start;
+                  flex-shrink: 0;
+                  position: relative;
+                "
+              >
+                <div
+                  style="
+                    color: #000000;
+                    text-align: left;
+                    font-family: 'DmSans-Medium', sans-serif;
+                    font-size: 19px;
+                    line-height: 40px;
+                    font-weight: 500;
+                    position: relative;
+                    width: 125px;
+                    height: 23px;
+                    display: flex;
+                    align-items: center;
+                    justify-content: flex-start;
+                  "
+                >
+                  이메일 주소
+                </div>
+                <div
+                  style="
+                    background: #ffffff;
+                    border-radius: 10px;
+                    border-style: solid;
+                    border-color: #000000;
+                    border-width: 1px;
+                    padding: 1px 43px 1px 43px;
+                    display: flex;
+                    flex-direction: row;
+                    gap: 10px;
+                    align-items: center;
+                    justify-content: flex-start;
+                    flex-shrink: 0;
+                    width: 337px;
+                    position: relative;
+                    overflow: hidden;
+                  "
+                >
+                <input type='email' bind:value={login_email} placeholder="이메일 입력"
+                style="
+                  color: rgba(0, 0, 0, 0.4);
+                  text-align: center;
+                  font-family: 'DmSans-Medium', sans-serif;
+                  font-size: 18px;
+                  line-height: 30px;
+                  font-weight: 500;
+                  position: relative;
+                  width: 100%;
+                  display: flex;
+                  align-items: center;
+                  justify-content: center;
+                  border: none;
+                "
+              />
+                </div>
+                <div
+                  style="
+                    color: #000000;
+                    text-align: left;
+                    font-family: 'DmSans-Medium', sans-serif;
+                    font-size: 19px;
+                    line-height: 40px;
+                    font-weight: 500;
+                    position: relative;
+                    width: 125px;
+                    height: 23px;
+                    display: flex;
+                    align-items: center;
+                    justify-content: flex-start;
+                  "
+                >
+                  비밀번호
+                </div>
+                <div
+                  style="
+                    background: #ffffff;
+                    border-radius: 10px;
+                    border-style: solid;
+                    border-color: #000000;
+                    border-width: 1px;
+                    padding: 1px 43px 1px 43px;
+                    display: flex;
+                    flex-direction: row;
+                    gap: 10px;
+                    align-items: center;
+                    justify-content: flex-start;
+                    flex-shrink: 0;
+                    width: 337px;
+                    position: relative;
+                    overflow: hidden;
+                  "
+                >
+                <input type='password' bind:value={login_pswd} placeholder="비밀번호 입력"
+                style="
+                  color: rgba(0, 0, 0, 0.4);
+                  text-align: center;
+                  font-family: 'DmSans-Medium', sans-serif;
+                  font-size: 18px;
+                  line-height: 30px;
+                  font-weight: 500;
+                  position: relative;
+                  width: 100%;
+                  display: flex;
+                  align-items: center;
+                  justify-content: center;
+                  margin: 0;
+                  background: transparent;
+                  -webkit-appearance: none;
+                  border: none;
+                "
+              />
+                </div>
+              </div>
+            </div>
+            <BasicFilled
+              styleVariant="filled"
+              style=
+              "background: var(--7b95b7, #6b6b6b);
+              flex-shrink: 0"
+              name="로그인"
+              type="submit"
+            ></BasicFilled>
+          </div>
+          <div
+            style="
+              background: #d9d9d9;
+              flex-shrink: 0;
+              width: 100px;
+              height: 100px;
+              position: relative;
+            "
+          ></div>
+        </div>
+        <PlaceholderImage
+          style="flex-shrink: 0; width: 405px; height: 405px" 
+          src="logo/login_f.png"
+        ></PlaceholderImage>
+      </div>
+    </div>
+  </div>
+</div>
+
+</form>
\ No newline at end of file
diff --git a/frontend/src/routes/login/PlaceholderImage.svelte b/frontend/src/routes/login/PlaceholderImage.svelte
new file mode 100644
index 0000000..3caa82e
--- /dev/null
+++ b/frontend/src/routes/login/PlaceholderImage.svelte
@@ -0,0 +1,12 @@
+<script>
+    let className = "";
+    export { className as class };
+    export let style;
+    export let src = 'logo/login_image.webp'
+  </script>
+  <img
+    class="{'placeholder-image ' + className}"
+    style="{'border-radius: 24px;padding: 10px; display: flex; flex-direction: row; gap: 10px; align-items: center; justify-content: center; align-self: stretch;flex: 1;height: auto; position: relative; overflow: visible;' + style}"
+    src={src}
+    alt="login_image"
+  />
\ No newline at end of file
diff --git a/frontend/src/routes/maintenance/+page.svelte b/frontend/src/routes/maintenance/+page.svelte
new file mode 100644
index 0000000..c0f2494
--- /dev/null
+++ b/frontend/src/routes/maintenance/+page.svelte
@@ -0,0 +1,53 @@
+<div
+style="{'background: var(--neutral-0, #ffffff);padding: 160px 0px 120px 0px; display: flex; flex-direction: column; gap: 70px; align-items: center; justify-content: flex-start; min-height: 100vh; position: relative ' }"
+>
+<div>
+  <img
+    class="image-22"
+    style="
+      flex-shrink: 0;
+      width: 320px;
+
+      position: relative;
+      object-fit: cover;
+      margin-left: auto;
+      margin-right: auto;
+    "
+    src="./logo/maintenance.png"
+    alt="maintenance"
+  />
+  <div
+    style="
+      color: #8c8686;
+      text-align: center;
+      font-family: var(--body-large-font-family, 'DmSans-Regular', sans-serif);
+      font-size: var(--body-large-font-size, 20px);
+      line-height: var(--body-large-line-height, 50px);
+      font-weight: var(--body-large-font-weight, 400);
+      position: relative;
+      width: 800px;
+      margin-left: auto;
+      margin-right: auto;
+    "
+  > 
+  <br>
+  <p 
+  style="
+    color: var(--6b6b6b, #000000);
+    text-align: center;
+    font-family: var(
+      --title-large-font-family,
+      'DmSans-Bold',
+      sans-serif );
+    font-size: var(--title-large-font-size, 50px);
+    line-height: var(--title-large-line-height, 76px);
+    font-weight: var(--title-large-font-weight, 700);
+    position: relative;
+    "
+  >
+  현재 페이지를 업데이트 중입니다!</p>
+  <p>불편을 드려 죄송합니다. 조금만 기다려 주세요.</p>
+  </div>
+</div>
+</div>
+
diff --git a/frontend/src/routes/result/+page.svelte b/frontend/src/routes/result/+page.svelte
new file mode 100644
index 0000000..6081248
--- /dev/null
+++ b/frontend/src/routes/result/+page.svelte
@@ -0,0 +1,711 @@
+<script>
+  import ButtonStyleFilled from "../join/ButtonStyleFilled.svelte";
+  import RequestFilled from "../../components/button/request_filled.svelte";
+  import PlaceholderImage from "./PlaceholderImage.svelte";
+  import Header from "../../components/header/header_login.svelte";
+  import StarRating from "../../components/rating/StarRating.svelte";
+  import SaveImage from "../../components/button/result_save.svelte";
+  import Survey from "../../components/survey/survey.svelte";
+  import { onMount } from 'svelte';
+  import { goto } from '$app/navigation';
+
+  let gt_stretch_path_m = "https://storage.makezenerator.com:9000/voice2face-public/site/result/tae_24fps_square.mp4"; 
+  let gt_stretch_path_w = "https://storage.makezenerator.com:9000/voice2face-public/site/result/hj_24fps_square.mp4"; 
+  let fail_path = "https://storage.makezenerator.com:9000/voice2face-public/site/result/sad.png"; 
+  let results = [];
+  let id;
+  let latest_id;
+  let gender;
+  let token = null;
+  
+  onMount(async () => {
+      token = sessionStorage.getItem('auth_token'); 
+      if (!token) {
+      alert(`세션이 만료되었습니다.\n다시 로그인 해주세요.`);
+      goto('/');
+      return
+    }
+      gender =sessionStorage.getItem('gender');
+      id = sessionStorage.getItem('id');
+      latest_id = sessionStorage.getItem('latest_id');
+
+    
+    const response = await fetch(`https://api.makezenerator.com/api/v1/mz-request/${id}/mz-result/${latest_id}`, {
+      method: 'GET',
+      headers: {
+                'Token': token,
+      },
+      
+    });
+
+    if (response.ok) {
+      const data = await response.json();
+      results = data.mz_result; 
+    } else if(response.status === 400) {
+      alert("데이터베이스 에러");
+    }
+      else {
+      console.error('데이터를 가져오는 데 실패했습니다.');
+      alert("요청 중 에러가 발생했습니다.")
+    }
+  });
+
+  
+
+
+</script>
+<div
+style="
+  background: var(--neutral-0, #ffffff);
+  padding: 0px 0px 120px 0px;
+  display: flex;
+  flex-direction: column;
+  gap: 30px;
+  align-items: center; 
+  justify-content: flex-start; 
+  width: 100%; 
+  height: auto;
+  position: relative;
+"
+>
+  <Header/>
+  <img
+    class="image-22"
+    style="
+      flex-shrink: 0;
+      width: 196px;
+      position: relative;
+      object-fit: cover;
+    "
+    src="/logo/logo1.png"
+    alt="logo"
+  />
+  <div
+    style="
+      display: flex;
+      flex-direction: column;
+      gap: 159px;
+      align-items: center;
+      justify-content: flex-start;
+      flex-shrink: 0;
+      position: relative;
+    "
+  >
+    <div
+      style="
+        display: flex;
+        flex-direction: row;
+        gap: 211px;
+        align-items: flex-start;
+        justify-content: flex-start;
+        flex-shrink: 0;
+        position: relative;
+      "
+    >
+      <div
+        style="
+          display: flex;
+          flex-direction: column;
+          gap: 13px;
+          align-items: center;
+          justify-content: flex-start;
+          flex-shrink: 0;
+          position: relative;
+        "
+      >
+        <div
+          style="
+            display: flex;
+            flex-direction: column;
+            gap: 15px;
+            align-items: center;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            position: relative;
+          "
+        >
+          <div
+            style="
+              display: flex;
+              flex-direction: column;
+              gap: 18px;
+              align-items: flex-start;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              position: relative;
+            "
+          >
+            <div
+              style="
+                color: #000000;
+                text-align: center;
+                font-family: 'DmSans-Bold', sans-serif;
+                font-size: 46px;
+                line-height: 40px;
+                font-weight: 700;
+                position: relative;
+                width: 294px;
+                height: 40px;
+              "
+            >
+              Voice
+            </div>
+            <div
+              style="
+                color: #8c8686;
+                text-align: center;
+                font-family: var(
+                  --body-large-font-family,
+                  'DmSans-Regular',
+                  sans-serif
+                );
+                font-size: var(--body-large-font-size, 20px);
+                line-height: var(--body-large-line-height, 32px);
+                font-weight: var(--body-large-font-weight, 400);
+                position: relative;
+                width: 294px;
+              "
+            >
+              목소리에 어울리는 얼굴
+            </div>
+          </div>
+          <PlaceholderImage
+            style="flex-shrink: 0; width: 390px; height: 390px"
+            targetPath={results.voice_image_url}
+          ></PlaceholderImage>
+        </div>
+        <div
+          style="
+            display: flex;
+            flex-direction: row;
+            gap: 70px;
+            align-items: flex-start;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            position: relative;
+          "
+        >
+          <div
+            style="
+              display: flex;
+              flex-direction: column;
+              align-items: center;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              position: relative;
+            "
+          >
+          <div
+            style="
+              display: flex;
+              flex-direction: row;
+              gap: 20px;
+              align-items: flex-end;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              position: relative;
+            "
+          >
+          <StarRating ABtype='voice' result_id={id} latest_id={latest_id} />
+          <SaveImage targetImage= {results.voice_image_url} fileName="voice"/>
+            
+          </div>
+            
+          </div>
+          
+        </div>
+      </div>
+      <div
+        style="
+          display: flex;
+          flex-direction: column;
+          gap: 13px;
+          align-items: center;
+          justify-content: flex-start;
+          flex-shrink: 0;
+          position: relative;
+        "
+      >
+        <div
+          style="
+            display: flex;
+            flex-direction: column;
+            gap: 15px;
+            align-items: center;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            position: relative;
+          "
+        >
+          <div
+            style="
+              display: flex;
+              flex-direction: column;
+              gap: 18px;
+              align-items: flex-start;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              position: relative;
+            "
+          >
+            <div
+              style="
+                color: #000000;
+                text-align: center;
+                font-family: 'DmSans-Bold', sans-serif;
+                font-size: 46px;
+                line-height: 40px;
+                font-weight: 700;
+                position: relative;
+                width: 294px;
+                height: 40px;
+              "
+            >
+              Condition
+            </div>
+            <div
+              style="
+                color: #8c8686;
+                text-align: center;
+                font-family: var(
+                  --body-large-font-family,
+                  'DmSans-Regular',
+                  sans-serif
+                );
+                font-size: var(--body-large-font-size, 20px);
+                line-height: var(--body-large-line-height, 32px);
+                font-weight: var(--body-large-font-weight, 400);
+                position: relative;
+                width: 294px;
+              "
+            >
+              성별, 나이만으로 만들어진 얼굴
+            </div>
+          </div>
+          <PlaceholderImage
+            style="flex-shrink: 0; width: 390px; height: 390px"
+            targetPath={results.condition_image_url}
+          ></PlaceholderImage>
+        </div>
+        <div
+          style="
+            display: flex;
+            flex-direction: row;
+            gap: 70px;
+            align-items: flex-start;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            position: relative;
+          "
+        >
+          <div
+            style="
+              display: flex;
+              flex-direction: column;
+              align-items: center;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              position: relative;
+            "
+          >
+          <div
+            style="
+              display: flex;
+              flex-direction: row;
+              gap: 20px;
+              align-items: flex-end;
+              justify-content: flex-start;
+              flex-shrink: 0;
+              position: relative;
+            "
+          >
+          <StarRating ABtype= 'condition' result_id={id} latest_id={latest_id}/>
+          <SaveImage targetImage={results.condition_image_url} fileName="condition"/>
+            
+          </div>
+            
+          </div>
+          
+        </div>
+      </div>
+    </div>
+    <div
+      style="
+        display: flex;
+        flex-direction: column;
+        gap: 119px;
+        align-items: center;
+        justify-content: flex-start;
+        flex-shrink: 0;
+        position: relative;
+      "
+    >
+      <div
+        style="
+          display: flex;
+          flex-direction: column;
+          gap: 10px;
+          align-items: center;
+          justify-content: flex-start;
+          flex-shrink: 0;
+          position: relative;
+        "
+      >
+        <div
+          style="
+            color: #000000;
+            text-align: center;
+            font-family: 'DmSans-Bold', sans-serif;
+            font-size: 52px;
+            line-height: 76px;
+            font-weight: 700;
+            position: relative;
+            width: 742px;
+            height: 65px;
+          "
+        >
+          Service Coming soon
+        </div>
+        <div
+          style="
+            color: #8c8686;
+            text-align: center;
+            font-family: var(
+              --body-large-font-family,
+              'DmSans-Regular',
+              sans-serif
+            );
+            font-size: var(--body-large-font-size, 20px);
+            line-height: var(--body-large-line-height, 32px);
+            font-weight: var(--body-large-font-weight, 400);
+            position: relative;
+            width: 615px;
+            height: 150px;
+          "
+        >
+          생성된 이미지를 영상에 입힐 수 있습니다
+          
+        </div>
+        <div
+          style="
+            display: flex;
+            flex-direction: row;
+            gap: 77px;
+            align-items: flex-end;
+            justify-content: flex-start;
+            flex-shrink: 0;
+            position: relative;
+          "
+        >
+          <div
+            style="
+              flex-shrink: 0;
+              width: 393px;
+              height: 498px;
+              position: relative;
+            "
+          >
+            <div
+              style="
+                display: flex;
+                flex-direction: column;
+                gap: 18px;
+                align-items: flex-start;
+                justify-content: flex-start;
+                position: absolute;
+                left: 49.5px;
+                top: 0px;
+              "
+            >
+              <div
+                style="
+                  color: #000000;
+                  text-align: center;
+                  font-family: 'DmSans-Bold', sans-serif;
+                  font-size: 46px;
+                  line-height: 40px;
+                  font-weight: 700;
+                  position: relative;
+                  width: 294px;
+                  height: 40px;
+                "
+              >
+                Original
+              </div>
+              <div
+                style="
+                  color: #8c8686;
+                  text-align: center;
+                  font-family: var(
+                    --body-large-font-family,
+                    'DmSans-Regular',
+                    sans-serif
+                  );
+                  font-size: var(--body-large-font-size, 20px);
+                  line-height: var(--body-large-line-height, 32px);
+                  font-weight: var(--body-large-font-weight, 400);
+                  position: relative;
+                  width: 294px;
+                "
+              >
+                스트레칭 동영상
+              </div>
+            </div>
+            {#if gender == "man"} <video 
+            style="
+            width: 393px;
+            height: 393px;
+            position: absolute;
+            left: 0px;
+            top: 105px;
+          "
+       src= {gt_stretch_path_m} type="video/mp4" autoplay loop muted />
+       {:else}
+       <video 
+                style="
+                width: 393px;
+                height: 393px;
+                position: absolute;
+                left: 0px;
+                top: 105px;
+              "
+           src= {gt_stretch_path_w} type="video/mp4" autoplay loop muted />
+       
+       {/if}
+            
+          </div>
+          <div
+            style="
+              flex-shrink: 0;
+              width: 393px;
+              height: 498px;
+              position: relative;
+            "
+          >
+            <div
+              style="
+                display: flex;
+                flex-direction: column;
+                gap: 18px;
+                align-items: flex-start;
+                justify-content: flex-start;
+                position: absolute;
+                left: 49.5px;
+                top: 0px;
+              "
+            >
+              <div
+                style="
+                  color: #000000;
+                  text-align: center;
+                  font-family: 'DmSans-Bold', sans-serif;
+                  font-size: 46px;
+                  line-height: 40px;
+                  font-weight: 700;
+                  position: relative;
+                  width: 294px;
+                  height: 40px;
+                "
+              >
+                Voice
+              </div>
+              <div
+                style="
+                  color: #8c8686;
+                  text-align: center;
+                  font-family: var(
+                    --body-large-font-family,
+                    'DmSans-Regular',
+                    sans-serif
+                  );
+                  font-size: var(--body-large-font-size, 20px);
+                  line-height: var(--body-large-line-height, 32px);
+                  font-weight: var(--body-large-font-weight, 400);
+                  position: relative;
+                  width: 294px;
+                "
+              >
+                목소리에 어울리는 얼굴
+              </div>
+            </div>
+            {#if results.voice_gif_url == null}
+            <PlaceholderImage
+            style="
+            flex-shrink: 0; 
+            width: 393px;
+            height: 393px;
+            <!-- position: absolute;
+            left: 0px;
+            top: 105px; -->
+                "
+            targetPath={fail_path}
+          ></PlaceholderImage>
+            {:else}
+
+            <video 
+                style="
+                width: 393px;
+                height: 393px;
+                position: absolute;
+                left: 0px;
+                top: 105px;
+              "
+            src= {results.voice_gif_url} type="video/mp4" autoplay loop muted />
+
+            {/if}
+            
+            
+          </div>
+          <div
+            style="
+              flex-shrink: 0;
+              width: 393px;
+              height: 498px;
+              position: relative;
+            "
+          >
+            <div
+              style="
+                display: flex;
+                flex-direction: column;
+                gap: 18px;
+                align-items: flex-start;
+                justify-content: flex-start;
+                position: absolute;
+                left: 49.5px;
+                top: 0px;
+              "
+            >
+              <div
+                style="
+                  color: #000000;
+                  text-align: center;
+                  font-family: 'DmSans-Bold', sans-serif;
+                  font-size: 46px;
+                  line-height: 40px;
+                  font-weight: 700;
+                  position: relative;
+                  width: 294px;
+                  height: 40px;
+                "
+              >
+                Condition
+              </div>
+              <div
+                style="
+                  color: #8c8686;
+                  text-align: center;
+                  font-family: var(
+                    --body-large-font-family,
+                    'DmSans-Regular',
+                    sans-serif
+                  );
+                  font-size: var(--body-large-font-size, 20px);
+                  line-height: var(--body-large-line-height, 32px);
+                  font-weight: var(--body-large-font-weight, 400);
+                  position: relative;
+                  width: 294px;
+                "
+              >
+              성별, 나이만으로 만들어진 얼굴
+              </div>
+            </div>
+
+            <video 
+                style="
+                width: 393px;
+                height: 393px;
+                position: absolute;
+                left: 0px;
+                top: 105px;
+              "
+           src= {results.condition_gif_url} type="video/mp4" autoplay loop muted />
+
+          </div>
+        </div>
+      </div>
+      <div
+        style="display: flex; items-align: center; justify-contents: center ; gap: 50px;  position: static"
+      >
+      
+      {#if results.updated_at == null}
+        <RequestFilled
+          styleVariant="filled"
+          style="
+            background: var(--7b95b7, #6b6b6b);
+            width: 190px;
+          "
+          name="다시 생성하기"
+        ></RequestFilled>
+      {/if}
+        <ButtonStyleFilled
+          styleVariant="filled"
+          style="
+            background: var(--7b95b7, #6b6b6b);
+            width: 190px;
+
+          "
+          targetPath="/resultlist"
+          name="결과 목록 보기"
+        ></ButtonStyleFilled>
+      </div>
+    </div>
+  </div>
+  {#if results.survey == 0}
+  <div><br><br></div>
+  <div
+          style="
+            color: #000000;
+            text-align: center;
+            font-family: 'DmSans-Bold', sans-serif;
+            font-size: 52px;
+            line-height: 76px;
+            font-weight: 700;
+            position: relative;
+            width: 742px;
+            height: 65px;
+          "
+        >
+          설문조사
+        </div>
+
+  <div style="text-align:center; font-size: 18pt; "> 
+    안녕하세요! 사용자의 목소리를 기반으로 가상의 얼굴을 생성하는 서비스, <br >
+    <span><strong>Voice2Face</strong></span>를 개발 중인 <span><strong>Make Zenerator</strong></span>팀입니다.
+    <br><br>
+    저희는 온라인 상에서 얼굴을 드러내고 싶지 않은 사람들에게 목소리를 기반으로 생성한 얼굴을 제공하여, 
+    <br />
+    목소리와 이미지 간의 이질감을 없애고 사생활을 보장받으며 온라인 상에서 활동할 수 있도록 하는 서비스를 제공하고자 합니다.
+    <br>
+    <br />
+    서비스 품질 향상을 위해 지금까지의 서비스 이용 경험을 바탕으로 아래의 설문 조사에 참여해주시면 감사드리겠습니다.
+    <br />
+    설문 예상 소요 시간은 <span><strong>약 5분 내외</strong></span>이며  
+    <br /> 
+    참여하신 분들 중 추첨을 통해 5분께 <span><strong>스타벅스 카페 아메리카노</strong></span> 기프티콘을 드릴 예정입니다.
+    <br />
+    (설문은 생성된 이미지 결과별로 제출할 수 있습니다.)
+    </div>
+
+    <div 
+      style="
+      display: flex; 
+      flex-direction: column; 
+      align-items: center; 
+      justify-content: center; 
+      text-align: center; 
+      width: 100%; 
+    ">
+    <div style="items-align:center; ">
+      
+      <Survey survey_id={id} survey_latest_id={latest_id} />
+    </div>
+
+    
+</div>
+{/if}
+</div>
+
+
+
+
diff --git a/frontend/src/routes/result/PlaceholderImage.svelte b/frontend/src/routes/result/PlaceholderImage.svelte
new file mode 100644
index 0000000..7d42b86
--- /dev/null
+++ b/frontend/src/routes/result/PlaceholderImage.svelte
@@ -0,0 +1,11 @@
+<script>
+  let className = "";
+  export { className as class };
+  export let style;
+  export let targetPath = "/join/placeholder-image.svg"
+</script>
+<img
+  class="{'placeholder-image ' + className}"
+  style="{'border-radius: 24px;padding: 10px; display: flex; flex-direction: row; gap: 10px; align-items: center; justify-content: center; flex-shrink: 0;height: 393px; position: relative; overflow: visible;' + style}"
+  src={targetPath}
+/>
diff --git a/frontend/src/routes/resultlist/+page.svelte b/frontend/src/routes/resultlist/+page.svelte
new file mode 100644
index 0000000..6345b8e
--- /dev/null
+++ b/frontend/src/routes/resultlist/+page.svelte
@@ -0,0 +1,132 @@
+<script>
+  import MovingFilled from "../../components/button/moving_filled.svelte";
+  import Header from "../../components/header/header_login.svelte";
+  import { onMount } from 'svelte';
+  import { goto } from '$app/navigation';
+  import { Table, TableBody, TableBodyCell, TableBodyRow, TableHead, TableHeadCell} from 'flowbite-svelte';
+
+  let items = [];
+
+  async function fetchData() {
+    const token = sessionStorage.getItem('auth_token'); 
+    const response = await fetch('https://api.makezenerator.com/api/v1/mz-request', {
+      method: 'GET',
+      headers: {
+        'Token': token,
+      },
+    });
+
+    if (response.ok) {
+      const data = await response.json();
+      items = data.mz_request_list; 
+    } else if(response.status === 400) {
+      alert("데이터베이스 에러");
+    } else if(response.status === 401) {
+      alert(`세션이 만료되었습니다. \n다시 로그인 해주세요`);
+      goto("/");
+      return
+    }
+    else {
+      console.error('데이터를 가져오는 데 실패했습니다.');
+      alert("요청 중 에러가 발생했습니다.");
+    }
+  }
+
+  onMount(() => {
+
+    fetchData(); 
+    const interval = setInterval(fetchData, 5000); 
+
+    return () => {
+      clearInterval(interval); 
+    };
+  });
+  </script>
+
+
+
+<div
+style="{'background: var(--neutral-0, #ffffff);padding: 0px 0px 120px 0px; display: flex; flex-direction: column; gap: 70px; align-items: center; justify-content: flex-start; min-height: 100vh; position: relative ' }"
+>
+<Header />
+<div>
+  <img
+    class="image-22"
+    style="
+      flex-shrink: 0;
+      width: 166px;
+
+      position: relative;
+      object-fit: cover;
+      margin-left: auto;
+      margin-right: auto;
+    "
+    src="./logo/logo1.png"
+    alt="logo"
+  />
+  <div
+    style="
+      color: #8c8686;
+      text-align: center;
+      font-family: var(--body-large-font-family, 'DmSans-Regular', sans-serif);
+      font-size: var(--body-large-font-size, 20px);
+      line-height: var(--body-large-line-height, 32px);
+      font-weight: var(--body-large-font-weight, 400);
+      position: relative;
+      width: 612px;
+      margin-left: auto;
+      margin-right: auto;
+    "
+  > 
+  <br>
+    생성 이미지는 선택 버튼을 통해 확인하실 수 있습니다.
+  </div>
+</div>
+
+  <Table hoverable={true}
+    style="
+        flex-shrink: 0;
+        width: 1400px;
+        font-size: 14pt;
+    ">
+    <TableHead>
+      <TableHeadCell style="width: 15%; font-size:14pt;">요청 시간</TableHeadCell>
+      <TableHeadCell style="width: 15%; font-size:14pt;">완료 시간</TableHeadCell>
+      <TableHeadCell style="width: 8%; font-size:14pt;">성별</TableHeadCell>
+      <TableHeadCell style="width: 8%; font-size:14pt;">나이</TableHeadCell>
+      <TableHeadCell style="width: 10%; font-size:14pt;">진행 상태</TableHeadCell>
+      <TableHeadCell style="font-size:14pt;">목소리 듣기</TableHeadCell>
+      <TableHeadCell style="font-size:14pt;">결과 보기</TableHeadCell>
+    </TableHead>
+    <TableBody >
+      {#each items as item}
+        <TableBodyRow>
+          <TableBodyCell>{item.created_at.replace('T', ' ')}</TableBodyCell>
+          <TableBodyCell>
+            {#if item.ata != null} {item.ata.replace('T', ' ')}{/if}
+          </TableBodyCell>
+          <TableBodyCell>{#if item.gender == "man" } 남성 {:else } 여성 {/if} </TableBodyCell>
+          <TableBodyCell>{item.age}</TableBodyCell>
+
+          <TableBodyCell>
+            {#if item.status == "Proceeding"}<img src = "/resultlist/생성중.png" style="width: 100%; height: 100%; max-width: 100%; max-height: 100%; " alt="생성 중"/>
+            {:else if item.status == "Success"}<img src = "/resultlist/생성완료.png" style="width: 100%; height: 100%; max-width: 100%; max-height: 100%;" alt="생성 완료"/>
+            {:else} <img src = "/resultlist/생성실패.png "style="width: 100%; height: 45%; max-width: 100%; max-height: 100%;" alt="생성 실패"/>
+            {/if}
+          </TableBodyCell>
+          <TableBodyCell>
+              <audio src={item.voice_url} controls></audio>
+            </TableBodyCell>
+          <TableBodyCell>
+            {#if item.status =="Success"}
+              <MovingFilled targetPath='/result' name="결과 확인" id = {item.id} latest_id = {item.latest_mz_result_id} gender={item.gender}> </MovingFilled>
+            <!-- {:else if item.status == "Failed"} -->
+              {/if}
+            </TableBodyCell>
+        </TableBodyRow>
+          {/each}
+    </TableBody>
+  </Table>
+</div>
+
+
diff --git a/frontend/src/routes/resultlist/ButtonStyleFilled.svelte b/frontend/src/routes/resultlist/ButtonStyleFilled.svelte
new file mode 100644
index 0000000..984c087
--- /dev/null
+++ b/frontend/src/routes/resultlist/ButtonStyleFilled.svelte
@@ -0,0 +1,32 @@
+<script>
+  export let styleVariant = "outlined";
+  const variantsClassName = "style-variant-" + styleVariant;
+  export let name = "Do something";
+  import { goto } from '$app/navigation';
+  export let targetPath = "/";
+  function handleClick() {
+    goto(targetPath); // 버튼 클릭 시 targetPath로 이동
+  }
+</script>
+<link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+<link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&family=Titan+One&display=swap" rel="stylesheet">
+
+<button
+on:click={handleClick} 
+  style="{'background: var(--neutral-10, #486284);border-radius: 50px; border-style: solid; border-color: var(--neutral-10, #486284); border-width: 1px; padding: 12px 20px 12px 20px; display: flex; flex-direction: row; gap: 10px; align-items: center; justify-content: center; position: relative; overflow: hidden;' }"
+>
+  <div
+    style="
+      color: var(--neutral-0, #ffffff);
+      text-align: left;
+      font-family: var(--body-small-font-family, 'DmSans-Regular', sans-serif);
+      font-size: var(--body-small-font-size, 24px);
+      line-height: var(--body-small-line-height, 24px);
+      font-weight: var(--body-small-font-weight, 400);
+      position: relative;
+    "
+  >
+    {name}
+  </div>
+</button>
\ No newline at end of file
diff --git a/frontend/src/routes/styles.css b/frontend/src/routes/styles.css
new file mode 100644
index 0000000..1441d94
--- /dev/null
+++ b/frontend/src/routes/styles.css
@@ -0,0 +1,107 @@
+@import '@fontsource/fira-mono';
+
+:root {
+	--font-body: Arial, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu,
+		Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;
+	--font-mono: 'Fira Mono', monospace;
+	--color-bg-0: rgb(202, 216, 228);
+	--color-bg-1: hsl(209, 36%, 86%);
+	--color-bg-2: hsl(224, 44%, 95%);
+	--color-theme-1: #ff3e00;
+	--color-theme-2: #4075a6;
+	--color-text: rgba(0, 0, 0, 0.7);
+	--column-width: 42rem;
+	--column-margin-top: 4rem;
+	font-family: var(--font-body);
+	color: var(--color-text);
+}
+
+body {
+	min-height: 100vh;
+	margin: 0;
+	background-attachment: fixed;
+	background-color: var(--color-bg-1);
+	background-size: 100vw 100vh;
+	background-image: radial-gradient(
+			50% 50% at 50% 50%,
+			rgba(255, 255, 255, 0.75) 0%,
+			rgba(255, 255, 255, 0) 100%
+		),
+		linear-gradient(180deg, var(--color-bg-0) 0%, var(--color-bg-1) 15%, var(--color-bg-2) 50%);
+}
+
+h1,
+h2,
+p {
+	font-weight: 400;
+}
+
+p {
+	line-height: 1.5;
+}
+
+a {
+	color: var(--color-theme-1);
+	text-decoration: none;
+}
+
+a:hover {
+	text-decoration: underline;
+}
+
+h1 {
+	font-size: 2rem;
+	text-align: center;
+}
+
+h2 {
+	font-size: 1rem;
+}
+
+pre {
+	font-size: 16px;
+	font-family: var(--font-mono);
+	background-color: rgba(255, 255, 255, 0.45);
+	border-radius: 3px;
+	box-shadow: 2px 2px 6px rgb(255 255 255 / 25%);
+	padding: 0.5em;
+	overflow-x: auto;
+	color: var(--color-text);
+}
+
+.text-column {
+	display: flex;
+	max-width: 48rem;
+	flex: 0.6;
+	flex-direction: column;
+	justify-content: center;
+	margin: 0 auto;
+}
+
+input,
+button {
+	font-size: inherit;
+	font-family: inherit;
+}
+
+button:focus:not(:focus-visible) {
+	outline: none;
+}
+
+@media (min-width: 720px) {
+	h1 {
+		font-size: 2.4rem;
+	}
+}
+
+.visually-hidden {
+	border: 0;
+	clip: rect(0 0 0 0);
+	height: auto;
+	margin: 0;
+	overflow: hidden;
+	padding: 0;
+	position: absolute;
+	width: 1px;
+	white-space: nowrap;
+}
diff --git a/frontend/static/join/frame-60.svg b/frontend/static/join/frame-60.svg
new file mode 100644
index 0000000..a4f128d
--- /dev/null
+++ b/frontend/static/join/frame-60.svg
@@ -0,0 +1,4 @@
+<svg width="50" height="50" viewBox="0 0 50 50" fill="none" xmlns="http://www.w3.org/2000/svg">
+<circle cx="25" cy="25" r="25" fill="#D9D9D9"/>
+<path d="M24.5414 10.186C24.3493 10.2137 24.1595 10.2528 23.9732 10.3031C22.9873 10.5013 22.1087 10.9986 21.4871 11.7102C20.8656 12.4218 20.5394 13.3037 20.564 14.2059V22.0116C20.564 23.0467 21.0245 24.0394 21.8442 24.7713C22.6639 25.5033 23.7756 25.9145 24.9348 25.9145C26.094 25.9145 27.2057 25.5033 28.0254 24.7713C28.8451 24.0394 29.3056 23.0467 29.3056 22.0116V14.2059C29.3267 13.6527 29.2158 13.1018 28.9802 12.5897C28.7447 12.0777 28.3898 11.6163 27.9393 11.2361C27.4887 10.8559 26.9527 10.5656 26.3669 10.3846C25.7811 10.2035 25.1588 10.1358 24.5414 10.186ZM13.3523 18.1088C12.9 18.2445 12.5099 18.5088 12.2415 18.8612C11.9732 19.2135 11.8414 19.6346 11.8663 20.0602V22.0116C11.8663 27.7878 16.6304 32.5103 22.7931 33.486V37.623H20.6078C18.2038 37.623 16.237 39.3793 16.237 41.5259H33.7637C33.7637 39.3793 31.7969 37.623 29.393 37.623H27.2076V33.486C33.3703 32.5493 38.1345 27.7878 38.1345 22.0116V20.0602C38.1345 19.5426 37.9042 19.0463 37.4944 18.6803C37.0845 18.3143 36.5287 18.1088 35.9491 18.1088C35.3695 18.1088 34.8136 18.3143 34.4038 18.6803C33.994 19.0463 33.7637 19.5426 33.7637 20.0602V22.0116C33.7637 26.3438 29.8737 29.8173 25.0222 29.8173C20.1707 29.8173 16.2807 26.3438 16.2807 22.0116V20.0602C16.286 19.7715 16.2195 19.4853 16.0859 19.2224C15.9524 18.9594 15.7552 18.7262 15.5085 18.5395C15.2618 18.3529 14.9718 18.2174 14.6594 18.1429C14.347 18.0684 14.02 18.0567 13.702 18.1088C13.6146 18.1041 13.5271 18.1041 13.4397 18.1088H13.3523Z" fill="#3D3D3D"/>
+</svg>
diff --git a/frontend/static/join/mic.svg b/frontend/static/join/mic.svg
new file mode 100644
index 0000000..a4f128d
--- /dev/null
+++ b/frontend/static/join/mic.svg
@@ -0,0 +1,4 @@
+<svg width="50" height="50" viewBox="0 0 50 50" fill="none" xmlns="http://www.w3.org/2000/svg">
+<circle cx="25" cy="25" r="25" fill="#D9D9D9"/>
+<path d="M24.5414 10.186C24.3493 10.2137 24.1595 10.2528 23.9732 10.3031C22.9873 10.5013 22.1087 10.9986 21.4871 11.7102C20.8656 12.4218 20.5394 13.3037 20.564 14.2059V22.0116C20.564 23.0467 21.0245 24.0394 21.8442 24.7713C22.6639 25.5033 23.7756 25.9145 24.9348 25.9145C26.094 25.9145 27.2057 25.5033 28.0254 24.7713C28.8451 24.0394 29.3056 23.0467 29.3056 22.0116V14.2059C29.3267 13.6527 29.2158 13.1018 28.9802 12.5897C28.7447 12.0777 28.3898 11.6163 27.9393 11.2361C27.4887 10.8559 26.9527 10.5656 26.3669 10.3846C25.7811 10.2035 25.1588 10.1358 24.5414 10.186ZM13.3523 18.1088C12.9 18.2445 12.5099 18.5088 12.2415 18.8612C11.9732 19.2135 11.8414 19.6346 11.8663 20.0602V22.0116C11.8663 27.7878 16.6304 32.5103 22.7931 33.486V37.623H20.6078C18.2038 37.623 16.237 39.3793 16.237 41.5259H33.7637C33.7637 39.3793 31.7969 37.623 29.393 37.623H27.2076V33.486C33.3703 32.5493 38.1345 27.7878 38.1345 22.0116V20.0602C38.1345 19.5426 37.9042 19.0463 37.4944 18.6803C37.0845 18.3143 36.5287 18.1088 35.9491 18.1088C35.3695 18.1088 34.8136 18.3143 34.4038 18.6803C33.994 19.0463 33.7637 19.5426 33.7637 20.0602V22.0116C33.7637 26.3438 29.8737 29.8173 25.0222 29.8173C20.1707 29.8173 16.2807 26.3438 16.2807 22.0116V20.0602C16.286 19.7715 16.2195 19.4853 16.0859 19.2224C15.9524 18.9594 15.7552 18.7262 15.5085 18.5395C15.2618 18.3529 14.9718 18.2174 14.6594 18.1429C14.347 18.0684 14.02 18.0567 13.702 18.1088C13.6146 18.1041 13.5271 18.1041 13.4397 18.1088H13.3523Z" fill="#3D3D3D"/>
+</svg>
diff --git a/frontend/static/join/placeholder-image.svg b/frontend/static/join/placeholder-image.svg
new file mode 100644
index 0000000..08cfe68
--- /dev/null
+++ b/frontend/static/join/placeholder-image.svg
@@ -0,0 +1,4 @@
+<svg width="564" height="308" viewBox="0 0 564 308" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect width="564" height="308" rx="24" fill="#EFF2F6"/>
+<path d="M330 191.333V116.667C330 110.8 325.2 106 319.333 106H244.667C238.8 106 234 110.8 234 116.667V191.333C234 197.2 238.8 202 244.667 202H319.333C325.2 202 330 197.2 330 191.333ZM263.333 162L276.667 178.053L295.333 154L319.333 186H244.667L263.333 162Z" fill="#7E7676"/>
+</svg>
diff --git a/frontend/static/join/placeholder-image0.svg b/frontend/static/join/placeholder-image0.svg
new file mode 100644
index 0000000..08cfe68
--- /dev/null
+++ b/frontend/static/join/placeholder-image0.svg
@@ -0,0 +1,4 @@
+<svg width="564" height="308" viewBox="0 0 564 308" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect width="564" height="308" rx="24" fill="#EFF2F6"/>
+<path d="M330 191.333V116.667C330 110.8 325.2 106 319.333 106H244.667C238.8 106 234 110.8 234 116.667V191.333C234 197.2 238.8 202 244.667 202H319.333C325.2 202 330 197.2 330 191.333ZM263.333 162L276.667 178.053L295.333 154L319.333 186H244.667L263.333 162Z" fill="#7E7676"/>
+</svg>
diff --git a/frontend/static/join/placeholder-image1.svg b/frontend/static/join/placeholder-image1.svg
new file mode 100644
index 0000000..e2c5c43
--- /dev/null
+++ b/frontend/static/join/placeholder-image1.svg
@@ -0,0 +1,4 @@
+<svg width="344" height="343" viewBox="0 0 344 343" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect x="0.5" width="343" height="343" rx="24" fill="#EFF2F6"/>
+<path d="M220 208.833V134.167C220 128.3 215.2 123.5 209.333 123.5H134.667C128.8 123.5 124 128.3 124 134.167V208.833C124 214.7 128.8 219.5 134.667 219.5H209.333C215.2 219.5 220 214.7 220 208.833ZM153.333 179.5L166.667 195.553L185.333 171.5L209.333 203.5H134.667L153.333 179.5Z" fill="#6B6B6B"/>
+</svg>
diff --git a/frontend/static/join/placeholder-image2.svg b/frontend/static/join/placeholder-image2.svg
new file mode 100644
index 0000000..e2c5c43
--- /dev/null
+++ b/frontend/static/join/placeholder-image2.svg
@@ -0,0 +1,4 @@
+<svg width="344" height="343" viewBox="0 0 344 343" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect x="0.5" width="343" height="343" rx="24" fill="#EFF2F6"/>
+<path d="M220 208.833V134.167C220 128.3 215.2 123.5 209.333 123.5H134.667C128.8 123.5 124 128.3 124 134.167V208.833C124 214.7 128.8 219.5 134.667 219.5H209.333C215.2 219.5 220 214.7 220 208.833ZM153.333 179.5L166.667 195.553L185.333 171.5L209.333 203.5H134.667L153.333 179.5Z" fill="#6B6B6B"/>
+</svg>
diff --git a/frontend/static/join/placeholder-image3.svg b/frontend/static/join/placeholder-image3.svg
new file mode 100644
index 0000000..e2c5c43
--- /dev/null
+++ b/frontend/static/join/placeholder-image3.svg
@@ -0,0 +1,4 @@
+<svg width="344" height="343" viewBox="0 0 344 343" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect x="0.5" width="343" height="343" rx="24" fill="#EFF2F6"/>
+<path d="M220 208.833V134.167C220 128.3 215.2 123.5 209.333 123.5H134.667C128.8 123.5 124 128.3 124 134.167V208.833C124 214.7 128.8 219.5 134.667 219.5H209.333C215.2 219.5 220 214.7 220 208.833ZM153.333 179.5L166.667 195.553L185.333 171.5L209.333 203.5H134.667L153.333 179.5Z" fill="#6B6B6B"/>
+</svg>
diff --git a/frontend/static/join/placeholder-image4.svg b/frontend/static/join/placeholder-image4.svg
new file mode 100644
index 0000000..e2c5c43
--- /dev/null
+++ b/frontend/static/join/placeholder-image4.svg
@@ -0,0 +1,4 @@
+<svg width="344" height="343" viewBox="0 0 344 343" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect x="0.5" width="343" height="343" rx="24" fill="#EFF2F6"/>
+<path d="M220 208.833V134.167C220 128.3 215.2 123.5 209.333 123.5H134.667C128.8 123.5 124 128.3 124 134.167V208.833C124 214.7 128.8 219.5 134.667 219.5H209.333C215.2 219.5 220 214.7 220 208.833ZM153.333 179.5L166.667 195.553L185.333 171.5L209.333 203.5H134.667L153.333 179.5Z" fill="#6B6B6B"/>
+</svg>
diff --git a/frontend/static/join/placeholder-image5.svg b/frontend/static/join/placeholder-image5.svg
new file mode 100644
index 0000000..e2c5c43
--- /dev/null
+++ b/frontend/static/join/placeholder-image5.svg
@@ -0,0 +1,4 @@
+<svg width="344" height="343" viewBox="0 0 344 343" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect x="0.5" width="343" height="343" rx="24" fill="#EFF2F6"/>
+<path d="M220 208.833V134.167C220 128.3 215.2 123.5 209.333 123.5H134.667C128.8 123.5 124 128.3 124 134.167V208.833C124 214.7 128.8 219.5 134.667 219.5H209.333C215.2 219.5 220 214.7 220 208.833ZM153.333 179.5L166.667 195.553L185.333 171.5L209.333 203.5H134.667L153.333 179.5Z" fill="#6B6B6B"/>
+</svg>
diff --git a/frontend/static/join/placeholder-image6.svg b/frontend/static/join/placeholder-image6.svg
new file mode 100644
index 0000000..e2c5c43
--- /dev/null
+++ b/frontend/static/join/placeholder-image6.svg
@@ -0,0 +1,4 @@
+<svg width="344" height="343" viewBox="0 0 344 343" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect x="0.5" width="343" height="343" rx="24" fill="#EFF2F6"/>
+<path d="M220 208.833V134.167C220 128.3 215.2 123.5 209.333 123.5H134.667C128.8 123.5 124 128.3 124 134.167V208.833C124 214.7 128.8 219.5 134.667 219.5H209.333C215.2 219.5 220 214.7 220 208.833ZM153.333 179.5L166.667 195.553L185.333 171.5L209.333 203.5H134.667L153.333 179.5Z" fill="#6B6B6B"/>
+</svg>
diff --git a/frontend/static/join/readme.md b/frontend/static/join/readme.md
new file mode 100644
index 0000000..2dbf495
--- /dev/null
+++ b/frontend/static/join/readme.md
@@ -0,0 +1 @@
+In this folder you can store your assets...
\ No newline at end of file
diff --git a/frontend/static/join/restart.png b/frontend/static/join/restart.png
new file mode 100644
index 0000000..7238deb
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diff --git a/frontend/static/join/social-property-1-github.svg b/frontend/static/join/social-property-1-github.svg
new file mode 100644
index 0000000..188ffdb
--- /dev/null
+++ b/frontend/static/join/social-property-1-github.svg
@@ -0,0 +1,3 @@
+<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M16 22.0001V18.1301C16.0375 17.6532 15.9731 17.1739 15.811 16.7239C15.6489 16.2738 15.3929 15.8635 15.06 15.5201C18.2 15.1701 21.5 13.9801 21.5 8.52006C21.4997 7.12389 20.9627 5.78126 20 4.77006C20.4559 3.54857 20.4236 2.19841 19.91 1.00006C19.91 1.00006 18.73 0.65006 16 2.48006C13.708 1.85888 11.292 1.85888 9 2.48006C6.27 0.65006 5.09 1.00006 5.09 1.00006C4.57638 2.19841 4.54414 3.54857 5 4.77006C4.03013 5.78876 3.49252 7.14352 3.5 8.55006C3.5 13.9701 6.8 15.1601 9.94 15.5501C9.611 15.89 9.35726 16.2955 9.19531 16.74C9.03335 17.1845 8.96681 17.6581 9 18.1301V22.0001M9 19.0001C4 20.5001 4 16.5001 2 16.0001L9 19.0001Z" stroke="black" stroke-linecap="round" stroke-linejoin="round"/>
+</svg>
diff --git a/frontend/static/join/tabler-download0.svg b/frontend/static/join/tabler-download0.svg
new file mode 100644
index 0000000..842143b
--- /dev/null
+++ b/frontend/static/join/tabler-download0.svg
@@ -0,0 +1,3 @@
+<svg width="49" height="48" viewBox="0 0 49 48" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M8.5 34V38C8.5 39.0609 8.92143 40.0783 9.67157 40.8284C10.4217 41.5786 11.4391 42 12.5 42H36.5C37.5609 42 38.5783 41.5786 39.3284 40.8284C40.0786 40.0783 40.5 39.0609 40.5 38V34M14.5 22L24.5 32M24.5 32L34.5 22M24.5 32V8" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+</svg>
diff --git a/frontend/static/join/tabler-download1.svg b/frontend/static/join/tabler-download1.svg
new file mode 100644
index 0000000..842143b
--- /dev/null
+++ b/frontend/static/join/tabler-download1.svg
@@ -0,0 +1,3 @@
+<svg width="49" height="48" viewBox="0 0 49 48" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M8.5 34V38C8.5 39.0609 8.92143 40.0783 9.67157 40.8284C10.4217 41.5786 11.4391 42 12.5 42H36.5C37.5609 42 38.5783 41.5786 39.3284 40.8284C40.0786 40.0783 40.5 39.0609 40.5 38V34M14.5 22L24.5 32M24.5 32L34.5 22M24.5 32V8" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+</svg>
diff --git a/frontend/static/logo/MZ_aboutus.jpg b/frontend/static/logo/MZ_aboutus.jpg
new file mode 100644
index 0000000..af7a2e0
Binary files /dev/null and b/frontend/static/logo/MZ_aboutus.jpg differ
diff --git a/frontend/static/logo/boostcampLogo.jpg b/frontend/static/logo/boostcampLogo.jpg
new file mode 100644
index 0000000..9927581
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diff --git a/frontend/static/logo/github.png b/frontend/static/logo/github.png
new file mode 100644
index 0000000..6cb3b70
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diff --git a/frontend/static/logo/join.png b/frontend/static/logo/join.png
new file mode 100644
index 0000000..8086a4d
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diff --git a/frontend/static/logo/loading.png b/frontend/static/logo/loading.png
new file mode 100644
index 0000000..d22271b
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diff --git a/frontend/static/logo/login_f.png b/frontend/static/logo/login_f.png
new file mode 100644
index 0000000..f4855a6
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diff --git a/frontend/static/logo/login_origin.png b/frontend/static/logo/login_origin.png
new file mode 100644
index 0000000..5f90cc1
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diff --git a/frontend/static/logo/logo.png b/frontend/static/logo/logo.png
new file mode 100644
index 0000000..fd7f7ef
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diff --git a/frontend/static/logo/logo1.png b/frontend/static/logo/logo1.png
new file mode 100644
index 0000000..435f91b
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diff --git a/frontend/static/logo/logo_o.png b/frontend/static/logo/logo_o.png
new file mode 100644
index 0000000..435f91b
Binary files /dev/null and b/frontend/static/logo/logo_o.png differ
diff --git a/frontend/static/logo/maintenance.png b/frontend/static/logo/maintenance.png
new file mode 100644
index 0000000..89d5d97
Binary files /dev/null and b/frontend/static/logo/maintenance.png differ
diff --git a/frontend/static/resultlist/Frame_7.png b/frontend/static/resultlist/Frame_7.png
new file mode 100644
index 0000000..82cc369
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diff --git "a/frontend/static/resultlist/\354\203\235\354\204\261\354\213\244\355\214\250.png" "b/frontend/static/resultlist/\354\203\235\354\204\261\354\213\244\355\214\250.png"
new file mode 100644
index 0000000..68f7d15
Binary files /dev/null and "b/frontend/static/resultlist/\354\203\235\354\204\261\354\213\244\355\214\250.png" differ
diff --git "a/frontend/static/resultlist/\354\203\235\354\204\261\354\231\204\353\243\214.png" "b/frontend/static/resultlist/\354\203\235\354\204\261\354\231\204\353\243\214.png"
new file mode 100644
index 0000000..9d82880
Binary files /dev/null and "b/frontend/static/resultlist/\354\203\235\354\204\261\354\231\204\353\243\214.png" differ
diff --git "a/frontend/static/resultlist/\354\203\235\354\204\261\354\244\221.png" "b/frontend/static/resultlist/\354\203\235\354\204\261\354\244\221.png"
new file mode 100644
index 0000000..c55e3cc
Binary files /dev/null and "b/frontend/static/resultlist/\354\203\235\354\204\261\354\244\221.png" differ
diff --git a/frontend/svelte.config.js b/frontend/svelte.config.js
new file mode 100644
index 0000000..65a3996
--- /dev/null
+++ b/frontend/svelte.config.js
@@ -0,0 +1,16 @@
+import adapter from "@sveltejs/adapter-node";
+import { vitePreprocess } from "@sveltejs/vite-plugin-svelte";
+
+/** @type {import('@sveltejs/kit').Config} */
+const config = {
+  kit: {
+    // adapter-auto only supports some environments, see https://kit.svelte.dev/docs/adapter-auto for a list.
+    // If your environment is not supported or you settled on a specific environment, switch out the adapter.
+    // See https://kit.svelte.dev/docs/adapters for more information about adapters.
+    adapter: adapter(),
+  },
+
+  preprocess: [vitePreprocess({})],
+};
+
+export default config;
diff --git a/frontend/tailwind.config.cjs b/frontend/tailwind.config.cjs
new file mode 100644
index 0000000..6bc3da9
--- /dev/null
+++ b/frontend/tailwind.config.cjs
@@ -0,0 +1,30 @@
+/** @type {import('tailwindcss').Config}*/
+const config = {
+  content: ['./src/**/*.{html,js,svelte,ts}', './node_modules/flowbite-svelte/**/*.{html,js,svelte,ts}'],
+
+  plugins: [require('flowbite/plugin')],
+
+  darkMode: 'class',
+
+  theme: {
+    extend: {
+      colors: {
+        // flowbite-svelte
+        primary: {
+          50: '#FFF5F2',
+          100: '#FFF1EE',
+          200: '#FFE4DE',
+          300: '#FFD5CC',
+          400: '#FFBCAD',
+          500: '#FE795D',
+          600: '#EF562F',
+          700: '#EB4F27',
+          800: '#CC4522',
+          900: '#A5371B'
+        }
+      }
+    }
+  }
+};
+
+module.exports = config;
\ No newline at end of file
diff --git a/frontend/vite.config.js b/frontend/vite.config.js
new file mode 100644
index 0000000..bbf8c7d
--- /dev/null
+++ b/frontend/vite.config.js
@@ -0,0 +1,6 @@
+import { sveltekit } from '@sveltejs/kit/vite';
+import { defineConfig } from 'vite';
+
+export default defineConfig({
+	plugins: [sveltekit()]
+});
diff --git a/mlops/README.md b/mlops/README.md
new file mode 100644
index 0000000..da5bd22
--- /dev/null
+++ b/mlops/README.md
@@ -0,0 +1 @@
+# voice2face-mlops
\ No newline at end of file
diff --git a/mlops/docker/mlflow/DockerFile.mlflow b/mlops/docker/mlflow/DockerFile.mlflow
new file mode 100644
index 0000000..4eb5baf
--- /dev/null
+++ b/mlops/docker/mlflow/DockerFile.mlflow
@@ -0,0 +1,14 @@
+FROM amd64/python:3.9-slim
+
+RUN apt-get update && apt-get install -y \
+    git \
+    wget \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN pip install -U pip &&\
+    pip install boto3==1.26.8 mlflow==1.30.0 psycopg2-binary
+
+RUN cd /tmp && \
+    wget https://dl.min.io/client/mc/release/linux-amd64/mc && \
+    chmod +x mc && \
+    mv mc /usr/bin/mc
diff --git a/mlops/docker/mlflow/docker-compose_mlflow.yaml b/mlops/docker/mlflow/docker-compose_mlflow.yaml
new file mode 100644
index 0000000..6bb5fb3
--- /dev/null
+++ b/mlops/docker/mlflow/docker-compose_mlflow.yaml
@@ -0,0 +1,58 @@
+version: "3"
+
+services:
+  mlflow-backend-store:
+    image: postgres:14.0
+    container_name: mlflow-backend-store
+    environment:
+      POSTGRES_USER: mlflowuser
+      POSTGRES_PASSWORD: mlflowpassword
+      POSTGRES_DB: mlflowdatabase
+    healthcheck:
+      test: ["CMD", "pg_isready", "-q", "-U", "mlflowuser", "-d", "mlflowdatabase"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+
+  mlflow-artifact-store:
+    image: minio/minio:RELEASE.2024-01-18T22-51-28Z
+    container_name: mlflow-artifact-store
+    ports:
+      - 9000:9000
+      - 9001:9001
+    environment:
+      MINIO_ROOT_USER: # write username
+      MINIO_ROOT_PASSWORD: # write password
+    command: server /data/minio --console-address :9001
+    healthcheck:
+      test: ["CMD", "mc", "ready", "local"]
+      interval: 5s
+      timeout: 5s
+      retries: 5
+
+  mlflow-server:
+    build:
+      context: .
+      dockerfile: DockerFile_mlflow
+    container_name: mlflow-server
+    depends_on:
+      mlflow-backend-store:
+        condition: service_healthy
+      mlflow-artifact-store:
+        condition: service_healthy
+    ports:
+      - 5001:5000
+    environment:
+      AWS_ACCESS_KEY: AKIA3FLD32HPRN22NJQ7
+      AWS_SECRET_ACCESS_KEY: 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
+      MLFLOW_S3_ENDPOINT_URL: http://mlflow-artifact-store:9000
+    command:
+      - /bin/sh
+      - -c
+      - |
+        mc config host add mlflowminio http://mlflow-artifact-store:9000 minio miniostorage &&
+        mc mb --ignore-existing mlflowminio/mlflow
+        mlflow server \
+        --backend-store-uri postgresql://mlflowuser:mlflowpassword@mlflow-backend-store/mlflowdatabase \
+        --default-artifact-root s3://mlflow/ \
+        --host 0.0.0.0
diff --git a/mlops/docker/monitoring/alertmanager/config/alertmanager.yml b/mlops/docker/monitoring/alertmanager/config/alertmanager.yml
new file mode 100644
index 0000000..d4643d7
--- /dev/null
+++ b/mlops/docker/monitoring/alertmanager/config/alertmanager.yml
@@ -0,0 +1,35 @@
+global:
+  resolve_timeout: 1m
+
+route:
+  group_by: ['alertname', 'instance']
+  group_wait: 10s
+  group_interval: 5m
+  repeat_interval: 1h
+  receiver: 'slack-notifications'
+  routes:
+    - match:
+        severity: 'critical'
+      receiver: 'slack-notifications'
+
+receivers:
+- name: 'slack-notifications'
+  slack_configs:
+  - api_url: 'https://discord.com/api/webhooks/1222157548657049611/TAhDV5DnL1sAVNBYJivf3CYe7877PKoBSsp0QZ9DgEMaVNaslR6wlBZuaSmk6NiQZ7zZ' # Discord webhook URL을 여기에 입력하세요.
+    channel: '#alerts'
+    send_resolved: true
+    title: '[{{ .Status | toUpper }}{{ if eq .Status "firing" }}:{{ .Alerts.Firing | len }}{{ end }}] Monitoring Alert - {{ .CommonLabels.alertname }} for {{ .CommonLabels.instance }}'
+    text: >-
+      {{ range .Alerts }}
+        *Alert:* {{ .Annotations.summary }}\n
+        *Description:* {{ .Annotations.description }}\n
+        *Details:*
+        {{ range .Labels.SortedPairs }} • *{{ .Name }}:* {{ .Value }}\n{{ end }}
+      {{ end }}
+
+inhibit_rules:
+  - source_match:
+      severity: 'critical'
+    target_match:
+      severity: 'warning'
+    equal: ['alertname', 'instance']
\ No newline at end of file
diff --git a/mlops/docker/monitoring/docker-compose_monitoring.yaml b/mlops/docker/monitoring/docker-compose_monitoring.yaml
new file mode 100644
index 0000000..48c5256
--- /dev/null
+++ b/mlops/docker/monitoring/docker-compose_monitoring.yaml
@@ -0,0 +1,47 @@
+version: '3.7'
+
+services:
+  prometheus:
+    image: prom/prometheus
+    container_name: prometheus
+    volumes:
+      - ./prometheus/config/:/etc/prometheus/
+      - ./prometheus/prometheus-volume:/prometheus
+    ports:
+      - 9090:9090
+    command:
+      - "--web.enable-lifecycle"
+      - '--config.file=/etc/prometheus/prometheus.yml'
+    restart: always
+    networks:
+      - promnet
+    user: root
+
+  grafana:
+    image: grafana/grafana
+    container_name: grafana
+    volumes:
+      - ./grafana-volume:/var/lib/grafana
+    restart: always
+    networks:
+      - promnet
+    ports:
+      - 3300:3000
+    user: root
+  alertmanager:
+    image: prom/alertmanager
+    container_name: alertmanager
+    user: root
+    ports: 
+      - 9093:9093
+    volumes:
+      - ./alertmanager/config/:/etc/alertmanager/
+    networks:
+      - promnet
+    restart: always
+    command:
+      - '--config.file=/etc/alertmanager/alertmanager.yml'
+
+networks:
+  promnet:
+    driver: bridge
\ No newline at end of file
diff --git a/mlops/docker/monitoring/docker-compose_node_exporter.yaml b/mlops/docker/monitoring/docker-compose_node_exporter.yaml
new file mode 100644
index 0000000..027e535
--- /dev/null
+++ b/mlops/docker/monitoring/docker-compose_node_exporter.yaml
@@ -0,0 +1,26 @@
+version: '3.7'
+services:
+  node:
+   image: prom/node-exporter
+   container_name: node-exporter
+   ports:
+     - 9100:9100
+   networks:
+     - promnet
+  dcgm:
+    image : nvcr.io/nvidia/k8s/dcgm-exporter:3.2.6-3.1.9-ubuntu20.04
+    container_name : dcgm-exporter
+    ports:
+      - 9400:9400
+    deploy:
+      resources:
+        reservations:
+          devices:
+          - driver: nvidia
+            count: 1
+            capabilities: [gpu]
+    networks:
+      - promnet
+networks:
+  promnet:
+    driver: bridge
\ No newline at end of file
diff --git a/mlops/docker/monitoring/prometheus/config/prometheus.yml b/mlops/docker/monitoring/prometheus/config/prometheus.yml
new file mode 100644
index 0000000..5febbe7
--- /dev/null
+++ b/mlops/docker/monitoring/prometheus/config/prometheus.yml
@@ -0,0 +1,55 @@
+# default 값 설정하기 - 여기 부분은 전부 설정 안해줘도 상관없음
+global:
+  scrape_interval: 15s # scrap target의 기본 interval을 15초로 변경 / default = 1m
+  scrape_timeout: 15s # scrap request 가 timeout 나는 길이 / default = 10s
+  evaluation_interval: 2m # rule 을 얼마나 빈번하게 검증하는지 / default = 1m
+
+  # Attach these labels to any time series or alerts when communicating with
+  # external systems (federation, remote storage, Alertmanager).
+  external_labels:
+    monitor: 'codelab-monitor' # 기본적으로 붙여줄 라벨
+  # query_log_file: 로그가저장될파일주소.log # prometheus의 쿼리 로그들을 기록, 없으면 기록안함
+
+# 규칙을 로딩하고 'evaluation_interval' 설정에 따라 정기적으로 평가한다.
+rule_files:
+  - "rule.yml" # 파일위치는 prometheus.yml 이 있는 곳과 동일 위치
+
+# 매트릭을 수집할 엔드포인드로 여기선 Prometheus 서버 자신을 가리킨다.
+
+scrape_configs:
+  - job_name: 'inference_node_exporter'
+    metrics_path: /metrics
+    static_configs:
+      - targets: ['175.45.193.25:9100']
+  - job_name: 'web_node_exporter'
+    metrics_path: /metrics
+    static_configs:
+      - targets: ['175.45.194.59:9100']
+  - job_name: 'minio_node_exporter'
+    metrics_path: /metrics
+    static_configs:
+      - targets: ['223.130.133.236:9100']
+  - job_name: 'gpu_exporter'
+    metrics_path: /metrics
+    static_configs:
+      - targets: ['175.45.193.25:9400']
+
+    # 그 외에도 authorization 설정 
+    # service discovery 설정(sd)
+
+    # 실제 scrap 하는 타겟에 관한 설정
+    # static_configs:
+    #   - targets: ['192.168.0.80:3300', 'localhost:9100', 'localhost:80'] // prometheus, node-exporter, cadvisor  
+    #     labels: # 옵션 - scrap 해서 가져올 metrics 들 전부에게 붙여줄 라벨
+    #       service : 'monitor-1'
+    
+    # relabel_config - 스크랩되기 전의 label들을 수정
+    # metric_relabel_configs - 가져오는 대상들의 레이블들을 동적으로 다시작성하는 설정(drop, replace, labeldrop)
+
+
+# # Alerting specifies settings related to the Alertmanager.
+# alerting:
+#   alert_relabel_configs:
+#     [ - <relabel_config> ... ]
+#   alertmanagers:
+#     [ - <alertmanager_config> ... ]
\ No newline at end of file
diff --git a/mlops/docker/monitoring/prometheus/config/rule.yml b/mlops/docker/monitoring/prometheus/config/rule.yml
new file mode 100644
index 0000000..1155e3a
--- /dev/null
+++ b/mlops/docker/monitoring/prometheus/config/rule.yml
@@ -0,0 +1,21 @@
+groups:
+- name: example # 파일 내에서 unique 해야함
+  rules:
+
+  # Alert for any instance that is unreachable for >5 minutes.
+  - alert: InstanceDown
+    expr: up == 0
+    for: 5m
+    labels:
+      severity: page
+    annotations:
+      summary: "Instance {{ $labels.instance }} down"
+      description: "{{ $labels.instance }} of job {{ $labels.job }} has been down for more than 5 minutes."
+
+  # Alert for any instance that has a median request latency >1s.
+  - alert: APIHighRequestLatency
+    expr: api_http_request_latencies_second{quantile="0.5"} > 1
+    for: 10m
+    annotations:
+      summary: "High request latency on {{ $labels.instance }}"
+      description: "{{ $labels.instance }} has a median request latency above 1s (current value: {{ $value }}s)"
\ No newline at end of file
diff --git a/mlops/docker/pipeline/Dockerfile.sf2f b/mlops/docker/pipeline/Dockerfile.sf2f
new file mode 100644
index 0000000..cd8b00c
--- /dev/null
+++ b/mlops/docker/pipeline/Dockerfile.sf2f
@@ -0,0 +1,23 @@
+FROM pytorch/pytorch:2.2.1-cuda11.8-cudnn8-runtime
+
+ENV base_path /workspace/
+COPY . ${base_path}
+
+RUN apt update -y
+RUN apt install -y gcc
+
+RUN pip install glog \
+python_speech_features \
+webrtcvad \
+pydub \
+mlflow \
+minio \
+boto3 \
+flask \
+flask_cors \
+gunicorn \
+python-dotenv
+
+EXPOSE 3002
+
+# CMD [ "python", "inference.py" ]
\ No newline at end of file
diff --git a/mlops/docker/pipeline/Dockerfile.swimswap b/mlops/docker/pipeline/Dockerfile.swimswap
new file mode 100644
index 0000000..73d85e1
--- /dev/null
+++ b/mlops/docker/pipeline/Dockerfile.swimswap
@@ -0,0 +1,37 @@
+FROM pytorch/pytorch:2.2.1-cuda11.8-cudnn8-runtime
+
+ENV base_path /workspace
+COPY . ${base_path}
+
+RUN mkdir -p ${base_path}/insightface_func/models \
+${base_path}/parsing_model/checkpoint \
+${base_path}/arcface_model
+
+RUN apt update -y
+RUN apt install -y libgl1-mesa-glx \
+libglib2.0-0 \
+unzip
+
+ADD https://storage.makezenerator.com:9000/voice2face-public/model_resource/swimswap/79999_iter.pth ${base_path}/parsing_model/checkpoint
+
+ADD https://storage.makezenerator.com:9000/voice2face-public/model_resource/swimswap/antelope.zip ${base_path}/insightface_func/models
+
+RUN unzip ${base_path}/insightface_func/models/antelope.zip -d ${base_path}/insightface_func/models/
+
+
+RUN pip install imageio \
+imageio-ffmpeg \
+insightface==0.2.1 \
+onnxruntime \
+onnxruntime-gpu \
+mlflow \
+minio \
+boto3 \
+flask \
+flask_cors \
+gunicorn \
+python-dotenv
+
+
+# ENTRYPOINT gunicorn -w 1 -b 0.0.0.0:3001 app:app --reload --timeout 90
+# CMD [ "python", "inference.py" ]
\ No newline at end of file
diff --git a/mlops/docker/pipeline/docker-compose_serving.yaml b/mlops/docker/pipeline/docker-compose_serving.yaml
new file mode 100644
index 0000000..4c88dae
--- /dev/null
+++ b/mlops/docker/pipeline/docker-compose_serving.yaml
@@ -0,0 +1,46 @@
+version: "3"
+
+services:
+  sf2f:
+    build:
+      context: ./sf2f/
+      dockerfile: Dockerfile.sf2f
+    container_name: sf2f-server
+    # ports:
+    #   - 3002:3002
+    env_file:
+      .env.serving
+    command: gunicorn -w 4 -b 0.0.0.0:3002 app:app --reload --timeout 2000
+    deploy:
+      resources:
+        reservations:
+          devices:
+          - driver: nvidia
+            count: 1
+            capabilities: [gpu]
+    # networks:
+    #   - inference
+    network_mode: "host"
+  swimswap:
+    build:
+      context: ./SwimSwap/
+      dockerfile: Dockerfile.swimswap
+    container_name: swimswap-server
+    # ports:
+    #   - 3001:3001
+    env_file:
+      .env.serving
+    command: gunicorn -w 4 -b 0.0.0.0:3001 app:app --timeout 2700
+    deploy:
+      resources:
+        reservations:
+          devices:
+          - driver: nvidia
+            count: 1
+            capabilities: [gpu]
+    network_mode: "host"
+#     networks:
+#       - inference
+# networks:
+#   inference:
+#     driver: host
\ No newline at end of file
diff --git a/mlops/mlflow/registry/Swimswap/checkpoints/people/iter.txt b/mlops/mlflow/registry/Swimswap/checkpoints/people/iter.txt
new file mode 100644
index 0000000..f05eb86
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/checkpoints/people/iter.txt
@@ -0,0 +1,2 @@
+519
+4062
diff --git a/mlops/mlflow/registry/Swimswap/checkpoints/people/loss_log.txt b/mlops/mlflow/registry/Swimswap/checkpoints/people/loss_log.txt
new file mode 100644
index 0000000..0585ac8
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/checkpoints/people/loss_log.txt
@@ -0,0 +1,11208 @@
+================ Training Loss (Sun Apr 19 12:26:39 2020) ================
+(epoch: 1, iters: 400, time: 0.075) G_GAN: -0.149 G_GAN_Feat: 2.780 G_ID: 1.016 G_Rec: 0.992 D_GP: 0.019 D_real: 0.583 D_fake: 1.174 
+(epoch: 1, iters: 800, time: 0.063) G_GAN: -0.172 G_GAN_Feat: 2.544 G_ID: 0.748 G_Rec: 1.044 D_GP: 0.031 D_real: 0.281 D_fake: 1.176 
+(epoch: 1, iters: 1200, time: 0.063) G_GAN: 0.689 G_GAN_Feat: 2.089 G_ID: 1.005 G_Rec: 0.932 D_GP: 0.028 D_real: 1.355 D_fake: 0.362 
+(epoch: 1, iters: 1600, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 1.997 G_ID: 0.609 G_Rec: 0.903 D_GP: 0.028 D_real: 1.281 D_fake: 0.633 
+(epoch: 1, iters: 2000, time: 0.063) G_GAN: 0.175 G_GAN_Feat: 1.587 G_ID: 0.981 G_Rec: 0.788 D_GP: 0.017 D_real: 0.824 D_fake: 0.841 
+(epoch: 1, iters: 2400, time: 0.063) G_GAN: 0.010 G_GAN_Feat: 1.704 G_ID: 0.563 G_Rec: 0.708 D_GP: 0.034 D_real: 0.683 D_fake: 0.991 
+(epoch: 1, iters: 2800, time: 0.063) G_GAN: 0.335 G_GAN_Feat: 1.430 G_ID: 0.991 G_Rec: 0.668 D_GP: 0.022 D_real: 1.130 D_fake: 0.671 
+(epoch: 1, iters: 3200, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 1.707 G_ID: 0.592 G_Rec: 0.958 D_GP: 0.050 D_real: 0.967 D_fake: 0.883 
+(epoch: 1, iters: 3600, time: 0.063) G_GAN: -0.091 G_GAN_Feat: 1.385 G_ID: 0.983 G_Rec: 0.648 D_GP: 0.020 D_real: 0.696 D_fake: 1.091 
+(epoch: 1, iters: 4000, time: 0.063) G_GAN: 0.290 G_GAN_Feat: 1.444 G_ID: 0.584 G_Rec: 0.722 D_GP: 0.040 D_real: 0.886 D_fake: 0.713 
+(epoch: 1, iters: 4400, time: 0.063) G_GAN: -0.082 G_GAN_Feat: 1.536 G_ID: 0.997 G_Rec: 0.635 D_GP: 0.044 D_real: 0.781 D_fake: 1.082 
+(epoch: 1, iters: 4800, time: 0.064) G_GAN: -0.138 G_GAN_Feat: 1.452 G_ID: 0.654 G_Rec: 0.626 D_GP: 0.053 D_real: 0.733 D_fake: 1.138 
+(epoch: 1, iters: 5200, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 1.153 G_ID: 1.004 G_Rec: 0.532 D_GP: 0.031 D_real: 0.769 D_fake: 0.990 
+(epoch: 1, iters: 5600, time: 0.063) G_GAN: -0.038 G_GAN_Feat: 1.194 G_ID: 0.619 G_Rec: 0.553 D_GP: 0.036 D_real: 0.926 D_fake: 1.040 
+(epoch: 1, iters: 6000, time: 0.063) G_GAN: -0.102 G_GAN_Feat: 1.214 G_ID: 0.981 G_Rec: 0.904 D_GP: 0.056 D_real: 0.589 D_fake: 1.102 
+(epoch: 1, iters: 6400, time: 0.064) G_GAN: 0.025 G_GAN_Feat: 1.220 G_ID: 0.544 G_Rec: 0.605 D_GP: 0.037 D_real: 0.987 D_fake: 0.975 
+(epoch: 1, iters: 6800, time: 0.063) G_GAN: 0.309 G_GAN_Feat: 1.110 G_ID: 1.040 G_Rec: 0.548 D_GP: 0.027 D_real: 1.106 D_fake: 0.695 
+(epoch: 1, iters: 7200, time: 0.064) G_GAN: -0.014 G_GAN_Feat: 1.094 G_ID: 0.531 G_Rec: 0.562 D_GP: 0.036 D_real: 0.868 D_fake: 1.015 
+(epoch: 1, iters: 7600, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 1.040 G_ID: 1.029 G_Rec: 0.685 D_GP: 0.026 D_real: 0.821 D_fake: 0.964 
+(epoch: 1, iters: 8000, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 1.039 G_ID: 0.552 G_Rec: 0.505 D_GP: 0.035 D_real: 0.922 D_fake: 0.888 
+(epoch: 1, iters: 8400, time: 0.063) G_GAN: 0.206 G_GAN_Feat: 1.051 G_ID: 0.967 G_Rec: 0.502 D_GP: 0.034 D_real: 1.008 D_fake: 0.796 
+(epoch: 2, iters: 192, time: 0.063) G_GAN: 0.444 G_GAN_Feat: 0.948 G_ID: 0.473 G_Rec: 1.017 D_GP: 0.013 D_real: 1.357 D_fake: 0.582 
+(epoch: 2, iters: 592, time: 0.063) G_GAN: 0.275 G_GAN_Feat: 1.016 G_ID: 0.997 G_Rec: 0.490 D_GP: 0.030 D_real: 1.145 D_fake: 0.727 
+(epoch: 2, iters: 992, time: 0.064) G_GAN: -0.031 G_GAN_Feat: 1.141 G_ID: 0.524 G_Rec: 0.510 D_GP: 0.041 D_real: 0.697 D_fake: 1.032 
+(epoch: 2, iters: 1392, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 1.060 G_ID: 1.025 G_Rec: 0.422 D_GP: 0.053 D_real: 1.016 D_fake: 0.677 
+(epoch: 2, iters: 1792, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.843 G_ID: 0.505 G_Rec: 0.653 D_GP: 0.013 D_real: 1.128 D_fake: 0.803 
+(epoch: 2, iters: 2192, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.990 G_ID: 0.992 G_Rec: 0.692 D_GP: 0.039 D_real: 0.982 D_fake: 0.745 
+(epoch: 2, iters: 2592, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 1.025 G_ID: 0.453 G_Rec: 0.583 D_GP: 0.048 D_real: 0.910 D_fake: 0.914 
+(epoch: 2, iters: 2992, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 1.077 G_ID: 0.993 G_Rec: 0.510 D_GP: 0.073 D_real: 0.690 D_fake: 0.865 
+(epoch: 2, iters: 3392, time: 0.064) G_GAN: -0.145 G_GAN_Feat: 0.977 G_ID: 0.442 G_Rec: 0.431 D_GP: 0.046 D_real: 0.611 D_fake: 1.145 
+(epoch: 2, iters: 3792, time: 0.064) G_GAN: 0.089 G_GAN_Feat: 0.892 G_ID: 1.006 G_Rec: 0.590 D_GP: 0.020 D_real: 1.005 D_fake: 0.915 
+(epoch: 2, iters: 4192, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.993 G_ID: 0.551 G_Rec: 0.589 D_GP: 0.040 D_real: 1.031 D_fake: 0.817 
+(epoch: 2, iters: 4592, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.996 G_ID: 0.995 G_Rec: 0.517 D_GP: 0.061 D_real: 1.158 D_fake: 0.614 
+(epoch: 2, iters: 4992, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 1.101 G_ID: 0.477 G_Rec: 0.843 D_GP: 0.041 D_real: 1.131 D_fake: 0.661 
+(epoch: 2, iters: 5392, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.819 G_ID: 0.987 G_Rec: 0.398 D_GP: 0.034 D_real: 0.857 D_fake: 0.898 
+(epoch: 2, iters: 5792, time: 0.064) G_GAN: 0.028 G_GAN_Feat: 0.885 G_ID: 0.463 G_Rec: 0.489 D_GP: 0.022 D_real: 0.821 D_fake: 0.973 
+(epoch: 2, iters: 6192, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.875 G_ID: 1.003 G_Rec: 0.398 D_GP: 0.045 D_real: 0.997 D_fake: 0.800 
+(epoch: 2, iters: 6592, time: 0.064) G_GAN: -0.040 G_GAN_Feat: 0.904 G_ID: 0.454 G_Rec: 0.434 D_GP: 0.031 D_real: 0.754 D_fake: 1.040 
+(epoch: 2, iters: 6992, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.948 G_ID: 0.934 G_Rec: 0.439 D_GP: 0.054 D_real: 0.975 D_fake: 0.891 
+(epoch: 2, iters: 7392, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 1.029 G_ID: 0.501 G_Rec: 0.452 D_GP: 0.061 D_real: 0.894 D_fake: 0.682 
+(epoch: 2, iters: 7792, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.882 G_ID: 1.016 G_Rec: 0.476 D_GP: 0.039 D_real: 0.923 D_fake: 0.820 
+(epoch: 2, iters: 8192, time: 0.064) G_GAN: 0.004 G_GAN_Feat: 0.991 G_ID: 0.541 G_Rec: 0.780 D_GP: 0.045 D_real: 0.800 D_fake: 0.996 
+(epoch: 2, iters: 8592, time: 0.064) G_GAN: -0.001 G_GAN_Feat: 0.880 G_ID: 1.019 G_Rec: 0.499 D_GP: 0.029 D_real: 0.743 D_fake: 1.001 
+(epoch: 3, iters: 384, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 1.037 G_ID: 0.490 G_Rec: 0.696 D_GP: 0.038 D_real: 0.917 D_fake: 0.837 
+(epoch: 3, iters: 784, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.865 G_ID: 0.990 G_Rec: 0.409 D_GP: 0.049 D_real: 0.909 D_fake: 0.849 
+(epoch: 3, iters: 1184, time: 0.064) G_GAN: -0.022 G_GAN_Feat: 0.814 G_ID: 0.442 G_Rec: 0.457 D_GP: 0.024 D_real: 0.774 D_fake: 1.022 
+(epoch: 3, iters: 1584, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.892 G_ID: 0.994 G_Rec: 0.673 D_GP: 0.037 D_real: 1.000 D_fake: 0.773 
+(epoch: 3, iters: 1984, time: 0.064) G_GAN: -0.190 G_GAN_Feat: 0.847 G_ID: 0.463 G_Rec: 0.375 D_GP: 0.026 D_real: 0.596 D_fake: 1.190 
+(epoch: 3, iters: 2384, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.909 G_ID: 0.992 G_Rec: 0.464 D_GP: 0.032 D_real: 1.266 D_fake: 0.577 
+(epoch: 3, iters: 2784, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.958 G_ID: 0.524 G_Rec: 0.490 D_GP: 0.068 D_real: 1.151 D_fake: 0.641 
+(epoch: 3, iters: 3184, time: 0.064) G_GAN: 0.120 G_GAN_Feat: 0.720 G_ID: 0.971 G_Rec: 0.545 D_GP: 0.013 D_real: 1.015 D_fake: 0.881 
+(epoch: 3, iters: 3584, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.940 G_ID: 0.439 G_Rec: 0.497 D_GP: 0.041 D_real: 0.908 D_fake: 0.883 
+(epoch: 3, iters: 3984, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.903 G_ID: 0.975 G_Rec: 0.459 D_GP: 0.043 D_real: 0.848 D_fake: 0.969 
+(epoch: 3, iters: 4384, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.898 G_ID: 0.422 G_Rec: 0.481 D_GP: 0.035 D_real: 0.877 D_fake: 0.838 
+(epoch: 3, iters: 4784, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 1.111 G_ID: 0.994 G_Rec: 0.512 D_GP: 0.060 D_real: 0.630 D_fake: 0.920 
+(epoch: 3, iters: 5184, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.968 G_ID: 0.462 G_Rec: 0.464 D_GP: 0.066 D_real: 0.938 D_fake: 0.728 
+(epoch: 3, iters: 5584, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 1.039 G_ID: 0.982 G_Rec: 0.621 D_GP: 0.054 D_real: 0.672 D_fake: 0.960 
+(epoch: 3, iters: 5984, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.823 G_ID: 0.424 G_Rec: 0.445 D_GP: 0.035 D_real: 0.974 D_fake: 0.779 
+(epoch: 3, iters: 6384, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.915 G_ID: 1.007 G_Rec: 0.432 D_GP: 0.078 D_real: 1.004 D_fake: 0.696 
+(epoch: 3, iters: 6784, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 1.005 G_ID: 0.473 G_Rec: 0.534 D_GP: 0.046 D_real: 1.028 D_fake: 0.785 
+(epoch: 3, iters: 7184, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.943 G_ID: 0.988 G_Rec: 0.523 D_GP: 0.039 D_real: 1.083 D_fake: 0.738 
+(epoch: 3, iters: 7584, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.810 G_ID: 0.444 G_Rec: 0.395 D_GP: 0.021 D_real: 1.115 D_fake: 0.681 
+(epoch: 3, iters: 7984, time: 0.064) G_GAN: 0.067 G_GAN_Feat: 0.844 G_ID: 0.998 G_Rec: 0.445 D_GP: 0.029 D_real: 0.793 D_fake: 0.933 
+(epoch: 3, iters: 8384, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.967 G_ID: 0.420 G_Rec: 0.580 D_GP: 0.051 D_real: 0.858 D_fake: 0.897 
+(epoch: 4, iters: 176, time: 0.064) G_GAN: 0.051 G_GAN_Feat: 0.797 G_ID: 0.993 G_Rec: 0.374 D_GP: 0.036 D_real: 0.758 D_fake: 0.949 
+(epoch: 4, iters: 576, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.879 G_ID: 0.526 G_Rec: 0.431 D_GP: 0.037 D_real: 1.062 D_fake: 0.807 
+(epoch: 4, iters: 976, time: 0.064) G_GAN: -0.062 G_GAN_Feat: 0.940 G_ID: 0.975 G_Rec: 0.481 D_GP: 0.042 D_real: 0.681 D_fake: 1.062 
+(epoch: 4, iters: 1376, time: 0.064) G_GAN: 0.017 G_GAN_Feat: 0.884 G_ID: 0.461 G_Rec: 0.519 D_GP: 0.022 D_real: 0.939 D_fake: 0.983 
+(epoch: 4, iters: 1776, time: 0.064) G_GAN: -0.003 G_GAN_Feat: 0.931 G_ID: 1.008 G_Rec: 0.499 D_GP: 0.036 D_real: 0.566 D_fake: 1.003 
+(epoch: 4, iters: 2176, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.685 G_ID: 0.473 G_Rec: 0.459 D_GP: 0.013 D_real: 0.998 D_fake: 0.870 
+(epoch: 4, iters: 2576, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.933 G_ID: 0.978 G_Rec: 0.546 D_GP: 0.046 D_real: 0.989 D_fake: 0.751 
+(epoch: 4, iters: 2976, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.849 G_ID: 0.517 G_Rec: 0.383 D_GP: 0.034 D_real: 1.061 D_fake: 0.753 
+(epoch: 4, iters: 3376, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.875 G_ID: 1.008 G_Rec: 0.442 D_GP: 0.057 D_real: 0.765 D_fake: 0.924 
+(epoch: 4, iters: 3776, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.901 G_ID: 0.517 G_Rec: 0.412 D_GP: 0.051 D_real: 1.254 D_fake: 0.484 
+(epoch: 4, iters: 4176, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.827 G_ID: 0.974 G_Rec: 0.446 D_GP: 0.056 D_real: 0.958 D_fake: 0.849 
+(epoch: 4, iters: 4576, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.848 G_ID: 0.480 G_Rec: 0.459 D_GP: 0.041 D_real: 1.059 D_fake: 0.829 
+(epoch: 4, iters: 4976, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 0.867 G_ID: 1.031 G_Rec: 0.513 D_GP: 0.030 D_real: 1.342 D_fake: 0.478 
+(epoch: 4, iters: 5376, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 1.050 G_ID: 0.511 G_Rec: 0.814 D_GP: 0.044 D_real: 0.877 D_fake: 0.928 
+(epoch: 4, iters: 5776, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.932 G_ID: 0.976 G_Rec: 0.472 D_GP: 0.052 D_real: 1.108 D_fake: 0.607 
+(epoch: 4, iters: 6176, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.963 G_ID: 0.456 G_Rec: 0.391 D_GP: 0.068 D_real: 0.705 D_fake: 0.917 
+(epoch: 4, iters: 6576, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 0.844 G_ID: 0.984 G_Rec: 0.406 D_GP: 0.041 D_real: 1.212 D_fake: 0.563 
+(epoch: 4, iters: 6976, time: 0.064) G_GAN: -0.016 G_GAN_Feat: 0.989 G_ID: 0.425 G_Rec: 0.501 D_GP: 0.042 D_real: 0.573 D_fake: 1.016 
+(epoch: 4, iters: 7376, time: 0.064) G_GAN: 0.610 G_GAN_Feat: 0.742 G_ID: 1.012 G_Rec: 0.383 D_GP: 0.023 D_real: 1.441 D_fake: 0.592 
+(epoch: 4, iters: 7776, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 1.001 G_ID: 0.431 G_Rec: 0.631 D_GP: 0.126 D_real: 0.979 D_fake: 0.770 
+(epoch: 4, iters: 8176, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.808 G_ID: 0.982 G_Rec: 0.436 D_GP: 0.023 D_real: 0.929 D_fake: 0.870 
+(epoch: 4, iters: 8576, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.811 G_ID: 0.495 G_Rec: 0.444 D_GP: 0.030 D_real: 1.010 D_fake: 0.794 
+(epoch: 5, iters: 368, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.792 G_ID: 1.018 G_Rec: 0.649 D_GP: 0.034 D_real: 0.835 D_fake: 0.907 
+(epoch: 5, iters: 768, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.759 G_ID: 0.467 G_Rec: 0.508 D_GP: 0.025 D_real: 1.117 D_fake: 0.699 
+(epoch: 5, iters: 1168, time: 0.064) G_GAN: 0.803 G_GAN_Feat: 0.758 G_ID: 0.983 G_Rec: 0.466 D_GP: 0.016 D_real: 1.650 D_fake: 0.218 
+(epoch: 5, iters: 1568, time: 0.064) G_GAN: -0.000 G_GAN_Feat: 0.901 G_ID: 0.407 G_Rec: 0.419 D_GP: 0.054 D_real: 0.693 D_fake: 1.000 
+(epoch: 5, iters: 1968, time: 0.064) G_GAN: -0.033 G_GAN_Feat: 0.799 G_ID: 0.979 G_Rec: 0.488 D_GP: 0.037 D_real: 0.805 D_fake: 1.033 
+(epoch: 5, iters: 2368, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.997 G_ID: 0.475 G_Rec: 0.546 D_GP: 0.074 D_real: 0.752 D_fake: 0.825 
+(epoch: 5, iters: 2768, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.872 G_ID: 1.023 G_Rec: 0.393 D_GP: 0.042 D_real: 0.813 D_fake: 0.923 
+(epoch: 5, iters: 3168, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.864 G_ID: 0.402 G_Rec: 0.476 D_GP: 0.031 D_real: 1.038 D_fake: 0.777 
+(epoch: 5, iters: 3568, time: 0.064) G_GAN: -0.044 G_GAN_Feat: 0.685 G_ID: 1.025 G_Rec: 0.601 D_GP: 0.019 D_real: 0.826 D_fake: 1.046 
+(epoch: 5, iters: 3968, time: 0.064) G_GAN: 0.006 G_GAN_Feat: 0.925 G_ID: 0.448 G_Rec: 0.388 D_GP: 0.056 D_real: 0.642 D_fake: 0.995 
+(epoch: 5, iters: 4368, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.798 G_ID: 0.937 G_Rec: 0.651 D_GP: 0.028 D_real: 1.142 D_fake: 0.694 
+(epoch: 5, iters: 4768, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 1.041 G_ID: 0.532 G_Rec: 0.514 D_GP: 0.072 D_real: 0.750 D_fake: 0.872 
+(epoch: 5, iters: 5168, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 0.925 G_ID: 0.989 G_Rec: 0.415 D_GP: 0.043 D_real: 1.188 D_fake: 0.525 
+(epoch: 5, iters: 5568, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.895 G_ID: 0.465 G_Rec: 0.568 D_GP: 0.037 D_real: 0.891 D_fake: 0.851 
+(epoch: 5, iters: 5968, time: 0.064) G_GAN: 0.019 G_GAN_Feat: 0.611 G_ID: 0.964 G_Rec: 0.388 D_GP: 0.015 D_real: 0.985 D_fake: 0.981 
+(epoch: 5, iters: 6368, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.893 G_ID: 0.479 G_Rec: 0.403 D_GP: 0.035 D_real: 0.899 D_fake: 0.845 
+(epoch: 5, iters: 6768, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.910 G_ID: 0.979 G_Rec: 0.648 D_GP: 0.033 D_real: 0.962 D_fake: 0.656 
+(epoch: 5, iters: 7168, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.862 G_ID: 0.464 G_Rec: 0.401 D_GP: 0.053 D_real: 1.161 D_fake: 0.630 
+(epoch: 5, iters: 7568, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.737 G_ID: 0.993 G_Rec: 0.439 D_GP: 0.023 D_real: 0.973 D_fake: 0.905 
+(epoch: 5, iters: 7968, time: 0.064) G_GAN: -0.164 G_GAN_Feat: 1.144 G_ID: 0.462 G_Rec: 0.829 D_GP: 0.102 D_real: 0.504 D_fake: 1.164 
+(epoch: 5, iters: 8368, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.947 G_ID: 1.013 G_Rec: 0.484 D_GP: 0.031 D_real: 1.347 D_fake: 0.682 
+(epoch: 6, iters: 160, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.823 G_ID: 0.357 G_Rec: 0.430 D_GP: 0.024 D_real: 0.856 D_fake: 0.905 
+(epoch: 6, iters: 560, time: 0.064) G_GAN: 0.648 G_GAN_Feat: 0.995 G_ID: 1.027 G_Rec: 0.461 D_GP: 0.046 D_real: 1.335 D_fake: 0.393 
+(epoch: 6, iters: 960, time: 0.064) G_GAN: -0.214 G_GAN_Feat: 0.687 G_ID: 0.403 G_Rec: 0.383 D_GP: 0.027 D_real: 0.612 D_fake: 1.214 
+(epoch: 6, iters: 1360, time: 0.064) G_GAN: 0.780 G_GAN_Feat: 0.840 G_ID: 1.001 G_Rec: 0.714 D_GP: 0.017 D_real: 1.650 D_fake: 0.497 
+(epoch: 6, iters: 1760, time: 0.064) G_GAN: -0.160 G_GAN_Feat: 0.776 G_ID: 0.490 G_Rec: 0.380 D_GP: 0.054 D_real: 0.604 D_fake: 1.160 
+(epoch: 6, iters: 2160, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.840 G_ID: 0.999 G_Rec: 0.646 D_GP: 0.040 D_real: 0.909 D_fake: 0.814 
+(epoch: 6, iters: 2560, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 1.001 G_ID: 0.480 G_Rec: 0.542 D_GP: 0.041 D_real: 1.250 D_fake: 0.655 
+(epoch: 6, iters: 2960, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.836 G_ID: 1.003 G_Rec: 0.374 D_GP: 0.042 D_real: 1.029 D_fake: 0.646 
+(epoch: 6, iters: 3360, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.821 G_ID: 0.502 G_Rec: 0.326 D_GP: 0.045 D_real: 1.002 D_fake: 0.735 
+(epoch: 6, iters: 3760, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.902 G_ID: 0.987 G_Rec: 0.426 D_GP: 0.028 D_real: 0.842 D_fake: 0.827 
+(epoch: 6, iters: 4160, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.954 G_ID: 0.518 G_Rec: 0.426 D_GP: 0.057 D_real: 1.052 D_fake: 0.826 
+(epoch: 6, iters: 4560, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.848 G_ID: 1.003 G_Rec: 0.456 D_GP: 0.036 D_real: 1.084 D_fake: 0.686 
+(epoch: 6, iters: 4960, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.948 G_ID: 0.395 G_Rec: 0.406 D_GP: 0.053 D_real: 0.904 D_fake: 0.655 
+(epoch: 6, iters: 5360, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.872 G_ID: 0.989 G_Rec: 0.381 D_GP: 0.057 D_real: 0.877 D_fake: 0.915 
+(epoch: 6, iters: 5760, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 0.791 G_ID: 0.422 G_Rec: 0.381 D_GP: 0.028 D_real: 1.343 D_fake: 0.584 
+(epoch: 6, iters: 6160, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.814 G_ID: 0.961 G_Rec: 0.363 D_GP: 0.043 D_real: 0.964 D_fake: 0.766 
+(epoch: 6, iters: 6560, time: 0.064) G_GAN: 0.067 G_GAN_Feat: 0.902 G_ID: 0.409 G_Rec: 0.356 D_GP: 0.079 D_real: 0.765 D_fake: 0.934 
+(epoch: 6, iters: 6960, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.794 G_ID: 1.007 G_Rec: 0.437 D_GP: 0.030 D_real: 0.954 D_fake: 0.838 
+(epoch: 6, iters: 7360, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.825 G_ID: 0.403 G_Rec: 0.483 D_GP: 0.036 D_real: 1.022 D_fake: 0.766 
+(epoch: 6, iters: 7760, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.910 G_ID: 1.008 G_Rec: 0.529 D_GP: 0.050 D_real: 0.708 D_fake: 0.898 
+(epoch: 6, iters: 8160, time: 0.064) G_GAN: -0.009 G_GAN_Feat: 0.811 G_ID: 0.552 G_Rec: 0.405 D_GP: 0.029 D_real: 0.735 D_fake: 1.010 
+(epoch: 6, iters: 8560, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.815 G_ID: 0.996 G_Rec: 0.488 D_GP: 0.038 D_real: 0.932 D_fake: 0.840 
+(epoch: 7, iters: 352, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.824 G_ID: 0.382 G_Rec: 0.375 D_GP: 0.043 D_real: 0.980 D_fake: 0.735 
+(epoch: 7, iters: 752, time: 0.064) G_GAN: -0.008 G_GAN_Feat: 1.127 G_ID: 0.990 G_Rec: 0.421 D_GP: 0.075 D_real: 0.556 D_fake: 1.008 
+(epoch: 7, iters: 1152, time: 0.064) G_GAN: -0.149 G_GAN_Feat: 0.800 G_ID: 0.477 G_Rec: 0.490 D_GP: 0.039 D_real: 0.623 D_fake: 1.149 
+(epoch: 7, iters: 1552, time: 0.064) G_GAN: -0.040 G_GAN_Feat: 0.750 G_ID: 0.952 G_Rec: 0.363 D_GP: 0.043 D_real: 0.831 D_fake: 1.040 
+(epoch: 7, iters: 1952, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.568 G_ID: 0.497 G_Rec: 0.390 D_GP: 0.014 D_real: 1.012 D_fake: 0.888 
+(epoch: 7, iters: 2352, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 0.783 G_ID: 0.985 G_Rec: 0.380 D_GP: 0.057 D_real: 0.715 D_fake: 0.934 
+(epoch: 7, iters: 2752, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 0.944 G_ID: 0.549 G_Rec: 0.444 D_GP: 0.068 D_real: 1.279 D_fake: 0.513 
+(epoch: 7, iters: 3152, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.846 G_ID: 0.973 G_Rec: 0.498 D_GP: 0.054 D_real: 1.202 D_fake: 0.642 
+(epoch: 7, iters: 3552, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 0.756 G_ID: 0.423 G_Rec: 0.434 D_GP: 0.036 D_real: 1.160 D_fake: 0.602 
+(epoch: 7, iters: 3952, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.951 G_ID: 0.975 G_Rec: 0.395 D_GP: 0.046 D_real: 0.926 D_fake: 0.639 
+(epoch: 7, iters: 4352, time: 0.064) G_GAN: -0.003 G_GAN_Feat: 0.828 G_ID: 0.453 G_Rec: 0.400 D_GP: 0.061 D_real: 0.746 D_fake: 1.003 
+(epoch: 7, iters: 4752, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 1.070 G_ID: 0.940 G_Rec: 0.408 D_GP: 0.147 D_real: 0.538 D_fake: 0.778 
+(epoch: 7, iters: 5152, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 1.008 G_ID: 0.382 G_Rec: 0.390 D_GP: 0.055 D_real: 0.757 D_fake: 0.707 
+(epoch: 7, iters: 5552, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 1.161 G_ID: 0.964 G_Rec: 0.361 D_GP: 0.076 D_real: 1.254 D_fake: 0.609 
+(epoch: 7, iters: 5952, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.850 G_ID: 0.422 G_Rec: 0.444 D_GP: 0.039 D_real: 1.139 D_fake: 0.600 
+(epoch: 7, iters: 6352, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.854 G_ID: 0.981 G_Rec: 0.423 D_GP: 0.034 D_real: 0.950 D_fake: 0.776 
+(epoch: 7, iters: 6752, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.887 G_ID: 0.557 G_Rec: 0.399 D_GP: 0.045 D_real: 0.905 D_fake: 0.858 
+(epoch: 7, iters: 7152, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 1.094 G_ID: 0.967 G_Rec: 0.373 D_GP: 0.099 D_real: 0.565 D_fake: 0.749 
+(epoch: 7, iters: 7552, time: 0.064) G_GAN: -0.019 G_GAN_Feat: 0.901 G_ID: 0.361 G_Rec: 0.404 D_GP: 0.079 D_real: 0.719 D_fake: 1.019 
+(epoch: 7, iters: 7952, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.677 G_ID: 0.997 G_Rec: 0.613 D_GP: 0.022 D_real: 1.013 D_fake: 0.808 
+(epoch: 7, iters: 8352, time: 0.064) G_GAN: -0.009 G_GAN_Feat: 0.767 G_ID: 0.407 G_Rec: 0.358 D_GP: 0.024 D_real: 0.750 D_fake: 1.009 
+(epoch: 8, iters: 144, time: 0.064) G_GAN: -0.207 G_GAN_Feat: 0.785 G_ID: 0.997 G_Rec: 0.381 D_GP: 0.048 D_real: 0.512 D_fake: 1.207 
+(epoch: 8, iters: 544, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.886 G_ID: 0.424 G_Rec: 0.372 D_GP: 0.048 D_real: 0.835 D_fake: 0.823 
+(epoch: 8, iters: 944, time: 0.064) G_GAN: -0.169 G_GAN_Feat: 0.695 G_ID: 0.988 G_Rec: 0.387 D_GP: 0.028 D_real: 0.694 D_fake: 1.169 
+(epoch: 8, iters: 1344, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.547 G_ID: 0.543 G_Rec: 0.533 D_GP: 0.009 D_real: 1.281 D_fake: 0.615 
+(epoch: 8, iters: 1744, time: 0.064) G_GAN: -0.024 G_GAN_Feat: 0.543 G_ID: 0.960 G_Rec: 0.428 D_GP: 0.018 D_real: 0.886 D_fake: 1.024 
+(epoch: 8, iters: 2144, time: 0.064) G_GAN: 0.019 G_GAN_Feat: 0.643 G_ID: 0.377 G_Rec: 0.350 D_GP: 0.056 D_real: 0.778 D_fake: 0.981 
+(epoch: 8, iters: 2544, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.679 G_ID: 0.968 G_Rec: 0.401 D_GP: 0.026 D_real: 1.266 D_fake: 0.582 
+(epoch: 8, iters: 2944, time: 0.064) G_GAN: 0.003 G_GAN_Feat: 0.913 G_ID: 0.405 G_Rec: 0.436 D_GP: 0.085 D_real: 0.566 D_fake: 0.997 
+(epoch: 8, iters: 3344, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 1.116 G_ID: 0.990 G_Rec: 0.471 D_GP: 0.078 D_real: 0.797 D_fake: 0.840 
+(epoch: 8, iters: 3744, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.842 G_ID: 0.383 G_Rec: 0.394 D_GP: 0.028 D_real: 1.033 D_fake: 0.649 
+(epoch: 8, iters: 4144, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.814 G_ID: 0.990 G_Rec: 0.360 D_GP: 0.027 D_real: 0.779 D_fake: 0.895 
+(epoch: 8, iters: 4544, time: 0.064) G_GAN: -0.019 G_GAN_Feat: 0.580 G_ID: 0.418 G_Rec: 0.501 D_GP: 0.019 D_real: 0.810 D_fake: 1.019 
+(epoch: 8, iters: 4944, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.641 G_ID: 0.969 G_Rec: 0.309 D_GP: 0.038 D_real: 0.999 D_fake: 0.766 
+(epoch: 8, iters: 5344, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.743 G_ID: 0.434 G_Rec: 0.368 D_GP: 0.068 D_real: 1.013 D_fake: 0.824 
+(epoch: 8, iters: 5744, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.949 G_ID: 0.951 G_Rec: 0.431 D_GP: 0.079 D_real: 0.675 D_fake: 0.811 
+(epoch: 8, iters: 6144, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.962 G_ID: 0.364 G_Rec: 0.389 D_GP: 0.076 D_real: 0.698 D_fake: 0.872 
+(epoch: 8, iters: 6544, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.974 G_ID: 0.984 G_Rec: 0.757 D_GP: 0.088 D_real: 0.624 D_fake: 0.857 
+(epoch: 8, iters: 6944, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 1.090 G_ID: 0.408 G_Rec: 0.428 D_GP: 0.090 D_real: 1.143 D_fake: 0.606 
+(epoch: 8, iters: 7344, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.806 G_ID: 0.952 G_Rec: 0.353 D_GP: 0.036 D_real: 0.814 D_fake: 0.849 
+(epoch: 8, iters: 7744, time: 0.064) G_GAN: 0.004 G_GAN_Feat: 0.896 G_ID: 0.420 G_Rec: 0.344 D_GP: 0.124 D_real: 0.733 D_fake: 0.996 
+(epoch: 8, iters: 8144, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.627 G_ID: 0.988 G_Rec: 0.394 D_GP: 0.016 D_real: 1.048 D_fake: 0.879 
+(epoch: 8, iters: 8544, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.891 G_ID: 0.428 G_Rec: 0.387 D_GP: 0.069 D_real: 0.870 D_fake: 0.774 
+(epoch: 9, iters: 336, time: 0.064) G_GAN: 0.058 G_GAN_Feat: 0.899 G_ID: 0.986 G_Rec: 0.346 D_GP: 0.107 D_real: 0.599 D_fake: 0.942 
+(epoch: 9, iters: 736, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.970 G_ID: 0.424 G_Rec: 0.330 D_GP: 0.095 D_real: 0.495 D_fake: 0.918 
+(epoch: 9, iters: 1136, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.625 G_ID: 0.986 G_Rec: 0.412 D_GP: 0.014 D_real: 1.022 D_fake: 0.858 
+(epoch: 9, iters: 1536, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.768 G_ID: 0.380 G_Rec: 0.401 D_GP: 0.048 D_real: 1.134 D_fake: 0.643 
+(epoch: 9, iters: 1936, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.872 G_ID: 0.963 G_Rec: 0.369 D_GP: 0.089 D_real: 0.799 D_fake: 0.783 
+(epoch: 9, iters: 2336, time: 0.064) G_GAN: -0.044 G_GAN_Feat: 0.710 G_ID: 0.437 G_Rec: 0.372 D_GP: 0.036 D_real: 0.717 D_fake: 1.044 
+(epoch: 9, iters: 2736, time: 0.064) G_GAN: -0.028 G_GAN_Feat: 1.063 G_ID: 0.975 G_Rec: 0.479 D_GP: 0.114 D_real: 0.497 D_fake: 1.028 
+(epoch: 9, iters: 3136, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.662 G_ID: 0.463 G_Rec: 0.388 D_GP: 0.028 D_real: 1.080 D_fake: 0.753 
+(epoch: 9, iters: 3536, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.589 G_ID: 0.967 G_Rec: 0.339 D_GP: 0.018 D_real: 0.929 D_fake: 0.989 
+(epoch: 9, iters: 3936, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.876 G_ID: 0.369 G_Rec: 0.400 D_GP: 0.114 D_real: 0.674 D_fake: 0.918 
+(epoch: 9, iters: 4336, time: 0.064) G_GAN: 0.108 G_GAN_Feat: 0.798 G_ID: 0.935 G_Rec: 0.468 D_GP: 0.050 D_real: 0.829 D_fake: 0.892 
+(epoch: 9, iters: 4736, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 1.019 G_ID: 0.445 G_Rec: 0.372 D_GP: 0.068 D_real: 0.558 D_fake: 0.912 
+(epoch: 9, iters: 5136, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 1.389 G_ID: 0.990 G_Rec: 0.432 D_GP: 0.351 D_real: 0.388 D_fake: 0.740 
+(epoch: 9, iters: 5536, time: 0.064) G_GAN: 0.056 G_GAN_Feat: 0.671 G_ID: 0.442 G_Rec: 0.450 D_GP: 0.030 D_real: 0.795 D_fake: 0.944 
+(epoch: 9, iters: 5936, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.817 G_ID: 0.935 G_Rec: 0.442 D_GP: 0.077 D_real: 0.890 D_fake: 0.698 
+(epoch: 9, iters: 6336, time: 0.064) G_GAN: -0.064 G_GAN_Feat: 0.747 G_ID: 0.453 G_Rec: 0.358 D_GP: 0.057 D_real: 0.649 D_fake: 1.064 
+(epoch: 9, iters: 6736, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.612 G_ID: 0.959 G_Rec: 0.324 D_GP: 0.019 D_real: 1.150 D_fake: 0.683 
+(epoch: 9, iters: 7136, time: 0.064) G_GAN: 0.718 G_GAN_Feat: 0.713 G_ID: 0.485 G_Rec: 0.537 D_GP: 0.019 D_real: 1.583 D_fake: 0.345 
+(epoch: 9, iters: 7536, time: 0.064) G_GAN: -0.217 G_GAN_Feat: 0.831 G_ID: 0.973 G_Rec: 0.392 D_GP: 0.050 D_real: 0.735 D_fake: 1.217 
+(epoch: 9, iters: 7936, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 1.072 G_ID: 0.352 G_Rec: 0.420 D_GP: 0.061 D_real: 0.520 D_fake: 0.918 
+(epoch: 9, iters: 8336, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.801 G_ID: 0.971 G_Rec: 0.339 D_GP: 0.052 D_real: 1.052 D_fake: 0.634 
+(epoch: 10, iters: 128, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.941 G_ID: 0.393 G_Rec: 0.391 D_GP: 0.078 D_real: 0.831 D_fake: 0.673 
+(epoch: 10, iters: 528, time: 0.064) G_GAN: 0.652 G_GAN_Feat: 1.110 G_ID: 0.961 G_Rec: 0.417 D_GP: 0.083 D_real: 1.016 D_fake: 0.461 
+(epoch: 10, iters: 928, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.717 G_ID: 0.400 G_Rec: 0.595 D_GP: 0.030 D_real: 1.115 D_fake: 0.688 
+(epoch: 10, iters: 1328, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.801 G_ID: 0.946 G_Rec: 0.391 D_GP: 0.069 D_real: 0.901 D_fake: 0.873 
+(epoch: 10, iters: 1728, time: 0.064) G_GAN: 0.549 G_GAN_Feat: 0.736 G_ID: 0.434 G_Rec: 0.341 D_GP: 0.023 D_real: 1.345 D_fake: 0.466 
+(epoch: 10, iters: 2128, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.791 G_ID: 0.983 G_Rec: 0.350 D_GP: 0.045 D_real: 1.103 D_fake: 0.585 
+(epoch: 10, iters: 2528, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.660 G_ID: 0.447 G_Rec: 0.382 D_GP: 0.018 D_real: 1.143 D_fake: 0.708 
+(epoch: 10, iters: 2928, time: 0.064) G_GAN: 0.586 G_GAN_Feat: 0.867 G_ID: 0.984 G_Rec: 0.452 D_GP: 0.058 D_real: 1.290 D_fake: 0.476 
+(epoch: 10, iters: 3328, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 0.872 G_ID: 0.392 G_Rec: 0.349 D_GP: 0.053 D_real: 1.103 D_fake: 0.609 
+(epoch: 10, iters: 3728, time: 0.064) G_GAN: 0.007 G_GAN_Feat: 0.709 G_ID: 0.947 G_Rec: 0.383 D_GP: 0.029 D_real: 0.783 D_fake: 0.993 
+(epoch: 10, iters: 4128, time: 0.064) G_GAN: 0.551 G_GAN_Feat: 0.736 G_ID: 0.465 G_Rec: 0.338 D_GP: 0.031 D_real: 1.343 D_fake: 0.554 
+(epoch: 10, iters: 4528, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.800 G_ID: 0.992 G_Rec: 0.582 D_GP: 0.051 D_real: 1.077 D_fake: 0.599 
+(epoch: 10, iters: 4928, time: 0.064) G_GAN: 0.586 G_GAN_Feat: 0.979 G_ID: 0.418 G_Rec: 0.478 D_GP: 0.041 D_real: 1.163 D_fake: 0.492 
+(epoch: 10, iters: 5328, time: 0.064) G_GAN: -0.007 G_GAN_Feat: 1.012 G_ID: 1.000 G_Rec: 0.497 D_GP: 0.099 D_real: 0.990 D_fake: 1.007 
+(epoch: 10, iters: 5728, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.857 G_ID: 0.398 G_Rec: 0.361 D_GP: 0.029 D_real: 1.090 D_fake: 0.548 
+(epoch: 10, iters: 6128, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.707 G_ID: 0.932 G_Rec: 0.407 D_GP: 0.015 D_real: 1.310 D_fake: 0.543 
+(epoch: 10, iters: 6528, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.672 G_ID: 0.488 G_Rec: 0.470 D_GP: 0.031 D_real: 1.157 D_fake: 0.616 
+(epoch: 10, iters: 6928, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.719 G_ID: 0.967 G_Rec: 0.395 D_GP: 0.039 D_real: 0.996 D_fake: 0.807 
+(epoch: 10, iters: 7328, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 1.028 G_ID: 0.369 G_Rec: 0.438 D_GP: 0.113 D_real: 0.618 D_fake: 0.738 
+(epoch: 10, iters: 7728, time: 0.064) G_GAN: 0.052 G_GAN_Feat: 0.922 G_ID: 0.971 G_Rec: 0.517 D_GP: 0.140 D_real: 0.553 D_fake: 0.948 
+(epoch: 10, iters: 8128, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.673 G_ID: 0.437 G_Rec: 0.476 D_GP: 0.018 D_real: 1.176 D_fake: 0.692 
+(epoch: 10, iters: 8528, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.720 G_ID: 0.994 G_Rec: 0.320 D_GP: 0.071 D_real: 0.860 D_fake: 0.870 
+(epoch: 11, iters: 320, time: 0.064) G_GAN: -0.044 G_GAN_Feat: 0.757 G_ID: 0.403 G_Rec: 0.407 D_GP: 0.042 D_real: 0.674 D_fake: 1.044 
+(epoch: 11, iters: 720, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.901 G_ID: 0.927 G_Rec: 0.421 D_GP: 0.087 D_real: 0.864 D_fake: 0.600 
+(epoch: 11, iters: 1120, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.961 G_ID: 0.398 G_Rec: 0.409 D_GP: 0.123 D_real: 0.835 D_fake: 0.571 
+(epoch: 11, iters: 1520, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.761 G_ID: 0.972 G_Rec: 0.473 D_GP: 0.043 D_real: 1.176 D_fake: 0.589 
+(epoch: 11, iters: 1920, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.707 G_ID: 0.472 G_Rec: 0.334 D_GP: 0.033 D_real: 1.243 D_fake: 0.588 
+(epoch: 11, iters: 2320, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 1.027 G_ID: 0.970 G_Rec: 0.354 D_GP: 0.048 D_real: 0.865 D_fake: 0.603 
+(epoch: 11, iters: 2720, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.907 G_ID: 0.327 G_Rec: 0.364 D_GP: 0.059 D_real: 0.959 D_fake: 0.731 
+(epoch: 11, iters: 3120, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.763 G_ID: 0.975 G_Rec: 0.399 D_GP: 0.029 D_real: 1.084 D_fake: 0.669 
+(epoch: 11, iters: 3520, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.696 G_ID: 0.405 G_Rec: 0.418 D_GP: 0.025 D_real: 1.009 D_fake: 0.763 
+(epoch: 11, iters: 3920, time: 0.064) G_GAN: -0.078 G_GAN_Feat: 0.918 G_ID: 0.940 G_Rec: 0.385 D_GP: 0.094 D_real: 0.494 D_fake: 1.078 
+(epoch: 11, iters: 4320, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.698 G_ID: 0.422 G_Rec: 0.413 D_GP: 0.026 D_real: 1.147 D_fake: 0.737 
+(epoch: 11, iters: 4720, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.892 G_ID: 0.946 G_Rec: 0.418 D_GP: 0.031 D_real: 1.174 D_fake: 0.498 
+(epoch: 11, iters: 5120, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.983 G_ID: 0.399 G_Rec: 0.364 D_GP: 0.104 D_real: 0.880 D_fake: 0.565 
+(epoch: 11, iters: 5520, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.489 G_ID: 0.971 G_Rec: 0.320 D_GP: 0.016 D_real: 1.151 D_fake: 0.786 
+(epoch: 11, iters: 5920, time: 0.064) G_GAN: -0.065 G_GAN_Feat: 0.613 G_ID: 0.418 G_Rec: 0.520 D_GP: 0.022 D_real: 0.700 D_fake: 1.065 
+(epoch: 11, iters: 6320, time: 0.064) G_GAN: -0.042 G_GAN_Feat: 0.618 G_ID: 0.939 G_Rec: 0.347 D_GP: 0.038 D_real: 0.823 D_fake: 1.042 
+(epoch: 11, iters: 6720, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.647 G_ID: 0.353 G_Rec: 0.312 D_GP: 0.033 D_real: 1.007 D_fake: 0.766 
+(epoch: 11, iters: 7120, time: 0.064) G_GAN: 0.555 G_GAN_Feat: 0.910 G_ID: 0.973 G_Rec: 0.477 D_GP: 0.042 D_real: 1.348 D_fake: 0.587 
+(epoch: 11, iters: 7520, time: 0.064) G_GAN: 0.026 G_GAN_Feat: 0.664 G_ID: 0.399 G_Rec: 0.484 D_GP: 0.034 D_real: 0.873 D_fake: 0.974 
+(epoch: 11, iters: 7920, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 1.114 G_ID: 0.997 G_Rec: 0.433 D_GP: 0.140 D_real: 0.488 D_fake: 0.727 
+(epoch: 11, iters: 8320, time: 0.064) G_GAN: 0.034 G_GAN_Feat: 0.838 G_ID: 0.351 G_Rec: 0.308 D_GP: 0.057 D_real: 0.595 D_fake: 0.966 
+(epoch: 12, iters: 112, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.763 G_ID: 0.950 G_Rec: 0.623 D_GP: 0.036 D_real: 1.038 D_fake: 0.756 
+(epoch: 12, iters: 512, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.668 G_ID: 0.428 G_Rec: 0.398 D_GP: 0.023 D_real: 1.084 D_fake: 0.722 
+(epoch: 12, iters: 912, time: 0.064) G_GAN: 0.499 G_GAN_Feat: 0.929 G_ID: 0.956 G_Rec: 0.461 D_GP: 0.078 D_real: 0.982 D_fake: 0.527 
+(epoch: 12, iters: 1312, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.977 G_ID: 0.431 G_Rec: 0.608 D_GP: 0.058 D_real: 0.514 D_fake: 0.968 
+(epoch: 12, iters: 1712, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.752 G_ID: 0.962 G_Rec: 0.310 D_GP: 0.060 D_real: 1.078 D_fake: 0.689 
+(epoch: 12, iters: 2112, time: 0.064) G_GAN: -0.177 G_GAN_Feat: 0.622 G_ID: 0.445 G_Rec: 0.379 D_GP: 0.022 D_real: 0.647 D_fake: 1.177 
+(epoch: 12, iters: 2512, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 1.198 G_ID: 0.928 G_Rec: 0.403 D_GP: 0.115 D_real: 0.388 D_fake: 0.934 
+(epoch: 12, iters: 2912, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.576 G_ID: 0.482 G_Rec: 0.400 D_GP: 0.015 D_real: 1.049 D_fake: 0.834 
+(epoch: 12, iters: 3312, time: 0.064) G_GAN: 0.078 G_GAN_Feat: 0.845 G_ID: 1.003 G_Rec: 0.604 D_GP: 0.082 D_real: 0.745 D_fake: 0.922 
+(epoch: 12, iters: 3712, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 1.101 G_ID: 0.395 G_Rec: 0.653 D_GP: 0.152 D_real: 0.473 D_fake: 0.807 
+(epoch: 12, iters: 4112, time: 0.064) G_GAN: 0.520 G_GAN_Feat: 0.851 G_ID: 0.952 G_Rec: 0.467 D_GP: 0.039 D_real: 1.284 D_fake: 0.484 
+(epoch: 12, iters: 4512, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.796 G_ID: 0.415 G_Rec: 0.349 D_GP: 0.047 D_real: 0.826 D_fake: 0.815 
+(epoch: 12, iters: 4912, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.435 G_ID: 0.953 G_Rec: 0.391 D_GP: 0.011 D_real: 1.190 D_fake: 0.733 
+(epoch: 12, iters: 5312, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.456 G_ID: 0.359 G_Rec: 0.340 D_GP: 0.012 D_real: 1.059 D_fake: 0.800 
+(epoch: 12, iters: 5712, time: 0.064) G_GAN: 0.189 G_GAN_Feat: 0.491 G_ID: 0.946 G_Rec: 0.366 D_GP: 0.015 D_real: 1.098 D_fake: 0.811 
+(epoch: 12, iters: 6112, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.615 G_ID: 0.389 G_Rec: 0.421 D_GP: 0.019 D_real: 1.009 D_fake: 0.968 
+(epoch: 12, iters: 6512, time: 0.064) G_GAN: -0.270 G_GAN_Feat: 0.532 G_ID: 0.948 G_Rec: 0.310 D_GP: 0.030 D_real: 0.566 D_fake: 1.270 
+(epoch: 12, iters: 6912, time: 0.064) G_GAN: 0.098 G_GAN_Feat: 0.603 G_ID: 0.375 G_Rec: 0.318 D_GP: 0.052 D_real: 0.875 D_fake: 0.902 
+(epoch: 12, iters: 7312, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 0.696 G_ID: 0.906 G_Rec: 0.388 D_GP: 0.064 D_real: 1.057 D_fake: 0.690 
+(epoch: 12, iters: 7712, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.674 G_ID: 0.363 G_Rec: 0.345 D_GP: 0.065 D_real: 1.027 D_fake: 0.731 
+(epoch: 12, iters: 8112, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.585 G_ID: 0.960 G_Rec: 0.344 D_GP: 0.021 D_real: 1.261 D_fake: 0.606 
+(epoch: 12, iters: 8512, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.770 G_ID: 0.412 G_Rec: 0.500 D_GP: 0.066 D_real: 0.880 D_fake: 0.760 
+(epoch: 13, iters: 304, time: 0.064) G_GAN: -0.080 G_GAN_Feat: 0.658 G_ID: 0.917 G_Rec: 0.354 D_GP: 0.049 D_real: 0.674 D_fake: 1.080 
+(epoch: 13, iters: 704, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.798 G_ID: 0.411 G_Rec: 0.471 D_GP: 0.069 D_real: 1.007 D_fake: 0.626 
+(epoch: 13, iters: 1104, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.740 G_ID: 0.947 G_Rec: 0.411 D_GP: 0.043 D_real: 1.147 D_fake: 0.612 
+(epoch: 13, iters: 1504, time: 0.064) G_GAN: 0.582 G_GAN_Feat: 1.012 G_ID: 0.453 G_Rec: 0.466 D_GP: 0.127 D_real: 0.883 D_fake: 0.436 
+(epoch: 13, iters: 1904, time: 0.064) G_GAN: -0.050 G_GAN_Feat: 0.735 G_ID: 0.946 G_Rec: 0.349 D_GP: 0.061 D_real: 0.624 D_fake: 1.050 
+(epoch: 13, iters: 2304, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.902 G_ID: 0.459 G_Rec: 0.346 D_GP: 0.143 D_real: 1.003 D_fake: 0.510 
+(epoch: 13, iters: 2704, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.679 G_ID: 0.972 G_Rec: 0.384 D_GP: 0.032 D_real: 1.033 D_fake: 0.711 
+(epoch: 13, iters: 3104, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.842 G_ID: 0.386 G_Rec: 0.571 D_GP: 0.085 D_real: 0.778 D_fake: 0.825 
+(epoch: 13, iters: 3504, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.767 G_ID: 0.920 G_Rec: 0.365 D_GP: 0.063 D_real: 0.998 D_fake: 0.641 
+(epoch: 13, iters: 3904, time: 0.064) G_GAN: 0.793 G_GAN_Feat: 0.705 G_ID: 0.459 G_Rec: 0.349 D_GP: 0.025 D_real: 1.559 D_fake: 0.277 
+(epoch: 13, iters: 4304, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.734 G_ID: 0.954 G_Rec: 0.367 D_GP: 0.048 D_real: 1.102 D_fake: 0.557 
+(epoch: 13, iters: 4704, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.709 G_ID: 0.393 G_Rec: 0.370 D_GP: 0.028 D_real: 0.854 D_fake: 0.912 
+(epoch: 13, iters: 5104, time: 0.064) G_GAN: 0.552 G_GAN_Feat: 0.694 G_ID: 0.933 G_Rec: 0.357 D_GP: 0.023 D_real: 1.362 D_fake: 0.499 
+(epoch: 13, iters: 5504, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.594 G_ID: 0.384 G_Rec: 0.450 D_GP: 0.024 D_real: 0.920 D_fake: 0.843 
+(epoch: 13, iters: 5904, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.664 G_ID: 0.949 G_Rec: 0.341 D_GP: 0.050 D_real: 0.960 D_fake: 0.751 
+(epoch: 13, iters: 6304, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.725 G_ID: 0.416 G_Rec: 0.339 D_GP: 0.079 D_real: 0.984 D_fake: 0.757 
+(epoch: 13, iters: 6704, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.644 G_ID: 0.938 G_Rec: 0.403 D_GP: 0.045 D_real: 0.741 D_fake: 0.959 
+(epoch: 13, iters: 7104, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.824 G_ID: 0.363 G_Rec: 0.347 D_GP: 0.096 D_real: 0.893 D_fake: 0.587 
+(epoch: 13, iters: 7504, time: 0.064) G_GAN: 0.060 G_GAN_Feat: 0.850 G_ID: 0.921 G_Rec: 0.431 D_GP: 0.026 D_real: 0.712 D_fake: 0.940 
+(epoch: 13, iters: 7904, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.787 G_ID: 0.478 G_Rec: 0.415 D_GP: 0.073 D_real: 1.030 D_fake: 0.761 
+(epoch: 13, iters: 8304, time: 0.064) G_GAN: 0.004 G_GAN_Feat: 0.784 G_ID: 0.950 G_Rec: 0.438 D_GP: 0.064 D_real: 0.609 D_fake: 0.996 
+(epoch: 14, iters: 96, time: 0.064) G_GAN: 0.523 G_GAN_Feat: 0.987 G_ID: 0.483 G_Rec: 0.440 D_GP: 0.047 D_real: 1.045 D_fake: 0.485 
+(epoch: 14, iters: 496, time: 0.064) G_GAN: 0.723 G_GAN_Feat: 0.740 G_ID: 0.919 G_Rec: 0.377 D_GP: 0.040 D_real: 1.437 D_fake: 0.411 
+(epoch: 14, iters: 896, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.934 G_ID: 0.408 G_Rec: 0.391 D_GP: 0.044 D_real: 0.695 D_fake: 0.780 
+(epoch: 14, iters: 1296, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.866 G_ID: 0.941 G_Rec: 0.367 D_GP: 0.035 D_real: 1.034 D_fake: 0.832 
+(epoch: 14, iters: 1696, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.824 G_ID: 0.402 G_Rec: 0.526 D_GP: 0.041 D_real: 0.929 D_fake: 0.898 
+(epoch: 14, iters: 2096, time: 0.064) G_GAN: -0.040 G_GAN_Feat: 0.887 G_ID: 0.923 G_Rec: 0.574 D_GP: 0.067 D_real: 0.415 D_fake: 1.040 
+(epoch: 14, iters: 2496, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 0.688 G_ID: 0.455 G_Rec: 0.353 D_GP: 0.038 D_real: 1.145 D_fake: 0.553 
+(epoch: 14, iters: 2896, time: 0.064) G_GAN: -0.044 G_GAN_Feat: 0.753 G_ID: 0.932 G_Rec: 0.371 D_GP: 0.038 D_real: 0.729 D_fake: 1.044 
+(epoch: 14, iters: 3296, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.665 G_ID: 0.454 G_Rec: 0.384 D_GP: 0.025 D_real: 0.971 D_fake: 0.781 
+(epoch: 14, iters: 3696, time: 0.064) G_GAN: -0.050 G_GAN_Feat: 0.984 G_ID: 0.944 G_Rec: 0.341 D_GP: 0.130 D_real: 0.384 D_fake: 1.050 
+(epoch: 14, iters: 4096, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.821 G_ID: 0.302 G_Rec: 0.367 D_GP: 0.038 D_real: 1.029 D_fake: 0.706 
+(epoch: 14, iters: 4496, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.533 G_ID: 0.931 G_Rec: 0.337 D_GP: 0.015 D_real: 1.137 D_fake: 0.772 
+(epoch: 14, iters: 4896, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.739 G_ID: 0.430 G_Rec: 0.615 D_GP: 0.041 D_real: 0.924 D_fake: 0.926 
+(epoch: 14, iters: 5296, time: 0.064) G_GAN: 0.581 G_GAN_Feat: 0.805 G_ID: 0.908 G_Rec: 0.390 D_GP: 0.055 D_real: 1.223 D_fake: 0.422 
+(epoch: 14, iters: 5696, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.681 G_ID: 0.456 G_Rec: 0.385 D_GP: 0.042 D_real: 0.937 D_fake: 0.795 
+(epoch: 14, iters: 6096, time: 0.064) G_GAN: -0.085 G_GAN_Feat: 0.681 G_ID: 0.943 G_Rec: 0.359 D_GP: 0.039 D_real: 0.773 D_fake: 1.085 
+(epoch: 14, iters: 6496, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 0.766 G_ID: 0.521 G_Rec: 0.381 D_GP: 0.074 D_real: 0.746 D_fake: 0.943 
+(epoch: 14, iters: 6896, time: 0.064) G_GAN: 0.028 G_GAN_Feat: 0.819 G_ID: 0.953 G_Rec: 0.352 D_GP: 0.053 D_real: 0.640 D_fake: 0.972 
+(epoch: 14, iters: 7296, time: 0.064) G_GAN: 0.014 G_GAN_Feat: 1.235 G_ID: 0.351 G_Rec: 0.343 D_GP: 0.088 D_real: 0.499 D_fake: 0.986 
+(epoch: 14, iters: 7696, time: 0.064) G_GAN: -0.037 G_GAN_Feat: 0.774 G_ID: 0.926 G_Rec: 0.504 D_GP: 0.049 D_real: 0.661 D_fake: 1.037 
+(epoch: 14, iters: 8096, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.720 G_ID: 0.438 G_Rec: 0.382 D_GP: 0.024 D_real: 0.844 D_fake: 0.822 
+(epoch: 14, iters: 8496, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 0.687 G_ID: 0.893 G_Rec: 0.352 D_GP: 0.027 D_real: 1.317 D_fake: 0.489 
+(epoch: 15, iters: 288, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.894 G_ID: 0.399 G_Rec: 0.495 D_GP: 0.104 D_real: 0.831 D_fake: 0.695 
+(epoch: 15, iters: 688, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.802 G_ID: 0.955 G_Rec: 0.341 D_GP: 0.040 D_real: 0.642 D_fake: 0.964 
+(epoch: 15, iters: 1088, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.952 G_ID: 0.429 G_Rec: 0.414 D_GP: 0.055 D_real: 0.640 D_fake: 0.830 
+(epoch: 15, iters: 1488, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.986 G_ID: 0.951 G_Rec: 0.325 D_GP: 0.103 D_real: 0.576 D_fake: 0.911 
+(epoch: 15, iters: 1888, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.703 G_ID: 0.413 G_Rec: 0.387 D_GP: 0.037 D_real: 0.862 D_fake: 0.816 
+(epoch: 15, iters: 2288, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.752 G_ID: 0.919 G_Rec: 0.403 D_GP: 0.041 D_real: 0.823 D_fake: 0.908 
+(epoch: 15, iters: 2688, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.580 G_ID: 0.486 G_Rec: 0.344 D_GP: 0.018 D_real: 1.006 D_fake: 0.866 
+(epoch: 15, iters: 3088, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.708 G_ID: 0.935 G_Rec: 0.370 D_GP: 0.057 D_real: 1.000 D_fake: 0.648 
+(epoch: 15, iters: 3488, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.739 G_ID: 0.400 G_Rec: 0.606 D_GP: 0.060 D_real: 0.911 D_fake: 0.818 
+(epoch: 15, iters: 3888, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 1.123 G_ID: 0.932 G_Rec: 0.356 D_GP: 0.154 D_real: 0.459 D_fake: 0.826 
+(epoch: 15, iters: 4288, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.927 G_ID: 0.419 G_Rec: 0.420 D_GP: 0.045 D_real: 0.804 D_fake: 0.645 
+(epoch: 15, iters: 4688, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 1.369 G_ID: 0.959 G_Rec: 0.369 D_GP: 0.298 D_real: 0.493 D_fake: 0.565 
+(epoch: 15, iters: 5088, time: 0.064) G_GAN: 0.016 G_GAN_Feat: 0.663 G_ID: 0.421 G_Rec: 0.459 D_GP: 0.034 D_real: 0.744 D_fake: 0.984 
+(epoch: 15, iters: 5488, time: 0.064) G_GAN: 0.003 G_GAN_Feat: 0.826 G_ID: 0.912 G_Rec: 0.361 D_GP: 0.094 D_real: 0.455 D_fake: 0.997 
+(epoch: 15, iters: 5888, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.940 G_ID: 0.423 G_Rec: 0.705 D_GP: 0.032 D_real: 1.128 D_fake: 0.523 
+(epoch: 15, iters: 6288, time: 0.064) G_GAN: -0.014 G_GAN_Feat: 1.008 G_ID: 0.931 G_Rec: 0.368 D_GP: 0.170 D_real: 0.409 D_fake: 1.014 
+(epoch: 15, iters: 6688, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.762 G_ID: 0.365 G_Rec: 0.344 D_GP: 0.031 D_real: 1.202 D_fake: 0.672 
+(epoch: 15, iters: 7088, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.672 G_ID: 0.944 G_Rec: 0.373 D_GP: 0.017 D_real: 0.902 D_fake: 0.923 
+(epoch: 15, iters: 7488, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.755 G_ID: 0.332 G_Rec: 0.412 D_GP: 0.064 D_real: 0.798 D_fake: 0.878 
+(epoch: 15, iters: 7888, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.930 G_ID: 0.930 G_Rec: 0.441 D_GP: 0.128 D_real: 0.689 D_fake: 0.754 
+(epoch: 15, iters: 8288, time: 0.064) G_GAN: 0.068 G_GAN_Feat: 0.594 G_ID: 0.412 G_Rec: 0.315 D_GP: 0.016 D_real: 0.948 D_fake: 0.932 
+(epoch: 16, iters: 80, time: 0.064) G_GAN: 0.021 G_GAN_Feat: 0.790 G_ID: 0.924 G_Rec: 0.410 D_GP: 0.066 D_real: 0.744 D_fake: 0.979 
+(epoch: 16, iters: 480, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 1.100 G_ID: 0.361 G_Rec: 0.364 D_GP: 0.169 D_real: 0.652 D_fake: 0.669 
+(epoch: 16, iters: 880, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.729 G_ID: 0.922 G_Rec: 0.549 D_GP: 0.038 D_real: 0.772 D_fake: 0.977 
+(epoch: 16, iters: 1280, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.755 G_ID: 0.392 G_Rec: 0.320 D_GP: 0.061 D_real: 1.005 D_fake: 0.698 
+(epoch: 16, iters: 1680, time: 0.064) G_GAN: -0.661 G_GAN_Feat: 1.123 G_ID: 0.931 G_Rec: 0.370 D_GP: 0.111 D_real: 0.061 D_fake: 1.661 
+(epoch: 16, iters: 2080, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 1.046 G_ID: 0.379 G_Rec: 0.377 D_GP: 0.334 D_real: 0.503 D_fake: 0.768 
+(epoch: 16, iters: 2480, time: 0.064) G_GAN: 0.001 G_GAN_Feat: 0.892 G_ID: 0.903 G_Rec: 0.457 D_GP: 0.113 D_real: 0.884 D_fake: 1.000 
+(epoch: 16, iters: 2880, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.875 G_ID: 0.375 G_Rec: 0.420 D_GP: 0.045 D_real: 1.224 D_fake: 0.536 
+(epoch: 16, iters: 3280, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.820 G_ID: 0.907 G_Rec: 0.607 D_GP: 0.055 D_real: 1.017 D_fake: 0.596 
+(epoch: 16, iters: 3680, time: 0.064) G_GAN: -0.031 G_GAN_Feat: 0.915 G_ID: 0.353 G_Rec: 0.479 D_GP: 0.075 D_real: 0.821 D_fake: 1.031 
+(epoch: 16, iters: 4080, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.984 G_ID: 0.915 G_Rec: 0.369 D_GP: 0.028 D_real: 0.850 D_fake: 0.744 
+(epoch: 16, iters: 4480, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.957 G_ID: 0.377 G_Rec: 0.448 D_GP: 0.070 D_real: 0.789 D_fake: 0.665 
+(epoch: 16, iters: 4880, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.598 G_ID: 0.922 G_Rec: 0.455 D_GP: 0.015 D_real: 1.075 D_fake: 0.800 
+(epoch: 16, iters: 5280, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 0.837 G_ID: 0.404 G_Rec: 0.397 D_GP: 0.027 D_real: 1.247 D_fake: 0.544 
+(epoch: 16, iters: 5680, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.788 G_ID: 0.924 G_Rec: 0.358 D_GP: 0.057 D_real: 0.869 D_fake: 0.812 
+(epoch: 16, iters: 6080, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.840 G_ID: 0.338 G_Rec: 0.373 D_GP: 0.052 D_real: 0.934 D_fake: 0.679 
+(epoch: 16, iters: 6480, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.694 G_ID: 0.881 G_Rec: 0.457 D_GP: 0.061 D_real: 0.966 D_fake: 0.795 
+(epoch: 16, iters: 6880, time: 0.064) G_GAN: 0.019 G_GAN_Feat: 0.823 G_ID: 0.408 G_Rec: 0.355 D_GP: 0.078 D_real: 0.782 D_fake: 0.981 
+(epoch: 16, iters: 7280, time: 0.064) G_GAN: -0.045 G_GAN_Feat: 1.056 G_ID: 0.942 G_Rec: 0.450 D_GP: 0.176 D_real: 0.262 D_fake: 1.045 
+(epoch: 16, iters: 7680, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 1.206 G_ID: 0.426 G_Rec: 0.366 D_GP: 0.274 D_real: 0.496 D_fake: 0.909 
+(epoch: 16, iters: 8080, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.804 G_ID: 0.960 G_Rec: 0.446 D_GP: 0.057 D_real: 1.057 D_fake: 0.624 
+(epoch: 16, iters: 8480, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.951 G_ID: 0.418 G_Rec: 0.341 D_GP: 0.069 D_real: 1.102 D_fake: 0.493 
+(epoch: 17, iters: 272, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.850 G_ID: 0.894 G_Rec: 0.388 D_GP: 0.051 D_real: 0.903 D_fake: 0.676 
+(epoch: 17, iters: 672, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.897 G_ID: 0.372 G_Rec: 0.426 D_GP: 0.059 D_real: 0.971 D_fake: 0.552 
+(epoch: 17, iters: 1072, time: 0.064) G_GAN: 0.624 G_GAN_Feat: 0.794 G_ID: 0.946 G_Rec: 0.402 D_GP: 0.048 D_real: 1.279 D_fake: 0.380 
+(epoch: 17, iters: 1472, time: 0.064) G_GAN: 0.530 G_GAN_Feat: 0.656 G_ID: 0.447 G_Rec: 0.423 D_GP: 0.031 D_real: 1.319 D_fake: 0.471 
+(epoch: 17, iters: 1872, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.845 G_ID: 0.934 G_Rec: 0.436 D_GP: 0.041 D_real: 0.710 D_fake: 0.882 
+(epoch: 17, iters: 2272, time: 0.064) G_GAN: -0.163 G_GAN_Feat: 0.713 G_ID: 0.461 G_Rec: 0.348 D_GP: 0.024 D_real: 0.706 D_fake: 1.164 
+(epoch: 17, iters: 2672, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.838 G_ID: 0.896 G_Rec: 0.330 D_GP: 0.030 D_real: 1.107 D_fake: 0.530 
+(epoch: 17, iters: 3072, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.832 G_ID: 0.404 G_Rec: 0.342 D_GP: 0.053 D_real: 1.197 D_fake: 0.545 
+(epoch: 17, iters: 3472, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.851 G_ID: 0.902 G_Rec: 0.425 D_GP: 0.043 D_real: 1.127 D_fake: 0.655 
+(epoch: 17, iters: 3872, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.923 G_ID: 0.436 G_Rec: 0.382 D_GP: 0.026 D_real: 0.931 D_fake: 0.684 
+(epoch: 17, iters: 4272, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.857 G_ID: 0.920 G_Rec: 0.520 D_GP: 0.041 D_real: 0.956 D_fake: 0.705 
+(epoch: 17, iters: 4672, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.497 G_ID: 0.400 G_Rec: 0.337 D_GP: 0.021 D_real: 1.138 D_fake: 0.681 
+(epoch: 17, iters: 5072, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.704 G_ID: 0.909 G_Rec: 0.431 D_GP: 0.047 D_real: 0.917 D_fake: 0.761 
+(epoch: 17, iters: 5472, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 0.874 G_ID: 0.426 G_Rec: 0.348 D_GP: 0.120 D_real: 0.553 D_fake: 0.989 
+(epoch: 17, iters: 5872, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.883 G_ID: 0.886 G_Rec: 0.363 D_GP: 0.312 D_real: 0.708 D_fake: 0.665 
+(epoch: 17, iters: 6272, time: 0.064) G_GAN: 0.076 G_GAN_Feat: 0.851 G_ID: 0.341 G_Rec: 0.374 D_GP: 0.054 D_real: 0.519 D_fake: 0.924 
+(epoch: 17, iters: 6672, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 1.015 G_ID: 0.854 G_Rec: 0.343 D_GP: 0.179 D_real: 0.682 D_fake: 0.677 
+(epoch: 17, iters: 7072, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.689 G_ID: 0.486 G_Rec: 0.440 D_GP: 0.027 D_real: 1.171 D_fake: 0.646 
+(epoch: 17, iters: 7472, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.867 G_ID: 0.894 G_Rec: 0.353 D_GP: 0.079 D_real: 0.957 D_fake: 0.698 
+(epoch: 17, iters: 7872, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.621 G_ID: 0.461 G_Rec: 0.378 D_GP: 0.018 D_real: 0.991 D_fake: 0.814 
+(epoch: 17, iters: 8272, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.831 G_ID: 0.915 G_Rec: 0.437 D_GP: 0.052 D_real: 0.827 D_fake: 0.744 
+(epoch: 18, iters: 64, time: 0.064) G_GAN: -0.060 G_GAN_Feat: 0.583 G_ID: 0.418 G_Rec: 0.453 D_GP: 0.020 D_real: 0.810 D_fake: 1.060 
+(epoch: 18, iters: 464, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.730 G_ID: 0.892 G_Rec: 0.340 D_GP: 0.046 D_real: 1.128 D_fake: 0.594 
+(epoch: 18, iters: 864, time: 0.064) G_GAN: 0.672 G_GAN_Feat: 1.016 G_ID: 0.456 G_Rec: 0.387 D_GP: 0.254 D_real: 1.252 D_fake: 0.362 
+(epoch: 18, iters: 1264, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.610 G_ID: 0.903 G_Rec: 0.353 D_GP: 0.024 D_real: 1.103 D_fake: 0.714 
+(epoch: 18, iters: 1664, time: 0.064) G_GAN: 0.035 G_GAN_Feat: 0.764 G_ID: 0.418 G_Rec: 0.436 D_GP: 0.062 D_real: 0.817 D_fake: 0.966 
+(epoch: 18, iters: 2064, time: 0.064) G_GAN: 0.670 G_GAN_Feat: 0.836 G_ID: 0.907 G_Rec: 0.700 D_GP: 0.036 D_real: 1.427 D_fake: 0.339 
+(epoch: 18, iters: 2464, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 0.656 G_ID: 0.421 G_Rec: 0.382 D_GP: 0.036 D_real: 1.046 D_fake: 0.689 
+(epoch: 18, iters: 2864, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.901 G_ID: 0.875 G_Rec: 0.400 D_GP: 0.107 D_real: 0.648 D_fake: 0.785 
+(epoch: 18, iters: 3264, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.563 G_ID: 0.333 G_Rec: 0.560 D_GP: 0.020 D_real: 1.323 D_fake: 0.599 
+(epoch: 18, iters: 3664, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.770 G_ID: 0.932 G_Rec: 0.422 D_GP: 0.056 D_real: 1.163 D_fake: 0.577 
+(epoch: 18, iters: 4064, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 1.142 G_ID: 0.406 G_Rec: 0.463 D_GP: 0.168 D_real: 0.481 D_fake: 0.755 
+(epoch: 18, iters: 4464, time: 0.064) G_GAN: -0.035 G_GAN_Feat: 0.798 G_ID: 0.922 G_Rec: 0.424 D_GP: 0.055 D_real: 0.631 D_fake: 1.035 
+(epoch: 18, iters: 4864, time: 0.064) G_GAN: 0.054 G_GAN_Feat: 0.667 G_ID: 0.360 G_Rec: 0.397 D_GP: 0.022 D_real: 0.807 D_fake: 0.946 
+(epoch: 18, iters: 5264, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.662 G_ID: 0.909 G_Rec: 0.358 D_GP: 0.041 D_real: 0.907 D_fake: 0.881 
+(epoch: 18, iters: 5664, time: 0.064) G_GAN: -0.071 G_GAN_Feat: 0.711 G_ID: 0.430 G_Rec: 0.337 D_GP: 0.071 D_real: 0.592 D_fake: 1.071 
+(epoch: 18, iters: 6064, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.889 G_ID: 0.872 G_Rec: 0.386 D_GP: 0.090 D_real: 0.869 D_fake: 0.559 
+(epoch: 18, iters: 6464, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.674 G_ID: 0.389 G_Rec: 0.371 D_GP: 0.027 D_real: 1.155 D_fake: 0.617 
+(epoch: 18, iters: 6864, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.676 G_ID: 0.888 G_Rec: 0.414 D_GP: 0.020 D_real: 1.275 D_fake: 0.697 
+(epoch: 18, iters: 7264, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.726 G_ID: 0.363 G_Rec: 0.338 D_GP: 0.048 D_real: 0.834 D_fake: 0.830 
+(epoch: 18, iters: 7664, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.853 G_ID: 0.881 G_Rec: 0.444 D_GP: 0.080 D_real: 0.592 D_fake: 0.881 
+(epoch: 18, iters: 8064, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.848 G_ID: 0.351 G_Rec: 0.336 D_GP: 0.073 D_real: 0.890 D_fake: 0.830 
+(epoch: 18, iters: 8464, time: 0.064) G_GAN: 0.749 G_GAN_Feat: 0.847 G_ID: 0.882 G_Rec: 0.514 D_GP: 0.037 D_real: 1.372 D_fake: 0.353 
+(epoch: 19, iters: 256, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.766 G_ID: 0.425 G_Rec: 0.405 D_GP: 0.056 D_real: 0.997 D_fake: 0.627 
+(epoch: 19, iters: 656, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.746 G_ID: 0.883 G_Rec: 0.359 D_GP: 0.067 D_real: 0.695 D_fake: 0.952 
+(epoch: 19, iters: 1056, time: 0.064) G_GAN: 0.564 G_GAN_Feat: 0.877 G_ID: 0.413 G_Rec: 0.410 D_GP: 0.071 D_real: 1.042 D_fake: 0.449 
+(epoch: 19, iters: 1456, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.663 G_ID: 0.873 G_Rec: 0.317 D_GP: 0.045 D_real: 1.110 D_fake: 0.609 
+(epoch: 19, iters: 1856, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.874 G_ID: 0.460 G_Rec: 0.330 D_GP: 0.117 D_real: 0.739 D_fake: 0.694 
+(epoch: 19, iters: 2256, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 1.014 G_ID: 0.894 G_Rec: 0.388 D_GP: 0.067 D_real: 0.453 D_fake: 0.868 
+(epoch: 19, iters: 2656, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.857 G_ID: 0.410 G_Rec: 0.355 D_GP: 0.115 D_real: 1.046 D_fake: 0.750 
+(epoch: 19, iters: 3056, time: 0.064) G_GAN: -0.001 G_GAN_Feat: 0.785 G_ID: 0.885 G_Rec: 0.373 D_GP: 0.055 D_real: 0.583 D_fake: 1.001 
+(epoch: 19, iters: 3456, time: 0.064) G_GAN: 0.020 G_GAN_Feat: 0.887 G_ID: 0.396 G_Rec: 0.366 D_GP: 0.061 D_real: 0.521 D_fake: 0.980 
+(epoch: 19, iters: 3856, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.720 G_ID: 0.858 G_Rec: 0.404 D_GP: 0.015 D_real: 1.081 D_fake: 0.777 
+(epoch: 19, iters: 4256, time: 0.064) G_GAN: -0.039 G_GAN_Feat: 0.914 G_ID: 0.362 G_Rec: 0.340 D_GP: 0.067 D_real: 0.444 D_fake: 1.039 
+(epoch: 19, iters: 4656, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.627 G_ID: 0.906 G_Rec: 0.374 D_GP: 0.022 D_real: 1.281 D_fake: 0.563 
+(epoch: 19, iters: 5056, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.851 G_ID: 0.355 G_Rec: 0.333 D_GP: 0.037 D_real: 1.045 D_fake: 0.597 
+(epoch: 19, iters: 5456, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.805 G_ID: 0.898 G_Rec: 0.366 D_GP: 0.036 D_real: 1.182 D_fake: 0.478 
+(epoch: 19, iters: 5856, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.647 G_ID: 0.399 G_Rec: 0.388 D_GP: 0.033 D_real: 1.014 D_fake: 0.718 
+(epoch: 19, iters: 6256, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.705 G_ID: 0.894 G_Rec: 0.340 D_GP: 0.050 D_real: 1.087 D_fake: 0.656 
+(epoch: 19, iters: 6656, time: 0.064) G_GAN: -0.214 G_GAN_Feat: 0.677 G_ID: 0.339 G_Rec: 0.327 D_GP: 0.045 D_real: 0.536 D_fake: 1.214 
+(epoch: 19, iters: 7056, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.696 G_ID: 0.903 G_Rec: 0.344 D_GP: 0.027 D_real: 1.227 D_fake: 0.605 
+(epoch: 19, iters: 7456, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.458 G_ID: 0.440 G_Rec: 0.413 D_GP: 0.015 D_real: 1.362 D_fake: 0.606 
+(epoch: 19, iters: 7856, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.535 G_ID: 0.883 G_Rec: 0.355 D_GP: 0.018 D_real: 1.121 D_fake: 0.718 
+(epoch: 19, iters: 8256, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.655 G_ID: 0.348 G_Rec: 0.558 D_GP: 0.054 D_real: 0.967 D_fake: 0.783 
+(epoch: 20, iters: 48, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.851 G_ID: 0.884 G_Rec: 0.379 D_GP: 0.122 D_real: 0.541 D_fake: 0.900 
+(epoch: 20, iters: 448, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.736 G_ID: 0.402 G_Rec: 0.396 D_GP: 0.048 D_real: 0.860 D_fake: 0.838 
+(epoch: 20, iters: 848, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.635 G_ID: 0.879 G_Rec: 0.429 D_GP: 0.048 D_real: 1.136 D_fake: 0.602 
+(epoch: 20, iters: 1248, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.682 G_ID: 0.341 G_Rec: 0.331 D_GP: 0.038 D_real: 0.875 D_fake: 0.814 
+(epoch: 20, iters: 1648, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.769 G_ID: 0.858 G_Rec: 0.357 D_GP: 0.071 D_real: 0.812 D_fake: 0.772 
+(epoch: 20, iters: 2048, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.580 G_ID: 0.444 G_Rec: 0.297 D_GP: 0.029 D_real: 1.030 D_fake: 0.774 
+(epoch: 20, iters: 2448, time: 0.064) G_GAN: -0.072 G_GAN_Feat: 0.886 G_ID: 0.848 G_Rec: 0.377 D_GP: 0.058 D_real: 0.324 D_fake: 1.072 
+(epoch: 20, iters: 2848, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.672 G_ID: 0.447 G_Rec: 0.368 D_GP: 0.038 D_real: 0.940 D_fake: 0.781 
+(epoch: 20, iters: 3248, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.885 G_ID: 0.864 G_Rec: 0.393 D_GP: 0.065 D_real: 0.967 D_fake: 0.642 
+(epoch: 20, iters: 3648, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 0.804 G_ID: 0.374 G_Rec: 0.368 D_GP: 0.033 D_real: 1.300 D_fake: 0.431 
+(epoch: 20, iters: 4048, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 1.070 G_ID: 0.848 G_Rec: 0.365 D_GP: 0.096 D_real: 0.782 D_fake: 0.518 
+(epoch: 20, iters: 4448, time: 0.064) G_GAN: 0.005 G_GAN_Feat: 0.760 G_ID: 0.411 G_Rec: 0.345 D_GP: 0.054 D_real: 0.551 D_fake: 0.995 
+(epoch: 20, iters: 4848, time: 0.064) G_GAN: 0.120 G_GAN_Feat: 0.672 G_ID: 0.890 G_Rec: 0.342 D_GP: 0.021 D_real: 1.045 D_fake: 0.880 
+(epoch: 20, iters: 5248, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.732 G_ID: 0.312 G_Rec: 0.347 D_GP: 0.028 D_real: 1.055 D_fake: 0.763 
+(epoch: 20, iters: 5648, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.791 G_ID: 0.868 G_Rec: 0.350 D_GP: 0.036 D_real: 1.002 D_fake: 0.837 
+(epoch: 20, iters: 6048, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.883 G_ID: 0.374 G_Rec: 0.337 D_GP: 0.060 D_real: 0.823 D_fake: 0.788 
+(epoch: 20, iters: 6448, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.761 G_ID: 0.824 G_Rec: 0.365 D_GP: 0.038 D_real: 1.163 D_fake: 0.622 
+(epoch: 20, iters: 6848, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.901 G_ID: 0.392 G_Rec: 0.551 D_GP: 0.077 D_real: 0.670 D_fake: 0.926 
+(epoch: 20, iters: 7248, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.819 G_ID: 0.828 G_Rec: 0.368 D_GP: 0.077 D_real: 0.687 D_fake: 0.989 
+(epoch: 20, iters: 7648, time: 0.064) G_GAN: -0.041 G_GAN_Feat: 0.801 G_ID: 0.395 G_Rec: 0.362 D_GP: 0.065 D_real: 0.703 D_fake: 1.041 
+(epoch: 20, iters: 8048, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.840 G_ID: 0.835 G_Rec: 0.361 D_GP: 0.041 D_real: 0.968 D_fake: 0.766 
+(epoch: 20, iters: 8448, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 1.023 G_ID: 0.479 G_Rec: 0.378 D_GP: 0.076 D_real: 0.750 D_fake: 0.742 
+(epoch: 21, iters: 240, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.861 G_ID: 0.831 G_Rec: 0.343 D_GP: 0.044 D_real: 1.051 D_fake: 0.603 
+(epoch: 21, iters: 640, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 1.022 G_ID: 0.368 G_Rec: 0.449 D_GP: 0.111 D_real: 1.136 D_fake: 0.743 
+(epoch: 21, iters: 1040, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.983 G_ID: 0.853 G_Rec: 0.399 D_GP: 0.063 D_real: 0.908 D_fake: 0.616 
+(epoch: 21, iters: 1440, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.867 G_ID: 0.350 G_Rec: 0.409 D_GP: 0.069 D_real: 0.898 D_fake: 0.769 
+(epoch: 21, iters: 1840, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.905 G_ID: 0.815 G_Rec: 0.357 D_GP: 0.038 D_real: 0.951 D_fake: 0.658 
+(epoch: 21, iters: 2240, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.762 G_ID: 0.352 G_Rec: 0.327 D_GP: 0.044 D_real: 1.094 D_fake: 0.717 
+(epoch: 21, iters: 2640, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.693 G_ID: 0.865 G_Rec: 0.349 D_GP: 0.032 D_real: 0.918 D_fake: 0.826 
+(epoch: 21, iters: 3040, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.959 G_ID: 0.499 G_Rec: 0.417 D_GP: 0.078 D_real: 0.715 D_fake: 0.617 
+(epoch: 21, iters: 3440, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 1.118 G_ID: 0.878 G_Rec: 0.399 D_GP: 0.138 D_real: 0.589 D_fake: 0.748 
+(epoch: 21, iters: 3840, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.643 G_ID: 0.382 G_Rec: 0.417 D_GP: 0.022 D_real: 1.010 D_fake: 0.773 
+(epoch: 21, iters: 4240, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.765 G_ID: 0.883 G_Rec: 0.513 D_GP: 0.069 D_real: 0.975 D_fake: 0.746 
+(epoch: 21, iters: 4640, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.994 G_ID: 0.423 G_Rec: 0.373 D_GP: 0.093 D_real: 0.548 D_fake: 0.816 
+(epoch: 21, iters: 5040, time: 0.064) G_GAN: -0.040 G_GAN_Feat: 0.632 G_ID: 0.854 G_Rec: 0.431 D_GP: 0.023 D_real: 0.769 D_fake: 1.040 
+(epoch: 21, iters: 5440, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.735 G_ID: 0.429 G_Rec: 0.370 D_GP: 0.036 D_real: 1.028 D_fake: 0.720 
+(epoch: 21, iters: 5840, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.769 G_ID: 0.833 G_Rec: 0.368 D_GP: 0.030 D_real: 1.263 D_fake: 0.485 
+(epoch: 21, iters: 6240, time: 0.064) G_GAN: 0.692 G_GAN_Feat: 0.502 G_ID: 0.379 G_Rec: 0.391 D_GP: 0.019 D_real: 1.636 D_fake: 0.337 
+(epoch: 21, iters: 6640, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.536 G_ID: 0.835 G_Rec: 0.457 D_GP: 0.016 D_real: 1.192 D_fake: 0.680 
+(epoch: 21, iters: 7040, time: 0.064) G_GAN: 0.135 G_GAN_Feat: 0.487 G_ID: 0.366 G_Rec: 0.478 D_GP: 0.018 D_real: 1.017 D_fake: 0.865 
+(epoch: 21, iters: 7440, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.545 G_ID: 0.845 G_Rec: 0.376 D_GP: 0.027 D_real: 1.000 D_fake: 0.798 
+(epoch: 21, iters: 7840, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.565 G_ID: 0.463 G_Rec: 0.373 D_GP: 0.035 D_real: 1.184 D_fake: 0.643 
+(epoch: 21, iters: 8240, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.634 G_ID: 0.801 G_Rec: 0.347 D_GP: 0.059 D_real: 0.973 D_fake: 0.776 
+(epoch: 22, iters: 32, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.768 G_ID: 0.390 G_Rec: 0.398 D_GP: 0.056 D_real: 0.782 D_fake: 0.815 
+(epoch: 22, iters: 432, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.669 G_ID: 0.887 G_Rec: 0.433 D_GP: 0.043 D_real: 1.042 D_fake: 0.760 
+(epoch: 22, iters: 832, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.821 G_ID: 0.403 G_Rec: 0.357 D_GP: 0.067 D_real: 0.795 D_fake: 0.732 
+(epoch: 22, iters: 1232, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.794 G_ID: 0.808 G_Rec: 0.652 D_GP: 0.070 D_real: 0.748 D_fake: 0.781 
+(epoch: 22, iters: 1632, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.734 G_ID: 0.425 G_Rec: 0.356 D_GP: 0.067 D_real: 1.018 D_fake: 0.616 
+(epoch: 22, iters: 2032, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.731 G_ID: 0.862 G_Rec: 0.339 D_GP: 0.061 D_real: 0.920 D_fake: 0.791 
+(epoch: 22, iters: 2432, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 1.068 G_ID: 0.413 G_Rec: 0.420 D_GP: 0.102 D_real: 0.496 D_fake: 0.750 
+(epoch: 22, iters: 2832, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.706 G_ID: 0.837 G_Rec: 0.344 D_GP: 0.026 D_real: 0.959 D_fake: 0.772 
+(epoch: 22, iters: 3232, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 0.773 G_ID: 0.412 G_Rec: 0.377 D_GP: 0.065 D_real: 1.115 D_fake: 0.499 
+(epoch: 22, iters: 3632, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.480 G_ID: 0.856 G_Rec: 0.330 D_GP: 0.016 D_real: 1.147 D_fake: 0.758 
+(epoch: 22, iters: 4032, time: 0.064) G_GAN: 0.020 G_GAN_Feat: 0.553 G_ID: 0.332 G_Rec: 0.406 D_GP: 0.029 D_real: 0.906 D_fake: 0.980 
+(epoch: 22, iters: 4432, time: 0.064) G_GAN: 0.905 G_GAN_Feat: 0.584 G_ID: 0.846 G_Rec: 0.438 D_GP: 0.023 D_real: 1.698 D_fake: 0.215 
+(epoch: 22, iters: 4832, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.628 G_ID: 0.325 G_Rec: 0.452 D_GP: 0.049 D_real: 0.983 D_fake: 0.717 
+(epoch: 22, iters: 5232, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.653 G_ID: 0.857 G_Rec: 0.425 D_GP: 0.054 D_real: 1.065 D_fake: 0.616 
+(epoch: 22, iters: 5632, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.570 G_ID: 0.414 G_Rec: 0.338 D_GP: 0.041 D_real: 1.065 D_fake: 0.757 
+(epoch: 22, iters: 6032, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.709 G_ID: 0.848 G_Rec: 0.353 D_GP: 0.076 D_real: 0.758 D_fake: 0.878 
+(epoch: 22, iters: 6432, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 0.785 G_ID: 0.388 G_Rec: 0.401 D_GP: 0.106 D_real: 1.011 D_fake: 0.596 
+(epoch: 22, iters: 6832, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.671 G_ID: 0.822 G_Rec: 0.421 D_GP: 0.066 D_real: 0.917 D_fake: 0.805 
+(epoch: 22, iters: 7232, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 1.041 G_ID: 0.399 G_Rec: 0.378 D_GP: 0.193 D_real: 0.479 D_fake: 0.806 
+(epoch: 22, iters: 7632, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.737 G_ID: 0.818 G_Rec: 0.329 D_GP: 0.042 D_real: 1.092 D_fake: 0.674 
+(epoch: 22, iters: 8032, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.731 G_ID: 0.391 G_Rec: 0.381 D_GP: 0.046 D_real: 1.178 D_fake: 0.633 
+(epoch: 22, iters: 8432, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.703 G_ID: 0.811 G_Rec: 0.468 D_GP: 0.063 D_real: 0.884 D_fake: 0.840 
+(epoch: 23, iters: 224, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.809 G_ID: 0.400 G_Rec: 0.412 D_GP: 0.089 D_real: 0.695 D_fake: 0.763 
+(epoch: 23, iters: 624, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.728 G_ID: 0.839 G_Rec: 0.418 D_GP: 0.036 D_real: 1.034 D_fake: 0.683 
+(epoch: 23, iters: 1024, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.582 G_ID: 0.381 G_Rec: 0.395 D_GP: 0.022 D_real: 1.328 D_fake: 0.579 
+(epoch: 23, iters: 1424, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 0.611 G_ID: 0.798 G_Rec: 0.431 D_GP: 0.024 D_real: 1.329 D_fake: 0.490 
+(epoch: 23, iters: 1824, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.681 G_ID: 0.467 G_Rec: 0.410 D_GP: 0.046 D_real: 1.020 D_fake: 0.779 
+(epoch: 23, iters: 2224, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.639 G_ID: 0.834 G_Rec: 0.427 D_GP: 0.039 D_real: 0.776 D_fake: 0.913 
+(epoch: 23, iters: 2624, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.852 G_ID: 0.429 G_Rec: 0.376 D_GP: 0.056 D_real: 0.799 D_fake: 0.717 
+(epoch: 23, iters: 3024, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 1.085 G_ID: 0.835 G_Rec: 0.382 D_GP: 0.167 D_real: 0.587 D_fake: 0.675 
+(epoch: 23, iters: 3424, time: 0.064) G_GAN: 0.042 G_GAN_Feat: 0.760 G_ID: 0.417 G_Rec: 0.346 D_GP: 0.077 D_real: 0.567 D_fake: 0.960 
+(epoch: 23, iters: 3824, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.648 G_ID: 0.824 G_Rec: 0.409 D_GP: 0.035 D_real: 0.948 D_fake: 0.849 
+(epoch: 23, iters: 4224, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.823 G_ID: 0.461 G_Rec: 0.451 D_GP: 0.068 D_real: 0.856 D_fake: 0.606 
+(epoch: 23, iters: 4624, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.757 G_ID: 0.801 G_Rec: 0.342 D_GP: 0.071 D_real: 0.830 D_fake: 0.701 
+(epoch: 23, iters: 5024, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.829 G_ID: 0.381 G_Rec: 0.405 D_GP: 0.040 D_real: 0.625 D_fake: 0.916 
+(epoch: 23, iters: 5424, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 0.692 G_ID: 0.868 G_Rec: 0.361 D_GP: 0.040 D_real: 0.796 D_fake: 0.935 
+(epoch: 23, iters: 5824, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 1.091 G_ID: 0.367 G_Rec: 0.384 D_GP: 0.086 D_real: 0.455 D_fake: 0.860 
+(epoch: 23, iters: 6224, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.621 G_ID: 0.817 G_Rec: 0.357 D_GP: 0.024 D_real: 1.015 D_fake: 0.800 
+(epoch: 23, iters: 6624, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 0.837 G_ID: 0.341 G_Rec: 0.379 D_GP: 0.107 D_real: 1.114 D_fake: 0.455 
+(epoch: 23, iters: 7024, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 1.085 G_ID: 0.798 G_Rec: 0.397 D_GP: 0.105 D_real: 0.643 D_fake: 0.727 
+(epoch: 23, iters: 7424, time: 0.064) G_GAN: 0.612 G_GAN_Feat: 0.521 G_ID: 0.412 G_Rec: 0.445 D_GP: 0.018 D_real: 1.482 D_fake: 0.393 
+(epoch: 23, iters: 7824, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.536 G_ID: 0.777 G_Rec: 0.383 D_GP: 0.020 D_real: 0.997 D_fake: 0.874 
+(epoch: 23, iters: 8224, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.503 G_ID: 0.437 G_Rec: 0.324 D_GP: 0.025 D_real: 1.040 D_fake: 0.811 
+(epoch: 24, iters: 16, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.548 G_ID: 0.858 G_Rec: 0.339 D_GP: 0.031 D_real: 1.196 D_fake: 0.594 
+(epoch: 24, iters: 416, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.530 G_ID: 0.429 G_Rec: 0.309 D_GP: 0.031 D_real: 1.005 D_fake: 0.837 
+(epoch: 24, iters: 816, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 0.601 G_ID: 0.829 G_Rec: 0.367 D_GP: 0.042 D_real: 1.186 D_fake: 0.567 
+(epoch: 24, iters: 1216, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.648 G_ID: 0.337 G_Rec: 0.367 D_GP: 0.038 D_real: 1.063 D_fake: 0.709 
+(epoch: 24, iters: 1616, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.672 G_ID: 0.821 G_Rec: 0.377 D_GP: 0.055 D_real: 0.846 D_fake: 0.813 
+(epoch: 24, iters: 2016, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.613 G_ID: 0.377 G_Rec: 0.336 D_GP: 0.060 D_real: 0.917 D_fake: 0.849 
+(epoch: 24, iters: 2416, time: 0.064) G_GAN: 0.060 G_GAN_Feat: 0.614 G_ID: 0.823 G_Rec: 0.316 D_GP: 0.063 D_real: 0.854 D_fake: 0.940 
+(epoch: 24, iters: 2816, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.802 G_ID: 0.403 G_Rec: 0.462 D_GP: 0.046 D_real: 1.095 D_fake: 0.654 
+(epoch: 24, iters: 3216, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 1.155 G_ID: 0.808 G_Rec: 0.372 D_GP: 0.227 D_real: 0.507 D_fake: 0.817 
+(epoch: 24, iters: 3616, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.616 G_ID: 0.358 G_Rec: 0.472 D_GP: 0.044 D_real: 0.885 D_fake: 0.910 
+(epoch: 24, iters: 4016, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.740 G_ID: 0.790 G_Rec: 0.530 D_GP: 0.030 D_real: 1.249 D_fake: 0.528 
+(epoch: 24, iters: 4416, time: 0.064) G_GAN: 0.552 G_GAN_Feat: 0.692 G_ID: 0.360 G_Rec: 0.366 D_GP: 0.066 D_real: 1.223 D_fake: 0.453 
+(epoch: 24, iters: 4816, time: 0.064) G_GAN: 0.499 G_GAN_Feat: 0.877 G_ID: 0.834 G_Rec: 0.381 D_GP: 0.050 D_real: 1.068 D_fake: 0.523 
+(epoch: 24, iters: 5216, time: 0.064) G_GAN: -0.163 G_GAN_Feat: 0.733 G_ID: 0.419 G_Rec: 0.388 D_GP: 0.084 D_real: 0.455 D_fake: 1.164 
+(epoch: 24, iters: 5616, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.861 G_ID: 0.789 G_Rec: 0.387 D_GP: 0.042 D_real: 1.048 D_fake: 0.722 
+(epoch: 24, iters: 6016, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 0.684 G_ID: 0.385 G_Rec: 0.419 D_GP: 0.041 D_real: 1.225 D_fake: 0.522 
+(epoch: 24, iters: 6416, time: 0.064) G_GAN: 0.096 G_GAN_Feat: 0.729 G_ID: 0.795 G_Rec: 0.389 D_GP: 0.039 D_real: 0.780 D_fake: 0.906 
+(epoch: 24, iters: 6816, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 0.724 G_ID: 0.413 G_Rec: 0.463 D_GP: 0.025 D_real: 1.323 D_fake: 0.418 
+(epoch: 24, iters: 7216, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.694 G_ID: 0.798 G_Rec: 0.473 D_GP: 0.024 D_real: 1.140 D_fake: 0.654 
+(epoch: 24, iters: 7616, time: 0.064) G_GAN: 0.723 G_GAN_Feat: 0.692 G_ID: 0.372 G_Rec: 0.351 D_GP: 0.040 D_real: 1.416 D_fake: 0.303 
+(epoch: 24, iters: 8016, time: 0.064) G_GAN: 0.487 G_GAN_Feat: 0.668 G_ID: 0.813 G_Rec: 0.334 D_GP: 0.028 D_real: 1.284 D_fake: 0.514 
+(epoch: 24, iters: 8416, time: 0.064) G_GAN: 0.026 G_GAN_Feat: 0.897 G_ID: 0.326 G_Rec: 0.504 D_GP: 0.114 D_real: 0.510 D_fake: 0.975 
+(epoch: 25, iters: 208, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.655 G_ID: 0.834 G_Rec: 0.341 D_GP: 0.051 D_real: 0.936 D_fake: 0.766 
+(epoch: 25, iters: 608, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.835 G_ID: 0.417 G_Rec: 0.336 D_GP: 0.108 D_real: 0.977 D_fake: 0.683 
+(epoch: 25, iters: 1008, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 1.075 G_ID: 0.757 G_Rec: 0.405 D_GP: 0.326 D_real: 0.554 D_fake: 0.840 
+(epoch: 25, iters: 1408, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 1.259 G_ID: 0.407 G_Rec: 0.430 D_GP: 0.367 D_real: 0.306 D_fake: 0.782 
+(epoch: 25, iters: 1808, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.793 G_ID: 0.824 G_Rec: 0.465 D_GP: 0.083 D_real: 0.757 D_fake: 0.768 
+(epoch: 25, iters: 2208, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.636 G_ID: 0.390 G_Rec: 0.354 D_GP: 0.024 D_real: 1.040 D_fake: 0.842 
+(epoch: 25, iters: 2608, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 0.760 G_ID: 0.782 G_Rec: 0.336 D_GP: 0.070 D_real: 1.186 D_fake: 0.422 
+(epoch: 25, iters: 3008, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.589 G_ID: 0.351 G_Rec: 0.326 D_GP: 0.035 D_real: 1.026 D_fake: 0.770 
+(epoch: 25, iters: 3408, time: 0.064) G_GAN: 0.664 G_GAN_Feat: 0.724 G_ID: 0.786 G_Rec: 0.385 D_GP: 0.034 D_real: 1.442 D_fake: 0.377 
+(epoch: 25, iters: 3808, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 0.882 G_ID: 0.333 G_Rec: 0.409 D_GP: 0.040 D_real: 1.010 D_fake: 0.601 
+(epoch: 25, iters: 4208, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 1.238 G_ID: 0.777 G_Rec: 0.521 D_GP: 0.239 D_real: 0.593 D_fake: 0.613 
+(epoch: 25, iters: 4608, time: 0.064) G_GAN: 0.470 G_GAN_Feat: 0.855 G_ID: 0.359 G_Rec: 0.328 D_GP: 0.076 D_real: 1.008 D_fake: 0.530 
+(epoch: 25, iters: 5008, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 0.744 G_ID: 0.835 G_Rec: 0.351 D_GP: 0.040 D_real: 1.247 D_fake: 0.534 
+(epoch: 25, iters: 5408, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 0.580 G_ID: 0.351 G_Rec: 0.347 D_GP: 0.022 D_real: 1.313 D_fake: 0.480 
+(epoch: 25, iters: 5808, time: 0.064) G_GAN: 0.033 G_GAN_Feat: 0.881 G_ID: 0.819 G_Rec: 0.385 D_GP: 0.082 D_real: 0.444 D_fake: 0.967 
+(epoch: 25, iters: 6208, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.725 G_ID: 0.348 G_Rec: 0.372 D_GP: 0.059 D_real: 0.869 D_fake: 0.767 
+(epoch: 25, iters: 6608, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.830 G_ID: 0.796 G_Rec: 0.354 D_GP: 0.047 D_real: 1.040 D_fake: 0.937 
+(epoch: 25, iters: 7008, time: 0.064) G_GAN: 0.120 G_GAN_Feat: 0.949 G_ID: 0.323 G_Rec: 0.494 D_GP: 0.095 D_real: 0.573 D_fake: 0.881 
+(epoch: 25, iters: 7408, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.631 G_ID: 0.774 G_Rec: 0.407 D_GP: 0.026 D_real: 1.057 D_fake: 0.715 
+(epoch: 25, iters: 7808, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 1.059 G_ID: 0.431 G_Rec: 0.440 D_GP: 0.110 D_real: 0.699 D_fake: 0.726 
+(epoch: 25, iters: 8208, time: 0.064) G_GAN: 0.653 G_GAN_Feat: 0.834 G_ID: 0.800 G_Rec: 0.370 D_GP: 0.096 D_real: 1.109 D_fake: 0.365 
+(epoch: 25, iters: 8608, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.560 G_ID: 0.421 G_Rec: 0.384 D_GP: 0.024 D_real: 1.190 D_fake: 0.612 
+(epoch: 26, iters: 400, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 1.221 G_ID: 0.802 G_Rec: 0.499 D_GP: 0.127 D_real: 0.442 D_fake: 0.843 
+(epoch: 26, iters: 800, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 1.063 G_ID: 0.365 G_Rec: 0.446 D_GP: 0.058 D_real: 1.194 D_fake: 0.642 
+(epoch: 26, iters: 1200, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 1.135 G_ID: 0.776 G_Rec: 0.353 D_GP: 0.092 D_real: 1.084 D_fake: 0.660 
+(epoch: 26, iters: 1600, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.686 G_ID: 0.368 G_Rec: 0.500 D_GP: 0.039 D_real: 0.814 D_fake: 0.871 
+(epoch: 26, iters: 2000, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.748 G_ID: 0.783 G_Rec: 0.364 D_GP: 0.043 D_real: 1.035 D_fake: 0.610 
+(epoch: 26, iters: 2400, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.584 G_ID: 0.334 G_Rec: 0.376 D_GP: 0.017 D_real: 1.270 D_fake: 0.593 
+(epoch: 26, iters: 2800, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.783 G_ID: 0.789 G_Rec: 0.398 D_GP: 0.040 D_real: 1.142 D_fake: 0.687 
+(epoch: 26, iters: 3200, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.947 G_ID: 0.386 G_Rec: 0.475 D_GP: 0.425 D_real: 0.524 D_fake: 0.931 
+(epoch: 26, iters: 3600, time: 0.064) G_GAN: 0.575 G_GAN_Feat: 0.703 G_ID: 0.747 G_Rec: 0.374 D_GP: 0.028 D_real: 1.354 D_fake: 0.438 
+(epoch: 26, iters: 4000, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.837 G_ID: 0.394 G_Rec: 0.388 D_GP: 0.070 D_real: 0.965 D_fake: 0.649 
+(epoch: 26, iters: 4400, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.749 G_ID: 0.784 G_Rec: 0.431 D_GP: 0.038 D_real: 0.883 D_fake: 0.753 
+(epoch: 26, iters: 4800, time: 0.064) G_GAN: 0.663 G_GAN_Feat: 0.804 G_ID: 0.396 G_Rec: 0.530 D_GP: 0.038 D_real: 1.429 D_fake: 0.356 
+(epoch: 26, iters: 5200, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.658 G_ID: 0.762 G_Rec: 0.356 D_GP: 0.033 D_real: 1.037 D_fake: 0.702 
+(epoch: 26, iters: 5600, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.739 G_ID: 0.355 G_Rec: 0.356 D_GP: 0.051 D_real: 1.091 D_fake: 0.592 
+(epoch: 26, iters: 6000, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.751 G_ID: 0.751 G_Rec: 0.378 D_GP: 0.051 D_real: 0.970 D_fake: 0.764 
+(epoch: 26, iters: 6400, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 1.151 G_ID: 0.366 G_Rec: 0.355 D_GP: 0.361 D_real: 0.296 D_fake: 0.670 
+(epoch: 26, iters: 6800, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 0.756 G_ID: 0.798 G_Rec: 0.376 D_GP: 0.051 D_real: 1.095 D_fake: 0.547 
+(epoch: 26, iters: 7200, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.633 G_ID: 0.396 G_Rec: 0.361 D_GP: 0.024 D_real: 0.970 D_fake: 0.771 
+(epoch: 26, iters: 7600, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.693 G_ID: 0.769 G_Rec: 0.322 D_GP: 0.051 D_real: 0.861 D_fake: 0.843 
+(epoch: 26, iters: 8000, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 1.083 G_ID: 0.355 G_Rec: 0.425 D_GP: 0.239 D_real: 0.565 D_fake: 0.588 
+(epoch: 26, iters: 8400, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.521 G_ID: 0.783 G_Rec: 0.337 D_GP: 0.022 D_real: 1.211 D_fake: 0.672 
+(epoch: 27, iters: 192, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.554 G_ID: 0.374 G_Rec: 0.358 D_GP: 0.026 D_real: 1.152 D_fake: 0.694 
+(epoch: 27, iters: 592, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.578 G_ID: 0.771 G_Rec: 0.345 D_GP: 0.040 D_real: 0.937 D_fake: 0.835 
+(epoch: 27, iters: 992, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.661 G_ID: 0.330 G_Rec: 0.367 D_GP: 0.045 D_real: 1.215 D_fake: 0.523 
+(epoch: 27, iters: 1392, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.677 G_ID: 0.725 G_Rec: 0.350 D_GP: 0.044 D_real: 1.247 D_fake: 0.479 
+(epoch: 27, iters: 1792, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.927 G_ID: 0.343 G_Rec: 0.353 D_GP: 0.061 D_real: 0.773 D_fake: 0.652 
+(epoch: 27, iters: 2192, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.740 G_ID: 0.758 G_Rec: 0.386 D_GP: 0.039 D_real: 0.976 D_fake: 0.691 
+(epoch: 27, iters: 2592, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 0.904 G_ID: 0.390 G_Rec: 0.431 D_GP: 0.118 D_real: 0.838 D_fake: 0.523 
+(epoch: 27, iters: 2992, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 1.200 G_ID: 0.780 G_Rec: 0.403 D_GP: 0.243 D_real: 0.500 D_fake: 0.545 
+(epoch: 27, iters: 3392, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.778 G_ID: 0.362 G_Rec: 0.599 D_GP: 0.028 D_real: 1.114 D_fake: 0.638 
+(epoch: 27, iters: 3792, time: 0.064) G_GAN: 0.604 G_GAN_Feat: 0.776 G_ID: 0.793 G_Rec: 0.378 D_GP: 0.091 D_real: 1.221 D_fake: 0.495 
+(epoch: 27, iters: 4192, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.826 G_ID: 0.318 G_Rec: 0.345 D_GP: 0.095 D_real: 0.823 D_fake: 0.670 
+(epoch: 27, iters: 4592, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.825 G_ID: 0.733 G_Rec: 0.423 D_GP: 0.029 D_real: 1.032 D_fake: 0.569 
+(epoch: 27, iters: 4992, time: 0.064) G_GAN: -0.152 G_GAN_Feat: 0.825 G_ID: 0.391 G_Rec: 0.472 D_GP: 0.088 D_real: 0.591 D_fake: 1.152 
+(epoch: 27, iters: 5392, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.644 G_ID: 0.801 G_Rec: 0.363 D_GP: 0.028 D_real: 1.231 D_fake: 0.549 
+(epoch: 27, iters: 5792, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.734 G_ID: 0.353 G_Rec: 0.391 D_GP: 0.039 D_real: 0.727 D_fake: 0.953 
+(epoch: 27, iters: 6192, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.696 G_ID: 0.752 G_Rec: 0.410 D_GP: 0.023 D_real: 0.866 D_fake: 0.847 
+(epoch: 27, iters: 6592, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 0.787 G_ID: 0.360 G_Rec: 0.405 D_GP: 0.045 D_real: 1.326 D_fake: 0.451 
+(epoch: 27, iters: 6992, time: 0.064) G_GAN: 0.624 G_GAN_Feat: 0.634 G_ID: 0.790 G_Rec: 0.447 D_GP: 0.020 D_real: 1.534 D_fake: 0.425 
+(epoch: 27, iters: 7392, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 1.169 G_ID: 0.390 G_Rec: 0.394 D_GP: 0.103 D_real: 0.490 D_fake: 0.721 
+(epoch: 27, iters: 7792, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.840 G_ID: 0.699 G_Rec: 0.383 D_GP: 0.049 D_real: 1.191 D_fake: 0.529 
+(epoch: 27, iters: 8192, time: 0.064) G_GAN: 0.733 G_GAN_Feat: 0.737 G_ID: 0.330 G_Rec: 0.343 D_GP: 0.042 D_real: 1.375 D_fake: 0.275 
+(epoch: 27, iters: 8592, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.871 G_ID: 0.764 G_Rec: 0.353 D_GP: 0.057 D_real: 1.072 D_fake: 0.576 
+(epoch: 28, iters: 384, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.629 G_ID: 0.309 G_Rec: 0.358 D_GP: 0.035 D_real: 0.997 D_fake: 0.862 
+(epoch: 28, iters: 784, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.719 G_ID: 0.798 G_Rec: 0.372 D_GP: 0.063 D_real: 1.086 D_fake: 0.590 
+(epoch: 28, iters: 1184, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 1.014 G_ID: 0.352 G_Rec: 0.432 D_GP: 0.261 D_real: 0.895 D_fake: 0.701 
+(epoch: 28, iters: 1584, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.924 G_ID: 0.712 G_Rec: 0.426 D_GP: 0.076 D_real: 1.048 D_fake: 0.711 
+(epoch: 28, iters: 1984, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.817 G_ID: 0.299 G_Rec: 0.425 D_GP: 0.058 D_real: 1.172 D_fake: 0.564 
+(epoch: 28, iters: 2384, time: 0.064) G_GAN: -0.048 G_GAN_Feat: 0.914 G_ID: 0.763 G_Rec: 0.392 D_GP: 0.155 D_real: 0.513 D_fake: 1.049 
+(epoch: 28, iters: 2784, time: 0.064) G_GAN: 0.676 G_GAN_Feat: 1.238 G_ID: 0.367 G_Rec: 0.409 D_GP: 0.060 D_real: 1.449 D_fake: 0.447 
+(epoch: 28, iters: 3184, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 0.903 G_ID: 0.699 G_Rec: 0.358 D_GP: 0.121 D_real: 1.125 D_fake: 0.488 
+(epoch: 28, iters: 3584, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.568 G_ID: 0.361 G_Rec: 0.358 D_GP: 0.025 D_real: 1.173 D_fake: 0.653 
+(epoch: 28, iters: 3984, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.729 G_ID: 0.725 G_Rec: 0.356 D_GP: 0.034 D_real: 1.168 D_fake: 0.612 
+(epoch: 28, iters: 4384, time: 0.064) G_GAN: 0.189 G_GAN_Feat: 0.567 G_ID: 0.344 G_Rec: 0.415 D_GP: 0.030 D_real: 0.979 D_fake: 0.812 
+(epoch: 28, iters: 4784, time: 0.064) G_GAN: -0.116 G_GAN_Feat: 0.717 G_ID: 0.715 G_Rec: 0.452 D_GP: 0.048 D_real: 0.472 D_fake: 1.116 
+(epoch: 28, iters: 5184, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.584 G_ID: 0.345 G_Rec: 0.482 D_GP: 0.031 D_real: 0.906 D_fake: 0.883 
+(epoch: 28, iters: 5584, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.824 G_ID: 0.717 G_Rec: 0.357 D_GP: 0.047 D_real: 0.630 D_fake: 0.877 
+(epoch: 28, iters: 5984, time: 0.064) G_GAN: 0.563 G_GAN_Feat: 0.769 G_ID: 0.383 G_Rec: 0.373 D_GP: 0.045 D_real: 1.135 D_fake: 0.477 
+(epoch: 28, iters: 6384, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.699 G_ID: 0.711 G_Rec: 0.399 D_GP: 0.033 D_real: 1.115 D_fake: 0.780 
+(epoch: 28, iters: 6784, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 0.680 G_ID: 0.352 G_Rec: 0.380 D_GP: 0.022 D_real: 1.307 D_fake: 0.478 
+(epoch: 28, iters: 7184, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.570 G_ID: 0.675 G_Rec: 0.356 D_GP: 0.028 D_real: 1.028 D_fake: 0.762 
+(epoch: 28, iters: 7584, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.589 G_ID: 0.358 G_Rec: 0.354 D_GP: 0.037 D_real: 1.243 D_fake: 0.536 
+(epoch: 28, iters: 7984, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 1.147 G_ID: 0.688 G_Rec: 0.435 D_GP: 0.548 D_real: 0.331 D_fake: 0.921 
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+(epoch: 29, iters: 176, time: 0.064) G_GAN: 0.685 G_GAN_Feat: 0.642 G_ID: 0.733 G_Rec: 0.383 D_GP: 0.035 D_real: 1.471 D_fake: 0.327 
+(epoch: 29, iters: 576, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.594 G_ID: 0.248 G_Rec: 0.327 D_GP: 0.029 D_real: 0.915 D_fake: 0.813 
+(epoch: 29, iters: 976, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.781 G_ID: 0.787 G_Rec: 0.349 D_GP: 0.095 D_real: 0.862 D_fake: 0.696 
+(epoch: 29, iters: 1376, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.708 G_ID: 0.335 G_Rec: 0.440 D_GP: 0.040 D_real: 1.152 D_fake: 0.496 
+(epoch: 29, iters: 1776, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 1.163 G_ID: 0.743 G_Rec: 0.426 D_GP: 0.072 D_real: 0.345 D_fake: 0.714 
+(epoch: 29, iters: 2176, time: 0.064) G_GAN: 0.853 G_GAN_Feat: 0.822 G_ID: 0.304 G_Rec: 0.378 D_GP: 0.047 D_real: 1.446 D_fake: 0.482 
+(epoch: 29, iters: 2576, time: 0.064) G_GAN: 0.634 G_GAN_Feat: 0.781 G_ID: 0.711 G_Rec: 0.429 D_GP: 0.046 D_real: 1.331 D_fake: 0.496 
+(epoch: 29, iters: 2976, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.692 G_ID: 0.333 G_Rec: 0.360 D_GP: 0.029 D_real: 0.825 D_fake: 0.912 
+(epoch: 29, iters: 3376, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.715 G_ID: 0.722 G_Rec: 0.463 D_GP: 0.041 D_real: 0.977 D_fake: 0.655 
+(epoch: 29, iters: 3776, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.794 G_ID: 0.360 G_Rec: 0.395 D_GP: 0.051 D_real: 0.942 D_fake: 0.568 
+(epoch: 29, iters: 4176, time: 0.064) G_GAN: 0.396 G_GAN_Feat: 0.706 G_ID: 0.702 G_Rec: 0.391 D_GP: 0.042 D_real: 1.098 D_fake: 0.604 
+(epoch: 29, iters: 4576, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.802 G_ID: 0.354 G_Rec: 0.360 D_GP: 0.049 D_real: 1.097 D_fake: 0.766 
+(epoch: 29, iters: 4976, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.840 G_ID: 0.726 G_Rec: 0.382 D_GP: 0.041 D_real: 0.831 D_fake: 0.774 
+(epoch: 29, iters: 5376, time: 0.064) G_GAN: 0.089 G_GAN_Feat: 0.757 G_ID: 0.326 G_Rec: 0.381 D_GP: 0.071 D_real: 0.664 D_fake: 0.911 
+(epoch: 29, iters: 5776, time: 0.064) G_GAN: -0.004 G_GAN_Feat: 0.820 G_ID: 0.686 G_Rec: 0.381 D_GP: 0.129 D_real: 0.469 D_fake: 1.004 
+(epoch: 29, iters: 6176, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.538 G_ID: 0.316 G_Rec: 0.348 D_GP: 0.019 D_real: 1.269 D_fake: 0.635 
+(epoch: 29, iters: 6576, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.602 G_ID: 0.669 G_Rec: 0.358 D_GP: 0.019 D_real: 0.991 D_fake: 0.853 
+(epoch: 29, iters: 6976, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.734 G_ID: 0.406 G_Rec: 0.472 D_GP: 0.040 D_real: 0.756 D_fake: 0.853 
+(epoch: 29, iters: 7376, time: 0.064) G_GAN: 0.781 G_GAN_Feat: 1.135 G_ID: 0.689 G_Rec: 0.478 D_GP: 0.079 D_real: 1.044 D_fake: 0.379 
+(epoch: 29, iters: 7776, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.805 G_ID: 0.359 G_Rec: 0.360 D_GP: 0.081 D_real: 0.721 D_fake: 0.818 
+(epoch: 29, iters: 8176, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 1.066 G_ID: 0.779 G_Rec: 0.365 D_GP: 0.194 D_real: 0.518 D_fake: 0.885 
+(epoch: 29, iters: 8576, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 1.011 G_ID: 0.362 G_Rec: 0.422 D_GP: 0.076 D_real: 0.634 D_fake: 0.529 
+(epoch: 30, iters: 368, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 1.532 G_ID: 0.704 G_Rec: 0.360 D_GP: 0.287 D_real: 0.491 D_fake: 0.660 
+(epoch: 30, iters: 768, time: 0.064) G_GAN: 0.806 G_GAN_Feat: 0.725 G_ID: 0.338 G_Rec: 0.384 D_GP: 0.034 D_real: 1.540 D_fake: 0.427 
+(epoch: 30, iters: 1168, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 1.095 G_ID: 0.739 G_Rec: 0.541 D_GP: 0.156 D_real: 0.422 D_fake: 0.813 
+(epoch: 30, iters: 1568, time: 0.064) G_GAN: 0.470 G_GAN_Feat: 0.681 G_ID: 0.327 G_Rec: 0.379 D_GP: 0.031 D_real: 1.217 D_fake: 0.535 
+(epoch: 30, iters: 1968, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.820 G_ID: 0.696 G_Rec: 0.374 D_GP: 0.086 D_real: 1.064 D_fake: 0.495 
+(epoch: 30, iters: 2368, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.683 G_ID: 0.351 G_Rec: 0.378 D_GP: 0.030 D_real: 0.996 D_fake: 0.820 
+(epoch: 30, iters: 2768, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.737 G_ID: 0.755 G_Rec: 0.388 D_GP: 0.027 D_real: 1.113 D_fake: 0.623 
+(epoch: 30, iters: 3168, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.686 G_ID: 0.335 G_Rec: 0.418 D_GP: 0.051 D_real: 0.879 D_fake: 0.779 
+(epoch: 30, iters: 3568, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.595 G_ID: 0.704 G_Rec: 0.368 D_GP: 0.024 D_real: 1.221 D_fake: 0.600 
+(epoch: 30, iters: 3968, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 1.174 G_ID: 0.318 G_Rec: 0.448 D_GP: 0.237 D_real: 0.539 D_fake: 0.577 
+(epoch: 30, iters: 4368, time: 0.064) G_GAN: -0.119 G_GAN_Feat: 0.937 G_ID: 0.725 G_Rec: 0.371 D_GP: 0.158 D_real: 0.546 D_fake: 1.119 
+(epoch: 30, iters: 4768, time: 0.064) G_GAN: -0.039 G_GAN_Feat: 0.692 G_ID: 0.354 G_Rec: 0.490 D_GP: 0.032 D_real: 0.683 D_fake: 1.039 
+(epoch: 30, iters: 5168, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.686 G_ID: 0.679 G_Rec: 0.451 D_GP: 0.027 D_real: 1.020 D_fake: 0.816 
+(epoch: 30, iters: 5568, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.666 G_ID: 0.338 G_Rec: 0.433 D_GP: 0.032 D_real: 1.109 D_fake: 0.647 
+(epoch: 30, iters: 5968, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.716 G_ID: 0.661 G_Rec: 0.378 D_GP: 0.035 D_real: 1.195 D_fake: 0.532 
+(epoch: 30, iters: 6368, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.682 G_ID: 0.310 G_Rec: 0.354 D_GP: 0.048 D_real: 0.934 D_fake: 0.718 
+(epoch: 30, iters: 6768, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.973 G_ID: 0.666 G_Rec: 0.403 D_GP: 0.173 D_real: 0.867 D_fake: 0.437 
+(epoch: 30, iters: 7168, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.856 G_ID: 0.272 G_Rec: 0.436 D_GP: 0.048 D_real: 0.773 D_fake: 0.765 
+(epoch: 30, iters: 7568, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.558 G_ID: 0.697 G_Rec: 0.351 D_GP: 0.018 D_real: 1.201 D_fake: 0.698 
+(epoch: 30, iters: 7968, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.598 G_ID: 0.332 G_Rec: 0.384 D_GP: 0.028 D_real: 1.389 D_fake: 0.477 
+(epoch: 30, iters: 8368, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.774 G_ID: 0.694 G_Rec: 0.366 D_GP: 0.055 D_real: 1.054 D_fake: 0.588 
+(epoch: 31, iters: 160, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.841 G_ID: 0.321 G_Rec: 0.387 D_GP: 0.054 D_real: 1.006 D_fake: 0.509 
+(epoch: 31, iters: 560, time: 0.064) G_GAN: 0.558 G_GAN_Feat: 1.008 G_ID: 0.713 G_Rec: 0.392 D_GP: 0.115 D_real: 0.739 D_fake: 0.450 
+(epoch: 31, iters: 960, time: 0.064) G_GAN: 0.561 G_GAN_Feat: 0.823 G_ID: 0.360 G_Rec: 0.393 D_GP: 0.044 D_real: 1.211 D_fake: 0.442 
+(epoch: 31, iters: 1360, time: 0.064) G_GAN: 0.647 G_GAN_Feat: 0.861 G_ID: 0.677 G_Rec: 0.362 D_GP: 0.045 D_real: 1.428 D_fake: 0.470 
+(epoch: 31, iters: 1760, time: 0.064) G_GAN: 0.055 G_GAN_Feat: 0.854 G_ID: 0.371 G_Rec: 0.356 D_GP: 0.083 D_real: 0.605 D_fake: 0.945 
+(epoch: 31, iters: 2160, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 0.522 G_ID: 0.709 G_Rec: 0.391 D_GP: 0.018 D_real: 1.444 D_fake: 0.487 
+(epoch: 31, iters: 2560, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.542 G_ID: 0.340 G_Rec: 0.374 D_GP: 0.022 D_real: 1.078 D_fake: 0.740 
+(epoch: 31, iters: 2960, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.591 G_ID: 0.651 G_Rec: 0.326 D_GP: 0.034 D_real: 1.009 D_fake: 0.792 
+(epoch: 31, iters: 3360, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.731 G_ID: 0.350 G_Rec: 0.381 D_GP: 0.092 D_real: 0.909 D_fake: 0.661 
+(epoch: 31, iters: 3760, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.742 G_ID: 0.713 G_Rec: 0.405 D_GP: 0.054 D_real: 0.903 D_fake: 0.855 
+(epoch: 31, iters: 4160, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 0.675 G_ID: 0.301 G_Rec: 0.402 D_GP: 0.036 D_real: 1.278 D_fake: 0.475 
+(epoch: 31, iters: 4560, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.740 G_ID: 0.648 G_Rec: 0.396 D_GP: 0.034 D_real: 1.047 D_fake: 0.664 
+(epoch: 31, iters: 4960, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.807 G_ID: 0.353 G_Rec: 0.360 D_GP: 0.064 D_real: 0.996 D_fake: 0.553 
+(epoch: 31, iters: 5360, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.905 G_ID: 0.668 G_Rec: 0.375 D_GP: 0.108 D_real: 1.019 D_fake: 0.467 
+(epoch: 31, iters: 5760, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.745 G_ID: 0.322 G_Rec: 0.325 D_GP: 0.063 D_real: 0.905 D_fake: 0.812 
+(epoch: 31, iters: 6160, time: 0.064) G_GAN: 0.688 G_GAN_Feat: 0.725 G_ID: 0.736 G_Rec: 0.382 D_GP: 0.031 D_real: 1.440 D_fake: 0.395 
+(epoch: 31, iters: 6560, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 0.766 G_ID: 0.296 G_Rec: 0.371 D_GP: 0.073 D_real: 1.034 D_fake: 0.461 
+(epoch: 31, iters: 6960, time: 0.064) G_GAN: 1.075 G_GAN_Feat: 0.714 G_ID: 0.665 G_Rec: 0.389 D_GP: 0.023 D_real: 1.808 D_fake: 0.085 
+(epoch: 31, iters: 7360, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 0.658 G_ID: 0.356 G_Rec: 0.449 D_GP: 0.030 D_real: 1.357 D_fake: 0.465 
+(epoch: 31, iters: 7760, time: 0.064) G_GAN: 0.702 G_GAN_Feat: 0.788 G_ID: 0.661 G_Rec: 0.396 D_GP: 0.056 D_real: 1.351 D_fake: 0.336 
+(epoch: 31, iters: 8160, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.688 G_ID: 0.359 G_Rec: 0.359 D_GP: 0.037 D_real: 1.049 D_fake: 0.655 
+(epoch: 31, iters: 8560, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.635 G_ID: 0.672 G_Rec: 0.390 D_GP: 0.029 D_real: 1.207 D_fake: 0.643 
+(epoch: 32, iters: 352, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.698 G_ID: 0.342 G_Rec: 0.365 D_GP: 0.034 D_real: 1.164 D_fake: 0.576 
+(epoch: 32, iters: 752, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 1.048 G_ID: 0.693 G_Rec: 0.486 D_GP: 0.117 D_real: 0.361 D_fake: 0.871 
+(epoch: 32, iters: 1152, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.628 G_ID: 0.389 G_Rec: 0.408 D_GP: 0.019 D_real: 1.277 D_fake: 0.636 
+(epoch: 32, iters: 1552, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.567 G_ID: 0.683 G_Rec: 0.360 D_GP: 0.017 D_real: 1.183 D_fake: 0.669 
+(epoch: 32, iters: 1952, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.548 G_ID: 0.310 G_Rec: 0.394 D_GP: 0.022 D_real: 1.381 D_fake: 0.658 
+(epoch: 32, iters: 2352, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.603 G_ID: 0.619 G_Rec: 0.369 D_GP: 0.029 D_real: 1.087 D_fake: 0.730 
+(epoch: 32, iters: 2752, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.594 G_ID: 0.399 G_Rec: 0.366 D_GP: 0.029 D_real: 1.116 D_fake: 0.730 
+(epoch: 32, iters: 3152, time: 0.064) G_GAN: 0.072 G_GAN_Feat: 0.683 G_ID: 0.733 G_Rec: 0.355 D_GP: 0.058 D_real: 0.818 D_fake: 0.928 
+(epoch: 32, iters: 3552, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.753 G_ID: 0.311 G_Rec: 0.358 D_GP: 0.067 D_real: 1.017 D_fake: 0.712 
+(epoch: 32, iters: 3952, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.981 G_ID: 0.685 G_Rec: 0.395 D_GP: 0.065 D_real: 0.816 D_fake: 0.662 
+(epoch: 32, iters: 4352, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.738 G_ID: 0.331 G_Rec: 0.341 D_GP: 0.037 D_real: 0.831 D_fake: 0.803 
+(epoch: 32, iters: 4752, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 0.789 G_ID: 0.657 G_Rec: 0.479 D_GP: 0.061 D_real: 1.165 D_fake: 0.478 
+(epoch: 32, iters: 5152, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.731 G_ID: 0.352 G_Rec: 0.409 D_GP: 0.034 D_real: 1.105 D_fake: 0.543 
+(epoch: 32, iters: 5552, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.903 G_ID: 0.634 G_Rec: 0.365 D_GP: 0.083 D_real: 0.911 D_fake: 0.489 
+(epoch: 32, iters: 5952, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.618 G_ID: 0.278 G_Rec: 0.437 D_GP: 0.022 D_real: 1.033 D_fake: 0.791 
+(epoch: 32, iters: 6352, time: 0.064) G_GAN: 0.189 G_GAN_Feat: 0.660 G_ID: 0.642 G_Rec: 0.411 D_GP: 0.033 D_real: 1.046 D_fake: 0.811 
+(epoch: 32, iters: 6752, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.732 G_ID: 0.330 G_Rec: 0.455 D_GP: 0.049 D_real: 1.001 D_fake: 0.672 
+(epoch: 32, iters: 7152, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.775 G_ID: 0.679 G_Rec: 0.335 D_GP: 0.043 D_real: 0.975 D_fake: 0.686 
+(epoch: 32, iters: 7552, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.847 G_ID: 0.346 G_Rec: 0.432 D_GP: 0.042 D_real: 0.750 D_fake: 0.733 
+(epoch: 32, iters: 7952, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.605 G_ID: 0.640 G_Rec: 0.325 D_GP: 0.028 D_real: 1.266 D_fake: 0.565 
+(epoch: 32, iters: 8352, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.870 G_ID: 0.328 G_Rec: 0.363 D_GP: 0.099 D_real: 0.898 D_fake: 0.577 
+(epoch: 33, iters: 144, time: 0.064) G_GAN: -0.015 G_GAN_Feat: 0.844 G_ID: 0.682 G_Rec: 0.354 D_GP: 0.160 D_real: 0.530 D_fake: 1.015 
+(epoch: 33, iters: 544, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.924 G_ID: 0.323 G_Rec: 0.341 D_GP: 0.058 D_real: 0.568 D_fake: 0.824 
+(epoch: 33, iters: 944, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 1.024 G_ID: 0.693 G_Rec: 0.470 D_GP: 0.136 D_real: 0.581 D_fake: 0.646 
+(epoch: 33, iters: 1344, time: 0.064) G_GAN: 0.759 G_GAN_Feat: 1.042 G_ID: 0.324 G_Rec: 0.391 D_GP: 0.075 D_real: 0.951 D_fake: 0.285 
+(epoch: 33, iters: 1744, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.489 G_ID: 0.650 G_Rec: 0.362 D_GP: 0.017 D_real: 1.209 D_fake: 0.693 
+(epoch: 33, iters: 2144, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.494 G_ID: 0.303 G_Rec: 0.326 D_GP: 0.020 D_real: 1.077 D_fake: 0.744 
+(epoch: 33, iters: 2544, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 0.546 G_ID: 0.703 G_Rec: 0.398 D_GP: 0.022 D_real: 0.943 D_fake: 0.848 
+(epoch: 33, iters: 2944, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.560 G_ID: 0.311 G_Rec: 0.446 D_GP: 0.027 D_real: 1.281 D_fake: 0.594 
+(epoch: 33, iters: 3344, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.570 G_ID: 0.644 G_Rec: 0.410 D_GP: 0.029 D_real: 0.859 D_fake: 0.879 
+(epoch: 33, iters: 3744, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.648 G_ID: 0.361 G_Rec: 0.377 D_GP: 0.039 D_real: 0.953 D_fake: 0.745 
+(epoch: 33, iters: 4144, time: 0.064) G_GAN: 0.024 G_GAN_Feat: 0.654 G_ID: 0.683 G_Rec: 0.393 D_GP: 0.055 D_real: 0.723 D_fake: 0.976 
+(epoch: 33, iters: 4544, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.960 G_ID: 0.382 G_Rec: 0.382 D_GP: 0.120 D_real: 0.701 D_fake: 0.616 
+(epoch: 33, iters: 4944, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.800 G_ID: 0.605 G_Rec: 0.370 D_GP: 0.051 D_real: 1.115 D_fake: 0.574 
+(epoch: 33, iters: 5344, time: 0.064) G_GAN: 0.728 G_GAN_Feat: 0.789 G_ID: 0.301 G_Rec: 0.416 D_GP: 0.061 D_real: 1.268 D_fake: 0.444 
+(epoch: 33, iters: 5744, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.572 G_ID: 0.648 G_Rec: 0.353 D_GP: 0.020 D_real: 1.289 D_fake: 0.615 
+(epoch: 33, iters: 6144, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.643 G_ID: 0.319 G_Rec: 0.373 D_GP: 0.032 D_real: 1.178 D_fake: 0.650 
+(epoch: 33, iters: 6544, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.816 G_ID: 0.639 G_Rec: 0.376 D_GP: 0.049 D_real: 1.008 D_fake: 0.668 
+(epoch: 33, iters: 6944, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.872 G_ID: 0.331 G_Rec: 0.505 D_GP: 0.055 D_real: 0.700 D_fake: 0.780 
+(epoch: 33, iters: 7344, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.657 G_ID: 0.664 G_Rec: 0.405 D_GP: 0.038 D_real: 0.973 D_fake: 0.721 
+(epoch: 33, iters: 7744, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.729 G_ID: 0.270 G_Rec: 0.429 D_GP: 0.056 D_real: 1.037 D_fake: 0.579 
+(epoch: 33, iters: 8144, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.684 G_ID: 0.704 G_Rec: 0.373 D_GP: 0.041 D_real: 1.096 D_fake: 0.709 
+(epoch: 33, iters: 8544, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.675 G_ID: 0.367 G_Rec: 0.364 D_GP: 0.019 D_real: 0.964 D_fake: 0.850 
+(epoch: 34, iters: 336, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.647 G_ID: 0.618 G_Rec: 0.367 D_GP: 0.034 D_real: 0.807 D_fake: 0.825 
+(epoch: 34, iters: 736, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.866 G_ID: 0.294 G_Rec: 0.351 D_GP: 0.122 D_real: 0.740 D_fake: 0.541 
+(epoch: 34, iters: 1136, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.738 G_ID: 0.651 G_Rec: 0.448 D_GP: 0.028 D_real: 0.836 D_fake: 0.882 
+(epoch: 34, iters: 1536, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.753 G_ID: 0.346 G_Rec: 0.345 D_GP: 0.040 D_real: 1.145 D_fake: 0.555 
+(epoch: 34, iters: 1936, time: 0.064) G_GAN: 0.655 G_GAN_Feat: 0.730 G_ID: 0.644 G_Rec: 0.369 D_GP: 0.036 D_real: 1.423 D_fake: 0.354 
+(epoch: 34, iters: 2336, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.994 G_ID: 0.330 G_Rec: 0.375 D_GP: 0.107 D_real: 1.108 D_fake: 0.584 
+(epoch: 34, iters: 2736, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.770 G_ID: 0.648 G_Rec: 0.359 D_GP: 0.058 D_real: 0.891 D_fake: 0.821 
+(epoch: 34, iters: 3136, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.726 G_ID: 0.290 G_Rec: 0.404 D_GP: 0.031 D_real: 1.027 D_fake: 0.719 
+(epoch: 34, iters: 3536, time: 0.064) G_GAN: 0.667 G_GAN_Feat: 0.794 G_ID: 0.642 G_Rec: 0.351 D_GP: 0.046 D_real: 1.411 D_fake: 0.348 
+(epoch: 34, iters: 3936, time: 0.064) G_GAN: -0.065 G_GAN_Feat: 0.723 G_ID: 0.332 G_Rec: 0.356 D_GP: 0.031 D_real: 0.641 D_fake: 1.065 
+(epoch: 34, iters: 4336, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.762 G_ID: 0.607 G_Rec: 0.348 D_GP: 0.047 D_real: 0.974 D_fake: 0.626 
+(epoch: 34, iters: 4736, time: 0.064) G_GAN: 0.563 G_GAN_Feat: 1.106 G_ID: 0.295 G_Rec: 0.354 D_GP: 0.084 D_real: 0.690 D_fake: 0.449 
+(epoch: 34, iters: 5136, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.575 G_ID: 0.590 G_Rec: 0.353 D_GP: 0.030 D_real: 0.971 D_fake: 0.900 
+(epoch: 34, iters: 5536, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.716 G_ID: 0.358 G_Rec: 0.409 D_GP: 0.052 D_real: 1.086 D_fake: 0.659 
+(epoch: 34, iters: 5936, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.712 G_ID: 0.634 G_Rec: 0.389 D_GP: 0.056 D_real: 1.066 D_fake: 0.660 
+(epoch: 34, iters: 6336, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.783 G_ID: 0.320 G_Rec: 0.400 D_GP: 0.088 D_real: 0.850 D_fake: 0.636 
+(epoch: 34, iters: 6736, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.995 G_ID: 0.643 G_Rec: 0.486 D_GP: 0.086 D_real: 0.387 D_fake: 0.947 
+(epoch: 34, iters: 7136, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.784 G_ID: 0.325 G_Rec: 0.356 D_GP: 0.046 D_real: 0.974 D_fake: 0.672 
+(epoch: 34, iters: 7536, time: 0.064) G_GAN: 0.777 G_GAN_Feat: 0.729 G_ID: 0.572 G_Rec: 0.393 D_GP: 0.036 D_real: 1.523 D_fake: 0.393 
+(epoch: 34, iters: 7936, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.541 G_ID: 0.275 G_Rec: 0.314 D_GP: 0.028 D_real: 1.003 D_fake: 0.846 
+(epoch: 34, iters: 8336, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.702 G_ID: 0.681 G_Rec: 0.415 D_GP: 0.032 D_real: 0.776 D_fake: 0.909 
+(epoch: 35, iters: 128, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.668 G_ID: 0.358 G_Rec: 0.500 D_GP: 0.059 D_real: 0.983 D_fake: 0.695 
+(epoch: 35, iters: 528, time: 0.064) G_GAN: 0.537 G_GAN_Feat: 0.852 G_ID: 0.592 G_Rec: 0.420 D_GP: 0.091 D_real: 1.163 D_fake: 0.515 
+(epoch: 35, iters: 928, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.790 G_ID: 0.284 G_Rec: 0.419 D_GP: 0.066 D_real: 1.076 D_fake: 0.564 
+(epoch: 35, iters: 1328, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 0.569 G_ID: 0.653 G_Rec: 0.345 D_GP: 0.021 D_real: 1.390 D_fake: 0.479 
+(epoch: 35, iters: 1728, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.619 G_ID: 0.303 G_Rec: 0.452 D_GP: 0.035 D_real: 1.120 D_fake: 0.690 
+(epoch: 35, iters: 2128, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.624 G_ID: 0.609 G_Rec: 0.381 D_GP: 0.039 D_real: 1.149 D_fake: 0.515 
+(epoch: 35, iters: 2528, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.788 G_ID: 0.311 G_Rec: 0.474 D_GP: 0.087 D_real: 0.708 D_fake: 0.746 
+(epoch: 35, iters: 2928, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.746 G_ID: 0.612 G_Rec: 0.347 D_GP: 0.086 D_real: 0.875 D_fake: 0.767 
+(epoch: 35, iters: 3328, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.923 G_ID: 0.302 G_Rec: 0.352 D_GP: 0.072 D_real: 0.565 D_fake: 0.783 
+(epoch: 35, iters: 3728, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.855 G_ID: 0.623 G_Rec: 0.352 D_GP: 0.153 D_real: 0.774 D_fake: 0.674 
+(epoch: 35, iters: 4128, time: 0.064) G_GAN: 0.703 G_GAN_Feat: 0.697 G_ID: 0.331 G_Rec: 0.452 D_GP: 0.029 D_real: 1.490 D_fake: 0.313 
+(epoch: 35, iters: 4528, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 1.183 G_ID: 0.644 G_Rec: 0.370 D_GP: 0.301 D_real: 0.466 D_fake: 0.613 
+(epoch: 35, iters: 4928, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 1.097 G_ID: 0.362 G_Rec: 0.430 D_GP: 0.219 D_real: 0.451 D_fake: 0.571 
+(epoch: 35, iters: 5328, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.564 G_ID: 0.686 G_Rec: 0.450 D_GP: 0.019 D_real: 1.137 D_fake: 0.810 
+(epoch: 35, iters: 5728, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.569 G_ID: 0.325 G_Rec: 0.356 D_GP: 0.027 D_real: 1.020 D_fake: 0.826 
+(epoch: 35, iters: 6128, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.516 G_ID: 0.636 G_Rec: 0.451 D_GP: 0.021 D_real: 1.043 D_fake: 0.819 
+(epoch: 35, iters: 6528, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.553 G_ID: 0.290 G_Rec: 0.353 D_GP: 0.024 D_real: 1.162 D_fake: 0.761 
+(epoch: 35, iters: 6928, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.571 G_ID: 0.641 G_Rec: 0.409 D_GP: 0.034 D_real: 0.961 D_fake: 0.850 
+(epoch: 35, iters: 7328, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 0.629 G_ID: 0.276 G_Rec: 0.348 D_GP: 0.059 D_real: 0.870 D_fake: 0.935 
+(epoch: 35, iters: 7728, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.649 G_ID: 0.606 G_Rec: 0.365 D_GP: 0.056 D_real: 0.973 D_fake: 0.782 
+(epoch: 35, iters: 8128, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.654 G_ID: 0.291 G_Rec: 0.430 D_GP: 0.036 D_real: 1.039 D_fake: 0.755 
+(epoch: 35, iters: 8528, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.732 G_ID: 0.649 G_Rec: 0.366 D_GP: 0.059 D_real: 1.098 D_fake: 0.561 
+(epoch: 36, iters: 320, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.891 G_ID: 0.360 G_Rec: 0.368 D_GP: 0.100 D_real: 0.506 D_fake: 0.897 
+(epoch: 36, iters: 720, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 0.705 G_ID: 0.603 G_Rec: 0.357 D_GP: 0.074 D_real: 1.050 D_fake: 0.546 
+(epoch: 36, iters: 1120, time: 0.064) G_GAN: 0.582 G_GAN_Feat: 0.710 G_ID: 0.325 G_Rec: 0.333 D_GP: 0.060 D_real: 1.233 D_fake: 0.450 
+(epoch: 36, iters: 1520, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.719 G_ID: 0.609 G_Rec: 0.406 D_GP: 0.034 D_real: 1.041 D_fake: 0.722 
+(epoch: 36, iters: 1920, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.827 G_ID: 0.298 G_Rec: 0.385 D_GP: 0.093 D_real: 0.938 D_fake: 0.631 
+(epoch: 36, iters: 2320, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.651 G_ID: 0.568 G_Rec: 0.370 D_GP: 0.030 D_real: 1.197 D_fake: 0.598 
+(epoch: 36, iters: 2720, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.796 G_ID: 0.328 G_Rec: 0.374 D_GP: 0.103 D_real: 0.712 D_fake: 0.808 
+(epoch: 36, iters: 3120, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.781 G_ID: 0.599 G_Rec: 0.390 D_GP: 0.071 D_real: 0.805 D_fake: 0.679 
+(epoch: 36, iters: 3520, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.897 G_ID: 0.242 G_Rec: 0.400 D_GP: 0.110 D_real: 0.473 D_fake: 0.773 
+(epoch: 36, iters: 3920, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.689 G_ID: 0.664 G_Rec: 0.361 D_GP: 0.042 D_real: 0.819 D_fake: 0.875 
+(epoch: 36, iters: 4320, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.652 G_ID: 0.292 G_Rec: 0.409 D_GP: 0.032 D_real: 0.955 D_fake: 0.798 
+(epoch: 36, iters: 4720, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.810 G_ID: 0.636 G_Rec: 0.328 D_GP: 0.056 D_real: 1.225 D_fake: 0.596 
+(epoch: 36, iters: 5120, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.722 G_ID: 0.312 G_Rec: 0.338 D_GP: 0.042 D_real: 1.134 D_fake: 0.499 
+(epoch: 36, iters: 5520, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.796 G_ID: 0.619 G_Rec: 0.342 D_GP: 0.049 D_real: 0.950 D_fake: 0.678 
+(epoch: 36, iters: 5920, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.764 G_ID: 0.296 G_Rec: 0.480 D_GP: 0.029 D_real: 0.940 D_fake: 0.785 
+(epoch: 36, iters: 6320, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.895 G_ID: 0.602 G_Rec: 0.372 D_GP: 0.083 D_real: 0.768 D_fake: 0.693 
+(epoch: 36, iters: 6720, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.633 G_ID: 0.296 G_Rec: 0.332 D_GP: 0.025 D_real: 1.207 D_fake: 0.632 
+(epoch: 36, iters: 7120, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.623 G_ID: 0.628 G_Rec: 0.326 D_GP: 0.026 D_real: 1.114 D_fake: 0.777 
+(epoch: 36, iters: 7520, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.669 G_ID: 0.321 G_Rec: 0.374 D_GP: 0.020 D_real: 1.009 D_fake: 0.877 
+(epoch: 36, iters: 7920, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.791 G_ID: 0.625 G_Rec: 0.415 D_GP: 0.032 D_real: 0.815 D_fake: 0.897 
+(epoch: 36, iters: 8320, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.721 G_ID: 0.279 G_Rec: 0.416 D_GP: 0.049 D_real: 1.027 D_fake: 0.759 
+(epoch: 37, iters: 112, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.802 G_ID: 0.592 G_Rec: 0.370 D_GP: 0.073 D_real: 1.043 D_fake: 0.720 
+(epoch: 37, iters: 512, time: 0.064) G_GAN: 0.617 G_GAN_Feat: 0.774 G_ID: 0.327 G_Rec: 0.400 D_GP: 0.046 D_real: 1.309 D_fake: 0.401 
+(epoch: 37, iters: 912, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.738 G_ID: 0.615 G_Rec: 0.336 D_GP: 0.057 D_real: 0.741 D_fake: 0.914 
+(epoch: 37, iters: 1312, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.732 G_ID: 0.273 G_Rec: 0.346 D_GP: 0.035 D_real: 1.197 D_fake: 0.526 
+(epoch: 37, iters: 1712, time: 0.064) G_GAN: -0.008 G_GAN_Feat: 1.181 G_ID: 0.656 G_Rec: 0.374 D_GP: 0.154 D_real: 0.289 D_fake: 1.008 
+(epoch: 37, iters: 2112, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.738 G_ID: 0.304 G_Rec: 0.419 D_GP: 0.031 D_real: 0.976 D_fake: 0.762 
+(epoch: 37, iters: 2512, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.605 G_ID: 0.607 G_Rec: 0.331 D_GP: 0.022 D_real: 1.075 D_fake: 0.816 
+(epoch: 37, iters: 2912, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 1.518 G_ID: 0.284 G_Rec: 0.461 D_GP: 0.080 D_real: 0.462 D_fake: 0.516 
+(epoch: 37, iters: 3312, time: 0.064) G_GAN: 0.504 G_GAN_Feat: 0.602 G_ID: 0.635 G_Rec: 0.401 D_GP: 0.024 D_real: 1.335 D_fake: 0.499 
+(epoch: 37, iters: 3712, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.611 G_ID: 0.252 G_Rec: 0.366 D_GP: 0.034 D_real: 0.904 D_fake: 0.913 
+(epoch: 37, iters: 4112, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.636 G_ID: 0.626 G_Rec: 0.358 D_GP: 0.027 D_real: 1.218 D_fake: 0.568 
+(epoch: 37, iters: 4512, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.895 G_ID: 0.333 G_Rec: 0.360 D_GP: 0.075 D_real: 0.752 D_fake: 0.726 
+(epoch: 37, iters: 4912, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.842 G_ID: 0.557 G_Rec: 0.435 D_GP: 0.045 D_real: 0.964 D_fake: 0.633 
+(epoch: 37, iters: 5312, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.735 G_ID: 0.290 G_Rec: 0.363 D_GP: 0.062 D_real: 1.030 D_fake: 0.576 
+(epoch: 37, iters: 5712, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.787 G_ID: 0.620 G_Rec: 0.395 D_GP: 0.044 D_real: 1.289 D_fake: 0.409 
+(epoch: 37, iters: 6112, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.786 G_ID: 0.292 G_Rec: 0.437 D_GP: 0.033 D_real: 1.254 D_fake: 0.536 
+(epoch: 37, iters: 6512, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.752 G_ID: 0.615 G_Rec: 0.363 D_GP: 0.047 D_real: 1.223 D_fake: 0.571 
+(epoch: 37, iters: 6912, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.850 G_ID: 0.279 G_Rec: 0.375 D_GP: 0.053 D_real: 0.939 D_fake: 0.629 
+(epoch: 37, iters: 7312, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.781 G_ID: 0.583 G_Rec: 0.377 D_GP: 0.040 D_real: 0.730 D_fake: 0.846 
+(epoch: 37, iters: 7712, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.767 G_ID: 0.318 G_Rec: 0.381 D_GP: 0.070 D_real: 0.899 D_fake: 0.794 
+(epoch: 37, iters: 8112, time: 0.064) G_GAN: 0.540 G_GAN_Feat: 1.050 G_ID: 0.577 G_Rec: 0.463 D_GP: 0.195 D_real: 0.670 D_fake: 0.532 
+(epoch: 37, iters: 8512, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.697 G_ID: 0.278 G_Rec: 0.359 D_GP: 0.030 D_real: 1.238 D_fake: 0.492 
+(epoch: 38, iters: 304, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 1.157 G_ID: 0.573 G_Rec: 0.359 D_GP: 0.165 D_real: 0.457 D_fake: 0.832 
+(epoch: 38, iters: 704, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.777 G_ID: 0.328 G_Rec: 0.348 D_GP: 0.044 D_real: 0.764 D_fake: 0.824 
+(epoch: 38, iters: 1104, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.707 G_ID: 0.620 G_Rec: 0.379 D_GP: 0.030 D_real: 1.345 D_fake: 0.459 
+(epoch: 38, iters: 1504, time: 0.064) G_GAN: 0.469 G_GAN_Feat: 0.661 G_ID: 0.265 G_Rec: 0.363 D_GP: 0.029 D_real: 1.320 D_fake: 0.532 
+(epoch: 38, iters: 1904, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 1.124 G_ID: 0.573 G_Rec: 0.455 D_GP: 0.052 D_real: 0.677 D_fake: 0.565 
+(epoch: 38, iters: 2304, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.634 G_ID: 0.324 G_Rec: 0.381 D_GP: 0.022 D_real: 1.338 D_fake: 0.516 
+(epoch: 38, iters: 2704, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.825 G_ID: 0.650 G_Rec: 0.377 D_GP: 0.042 D_real: 0.733 D_fake: 0.815 
+(epoch: 38, iters: 3104, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.848 G_ID: 0.289 G_Rec: 0.434 D_GP: 0.075 D_real: 0.711 D_fake: 0.826 
+(epoch: 38, iters: 3504, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 0.842 G_ID: 0.617 G_Rec: 0.404 D_GP: 0.039 D_real: 0.585 D_fake: 0.988 
+(epoch: 38, iters: 3904, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.845 G_ID: 0.331 G_Rec: 0.395 D_GP: 0.039 D_real: 0.857 D_fake: 0.729 
+(epoch: 38, iters: 4304, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.894 G_ID: 0.587 G_Rec: 0.378 D_GP: 0.040 D_real: 1.047 D_fake: 0.515 
+(epoch: 38, iters: 4704, time: 0.064) G_GAN: 0.582 G_GAN_Feat: 0.554 G_ID: 0.304 G_Rec: 0.367 D_GP: 0.020 D_real: 1.494 D_fake: 0.420 
+(epoch: 38, iters: 5104, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.509 G_ID: 0.617 G_Rec: 0.352 D_GP: 0.021 D_real: 1.240 D_fake: 0.675 
+(epoch: 38, iters: 5504, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.550 G_ID: 0.291 G_Rec: 0.596 D_GP: 0.022 D_real: 1.294 D_fake: 0.565 
+(epoch: 38, iters: 5904, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.574 G_ID: 0.581 G_Rec: 0.405 D_GP: 0.022 D_real: 1.142 D_fake: 0.714 
+(epoch: 38, iters: 6304, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.645 G_ID: 0.303 G_Rec: 0.369 D_GP: 0.032 D_real: 1.081 D_fake: 0.769 
+(epoch: 38, iters: 6704, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.703 G_ID: 0.609 G_Rec: 0.431 D_GP: 0.040 D_real: 1.157 D_fake: 0.583 
+(epoch: 38, iters: 7104, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.660 G_ID: 0.310 G_Rec: 0.349 D_GP: 0.050 D_real: 1.110 D_fake: 0.629 
+(epoch: 38, iters: 7504, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 0.808 G_ID: 0.626 G_Rec: 0.406 D_GP: 0.040 D_real: 1.207 D_fake: 0.565 
+(epoch: 38, iters: 7904, time: 0.064) G_GAN: 0.592 G_GAN_Feat: 0.710 G_ID: 0.270 G_Rec: 0.392 D_GP: 0.030 D_real: 1.269 D_fake: 0.411 
+(epoch: 38, iters: 8304, time: 0.064) G_GAN: 0.591 G_GAN_Feat: 0.711 G_ID: 0.575 G_Rec: 0.374 D_GP: 0.035 D_real: 1.357 D_fake: 0.462 
+(epoch: 39, iters: 96, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.741 G_ID: 0.311 G_Rec: 0.505 D_GP: 0.076 D_real: 0.981 D_fake: 0.690 
+(epoch: 39, iters: 496, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.708 G_ID: 0.643 G_Rec: 0.347 D_GP: 0.064 D_real: 1.029 D_fake: 0.722 
+(epoch: 39, iters: 896, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.783 G_ID: 0.341 G_Rec: 0.358 D_GP: 0.077 D_real: 1.099 D_fake: 0.567 
+(epoch: 39, iters: 1296, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.770 G_ID: 0.622 G_Rec: 0.373 D_GP: 0.069 D_real: 1.013 D_fake: 0.621 
+(epoch: 39, iters: 1696, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.672 G_ID: 0.276 G_Rec: 0.382 D_GP: 0.035 D_real: 1.086 D_fake: 0.605 
+(epoch: 39, iters: 2096, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.646 G_ID: 0.545 G_Rec: 0.354 D_GP: 0.051 D_real: 1.117 D_fake: 0.683 
+(epoch: 39, iters: 2496, time: 0.064) G_GAN: 0.614 G_GAN_Feat: 0.832 G_ID: 0.261 G_Rec: 0.428 D_GP: 0.061 D_real: 1.080 D_fake: 0.452 
+(epoch: 39, iters: 2896, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 1.056 G_ID: 0.602 G_Rec: 0.362 D_GP: 0.137 D_real: 0.461 D_fake: 0.729 
+(epoch: 39, iters: 3296, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 0.888 G_ID: 0.267 G_Rec: 0.410 D_GP: 0.049 D_real: 1.179 D_fake: 0.481 
+(epoch: 39, iters: 3696, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.980 G_ID: 0.566 G_Rec: 0.412 D_GP: 0.064 D_real: 0.568 D_fake: 0.774 
+(epoch: 39, iters: 4096, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 1.037 G_ID: 0.289 G_Rec: 0.383 D_GP: 0.318 D_real: 0.700 D_fake: 0.525 
+(epoch: 39, iters: 4496, time: 0.064) G_GAN: 0.705 G_GAN_Feat: 1.442 G_ID: 0.637 G_Rec: 0.478 D_GP: 0.615 D_real: 0.428 D_fake: 0.445 
+(epoch: 39, iters: 4896, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.706 G_ID: 0.283 G_Rec: 0.379 D_GP: 0.054 D_real: 1.123 D_fake: 0.509 
+(epoch: 39, iters: 5296, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.689 G_ID: 0.556 G_Rec: 0.351 D_GP: 0.034 D_real: 1.175 D_fake: 0.557 
+(epoch: 39, iters: 5696, time: 0.064) G_GAN: 0.540 G_GAN_Feat: 0.643 G_ID: 0.265 G_Rec: 0.363 D_GP: 0.020 D_real: 1.345 D_fake: 0.465 
+(epoch: 39, iters: 6096, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.656 G_ID: 0.538 G_Rec: 0.407 D_GP: 0.031 D_real: 0.949 D_fake: 0.816 
+(epoch: 39, iters: 6496, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 1.075 G_ID: 0.311 G_Rec: 0.395 D_GP: 0.058 D_real: 0.474 D_fake: 0.784 
+(epoch: 39, iters: 6896, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.823 G_ID: 0.567 G_Rec: 0.395 D_GP: 0.034 D_real: 0.967 D_fake: 0.700 
+(epoch: 39, iters: 7296, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.788 G_ID: 0.284 G_Rec: 0.495 D_GP: 0.067 D_real: 1.087 D_fake: 0.526 
+(epoch: 39, iters: 7696, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.745 G_ID: 0.590 G_Rec: 0.412 D_GP: 0.034 D_real: 1.015 D_fake: 0.711 
+(epoch: 39, iters: 8096, time: 0.064) G_GAN: 0.759 G_GAN_Feat: 0.898 G_ID: 0.273 G_Rec: 0.354 D_GP: 0.049 D_real: 1.493 D_fake: 0.267 
+(epoch: 39, iters: 8496, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.670 G_ID: 0.586 G_Rec: 0.378 D_GP: 0.019 D_real: 1.218 D_fake: 0.583 
+(epoch: 40, iters: 288, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.550 G_ID: 0.307 G_Rec: 0.362 D_GP: 0.025 D_real: 1.007 D_fake: 0.807 
+(epoch: 40, iters: 688, time: 0.064) G_GAN: 0.009 G_GAN_Feat: 0.607 G_ID: 0.588 G_Rec: 0.307 D_GP: 0.031 D_real: 0.897 D_fake: 0.991 
+(epoch: 40, iters: 1088, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.739 G_ID: 0.274 G_Rec: 0.434 D_GP: 0.040 D_real: 0.909 D_fake: 0.727 
+(epoch: 40, iters: 1488, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.697 G_ID: 0.578 G_Rec: 0.381 D_GP: 0.034 D_real: 1.072 D_fake: 0.823 
+(epoch: 40, iters: 1888, time: 0.064) G_GAN: 0.072 G_GAN_Feat: 0.829 G_ID: 0.353 G_Rec: 0.380 D_GP: 0.277 D_real: 0.459 D_fake: 0.930 
+(epoch: 40, iters: 2288, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 1.009 G_ID: 0.620 G_Rec: 0.361 D_GP: 0.127 D_real: 0.573 D_fake: 0.513 
+(epoch: 40, iters: 2688, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.710 G_ID: 0.319 G_Rec: 0.400 D_GP: 0.041 D_real: 0.946 D_fake: 0.874 
+(epoch: 40, iters: 3088, time: 0.064) G_GAN: 0.752 G_GAN_Feat: 1.033 G_ID: 0.526 G_Rec: 0.421 D_GP: 0.110 D_real: 1.194 D_fake: 0.294 
+(epoch: 40, iters: 3488, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.652 G_ID: 0.335 G_Rec: 0.347 D_GP: 0.022 D_real: 0.964 D_fake: 0.886 
+(epoch: 40, iters: 3888, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.883 G_ID: 0.605 G_Rec: 0.434 D_GP: 0.068 D_real: 1.105 D_fake: 0.666 
+(epoch: 40, iters: 4288, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.744 G_ID: 0.260 G_Rec: 0.374 D_GP: 0.055 D_real: 0.743 D_fake: 0.977 
+(epoch: 40, iters: 4688, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 1.020 G_ID: 0.575 G_Rec: 0.456 D_GP: 0.178 D_real: 0.675 D_fake: 0.480 
+(epoch: 40, iters: 5088, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.726 G_ID: 0.255 G_Rec: 0.426 D_GP: 0.021 D_real: 1.249 D_fake: 0.560 
+(epoch: 40, iters: 5488, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.644 G_ID: 0.572 G_Rec: 0.351 D_GP: 0.028 D_real: 1.092 D_fake: 0.692 
+(epoch: 40, iters: 5888, time: 0.064) G_GAN: 0.494 G_GAN_Feat: 0.607 G_ID: 0.277 G_Rec: 0.363 D_GP: 0.030 D_real: 1.312 D_fake: 0.511 
+(epoch: 40, iters: 6288, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.769 G_ID: 0.568 G_Rec: 0.382 D_GP: 0.044 D_real: 1.220 D_fake: 0.496 
+(epoch: 40, iters: 6688, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.802 G_ID: 0.297 G_Rec: 0.393 D_GP: 0.071 D_real: 0.971 D_fake: 0.662 
+(epoch: 40, iters: 7088, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.669 G_ID: 0.555 G_Rec: 0.397 D_GP: 0.029 D_real: 1.244 D_fake: 0.651 
+(epoch: 40, iters: 7488, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.754 G_ID: 0.293 G_Rec: 0.352 D_GP: 0.081 D_real: 0.932 D_fake: 0.747 
+(epoch: 40, iters: 7888, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 1.500 G_ID: 0.559 G_Rec: 0.404 D_GP: 1.063 D_real: 0.541 D_fake: 0.992 
+(epoch: 40, iters: 8288, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.859 G_ID: 0.273 G_Rec: 0.444 D_GP: 0.075 D_real: 0.813 D_fake: 0.668 
+(epoch: 41, iters: 80, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.745 G_ID: 0.553 G_Rec: 0.360 D_GP: 0.036 D_real: 1.219 D_fake: 0.583 
+(epoch: 41, iters: 480, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.708 G_ID: 0.356 G_Rec: 0.353 D_GP: 0.043 D_real: 0.949 D_fake: 0.722 
+(epoch: 41, iters: 880, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.886 G_ID: 0.614 G_Rec: 0.371 D_GP: 0.050 D_real: 0.831 D_fake: 0.783 
+(epoch: 41, iters: 1280, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.948 G_ID: 0.309 G_Rec: 0.342 D_GP: 0.095 D_real: 0.825 D_fake: 0.789 
+(epoch: 41, iters: 1680, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.756 G_ID: 0.557 G_Rec: 0.433 D_GP: 0.028 D_real: 1.328 D_fake: 0.441 
+(epoch: 41, iters: 2080, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.989 G_ID: 0.291 G_Rec: 0.402 D_GP: 0.042 D_real: 0.763 D_fake: 0.687 
+(epoch: 41, iters: 2480, time: 0.064) G_GAN: 0.494 G_GAN_Feat: 0.743 G_ID: 0.501 G_Rec: 0.341 D_GP: 0.053 D_real: 1.188 D_fake: 0.507 
+(epoch: 41, iters: 2880, time: 0.064) G_GAN: 0.054 G_GAN_Feat: 0.706 G_ID: 0.305 G_Rec: 0.346 D_GP: 0.040 D_real: 0.788 D_fake: 0.946 
+(epoch: 41, iters: 3280, time: 0.064) G_GAN: 0.564 G_GAN_Feat: 0.873 G_ID: 0.553 G_Rec: 0.367 D_GP: 0.184 D_real: 0.965 D_fake: 0.439 
+(epoch: 41, iters: 3680, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.642 G_ID: 0.333 G_Rec: 0.385 D_GP: 0.033 D_real: 1.080 D_fake: 0.671 
+(epoch: 41, iters: 4080, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.927 G_ID: 0.531 G_Rec: 0.420 D_GP: 0.099 D_real: 0.766 D_fake: 0.736 
+(epoch: 41, iters: 4480, time: 0.064) G_GAN: 0.183 G_GAN_Feat: 0.605 G_ID: 0.268 G_Rec: 0.339 D_GP: 0.023 D_real: 0.940 D_fake: 0.817 
+(epoch: 41, iters: 4880, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.792 G_ID: 0.538 G_Rec: 0.384 D_GP: 0.037 D_real: 0.934 D_fake: 0.617 
+(epoch: 41, iters: 5280, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.759 G_ID: 0.257 G_Rec: 0.384 D_GP: 0.069 D_real: 0.885 D_fake: 0.849 
+(epoch: 41, iters: 5680, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.716 G_ID: 0.579 G_Rec: 0.379 D_GP: 0.047 D_real: 1.044 D_fake: 0.749 
+(epoch: 41, iters: 6080, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 0.926 G_ID: 0.308 G_Rec: 0.376 D_GP: 0.092 D_real: 0.827 D_fake: 0.548 
+(epoch: 41, iters: 6480, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.611 G_ID: 0.595 G_Rec: 0.347 D_GP: 0.025 D_real: 1.286 D_fake: 0.618 
+(epoch: 41, iters: 6880, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.564 G_ID: 0.289 G_Rec: 0.394 D_GP: 0.026 D_real: 1.018 D_fake: 0.808 
+(epoch: 41, iters: 7280, time: 0.064) G_GAN: -0.129 G_GAN_Feat: 0.623 G_ID: 0.539 G_Rec: 0.398 D_GP: 0.039 D_real: 0.642 D_fake: 1.129 
+(epoch: 41, iters: 7680, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.701 G_ID: 0.272 G_Rec: 0.389 D_GP: 0.049 D_real: 0.887 D_fake: 0.770 
+(epoch: 41, iters: 8080, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.729 G_ID: 0.547 G_Rec: 0.340 D_GP: 0.045 D_real: 1.183 D_fake: 0.556 
+(epoch: 41, iters: 8480, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.777 G_ID: 0.270 G_Rec: 0.357 D_GP: 0.092 D_real: 0.812 D_fake: 0.651 
+(epoch: 42, iters: 272, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.884 G_ID: 0.574 G_Rec: 0.351 D_GP: 0.043 D_real: 0.659 D_fake: 0.838 
+(epoch: 42, iters: 672, time: 0.064) G_GAN: 0.010 G_GAN_Feat: 0.725 G_ID: 0.334 G_Rec: 0.379 D_GP: 0.045 D_real: 0.777 D_fake: 0.990 
+(epoch: 42, iters: 1072, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.828 G_ID: 0.619 G_Rec: 0.430 D_GP: 0.074 D_real: 0.809 D_fake: 0.812 
+(epoch: 42, iters: 1472, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.867 G_ID: 0.230 G_Rec: 0.412 D_GP: 0.041 D_real: 0.912 D_fake: 0.623 
+(epoch: 42, iters: 1872, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.787 G_ID: 0.592 G_Rec: 0.426 D_GP: 0.029 D_real: 1.134 D_fake: 0.626 
+(epoch: 42, iters: 2272, time: 0.064) G_GAN: 0.139 G_GAN_Feat: 0.782 G_ID: 0.286 G_Rec: 0.348 D_GP: 0.079 D_real: 0.671 D_fake: 0.861 
+(epoch: 42, iters: 2672, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.790 G_ID: 0.581 G_Rec: 0.352 D_GP: 0.055 D_real: 1.134 D_fake: 0.545 
+(epoch: 42, iters: 3072, time: 0.064) G_GAN: 0.666 G_GAN_Feat: 0.796 G_ID: 0.293 G_Rec: 0.368 D_GP: 0.039 D_real: 1.467 D_fake: 0.340 
+(epoch: 42, iters: 3472, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.918 G_ID: 0.569 G_Rec: 0.365 D_GP: 0.046 D_real: 1.139 D_fake: 0.542 
+(epoch: 42, iters: 3872, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.918 G_ID: 0.270 G_Rec: 0.384 D_GP: 0.088 D_real: 0.642 D_fake: 0.711 
+(epoch: 42, iters: 4272, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.811 G_ID: 0.530 G_Rec: 0.391 D_GP: 0.040 D_real: 1.056 D_fake: 0.621 
+(epoch: 42, iters: 4672, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.725 G_ID: 0.242 G_Rec: 0.467 D_GP: 0.029 D_real: 1.151 D_fake: 0.543 
+(epoch: 42, iters: 5072, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.593 G_ID: 0.477 G_Rec: 0.378 D_GP: 0.021 D_real: 1.218 D_fake: 0.653 
+(epoch: 42, iters: 5472, time: 0.064) G_GAN: 0.756 G_GAN_Feat: 0.670 G_ID: 0.245 G_Rec: 0.485 D_GP: 0.028 D_real: 1.460 D_fake: 0.285 
+(epoch: 42, iters: 5872, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.673 G_ID: 0.538 G_Rec: 0.379 D_GP: 0.036 D_real: 1.222 D_fake: 0.614 
+(epoch: 42, iters: 6272, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.784 G_ID: 0.270 G_Rec: 0.374 D_GP: 0.073 D_real: 0.743 D_fake: 0.747 
+(epoch: 42, iters: 6672, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.800 G_ID: 0.612 G_Rec: 0.356 D_GP: 0.040 D_real: 1.085 D_fake: 0.552 
+(epoch: 42, iters: 7072, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.783 G_ID: 0.272 G_Rec: 0.375 D_GP: 0.044 D_real: 0.992 D_fake: 0.650 
+(epoch: 42, iters: 7472, time: 0.064) G_GAN: 0.643 G_GAN_Feat: 0.717 G_ID: 0.571 G_Rec: 0.395 D_GP: 0.037 D_real: 1.357 D_fake: 0.359 
+(epoch: 42, iters: 7872, time: 0.064) G_GAN: -0.068 G_GAN_Feat: 0.970 G_ID: 0.309 G_Rec: 0.383 D_GP: 0.114 D_real: 0.531 D_fake: 1.068 
+(epoch: 42, iters: 8272, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.813 G_ID: 0.546 G_Rec: 0.379 D_GP: 0.038 D_real: 0.775 D_fake: 0.779 
+(epoch: 43, iters: 64, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.949 G_ID: 0.274 G_Rec: 0.371 D_GP: 0.158 D_real: 0.368 D_fake: 0.724 
+(epoch: 43, iters: 464, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.866 G_ID: 0.559 G_Rec: 0.412 D_GP: 0.029 D_real: 1.099 D_fake: 0.721 
+(epoch: 43, iters: 864, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.892 G_ID: 0.326 G_Rec: 0.349 D_GP: 0.142 D_real: 0.408 D_fake: 0.978 
+(epoch: 43, iters: 1264, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 1.030 G_ID: 0.534 G_Rec: 0.378 D_GP: 0.051 D_real: 0.789 D_fake: 0.570 
+(epoch: 43, iters: 1664, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 1.053 G_ID: 0.245 G_Rec: 0.387 D_GP: 0.066 D_real: 0.578 D_fake: 0.612 
+(epoch: 43, iters: 2064, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.696 G_ID: 0.551 G_Rec: 0.395 D_GP: 0.024 D_real: 1.132 D_fake: 0.688 
+(epoch: 43, iters: 2464, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.592 G_ID: 0.249 G_Rec: 0.381 D_GP: 0.028 D_real: 1.130 D_fake: 0.768 
+(epoch: 43, iters: 2864, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.670 G_ID: 0.523 G_Rec: 0.401 D_GP: 0.036 D_real: 0.943 D_fake: 0.843 
+(epoch: 43, iters: 3264, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.747 G_ID: 0.278 G_Rec: 0.383 D_GP: 0.049 D_real: 1.077 D_fake: 0.553 
+(epoch: 43, iters: 3664, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.843 G_ID: 0.551 G_Rec: 0.405 D_GP: 0.097 D_real: 0.691 D_fake: 0.844 
+(epoch: 43, iters: 4064, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.832 G_ID: 0.284 G_Rec: 0.337 D_GP: 0.045 D_real: 0.635 D_fake: 0.884 
+(epoch: 43, iters: 4464, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.720 G_ID: 0.612 G_Rec: 0.357 D_GP: 0.039 D_real: 0.978 D_fake: 0.747 
+(epoch: 43, iters: 4864, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.806 G_ID: 0.320 G_Rec: 0.349 D_GP: 0.050 D_real: 0.758 D_fake: 0.701 
+(epoch: 43, iters: 5264, time: 0.064) G_GAN: 0.576 G_GAN_Feat: 0.740 G_ID: 0.599 G_Rec: 0.365 D_GP: 0.028 D_real: 1.379 D_fake: 0.458 
+(epoch: 43, iters: 5664, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.707 G_ID: 0.301 G_Rec: 0.376 D_GP: 0.032 D_real: 1.169 D_fake: 0.570 
+(epoch: 43, iters: 6064, time: 0.064) G_GAN: 0.043 G_GAN_Feat: 0.771 G_ID: 0.579 G_Rec: 0.387 D_GP: 0.025 D_real: 0.894 D_fake: 0.957 
+(epoch: 43, iters: 6464, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.847 G_ID: 0.276 G_Rec: 0.363 D_GP: 0.066 D_real: 0.928 D_fake: 0.601 
+(epoch: 43, iters: 6864, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.790 G_ID: 0.541 G_Rec: 0.398 D_GP: 0.027 D_real: 1.122 D_fake: 0.579 
+(epoch: 43, iters: 7264, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 0.879 G_ID: 0.263 G_Rec: 0.401 D_GP: 0.070 D_real: 0.990 D_fake: 0.517 
+(epoch: 43, iters: 7664, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.800 G_ID: 0.567 G_Rec: 0.382 D_GP: 0.026 D_real: 1.146 D_fake: 0.624 
+(epoch: 43, iters: 8064, time: 0.064) G_GAN: 0.783 G_GAN_Feat: 0.805 G_ID: 0.304 G_Rec: 0.427 D_GP: 0.039 D_real: 1.600 D_fake: 0.322 
+(epoch: 43, iters: 8464, time: 0.064) G_GAN: 0.543 G_GAN_Feat: 1.147 G_ID: 0.551 G_Rec: 0.381 D_GP: 0.101 D_real: 0.519 D_fake: 0.459 
+(epoch: 44, iters: 256, time: 0.064) G_GAN: -0.319 G_GAN_Feat: 0.838 G_ID: 0.274 G_Rec: 0.414 D_GP: 0.044 D_real: 0.506 D_fake: 1.319 
+(epoch: 44, iters: 656, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.640 G_ID: 0.565 G_Rec: 0.494 D_GP: 0.029 D_real: 1.141 D_fake: 0.700 
+(epoch: 44, iters: 1056, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.762 G_ID: 0.270 G_Rec: 0.357 D_GP: 0.089 D_real: 1.047 D_fake: 0.725 
+(epoch: 44, iters: 1456, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.888 G_ID: 0.542 G_Rec: 0.366 D_GP: 0.047 D_real: 0.921 D_fake: 0.634 
+(epoch: 44, iters: 1856, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.972 G_ID: 0.250 G_Rec: 0.341 D_GP: 0.158 D_real: 0.333 D_fake: 0.870 
+(epoch: 44, iters: 2256, time: 0.064) G_GAN: 0.047 G_GAN_Feat: 0.906 G_ID: 0.545 G_Rec: 0.348 D_GP: 0.179 D_real: 0.440 D_fake: 0.954 
+(epoch: 44, iters: 2656, time: 0.064) G_GAN: 0.743 G_GAN_Feat: 0.981 G_ID: 0.305 G_Rec: 0.373 D_GP: 0.064 D_real: 1.328 D_fake: 0.395 
+(epoch: 44, iters: 3056, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.892 G_ID: 0.584 G_Rec: 0.356 D_GP: 0.056 D_real: 0.951 D_fake: 0.562 
+(epoch: 44, iters: 3456, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.856 G_ID: 0.249 G_Rec: 0.381 D_GP: 0.045 D_real: 1.105 D_fake: 0.533 
+(epoch: 44, iters: 3856, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.659 G_ID: 0.560 G_Rec: 0.380 D_GP: 0.022 D_real: 1.135 D_fake: 0.748 
+(epoch: 44, iters: 4256, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.613 G_ID: 0.339 G_Rec: 0.330 D_GP: 0.030 D_real: 1.084 D_fake: 0.682 
+(epoch: 44, iters: 4656, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.859 G_ID: 0.530 G_Rec: 0.441 D_GP: 0.115 D_real: 0.945 D_fake: 0.629 
+(epoch: 44, iters: 5056, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.904 G_ID: 0.276 G_Rec: 0.366 D_GP: 0.044 D_real: 1.178 D_fake: 0.482 
+(epoch: 44, iters: 5456, time: 0.064) G_GAN: 0.630 G_GAN_Feat: 1.709 G_ID: 0.619 G_Rec: 0.512 D_GP: 1.071 D_real: 0.515 D_fake: 0.386 
+(epoch: 44, iters: 5856, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.576 G_ID: 0.268 G_Rec: 0.462 D_GP: 0.024 D_real: 1.046 D_fake: 0.782 
+(epoch: 44, iters: 6256, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.716 G_ID: 0.534 G_Rec: 0.380 D_GP: 0.052 D_real: 1.075 D_fake: 0.696 
+(epoch: 44, iters: 6656, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 0.831 G_ID: 0.265 G_Rec: 0.392 D_GP: 0.060 D_real: 0.983 D_fake: 0.505 
+(epoch: 44, iters: 7056, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.688 G_ID: 0.564 G_Rec: 0.410 D_GP: 0.035 D_real: 1.057 D_fake: 0.709 
+(epoch: 44, iters: 7456, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.759 G_ID: 0.249 G_Rec: 0.337 D_GP: 0.054 D_real: 0.747 D_fake: 0.881 
+(epoch: 44, iters: 7856, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 1.039 G_ID: 0.544 G_Rec: 0.386 D_GP: 0.216 D_real: 0.652 D_fake: 0.631 
+(epoch: 44, iters: 8256, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.800 G_ID: 0.284 G_Rec: 0.517 D_GP: 0.064 D_real: 0.956 D_fake: 0.634 
+(epoch: 45, iters: 48, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.946 G_ID: 0.527 G_Rec: 0.361 D_GP: 0.063 D_real: 0.752 D_fake: 0.705 
+(epoch: 45, iters: 448, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 1.196 G_ID: 0.282 G_Rec: 0.395 D_GP: 0.291 D_real: 0.517 D_fake: 0.723 
+(epoch: 45, iters: 848, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.697 G_ID: 0.518 G_Rec: 0.358 D_GP: 0.027 D_real: 1.217 D_fake: 0.548 
+(epoch: 45, iters: 1248, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.773 G_ID: 0.272 G_Rec: 0.372 D_GP: 0.054 D_real: 0.952 D_fake: 0.599 
+(epoch: 45, iters: 1648, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.729 G_ID: 0.547 G_Rec: 0.378 D_GP: 0.034 D_real: 1.132 D_fake: 0.608 
+(epoch: 45, iters: 2048, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.727 G_ID: 0.242 G_Rec: 0.339 D_GP: 0.057 D_real: 0.894 D_fake: 0.747 
+(epoch: 45, iters: 2448, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.581 G_ID: 0.521 G_Rec: 0.373 D_GP: 0.020 D_real: 1.097 D_fake: 0.784 
+(epoch: 45, iters: 2848, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.805 G_ID: 0.274 G_Rec: 0.383 D_GP: 0.048 D_real: 0.923 D_fake: 0.668 
+(epoch: 45, iters: 3248, time: 0.064) G_GAN: 0.046 G_GAN_Feat: 1.211 G_ID: 0.516 G_Rec: 0.395 D_GP: 0.298 D_real: 0.423 D_fake: 0.955 
+(epoch: 45, iters: 3648, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.915 G_ID: 0.293 G_Rec: 0.399 D_GP: 0.131 D_real: 0.935 D_fake: 0.494 
+(epoch: 45, iters: 4048, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.843 G_ID: 0.525 G_Rec: 0.414 D_GP: 0.025 D_real: 1.214 D_fake: 0.608 
+(epoch: 45, iters: 4448, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.670 G_ID: 0.238 G_Rec: 0.374 D_GP: 0.029 D_real: 1.127 D_fake: 0.653 
+(epoch: 45, iters: 4848, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.800 G_ID: 0.537 G_Rec: 0.401 D_GP: 0.031 D_real: 1.296 D_fake: 0.540 
+(epoch: 45, iters: 5248, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 0.753 G_ID: 0.235 G_Rec: 0.348 D_GP: 0.070 D_real: 1.248 D_fake: 0.426 
+(epoch: 45, iters: 5648, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.842 G_ID: 0.543 G_Rec: 0.365 D_GP: 0.070 D_real: 0.910 D_fake: 0.615 
+(epoch: 45, iters: 6048, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 0.840 G_ID: 0.285 G_Rec: 0.386 D_GP: 0.043 D_real: 1.093 D_fake: 0.595 
+(epoch: 45, iters: 6448, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.708 G_ID: 0.583 G_Rec: 0.372 D_GP: 0.030 D_real: 0.958 D_fake: 0.799 
+(epoch: 45, iters: 6848, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.624 G_ID: 0.240 G_Rec: 0.309 D_GP: 0.025 D_real: 1.266 D_fake: 0.580 
+(epoch: 45, iters: 7248, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.876 G_ID: 0.562 G_Rec: 0.349 D_GP: 0.078 D_real: 0.929 D_fake: 0.610 
+(epoch: 45, iters: 7648, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.751 G_ID: 0.227 G_Rec: 0.391 D_GP: 0.032 D_real: 1.075 D_fake: 0.760 
+(epoch: 45, iters: 8048, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.790 G_ID: 0.537 G_Rec: 0.338 D_GP: 0.035 D_real: 1.024 D_fake: 0.720 
+(epoch: 45, iters: 8448, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.977 G_ID: 0.288 G_Rec: 0.408 D_GP: 0.111 D_real: 0.693 D_fake: 0.589 
+(epoch: 46, iters: 240, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.746 G_ID: 0.590 G_Rec: 0.378 D_GP: 0.038 D_real: 1.212 D_fake: 0.540 
+(epoch: 46, iters: 640, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.710 G_ID: 0.262 G_Rec: 0.334 D_GP: 0.032 D_real: 1.159 D_fake: 0.600 
+(epoch: 46, iters: 1040, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.614 G_ID: 0.548 G_Rec: 0.388 D_GP: 0.024 D_real: 1.389 D_fake: 0.476 
+(epoch: 46, iters: 1440, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.785 G_ID: 0.293 G_Rec: 0.366 D_GP: 0.065 D_real: 0.979 D_fake: 0.594 
+(epoch: 46, iters: 1840, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.885 G_ID: 0.500 G_Rec: 0.363 D_GP: 0.148 D_real: 0.852 D_fake: 0.779 
+(epoch: 46, iters: 2240, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.725 G_ID: 0.291 G_Rec: 0.364 D_GP: 0.035 D_real: 0.771 D_fake: 0.915 
+(epoch: 46, iters: 2640, time: 0.064) G_GAN: 0.560 G_GAN_Feat: 0.840 G_ID: 0.519 G_Rec: 0.390 D_GP: 0.054 D_real: 1.165 D_fake: 0.443 
+(epoch: 46, iters: 3040, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.737 G_ID: 0.312 G_Rec: 0.387 D_GP: 0.036 D_real: 0.983 D_fake: 0.670 
+(epoch: 46, iters: 3440, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.832 G_ID: 0.587 G_Rec: 0.373 D_GP: 0.049 D_real: 0.857 D_fake: 0.681 
+(epoch: 46, iters: 3840, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 1.066 G_ID: 0.217 G_Rec: 0.396 D_GP: 0.310 D_real: 0.677 D_fake: 0.592 
+(epoch: 46, iters: 4240, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.789 G_ID: 0.510 G_Rec: 0.369 D_GP: 0.041 D_real: 0.889 D_fake: 0.681 
+(epoch: 46, iters: 4640, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.673 G_ID: 0.279 G_Rec: 0.400 D_GP: 0.033 D_real: 0.994 D_fake: 0.789 
+(epoch: 46, iters: 5040, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.957 G_ID: 0.529 G_Rec: 0.402 D_GP: 0.057 D_real: 0.687 D_fake: 0.698 
+(epoch: 46, iters: 5440, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.902 G_ID: 0.279 G_Rec: 0.371 D_GP: 0.075 D_real: 0.806 D_fake: 0.654 
+(epoch: 46, iters: 5840, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.819 G_ID: 0.551 G_Rec: 0.368 D_GP: 0.042 D_real: 1.148 D_fake: 0.520 
+(epoch: 46, iters: 6240, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.694 G_ID: 0.250 G_Rec: 0.338 D_GP: 0.039 D_real: 1.129 D_fake: 0.639 
+(epoch: 46, iters: 6640, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.940 G_ID: 0.495 G_Rec: 0.397 D_GP: 0.102 D_real: 0.549 D_fake: 0.744 
+(epoch: 46, iters: 7040, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 1.309 G_ID: 0.266 G_Rec: 0.388 D_GP: 0.107 D_real: 0.531 D_fake: 0.392 
+(epoch: 46, iters: 7440, time: 0.064) G_GAN: 0.475 G_GAN_Feat: 0.832 G_ID: 0.544 G_Rec: 0.361 D_GP: 0.058 D_real: 1.193 D_fake: 0.530 
+(epoch: 46, iters: 7840, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 0.784 G_ID: 0.246 G_Rec: 0.346 D_GP: 0.036 D_real: 1.198 D_fake: 0.476 
+(epoch: 46, iters: 8240, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.892 G_ID: 0.519 G_Rec: 0.395 D_GP: 0.044 D_real: 0.696 D_fake: 0.818 
+(epoch: 47, iters: 32, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 1.384 G_ID: 0.272 G_Rec: 0.360 D_GP: 0.099 D_real: 0.215 D_fake: 0.807 
+(epoch: 47, iters: 432, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.855 G_ID: 0.567 G_Rec: 0.339 D_GP: 0.067 D_real: 0.971 D_fake: 0.660 
+(epoch: 47, iters: 832, time: 0.064) G_GAN: 0.577 G_GAN_Feat: 0.820 G_ID: 0.280 G_Rec: 0.409 D_GP: 0.047 D_real: 1.259 D_fake: 0.447 
+(epoch: 47, iters: 1232, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.738 G_ID: 0.516 G_Rec: 0.440 D_GP: 0.034 D_real: 0.795 D_fake: 0.938 
+(epoch: 47, iters: 1632, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.632 G_ID: 0.284 G_Rec: 0.347 D_GP: 0.026 D_real: 1.184 D_fake: 0.621 
+(epoch: 47, iters: 2032, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.737 G_ID: 0.554 G_Rec: 0.361 D_GP: 0.054 D_real: 1.182 D_fake: 0.578 
+(epoch: 47, iters: 2432, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.811 G_ID: 0.230 G_Rec: 0.440 D_GP: 0.045 D_real: 0.663 D_fake: 0.873 
+(epoch: 47, iters: 2832, time: 0.064) G_GAN: 0.841 G_GAN_Feat: 1.052 G_ID: 0.490 G_Rec: 0.380 D_GP: 0.080 D_real: 1.423 D_fake: 0.273 
+(epoch: 47, iters: 3232, time: 0.064) G_GAN: 0.470 G_GAN_Feat: 0.610 G_ID: 0.284 G_Rec: 0.351 D_GP: 0.022 D_real: 1.406 D_fake: 0.532 
+(epoch: 47, iters: 3632, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.635 G_ID: 0.523 G_Rec: 0.369 D_GP: 0.022 D_real: 1.206 D_fake: 0.651 
+(epoch: 47, iters: 4032, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.572 G_ID: 0.263 G_Rec: 0.374 D_GP: 0.024 D_real: 1.142 D_fake: 0.719 
+(epoch: 47, iters: 4432, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.650 G_ID: 0.561 G_Rec: 0.420 D_GP: 0.037 D_real: 0.874 D_fake: 0.877 
+(epoch: 47, iters: 4832, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.655 G_ID: 0.280 G_Rec: 0.417 D_GP: 0.041 D_real: 0.771 D_fake: 0.964 
+(epoch: 47, iters: 5232, time: 0.064) G_GAN: 0.444 G_GAN_Feat: 0.696 G_ID: 0.493 G_Rec: 0.358 D_GP: 0.042 D_real: 1.145 D_fake: 0.560 
+(epoch: 47, iters: 5632, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.751 G_ID: 0.288 G_Rec: 0.423 D_GP: 0.067 D_real: 0.772 D_fake: 0.822 
+(epoch: 47, iters: 6032, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.682 G_ID: 0.589 G_Rec: 0.370 D_GP: 0.036 D_real: 1.037 D_fake: 0.746 
+(epoch: 47, iters: 6432, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.705 G_ID: 0.288 G_Rec: 0.345 D_GP: 0.046 D_real: 1.023 D_fake: 0.678 
+(epoch: 47, iters: 6832, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 1.209 G_ID: 0.544 G_Rec: 0.363 D_GP: 0.480 D_real: 0.486 D_fake: 0.738 
+(epoch: 47, iters: 7232, time: 0.064) G_GAN: -0.062 G_GAN_Feat: 0.693 G_ID: 0.270 G_Rec: 0.365 D_GP: 0.041 D_real: 0.670 D_fake: 1.062 
+(epoch: 47, iters: 7632, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 1.098 G_ID: 0.542 G_Rec: 0.374 D_GP: 0.092 D_real: 0.430 D_fake: 0.767 
+(epoch: 47, iters: 8032, time: 0.064) G_GAN: 0.631 G_GAN_Feat: 0.796 G_ID: 0.259 G_Rec: 0.442 D_GP: 0.053 D_real: 1.214 D_fake: 0.376 
+(epoch: 47, iters: 8432, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.773 G_ID: 0.508 G_Rec: 0.379 D_GP: 0.039 D_real: 1.155 D_fake: 0.560 
+(epoch: 48, iters: 224, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.707 G_ID: 0.266 G_Rec: 0.444 D_GP: 0.029 D_real: 0.850 D_fake: 0.840 
+(epoch: 48, iters: 624, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 0.829 G_ID: 0.525 G_Rec: 0.380 D_GP: 0.041 D_real: 1.105 D_fake: 0.508 
+(epoch: 48, iters: 1024, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.648 G_ID: 0.262 G_Rec: 0.400 D_GP: 0.024 D_real: 0.996 D_fake: 0.829 
+(epoch: 48, iters: 1424, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.745 G_ID: 0.507 G_Rec: 0.369 D_GP: 0.029 D_real: 0.929 D_fake: 0.848 
+(epoch: 48, iters: 1824, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.698 G_ID: 0.249 G_Rec: 0.370 D_GP: 0.030 D_real: 1.020 D_fake: 0.826 
+(epoch: 48, iters: 2224, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.859 G_ID: 0.509 G_Rec: 0.384 D_GP: 0.052 D_real: 0.912 D_fake: 0.804 
+(epoch: 48, iters: 2624, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.919 G_ID: 0.302 G_Rec: 0.354 D_GP: 0.037 D_real: 1.114 D_fake: 0.807 
+(epoch: 48, iters: 3024, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 1.001 G_ID: 0.501 G_Rec: 0.375 D_GP: 0.047 D_real: 0.974 D_fake: 0.614 
+(epoch: 48, iters: 3424, time: 0.064) G_GAN: 0.509 G_GAN_Feat: 0.772 G_ID: 0.255 G_Rec: 0.339 D_GP: 0.049 D_real: 1.243 D_fake: 0.544 
+(epoch: 48, iters: 3824, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.798 G_ID: 0.565 G_Rec: 0.374 D_GP: 0.032 D_real: 1.082 D_fake: 0.793 
+(epoch: 48, iters: 4224, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.794 G_ID: 0.289 G_Rec: 0.339 D_GP: 0.044 D_real: 0.949 D_fake: 0.712 
+(epoch: 48, iters: 4624, time: 0.064) G_GAN: -0.050 G_GAN_Feat: 0.874 G_ID: 0.490 G_Rec: 0.388 D_GP: 0.037 D_real: 0.548 D_fake: 1.050 
+(epoch: 48, iters: 5024, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.708 G_ID: 0.249 G_Rec: 0.346 D_GP: 0.023 D_real: 1.069 D_fake: 0.705 
+(epoch: 48, iters: 5424, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.704 G_ID: 0.496 G_Rec: 0.333 D_GP: 0.037 D_real: 0.955 D_fake: 0.875 
+(epoch: 48, iters: 5824, time: 0.064) G_GAN: -0.213 G_GAN_Feat: 0.858 G_ID: 0.253 G_Rec: 0.376 D_GP: 0.047 D_real: 0.513 D_fake: 1.213 
+(epoch: 48, iters: 6224, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.918 G_ID: 0.493 G_Rec: 0.348 D_GP: 0.052 D_real: 1.234 D_fake: 0.582 
+(epoch: 48, iters: 6624, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.590 G_ID: 0.259 G_Rec: 0.328 D_GP: 0.022 D_real: 1.079 D_fake: 0.789 
+(epoch: 48, iters: 7024, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.682 G_ID: 0.513 G_Rec: 0.458 D_GP: 0.026 D_real: 1.002 D_fake: 0.741 
+(epoch: 48, iters: 7424, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.721 G_ID: 0.291 G_Rec: 0.370 D_GP: 0.053 D_real: 0.915 D_fake: 0.812 
+(epoch: 48, iters: 7824, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.796 G_ID: 0.565 G_Rec: 0.380 D_GP: 0.039 D_real: 0.992 D_fake: 0.678 
+(epoch: 48, iters: 8224, time: 0.064) G_GAN: 0.666 G_GAN_Feat: 1.164 G_ID: 0.272 G_Rec: 0.438 D_GP: 0.114 D_real: 1.092 D_fake: 0.339 
+(epoch: 49, iters: 16, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.975 G_ID: 0.546 G_Rec: 0.350 D_GP: 0.107 D_real: 0.411 D_fake: 0.894 
+(epoch: 49, iters: 416, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.847 G_ID: 0.270 G_Rec: 0.400 D_GP: 0.046 D_real: 1.108 D_fake: 0.511 
+(epoch: 49, iters: 816, time: 0.064) G_GAN: 0.659 G_GAN_Feat: 0.967 G_ID: 0.535 G_Rec: 0.347 D_GP: 0.160 D_real: 0.997 D_fake: 0.448 
+(epoch: 49, iters: 1216, time: 0.064) G_GAN: 0.771 G_GAN_Feat: 0.986 G_ID: 0.304 G_Rec: 0.406 D_GP: 0.102 D_real: 1.035 D_fake: 0.256 
+(epoch: 49, iters: 1616, time: 0.064) G_GAN: 0.767 G_GAN_Feat: 1.042 G_ID: 0.509 G_Rec: 0.479 D_GP: 0.073 D_real: 1.235 D_fake: 0.446 
+(epoch: 49, iters: 2016, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.850 G_ID: 0.267 G_Rec: 0.348 D_GP: 0.060 D_real: 0.860 D_fake: 0.638 
+(epoch: 49, iters: 2416, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.890 G_ID: 0.528 G_Rec: 0.399 D_GP: 0.071 D_real: 0.779 D_fake: 0.684 
+(epoch: 49, iters: 2816, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.717 G_ID: 0.291 G_Rec: 0.369 D_GP: 0.029 D_real: 1.219 D_fake: 0.541 
+(epoch: 49, iters: 3216, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.637 G_ID: 0.534 G_Rec: 0.400 D_GP: 0.025 D_real: 1.210 D_fake: 0.660 
+(epoch: 49, iters: 3616, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.547 G_ID: 0.233 G_Rec: 0.489 D_GP: 0.020 D_real: 1.155 D_fake: 0.595 
+(epoch: 49, iters: 4016, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.598 G_ID: 0.532 G_Rec: 0.349 D_GP: 0.022 D_real: 1.290 D_fake: 0.656 
+(epoch: 49, iters: 4416, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.672 G_ID: 0.232 G_Rec: 0.346 D_GP: 0.030 D_real: 1.038 D_fake: 0.722 
+(epoch: 49, iters: 4816, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.753 G_ID: 0.481 G_Rec: 0.338 D_GP: 0.076 D_real: 0.857 D_fake: 0.728 
+(epoch: 49, iters: 5216, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.821 G_ID: 0.268 G_Rec: 0.384 D_GP: 0.066 D_real: 1.075 D_fake: 0.631 
+(epoch: 49, iters: 5616, time: 0.064) G_GAN: 0.721 G_GAN_Feat: 0.890 G_ID: 0.568 G_Rec: 0.440 D_GP: 0.084 D_real: 1.115 D_fake: 0.443 
+(epoch: 49, iters: 6016, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.725 G_ID: 0.233 G_Rec: 0.356 D_GP: 0.042 D_real: 1.021 D_fake: 0.754 
+(epoch: 49, iters: 6416, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.695 G_ID: 0.531 G_Rec: 0.359 D_GP: 0.030 D_real: 0.847 D_fake: 0.912 
+(epoch: 49, iters: 6816, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 0.672 G_ID: 0.262 G_Rec: 0.363 D_GP: 0.029 D_real: 1.305 D_fake: 0.512 
+(epoch: 49, iters: 7216, time: 0.064) G_GAN: 0.635 G_GAN_Feat: 0.903 G_ID: 0.488 G_Rec: 0.408 D_GP: 0.064 D_real: 1.338 D_fake: 0.386 
+(epoch: 49, iters: 7616, time: 0.064) G_GAN: 0.470 G_GAN_Feat: 0.694 G_ID: 0.277 G_Rec: 0.373 D_GP: 0.030 D_real: 1.190 D_fake: 0.531 
+(epoch: 49, iters: 8016, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.715 G_ID: 0.544 G_Rec: 0.337 D_GP: 0.029 D_real: 1.112 D_fake: 0.683 
+(epoch: 49, iters: 8416, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.987 G_ID: 0.242 G_Rec: 0.364 D_GP: 0.061 D_real: 0.585 D_fake: 0.675 
+(epoch: 50, iters: 208, time: 0.064) G_GAN: 0.761 G_GAN_Feat: 1.155 G_ID: 0.451 G_Rec: 0.411 D_GP: 0.059 D_real: 1.154 D_fake: 0.385 
+(epoch: 50, iters: 608, time: 0.064) G_GAN: 0.061 G_GAN_Feat: 0.697 G_ID: 0.263 G_Rec: 0.403 D_GP: 0.031 D_real: 0.892 D_fake: 0.940 
+(epoch: 50, iters: 1008, time: 0.064) G_GAN: 0.694 G_GAN_Feat: 0.848 G_ID: 0.512 G_Rec: 0.374 D_GP: 0.045 D_real: 1.268 D_fake: 0.335 
+(epoch: 50, iters: 1408, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.782 G_ID: 0.242 G_Rec: 0.369 D_GP: 0.037 D_real: 1.045 D_fake: 0.548 
+(epoch: 50, iters: 1808, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.807 G_ID: 0.550 G_Rec: 0.335 D_GP: 0.049 D_real: 1.120 D_fake: 0.490 
+(epoch: 50, iters: 2208, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.766 G_ID: 0.274 G_Rec: 0.453 D_GP: 0.047 D_real: 1.260 D_fake: 0.493 
+(epoch: 50, iters: 2608, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 1.035 G_ID: 0.502 G_Rec: 0.459 D_GP: 0.032 D_real: 1.054 D_fake: 0.881 
+(epoch: 50, iters: 3008, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.629 G_ID: 0.241 G_Rec: 0.379 D_GP: 0.020 D_real: 1.200 D_fake: 0.645 
+(epoch: 50, iters: 3408, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.625 G_ID: 0.520 G_Rec: 0.350 D_GP: 0.021 D_real: 0.994 D_fake: 0.889 
+(epoch: 50, iters: 3808, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.656 G_ID: 0.262 G_Rec: 0.343 D_GP: 0.029 D_real: 1.054 D_fake: 0.771 
+(epoch: 50, iters: 4208, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.766 G_ID: 0.501 G_Rec: 0.360 D_GP: 0.031 D_real: 1.136 D_fake: 0.689 
+(epoch: 50, iters: 4608, time: 0.064) G_GAN: 0.600 G_GAN_Feat: 0.793 G_ID: 0.275 G_Rec: 0.451 D_GP: 0.059 D_real: 1.283 D_fake: 0.408 
+(epoch: 50, iters: 5008, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.783 G_ID: 0.516 G_Rec: 0.404 D_GP: 0.083 D_real: 0.795 D_fake: 0.937 
+(epoch: 50, iters: 5408, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 1.103 G_ID: 0.254 G_Rec: 0.388 D_GP: 0.080 D_real: 0.584 D_fake: 0.691 
+(epoch: 50, iters: 5808, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 1.068 G_ID: 0.525 G_Rec: 0.456 D_GP: 0.092 D_real: 0.743 D_fake: 0.614 
+(epoch: 50, iters: 6208, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.743 G_ID: 0.227 G_Rec: 0.373 D_GP: 0.045 D_real: 0.978 D_fake: 0.738 
+(epoch: 50, iters: 6608, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.809 G_ID: 0.481 G_Rec: 0.393 D_GP: 0.062 D_real: 0.665 D_fake: 0.881 
+(epoch: 50, iters: 7008, time: 0.064) G_GAN: 0.774 G_GAN_Feat: 1.046 G_ID: 0.266 G_Rec: 0.502 D_GP: 0.282 D_real: 1.175 D_fake: 0.273 
+(epoch: 50, iters: 7408, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 1.229 G_ID: 0.510 G_Rec: 0.425 D_GP: 0.146 D_real: 0.299 D_fake: 0.854 
+(epoch: 50, iters: 7808, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.825 G_ID: 0.296 G_Rec: 0.369 D_GP: 0.052 D_real: 0.932 D_fake: 0.706 
+(epoch: 50, iters: 8208, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.771 G_ID: 0.524 G_Rec: 0.348 D_GP: 0.027 D_real: 1.042 D_fake: 0.700 
+(epoch: 50, iters: 8608, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.723 G_ID: 0.267 G_Rec: 0.377 D_GP: 0.039 D_real: 0.898 D_fake: 0.763 
+(epoch: 51, iters: 400, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.875 G_ID: 0.466 G_Rec: 0.363 D_GP: 0.048 D_real: 0.627 D_fake: 0.882 
+(epoch: 51, iters: 800, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.845 G_ID: 0.285 G_Rec: 0.368 D_GP: 0.060 D_real: 0.774 D_fake: 0.665 
+(epoch: 51, iters: 1200, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.790 G_ID: 0.540 G_Rec: 0.419 D_GP: 0.038 D_real: 1.254 D_fake: 0.495 
+(epoch: 51, iters: 1600, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 1.325 G_ID: 0.213 G_Rec: 0.374 D_GP: 0.357 D_real: 0.511 D_fake: 0.750 
+(epoch: 51, iters: 2000, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.620 G_ID: 0.482 G_Rec: 0.383 D_GP: 0.024 D_real: 1.238 D_fake: 0.570 
+(epoch: 51, iters: 2400, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 0.851 G_ID: 0.265 G_Rec: 0.330 D_GP: 0.033 D_real: 1.379 D_fake: 0.470 
+(epoch: 51, iters: 2800, time: 0.064) G_GAN: -0.086 G_GAN_Feat: 0.759 G_ID: 0.498 G_Rec: 0.432 D_GP: 0.047 D_real: 0.652 D_fake: 1.086 
+(epoch: 51, iters: 3200, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 0.832 G_ID: 0.222 G_Rec: 0.388 D_GP: 0.041 D_real: 1.068 D_fake: 0.561 
+(epoch: 51, iters: 3600, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 0.801 G_ID: 0.501 G_Rec: 0.419 D_GP: 0.059 D_real: 1.270 D_fake: 0.377 
+(epoch: 51, iters: 4000, time: 0.064) G_GAN: 0.709 G_GAN_Feat: 0.889 G_ID: 0.224 G_Rec: 0.439 D_GP: 0.080 D_real: 1.144 D_fake: 0.310 
+(epoch: 51, iters: 4400, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.819 G_ID: 0.439 G_Rec: 0.366 D_GP: 0.064 D_real: 0.936 D_fake: 0.801 
+(epoch: 51, iters: 4800, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.873 G_ID: 0.238 G_Rec: 0.401 D_GP: 0.037 D_real: 1.138 D_fake: 0.562 
+(epoch: 51, iters: 5200, time: 0.064) G_GAN: 0.911 G_GAN_Feat: 0.749 G_ID: 0.470 G_Rec: 0.429 D_GP: 0.026 D_real: 1.645 D_fake: 0.219 
+(epoch: 51, iters: 5600, time: 0.064) G_GAN: 0.473 G_GAN_Feat: 0.685 G_ID: 0.221 G_Rec: 0.444 D_GP: 0.024 D_real: 1.258 D_fake: 0.527 
+(epoch: 51, iters: 6000, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.823 G_ID: 0.550 G_Rec: 0.430 D_GP: 0.040 D_real: 1.012 D_fake: 0.657 
+(epoch: 51, iters: 6400, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.886 G_ID: 0.248 G_Rec: 0.370 D_GP: 0.107 D_real: 0.782 D_fake: 0.625 
+(epoch: 51, iters: 6800, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 1.157 G_ID: 0.510 G_Rec: 0.372 D_GP: 0.621 D_real: 0.273 D_fake: 0.706 
+(epoch: 51, iters: 7200, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 1.245 G_ID: 0.248 G_Rec: 0.374 D_GP: 0.592 D_real: 0.356 D_fake: 0.751 
+(epoch: 51, iters: 7600, time: 0.064) G_GAN: 0.815 G_GAN_Feat: 0.930 G_ID: 0.514 G_Rec: 0.379 D_GP: 0.039 D_real: 1.413 D_fake: 0.335 
+(epoch: 51, iters: 8000, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.861 G_ID: 0.319 G_Rec: 0.376 D_GP: 0.032 D_real: 1.088 D_fake: 0.602 
+(epoch: 51, iters: 8400, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 1.222 G_ID: 0.542 G_Rec: 0.401 D_GP: 0.142 D_real: 0.348 D_fake: 0.734 
+(epoch: 52, iters: 192, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.906 G_ID: 0.269 G_Rec: 0.360 D_GP: 0.045 D_real: 1.082 D_fake: 0.525 
+(epoch: 52, iters: 592, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.888 G_ID: 0.520 G_Rec: 0.377 D_GP: 0.067 D_real: 0.926 D_fake: 0.611 
+(epoch: 52, iters: 992, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 1.152 G_ID: 0.283 G_Rec: 0.411 D_GP: 0.137 D_real: 0.351 D_fake: 0.859 
+(epoch: 52, iters: 1392, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 1.085 G_ID: 0.502 G_Rec: 0.390 D_GP: 0.085 D_real: 0.651 D_fake: 0.613 
+(epoch: 52, iters: 1792, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.931 G_ID: 0.246 G_Rec: 0.413 D_GP: 0.028 D_real: 1.224 D_fake: 0.548 
+(epoch: 52, iters: 2192, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.798 G_ID: 0.542 G_Rec: 0.355 D_GP: 0.025 D_real: 1.119 D_fake: 0.621 
+(epoch: 52, iters: 2592, time: 0.064) G_GAN: 0.063 G_GAN_Feat: 0.989 G_ID: 0.267 G_Rec: 0.371 D_GP: 0.116 D_real: 0.407 D_fake: 0.937 
+(epoch: 52, iters: 2992, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.771 G_ID: 0.517 G_Rec: 0.388 D_GP: 0.026 D_real: 1.271 D_fake: 0.622 
+(epoch: 52, iters: 3392, time: 0.064) G_GAN: -0.042 G_GAN_Feat: 0.522 G_ID: 0.267 G_Rec: 0.343 D_GP: 0.022 D_real: 0.939 D_fake: 1.042 
+(epoch: 52, iters: 3792, time: 0.064) G_GAN: 0.623 G_GAN_Feat: 0.600 G_ID: 0.545 G_Rec: 0.397 D_GP: 0.018 D_real: 1.488 D_fake: 0.432 
+(epoch: 52, iters: 4192, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.541 G_ID: 0.216 G_Rec: 0.387 D_GP: 0.020 D_real: 1.044 D_fake: 0.826 
+(epoch: 52, iters: 4592, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.606 G_ID: 0.476 G_Rec: 0.510 D_GP: 0.026 D_real: 1.064 D_fake: 0.754 
+(epoch: 52, iters: 4992, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.671 G_ID: 0.267 G_Rec: 0.382 D_GP: 0.032 D_real: 1.036 D_fake: 0.678 
+(epoch: 52, iters: 5392, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.642 G_ID: 0.547 G_Rec: 0.364 D_GP: 0.044 D_real: 0.896 D_fake: 0.822 
+(epoch: 52, iters: 5792, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.637 G_ID: 0.230 G_Rec: 0.315 D_GP: 0.045 D_real: 1.062 D_fake: 0.659 
+(epoch: 52, iters: 6192, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.760 G_ID: 0.512 G_Rec: 0.393 D_GP: 0.051 D_real: 0.845 D_fake: 0.838 
+(epoch: 52, iters: 6592, time: 0.064) G_GAN: -0.081 G_GAN_Feat: 0.839 G_ID: 0.228 G_Rec: 0.446 D_GP: 0.080 D_real: 0.308 D_fake: 1.081 
+(epoch: 52, iters: 6992, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.919 G_ID: 0.532 G_Rec: 0.361 D_GP: 0.069 D_real: 0.669 D_fake: 0.772 
+(epoch: 52, iters: 7392, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.648 G_ID: 0.265 G_Rec: 0.394 D_GP: 0.025 D_real: 1.012 D_fake: 0.751 
+(epoch: 52, iters: 7792, time: 0.064) G_GAN: 0.595 G_GAN_Feat: 0.771 G_ID: 0.502 G_Rec: 0.382 D_GP: 0.055 D_real: 1.312 D_fake: 0.408 
+(epoch: 52, iters: 8192, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.873 G_ID: 0.254 G_Rec: 0.503 D_GP: 0.039 D_real: 1.159 D_fake: 0.600 
+(epoch: 52, iters: 8592, time: 0.064) G_GAN: 0.562 G_GAN_Feat: 0.733 G_ID: 0.488 G_Rec: 0.353 D_GP: 0.044 D_real: 1.305 D_fake: 0.461 
+(epoch: 53, iters: 384, time: 0.064) G_GAN: 0.060 G_GAN_Feat: 1.258 G_ID: 0.229 G_Rec: 0.350 D_GP: 0.112 D_real: 0.348 D_fake: 0.940 
+(epoch: 53, iters: 784, time: 0.064) G_GAN: 0.480 G_GAN_Feat: 0.709 G_ID: 0.477 G_Rec: 0.430 D_GP: 0.030 D_real: 1.341 D_fake: 0.521 
+(epoch: 53, iters: 1184, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.882 G_ID: 0.256 G_Rec: 0.395 D_GP: 0.048 D_real: 1.080 D_fake: 0.594 
+(epoch: 53, iters: 1584, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.823 G_ID: 0.520 G_Rec: 0.407 D_GP: 0.030 D_real: 1.040 D_fake: 0.664 
+(epoch: 53, iters: 1984, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.684 G_ID: 0.248 G_Rec: 0.419 D_GP: 0.027 D_real: 1.025 D_fake: 0.729 
+(epoch: 53, iters: 2384, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.832 G_ID: 0.508 G_Rec: 0.368 D_GP: 0.054 D_real: 0.931 D_fake: 0.711 
+(epoch: 53, iters: 2784, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 1.146 G_ID: 0.295 G_Rec: 0.393 D_GP: 0.055 D_real: 0.469 D_fake: 0.583 
+(epoch: 53, iters: 3184, time: 0.064) G_GAN: 0.725 G_GAN_Feat: 0.985 G_ID: 0.504 G_Rec: 0.372 D_GP: 0.090 D_real: 1.224 D_fake: 0.311 
+(epoch: 53, iters: 3584, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 0.745 G_ID: 0.242 G_Rec: 0.395 D_GP: 0.035 D_real: 1.345 D_fake: 0.435 
+(epoch: 53, iters: 3984, time: 0.064) G_GAN: 0.735 G_GAN_Feat: 1.027 G_ID: 0.510 G_Rec: 0.418 D_GP: 0.033 D_real: 1.309 D_fake: 0.503 
+(epoch: 53, iters: 4384, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.953 G_ID: 0.252 G_Rec: 0.383 D_GP: 0.051 D_real: 0.905 D_fake: 0.617 
+(epoch: 53, iters: 4784, time: 0.064) G_GAN: 1.063 G_GAN_Feat: 0.949 G_ID: 0.446 G_Rec: 0.391 D_GP: 0.096 D_real: 1.526 D_fake: 0.229 
+(epoch: 53, iters: 5184, time: 0.064) G_GAN: 0.582 G_GAN_Feat: 0.802 G_ID: 0.201 G_Rec: 0.426 D_GP: 0.040 D_real: 1.252 D_fake: 0.429 
+(epoch: 53, iters: 5584, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.726 G_ID: 0.509 G_Rec: 0.349 D_GP: 0.028 D_real: 1.211 D_fake: 0.572 
+(epoch: 53, iters: 5984, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.874 G_ID: 0.246 G_Rec: 0.405 D_GP: 0.037 D_real: 0.928 D_fake: 0.618 
+(epoch: 53, iters: 6384, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.832 G_ID: 0.495 G_Rec: 0.351 D_GP: 0.035 D_real: 1.194 D_fake: 0.594 
+(epoch: 53, iters: 6784, time: 0.064) G_GAN: 0.811 G_GAN_Feat: 0.852 G_ID: 0.275 G_Rec: 0.361 D_GP: 0.066 D_real: 1.361 D_fake: 0.293 
+(epoch: 53, iters: 7184, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.768 G_ID: 0.456 G_Rec: 0.363 D_GP: 0.056 D_real: 0.838 D_fake: 0.795 
+(epoch: 53, iters: 7584, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.725 G_ID: 0.235 G_Rec: 0.460 D_GP: 0.022 D_real: 0.962 D_fake: 0.845 
+(epoch: 53, iters: 7984, time: 0.064) G_GAN: 0.044 G_GAN_Feat: 0.971 G_ID: 0.470 G_Rec: 0.385 D_GP: 0.175 D_real: 0.508 D_fake: 0.956 
+(epoch: 53, iters: 8384, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 1.015 G_ID: 0.219 G_Rec: 0.455 D_GP: 0.108 D_real: 0.558 D_fake: 0.630 
+(epoch: 54, iters: 176, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.934 G_ID: 0.483 G_Rec: 0.427 D_GP: 0.032 D_real: 0.833 D_fake: 0.844 
+(epoch: 54, iters: 576, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 0.846 G_ID: 0.226 G_Rec: 0.356 D_GP: 0.046 D_real: 1.087 D_fake: 0.501 
+(epoch: 54, iters: 976, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.828 G_ID: 0.484 G_Rec: 0.412 D_GP: 0.026 D_real: 0.981 D_fake: 0.813 
+(epoch: 54, iters: 1376, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 0.666 G_ID: 0.228 G_Rec: 0.345 D_GP: 0.027 D_real: 1.301 D_fake: 0.479 
+(epoch: 54, iters: 1776, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.856 G_ID: 0.507 G_Rec: 0.419 D_GP: 0.039 D_real: 1.230 D_fake: 0.506 
+(epoch: 54, iters: 2176, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.843 G_ID: 0.238 G_Rec: 0.408 D_GP: 0.068 D_real: 1.014 D_fake: 0.624 
+(epoch: 54, iters: 2576, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.688 G_ID: 0.473 G_Rec: 0.426 D_GP: 0.020 D_real: 1.334 D_fake: 0.594 
+(epoch: 54, iters: 2976, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.656 G_ID: 0.221 G_Rec: 0.390 D_GP: 0.025 D_real: 1.078 D_fake: 0.729 
+(epoch: 54, iters: 3376, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.745 G_ID: 0.512 G_Rec: 0.398 D_GP: 0.043 D_real: 0.995 D_fake: 0.719 
+(epoch: 54, iters: 3776, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.791 G_ID: 0.213 G_Rec: 0.384 D_GP: 0.057 D_real: 0.849 D_fake: 0.717 
+(epoch: 54, iters: 4176, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 1.036 G_ID: 0.472 G_Rec: 0.432 D_GP: 0.039 D_real: 0.736 D_fake: 0.659 
+(epoch: 54, iters: 4576, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.872 G_ID: 0.212 G_Rec: 0.389 D_GP: 0.033 D_real: 1.123 D_fake: 0.577 
+(epoch: 54, iters: 4976, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.910 G_ID: 0.491 G_Rec: 0.376 D_GP: 0.094 D_real: 0.729 D_fake: 0.716 
+(epoch: 54, iters: 5376, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.558 G_ID: 0.238 G_Rec: 0.344 D_GP: 0.020 D_real: 1.082 D_fake: 0.852 
+(epoch: 54, iters: 5776, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.680 G_ID: 0.514 G_Rec: 0.369 D_GP: 0.029 D_real: 1.007 D_fake: 0.739 
+(epoch: 54, iters: 6176, time: 0.064) G_GAN: -0.017 G_GAN_Feat: 0.749 G_ID: 0.268 G_Rec: 0.467 D_GP: 0.067 D_real: 0.560 D_fake: 1.017 
+(epoch: 54, iters: 6576, time: 0.064) G_GAN: -0.172 G_GAN_Feat: 0.793 G_ID: 0.486 G_Rec: 0.363 D_GP: 0.238 D_real: 0.646 D_fake: 1.172 
+(epoch: 54, iters: 6976, time: 0.064) G_GAN: -0.296 G_GAN_Feat: 0.828 G_ID: 0.218 G_Rec: 0.425 D_GP: 0.108 D_real: 0.515 D_fake: 1.296 
+(epoch: 54, iters: 7376, time: 0.064) G_GAN: 0.755 G_GAN_Feat: 0.721 G_ID: 0.467 G_Rec: 0.364 D_GP: 0.034 D_real: 1.498 D_fake: 0.279 
+(epoch: 54, iters: 7776, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.761 G_ID: 0.233 G_Rec: 0.391 D_GP: 0.040 D_real: 1.053 D_fake: 0.684 
+(epoch: 54, iters: 8176, time: 0.064) G_GAN: 0.462 G_GAN_Feat: 1.009 G_ID: 0.442 G_Rec: 0.413 D_GP: 0.057 D_real: 0.768 D_fake: 0.540 
+(epoch: 54, iters: 8576, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.666 G_ID: 0.258 G_Rec: 0.378 D_GP: 0.030 D_real: 1.099 D_fake: 0.666 
+(epoch: 55, iters: 368, time: 0.064) G_GAN: 0.631 G_GAN_Feat: 0.889 G_ID: 0.476 G_Rec: 0.411 D_GP: 0.060 D_real: 1.222 D_fake: 0.456 
+(epoch: 55, iters: 768, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 1.137 G_ID: 0.233 G_Rec: 0.403 D_GP: 0.449 D_real: 0.636 D_fake: 0.669 
+(epoch: 55, iters: 1168, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 1.004 G_ID: 0.506 G_Rec: 0.378 D_GP: 0.063 D_real: 0.683 D_fake: 0.676 
+(epoch: 55, iters: 1568, time: 0.064) G_GAN: 0.550 G_GAN_Feat: 0.932 G_ID: 0.241 G_Rec: 0.357 D_GP: 0.063 D_real: 0.990 D_fake: 0.468 
+(epoch: 55, iters: 1968, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.804 G_ID: 0.500 G_Rec: 0.406 D_GP: 0.037 D_real: 0.983 D_fake: 0.754 
+(epoch: 55, iters: 2368, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.752 G_ID: 0.256 G_Rec: 0.362 D_GP: 0.031 D_real: 1.103 D_fake: 0.630 
+(epoch: 55, iters: 2768, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.820 G_ID: 0.454 G_Rec: 0.493 D_GP: 0.056 D_real: 0.712 D_fake: 0.907 
+(epoch: 55, iters: 3168, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 0.818 G_ID: 0.319 G_Rec: 0.368 D_GP: 0.042 D_real: 1.191 D_fake: 0.508 
+(epoch: 55, iters: 3568, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.788 G_ID: 0.458 G_Rec: 0.345 D_GP: 0.030 D_real: 0.966 D_fake: 0.717 
+(epoch: 55, iters: 3968, time: 0.064) G_GAN: 0.965 G_GAN_Feat: 0.896 G_ID: 0.214 G_Rec: 0.382 D_GP: 0.037 D_real: 1.536 D_fake: 0.297 
+(epoch: 55, iters: 4368, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.824 G_ID: 0.454 G_Rec: 0.382 D_GP: 0.020 D_real: 1.041 D_fake: 0.755 
+(epoch: 55, iters: 4768, time: 0.064) G_GAN: -0.098 G_GAN_Feat: 0.719 G_ID: 0.261 G_Rec: 0.372 D_GP: 0.026 D_real: 0.637 D_fake: 1.098 
+(epoch: 55, iters: 5168, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 0.839 G_ID: 0.481 G_Rec: 0.378 D_GP: 0.077 D_real: 0.654 D_fake: 0.943 
+(epoch: 55, iters: 5568, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.633 G_ID: 0.220 G_Rec: 0.336 D_GP: 0.027 D_real: 1.162 D_fake: 0.666 
+(epoch: 55, iters: 5968, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.836 G_ID: 0.452 G_Rec: 0.390 D_GP: 0.073 D_real: 0.926 D_fake: 0.672 
+(epoch: 55, iters: 6368, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.854 G_ID: 0.275 G_Rec: 0.428 D_GP: 0.051 D_real: 0.803 D_fake: 0.766 
+(epoch: 55, iters: 6768, time: 0.064) G_GAN: 0.764 G_GAN_Feat: 0.690 G_ID: 0.449 G_Rec: 0.524 D_GP: 0.022 D_real: 1.564 D_fake: 0.344 
+(epoch: 55, iters: 7168, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 1.077 G_ID: 0.261 G_Rec: 0.375 D_GP: 0.046 D_real: 0.773 D_fake: 0.735 
+(epoch: 55, iters: 7568, time: 0.064) G_GAN: 0.622 G_GAN_Feat: 0.777 G_ID: 0.496 G_Rec: 0.378 D_GP: 0.037 D_real: 1.248 D_fake: 0.382 
+(epoch: 55, iters: 7968, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.775 G_ID: 0.271 G_Rec: 0.321 D_GP: 0.041 D_real: 1.207 D_fake: 0.581 
+(epoch: 55, iters: 8368, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.849 G_ID: 0.466 G_Rec: 0.379 D_GP: 0.042 D_real: 1.117 D_fake: 0.479 
+(epoch: 56, iters: 160, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.951 G_ID: 0.246 G_Rec: 0.399 D_GP: 0.052 D_real: 0.534 D_fake: 0.754 
+(epoch: 56, iters: 560, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.705 G_ID: 0.496 G_Rec: 0.391 D_GP: 0.029 D_real: 1.185 D_fake: 0.716 
+(epoch: 56, iters: 960, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.609 G_ID: 0.288 G_Rec: 0.297 D_GP: 0.025 D_real: 1.349 D_fake: 0.569 
+(epoch: 56, iters: 1360, time: 0.064) G_GAN: 0.462 G_GAN_Feat: 0.857 G_ID: 0.480 G_Rec: 0.418 D_GP: 0.034 D_real: 1.105 D_fake: 0.538 
+(epoch: 56, iters: 1760, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.851 G_ID: 0.253 G_Rec: 0.350 D_GP: 0.105 D_real: 0.628 D_fake: 0.839 
+(epoch: 56, iters: 2160, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.897 G_ID: 0.496 G_Rec: 0.377 D_GP: 0.033 D_real: 1.076 D_fake: 0.603 
+(epoch: 56, iters: 2560, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 0.763 G_ID: 0.260 G_Rec: 0.390 D_GP: 0.037 D_real: 1.147 D_fake: 0.513 
+(epoch: 56, iters: 2960, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.810 G_ID: 0.473 G_Rec: 0.362 D_GP: 0.076 D_real: 1.150 D_fake: 0.551 
+(epoch: 56, iters: 3360, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.933 G_ID: 0.240 G_Rec: 0.348 D_GP: 0.050 D_real: 0.997 D_fake: 0.642 
+(epoch: 56, iters: 3760, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 1.205 G_ID: 0.467 G_Rec: 0.387 D_GP: 0.090 D_real: 0.604 D_fake: 0.565 
+(epoch: 56, iters: 4160, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 1.041 G_ID: 0.243 G_Rec: 0.379 D_GP: 0.320 D_real: 0.408 D_fake: 0.662 
+(epoch: 56, iters: 4560, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.742 G_ID: 0.476 G_Rec: 0.358 D_GP: 0.034 D_real: 0.857 D_fake: 0.927 
+(epoch: 56, iters: 4960, time: 0.064) G_GAN: 0.623 G_GAN_Feat: 0.891 G_ID: 0.221 G_Rec: 0.406 D_GP: 0.048 D_real: 1.204 D_fake: 0.392 
+(epoch: 56, iters: 5360, time: 0.064) G_GAN: 0.706 G_GAN_Feat: 0.696 G_ID: 0.501 G_Rec: 0.409 D_GP: 0.021 D_real: 1.580 D_fake: 0.307 
+(epoch: 56, iters: 5760, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.713 G_ID: 0.277 G_Rec: 0.340 D_GP: 0.037 D_real: 1.125 D_fake: 0.627 
+(epoch: 56, iters: 6160, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.708 G_ID: 0.502 G_Rec: 0.376 D_GP: 0.028 D_real: 1.115 D_fake: 0.687 
+(epoch: 56, iters: 6560, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.877 G_ID: 0.214 G_Rec: 0.382 D_GP: 0.061 D_real: 0.781 D_fake: 0.790 
+(epoch: 56, iters: 6960, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.698 G_ID: 0.476 G_Rec: 0.381 D_GP: 0.024 D_real: 1.121 D_fake: 0.662 
+(epoch: 56, iters: 7360, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 0.593 G_ID: 0.237 G_Rec: 0.337 D_GP: 0.023 D_real: 0.978 D_fake: 0.919 
+(epoch: 56, iters: 7760, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.841 G_ID: 0.487 G_Rec: 0.366 D_GP: 0.051 D_real: 1.147 D_fake: 0.517 
+(epoch: 56, iters: 8160, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.700 G_ID: 0.233 G_Rec: 0.370 D_GP: 0.031 D_real: 1.363 D_fake: 0.485 
+(epoch: 56, iters: 8560, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.851 G_ID: 0.469 G_Rec: 0.355 D_GP: 0.066 D_real: 0.879 D_fake: 0.649 
+(epoch: 57, iters: 352, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.775 G_ID: 0.252 G_Rec: 0.406 D_GP: 0.028 D_real: 1.206 D_fake: 0.623 
+(epoch: 57, iters: 752, time: 0.064) G_GAN: 0.806 G_GAN_Feat: 0.765 G_ID: 0.488 G_Rec: 0.412 D_GP: 0.027 D_real: 1.577 D_fake: 0.437 
+(epoch: 57, iters: 1152, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.597 G_ID: 0.238 G_Rec: 0.344 D_GP: 0.027 D_real: 1.162 D_fake: 0.673 
+(epoch: 57, iters: 1552, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.877 G_ID: 0.454 G_Rec: 0.434 D_GP: 0.092 D_real: 0.692 D_fake: 0.844 
+(epoch: 57, iters: 1952, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.784 G_ID: 0.266 G_Rec: 0.471 D_GP: 0.037 D_real: 1.076 D_fake: 0.686 
+(epoch: 57, iters: 2352, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.888 G_ID: 0.455 G_Rec: 0.399 D_GP: 0.054 D_real: 1.139 D_fake: 0.493 
+(epoch: 57, iters: 2752, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.981 G_ID: 0.279 G_Rec: 0.363 D_GP: 0.070 D_real: 0.671 D_fake: 0.554 
+(epoch: 57, iters: 3152, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.705 G_ID: 0.461 G_Rec: 0.376 D_GP: 0.021 D_real: 1.272 D_fake: 0.544 
+(epoch: 57, iters: 3552, time: 0.064) G_GAN: 0.690 G_GAN_Feat: 0.965 G_ID: 0.244 G_Rec: 0.384 D_GP: 0.045 D_real: 1.383 D_fake: 0.353 
+(epoch: 57, iters: 3952, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 1.094 G_ID: 0.490 G_Rec: 0.408 D_GP: 0.142 D_real: 0.583 D_fake: 0.804 
+(epoch: 57, iters: 4352, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.773 G_ID: 0.258 G_Rec: 0.381 D_GP: 0.028 D_real: 1.019 D_fake: 0.799 
+(epoch: 57, iters: 4752, time: 0.064) G_GAN: 0.822 G_GAN_Feat: 0.847 G_ID: 0.456 G_Rec: 0.384 D_GP: 0.033 D_real: 1.484 D_fake: 0.424 
+(epoch: 57, iters: 5152, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.654 G_ID: 0.266 G_Rec: 0.392 D_GP: 0.027 D_real: 1.097 D_fake: 0.668 
+(epoch: 57, iters: 5552, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.665 G_ID: 0.523 G_Rec: 0.349 D_GP: 0.026 D_real: 0.929 D_fake: 0.860 
+(epoch: 57, iters: 5952, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.692 G_ID: 0.214 G_Rec: 0.391 D_GP: 0.033 D_real: 1.165 D_fake: 0.676 
+(epoch: 57, iters: 6352, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.837 G_ID: 0.498 G_Rec: 0.447 D_GP: 0.057 D_real: 0.957 D_fake: 0.642 
+(epoch: 57, iters: 6752, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.735 G_ID: 0.268 G_Rec: 0.374 D_GP: 0.027 D_real: 1.131 D_fake: 0.648 
+(epoch: 57, iters: 7152, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.702 G_ID: 0.467 G_Rec: 0.362 D_GP: 0.030 D_real: 1.305 D_fake: 0.569 
+(epoch: 57, iters: 7552, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.723 G_ID: 0.228 G_Rec: 0.361 D_GP: 0.039 D_real: 1.097 D_fake: 0.721 
+(epoch: 57, iters: 7952, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.798 G_ID: 0.509 G_Rec: 0.359 D_GP: 0.037 D_real: 0.925 D_fake: 0.771 
+(epoch: 57, iters: 8352, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.742 G_ID: 0.237 G_Rec: 0.330 D_GP: 0.053 D_real: 1.033 D_fake: 0.719 
+(epoch: 58, iters: 144, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.741 G_ID: 0.497 G_Rec: 0.401 D_GP: 0.032 D_real: 1.072 D_fake: 0.738 
+(epoch: 58, iters: 544, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.781 G_ID: 0.270 G_Rec: 0.371 D_GP: 0.044 D_real: 1.251 D_fake: 0.538 
+(epoch: 58, iters: 944, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.979 G_ID: 0.483 G_Rec: 0.404 D_GP: 0.277 D_real: 0.531 D_fake: 0.707 
+(epoch: 58, iters: 1344, time: 0.064) G_GAN: 0.784 G_GAN_Feat: 0.806 G_ID: 0.226 G_Rec: 0.362 D_GP: 0.042 D_real: 1.398 D_fake: 0.234 
+(epoch: 58, iters: 1744, time: 0.064) G_GAN: 0.589 G_GAN_Feat: 0.956 G_ID: 0.467 G_Rec: 0.384 D_GP: 0.096 D_real: 0.961 D_fake: 0.523 
+(epoch: 58, iters: 2144, time: 0.064) G_GAN: 0.109 G_GAN_Feat: 0.790 G_ID: 0.231 G_Rec: 0.361 D_GP: 0.034 D_real: 0.724 D_fake: 0.891 
+(epoch: 58, iters: 2544, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.044 G_ID: 0.491 G_Rec: 0.400 D_GP: 0.072 D_real: 0.454 D_fake: 0.651 
+(epoch: 58, iters: 2944, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.726 G_ID: 0.256 G_Rec: 0.399 D_GP: 0.029 D_real: 1.203 D_fake: 0.613 
+(epoch: 58, iters: 3344, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 1.014 G_ID: 0.441 G_Rec: 0.366 D_GP: 0.098 D_real: 0.556 D_fake: 0.732 
+(epoch: 58, iters: 3744, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.925 G_ID: 0.244 G_Rec: 0.360 D_GP: 0.049 D_real: 0.547 D_fake: 0.862 
+(epoch: 58, iters: 4144, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 1.150 G_ID: 0.494 G_Rec: 0.435 D_GP: 0.149 D_real: 0.474 D_fake: 0.579 
+(epoch: 58, iters: 4544, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.614 G_ID: 0.222 G_Rec: 0.341 D_GP: 0.025 D_real: 1.124 D_fake: 0.685 
+(epoch: 58, iters: 4944, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.689 G_ID: 0.467 G_Rec: 0.352 D_GP: 0.032 D_real: 1.091 D_fake: 0.716 
+(epoch: 58, iters: 5344, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.777 G_ID: 0.258 G_Rec: 0.363 D_GP: 0.036 D_real: 1.025 D_fake: 0.659 
+(epoch: 58, iters: 5744, time: 0.064) G_GAN: 0.532 G_GAN_Feat: 1.180 G_ID: 0.483 G_Rec: 0.396 D_GP: 0.150 D_real: 0.681 D_fake: 0.495 
+(epoch: 58, iters: 6144, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.908 G_ID: 0.221 G_Rec: 0.395 D_GP: 0.100 D_real: 0.607 D_fake: 0.802 
+(epoch: 58, iters: 6544, time: 0.064) G_GAN: 0.596 G_GAN_Feat: 0.670 G_ID: 0.469 G_Rec: 0.370 D_GP: 0.021 D_real: 1.468 D_fake: 0.438 
+(epoch: 58, iters: 6944, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.782 G_ID: 0.210 G_Rec: 0.422 D_GP: 0.034 D_real: 1.301 D_fake: 0.543 
+(epoch: 58, iters: 7344, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.815 G_ID: 0.478 G_Rec: 0.392 D_GP: 0.090 D_real: 0.893 D_fake: 0.680 
+(epoch: 58, iters: 7744, time: 0.064) G_GAN: 0.630 G_GAN_Feat: 0.813 G_ID: 0.248 G_Rec: 0.415 D_GP: 0.047 D_real: 1.299 D_fake: 0.376 
+(epoch: 58, iters: 8144, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.909 G_ID: 0.511 G_Rec: 0.402 D_GP: 0.166 D_real: 0.651 D_fake: 0.632 
+(epoch: 58, iters: 8544, time: 0.064) G_GAN: 0.582 G_GAN_Feat: 1.024 G_ID: 0.230 G_Rec: 0.390 D_GP: 0.095 D_real: 0.733 D_fake: 0.424 
+(epoch: 59, iters: 336, time: 0.064) G_GAN: 0.108 G_GAN_Feat: 0.856 G_ID: 0.438 G_Rec: 0.370 D_GP: 0.073 D_real: 0.701 D_fake: 0.892 
+(epoch: 59, iters: 736, time: 0.064) G_GAN: 0.830 G_GAN_Feat: 1.335 G_ID: 0.217 G_Rec: 0.391 D_GP: 0.405 D_real: 0.730 D_fake: 0.225 
+(epoch: 59, iters: 1136, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 1.002 G_ID: 0.470 G_Rec: 0.388 D_GP: 0.040 D_real: 0.750 D_fake: 0.691 
+(epoch: 59, iters: 1536, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.772 G_ID: 0.276 G_Rec: 0.356 D_GP: 0.040 D_real: 1.147 D_fake: 0.566 
+(epoch: 59, iters: 1936, time: 0.064) G_GAN: 0.661 G_GAN_Feat: 0.642 G_ID: 0.496 G_Rec: 0.400 D_GP: 0.020 D_real: 1.477 D_fake: 0.397 
+(epoch: 59, iters: 2336, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.614 G_ID: 0.252 G_Rec: 0.420 D_GP: 0.028 D_real: 1.127 D_fake: 0.744 
+(epoch: 59, iters: 2736, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.860 G_ID: 0.483 G_Rec: 0.432 D_GP: 0.065 D_real: 0.914 D_fake: 0.553 
+(epoch: 59, iters: 3136, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.835 G_ID: 0.195 G_Rec: 0.399 D_GP: 0.043 D_real: 0.960 D_fake: 0.638 
+(epoch: 59, iters: 3536, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.813 G_ID: 0.517 G_Rec: 0.365 D_GP: 0.060 D_real: 0.814 D_fake: 0.923 
+(epoch: 59, iters: 3936, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.841 G_ID: 0.269 G_Rec: 0.458 D_GP: 0.025 D_real: 0.792 D_fake: 0.856 
+(epoch: 59, iters: 4336, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.871 G_ID: 0.469 G_Rec: 0.341 D_GP: 0.083 D_real: 0.782 D_fake: 0.730 
+(epoch: 59, iters: 4736, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.923 G_ID: 0.278 G_Rec: 0.406 D_GP: 0.069 D_real: 0.614 D_fake: 0.729 
+(epoch: 59, iters: 5136, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.956 G_ID: 0.455 G_Rec: 0.352 D_GP: 0.116 D_real: 0.773 D_fake: 0.587 
+(epoch: 59, iters: 5536, time: 0.064) G_GAN: 0.496 G_GAN_Feat: 0.544 G_ID: 0.237 G_Rec: 0.378 D_GP: 0.015 D_real: 1.431 D_fake: 0.514 
+(epoch: 59, iters: 5936, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 0.585 G_ID: 0.486 G_Rec: 0.371 D_GP: 0.018 D_real: 1.286 D_fake: 0.568 
+(epoch: 59, iters: 6336, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.584 G_ID: 0.183 G_Rec: 0.385 D_GP: 0.022 D_real: 1.267 D_fake: 0.710 
+(epoch: 59, iters: 6736, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.687 G_ID: 0.457 G_Rec: 0.370 D_GP: 0.045 D_real: 0.925 D_fake: 0.836 
+(epoch: 59, iters: 7136, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.697 G_ID: 0.235 G_Rec: 0.372 D_GP: 0.039 D_real: 1.043 D_fake: 0.718 
+(epoch: 59, iters: 7536, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.737 G_ID: 0.467 G_Rec: 0.408 D_GP: 0.075 D_real: 0.964 D_fake: 0.639 
+(epoch: 59, iters: 7936, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.751 G_ID: 0.257 G_Rec: 0.384 D_GP: 0.070 D_real: 0.729 D_fake: 0.816 
+(epoch: 59, iters: 8336, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.934 G_ID: 0.494 G_Rec: 0.382 D_GP: 0.080 D_real: 0.893 D_fake: 0.493 
+(epoch: 60, iters: 128, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 1.045 G_ID: 0.268 G_Rec: 0.377 D_GP: 0.060 D_real: 0.693 D_fake: 0.564 
+(epoch: 60, iters: 528, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.748 G_ID: 0.481 G_Rec: 0.383 D_GP: 0.032 D_real: 1.023 D_fake: 0.710 
+(epoch: 60, iters: 928, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.688 G_ID: 0.264 G_Rec: 0.348 D_GP: 0.030 D_real: 1.275 D_fake: 0.477 
+(epoch: 60, iters: 1328, time: 0.064) G_GAN: 0.729 G_GAN_Feat: 0.968 G_ID: 0.465 G_Rec: 0.371 D_GP: 0.120 D_real: 1.170 D_fake: 0.491 
+(epoch: 60, iters: 1728, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.698 G_ID: 0.278 G_Rec: 0.368 D_GP: 0.024 D_real: 1.142 D_fake: 0.742 
+(epoch: 60, iters: 2128, time: 0.064) G_GAN: 0.008 G_GAN_Feat: 0.700 G_ID: 0.448 G_Rec: 0.385 D_GP: 0.044 D_real: 0.855 D_fake: 0.992 
+(epoch: 60, iters: 2528, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.766 G_ID: 0.225 G_Rec: 0.386 D_GP: 0.062 D_real: 0.794 D_fake: 0.844 
+(epoch: 60, iters: 2928, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 1.343 G_ID: 0.499 G_Rec: 0.401 D_GP: 0.776 D_real: 0.430 D_fake: 0.829 
+(epoch: 60, iters: 3328, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.649 G_ID: 0.250 G_Rec: 0.387 D_GP: 0.024 D_real: 1.060 D_fake: 0.691 
+(epoch: 60, iters: 3728, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.725 G_ID: 0.495 G_Rec: 0.378 D_GP: 0.043 D_real: 0.890 D_fake: 0.833 
+(epoch: 60, iters: 4128, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.725 G_ID: 0.260 G_Rec: 0.395 D_GP: 0.042 D_real: 0.992 D_fake: 0.735 
+(epoch: 60, iters: 4528, time: 0.064) G_GAN: 0.078 G_GAN_Feat: 0.778 G_ID: 0.432 G_Rec: 0.372 D_GP: 0.056 D_real: 0.739 D_fake: 0.922 
+(epoch: 60, iters: 4928, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.912 G_ID: 0.260 G_Rec: 0.385 D_GP: 0.058 D_real: 0.741 D_fake: 0.743 
+(epoch: 60, iters: 5328, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.748 G_ID: 0.465 G_Rec: 0.349 D_GP: 0.047 D_real: 0.992 D_fake: 0.658 
+(epoch: 60, iters: 5728, time: 0.064) G_GAN: 0.660 G_GAN_Feat: 0.874 G_ID: 0.260 G_Rec: 0.373 D_GP: 0.081 D_real: 1.166 D_fake: 0.396 
+(epoch: 60, iters: 6128, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 1.213 G_ID: 0.454 G_Rec: 0.430 D_GP: 0.837 D_real: 0.389 D_fake: 0.671 
+(epoch: 60, iters: 6528, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.688 G_ID: 0.189 G_Rec: 0.352 D_GP: 0.031 D_real: 1.111 D_fake: 0.807 
+(epoch: 60, iters: 6928, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 1.018 G_ID: 0.407 G_Rec: 0.411 D_GP: 0.262 D_real: 0.478 D_fake: 0.637 
+(epoch: 60, iters: 7328, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.968 G_ID: 0.238 G_Rec: 0.375 D_GP: 0.120 D_real: 0.415 D_fake: 0.917 
+(epoch: 60, iters: 7728, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.730 G_ID: 0.469 G_Rec: 0.421 D_GP: 0.033 D_real: 0.981 D_fake: 0.760 
+(epoch: 60, iters: 8128, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.890 G_ID: 0.241 G_Rec: 0.375 D_GP: 0.031 D_real: 1.034 D_fake: 0.617 
+(epoch: 60, iters: 8528, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 1.101 G_ID: 0.490 G_Rec: 0.412 D_GP: 0.079 D_real: 0.454 D_fake: 0.885 
+(epoch: 61, iters: 320, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.981 G_ID: 0.244 G_Rec: 0.398 D_GP: 0.156 D_real: 0.734 D_fake: 0.712 
+(epoch: 61, iters: 720, time: 0.064) G_GAN: 0.652 G_GAN_Feat: 0.814 G_ID: 0.446 G_Rec: 0.360 D_GP: 0.051 D_real: 1.321 D_fake: 0.358 
+(epoch: 61, iters: 1120, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.930 G_ID: 0.227 G_Rec: 0.380 D_GP: 0.043 D_real: 0.886 D_fake: 0.552 
+(epoch: 61, iters: 1520, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 1.078 G_ID: 0.460 G_Rec: 0.404 D_GP: 0.064 D_real: 0.660 D_fake: 0.680 
+(epoch: 61, iters: 1920, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 0.733 G_ID: 0.240 G_Rec: 0.372 D_GP: 0.031 D_real: 1.115 D_fake: 0.625 
+(epoch: 61, iters: 2320, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.634 G_ID: 0.428 G_Rec: 0.400 D_GP: 0.022 D_real: 1.347 D_fake: 0.512 
+(epoch: 61, iters: 2720, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.654 G_ID: 0.244 G_Rec: 0.406 D_GP: 0.028 D_real: 0.992 D_fake: 0.780 
+(epoch: 61, iters: 3120, time: 0.064) G_GAN: 0.563 G_GAN_Feat: 0.851 G_ID: 0.464 G_Rec: 0.389 D_GP: 0.081 D_real: 1.056 D_fake: 0.445 
+(epoch: 61, iters: 3520, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.730 G_ID: 0.198 G_Rec: 0.373 D_GP: 0.038 D_real: 1.122 D_fake: 0.566 
+(epoch: 61, iters: 3920, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.932 G_ID: 0.492 G_Rec: 0.396 D_GP: 0.075 D_real: 0.498 D_fake: 0.868 
+(epoch: 61, iters: 4320, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.657 G_ID: 0.257 G_Rec: 0.376 D_GP: 0.025 D_real: 1.068 D_fake: 0.768 
+(epoch: 61, iters: 4720, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.705 G_ID: 0.456 G_Rec: 0.372 D_GP: 0.036 D_real: 0.981 D_fake: 0.737 
+(epoch: 61, iters: 5120, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.672 G_ID: 0.229 G_Rec: 0.333 D_GP: 0.042 D_real: 0.965 D_fake: 0.792 
+(epoch: 61, iters: 5520, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.858 G_ID: 0.496 G_Rec: 0.376 D_GP: 0.041 D_real: 0.915 D_fake: 0.678 
+(epoch: 61, iters: 5920, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.797 G_ID: 0.246 G_Rec: 0.368 D_GP: 0.051 D_real: 1.193 D_fake: 0.659 
+(epoch: 61, iters: 6320, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.821 G_ID: 0.429 G_Rec: 0.362 D_GP: 0.105 D_real: 0.963 D_fake: 0.591 
+(epoch: 61, iters: 6720, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.624 G_ID: 0.236 G_Rec: 0.447 D_GP: 0.020 D_real: 1.115 D_fake: 0.791 
+(epoch: 61, iters: 7120, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.862 G_ID: 0.431 G_Rec: 0.401 D_GP: 0.040 D_real: 1.003 D_fake: 0.693 
+(epoch: 61, iters: 7520, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.816 G_ID: 0.265 G_Rec: 0.391 D_GP: 0.052 D_real: 0.829 D_fake: 0.801 
+(epoch: 61, iters: 7920, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.830 G_ID: 0.435 G_Rec: 0.403 D_GP: 0.046 D_real: 1.099 D_fake: 0.607 
+(epoch: 61, iters: 8320, time: 0.064) G_GAN: 0.470 G_GAN_Feat: 0.767 G_ID: 0.290 G_Rec: 0.366 D_GP: 0.048 D_real: 1.142 D_fake: 0.547 
+(epoch: 62, iters: 112, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.840 G_ID: 0.480 G_Rec: 0.424 D_GP: 0.037 D_real: 1.037 D_fake: 0.639 
+(epoch: 62, iters: 512, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 0.730 G_ID: 0.221 G_Rec: 0.325 D_GP: 0.042 D_real: 1.127 D_fake: 0.709 
+(epoch: 62, iters: 912, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.862 G_ID: 0.446 G_Rec: 0.405 D_GP: 0.034 D_real: 0.864 D_fake: 0.781 
+(epoch: 62, iters: 1312, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.883 G_ID: 0.253 G_Rec: 0.395 D_GP: 0.030 D_real: 1.030 D_fake: 0.663 
+(epoch: 62, iters: 1712, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 0.890 G_ID: 0.439 G_Rec: 0.363 D_GP: 0.085 D_real: 0.642 D_fake: 0.808 
+(epoch: 62, iters: 2112, time: 0.064) G_GAN: 0.658 G_GAN_Feat: 1.064 G_ID: 0.250 G_Rec: 0.397 D_GP: 0.060 D_real: 1.323 D_fake: 0.558 
+(epoch: 62, iters: 2512, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 1.194 G_ID: 0.480 G_Rec: 0.424 D_GP: 0.146 D_real: 0.494 D_fake: 0.634 
+(epoch: 62, iters: 2912, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 1.057 G_ID: 0.220 G_Rec: 0.443 D_GP: 0.105 D_real: 0.243 D_fake: 0.885 
+(epoch: 62, iters: 3312, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.762 G_ID: 0.498 G_Rec: 0.390 D_GP: 0.024 D_real: 1.044 D_fake: 0.716 
+(epoch: 62, iters: 3712, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.746 G_ID: 0.240 G_Rec: 0.358 D_GP: 0.027 D_real: 1.174 D_fake: 0.555 
+(epoch: 62, iters: 4112, time: 0.064) G_GAN: 0.550 G_GAN_Feat: 0.896 G_ID: 0.445 G_Rec: 0.362 D_GP: 0.051 D_real: 1.118 D_fake: 0.451 
+(epoch: 62, iters: 4512, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.685 G_ID: 0.290 G_Rec: 0.372 D_GP: 0.022 D_real: 1.123 D_fake: 0.655 
+(epoch: 62, iters: 4912, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.815 G_ID: 0.432 G_Rec: 0.387 D_GP: 0.101 D_real: 0.593 D_fake: 0.912 
+(epoch: 62, iters: 5312, time: 0.064) G_GAN: 0.572 G_GAN_Feat: 0.715 G_ID: 0.223 G_Rec: 0.362 D_GP: 0.035 D_real: 1.371 D_fake: 0.430 
+(epoch: 62, iters: 5712, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 1.145 G_ID: 0.411 G_Rec: 0.434 D_GP: 0.106 D_real: 0.491 D_fake: 0.686 
+(epoch: 62, iters: 6112, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.857 G_ID: 0.220 G_Rec: 0.481 D_GP: 0.056 D_real: 0.908 D_fake: 0.649 
+(epoch: 62, iters: 6512, time: 0.064) G_GAN: 0.664 G_GAN_Feat: 1.151 G_ID: 0.440 G_Rec: 0.405 D_GP: 0.298 D_real: 0.664 D_fake: 0.386 
+(epoch: 62, iters: 6912, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 1.093 G_ID: 0.232 G_Rec: 0.438 D_GP: 0.096 D_real: 0.467 D_fake: 0.586 
+(epoch: 62, iters: 7312, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 1.073 G_ID: 0.440 G_Rec: 0.402 D_GP: 0.055 D_real: 0.539 D_fake: 0.762 
+(epoch: 62, iters: 7712, time: 0.064) G_GAN: 0.685 G_GAN_Feat: 0.764 G_ID: 0.218 G_Rec: 0.331 D_GP: 0.047 D_real: 1.400 D_fake: 0.353 
+(epoch: 62, iters: 8112, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.674 G_ID: 0.446 G_Rec: 0.405 D_GP: 0.024 D_real: 1.056 D_fake: 0.768 
+(epoch: 62, iters: 8512, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.842 G_ID: 0.208 G_Rec: 0.365 D_GP: 0.057 D_real: 0.755 D_fake: 0.702 
+(epoch: 63, iters: 304, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.654 G_ID: 0.442 G_Rec: 0.382 D_GP: 0.027 D_real: 1.212 D_fake: 0.584 
+(epoch: 63, iters: 704, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.847 G_ID: 0.248 G_Rec: 0.380 D_GP: 0.080 D_real: 0.780 D_fake: 0.725 
+(epoch: 63, iters: 1104, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.805 G_ID: 0.460 G_Rec: 0.380 D_GP: 0.035 D_real: 0.929 D_fake: 0.734 
+(epoch: 63, iters: 1504, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.626 G_ID: 0.308 G_Rec: 0.348 D_GP: 0.024 D_real: 1.162 D_fake: 0.591 
+(epoch: 63, iters: 1904, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.659 G_ID: 0.427 G_Rec: 0.358 D_GP: 0.031 D_real: 1.043 D_fake: 0.749 
+(epoch: 63, iters: 2304, time: 0.064) G_GAN: 0.000 G_GAN_Feat: 0.738 G_ID: 0.224 G_Rec: 0.334 D_GP: 0.050 D_real: 0.850 D_fake: 1.000 
+(epoch: 63, iters: 2704, time: 0.064) G_GAN: 0.038 G_GAN_Feat: 0.793 G_ID: 0.426 G_Rec: 0.370 D_GP: 0.060 D_real: 0.714 D_fake: 0.962 
+(epoch: 63, iters: 3104, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.750 G_ID: 0.228 G_Rec: 0.369 D_GP: 0.039 D_real: 1.154 D_fake: 0.537 
+(epoch: 63, iters: 3504, time: 0.064) G_GAN: 0.620 G_GAN_Feat: 0.893 G_ID: 0.414 G_Rec: 0.411 D_GP: 0.077 D_real: 1.113 D_fake: 0.414 
+(epoch: 63, iters: 3904, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.994 G_ID: 0.195 G_Rec: 0.375 D_GP: 0.042 D_real: 0.785 D_fake: 0.590 
+(epoch: 63, iters: 4304, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.833 G_ID: 0.513 G_Rec: 0.369 D_GP: 0.044 D_real: 1.149 D_fake: 0.577 
+(epoch: 63, iters: 4704, time: 0.064) G_GAN: 0.444 G_GAN_Feat: 0.849 G_ID: 0.200 G_Rec: 0.361 D_GP: 0.089 D_real: 0.910 D_fake: 0.632 
+(epoch: 63, iters: 5104, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 1.236 G_ID: 0.482 G_Rec: 0.361 D_GP: 0.070 D_real: 0.429 D_fake: 0.604 
+(epoch: 63, iters: 5504, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.718 G_ID: 0.212 G_Rec: 0.382 D_GP: 0.022 D_real: 0.924 D_fake: 0.893 
+(epoch: 63, iters: 5904, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.759 G_ID: 0.487 G_Rec: 0.363 D_GP: 0.036 D_real: 1.212 D_fake: 0.515 
+(epoch: 63, iters: 6304, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.840 G_ID: 0.233 G_Rec: 0.417 D_GP: 0.030 D_real: 1.122 D_fake: 0.578 
+(epoch: 63, iters: 6704, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.686 G_ID: 0.428 G_Rec: 0.425 D_GP: 0.022 D_real: 1.167 D_fake: 0.697 
+(epoch: 63, iters: 7104, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.703 G_ID: 0.249 G_Rec: 0.352 D_GP: 0.027 D_real: 1.262 D_fake: 0.505 
+(epoch: 63, iters: 7504, time: 0.064) G_GAN: 0.550 G_GAN_Feat: 0.698 G_ID: 0.432 G_Rec: 0.338 D_GP: 0.032 D_real: 1.329 D_fake: 0.453 
+(epoch: 63, iters: 7904, time: 0.064) G_GAN: 0.733 G_GAN_Feat: 0.864 G_ID: 0.256 G_Rec: 0.394 D_GP: 0.085 D_real: 1.191 D_fake: 0.300 
+(epoch: 63, iters: 8304, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 0.931 G_ID: 0.447 G_Rec: 0.406 D_GP: 0.041 D_real: 0.968 D_fake: 0.563 
+(epoch: 64, iters: 96, time: 0.064) G_GAN: -0.106 G_GAN_Feat: 1.209 G_ID: 0.261 G_Rec: 0.390 D_GP: 0.300 D_real: 0.335 D_fake: 1.106 
+(epoch: 64, iters: 496, time: 0.064) G_GAN: 0.727 G_GAN_Feat: 0.886 G_ID: 0.450 G_Rec: 0.402 D_GP: 0.033 D_real: 1.333 D_fake: 0.296 
+(epoch: 64, iters: 896, time: 0.064) G_GAN: 0.852 G_GAN_Feat: 0.846 G_ID: 0.240 G_Rec: 0.372 D_GP: 0.074 D_real: 1.486 D_fake: 0.315 
+(epoch: 64, iters: 1296, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 1.165 G_ID: 0.482 G_Rec: 0.389 D_GP: 0.086 D_real: 0.376 D_fake: 0.620 
+(epoch: 64, iters: 1696, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.721 G_ID: 0.239 G_Rec: 0.399 D_GP: 0.021 D_real: 1.002 D_fake: 0.835 
+(epoch: 64, iters: 2096, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.726 G_ID: 0.468 G_Rec: 0.402 D_GP: 0.027 D_real: 0.989 D_fake: 0.717 
+(epoch: 64, iters: 2496, time: 0.064) G_GAN: 0.108 G_GAN_Feat: 0.682 G_ID: 0.241 G_Rec: 0.332 D_GP: 0.033 D_real: 0.925 D_fake: 0.892 
+(epoch: 64, iters: 2896, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.837 G_ID: 0.462 G_Rec: 0.386 D_GP: 0.071 D_real: 0.926 D_fake: 0.701 
+(epoch: 64, iters: 3296, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 0.731 G_ID: 0.219 G_Rec: 0.409 D_GP: 0.037 D_real: 1.196 D_fake: 0.519 
+(epoch: 64, iters: 3696, time: 0.064) G_GAN: 0.747 G_GAN_Feat: 0.978 G_ID: 0.462 G_Rec: 0.406 D_GP: 0.098 D_real: 0.972 D_fake: 0.351 
+(epoch: 64, iters: 4096, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.877 G_ID: 0.241 G_Rec: 0.426 D_GP: 0.040 D_real: 0.923 D_fake: 0.774 
+(epoch: 64, iters: 4496, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.881 G_ID: 0.475 G_Rec: 0.347 D_GP: 0.092 D_real: 0.973 D_fake: 0.483 
+(epoch: 64, iters: 4896, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.573 G_ID: 0.221 G_Rec: 0.406 D_GP: 0.023 D_real: 1.194 D_fake: 0.714 
+(epoch: 64, iters: 5296, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.655 G_ID: 0.435 G_Rec: 0.371 D_GP: 0.021 D_real: 1.159 D_fake: 0.630 
+(epoch: 64, iters: 5696, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.611 G_ID: 0.225 G_Rec: 0.391 D_GP: 0.027 D_real: 1.039 D_fake: 0.822 
+(epoch: 64, iters: 6096, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.607 G_ID: 0.436 G_Rec: 0.352 D_GP: 0.029 D_real: 1.069 D_fake: 0.743 
+(epoch: 64, iters: 6496, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.674 G_ID: 0.215 G_Rec: 0.446 D_GP: 0.043 D_real: 1.021 D_fake: 0.714 
+(epoch: 64, iters: 6896, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.759 G_ID: 0.426 G_Rec: 0.389 D_GP: 0.044 D_real: 0.893 D_fake: 0.750 
+(epoch: 64, iters: 7296, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.920 G_ID: 0.255 G_Rec: 0.346 D_GP: 0.126 D_real: 0.575 D_fake: 0.715 
+(epoch: 64, iters: 7696, time: 0.064) G_GAN: 0.567 G_GAN_Feat: 0.734 G_ID: 0.446 G_Rec: 0.357 D_GP: 0.053 D_real: 1.260 D_fake: 0.438 
+(epoch: 64, iters: 8096, time: 0.064) G_GAN: 0.603 G_GAN_Feat: 0.864 G_ID: 0.236 G_Rec: 0.372 D_GP: 0.068 D_real: 1.199 D_fake: 0.445 
+(epoch: 64, iters: 8496, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.779 G_ID: 0.483 G_Rec: 0.384 D_GP: 0.039 D_real: 1.068 D_fake: 0.669 
+(epoch: 65, iters: 288, time: 0.064) G_GAN: 0.787 G_GAN_Feat: 0.860 G_ID: 0.233 G_Rec: 0.358 D_GP: 0.060 D_real: 1.353 D_fake: 0.409 
+(epoch: 65, iters: 688, time: 0.064) G_GAN: 0.621 G_GAN_Feat: 0.974 G_ID: 0.463 G_Rec: 0.374 D_GP: 0.056 D_real: 0.990 D_fake: 0.416 
+(epoch: 65, iters: 1088, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.739 G_ID: 0.221 G_Rec: 0.425 D_GP: 0.032 D_real: 1.264 D_fake: 0.671 
+(epoch: 65, iters: 1488, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.718 G_ID: 0.415 G_Rec: 0.369 D_GP: 0.026 D_real: 1.063 D_fake: 0.728 
+(epoch: 65, iters: 1888, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 1.117 G_ID: 0.205 G_Rec: 0.384 D_GP: 0.212 D_real: 0.556 D_fake: 0.569 
+(epoch: 65, iters: 2288, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 0.964 G_ID: 0.473 G_Rec: 0.425 D_GP: 0.105 D_real: 0.289 D_fake: 0.934 
+(epoch: 65, iters: 2688, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.835 G_ID: 0.255 G_Rec: 0.396 D_GP: 0.065 D_real: 0.688 D_fake: 0.794 
+(epoch: 65, iters: 3088, time: 0.064) G_GAN: 0.726 G_GAN_Feat: 0.769 G_ID: 0.416 G_Rec: 0.414 D_GP: 0.034 D_real: 1.426 D_fake: 0.310 
+(epoch: 65, iters: 3488, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.661 G_ID: 0.195 G_Rec: 0.358 D_GP: 0.026 D_real: 1.282 D_fake: 0.581 
+(epoch: 65, iters: 3888, time: 0.064) G_GAN: 0.933 G_GAN_Feat: 1.078 G_ID: 0.378 G_Rec: 0.403 D_GP: 0.082 D_real: 1.074 D_fake: 0.200 
+(epoch: 65, iters: 4288, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.991 G_ID: 0.229 G_Rec: 0.375 D_GP: 0.152 D_real: 0.367 D_fake: 0.791 
+(epoch: 65, iters: 4688, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.759 G_ID: 0.438 G_Rec: 0.458 D_GP: 0.042 D_real: 0.967 D_fake: 0.686 
+(epoch: 65, iters: 5088, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.768 G_ID: 0.247 G_Rec: 0.337 D_GP: 0.045 D_real: 1.139 D_fake: 0.568 
+(epoch: 65, iters: 5488, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.929 G_ID: 0.399 G_Rec: 0.375 D_GP: 0.080 D_real: 0.917 D_fake: 0.848 
+(epoch: 65, iters: 5888, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.956 G_ID: 0.229 G_Rec: 0.374 D_GP: 0.063 D_real: 0.647 D_fake: 0.808 
+(epoch: 65, iters: 6288, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 1.133 G_ID: 0.437 G_Rec: 0.457 D_GP: 0.044 D_real: 0.532 D_fake: 0.666 
+(epoch: 65, iters: 6688, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.718 G_ID: 0.238 G_Rec: 0.353 D_GP: 0.025 D_real: 1.065 D_fake: 0.855 
+(epoch: 65, iters: 7088, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.808 G_ID: 0.450 G_Rec: 0.361 D_GP: 0.031 D_real: 0.966 D_fake: 0.719 
+(epoch: 65, iters: 7488, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.670 G_ID: 0.283 G_Rec: 0.345 D_GP: 0.026 D_real: 0.923 D_fake: 0.844 
+(epoch: 65, iters: 7888, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.760 G_ID: 0.417 G_Rec: 0.344 D_GP: 0.036 D_real: 1.190 D_fake: 0.525 
+(epoch: 65, iters: 8288, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.711 G_ID: 0.236 G_Rec: 0.361 D_GP: 0.032 D_real: 1.045 D_fake: 0.768 
+(epoch: 66, iters: 80, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.777 G_ID: 0.412 G_Rec: 0.358 D_GP: 0.066 D_real: 0.837 D_fake: 0.763 
+(epoch: 66, iters: 480, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.996 G_ID: 0.207 G_Rec: 0.417 D_GP: 0.037 D_real: 0.570 D_fake: 0.732 
+(epoch: 66, iters: 880, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.670 G_ID: 0.410 G_Rec: 0.380 D_GP: 0.020 D_real: 1.110 D_fake: 0.679 
+(epoch: 66, iters: 1280, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.899 G_ID: 0.218 G_Rec: 0.351 D_GP: 0.069 D_real: 0.722 D_fake: 0.741 
+(epoch: 66, iters: 1680, time: 0.064) G_GAN: 0.820 G_GAN_Feat: 1.213 G_ID: 0.444 G_Rec: 0.438 D_GP: 0.120 D_real: 0.699 D_fake: 0.235 
+(epoch: 66, iters: 2080, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 1.223 G_ID: 0.216 G_Rec: 0.405 D_GP: 0.200 D_real: 0.499 D_fake: 0.816 
+(epoch: 66, iters: 2480, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 1.461 G_ID: 0.441 G_Rec: 0.387 D_GP: 1.862 D_real: 0.440 D_fake: 0.579 
+(epoch: 66, iters: 2880, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.597 G_ID: 0.218 G_Rec: 0.381 D_GP: 0.025 D_real: 1.174 D_fake: 0.676 
+(epoch: 66, iters: 3280, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 0.614 G_ID: 0.462 G_Rec: 0.426 D_GP: 0.022 D_real: 1.067 D_fake: 0.848 
+(epoch: 66, iters: 3680, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.574 G_ID: 0.222 G_Rec: 0.396 D_GP: 0.019 D_real: 1.238 D_fake: 0.642 
+(epoch: 66, iters: 4080, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 0.725 G_ID: 0.423 G_Rec: 0.409 D_GP: 0.043 D_real: 1.227 D_fake: 0.554 
+(epoch: 66, iters: 4480, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.776 G_ID: 0.219 G_Rec: 0.396 D_GP: 0.053 D_real: 1.110 D_fake: 0.586 
+(epoch: 66, iters: 4880, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.762 G_ID: 0.428 G_Rec: 0.367 D_GP: 0.054 D_real: 1.240 D_fake: 0.527 
+(epoch: 66, iters: 5280, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.644 G_ID: 0.251 G_Rec: 0.373 D_GP: 0.024 D_real: 0.934 D_fake: 0.879 
+(epoch: 66, iters: 5680, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.968 G_ID: 0.465 G_Rec: 0.365 D_GP: 0.049 D_real: 0.869 D_fake: 0.596 
+(epoch: 66, iters: 6080, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.725 G_ID: 0.245 G_Rec: 0.369 D_GP: 0.044 D_real: 0.908 D_fake: 0.859 
+(epoch: 66, iters: 6480, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.828 G_ID: 0.497 G_Rec: 0.371 D_GP: 0.061 D_real: 0.956 D_fake: 0.630 
+(epoch: 66, iters: 6880, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.667 G_ID: 0.223 G_Rec: 0.339 D_GP: 0.030 D_real: 1.074 D_fake: 0.774 
+(epoch: 66, iters: 7280, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.863 G_ID: 0.404 G_Rec: 0.383 D_GP: 0.041 D_real: 1.000 D_fake: 0.577 
+(epoch: 66, iters: 7680, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.796 G_ID: 0.226 G_Rec: 0.347 D_GP: 0.062 D_real: 0.961 D_fake: 0.599 
+(epoch: 66, iters: 8080, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.883 G_ID: 0.429 G_Rec: 0.430 D_GP: 0.038 D_real: 0.964 D_fake: 0.574 
+(epoch: 66, iters: 8480, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.748 G_ID: 0.205 G_Rec: 0.431 D_GP: 0.034 D_real: 1.134 D_fake: 0.622 
+(epoch: 67, iters: 272, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.929 G_ID: 0.434 G_Rec: 0.370 D_GP: 0.065 D_real: 1.028 D_fake: 0.691 
+(epoch: 67, iters: 672, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 1.081 G_ID: 0.198 G_Rec: 0.374 D_GP: 0.052 D_real: 0.722 D_fake: 0.620 
+(epoch: 67, iters: 1072, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 1.121 G_ID: 0.462 G_Rec: 0.424 D_GP: 0.082 D_real: 0.217 D_fake: 0.824 
+(epoch: 67, iters: 1472, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.829 G_ID: 0.194 G_Rec: 0.408 D_GP: 0.051 D_real: 0.991 D_fake: 0.546 
+(epoch: 67, iters: 1872, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.784 G_ID: 0.428 G_Rec: 0.403 D_GP: 0.029 D_real: 1.151 D_fake: 0.602 
+(epoch: 67, iters: 2272, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 1.408 G_ID: 0.249 G_Rec: 0.365 D_GP: 0.237 D_real: 0.239 D_fake: 0.918 
+(epoch: 67, iters: 2672, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 1.056 G_ID: 0.393 G_Rec: 0.394 D_GP: 0.068 D_real: 0.634 D_fake: 0.576 
+(epoch: 67, iters: 3072, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.662 G_ID: 0.213 G_Rec: 0.358 D_GP: 0.022 D_real: 1.254 D_fake: 0.548 
+(epoch: 67, iters: 3472, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.746 G_ID: 0.392 G_Rec: 0.405 D_GP: 0.025 D_real: 1.144 D_fake: 0.553 
+(epoch: 67, iters: 3872, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.826 G_ID: 0.255 G_Rec: 0.361 D_GP: 0.051 D_real: 0.954 D_fake: 0.618 
+(epoch: 67, iters: 4272, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.997 G_ID: 0.425 G_Rec: 0.393 D_GP: 0.105 D_real: 0.762 D_fake: 0.834 
+(epoch: 67, iters: 4672, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.684 G_ID: 0.250 G_Rec: 0.402 D_GP: 0.029 D_real: 1.191 D_fake: 0.739 
+(epoch: 67, iters: 5072, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.717 G_ID: 0.381 G_Rec: 0.384 D_GP: 0.026 D_real: 1.348 D_fake: 0.477 
+(epoch: 67, iters: 5472, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.784 G_ID: 0.237 G_Rec: 0.361 D_GP: 0.060 D_real: 1.067 D_fake: 0.714 
+(epoch: 67, iters: 5872, time: 0.064) G_GAN: 0.520 G_GAN_Feat: 0.897 G_ID: 0.459 G_Rec: 0.436 D_GP: 0.038 D_real: 1.151 D_fake: 0.484 
+(epoch: 67, iters: 6272, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.742 G_ID: 0.231 G_Rec: 0.376 D_GP: 0.027 D_real: 1.157 D_fake: 0.634 
+(epoch: 67, iters: 6672, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.806 G_ID: 0.421 G_Rec: 0.403 D_GP: 0.047 D_real: 1.074 D_fake: 0.588 
+(epoch: 67, iters: 7072, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.804 G_ID: 0.244 G_Rec: 0.437 D_GP: 0.093 D_real: 1.065 D_fake: 0.608 
+(epoch: 67, iters: 7472, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 1.086 G_ID: 0.421 G_Rec: 0.431 D_GP: 0.097 D_real: 0.534 D_fake: 0.612 
+(epoch: 67, iters: 7872, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.961 G_ID: 0.217 G_Rec: 0.370 D_GP: 0.069 D_real: 0.832 D_fake: 0.702 
+(epoch: 67, iters: 8272, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.757 G_ID: 0.429 G_Rec: 0.369 D_GP: 0.040 D_real: 1.113 D_fake: 0.671 
+(epoch: 68, iters: 64, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.975 G_ID: 0.240 G_Rec: 0.358 D_GP: 0.062 D_real: 0.651 D_fake: 0.726 
+(epoch: 68, iters: 464, time: 0.064) G_GAN: 0.765 G_GAN_Feat: 0.818 G_ID: 0.415 G_Rec: 0.396 D_GP: 0.031 D_real: 1.509 D_fake: 0.262 
+(epoch: 68, iters: 864, time: 0.064) G_GAN: 0.696 G_GAN_Feat: 0.869 G_ID: 0.208 G_Rec: 0.399 D_GP: 0.076 D_real: 1.111 D_fake: 0.362 
+(epoch: 68, iters: 1264, time: 0.064) G_GAN: 0.618 G_GAN_Feat: 0.812 G_ID: 0.449 G_Rec: 0.419 D_GP: 0.031 D_real: 1.323 D_fake: 0.399 
+(epoch: 68, iters: 1664, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.737 G_ID: 0.230 G_Rec: 0.369 D_GP: 0.030 D_real: 1.150 D_fake: 0.622 
+(epoch: 68, iters: 2064, time: 0.064) G_GAN: 0.496 G_GAN_Feat: 0.826 G_ID: 0.412 G_Rec: 0.385 D_GP: 0.038 D_real: 1.140 D_fake: 0.509 
+(epoch: 68, iters: 2464, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.687 G_ID: 0.252 G_Rec: 0.366 D_GP: 0.029 D_real: 1.079 D_fake: 0.683 
+(epoch: 68, iters: 2864, time: 0.064) G_GAN: 0.747 G_GAN_Feat: 1.004 G_ID: 0.436 G_Rec: 0.397 D_GP: 0.069 D_real: 1.359 D_fake: 0.305 
+(epoch: 68, iters: 3264, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.826 G_ID: 0.236 G_Rec: 0.364 D_GP: 0.055 D_real: 1.091 D_fake: 0.614 
+(epoch: 68, iters: 3664, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 0.971 G_ID: 0.465 G_Rec: 0.406 D_GP: 0.094 D_real: 1.029 D_fake: 0.456 
+(epoch: 68, iters: 4064, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.678 G_ID: 0.259 G_Rec: 0.401 D_GP: 0.022 D_real: 1.074 D_fake: 0.839 
+(epoch: 68, iters: 4464, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.994 G_ID: 0.413 G_Rec: 0.414 D_GP: 0.075 D_real: 0.623 D_fake: 0.652 
+(epoch: 68, iters: 4864, time: 0.064) G_GAN: 0.499 G_GAN_Feat: 0.817 G_ID: 0.235 G_Rec: 0.378 D_GP: 0.054 D_real: 1.148 D_fake: 0.506 
+(epoch: 68, iters: 5264, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.825 G_ID: 0.442 G_Rec: 0.416 D_GP: 0.035 D_real: 1.084 D_fake: 0.541 
+(epoch: 68, iters: 5664, time: 0.064) G_GAN: 0.591 G_GAN_Feat: 0.926 G_ID: 0.203 G_Rec: 0.408 D_GP: 0.080 D_real: 0.970 D_fake: 0.519 
+(epoch: 68, iters: 6064, time: 0.064) G_GAN: 0.694 G_GAN_Feat: 0.977 G_ID: 0.388 G_Rec: 0.378 D_GP: 0.034 D_real: 1.511 D_fake: 0.316 
+(epoch: 68, iters: 6464, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 1.100 G_ID: 0.252 G_Rec: 0.392 D_GP: 0.086 D_real: 0.433 D_fake: 0.823 
+(epoch: 68, iters: 6864, time: 0.064) G_GAN: 0.049 G_GAN_Feat: 0.866 G_ID: 0.443 G_Rec: 0.436 D_GP: 0.032 D_real: 0.758 D_fake: 0.951 
+(epoch: 68, iters: 7264, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.909 G_ID: 0.226 G_Rec: 0.386 D_GP: 0.098 D_real: 0.702 D_fake: 0.847 
+(epoch: 68, iters: 7664, time: 0.064) G_GAN: 0.096 G_GAN_Feat: 0.767 G_ID: 0.392 G_Rec: 0.364 D_GP: 0.037 D_real: 0.773 D_fake: 0.904 
+(epoch: 68, iters: 8064, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.833 G_ID: 0.261 G_Rec: 0.431 D_GP: 0.053 D_real: 0.958 D_fake: 0.598 
+(epoch: 68, iters: 8464, time: 0.064) G_GAN: 0.864 G_GAN_Feat: 1.298 G_ID: 0.402 G_Rec: 0.394 D_GP: 0.115 D_real: 1.144 D_fake: 0.249 
+(epoch: 69, iters: 256, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.666 G_ID: 0.206 G_Rec: 0.365 D_GP: 0.029 D_real: 1.035 D_fake: 0.788 
+(epoch: 69, iters: 656, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.762 G_ID: 0.387 G_Rec: 0.510 D_GP: 0.035 D_real: 1.274 D_fake: 0.547 
+(epoch: 69, iters: 1056, time: 0.064) G_GAN: -0.006 G_GAN_Feat: 1.153 G_ID: 0.293 G_Rec: 0.388 D_GP: 0.183 D_real: 0.258 D_fake: 1.006 
+(epoch: 69, iters: 1456, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 0.954 G_ID: 0.443 G_Rec: 0.460 D_GP: 0.032 D_real: 1.243 D_fake: 0.476 
+(epoch: 69, iters: 1856, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.688 G_ID: 0.254 G_Rec: 0.338 D_GP: 0.027 D_real: 1.227 D_fake: 0.596 
+(epoch: 69, iters: 2256, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.965 G_ID: 0.410 G_Rec: 0.364 D_GP: 0.089 D_real: 0.951 D_fake: 0.497 
+(epoch: 69, iters: 2656, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.941 G_ID: 0.224 G_Rec: 0.329 D_GP: 0.040 D_real: 0.758 D_fake: 0.750 
+(epoch: 69, iters: 3056, time: 0.064) G_GAN: 0.674 G_GAN_Feat: 0.900 G_ID: 0.428 G_Rec: 0.396 D_GP: 0.062 D_real: 1.276 D_fake: 0.350 
+(epoch: 69, iters: 3456, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.723 G_ID: 0.184 G_Rec: 0.357 D_GP: 0.030 D_real: 1.182 D_fake: 0.538 
+(epoch: 69, iters: 3856, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.856 G_ID: 0.414 G_Rec: 0.371 D_GP: 0.039 D_real: 1.112 D_fake: 0.564 
+(epoch: 69, iters: 4256, time: 0.064) G_GAN: 0.656 G_GAN_Feat: 0.977 G_ID: 0.194 G_Rec: 0.391 D_GP: 0.109 D_real: 0.874 D_fake: 0.408 
+(epoch: 69, iters: 4656, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.699 G_ID: 0.414 G_Rec: 0.377 D_GP: 0.024 D_real: 1.355 D_fake: 0.501 
+(epoch: 69, iters: 5056, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 1.043 G_ID: 0.211 G_Rec: 0.429 D_GP: 0.183 D_real: 0.415 D_fake: 0.710 
+(epoch: 69, iters: 5456, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.922 G_ID: 0.417 G_Rec: 0.447 D_GP: 0.035 D_real: 1.123 D_fake: 0.666 
+(epoch: 69, iters: 5856, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 0.727 G_ID: 0.237 G_Rec: 0.341 D_GP: 0.041 D_real: 1.188 D_fake: 0.546 
+(epoch: 69, iters: 6256, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.845 G_ID: 0.397 G_Rec: 0.413 D_GP: 0.036 D_real: 1.168 D_fake: 0.533 
+(epoch: 69, iters: 6656, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.928 G_ID: 0.201 G_Rec: 0.361 D_GP: 0.069 D_real: 0.507 D_fake: 0.913 
+(epoch: 69, iters: 7056, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.775 G_ID: 0.420 G_Rec: 0.372 D_GP: 0.029 D_real: 1.042 D_fake: 0.694 
+(epoch: 69, iters: 7456, time: 0.064) G_GAN: 0.131 G_GAN_Feat: 0.761 G_ID: 0.262 G_Rec: 0.367 D_GP: 0.035 D_real: 0.827 D_fake: 0.869 
+(epoch: 69, iters: 7856, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.783 G_ID: 0.419 G_Rec: 0.373 D_GP: 0.055 D_real: 0.809 D_fake: 0.799 
+(epoch: 69, iters: 8256, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.783 G_ID: 0.221 G_Rec: 0.356 D_GP: 0.042 D_real: 1.201 D_fake: 0.518 
+(epoch: 70, iters: 48, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.749 G_ID: 0.410 G_Rec: 0.385 D_GP: 0.026 D_real: 1.062 D_fake: 0.618 
+(epoch: 70, iters: 448, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.785 G_ID: 0.223 G_Rec: 0.366 D_GP: 0.056 D_real: 1.189 D_fake: 0.521 
+(epoch: 70, iters: 848, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 1.511 G_ID: 0.400 G_Rec: 0.431 D_GP: 0.226 D_real: 0.450 D_fake: 0.637 
+(epoch: 70, iters: 1248, time: 0.064) G_GAN: 0.779 G_GAN_Feat: 0.901 G_ID: 0.190 G_Rec: 0.341 D_GP: 0.049 D_real: 1.324 D_fake: 0.238 
+(epoch: 70, iters: 1648, time: 0.064) G_GAN: 1.037 G_GAN_Feat: 1.149 G_ID: 0.411 G_Rec: 0.371 D_GP: 0.054 D_real: 1.550 D_fake: 0.220 
+(epoch: 70, iters: 2048, time: 0.064) G_GAN: 0.621 G_GAN_Feat: 0.724 G_ID: 0.213 G_Rec: 0.393 D_GP: 0.018 D_real: 1.536 D_fake: 0.391 
+(epoch: 70, iters: 2448, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.642 G_ID: 0.441 G_Rec: 0.410 D_GP: 0.018 D_real: 1.140 D_fake: 0.659 
+(epoch: 70, iters: 2848, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.615 G_ID: 0.205 G_Rec: 0.353 D_GP: 0.025 D_real: 1.155 D_fake: 0.700 
+(epoch: 70, iters: 3248, time: 0.064) G_GAN: 0.029 G_GAN_Feat: 0.775 G_ID: 0.442 G_Rec: 0.395 D_GP: 0.042 D_real: 0.740 D_fake: 0.971 
+(epoch: 70, iters: 3648, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.663 G_ID: 0.193 G_Rec: 0.364 D_GP: 0.032 D_real: 1.098 D_fake: 0.727 
+(epoch: 70, iters: 4048, time: 0.064) G_GAN: 0.590 G_GAN_Feat: 0.818 G_ID: 0.374 G_Rec: 0.378 D_GP: 0.053 D_real: 1.209 D_fake: 0.425 
+(epoch: 70, iters: 4448, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.763 G_ID: 0.189 G_Rec: 0.385 D_GP: 0.030 D_real: 1.174 D_fake: 0.642 
+(epoch: 70, iters: 4848, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.822 G_ID: 0.377 G_Rec: 0.401 D_GP: 0.075 D_real: 0.827 D_fake: 0.741 
+(epoch: 70, iters: 5248, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 0.771 G_ID: 0.217 G_Rec: 0.372 D_GP: 0.052 D_real: 1.094 D_fake: 0.545 
+(epoch: 70, iters: 5648, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.883 G_ID: 0.375 G_Rec: 0.375 D_GP: 0.082 D_real: 0.878 D_fake: 0.635 
+(epoch: 70, iters: 6048, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.823 G_ID: 0.254 G_Rec: 0.396 D_GP: 0.067 D_real: 0.911 D_fake: 0.736 
+(epoch: 70, iters: 6448, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 0.919 G_ID: 0.379 G_Rec: 0.445 D_GP: 0.052 D_real: 0.972 D_fake: 0.507 
+(epoch: 70, iters: 6848, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.821 G_ID: 0.210 G_Rec: 0.384 D_GP: 0.065 D_real: 0.895 D_fake: 0.653 
+(epoch: 70, iters: 7248, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.904 G_ID: 0.385 G_Rec: 0.380 D_GP: 0.056 D_real: 0.832 D_fake: 0.726 
+(epoch: 70, iters: 7648, time: 0.064) G_GAN: 0.509 G_GAN_Feat: 0.845 G_ID: 0.217 G_Rec: 0.351 D_GP: 0.051 D_real: 1.210 D_fake: 0.495 
+(epoch: 70, iters: 8048, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.815 G_ID: 0.428 G_Rec: 0.353 D_GP: 0.029 D_real: 1.200 D_fake: 0.598 
+(epoch: 70, iters: 8448, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.917 G_ID: 0.197 G_Rec: 0.371 D_GP: 0.088 D_real: 0.608 D_fake: 0.769 
+(epoch: 71, iters: 240, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 1.406 G_ID: 0.420 G_Rec: 0.489 D_GP: 2.646 D_real: 0.458 D_fake: 0.953 
+(epoch: 71, iters: 640, time: 0.064) G_GAN: 0.135 G_GAN_Feat: 0.899 G_ID: 0.209 G_Rec: 0.423 D_GP: 0.027 D_real: 0.900 D_fake: 0.866 
+(epoch: 71, iters: 1040, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.847 G_ID: 0.420 G_Rec: 0.363 D_GP: 0.036 D_real: 0.904 D_fake: 0.809 
+(epoch: 71, iters: 1440, time: 0.064) G_GAN: -0.206 G_GAN_Feat: 0.897 G_ID: 0.238 G_Rec: 0.389 D_GP: 0.057 D_real: 0.656 D_fake: 1.206 
+(epoch: 71, iters: 1840, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.988 G_ID: 0.387 G_Rec: 0.399 D_GP: 0.089 D_real: 0.713 D_fake: 0.837 
+(epoch: 71, iters: 2240, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.834 G_ID: 0.232 G_Rec: 0.375 D_GP: 0.049 D_real: 1.041 D_fake: 0.650 
+(epoch: 71, iters: 2640, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.802 G_ID: 0.350 G_Rec: 0.434 D_GP: 0.030 D_real: 1.247 D_fake: 0.563 
+(epoch: 71, iters: 3040, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 1.129 G_ID: 0.221 G_Rec: 0.371 D_GP: 0.093 D_real: 0.774 D_fake: 0.711 
+(epoch: 71, iters: 3440, time: 0.064) G_GAN: 0.467 G_GAN_Feat: 0.848 G_ID: 0.382 G_Rec: 0.396 D_GP: 0.044 D_real: 1.176 D_fake: 0.547 
+(epoch: 71, iters: 3840, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.987 G_ID: 0.255 G_Rec: 0.370 D_GP: 0.058 D_real: 0.705 D_fake: 0.886 
+(epoch: 71, iters: 4240, time: 0.064) G_GAN: 0.664 G_GAN_Feat: 0.885 G_ID: 0.394 G_Rec: 0.365 D_GP: 0.041 D_real: 1.376 D_fake: 0.372 
+(epoch: 71, iters: 4640, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.768 G_ID: 0.253 G_Rec: 0.369 D_GP: 0.032 D_real: 1.029 D_fake: 0.829 
+(epoch: 71, iters: 5040, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.796 G_ID: 0.338 G_Rec: 0.391 D_GP: 0.038 D_real: 0.973 D_fake: 0.780 
+(epoch: 71, iters: 5440, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.816 G_ID: 0.220 G_Rec: 0.368 D_GP: 0.057 D_real: 0.898 D_fake: 0.774 
+(epoch: 71, iters: 5840, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.766 G_ID: 0.414 G_Rec: 0.370 D_GP: 0.034 D_real: 0.777 D_fake: 0.886 
+(epoch: 71, iters: 6240, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.867 G_ID: 0.200 G_Rec: 0.414 D_GP: 0.058 D_real: 0.816 D_fake: 0.744 
+(epoch: 71, iters: 6640, time: 0.064) G_GAN: -0.123 G_GAN_Feat: 0.943 G_ID: 0.424 G_Rec: 0.376 D_GP: 0.090 D_real: 0.694 D_fake: 1.123 
+(epoch: 71, iters: 7040, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.922 G_ID: 0.195 G_Rec: 0.361 D_GP: 0.047 D_real: 0.738 D_fake: 0.764 
+(epoch: 71, iters: 7440, time: 0.064) G_GAN: 0.624 G_GAN_Feat: 0.845 G_ID: 0.398 G_Rec: 0.392 D_GP: 0.035 D_real: 1.334 D_fake: 0.383 
+(epoch: 71, iters: 7840, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 1.004 G_ID: 0.257 G_Rec: 0.371 D_GP: 0.037 D_real: 1.238 D_fake: 0.442 
+(epoch: 71, iters: 8240, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.774 G_ID: 0.415 G_Rec: 0.466 D_GP: 0.028 D_real: 0.895 D_fake: 0.809 
+(epoch: 72, iters: 32, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.939 G_ID: 0.209 G_Rec: 0.352 D_GP: 0.031 D_real: 1.029 D_fake: 0.555 
+(epoch: 72, iters: 432, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.845 G_ID: 0.381 G_Rec: 0.433 D_GP: 0.032 D_real: 0.977 D_fake: 0.735 
+(epoch: 72, iters: 832, time: 0.064) G_GAN: 0.804 G_GAN_Feat: 0.854 G_ID: 0.210 G_Rec: 0.388 D_GP: 0.052 D_real: 1.471 D_fake: 0.259 
+(epoch: 72, iters: 1232, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 1.358 G_ID: 0.403 G_Rec: 0.410 D_GP: 0.230 D_real: 0.474 D_fake: 0.690 
+(epoch: 72, iters: 1632, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 1.152 G_ID: 0.190 G_Rec: 0.368 D_GP: 0.065 D_real: 0.462 D_fake: 0.824 
+(epoch: 72, iters: 2032, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.710 G_ID: 0.438 G_Rec: 0.371 D_GP: 0.029 D_real: 0.913 D_fake: 0.873 
+(epoch: 72, iters: 2432, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.838 G_ID: 0.198 G_Rec: 0.395 D_GP: 0.058 D_real: 0.715 D_fake: 0.907 
+(epoch: 72, iters: 2832, time: 0.064) G_GAN: -0.050 G_GAN_Feat: 0.804 G_ID: 0.337 G_Rec: 0.438 D_GP: 0.024 D_real: 0.869 D_fake: 1.050 
+(epoch: 72, iters: 3232, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.701 G_ID: 0.176 G_Rec: 0.355 D_GP: 0.029 D_real: 0.938 D_fake: 0.831 
+(epoch: 72, iters: 3632, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.800 G_ID: 0.391 G_Rec: 0.427 D_GP: 0.038 D_real: 0.872 D_fake: 0.801 
+(epoch: 72, iters: 4032, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.805 G_ID: 0.217 G_Rec: 0.359 D_GP: 0.031 D_real: 1.127 D_fake: 0.591 
+(epoch: 72, iters: 4432, time: 0.064) G_GAN: 0.700 G_GAN_Feat: 0.991 G_ID: 0.407 G_Rec: 0.468 D_GP: 0.079 D_real: 1.180 D_fake: 0.426 
+(epoch: 72, iters: 4832, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.815 G_ID: 0.246 G_Rec: 0.384 D_GP: 0.038 D_real: 0.937 D_fake: 0.706 
+(epoch: 72, iters: 5232, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 1.166 G_ID: 0.388 G_Rec: 0.406 D_GP: 0.162 D_real: 0.362 D_fake: 0.798 
+(epoch: 72, iters: 5632, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.727 G_ID: 0.244 G_Rec: 0.345 D_GP: 0.033 D_real: 0.936 D_fake: 0.837 
+(epoch: 72, iters: 6032, time: 0.064) G_GAN: 0.613 G_GAN_Feat: 0.716 G_ID: 0.384 G_Rec: 0.460 D_GP: 0.027 D_real: 1.412 D_fake: 0.395 
+(epoch: 72, iters: 6432, time: 0.064) G_GAN: -0.041 G_GAN_Feat: 0.761 G_ID: 0.223 G_Rec: 0.400 D_GP: 0.034 D_real: 0.840 D_fake: 1.041 
+(epoch: 72, iters: 6832, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.731 G_ID: 0.385 G_Rec: 0.382 D_GP: 0.029 D_real: 1.174 D_fake: 0.583 
+(epoch: 72, iters: 7232, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.722 G_ID: 0.236 G_Rec: 0.353 D_GP: 0.028 D_real: 0.990 D_fake: 0.790 
+(epoch: 72, iters: 7632, time: 0.064) G_GAN: -0.273 G_GAN_Feat: 0.991 G_ID: 0.382 G_Rec: 0.435 D_GP: 0.190 D_real: 0.310 D_fake: 1.273 
+(epoch: 72, iters: 8032, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 1.227 G_ID: 0.208 G_Rec: 0.409 D_GP: 0.077 D_real: 0.358 D_fake: 0.923 
+(epoch: 72, iters: 8432, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.984 G_ID: 0.421 G_Rec: 0.380 D_GP: 0.032 D_real: 0.886 D_fake: 0.688 
+(epoch: 73, iters: 224, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.794 G_ID: 0.245 G_Rec: 0.348 D_GP: 0.049 D_real: 0.770 D_fake: 0.871 
+(epoch: 73, iters: 624, time: 0.064) G_GAN: 0.494 G_GAN_Feat: 1.083 G_ID: 0.387 G_Rec: 0.414 D_GP: 0.295 D_real: 0.543 D_fake: 0.511 
+(epoch: 73, iters: 1024, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.874 G_ID: 0.213 G_Rec: 0.377 D_GP: 0.034 D_real: 0.881 D_fake: 0.663 
+(epoch: 73, iters: 1424, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.848 G_ID: 0.396 G_Rec: 0.384 D_GP: 0.051 D_real: 0.928 D_fake: 0.800 
+(epoch: 73, iters: 1824, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.676 G_ID: 0.226 G_Rec: 0.337 D_GP: 0.031 D_real: 1.214 D_fake: 0.606 
+(epoch: 73, iters: 2224, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 1.215 G_ID: 0.393 G_Rec: 0.397 D_GP: 0.074 D_real: 0.512 D_fake: 0.539 
+(epoch: 73, iters: 2624, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.934 G_ID: 0.203 G_Rec: 0.458 D_GP: 0.079 D_real: 0.698 D_fake: 0.691 
+(epoch: 73, iters: 3024, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.914 G_ID: 0.440 G_Rec: 0.413 D_GP: 0.036 D_real: 0.928 D_fake: 0.684 
+(epoch: 73, iters: 3424, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.880 G_ID: 0.256 G_Rec: 0.356 D_GP: 0.044 D_real: 1.068 D_fake: 0.470 
+(epoch: 73, iters: 3824, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.857 G_ID: 0.422 G_Rec: 0.411 D_GP: 0.028 D_real: 1.028 D_fake: 0.623 
+(epoch: 73, iters: 4224, time: 0.064) G_GAN: 0.583 G_GAN_Feat: 0.788 G_ID: 0.237 G_Rec: 0.397 D_GP: 0.038 D_real: 1.353 D_fake: 0.421 
+(epoch: 73, iters: 4624, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.808 G_ID: 0.379 G_Rec: 0.406 D_GP: 0.026 D_real: 1.011 D_fake: 0.813 
+(epoch: 73, iters: 5024, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.678 G_ID: 0.205 G_Rec: 0.397 D_GP: 0.022 D_real: 1.117 D_fake: 0.735 
+(epoch: 73, iters: 5424, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.825 G_ID: 0.343 G_Rec: 0.410 D_GP: 0.030 D_real: 1.010 D_fake: 0.670 
+(epoch: 73, iters: 5824, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.755 G_ID: 0.177 G_Rec: 0.359 D_GP: 0.048 D_real: 1.147 D_fake: 0.542 
+(epoch: 73, iters: 6224, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.895 G_ID: 0.395 G_Rec: 0.373 D_GP: 0.029 D_real: 0.982 D_fake: 0.647 
+(epoch: 73, iters: 6624, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.929 G_ID: 0.293 G_Rec: 0.376 D_GP: 0.067 D_real: 0.922 D_fake: 0.744 
+(epoch: 73, iters: 7024, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 1.020 G_ID: 0.378 G_Rec: 0.371 D_GP: 0.043 D_real: 0.634 D_fake: 0.669 
+(epoch: 73, iters: 7424, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.677 G_ID: 0.230 G_Rec: 0.349 D_GP: 0.026 D_real: 1.251 D_fake: 0.623 
+(epoch: 73, iters: 7824, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.908 G_ID: 0.348 G_Rec: 0.410 D_GP: 0.030 D_real: 0.754 D_fake: 0.838 
+(epoch: 73, iters: 8224, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.813 G_ID: 0.278 G_Rec: 0.359 D_GP: 0.032 D_real: 1.038 D_fake: 0.666 
+(epoch: 74, iters: 16, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.755 G_ID: 0.385 G_Rec: 0.441 D_GP: 0.027 D_real: 1.197 D_fake: 0.574 
+(epoch: 74, iters: 416, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.631 G_ID: 0.238 G_Rec: 0.348 D_GP: 0.024 D_real: 1.187 D_fake: 0.705 
+(epoch: 74, iters: 816, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 1.044 G_ID: 0.391 G_Rec: 0.472 D_GP: 0.058 D_real: 1.045 D_fake: 0.548 
+(epoch: 74, iters: 1216, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.723 G_ID: 0.206 G_Rec: 0.430 D_GP: 0.031 D_real: 1.097 D_fake: 0.671 
+(epoch: 74, iters: 1616, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.865 G_ID: 0.378 G_Rec: 0.376 D_GP: 0.046 D_real: 1.098 D_fake: 0.516 
+(epoch: 74, iters: 2016, time: 0.064) G_GAN: 0.473 G_GAN_Feat: 0.879 G_ID: 0.221 G_Rec: 0.354 D_GP: 0.084 D_real: 1.005 D_fake: 0.530 
+(epoch: 74, iters: 2416, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.896 G_ID: 0.431 G_Rec: 0.382 D_GP: 0.071 D_real: 0.906 D_fake: 0.613 
+(epoch: 74, iters: 2816, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.675 G_ID: 0.185 G_Rec: 0.370 D_GP: 0.024 D_real: 1.202 D_fake: 0.669 
+(epoch: 74, iters: 3216, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.784 G_ID: 0.356 G_Rec: 0.397 D_GP: 0.031 D_real: 0.980 D_fake: 0.708 
+(epoch: 74, iters: 3616, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.979 G_ID: 0.292 G_Rec: 0.393 D_GP: 0.052 D_real: 1.045 D_fake: 0.735 
+(epoch: 74, iters: 4016, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 1.154 G_ID: 0.353 G_Rec: 0.530 D_GP: 0.111 D_real: 0.533 D_fake: 0.634 
+(epoch: 74, iters: 4416, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 1.098 G_ID: 0.220 G_Rec: 0.400 D_GP: 0.554 D_real: 0.497 D_fake: 0.788 
+(epoch: 74, iters: 4816, time: 0.064) G_GAN: 0.016 G_GAN_Feat: 1.093 G_ID: 0.365 G_Rec: 0.413 D_GP: 0.102 D_real: 0.321 D_fake: 0.986 
+(epoch: 74, iters: 5216, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 0.754 G_ID: 0.187 G_Rec: 0.375 D_GP: 0.022 D_real: 1.255 D_fake: 0.592 
+(epoch: 74, iters: 5616, time: 0.064) G_GAN: 0.580 G_GAN_Feat: 0.977 G_ID: 0.378 G_Rec: 0.412 D_GP: 0.079 D_real: 1.005 D_fake: 0.436 
+(epoch: 74, iters: 6016, time: 0.064) G_GAN: 0.769 G_GAN_Feat: 1.065 G_ID: 0.218 G_Rec: 0.398 D_GP: 0.139 D_real: 0.731 D_fake: 0.330 
+(epoch: 74, iters: 6416, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.969 G_ID: 0.374 G_Rec: 0.425 D_GP: 0.058 D_real: 0.500 D_fake: 0.905 
+(epoch: 74, iters: 6816, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 1.009 G_ID: 0.202 G_Rec: 0.393 D_GP: 0.076 D_real: 0.504 D_fake: 0.705 
+(epoch: 74, iters: 7216, time: 0.064) G_GAN: 0.709 G_GAN_Feat: 0.979 G_ID: 0.399 G_Rec: 0.396 D_GP: 0.052 D_real: 1.025 D_fake: 0.370 
+(epoch: 74, iters: 7616, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.664 G_ID: 0.224 G_Rec: 0.365 D_GP: 0.020 D_real: 1.205 D_fake: 0.672 
+(epoch: 74, iters: 8016, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.692 G_ID: 0.403 G_Rec: 0.379 D_GP: 0.023 D_real: 1.025 D_fake: 0.789 
+(epoch: 74, iters: 8416, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.776 G_ID: 0.231 G_Rec: 0.399 D_GP: 0.059 D_real: 0.854 D_fake: 0.821 
+(epoch: 75, iters: 208, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.762 G_ID: 0.359 G_Rec: 0.375 D_GP: 0.026 D_real: 1.185 D_fake: 0.618 
+(epoch: 75, iters: 608, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 1.079 G_ID: 0.181 G_Rec: 0.403 D_GP: 0.467 D_real: 0.530 D_fake: 0.650 
+(epoch: 75, iters: 1008, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 0.921 G_ID: 0.320 G_Rec: 0.437 D_GP: 0.091 D_real: 0.915 D_fake: 0.490 
+(epoch: 75, iters: 1408, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.678 G_ID: 0.191 G_Rec: 0.366 D_GP: 0.028 D_real: 1.130 D_fake: 0.758 
+(epoch: 75, iters: 1808, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.761 G_ID: 0.369 G_Rec: 0.337 D_GP: 0.034 D_real: 1.097 D_fake: 0.626 
+(epoch: 75, iters: 2208, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 1.020 G_ID: 0.181 G_Rec: 0.385 D_GP: 0.091 D_real: 0.498 D_fake: 0.707 
+(epoch: 75, iters: 2608, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.759 G_ID: 0.334 G_Rec: 0.379 D_GP: 0.025 D_real: 0.825 D_fake: 0.896 
+(epoch: 75, iters: 3008, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 1.146 G_ID: 0.223 G_Rec: 0.421 D_GP: 0.149 D_real: 0.437 D_fake: 0.879 
+(epoch: 75, iters: 3408, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.866 G_ID: 0.432 G_Rec: 0.415 D_GP: 0.039 D_real: 1.090 D_fake: 0.648 
+(epoch: 75, iters: 3808, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.846 G_ID: 0.202 G_Rec: 0.373 D_GP: 0.036 D_real: 1.143 D_fake: 0.530 
+(epoch: 75, iters: 4208, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.940 G_ID: 0.426 G_Rec: 0.386 D_GP: 0.093 D_real: 0.429 D_fake: 0.893 
+(epoch: 75, iters: 4608, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.759 G_ID: 0.222 G_Rec: 0.357 D_GP: 0.033 D_real: 1.019 D_fake: 0.774 
+(epoch: 75, iters: 5008, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.815 G_ID: 0.347 G_Rec: 0.408 D_GP: 0.034 D_real: 1.263 D_fake: 0.515 
+(epoch: 75, iters: 5408, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.786 G_ID: 0.218 G_Rec: 0.334 D_GP: 0.044 D_real: 1.048 D_fake: 0.599 
+(epoch: 75, iters: 5808, time: 0.064) G_GAN: 0.813 G_GAN_Feat: 0.948 G_ID: 0.318 G_Rec: 0.397 D_GP: 0.056 D_real: 1.335 D_fake: 0.232 
+(epoch: 75, iters: 6208, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.648 G_ID: 0.205 G_Rec: 0.373 D_GP: 0.021 D_real: 1.229 D_fake: 0.702 
+(epoch: 75, iters: 6608, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 1.146 G_ID: 0.400 G_Rec: 0.423 D_GP: 0.051 D_real: 0.837 D_fake: 0.672 
+(epoch: 75, iters: 7008, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.960 G_ID: 0.200 G_Rec: 0.377 D_GP: 0.069 D_real: 1.132 D_fake: 0.663 
+(epoch: 75, iters: 7408, time: 0.064) G_GAN: -0.194 G_GAN_Feat: 0.851 G_ID: 0.413 G_Rec: 0.397 D_GP: 0.060 D_real: 0.563 D_fake: 1.194 
+(epoch: 75, iters: 7808, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.787 G_ID: 0.242 G_Rec: 0.370 D_GP: 0.070 D_real: 0.795 D_fake: 0.830 
+(epoch: 75, iters: 8208, time: 0.064) G_GAN: 0.618 G_GAN_Feat: 0.741 G_ID: 0.357 G_Rec: 0.346 D_GP: 0.030 D_real: 1.364 D_fake: 0.391 
+(epoch: 75, iters: 8608, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 1.012 G_ID: 0.193 G_Rec: 0.347 D_GP: 0.056 D_real: 0.624 D_fake: 0.712 
+(epoch: 76, iters: 400, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.883 G_ID: 0.421 G_Rec: 0.428 D_GP: 0.029 D_real: 0.934 D_fake: 0.673 
+(epoch: 76, iters: 800, time: 0.064) G_GAN: 0.131 G_GAN_Feat: 0.688 G_ID: 0.197 G_Rec: 0.349 D_GP: 0.027 D_real: 0.939 D_fake: 0.869 
+(epoch: 76, iters: 1200, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.946 G_ID: 0.350 G_Rec: 0.395 D_GP: 0.065 D_real: 0.823 D_fake: 0.735 
+(epoch: 76, iters: 1600, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.936 G_ID: 0.206 G_Rec: 0.381 D_GP: 0.168 D_real: 0.722 D_fake: 0.865 
+(epoch: 76, iters: 2000, time: 0.064) G_GAN: 0.651 G_GAN_Feat: 0.780 G_ID: 0.327 G_Rec: 0.351 D_GP: 0.057 D_real: 1.356 D_fake: 0.379 
+(epoch: 76, iters: 2400, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 1.162 G_ID: 0.248 G_Rec: 0.368 D_GP: 0.173 D_real: 0.333 D_fake: 0.777 
+(epoch: 76, iters: 2800, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.882 G_ID: 0.306 G_Rec: 0.398 D_GP: 0.030 D_real: 1.104 D_fake: 0.571 
+(epoch: 76, iters: 3200, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.942 G_ID: 0.236 G_Rec: 0.377 D_GP: 0.087 D_real: 0.926 D_fake: 0.683 
+(epoch: 76, iters: 3600, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.860 G_ID: 0.437 G_Rec: 0.421 D_GP: 0.038 D_real: 0.988 D_fake: 0.645 
+(epoch: 76, iters: 4000, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 1.060 G_ID: 0.211 G_Rec: 0.392 D_GP: 0.404 D_real: 0.395 D_fake: 0.915 
+(epoch: 76, iters: 4400, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 0.931 G_ID: 0.355 G_Rec: 0.388 D_GP: 0.045 D_real: 0.963 D_fake: 0.539 
+(epoch: 76, iters: 4800, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.696 G_ID: 0.184 G_Rec: 0.346 D_GP: 0.025 D_real: 1.148 D_fake: 0.737 
+(epoch: 76, iters: 5200, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.725 G_ID: 0.326 G_Rec: 0.397 D_GP: 0.022 D_real: 1.292 D_fake: 0.483 
+(epoch: 76, iters: 5600, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.632 G_ID: 0.216 G_Rec: 0.350 D_GP: 0.025 D_real: 1.085 D_fake: 0.709 
+(epoch: 76, iters: 6000, time: 0.064) G_GAN: 0.545 G_GAN_Feat: 0.819 G_ID: 0.359 G_Rec: 0.450 D_GP: 0.041 D_real: 1.243 D_fake: 0.492 
+(epoch: 76, iters: 6400, time: 0.064) G_GAN: 0.528 G_GAN_Feat: 0.746 G_ID: 0.218 G_Rec: 0.355 D_GP: 0.031 D_real: 1.321 D_fake: 0.479 
+(epoch: 76, iters: 6800, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.906 G_ID: 0.354 G_Rec: 0.401 D_GP: 0.043 D_real: 1.089 D_fake: 0.612 
+(epoch: 76, iters: 7200, time: 0.064) G_GAN: -0.272 G_GAN_Feat: 0.835 G_ID: 0.216 G_Rec: 0.396 D_GP: 0.066 D_real: 0.402 D_fake: 1.272 
+(epoch: 76, iters: 7600, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.956 G_ID: 0.332 G_Rec: 0.384 D_GP: 0.032 D_real: 1.163 D_fake: 0.714 
+(epoch: 76, iters: 8000, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.881 G_ID: 0.238 G_Rec: 0.437 D_GP: 0.034 D_real: 0.907 D_fake: 0.732 
+(epoch: 76, iters: 8400, time: 0.064) G_GAN: 0.732 G_GAN_Feat: 0.868 G_ID: 0.333 G_Rec: 0.380 D_GP: 0.072 D_real: 1.407 D_fake: 0.309 
+(epoch: 77, iters: 192, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.759 G_ID: 0.209 G_Rec: 0.362 D_GP: 0.034 D_real: 1.315 D_fake: 0.478 
+(epoch: 77, iters: 592, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.940 G_ID: 0.314 G_Rec: 0.388 D_GP: 0.059 D_real: 0.652 D_fake: 0.762 
+(epoch: 77, iters: 992, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.810 G_ID: 0.237 G_Rec: 0.367 D_GP: 0.064 D_real: 0.879 D_fake: 0.706 
+(epoch: 77, iters: 1392, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.856 G_ID: 0.399 G_Rec: 0.384 D_GP: 0.045 D_real: 1.092 D_fake: 0.641 
+(epoch: 77, iters: 1792, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.752 G_ID: 0.228 G_Rec: 0.343 D_GP: 0.033 D_real: 1.115 D_fake: 0.673 
+(epoch: 77, iters: 2192, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.755 G_ID: 0.378 G_Rec: 0.383 D_GP: 0.026 D_real: 1.132 D_fake: 0.711 
+(epoch: 77, iters: 2592, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.798 G_ID: 0.242 G_Rec: 0.406 D_GP: 0.044 D_real: 1.038 D_fake: 0.654 
+(epoch: 77, iters: 2992, time: 0.064) G_GAN: 0.704 G_GAN_Feat: 0.891 G_ID: 0.345 G_Rec: 0.396 D_GP: 0.073 D_real: 1.208 D_fake: 0.314 
+(epoch: 77, iters: 3392, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.771 G_ID: 0.228 G_Rec: 0.359 D_GP: 0.034 D_real: 0.974 D_fake: 0.758 
+(epoch: 77, iters: 3792, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.996 G_ID: 0.330 G_Rec: 0.426 D_GP: 0.101 D_real: 0.266 D_fake: 0.921 
+(epoch: 77, iters: 4192, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.766 G_ID: 0.187 G_Rec: 0.369 D_GP: 0.039 D_real: 1.047 D_fake: 0.715 
+(epoch: 77, iters: 4592, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.770 G_ID: 0.369 G_Rec: 0.393 D_GP: 0.034 D_real: 1.112 D_fake: 0.618 
+(epoch: 77, iters: 4992, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.887 G_ID: 0.202 G_Rec: 0.364 D_GP: 0.088 D_real: 0.668 D_fake: 0.744 
+(epoch: 77, iters: 5392, time: 0.064) G_GAN: 0.635 G_GAN_Feat: 0.906 G_ID: 0.367 G_Rec: 0.380 D_GP: 0.054 D_real: 1.263 D_fake: 0.377 
+(epoch: 77, iters: 5792, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 1.132 G_ID: 0.182 G_Rec: 0.420 D_GP: 0.354 D_real: 0.814 D_fake: 0.501 
+(epoch: 77, iters: 6192, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 1.277 G_ID: 0.382 G_Rec: 0.421 D_GP: 0.127 D_real: 0.255 D_fake: 0.842 
+(epoch: 77, iters: 6592, time: 0.064) G_GAN: -0.065 G_GAN_Feat: 0.975 G_ID: 0.226 G_Rec: 0.379 D_GP: 0.138 D_real: 0.461 D_fake: 1.065 
+(epoch: 77, iters: 6992, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 1.517 G_ID: 0.382 G_Rec: 0.438 D_GP: 0.386 D_real: 0.553 D_fake: 0.595 
+(epoch: 77, iters: 7392, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.857 G_ID: 0.215 G_Rec: 0.354 D_GP: 0.221 D_real: 0.865 D_fake: 0.684 
+(epoch: 77, iters: 7792, time: 0.064) G_GAN: 0.731 G_GAN_Feat: 1.057 G_ID: 0.324 G_Rec: 0.432 D_GP: 0.128 D_real: 0.861 D_fake: 0.315 
+(epoch: 77, iters: 8192, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.707 G_ID: 0.216 G_Rec: 0.359 D_GP: 0.031 D_real: 1.009 D_fake: 0.879 
+(epoch: 77, iters: 8592, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.755 G_ID: 0.391 G_Rec: 0.375 D_GP: 0.026 D_real: 1.165 D_fake: 0.677 
+(epoch: 78, iters: 384, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.875 G_ID: 0.198 G_Rec: 0.377 D_GP: 0.058 D_real: 0.864 D_fake: 0.729 
+(epoch: 78, iters: 784, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 0.781 G_ID: 0.344 G_Rec: 0.427 D_GP: 0.027 D_real: 1.248 D_fake: 0.559 
+(epoch: 78, iters: 1184, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.776 G_ID: 0.181 G_Rec: 0.351 D_GP: 0.034 D_real: 0.883 D_fake: 0.790 
+(epoch: 78, iters: 1584, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.753 G_ID: 0.344 G_Rec: 0.386 D_GP: 0.024 D_real: 1.178 D_fake: 0.614 
+(epoch: 78, iters: 1984, time: 0.064) G_GAN: 0.576 G_GAN_Feat: 0.873 G_ID: 0.267 G_Rec: 0.382 D_GP: 0.036 D_real: 1.166 D_fake: 0.474 
+(epoch: 78, iters: 2384, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.898 G_ID: 0.358 G_Rec: 0.392 D_GP: 0.042 D_real: 1.012 D_fake: 0.513 
+(epoch: 78, iters: 2784, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.749 G_ID: 0.258 G_Rec: 0.399 D_GP: 0.025 D_real: 1.260 D_fake: 0.578 
+(epoch: 78, iters: 3184, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.841 G_ID: 0.368 G_Rec: 0.375 D_GP: 0.030 D_real: 1.053 D_fake: 0.721 
+(epoch: 78, iters: 3584, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.928 G_ID: 0.193 G_Rec: 0.377 D_GP: 0.050 D_real: 0.797 D_fake: 0.649 
+(epoch: 78, iters: 3984, time: 0.064) G_GAN: 0.811 G_GAN_Feat: 1.147 G_ID: 0.351 G_Rec: 0.398 D_GP: 0.181 D_real: 0.765 D_fake: 0.357 
+(epoch: 78, iters: 4384, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 1.130 G_ID: 0.233 G_Rec: 0.396 D_GP: 0.074 D_real: 0.360 D_fake: 0.746 
+(epoch: 78, iters: 4784, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.920 G_ID: 0.355 G_Rec: 0.386 D_GP: 0.076 D_real: 0.684 D_fake: 0.868 
+(epoch: 78, iters: 5184, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.861 G_ID: 0.263 G_Rec: 0.393 D_GP: 0.025 D_real: 0.919 D_fake: 0.850 
+(epoch: 78, iters: 5584, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 1.217 G_ID: 0.356 G_Rec: 0.470 D_GP: 0.043 D_real: 0.518 D_fake: 0.707 
+(epoch: 78, iters: 5984, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.787 G_ID: 0.193 G_Rec: 0.388 D_GP: 0.023 D_real: 1.079 D_fake: 0.761 
+(epoch: 78, iters: 6384, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.808 G_ID: 0.330 G_Rec: 0.364 D_GP: 0.031 D_real: 0.963 D_fake: 0.790 
+(epoch: 78, iters: 6784, time: 0.064) G_GAN: 0.543 G_GAN_Feat: 0.734 G_ID: 0.203 G_Rec: 0.384 D_GP: 0.025 D_real: 1.372 D_fake: 0.458 
+(epoch: 78, iters: 7184, time: 0.064) G_GAN: 0.603 G_GAN_Feat: 0.904 G_ID: 0.331 G_Rec: 0.414 D_GP: 0.035 D_real: 1.223 D_fake: 0.406 
+(epoch: 78, iters: 7584, time: 0.064) G_GAN: 0.529 G_GAN_Feat: 0.870 G_ID: 0.186 G_Rec: 0.379 D_GP: 0.080 D_real: 0.955 D_fake: 0.473 
+(epoch: 78, iters: 7984, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.920 G_ID: 0.370 G_Rec: 0.383 D_GP: 0.045 D_real: 0.807 D_fake: 0.759 
+(epoch: 78, iters: 8384, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.762 G_ID: 0.213 G_Rec: 0.369 D_GP: 0.047 D_real: 1.046 D_fake: 0.662 
+(epoch: 79, iters: 176, time: 0.064) G_GAN: 0.165 G_GAN_Feat: 0.807 G_ID: 0.425 G_Rec: 0.398 D_GP: 0.026 D_real: 0.884 D_fake: 0.835 
+(epoch: 79, iters: 576, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.917 G_ID: 0.203 G_Rec: 0.349 D_GP: 0.092 D_real: 0.559 D_fake: 0.799 
+(epoch: 79, iters: 976, time: 0.064) G_GAN: 0.897 G_GAN_Feat: 1.038 G_ID: 0.329 G_Rec: 0.406 D_GP: 0.050 D_real: 1.438 D_fake: 0.187 
+(epoch: 79, iters: 1376, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 1.265 G_ID: 0.223 G_Rec: 0.422 D_GP: 0.061 D_real: 0.404 D_fake: 0.626 
+(epoch: 79, iters: 1776, time: 0.064) G_GAN: 0.791 G_GAN_Feat: 0.783 G_ID: 0.360 G_Rec: 0.394 D_GP: 0.026 D_real: 1.563 D_fake: 0.282 
+(epoch: 79, iters: 2176, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 0.802 G_ID: 0.178 G_Rec: 0.365 D_GP: 0.043 D_real: 1.214 D_fake: 0.440 
+(epoch: 79, iters: 2576, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.943 G_ID: 0.348 G_Rec: 0.427 D_GP: 0.055 D_real: 0.545 D_fake: 0.882 
+(epoch: 79, iters: 2976, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.917 G_ID: 0.226 G_Rec: 0.387 D_GP: 0.042 D_real: 1.105 D_fake: 0.687 
+(epoch: 79, iters: 3376, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.996 G_ID: 0.395 G_Rec: 0.400 D_GP: 0.079 D_real: 0.571 D_fake: 0.614 
+(epoch: 79, iters: 3776, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.893 G_ID: 0.235 G_Rec: 0.395 D_GP: 0.149 D_real: 0.779 D_fake: 0.773 
+(epoch: 79, iters: 4176, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.826 G_ID: 0.320 G_Rec: 0.483 D_GP: 0.032 D_real: 1.133 D_fake: 0.532 
+(epoch: 79, iters: 4576, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.993 G_ID: 0.202 G_Rec: 0.346 D_GP: 0.040 D_real: 0.811 D_fake: 0.614 
+(epoch: 79, iters: 4976, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.823 G_ID: 0.399 G_Rec: 0.385 D_GP: 0.039 D_real: 1.029 D_fake: 0.726 
+(epoch: 79, iters: 5376, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.776 G_ID: 0.173 G_Rec: 0.388 D_GP: 0.041 D_real: 0.983 D_fake: 0.696 
+(epoch: 79, iters: 5776, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 1.278 G_ID: 0.344 G_Rec: 0.431 D_GP: 0.292 D_real: 0.436 D_fake: 0.718 
+(epoch: 79, iters: 6176, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.761 G_ID: 0.192 G_Rec: 0.390 D_GP: 0.021 D_real: 1.275 D_fake: 0.643 
+(epoch: 79, iters: 6576, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.836 G_ID: 0.302 G_Rec: 0.391 D_GP: 0.050 D_real: 1.157 D_fake: 0.571 
+(epoch: 79, iters: 6976, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.801 G_ID: 0.196 G_Rec: 0.356 D_GP: 0.045 D_real: 1.122 D_fake: 0.588 
+(epoch: 79, iters: 7376, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.837 G_ID: 0.401 G_Rec: 0.445 D_GP: 0.028 D_real: 1.049 D_fake: 0.612 
+(epoch: 79, iters: 7776, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.884 G_ID: 0.185 G_Rec: 0.432 D_GP: 0.044 D_real: 1.072 D_fake: 0.592 
+(epoch: 79, iters: 8176, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.838 G_ID: 0.349 G_Rec: 0.366 D_GP: 0.027 D_real: 1.058 D_fake: 0.598 
+(epoch: 79, iters: 8576, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 1.235 G_ID: 0.187 G_Rec: 0.384 D_GP: 0.275 D_real: 0.308 D_fake: 0.730 
+(epoch: 80, iters: 368, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.801 G_ID: 0.323 G_Rec: 0.381 D_GP: 0.032 D_real: 1.183 D_fake: 0.593 
+(epoch: 80, iters: 768, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 0.635 G_ID: 0.193 G_Rec: 0.363 D_GP: 0.020 D_real: 1.346 D_fake: 0.535 
+(epoch: 80, iters: 1168, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.895 G_ID: 0.369 G_Rec: 0.502 D_GP: 0.051 D_real: 0.938 D_fake: 0.729 
+(epoch: 80, iters: 1568, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.854 G_ID: 0.209 G_Rec: 0.432 D_GP: 0.090 D_real: 0.724 D_fake: 0.809 
+(epoch: 80, iters: 1968, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.813 G_ID: 0.299 G_Rec: 0.390 D_GP: 0.034 D_real: 1.304 D_fake: 0.514 
+(epoch: 80, iters: 2368, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.647 G_ID: 0.197 G_Rec: 0.393 D_GP: 0.022 D_real: 1.167 D_fake: 0.644 
+(epoch: 80, iters: 2768, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.739 G_ID: 0.340 G_Rec: 0.385 D_GP: 0.028 D_real: 1.156 D_fake: 0.640 
+(epoch: 80, iters: 3168, time: 0.064) G_GAN: -0.269 G_GAN_Feat: 0.643 G_ID: 0.207 G_Rec: 0.326 D_GP: 0.024 D_real: 0.858 D_fake: 1.269 
+(epoch: 80, iters: 3568, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 1.044 G_ID: 0.329 G_Rec: 0.394 D_GP: 0.052 D_real: 0.696 D_fake: 0.598 
+(epoch: 80, iters: 3968, time: 0.064) G_GAN: 0.139 G_GAN_Feat: 0.720 G_ID: 0.186 G_Rec: 0.418 D_GP: 0.040 D_real: 0.937 D_fake: 0.861 
+(epoch: 80, iters: 4368, time: 0.064) G_GAN: 0.131 G_GAN_Feat: 0.805 G_ID: 0.377 G_Rec: 0.425 D_GP: 0.048 D_real: 0.775 D_fake: 0.869 
+(epoch: 80, iters: 4768, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.633 G_ID: 0.220 G_Rec: 0.337 D_GP: 0.031 D_real: 1.347 D_fake: 0.533 
+(epoch: 80, iters: 5168, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.873 G_ID: 0.371 G_Rec: 0.390 D_GP: 0.036 D_real: 1.042 D_fake: 0.593 
+(epoch: 80, iters: 5568, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.897 G_ID: 0.210 G_Rec: 0.413 D_GP: 0.057 D_real: 0.844 D_fake: 0.620 
+(epoch: 80, iters: 5968, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.876 G_ID: 0.361 G_Rec: 0.425 D_GP: 0.062 D_real: 0.790 D_fake: 0.796 
+(epoch: 80, iters: 6368, time: 0.064) G_GAN: 0.600 G_GAN_Feat: 1.043 G_ID: 0.184 G_Rec: 0.427 D_GP: 0.223 D_real: 0.755 D_fake: 0.523 
+(epoch: 80, iters: 6768, time: 0.064) G_GAN: 0.494 G_GAN_Feat: 0.896 G_ID: 0.340 G_Rec: 0.376 D_GP: 0.041 D_real: 1.125 D_fake: 0.510 
+(epoch: 80, iters: 7168, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.821 G_ID: 0.187 G_Rec: 0.373 D_GP: 0.040 D_real: 1.035 D_fake: 0.664 
+(epoch: 80, iters: 7568, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.828 G_ID: 0.336 G_Rec: 0.410 D_GP: 0.040 D_real: 1.091 D_fake: 0.670 
+(epoch: 80, iters: 7968, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.987 G_ID: 0.234 G_Rec: 0.378 D_GP: 0.121 D_real: 0.379 D_fake: 0.824 
+(epoch: 80, iters: 8368, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.887 G_ID: 0.372 G_Rec: 0.418 D_GP: 0.049 D_real: 1.072 D_fake: 0.552 
+(epoch: 81, iters: 160, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.688 G_ID: 0.199 G_Rec: 0.329 D_GP: 0.034 D_real: 1.137 D_fake: 0.785 
+(epoch: 81, iters: 560, time: 0.064) G_GAN: 0.554 G_GAN_Feat: 0.718 G_ID: 0.375 G_Rec: 0.404 D_GP: 0.024 D_real: 1.415 D_fake: 0.450 
+(epoch: 81, iters: 960, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.705 G_ID: 0.227 G_Rec: 0.356 D_GP: 0.022 D_real: 1.109 D_fake: 0.797 
+(epoch: 81, iters: 1360, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.723 G_ID: 0.305 G_Rec: 0.376 D_GP: 0.027 D_real: 1.293 D_fake: 0.477 
+(epoch: 81, iters: 1760, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.695 G_ID: 0.240 G_Rec: 0.361 D_GP: 0.028 D_real: 1.142 D_fake: 0.686 
+(epoch: 81, iters: 2160, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.726 G_ID: 0.351 G_Rec: 0.353 D_GP: 0.032 D_real: 1.195 D_fake: 0.591 
+(epoch: 81, iters: 2560, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.757 G_ID: 0.219 G_Rec: 0.366 D_GP: 0.032 D_real: 1.234 D_fake: 0.559 
+(epoch: 81, iters: 2960, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.992 G_ID: 0.365 G_Rec: 0.422 D_GP: 0.239 D_real: 0.549 D_fake: 0.865 
+(epoch: 81, iters: 3360, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.794 G_ID: 0.250 G_Rec: 0.361 D_GP: 0.036 D_real: 1.077 D_fake: 0.698 
+(epoch: 81, iters: 3760, time: 0.064) G_GAN: 0.688 G_GAN_Feat: 0.969 G_ID: 0.324 G_Rec: 0.413 D_GP: 0.129 D_real: 1.074 D_fake: 0.325 
+(epoch: 81, iters: 4160, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.817 G_ID: 0.190 G_Rec: 0.460 D_GP: 0.046 D_real: 1.080 D_fake: 0.725 
+(epoch: 81, iters: 4560, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 0.730 G_ID: 0.335 G_Rec: 0.378 D_GP: 0.023 D_real: 1.372 D_fake: 0.476 
+(epoch: 81, iters: 4960, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.696 G_ID: 0.170 G_Rec: 0.325 D_GP: 0.034 D_real: 1.218 D_fake: 0.545 
+(epoch: 81, iters: 5360, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.983 G_ID: 0.385 G_Rec: 0.400 D_GP: 0.057 D_real: 0.769 D_fake: 0.725 
+(epoch: 81, iters: 5760, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 1.241 G_ID: 0.215 G_Rec: 0.408 D_GP: 0.472 D_real: 0.242 D_fake: 0.781 
+(epoch: 81, iters: 6160, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 1.043 G_ID: 0.320 G_Rec: 0.426 D_GP: 0.116 D_real: 0.358 D_fake: 0.732 
+(epoch: 81, iters: 6560, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.990 G_ID: 0.191 G_Rec: 0.432 D_GP: 0.116 D_real: 1.011 D_fake: 0.542 
+(epoch: 81, iters: 6960, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.904 G_ID: 0.355 G_Rec: 0.390 D_GP: 0.036 D_real: 0.835 D_fake: 0.776 
+(epoch: 81, iters: 7360, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.856 G_ID: 0.178 G_Rec: 0.405 D_GP: 0.071 D_real: 0.865 D_fake: 0.727 
+(epoch: 81, iters: 7760, time: 0.064) G_GAN: 0.535 G_GAN_Feat: 0.900 G_ID: 0.357 G_Rec: 0.377 D_GP: 0.053 D_real: 1.033 D_fake: 0.473 
+(epoch: 81, iters: 8160, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.743 G_ID: 0.185 G_Rec: 0.376 D_GP: 0.035 D_real: 1.251 D_fake: 0.571 
+(epoch: 81, iters: 8560, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.861 G_ID: 0.339 G_Rec: 0.464 D_GP: 0.035 D_real: 1.311 D_fake: 0.543 
+(epoch: 82, iters: 352, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.759 G_ID: 0.193 G_Rec: 0.394 D_GP: 0.025 D_real: 0.985 D_fake: 0.798 
+(epoch: 82, iters: 752, time: 0.064) G_GAN: 0.606 G_GAN_Feat: 0.797 G_ID: 0.322 G_Rec: 0.413 D_GP: 0.024 D_real: 1.345 D_fake: 0.433 
+(epoch: 82, iters: 1152, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.779 G_ID: 0.174 G_Rec: 0.346 D_GP: 0.037 D_real: 1.090 D_fake: 0.724 
+(epoch: 82, iters: 1552, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 1.059 G_ID: 0.287 G_Rec: 0.421 D_GP: 0.047 D_real: 1.010 D_fake: 0.575 
+(epoch: 82, iters: 1952, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.990 G_ID: 0.188 G_Rec: 0.387 D_GP: 0.089 D_real: 1.004 D_fake: 0.688 
+(epoch: 82, iters: 2352, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.908 G_ID: 0.312 G_Rec: 0.406 D_GP: 0.066 D_real: 0.721 D_fake: 0.804 
+(epoch: 82, iters: 2752, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 0.988 G_ID: 0.191 G_Rec: 0.385 D_GP: 0.197 D_real: 0.825 D_fake: 0.517 
+(epoch: 82, iters: 3152, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 0.823 G_ID: 0.312 G_Rec: 0.373 D_GP: 0.024 D_real: 1.267 D_fake: 0.476 
+(epoch: 82, iters: 3552, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.718 G_ID: 0.226 G_Rec: 0.383 D_GP: 0.033 D_real: 1.061 D_fake: 0.787 
+(epoch: 82, iters: 3952, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.750 G_ID: 0.368 G_Rec: 0.369 D_GP: 0.030 D_real: 1.159 D_fake: 0.599 
+(epoch: 82, iters: 4352, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.767 G_ID: 0.229 G_Rec: 0.340 D_GP: 0.034 D_real: 1.233 D_fake: 0.659 
+(epoch: 82, iters: 4752, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 0.801 G_ID: 0.330 G_Rec: 0.340 D_GP: 0.029 D_real: 1.286 D_fake: 0.459 
+(epoch: 82, iters: 5152, time: 0.064) G_GAN: 0.571 G_GAN_Feat: 0.768 G_ID: 0.184 G_Rec: 0.351 D_GP: 0.026 D_real: 1.474 D_fake: 0.484 
+(epoch: 82, iters: 5552, time: 0.064) G_GAN: 0.576 G_GAN_Feat: 0.833 G_ID: 0.294 G_Rec: 0.393 D_GP: 0.027 D_real: 1.209 D_fake: 0.439 
+(epoch: 82, iters: 5952, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.806 G_ID: 0.163 G_Rec: 0.397 D_GP: 0.062 D_real: 1.115 D_fake: 0.643 
+(epoch: 82, iters: 6352, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.923 G_ID: 0.378 G_Rec: 0.401 D_GP: 0.045 D_real: 0.638 D_fake: 0.839 
+(epoch: 82, iters: 6752, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.877 G_ID: 0.183 G_Rec: 0.356 D_GP: 0.054 D_real: 0.901 D_fake: 0.618 
+(epoch: 82, iters: 7152, time: 0.064) G_GAN: 0.894 G_GAN_Feat: 0.715 G_ID: 0.314 G_Rec: 0.393 D_GP: 0.024 D_real: 1.620 D_fake: 0.162 
+(epoch: 82, iters: 7552, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.643 G_ID: 0.178 G_Rec: 0.346 D_GP: 0.025 D_real: 1.233 D_fake: 0.669 
+(epoch: 82, iters: 7952, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 1.045 G_ID: 0.366 G_Rec: 0.469 D_GP: 0.063 D_real: 0.591 D_fake: 0.714 
+(epoch: 82, iters: 8352, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.813 G_ID: 0.214 G_Rec: 0.392 D_GP: 0.062 D_real: 0.795 D_fake: 0.927 
+(epoch: 83, iters: 144, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 1.152 G_ID: 0.375 G_Rec: 0.430 D_GP: 0.269 D_real: 0.503 D_fake: 0.589 
+(epoch: 83, iters: 544, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.770 G_ID: 0.173 G_Rec: 0.331 D_GP: 0.048 D_real: 1.188 D_fake: 0.510 
+(epoch: 83, iters: 944, time: 0.064) G_GAN: -0.063 G_GAN_Feat: 0.809 G_ID: 0.299 G_Rec: 0.441 D_GP: 0.030 D_real: 0.769 D_fake: 1.063 
+(epoch: 83, iters: 1344, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.812 G_ID: 0.203 G_Rec: 0.385 D_GP: 0.045 D_real: 0.997 D_fake: 0.670 
+(epoch: 83, iters: 1744, time: 0.064) G_GAN: 0.027 G_GAN_Feat: 0.847 G_ID: 0.350 G_Rec: 0.387 D_GP: 0.081 D_real: 0.571 D_fake: 0.973 
+(epoch: 83, iters: 2144, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.913 G_ID: 0.209 G_Rec: 0.337 D_GP: 0.047 D_real: 0.703 D_fake: 0.680 
+(epoch: 83, iters: 2544, time: 0.064) G_GAN: 0.693 G_GAN_Feat: 0.719 G_ID: 0.352 G_Rec: 0.370 D_GP: 0.026 D_real: 1.497 D_fake: 0.319 
+(epoch: 83, iters: 2944, time: 0.064) G_GAN: 0.712 G_GAN_Feat: 0.878 G_ID: 0.220 G_Rec: 0.367 D_GP: 0.055 D_real: 1.270 D_fake: 0.333 
+(epoch: 83, iters: 3344, time: 0.064) G_GAN: -0.064 G_GAN_Feat: 0.887 G_ID: 0.434 G_Rec: 0.406 D_GP: 0.072 D_real: 0.570 D_fake: 1.064 
+(epoch: 83, iters: 3744, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 1.000 G_ID: 0.228 G_Rec: 0.405 D_GP: 0.067 D_real: 0.636 D_fake: 0.611 
+(epoch: 83, iters: 4144, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 1.060 G_ID: 0.324 G_Rec: 0.411 D_GP: 0.127 D_real: 0.464 D_fake: 0.627 
+(epoch: 83, iters: 4544, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.780 G_ID: 0.208 G_Rec: 0.350 D_GP: 0.035 D_real: 1.025 D_fake: 0.724 
+(epoch: 83, iters: 4944, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 1.089 G_ID: 0.334 G_Rec: 0.413 D_GP: 0.098 D_real: 0.593 D_fake: 0.474 
+(epoch: 83, iters: 5344, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.754 G_ID: 0.211 G_Rec: 0.429 D_GP: 0.023 D_real: 0.980 D_fake: 0.906 
+(epoch: 83, iters: 5744, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.739 G_ID: 0.307 G_Rec: 0.453 D_GP: 0.024 D_real: 1.177 D_fake: 0.535 
+(epoch: 83, iters: 6144, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.697 G_ID: 0.172 G_Rec: 0.356 D_GP: 0.027 D_real: 1.133 D_fake: 0.689 
+(epoch: 83, iters: 6544, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.845 G_ID: 0.354 G_Rec: 0.404 D_GP: 0.069 D_real: 0.805 D_fake: 0.836 
+(epoch: 83, iters: 6944, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 0.826 G_ID: 0.157 G_Rec: 0.357 D_GP: 0.037 D_real: 0.988 D_fake: 0.675 
+(epoch: 83, iters: 7344, time: 0.064) G_GAN: 0.985 G_GAN_Feat: 1.213 G_ID: 0.345 G_Rec: 0.466 D_GP: 0.405 D_real: 0.853 D_fake: 0.135 
+(epoch: 83, iters: 7744, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.885 G_ID: 0.249 G_Rec: 0.424 D_GP: 0.062 D_real: 0.694 D_fake: 0.800 
+(epoch: 83, iters: 8144, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 0.817 G_ID: 0.302 G_Rec: 0.378 D_GP: 0.034 D_real: 1.215 D_fake: 0.554 
+(epoch: 83, iters: 8544, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.731 G_ID: 0.193 G_Rec: 0.375 D_GP: 0.033 D_real: 1.272 D_fake: 0.557 
+(epoch: 84, iters: 336, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 1.004 G_ID: 0.354 G_Rec: 0.400 D_GP: 0.063 D_real: 0.651 D_fake: 0.720 
+(epoch: 84, iters: 736, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.944 G_ID: 0.184 G_Rec: 0.366 D_GP: 0.035 D_real: 1.038 D_fake: 0.635 
+(epoch: 84, iters: 1136, time: 0.064) G_GAN: -0.063 G_GAN_Feat: 0.832 G_ID: 0.363 G_Rec: 0.417 D_GP: 0.037 D_real: 0.663 D_fake: 1.065 
+(epoch: 84, iters: 1536, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.790 G_ID: 0.222 G_Rec: 0.379 D_GP: 0.039 D_real: 1.100 D_fake: 0.757 
+(epoch: 84, iters: 1936, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.795 G_ID: 0.313 G_Rec: 0.397 D_GP: 0.026 D_real: 1.131 D_fake: 0.639 
+(epoch: 84, iters: 2336, time: 0.064) G_GAN: -0.136 G_GAN_Feat: 0.881 G_ID: 0.235 G_Rec: 0.407 D_GP: 0.064 D_real: 0.615 D_fake: 1.136 
+(epoch: 84, iters: 2736, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 0.957 G_ID: 0.353 G_Rec: 0.402 D_GP: 0.068 D_real: 1.199 D_fake: 0.420 
+(epoch: 84, iters: 3136, time: 0.064) G_GAN: 0.110 G_GAN_Feat: 0.979 G_ID: 0.187 G_Rec: 0.425 D_GP: 0.026 D_real: 0.669 D_fake: 0.890 
+(epoch: 84, iters: 3536, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.791 G_ID: 0.364 G_Rec: 0.374 D_GP: 0.031 D_real: 1.034 D_fake: 0.753 
+(epoch: 84, iters: 3936, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.867 G_ID: 0.209 G_Rec: 0.383 D_GP: 0.036 D_real: 1.138 D_fake: 0.626 
+(epoch: 84, iters: 4336, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 1.132 G_ID: 0.302 G_Rec: 0.428 D_GP: 0.095 D_real: 0.463 D_fake: 0.547 
+(epoch: 84, iters: 4736, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.867 G_ID: 0.181 G_Rec: 0.410 D_GP: 0.035 D_real: 0.892 D_fake: 0.697 
+(epoch: 84, iters: 5136, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.995 G_ID: 0.350 G_Rec: 0.436 D_GP: 0.148 D_real: 0.682 D_fake: 0.746 
+(epoch: 84, iters: 5536, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.830 G_ID: 0.193 G_Rec: 0.362 D_GP: 0.066 D_real: 0.748 D_fake: 0.908 
+(epoch: 84, iters: 5936, time: 0.064) G_GAN: 0.585 G_GAN_Feat: 0.961 G_ID: 0.321 G_Rec: 0.483 D_GP: 0.054 D_real: 0.973 D_fake: 0.430 
+(epoch: 84, iters: 6336, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.867 G_ID: 0.178 G_Rec: 0.374 D_GP: 0.033 D_real: 0.668 D_fake: 0.910 
+(epoch: 84, iters: 6736, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.915 G_ID: 0.348 G_Rec: 0.367 D_GP: 0.035 D_real: 1.052 D_fake: 0.605 
+(epoch: 84, iters: 7136, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.751 G_ID: 0.193 G_Rec: 0.363 D_GP: 0.023 D_real: 1.251 D_fake: 0.620 
+(epoch: 84, iters: 7536, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 1.016 G_ID: 0.353 G_Rec: 0.412 D_GP: 0.046 D_real: 1.069 D_fake: 0.528 
+(epoch: 84, iters: 7936, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.819 G_ID: 0.184 G_Rec: 0.357 D_GP: 0.075 D_real: 0.856 D_fake: 0.800 
+(epoch: 84, iters: 8336, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 0.998 G_ID: 0.351 G_Rec: 0.434 D_GP: 0.066 D_real: 0.736 D_fake: 0.584 
+(epoch: 85, iters: 128, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 1.168 G_ID: 0.223 G_Rec: 0.360 D_GP: 0.102 D_real: 0.305 D_fake: 0.795 
+(epoch: 85, iters: 528, time: 0.064) G_GAN: -0.478 G_GAN_Feat: 1.075 G_ID: 0.339 G_Rec: 0.469 D_GP: 0.258 D_real: 0.179 D_fake: 1.478 
+(epoch: 85, iters: 928, time: 0.064) G_GAN: 0.537 G_GAN_Feat: 0.758 G_ID: 0.168 G_Rec: 0.348 D_GP: 0.030 D_real: 1.364 D_fake: 0.465 
+(epoch: 85, iters: 1328, time: 0.064) G_GAN: 0.555 G_GAN_Feat: 0.812 G_ID: 0.319 G_Rec: 0.400 D_GP: 0.029 D_real: 1.233 D_fake: 0.448 
+(epoch: 85, iters: 1728, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.707 G_ID: 0.208 G_Rec: 0.391 D_GP: 0.024 D_real: 1.416 D_fake: 0.485 
+(epoch: 85, iters: 2128, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.978 G_ID: 0.347 G_Rec: 0.404 D_GP: 0.080 D_real: 0.624 D_fake: 0.726 
+(epoch: 85, iters: 2528, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 0.894 G_ID: 0.208 G_Rec: 0.387 D_GP: 0.072 D_real: 1.047 D_fake: 0.509 
+(epoch: 85, iters: 2928, time: 0.064) G_GAN: 0.778 G_GAN_Feat: 0.980 G_ID: 0.277 G_Rec: 0.442 D_GP: 0.044 D_real: 1.342 D_fake: 0.300 
+(epoch: 85, iters: 3328, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.611 G_ID: 0.208 G_Rec: 0.390 D_GP: 0.022 D_real: 0.976 D_fake: 0.823 
+(epoch: 85, iters: 3728, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.719 G_ID: 0.299 G_Rec: 0.398 D_GP: 0.020 D_real: 1.121 D_fake: 0.610 
+(epoch: 85, iters: 4128, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.636 G_ID: 0.170 G_Rec: 0.345 D_GP: 0.023 D_real: 1.262 D_fake: 0.579 
+(epoch: 85, iters: 4528, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.966 G_ID: 0.278 G_Rec: 0.526 D_GP: 0.216 D_real: 0.903 D_fake: 0.584 
+(epoch: 85, iters: 4928, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.753 G_ID: 0.191 G_Rec: 0.339 D_GP: 0.036 D_real: 1.026 D_fake: 0.749 
+(epoch: 85, iters: 5328, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.795 G_ID: 0.357 G_Rec: 0.366 D_GP: 0.029 D_real: 1.131 D_fake: 0.619 
+(epoch: 85, iters: 5728, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.696 G_ID: 0.163 G_Rec: 0.362 D_GP: 0.024 D_real: 1.287 D_fake: 0.515 
+(epoch: 85, iters: 6128, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 1.178 G_ID: 0.321 G_Rec: 0.432 D_GP: 0.477 D_real: 0.587 D_fake: 0.513 
+(epoch: 85, iters: 6528, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.757 G_ID: 0.188 G_Rec: 0.351 D_GP: 0.035 D_real: 1.221 D_fake: 0.568 
+(epoch: 85, iters: 6928, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 1.059 G_ID: 0.331 G_Rec: 0.426 D_GP: 0.052 D_real: 0.546 D_fake: 0.737 
+(epoch: 85, iters: 7328, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.810 G_ID: 0.211 G_Rec: 0.396 D_GP: 0.024 D_real: 1.079 D_fake: 0.691 
+(epoch: 85, iters: 7728, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 0.816 G_ID: 0.323 G_Rec: 0.368 D_GP: 0.036 D_real: 1.283 D_fake: 0.391 
+(epoch: 85, iters: 8128, time: 0.064) G_GAN: 0.017 G_GAN_Feat: 1.081 G_ID: 0.228 G_Rec: 0.348 D_GP: 0.048 D_real: 0.363 D_fake: 0.983 
+(epoch: 85, iters: 8528, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.823 G_ID: 0.336 G_Rec: 0.421 D_GP: 0.023 D_real: 1.232 D_fake: 0.605 
+(epoch: 86, iters: 320, time: 0.064) G_GAN: 0.008 G_GAN_Feat: 0.733 G_ID: 0.202 G_Rec: 0.343 D_GP: 0.034 D_real: 0.890 D_fake: 0.992 
+(epoch: 86, iters: 720, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.879 G_ID: 0.371 G_Rec: 0.405 D_GP: 0.026 D_real: 1.201 D_fake: 0.487 
+(epoch: 86, iters: 1120, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.897 G_ID: 0.212 G_Rec: 0.359 D_GP: 0.065 D_real: 0.800 D_fake: 0.662 
+(epoch: 86, iters: 1520, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.785 G_ID: 0.304 G_Rec: 0.363 D_GP: 0.033 D_real: 1.313 D_fake: 0.396 
+(epoch: 86, iters: 1920, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.897 G_ID: 0.209 G_Rec: 0.368 D_GP: 0.053 D_real: 0.786 D_fake: 0.753 
+(epoch: 86, iters: 2320, time: 0.064) G_GAN: 0.548 G_GAN_Feat: 0.868 G_ID: 0.341 G_Rec: 0.394 D_GP: 0.038 D_real: 1.209 D_fake: 0.457 
+(epoch: 86, iters: 2720, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 0.684 G_ID: 0.205 G_Rec: 0.392 D_GP: 0.023 D_real: 1.226 D_fake: 0.673 
+(epoch: 86, iters: 3120, time: 0.064) G_GAN: 0.580 G_GAN_Feat: 1.041 G_ID: 0.313 G_Rec: 0.443 D_GP: 0.074 D_real: 0.910 D_fake: 0.427 
+(epoch: 86, iters: 3520, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.822 G_ID: 0.187 G_Rec: 0.379 D_GP: 0.035 D_real: 0.922 D_fake: 0.826 
+(epoch: 86, iters: 3920, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 1.131 G_ID: 0.356 G_Rec: 0.387 D_GP: 0.064 D_real: 0.395 D_fake: 0.751 
+(epoch: 86, iters: 4320, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.712 G_ID: 0.193 G_Rec: 0.400 D_GP: 0.024 D_real: 1.262 D_fake: 0.626 
+(epoch: 86, iters: 4720, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.848 G_ID: 0.285 G_Rec: 0.428 D_GP: 0.034 D_real: 1.042 D_fake: 0.593 
+(epoch: 86, iters: 5120, time: 0.064) G_GAN: -0.022 G_GAN_Feat: 1.048 G_ID: 0.223 G_Rec: 0.423 D_GP: 0.131 D_real: 0.169 D_fake: 1.022 
+(epoch: 86, iters: 5520, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.900 G_ID: 0.370 G_Rec: 0.399 D_GP: 0.035 D_real: 0.896 D_fake: 0.708 
+(epoch: 86, iters: 5920, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.774 G_ID: 0.195 G_Rec: 0.340 D_GP: 0.039 D_real: 1.017 D_fake: 0.634 
+(epoch: 86, iters: 6320, time: 0.064) G_GAN: 0.622 G_GAN_Feat: 0.842 G_ID: 0.325 G_Rec: 0.438 D_GP: 0.032 D_real: 1.288 D_fake: 0.386 
+(epoch: 86, iters: 6720, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.739 G_ID: 0.231 G_Rec: 0.353 D_GP: 0.037 D_real: 1.285 D_fake: 0.533 
+(epoch: 86, iters: 7120, time: 0.064) G_GAN: 0.690 G_GAN_Feat: 0.859 G_ID: 0.332 G_Rec: 0.421 D_GP: 0.036 D_real: 1.312 D_fake: 0.322 
+(epoch: 86, iters: 7520, time: 0.064) G_GAN: 0.563 G_GAN_Feat: 0.831 G_ID: 0.200 G_Rec: 0.391 D_GP: 0.027 D_real: 1.204 D_fake: 0.448 
+(epoch: 86, iters: 7920, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 1.238 G_ID: 0.312 G_Rec: 0.415 D_GP: 0.060 D_real: 0.450 D_fake: 0.698 
+(epoch: 86, iters: 8320, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 1.133 G_ID: 0.172 G_Rec: 0.361 D_GP: 0.097 D_real: 0.426 D_fake: 0.580 
+(epoch: 87, iters: 112, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.761 G_ID: 0.341 G_Rec: 0.399 D_GP: 0.025 D_real: 1.151 D_fake: 0.704 
+(epoch: 87, iters: 512, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.679 G_ID: 0.175 G_Rec: 0.357 D_GP: 0.024 D_real: 1.027 D_fake: 0.839 
+(epoch: 87, iters: 912, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 1.003 G_ID: 0.309 G_Rec: 0.466 D_GP: 0.065 D_real: 0.597 D_fake: 0.714 
+(epoch: 87, iters: 1312, time: 0.064) G_GAN: 0.467 G_GAN_Feat: 0.754 G_ID: 0.182 G_Rec: 0.370 D_GP: 0.025 D_real: 1.379 D_fake: 0.533 
+(epoch: 87, iters: 1712, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.769 G_ID: 0.305 G_Rec: 0.362 D_GP: 0.034 D_real: 1.095 D_fake: 0.693 
+(epoch: 87, iters: 2112, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.659 G_ID: 0.200 G_Rec: 0.345 D_GP: 0.027 D_real: 1.029 D_fake: 0.811 
+(epoch: 87, iters: 2512, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.842 G_ID: 0.304 G_Rec: 0.359 D_GP: 0.067 D_real: 0.935 D_fake: 0.632 
+(epoch: 87, iters: 2912, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.840 G_ID: 0.180 G_Rec: 0.360 D_GP: 0.038 D_real: 1.094 D_fake: 0.709 
+(epoch: 87, iters: 3312, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 0.900 G_ID: 0.311 G_Rec: 0.383 D_GP: 0.084 D_real: 0.983 D_fake: 0.524 
+(epoch: 87, iters: 3712, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.627 G_ID: 0.260 G_Rec: 0.342 D_GP: 0.023 D_real: 1.091 D_fake: 0.790 
+(epoch: 87, iters: 4112, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.674 G_ID: 0.344 G_Rec: 0.362 D_GP: 0.025 D_real: 1.299 D_fake: 0.582 
+(epoch: 87, iters: 4512, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 0.739 G_ID: 0.182 G_Rec: 0.353 D_GP: 0.065 D_real: 0.909 D_fake: 0.849 
+(epoch: 87, iters: 4912, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.705 G_ID: 0.331 G_Rec: 0.436 D_GP: 0.023 D_real: 1.163 D_fake: 0.621 
+(epoch: 87, iters: 5312, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.702 G_ID: 0.186 G_Rec: 0.365 D_GP: 0.035 D_real: 1.254 D_fake: 0.580 
+(epoch: 87, iters: 5712, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.875 G_ID: 0.298 G_Rec: 0.397 D_GP: 0.043 D_real: 1.123 D_fake: 0.575 
+(epoch: 87, iters: 6112, time: 0.064) G_GAN: 0.072 G_GAN_Feat: 0.681 G_ID: 0.226 G_Rec: 0.376 D_GP: 0.026 D_real: 0.954 D_fake: 0.928 
+(epoch: 87, iters: 6512, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 1.263 G_ID: 0.306 G_Rec: 0.456 D_GP: 0.148 D_real: 0.517 D_fake: 0.405 
+(epoch: 87, iters: 6912, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 1.052 G_ID: 0.212 G_Rec: 0.381 D_GP: 0.183 D_real: 0.255 D_fake: 0.907 
+(epoch: 87, iters: 7312, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 0.941 G_ID: 0.294 G_Rec: 0.415 D_GP: 0.071 D_real: 0.667 D_fake: 0.674 
+(epoch: 87, iters: 7712, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 0.624 G_ID: 0.181 G_Rec: 0.342 D_GP: 0.022 D_real: 1.476 D_fake: 0.484 
+(epoch: 87, iters: 8112, time: 0.064) G_GAN: 0.613 G_GAN_Feat: 0.960 G_ID: 0.290 G_Rec: 0.367 D_GP: 0.060 D_real: 0.984 D_fake: 0.391 
+(epoch: 87, iters: 8512, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.729 G_ID: 0.202 G_Rec: 0.395 D_GP: 0.026 D_real: 1.178 D_fake: 0.660 
+(epoch: 88, iters: 304, time: 0.064) G_GAN: -0.161 G_GAN_Feat: 0.940 G_ID: 0.320 G_Rec: 0.423 D_GP: 0.077 D_real: 0.316 D_fake: 1.161 
+(epoch: 88, iters: 704, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.772 G_ID: 0.197 G_Rec: 0.360 D_GP: 0.024 D_real: 1.047 D_fake: 0.760 
+(epoch: 88, iters: 1104, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.804 G_ID: 0.327 G_Rec: 0.464 D_GP: 0.042 D_real: 1.063 D_fake: 0.600 
+(epoch: 88, iters: 1504, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.828 G_ID: 0.193 G_Rec: 0.367 D_GP: 0.056 D_real: 1.043 D_fake: 0.586 
+(epoch: 88, iters: 1904, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.729 G_ID: 0.294 G_Rec: 0.396 D_GP: 0.026 D_real: 1.077 D_fake: 0.717 
+(epoch: 88, iters: 2304, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.780 G_ID: 0.192 G_Rec: 0.443 D_GP: 0.061 D_real: 0.949 D_fake: 0.715 
+(epoch: 88, iters: 2704, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.918 G_ID: 0.319 G_Rec: 0.404 D_GP: 0.027 D_real: 0.773 D_fake: 0.758 
+(epoch: 88, iters: 3104, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.725 G_ID: 0.188 G_Rec: 0.380 D_GP: 0.043 D_real: 1.006 D_fake: 0.827 
+(epoch: 88, iters: 3504, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 1.234 G_ID: 0.309 G_Rec: 0.389 D_GP: 0.079 D_real: 0.308 D_fake: 0.731 
+(epoch: 88, iters: 3904, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.992 G_ID: 0.199 G_Rec: 0.400 D_GP: 0.114 D_real: 0.378 D_fake: 0.870 
+(epoch: 88, iters: 4304, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.868 G_ID: 0.350 G_Rec: 0.416 D_GP: 0.031 D_real: 1.062 D_fake: 0.594 
+(epoch: 88, iters: 4704, time: 0.064) G_GAN: 0.687 G_GAN_Feat: 1.040 G_ID: 0.160 G_Rec: 0.412 D_GP: 0.115 D_real: 1.227 D_fake: 0.473 
+(epoch: 88, iters: 5104, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.894 G_ID: 0.303 G_Rec: 0.440 D_GP: 0.045 D_real: 0.848 D_fake: 0.662 
+(epoch: 88, iters: 5504, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.691 G_ID: 0.204 G_Rec: 0.347 D_GP: 0.032 D_real: 0.848 D_fake: 0.964 
+(epoch: 88, iters: 5904, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.818 G_ID: 0.280 G_Rec: 0.395 D_GP: 0.028 D_real: 1.246 D_fake: 0.552 
+(epoch: 88, iters: 6304, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.877 G_ID: 0.228 G_Rec: 0.407 D_GP: 0.062 D_real: 1.074 D_fake: 0.533 
+(epoch: 88, iters: 6704, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.873 G_ID: 0.352 G_Rec: 0.371 D_GP: 0.070 D_real: 0.735 D_fake: 0.721 
+(epoch: 88, iters: 7104, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.928 G_ID: 0.189 G_Rec: 0.359 D_GP: 0.060 D_real: 0.573 D_fake: 0.699 
+(epoch: 88, iters: 7504, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 0.953 G_ID: 0.310 G_Rec: 0.388 D_GP: 0.057 D_real: 0.881 D_fake: 0.486 
+(epoch: 88, iters: 7904, time: 0.064) G_GAN: 0.650 G_GAN_Feat: 0.891 G_ID: 0.203 G_Rec: 0.359 D_GP: 0.087 D_real: 1.188 D_fake: 0.390 
+(epoch: 88, iters: 8304, time: 0.064) G_GAN: 0.632 G_GAN_Feat: 1.012 G_ID: 0.328 G_Rec: 0.411 D_GP: 0.111 D_real: 0.964 D_fake: 0.433 
+(epoch: 89, iters: 96, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.810 G_ID: 0.187 G_Rec: 0.371 D_GP: 0.045 D_real: 1.204 D_fake: 0.622 
+(epoch: 89, iters: 496, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.796 G_ID: 0.316 G_Rec: 0.367 D_GP: 0.032 D_real: 1.121 D_fake: 0.773 
+(epoch: 89, iters: 896, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.744 G_ID: 0.168 G_Rec: 0.353 D_GP: 0.026 D_real: 0.894 D_fake: 0.926 
+(epoch: 89, iters: 1296, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 1.238 G_ID: 0.317 G_Rec: 0.483 D_GP: 0.948 D_real: 0.587 D_fake: 0.836 
+(epoch: 89, iters: 1696, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.724 G_ID: 0.201 G_Rec: 0.407 D_GP: 0.026 D_real: 0.959 D_fake: 0.863 
+(epoch: 89, iters: 2096, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.950 G_ID: 0.314 G_Rec: 0.405 D_GP: 0.074 D_real: 0.876 D_fake: 0.576 
+(epoch: 89, iters: 2496, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.696 G_ID: 0.177 G_Rec: 0.328 D_GP: 0.029 D_real: 1.195 D_fake: 0.708 
+(epoch: 89, iters: 2896, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.887 G_ID: 0.346 G_Rec: 0.472 D_GP: 0.039 D_real: 1.030 D_fake: 0.686 
+(epoch: 89, iters: 3296, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.977 G_ID: 0.194 G_Rec: 0.366 D_GP: 0.041 D_real: 0.510 D_fake: 0.920 
+(epoch: 89, iters: 3696, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.816 G_ID: 0.325 G_Rec: 0.375 D_GP: 0.030 D_real: 1.151 D_fake: 0.594 
+(epoch: 89, iters: 4096, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.768 G_ID: 0.163 G_Rec: 0.394 D_GP: 0.023 D_real: 1.132 D_fake: 0.684 
+(epoch: 89, iters: 4496, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.802 G_ID: 0.287 G_Rec: 0.403 D_GP: 0.028 D_real: 1.096 D_fake: 0.662 
+(epoch: 89, iters: 4896, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.723 G_ID: 0.189 G_Rec: 0.381 D_GP: 0.028 D_real: 1.138 D_fake: 0.651 
+(epoch: 89, iters: 5296, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 0.879 G_ID: 0.337 G_Rec: 0.371 D_GP: 0.030 D_real: 1.226 D_fake: 0.437 
+(epoch: 89, iters: 5696, time: 0.064) G_GAN: -0.010 G_GAN_Feat: 0.669 G_ID: 0.241 G_Rec: 0.376 D_GP: 0.025 D_real: 0.948 D_fake: 1.011 
+(epoch: 89, iters: 6096, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.741 G_ID: 0.300 G_Rec: 0.402 D_GP: 0.025 D_real: 1.225 D_fake: 0.584 
+(epoch: 89, iters: 6496, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.836 G_ID: 0.188 G_Rec: 0.357 D_GP: 0.069 D_real: 0.883 D_fake: 0.713 
+(epoch: 89, iters: 6896, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.954 G_ID: 0.307 G_Rec: 0.420 D_GP: 0.069 D_real: 0.887 D_fake: 0.506 
+(epoch: 89, iters: 7296, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.787 G_ID: 0.158 G_Rec: 0.341 D_GP: 0.034 D_real: 0.843 D_fake: 0.853 
+(epoch: 89, iters: 7696, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.952 G_ID: 0.284 G_Rec: 0.429 D_GP: 0.098 D_real: 1.136 D_fake: 0.488 
+(epoch: 89, iters: 8096, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 1.019 G_ID: 0.204 G_Rec: 0.386 D_GP: 0.100 D_real: 1.039 D_fake: 0.830 
+(epoch: 89, iters: 8496, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.919 G_ID: 0.299 G_Rec: 0.436 D_GP: 0.050 D_real: 0.799 D_fake: 0.833 
+(epoch: 90, iters: 288, time: 0.064) G_GAN: 0.827 G_GAN_Feat: 1.069 G_ID: 0.218 G_Rec: 0.399 D_GP: 0.236 D_real: 1.116 D_fake: 0.605 
+(epoch: 90, iters: 688, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.726 G_ID: 0.310 G_Rec: 0.347 D_GP: 0.028 D_real: 1.209 D_fake: 0.504 
+(epoch: 90, iters: 1088, time: 0.064) G_GAN: 0.543 G_GAN_Feat: 0.681 G_ID: 0.159 G_Rec: 0.380 D_GP: 0.026 D_real: 1.376 D_fake: 0.459 
+(epoch: 90, iters: 1488, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.991 G_ID: 0.336 G_Rec: 0.389 D_GP: 0.111 D_real: 0.585 D_fake: 0.660 
+(epoch: 90, iters: 1888, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 1.040 G_ID: 0.193 G_Rec: 0.382 D_GP: 0.114 D_real: 0.553 D_fake: 0.627 
+(epoch: 90, iters: 2288, time: 0.064) G_GAN: 0.475 G_GAN_Feat: 1.033 G_ID: 0.309 G_Rec: 0.420 D_GP: 0.070 D_real: 0.833 D_fake: 0.533 
+(epoch: 90, iters: 2688, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 1.022 G_ID: 0.179 G_Rec: 0.366 D_GP: 0.103 D_real: 0.557 D_fake: 0.718 
+(epoch: 90, iters: 3088, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.829 G_ID: 0.338 G_Rec: 0.383 D_GP: 0.033 D_real: 1.057 D_fake: 0.592 
+(epoch: 90, iters: 3488, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.769 G_ID: 0.177 G_Rec: 0.386 D_GP: 0.027 D_real: 1.186 D_fake: 0.582 
+(epoch: 90, iters: 3888, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 0.864 G_ID: 0.296 G_Rec: 0.419 D_GP: 0.043 D_real: 1.161 D_fake: 0.521 
+(epoch: 90, iters: 4288, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.731 G_ID: 0.214 G_Rec: 0.356 D_GP: 0.029 D_real: 1.043 D_fake: 0.767 
+(epoch: 90, iters: 4688, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 1.026 G_ID: 0.274 G_Rec: 0.442 D_GP: 0.138 D_real: 0.567 D_fake: 0.611 
+(epoch: 90, iters: 5088, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.780 G_ID: 0.198 G_Rec: 0.359 D_GP: 0.042 D_real: 1.122 D_fake: 0.658 
+(epoch: 90, iters: 5488, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.819 G_ID: 0.304 G_Rec: 0.378 D_GP: 0.030 D_real: 0.989 D_fake: 0.749 
+(epoch: 90, iters: 5888, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 1.083 G_ID: 0.218 G_Rec: 0.430 D_GP: 0.183 D_real: 0.231 D_fake: 0.898 
+(epoch: 90, iters: 6288, time: 0.064) G_GAN: 0.462 G_GAN_Feat: 0.905 G_ID: 0.311 G_Rec: 0.437 D_GP: 0.044 D_real: 1.145 D_fake: 0.638 
+(epoch: 90, iters: 6688, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 1.139 G_ID: 0.176 G_Rec: 0.376 D_GP: 0.716 D_real: 0.492 D_fake: 0.936 
+(epoch: 90, iters: 7088, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.922 G_ID: 0.301 G_Rec: 0.416 D_GP: 0.026 D_real: 1.109 D_fake: 0.591 
+(epoch: 90, iters: 7488, time: 0.064) G_GAN: 0.555 G_GAN_Feat: 0.943 G_ID: 0.191 G_Rec: 0.350 D_GP: 0.102 D_real: 1.166 D_fake: 0.462 
+(epoch: 90, iters: 7888, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.806 G_ID: 0.303 G_Rec: 0.392 D_GP: 0.028 D_real: 1.219 D_fake: 0.496 
+(epoch: 90, iters: 8288, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.995 G_ID: 0.199 G_Rec: 0.383 D_GP: 0.044 D_real: 0.506 D_fake: 0.866 
+(epoch: 91, iters: 80, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.869 G_ID: 0.295 G_Rec: 0.421 D_GP: 0.024 D_real: 0.955 D_fake: 0.731 
+(epoch: 91, iters: 480, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.800 G_ID: 0.175 G_Rec: 0.376 D_GP: 0.038 D_real: 0.877 D_fake: 0.850 
+(epoch: 91, iters: 880, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.891 G_ID: 0.305 G_Rec: 0.361 D_GP: 0.040 D_real: 1.033 D_fake: 0.554 
+(epoch: 91, iters: 1280, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.934 G_ID: 0.212 G_Rec: 0.376 D_GP: 0.118 D_real: 0.639 D_fake: 0.852 
+(epoch: 91, iters: 1680, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 1.260 G_ID: 0.281 G_Rec: 0.414 D_GP: 0.204 D_real: 0.514 D_fake: 0.472 
+(epoch: 91, iters: 2080, time: 0.064) G_GAN: 0.133 G_GAN_Feat: 0.717 G_ID: 0.192 G_Rec: 0.342 D_GP: 0.027 D_real: 1.013 D_fake: 0.867 
+(epoch: 91, iters: 2480, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.849 G_ID: 0.300 G_Rec: 0.392 D_GP: 0.029 D_real: 1.187 D_fake: 0.476 
+(epoch: 91, iters: 2880, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.831 G_ID: 0.177 G_Rec: 0.361 D_GP: 0.039 D_real: 1.188 D_fake: 0.631 
+(epoch: 91, iters: 3280, time: 0.064) G_GAN: 0.644 G_GAN_Feat: 0.865 G_ID: 0.317 G_Rec: 0.464 D_GP: 0.023 D_real: 1.450 D_fake: 0.363 
+(epoch: 91, iters: 3680, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.830 G_ID: 0.175 G_Rec: 0.427 D_GP: 0.050 D_real: 1.129 D_fake: 0.579 
+(epoch: 91, iters: 4080, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.800 G_ID: 0.299 G_Rec: 0.381 D_GP: 0.025 D_real: 1.069 D_fake: 0.653 
+(epoch: 91, iters: 4480, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 0.800 G_ID: 0.187 G_Rec: 0.375 D_GP: 0.035 D_real: 1.097 D_fake: 0.593 
+(epoch: 91, iters: 4880, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 1.155 G_ID: 0.344 G_Rec: 0.381 D_GP: 0.069 D_real: 0.504 D_fake: 0.485 
+(epoch: 91, iters: 5280, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.706 G_ID: 0.204 G_Rec: 0.446 D_GP: 0.023 D_real: 1.194 D_fake: 0.678 
+(epoch: 91, iters: 5680, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.959 G_ID: 0.283 G_Rec: 0.384 D_GP: 0.040 D_real: 0.904 D_fake: 0.550 
+(epoch: 91, iters: 6080, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 1.003 G_ID: 0.197 G_Rec: 0.421 D_GP: 0.040 D_real: 1.167 D_fake: 0.656 
+(epoch: 91, iters: 6480, time: 0.064) G_GAN: 0.547 G_GAN_Feat: 1.039 G_ID: 0.325 G_Rec: 0.398 D_GP: 0.079 D_real: 0.840 D_fake: 0.478 
+(epoch: 91, iters: 6880, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.770 G_ID: 0.158 G_Rec: 0.363 D_GP: 0.024 D_real: 1.252 D_fake: 0.530 
+(epoch: 91, iters: 7280, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 1.223 G_ID: 0.311 G_Rec: 0.419 D_GP: 0.066 D_real: 0.554 D_fake: 0.548 
+(epoch: 91, iters: 7680, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 1.147 G_ID: 0.177 G_Rec: 0.434 D_GP: 0.385 D_real: 0.432 D_fake: 0.805 
+(epoch: 91, iters: 8080, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.811 G_ID: 0.334 G_Rec: 0.386 D_GP: 0.037 D_real: 0.806 D_fake: 0.926 
+(epoch: 91, iters: 8480, time: 0.064) G_GAN: 0.035 G_GAN_Feat: 0.751 G_ID: 0.192 G_Rec: 0.363 D_GP: 0.027 D_real: 0.872 D_fake: 0.965 
+(epoch: 92, iters: 272, time: 0.064) G_GAN: 0.664 G_GAN_Feat: 0.931 G_ID: 0.308 G_Rec: 0.387 D_GP: 0.042 D_real: 1.213 D_fake: 0.348 
+(epoch: 92, iters: 672, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.654 G_ID: 0.199 G_Rec: 0.360 D_GP: 0.023 D_real: 1.361 D_fake: 0.617 
+(epoch: 92, iters: 1072, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.900 G_ID: 0.333 G_Rec: 0.375 D_GP: 0.037 D_real: 1.035 D_fake: 0.720 
+(epoch: 92, iters: 1472, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.668 G_ID: 0.245 G_Rec: 0.345 D_GP: 0.024 D_real: 1.284 D_fake: 0.602 
+(epoch: 92, iters: 1872, time: 0.064) G_GAN: 0.487 G_GAN_Feat: 0.740 G_ID: 0.311 G_Rec: 0.387 D_GP: 0.025 D_real: 1.233 D_fake: 0.514 
+(epoch: 92, iters: 2272, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.870 G_ID: 0.194 G_Rec: 0.361 D_GP: 0.060 D_real: 0.840 D_fake: 0.690 
+(epoch: 92, iters: 2672, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 1.151 G_ID: 0.278 G_Rec: 0.399 D_GP: 0.281 D_real: 0.467 D_fake: 0.448 
+(epoch: 92, iters: 3072, time: 0.064) G_GAN: 0.540 G_GAN_Feat: 0.850 G_ID: 0.230 G_Rec: 0.415 D_GP: 0.046 D_real: 1.235 D_fake: 0.496 
+(epoch: 92, iters: 3472, time: 0.064) G_GAN: 0.761 G_GAN_Feat: 1.052 G_ID: 0.303 G_Rec: 0.437 D_GP: 0.169 D_real: 1.014 D_fake: 0.338 
+(epoch: 92, iters: 3872, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.585 G_ID: 0.165 G_Rec: 0.414 D_GP: 0.016 D_real: 1.260 D_fake: 0.654 
+(epoch: 92, iters: 4272, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.683 G_ID: 0.288 G_Rec: 0.416 D_GP: 0.018 D_real: 1.316 D_fake: 0.592 
+(epoch: 92, iters: 4672, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.737 G_ID: 0.173 G_Rec: 0.406 D_GP: 0.023 D_real: 1.151 D_fake: 0.709 
+(epoch: 92, iters: 5072, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.766 G_ID: 0.292 G_Rec: 0.419 D_GP: 0.027 D_real: 0.906 D_fake: 0.823 
+(epoch: 92, iters: 5472, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.716 G_ID: 0.193 G_Rec: 0.344 D_GP: 0.040 D_real: 1.026 D_fake: 0.719 
+(epoch: 92, iters: 5872, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.826 G_ID: 0.302 G_Rec: 0.377 D_GP: 0.054 D_real: 0.852 D_fake: 0.763 
+(epoch: 92, iters: 6272, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.914 G_ID: 0.170 G_Rec: 0.404 D_GP: 0.075 D_real: 0.739 D_fake: 0.704 
+(epoch: 92, iters: 6672, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.873 G_ID: 0.313 G_Rec: 0.387 D_GP: 0.043 D_real: 1.039 D_fake: 0.693 
+(epoch: 92, iters: 7072, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.722 G_ID: 0.202 G_Rec: 0.367 D_GP: 0.029 D_real: 1.293 D_fake: 0.541 
+(epoch: 92, iters: 7472, time: 0.064) G_GAN: 0.614 G_GAN_Feat: 0.937 G_ID: 0.309 G_Rec: 0.415 D_GP: 0.086 D_real: 0.985 D_fake: 0.395 
+(epoch: 92, iters: 7872, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.710 G_ID: 0.172 G_Rec: 0.391 D_GP: 0.025 D_real: 1.147 D_fake: 0.696 
+(epoch: 92, iters: 8272, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.787 G_ID: 0.303 G_Rec: 0.400 D_GP: 0.033 D_real: 0.955 D_fake: 0.774 
+(epoch: 93, iters: 64, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.836 G_ID: 0.182 G_Rec: 0.396 D_GP: 0.108 D_real: 0.759 D_fake: 0.837 
+(epoch: 93, iters: 464, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.854 G_ID: 0.333 G_Rec: 0.405 D_GP: 0.023 D_real: 1.024 D_fake: 0.691 
+(epoch: 93, iters: 864, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.863 G_ID: 0.220 G_Rec: 0.397 D_GP: 0.049 D_real: 0.974 D_fake: 0.655 
+(epoch: 93, iters: 1264, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.971 G_ID: 0.319 G_Rec: 0.387 D_GP: 0.207 D_real: 0.612 D_fake: 0.821 
+(epoch: 93, iters: 1664, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.706 G_ID: 0.205 G_Rec: 0.333 D_GP: 0.033 D_real: 1.022 D_fake: 0.795 
+(epoch: 93, iters: 2064, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 0.753 G_ID: 0.338 G_Rec: 0.428 D_GP: 0.025 D_real: 0.879 D_fake: 0.901 
+(epoch: 93, iters: 2464, time: 0.064) G_GAN: 0.031 G_GAN_Feat: 0.666 G_ID: 0.154 G_Rec: 0.360 D_GP: 0.025 D_real: 0.872 D_fake: 0.969 
+(epoch: 93, iters: 2864, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.853 G_ID: 0.309 G_Rec: 0.378 D_GP: 0.047 D_real: 1.020 D_fake: 0.631 
+(epoch: 93, iters: 3264, time: 0.064) G_GAN: 0.000 G_GAN_Feat: 0.748 G_ID: 0.155 G_Rec: 0.370 D_GP: 0.053 D_real: 0.699 D_fake: 1.000 
+(epoch: 93, iters: 3664, time: 0.064) G_GAN: 0.802 G_GAN_Feat: 0.808 G_ID: 0.305 G_Rec: 0.357 D_GP: 0.032 D_real: 1.417 D_fake: 0.232 
+(epoch: 93, iters: 4064, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.798 G_ID: 0.188 G_Rec: 0.339 D_GP: 0.032 D_real: 1.269 D_fake: 0.622 
+(epoch: 93, iters: 4464, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.936 G_ID: 0.248 G_Rec: 0.376 D_GP: 0.032 D_real: 0.734 D_fake: 0.906 
+(epoch: 93, iters: 4864, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.846 G_ID: 0.199 G_Rec: 0.398 D_GP: 0.058 D_real: 1.208 D_fake: 0.579 
+(epoch: 93, iters: 5264, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 1.089 G_ID: 0.268 G_Rec: 0.414 D_GP: 0.083 D_real: 0.365 D_fake: 0.670 
+(epoch: 93, iters: 5664, time: 0.064) G_GAN: -0.014 G_GAN_Feat: 0.766 G_ID: 0.194 G_Rec: 0.446 D_GP: 0.025 D_real: 0.779 D_fake: 1.014 
+(epoch: 93, iters: 6064, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.839 G_ID: 0.294 G_Rec: 0.368 D_GP: 0.036 D_real: 0.707 D_fake: 0.959 
+(epoch: 93, iters: 6464, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 1.039 G_ID: 0.174 G_Rec: 0.393 D_GP: 0.205 D_real: 0.538 D_fake: 0.625 
+(epoch: 93, iters: 6864, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.975 G_ID: 0.297 G_Rec: 0.445 D_GP: 0.049 D_real: 0.690 D_fake: 0.784 
+(epoch: 93, iters: 7264, time: 0.064) G_GAN: 0.664 G_GAN_Feat: 0.872 G_ID: 0.183 G_Rec: 0.342 D_GP: 0.063 D_real: 1.233 D_fake: 0.496 
+(epoch: 93, iters: 7664, time: 0.064) G_GAN: 0.685 G_GAN_Feat: 1.040 G_ID: 0.312 G_Rec: 0.446 D_GP: 0.112 D_real: 1.056 D_fake: 0.388 
+(epoch: 93, iters: 8064, time: 0.064) G_GAN: 0.856 G_GAN_Feat: 0.879 G_ID: 0.210 G_Rec: 0.381 D_GP: 0.064 D_real: 1.354 D_fake: 0.403 
+(epoch: 93, iters: 8464, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 1.010 G_ID: 0.278 G_Rec: 0.413 D_GP: 0.035 D_real: 0.783 D_fake: 0.704 
+(epoch: 94, iters: 256, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.619 G_ID: 0.221 G_Rec: 0.408 D_GP: 0.020 D_real: 1.334 D_fake: 0.551 
+(epoch: 94, iters: 656, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 0.762 G_ID: 0.302 G_Rec: 0.398 D_GP: 0.027 D_real: 1.233 D_fake: 0.514 
+(epoch: 94, iters: 1056, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.969 G_ID: 0.200 G_Rec: 0.378 D_GP: 0.070 D_real: 0.793 D_fake: 0.670 
+(epoch: 94, iters: 1456, time: 0.064) G_GAN: 0.674 G_GAN_Feat: 0.747 G_ID: 0.270 G_Rec: 0.411 D_GP: 0.026 D_real: 1.398 D_fake: 0.345 
+(epoch: 94, iters: 1856, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.712 G_ID: 0.165 G_Rec: 0.364 D_GP: 0.033 D_real: 1.375 D_fake: 0.475 
+(epoch: 94, iters: 2256, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.728 G_ID: 0.305 G_Rec: 0.347 D_GP: 0.029 D_real: 1.250 D_fake: 0.569 
+(epoch: 94, iters: 2656, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.665 G_ID: 0.197 G_Rec: 0.361 D_GP: 0.025 D_real: 1.078 D_fake: 0.704 
+(epoch: 94, iters: 3056, time: 0.064) G_GAN: 0.588 G_GAN_Feat: 0.961 G_ID: 0.310 G_Rec: 0.389 D_GP: 0.054 D_real: 1.054 D_fake: 0.423 
+(epoch: 94, iters: 3456, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.745 G_ID: 0.185 G_Rec: 0.354 D_GP: 0.036 D_real: 0.958 D_fake: 0.781 
+(epoch: 94, iters: 3856, time: 0.064) G_GAN: 0.568 G_GAN_Feat: 0.804 G_ID: 0.263 G_Rec: 0.368 D_GP: 0.035 D_real: 1.342 D_fake: 0.443 
+(epoch: 94, iters: 4256, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.630 G_ID: 0.186 G_Rec: 0.366 D_GP: 0.023 D_real: 1.114 D_fake: 0.728 
+(epoch: 94, iters: 4656, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.819 G_ID: 0.320 G_Rec: 0.387 D_GP: 0.043 D_real: 0.773 D_fake: 0.847 
+(epoch: 94, iters: 5056, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.642 G_ID: 0.172 G_Rec: 0.378 D_GP: 0.030 D_real: 1.077 D_fake: 0.813 
+(epoch: 94, iters: 5456, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.892 G_ID: 0.316 G_Rec: 0.393 D_GP: 0.062 D_real: 1.066 D_fake: 0.499 
+(epoch: 94, iters: 5856, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.680 G_ID: 0.183 G_Rec: 0.374 D_GP: 0.027 D_real: 1.086 D_fake: 0.704 
+(epoch: 94, iters: 6256, time: 0.064) G_GAN: 0.595 G_GAN_Feat: 0.836 G_ID: 0.313 G_Rec: 0.375 D_GP: 0.044 D_real: 1.223 D_fake: 0.409 
+(epoch: 94, iters: 6656, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 1.022 G_ID: 0.193 G_Rec: 0.379 D_GP: 0.201 D_real: 0.454 D_fake: 0.694 
+(epoch: 94, iters: 7056, time: 0.064) G_GAN: 0.556 G_GAN_Feat: 1.168 G_ID: 0.271 G_Rec: 0.419 D_GP: 0.263 D_real: 0.653 D_fake: 0.457 
+(epoch: 94, iters: 7456, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.640 G_ID: 0.161 G_Rec: 0.346 D_GP: 0.024 D_real: 1.238 D_fake: 0.662 
+(epoch: 94, iters: 7856, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 0.787 G_ID: 0.275 G_Rec: 0.381 D_GP: 0.034 D_real: 1.293 D_fake: 0.488 
+(epoch: 94, iters: 8256, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.796 G_ID: 0.241 G_Rec: 0.357 D_GP: 0.054 D_real: 1.208 D_fake: 0.526 
+(epoch: 95, iters: 48, time: 0.064) G_GAN: 0.480 G_GAN_Feat: 1.107 G_ID: 0.267 G_Rec: 0.435 D_GP: 0.122 D_real: 0.533 D_fake: 0.521 
+(epoch: 95, iters: 448, time: 0.064) G_GAN: -0.071 G_GAN_Feat: 0.872 G_ID: 0.200 G_Rec: 0.391 D_GP: 0.070 D_real: 0.561 D_fake: 1.071 
+(epoch: 95, iters: 848, time: 0.064) G_GAN: 0.634 G_GAN_Feat: 1.181 G_ID: 0.275 G_Rec: 0.387 D_GP: 0.449 D_real: 0.591 D_fake: 0.382 
+(epoch: 95, iters: 1248, time: 0.064) G_GAN: 0.024 G_GAN_Feat: 0.871 G_ID: 0.226 G_Rec: 0.383 D_GP: 0.093 D_real: 0.593 D_fake: 0.976 
+(epoch: 95, iters: 1648, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.670 G_ID: 0.275 G_Rec: 0.416 D_GP: 0.018 D_real: 1.343 D_fake: 0.567 
+(epoch: 95, iters: 2048, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.624 G_ID: 0.223 G_Rec: 0.356 D_GP: 0.019 D_real: 1.301 D_fake: 0.574 
+(epoch: 95, iters: 2448, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.729 G_ID: 0.261 G_Rec: 0.403 D_GP: 0.023 D_real: 0.976 D_fake: 0.877 
+(epoch: 95, iters: 2848, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.634 G_ID: 0.166 G_Rec: 0.359 D_GP: 0.025 D_real: 1.165 D_fake: 0.709 
+(epoch: 95, iters: 3248, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.744 G_ID: 0.309 G_Rec: 0.383 D_GP: 0.031 D_real: 0.952 D_fake: 0.830 
+(epoch: 95, iters: 3648, time: 0.064) G_GAN: -0.005 G_GAN_Feat: 0.720 G_ID: 0.225 G_Rec: 0.390 D_GP: 0.047 D_real: 0.750 D_fake: 1.005 
+(epoch: 95, iters: 4048, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.752 G_ID: 0.306 G_Rec: 0.403 D_GP: 0.022 D_real: 0.986 D_fake: 0.685 
+(epoch: 95, iters: 4448, time: 0.064) G_GAN: -0.037 G_GAN_Feat: 0.797 G_ID: 0.160 G_Rec: 0.362 D_GP: 0.081 D_real: 0.670 D_fake: 1.037 
+(epoch: 95, iters: 4848, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.805 G_ID: 0.330 G_Rec: 0.418 D_GP: 0.033 D_real: 1.093 D_fake: 0.682 
+(epoch: 95, iters: 5248, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.822 G_ID: 0.215 G_Rec: 0.353 D_GP: 0.040 D_real: 0.965 D_fake: 0.727 
+(epoch: 95, iters: 5648, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.815 G_ID: 0.268 G_Rec: 0.384 D_GP: 0.039 D_real: 1.038 D_fake: 0.677 
+(epoch: 95, iters: 6048, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.784 G_ID: 0.200 G_Rec: 0.335 D_GP: 0.041 D_real: 0.935 D_fake: 0.675 
+(epoch: 95, iters: 6448, time: 0.064) G_GAN: -0.013 G_GAN_Feat: 1.069 G_ID: 0.309 G_Rec: 0.388 D_GP: 0.047 D_real: 0.220 D_fake: 1.013 
+(epoch: 95, iters: 6848, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.676 G_ID: 0.200 G_Rec: 0.324 D_GP: 0.020 D_real: 1.170 D_fake: 0.682 
+(epoch: 95, iters: 7248, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 0.870 G_ID: 0.270 G_Rec: 0.405 D_GP: 0.029 D_real: 1.116 D_fake: 0.457 
+(epoch: 95, iters: 7648, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 0.790 G_ID: 0.170 G_Rec: 0.350 D_GP: 0.075 D_real: 1.140 D_fake: 0.549 
+(epoch: 95, iters: 8048, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 1.308 G_ID: 0.320 G_Rec: 0.402 D_GP: 0.421 D_real: 0.552 D_fake: 0.678 
+(epoch: 95, iters: 8448, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 1.082 G_ID: 0.167 G_Rec: 0.391 D_GP: 0.193 D_real: 0.457 D_fake: 0.653 
+(epoch: 96, iters: 240, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 1.021 G_ID: 0.273 G_Rec: 0.432 D_GP: 0.111 D_real: 1.027 D_fake: 0.559 
+(epoch: 96, iters: 640, time: 0.064) G_GAN: 0.037 G_GAN_Feat: 0.747 G_ID: 0.173 G_Rec: 0.337 D_GP: 0.031 D_real: 0.857 D_fake: 0.963 
+(epoch: 96, iters: 1040, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.729 G_ID: 0.336 G_Rec: 0.393 D_GP: 0.022 D_real: 1.153 D_fake: 0.677 
+(epoch: 96, iters: 1440, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.624 G_ID: 0.165 G_Rec: 0.367 D_GP: 0.020 D_real: 1.308 D_fake: 0.620 
+(epoch: 96, iters: 1840, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.759 G_ID: 0.307 G_Rec: 0.394 D_GP: 0.027 D_real: 1.066 D_fake: 0.636 
+(epoch: 96, iters: 2240, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.727 G_ID: 0.190 G_Rec: 0.369 D_GP: 0.048 D_real: 1.012 D_fake: 0.815 
+(epoch: 96, iters: 2640, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.898 G_ID: 0.277 G_Rec: 0.390 D_GP: 0.069 D_real: 1.019 D_fake: 0.531 
+(epoch: 96, iters: 3040, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.751 G_ID: 0.196 G_Rec: 0.351 D_GP: 0.050 D_real: 1.047 D_fake: 0.675 
+(epoch: 96, iters: 3440, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 0.903 G_ID: 0.279 G_Rec: 0.395 D_GP: 0.043 D_real: 0.955 D_fake: 0.593 
+(epoch: 96, iters: 3840, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.867 G_ID: 0.190 G_Rec: 0.374 D_GP: 0.048 D_real: 0.782 D_fake: 0.727 
+(epoch: 96, iters: 4240, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.755 G_ID: 0.293 G_Rec: 0.380 D_GP: 0.028 D_real: 0.813 D_fake: 0.913 
+(epoch: 96, iters: 4640, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.962 G_ID: 0.168 G_Rec: 0.394 D_GP: 0.141 D_real: 0.509 D_fake: 0.776 
+(epoch: 96, iters: 5040, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.806 G_ID: 0.297 G_Rec: 0.403 D_GP: 0.035 D_real: 1.100 D_fake: 0.686 
+(epoch: 96, iters: 5440, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.690 G_ID: 0.183 G_Rec: 0.334 D_GP: 0.024 D_real: 1.002 D_fake: 0.853 
+(epoch: 96, iters: 5840, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 1.015 G_ID: 0.274 G_Rec: 0.397 D_GP: 0.139 D_real: 0.762 D_fake: 0.553 
+(epoch: 96, iters: 6240, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.667 G_ID: 0.185 G_Rec: 0.350 D_GP: 0.027 D_real: 1.164 D_fake: 0.719 
+(epoch: 96, iters: 6640, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.867 G_ID: 0.264 G_Rec: 0.406 D_GP: 0.044 D_real: 0.978 D_fake: 0.661 
+(epoch: 96, iters: 7040, time: 0.064) G_GAN: 0.496 G_GAN_Feat: 0.764 G_ID: 0.189 G_Rec: 0.358 D_GP: 0.031 D_real: 1.257 D_fake: 0.513 
+(epoch: 96, iters: 7440, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.821 G_ID: 0.243 G_Rec: 0.365 D_GP: 0.034 D_real: 1.058 D_fake: 0.698 
+(epoch: 96, iters: 7840, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.901 G_ID: 0.174 G_Rec: 0.361 D_GP: 0.043 D_real: 1.159 D_fake: 0.581 
+(epoch: 96, iters: 8240, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.999 G_ID: 0.304 G_Rec: 0.393 D_GP: 0.088 D_real: 0.528 D_fake: 0.688 
+(epoch: 97, iters: 32, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.786 G_ID: 0.190 G_Rec: 0.364 D_GP: 0.048 D_real: 1.145 D_fake: 0.663 
+(epoch: 97, iters: 432, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.799 G_ID: 0.333 G_Rec: 0.397 D_GP: 0.041 D_real: 0.840 D_fake: 0.861 
+(epoch: 97, iters: 832, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 1.120 G_ID: 0.218 G_Rec: 0.404 D_GP: 0.161 D_real: 0.382 D_fake: 0.836 
+(epoch: 97, iters: 1232, time: 0.064) G_GAN: 0.655 G_GAN_Feat: 0.976 G_ID: 0.276 G_Rec: 0.412 D_GP: 0.050 D_real: 1.069 D_fake: 0.361 
+(epoch: 97, iters: 1632, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.914 G_ID: 0.200 G_Rec: 0.335 D_GP: 0.046 D_real: 0.964 D_fake: 0.671 
+(epoch: 97, iters: 2032, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.716 G_ID: 0.228 G_Rec: 0.357 D_GP: 0.024 D_real: 1.333 D_fake: 0.498 
+(epoch: 97, iters: 2432, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.658 G_ID: 0.186 G_Rec: 0.332 D_GP: 0.025 D_real: 1.149 D_fake: 0.703 
+(epoch: 97, iters: 2832, time: 0.064) G_GAN: 0.612 G_GAN_Feat: 0.833 G_ID: 0.295 G_Rec: 0.374 D_GP: 0.035 D_real: 1.345 D_fake: 0.393 
+(epoch: 97, iters: 3232, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.959 G_ID: 0.160 G_Rec: 0.376 D_GP: 0.054 D_real: 0.683 D_fake: 0.642 
+(epoch: 97, iters: 3632, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.839 G_ID: 0.302 G_Rec: 0.397 D_GP: 0.032 D_real: 1.124 D_fake: 0.588 
+(epoch: 97, iters: 4032, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.888 G_ID: 0.150 G_Rec: 0.341 D_GP: 0.102 D_real: 0.751 D_fake: 0.724 
+(epoch: 97, iters: 4432, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.965 G_ID: 0.296 G_Rec: 0.396 D_GP: 0.045 D_real: 1.031 D_fake: 0.548 
+(epoch: 97, iters: 4832, time: 0.064) G_GAN: 0.577 G_GAN_Feat: 0.789 G_ID: 0.209 G_Rec: 0.388 D_GP: 0.062 D_real: 1.308 D_fake: 0.443 
+(epoch: 97, iters: 5232, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.832 G_ID: 0.285 G_Rec: 0.393 D_GP: 0.032 D_real: 0.723 D_fake: 0.914 
+(epoch: 97, iters: 5632, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.754 G_ID: 0.187 G_Rec: 0.388 D_GP: 0.041 D_real: 1.057 D_fake: 0.683 
+(epoch: 97, iters: 6032, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.989 G_ID: 0.318 G_Rec: 0.381 D_GP: 0.086 D_real: 0.670 D_fake: 0.555 
+(epoch: 97, iters: 6432, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.682 G_ID: 0.179 G_Rec: 0.369 D_GP: 0.023 D_real: 1.106 D_fake: 0.799 
+(epoch: 97, iters: 6832, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.758 G_ID: 0.270 G_Rec: 0.387 D_GP: 0.025 D_real: 1.109 D_fake: 0.604 
+(epoch: 97, iters: 7232, time: 0.064) G_GAN: 0.015 G_GAN_Feat: 0.906 G_ID: 0.178 G_Rec: 0.390 D_GP: 0.190 D_real: 0.594 D_fake: 0.985 
+(epoch: 97, iters: 7632, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 0.876 G_ID: 0.317 G_Rec: 0.408 D_GP: 0.050 D_real: 1.175 D_fake: 0.502 
+(epoch: 97, iters: 8032, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.786 G_ID: 0.173 G_Rec: 0.348 D_GP: 0.033 D_real: 1.290 D_fake: 0.488 
+(epoch: 97, iters: 8432, time: 0.064) G_GAN: 0.585 G_GAN_Feat: 1.036 G_ID: 0.307 G_Rec: 0.432 D_GP: 0.125 D_real: 0.794 D_fake: 0.428 
+(epoch: 98, iters: 224, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.692 G_ID: 0.173 G_Rec: 0.345 D_GP: 0.030 D_real: 1.170 D_fake: 0.626 
+(epoch: 98, iters: 624, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.762 G_ID: 0.321 G_Rec: 0.407 D_GP: 0.027 D_real: 1.108 D_fake: 0.649 
+(epoch: 98, iters: 1024, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.962 G_ID: 0.182 G_Rec: 0.399 D_GP: 0.076 D_real: 0.500 D_fake: 0.823 
+(epoch: 98, iters: 1424, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.978 G_ID: 0.261 G_Rec: 0.443 D_GP: 0.045 D_real: 0.920 D_fake: 0.577 
+(epoch: 98, iters: 1824, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.927 G_ID: 0.174 G_Rec: 0.391 D_GP: 0.028 D_real: 1.051 D_fake: 0.710 
+(epoch: 98, iters: 2224, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.780 G_ID: 0.288 G_Rec: 0.402 D_GP: 0.022 D_real: 0.942 D_fake: 0.833 
+(epoch: 98, iters: 2624, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.835 G_ID: 0.176 G_Rec: 0.361 D_GP: 0.055 D_real: 0.792 D_fake: 0.857 
+(epoch: 98, iters: 3024, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 1.069 G_ID: 0.318 G_Rec: 0.459 D_GP: 0.140 D_real: 0.429 D_fake: 0.762 
+(epoch: 98, iters: 3424, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.826 G_ID: 0.164 G_Rec: 0.374 D_GP: 0.037 D_real: 1.342 D_fake: 0.423 
+(epoch: 98, iters: 3824, time: 0.064) G_GAN: 0.473 G_GAN_Feat: 0.926 G_ID: 0.330 G_Rec: 0.387 D_GP: 0.049 D_real: 0.994 D_fake: 0.528 
+(epoch: 98, iters: 4224, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.760 G_ID: 0.191 G_Rec: 0.357 D_GP: 0.031 D_real: 1.024 D_fake: 0.715 
+(epoch: 98, iters: 4624, time: 0.064) G_GAN: 0.594 G_GAN_Feat: 1.090 G_ID: 0.263 G_Rec: 0.401 D_GP: 0.103 D_real: 0.863 D_fake: 0.442 
+(epoch: 98, iters: 5024, time: 0.064) G_GAN: 0.049 G_GAN_Feat: 0.795 G_ID: 0.163 G_Rec: 0.347 D_GP: 0.045 D_real: 0.762 D_fake: 0.952 
+(epoch: 98, iters: 5424, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.824 G_ID: 0.287 G_Rec: 0.372 D_GP: 0.030 D_real: 1.030 D_fake: 0.659 
+(epoch: 98, iters: 5824, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.910 G_ID: 0.168 G_Rec: 0.425 D_GP: 0.061 D_real: 0.591 D_fake: 0.916 
+(epoch: 98, iters: 6224, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 1.198 G_ID: 0.255 G_Rec: 0.386 D_GP: 0.368 D_real: 0.549 D_fake: 0.735 
+(epoch: 98, iters: 6624, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 0.751 G_ID: 0.188 G_Rec: 0.372 D_GP: 0.030 D_real: 1.237 D_fake: 0.604 
+(epoch: 98, iters: 7024, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.812 G_ID: 0.260 G_Rec: 0.401 D_GP: 0.032 D_real: 0.875 D_fake: 0.846 
+(epoch: 98, iters: 7424, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.841 G_ID: 0.172 G_Rec: 0.356 D_GP: 0.062 D_real: 0.803 D_fake: 0.754 
+(epoch: 98, iters: 7824, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.709 G_ID: 0.310 G_Rec: 0.393 D_GP: 0.020 D_real: 1.324 D_fake: 0.504 
+(epoch: 98, iters: 8224, time: 0.064) G_GAN: 0.024 G_GAN_Feat: 0.717 G_ID: 0.170 G_Rec: 0.374 D_GP: 0.023 D_real: 0.944 D_fake: 0.976 
+(epoch: 99, iters: 16, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.863 G_ID: 0.308 G_Rec: 0.400 D_GP: 0.034 D_real: 0.918 D_fake: 0.713 
+(epoch: 99, iters: 416, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.781 G_ID: 0.160 G_Rec: 0.361 D_GP: 0.037 D_real: 1.394 D_fake: 0.434 
+(epoch: 99, iters: 816, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.903 G_ID: 0.290 G_Rec: 0.366 D_GP: 0.055 D_real: 0.860 D_fake: 0.703 
+(epoch: 99, iters: 1216, time: 0.064) G_GAN: 0.735 G_GAN_Feat: 0.858 G_ID: 0.168 G_Rec: 0.344 D_GP: 0.078 D_real: 1.299 D_fake: 0.336 
+(epoch: 99, iters: 1616, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 1.304 G_ID: 0.291 G_Rec: 0.384 D_GP: 0.108 D_real: 0.440 D_fake: 0.631 
+(epoch: 99, iters: 2016, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.617 G_ID: 0.162 G_Rec: 0.391 D_GP: 0.024 D_real: 1.259 D_fake: 0.706 
+(epoch: 99, iters: 2416, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.957 G_ID: 0.313 G_Rec: 0.451 D_GP: 0.059 D_real: 0.790 D_fake: 0.773 
+(epoch: 99, iters: 2816, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.720 G_ID: 0.185 G_Rec: 0.355 D_GP: 0.026 D_real: 1.118 D_fake: 0.692 
+(epoch: 99, iters: 3216, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 0.816 G_ID: 0.270 G_Rec: 0.356 D_GP: 0.053 D_real: 1.133 D_fake: 0.522 
+(epoch: 99, iters: 3616, time: 0.064) G_GAN: 0.042 G_GAN_Feat: 0.754 G_ID: 0.216 G_Rec: 0.380 D_GP: 0.031 D_real: 0.903 D_fake: 0.958 
+(epoch: 99, iters: 4016, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.868 G_ID: 0.240 G_Rec: 0.386 D_GP: 0.033 D_real: 1.036 D_fake: 0.608 
+(epoch: 99, iters: 4416, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.806 G_ID: 0.169 G_Rec: 0.362 D_GP: 0.038 D_real: 1.211 D_fake: 0.681 
+(epoch: 99, iters: 4816, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 1.025 G_ID: 0.298 G_Rec: 0.392 D_GP: 0.075 D_real: 0.773 D_fake: 0.588 
+(epoch: 99, iters: 5216, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.957 G_ID: 0.183 G_Rec: 0.356 D_GP: 0.087 D_real: 0.868 D_fake: 0.652 
+(epoch: 99, iters: 5616, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.986 G_ID: 0.304 G_Rec: 0.422 D_GP: 0.112 D_real: 0.756 D_fake: 0.756 
+(epoch: 99, iters: 6016, time: 0.064) G_GAN: 0.633 G_GAN_Feat: 1.019 G_ID: 0.175 G_Rec: 0.400 D_GP: 0.139 D_real: 1.024 D_fake: 0.499 
+(epoch: 99, iters: 6416, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 1.122 G_ID: 0.290 G_Rec: 0.450 D_GP: 0.189 D_real: 0.560 D_fake: 0.745 
+(epoch: 99, iters: 6816, time: 0.064) G_GAN: 0.071 G_GAN_Feat: 0.854 G_ID: 0.179 G_Rec: 0.342 D_GP: 0.043 D_real: 0.693 D_fake: 0.929 
+(epoch: 99, iters: 7216, time: 0.064) G_GAN: 0.434 G_GAN_Feat: 1.059 G_ID: 0.274 G_Rec: 0.374 D_GP: 0.062 D_real: 0.752 D_fake: 0.578 
+(epoch: 99, iters: 7616, time: 0.064) G_GAN: 0.691 G_GAN_Feat: 1.015 G_ID: 0.178 G_Rec: 0.370 D_GP: 0.115 D_real: 0.837 D_fake: 0.331 
+(epoch: 99, iters: 8016, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.936 G_ID: 0.328 G_Rec: 0.374 D_GP: 0.093 D_real: 0.807 D_fake: 0.579 
+(epoch: 99, iters: 8416, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.873 G_ID: 0.160 G_Rec: 0.381 D_GP: 0.031 D_real: 1.032 D_fake: 0.676 
+(epoch: 100, iters: 208, time: 0.064) G_GAN: 0.745 G_GAN_Feat: 0.973 G_ID: 0.293 G_Rec: 0.393 D_GP: 0.050 D_real: 1.343 D_fake: 0.344 
+(epoch: 100, iters: 608, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.784 G_ID: 0.187 G_Rec: 0.360 D_GP: 0.045 D_real: 1.105 D_fake: 0.634 
+(epoch: 100, iters: 1008, time: 0.064) G_GAN: 0.634 G_GAN_Feat: 1.135 G_ID: 0.303 G_Rec: 0.407 D_GP: 0.197 D_real: 0.779 D_fake: 0.448 
+(epoch: 100, iters: 1408, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.724 G_ID: 0.160 G_Rec: 0.402 D_GP: 0.029 D_real: 1.147 D_fake: 0.689 
+(epoch: 100, iters: 1808, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.993 G_ID: 0.274 G_Rec: 0.387 D_GP: 0.046 D_real: 0.629 D_fake: 0.681 
+(epoch: 100, iters: 2208, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 1.004 G_ID: 0.186 G_Rec: 0.400 D_GP: 0.101 D_real: 0.568 D_fake: 0.763 
+(epoch: 100, iters: 2608, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.894 G_ID: 0.344 G_Rec: 0.399 D_GP: 0.024 D_real: 0.935 D_fake: 0.822 
+(epoch: 100, iters: 3008, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.778 G_ID: 0.180 G_Rec: 0.366 D_GP: 0.032 D_real: 1.063 D_fake: 0.847 
+(epoch: 100, iters: 3408, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.838 G_ID: 0.273 G_Rec: 0.444 D_GP: 0.020 D_real: 1.137 D_fake: 0.713 
+(epoch: 100, iters: 3808, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.832 G_ID: 0.194 G_Rec: 0.389 D_GP: 0.031 D_real: 1.034 D_fake: 0.714 
+(epoch: 100, iters: 4208, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.820 G_ID: 0.300 G_Rec: 0.448 D_GP: 0.022 D_real: 1.021 D_fake: 0.710 
+(epoch: 100, iters: 4608, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.716 G_ID: 0.170 G_Rec: 0.317 D_GP: 0.036 D_real: 1.090 D_fake: 0.698 
+(epoch: 100, iters: 5008, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 1.058 G_ID: 0.261 G_Rec: 0.405 D_GP: 0.050 D_real: 0.481 D_fake: 0.662 
+(epoch: 100, iters: 5408, time: 0.064) G_GAN: 0.528 G_GAN_Feat: 1.171 G_ID: 0.172 G_Rec: 0.401 D_GP: 0.259 D_real: 1.067 D_fake: 0.523 
+(epoch: 100, iters: 5808, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 0.989 G_ID: 0.294 G_Rec: 0.399 D_GP: 0.029 D_real: 1.040 D_fake: 0.411 
+(epoch: 100, iters: 6208, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 0.846 G_ID: 0.172 G_Rec: 0.403 D_GP: 0.049 D_real: 1.210 D_fake: 0.552 
+(epoch: 100, iters: 6608, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.954 G_ID: 0.265 G_Rec: 0.454 D_GP: 0.033 D_real: 0.674 D_fake: 0.762 
+(epoch: 100, iters: 7008, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.745 G_ID: 0.151 G_Rec: 0.356 D_GP: 0.022 D_real: 1.046 D_fake: 0.786 
+(epoch: 100, iters: 7408, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.738 G_ID: 0.286 G_Rec: 0.356 D_GP: 0.027 D_real: 0.928 D_fake: 0.894 
+(epoch: 100, iters: 7808, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 1.120 G_ID: 0.187 G_Rec: 0.370 D_GP: 0.102 D_real: 0.413 D_fake: 0.866 
+(epoch: 100, iters: 8208, time: 0.064) G_GAN: 0.051 G_GAN_Feat: 1.090 G_ID: 0.322 G_Rec: 0.414 D_GP: 0.390 D_real: 0.526 D_fake: 0.950 
+(epoch: 100, iters: 8608, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.715 G_ID: 0.187 G_Rec: 0.339 D_GP: 0.033 D_real: 1.008 D_fake: 0.810 
+(epoch: 101, iters: 400, time: 0.064) G_GAN: 0.617 G_GAN_Feat: 0.800 G_ID: 0.295 G_Rec: 0.403 D_GP: 0.030 D_real: 1.377 D_fake: 0.389 
+(epoch: 101, iters: 800, time: 0.064) G_GAN: 0.827 G_GAN_Feat: 0.825 G_ID: 0.156 G_Rec: 0.389 D_GP: 0.021 D_real: 1.616 D_fake: 0.234 
+(epoch: 101, iters: 1200, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.776 G_ID: 0.267 G_Rec: 0.404 D_GP: 0.029 D_real: 1.045 D_fake: 0.670 
+(epoch: 101, iters: 1600, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.865 G_ID: 0.164 G_Rec: 0.411 D_GP: 0.067 D_real: 0.733 D_fake: 0.752 
+(epoch: 101, iters: 2000, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.930 G_ID: 0.279 G_Rec: 0.407 D_GP: 0.084 D_real: 0.772 D_fake: 0.629 
+(epoch: 101, iters: 2400, time: 0.064) G_GAN: 0.677 G_GAN_Feat: 0.845 G_ID: 0.218 G_Rec: 0.335 D_GP: 0.041 D_real: 1.510 D_fake: 0.357 
+(epoch: 101, iters: 2800, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 1.006 G_ID: 0.289 G_Rec: 0.417 D_GP: 0.036 D_real: 0.857 D_fake: 0.707 
+(epoch: 101, iters: 3200, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.875 G_ID: 0.176 G_Rec: 0.368 D_GP: 0.027 D_real: 1.063 D_fake: 0.836 
+(epoch: 101, iters: 3600, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 1.347 G_ID: 0.275 G_Rec: 0.463 D_GP: 0.059 D_real: 0.580 D_fake: 0.560 
+(epoch: 101, iters: 4000, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.705 G_ID: 0.172 G_Rec: 0.354 D_GP: 0.028 D_real: 1.209 D_fake: 0.648 
+(epoch: 101, iters: 4400, time: 0.064) G_GAN: 0.561 G_GAN_Feat: 1.155 G_ID: 0.265 G_Rec: 0.396 D_GP: 0.069 D_real: 0.738 D_fake: 0.443 
+(epoch: 101, iters: 4800, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.706 G_ID: 0.188 G_Rec: 0.347 D_GP: 0.024 D_real: 0.971 D_fake: 0.862 
+(epoch: 101, iters: 5200, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 1.010 G_ID: 0.255 G_Rec: 0.434 D_GP: 0.104 D_real: 0.591 D_fake: 0.755 
+(epoch: 101, iters: 5600, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.700 G_ID: 0.185 G_Rec: 0.308 D_GP: 0.025 D_real: 1.125 D_fake: 0.824 
+(epoch: 101, iters: 6000, time: 0.064) G_GAN: 0.726 G_GAN_Feat: 1.096 G_ID: 0.244 G_Rec: 0.431 D_GP: 0.114 D_real: 0.923 D_fake: 0.311 
+(epoch: 101, iters: 6400, time: 0.064) G_GAN: 0.841 G_GAN_Feat: 0.856 G_ID: 0.164 G_Rec: 0.372 D_GP: 0.031 D_real: 1.522 D_fake: 0.360 
+(epoch: 101, iters: 6800, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.881 G_ID: 0.274 G_Rec: 0.416 D_GP: 0.042 D_real: 1.061 D_fake: 0.633 
+(epoch: 101, iters: 7200, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.779 G_ID: 0.164 G_Rec: 0.350 D_GP: 0.035 D_real: 1.232 D_fake: 0.557 
+(epoch: 101, iters: 7600, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.907 G_ID: 0.254 G_Rec: 0.397 D_GP: 0.039 D_real: 0.846 D_fake: 0.804 
+(epoch: 101, iters: 8000, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.740 G_ID: 0.193 G_Rec: 0.423 D_GP: 0.020 D_real: 1.117 D_fake: 0.783 
+(epoch: 101, iters: 8400, time: 0.064) G_GAN: 0.600 G_GAN_Feat: 0.707 G_ID: 0.268 G_Rec: 0.473 D_GP: 0.020 D_real: 1.385 D_fake: 0.405 
+(epoch: 102, iters: 192, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.614 G_ID: 0.199 G_Rec: 0.328 D_GP: 0.021 D_real: 1.336 D_fake: 0.613 
+(epoch: 102, iters: 592, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.735 G_ID: 0.252 G_Rec: 0.363 D_GP: 0.026 D_real: 1.138 D_fake: 0.725 
+(epoch: 102, iters: 992, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.637 G_ID: 0.204 G_Rec: 0.351 D_GP: 0.027 D_real: 1.137 D_fake: 0.722 
+(epoch: 102, iters: 1392, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 0.850 G_ID: 0.243 G_Rec: 0.413 D_GP: 0.042 D_real: 1.137 D_fake: 0.465 
+(epoch: 102, iters: 1792, time: 0.064) G_GAN: -0.030 G_GAN_Feat: 0.881 G_ID: 0.199 G_Rec: 0.389 D_GP: 0.272 D_real: 0.540 D_fake: 1.030 
+(epoch: 102, iters: 2192, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 0.794 G_ID: 0.283 G_Rec: 0.402 D_GP: 0.025 D_real: 1.340 D_fake: 0.479 
+(epoch: 102, iters: 2592, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.798 G_ID: 0.193 G_Rec: 0.369 D_GP: 0.042 D_real: 0.971 D_fake: 0.739 
+(epoch: 102, iters: 2992, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.913 G_ID: 0.269 G_Rec: 0.403 D_GP: 0.078 D_real: 0.838 D_fake: 0.544 
+(epoch: 102, iters: 3392, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.891 G_ID: 0.159 G_Rec: 0.378 D_GP: 0.122 D_real: 0.953 D_fake: 0.658 
+(epoch: 102, iters: 3792, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.992 G_ID: 0.293 G_Rec: 0.440 D_GP: 0.084 D_real: 0.596 D_fake: 0.787 
+(epoch: 102, iters: 4192, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.735 G_ID: 0.196 G_Rec: 0.410 D_GP: 0.040 D_real: 1.128 D_fake: 0.777 
+(epoch: 102, iters: 4592, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.789 G_ID: 0.249 G_Rec: 0.370 D_GP: 0.034 D_real: 1.126 D_fake: 0.553 
+(epoch: 102, iters: 4992, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.874 G_ID: 0.178 G_Rec: 0.344 D_GP: 0.063 D_real: 1.047 D_fake: 0.516 
+(epoch: 102, iters: 5392, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.783 G_ID: 0.300 G_Rec: 0.440 D_GP: 0.026 D_real: 1.064 D_fake: 0.649 
+(epoch: 102, iters: 5792, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.726 G_ID: 0.172 G_Rec: 0.366 D_GP: 0.034 D_real: 1.069 D_fake: 0.772 
+(epoch: 102, iters: 6192, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.853 G_ID: 0.304 G_Rec: 0.425 D_GP: 0.033 D_real: 1.101 D_fake: 0.594 
+(epoch: 102, iters: 6592, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.798 G_ID: 0.186 G_Rec: 0.356 D_GP: 0.052 D_real: 0.867 D_fake: 0.829 
+(epoch: 102, iters: 6992, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 1.008 G_ID: 0.275 G_Rec: 0.400 D_GP: 0.086 D_real: 0.571 D_fake: 0.740 
+(epoch: 102, iters: 7392, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.804 G_ID: 0.169 G_Rec: 0.334 D_GP: 0.064 D_real: 1.192 D_fake: 0.440 
+(epoch: 102, iters: 7792, time: 0.064) G_GAN: 0.551 G_GAN_Feat: 0.985 G_ID: 0.289 G_Rec: 0.429 D_GP: 0.039 D_real: 0.975 D_fake: 0.466 
+(epoch: 102, iters: 8192, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.913 G_ID: 0.183 G_Rec: 0.347 D_GP: 0.038 D_real: 0.659 D_fake: 0.868 
+(epoch: 102, iters: 8592, time: 0.064) G_GAN: 0.547 G_GAN_Feat: 1.145 G_ID: 0.249 G_Rec: 0.424 D_GP: 0.023 D_real: 1.071 D_fake: 0.454 
+(epoch: 103, iters: 384, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.950 G_ID: 0.172 G_Rec: 0.396 D_GP: 0.473 D_real: 0.577 D_fake: 0.756 
+(epoch: 103, iters: 784, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.992 G_ID: 0.283 G_Rec: 0.395 D_GP: 0.121 D_real: 0.435 D_fake: 0.921 
+(epoch: 103, iters: 1184, time: 0.064) G_GAN: 0.067 G_GAN_Feat: 0.902 G_ID: 0.231 G_Rec: 0.362 D_GP: 0.087 D_real: 0.705 D_fake: 0.934 
+(epoch: 103, iters: 1584, time: 0.064) G_GAN: 0.810 G_GAN_Feat: 0.876 G_ID: 0.301 G_Rec: 0.390 D_GP: 0.033 D_real: 1.448 D_fake: 0.235 
+(epoch: 103, iters: 1984, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.710 G_ID: 0.223 G_Rec: 0.331 D_GP: 0.023 D_real: 1.098 D_fake: 0.693 
+(epoch: 103, iters: 2384, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 0.711 G_ID: 0.249 G_Rec: 0.351 D_GP: 0.023 D_real: 1.355 D_fake: 0.412 
+(epoch: 103, iters: 2784, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.748 G_ID: 0.163 G_Rec: 0.356 D_GP: 0.034 D_real: 1.219 D_fake: 0.583 
+(epoch: 103, iters: 3184, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 1.020 G_ID: 0.293 G_Rec: 0.392 D_GP: 0.062 D_real: 0.639 D_fake: 0.648 
+(epoch: 103, iters: 3584, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.620 G_ID: 0.187 G_Rec: 0.345 D_GP: 0.020 D_real: 1.312 D_fake: 0.650 
+(epoch: 103, iters: 3984, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.813 G_ID: 0.269 G_Rec: 0.454 D_GP: 0.028 D_real: 0.983 D_fake: 0.733 
+(epoch: 103, iters: 4384, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.795 G_ID: 0.173 G_Rec: 0.472 D_GP: 0.052 D_real: 1.046 D_fake: 0.754 
+(epoch: 103, iters: 4784, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 0.925 G_ID: 0.264 G_Rec: 0.421 D_GP: 0.045 D_real: 1.124 D_fake: 0.480 
+(epoch: 103, iters: 5184, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.756 G_ID: 0.178 G_Rec: 0.365 D_GP: 0.024 D_real: 0.986 D_fake: 0.851 
+(epoch: 103, iters: 5584, time: 0.064) G_GAN: 0.764 G_GAN_Feat: 0.828 G_ID: 0.283 G_Rec: 0.375 D_GP: 0.032 D_real: 1.475 D_fake: 0.270 
+(epoch: 103, iters: 5984, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 1.027 G_ID: 0.161 G_Rec: 0.371 D_GP: 0.055 D_real: 0.583 D_fake: 0.756 
+(epoch: 103, iters: 6384, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.818 G_ID: 0.280 G_Rec: 0.381 D_GP: 0.039 D_real: 0.959 D_fake: 0.731 
+(epoch: 103, iters: 6784, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.754 G_ID: 0.199 G_Rec: 0.320 D_GP: 0.042 D_real: 1.219 D_fake: 0.531 
+(epoch: 103, iters: 7184, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.889 G_ID: 0.266 G_Rec: 0.389 D_GP: 0.029 D_real: 0.958 D_fake: 0.682 
+(epoch: 103, iters: 7584, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.814 G_ID: 0.190 G_Rec: 0.326 D_GP: 0.052 D_real: 0.972 D_fake: 0.652 
+(epoch: 103, iters: 7984, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.838 G_ID: 0.258 G_Rec: 0.439 D_GP: 0.027 D_real: 1.166 D_fake: 0.551 
+(epoch: 103, iters: 8384, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.758 G_ID: 0.169 G_Rec: 0.355 D_GP: 0.044 D_real: 0.950 D_fake: 0.916 
+(epoch: 104, iters: 176, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.813 G_ID: 0.330 G_Rec: 0.369 D_GP: 0.030 D_real: 1.014 D_fake: 0.720 
+(epoch: 104, iters: 576, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 0.658 G_ID: 0.181 G_Rec: 0.326 D_GP: 0.028 D_real: 1.349 D_fake: 0.520 
+(epoch: 104, iters: 976, time: 0.064) G_GAN: 0.529 G_GAN_Feat: 0.759 G_ID: 0.259 G_Rec: 0.375 D_GP: 0.035 D_real: 1.252 D_fake: 0.480 
+(epoch: 104, iters: 1376, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.884 G_ID: 0.152 G_Rec: 0.326 D_GP: 0.236 D_real: 0.509 D_fake: 0.785 
+(epoch: 104, iters: 1776, time: 0.064) G_GAN: 0.594 G_GAN_Feat: 0.768 G_ID: 0.271 G_Rec: 0.433 D_GP: 0.023 D_real: 1.310 D_fake: 0.413 
+(epoch: 104, iters: 2176, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.871 G_ID: 0.170 G_Rec: 0.353 D_GP: 0.033 D_real: 1.054 D_fake: 0.654 
+(epoch: 104, iters: 2576, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 0.875 G_ID: 0.317 G_Rec: 0.383 D_GP: 0.026 D_real: 1.253 D_fake: 0.515 
+(epoch: 104, iters: 2976, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 1.163 G_ID: 0.174 G_Rec: 0.383 D_GP: 3.192 D_real: 0.560 D_fake: 0.883 
+(epoch: 104, iters: 3376, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.641 G_ID: 0.301 G_Rec: 0.345 D_GP: 0.020 D_real: 1.168 D_fake: 0.687 
+(epoch: 104, iters: 3776, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.651 G_ID: 0.160 G_Rec: 0.372 D_GP: 0.022 D_real: 1.216 D_fake: 0.641 
+(epoch: 104, iters: 4176, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 0.669 G_ID: 0.273 G_Rec: 0.365 D_GP: 0.023 D_real: 1.266 D_fake: 0.563 
+(epoch: 104, iters: 4576, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.618 G_ID: 0.188 G_Rec: 0.331 D_GP: 0.023 D_real: 1.231 D_fake: 0.681 
+(epoch: 104, iters: 4976, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.845 G_ID: 0.243 G_Rec: 0.396 D_GP: 0.038 D_real: 1.059 D_fake: 0.660 
+(epoch: 104, iters: 5376, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.752 G_ID: 0.172 G_Rec: 0.366 D_GP: 0.055 D_real: 1.005 D_fake: 0.649 
+(epoch: 104, iters: 5776, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.883 G_ID: 0.242 G_Rec: 0.412 D_GP: 0.057 D_real: 0.856 D_fake: 0.824 
+(epoch: 104, iters: 6176, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.955 G_ID: 0.187 G_Rec: 0.385 D_GP: 0.080 D_real: 0.628 D_fake: 0.699 
+(epoch: 104, iters: 6576, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 1.101 G_ID: 0.229 G_Rec: 0.444 D_GP: 0.232 D_real: 0.509 D_fake: 0.699 
+(epoch: 104, iters: 6976, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.830 G_ID: 0.160 G_Rec: 0.349 D_GP: 0.056 D_real: 1.042 D_fake: 0.771 
+(epoch: 104, iters: 7376, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.917 G_ID: 0.287 G_Rec: 0.409 D_GP: 0.076 D_real: 0.739 D_fake: 0.813 
+(epoch: 104, iters: 7776, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.765 G_ID: 0.143 G_Rec: 0.377 D_GP: 0.034 D_real: 1.172 D_fake: 0.529 
+(epoch: 104, iters: 8176, time: 0.064) G_GAN: 0.774 G_GAN_Feat: 0.906 G_ID: 0.255 G_Rec: 0.397 D_GP: 0.041 D_real: 1.385 D_fake: 0.283 
+(epoch: 104, iters: 8576, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.977 G_ID: 0.200 G_Rec: 0.367 D_GP: 0.112 D_real: 0.408 D_fake: 0.841 
+(epoch: 105, iters: 368, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.988 G_ID: 0.312 G_Rec: 0.369 D_GP: 0.047 D_real: 0.992 D_fake: 0.534 
+(epoch: 105, iters: 768, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.855 G_ID: 0.160 G_Rec: 0.341 D_GP: 0.058 D_real: 1.197 D_fake: 0.694 
+(epoch: 105, iters: 1168, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.937 G_ID: 0.295 G_Rec: 0.389 D_GP: 0.036 D_real: 1.003 D_fake: 0.603 
+(epoch: 105, iters: 1568, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.936 G_ID: 0.177 G_Rec: 0.362 D_GP: 0.100 D_real: 0.530 D_fake: 0.906 
+(epoch: 105, iters: 1968, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 1.135 G_ID: 0.294 G_Rec: 0.422 D_GP: 0.068 D_real: 0.757 D_fake: 0.372 
+(epoch: 105, iters: 2368, time: 0.064) G_GAN: -0.263 G_GAN_Feat: 0.875 G_ID: 0.209 G_Rec: 0.430 D_GP: 0.119 D_real: 0.577 D_fake: 1.268 
+(epoch: 105, iters: 2768, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 0.841 G_ID: 0.251 G_Rec: 0.379 D_GP: 0.031 D_real: 1.313 D_fake: 0.449 
+(epoch: 105, iters: 3168, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.823 G_ID: 0.170 G_Rec: 0.338 D_GP: 0.042 D_real: 0.904 D_fake: 0.788 
+(epoch: 105, iters: 3568, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.936 G_ID: 0.287 G_Rec: 0.418 D_GP: 0.037 D_real: 0.769 D_fake: 0.791 
+(epoch: 105, iters: 3968, time: 0.064) G_GAN: -0.465 G_GAN_Feat: 0.915 G_ID: 0.215 G_Rec: 0.360 D_GP: 0.115 D_real: 0.177 D_fake: 1.465 
+(epoch: 105, iters: 4368, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.964 G_ID: 0.273 G_Rec: 0.424 D_GP: 0.080 D_real: 0.593 D_fake: 0.882 
+(epoch: 105, iters: 4768, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.767 G_ID: 0.154 G_Rec: 0.326 D_GP: 0.027 D_real: 1.232 D_fake: 0.617 
+(epoch: 105, iters: 5168, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 0.841 G_ID: 0.326 G_Rec: 0.405 D_GP: 0.023 D_real: 1.116 D_fake: 0.673 
+(epoch: 105, iters: 5568, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.789 G_ID: 0.177 G_Rec: 0.366 D_GP: 0.031 D_real: 0.988 D_fake: 0.791 
+(epoch: 105, iters: 5968, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.892 G_ID: 0.223 G_Rec: 0.417 D_GP: 0.028 D_real: 0.935 D_fake: 0.669 
+(epoch: 105, iters: 6368, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.750 G_ID: 0.191 G_Rec: 0.349 D_GP: 0.050 D_real: 0.942 D_fake: 0.782 
+(epoch: 105, iters: 6768, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.811 G_ID: 0.257 G_Rec: 0.398 D_GP: 0.032 D_real: 0.985 D_fake: 0.631 
+(epoch: 105, iters: 7168, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 1.161 G_ID: 0.184 G_Rec: 0.379 D_GP: 0.848 D_real: 0.369 D_fake: 0.756 
+(epoch: 105, iters: 7568, time: 0.064) G_GAN: 0.496 G_GAN_Feat: 1.034 G_ID: 0.285 G_Rec: 0.404 D_GP: 0.031 D_real: 1.040 D_fake: 0.507 
+(epoch: 105, iters: 7968, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.724 G_ID: 0.142 G_Rec: 0.324 D_GP: 0.027 D_real: 1.330 D_fake: 0.586 
+(epoch: 105, iters: 8368, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 1.019 G_ID: 0.251 G_Rec: 0.373 D_GP: 0.107 D_real: 0.283 D_fake: 0.762 
+(epoch: 106, iters: 160, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.857 G_ID: 0.177 G_Rec: 0.412 D_GP: 0.038 D_real: 1.185 D_fake: 0.666 
+(epoch: 106, iters: 560, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 0.852 G_ID: 0.266 G_Rec: 0.383 D_GP: 0.032 D_real: 1.239 D_fake: 0.488 
+(epoch: 106, iters: 960, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 1.048 G_ID: 0.180 G_Rec: 0.395 D_GP: 0.095 D_real: 0.549 D_fake: 0.780 
+(epoch: 106, iters: 1360, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 0.871 G_ID: 0.291 G_Rec: 0.406 D_GP: 0.022 D_real: 1.202 D_fake: 0.471 
+(epoch: 106, iters: 1760, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.747 G_ID: 0.214 G_Rec: 0.326 D_GP: 0.036 D_real: 1.089 D_fake: 0.714 
+(epoch: 106, iters: 2160, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.871 G_ID: 0.285 G_Rec: 0.416 D_GP: 0.029 D_real: 1.122 D_fake: 0.697 
+(epoch: 106, iters: 2560, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.910 G_ID: 0.170 G_Rec: 0.365 D_GP: 0.059 D_real: 1.072 D_fake: 0.521 
+(epoch: 106, iters: 2960, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.879 G_ID: 0.282 G_Rec: 0.397 D_GP: 0.051 D_real: 0.952 D_fake: 0.724 
+(epoch: 106, iters: 3360, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.712 G_ID: 0.169 G_Rec: 0.321 D_GP: 0.029 D_real: 0.979 D_fake: 0.866 
+(epoch: 106, iters: 3760, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.968 G_ID: 0.324 G_Rec: 0.408 D_GP: 0.061 D_real: 0.952 D_fake: 0.569 
+(epoch: 106, iters: 4160, time: 0.064) G_GAN: -0.348 G_GAN_Feat: 0.800 G_ID: 0.159 G_Rec: 0.384 D_GP: 0.033 D_real: 0.446 D_fake: 1.348 
+(epoch: 106, iters: 4560, time: 0.064) G_GAN: 0.044 G_GAN_Feat: 0.852 G_ID: 0.259 G_Rec: 0.435 D_GP: 0.031 D_real: 0.684 D_fake: 0.956 
+(epoch: 106, iters: 4960, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.654 G_ID: 0.183 G_Rec: 0.347 D_GP: 0.021 D_real: 1.380 D_fake: 0.500 
+(epoch: 106, iters: 5360, time: 0.064) G_GAN: 0.603 G_GAN_Feat: 0.828 G_ID: 0.222 G_Rec: 0.407 D_GP: 0.027 D_real: 1.258 D_fake: 0.406 
+(epoch: 106, iters: 5760, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.636 G_ID: 0.189 G_Rec: 0.346 D_GP: 0.024 D_real: 1.170 D_fake: 0.740 
+(epoch: 106, iters: 6160, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 1.017 G_ID: 0.258 G_Rec: 0.396 D_GP: 0.080 D_real: 0.736 D_fake: 0.590 
+(epoch: 106, iters: 6560, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.911 G_ID: 0.159 G_Rec: 0.346 D_GP: 0.039 D_real: 1.035 D_fake: 0.493 
+(epoch: 106, iters: 6960, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 1.052 G_ID: 0.234 G_Rec: 0.415 D_GP: 0.119 D_real: 0.731 D_fake: 0.438 
+(epoch: 106, iters: 7360, time: 0.064) G_GAN: -0.261 G_GAN_Feat: 1.081 G_ID: 0.160 G_Rec: 0.384 D_GP: 0.074 D_real: 0.097 D_fake: 1.261 
+(epoch: 106, iters: 7760, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 1.044 G_ID: 0.260 G_Rec: 0.399 D_GP: 0.057 D_real: 0.886 D_fake: 0.495 
+(epoch: 106, iters: 8160, time: 0.064) G_GAN: -0.028 G_GAN_Feat: 0.749 G_ID: 0.176 G_Rec: 0.369 D_GP: 0.022 D_real: 0.792 D_fake: 1.028 
+(epoch: 106, iters: 8560, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.913 G_ID: 0.277 G_Rec: 0.442 D_GP: 0.038 D_real: 0.839 D_fake: 0.688 
+(epoch: 107, iters: 352, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 1.035 G_ID: 0.147 G_Rec: 0.361 D_GP: 0.133 D_real: 0.387 D_fake: 0.739 
+(epoch: 107, iters: 752, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.924 G_ID: 0.286 G_Rec: 0.416 D_GP: 0.035 D_real: 0.921 D_fake: 0.814 
+(epoch: 107, iters: 1152, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.802 G_ID: 0.155 G_Rec: 0.352 D_GP: 0.087 D_real: 1.089 D_fake: 0.647 
+(epoch: 107, iters: 1552, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.861 G_ID: 0.287 G_Rec: 0.416 D_GP: 0.061 D_real: 0.953 D_fake: 0.623 
+(epoch: 107, iters: 1952, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.795 G_ID: 0.156 G_Rec: 0.351 D_GP: 0.046 D_real: 0.887 D_fake: 0.706 
+(epoch: 107, iters: 2352, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.970 G_ID: 0.240 G_Rec: 0.444 D_GP: 0.046 D_real: 0.991 D_fake: 0.449 
+(epoch: 107, iters: 2752, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 0.666 G_ID: 0.174 G_Rec: 0.331 D_GP: 0.026 D_real: 1.203 D_fake: 0.689 
+(epoch: 107, iters: 3152, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.938 G_ID: 0.251 G_Rec: 0.373 D_GP: 0.065 D_real: 1.034 D_fake: 0.509 
+(epoch: 107, iters: 3552, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 1.182 G_ID: 0.175 G_Rec: 0.364 D_GP: 0.188 D_real: 1.159 D_fake: 0.750 
+(epoch: 107, iters: 3952, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.694 G_ID: 0.285 G_Rec: 0.406 D_GP: 0.021 D_real: 1.284 D_fake: 0.609 
+(epoch: 107, iters: 4352, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.667 G_ID: 0.141 G_Rec: 0.343 D_GP: 0.026 D_real: 1.108 D_fake: 0.742 
+(epoch: 107, iters: 4752, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.882 G_ID: 0.277 G_Rec: 0.474 D_GP: 0.033 D_real: 0.999 D_fake: 0.502 
+(epoch: 107, iters: 5152, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.658 G_ID: 0.170 G_Rec: 0.349 D_GP: 0.024 D_real: 1.021 D_fake: 0.839 
+(epoch: 107, iters: 5552, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.959 G_ID: 0.273 G_Rec: 0.393 D_GP: 0.068 D_real: 0.794 D_fake: 0.552 
+(epoch: 107, iters: 5952, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.814 G_ID: 0.156 G_Rec: 0.346 D_GP: 0.048 D_real: 1.112 D_fake: 0.550 
+(epoch: 107, iters: 6352, time: 0.064) G_GAN: 0.652 G_GAN_Feat: 0.750 G_ID: 0.252 G_Rec: 0.361 D_GP: 0.024 D_real: 1.383 D_fake: 0.354 
+(epoch: 107, iters: 6752, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.964 G_ID: 0.162 G_Rec: 0.330 D_GP: 0.046 D_real: 0.620 D_fake: 0.841 
+(epoch: 107, iters: 7152, time: 0.064) G_GAN: 0.805 G_GAN_Feat: 0.828 G_ID: 0.249 G_Rec: 0.409 D_GP: 0.027 D_real: 1.548 D_fake: 0.238 
+(epoch: 107, iters: 7552, time: 0.064) G_GAN: 0.124 G_GAN_Feat: 1.030 G_ID: 0.151 G_Rec: 0.384 D_GP: 0.172 D_real: 0.409 D_fake: 0.876 
+(epoch: 107, iters: 7952, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.907 G_ID: 0.263 G_Rec: 0.398 D_GP: 0.032 D_real: 0.850 D_fake: 0.755 
+(epoch: 107, iters: 8352, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.686 G_ID: 0.173 G_Rec: 0.335 D_GP: 0.027 D_real: 1.079 D_fake: 0.801 
+(epoch: 108, iters: 144, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 1.008 G_ID: 0.299 G_Rec: 0.391 D_GP: 0.037 D_real: 0.693 D_fake: 0.734 
+(epoch: 108, iters: 544, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 0.750 G_ID: 0.186 G_Rec: 0.415 D_GP: 0.033 D_real: 1.378 D_fake: 0.475 
+(epoch: 108, iters: 944, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.925 G_ID: 0.249 G_Rec: 0.420 D_GP: 0.023 D_real: 0.796 D_fake: 0.786 
+(epoch: 108, iters: 1344, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.915 G_ID: 0.183 G_Rec: 0.350 D_GP: 0.032 D_real: 0.865 D_fake: 0.638 
+(epoch: 108, iters: 1744, time: 0.064) G_GAN: 0.548 G_GAN_Feat: 0.902 G_ID: 0.255 G_Rec: 0.384 D_GP: 0.029 D_real: 1.200 D_fake: 0.454 
+(epoch: 108, iters: 2144, time: 0.064) G_GAN: 0.592 G_GAN_Feat: 0.814 G_ID: 0.163 G_Rec: 0.457 D_GP: 0.026 D_real: 1.329 D_fake: 0.426 
+(epoch: 108, iters: 2544, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 1.065 G_ID: 0.263 G_Rec: 0.382 D_GP: 0.122 D_real: 0.791 D_fake: 0.424 
+(epoch: 108, iters: 2944, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.710 G_ID: 0.147 G_Rec: 0.377 D_GP: 0.025 D_real: 1.125 D_fake: 0.731 
+(epoch: 108, iters: 3344, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.963 G_ID: 0.249 G_Rec: 0.397 D_GP: 0.049 D_real: 1.029 D_fake: 0.624 
+(epoch: 108, iters: 3744, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.776 G_ID: 0.147 G_Rec: 0.348 D_GP: 0.024 D_real: 1.078 D_fake: 0.770 
+(epoch: 108, iters: 4144, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 0.884 G_ID: 0.265 G_Rec: 0.423 D_GP: 0.029 D_real: 1.110 D_fake: 0.542 
+(epoch: 108, iters: 4544, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.797 G_ID: 0.159 G_Rec: 0.387 D_GP: 0.047 D_real: 0.943 D_fake: 0.852 
+(epoch: 108, iters: 4944, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.872 G_ID: 0.272 G_Rec: 0.422 D_GP: 0.028 D_real: 0.906 D_fake: 0.831 
+(epoch: 108, iters: 5344, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.869 G_ID: 0.207 G_Rec: 0.368 D_GP: 0.094 D_real: 0.745 D_fake: 0.788 
+(epoch: 108, iters: 5744, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.857 G_ID: 0.247 G_Rec: 0.370 D_GP: 0.035 D_real: 0.951 D_fake: 0.615 
+(epoch: 108, iters: 6144, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.863 G_ID: 0.173 G_Rec: 0.398 D_GP: 0.057 D_real: 1.006 D_fake: 0.632 
+(epoch: 108, iters: 6544, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.956 G_ID: 0.232 G_Rec: 0.476 D_GP: 0.080 D_real: 0.788 D_fake: 0.666 
+(epoch: 108, iters: 6944, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 1.266 G_ID: 0.153 G_Rec: 0.640 D_GP: 0.204 D_real: 0.391 D_fake: 0.680 
+(epoch: 108, iters: 7344, time: 0.064) G_GAN: 0.495 G_GAN_Feat: 1.201 G_ID: 0.289 G_Rec: 0.440 D_GP: 0.043 D_real: 0.925 D_fake: 0.507 
+(epoch: 108, iters: 7744, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 0.946 G_ID: 0.192 G_Rec: 0.365 D_GP: 0.228 D_real: 0.510 D_fake: 0.808 
+(epoch: 108, iters: 8144, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 0.806 G_ID: 0.250 G_Rec: 0.420 D_GP: 0.025 D_real: 1.372 D_fake: 0.350 
+(epoch: 108, iters: 8544, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.820 G_ID: 0.154 G_Rec: 0.377 D_GP: 0.046 D_real: 0.989 D_fake: 0.699 
+(epoch: 109, iters: 336, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.825 G_ID: 0.256 G_Rec: 0.415 D_GP: 0.023 D_real: 1.121 D_fake: 0.609 
+(epoch: 109, iters: 736, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.839 G_ID: 0.150 G_Rec: 0.338 D_GP: 0.055 D_real: 0.784 D_fake: 0.813 
+(epoch: 109, iters: 1136, time: 0.064) G_GAN: 0.504 G_GAN_Feat: 1.022 G_ID: 0.238 G_Rec: 0.400 D_GP: 0.065 D_real: 0.811 D_fake: 0.499 
+(epoch: 109, iters: 1536, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.793 G_ID: 0.147 G_Rec: 0.325 D_GP: 0.033 D_real: 1.018 D_fake: 0.749 
+(epoch: 109, iters: 1936, time: 0.064) G_GAN: 0.629 G_GAN_Feat: 0.924 G_ID: 0.239 G_Rec: 0.415 D_GP: 0.035 D_real: 1.086 D_fake: 0.379 
+(epoch: 109, iters: 2336, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.724 G_ID: 0.175 G_Rec: 0.385 D_GP: 0.028 D_real: 1.127 D_fake: 0.769 
+(epoch: 109, iters: 2736, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.834 G_ID: 0.306 G_Rec: 0.446 D_GP: 0.025 D_real: 1.130 D_fake: 0.656 
+(epoch: 109, iters: 3136, time: 0.064) G_GAN: -0.056 G_GAN_Feat: 0.843 G_ID: 0.141 G_Rec: 0.360 D_GP: 0.145 D_real: 0.639 D_fake: 1.056 
+(epoch: 109, iters: 3536, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 1.003 G_ID: 0.302 G_Rec: 0.421 D_GP: 0.048 D_real: 1.201 D_fake: 0.531 
+(epoch: 109, iters: 3936, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.675 G_ID: 0.195 G_Rec: 0.330 D_GP: 0.024 D_real: 1.019 D_fake: 0.814 
+(epoch: 109, iters: 4336, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 1.299 G_ID: 0.241 G_Rec: 0.430 D_GP: 0.692 D_real: 0.609 D_fake: 0.495 
+(epoch: 109, iters: 4736, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.744 G_ID: 0.154 G_Rec: 0.357 D_GP: 0.027 D_real: 1.154 D_fake: 0.659 
+(epoch: 109, iters: 5136, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.915 G_ID: 0.280 G_Rec: 0.419 D_GP: 0.028 D_real: 0.936 D_fake: 0.719 
+(epoch: 109, iters: 5536, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 0.882 G_ID: 0.165 G_Rec: 0.362 D_GP: 0.078 D_real: 0.968 D_fake: 0.483 
+(epoch: 109, iters: 5936, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 0.874 G_ID: 0.245 G_Rec: 0.431 D_GP: 0.023 D_real: 1.193 D_fake: 0.466 
+(epoch: 109, iters: 6336, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.752 G_ID: 0.166 G_Rec: 0.346 D_GP: 0.030 D_real: 1.219 D_fake: 0.593 
+(epoch: 109, iters: 6736, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.873 G_ID: 0.254 G_Rec: 0.393 D_GP: 0.036 D_real: 1.069 D_fake: 0.578 
+(epoch: 109, iters: 7136, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.790 G_ID: 0.151 G_Rec: 0.350 D_GP: 0.057 D_real: 0.993 D_fake: 0.693 
+(epoch: 109, iters: 7536, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.699 G_ID: 0.281 G_Rec: 0.369 D_GP: 0.025 D_real: 1.148 D_fake: 0.648 
+(epoch: 109, iters: 7936, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.772 G_ID: 0.175 G_Rec: 0.359 D_GP: 0.054 D_real: 0.972 D_fake: 0.808 
+(epoch: 109, iters: 8336, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.849 G_ID: 0.262 G_Rec: 0.375 D_GP: 0.029 D_real: 1.177 D_fake: 0.569 
+(epoch: 110, iters: 128, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.761 G_ID: 0.177 G_Rec: 0.332 D_GP: 0.039 D_real: 1.112 D_fake: 0.630 
+(epoch: 110, iters: 528, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.847 G_ID: 0.259 G_Rec: 0.420 D_GP: 0.038 D_real: 0.945 D_fake: 0.781 
+(epoch: 110, iters: 928, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.899 G_ID: 0.145 G_Rec: 0.360 D_GP: 0.128 D_real: 0.826 D_fake: 0.600 
+(epoch: 110, iters: 1328, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.842 G_ID: 0.272 G_Rec: 0.407 D_GP: 0.028 D_real: 1.231 D_fake: 0.545 
+(epoch: 110, iters: 1728, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.698 G_ID: 0.189 G_Rec: 0.319 D_GP: 0.033 D_real: 1.023 D_fake: 0.802 
+(epoch: 110, iters: 2128, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.927 G_ID: 0.210 G_Rec: 0.408 D_GP: 0.037 D_real: 0.940 D_fake: 0.576 
+(epoch: 110, iters: 2528, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 0.696 G_ID: 0.165 G_Rec: 0.341 D_GP: 0.024 D_real: 1.342 D_fake: 0.510 
+(epoch: 110, iters: 2928, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.889 G_ID: 0.254 G_Rec: 0.388 D_GP: 0.036 D_real: 1.164 D_fake: 0.494 
+(epoch: 110, iters: 3328, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.863 G_ID: 0.175 G_Rec: 0.366 D_GP: 0.053 D_real: 0.717 D_fake: 0.842 
+(epoch: 110, iters: 3728, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.912 G_ID: 0.302 G_Rec: 0.401 D_GP: 0.056 D_real: 1.032 D_fake: 0.634 
+(epoch: 110, iters: 4128, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.888 G_ID: 0.172 G_Rec: 0.345 D_GP: 0.051 D_real: 1.055 D_fake: 0.555 
+(epoch: 110, iters: 4528, time: 0.064) G_GAN: 0.530 G_GAN_Feat: 0.929 G_ID: 0.247 G_Rec: 0.377 D_GP: 0.029 D_real: 1.207 D_fake: 0.472 
+(epoch: 110, iters: 4928, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 0.655 G_ID: 0.196 G_Rec: 0.309 D_GP: 0.026 D_real: 1.348 D_fake: 0.541 
+(epoch: 110, iters: 5328, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.948 G_ID: 0.250 G_Rec: 0.390 D_GP: 0.046 D_real: 1.030 D_fake: 0.686 
+(epoch: 110, iters: 5728, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.765 G_ID: 0.152 G_Rec: 0.367 D_GP: 0.024 D_real: 1.237 D_fake: 0.598 
+(epoch: 110, iters: 6128, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.832 G_ID: 0.229 G_Rec: 0.385 D_GP: 0.029 D_real: 1.241 D_fake: 0.495 
+(epoch: 110, iters: 6528, time: 0.064) G_GAN: -0.085 G_GAN_Feat: 0.932 G_ID: 0.177 G_Rec: 0.355 D_GP: 0.073 D_real: 0.992 D_fake: 1.085 
+(epoch: 110, iters: 6928, time: 0.064) G_GAN: 0.864 G_GAN_Feat: 0.975 G_ID: 0.265 G_Rec: 0.410 D_GP: 0.046 D_real: 1.439 D_fake: 0.183 
+(epoch: 110, iters: 7328, time: 0.064) G_GAN: 0.561 G_GAN_Feat: 0.816 G_ID: 0.177 G_Rec: 0.362 D_GP: 0.042 D_real: 1.295 D_fake: 0.494 
+(epoch: 110, iters: 7728, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 0.765 G_ID: 0.264 G_Rec: 0.377 D_GP: 0.023 D_real: 1.377 D_fake: 0.434 
+(epoch: 110, iters: 8128, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.895 G_ID: 0.147 G_Rec: 0.342 D_GP: 0.056 D_real: 0.750 D_fake: 0.746 
+(epoch: 110, iters: 8528, time: 0.064) G_GAN: 0.787 G_GAN_Feat: 1.238 G_ID: 0.258 G_Rec: 0.464 D_GP: 0.154 D_real: 0.614 D_fake: 0.319 
+(epoch: 111, iters: 320, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.786 G_ID: 0.165 G_Rec: 0.402 D_GP: 0.030 D_real: 1.214 D_fake: 0.636 
+(epoch: 111, iters: 720, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 0.870 G_ID: 0.263 G_Rec: 0.371 D_GP: 0.039 D_real: 1.075 D_fake: 0.592 
+(epoch: 111, iters: 1120, time: 0.064) G_GAN: -0.811 G_GAN_Feat: 1.135 G_ID: 0.174 G_Rec: 0.456 D_GP: 0.940 D_real: 0.077 D_fake: 1.811 
+(epoch: 111, iters: 1520, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.738 G_ID: 0.247 G_Rec: 0.368 D_GP: 0.022 D_real: 1.113 D_fake: 0.640 
+(epoch: 111, iters: 1920, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.620 G_ID: 0.173 G_Rec: 0.310 D_GP: 0.027 D_real: 1.412 D_fake: 0.521 
+(epoch: 111, iters: 2320, time: 0.064) G_GAN: 0.467 G_GAN_Feat: 0.814 G_ID: 0.269 G_Rec: 0.396 D_GP: 0.054 D_real: 1.034 D_fake: 0.549 
+(epoch: 111, iters: 2720, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.889 G_ID: 0.184 G_Rec: 0.424 D_GP: 0.034 D_real: 0.662 D_fake: 0.882 
+(epoch: 111, iters: 3120, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.789 G_ID: 0.227 G_Rec: 0.395 D_GP: 0.026 D_real: 1.232 D_fake: 0.518 
+(epoch: 111, iters: 3520, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.956 G_ID: 0.170 G_Rec: 0.391 D_GP: 0.072 D_real: 0.717 D_fake: 0.768 
+(epoch: 111, iters: 3920, time: 0.064) G_GAN: 0.633 G_GAN_Feat: 1.234 G_ID: 0.255 G_Rec: 0.466 D_GP: 0.141 D_real: 0.863 D_fake: 0.452 
+(epoch: 111, iters: 4320, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 0.871 G_ID: 0.154 G_Rec: 0.337 D_GP: 0.033 D_real: 1.393 D_fake: 0.540 
+(epoch: 111, iters: 4720, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.745 G_ID: 0.269 G_Rec: 0.385 D_GP: 0.023 D_real: 0.853 D_fake: 0.987 
+(epoch: 111, iters: 5120, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.697 G_ID: 0.154 G_Rec: 0.357 D_GP: 0.027 D_real: 0.978 D_fake: 0.846 
+(epoch: 111, iters: 5520, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.829 G_ID: 0.257 G_Rec: 0.397 D_GP: 0.031 D_real: 0.958 D_fake: 0.647 
+(epoch: 111, iters: 5920, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.714 G_ID: 0.151 G_Rec: 0.309 D_GP: 0.026 D_real: 1.184 D_fake: 0.742 
+(epoch: 111, iters: 6320, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.881 G_ID: 0.250 G_Rec: 0.430 D_GP: 0.061 D_real: 1.010 D_fake: 0.628 
+(epoch: 111, iters: 6720, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.844 G_ID: 0.152 G_Rec: 0.372 D_GP: 0.074 D_real: 1.107 D_fake: 0.521 
+(epoch: 111, iters: 7120, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.852 G_ID: 0.252 G_Rec: 0.386 D_GP: 0.027 D_real: 1.010 D_fake: 0.739 
+(epoch: 111, iters: 7520, time: 0.064) G_GAN: 0.735 G_GAN_Feat: 0.796 G_ID: 0.170 G_Rec: 0.341 D_GP: 0.037 D_real: 1.465 D_fake: 0.303 
+(epoch: 111, iters: 7920, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.834 G_ID: 0.269 G_Rec: 0.418 D_GP: 0.028 D_real: 1.163 D_fake: 0.594 
+(epoch: 111, iters: 8320, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.849 G_ID: 0.178 G_Rec: 0.396 D_GP: 0.059 D_real: 0.796 D_fake: 0.794 
+(epoch: 112, iters: 112, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.906 G_ID: 0.222 G_Rec: 0.427 D_GP: 0.043 D_real: 1.210 D_fake: 0.405 
+(epoch: 112, iters: 512, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.813 G_ID: 0.154 G_Rec: 0.339 D_GP: 0.033 D_real: 0.982 D_fake: 0.681 
+(epoch: 112, iters: 912, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.713 G_ID: 0.277 G_Rec: 0.390 D_GP: 0.023 D_real: 1.108 D_fake: 0.768 
+(epoch: 112, iters: 1312, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.815 G_ID: 0.179 G_Rec: 0.381 D_GP: 0.062 D_real: 0.844 D_fake: 0.895 
+(epoch: 112, iters: 1712, time: 0.064) G_GAN: 0.675 G_GAN_Feat: 0.824 G_ID: 0.280 G_Rec: 0.387 D_GP: 0.031 D_real: 1.358 D_fake: 0.349 
+(epoch: 112, iters: 2112, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.795 G_ID: 0.178 G_Rec: 0.349 D_GP: 0.039 D_real: 1.030 D_fake: 0.701 
+(epoch: 112, iters: 2512, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.949 G_ID: 0.273 G_Rec: 0.404 D_GP: 0.033 D_real: 0.839 D_fake: 0.672 
+(epoch: 112, iters: 2912, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 1.091 G_ID: 0.220 G_Rec: 0.356 D_GP: 0.481 D_real: 0.246 D_fake: 0.902 
+(epoch: 112, iters: 3312, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 1.155 G_ID: 0.232 G_Rec: 0.394 D_GP: 0.086 D_real: 0.450 D_fake: 0.620 
+(epoch: 112, iters: 3712, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.762 G_ID: 0.153 G_Rec: 0.342 D_GP: 0.027 D_real: 1.257 D_fake: 0.548 
+(epoch: 112, iters: 4112, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.740 G_ID: 0.241 G_Rec: 0.406 D_GP: 0.026 D_real: 1.170 D_fake: 0.660 
+(epoch: 112, iters: 4512, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.751 G_ID: 0.156 G_Rec: 0.348 D_GP: 0.031 D_real: 1.138 D_fake: 0.594 
+(epoch: 112, iters: 4912, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.885 G_ID: 0.270 G_Rec: 0.388 D_GP: 0.027 D_real: 1.025 D_fake: 0.653 
+(epoch: 112, iters: 5312, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 0.674 G_ID: 0.183 G_Rec: 0.381 D_GP: 0.026 D_real: 1.066 D_fake: 0.807 
+(epoch: 112, iters: 5712, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.844 G_ID: 0.262 G_Rec: 0.386 D_GP: 0.043 D_real: 0.924 D_fake: 0.735 
+(epoch: 112, iters: 6112, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.777 G_ID: 0.157 G_Rec: 0.346 D_GP: 0.033 D_real: 1.286 D_fake: 0.545 
+(epoch: 112, iters: 6512, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.976 G_ID: 0.258 G_Rec: 0.449 D_GP: 0.031 D_real: 0.991 D_fake: 0.543 
+(epoch: 112, iters: 6912, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.886 G_ID: 0.157 G_Rec: 0.367 D_GP: 0.083 D_real: 0.728 D_fake: 0.779 
+(epoch: 112, iters: 7312, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.834 G_ID: 0.240 G_Rec: 0.377 D_GP: 0.031 D_real: 1.090 D_fake: 0.678 
+(epoch: 112, iters: 7712, time: 0.064) G_GAN: 0.204 G_GAN_Feat: 0.710 G_ID: 0.198 G_Rec: 0.329 D_GP: 0.030 D_real: 1.054 D_fake: 0.796 
+(epoch: 112, iters: 8112, time: 0.064) G_GAN: 0.959 G_GAN_Feat: 0.929 G_ID: 0.253 G_Rec: 0.389 D_GP: 0.050 D_real: 1.422 D_fake: 0.139 
+(epoch: 112, iters: 8512, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.719 G_ID: 0.148 G_Rec: 0.341 D_GP: 0.027 D_real: 1.309 D_fake: 0.586 
+(epoch: 113, iters: 304, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.948 G_ID: 0.243 G_Rec: 0.409 D_GP: 0.046 D_real: 0.874 D_fake: 0.602 
+(epoch: 113, iters: 704, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.766 G_ID: 0.194 G_Rec: 0.359 D_GP: 0.035 D_real: 1.128 D_fake: 0.734 
+(epoch: 113, iters: 1104, time: 0.064) G_GAN: 0.560 G_GAN_Feat: 0.942 G_ID: 0.255 G_Rec: 0.369 D_GP: 0.059 D_real: 1.017 D_fake: 0.498 
+(epoch: 113, iters: 1504, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.757 G_ID: 0.163 G_Rec: 0.330 D_GP: 0.036 D_real: 1.142 D_fake: 0.715 
+(epoch: 113, iters: 1904, time: 0.064) G_GAN: 0.536 G_GAN_Feat: 0.842 G_ID: 0.273 G_Rec: 0.437 D_GP: 0.023 D_real: 1.310 D_fake: 0.467 
+(epoch: 113, iters: 2304, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.902 G_ID: 0.171 G_Rec: 0.369 D_GP: 0.092 D_real: 0.614 D_fake: 0.725 
+(epoch: 113, iters: 2704, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 1.052 G_ID: 0.271 G_Rec: 0.455 D_GP: 0.078 D_real: 0.960 D_fake: 0.520 
+(epoch: 113, iters: 3104, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.982 G_ID: 0.164 G_Rec: 0.351 D_GP: 0.040 D_real: 0.803 D_fake: 0.679 
+(epoch: 113, iters: 3504, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.869 G_ID: 0.221 G_Rec: 0.364 D_GP: 0.028 D_real: 0.977 D_fake: 0.674 
+(epoch: 113, iters: 3904, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.760 G_ID: 0.167 G_Rec: 0.354 D_GP: 0.027 D_real: 1.275 D_fake: 0.603 
+(epoch: 113, iters: 4304, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.898 G_ID: 0.242 G_Rec: 0.401 D_GP: 0.031 D_real: 0.938 D_fake: 0.678 
+(epoch: 113, iters: 4704, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.720 G_ID: 0.140 G_Rec: 0.369 D_GP: 0.022 D_real: 1.104 D_fake: 0.720 
+(epoch: 113, iters: 5104, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.851 G_ID: 0.271 G_Rec: 0.406 D_GP: 0.032 D_real: 1.184 D_fake: 0.529 
+(epoch: 113, iters: 5504, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.928 G_ID: 0.167 G_Rec: 0.399 D_GP: 0.067 D_real: 0.853 D_fake: 0.691 
+(epoch: 113, iters: 5904, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.823 G_ID: 0.277 G_Rec: 0.407 D_GP: 0.021 D_real: 1.084 D_fake: 0.576 
+(epoch: 113, iters: 6304, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.797 G_ID: 0.160 G_Rec: 0.362 D_GP: 0.022 D_real: 1.103 D_fake: 0.714 
+(epoch: 113, iters: 6704, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.885 G_ID: 0.231 G_Rec: 0.386 D_GP: 0.030 D_real: 1.138 D_fake: 0.598 
+(epoch: 113, iters: 7104, time: 0.064) G_GAN: -0.012 G_GAN_Feat: 0.814 G_ID: 0.164 G_Rec: 0.352 D_GP: 0.053 D_real: 0.757 D_fake: 1.012 
+(epoch: 113, iters: 7504, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.783 G_ID: 0.284 G_Rec: 0.392 D_GP: 0.026 D_real: 1.135 D_fake: 0.664 
+(epoch: 113, iters: 7904, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.744 G_ID: 0.218 G_Rec: 0.379 D_GP: 0.032 D_real: 1.068 D_fake: 0.743 
+(epoch: 113, iters: 8304, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 1.091 G_ID: 0.239 G_Rec: 0.500 D_GP: 0.090 D_real: 0.917 D_fake: 0.618 
+(epoch: 114, iters: 96, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.811 G_ID: 0.159 G_Rec: 0.401 D_GP: 0.038 D_real: 1.014 D_fake: 0.720 
+(epoch: 114, iters: 496, time: 0.064) G_GAN: 0.621 G_GAN_Feat: 1.083 G_ID: 0.267 G_Rec: 0.424 D_GP: 0.134 D_real: 0.898 D_fake: 0.420 
+(epoch: 114, iters: 896, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 1.020 G_ID: 0.177 G_Rec: 0.423 D_GP: 0.290 D_real: 0.845 D_fake: 0.529 
+(epoch: 114, iters: 1296, time: 0.064) G_GAN: 0.690 G_GAN_Feat: 0.862 G_ID: 0.240 G_Rec: 0.383 D_GP: 0.032 D_real: 1.412 D_fake: 0.333 
+(epoch: 114, iters: 1696, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 1.013 G_ID: 0.164 G_Rec: 0.365 D_GP: 0.122 D_real: 0.470 D_fake: 0.862 
+(epoch: 114, iters: 2096, time: 0.064) G_GAN: 0.677 G_GAN_Feat: 0.745 G_ID: 0.272 G_Rec: 0.371 D_GP: 0.022 D_real: 1.484 D_fake: 0.346 
+(epoch: 114, iters: 2496, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.730 G_ID: 0.166 G_Rec: 0.389 D_GP: 0.031 D_real: 1.136 D_fake: 0.633 
+(epoch: 114, iters: 2896, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.905 G_ID: 0.241 G_Rec: 0.380 D_GP: 0.060 D_real: 1.170 D_fake: 0.527 
+(epoch: 114, iters: 3296, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.734 G_ID: 0.172 G_Rec: 0.357 D_GP: 0.030 D_real: 1.215 D_fake: 0.610 
+(epoch: 114, iters: 3696, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.867 G_ID: 0.223 G_Rec: 0.403 D_GP: 0.050 D_real: 1.165 D_fake: 0.493 
+(epoch: 114, iters: 4096, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.754 G_ID: 0.132 G_Rec: 0.374 D_GP: 0.028 D_real: 1.192 D_fake: 0.650 
+(epoch: 114, iters: 4496, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.880 G_ID: 0.278 G_Rec: 0.389 D_GP: 0.043 D_real: 0.980 D_fake: 0.691 
+(epoch: 114, iters: 4896, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.921 G_ID: 0.177 G_Rec: 0.381 D_GP: 0.046 D_real: 0.772 D_fake: 0.781 
+(epoch: 114, iters: 5296, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.931 G_ID: 0.221 G_Rec: 0.390 D_GP: 0.060 D_real: 0.643 D_fake: 0.776 
+(epoch: 114, iters: 5696, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 1.089 G_ID: 0.184 G_Rec: 0.396 D_GP: 0.518 D_real: 0.335 D_fake: 0.876 
+(epoch: 114, iters: 6096, time: 0.064) G_GAN: 0.072 G_GAN_Feat: 1.178 G_ID: 0.209 G_Rec: 0.423 D_GP: 0.137 D_real: 0.415 D_fake: 0.928 
+(epoch: 114, iters: 6496, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.844 G_ID: 0.134 G_Rec: 0.356 D_GP: 0.068 D_real: 0.833 D_fake: 0.715 
+(epoch: 114, iters: 6896, time: 0.064) G_GAN: 0.606 G_GAN_Feat: 0.947 G_ID: 0.299 G_Rec: 0.434 D_GP: 0.030 D_real: 1.152 D_fake: 0.405 
+(epoch: 114, iters: 7296, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.900 G_ID: 0.155 G_Rec: 0.424 D_GP: 0.048 D_real: 0.735 D_fake: 0.705 
+(epoch: 114, iters: 7696, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.835 G_ID: 0.245 G_Rec: 0.366 D_GP: 0.041 D_real: 1.078 D_fake: 0.729 
+(epoch: 114, iters: 8096, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.759 G_ID: 0.188 G_Rec: 0.372 D_GP: 0.026 D_real: 1.168 D_fake: 0.677 
+(epoch: 114, iters: 8496, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 1.087 G_ID: 0.237 G_Rec: 0.396 D_GP: 0.072 D_real: 0.570 D_fake: 0.564 
+(epoch: 115, iters: 288, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.814 G_ID: 0.159 G_Rec: 0.342 D_GP: 0.037 D_real: 0.921 D_fake: 0.780 
+(epoch: 115, iters: 688, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.873 G_ID: 0.272 G_Rec: 0.372 D_GP: 0.037 D_real: 1.181 D_fake: 0.531 
+(epoch: 115, iters: 1088, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.800 G_ID: 0.153 G_Rec: 0.418 D_GP: 0.028 D_real: 0.870 D_fake: 0.941 
+(epoch: 115, iters: 1488, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 0.974 G_ID: 0.264 G_Rec: 0.413 D_GP: 0.077 D_real: 0.861 D_fake: 0.595 
+(epoch: 115, iters: 1888, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 1.015 G_ID: 0.148 G_Rec: 0.406 D_GP: 0.029 D_real: 0.855 D_fake: 0.619 
+(epoch: 115, iters: 2288, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.759 G_ID: 0.252 G_Rec: 0.374 D_GP: 0.025 D_real: 1.268 D_fake: 0.495 
+(epoch: 115, iters: 2688, time: 0.064) G_GAN: 0.663 G_GAN_Feat: 0.903 G_ID: 0.162 G_Rec: 0.374 D_GP: 0.121 D_real: 1.110 D_fake: 0.424 
+(epoch: 115, iters: 3088, time: 0.064) G_GAN: 0.734 G_GAN_Feat: 0.917 G_ID: 0.252 G_Rec: 0.397 D_GP: 0.028 D_real: 1.366 D_fake: 0.339 
+(epoch: 115, iters: 3488, time: 0.064) G_GAN: 0.075 G_GAN_Feat: 0.678 G_ID: 0.144 G_Rec: 0.428 D_GP: 0.026 D_real: 0.940 D_fake: 0.925 
+(epoch: 115, iters: 3888, time: 0.064) G_GAN: 0.614 G_GAN_Feat: 0.917 G_ID: 0.241 G_Rec: 0.392 D_GP: 0.029 D_real: 1.264 D_fake: 0.392 
+(epoch: 115, iters: 4288, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.715 G_ID: 0.188 G_Rec: 0.322 D_GP: 0.027 D_real: 1.046 D_fake: 0.737 
+(epoch: 115, iters: 4688, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.891 G_ID: 0.245 G_Rec: 0.415 D_GP: 0.030 D_real: 0.920 D_fake: 0.737 
+(epoch: 115, iters: 5088, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.806 G_ID: 0.171 G_Rec: 0.361 D_GP: 0.042 D_real: 1.054 D_fake: 0.705 
+(epoch: 115, iters: 5488, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 1.214 G_ID: 0.241 G_Rec: 0.453 D_GP: 0.114 D_real: 0.346 D_fake: 0.833 
+(epoch: 115, iters: 5888, time: 0.064) G_GAN: -0.023 G_GAN_Feat: 0.785 G_ID: 0.189 G_Rec: 0.525 D_GP: 0.033 D_real: 0.822 D_fake: 1.023 
+(epoch: 115, iters: 6288, time: 0.064) G_GAN: 0.646 G_GAN_Feat: 1.036 G_ID: 0.249 G_Rec: 0.448 D_GP: 0.061 D_real: 0.941 D_fake: 0.364 
+(epoch: 115, iters: 6688, time: 0.064) G_GAN: 0.029 G_GAN_Feat: 1.008 G_ID: 0.169 G_Rec: 0.343 D_GP: 0.049 D_real: 0.378 D_fake: 0.971 
+(epoch: 115, iters: 7088, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.936 G_ID: 0.234 G_Rec: 0.429 D_GP: 0.048 D_real: 1.056 D_fake: 0.615 
+(epoch: 115, iters: 7488, time: 0.064) G_GAN: 0.645 G_GAN_Feat: 0.900 G_ID: 0.170 G_Rec: 0.361 D_GP: 0.056 D_real: 1.142 D_fake: 0.377 
+(epoch: 115, iters: 7888, time: 0.064) G_GAN: 0.603 G_GAN_Feat: 0.970 G_ID: 0.244 G_Rec: 0.381 D_GP: 0.052 D_real: 0.996 D_fake: 0.440 
+(epoch: 115, iters: 8288, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.581 G_ID: 0.151 G_Rec: 0.333 D_GP: 0.017 D_real: 1.387 D_fake: 0.601 
+(epoch: 116, iters: 80, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.681 G_ID: 0.235 G_Rec: 0.402 D_GP: 0.018 D_real: 1.158 D_fake: 0.724 
+(epoch: 116, iters: 480, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 0.619 G_ID: 0.141 G_Rec: 0.369 D_GP: 0.019 D_real: 1.328 D_fake: 0.626 
+(epoch: 116, iters: 880, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.723 G_ID: 0.271 G_Rec: 0.386 D_GP: 0.025 D_real: 1.036 D_fake: 0.741 
+(epoch: 116, iters: 1280, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.608 G_ID: 0.215 G_Rec: 0.350 D_GP: 0.024 D_real: 0.912 D_fake: 0.931 
+(epoch: 116, iters: 1680, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.809 G_ID: 0.241 G_Rec: 0.406 D_GP: 0.044 D_real: 0.893 D_fake: 0.690 
+(epoch: 116, iters: 2080, time: 0.064) G_GAN: 0.060 G_GAN_Feat: 0.611 G_ID: 0.165 G_Rec: 0.336 D_GP: 0.033 D_real: 0.983 D_fake: 0.940 
+(epoch: 116, iters: 2480, time: 0.064) G_GAN: 0.068 G_GAN_Feat: 0.697 G_ID: 0.264 G_Rec: 0.373 D_GP: 0.034 D_real: 0.810 D_fake: 0.932 
+(epoch: 116, iters: 2880, time: 0.064) G_GAN: -0.023 G_GAN_Feat: 0.690 G_ID: 0.175 G_Rec: 0.351 D_GP: 0.045 D_real: 0.776 D_fake: 1.023 
+(epoch: 116, iters: 3280, time: 0.064) G_GAN: 0.010 G_GAN_Feat: 0.792 G_ID: 0.239 G_Rec: 0.412 D_GP: 0.039 D_real: 0.787 D_fake: 0.990 
+(epoch: 116, iters: 3680, time: 0.064) G_GAN: 0.018 G_GAN_Feat: 0.804 G_ID: 0.207 G_Rec: 0.359 D_GP: 0.102 D_real: 0.575 D_fake: 0.985 
+(epoch: 116, iters: 4080, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 0.836 G_ID: 0.281 G_Rec: 0.394 D_GP: 0.032 D_real: 1.249 D_fake: 0.467 
+(epoch: 116, iters: 4480, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.811 G_ID: 0.179 G_Rec: 0.363 D_GP: 0.057 D_real: 1.028 D_fake: 0.767 
+(epoch: 116, iters: 4880, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 1.089 G_ID: 0.231 G_Rec: 0.429 D_GP: 0.092 D_real: 0.643 D_fake: 0.634 
+(epoch: 116, iters: 5280, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.863 G_ID: 0.152 G_Rec: 0.413 D_GP: 0.098 D_real: 0.988 D_fake: 0.736 
+(epoch: 116, iters: 5680, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 1.049 G_ID: 0.266 G_Rec: 0.409 D_GP: 0.074 D_real: 0.634 D_fake: 0.741 
+(epoch: 116, iters: 6080, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 0.838 G_ID: 0.180 G_Rec: 0.365 D_GP: 0.094 D_real: 1.119 D_fake: 0.466 
+(epoch: 116, iters: 6480, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.748 G_ID: 0.241 G_Rec: 0.383 D_GP: 0.023 D_real: 0.959 D_fake: 0.754 
+(epoch: 116, iters: 6880, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.695 G_ID: 0.153 G_Rec: 0.331 D_GP: 0.035 D_real: 0.897 D_fake: 0.923 
+(epoch: 116, iters: 7280, time: 0.064) G_GAN: 0.676 G_GAN_Feat: 0.862 G_ID: 0.252 G_Rec: 0.411 D_GP: 0.029 D_real: 1.423 D_fake: 0.345 
+(epoch: 116, iters: 7680, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.713 G_ID: 0.168 G_Rec: 0.467 D_GP: 0.028 D_real: 1.149 D_fake: 0.751 
+(epoch: 116, iters: 8080, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 1.079 G_ID: 0.261 G_Rec: 0.420 D_GP: 0.152 D_real: 0.575 D_fake: 0.550 
+(epoch: 116, iters: 8480, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.895 G_ID: 0.132 G_Rec: 0.339 D_GP: 0.100 D_real: 0.743 D_fake: 0.662 
+(epoch: 117, iters: 272, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.735 G_ID: 0.259 G_Rec: 0.421 D_GP: 0.022 D_real: 1.059 D_fake: 0.725 
+(epoch: 117, iters: 672, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.644 G_ID: 0.184 G_Rec: 0.330 D_GP: 0.026 D_real: 1.047 D_fake: 0.872 
+(epoch: 117, iters: 1072, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.805 G_ID: 0.280 G_Rec: 0.400 D_GP: 0.045 D_real: 1.187 D_fake: 0.556 
+(epoch: 117, iters: 1472, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.721 G_ID: 0.160 G_Rec: 0.366 D_GP: 0.046 D_real: 1.229 D_fake: 0.560 
+(epoch: 117, iters: 1872, time: 0.064) G_GAN: 0.778 G_GAN_Feat: 0.825 G_ID: 0.229 G_Rec: 0.398 D_GP: 0.042 D_real: 1.411 D_fake: 0.273 
+(epoch: 117, iters: 2272, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.708 G_ID: 0.161 G_Rec: 0.345 D_GP: 0.039 D_real: 1.106 D_fake: 0.729 
+(epoch: 117, iters: 2672, time: 0.064) G_GAN: 0.560 G_GAN_Feat: 0.835 G_ID: 0.220 G_Rec: 0.376 D_GP: 0.040 D_real: 1.151 D_fake: 0.455 
+(epoch: 117, iters: 3072, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.641 G_ID: 0.149 G_Rec: 0.316 D_GP: 0.023 D_real: 1.128 D_fake: 0.774 
+(epoch: 117, iters: 3472, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.985 G_ID: 0.255 G_Rec: 0.381 D_GP: 0.289 D_real: 0.539 D_fake: 0.689 
+(epoch: 117, iters: 3872, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.874 G_ID: 0.143 G_Rec: 0.360 D_GP: 0.233 D_real: 0.502 D_fake: 0.860 
+(epoch: 117, iters: 4272, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.952 G_ID: 0.249 G_Rec: 0.442 D_GP: 0.088 D_real: 0.792 D_fake: 0.583 
+(epoch: 117, iters: 4672, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.672 G_ID: 0.187 G_Rec: 0.336 D_GP: 0.025 D_real: 1.113 D_fake: 0.739 
+(epoch: 117, iters: 5072, time: 0.064) G_GAN: 0.812 G_GAN_Feat: 0.880 G_ID: 0.214 G_Rec: 0.409 D_GP: 0.040 D_real: 1.355 D_fake: 0.287 
+(epoch: 117, iters: 5472, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 1.045 G_ID: 0.174 G_Rec: 0.388 D_GP: 0.333 D_real: 0.484 D_fake: 0.715 
+(epoch: 117, iters: 5872, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.908 G_ID: 0.314 G_Rec: 0.369 D_GP: 0.049 D_real: 0.722 D_fake: 0.785 
+(epoch: 117, iters: 6272, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 0.843 G_ID: 0.178 G_Rec: 0.369 D_GP: 0.152 D_real: 0.643 D_fake: 0.849 
+(epoch: 117, iters: 6672, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 1.211 G_ID: 0.276 G_Rec: 0.471 D_GP: 0.147 D_real: 1.091 D_fake: 0.516 
+(epoch: 117, iters: 7072, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.740 G_ID: 0.192 G_Rec: 0.402 D_GP: 0.035 D_real: 1.114 D_fake: 0.647 
+(epoch: 117, iters: 7472, time: 0.064) G_GAN: 0.643 G_GAN_Feat: 0.933 G_ID: 0.301 G_Rec: 0.390 D_GP: 0.071 D_real: 1.061 D_fake: 0.378 
+(epoch: 117, iters: 7872, time: 0.064) G_GAN: 0.434 G_GAN_Feat: 0.817 G_ID: 0.162 G_Rec: 0.356 D_GP: 0.047 D_real: 1.080 D_fake: 0.569 
+(epoch: 117, iters: 8272, time: 0.064) G_GAN: 0.540 G_GAN_Feat: 0.773 G_ID: 0.243 G_Rec: 0.393 D_GP: 0.027 D_real: 1.321 D_fake: 0.464 
+(epoch: 118, iters: 64, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.729 G_ID: 0.189 G_Rec: 0.327 D_GP: 0.042 D_real: 1.058 D_fake: 0.784 
+(epoch: 118, iters: 464, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 1.131 G_ID: 0.255 G_Rec: 0.396 D_GP: 0.190 D_real: 0.175 D_fake: 0.846 
+(epoch: 118, iters: 864, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.923 G_ID: 0.161 G_Rec: 0.370 D_GP: 0.136 D_real: 0.778 D_fake: 0.633 
+(epoch: 118, iters: 1264, time: 0.064) G_GAN: 0.551 G_GAN_Feat: 0.919 G_ID: 0.214 G_Rec: 0.405 D_GP: 0.026 D_real: 1.308 D_fake: 0.452 
+(epoch: 118, iters: 1664, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.796 G_ID: 0.138 G_Rec: 0.328 D_GP: 0.061 D_real: 1.153 D_fake: 0.484 
+(epoch: 118, iters: 2064, time: 0.064) G_GAN: 0.545 G_GAN_Feat: 0.857 G_ID: 0.215 G_Rec: 0.394 D_GP: 0.032 D_real: 1.126 D_fake: 0.458 
+(epoch: 118, iters: 2464, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.761 G_ID: 0.169 G_Rec: 0.396 D_GP: 0.028 D_real: 1.169 D_fake: 0.608 
+(epoch: 118, iters: 2864, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 0.762 G_ID: 0.224 G_Rec: 0.396 D_GP: 0.023 D_real: 1.411 D_fake: 0.352 
+(epoch: 118, iters: 3264, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.584 G_ID: 0.164 G_Rec: 0.350 D_GP: 0.022 D_real: 1.154 D_fake: 0.780 
+(epoch: 118, iters: 3664, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.753 G_ID: 0.263 G_Rec: 0.370 D_GP: 0.029 D_real: 1.070 D_fake: 0.587 
+(epoch: 118, iters: 4064, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.746 G_ID: 0.174 G_Rec: 0.337 D_GP: 0.045 D_real: 0.990 D_fake: 0.799 
+(epoch: 118, iters: 4464, time: 0.064) G_GAN: 0.598 G_GAN_Feat: 0.941 G_ID: 0.224 G_Rec: 0.386 D_GP: 0.250 D_real: 0.879 D_fake: 0.422 
+(epoch: 118, iters: 4864, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.779 G_ID: 0.149 G_Rec: 0.373 D_GP: 0.051 D_real: 0.986 D_fake: 0.718 
+(epoch: 118, iters: 5264, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.894 G_ID: 0.255 G_Rec: 0.371 D_GP: 0.080 D_real: 0.820 D_fake: 0.662 
+(epoch: 118, iters: 5664, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.820 G_ID: 0.168 G_Rec: 0.392 D_GP: 0.047 D_real: 0.724 D_fake: 0.878 
+(epoch: 118, iters: 6064, time: 0.064) G_GAN: 0.924 G_GAN_Feat: 1.193 G_ID: 0.223 G_Rec: 0.467 D_GP: 0.052 D_real: 1.473 D_fake: 0.244 
+(epoch: 118, iters: 6464, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.945 G_ID: 0.131 G_Rec: 0.339 D_GP: 0.116 D_real: 0.448 D_fake: 0.887 
+(epoch: 118, iters: 6864, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.689 G_ID: 0.243 G_Rec: 0.415 D_GP: 0.018 D_real: 1.109 D_fake: 0.668 
+(epoch: 118, iters: 7264, time: 0.064) G_GAN: 0.047 G_GAN_Feat: 0.692 G_ID: 0.182 G_Rec: 0.363 D_GP: 0.023 D_real: 0.972 D_fake: 0.953 
+(epoch: 118, iters: 7664, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.777 G_ID: 0.204 G_Rec: 0.408 D_GP: 0.023 D_real: 1.165 D_fake: 0.620 
+(epoch: 118, iters: 8064, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.653 G_ID: 0.168 G_Rec: 0.365 D_GP: 0.022 D_real: 0.970 D_fake: 0.895 
+(epoch: 118, iters: 8464, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.796 G_ID: 0.235 G_Rec: 0.415 D_GP: 0.034 D_real: 0.912 D_fake: 0.826 
+(epoch: 119, iters: 256, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.740 G_ID: 0.154 G_Rec: 0.375 D_GP: 0.078 D_real: 0.915 D_fake: 0.788 
+(epoch: 119, iters: 656, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 1.097 G_ID: 0.216 G_Rec: 0.418 D_GP: 0.075 D_real: 0.446 D_fake: 0.670 
+(epoch: 119, iters: 1056, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.703 G_ID: 0.197 G_Rec: 0.333 D_GP: 0.033 D_real: 1.051 D_fake: 0.842 
+(epoch: 119, iters: 1456, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 1.001 G_ID: 0.207 G_Rec: 0.396 D_GP: 0.049 D_real: 0.765 D_fake: 0.547 
+(epoch: 119, iters: 1856, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.712 G_ID: 0.171 G_Rec: 0.326 D_GP: 0.027 D_real: 1.252 D_fake: 0.621 
+(epoch: 119, iters: 2256, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.713 G_ID: 0.249 G_Rec: 0.367 D_GP: 0.027 D_real: 1.059 D_fake: 0.768 
+(epoch: 119, iters: 2656, time: 0.064) G_GAN: 0.104 G_GAN_Feat: 0.695 G_ID: 0.138 G_Rec: 0.353 D_GP: 0.037 D_real: 0.932 D_fake: 0.896 
+(epoch: 119, iters: 3056, time: 0.064) G_GAN: 0.580 G_GAN_Feat: 0.882 G_ID: 0.276 G_Rec: 0.392 D_GP: 0.060 D_real: 1.073 D_fake: 0.459 
+(epoch: 119, iters: 3456, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.707 G_ID: 0.166 G_Rec: 0.333 D_GP: 0.037 D_real: 0.947 D_fake: 0.823 
+(epoch: 119, iters: 3856, time: 0.064) G_GAN: 0.189 G_GAN_Feat: 0.859 G_ID: 0.260 G_Rec: 0.439 D_GP: 0.038 D_real: 0.852 D_fake: 0.811 
+(epoch: 119, iters: 4256, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.701 G_ID: 0.138 G_Rec: 0.308 D_GP: 0.034 D_real: 1.027 D_fake: 0.777 
+(epoch: 119, iters: 4656, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.922 G_ID: 0.260 G_Rec: 0.384 D_GP: 0.057 D_real: 0.966 D_fake: 0.726 
+(epoch: 119, iters: 5056, time: 0.064) G_GAN: -0.072 G_GAN_Feat: 0.697 G_ID: 0.176 G_Rec: 0.346 D_GP: 0.028 D_real: 0.742 D_fake: 1.072 
+(epoch: 119, iters: 5456, time: 0.064) G_GAN: 0.740 G_GAN_Feat: 0.805 G_ID: 0.221 G_Rec: 0.376 D_GP: 0.028 D_real: 1.429 D_fake: 0.280 
+(epoch: 119, iters: 5856, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.770 G_ID: 0.152 G_Rec: 0.347 D_GP: 0.049 D_real: 1.043 D_fake: 0.777 
+(epoch: 119, iters: 6256, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.838 G_ID: 0.282 G_Rec: 0.384 D_GP: 0.026 D_real: 1.144 D_fake: 0.591 
+(epoch: 119, iters: 6656, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.912 G_ID: 0.157 G_Rec: 0.386 D_GP: 0.038 D_real: 0.595 D_fake: 0.789 
+(epoch: 119, iters: 7056, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 1.007 G_ID: 0.213 G_Rec: 0.367 D_GP: 0.063 D_real: 0.496 D_fake: 0.774 
+(epoch: 119, iters: 7456, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.867 G_ID: 0.174 G_Rec: 0.348 D_GP: 0.049 D_real: 0.771 D_fake: 0.694 
+(epoch: 119, iters: 7856, time: 0.064) G_GAN: 0.629 G_GAN_Feat: 0.988 G_ID: 0.244 G_Rec: 0.418 D_GP: 0.036 D_real: 1.101 D_fake: 0.379 
+(epoch: 119, iters: 8256, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.845 G_ID: 0.155 G_Rec: 0.349 D_GP: 0.051 D_real: 0.841 D_fake: 0.693 
+(epoch: 120, iters: 48, time: 0.064) G_GAN: 0.504 G_GAN_Feat: 0.841 G_ID: 0.196 G_Rec: 0.402 D_GP: 0.024 D_real: 1.160 D_fake: 0.500 
+(epoch: 120, iters: 448, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 1.148 G_ID: 0.161 G_Rec: 0.401 D_GP: 1.558 D_real: 0.469 D_fake: 0.724 
+(epoch: 120, iters: 848, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.824 G_ID: 0.248 G_Rec: 0.418 D_GP: 0.025 D_real: 1.095 D_fake: 0.656 
+(epoch: 120, iters: 1248, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.770 G_ID: 0.159 G_Rec: 0.386 D_GP: 0.044 D_real: 1.215 D_fake: 0.537 
+(epoch: 120, iters: 1648, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.898 G_ID: 0.225 G_Rec: 0.415 D_GP: 0.039 D_real: 0.968 D_fake: 0.596 
+(epoch: 120, iters: 2048, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.745 G_ID: 0.153 G_Rec: 0.366 D_GP: 0.041 D_real: 1.021 D_fake: 0.722 
+(epoch: 120, iters: 2448, time: 0.064) G_GAN: 0.589 G_GAN_Feat: 0.986 G_ID: 0.257 G_Rec: 0.397 D_GP: 0.067 D_real: 0.998 D_fake: 0.443 
+(epoch: 120, iters: 2848, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.699 G_ID: 0.165 G_Rec: 0.327 D_GP: 0.034 D_real: 1.106 D_fake: 0.800 
+(epoch: 120, iters: 3248, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.830 G_ID: 0.217 G_Rec: 0.388 D_GP: 0.027 D_real: 1.312 D_fake: 0.485 
+(epoch: 120, iters: 3648, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.768 G_ID: 0.148 G_Rec: 0.347 D_GP: 0.038 D_real: 1.274 D_fake: 0.520 
+(epoch: 120, iters: 4048, time: 0.064) G_GAN: 0.618 G_GAN_Feat: 0.885 G_ID: 0.206 G_Rec: 0.389 D_GP: 0.054 D_real: 1.253 D_fake: 0.427 
+(epoch: 120, iters: 4448, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 0.879 G_ID: 0.164 G_Rec: 0.366 D_GP: 0.064 D_real: 0.966 D_fake: 0.529 
+(epoch: 120, iters: 4848, time: 0.064) G_GAN: 0.647 G_GAN_Feat: 0.769 G_ID: 0.244 G_Rec: 0.374 D_GP: 0.026 D_real: 1.443 D_fake: 0.356 
+(epoch: 120, iters: 5248, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.639 G_ID: 0.175 G_Rec: 0.327 D_GP: 0.023 D_real: 1.203 D_fake: 0.741 
+(epoch: 120, iters: 5648, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 0.933 G_ID: 0.275 G_Rec: 0.392 D_GP: 0.052 D_real: 0.865 D_fake: 0.625 
+(epoch: 120, iters: 6048, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.968 G_ID: 0.208 G_Rec: 0.355 D_GP: 0.085 D_real: 0.509 D_fake: 0.700 
+(epoch: 120, iters: 6448, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.804 G_ID: 0.230 G_Rec: 0.385 D_GP: 0.033 D_real: 1.028 D_fake: 0.799 
+(epoch: 120, iters: 6848, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.930 G_ID: 0.153 G_Rec: 0.366 D_GP: 0.245 D_real: 0.590 D_fake: 0.733 
+(epoch: 120, iters: 7248, time: 0.064) G_GAN: 0.698 G_GAN_Feat: 0.902 G_ID: 0.240 G_Rec: 0.401 D_GP: 0.036 D_real: 1.237 D_fake: 0.315 
+(epoch: 120, iters: 7648, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.748 G_ID: 0.196 G_Rec: 0.400 D_GP: 0.030 D_real: 0.959 D_fake: 0.845 
+(epoch: 120, iters: 8048, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.869 G_ID: 0.262 G_Rec: 0.364 D_GP: 0.048 D_real: 0.917 D_fake: 0.660 
+(epoch: 120, iters: 8448, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.700 G_ID: 0.151 G_Rec: 0.312 D_GP: 0.024 D_real: 1.153 D_fake: 0.669 
+(epoch: 121, iters: 240, time: 0.064) G_GAN: 0.146 G_GAN_Feat: 0.771 G_ID: 0.226 G_Rec: 0.383 D_GP: 0.022 D_real: 0.931 D_fake: 0.854 
+(epoch: 121, iters: 640, time: 0.064) G_GAN: 0.056 G_GAN_Feat: 0.733 G_ID: 0.182 G_Rec: 0.317 D_GP: 0.040 D_real: 0.951 D_fake: 0.944 
+(epoch: 121, iters: 1040, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.871 G_ID: 0.251 G_Rec: 0.373 D_GP: 0.023 D_real: 1.073 D_fake: 0.657 
+(epoch: 121, iters: 1440, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.853 G_ID: 0.156 G_Rec: 0.335 D_GP: 0.059 D_real: 1.016 D_fake: 0.488 
+(epoch: 121, iters: 1840, time: 0.064) G_GAN: 0.528 G_GAN_Feat: 0.852 G_ID: 0.214 G_Rec: 0.373 D_GP: 0.028 D_real: 1.241 D_fake: 0.516 
+(epoch: 121, iters: 2240, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.806 G_ID: 0.176 G_Rec: 0.361 D_GP: 0.028 D_real: 0.956 D_fake: 0.753 
+(epoch: 121, iters: 2640, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.819 G_ID: 0.248 G_Rec: 0.391 D_GP: 0.022 D_real: 1.080 D_fake: 0.650 
+(epoch: 121, iters: 3040, time: 0.064) G_GAN: 0.042 G_GAN_Feat: 0.892 G_ID: 0.158 G_Rec: 0.338 D_GP: 0.052 D_real: 0.393 D_fake: 0.958 
+(epoch: 121, iters: 3440, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.853 G_ID: 0.231 G_Rec: 0.422 D_GP: 0.026 D_real: 1.124 D_fake: 0.614 
+(epoch: 121, iters: 3840, time: 0.064) G_GAN: 0.704 G_GAN_Feat: 0.712 G_ID: 0.159 G_Rec: 0.308 D_GP: 0.025 D_real: 1.548 D_fake: 0.324 
+(epoch: 121, iters: 4240, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.828 G_ID: 0.241 G_Rec: 0.379 D_GP: 0.036 D_real: 1.112 D_fake: 0.597 
+(epoch: 121, iters: 4640, time: 0.064) G_GAN: 0.650 G_GAN_Feat: 0.837 G_ID: 0.140 G_Rec: 0.361 D_GP: 0.045 D_real: 1.312 D_fake: 0.425 
+(epoch: 121, iters: 5040, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.802 G_ID: 0.218 G_Rec: 0.390 D_GP: 0.028 D_real: 1.203 D_fake: 0.526 
+(epoch: 121, iters: 5440, time: 0.064) G_GAN: -0.101 G_GAN_Feat: 0.659 G_ID: 0.219 G_Rec: 0.354 D_GP: 0.024 D_real: 0.854 D_fake: 1.101 
+(epoch: 121, iters: 5840, time: 0.064) G_GAN: 0.583 G_GAN_Feat: 0.876 G_ID: 0.235 G_Rec: 0.398 D_GP: 0.039 D_real: 1.189 D_fake: 0.422 
+(epoch: 121, iters: 6240, time: 0.064) G_GAN: 0.146 G_GAN_Feat: 0.819 G_ID: 0.162 G_Rec: 0.335 D_GP: 0.067 D_real: 0.791 D_fake: 0.854 
+(epoch: 121, iters: 6640, time: 0.064) G_GAN: 0.738 G_GAN_Feat: 0.931 G_ID: 0.228 G_Rec: 0.384 D_GP: 0.030 D_real: 1.234 D_fake: 0.300 
+(epoch: 121, iters: 7040, time: 0.064) G_GAN: 0.605 G_GAN_Feat: 0.720 G_ID: 0.156 G_Rec: 0.388 D_GP: 0.025 D_real: 1.408 D_fake: 0.439 
+(epoch: 121, iters: 7440, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.972 G_ID: 0.285 G_Rec: 0.416 D_GP: 0.051 D_real: 0.950 D_fake: 0.526 
+(epoch: 121, iters: 7840, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 0.766 G_ID: 0.151 G_Rec: 0.330 D_GP: 0.034 D_real: 1.207 D_fake: 0.593 
+(epoch: 121, iters: 8240, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.857 G_ID: 0.221 G_Rec: 0.500 D_GP: 0.024 D_real: 1.224 D_fake: 0.577 
+(epoch: 122, iters: 32, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.668 G_ID: 0.166 G_Rec: 0.356 D_GP: 0.025 D_real: 1.008 D_fake: 0.810 
+(epoch: 122, iters: 432, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.999 G_ID: 0.272 G_Rec: 0.401 D_GP: 0.068 D_real: 0.868 D_fake: 0.612 
+(epoch: 122, iters: 832, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.745 G_ID: 0.134 G_Rec: 0.327 D_GP: 0.041 D_real: 0.995 D_fake: 0.791 
+(epoch: 122, iters: 1232, time: 0.064) G_GAN: 0.693 G_GAN_Feat: 0.963 G_ID: 0.248 G_Rec: 0.376 D_GP: 0.031 D_real: 1.188 D_fake: 0.345 
+(epoch: 122, iters: 1632, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.650 G_ID: 0.152 G_Rec: 0.339 D_GP: 0.022 D_real: 1.449 D_fake: 0.496 
+(epoch: 122, iters: 2032, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.867 G_ID: 0.240 G_Rec: 0.378 D_GP: 0.055 D_real: 1.062 D_fake: 0.520 
+(epoch: 122, iters: 2432, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.756 G_ID: 0.169 G_Rec: 0.361 D_GP: 0.030 D_real: 1.087 D_fake: 0.749 
+(epoch: 122, iters: 2832, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 1.122 G_ID: 0.237 G_Rec: 0.438 D_GP: 0.254 D_real: 0.546 D_fake: 0.483 
+(epoch: 122, iters: 3232, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.723 G_ID: 0.145 G_Rec: 0.327 D_GP: 0.030 D_real: 1.059 D_fake: 0.770 
+(epoch: 122, iters: 3632, time: 0.064) G_GAN: 0.626 G_GAN_Feat: 0.824 G_ID: 0.268 G_Rec: 0.415 D_GP: 0.026 D_real: 1.386 D_fake: 0.390 
+(epoch: 122, iters: 4032, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.703 G_ID: 0.158 G_Rec: 0.315 D_GP: 0.028 D_real: 1.148 D_fake: 0.733 
+(epoch: 122, iters: 4432, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.995 G_ID: 0.266 G_Rec: 0.417 D_GP: 0.087 D_real: 0.426 D_fake: 0.846 
+(epoch: 122, iters: 4832, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 0.730 G_ID: 0.155 G_Rec: 0.340 D_GP: 0.025 D_real: 1.278 D_fake: 0.586 
+(epoch: 122, iters: 5232, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.821 G_ID: 0.223 G_Rec: 0.415 D_GP: 0.020 D_real: 1.282 D_fake: 0.538 
+(epoch: 122, iters: 5632, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.968 G_ID: 0.157 G_Rec: 0.360 D_GP: 0.326 D_real: 0.536 D_fake: 0.729 
+(epoch: 122, iters: 6032, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.875 G_ID: 0.218 G_Rec: 0.396 D_GP: 0.041 D_real: 0.949 D_fake: 0.771 
+(epoch: 122, iters: 6432, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.818 G_ID: 0.156 G_Rec: 0.363 D_GP: 0.037 D_real: 1.086 D_fake: 0.577 
+(epoch: 122, iters: 6832, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.976 G_ID: 0.223 G_Rec: 0.414 D_GP: 0.086 D_real: 0.771 D_fake: 0.552 
+(epoch: 122, iters: 7232, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.679 G_ID: 0.157 G_Rec: 0.343 D_GP: 0.034 D_real: 0.932 D_fake: 0.913 
+(epoch: 122, iters: 7632, time: 0.064) G_GAN: 0.037 G_GAN_Feat: 0.902 G_ID: 0.243 G_Rec: 0.404 D_GP: 0.084 D_real: 0.483 D_fake: 0.963 
+(epoch: 122, iters: 8032, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.738 G_ID: 0.164 G_Rec: 0.343 D_GP: 0.035 D_real: 1.134 D_fake: 0.738 
+(epoch: 122, iters: 8432, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.948 G_ID: 0.236 G_Rec: 0.410 D_GP: 0.061 D_real: 0.767 D_fake: 0.620 
+(epoch: 123, iters: 224, time: 0.064) G_GAN: 0.609 G_GAN_Feat: 1.026 G_ID: 0.168 G_Rec: 0.396 D_GP: 0.749 D_real: 0.695 D_fake: 0.466 
+(epoch: 123, iters: 624, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 1.014 G_ID: 0.270 G_Rec: 0.414 D_GP: 0.184 D_real: 0.294 D_fake: 0.938 
+(epoch: 123, iters: 1024, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.857 G_ID: 0.153 G_Rec: 0.341 D_GP: 0.054 D_real: 1.074 D_fake: 0.635 
+(epoch: 123, iters: 1424, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.783 G_ID: 0.212 G_Rec: 0.411 D_GP: 0.021 D_real: 1.257 D_fake: 0.522 
+(epoch: 123, iters: 1824, time: 0.064) G_GAN: -0.062 G_GAN_Feat: 0.740 G_ID: 0.167 G_Rec: 0.323 D_GP: 0.032 D_real: 0.855 D_fake: 1.062 
+(epoch: 123, iters: 2224, time: 0.064) G_GAN: 0.885 G_GAN_Feat: 1.047 G_ID: 0.258 G_Rec: 0.424 D_GP: 0.183 D_real: 0.945 D_fake: 0.274 
+(epoch: 123, iters: 2624, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.823 G_ID: 0.153 G_Rec: 0.353 D_GP: 0.033 D_real: 1.139 D_fake: 0.541 
+(epoch: 123, iters: 3024, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.904 G_ID: 0.223 G_Rec: 0.403 D_GP: 0.032 D_real: 1.142 D_fake: 0.494 
+(epoch: 123, iters: 3424, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.931 G_ID: 0.147 G_Rec: 0.354 D_GP: 0.195 D_real: 0.833 D_fake: 0.502 
+(epoch: 123, iters: 3824, time: 0.064) G_GAN: 0.683 G_GAN_Feat: 0.973 G_ID: 0.260 G_Rec: 0.461 D_GP: 0.039 D_real: 1.352 D_fake: 0.350 
+(epoch: 123, iters: 4224, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.679 G_ID: 0.163 G_Rec: 0.333 D_GP: 0.028 D_real: 1.145 D_fake: 0.686 
+(epoch: 123, iters: 4624, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.918 G_ID: 0.240 G_Rec: 0.383 D_GP: 0.058 D_real: 0.972 D_fake: 0.511 
+(epoch: 123, iters: 5024, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.627 G_ID: 0.150 G_Rec: 0.363 D_GP: 0.019 D_real: 1.272 D_fake: 0.575 
+(epoch: 123, iters: 5424, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 0.821 G_ID: 0.233 G_Rec: 0.385 D_GP: 0.035 D_real: 1.116 D_fake: 0.485 
+(epoch: 123, iters: 5824, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.703 G_ID: 0.142 G_Rec: 0.324 D_GP: 0.037 D_real: 0.939 D_fake: 0.881 
+(epoch: 123, iters: 6224, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.826 G_ID: 0.247 G_Rec: 0.404 D_GP: 0.023 D_real: 1.339 D_fake: 0.435 
+(epoch: 123, iters: 6624, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.805 G_ID: 0.169 G_Rec: 0.358 D_GP: 0.055 D_real: 0.939 D_fake: 0.704 
+(epoch: 123, iters: 7024, time: 0.064) G_GAN: 0.756 G_GAN_Feat: 1.036 G_ID: 0.235 G_Rec: 0.421 D_GP: 0.121 D_real: 0.878 D_fake: 0.322 
+(epoch: 123, iters: 7424, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.700 G_ID: 0.169 G_Rec: 0.352 D_GP: 0.031 D_real: 1.088 D_fake: 0.756 
+(epoch: 123, iters: 7824, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 1.001 G_ID: 0.231 G_Rec: 0.398 D_GP: 0.068 D_real: 0.808 D_fake: 0.516 
+(epoch: 123, iters: 8224, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 0.790 G_ID: 0.168 G_Rec: 0.337 D_GP: 0.036 D_real: 1.253 D_fake: 0.511 
+(epoch: 124, iters: 16, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 0.947 G_ID: 0.224 G_Rec: 0.411 D_GP: 0.046 D_real: 1.129 D_fake: 0.462 
+(epoch: 124, iters: 416, time: 0.064) G_GAN: 0.003 G_GAN_Feat: 0.846 G_ID: 0.239 G_Rec: 0.345 D_GP: 0.055 D_real: 0.625 D_fake: 0.999 
+(epoch: 124, iters: 816, time: 0.064) G_GAN: 0.741 G_GAN_Feat: 1.034 G_ID: 0.251 G_Rec: 0.387 D_GP: 0.121 D_real: 1.060 D_fake: 0.287 
+(epoch: 124, iters: 1216, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.682 G_ID: 0.148 G_Rec: 0.379 D_GP: 0.021 D_real: 1.194 D_fake: 0.762 
+(epoch: 124, iters: 1616, time: 0.064) G_GAN: 0.532 G_GAN_Feat: 1.000 G_ID: 0.237 G_Rec: 0.438 D_GP: 0.139 D_real: 0.971 D_fake: 0.473 
+(epoch: 124, iters: 2016, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.727 G_ID: 0.158 G_Rec: 0.318 D_GP: 0.047 D_real: 0.946 D_fake: 0.830 
+(epoch: 124, iters: 2416, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.979 G_ID: 0.239 G_Rec: 0.419 D_GP: 0.067 D_real: 0.881 D_fake: 0.571 
+(epoch: 124, iters: 2816, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.820 G_ID: 0.153 G_Rec: 0.328 D_GP: 0.040 D_real: 1.148 D_fake: 0.644 
+(epoch: 124, iters: 3216, time: 0.064) G_GAN: 0.608 G_GAN_Feat: 0.981 G_ID: 0.216 G_Rec: 0.381 D_GP: 0.066 D_real: 0.851 D_fake: 0.417 
+(epoch: 124, iters: 3616, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.709 G_ID: 0.182 G_Rec: 0.360 D_GP: 0.028 D_real: 1.210 D_fake: 0.671 
+(epoch: 124, iters: 4016, time: 0.064) G_GAN: 0.633 G_GAN_Feat: 0.839 G_ID: 0.237 G_Rec: 0.399 D_GP: 0.026 D_real: 1.321 D_fake: 0.398 
+(epoch: 124, iters: 4416, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.790 G_ID: 0.173 G_Rec: 0.359 D_GP: 0.043 D_real: 1.077 D_fake: 0.685 
+(epoch: 124, iters: 4816, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.934 G_ID: 0.265 G_Rec: 0.398 D_GP: 0.060 D_real: 0.726 D_fake: 0.704 
+(epoch: 124, iters: 5216, time: 0.064) G_GAN: -0.025 G_GAN_Feat: 0.862 G_ID: 0.168 G_Rec: 0.373 D_GP: 0.066 D_real: 0.758 D_fake: 1.025 
+(epoch: 124, iters: 5616, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.802 G_ID: 0.237 G_Rec: 0.348 D_GP: 0.029 D_real: 1.017 D_fake: 0.721 
+(epoch: 124, iters: 6016, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.806 G_ID: 0.149 G_Rec: 0.359 D_GP: 0.063 D_real: 1.078 D_fake: 0.624 
+(epoch: 124, iters: 6416, time: 0.064) G_GAN: 0.588 G_GAN_Feat: 1.019 G_ID: 0.237 G_Rec: 0.441 D_GP: 0.106 D_real: 1.096 D_fake: 0.473 
+(epoch: 124, iters: 6816, time: 0.064) G_GAN: -0.010 G_GAN_Feat: 0.873 G_ID: 0.157 G_Rec: 0.369 D_GP: 0.030 D_real: 0.750 D_fake: 1.012 
+(epoch: 124, iters: 7216, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 1.126 G_ID: 0.269 G_Rec: 0.380 D_GP: 0.097 D_real: 0.622 D_fake: 0.444 
+(epoch: 124, iters: 7616, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.726 G_ID: 0.125 G_Rec: 0.330 D_GP: 0.024 D_real: 1.026 D_fake: 0.856 
+(epoch: 124, iters: 8016, time: 0.064) G_GAN: 0.604 G_GAN_Feat: 0.912 G_ID: 0.217 G_Rec: 0.393 D_GP: 0.034 D_real: 1.225 D_fake: 0.403 
+(epoch: 124, iters: 8416, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.945 G_ID: 0.161 G_Rec: 0.373 D_GP: 0.366 D_real: 0.520 D_fake: 0.848 
+(epoch: 125, iters: 208, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.916 G_ID: 0.239 G_Rec: 0.384 D_GP: 0.035 D_real: 1.059 D_fake: 0.530 
+(epoch: 125, iters: 608, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 0.702 G_ID: 0.178 G_Rec: 0.336 D_GP: 0.027 D_real: 0.980 D_fake: 0.919 
+(epoch: 125, iters: 1008, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 1.068 G_ID: 0.216 G_Rec: 0.429 D_GP: 0.066 D_real: 0.766 D_fake: 0.771 
+(epoch: 125, iters: 1408, time: 0.064) G_GAN: -0.032 G_GAN_Feat: 0.886 G_ID: 0.153 G_Rec: 0.337 D_GP: 0.053 D_real: 0.454 D_fake: 1.032 
+(epoch: 125, iters: 1808, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 1.065 G_ID: 0.253 G_Rec: 0.390 D_GP: 0.069 D_real: 0.426 D_fake: 0.820 
+(epoch: 125, iters: 2208, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.987 G_ID: 0.165 G_Rec: 0.334 D_GP: 0.194 D_real: 0.516 D_fake: 0.872 
+(epoch: 125, iters: 2608, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.908 G_ID: 0.242 G_Rec: 0.424 D_GP: 0.025 D_real: 1.086 D_fake: 0.632 
+(epoch: 125, iters: 3008, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.632 G_ID: 0.183 G_Rec: 0.342 D_GP: 0.020 D_real: 1.102 D_fake: 0.821 
+(epoch: 125, iters: 3408, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.764 G_ID: 0.268 G_Rec: 0.372 D_GP: 0.024 D_real: 1.107 D_fake: 0.652 
+(epoch: 125, iters: 3808, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.732 G_ID: 0.135 G_Rec: 0.336 D_GP: 0.062 D_real: 0.869 D_fake: 0.833 
+(epoch: 125, iters: 4208, time: 0.064) G_GAN: 0.585 G_GAN_Feat: 0.800 G_ID: 0.249 G_Rec: 0.382 D_GP: 0.027 D_real: 1.313 D_fake: 0.424 
+(epoch: 125, iters: 4608, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 1.047 G_ID: 0.183 G_Rec: 0.351 D_GP: 0.093 D_real: 0.528 D_fake: 0.821 
+(epoch: 125, iters: 5008, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 0.866 G_ID: 0.245 G_Rec: 0.379 D_GP: 0.049 D_real: 1.131 D_fake: 0.476 
+(epoch: 125, iters: 5408, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.869 G_ID: 0.147 G_Rec: 0.358 D_GP: 0.043 D_real: 1.127 D_fake: 0.549 
+(epoch: 125, iters: 5808, time: 0.064) G_GAN: 0.549 G_GAN_Feat: 1.077 G_ID: 0.257 G_Rec: 0.471 D_GP: 0.210 D_real: 0.810 D_fake: 0.460 
+(epoch: 125, iters: 6208, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.855 G_ID: 0.170 G_Rec: 0.348 D_GP: 0.068 D_real: 0.863 D_fake: 0.717 
+(epoch: 125, iters: 6608, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 0.831 G_ID: 0.213 G_Rec: 0.361 D_GP: 0.026 D_real: 0.885 D_fake: 0.919 
+(epoch: 125, iters: 7008, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.813 G_ID: 0.141 G_Rec: 0.376 D_GP: 0.040 D_real: 1.153 D_fake: 0.605 
+(epoch: 125, iters: 7408, time: 0.064) G_GAN: 0.831 G_GAN_Feat: 1.029 G_ID: 0.268 G_Rec: 0.425 D_GP: 0.088 D_real: 1.023 D_fake: 0.254 
+(epoch: 125, iters: 7808, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.799 G_ID: 0.149 G_Rec: 0.358 D_GP: 0.033 D_real: 0.979 D_fake: 0.718 
+(epoch: 125, iters: 8208, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 1.034 G_ID: 0.257 G_Rec: 0.410 D_GP: 0.046 D_real: 0.767 D_fake: 0.551 
+(epoch: 125, iters: 8608, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.723 G_ID: 0.174 G_Rec: 0.347 D_GP: 0.025 D_real: 1.328 D_fake: 0.608 
+(epoch: 126, iters: 400, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.938 G_ID: 0.241 G_Rec: 0.383 D_GP: 0.046 D_real: 0.953 D_fake: 0.755 
+(epoch: 126, iters: 800, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.855 G_ID: 0.150 G_Rec: 0.364 D_GP: 0.046 D_real: 1.070 D_fake: 0.627 
+(epoch: 126, iters: 1200, time: 0.064) G_GAN: 0.648 G_GAN_Feat: 1.118 G_ID: 0.217 G_Rec: 0.415 D_GP: 0.097 D_real: 0.801 D_fake: 0.414 
+(epoch: 126, iters: 1600, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.835 G_ID: 0.147 G_Rec: 0.363 D_GP: 0.029 D_real: 1.040 D_fake: 0.720 
+(epoch: 126, iters: 2000, time: 0.064) G_GAN: 0.591 G_GAN_Feat: 0.918 G_ID: 0.245 G_Rec: 0.404 D_GP: 0.027 D_real: 1.183 D_fake: 0.414 
+(epoch: 126, iters: 2400, time: 0.064) G_GAN: 0.627 G_GAN_Feat: 0.839 G_ID: 0.136 G_Rec: 0.351 D_GP: 0.058 D_real: 1.328 D_fake: 0.386 
+(epoch: 126, iters: 2800, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.966 G_ID: 0.227 G_Rec: 0.431 D_GP: 0.039 D_real: 0.905 D_fake: 0.624 
+(epoch: 126, iters: 3200, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.651 G_ID: 0.165 G_Rec: 0.337 D_GP: 0.021 D_real: 1.219 D_fake: 0.664 
+(epoch: 126, iters: 3600, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.981 G_ID: 0.233 G_Rec: 0.442 D_GP: 0.056 D_real: 0.843 D_fake: 0.586 
+(epoch: 126, iters: 4000, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.709 G_ID: 0.189 G_Rec: 0.325 D_GP: 0.027 D_real: 1.094 D_fake: 0.768 
+(epoch: 126, iters: 4400, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.831 G_ID: 0.257 G_Rec: 0.397 D_GP: 0.035 D_real: 1.044 D_fake: 0.685 
+(epoch: 126, iters: 4800, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.818 G_ID: 0.192 G_Rec: 0.343 D_GP: 0.038 D_real: 1.091 D_fake: 0.654 
+(epoch: 126, iters: 5200, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.921 G_ID: 0.222 G_Rec: 0.376 D_GP: 0.040 D_real: 0.954 D_fake: 0.608 
+(epoch: 126, iters: 5600, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.715 G_ID: 0.190 G_Rec: 0.350 D_GP: 0.026 D_real: 1.051 D_fake: 0.887 
+(epoch: 126, iters: 6000, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.831 G_ID: 0.193 G_Rec: 0.394 D_GP: 0.036 D_real: 1.003 D_fake: 0.652 
+(epoch: 126, iters: 6400, time: 0.064) G_GAN: 0.396 G_GAN_Feat: 0.861 G_ID: 0.144 G_Rec: 0.371 D_GP: 0.052 D_real: 0.966 D_fake: 0.633 
+(epoch: 126, iters: 6800, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 1.216 G_ID: 0.290 G_Rec: 0.424 D_GP: 0.241 D_real: 0.306 D_fake: 0.828 
+(epoch: 126, iters: 7200, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.779 G_ID: 0.144 G_Rec: 0.326 D_GP: 0.030 D_real: 1.037 D_fake: 0.728 
+(epoch: 126, iters: 7600, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.866 G_ID: 0.236 G_Rec: 0.398 D_GP: 0.030 D_real: 1.095 D_fake: 0.594 
+(epoch: 126, iters: 8000, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.929 G_ID: 0.155 G_Rec: 0.352 D_GP: 0.051 D_real: 0.645 D_fake: 0.709 
+(epoch: 126, iters: 8400, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.869 G_ID: 0.207 G_Rec: 0.355 D_GP: 0.033 D_real: 1.058 D_fake: 0.673 
+(epoch: 127, iters: 192, time: 0.064) G_GAN: 0.595 G_GAN_Feat: 0.758 G_ID: 0.141 G_Rec: 0.381 D_GP: 0.037 D_real: 1.391 D_fake: 0.413 
+(epoch: 127, iters: 592, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.877 G_ID: 0.232 G_Rec: 0.430 D_GP: 0.026 D_real: 0.967 D_fake: 0.733 
+(epoch: 127, iters: 992, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.610 G_ID: 0.140 G_Rec: 0.310 D_GP: 0.025 D_real: 1.056 D_fake: 0.830 
+(epoch: 127, iters: 1392, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.724 G_ID: 0.195 G_Rec: 0.354 D_GP: 0.026 D_real: 1.007 D_fake: 0.714 
+(epoch: 127, iters: 1792, time: 0.064) G_GAN: -0.018 G_GAN_Feat: 0.745 G_ID: 0.160 G_Rec: 0.337 D_GP: 0.059 D_real: 0.753 D_fake: 1.018 
+(epoch: 127, iters: 2192, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.912 G_ID: 0.271 G_Rec: 0.415 D_GP: 0.058 D_real: 0.855 D_fake: 0.620 
+(epoch: 127, iters: 2592, time: 0.064) G_GAN: 0.605 G_GAN_Feat: 0.773 G_ID: 0.131 G_Rec: 0.364 D_GP: 0.048 D_real: 1.401 D_fake: 0.412 
+(epoch: 127, iters: 2992, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.864 G_ID: 0.248 G_Rec: 0.396 D_GP: 0.034 D_real: 1.134 D_fake: 0.584 
+(epoch: 127, iters: 3392, time: 0.064) G_GAN: 0.056 G_GAN_Feat: 0.943 G_ID: 0.147 G_Rec: 0.332 D_GP: 0.116 D_real: 0.360 D_fake: 0.944 
+(epoch: 127, iters: 3792, time: 0.064) G_GAN: 0.632 G_GAN_Feat: 0.949 G_ID: 0.204 G_Rec: 0.392 D_GP: 0.064 D_real: 1.086 D_fake: 0.394 
+(epoch: 127, iters: 4192, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 1.055 G_ID: 0.168 G_Rec: 0.354 D_GP: 0.246 D_real: 0.424 D_fake: 0.907 
+(epoch: 127, iters: 4592, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.864 G_ID: 0.233 G_Rec: 0.401 D_GP: 0.027 D_real: 1.162 D_fake: 0.609 
+(epoch: 127, iters: 4992, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.948 G_ID: 0.174 G_Rec: 0.381 D_GP: 0.210 D_real: 0.481 D_fake: 0.928 
+(epoch: 127, iters: 5392, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 1.121 G_ID: 0.247 G_Rec: 0.438 D_GP: 0.332 D_real: 0.563 D_fake: 0.712 
+(epoch: 127, iters: 5792, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.798 G_ID: 0.178 G_Rec: 0.334 D_GP: 0.040 D_real: 1.050 D_fake: 0.683 
+(epoch: 127, iters: 6192, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 0.914 G_ID: 0.201 G_Rec: 0.393 D_GP: 0.040 D_real: 1.037 D_fake: 0.439 
+(epoch: 127, iters: 6592, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.732 G_ID: 0.173 G_Rec: 0.310 D_GP: 0.032 D_real: 1.135 D_fake: 0.727 
+(epoch: 127, iters: 6992, time: 0.064) G_GAN: 0.547 G_GAN_Feat: 1.269 G_ID: 0.209 G_Rec: 0.420 D_GP: 0.058 D_real: 0.808 D_fake: 0.455 
+(epoch: 127, iters: 7392, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.971 G_ID: 0.133 G_Rec: 0.366 D_GP: 0.055 D_real: 0.626 D_fake: 0.777 
+(epoch: 127, iters: 7792, time: 0.064) G_GAN: 0.873 G_GAN_Feat: 1.044 G_ID: 0.224 G_Rec: 0.441 D_GP: 0.157 D_real: 1.217 D_fake: 0.369 
+(epoch: 127, iters: 8192, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.974 G_ID: 0.149 G_Rec: 0.410 D_GP: 0.067 D_real: 0.627 D_fake: 0.609 
+(epoch: 127, iters: 8592, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 1.075 G_ID: 0.288 G_Rec: 0.412 D_GP: 0.088 D_real: 0.534 D_fake: 0.605 
+(epoch: 128, iters: 384, time: 0.064) G_GAN: -0.048 G_GAN_Feat: 0.785 G_ID: 0.205 G_Rec: 0.389 D_GP: 0.063 D_real: 0.935 D_fake: 1.051 
+(epoch: 128, iters: 784, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.803 G_ID: 0.232 G_Rec: 0.388 D_GP: 0.033 D_real: 0.994 D_fake: 0.698 
+(epoch: 128, iters: 1184, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.819 G_ID: 0.167 G_Rec: 0.368 D_GP: 0.039 D_real: 1.056 D_fake: 0.681 
+(epoch: 128, iters: 1584, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 1.005 G_ID: 0.205 G_Rec: 0.403 D_GP: 0.032 D_real: 0.840 D_fake: 0.906 
+(epoch: 128, iters: 1984, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.969 G_ID: 0.150 G_Rec: 0.374 D_GP: 0.041 D_real: 1.124 D_fake: 0.585 
+(epoch: 128, iters: 2384, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.877 G_ID: 0.230 G_Rec: 0.367 D_GP: 0.044 D_real: 0.924 D_fake: 0.666 
+(epoch: 128, iters: 2784, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 1.163 G_ID: 0.154 G_Rec: 0.368 D_GP: 0.139 D_real: 1.110 D_fake: 0.947 
+(epoch: 128, iters: 3184, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.990 G_ID: 0.234 G_Rec: 0.421 D_GP: 0.047 D_real: 1.118 D_fake: 0.625 
+(epoch: 128, iters: 3584, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.734 G_ID: 0.158 G_Rec: 0.357 D_GP: 0.026 D_real: 1.105 D_fake: 0.753 
+(epoch: 128, iters: 3984, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.848 G_ID: 0.255 G_Rec: 0.404 D_GP: 0.032 D_real: 0.916 D_fake: 0.784 
+(epoch: 128, iters: 4384, time: 0.064) G_GAN: -0.008 G_GAN_Feat: 0.767 G_ID: 0.170 G_Rec: 0.371 D_GP: 0.032 D_real: 0.742 D_fake: 1.009 
+(epoch: 128, iters: 4784, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 1.194 G_ID: 0.232 G_Rec: 0.369 D_GP: 0.072 D_real: 0.439 D_fake: 0.437 
+(epoch: 128, iters: 5184, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.677 G_ID: 0.156 G_Rec: 0.374 D_GP: 0.021 D_real: 1.207 D_fake: 0.700 
+(epoch: 128, iters: 5584, time: 0.064) G_GAN: 0.496 G_GAN_Feat: 0.792 G_ID: 0.215 G_Rec: 0.390 D_GP: 0.027 D_real: 1.174 D_fake: 0.522 
+(epoch: 128, iters: 5984, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.696 G_ID: 0.133 G_Rec: 0.336 D_GP: 0.049 D_real: 1.119 D_fake: 0.724 
+(epoch: 128, iters: 6384, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.843 G_ID: 0.235 G_Rec: 0.411 D_GP: 0.025 D_real: 1.090 D_fake: 0.665 
+(epoch: 128, iters: 6784, time: 0.064) G_GAN: -0.064 G_GAN_Feat: 0.831 G_ID: 0.181 G_Rec: 0.348 D_GP: 0.133 D_real: 0.521 D_fake: 1.065 
+(epoch: 128, iters: 7184, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.751 G_ID: 0.248 G_Rec: 0.378 D_GP: 0.020 D_real: 1.151 D_fake: 0.669 
+(epoch: 128, iters: 7584, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.675 G_ID: 0.165 G_Rec: 0.307 D_GP: 0.041 D_real: 1.054 D_fake: 0.748 
+(epoch: 128, iters: 7984, time: 0.064) G_GAN: -0.039 G_GAN_Feat: 0.986 G_ID: 0.226 G_Rec: 0.382 D_GP: 0.229 D_real: 0.313 D_fake: 1.040 
+(epoch: 128, iters: 8384, time: 0.064) G_GAN: 0.026 G_GAN_Feat: 1.019 G_ID: 0.146 G_Rec: 0.364 D_GP: 0.469 D_real: 0.464 D_fake: 0.974 
+(epoch: 129, iters: 176, time: 0.064) G_GAN: 0.567 G_GAN_Feat: 0.870 G_ID: 0.249 G_Rec: 0.363 D_GP: 0.061 D_real: 0.943 D_fake: 0.444 
+(epoch: 129, iters: 576, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.801 G_ID: 0.161 G_Rec: 0.357 D_GP: 0.030 D_real: 1.257 D_fake: 0.553 
+(epoch: 129, iters: 976, time: 0.064) G_GAN: 0.537 G_GAN_Feat: 0.980 G_ID: 0.214 G_Rec: 0.424 D_GP: 0.034 D_real: 0.985 D_fake: 0.470 
+(epoch: 129, iters: 1376, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.840 G_ID: 0.154 G_Rec: 0.360 D_GP: 0.031 D_real: 1.073 D_fake: 0.657 
+(epoch: 129, iters: 1776, time: 0.064) G_GAN: 0.469 G_GAN_Feat: 1.039 G_ID: 0.215 G_Rec: 0.442 D_GP: 0.091 D_real: 0.830 D_fake: 0.562 
+(epoch: 129, iters: 2176, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.863 G_ID: 0.146 G_Rec: 0.330 D_GP: 0.033 D_real: 0.972 D_fake: 0.695 
+(epoch: 129, iters: 2576, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.838 G_ID: 0.245 G_Rec: 0.393 D_GP: 0.033 D_real: 0.881 D_fake: 0.790 
+(epoch: 129, iters: 2976, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.788 G_ID: 0.175 G_Rec: 0.313 D_GP: 0.040 D_real: 1.011 D_fake: 0.732 
+(epoch: 129, iters: 3376, time: 0.064) G_GAN: 0.511 G_GAN_Feat: 0.835 G_ID: 0.248 G_Rec: 0.373 D_GP: 0.030 D_real: 1.278 D_fake: 0.492 
+(epoch: 129, iters: 3776, time: 0.064) G_GAN: 0.146 G_GAN_Feat: 0.751 G_ID: 0.148 G_Rec: 0.359 D_GP: 0.028 D_real: 0.973 D_fake: 0.854 
+(epoch: 129, iters: 4176, time: 0.064) G_GAN: 0.714 G_GAN_Feat: 1.004 G_ID: 0.177 G_Rec: 0.404 D_GP: 0.042 D_real: 1.085 D_fake: 0.316 
+(epoch: 129, iters: 4576, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.836 G_ID: 0.147 G_Rec: 0.348 D_GP: 0.041 D_real: 0.867 D_fake: 0.895 
+(epoch: 129, iters: 4976, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 1.134 G_ID: 0.210 G_Rec: 0.418 D_GP: 0.332 D_real: 0.543 D_fake: 0.615 
+(epoch: 129, iters: 5376, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.689 G_ID: 0.165 G_Rec: 0.366 D_GP: 0.025 D_real: 1.103 D_fake: 0.783 
+(epoch: 129, iters: 5776, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.938 G_ID: 0.216 G_Rec: 0.399 D_GP: 0.069 D_real: 0.929 D_fake: 0.633 
+(epoch: 129, iters: 6176, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.860 G_ID: 0.165 G_Rec: 0.314 D_GP: 0.107 D_real: 0.736 D_fake: 0.705 
+(epoch: 129, iters: 6576, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 0.952 G_ID: 0.239 G_Rec: 0.431 D_GP: 0.037 D_real: 0.934 D_fake: 0.512 
+(epoch: 129, iters: 6976, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.737 G_ID: 0.171 G_Rec: 0.347 D_GP: 0.031 D_real: 1.012 D_fake: 0.790 
+(epoch: 129, iters: 7376, time: 0.064) G_GAN: 0.605 G_GAN_Feat: 0.866 G_ID: 0.206 G_Rec: 0.381 D_GP: 0.032 D_real: 1.196 D_fake: 0.399 
+(epoch: 129, iters: 7776, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.641 G_ID: 0.167 G_Rec: 0.336 D_GP: 0.018 D_real: 1.226 D_fake: 0.670 
+(epoch: 129, iters: 8176, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.858 G_ID: 0.277 G_Rec: 0.418 D_GP: 0.023 D_real: 1.206 D_fake: 0.509 
+(epoch: 129, iters: 8576, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.721 G_ID: 0.157 G_Rec: 0.328 D_GP: 0.039 D_real: 1.083 D_fake: 0.683 
+(epoch: 130, iters: 368, time: 0.064) G_GAN: 0.487 G_GAN_Feat: 0.923 G_ID: 0.225 G_Rec: 0.394 D_GP: 0.042 D_real: 0.917 D_fake: 0.515 
+(epoch: 130, iters: 768, time: 0.064) G_GAN: 0.135 G_GAN_Feat: 0.706 G_ID: 0.197 G_Rec: 0.328 D_GP: 0.035 D_real: 0.989 D_fake: 0.866 
+(epoch: 130, iters: 1168, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.876 G_ID: 0.218 G_Rec: 0.386 D_GP: 0.044 D_real: 0.870 D_fake: 0.793 
+(epoch: 130, iters: 1568, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.886 G_ID: 0.159 G_Rec: 0.336 D_GP: 0.168 D_real: 0.707 D_fake: 0.826 
+(epoch: 130, iters: 1968, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 1.193 G_ID: 0.237 G_Rec: 0.394 D_GP: 0.165 D_real: 0.257 D_fake: 0.748 
+(epoch: 130, iters: 2368, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 1.080 G_ID: 0.134 G_Rec: 0.354 D_GP: 0.071 D_real: 0.403 D_fake: 0.730 
+(epoch: 130, iters: 2768, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.843 G_ID: 0.224 G_Rec: 0.398 D_GP: 0.027 D_real: 1.162 D_fake: 0.547 
+(epoch: 130, iters: 3168, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.777 G_ID: 0.172 G_Rec: 0.351 D_GP: 0.038 D_real: 1.197 D_fake: 0.626 
+(epoch: 130, iters: 3568, time: 0.064) G_GAN: 0.748 G_GAN_Feat: 1.039 G_ID: 0.195 G_Rec: 0.394 D_GP: 0.279 D_real: 0.878 D_fake: 0.387 
+(epoch: 130, iters: 3968, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.795 G_ID: 0.159 G_Rec: 0.328 D_GP: 0.038 D_real: 1.155 D_fake: 0.650 
+(epoch: 130, iters: 4368, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.882 G_ID: 0.211 G_Rec: 0.442 D_GP: 0.033 D_real: 1.191 D_fake: 0.594 
+(epoch: 130, iters: 4768, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.685 G_ID: 0.153 G_Rec: 0.328 D_GP: 0.022 D_real: 1.279 D_fake: 0.656 
+(epoch: 130, iters: 5168, time: 0.064) G_GAN: 0.729 G_GAN_Feat: 0.883 G_ID: 0.207 G_Rec: 0.395 D_GP: 0.027 D_real: 1.403 D_fake: 0.288 
+(epoch: 130, iters: 5568, time: 0.064) G_GAN: -0.156 G_GAN_Feat: 0.928 G_ID: 0.159 G_Rec: 0.360 D_GP: 0.062 D_real: 0.958 D_fake: 1.156 
+(epoch: 130, iters: 5968, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 1.093 G_ID: 0.190 G_Rec: 0.394 D_GP: 0.042 D_real: 0.654 D_fake: 0.468 
+(epoch: 130, iters: 6368, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.680 G_ID: 0.154 G_Rec: 0.355 D_GP: 0.022 D_real: 1.162 D_fake: 0.794 
+(epoch: 130, iters: 6768, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.826 G_ID: 0.227 G_Rec: 0.388 D_GP: 0.027 D_real: 1.005 D_fake: 0.724 
+(epoch: 130, iters: 7168, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.670 G_ID: 0.147 G_Rec: 0.335 D_GP: 0.024 D_real: 1.221 D_fake: 0.699 
+(epoch: 130, iters: 7568, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.878 G_ID: 0.246 G_Rec: 0.400 D_GP: 0.028 D_real: 1.217 D_fake: 0.514 
+(epoch: 130, iters: 7968, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.751 G_ID: 0.150 G_Rec: 0.341 D_GP: 0.032 D_real: 1.093 D_fake: 0.777 
+(epoch: 130, iters: 8368, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.892 G_ID: 0.242 G_Rec: 0.395 D_GP: 0.047 D_real: 0.900 D_fake: 0.631 
+(epoch: 131, iters: 160, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.778 G_ID: 0.124 G_Rec: 0.339 D_GP: 0.040 D_real: 1.137 D_fake: 0.673 
+(epoch: 131, iters: 560, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.828 G_ID: 0.228 G_Rec: 0.388 D_GP: 0.034 D_real: 1.080 D_fake: 0.662 
+(epoch: 131, iters: 960, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.766 G_ID: 0.144 G_Rec: 0.323 D_GP: 0.038 D_real: 1.056 D_fake: 0.672 
+(epoch: 131, iters: 1360, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.894 G_ID: 0.218 G_Rec: 0.408 D_GP: 0.040 D_real: 1.118 D_fake: 0.491 
+(epoch: 131, iters: 1760, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.928 G_ID: 0.152 G_Rec: 0.353 D_GP: 0.123 D_real: 0.694 D_fake: 0.657 
+(epoch: 131, iters: 2160, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.981 G_ID: 0.201 G_Rec: 0.432 D_GP: 0.051 D_real: 0.885 D_fake: 0.551 
+(epoch: 131, iters: 2560, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.752 G_ID: 0.155 G_Rec: 0.312 D_GP: 0.030 D_real: 1.136 D_fake: 0.641 
+(epoch: 131, iters: 2960, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.993 G_ID: 0.232 G_Rec: 0.379 D_GP: 0.042 D_real: 0.918 D_fake: 0.532 
+(epoch: 131, iters: 3360, time: 0.064) G_GAN: 0.026 G_GAN_Feat: 0.619 G_ID: 0.167 G_Rec: 0.317 D_GP: 0.023 D_real: 0.973 D_fake: 0.974 
+(epoch: 131, iters: 3760, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.817 G_ID: 0.202 G_Rec: 0.387 D_GP: 0.022 D_real: 1.136 D_fake: 0.670 
+(epoch: 131, iters: 4160, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 0.715 G_ID: 0.152 G_Rec: 0.363 D_GP: 0.043 D_real: 0.923 D_fake: 0.902 
+(epoch: 131, iters: 4560, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.888 G_ID: 0.242 G_Rec: 0.398 D_GP: 0.094 D_real: 0.911 D_fake: 0.620 
+(epoch: 131, iters: 4960, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.813 G_ID: 0.133 G_Rec: 0.329 D_GP: 0.092 D_real: 0.833 D_fake: 0.831 
+(epoch: 131, iters: 5360, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.893 G_ID: 0.220 G_Rec: 0.391 D_GP: 0.029 D_real: 0.944 D_fake: 0.676 
+(epoch: 131, iters: 5760, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 1.061 G_ID: 0.160 G_Rec: 0.432 D_GP: 1.313 D_real: 0.363 D_fake: 0.989 
+(epoch: 131, iters: 6160, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.970 G_ID: 0.250 G_Rec: 0.439 D_GP: 0.037 D_real: 0.775 D_fake: 0.692 
+(epoch: 131, iters: 6560, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.724 G_ID: 0.170 G_Rec: 0.315 D_GP: 0.035 D_real: 1.073 D_fake: 0.681 
+(epoch: 131, iters: 6960, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.990 G_ID: 0.268 G_Rec: 0.366 D_GP: 0.036 D_real: 0.901 D_fake: 0.631 
+(epoch: 131, iters: 7360, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.977 G_ID: 0.146 G_Rec: 0.355 D_GP: 0.198 D_real: 0.677 D_fake: 0.548 
+(epoch: 131, iters: 7760, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 1.001 G_ID: 0.209 G_Rec: 0.421 D_GP: 0.050 D_real: 0.809 D_fake: 0.552 
+(epoch: 131, iters: 8160, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.923 G_ID: 0.151 G_Rec: 0.355 D_GP: 0.030 D_real: 0.636 D_fake: 0.810 
+(epoch: 131, iters: 8560, time: 0.064) G_GAN: 1.133 G_GAN_Feat: 0.864 G_ID: 0.241 G_Rec: 0.375 D_GP: 0.031 D_real: 1.804 D_fake: 0.135 
+(epoch: 132, iters: 352, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.928 G_ID: 0.141 G_Rec: 0.357 D_GP: 0.056 D_real: 0.756 D_fake: 0.592 
+(epoch: 132, iters: 752, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.833 G_ID: 0.259 G_Rec: 0.377 D_GP: 0.026 D_real: 0.975 D_fake: 0.829 
+(epoch: 132, iters: 1152, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.732 G_ID: 0.166 G_Rec: 0.325 D_GP: 0.024 D_real: 1.311 D_fake: 0.603 
+(epoch: 132, iters: 1552, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.877 G_ID: 0.189 G_Rec: 0.402 D_GP: 0.028 D_real: 1.208 D_fake: 0.463 
+(epoch: 132, iters: 1952, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.788 G_ID: 0.180 G_Rec: 0.329 D_GP: 0.043 D_real: 0.911 D_fake: 0.848 
+(epoch: 132, iters: 2352, time: 0.064) G_GAN: 0.736 G_GAN_Feat: 1.027 G_ID: 0.257 G_Rec: 0.393 D_GP: 0.052 D_real: 0.881 D_fake: 0.357 
+(epoch: 132, iters: 2752, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.818 G_ID: 0.189 G_Rec: 0.365 D_GP: 0.025 D_real: 1.148 D_fake: 0.717 
+(epoch: 132, iters: 3152, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 0.850 G_ID: 0.210 G_Rec: 0.343 D_GP: 0.029 D_real: 1.214 D_fake: 0.510 
+(epoch: 132, iters: 3552, time: 0.064) G_GAN: 0.653 G_GAN_Feat: 1.002 G_ID: 0.137 G_Rec: 0.334 D_GP: 0.104 D_real: 0.783 D_fake: 0.409 
+(epoch: 132, iters: 3952, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.706 G_ID: 0.207 G_Rec: 0.354 D_GP: 0.021 D_real: 1.307 D_fake: 0.494 
+(epoch: 132, iters: 4352, time: 0.064) G_GAN: 0.045 G_GAN_Feat: 0.805 G_ID: 0.152 G_Rec: 0.376 D_GP: 0.069 D_real: 0.786 D_fake: 0.955 
+(epoch: 132, iters: 4752, time: 0.064) G_GAN: -0.031 G_GAN_Feat: 1.121 G_ID: 0.195 G_Rec: 0.387 D_GP: 0.049 D_real: 1.077 D_fake: 1.032 
+(epoch: 132, iters: 5152, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.813 G_ID: 0.130 G_Rec: 0.347 D_GP: 0.029 D_real: 1.005 D_fake: 0.770 
+(epoch: 132, iters: 5552, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 1.189 G_ID: 0.228 G_Rec: 0.430 D_GP: 0.205 D_real: 0.479 D_fake: 0.613 
+(epoch: 132, iters: 5952, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.934 G_ID: 0.157 G_Rec: 0.341 D_GP: 0.059 D_real: 1.041 D_fake: 0.605 
+(epoch: 132, iters: 6352, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.883 G_ID: 0.214 G_Rec: 0.382 D_GP: 0.026 D_real: 1.092 D_fake: 0.670 
+(epoch: 132, iters: 6752, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 0.825 G_ID: 0.141 G_Rec: 0.344 D_GP: 0.033 D_real: 0.915 D_fake: 0.673 
+(epoch: 132, iters: 7152, time: 0.064) G_GAN: 0.603 G_GAN_Feat: 0.852 G_ID: 0.212 G_Rec: 0.537 D_GP: 0.024 D_real: 1.328 D_fake: 0.404 
+(epoch: 132, iters: 7552, time: 0.064) G_GAN: -0.014 G_GAN_Feat: 0.749 G_ID: 0.184 G_Rec: 0.317 D_GP: 0.036 D_real: 0.905 D_fake: 1.016 
+(epoch: 132, iters: 7952, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.853 G_ID: 0.214 G_Rec: 0.369 D_GP: 0.027 D_real: 1.024 D_fake: 0.698 
+(epoch: 132, iters: 8352, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 0.753 G_ID: 0.167 G_Rec: 0.357 D_GP: 0.025 D_real: 1.061 D_fake: 0.794 
+(epoch: 133, iters: 144, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.854 G_ID: 0.217 G_Rec: 0.370 D_GP: 0.027 D_real: 1.130 D_fake: 0.607 
+(epoch: 133, iters: 544, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 1.079 G_ID: 0.177 G_Rec: 0.374 D_GP: 1.310 D_real: 0.565 D_fake: 0.570 
+(epoch: 133, iters: 944, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.860 G_ID: 0.224 G_Rec: 0.436 D_GP: 0.031 D_real: 1.062 D_fake: 0.574 
+(epoch: 133, iters: 1344, time: 0.064) G_GAN: 0.742 G_GAN_Feat: 0.850 G_ID: 0.159 G_Rec: 0.363 D_GP: 0.059 D_real: 1.310 D_fake: 0.307 
+(epoch: 133, iters: 1744, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.806 G_ID: 0.242 G_Rec: 0.386 D_GP: 0.021 D_real: 1.046 D_fake: 0.741 
+(epoch: 133, iters: 2144, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.702 G_ID: 0.154 G_Rec: 0.323 D_GP: 0.026 D_real: 0.943 D_fake: 0.916 
+(epoch: 133, iters: 2544, time: 0.064) G_GAN: 0.667 G_GAN_Feat: 1.187 G_ID: 0.259 G_Rec: 0.431 D_GP: 0.405 D_real: 0.625 D_fake: 0.357 
+(epoch: 133, iters: 2944, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.816 G_ID: 0.145 G_Rec: 0.343 D_GP: 0.035 D_real: 1.183 D_fake: 0.499 
+(epoch: 133, iters: 3344, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.877 G_ID: 0.218 G_Rec: 0.393 D_GP: 0.078 D_real: 0.786 D_fake: 0.868 
+(epoch: 133, iters: 3744, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.718 G_ID: 0.186 G_Rec: 0.310 D_GP: 0.029 D_real: 1.051 D_fake: 0.769 
+(epoch: 133, iters: 4144, time: 0.064) G_GAN: 0.606 G_GAN_Feat: 0.985 G_ID: 0.215 G_Rec: 0.409 D_GP: 0.046 D_real: 1.004 D_fake: 0.403 
+(epoch: 133, iters: 4544, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.941 G_ID: 0.171 G_Rec: 0.351 D_GP: 0.193 D_real: 0.682 D_fake: 0.729 
+(epoch: 133, iters: 4944, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 0.914 G_ID: 0.190 G_Rec: 0.387 D_GP: 0.034 D_real: 1.115 D_fake: 0.535 
+(epoch: 133, iters: 5344, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.873 G_ID: 0.153 G_Rec: 0.348 D_GP: 0.053 D_real: 0.723 D_fake: 0.849 
+(epoch: 133, iters: 5744, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 1.133 G_ID: 0.216 G_Rec: 0.449 D_GP: 0.402 D_real: 0.515 D_fake: 0.527 
+(epoch: 133, iters: 6144, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.857 G_ID: 0.157 G_Rec: 0.325 D_GP: 0.028 D_real: 0.906 D_fake: 0.792 
+(epoch: 133, iters: 6544, time: 0.064) G_GAN: 0.543 G_GAN_Feat: 1.165 G_ID: 0.231 G_Rec: 0.409 D_GP: 0.070 D_real: 0.826 D_fake: 0.464 
+(epoch: 133, iters: 6944, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.735 G_ID: 0.158 G_Rec: 0.326 D_GP: 0.030 D_real: 1.225 D_fake: 0.731 
+(epoch: 133, iters: 7344, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.918 G_ID: 0.203 G_Rec: 0.379 D_GP: 0.034 D_real: 1.107 D_fake: 0.578 
+(epoch: 133, iters: 7744, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.813 G_ID: 0.159 G_Rec: 0.319 D_GP: 0.044 D_real: 1.082 D_fake: 0.593 
+(epoch: 133, iters: 8144, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.981 G_ID: 0.221 G_Rec: 0.409 D_GP: 0.048 D_real: 0.962 D_fake: 0.486 
+(epoch: 133, iters: 8544, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.852 G_ID: 0.158 G_Rec: 0.336 D_GP: 0.028 D_real: 1.046 D_fake: 0.667 
+(epoch: 134, iters: 336, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 1.112 G_ID: 0.223 G_Rec: 0.424 D_GP: 0.046 D_real: 0.419 D_fake: 0.761 
+(epoch: 134, iters: 736, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.931 G_ID: 0.149 G_Rec: 0.354 D_GP: 0.066 D_real: 0.606 D_fake: 0.752 
+(epoch: 134, iters: 1136, time: 0.064) G_GAN: -0.017 G_GAN_Feat: 0.859 G_ID: 0.209 G_Rec: 0.396 D_GP: 0.030 D_real: 0.818 D_fake: 1.017 
+(epoch: 134, iters: 1536, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.707 G_ID: 0.131 G_Rec: 0.335 D_GP: 0.028 D_real: 1.054 D_fake: 0.809 
+(epoch: 134, iters: 1936, time: 0.064) G_GAN: 0.728 G_GAN_Feat: 1.032 G_ID: 0.224 G_Rec: 0.433 D_GP: 0.051 D_real: 1.223 D_fake: 0.313 
+(epoch: 134, iters: 2336, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.901 G_ID: 0.159 G_Rec: 0.407 D_GP: 0.069 D_real: 0.787 D_fake: 0.698 
+(epoch: 134, iters: 2736, time: 0.064) G_GAN: 0.644 G_GAN_Feat: 1.087 G_ID: 0.211 G_Rec: 0.416 D_GP: 0.068 D_real: 0.816 D_fake: 0.366 
+(epoch: 134, iters: 3136, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.763 G_ID: 0.142 G_Rec: 0.382 D_GP: 0.027 D_real: 0.959 D_fake: 0.870 
+(epoch: 134, iters: 3536, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.826 G_ID: 0.233 G_Rec: 0.351 D_GP: 0.027 D_real: 1.116 D_fake: 0.645 
+(epoch: 134, iters: 3936, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 1.020 G_ID: 0.154 G_Rec: 0.380 D_GP: 0.119 D_real: 0.494 D_fake: 0.787 
+(epoch: 134, iters: 4336, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.916 G_ID: 0.259 G_Rec: 0.378 D_GP: 0.027 D_real: 0.931 D_fake: 0.627 
+(epoch: 134, iters: 4736, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 1.045 G_ID: 0.175 G_Rec: 0.321 D_GP: 0.150 D_real: 0.363 D_fake: 0.830 
+(epoch: 134, iters: 5136, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.928 G_ID: 0.211 G_Rec: 0.426 D_GP: 0.050 D_real: 0.679 D_fake: 0.749 
+(epoch: 134, iters: 5536, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.818 G_ID: 0.140 G_Rec: 0.359 D_GP: 0.062 D_real: 0.731 D_fake: 0.848 
+(epoch: 134, iters: 5936, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.978 G_ID: 0.199 G_Rec: 0.430 D_GP: 0.037 D_real: 0.844 D_fake: 0.692 
+(epoch: 134, iters: 6336, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.779 G_ID: 0.130 G_Rec: 0.328 D_GP: 0.029 D_real: 1.305 D_fake: 0.501 
+(epoch: 134, iters: 6736, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.757 G_ID: 0.212 G_Rec: 0.390 D_GP: 0.021 D_real: 1.258 D_fake: 0.564 
+(epoch: 134, iters: 7136, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.932 G_ID: 0.189 G_Rec: 0.348 D_GP: 0.052 D_real: 0.613 D_fake: 0.658 
+(epoch: 134, iters: 7536, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 1.105 G_ID: 0.288 G_Rec: 0.411 D_GP: 0.112 D_real: 0.832 D_fake: 0.596 
+(epoch: 134, iters: 7936, time: 0.064) G_GAN: 0.434 G_GAN_Feat: 0.812 G_ID: 0.162 G_Rec: 0.320 D_GP: 0.029 D_real: 1.189 D_fake: 0.566 
+(epoch: 134, iters: 8336, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.981 G_ID: 0.244 G_Rec: 0.431 D_GP: 0.059 D_real: 0.947 D_fake: 0.483 
+(epoch: 135, iters: 128, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.715 G_ID: 0.154 G_Rec: 0.322 D_GP: 0.026 D_real: 1.109 D_fake: 0.762 
+(epoch: 135, iters: 528, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.971 G_ID: 0.224 G_Rec: 0.381 D_GP: 0.031 D_real: 1.267 D_fake: 0.623 
+(epoch: 135, iters: 928, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.730 G_ID: 0.164 G_Rec: 0.307 D_GP: 0.032 D_real: 1.111 D_fake: 0.745 
+(epoch: 135, iters: 1328, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.830 G_ID: 0.246 G_Rec: 0.381 D_GP: 0.026 D_real: 0.839 D_fake: 0.826 
+(epoch: 135, iters: 1728, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.883 G_ID: 0.157 G_Rec: 0.347 D_GP: 0.085 D_real: 0.821 D_fake: 0.494 
+(epoch: 135, iters: 2128, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.853 G_ID: 0.212 G_Rec: 0.392 D_GP: 0.022 D_real: 1.112 D_fake: 0.636 
+(epoch: 135, iters: 2528, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.850 G_ID: 0.172 G_Rec: 0.320 D_GP: 0.037 D_real: 0.756 D_fake: 0.766 
+(epoch: 135, iters: 2928, time: 0.064) G_GAN: 0.560 G_GAN_Feat: 0.930 G_ID: 0.224 G_Rec: 0.381 D_GP: 0.039 D_real: 1.047 D_fake: 0.447 
+(epoch: 135, iters: 3328, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.708 G_ID: 0.172 G_Rec: 0.328 D_GP: 0.028 D_real: 1.020 D_fake: 0.873 
+(epoch: 135, iters: 3728, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 1.139 G_ID: 0.203 G_Rec: 0.434 D_GP: 0.183 D_real: 0.453 D_fake: 0.598 
+(epoch: 135, iters: 4128, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.758 G_ID: 0.161 G_Rec: 0.303 D_GP: 0.036 D_real: 1.082 D_fake: 0.874 
+(epoch: 135, iters: 4528, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 1.092 G_ID: 0.202 G_Rec: 0.416 D_GP: 0.143 D_real: 0.882 D_fake: 0.642 
+(epoch: 135, iters: 4928, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.708 G_ID: 0.152 G_Rec: 0.342 D_GP: 0.021 D_real: 1.143 D_fake: 0.798 
+(epoch: 135, iters: 5328, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.792 G_ID: 0.231 G_Rec: 0.351 D_GP: 0.025 D_real: 1.222 D_fake: 0.553 
+(epoch: 135, iters: 5728, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.686 G_ID: 0.156 G_Rec: 0.321 D_GP: 0.025 D_real: 1.194 D_fake: 0.711 
+(epoch: 135, iters: 6128, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 1.040 G_ID: 0.245 G_Rec: 0.430 D_GP: 0.041 D_real: 1.050 D_fake: 0.452 
+(epoch: 135, iters: 6528, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.934 G_ID: 0.135 G_Rec: 0.301 D_GP: 0.045 D_real: 0.626 D_fake: 0.786 
+(epoch: 135, iters: 6928, time: 0.064) G_GAN: 0.616 G_GAN_Feat: 0.891 G_ID: 0.200 G_Rec: 0.385 D_GP: 0.035 D_real: 1.247 D_fake: 0.409 
+(epoch: 135, iters: 7328, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.910 G_ID: 0.139 G_Rec: 0.352 D_GP: 0.069 D_real: 0.896 D_fake: 0.688 
+(epoch: 135, iters: 7728, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.891 G_ID: 0.251 G_Rec: 0.383 D_GP: 0.032 D_real: 0.779 D_fake: 0.787 
+(epoch: 135, iters: 8128, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.853 G_ID: 0.144 G_Rec: 0.326 D_GP: 0.034 D_real: 0.938 D_fake: 0.776 
+(epoch: 135, iters: 8528, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 1.028 G_ID: 0.221 G_Rec: 0.442 D_GP: 0.056 D_real: 0.899 D_fake: 0.647 
+(epoch: 136, iters: 320, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.836 G_ID: 0.141 G_Rec: 0.326 D_GP: 0.029 D_real: 0.817 D_fake: 0.821 
+(epoch: 136, iters: 720, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.838 G_ID: 0.205 G_Rec: 0.408 D_GP: 0.024 D_real: 1.227 D_fake: 0.490 
+(epoch: 136, iters: 1120, time: 0.064) G_GAN: 0.071 G_GAN_Feat: 0.929 G_ID: 0.138 G_Rec: 0.429 D_GP: 0.136 D_real: 0.402 D_fake: 0.929 
+(epoch: 136, iters: 1520, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 1.096 G_ID: 0.204 G_Rec: 0.405 D_GP: 0.132 D_real: 0.459 D_fake: 0.541 
+(epoch: 136, iters: 1920, time: 0.064) G_GAN: 0.010 G_GAN_Feat: 0.665 G_ID: 0.154 G_Rec: 0.299 D_GP: 0.027 D_real: 0.928 D_fake: 0.990 
+(epoch: 136, iters: 2320, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 0.963 G_ID: 0.221 G_Rec: 0.416 D_GP: 0.043 D_real: 1.027 D_fake: 0.447 
+(epoch: 136, iters: 2720, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.726 G_ID: 0.140 G_Rec: 0.325 D_GP: 0.026 D_real: 1.305 D_fake: 0.554 
+(epoch: 136, iters: 3120, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.886 G_ID: 0.218 G_Rec: 0.377 D_GP: 0.040 D_real: 1.111 D_fake: 0.544 
+(epoch: 136, iters: 3520, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.675 G_ID: 0.154 G_Rec: 0.333 D_GP: 0.022 D_real: 1.258 D_fake: 0.731 
+(epoch: 136, iters: 3920, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.793 G_ID: 0.224 G_Rec: 0.394 D_GP: 0.023 D_real: 0.916 D_fake: 0.801 
+(epoch: 136, iters: 4320, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.712 G_ID: 0.161 G_Rec: 0.414 D_GP: 0.049 D_real: 0.918 D_fake: 0.881 
+(epoch: 136, iters: 4720, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.867 G_ID: 0.251 G_Rec: 0.420 D_GP: 0.036 D_real: 0.743 D_fake: 0.894 
+(epoch: 136, iters: 5120, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.699 G_ID: 0.161 G_Rec: 0.351 D_GP: 0.028 D_real: 1.021 D_fake: 0.787 
+(epoch: 136, iters: 5520, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 1.095 G_ID: 0.251 G_Rec: 0.461 D_GP: 0.131 D_real: 1.025 D_fake: 0.478 
+(epoch: 136, iters: 5920, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.953 G_ID: 0.168 G_Rec: 0.362 D_GP: 0.225 D_real: 0.603 D_fake: 0.906 
+(epoch: 136, iters: 6320, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.828 G_ID: 0.246 G_Rec: 0.340 D_GP: 0.049 D_real: 0.899 D_fake: 0.765 
+(epoch: 136, iters: 6720, time: 0.064) G_GAN: 0.575 G_GAN_Feat: 0.834 G_ID: 0.157 G_Rec: 0.369 D_GP: 0.051 D_real: 1.159 D_fake: 0.452 
+(epoch: 136, iters: 7120, time: 0.064) G_GAN: 0.803 G_GAN_Feat: 0.902 G_ID: 0.220 G_Rec: 0.399 D_GP: 0.033 D_real: 1.396 D_fake: 0.316 
+(epoch: 136, iters: 7520, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.726 G_ID: 0.174 G_Rec: 0.302 D_GP: 0.035 D_real: 0.891 D_fake: 0.947 
+(epoch: 136, iters: 7920, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.912 G_ID: 0.223 G_Rec: 0.393 D_GP: 0.049 D_real: 0.948 D_fake: 0.643 
+(epoch: 136, iters: 8320, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.707 G_ID: 0.179 G_Rec: 0.340 D_GP: 0.023 D_real: 1.374 D_fake: 0.555 
+(epoch: 137, iters: 112, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.946 G_ID: 0.222 G_Rec: 0.402 D_GP: 0.030 D_real: 1.026 D_fake: 0.607 
+(epoch: 137, iters: 512, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 1.035 G_ID: 0.178 G_Rec: 0.354 D_GP: 0.191 D_real: 0.446 D_fake: 0.675 
+(epoch: 137, iters: 912, time: 0.064) G_GAN: 0.755 G_GAN_Feat: 1.053 G_ID: 0.244 G_Rec: 0.427 D_GP: 0.040 D_real: 1.309 D_fake: 0.348 
+(epoch: 137, iters: 1312, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.859 G_ID: 0.170 G_Rec: 0.319 D_GP: 0.063 D_real: 0.724 D_fake: 0.768 
+(epoch: 137, iters: 1712, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 1.009 G_ID: 0.206 G_Rec: 0.395 D_GP: 0.064 D_real: 0.669 D_fake: 0.624 
+(epoch: 137, iters: 2112, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.841 G_ID: 0.191 G_Rec: 0.365 D_GP: 0.037 D_real: 0.719 D_fake: 0.910 
+(epoch: 137, iters: 2512, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 1.115 G_ID: 0.270 G_Rec: 0.424 D_GP: 0.272 D_real: 0.387 D_fake: 0.771 
+(epoch: 137, iters: 2912, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 0.818 G_ID: 0.150 G_Rec: 0.298 D_GP: 0.082 D_real: 1.065 D_fake: 0.562 
+(epoch: 137, iters: 3312, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.804 G_ID: 0.247 G_Rec: 0.392 D_GP: 0.024 D_real: 1.250 D_fake: 0.527 
+(epoch: 137, iters: 3712, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.799 G_ID: 0.140 G_Rec: 0.339 D_GP: 0.046 D_real: 0.788 D_fake: 0.917 
+(epoch: 137, iters: 4112, time: 0.064) G_GAN: 0.786 G_GAN_Feat: 0.931 G_ID: 0.192 G_Rec: 0.401 D_GP: 0.028 D_real: 1.435 D_fake: 0.244 
+(epoch: 137, iters: 4512, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.827 G_ID: 0.181 G_Rec: 0.345 D_GP: 0.045 D_real: 1.082 D_fake: 0.580 
+(epoch: 137, iters: 4912, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 1.103 G_ID: 0.237 G_Rec: 0.421 D_GP: 0.332 D_real: 0.467 D_fake: 0.543 
+(epoch: 137, iters: 5312, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.676 G_ID: 0.141 G_Rec: 0.328 D_GP: 0.024 D_real: 1.245 D_fake: 0.661 
+(epoch: 137, iters: 5712, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.899 G_ID: 0.199 G_Rec: 0.374 D_GP: 0.065 D_real: 0.849 D_fake: 0.752 
+(epoch: 137, iters: 6112, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.816 G_ID: 0.123 G_Rec: 0.340 D_GP: 0.054 D_real: 0.779 D_fake: 0.840 
+(epoch: 137, iters: 6512, time: 0.064) G_GAN: 0.564 G_GAN_Feat: 0.937 G_ID: 0.196 G_Rec: 0.400 D_GP: 0.038 D_real: 1.159 D_fake: 0.448 
+(epoch: 137, iters: 6912, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.772 G_ID: 0.194 G_Rec: 0.340 D_GP: 0.035 D_real: 1.127 D_fake: 0.766 
+(epoch: 137, iters: 7312, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 0.883 G_ID: 0.197 G_Rec: 0.408 D_GP: 0.023 D_real: 1.303 D_fake: 0.471 
+(epoch: 137, iters: 7712, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 1.032 G_ID: 0.205 G_Rec: 0.384 D_GP: 0.381 D_real: 0.346 D_fake: 0.641 
+(epoch: 137, iters: 8112, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.785 G_ID: 0.214 G_Rec: 0.379 D_GP: 0.023 D_real: 0.929 D_fake: 0.864 
+(epoch: 137, iters: 8512, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.678 G_ID: 0.196 G_Rec: 0.330 D_GP: 0.023 D_real: 1.171 D_fake: 0.657 
+(epoch: 138, iters: 304, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.909 G_ID: 0.254 G_Rec: 0.418 D_GP: 0.029 D_real: 0.996 D_fake: 0.700 
+(epoch: 138, iters: 704, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.671 G_ID: 0.160 G_Rec: 0.344 D_GP: 0.023 D_real: 1.128 D_fake: 0.789 
+(epoch: 138, iters: 1104, time: 0.064) G_GAN: 0.587 G_GAN_Feat: 1.110 G_ID: 0.201 G_Rec: 0.417 D_GP: 0.155 D_real: 0.622 D_fake: 0.423 
+(epoch: 138, iters: 1504, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.911 G_ID: 0.171 G_Rec: 0.353 D_GP: 0.090 D_real: 1.020 D_fake: 0.709 
+(epoch: 138, iters: 1904, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.990 G_ID: 0.205 G_Rec: 0.423 D_GP: 0.034 D_real: 1.069 D_fake: 0.540 
+(epoch: 138, iters: 2304, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.699 G_ID: 0.142 G_Rec: 0.322 D_GP: 0.027 D_real: 1.318 D_fake: 0.655 
+(epoch: 138, iters: 2704, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 1.059 G_ID: 0.214 G_Rec: 0.447 D_GP: 0.037 D_real: 0.804 D_fake: 0.545 
+(epoch: 138, iters: 3104, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.795 G_ID: 0.141 G_Rec: 0.310 D_GP: 0.046 D_real: 1.201 D_fake: 0.485 
+(epoch: 138, iters: 3504, time: 0.064) G_GAN: 0.071 G_GAN_Feat: 0.850 G_ID: 0.248 G_Rec: 0.402 D_GP: 0.025 D_real: 0.851 D_fake: 0.929 
+(epoch: 138, iters: 3904, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.908 G_ID: 0.151 G_Rec: 0.345 D_GP: 0.074 D_real: 0.864 D_fake: 0.647 
+(epoch: 138, iters: 4304, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 0.810 G_ID: 0.254 G_Rec: 0.390 D_GP: 0.027 D_real: 1.202 D_fake: 0.562 
+(epoch: 138, iters: 4704, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.678 G_ID: 0.143 G_Rec: 0.351 D_GP: 0.029 D_real: 0.925 D_fake: 0.923 
+(epoch: 138, iters: 5104, time: 0.064) G_GAN: 0.641 G_GAN_Feat: 0.942 G_ID: 0.204 G_Rec: 0.425 D_GP: 0.031 D_real: 1.185 D_fake: 0.384 
+(epoch: 138, iters: 5504, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 1.229 G_ID: 0.176 G_Rec: 0.388 D_GP: 0.662 D_real: 0.309 D_fake: 0.953 
+(epoch: 138, iters: 5904, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.963 G_ID: 0.194 G_Rec: 0.406 D_GP: 0.036 D_real: 0.918 D_fake: 0.611 
+(epoch: 138, iters: 6304, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.861 G_ID: 0.184 G_Rec: 0.327 D_GP: 0.078 D_real: 0.826 D_fake: 0.896 
+(epoch: 138, iters: 6704, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.871 G_ID: 0.234 G_Rec: 0.412 D_GP: 0.029 D_real: 1.032 D_fake: 0.646 
+(epoch: 138, iters: 7104, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 1.124 G_ID: 0.164 G_Rec: 0.367 D_GP: 0.288 D_real: 0.476 D_fake: 0.639 
+(epoch: 138, iters: 7504, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.923 G_ID: 0.216 G_Rec: 0.400 D_GP: 0.038 D_real: 0.923 D_fake: 0.590 
+(epoch: 138, iters: 7904, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.039 G_ID: 0.154 G_Rec: 0.383 D_GP: 0.876 D_real: 0.382 D_fake: 0.665 
+(epoch: 138, iters: 8304, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.890 G_ID: 0.223 G_Rec: 0.390 D_GP: 0.034 D_real: 1.006 D_fake: 0.570 
+(epoch: 139, iters: 96, time: 0.064) G_GAN: 0.183 G_GAN_Feat: 0.875 G_ID: 0.158 G_Rec: 0.321 D_GP: 0.115 D_real: 0.683 D_fake: 0.817 
+(epoch: 139, iters: 496, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.801 G_ID: 0.202 G_Rec: 0.378 D_GP: 0.025 D_real: 1.137 D_fake: 0.627 
+(epoch: 139, iters: 896, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.812 G_ID: 0.137 G_Rec: 0.317 D_GP: 0.050 D_real: 0.827 D_fake: 0.826 
+(epoch: 139, iters: 1296, time: 0.064) G_GAN: 0.511 G_GAN_Feat: 0.846 G_ID: 0.244 G_Rec: 0.397 D_GP: 0.026 D_real: 1.175 D_fake: 0.504 
+(epoch: 139, iters: 1696, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.776 G_ID: 0.160 G_Rec: 0.374 D_GP: 0.023 D_real: 1.122 D_fake: 0.774 
+(epoch: 139, iters: 2096, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.927 G_ID: 0.276 G_Rec: 0.411 D_GP: 0.029 D_real: 0.874 D_fake: 0.724 
+(epoch: 139, iters: 2496, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.654 G_ID: 0.148 G_Rec: 0.296 D_GP: 0.027 D_real: 1.147 D_fake: 0.707 
+(epoch: 139, iters: 2896, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.833 G_ID: 0.224 G_Rec: 0.380 D_GP: 0.036 D_real: 0.917 D_fake: 0.821 
+(epoch: 139, iters: 3296, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.695 G_ID: 0.182 G_Rec: 0.371 D_GP: 0.026 D_real: 1.048 D_fake: 0.816 
+(epoch: 139, iters: 3696, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 0.954 G_ID: 0.203 G_Rec: 0.401 D_GP: 0.048 D_real: 0.986 D_fake: 0.482 
+(epoch: 139, iters: 4096, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.707 G_ID: 0.151 G_Rec: 0.442 D_GP: 0.027 D_real: 1.083 D_fake: 0.782 
+(epoch: 139, iters: 4496, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 1.039 G_ID: 0.210 G_Rec: 0.419 D_GP: 0.031 D_real: 0.968 D_fake: 0.612 
+(epoch: 139, iters: 4896, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.763 G_ID: 0.153 G_Rec: 0.362 D_GP: 0.032 D_real: 1.219 D_fake: 0.612 
+(epoch: 139, iters: 5296, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.803 G_ID: 0.209 G_Rec: 0.397 D_GP: 0.022 D_real: 1.285 D_fake: 0.547 
+(epoch: 139, iters: 5696, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.882 G_ID: 0.135 G_Rec: 0.345 D_GP: 0.048 D_real: 0.964 D_fake: 0.657 
+(epoch: 139, iters: 6096, time: 0.064) G_GAN: 0.768 G_GAN_Feat: 0.943 G_ID: 0.188 G_Rec: 0.406 D_GP: 0.034 D_real: 1.235 D_fake: 0.272 
+(epoch: 139, iters: 6496, time: 0.064) G_GAN: -0.027 G_GAN_Feat: 0.975 G_ID: 0.159 G_Rec: 0.327 D_GP: 0.079 D_real: 1.021 D_fake: 1.028 
+(epoch: 139, iters: 6896, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 1.193 G_ID: 0.221 G_Rec: 0.488 D_GP: 0.063 D_real: 0.684 D_fake: 0.552 
+(epoch: 139, iters: 7296, time: 0.064) G_GAN: -0.019 G_GAN_Feat: 0.874 G_ID: 0.163 G_Rec: 0.317 D_GP: 0.079 D_real: 0.423 D_fake: 1.019 
+(epoch: 139, iters: 7696, time: 0.064) G_GAN: 0.661 G_GAN_Feat: 0.952 G_ID: 0.217 G_Rec: 0.396 D_GP: 0.034 D_real: 1.306 D_fake: 0.345 
+(epoch: 139, iters: 8096, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.771 G_ID: 0.175 G_Rec: 0.343 D_GP: 0.044 D_real: 0.952 D_fake: 0.813 
+(epoch: 139, iters: 8496, time: 0.064) G_GAN: 0.658 G_GAN_Feat: 0.881 G_ID: 0.275 G_Rec: 0.408 D_GP: 0.024 D_real: 1.409 D_fake: 0.383 
+(epoch: 140, iters: 288, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.905 G_ID: 0.163 G_Rec: 0.355 D_GP: 0.060 D_real: 0.546 D_fake: 0.910 
+(epoch: 140, iters: 688, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 0.789 G_ID: 0.226 G_Rec: 0.404 D_GP: 0.023 D_real: 1.138 D_fake: 0.604 
+(epoch: 140, iters: 1088, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.565 G_ID: 0.142 G_Rec: 0.289 D_GP: 0.021 D_real: 1.171 D_fake: 0.791 
+(epoch: 140, iters: 1488, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.792 G_ID: 0.239 G_Rec: 0.417 D_GP: 0.028 D_real: 1.104 D_fake: 0.636 
+(epoch: 140, iters: 1888, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.640 G_ID: 0.147 G_Rec: 0.346 D_GP: 0.025 D_real: 1.090 D_fake: 0.761 
+(epoch: 140, iters: 2288, time: 0.064) G_GAN: 0.141 G_GAN_Feat: 0.895 G_ID: 0.222 G_Rec: 0.457 D_GP: 0.047 D_real: 0.779 D_fake: 0.859 
+(epoch: 140, iters: 2688, time: 0.064) G_GAN: -0.026 G_GAN_Feat: 0.741 G_ID: 0.152 G_Rec: 0.328 D_GP: 0.034 D_real: 0.932 D_fake: 1.026 
+(epoch: 140, iters: 3088, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.940 G_ID: 0.227 G_Rec: 0.424 D_GP: 0.035 D_real: 1.047 D_fake: 0.614 
+(epoch: 140, iters: 3488, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.879 G_ID: 0.161 G_Rec: 0.345 D_GP: 0.036 D_real: 0.974 D_fake: 0.543 
+(epoch: 140, iters: 3888, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.993 G_ID: 0.230 G_Rec: 0.404 D_GP: 0.045 D_real: 0.722 D_fake: 0.647 
+(epoch: 140, iters: 4288, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.803 G_ID: 0.164 G_Rec: 0.326 D_GP: 0.031 D_real: 0.948 D_fake: 0.800 
+(epoch: 140, iters: 4688, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 1.266 G_ID: 0.172 G_Rec: 0.461 D_GP: 0.165 D_real: 0.564 D_fake: 0.492 
+(epoch: 140, iters: 5088, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.786 G_ID: 0.134 G_Rec: 0.310 D_GP: 0.038 D_real: 1.075 D_fake: 0.654 
+(epoch: 140, iters: 5488, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.762 G_ID: 0.216 G_Rec: 0.391 D_GP: 0.024 D_real: 0.976 D_fake: 0.843 
+(epoch: 140, iters: 5888, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.735 G_ID: 0.159 G_Rec: 0.351 D_GP: 0.030 D_real: 1.056 D_fake: 0.803 
+(epoch: 140, iters: 6288, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.911 G_ID: 0.234 G_Rec: 0.397 D_GP: 0.061 D_real: 0.695 D_fake: 0.913 
+(epoch: 140, iters: 6688, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.786 G_ID: 0.192 G_Rec: 0.318 D_GP: 0.034 D_real: 1.113 D_fake: 0.841 
+(epoch: 140, iters: 7088, time: 0.064) G_GAN: 0.529 G_GAN_Feat: 1.075 G_ID: 0.223 G_Rec: 0.431 D_GP: 0.141 D_real: 0.578 D_fake: 0.475 
+(epoch: 140, iters: 7488, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.701 G_ID: 0.135 G_Rec: 0.303 D_GP: 0.027 D_real: 1.073 D_fake: 0.808 
+(epoch: 140, iters: 7888, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.999 G_ID: 0.225 G_Rec: 0.381 D_GP: 0.089 D_real: 0.947 D_fake: 0.518 
+(epoch: 140, iters: 8288, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.698 G_ID: 0.159 G_Rec: 0.324 D_GP: 0.025 D_real: 1.181 D_fake: 0.751 
+(epoch: 141, iters: 80, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 1.028 G_ID: 0.212 G_Rec: 0.439 D_GP: 0.031 D_real: 0.857 D_fake: 0.621 
+(epoch: 141, iters: 480, time: 0.064) G_GAN: 0.189 G_GAN_Feat: 0.778 G_ID: 0.178 G_Rec: 0.334 D_GP: 0.031 D_real: 0.982 D_fake: 0.811 
+(epoch: 141, iters: 880, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 0.822 G_ID: 0.213 G_Rec: 0.376 D_GP: 0.025 D_real: 1.137 D_fake: 0.521 
+(epoch: 141, iters: 1280, time: 0.064) G_GAN: 0.189 G_GAN_Feat: 0.684 G_ID: 0.158 G_Rec: 0.301 D_GP: 0.024 D_real: 1.064 D_fake: 0.812 
+(epoch: 141, iters: 1680, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.832 G_ID: 0.211 G_Rec: 0.356 D_GP: 0.030 D_real: 1.199 D_fake: 0.620 
+(epoch: 141, iters: 2080, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.844 G_ID: 0.170 G_Rec: 0.379 D_GP: 0.055 D_real: 0.768 D_fake: 0.776 
+(epoch: 141, iters: 2480, time: 0.064) G_GAN: 0.645 G_GAN_Feat: 1.002 G_ID: 0.224 G_Rec: 0.408 D_GP: 0.045 D_real: 1.097 D_fake: 0.392 
+(epoch: 141, iters: 2880, time: 0.064) G_GAN: 0.075 G_GAN_Feat: 0.689 G_ID: 0.154 G_Rec: 0.352 D_GP: 0.030 D_real: 0.915 D_fake: 0.926 
+(epoch: 141, iters: 3280, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.895 G_ID: 0.218 G_Rec: 0.434 D_GP: 0.033 D_real: 1.130 D_fake: 0.555 
+(epoch: 141, iters: 3680, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.620 G_ID: 0.152 G_Rec: 0.336 D_GP: 0.030 D_real: 1.119 D_fake: 0.804 
+(epoch: 141, iters: 4080, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.853 G_ID: 0.223 G_Rec: 0.402 D_GP: 0.053 D_real: 0.782 D_fake: 0.815 
+(epoch: 141, iters: 4480, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.866 G_ID: 0.153 G_Rec: 0.416 D_GP: 0.105 D_real: 0.519 D_fake: 0.855 
+(epoch: 141, iters: 4880, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.836 G_ID: 0.232 G_Rec: 0.371 D_GP: 0.027 D_real: 1.201 D_fake: 0.442 
+(epoch: 141, iters: 5280, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.759 G_ID: 0.136 G_Rec: 0.314 D_GP: 0.048 D_real: 0.979 D_fake: 0.779 
+(epoch: 141, iters: 5680, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 1.018 G_ID: 0.216 G_Rec: 0.442 D_GP: 0.100 D_real: 0.556 D_fake: 0.584 
+(epoch: 141, iters: 6080, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.855 G_ID: 0.147 G_Rec: 0.406 D_GP: 0.291 D_real: 0.801 D_fake: 0.833 
+(epoch: 141, iters: 6480, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.987 G_ID: 0.217 G_Rec: 0.431 D_GP: 0.042 D_real: 0.881 D_fake: 0.608 
+(epoch: 141, iters: 6880, time: 0.064) G_GAN: 0.003 G_GAN_Feat: 0.928 G_ID: 0.159 G_Rec: 0.357 D_GP: 0.131 D_real: 0.391 D_fake: 0.997 
+(epoch: 141, iters: 7280, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 0.985 G_ID: 0.247 G_Rec: 0.404 D_GP: 0.032 D_real: 0.808 D_fake: 0.690 
+(epoch: 141, iters: 7680, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.936 G_ID: 0.187 G_Rec: 0.345 D_GP: 0.065 D_real: 0.739 D_fake: 0.584 
+(epoch: 141, iters: 8080, time: 0.064) G_GAN: 0.555 G_GAN_Feat: 0.943 G_ID: 0.214 G_Rec: 0.386 D_GP: 0.028 D_real: 1.162 D_fake: 0.454 
+(epoch: 141, iters: 8480, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 0.703 G_ID: 0.147 G_Rec: 0.361 D_GP: 0.020 D_real: 1.413 D_fake: 0.509 
+(epoch: 142, iters: 272, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 1.042 G_ID: 0.189 G_Rec: 0.431 D_GP: 0.100 D_real: 0.706 D_fake: 0.705 
+(epoch: 142, iters: 672, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.809 G_ID: 0.160 G_Rec: 0.356 D_GP: 0.028 D_real: 0.901 D_fake: 0.717 
+(epoch: 142, iters: 1072, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.894 G_ID: 0.203 G_Rec: 0.374 D_GP: 0.038 D_real: 1.088 D_fake: 0.494 
+(epoch: 142, iters: 1472, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.731 G_ID: 0.152 G_Rec: 0.365 D_GP: 0.019 D_real: 1.167 D_fake: 0.718 
+(epoch: 142, iters: 1872, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.775 G_ID: 0.224 G_Rec: 0.358 D_GP: 0.027 D_real: 1.086 D_fake: 0.687 
+(epoch: 142, iters: 2272, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.734 G_ID: 0.177 G_Rec: 0.387 D_GP: 0.037 D_real: 0.929 D_fake: 0.860 
+(epoch: 142, iters: 2672, time: 0.064) G_GAN: 0.823 G_GAN_Feat: 0.892 G_ID: 0.206 G_Rec: 0.389 D_GP: 0.042 D_real: 1.342 D_fake: 0.334 
+(epoch: 142, iters: 3072, time: 0.064) G_GAN: 0.049 G_GAN_Feat: 0.823 G_ID: 0.157 G_Rec: 0.382 D_GP: 0.066 D_real: 1.018 D_fake: 0.953 
+(epoch: 142, iters: 3472, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.857 G_ID: 0.208 G_Rec: 0.429 D_GP: 0.029 D_real: 1.114 D_fake: 0.617 
+(epoch: 142, iters: 3872, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.799 G_ID: 0.153 G_Rec: 0.324 D_GP: 0.031 D_real: 1.048 D_fake: 0.797 
+(epoch: 142, iters: 4272, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.931 G_ID: 0.199 G_Rec: 0.439 D_GP: 0.042 D_real: 1.013 D_fake: 0.493 
+(epoch: 142, iters: 4672, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.686 G_ID: 0.164 G_Rec: 0.304 D_GP: 0.025 D_real: 1.100 D_fake: 0.768 
+(epoch: 142, iters: 5072, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.831 G_ID: 0.244 G_Rec: 0.387 D_GP: 0.027 D_real: 1.255 D_fake: 0.488 
+(epoch: 142, iters: 5472, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.735 G_ID: 0.122 G_Rec: 0.301 D_GP: 0.044 D_real: 0.938 D_fake: 0.858 
+(epoch: 142, iters: 5872, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 0.882 G_ID: 0.248 G_Rec: 0.414 D_GP: 0.026 D_real: 1.230 D_fake: 0.481 
+(epoch: 142, iters: 6272, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.878 G_ID: 0.155 G_Rec: 0.311 D_GP: 0.092 D_real: 0.454 D_fake: 0.937 
+(epoch: 142, iters: 6672, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.997 G_ID: 0.211 G_Rec: 0.417 D_GP: 0.077 D_real: 0.787 D_fake: 0.856 
+(epoch: 142, iters: 7072, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.631 G_ID: 0.191 G_Rec: 0.305 D_GP: 0.024 D_real: 1.139 D_fake: 0.742 
+(epoch: 142, iters: 7472, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 1.085 G_ID: 0.241 G_Rec: 0.392 D_GP: 0.083 D_real: 0.417 D_fake: 0.660 
+(epoch: 142, iters: 7872, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.860 G_ID: 0.176 G_Rec: 0.342 D_GP: 0.074 D_real: 0.816 D_fake: 0.806 
+(epoch: 142, iters: 8272, time: 0.064) G_GAN: 0.735 G_GAN_Feat: 0.860 G_ID: 0.247 G_Rec: 0.414 D_GP: 0.024 D_real: 1.431 D_fake: 0.355 
+(epoch: 143, iters: 64, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.747 G_ID: 0.175 G_Rec: 0.349 D_GP: 0.034 D_real: 0.935 D_fake: 0.841 
+(epoch: 143, iters: 464, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 1.016 G_ID: 0.222 G_Rec: 0.438 D_GP: 0.078 D_real: 0.885 D_fake: 0.850 
+(epoch: 143, iters: 864, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.665 G_ID: 0.147 G_Rec: 0.315 D_GP: 0.021 D_real: 1.265 D_fake: 0.675 
+(epoch: 143, iters: 1264, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 1.109 G_ID: 0.203 G_Rec: 0.377 D_GP: 0.048 D_real: 0.565 D_fake: 0.630 
+(epoch: 143, iters: 1664, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 1.075 G_ID: 0.159 G_Rec: 0.410 D_GP: 0.068 D_real: 1.299 D_fake: 0.546 
+(epoch: 143, iters: 2064, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.817 G_ID: 0.208 G_Rec: 0.399 D_GP: 0.021 D_real: 1.257 D_fake: 0.574 
+(epoch: 143, iters: 2464, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.908 G_ID: 0.151 G_Rec: 0.349 D_GP: 0.070 D_real: 0.741 D_fake: 0.780 
+(epoch: 143, iters: 2864, time: 0.064) G_GAN: 0.682 G_GAN_Feat: 1.008 G_ID: 0.214 G_Rec: 0.432 D_GP: 0.056 D_real: 1.047 D_fake: 0.365 
+(epoch: 143, iters: 3264, time: 0.064) G_GAN: -0.041 G_GAN_Feat: 0.802 G_ID: 0.131 G_Rec: 0.409 D_GP: 0.033 D_real: 0.696 D_fake: 1.041 
+(epoch: 143, iters: 3664, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.818 G_ID: 0.221 G_Rec: 0.392 D_GP: 0.023 D_real: 1.182 D_fake: 0.545 
+(epoch: 143, iters: 4064, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.807 G_ID: 0.151 G_Rec: 0.319 D_GP: 0.050 D_real: 0.813 D_fake: 0.764 
+(epoch: 143, iters: 4464, time: 0.064) G_GAN: 0.556 G_GAN_Feat: 0.902 G_ID: 0.212 G_Rec: 0.437 D_GP: 0.023 D_real: 1.200 D_fake: 0.448 
+(epoch: 143, iters: 4864, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.818 G_ID: 0.152 G_Rec: 0.328 D_GP: 0.052 D_real: 0.963 D_fake: 0.759 
+(epoch: 143, iters: 5264, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.939 G_ID: 0.239 G_Rec: 0.423 D_GP: 0.031 D_real: 0.860 D_fake: 0.803 
+(epoch: 143, iters: 5664, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.855 G_ID: 0.117 G_Rec: 0.334 D_GP: 0.090 D_real: 0.825 D_fake: 0.923 
+(epoch: 143, iters: 6064, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.917 G_ID: 0.216 G_Rec: 0.461 D_GP: 0.026 D_real: 1.159 D_fake: 0.703 
+(epoch: 143, iters: 6464, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.697 G_ID: 0.145 G_Rec: 0.326 D_GP: 0.024 D_real: 1.112 D_fake: 0.810 
+(epoch: 143, iters: 6864, time: 0.064) G_GAN: 0.641 G_GAN_Feat: 0.902 G_ID: 0.230 G_Rec: 0.404 D_GP: 0.040 D_real: 1.127 D_fake: 0.394 
+(epoch: 143, iters: 7264, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 1.085 G_ID: 0.134 G_Rec: 0.343 D_GP: 0.109 D_real: 0.312 D_fake: 0.850 
+(epoch: 143, iters: 7664, time: 0.064) G_GAN: 0.661 G_GAN_Feat: 0.939 G_ID: 0.198 G_Rec: 0.439 D_GP: 0.029 D_real: 1.348 D_fake: 0.345 
+(epoch: 143, iters: 8064, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.831 G_ID: 0.149 G_Rec: 0.357 D_GP: 0.058 D_real: 0.989 D_fake: 0.696 
+(epoch: 143, iters: 8464, time: 0.064) G_GAN: 0.820 G_GAN_Feat: 0.945 G_ID: 0.222 G_Rec: 0.415 D_GP: 0.043 D_real: 1.384 D_fake: 0.221 
+(epoch: 144, iters: 256, time: 0.064) G_GAN: 0.070 G_GAN_Feat: 1.022 G_ID: 0.178 G_Rec: 0.328 D_GP: 0.064 D_real: 0.293 D_fake: 0.930 
+(epoch: 144, iters: 656, time: 0.064) G_GAN: 0.648 G_GAN_Feat: 0.924 G_ID: 0.204 G_Rec: 0.422 D_GP: 0.025 D_real: 1.330 D_fake: 0.361 
+(epoch: 144, iters: 1056, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.639 G_ID: 0.136 G_Rec: 0.298 D_GP: 0.026 D_real: 1.335 D_fake: 0.594 
+(epoch: 144, iters: 1456, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.845 G_ID: 0.213 G_Rec: 0.402 D_GP: 0.037 D_real: 0.853 D_fake: 0.855 
+(epoch: 144, iters: 1856, time: 0.064) G_GAN: -0.023 G_GAN_Feat: 0.724 G_ID: 0.172 G_Rec: 0.310 D_GP: 0.045 D_real: 0.870 D_fake: 1.023 
+(epoch: 144, iters: 2256, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 0.840 G_ID: 0.192 G_Rec: 0.392 D_GP: 0.030 D_real: 1.155 D_fake: 0.576 
+(epoch: 144, iters: 2656, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.814 G_ID: 0.173 G_Rec: 0.340 D_GP: 0.038 D_real: 1.035 D_fake: 0.704 
+(epoch: 144, iters: 3056, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.949 G_ID: 0.232 G_Rec: 0.401 D_GP: 0.074 D_real: 0.746 D_fake: 0.771 
+(epoch: 144, iters: 3456, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.746 G_ID: 0.164 G_Rec: 0.327 D_GP: 0.037 D_real: 1.046 D_fake: 0.714 
+(epoch: 144, iters: 3856, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 1.013 G_ID: 0.229 G_Rec: 0.420 D_GP: 0.135 D_real: 0.831 D_fake: 0.615 
+(epoch: 144, iters: 4256, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.654 G_ID: 0.153 G_Rec: 0.320 D_GP: 0.026 D_real: 1.165 D_fake: 0.740 
+(epoch: 144, iters: 4656, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 1.095 G_ID: 0.213 G_Rec: 0.452 D_GP: 0.203 D_real: 0.293 D_fake: 0.919 
+(epoch: 144, iters: 5056, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.949 G_ID: 0.154 G_Rec: 0.359 D_GP: 0.097 D_real: 0.541 D_fake: 0.825 
+(epoch: 144, iters: 5456, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 1.064 G_ID: 0.219 G_Rec: 0.460 D_GP: 0.212 D_real: 0.523 D_fake: 0.750 
+(epoch: 144, iters: 5856, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.651 G_ID: 0.151 G_Rec: 0.289 D_GP: 0.026 D_real: 1.266 D_fake: 0.595 
+(epoch: 144, iters: 6256, time: 0.064) G_GAN: 0.804 G_GAN_Feat: 1.326 G_ID: 0.239 G_Rec: 0.466 D_GP: 0.497 D_real: 0.731 D_fake: 0.246 
+(epoch: 144, iters: 6656, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.883 G_ID: 0.156 G_Rec: 0.360 D_GP: 0.042 D_real: 0.739 D_fake: 0.673 
+(epoch: 144, iters: 7056, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 1.109 G_ID: 0.219 G_Rec: 0.410 D_GP: 0.501 D_real: 0.427 D_fake: 0.648 
+(epoch: 144, iters: 7456, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.702 G_ID: 0.151 G_Rec: 0.329 D_GP: 0.021 D_real: 1.173 D_fake: 0.692 
+(epoch: 144, iters: 7856, time: 0.064) G_GAN: 0.473 G_GAN_Feat: 1.036 G_ID: 0.189 G_Rec: 0.417 D_GP: 0.144 D_real: 0.781 D_fake: 0.533 
+(epoch: 144, iters: 8256, time: 0.064) G_GAN: 0.101 G_GAN_Feat: 0.631 G_ID: 0.177 G_Rec: 0.288 D_GP: 0.024 D_real: 1.019 D_fake: 0.899 
+(epoch: 145, iters: 48, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.957 G_ID: 0.193 G_Rec: 0.435 D_GP: 0.047 D_real: 1.067 D_fake: 0.669 
+(epoch: 145, iters: 448, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.614 G_ID: 0.161 G_Rec: 0.418 D_GP: 0.021 D_real: 1.177 D_fake: 0.735 
+(epoch: 145, iters: 848, time: 0.064) G_GAN: 0.601 G_GAN_Feat: 0.787 G_ID: 0.231 G_Rec: 0.390 D_GP: 0.022 D_real: 1.272 D_fake: 0.422 
+(epoch: 145, iters: 1248, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.737 G_ID: 0.170 G_Rec: 0.325 D_GP: 0.080 D_real: 0.975 D_fake: 0.730 
+(epoch: 145, iters: 1648, time: 0.064) G_GAN: 0.676 G_GAN_Feat: 1.044 G_ID: 0.213 G_Rec: 0.426 D_GP: 0.070 D_real: 0.969 D_fake: 0.366 
+(epoch: 145, iters: 2048, time: 0.064) G_GAN: 0.561 G_GAN_Feat: 0.906 G_ID: 0.137 G_Rec: 0.337 D_GP: 0.059 D_real: 1.327 D_fake: 0.448 
+(epoch: 145, iters: 2448, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.877 G_ID: 0.192 G_Rec: 0.400 D_GP: 0.031 D_real: 1.080 D_fake: 0.553 
+(epoch: 145, iters: 2848, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.967 G_ID: 0.176 G_Rec: 0.357 D_GP: 0.105 D_real: 0.629 D_fake: 0.693 
+(epoch: 145, iters: 3248, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.895 G_ID: 0.197 G_Rec: 0.416 D_GP: 0.032 D_real: 0.929 D_fake: 0.623 
+(epoch: 145, iters: 3648, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.812 G_ID: 0.139 G_Rec: 0.379 D_GP: 0.033 D_real: 1.088 D_fake: 0.680 
+(epoch: 145, iters: 4048, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.806 G_ID: 0.202 G_Rec: 0.377 D_GP: 0.025 D_real: 0.963 D_fake: 0.808 
+(epoch: 145, iters: 4448, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.749 G_ID: 0.146 G_Rec: 0.339 D_GP: 0.044 D_real: 0.882 D_fake: 0.901 
+(epoch: 145, iters: 4848, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.919 G_ID: 0.218 G_Rec: 0.415 D_GP: 0.030 D_real: 1.050 D_fake: 0.627 
+(epoch: 145, iters: 5248, time: 0.064) G_GAN: 0.063 G_GAN_Feat: 0.750 G_ID: 0.144 G_Rec: 0.323 D_GP: 0.032 D_real: 0.874 D_fake: 0.937 
+(epoch: 145, iters: 5648, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 0.946 G_ID: 0.206 G_Rec: 0.392 D_GP: 0.055 D_real: 1.223 D_fake: 0.442 
+(epoch: 145, iters: 6048, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.880 G_ID: 0.143 G_Rec: 0.324 D_GP: 0.039 D_real: 0.770 D_fake: 0.807 
+(epoch: 145, iters: 6448, time: 0.064) G_GAN: 0.665 G_GAN_Feat: 1.094 G_ID: 0.215 G_Rec: 0.425 D_GP: 0.113 D_real: 0.958 D_fake: 0.414 
+(epoch: 145, iters: 6848, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.796 G_ID: 0.145 G_Rec: 0.330 D_GP: 0.027 D_real: 1.155 D_fake: 0.685 
+(epoch: 145, iters: 7248, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.826 G_ID: 0.203 G_Rec: 0.410 D_GP: 0.022 D_real: 0.997 D_fake: 0.747 
+(epoch: 145, iters: 7648, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.622 G_ID: 0.152 G_Rec: 0.321 D_GP: 0.027 D_real: 1.032 D_fake: 0.862 
+(epoch: 145, iters: 8048, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.845 G_ID: 0.191 G_Rec: 0.394 D_GP: 0.036 D_real: 0.898 D_fake: 0.757 
+(epoch: 145, iters: 8448, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.854 G_ID: 0.181 G_Rec: 0.344 D_GP: 0.037 D_real: 0.869 D_fake: 0.747 
+(epoch: 146, iters: 240, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 0.853 G_ID: 0.185 G_Rec: 0.423 D_GP: 0.022 D_real: 1.189 D_fake: 0.454 
+(epoch: 146, iters: 640, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.758 G_ID: 0.158 G_Rec: 0.333 D_GP: 0.051 D_real: 1.076 D_fake: 0.670 
+(epoch: 146, iters: 1040, time: 0.064) G_GAN: 0.666 G_GAN_Feat: 1.046 G_ID: 0.217 G_Rec: 0.430 D_GP: 0.108 D_real: 0.980 D_fake: 0.345 
+(epoch: 146, iters: 1440, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.873 G_ID: 0.129 G_Rec: 0.323 D_GP: 0.055 D_real: 0.760 D_fake: 0.618 
+(epoch: 146, iters: 1840, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 0.832 G_ID: 0.214 G_Rec: 0.390 D_GP: 0.027 D_real: 1.285 D_fake: 0.483 
+(epoch: 146, iters: 2240, time: 0.064) G_GAN: 0.038 G_GAN_Feat: 0.911 G_ID: 0.163 G_Rec: 0.337 D_GP: 0.096 D_real: 0.499 D_fake: 0.963 
+(epoch: 146, iters: 2640, time: 0.064) G_GAN: 0.669 G_GAN_Feat: 1.052 G_ID: 0.197 G_Rec: 0.401 D_GP: 0.031 D_real: 1.038 D_fake: 0.362 
+(epoch: 146, iters: 3040, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 1.187 G_ID: 0.145 G_Rec: 0.422 D_GP: 1.129 D_real: 0.859 D_fake: 0.876 
+(epoch: 146, iters: 3440, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.862 G_ID: 0.236 G_Rec: 0.419 D_GP: 0.026 D_real: 1.088 D_fake: 0.713 
+(epoch: 146, iters: 3840, time: 0.064) G_GAN: 0.014 G_GAN_Feat: 0.752 G_ID: 0.169 G_Rec: 0.354 D_GP: 0.038 D_real: 0.792 D_fake: 0.986 
+(epoch: 146, iters: 4240, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.966 G_ID: 0.208 G_Rec: 0.410 D_GP: 0.053 D_real: 0.955 D_fake: 0.605 
+(epoch: 146, iters: 4640, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.715 G_ID: 0.140 G_Rec: 0.294 D_GP: 0.033 D_real: 1.131 D_fake: 0.737 
+(epoch: 146, iters: 5040, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 0.930 G_ID: 0.265 G_Rec: 0.415 D_GP: 0.033 D_real: 0.869 D_fake: 0.708 
+(epoch: 146, iters: 5440, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.828 G_ID: 0.153 G_Rec: 0.315 D_GP: 0.027 D_real: 1.078 D_fake: 0.697 
+(epoch: 146, iters: 5840, time: 0.064) G_GAN: 0.711 G_GAN_Feat: 0.846 G_ID: 0.206 G_Rec: 0.396 D_GP: 0.025 D_real: 1.345 D_fake: 0.302 
+(epoch: 146, iters: 6240, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.671 G_ID: 0.152 G_Rec: 0.322 D_GP: 0.023 D_real: 1.231 D_fake: 0.685 
+(epoch: 146, iters: 6640, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.993 G_ID: 0.207 G_Rec: 0.418 D_GP: 0.124 D_real: 0.830 D_fake: 0.491 
+(epoch: 146, iters: 7040, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.786 G_ID: 0.128 G_Rec: 0.349 D_GP: 0.036 D_real: 1.019 D_fake: 0.841 
+(epoch: 146, iters: 7440, time: 0.064) G_GAN: 0.010 G_GAN_Feat: 1.240 G_ID: 0.223 G_Rec: 0.411 D_GP: 0.057 D_real: 1.244 D_fake: 0.991 
+(epoch: 146, iters: 7840, time: 0.064) G_GAN: -0.070 G_GAN_Feat: 0.650 G_ID: 0.173 G_Rec: 0.338 D_GP: 0.020 D_real: 0.894 D_fake: 1.070 
+(epoch: 146, iters: 8240, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.957 G_ID: 0.224 G_Rec: 0.425 D_GP: 0.069 D_real: 0.712 D_fake: 0.772 
+(epoch: 147, iters: 32, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.680 G_ID: 0.128 G_Rec: 0.337 D_GP: 0.024 D_real: 1.338 D_fake: 0.600 
+(epoch: 147, iters: 432, time: 0.064) G_GAN: 0.792 G_GAN_Feat: 1.117 G_ID: 0.217 G_Rec: 0.436 D_GP: 0.095 D_real: 0.850 D_fake: 0.263 
+(epoch: 147, iters: 832, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.743 G_ID: 0.145 G_Rec: 0.352 D_GP: 0.028 D_real: 1.208 D_fake: 0.721 
+(epoch: 147, iters: 1232, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.824 G_ID: 0.218 G_Rec: 0.376 D_GP: 0.027 D_real: 1.030 D_fake: 0.750 
+(epoch: 147, iters: 1632, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.869 G_ID: 0.171 G_Rec: 0.320 D_GP: 0.045 D_real: 0.688 D_fake: 0.846 
+(epoch: 147, iters: 2032, time: 0.064) G_GAN: 0.204 G_GAN_Feat: 1.225 G_ID: 0.226 G_Rec: 0.467 D_GP: 0.851 D_real: 0.573 D_fake: 0.825 
+(epoch: 147, iters: 2432, time: 0.064) G_GAN: 0.089 G_GAN_Feat: 0.727 G_ID: 0.146 G_Rec: 0.302 D_GP: 0.037 D_real: 0.941 D_fake: 0.911 
+(epoch: 147, iters: 2832, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.760 G_ID: 0.203 G_Rec: 0.399 D_GP: 0.021 D_real: 1.260 D_fake: 0.526 
+(epoch: 147, iters: 3232, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.642 G_ID: 0.146 G_Rec: 0.301 D_GP: 0.023 D_real: 1.053 D_fake: 0.887 
+(epoch: 147, iters: 3632, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.795 G_ID: 0.216 G_Rec: 0.407 D_GP: 0.032 D_real: 0.919 D_fake: 0.734 
+(epoch: 147, iters: 4032, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.742 G_ID: 0.154 G_Rec: 0.328 D_GP: 0.081 D_real: 0.894 D_fake: 0.918 
+(epoch: 147, iters: 4432, time: 0.064) G_GAN: 0.643 G_GAN_Feat: 0.847 G_ID: 0.223 G_Rec: 0.386 D_GP: 0.026 D_real: 1.351 D_fake: 0.362 
+(epoch: 147, iters: 4832, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.824 G_ID: 0.182 G_Rec: 0.343 D_GP: 0.047 D_real: 0.936 D_fake: 0.700 
+(epoch: 147, iters: 5232, time: 0.064) G_GAN: 0.595 G_GAN_Feat: 0.861 G_ID: 0.210 G_Rec: 0.391 D_GP: 0.027 D_real: 1.307 D_fake: 0.425 
+(epoch: 147, iters: 5632, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.793 G_ID: 0.167 G_Rec: 0.331 D_GP: 0.044 D_real: 0.885 D_fake: 0.831 
+(epoch: 147, iters: 6032, time: 0.064) G_GAN: 0.617 G_GAN_Feat: 0.851 G_ID: 0.197 G_Rec: 0.415 D_GP: 0.026 D_real: 1.357 D_fake: 0.413 
+(epoch: 147, iters: 6432, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 0.830 G_ID: 0.138 G_Rec: 0.310 D_GP: 0.131 D_real: 0.447 D_fake: 0.936 
+(epoch: 147, iters: 6832, time: 0.064) G_GAN: 0.557 G_GAN_Feat: 0.882 G_ID: 0.186 G_Rec: 0.456 D_GP: 0.022 D_real: 1.192 D_fake: 0.454 
+(epoch: 147, iters: 7232, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.602 G_ID: 0.154 G_Rec: 0.299 D_GP: 0.022 D_real: 1.221 D_fake: 0.701 
+(epoch: 147, iters: 7632, time: 0.064) G_GAN: 0.707 G_GAN_Feat: 0.810 G_ID: 0.216 G_Rec: 0.358 D_GP: 0.028 D_real: 1.385 D_fake: 0.336 
+(epoch: 147, iters: 8032, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.807 G_ID: 0.163 G_Rec: 0.332 D_GP: 0.059 D_real: 0.822 D_fake: 0.748 
+(epoch: 147, iters: 8432, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.801 G_ID: 0.244 G_Rec: 0.426 D_GP: 0.031 D_real: 0.880 D_fake: 0.797 
+(epoch: 148, iters: 224, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.654 G_ID: 0.132 G_Rec: 0.296 D_GP: 0.027 D_real: 1.160 D_fake: 0.756 
+(epoch: 148, iters: 624, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.900 G_ID: 0.237 G_Rec: 0.395 D_GP: 0.028 D_real: 1.196 D_fake: 0.532 
+(epoch: 148, iters: 1024, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.707 G_ID: 0.146 G_Rec: 0.332 D_GP: 0.025 D_real: 1.089 D_fake: 0.808 
+(epoch: 148, iters: 1424, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.974 G_ID: 0.206 G_Rec: 0.407 D_GP: 0.044 D_real: 0.804 D_fake: 0.645 
+(epoch: 148, iters: 1824, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.836 G_ID: 0.163 G_Rec: 0.353 D_GP: 0.032 D_real: 1.149 D_fake: 0.696 
+(epoch: 148, iters: 2224, time: 0.064) G_GAN: 0.684 G_GAN_Feat: 1.097 G_ID: 0.212 G_Rec: 0.436 D_GP: 0.091 D_real: 0.733 D_fake: 0.359 
+(epoch: 148, iters: 2624, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.750 G_ID: 0.157 G_Rec: 0.356 D_GP: 0.031 D_real: 0.998 D_fake: 0.829 
+(epoch: 148, iters: 3024, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.897 G_ID: 0.206 G_Rec: 0.410 D_GP: 0.060 D_real: 0.668 D_fake: 0.828 
+(epoch: 148, iters: 3424, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.751 G_ID: 0.133 G_Rec: 0.313 D_GP: 0.096 D_real: 0.862 D_fake: 0.796 
+(epoch: 148, iters: 3824, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.985 G_ID: 0.225 G_Rec: 0.382 D_GP: 0.058 D_real: 1.019 D_fake: 0.408 
+(epoch: 148, iters: 4224, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.763 G_ID: 0.159 G_Rec: 0.321 D_GP: 0.026 D_real: 1.154 D_fake: 0.718 
+(epoch: 148, iters: 4624, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.861 G_ID: 0.249 G_Rec: 0.394 D_GP: 0.027 D_real: 1.001 D_fake: 0.676 
+(epoch: 148, iters: 5024, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.878 G_ID: 0.138 G_Rec: 0.329 D_GP: 0.060 D_real: 0.704 D_fake: 0.829 
+(epoch: 148, iters: 5424, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 1.043 G_ID: 0.219 G_Rec: 0.416 D_GP: 0.054 D_real: 0.676 D_fake: 0.659 
+(epoch: 148, iters: 5824, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 0.835 G_ID: 0.145 G_Rec: 0.315 D_GP: 0.073 D_real: 1.027 D_fake: 0.493 
+(epoch: 148, iters: 6224, time: 0.064) G_GAN: 0.499 G_GAN_Feat: 0.966 G_ID: 0.207 G_Rec: 0.414 D_GP: 0.055 D_real: 0.887 D_fake: 0.507 
+(epoch: 148, iters: 6624, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.752 G_ID: 0.126 G_Rec: 0.342 D_GP: 0.028 D_real: 0.938 D_fake: 0.918 
+(epoch: 148, iters: 7024, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.881 G_ID: 0.228 G_Rec: 0.423 D_GP: 0.024 D_real: 1.043 D_fake: 0.618 
+(epoch: 148, iters: 7424, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.715 G_ID: 0.140 G_Rec: 0.342 D_GP: 0.035 D_real: 1.059 D_fake: 0.709 
+(epoch: 148, iters: 7824, time: 0.064) G_GAN: 0.588 G_GAN_Feat: 0.887 G_ID: 0.181 G_Rec: 0.412 D_GP: 0.027 D_real: 1.220 D_fake: 0.427 
+(epoch: 148, iters: 8224, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.739 G_ID: 0.154 G_Rec: 0.340 D_GP: 0.025 D_real: 1.060 D_fake: 0.782 
+(epoch: 149, iters: 16, time: 0.064) G_GAN: 0.703 G_GAN_Feat: 0.783 G_ID: 0.235 G_Rec: 0.402 D_GP: 0.026 D_real: 1.415 D_fake: 0.364 
+(epoch: 149, iters: 416, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.688 G_ID: 0.154 G_Rec: 0.328 D_GP: 0.020 D_real: 1.184 D_fake: 0.728 
+(epoch: 149, iters: 816, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.941 G_ID: 0.192 G_Rec: 0.444 D_GP: 0.025 D_real: 0.868 D_fake: 0.668 
+(epoch: 149, iters: 1216, time: 0.064) G_GAN: 0.005 G_GAN_Feat: 0.843 G_ID: 0.133 G_Rec: 0.361 D_GP: 0.098 D_real: 0.481 D_fake: 0.995 
+(epoch: 149, iters: 1616, time: 0.064) G_GAN: 0.530 G_GAN_Feat: 0.862 G_ID: 0.211 G_Rec: 0.392 D_GP: 0.026 D_real: 1.154 D_fake: 0.479 
+(epoch: 149, iters: 2016, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.935 G_ID: 0.136 G_Rec: 0.366 D_GP: 0.198 D_real: 0.477 D_fake: 0.794 
+(epoch: 149, iters: 2416, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.900 G_ID: 0.216 G_Rec: 0.384 D_GP: 0.058 D_real: 0.920 D_fake: 0.547 
+(epoch: 149, iters: 2816, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 0.732 G_ID: 0.148 G_Rec: 0.298 D_GP: 0.027 D_real: 1.152 D_fake: 0.689 
+(epoch: 149, iters: 3216, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 0.798 G_ID: 0.210 G_Rec: 0.346 D_GP: 0.028 D_real: 1.077 D_fake: 0.624 
+(epoch: 149, iters: 3616, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.967 G_ID: 0.149 G_Rec: 0.415 D_GP: 0.063 D_real: 0.768 D_fake: 0.764 
+(epoch: 149, iters: 4016, time: 0.064) G_GAN: 1.019 G_GAN_Feat: 1.046 G_ID: 0.213 G_Rec: 0.421 D_GP: 0.062 D_real: 1.202 D_fake: 0.127 
+(epoch: 149, iters: 4416, time: 0.064) G_GAN: 0.496 G_GAN_Feat: 0.974 G_ID: 0.162 G_Rec: 0.375 D_GP: 0.085 D_real: 0.555 D_fake: 0.545 
+(epoch: 149, iters: 4816, time: 0.064) G_GAN: 0.622 G_GAN_Feat: 0.863 G_ID: 0.188 G_Rec: 0.420 D_GP: 0.025 D_real: 1.388 D_fake: 0.402 
+(epoch: 149, iters: 5216, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.879 G_ID: 0.158 G_Rec: 0.353 D_GP: 0.042 D_real: 1.013 D_fake: 0.740 
+(epoch: 149, iters: 5616, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 1.059 G_ID: 0.223 G_Rec: 0.423 D_GP: 0.063 D_real: 0.362 D_fake: 0.905 
+(epoch: 149, iters: 6016, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.825 G_ID: 0.150 G_Rec: 0.377 D_GP: 0.055 D_real: 0.994 D_fake: 0.732 
+(epoch: 149, iters: 6416, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.879 G_ID: 0.235 G_Rec: 0.450 D_GP: 0.025 D_real: 1.147 D_fake: 0.562 
+(epoch: 149, iters: 6816, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.849 G_ID: 0.151 G_Rec: 0.404 D_GP: 0.031 D_real: 1.053 D_fake: 0.658 
+(epoch: 149, iters: 7216, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 0.902 G_ID: 0.187 G_Rec: 0.383 D_GP: 0.028 D_real: 1.156 D_fake: 0.420 
+(epoch: 149, iters: 7616, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.838 G_ID: 0.143 G_Rec: 0.366 D_GP: 0.061 D_real: 0.760 D_fake: 0.873 
+(epoch: 149, iters: 8016, time: 0.064) G_GAN: 0.603 G_GAN_Feat: 0.864 G_ID: 0.212 G_Rec: 0.425 D_GP: 0.025 D_real: 1.375 D_fake: 0.414 
+(epoch: 149, iters: 8416, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.678 G_ID: 0.144 G_Rec: 0.332 D_GP: 0.021 D_real: 1.037 D_fake: 0.841 
+(epoch: 150, iters: 208, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.764 G_ID: 0.203 G_Rec: 0.378 D_GP: 0.029 D_real: 1.069 D_fake: 0.713 
+(epoch: 150, iters: 608, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.645 G_ID: 0.169 G_Rec: 0.362 D_GP: 0.021 D_real: 1.127 D_fake: 0.836 
+(epoch: 150, iters: 1008, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.909 G_ID: 0.216 G_Rec: 0.415 D_GP: 0.065 D_real: 0.896 D_fake: 0.692 
+(epoch: 150, iters: 1408, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.739 G_ID: 0.130 G_Rec: 0.331 D_GP: 0.033 D_real: 1.045 D_fake: 0.838 
+(epoch: 150, iters: 1808, time: 0.064) G_GAN: 0.680 G_GAN_Feat: 1.296 G_ID: 0.187 G_Rec: 0.429 D_GP: 0.589 D_real: 0.471 D_fake: 0.381 
+(epoch: 150, iters: 2208, time: 0.064) G_GAN: -0.039 G_GAN_Feat: 0.916 G_ID: 0.144 G_Rec: 0.351 D_GP: 0.029 D_real: 0.641 D_fake: 1.039 
+(epoch: 150, iters: 2608, time: 0.064) G_GAN: 0.545 G_GAN_Feat: 0.924 G_ID: 0.214 G_Rec: 0.385 D_GP: 0.032 D_real: 1.149 D_fake: 0.474 
+(epoch: 150, iters: 3008, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.685 G_ID: 0.143 G_Rec: 0.343 D_GP: 0.023 D_real: 1.261 D_fake: 0.646 
+(epoch: 150, iters: 3408, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.840 G_ID: 0.227 G_Rec: 0.417 D_GP: 0.031 D_real: 1.456 D_fake: 0.410 
+(epoch: 150, iters: 3808, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.674 G_ID: 0.136 G_Rec: 0.344 D_GP: 0.028 D_real: 1.061 D_fake: 0.779 
+(epoch: 150, iters: 4208, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.897 G_ID: 0.198 G_Rec: 0.406 D_GP: 0.044 D_real: 1.115 D_fake: 0.478 
+(epoch: 150, iters: 4608, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.876 G_ID: 0.138 G_Rec: 0.331 D_GP: 0.221 D_real: 0.881 D_fake: 0.627 
+(epoch: 150, iters: 5008, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.975 G_ID: 0.200 G_Rec: 0.395 D_GP: 0.056 D_real: 0.731 D_fake: 0.791 
+(epoch: 150, iters: 5408, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.692 G_ID: 0.154 G_Rec: 0.320 D_GP: 0.035 D_real: 1.189 D_fake: 0.713 
+(epoch: 150, iters: 5808, time: 0.064) G_GAN: 0.724 G_GAN_Feat: 1.092 G_ID: 0.192 G_Rec: 0.412 D_GP: 0.048 D_real: 0.846 D_fake: 0.318 
+(epoch: 150, iters: 6208, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.911 G_ID: 0.137 G_Rec: 0.332 D_GP: 0.033 D_real: 0.928 D_fake: 0.675 
+(epoch: 150, iters: 6608, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 1.071 G_ID: 0.209 G_Rec: 0.409 D_GP: 0.049 D_real: 0.630 D_fake: 0.573 
+(epoch: 150, iters: 7008, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.673 G_ID: 0.132 G_Rec: 0.327 D_GP: 0.024 D_real: 1.087 D_fake: 0.878 
+(epoch: 150, iters: 7408, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 1.043 G_ID: 0.199 G_Rec: 0.420 D_GP: 0.185 D_real: 0.853 D_fake: 0.487 
+(epoch: 150, iters: 7808, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 1.065 G_ID: 0.161 G_Rec: 0.391 D_GP: 0.201 D_real: 0.364 D_fake: 0.889 
+(epoch: 150, iters: 8208, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 1.018 G_ID: 0.172 G_Rec: 0.436 D_GP: 0.024 D_real: 0.848 D_fake: 0.778 
+(epoch: 150, iters: 8608, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.854 G_ID: 0.148 G_Rec: 0.325 D_GP: 0.038 D_real: 1.112 D_fake: 0.831 
+(epoch: 151, iters: 400, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.817 G_ID: 0.169 G_Rec: 0.373 D_GP: 0.024 D_real: 1.196 D_fake: 0.539 
+(epoch: 151, iters: 800, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.762 G_ID: 0.173 G_Rec: 0.372 D_GP: 0.026 D_real: 0.957 D_fake: 0.885 
+(epoch: 151, iters: 1200, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 1.035 G_ID: 0.227 G_Rec: 0.401 D_GP: 0.087 D_real: 0.465 D_fake: 0.621 
+(epoch: 151, iters: 1600, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.812 G_ID: 0.128 G_Rec: 0.343 D_GP: 0.039 D_real: 1.103 D_fake: 0.688 
+(epoch: 151, iters: 2000, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.806 G_ID: 0.186 G_Rec: 0.389 D_GP: 0.025 D_real: 1.259 D_fake: 0.602 
+(epoch: 151, iters: 2400, time: 0.064) G_GAN: -0.120 G_GAN_Feat: 0.876 G_ID: 0.147 G_Rec: 0.313 D_GP: 0.049 D_real: 0.380 D_fake: 1.120 
+(epoch: 151, iters: 2800, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.898 G_ID: 0.208 G_Rec: 0.406 D_GP: 0.037 D_real: 1.251 D_fake: 0.461 
+(epoch: 151, iters: 3200, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.759 G_ID: 0.150 G_Rec: 0.332 D_GP: 0.035 D_real: 1.087 D_fake: 0.731 
+(epoch: 151, iters: 3600, time: 0.064) G_GAN: 0.663 G_GAN_Feat: 0.910 G_ID: 0.188 G_Rec: 0.397 D_GP: 0.038 D_real: 1.130 D_fake: 0.354 
+(epoch: 151, iters: 4000, time: 0.064) G_GAN: 0.015 G_GAN_Feat: 1.077 G_ID: 0.159 G_Rec: 0.353 D_GP: 0.299 D_real: 0.226 D_fake: 0.985 
+(epoch: 151, iters: 4400, time: 0.064) G_GAN: 0.637 G_GAN_Feat: 1.154 G_ID: 0.200 G_Rec: 0.507 D_GP: 0.132 D_real: 1.082 D_fake: 0.397 
+(epoch: 151, iters: 4800, time: 0.064) G_GAN: -0.154 G_GAN_Feat: 0.822 G_ID: 0.162 G_Rec: 0.366 D_GP: 0.076 D_real: 0.446 D_fake: 1.154 
+(epoch: 151, iters: 5200, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 1.012 G_ID: 0.252 G_Rec: 0.442 D_GP: 0.043 D_real: 0.799 D_fake: 0.803 
+(epoch: 151, iters: 5600, time: 0.064) G_GAN: -0.118 G_GAN_Feat: 0.742 G_ID: 0.144 G_Rec: 0.336 D_GP: 0.040 D_real: 0.762 D_fake: 1.118 
+(epoch: 151, iters: 6000, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.819 G_ID: 0.194 G_Rec: 0.419 D_GP: 0.024 D_real: 1.200 D_fake: 0.529 
+(epoch: 151, iters: 6400, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.703 G_ID: 0.160 G_Rec: 0.327 D_GP: 0.036 D_real: 1.093 D_fake: 0.761 
+(epoch: 151, iters: 6800, time: 0.064) G_GAN: 0.469 G_GAN_Feat: 0.867 G_ID: 0.200 G_Rec: 0.395 D_GP: 0.035 D_real: 1.136 D_fake: 0.533 
+(epoch: 151, iters: 7200, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.708 G_ID: 0.132 G_Rec: 0.324 D_GP: 0.025 D_real: 1.014 D_fake: 0.871 
+(epoch: 151, iters: 7600, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.837 G_ID: 0.181 G_Rec: 0.391 D_GP: 0.033 D_real: 1.183 D_fake: 0.543 
+(epoch: 151, iters: 8000, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.727 G_ID: 0.150 G_Rec: 0.318 D_GP: 0.035 D_real: 0.896 D_fake: 0.856 
+(epoch: 151, iters: 8400, time: 0.064) G_GAN: 0.535 G_GAN_Feat: 1.021 G_ID: 0.212 G_Rec: 0.420 D_GP: 0.053 D_real: 0.845 D_fake: 0.469 
+(epoch: 152, iters: 192, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 1.190 G_ID: 0.145 G_Rec: 0.347 D_GP: 0.189 D_real: 0.698 D_fake: 0.662 
+(epoch: 152, iters: 592, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.963 G_ID: 0.212 G_Rec: 0.378 D_GP: 0.041 D_real: 1.100 D_fake: 0.496 
+(epoch: 152, iters: 992, time: 0.064) G_GAN: -0.044 G_GAN_Feat: 0.722 G_ID: 0.133 G_Rec: 0.342 D_GP: 0.021 D_real: 0.849 D_fake: 1.044 
+(epoch: 152, iters: 1392, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 1.248 G_ID: 0.190 G_Rec: 0.459 D_GP: 0.140 D_real: 0.376 D_fake: 0.871 
+(epoch: 152, iters: 1792, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.719 G_ID: 0.145 G_Rec: 0.330 D_GP: 0.026 D_real: 1.245 D_fake: 0.622 
+(epoch: 152, iters: 2192, time: 0.064) G_GAN: 0.730 G_GAN_Feat: 0.953 G_ID: 0.200 G_Rec: 0.408 D_GP: 0.030 D_real: 1.307 D_fake: 0.333 
+(epoch: 152, iters: 2592, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.838 G_ID: 0.161 G_Rec: 0.367 D_GP: 0.031 D_real: 0.930 D_fake: 0.605 
+(epoch: 152, iters: 2992, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.749 G_ID: 0.215 G_Rec: 0.359 D_GP: 0.037 D_real: 1.196 D_fake: 0.619 
+(epoch: 152, iters: 3392, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.732 G_ID: 0.135 G_Rec: 0.314 D_GP: 0.041 D_real: 0.985 D_fake: 0.879 
+(epoch: 152, iters: 3792, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.849 G_ID: 0.211 G_Rec: 0.391 D_GP: 0.045 D_real: 1.121 D_fake: 0.561 
+(epoch: 152, iters: 4192, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.896 G_ID: 0.146 G_Rec: 0.354 D_GP: 0.106 D_real: 0.788 D_fake: 0.827 
+(epoch: 152, iters: 4592, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.900 G_ID: 0.201 G_Rec: 0.420 D_GP: 0.030 D_real: 1.236 D_fake: 0.509 
+(epoch: 152, iters: 4992, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.758 G_ID: 0.172 G_Rec: 0.324 D_GP: 0.027 D_real: 1.080 D_fake: 0.794 
+(epoch: 152, iters: 5392, time: 0.064) G_GAN: 0.499 G_GAN_Feat: 0.954 G_ID: 0.188 G_Rec: 0.390 D_GP: 0.042 D_real: 1.064 D_fake: 0.511 
+(epoch: 152, iters: 5792, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.691 G_ID: 0.160 G_Rec: 0.308 D_GP: 0.022 D_real: 1.181 D_fake: 0.674 
+(epoch: 152, iters: 6192, time: 0.064) G_GAN: 0.540 G_GAN_Feat: 1.061 G_ID: 0.218 G_Rec: 0.403 D_GP: 0.092 D_real: 1.075 D_fake: 0.556 
+(epoch: 152, iters: 6592, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.648 G_ID: 0.152 G_Rec: 0.334 D_GP: 0.020 D_real: 1.377 D_fake: 0.613 
+(epoch: 152, iters: 6992, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.838 G_ID: 0.200 G_Rec: 0.407 D_GP: 0.020 D_real: 1.181 D_fake: 0.538 
+(epoch: 152, iters: 7392, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.749 G_ID: 0.186 G_Rec: 0.381 D_GP: 0.028 D_real: 0.936 D_fake: 0.881 
+(epoch: 152, iters: 7792, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.834 G_ID: 0.235 G_Rec: 0.402 D_GP: 0.036 D_real: 0.883 D_fake: 0.782 
+(epoch: 152, iters: 8192, time: 0.064) G_GAN: 0.063 G_GAN_Feat: 0.634 G_ID: 0.142 G_Rec: 0.317 D_GP: 0.026 D_real: 0.978 D_fake: 0.937 
+(epoch: 152, iters: 8592, time: 0.064) G_GAN: 0.558 G_GAN_Feat: 0.985 G_ID: 0.197 G_Rec: 0.393 D_GP: 0.089 D_real: 0.898 D_fake: 0.460 
+(epoch: 153, iters: 384, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.972 G_ID: 0.139 G_Rec: 0.324 D_GP: 0.064 D_real: 0.507 D_fake: 0.775 
+(epoch: 153, iters: 784, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.805 G_ID: 0.221 G_Rec: 0.357 D_GP: 0.031 D_real: 1.110 D_fake: 0.734 
+(epoch: 153, iters: 1184, time: 0.064) G_GAN: -0.074 G_GAN_Feat: 0.875 G_ID: 0.157 G_Rec: 0.355 D_GP: 0.050 D_real: 0.647 D_fake: 1.074 
+(epoch: 153, iters: 1584, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 1.022 G_ID: 0.202 G_Rec: 0.433 D_GP: 0.041 D_real: 0.894 D_fake: 0.499 
+(epoch: 153, iters: 1984, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.703 G_ID: 0.160 G_Rec: 0.316 D_GP: 0.025 D_real: 0.967 D_fake: 0.968 
+(epoch: 153, iters: 2384, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 0.923 G_ID: 0.221 G_Rec: 0.402 D_GP: 0.031 D_real: 1.109 D_fake: 0.500 
+(epoch: 153, iters: 2784, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.946 G_ID: 0.161 G_Rec: 0.346 D_GP: 0.198 D_real: 0.399 D_fake: 0.884 
+(epoch: 153, iters: 3184, time: 0.064) G_GAN: 0.480 G_GAN_Feat: 0.847 G_ID: 0.181 G_Rec: 0.352 D_GP: 0.030 D_real: 1.187 D_fake: 0.533 
+(epoch: 153, iters: 3584, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.951 G_ID: 0.177 G_Rec: 0.361 D_GP: 0.274 D_real: 0.389 D_fake: 0.771 
+(epoch: 153, iters: 3984, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.884 G_ID: 0.208 G_Rec: 0.377 D_GP: 0.027 D_real: 1.107 D_fake: 0.666 
+(epoch: 153, iters: 4384, time: 0.064) G_GAN: 0.499 G_GAN_Feat: 0.917 G_ID: 0.139 G_Rec: 0.332 D_GP: 0.042 D_real: 0.688 D_fake: 0.506 
+(epoch: 153, iters: 4784, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 1.066 G_ID: 0.199 G_Rec: 0.423 D_GP: 0.047 D_real: 0.345 D_fake: 0.798 
+(epoch: 153, iters: 5184, time: 0.064) G_GAN: 0.030 G_GAN_Feat: 0.685 G_ID: 0.171 G_Rec: 0.325 D_GP: 0.022 D_real: 1.024 D_fake: 0.970 
+(epoch: 153, iters: 5584, time: 0.064) G_GAN: 0.096 G_GAN_Feat: 1.005 G_ID: 0.236 G_Rec: 0.425 D_GP: 0.040 D_real: 0.537 D_fake: 0.904 
+(epoch: 153, iters: 5984, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.726 G_ID: 0.135 G_Rec: 0.333 D_GP: 0.025 D_real: 1.226 D_fake: 0.600 
+(epoch: 153, iters: 6384, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.840 G_ID: 0.188 G_Rec: 0.473 D_GP: 0.021 D_real: 1.055 D_fake: 0.675 
+(epoch: 153, iters: 6784, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.819 G_ID: 0.157 G_Rec: 0.328 D_GP: 0.026 D_real: 1.101 D_fake: 0.657 
+(epoch: 153, iters: 7184, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.865 G_ID: 0.180 G_Rec: 0.407 D_GP: 0.021 D_real: 1.310 D_fake: 0.496 
+(epoch: 153, iters: 7584, time: 0.064) G_GAN: 0.667 G_GAN_Feat: 1.064 G_ID: 0.148 G_Rec: 0.343 D_GP: 0.630 D_real: 0.558 D_fake: 0.568 
+(epoch: 153, iters: 7984, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.869 G_ID: 0.205 G_Rec: 0.419 D_GP: 0.021 D_real: 0.821 D_fake: 0.885 
+(epoch: 153, iters: 8384, time: 0.064) G_GAN: 0.052 G_GAN_Feat: 0.745 G_ID: 0.119 G_Rec: 0.301 D_GP: 0.054 D_real: 0.797 D_fake: 0.948 
+(epoch: 154, iters: 176, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.966 G_ID: 0.232 G_Rec: 0.418 D_GP: 0.046 D_real: 0.711 D_fake: 0.898 
+(epoch: 154, iters: 576, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.872 G_ID: 0.136 G_Rec: 0.326 D_GP: 0.038 D_real: 0.861 D_fake: 0.724 
+(epoch: 154, iters: 976, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.824 G_ID: 0.210 G_Rec: 0.408 D_GP: 0.022 D_real: 1.232 D_fake: 0.511 
+(epoch: 154, iters: 1376, time: 0.064) G_GAN: 0.035 G_GAN_Feat: 0.681 G_ID: 0.167 G_Rec: 0.330 D_GP: 0.026 D_real: 0.951 D_fake: 0.965 
+(epoch: 154, iters: 1776, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.846 G_ID: 0.190 G_Rec: 0.406 D_GP: 0.034 D_real: 0.915 D_fake: 0.656 
+(epoch: 154, iters: 2176, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.759 G_ID: 0.139 G_Rec: 0.362 D_GP: 0.038 D_real: 1.043 D_fake: 0.799 
+(epoch: 154, iters: 2576, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 0.831 G_ID: 0.210 G_Rec: 0.394 D_GP: 0.028 D_real: 1.110 D_fake: 0.593 
+(epoch: 154, iters: 2976, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.729 G_ID: 0.160 G_Rec: 0.321 D_GP: 0.036 D_real: 0.952 D_fake: 0.914 
+(epoch: 154, iters: 3376, time: 0.064) G_GAN: 0.682 G_GAN_Feat: 0.879 G_ID: 0.192 G_Rec: 0.408 D_GP: 0.028 D_real: 1.454 D_fake: 0.353 
+(epoch: 154, iters: 3776, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.922 G_ID: 0.151 G_Rec: 0.358 D_GP: 0.047 D_real: 0.824 D_fake: 0.575 
+(epoch: 154, iters: 4176, time: 0.064) G_GAN: 0.687 G_GAN_Feat: 0.928 G_ID: 0.196 G_Rec: 0.404 D_GP: 0.031 D_real: 1.212 D_fake: 0.346 
+(epoch: 154, iters: 4576, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.596 G_ID: 0.143 G_Rec: 0.368 D_GP: 0.018 D_real: 1.198 D_fake: 0.794 
+(epoch: 154, iters: 4976, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.827 G_ID: 0.214 G_Rec: 0.382 D_GP: 0.026 D_real: 1.071 D_fake: 0.609 
+(epoch: 154, iters: 5376, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.665 G_ID: 0.132 G_Rec: 0.308 D_GP: 0.022 D_real: 1.092 D_fake: 0.802 
+(epoch: 154, iters: 5776, time: 0.064) G_GAN: 0.613 G_GAN_Feat: 0.900 G_ID: 0.180 G_Rec: 0.439 D_GP: 0.025 D_real: 1.196 D_fake: 0.397 
+(epoch: 154, iters: 6176, time: 0.064) G_GAN: -0.055 G_GAN_Feat: 0.713 G_ID: 0.167 G_Rec: 0.307 D_GP: 0.031 D_real: 0.806 D_fake: 1.055 
+(epoch: 154, iters: 6576, time: 0.064) G_GAN: 0.787 G_GAN_Feat: 0.840 G_ID: 0.235 G_Rec: 0.386 D_GP: 0.024 D_real: 1.544 D_fake: 0.248 
+(epoch: 154, iters: 6976, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.750 G_ID: 0.172 G_Rec: 0.319 D_GP: 0.036 D_real: 0.913 D_fake: 0.852 
+(epoch: 154, iters: 7376, time: 0.064) G_GAN: 0.434 G_GAN_Feat: 0.869 G_ID: 0.194 G_Rec: 0.381 D_GP: 0.037 D_real: 1.064 D_fake: 0.567 
+(epoch: 154, iters: 7776, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.867 G_ID: 0.176 G_Rec: 0.340 D_GP: 0.059 D_real: 0.782 D_fake: 0.819 
+(epoch: 154, iters: 8176, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 1.054 G_ID: 0.232 G_Rec: 0.468 D_GP: 0.024 D_real: 1.043 D_fake: 0.544 
+(epoch: 154, iters: 8576, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.689 G_ID: 0.143 G_Rec: 0.304 D_GP: 0.025 D_real: 1.174 D_fake: 0.714 
+(epoch: 155, iters: 368, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 1.207 G_ID: 0.210 G_Rec: 0.407 D_GP: 0.257 D_real: 0.465 D_fake: 0.596 
+(epoch: 155, iters: 768, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.651 G_ID: 0.132 G_Rec: 0.304 D_GP: 0.023 D_real: 1.111 D_fake: 0.778 
+(epoch: 155, iters: 1168, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 1.075 G_ID: 0.184 G_Rec: 0.464 D_GP: 0.126 D_real: 0.487 D_fake: 0.790 
+(epoch: 155, iters: 1568, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.642 G_ID: 0.126 G_Rec: 0.300 D_GP: 0.023 D_real: 1.223 D_fake: 0.736 
+(epoch: 155, iters: 1968, time: 0.064) G_GAN: 0.768 G_GAN_Feat: 0.837 G_ID: 0.191 G_Rec: 0.409 D_GP: 0.026 D_real: 1.509 D_fake: 0.318 
+(epoch: 155, iters: 2368, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.785 G_ID: 0.132 G_Rec: 0.321 D_GP: 0.035 D_real: 1.241 D_fake: 0.589 
+(epoch: 155, iters: 2768, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 0.853 G_ID: 0.200 G_Rec: 0.382 D_GP: 0.024 D_real: 1.394 D_fake: 0.429 
+(epoch: 155, iters: 3168, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.711 G_ID: 0.146 G_Rec: 0.341 D_GP: 0.024 D_real: 1.124 D_fake: 0.713 
+(epoch: 155, iters: 3568, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.918 G_ID: 0.190 G_Rec: 0.388 D_GP: 0.029 D_real: 1.188 D_fake: 0.546 
+(epoch: 155, iters: 3968, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.752 G_ID: 0.146 G_Rec: 0.332 D_GP: 0.031 D_real: 1.030 D_fake: 0.810 
+(epoch: 155, iters: 4368, time: 0.064) G_GAN: 0.880 G_GAN_Feat: 0.833 G_ID: 0.180 G_Rec: 0.362 D_GP: 0.035 D_real: 1.615 D_fake: 0.232 
+(epoch: 155, iters: 4768, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.677 G_ID: 0.155 G_Rec: 0.344 D_GP: 0.025 D_real: 1.360 D_fake: 0.580 
+(epoch: 155, iters: 5168, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.920 G_ID: 0.221 G_Rec: 0.451 D_GP: 0.037 D_real: 0.766 D_fake: 0.810 
+(epoch: 155, iters: 5568, time: 0.064) G_GAN: 0.557 G_GAN_Feat: 0.916 G_ID: 0.137 G_Rec: 0.356 D_GP: 0.440 D_real: 0.656 D_fake: 0.459 
+(epoch: 155, iters: 5968, time: 0.064) G_GAN: 0.532 G_GAN_Feat: 0.871 G_ID: 0.191 G_Rec: 0.399 D_GP: 0.022 D_real: 1.177 D_fake: 0.471 
+(epoch: 155, iters: 6368, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 1.064 G_ID: 0.146 G_Rec: 0.314 D_GP: 0.148 D_real: 0.409 D_fake: 0.751 
+(epoch: 155, iters: 6768, time: 0.064) G_GAN: 0.480 G_GAN_Feat: 0.867 G_ID: 0.229 G_Rec: 0.437 D_GP: 0.021 D_real: 1.212 D_fake: 0.522 
+(epoch: 155, iters: 7168, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.659 G_ID: 0.141 G_Rec: 0.297 D_GP: 0.025 D_real: 1.222 D_fake: 0.644 
+(epoch: 155, iters: 7568, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.819 G_ID: 0.172 G_Rec: 0.391 D_GP: 0.025 D_real: 1.096 D_fake: 0.647 
+(epoch: 155, iters: 7968, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.905 G_ID: 0.129 G_Rec: 0.336 D_GP: 0.123 D_real: 0.897 D_fake: 0.701 
+(epoch: 155, iters: 8368, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 0.873 G_ID: 0.193 G_Rec: 0.395 D_GP: 0.027 D_real: 1.272 D_fake: 0.427 
+(epoch: 156, iters: 160, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.854 G_ID: 0.183 G_Rec: 0.313 D_GP: 0.038 D_real: 0.600 D_fake: 0.916 
+(epoch: 156, iters: 560, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 0.794 G_ID: 0.214 G_Rec: 0.398 D_GP: 0.023 D_real: 1.241 D_fake: 0.530 
+(epoch: 156, iters: 960, time: 0.064) G_GAN: -0.086 G_GAN_Feat: 0.748 G_ID: 0.151 G_Rec: 0.336 D_GP: 0.051 D_real: 0.646 D_fake: 1.086 
+(epoch: 156, iters: 1360, time: 0.064) G_GAN: 0.666 G_GAN_Feat: 0.907 G_ID: 0.197 G_Rec: 0.382 D_GP: 0.043 D_real: 1.275 D_fake: 0.351 
+(epoch: 156, iters: 1760, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.803 G_ID: 0.140 G_Rec: 0.322 D_GP: 0.043 D_real: 0.886 D_fake: 0.783 
+(epoch: 156, iters: 2160, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 1.125 G_ID: 0.216 G_Rec: 0.450 D_GP: 0.100 D_real: 0.434 D_fake: 0.681 
+(epoch: 156, iters: 2560, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.792 G_ID: 0.130 G_Rec: 0.314 D_GP: 0.041 D_real: 1.025 D_fake: 0.520 
+(epoch: 156, iters: 2960, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 0.742 G_ID: 0.230 G_Rec: 0.417 D_GP: 0.020 D_real: 1.377 D_fake: 0.388 
+(epoch: 156, iters: 3360, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.577 G_ID: 0.166 G_Rec: 0.287 D_GP: 0.020 D_real: 1.224 D_fake: 0.732 
+(epoch: 156, iters: 3760, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.792 G_ID: 0.189 G_Rec: 0.415 D_GP: 0.027 D_real: 1.045 D_fake: 0.681 
+(epoch: 156, iters: 4160, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.594 G_ID: 0.166 G_Rec: 0.294 D_GP: 0.022 D_real: 1.065 D_fake: 0.879 
+(epoch: 156, iters: 4560, time: 0.064) G_GAN: 0.434 G_GAN_Feat: 0.851 G_ID: 0.170 G_Rec: 0.405 D_GP: 0.052 D_real: 1.070 D_fake: 0.569 
+(epoch: 156, iters: 4960, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.739 G_ID: 0.160 G_Rec: 0.369 D_GP: 0.030 D_real: 1.059 D_fake: 0.860 
+(epoch: 156, iters: 5360, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 0.957 G_ID: 0.212 G_Rec: 0.437 D_GP: 0.047 D_real: 0.972 D_fake: 0.528 
+(epoch: 156, iters: 5760, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.675 G_ID: 0.142 G_Rec: 0.321 D_GP: 0.020 D_real: 1.079 D_fake: 0.819 
+(epoch: 156, iters: 6160, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 0.901 G_ID: 0.193 G_Rec: 0.389 D_GP: 0.045 D_real: 1.098 D_fake: 0.527 
+(epoch: 156, iters: 6560, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.808 G_ID: 0.142 G_Rec: 0.306 D_GP: 0.039 D_real: 0.983 D_fake: 0.720 
+(epoch: 156, iters: 6960, time: 0.064) G_GAN: 0.530 G_GAN_Feat: 0.784 G_ID: 0.174 G_Rec: 0.396 D_GP: 0.024 D_real: 1.288 D_fake: 0.470 
+(epoch: 156, iters: 7360, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.914 G_ID: 0.183 G_Rec: 0.360 D_GP: 0.052 D_real: 0.926 D_fake: 0.686 
+(epoch: 156, iters: 7760, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.914 G_ID: 0.189 G_Rec: 0.410 D_GP: 0.030 D_real: 1.085 D_fake: 0.587 
+(epoch: 156, iters: 8160, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.820 G_ID: 0.148 G_Rec: 0.315 D_GP: 0.050 D_real: 1.025 D_fake: 0.578 
+(epoch: 156, iters: 8560, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.920 G_ID: 0.177 G_Rec: 0.385 D_GP: 0.040 D_real: 1.086 D_fake: 0.519 
+(epoch: 157, iters: 352, time: 0.064) G_GAN: 0.675 G_GAN_Feat: 0.856 G_ID: 0.147 G_Rec: 0.370 D_GP: 0.081 D_real: 1.232 D_fake: 0.350 
+(epoch: 157, iters: 752, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.881 G_ID: 0.220 G_Rec: 0.393 D_GP: 0.046 D_real: 1.095 D_fake: 0.576 
+(epoch: 157, iters: 1152, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 0.805 G_ID: 0.152 G_Rec: 0.317 D_GP: 0.034 D_real: 1.347 D_fake: 0.532 
+(epoch: 157, iters: 1552, time: 0.064) G_GAN: 0.632 G_GAN_Feat: 1.200 G_ID: 0.186 G_Rec: 0.454 D_GP: 0.078 D_real: 0.388 D_fake: 0.423 
+(epoch: 157, iters: 1952, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.819 G_ID: 0.141 G_Rec: 0.324 D_GP: 0.036 D_real: 1.124 D_fake: 0.645 
+(epoch: 157, iters: 2352, time: 0.064) G_GAN: 0.528 G_GAN_Feat: 0.824 G_ID: 0.189 G_Rec: 0.395 D_GP: 0.035 D_real: 1.254 D_fake: 0.492 
+(epoch: 157, iters: 2752, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.677 G_ID: 0.142 G_Rec: 0.305 D_GP: 0.031 D_real: 1.021 D_fake: 0.926 
+(epoch: 157, iters: 3152, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 1.067 G_ID: 0.204 G_Rec: 0.426 D_GP: 0.048 D_real: 1.129 D_fake: 0.522 
+(epoch: 157, iters: 3552, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.782 G_ID: 0.148 G_Rec: 0.325 D_GP: 0.036 D_real: 1.035 D_fake: 0.617 
+(epoch: 157, iters: 3952, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.992 G_ID: 0.239 G_Rec: 0.408 D_GP: 0.091 D_real: 0.460 D_fake: 0.857 
+(epoch: 157, iters: 4352, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.799 G_ID: 0.137 G_Rec: 0.331 D_GP: 0.030 D_real: 1.316 D_fake: 0.503 
+(epoch: 157, iters: 4752, time: 0.064) G_GAN: 0.676 G_GAN_Feat: 1.242 G_ID: 0.234 G_Rec: 0.414 D_GP: 0.103 D_real: 0.703 D_fake: 0.338 
+(epoch: 157, iters: 5152, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.835 G_ID: 0.135 G_Rec: 0.318 D_GP: 0.046 D_real: 1.068 D_fake: 0.620 
+(epoch: 157, iters: 5552, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 1.192 G_ID: 0.199 G_Rec: 0.483 D_GP: 0.165 D_real: 0.447 D_fake: 0.585 
+(epoch: 157, iters: 5952, time: 0.064) G_GAN: -0.126 G_GAN_Feat: 0.763 G_ID: 0.132 G_Rec: 0.333 D_GP: 0.026 D_real: 0.718 D_fake: 1.126 
+(epoch: 157, iters: 6352, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.856 G_ID: 0.200 G_Rec: 0.397 D_GP: 0.027 D_real: 0.960 D_fake: 0.874 
+(epoch: 157, iters: 6752, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.664 G_ID: 0.167 G_Rec: 0.324 D_GP: 0.023 D_real: 0.932 D_fake: 0.964 
+(epoch: 157, iters: 7152, time: 0.064) G_GAN: 0.467 G_GAN_Feat: 0.982 G_ID: 0.211 G_Rec: 0.438 D_GP: 0.045 D_real: 0.880 D_fake: 0.552 
+(epoch: 157, iters: 7552, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.724 G_ID: 0.126 G_Rec: 0.314 D_GP: 0.022 D_real: 1.172 D_fake: 0.694 
+(epoch: 157, iters: 7952, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 0.927 G_ID: 0.224 G_Rec: 0.401 D_GP: 0.028 D_real: 1.109 D_fake: 0.522 
+(epoch: 157, iters: 8352, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.777 G_ID: 0.143 G_Rec: 0.317 D_GP: 0.052 D_real: 1.149 D_fake: 0.618 
+(epoch: 158, iters: 144, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.938 G_ID: 0.215 G_Rec: 0.461 D_GP: 0.043 D_real: 0.928 D_fake: 0.677 
+(epoch: 158, iters: 544, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.730 G_ID: 0.157 G_Rec: 0.337 D_GP: 0.070 D_real: 1.008 D_fake: 0.758 
+(epoch: 158, iters: 944, time: 0.064) G_GAN: 0.568 G_GAN_Feat: 1.000 G_ID: 0.206 G_Rec: 0.409 D_GP: 0.114 D_real: 0.860 D_fake: 0.436 
+(epoch: 158, iters: 1344, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.890 G_ID: 0.128 G_Rec: 0.351 D_GP: 0.112 D_real: 0.770 D_fake: 0.571 
+(epoch: 158, iters: 1744, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.941 G_ID: 0.207 G_Rec: 0.411 D_GP: 0.033 D_real: 0.929 D_fake: 0.773 
+(epoch: 158, iters: 2144, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.781 G_ID: 0.150 G_Rec: 0.315 D_GP: 0.028 D_real: 0.927 D_fake: 0.807 
+(epoch: 158, iters: 2544, time: 0.064) G_GAN: 0.706 G_GAN_Feat: 1.024 G_ID: 0.218 G_Rec: 0.411 D_GP: 0.080 D_real: 0.984 D_fake: 0.371 
+(epoch: 158, iters: 2944, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.664 G_ID: 0.140 G_Rec: 0.323 D_GP: 0.022 D_real: 1.002 D_fake: 0.987 
+(epoch: 158, iters: 3344, time: 0.064) G_GAN: 0.579 G_GAN_Feat: 0.784 G_ID: 0.189 G_Rec: 0.380 D_GP: 0.025 D_real: 1.289 D_fake: 0.433 
+(epoch: 158, iters: 3744, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.882 G_ID: 0.137 G_Rec: 0.389 D_GP: 0.113 D_real: 0.410 D_fake: 0.910 
+(epoch: 158, iters: 4144, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 1.228 G_ID: 0.237 G_Rec: 0.451 D_GP: 0.064 D_real: 1.043 D_fake: 0.674 
+(epoch: 158, iters: 4544, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.921 G_ID: 0.146 G_Rec: 0.350 D_GP: 0.119 D_real: 0.717 D_fake: 0.533 
+(epoch: 158, iters: 4944, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.852 G_ID: 0.208 G_Rec: 0.411 D_GP: 0.025 D_real: 1.027 D_fake: 0.737 
+(epoch: 158, iters: 5344, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.737 G_ID: 0.169 G_Rec: 0.359 D_GP: 0.030 D_real: 0.865 D_fake: 0.912 
+(epoch: 158, iters: 5744, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.795 G_ID: 0.198 G_Rec: 0.374 D_GP: 0.023 D_real: 1.066 D_fake: 0.719 
+(epoch: 158, iters: 6144, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.678 G_ID: 0.124 G_Rec: 0.321 D_GP: 0.037 D_real: 1.049 D_fake: 0.858 
+(epoch: 158, iters: 6544, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.873 G_ID: 0.190 G_Rec: 0.390 D_GP: 0.029 D_real: 1.010 D_fake: 0.664 
+(epoch: 158, iters: 6944, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.749 G_ID: 0.125 G_Rec: 0.335 D_GP: 0.030 D_real: 1.207 D_fake: 0.638 
+(epoch: 158, iters: 7344, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.970 G_ID: 0.196 G_Rec: 0.376 D_GP: 0.059 D_real: 0.797 D_fake: 0.565 
+(epoch: 158, iters: 7744, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.842 G_ID: 0.180 G_Rec: 0.326 D_GP: 0.039 D_real: 0.875 D_fake: 1.013 
+(epoch: 158, iters: 8144, time: 0.064) G_GAN: 0.909 G_GAN_Feat: 1.059 G_ID: 0.198 G_Rec: 0.438 D_GP: 0.033 D_real: 1.252 D_fake: 0.171 
+(epoch: 158, iters: 8544, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.716 G_ID: 0.152 G_Rec: 0.329 D_GP: 0.023 D_real: 1.149 D_fake: 0.661 
+(epoch: 159, iters: 336, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.898 G_ID: 0.168 G_Rec: 0.435 D_GP: 0.031 D_real: 1.041 D_fake: 0.593 
+(epoch: 159, iters: 736, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.678 G_ID: 0.137 G_Rec: 0.320 D_GP: 0.026 D_real: 1.175 D_fake: 0.637 
+(epoch: 159, iters: 1136, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.773 G_ID: 0.209 G_Rec: 0.390 D_GP: 0.025 D_real: 1.171 D_fake: 0.576 
+(epoch: 159, iters: 1536, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.628 G_ID: 0.140 G_Rec: 0.287 D_GP: 0.025 D_real: 1.123 D_fake: 0.803 
+(epoch: 159, iters: 1936, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.819 G_ID: 0.207 G_Rec: 0.422 D_GP: 0.020 D_real: 1.111 D_fake: 0.624 
+(epoch: 159, iters: 2336, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.679 G_ID: 0.148 G_Rec: 0.296 D_GP: 0.032 D_real: 0.957 D_fake: 0.822 
+(epoch: 159, iters: 2736, time: 0.064) G_GAN: 0.601 G_GAN_Feat: 1.109 G_ID: 0.226 G_Rec: 0.442 D_GP: 0.041 D_real: 1.181 D_fake: 0.448 
+(epoch: 159, iters: 3136, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.748 G_ID: 0.128 G_Rec: 0.305 D_GP: 0.032 D_real: 1.271 D_fake: 0.469 
+(epoch: 159, iters: 3536, time: 0.064) G_GAN: 0.579 G_GAN_Feat: 1.004 G_ID: 0.214 G_Rec: 0.409 D_GP: 0.037 D_real: 1.034 D_fake: 0.434 
+(epoch: 159, iters: 3936, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.904 G_ID: 0.154 G_Rec: 0.369 D_GP: 0.065 D_real: 0.794 D_fake: 0.656 
+(epoch: 159, iters: 4336, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.752 G_ID: 0.227 G_Rec: 0.370 D_GP: 0.024 D_real: 1.097 D_fake: 0.720 
+(epoch: 159, iters: 4736, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.643 G_ID: 0.152 G_Rec: 0.322 D_GP: 0.027 D_real: 1.091 D_fake: 0.814 
+(epoch: 159, iters: 5136, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.796 G_ID: 0.202 G_Rec: 0.399 D_GP: 0.024 D_real: 1.010 D_fake: 0.781 
+(epoch: 159, iters: 5536, time: 0.064) G_GAN: 0.037 G_GAN_Feat: 0.680 G_ID: 0.139 G_Rec: 0.308 D_GP: 0.032 D_real: 0.965 D_fake: 0.963 
+(epoch: 159, iters: 5936, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.750 G_ID: 0.188 G_Rec: 0.355 D_GP: 0.028 D_real: 1.144 D_fake: 0.689 
+(epoch: 159, iters: 6336, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.758 G_ID: 0.136 G_Rec: 0.332 D_GP: 0.053 D_real: 0.860 D_fake: 0.941 
+(epoch: 159, iters: 6736, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.906 G_ID: 0.202 G_Rec: 0.375 D_GP: 0.056 D_real: 0.990 D_fake: 0.820 
+(epoch: 159, iters: 7136, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.663 G_ID: 0.123 G_Rec: 0.298 D_GP: 0.028 D_real: 1.061 D_fake: 0.874 
+(epoch: 159, iters: 7536, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.910 G_ID: 0.208 G_Rec: 0.404 D_GP: 0.029 D_real: 1.137 D_fake: 0.609 
+(epoch: 159, iters: 7936, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 0.676 G_ID: 0.133 G_Rec: 0.314 D_GP: 0.027 D_real: 1.100 D_fake: 0.793 
+(epoch: 159, iters: 8336, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.852 G_ID: 0.211 G_Rec: 0.401 D_GP: 0.029 D_real: 1.118 D_fake: 0.580 
+(epoch: 160, iters: 128, time: 0.064) G_GAN: 0.049 G_GAN_Feat: 0.768 G_ID: 0.123 G_Rec: 0.340 D_GP: 0.059 D_real: 0.899 D_fake: 0.951 
+(epoch: 160, iters: 528, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.964 G_ID: 0.203 G_Rec: 0.455 D_GP: 0.046 D_real: 0.871 D_fake: 0.652 
+(epoch: 160, iters: 928, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 1.082 G_ID: 0.139 G_Rec: 0.353 D_GP: 0.036 D_real: 0.824 D_fake: 0.798 
+(epoch: 160, iters: 1328, time: 0.064) G_GAN: 0.600 G_GAN_Feat: 0.987 G_ID: 0.182 G_Rec: 0.382 D_GP: 0.031 D_real: 1.209 D_fake: 0.410 
+(epoch: 160, iters: 1728, time: 0.064) G_GAN: 0.044 G_GAN_Feat: 0.917 G_ID: 0.153 G_Rec: 0.346 D_GP: 0.225 D_real: 0.468 D_fake: 0.956 
+(epoch: 160, iters: 2128, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.874 G_ID: 0.194 G_Rec: 0.393 D_GP: 0.029 D_real: 0.924 D_fake: 0.724 
+(epoch: 160, iters: 2528, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.737 G_ID: 0.116 G_Rec: 0.417 D_GP: 0.028 D_real: 0.971 D_fake: 0.749 
+(epoch: 160, iters: 2928, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.890 G_ID: 0.191 G_Rec: 0.413 D_GP: 0.030 D_real: 1.123 D_fake: 0.591 
+(epoch: 160, iters: 3328, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.703 G_ID: 0.162 G_Rec: 0.326 D_GP: 0.021 D_real: 1.214 D_fake: 0.719 
+(epoch: 160, iters: 3728, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 0.842 G_ID: 0.193 G_Rec: 0.398 D_GP: 0.028 D_real: 1.179 D_fake: 0.560 
+(epoch: 160, iters: 4128, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.739 G_ID: 0.138 G_Rec: 0.356 D_GP: 0.035 D_real: 1.221 D_fake: 0.611 
+(epoch: 160, iters: 4528, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 1.166 G_ID: 0.198 G_Rec: 0.421 D_GP: 0.244 D_real: 0.302 D_fake: 0.709 
+(epoch: 160, iters: 4928, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.845 G_ID: 0.143 G_Rec: 0.327 D_GP: 0.093 D_real: 0.785 D_fake: 0.706 
+(epoch: 160, iters: 5328, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.835 G_ID: 0.201 G_Rec: 0.399 D_GP: 0.021 D_real: 1.108 D_fake: 0.713 
+(epoch: 160, iters: 5728, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.645 G_ID: 0.142 G_Rec: 0.319 D_GP: 0.033 D_real: 1.092 D_fake: 0.749 
+(epoch: 160, iters: 6128, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.858 G_ID: 0.205 G_Rec: 0.395 D_GP: 0.029 D_real: 0.995 D_fake: 0.782 
+(epoch: 160, iters: 6528, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.768 G_ID: 0.136 G_Rec: 0.305 D_GP: 0.109 D_real: 0.837 D_fake: 0.873 
+(epoch: 160, iters: 6928, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.996 G_ID: 0.198 G_Rec: 0.422 D_GP: 0.035 D_real: 1.105 D_fake: 0.492 
+(epoch: 160, iters: 7328, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.849 G_ID: 0.137 G_Rec: 0.404 D_GP: 0.052 D_real: 0.714 D_fake: 0.908 
+(epoch: 160, iters: 7728, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.981 G_ID: 0.179 G_Rec: 0.424 D_GP: 0.067 D_real: 0.784 D_fake: 0.616 
+(epoch: 160, iters: 8128, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.850 G_ID: 0.134 G_Rec: 0.320 D_GP: 0.116 D_real: 0.848 D_fake: 0.679 
+(epoch: 160, iters: 8528, time: 0.064) G_GAN: 0.583 G_GAN_Feat: 0.883 G_ID: 0.163 G_Rec: 0.410 D_GP: 0.025 D_real: 1.338 D_fake: 0.422 
+(epoch: 161, iters: 320, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.741 G_ID: 0.147 G_Rec: 0.307 D_GP: 0.041 D_real: 0.940 D_fake: 0.787 
+(epoch: 161, iters: 720, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.872 G_ID: 0.190 G_Rec: 0.376 D_GP: 0.039 D_real: 1.028 D_fake: 0.786 
+(epoch: 161, iters: 1120, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.882 G_ID: 0.173 G_Rec: 0.340 D_GP: 0.029 D_real: 1.091 D_fake: 0.774 
+(epoch: 161, iters: 1520, time: 0.064) G_GAN: 0.779 G_GAN_Feat: 1.058 G_ID: 0.190 G_Rec: 0.444 D_GP: 0.032 D_real: 1.155 D_fake: 0.251 
+(epoch: 161, iters: 1920, time: 0.064) G_GAN: -0.040 G_GAN_Feat: 0.896 G_ID: 0.152 G_Rec: 0.338 D_GP: 0.048 D_real: 0.418 D_fake: 1.040 
+(epoch: 161, iters: 2320, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.798 G_ID: 0.208 G_Rec: 0.406 D_GP: 0.023 D_real: 1.194 D_fake: 0.585 
+(epoch: 161, iters: 2720, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.644 G_ID: 0.145 G_Rec: 0.321 D_GP: 0.026 D_real: 1.062 D_fake: 0.881 
+(epoch: 161, iters: 3120, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.812 G_ID: 0.168 G_Rec: 0.393 D_GP: 0.031 D_real: 1.304 D_fake: 0.491 
+(epoch: 161, iters: 3520, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.728 G_ID: 0.141 G_Rec: 0.329 D_GP: 0.046 D_real: 1.002 D_fake: 0.862 
+(epoch: 161, iters: 3920, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.855 G_ID: 0.211 G_Rec: 0.411 D_GP: 0.027 D_real: 0.893 D_fake: 0.825 
+(epoch: 161, iters: 4320, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.704 G_ID: 0.149 G_Rec: 0.302 D_GP: 0.025 D_real: 1.133 D_fake: 0.778 
+(epoch: 161, iters: 4720, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.937 G_ID: 0.185 G_Rec: 0.454 D_GP: 0.055 D_real: 0.986 D_fake: 0.581 
+(epoch: 161, iters: 5120, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.791 G_ID: 0.162 G_Rec: 0.361 D_GP: 0.081 D_real: 0.930 D_fake: 0.736 
+(epoch: 161, iters: 5520, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.904 G_ID: 0.211 G_Rec: 0.393 D_GP: 0.036 D_real: 0.819 D_fake: 0.857 
+(epoch: 161, iters: 5920, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.754 G_ID: 0.159 G_Rec: 0.370 D_GP: 0.039 D_real: 0.911 D_fake: 0.785 
+(epoch: 161, iters: 6320, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.866 G_ID: 0.193 G_Rec: 0.408 D_GP: 0.052 D_real: 0.962 D_fake: 0.665 
+(epoch: 161, iters: 6720, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.742 G_ID: 0.142 G_Rec: 0.345 D_GP: 0.025 D_real: 1.092 D_fake: 0.815 
+(epoch: 161, iters: 7120, time: 0.064) G_GAN: 0.536 G_GAN_Feat: 0.951 G_ID: 0.247 G_Rec: 0.393 D_GP: 0.050 D_real: 0.966 D_fake: 0.478 
+(epoch: 161, iters: 7520, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.881 G_ID: 0.155 G_Rec: 0.342 D_GP: 0.033 D_real: 0.731 D_fake: 0.916 
+(epoch: 161, iters: 7920, time: 0.064) G_GAN: 0.656 G_GAN_Feat: 0.993 G_ID: 0.218 G_Rec: 0.435 D_GP: 0.039 D_real: 1.069 D_fake: 0.368 
+(epoch: 161, iters: 8320, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.650 G_ID: 0.133 G_Rec: 0.290 D_GP: 0.020 D_real: 1.297 D_fake: 0.671 
+(epoch: 162, iters: 112, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.976 G_ID: 0.225 G_Rec: 0.428 D_GP: 0.071 D_real: 0.720 D_fake: 0.655 
+(epoch: 162, iters: 512, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.670 G_ID: 0.172 G_Rec: 0.299 D_GP: 0.021 D_real: 1.191 D_fake: 0.751 
+(epoch: 162, iters: 912, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.839 G_ID: 0.240 G_Rec: 0.379 D_GP: 0.046 D_real: 1.001 D_fake: 0.640 
+(epoch: 162, iters: 1312, time: 0.064) G_GAN: 0.697 G_GAN_Feat: 0.774 G_ID: 0.138 G_Rec: 0.342 D_GP: 0.025 D_real: 1.562 D_fake: 0.422 
+(epoch: 162, iters: 1712, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 1.069 G_ID: 0.231 G_Rec: 0.405 D_GP: 0.065 D_real: 0.415 D_fake: 0.788 
+(epoch: 162, iters: 2112, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 0.782 G_ID: 0.165 G_Rec: 0.333 D_GP: 0.057 D_real: 1.025 D_fake: 0.645 
+(epoch: 162, iters: 2512, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.945 G_ID: 0.187 G_Rec: 0.427 D_GP: 0.049 D_real: 1.061 D_fake: 0.512 
+(epoch: 162, iters: 2912, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.716 G_ID: 0.154 G_Rec: 0.351 D_GP: 0.021 D_real: 0.989 D_fake: 0.917 
+(epoch: 162, iters: 3312, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.468 G_ID: 0.177 G_Rec: 0.454 D_GP: 0.124 D_real: 0.483 D_fake: 0.659 
+(epoch: 162, iters: 3712, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.804 G_ID: 0.156 G_Rec: 0.323 D_GP: 0.085 D_real: 1.005 D_fake: 0.598 
+(epoch: 162, iters: 4112, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.868 G_ID: 0.209 G_Rec: 0.429 D_GP: 0.035 D_real: 1.015 D_fake: 0.653 
+(epoch: 162, iters: 4512, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.732 G_ID: 0.145 G_Rec: 0.296 D_GP: 0.067 D_real: 0.864 D_fake: 0.828 
+(epoch: 162, iters: 4912, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 0.895 G_ID: 0.212 G_Rec: 0.444 D_GP: 0.032 D_real: 1.281 D_fake: 0.409 
+(epoch: 162, iters: 5312, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.639 G_ID: 0.159 G_Rec: 0.312 D_GP: 0.023 D_real: 1.002 D_fake: 0.936 
+(epoch: 162, iters: 5712, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.855 G_ID: 0.167 G_Rec: 0.362 D_GP: 0.046 D_real: 0.956 D_fake: 0.674 
+(epoch: 162, iters: 6112, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.681 G_ID: 0.139 G_Rec: 0.317 D_GP: 0.033 D_real: 1.058 D_fake: 0.834 
+(epoch: 162, iters: 6512, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 0.998 G_ID: 0.225 G_Rec: 0.422 D_GP: 0.135 D_real: 0.664 D_fake: 0.562 
+(epoch: 162, iters: 6912, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.842 G_ID: 0.166 G_Rec: 0.340 D_GP: 0.057 D_real: 0.740 D_fake: 0.800 
+(epoch: 162, iters: 7312, time: 0.064) G_GAN: 0.628 G_GAN_Feat: 0.977 G_ID: 0.198 G_Rec: 0.392 D_GP: 0.037 D_real: 1.004 D_fake: 0.377 
+(epoch: 162, iters: 7712, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.609 G_ID: 0.138 G_Rec: 0.294 D_GP: 0.021 D_real: 1.123 D_fake: 0.836 
+(epoch: 162, iters: 8112, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.829 G_ID: 0.199 G_Rec: 0.381 D_GP: 0.030 D_real: 0.842 D_fake: 0.789 
+(epoch: 162, iters: 8512, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.802 G_ID: 0.144 G_Rec: 0.340 D_GP: 0.122 D_real: 0.763 D_fake: 0.879 
+(epoch: 163, iters: 304, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.941 G_ID: 0.199 G_Rec: 0.406 D_GP: 0.044 D_real: 1.051 D_fake: 0.550 
+(epoch: 163, iters: 704, time: 0.064) G_GAN: 0.068 G_GAN_Feat: 0.823 G_ID: 0.140 G_Rec: 0.360 D_GP: 0.047 D_real: 0.638 D_fake: 0.932 
+(epoch: 163, iters: 1104, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.988 G_ID: 0.209 G_Rec: 0.444 D_GP: 0.079 D_real: 0.771 D_fake: 0.775 
+(epoch: 163, iters: 1504, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.732 G_ID: 0.137 G_Rec: 0.319 D_GP: 0.025 D_real: 1.151 D_fake: 0.725 
+(epoch: 163, iters: 1904, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.916 G_ID: 0.239 G_Rec: 0.438 D_GP: 0.022 D_real: 1.035 D_fake: 0.656 
+(epoch: 163, iters: 2304, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.989 G_ID: 0.146 G_Rec: 0.342 D_GP: 0.054 D_real: 0.730 D_fake: 0.631 
+(epoch: 163, iters: 2704, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.877 G_ID: 0.217 G_Rec: 0.433 D_GP: 0.029 D_real: 1.081 D_fake: 0.573 
+(epoch: 163, iters: 3104, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.906 G_ID: 0.185 G_Rec: 0.353 D_GP: 0.053 D_real: 0.692 D_fake: 0.781 
+(epoch: 163, iters: 3504, time: 0.064) G_GAN: 0.920 G_GAN_Feat: 0.931 G_ID: 0.190 G_Rec: 0.425 D_GP: 0.029 D_real: 1.655 D_fake: 0.167 
+(epoch: 163, iters: 3904, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.771 G_ID: 0.143 G_Rec: 0.325 D_GP: 0.035 D_real: 1.061 D_fake: 0.857 
+(epoch: 163, iters: 4304, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 1.005 G_ID: 0.193 G_Rec: 0.420 D_GP: 0.096 D_real: 0.856 D_fake: 0.495 
+(epoch: 163, iters: 4704, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 0.895 G_ID: 0.163 G_Rec: 0.323 D_GP: 0.072 D_real: 1.021 D_fake: 0.573 
+(epoch: 163, iters: 5104, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.840 G_ID: 0.207 G_Rec: 0.374 D_GP: 0.026 D_real: 1.133 D_fake: 0.701 
+(epoch: 163, iters: 5504, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.765 G_ID: 0.133 G_Rec: 0.322 D_GP: 0.028 D_real: 0.945 D_fake: 0.893 
+(epoch: 163, iters: 5904, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.924 G_ID: 0.201 G_Rec: 0.408 D_GP: 0.048 D_real: 1.060 D_fake: 0.552 
+(epoch: 163, iters: 6304, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.694 G_ID: 0.153 G_Rec: 0.322 D_GP: 0.022 D_real: 1.087 D_fake: 0.776 
+(epoch: 163, iters: 6704, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.931 G_ID: 0.196 G_Rec: 0.482 D_GP: 0.022 D_real: 1.107 D_fake: 0.619 
+(epoch: 163, iters: 7104, time: 0.064) G_GAN: -0.076 G_GAN_Feat: 0.861 G_ID: 0.148 G_Rec: 0.348 D_GP: 0.085 D_real: 0.470 D_fake: 1.076 
+(epoch: 163, iters: 7504, time: 0.064) G_GAN: 0.615 G_GAN_Feat: 0.941 G_ID: 0.213 G_Rec: 0.428 D_GP: 0.031 D_real: 1.325 D_fake: 0.425 
+(epoch: 163, iters: 7904, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.805 G_ID: 0.126 G_Rec: 0.383 D_GP: 0.035 D_real: 0.805 D_fake: 0.893 
+(epoch: 163, iters: 8304, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.980 G_ID: 0.188 G_Rec: 0.399 D_GP: 0.077 D_real: 1.070 D_fake: 0.607 
+(epoch: 164, iters: 96, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.908 G_ID: 0.147 G_Rec: 0.361 D_GP: 0.088 D_real: 0.568 D_fake: 0.718 
+(epoch: 164, iters: 496, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.834 G_ID: 0.206 G_Rec: 0.396 D_GP: 0.021 D_real: 1.184 D_fake: 0.554 
+(epoch: 164, iters: 896, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.706 G_ID: 0.144 G_Rec: 0.302 D_GP: 0.030 D_real: 1.135 D_fake: 0.732 
+(epoch: 164, iters: 1296, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.877 G_ID: 0.212 G_Rec: 0.385 D_GP: 0.026 D_real: 1.121 D_fake: 0.658 
+(epoch: 164, iters: 1696, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.747 G_ID: 0.137 G_Rec: 0.292 D_GP: 0.039 D_real: 1.112 D_fake: 0.642 
+(epoch: 164, iters: 2096, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.763 G_ID: 0.230 G_Rec: 0.386 D_GP: 0.023 D_real: 0.988 D_fake: 0.834 
+(epoch: 164, iters: 2496, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.680 G_ID: 0.155 G_Rec: 0.324 D_GP: 0.035 D_real: 0.960 D_fake: 0.884 
+(epoch: 164, iters: 2896, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.855 G_ID: 0.214 G_Rec: 0.412 D_GP: 0.034 D_real: 0.987 D_fake: 0.702 
+(epoch: 164, iters: 3296, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.641 G_ID: 0.161 G_Rec: 0.285 D_GP: 0.025 D_real: 1.267 D_fake: 0.629 
+(epoch: 164, iters: 3696, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 1.061 G_ID: 0.199 G_Rec: 0.520 D_GP: 0.031 D_real: 0.904 D_fake: 0.632 
+(epoch: 164, iters: 4096, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.746 G_ID: 0.137 G_Rec: 0.311 D_GP: 0.034 D_real: 0.898 D_fake: 0.808 
+(epoch: 164, iters: 4496, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.953 G_ID: 0.198 G_Rec: 0.392 D_GP: 0.044 D_real: 1.037 D_fake: 0.442 
+(epoch: 164, iters: 4896, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.812 G_ID: 0.132 G_Rec: 0.337 D_GP: 0.026 D_real: 1.119 D_fake: 0.633 
+(epoch: 164, iters: 5296, time: 0.064) G_GAN: 0.511 G_GAN_Feat: 0.982 G_ID: 0.193 G_Rec: 0.476 D_GP: 0.028 D_real: 1.001 D_fake: 0.497 
+(epoch: 164, iters: 5696, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.657 G_ID: 0.124 G_Rec: 0.283 D_GP: 0.025 D_real: 1.066 D_fake: 0.815 
+(epoch: 164, iters: 6096, time: 0.064) G_GAN: 0.669 G_GAN_Feat: 0.846 G_ID: 0.219 G_Rec: 0.402 D_GP: 0.026 D_real: 1.301 D_fake: 0.348 
+(epoch: 164, iters: 6496, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.894 G_ID: 0.141 G_Rec: 0.343 D_GP: 0.047 D_real: 0.694 D_fake: 0.830 
+(epoch: 164, iters: 6896, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.905 G_ID: 0.176 G_Rec: 0.423 D_GP: 0.030 D_real: 0.884 D_fake: 0.731 
+(epoch: 164, iters: 7296, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.921 G_ID: 0.136 G_Rec: 0.373 D_GP: 0.159 D_real: 0.902 D_fake: 0.624 
+(epoch: 164, iters: 7696, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 1.067 G_ID: 0.245 G_Rec: 0.389 D_GP: 0.061 D_real: 0.475 D_fake: 0.732 
+(epoch: 164, iters: 8096, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.614 G_ID: 0.156 G_Rec: 0.301 D_GP: 0.020 D_real: 1.139 D_fake: 0.855 
+(epoch: 164, iters: 8496, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.825 G_ID: 0.189 G_Rec: 0.401 D_GP: 0.026 D_real: 1.107 D_fake: 0.558 
+(epoch: 165, iters: 288, time: 0.064) G_GAN: 0.098 G_GAN_Feat: 0.696 G_ID: 0.144 G_Rec: 0.310 D_GP: 0.026 D_real: 0.993 D_fake: 0.902 
+(epoch: 165, iters: 688, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.831 G_ID: 0.215 G_Rec: 0.382 D_GP: 0.026 D_real: 1.049 D_fake: 0.667 
+(epoch: 165, iters: 1088, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.721 G_ID: 0.154 G_Rec: 0.315 D_GP: 0.021 D_real: 0.972 D_fake: 0.887 
+(epoch: 165, iters: 1488, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.935 G_ID: 0.207 G_Rec: 0.443 D_GP: 0.124 D_real: 0.746 D_fake: 0.582 
+(epoch: 165, iters: 1888, time: 0.064) G_GAN: -0.250 G_GAN_Feat: 0.733 G_ID: 0.161 G_Rec: 0.308 D_GP: 0.023 D_real: 0.736 D_fake: 1.250 
+(epoch: 165, iters: 2288, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.915 G_ID: 0.204 G_Rec: 0.402 D_GP: 0.024 D_real: 1.191 D_fake: 0.470 
+(epoch: 165, iters: 2688, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.726 G_ID: 0.143 G_Rec: 0.340 D_GP: 0.025 D_real: 1.331 D_fake: 0.655 
+(epoch: 165, iters: 3088, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.829 G_ID: 0.200 G_Rec: 0.394 D_GP: 0.029 D_real: 1.091 D_fake: 0.603 
+(epoch: 165, iters: 3488, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.865 G_ID: 0.156 G_Rec: 0.336 D_GP: 0.142 D_real: 0.523 D_fake: 0.851 
+(epoch: 165, iters: 3888, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 1.203 G_ID: 0.202 G_Rec: 0.447 D_GP: 0.640 D_real: 0.301 D_fake: 0.725 
+(epoch: 165, iters: 4288, time: 0.064) G_GAN: -0.151 G_GAN_Feat: 0.806 G_ID: 0.127 G_Rec: 0.357 D_GP: 0.042 D_real: 0.516 D_fake: 1.151 
+(epoch: 165, iters: 4688, time: 0.064) G_GAN: 0.855 G_GAN_Feat: 1.045 G_ID: 0.210 G_Rec: 0.426 D_GP: 0.036 D_real: 1.361 D_fake: 0.221 
+(epoch: 165, iters: 5088, time: 0.064) G_GAN: 0.044 G_GAN_Feat: 0.801 G_ID: 0.124 G_Rec: 0.336 D_GP: 0.050 D_real: 0.744 D_fake: 0.956 
+(epoch: 165, iters: 5488, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 0.789 G_ID: 0.190 G_Rec: 0.382 D_GP: 0.022 D_real: 1.297 D_fake: 0.528 
+(epoch: 165, iters: 5888, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.650 G_ID: 0.122 G_Rec: 0.318 D_GP: 0.027 D_real: 1.139 D_fake: 0.773 
+(epoch: 165, iters: 6288, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.872 G_ID: 0.176 G_Rec: 0.382 D_GP: 0.027 D_real: 1.193 D_fake: 0.564 
+(epoch: 165, iters: 6688, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.702 G_ID: 0.168 G_Rec: 0.311 D_GP: 0.030 D_real: 1.061 D_fake: 0.694 
+(epoch: 165, iters: 7088, time: 0.064) G_GAN: 0.587 G_GAN_Feat: 1.025 G_ID: 0.191 G_Rec: 0.428 D_GP: 0.078 D_real: 0.824 D_fake: 0.431 
+(epoch: 165, iters: 7488, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.937 G_ID: 0.165 G_Rec: 0.343 D_GP: 0.067 D_real: 0.960 D_fake: 0.741 
+(epoch: 165, iters: 7888, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.908 G_ID: 0.206 G_Rec: 0.425 D_GP: 0.024 D_real: 1.050 D_fake: 0.649 
+(epoch: 165, iters: 8288, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.756 G_ID: 0.138 G_Rec: 0.318 D_GP: 0.032 D_real: 1.050 D_fake: 0.769 
+(epoch: 166, iters: 80, time: 0.064) G_GAN: 0.504 G_GAN_Feat: 0.909 G_ID: 0.181 G_Rec: 0.429 D_GP: 0.054 D_real: 1.121 D_fake: 0.498 
+(epoch: 166, iters: 480, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.740 G_ID: 0.158 G_Rec: 0.300 D_GP: 0.089 D_real: 1.035 D_fake: 0.677 
+(epoch: 166, iters: 880, time: 0.064) G_GAN: 0.496 G_GAN_Feat: 0.950 G_ID: 0.175 G_Rec: 0.431 D_GP: 0.072 D_real: 0.969 D_fake: 0.508 
+(epoch: 166, iters: 1280, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.824 G_ID: 0.149 G_Rec: 0.341 D_GP: 0.039 D_real: 1.016 D_fake: 0.700 
+(epoch: 166, iters: 1680, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.906 G_ID: 0.194 G_Rec: 0.381 D_GP: 0.036 D_real: 0.978 D_fake: 0.636 
+(epoch: 166, iters: 2080, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.656 G_ID: 0.132 G_Rec: 0.297 D_GP: 0.022 D_real: 1.092 D_fake: 0.808 
+(epoch: 166, iters: 2480, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 0.794 G_ID: 0.198 G_Rec: 0.384 D_GP: 0.027 D_real: 1.303 D_fake: 0.528 
+(epoch: 166, iters: 2880, time: 0.064) G_GAN: -0.007 G_GAN_Feat: 0.819 G_ID: 0.131 G_Rec: 0.375 D_GP: 0.109 D_real: 0.701 D_fake: 1.007 
+(epoch: 166, iters: 3280, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.963 G_ID: 0.186 G_Rec: 0.417 D_GP: 0.037 D_real: 0.992 D_fake: 0.639 
+(epoch: 166, iters: 3680, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.816 G_ID: 0.149 G_Rec: 0.359 D_GP: 0.094 D_real: 0.782 D_fake: 0.874 
+(epoch: 166, iters: 4080, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.831 G_ID: 0.186 G_Rec: 0.395 D_GP: 0.025 D_real: 1.160 D_fake: 0.551 
+(epoch: 166, iters: 4480, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.665 G_ID: 0.124 G_Rec: 0.308 D_GP: 0.022 D_real: 1.064 D_fake: 0.792 
+(epoch: 166, iters: 4880, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.973 G_ID: 0.227 G_Rec: 0.448 D_GP: 0.066 D_real: 0.888 D_fake: 0.565 
+(epoch: 166, iters: 5280, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.815 G_ID: 0.142 G_Rec: 0.350 D_GP: 0.032 D_real: 1.007 D_fake: 0.718 
+(epoch: 166, iters: 5680, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 1.040 G_ID: 0.171 G_Rec: 0.407 D_GP: 0.040 D_real: 0.676 D_fake: 0.607 
+(epoch: 166, iters: 6080, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.714 G_ID: 0.166 G_Rec: 0.310 D_GP: 0.025 D_real: 1.099 D_fake: 0.816 
+(epoch: 166, iters: 6480, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.928 G_ID: 0.216 G_Rec: 0.374 D_GP: 0.030 D_real: 1.000 D_fake: 0.585 
+(epoch: 166, iters: 6880, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.724 G_ID: 0.131 G_Rec: 0.345 D_GP: 0.028 D_real: 1.212 D_fake: 0.705 
+(epoch: 166, iters: 7280, time: 0.064) G_GAN: 0.765 G_GAN_Feat: 0.814 G_ID: 0.222 G_Rec: 0.382 D_GP: 0.022 D_real: 1.504 D_fake: 0.332 
+(epoch: 166, iters: 7680, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.812 G_ID: 0.140 G_Rec: 0.323 D_GP: 0.070 D_real: 0.725 D_fake: 0.863 
+(epoch: 166, iters: 8080, time: 0.064) G_GAN: 0.576 G_GAN_Feat: 1.225 G_ID: 0.184 G_Rec: 0.475 D_GP: 0.438 D_real: 0.440 D_fake: 0.445 
+(epoch: 166, iters: 8480, time: 0.064) G_GAN: 0.039 G_GAN_Feat: 0.807 G_ID: 0.185 G_Rec: 0.309 D_GP: 0.025 D_real: 0.815 D_fake: 0.961 
+(epoch: 167, iters: 272, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 1.132 G_ID: 0.198 G_Rec: 0.465 D_GP: 0.302 D_real: 0.469 D_fake: 0.487 
+(epoch: 167, iters: 672, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.817 G_ID: 0.129 G_Rec: 0.325 D_GP: 0.022 D_real: 1.055 D_fake: 0.718 
+(epoch: 167, iters: 1072, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.735 G_ID: 0.205 G_Rec: 0.405 D_GP: 0.019 D_real: 1.221 D_fake: 0.685 
+(epoch: 167, iters: 1472, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.567 G_ID: 0.163 G_Rec: 0.279 D_GP: 0.021 D_real: 1.221 D_fake: 0.719 
+(epoch: 167, iters: 1872, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.837 G_ID: 0.180 G_Rec: 0.384 D_GP: 0.023 D_real: 1.130 D_fake: 0.616 
+(epoch: 167, iters: 2272, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.644 G_ID: 0.147 G_Rec: 0.336 D_GP: 0.025 D_real: 1.290 D_fake: 0.685 
+(epoch: 167, iters: 2672, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 0.753 G_ID: 0.185 G_Rec: 0.375 D_GP: 0.026 D_real: 1.356 D_fake: 0.432 
+(epoch: 167, iters: 3072, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.643 G_ID: 0.154 G_Rec: 0.320 D_GP: 0.025 D_real: 1.031 D_fake: 0.844 
+(epoch: 167, iters: 3472, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 0.987 G_ID: 0.177 G_Rec: 0.420 D_GP: 0.051 D_real: 1.297 D_fake: 0.421 
+(epoch: 167, iters: 3872, time: 0.064) G_GAN: 0.037 G_GAN_Feat: 0.737 G_ID: 0.177 G_Rec: 0.320 D_GP: 0.047 D_real: 0.787 D_fake: 0.963 
+(epoch: 167, iters: 4272, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 0.925 G_ID: 0.203 G_Rec: 0.417 D_GP: 0.044 D_real: 1.175 D_fake: 0.442 
+(epoch: 167, iters: 4672, time: 0.064) G_GAN: -0.319 G_GAN_Feat: 1.039 G_ID: 0.137 G_Rec: 0.447 D_GP: 0.318 D_real: 0.343 D_fake: 1.319 
+(epoch: 167, iters: 5072, time: 0.064) G_GAN: 0.035 G_GAN_Feat: 0.992 G_ID: 0.225 G_Rec: 0.448 D_GP: 0.041 D_real: 0.503 D_fake: 0.965 
+(epoch: 167, iters: 5472, time: 0.064) G_GAN: -0.107 G_GAN_Feat: 0.858 G_ID: 0.132 G_Rec: 0.340 D_GP: 0.133 D_real: 0.507 D_fake: 1.107 
+(epoch: 167, iters: 5872, time: 0.064) G_GAN: 0.637 G_GAN_Feat: 0.906 G_ID: 0.178 G_Rec: 0.382 D_GP: 0.083 D_real: 1.198 D_fake: 0.383 
+(epoch: 167, iters: 6272, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.730 G_ID: 0.152 G_Rec: 0.344 D_GP: 0.030 D_real: 0.961 D_fake: 0.827 
+(epoch: 167, iters: 6672, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 1.008 G_ID: 0.194 G_Rec: 0.457 D_GP: 0.099 D_real: 0.940 D_fake: 0.488 
+(epoch: 167, iters: 7072, time: 0.064) G_GAN: -0.060 G_GAN_Feat: 0.796 G_ID: 0.135 G_Rec: 0.316 D_GP: 0.089 D_real: 0.590 D_fake: 1.060 
+(epoch: 167, iters: 7472, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.908 G_ID: 0.206 G_Rec: 0.393 D_GP: 0.098 D_real: 0.854 D_fake: 0.837 
+(epoch: 167, iters: 7872, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.829 G_ID: 0.151 G_Rec: 0.311 D_GP: 0.034 D_real: 0.749 D_fake: 0.884 
+(epoch: 167, iters: 8272, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 1.158 G_ID: 0.196 G_Rec: 0.454 D_GP: 0.221 D_real: 0.555 D_fake: 0.522 
+(epoch: 168, iters: 64, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.698 G_ID: 0.162 G_Rec: 0.300 D_GP: 0.029 D_real: 1.097 D_fake: 0.786 
+(epoch: 168, iters: 464, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.964 G_ID: 0.215 G_Rec: 0.436 D_GP: 0.030 D_real: 0.848 D_fake: 0.775 
+(epoch: 168, iters: 864, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.776 G_ID: 0.150 G_Rec: 0.341 D_GP: 0.075 D_real: 0.958 D_fake: 0.795 
+(epoch: 168, iters: 1264, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.907 G_ID: 0.183 G_Rec: 0.378 D_GP: 0.029 D_real: 0.963 D_fake: 0.723 
+(epoch: 168, iters: 1664, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.839 G_ID: 0.139 G_Rec: 0.331 D_GP: 0.045 D_real: 1.087 D_fake: 0.687 
+(epoch: 168, iters: 2064, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.782 G_ID: 0.208 G_Rec: 0.393 D_GP: 0.021 D_real: 1.218 D_fake: 0.562 
+(epoch: 168, iters: 2464, time: 0.064) G_GAN: 0.008 G_GAN_Feat: 0.629 G_ID: 0.135 G_Rec: 0.369 D_GP: 0.025 D_real: 0.934 D_fake: 0.992 
+(epoch: 168, iters: 2864, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.877 G_ID: 0.196 G_Rec: 0.420 D_GP: 0.034 D_real: 1.027 D_fake: 0.720 
+(epoch: 168, iters: 3264, time: 0.064) G_GAN: 0.051 G_GAN_Feat: 0.622 G_ID: 0.150 G_Rec: 0.298 D_GP: 0.022 D_real: 0.958 D_fake: 0.949 
+(epoch: 168, iters: 3664, time: 0.064) G_GAN: 0.547 G_GAN_Feat: 0.757 G_ID: 0.195 G_Rec: 0.353 D_GP: 0.022 D_real: 1.337 D_fake: 0.454 
+(epoch: 168, iters: 4064, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.683 G_ID: 0.154 G_Rec: 0.416 D_GP: 0.025 D_real: 1.252 D_fake: 0.659 
+(epoch: 168, iters: 4464, time: 0.064) G_GAN: 0.612 G_GAN_Feat: 1.021 G_ID: 0.178 G_Rec: 0.409 D_GP: 0.174 D_real: 0.779 D_fake: 0.395 
+(epoch: 168, iters: 4864, time: 0.064) G_GAN: 0.031 G_GAN_Feat: 0.772 G_ID: 0.137 G_Rec: 0.354 D_GP: 0.023 D_real: 0.858 D_fake: 0.970 
+(epoch: 168, iters: 5264, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.860 G_ID: 0.193 G_Rec: 0.372 D_GP: 0.032 D_real: 1.087 D_fake: 0.617 
+(epoch: 168, iters: 5664, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.801 G_ID: 0.177 G_Rec: 0.353 D_GP: 0.046 D_real: 0.852 D_fake: 0.782 
+(epoch: 168, iters: 6064, time: 0.064) G_GAN: 0.604 G_GAN_Feat: 0.989 G_ID: 0.212 G_Rec: 0.410 D_GP: 0.043 D_real: 0.955 D_fake: 0.403 
+(epoch: 168, iters: 6464, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.783 G_ID: 0.163 G_Rec: 0.311 D_GP: 0.029 D_real: 1.070 D_fake: 0.724 
+(epoch: 168, iters: 6864, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 0.967 G_ID: 0.195 G_Rec: 0.379 D_GP: 0.061 D_real: 0.926 D_fake: 0.354 
+(epoch: 168, iters: 7264, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.738 G_ID: 0.149 G_Rec: 0.306 D_GP: 0.029 D_real: 1.103 D_fake: 0.713 
+(epoch: 168, iters: 7664, time: 0.064) G_GAN: 0.614 G_GAN_Feat: 0.894 G_ID: 0.186 G_Rec: 0.452 D_GP: 0.024 D_real: 1.313 D_fake: 0.390 
+(epoch: 168, iters: 8064, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.604 G_ID: 0.150 G_Rec: 0.289 D_GP: 0.018 D_real: 1.058 D_fake: 0.871 
+(epoch: 168, iters: 8464, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.907 G_ID: 0.189 G_Rec: 0.386 D_GP: 0.036 D_real: 0.964 D_fake: 0.758 
+(epoch: 169, iters: 256, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.711 G_ID: 0.153 G_Rec: 0.283 D_GP: 0.032 D_real: 1.018 D_fake: 0.787 
+(epoch: 169, iters: 656, time: 0.064) G_GAN: 0.763 G_GAN_Feat: 0.899 G_ID: 0.202 G_Rec: 0.426 D_GP: 0.039 D_real: 1.352 D_fake: 0.281 
+(epoch: 169, iters: 1056, time: 0.064) G_GAN: 0.065 G_GAN_Feat: 0.823 G_ID: 0.122 G_Rec: 0.308 D_GP: 0.055 D_real: 0.499 D_fake: 0.936 
+(epoch: 169, iters: 1456, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 1.072 G_ID: 0.198 G_Rec: 0.405 D_GP: 0.041 D_real: 0.606 D_fake: 0.634 
+(epoch: 169, iters: 1856, time: 0.064) G_GAN: 0.580 G_GAN_Feat: 0.945 G_ID: 0.126 G_Rec: 0.328 D_GP: 0.166 D_real: 0.557 D_fake: 0.550 
+(epoch: 169, iters: 2256, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.802 G_ID: 0.179 G_Rec: 0.406 D_GP: 0.017 D_real: 1.279 D_fake: 0.495 
+(epoch: 169, iters: 2656, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.679 G_ID: 0.155 G_Rec: 0.310 D_GP: 0.036 D_real: 1.068 D_fake: 0.875 
+(epoch: 169, iters: 3056, time: 0.064) G_GAN: 0.642 G_GAN_Feat: 1.058 G_ID: 0.178 G_Rec: 0.417 D_GP: 0.043 D_real: 1.171 D_fake: 0.380 
+(epoch: 169, iters: 3456, time: 0.064) G_GAN: 0.165 G_GAN_Feat: 0.747 G_ID: 0.151 G_Rec: 0.324 D_GP: 0.029 D_real: 0.996 D_fake: 0.835 
+(epoch: 169, iters: 3856, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.946 G_ID: 0.189 G_Rec: 0.428 D_GP: 0.026 D_real: 1.172 D_fake: 0.528 
+(epoch: 169, iters: 4256, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.856 G_ID: 0.142 G_Rec: 0.348 D_GP: 0.096 D_real: 0.585 D_fake: 0.910 
+(epoch: 169, iters: 4656, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 1.026 G_ID: 0.184 G_Rec: 0.416 D_GP: 0.043 D_real: 1.116 D_fake: 0.537 
+(epoch: 169, iters: 5056, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.709 G_ID: 0.180 G_Rec: 0.316 D_GP: 0.025 D_real: 1.086 D_fake: 0.764 
+(epoch: 169, iters: 5456, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.805 G_ID: 0.205 G_Rec: 0.416 D_GP: 0.028 D_real: 1.028 D_fake: 0.732 
+(epoch: 169, iters: 5856, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.733 G_ID: 0.141 G_Rec: 0.303 D_GP: 0.051 D_real: 1.061 D_fake: 0.661 
+(epoch: 169, iters: 6256, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.947 G_ID: 0.195 G_Rec: 0.365 D_GP: 0.032 D_real: 0.914 D_fake: 0.614 
+(epoch: 169, iters: 6656, time: 0.064) G_GAN: 0.547 G_GAN_Feat: 0.871 G_ID: 0.118 G_Rec: 0.320 D_GP: 0.133 D_real: 0.829 D_fake: 0.564 
+(epoch: 169, iters: 7056, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.841 G_ID: 0.201 G_Rec: 0.407 D_GP: 0.025 D_real: 1.063 D_fake: 0.631 
+(epoch: 169, iters: 7456, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.658 G_ID: 0.136 G_Rec: 0.316 D_GP: 0.025 D_real: 0.981 D_fake: 0.921 
+(epoch: 169, iters: 7856, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 1.058 G_ID: 0.150 G_Rec: 0.451 D_GP: 0.091 D_real: 0.778 D_fake: 0.572 
+(epoch: 169, iters: 8256, time: 0.064) G_GAN: -0.009 G_GAN_Feat: 0.785 G_ID: 0.134 G_Rec: 0.332 D_GP: 0.032 D_real: 0.737 D_fake: 1.009 
+(epoch: 170, iters: 48, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 1.171 G_ID: 0.188 G_Rec: 0.447 D_GP: 0.108 D_real: 0.427 D_fake: 0.461 
+(epoch: 170, iters: 448, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.774 G_ID: 0.123 G_Rec: 0.307 D_GP: 0.023 D_real: 0.984 D_fake: 0.908 
+(epoch: 170, iters: 848, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.898 G_ID: 0.213 G_Rec: 0.393 D_GP: 0.045 D_real: 0.631 D_fake: 0.856 
+(epoch: 170, iters: 1248, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.742 G_ID: 0.112 G_Rec: 0.320 D_GP: 0.018 D_real: 1.112 D_fake: 0.836 
+(epoch: 170, iters: 1648, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.871 G_ID: 0.205 G_Rec: 0.426 D_GP: 0.025 D_real: 0.927 D_fake: 0.753 
+(epoch: 170, iters: 2048, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.720 G_ID: 0.119 G_Rec: 0.301 D_GP: 0.048 D_real: 0.896 D_fake: 0.872 
+(epoch: 170, iters: 2448, time: 0.064) G_GAN: 0.548 G_GAN_Feat: 0.929 G_ID: 0.197 G_Rec: 0.390 D_GP: 0.030 D_real: 1.132 D_fake: 0.464 
+(epoch: 170, iters: 2848, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.947 G_ID: 0.123 G_Rec: 0.314 D_GP: 0.414 D_real: 0.492 D_fake: 0.692 
+(epoch: 170, iters: 3248, time: 0.064) G_GAN: 0.651 G_GAN_Feat: 0.863 G_ID: 0.175 G_Rec: 0.388 D_GP: 0.024 D_real: 1.472 D_fake: 0.359 
+(epoch: 170, iters: 3648, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.644 G_ID: 0.123 G_Rec: 0.321 D_GP: 0.019 D_real: 1.140 D_fake: 0.823 
+(epoch: 170, iters: 4048, time: 0.064) G_GAN: 0.689 G_GAN_Feat: 0.834 G_ID: 0.187 G_Rec: 0.385 D_GP: 0.023 D_real: 1.458 D_fake: 0.335 
+(epoch: 170, iters: 4448, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.644 G_ID: 0.140 G_Rec: 0.313 D_GP: 0.023 D_real: 1.118 D_fake: 0.772 
+(epoch: 170, iters: 4848, time: 0.064) G_GAN: 0.580 G_GAN_Feat: 1.055 G_ID: 0.209 G_Rec: 0.387 D_GP: 0.057 D_real: 1.147 D_fake: 0.518 
+(epoch: 170, iters: 5248, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.769 G_ID: 0.163 G_Rec: 0.315 D_GP: 0.027 D_real: 1.274 D_fake: 0.554 
+(epoch: 170, iters: 5648, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.802 G_ID: 0.200 G_Rec: 0.362 D_GP: 0.025 D_real: 1.034 D_fake: 0.741 
+(epoch: 170, iters: 6048, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.830 G_ID: 0.146 G_Rec: 0.353 D_GP: 0.056 D_real: 0.758 D_fake: 0.852 
+(epoch: 170, iters: 6448, time: 0.064) G_GAN: 0.709 G_GAN_Feat: 0.966 G_ID: 0.182 G_Rec: 0.410 D_GP: 0.027 D_real: 1.410 D_fake: 0.342 
+(epoch: 170, iters: 6848, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.734 G_ID: 0.126 G_Rec: 0.317 D_GP: 0.024 D_real: 1.320 D_fake: 0.640 
+(epoch: 170, iters: 7248, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.838 G_ID: 0.198 G_Rec: 0.389 D_GP: 0.029 D_real: 0.844 D_fake: 0.826 
+(epoch: 170, iters: 7648, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.746 G_ID: 0.179 G_Rec: 0.331 D_GP: 0.033 D_real: 1.066 D_fake: 0.812 
+(epoch: 170, iters: 8048, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.805 G_ID: 0.190 G_Rec: 0.362 D_GP: 0.023 D_real: 1.183 D_fake: 0.541 
+(epoch: 170, iters: 8448, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 0.729 G_ID: 0.146 G_Rec: 0.371 D_GP: 0.033 D_real: 1.065 D_fake: 0.759 
+(epoch: 171, iters: 240, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.947 G_ID: 0.176 G_Rec: 0.428 D_GP: 0.042 D_real: 0.984 D_fake: 0.617 
+(epoch: 171, iters: 640, time: 0.064) G_GAN: -0.068 G_GAN_Feat: 1.016 G_ID: 0.152 G_Rec: 0.398 D_GP: 0.204 D_real: 0.111 D_fake: 1.070 
+(epoch: 171, iters: 1040, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.767 G_ID: 0.218 G_Rec: 0.396 D_GP: 0.022 D_real: 1.103 D_fake: 0.687 
+(epoch: 171, iters: 1440, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.642 G_ID: 0.131 G_Rec: 0.292 D_GP: 0.030 D_real: 1.242 D_fake: 0.653 
+(epoch: 171, iters: 1840, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.868 G_ID: 0.219 G_Rec: 0.397 D_GP: 0.035 D_real: 1.080 D_fake: 0.618 
+(epoch: 171, iters: 2240, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.826 G_ID: 0.129 G_Rec: 0.313 D_GP: 0.057 D_real: 0.897 D_fake: 0.697 
+(epoch: 171, iters: 2640, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 0.773 G_ID: 0.189 G_Rec: 0.432 D_GP: 0.020 D_real: 1.365 D_fake: 0.389 
+(epoch: 171, iters: 3040, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.679 G_ID: 0.129 G_Rec: 0.305 D_GP: 0.026 D_real: 1.340 D_fake: 0.534 
+(epoch: 171, iters: 3440, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 1.036 G_ID: 0.213 G_Rec: 0.397 D_GP: 0.143 D_real: 0.361 D_fake: 0.828 
+(epoch: 171, iters: 3840, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 1.031 G_ID: 0.170 G_Rec: 0.379 D_GP: 0.229 D_real: 0.372 D_fake: 0.917 
+(epoch: 171, iters: 4240, time: 0.064) G_GAN: 0.746 G_GAN_Feat: 1.012 G_ID: 0.233 G_Rec: 0.421 D_GP: 0.170 D_real: 0.986 D_fake: 0.514 
+(epoch: 171, iters: 4640, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.909 G_ID: 0.145 G_Rec: 0.359 D_GP: 0.067 D_real: 0.591 D_fake: 0.798 
+(epoch: 171, iters: 5040, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 0.983 G_ID: 0.196 G_Rec: 0.407 D_GP: 0.032 D_real: 0.891 D_fake: 0.559 
+(epoch: 171, iters: 5440, time: 0.064) G_GAN: -0.026 G_GAN_Feat: 0.800 G_ID: 0.142 G_Rec: 0.319 D_GP: 0.076 D_real: 0.763 D_fake: 1.026 
+(epoch: 171, iters: 5840, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.928 G_ID: 0.178 G_Rec: 0.438 D_GP: 0.034 D_real: 0.989 D_fake: 0.588 
+(epoch: 171, iters: 6240, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.929 G_ID: 0.133 G_Rec: 0.358 D_GP: 0.350 D_real: 0.380 D_fake: 0.714 
+(epoch: 171, iters: 6640, time: 0.064) G_GAN: 0.684 G_GAN_Feat: 0.952 G_ID: 0.205 G_Rec: 0.426 D_GP: 0.032 D_real: 1.224 D_fake: 0.376 
+(epoch: 171, iters: 7040, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.666 G_ID: 0.138 G_Rec: 0.341 D_GP: 0.021 D_real: 1.115 D_fake: 0.852 
+(epoch: 171, iters: 7440, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.798 G_ID: 0.181 G_Rec: 0.387 D_GP: 0.026 D_real: 1.006 D_fake: 0.670 
+(epoch: 171, iters: 7840, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.737 G_ID: 0.132 G_Rec: 0.337 D_GP: 0.062 D_real: 0.718 D_fake: 0.952 
+(epoch: 171, iters: 8240, time: 0.064) G_GAN: 0.146 G_GAN_Feat: 0.815 G_ID: 0.217 G_Rec: 0.374 D_GP: 0.034 D_real: 0.884 D_fake: 0.854 
+(epoch: 172, iters: 32, time: 0.064) G_GAN: 0.070 G_GAN_Feat: 0.712 G_ID: 0.134 G_Rec: 0.323 D_GP: 0.032 D_real: 0.935 D_fake: 0.930 
+(epoch: 172, iters: 432, time: 0.064) G_GAN: 0.543 G_GAN_Feat: 0.806 G_ID: 0.203 G_Rec: 0.402 D_GP: 0.024 D_real: 1.262 D_fake: 0.468 
+(epoch: 172, iters: 832, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.708 G_ID: 0.154 G_Rec: 0.320 D_GP: 0.025 D_real: 1.170 D_fake: 0.730 
+(epoch: 172, iters: 1232, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.932 G_ID: 0.179 G_Rec: 0.391 D_GP: 0.026 D_real: 0.826 D_fake: 0.871 
+(epoch: 172, iters: 1632, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.678 G_ID: 0.163 G_Rec: 0.295 D_GP: 0.028 D_real: 0.936 D_fake: 0.947 
+(epoch: 172, iters: 2032, time: 0.064) G_GAN: 0.626 G_GAN_Feat: 0.891 G_ID: 0.210 G_Rec: 0.398 D_GP: 0.028 D_real: 1.260 D_fake: 0.387 
+(epoch: 172, iters: 2432, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.701 G_ID: 0.148 G_Rec: 0.299 D_GP: 0.022 D_real: 1.257 D_fake: 0.628 
+(epoch: 172, iters: 2832, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.905 G_ID: 0.194 G_Rec: 0.383 D_GP: 0.034 D_real: 1.033 D_fake: 0.556 
+(epoch: 172, iters: 3232, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.844 G_ID: 0.155 G_Rec: 0.338 D_GP: 0.058 D_real: 0.783 D_fake: 0.827 
+(epoch: 172, iters: 3632, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.916 G_ID: 0.192 G_Rec: 0.405 D_GP: 0.039 D_real: 0.922 D_fake: 0.628 
+(epoch: 172, iters: 4032, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.693 G_ID: 0.160 G_Rec: 0.306 D_GP: 0.024 D_real: 1.201 D_fake: 0.715 
+(epoch: 172, iters: 4432, time: 0.064) G_GAN: 0.509 G_GAN_Feat: 0.889 G_ID: 0.187 G_Rec: 0.382 D_GP: 0.037 D_real: 1.189 D_fake: 0.525 
+(epoch: 172, iters: 4832, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.681 G_ID: 0.152 G_Rec: 0.335 D_GP: 0.024 D_real: 1.104 D_fake: 0.844 
+(epoch: 172, iters: 5232, time: 0.064) G_GAN: 0.764 G_GAN_Feat: 1.108 G_ID: 0.201 G_Rec: 0.424 D_GP: 0.061 D_real: 0.830 D_fake: 0.298 
+(epoch: 172, iters: 5632, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.697 G_ID: 0.146 G_Rec: 0.310 D_GP: 0.024 D_real: 0.996 D_fake: 0.873 
+(epoch: 172, iters: 6032, time: 0.064) G_GAN: 0.504 G_GAN_Feat: 0.849 G_ID: 0.174 G_Rec: 0.389 D_GP: 0.037 D_real: 1.203 D_fake: 0.509 
+(epoch: 172, iters: 6432, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.896 G_ID: 0.111 G_Rec: 0.341 D_GP: 0.133 D_real: 0.631 D_fake: 0.797 
+(epoch: 172, iters: 6832, time: 0.064) G_GAN: 0.568 G_GAN_Feat: 0.974 G_ID: 0.195 G_Rec: 0.464 D_GP: 0.034 D_real: 1.096 D_fake: 0.436 
+(epoch: 172, iters: 7232, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.817 G_ID: 0.136 G_Rec: 0.364 D_GP: 0.019 D_real: 1.249 D_fake: 0.664 
+(epoch: 172, iters: 7632, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.832 G_ID: 0.170 G_Rec: 0.422 D_GP: 0.023 D_real: 1.176 D_fake: 0.594 
+(epoch: 172, iters: 8032, time: 0.064) G_GAN: -0.030 G_GAN_Feat: 0.643 G_ID: 0.143 G_Rec: 0.332 D_GP: 0.020 D_real: 0.832 D_fake: 1.030 
+(epoch: 172, iters: 8432, time: 0.064) G_GAN: 0.120 G_GAN_Feat: 0.814 G_ID: 0.194 G_Rec: 0.416 D_GP: 0.024 D_real: 0.854 D_fake: 0.881 
+(epoch: 173, iters: 224, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.653 G_ID: 0.126 G_Rec: 0.322 D_GP: 0.029 D_real: 0.911 D_fake: 0.942 
+(epoch: 173, iters: 624, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.897 G_ID: 0.177 G_Rec: 0.404 D_GP: 0.043 D_real: 0.821 D_fake: 0.715 
+(epoch: 173, iters: 1024, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.656 G_ID: 0.142 G_Rec: 0.297 D_GP: 0.030 D_real: 1.071 D_fake: 0.847 
+(epoch: 173, iters: 1424, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.893 G_ID: 0.177 G_Rec: 0.385 D_GP: 0.039 D_real: 1.088 D_fake: 0.482 
+(epoch: 173, iters: 1824, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.768 G_ID: 0.146 G_Rec: 0.297 D_GP: 0.025 D_real: 1.131 D_fake: 0.725 
+(epoch: 173, iters: 2224, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.837 G_ID: 0.190 G_Rec: 0.372 D_GP: 0.026 D_real: 1.048 D_fake: 0.652 
+(epoch: 173, iters: 2624, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.722 G_ID: 0.137 G_Rec: 0.334 D_GP: 0.025 D_real: 0.946 D_fake: 0.941 
+(epoch: 173, iters: 3024, time: 0.064) G_GAN: 0.587 G_GAN_Feat: 0.947 G_ID: 0.219 G_Rec: 0.399 D_GP: 0.031 D_real: 1.160 D_fake: 0.426 
+(epoch: 173, iters: 3424, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.703 G_ID: 0.144 G_Rec: 0.310 D_GP: 0.028 D_real: 0.928 D_fake: 0.977 
+(epoch: 173, iters: 3824, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.979 G_ID: 0.233 G_Rec: 0.429 D_GP: 0.068 D_real: 0.842 D_fake: 0.606 
+(epoch: 173, iters: 4224, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.843 G_ID: 0.141 G_Rec: 0.315 D_GP: 0.032 D_real: 0.910 D_fake: 0.721 
+(epoch: 173, iters: 4624, time: 0.064) G_GAN: 0.751 G_GAN_Feat: 0.874 G_ID: 0.177 G_Rec: 0.384 D_GP: 0.024 D_real: 1.467 D_fake: 0.351 
+(epoch: 173, iters: 5024, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.585 G_ID: 0.120 G_Rec: 0.306 D_GP: 0.020 D_real: 1.068 D_fake: 0.856 
+(epoch: 173, iters: 5424, time: 0.064) G_GAN: 0.469 G_GAN_Feat: 0.710 G_ID: 0.187 G_Rec: 0.370 D_GP: 0.020 D_real: 1.285 D_fake: 0.535 
+(epoch: 173, iters: 5824, time: 0.064) G_GAN: -0.010 G_GAN_Feat: 0.599 G_ID: 0.162 G_Rec: 0.324 D_GP: 0.024 D_real: 0.919 D_fake: 1.010 
+(epoch: 173, iters: 6224, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.770 G_ID: 0.194 G_Rec: 0.383 D_GP: 0.031 D_real: 1.034 D_fake: 0.703 
+(epoch: 173, iters: 6624, time: 0.064) G_GAN: -0.007 G_GAN_Feat: 0.685 G_ID: 0.139 G_Rec: 0.334 D_GP: 0.035 D_real: 0.813 D_fake: 1.007 
+(epoch: 173, iters: 7024, time: 0.064) G_GAN: 0.045 G_GAN_Feat: 0.938 G_ID: 0.189 G_Rec: 0.416 D_GP: 0.184 D_real: 0.517 D_fake: 0.955 
+(epoch: 173, iters: 7424, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.636 G_ID: 0.130 G_Rec: 0.317 D_GP: 0.066 D_real: 0.979 D_fake: 0.843 
+(epoch: 173, iters: 7824, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.776 G_ID: 0.200 G_Rec: 0.360 D_GP: 0.041 D_real: 0.861 D_fake: 0.779 
+(epoch: 173, iters: 8224, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.891 G_ID: 0.131 G_Rec: 0.340 D_GP: 0.083 D_real: 0.933 D_fake: 0.698 
+(epoch: 174, iters: 16, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.873 G_ID: 0.204 G_Rec: 0.390 D_GP: 0.033 D_real: 0.904 D_fake: 0.754 
+(epoch: 174, iters: 416, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.766 G_ID: 0.152 G_Rec: 0.320 D_GP: 0.040 D_real: 1.111 D_fake: 0.675 
+(epoch: 174, iters: 816, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.836 G_ID: 0.177 G_Rec: 0.419 D_GP: 0.023 D_real: 1.195 D_fake: 0.628 
+(epoch: 174, iters: 1216, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.610 G_ID: 0.158 G_Rec: 0.307 D_GP: 0.025 D_real: 1.149 D_fake: 0.747 
+(epoch: 174, iters: 1616, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.782 G_ID: 0.197 G_Rec: 0.385 D_GP: 0.031 D_real: 1.158 D_fake: 0.578 
+(epoch: 174, iters: 2016, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.721 G_ID: 0.116 G_Rec: 0.295 D_GP: 0.044 D_real: 0.926 D_fake: 0.872 
+(epoch: 174, iters: 2416, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.961 G_ID: 0.172 G_Rec: 0.452 D_GP: 0.037 D_real: 1.031 D_fake: 0.494 
+(epoch: 174, iters: 2816, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.908 G_ID: 0.109 G_Rec: 0.359 D_GP: 0.100 D_real: 0.757 D_fake: 0.623 
+(epoch: 174, iters: 3216, time: 0.064) G_GAN: 0.644 G_GAN_Feat: 0.895 G_ID: 0.260 G_Rec: 0.424 D_GP: 0.031 D_real: 1.301 D_fake: 0.386 
+(epoch: 174, iters: 3616, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.786 G_ID: 0.148 G_Rec: 0.304 D_GP: 0.066 D_real: 0.981 D_fake: 0.653 
+(epoch: 174, iters: 4016, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.875 G_ID: 0.197 G_Rec: 0.387 D_GP: 0.041 D_real: 0.916 D_fake: 0.617 
+(epoch: 174, iters: 4416, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.765 G_ID: 0.124 G_Rec: 0.325 D_GP: 0.066 D_real: 0.783 D_fake: 0.917 
+(epoch: 174, iters: 4816, time: 0.064) G_GAN: 0.602 G_GAN_Feat: 0.995 G_ID: 0.190 G_Rec: 0.410 D_GP: 0.076 D_real: 0.934 D_fake: 0.414 
+(epoch: 174, iters: 5216, time: 0.064) G_GAN: 0.597 G_GAN_Feat: 1.010 G_ID: 0.131 G_Rec: 0.406 D_GP: 0.870 D_real: 0.777 D_fake: 0.427 
+(epoch: 174, iters: 5616, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.994 G_ID: 0.189 G_Rec: 0.498 D_GP: 0.061 D_real: 0.923 D_fake: 0.582 
+(epoch: 174, iters: 6016, time: 0.064) G_GAN: 0.054 G_GAN_Feat: 0.733 G_ID: 0.122 G_Rec: 0.320 D_GP: 0.025 D_real: 1.036 D_fake: 0.946 
+(epoch: 174, iters: 6416, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.882 G_ID: 0.190 G_Rec: 0.428 D_GP: 0.024 D_real: 1.183 D_fake: 0.501 
+(epoch: 174, iters: 6816, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.911 G_ID: 0.155 G_Rec: 0.349 D_GP: 0.060 D_real: 0.715 D_fake: 0.634 
+(epoch: 174, iters: 7216, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.786 G_ID: 0.186 G_Rec: 0.410 D_GP: 0.019 D_real: 1.014 D_fake: 0.731 
+(epoch: 174, iters: 7616, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.691 G_ID: 0.145 G_Rec: 0.320 D_GP: 0.023 D_real: 1.227 D_fake: 0.766 
+(epoch: 174, iters: 8016, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.833 G_ID: 0.204 G_Rec: 0.383 D_GP: 0.025 D_real: 0.948 D_fake: 0.735 
+(epoch: 174, iters: 8416, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.709 G_ID: 0.146 G_Rec: 0.302 D_GP: 0.036 D_real: 1.084 D_fake: 0.741 
+(epoch: 175, iters: 208, time: 0.064) G_GAN: 0.620 G_GAN_Feat: 0.880 G_ID: 0.203 G_Rec: 0.391 D_GP: 0.027 D_real: 1.312 D_fake: 0.403 
+(epoch: 175, iters: 608, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.790 G_ID: 0.158 G_Rec: 0.339 D_GP: 0.037 D_real: 0.930 D_fake: 0.798 
+(epoch: 175, iters: 1008, time: 0.064) G_GAN: 0.636 G_GAN_Feat: 1.072 G_ID: 0.181 G_Rec: 0.468 D_GP: 0.069 D_real: 0.840 D_fake: 0.395 
+(epoch: 175, iters: 1408, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.751 G_ID: 0.143 G_Rec: 0.337 D_GP: 0.022 D_real: 1.155 D_fake: 0.704 
+(epoch: 175, iters: 1808, time: 0.064) G_GAN: 0.615 G_GAN_Feat: 0.932 G_ID: 0.174 G_Rec: 0.406 D_GP: 0.038 D_real: 1.246 D_fake: 0.416 
+(epoch: 175, iters: 2208, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.696 G_ID: 0.125 G_Rec: 0.315 D_GP: 0.025 D_real: 1.448 D_fake: 0.515 
+(epoch: 175, iters: 2608, time: 0.064) G_GAN: 0.592 G_GAN_Feat: 0.977 G_ID: 0.201 G_Rec: 0.422 D_GP: 0.040 D_real: 0.978 D_fake: 0.420 
+(epoch: 175, iters: 3008, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.686 G_ID: 0.130 G_Rec: 0.347 D_GP: 0.024 D_real: 1.213 D_fake: 0.716 
+(epoch: 175, iters: 3408, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.886 G_ID: 0.222 G_Rec: 0.367 D_GP: 0.047 D_real: 0.817 D_fake: 0.817 
+(epoch: 175, iters: 3808, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.680 G_ID: 0.130 G_Rec: 0.299 D_GP: 0.022 D_real: 1.295 D_fake: 0.625 
+(epoch: 175, iters: 4208, time: 0.064) G_GAN: 0.562 G_GAN_Feat: 0.902 G_ID: 0.185 G_Rec: 0.378 D_GP: 0.035 D_real: 1.212 D_fake: 0.448 
+(epoch: 175, iters: 4608, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.706 G_ID: 0.137 G_Rec: 0.295 D_GP: 0.022 D_real: 1.097 D_fake: 0.883 
+(epoch: 175, iters: 5008, time: 0.064) G_GAN: 0.104 G_GAN_Feat: 0.938 G_ID: 0.227 G_Rec: 0.409 D_GP: 0.049 D_real: 0.848 D_fake: 0.897 
+(epoch: 175, iters: 5408, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.662 G_ID: 0.152 G_Rec: 0.284 D_GP: 0.026 D_real: 1.303 D_fake: 0.654 
+(epoch: 175, iters: 5808, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.961 G_ID: 0.182 G_Rec: 0.428 D_GP: 0.072 D_real: 0.861 D_fake: 0.545 
+(epoch: 175, iters: 6208, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.766 G_ID: 0.138 G_Rec: 0.305 D_GP: 0.031 D_real: 1.289 D_fake: 0.502 
+(epoch: 175, iters: 6608, time: 0.064) G_GAN: 0.598 G_GAN_Feat: 0.806 G_ID: 0.206 G_Rec: 0.350 D_GP: 0.029 D_real: 1.389 D_fake: 0.419 
+(epoch: 175, iters: 7008, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.707 G_ID: 0.149 G_Rec: 0.296 D_GP: 0.023 D_real: 1.465 D_fake: 0.489 
+(epoch: 175, iters: 7408, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.780 G_ID: 0.181 G_Rec: 0.392 D_GP: 0.021 D_real: 1.218 D_fake: 0.611 
+(epoch: 175, iters: 7808, time: 0.064) G_GAN: -0.001 G_GAN_Feat: 0.688 G_ID: 0.142 G_Rec: 0.340 D_GP: 0.029 D_real: 0.905 D_fake: 1.001 
+(epoch: 175, iters: 8208, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.872 G_ID: 0.201 G_Rec: 0.403 D_GP: 0.043 D_real: 0.968 D_fake: 0.674 
+(epoch: 175, iters: 8608, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.790 G_ID: 0.147 G_Rec: 0.286 D_GP: 0.026 D_real: 1.098 D_fake: 0.648 
+(epoch: 176, iters: 400, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.862 G_ID: 0.182 G_Rec: 0.395 D_GP: 0.025 D_real: 1.133 D_fake: 0.657 
+(epoch: 176, iters: 800, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.680 G_ID: 0.141 G_Rec: 0.277 D_GP: 0.027 D_real: 1.344 D_fake: 0.550 
+(epoch: 176, iters: 1200, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.723 G_ID: 0.184 G_Rec: 0.376 D_GP: 0.022 D_real: 1.038 D_fake: 0.731 
+(epoch: 176, iters: 1600, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.712 G_ID: 0.138 G_Rec: 0.334 D_GP: 0.040 D_real: 0.885 D_fake: 0.896 
+(epoch: 176, iters: 2000, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.944 G_ID: 0.190 G_Rec: 0.419 D_GP: 0.271 D_real: 0.555 D_fake: 0.861 
+(epoch: 176, iters: 2400, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.803 G_ID: 0.157 G_Rec: 0.312 D_GP: 0.043 D_real: 0.921 D_fake: 0.692 
+(epoch: 176, iters: 2800, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.657 G_ID: 0.176 G_Rec: 0.366 D_GP: 0.021 D_real: 1.264 D_fake: 0.515 
+(epoch: 176, iters: 3200, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.531 G_ID: 0.140 G_Rec: 0.307 D_GP: 0.019 D_real: 1.175 D_fake: 0.782 
+(epoch: 176, iters: 3600, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.749 G_ID: 0.215 G_Rec: 0.403 D_GP: 0.026 D_real: 1.004 D_fake: 0.658 
+(epoch: 176, iters: 4000, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.626 G_ID: 0.146 G_Rec: 0.347 D_GP: 0.034 D_real: 1.001 D_fake: 0.907 
+(epoch: 176, iters: 4400, time: 0.064) G_GAN: 0.462 G_GAN_Feat: 0.743 G_ID: 0.185 G_Rec: 0.371 D_GP: 0.029 D_real: 1.218 D_fake: 0.541 
+(epoch: 176, iters: 4800, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.710 G_ID: 0.139 G_Rec: 0.314 D_GP: 0.071 D_real: 0.897 D_fake: 0.872 
+(epoch: 176, iters: 5200, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.832 G_ID: 0.197 G_Rec: 0.385 D_GP: 0.039 D_real: 1.066 D_fake: 0.662 
+(epoch: 176, iters: 5600, time: 0.064) G_GAN: 0.120 G_GAN_Feat: 0.680 G_ID: 0.142 G_Rec: 0.274 D_GP: 0.044 D_real: 0.870 D_fake: 0.880 
+(epoch: 176, iters: 6000, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.880 G_ID: 0.176 G_Rec: 0.382 D_GP: 0.036 D_real: 1.136 D_fake: 0.521 
+(epoch: 176, iters: 6400, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.699 G_ID: 0.122 G_Rec: 0.301 D_GP: 0.024 D_real: 1.241 D_fake: 0.665 
+(epoch: 176, iters: 6800, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.918 G_ID: 0.170 G_Rec: 0.432 D_GP: 0.030 D_real: 1.030 D_fake: 0.560 
+(epoch: 176, iters: 7200, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.641 G_ID: 0.141 G_Rec: 0.305 D_GP: 0.028 D_real: 1.090 D_fake: 0.828 
+(epoch: 176, iters: 7600, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 1.102 G_ID: 0.223 G_Rec: 0.416 D_GP: 0.100 D_real: 0.528 D_fake: 0.586 
+(epoch: 176, iters: 8000, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.617 G_ID: 0.173 G_Rec: 0.311 D_GP: 0.018 D_real: 1.091 D_fake: 0.822 
+(epoch: 176, iters: 8400, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.788 G_ID: 0.206 G_Rec: 0.380 D_GP: 0.028 D_real: 1.054 D_fake: 0.666 
+(epoch: 177, iters: 192, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.785 G_ID: 0.130 G_Rec: 0.339 D_GP: 0.061 D_real: 1.077 D_fake: 0.590 
+(epoch: 177, iters: 592, time: 0.064) G_GAN: 0.523 G_GAN_Feat: 1.016 G_ID: 0.173 G_Rec: 0.429 D_GP: 0.132 D_real: 0.780 D_fake: 0.485 
+(epoch: 177, iters: 992, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.840 G_ID: 0.162 G_Rec: 0.343 D_GP: 0.043 D_real: 0.880 D_fake: 0.750 
+(epoch: 177, iters: 1392, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.917 G_ID: 0.230 G_Rec: 0.392 D_GP: 0.042 D_real: 0.972 D_fake: 0.585 
+(epoch: 177, iters: 1792, time: 0.064) G_GAN: 0.067 G_GAN_Feat: 0.696 G_ID: 0.150 G_Rec: 0.309 D_GP: 0.022 D_real: 1.012 D_fake: 0.935 
+(epoch: 177, iters: 2192, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.858 G_ID: 0.185 G_Rec: 0.435 D_GP: 0.023 D_real: 0.973 D_fake: 0.732 
+(epoch: 177, iters: 2592, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 0.740 G_ID: 0.152 G_Rec: 0.323 D_GP: 0.032 D_real: 1.063 D_fake: 0.758 
+(epoch: 177, iters: 2992, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.796 G_ID: 0.191 G_Rec: 0.374 D_GP: 0.032 D_real: 1.078 D_fake: 0.680 
+(epoch: 177, iters: 3392, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.665 G_ID: 0.151 G_Rec: 0.287 D_GP: 0.025 D_real: 1.047 D_fake: 0.840 
+(epoch: 177, iters: 3792, time: 0.064) G_GAN: 0.139 G_GAN_Feat: 1.088 G_ID: 0.222 G_Rec: 0.419 D_GP: 0.379 D_real: 0.483 D_fake: 0.862 
+(epoch: 177, iters: 4192, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.684 G_ID: 0.175 G_Rec: 0.335 D_GP: 0.024 D_real: 1.175 D_fake: 0.813 
+(epoch: 177, iters: 4592, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.850 G_ID: 0.195 G_Rec: 0.357 D_GP: 0.028 D_real: 1.010 D_fake: 0.739 
+(epoch: 177, iters: 4992, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.894 G_ID: 0.161 G_Rec: 0.353 D_GP: 0.044 D_real: 0.742 D_fake: 0.858 
+(epoch: 177, iters: 5392, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.997 G_ID: 0.232 G_Rec: 0.415 D_GP: 0.058 D_real: 0.695 D_fake: 0.637 
+(epoch: 177, iters: 5792, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.807 G_ID: 0.138 G_Rec: 0.298 D_GP: 0.030 D_real: 0.872 D_fake: 0.812 
+(epoch: 177, iters: 6192, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.932 G_ID: 0.194 G_Rec: 0.390 D_GP: 0.030 D_real: 0.807 D_fake: 0.807 
+(epoch: 177, iters: 6592, time: 0.064) G_GAN: -0.117 G_GAN_Feat: 0.692 G_ID: 0.140 G_Rec: 0.330 D_GP: 0.036 D_real: 0.827 D_fake: 1.117 
+(epoch: 177, iters: 6992, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.866 G_ID: 0.191 G_Rec: 0.397 D_GP: 0.041 D_real: 0.881 D_fake: 0.825 
+(epoch: 177, iters: 7392, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.718 G_ID: 0.137 G_Rec: 0.281 D_GP: 0.054 D_real: 1.141 D_fake: 0.735 
+(epoch: 177, iters: 7792, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 1.102 G_ID: 0.183 G_Rec: 0.418 D_GP: 0.046 D_real: 1.263 D_fake: 0.679 
+(epoch: 177, iters: 8192, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.747 G_ID: 0.139 G_Rec: 0.394 D_GP: 0.026 D_real: 1.099 D_fake: 0.742 
+(epoch: 177, iters: 8592, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 1.002 G_ID: 0.188 G_Rec: 0.408 D_GP: 0.039 D_real: 0.746 D_fake: 0.818 
+(epoch: 178, iters: 384, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.780 G_ID: 0.119 G_Rec: 0.312 D_GP: 0.293 D_real: 0.762 D_fake: 0.817 
+(epoch: 178, iters: 784, time: 0.064) G_GAN: 0.616 G_GAN_Feat: 0.909 G_ID: 0.187 G_Rec: 0.393 D_GP: 0.046 D_real: 1.136 D_fake: 0.397 
+(epoch: 178, iters: 1184, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.793 G_ID: 0.147 G_Rec: 0.304 D_GP: 0.033 D_real: 0.997 D_fake: 0.653 
+(epoch: 178, iters: 1584, time: 0.064) G_GAN: 0.556 G_GAN_Feat: 1.062 G_ID: 0.175 G_Rec: 0.452 D_GP: 0.053 D_real: 0.782 D_fake: 0.448 
+(epoch: 178, iters: 1984, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.703 G_ID: 0.164 G_Rec: 0.324 D_GP: 0.046 D_real: 0.920 D_fake: 0.898 
+(epoch: 178, iters: 2384, time: 0.064) G_GAN: 0.657 G_GAN_Feat: 0.838 G_ID: 0.196 G_Rec: 0.395 D_GP: 0.046 D_real: 1.257 D_fake: 0.351 
+(epoch: 178, iters: 2784, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.836 G_ID: 0.140 G_Rec: 0.338 D_GP: 0.173 D_real: 0.537 D_fake: 0.829 
+(epoch: 178, iters: 3184, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.847 G_ID: 0.186 G_Rec: 0.416 D_GP: 0.022 D_real: 1.167 D_fake: 0.591 
+(epoch: 178, iters: 3584, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.733 G_ID: 0.170 G_Rec: 0.327 D_GP: 0.024 D_real: 1.108 D_fake: 0.751 
+(epoch: 178, iters: 3984, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.990 G_ID: 0.187 G_Rec: 0.431 D_GP: 0.055 D_real: 0.674 D_fake: 0.714 
+(epoch: 178, iters: 4384, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.976 G_ID: 0.161 G_Rec: 0.360 D_GP: 0.075 D_real: 0.722 D_fake: 0.807 
+(epoch: 178, iters: 4784, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.906 G_ID: 0.178 G_Rec: 0.389 D_GP: 0.032 D_real: 1.213 D_fake: 0.503 
+(epoch: 178, iters: 5184, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.812 G_ID: 0.142 G_Rec: 0.338 D_GP: 0.048 D_real: 1.214 D_fake: 0.601 
+(epoch: 178, iters: 5584, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 1.047 G_ID: 0.229 G_Rec: 0.412 D_GP: 0.132 D_real: 0.430 D_fake: 0.678 
+(epoch: 178, iters: 5984, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 1.019 G_ID: 0.123 G_Rec: 0.319 D_GP: 0.385 D_real: 0.299 D_fake: 0.708 
+(epoch: 178, iters: 6384, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.904 G_ID: 0.202 G_Rec: 0.387 D_GP: 0.034 D_real: 1.099 D_fake: 0.562 
+(epoch: 178, iters: 6784, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.777 G_ID: 0.143 G_Rec: 0.322 D_GP: 0.030 D_real: 1.026 D_fake: 0.724 
+(epoch: 178, iters: 7184, time: 0.064) G_GAN: 0.047 G_GAN_Feat: 0.896 G_ID: 0.202 G_Rec: 0.366 D_GP: 0.024 D_real: 0.848 D_fake: 0.953 
+(epoch: 178, iters: 7584, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.859 G_ID: 0.134 G_Rec: 0.324 D_GP: 0.036 D_real: 1.000 D_fake: 0.661 
+(epoch: 178, iters: 7984, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 1.125 G_ID: 0.192 G_Rec: 0.413 D_GP: 0.061 D_real: 0.676 D_fake: 0.461 
+(epoch: 178, iters: 8384, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.760 G_ID: 0.199 G_Rec: 0.357 D_GP: 0.031 D_real: 1.028 D_fake: 0.758 
+(epoch: 179, iters: 176, time: 0.064) G_GAN: 0.545 G_GAN_Feat: 0.941 G_ID: 0.197 G_Rec: 0.414 D_GP: 0.054 D_real: 1.031 D_fake: 0.459 
+(epoch: 179, iters: 576, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.706 G_ID: 0.129 G_Rec: 0.301 D_GP: 0.040 D_real: 1.228 D_fake: 0.581 
+(epoch: 179, iters: 976, time: 0.064) G_GAN: 0.666 G_GAN_Feat: 0.821 G_ID: 0.192 G_Rec: 0.383 D_GP: 0.023 D_real: 1.358 D_fake: 0.345 
+(epoch: 179, iters: 1376, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.775 G_ID: 0.141 G_Rec: 0.358 D_GP: 0.038 D_real: 0.977 D_fake: 0.715 
+(epoch: 179, iters: 1776, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 1.099 G_ID: 0.238 G_Rec: 0.407 D_GP: 0.179 D_real: 0.642 D_fake: 0.589 
+(epoch: 179, iters: 2176, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.891 G_ID: 0.132 G_Rec: 0.358 D_GP: 0.084 D_real: 0.737 D_fake: 0.692 
+(epoch: 179, iters: 2576, time: 0.064) G_GAN: 0.572 G_GAN_Feat: 0.949 G_ID: 0.206 G_Rec: 0.394 D_GP: 0.045 D_real: 1.100 D_fake: 0.438 
+(epoch: 179, iters: 2976, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.696 G_ID: 0.168 G_Rec: 0.308 D_GP: 0.023 D_real: 1.290 D_fake: 0.624 
+(epoch: 179, iters: 3376, time: 0.064) G_GAN: 0.509 G_GAN_Feat: 0.890 G_ID: 0.217 G_Rec: 0.393 D_GP: 0.024 D_real: 1.210 D_fake: 0.492 
+(epoch: 179, iters: 3776, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.639 G_ID: 0.154 G_Rec: 0.284 D_GP: 0.033 D_real: 1.173 D_fake: 0.740 
+(epoch: 179, iters: 4176, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.878 G_ID: 0.204 G_Rec: 0.395 D_GP: 0.035 D_real: 1.008 D_fake: 0.739 
+(epoch: 179, iters: 4576, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.833 G_ID: 0.122 G_Rec: 0.298 D_GP: 0.053 D_real: 1.117 D_fake: 0.443 
+(epoch: 179, iters: 4976, time: 0.064) G_GAN: 0.469 G_GAN_Feat: 0.908 G_ID: 0.173 G_Rec: 0.408 D_GP: 0.035 D_real: 1.105 D_fake: 0.534 
+(epoch: 179, iters: 5376, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.789 G_ID: 0.138 G_Rec: 0.323 D_GP: 0.037 D_real: 1.164 D_fake: 0.651 
+(epoch: 179, iters: 5776, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.732 G_ID: 0.193 G_Rec: 0.393 D_GP: 0.018 D_real: 1.068 D_fake: 0.807 
+(epoch: 179, iters: 6176, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.619 G_ID: 0.140 G_Rec: 0.289 D_GP: 0.019 D_real: 1.191 D_fake: 0.784 
+(epoch: 179, iters: 6576, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.771 G_ID: 0.193 G_Rec: 0.372 D_GP: 0.021 D_real: 1.173 D_fake: 0.619 
+(epoch: 179, iters: 6976, time: 0.064) G_GAN: 0.189 G_GAN_Feat: 0.726 G_ID: 0.161 G_Rec: 0.385 D_GP: 0.036 D_real: 1.074 D_fake: 0.811 
+(epoch: 179, iters: 7376, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.804 G_ID: 0.200 G_Rec: 0.353 D_GP: 0.040 D_real: 1.026 D_fake: 0.767 
+(epoch: 179, iters: 7776, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.755 G_ID: 0.181 G_Rec: 0.303 D_GP: 0.048 D_real: 0.981 D_fake: 0.707 
+(epoch: 179, iters: 8176, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.917 G_ID: 0.174 G_Rec: 0.399 D_GP: 0.031 D_real: 1.099 D_fake: 0.588 
+(epoch: 179, iters: 8576, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.852 G_ID: 0.134 G_Rec: 0.337 D_GP: 0.080 D_real: 0.974 D_fake: 0.877 
+(epoch: 180, iters: 368, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.805 G_ID: 0.228 G_Rec: 0.378 D_GP: 0.024 D_real: 1.073 D_fake: 0.704 
+(epoch: 180, iters: 768, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.731 G_ID: 0.151 G_Rec: 0.316 D_GP: 0.035 D_real: 1.009 D_fake: 0.843 
+(epoch: 180, iters: 1168, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.914 G_ID: 0.177 G_Rec: 0.413 D_GP: 0.038 D_real: 1.157 D_fake: 0.514 
+(epoch: 180, iters: 1568, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.736 G_ID: 0.160 G_Rec: 0.332 D_GP: 0.028 D_real: 1.023 D_fake: 0.827 
+(epoch: 180, iters: 1968, time: 0.064) G_GAN: 0.957 G_GAN_Feat: 0.945 G_ID: 0.183 G_Rec: 0.377 D_GP: 0.168 D_real: 1.364 D_fake: 0.190 
+(epoch: 180, iters: 2368, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.774 G_ID: 0.146 G_Rec: 0.375 D_GP: 0.034 D_real: 1.044 D_fake: 0.702 
+(epoch: 180, iters: 2768, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 0.930 G_ID: 0.197 G_Rec: 0.371 D_GP: 0.034 D_real: 1.075 D_fake: 0.472 
+(epoch: 180, iters: 3168, time: 0.064) G_GAN: 0.656 G_GAN_Feat: 0.855 G_ID: 0.147 G_Rec: 0.328 D_GP: 0.057 D_real: 1.220 D_fake: 0.363 
+(epoch: 180, iters: 3568, time: 0.064) G_GAN: 0.815 G_GAN_Feat: 0.903 G_ID: 0.211 G_Rec: 0.378 D_GP: 0.100 D_real: 1.391 D_fake: 0.346 
+(epoch: 180, iters: 3968, time: 0.064) G_GAN: -0.063 G_GAN_Feat: 0.669 G_ID: 0.130 G_Rec: 0.318 D_GP: 0.022 D_real: 0.933 D_fake: 1.063 
+(epoch: 180, iters: 4368, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.822 G_ID: 0.194 G_Rec: 0.433 D_GP: 0.024 D_real: 0.942 D_fake: 0.772 
+(epoch: 180, iters: 4768, time: 0.064) G_GAN: -0.006 G_GAN_Feat: 0.705 G_ID: 0.124 G_Rec: 0.322 D_GP: 0.037 D_real: 0.739 D_fake: 1.006 
+(epoch: 180, iters: 5168, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.871 G_ID: 0.181 G_Rec: 0.409 D_GP: 0.025 D_real: 0.967 D_fake: 0.710 
+(epoch: 180, iters: 5568, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.848 G_ID: 0.115 G_Rec: 0.309 D_GP: 0.032 D_real: 0.921 D_fake: 0.779 
+(epoch: 180, iters: 5968, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.837 G_ID: 0.194 G_Rec: 0.390 D_GP: 0.024 D_real: 1.207 D_fake: 0.495 
+(epoch: 180, iters: 6368, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.823 G_ID: 0.150 G_Rec: 0.314 D_GP: 0.039 D_real: 0.839 D_fake: 0.862 
+(epoch: 180, iters: 6768, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.932 G_ID: 0.185 G_Rec: 0.395 D_GP: 0.030 D_real: 0.972 D_fake: 0.521 
+(epoch: 180, iters: 7168, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.937 G_ID: 0.123 G_Rec: 0.329 D_GP: 0.120 D_real: 0.423 D_fake: 0.804 
+(epoch: 180, iters: 7568, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.762 G_ID: 0.184 G_Rec: 0.411 D_GP: 0.022 D_real: 1.074 D_fake: 0.718 
+(epoch: 180, iters: 7968, time: 0.064) G_GAN: -0.015 G_GAN_Feat: 0.647 G_ID: 0.163 G_Rec: 0.342 D_GP: 0.020 D_real: 0.956 D_fake: 1.015 
+(epoch: 180, iters: 8368, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.794 G_ID: 0.204 G_Rec: 0.388 D_GP: 0.025 D_real: 0.908 D_fake: 0.834 
+(epoch: 181, iters: 160, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.646 G_ID: 0.145 G_Rec: 0.287 D_GP: 0.025 D_real: 1.086 D_fake: 0.910 
+(epoch: 181, iters: 560, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.972 G_ID: 0.203 G_Rec: 0.407 D_GP: 0.150 D_real: 0.424 D_fake: 0.913 
+(epoch: 181, iters: 960, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.717 G_ID: 0.135 G_Rec: 0.311 D_GP: 0.022 D_real: 1.007 D_fake: 0.838 
+(epoch: 181, iters: 1360, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.893 G_ID: 0.179 G_Rec: 0.428 D_GP: 0.034 D_real: 1.032 D_fake: 0.586 
+(epoch: 181, iters: 1760, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 0.897 G_ID: 0.131 G_Rec: 0.331 D_GP: 0.109 D_real: 0.639 D_fake: 0.758 
+(epoch: 181, iters: 2160, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.860 G_ID: 0.205 G_Rec: 0.396 D_GP: 0.036 D_real: 1.058 D_fake: 0.603 
+(epoch: 181, iters: 2560, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 1.108 G_ID: 0.163 G_Rec: 0.373 D_GP: 0.217 D_real: 0.312 D_fake: 0.842 
+(epoch: 181, iters: 2960, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.938 G_ID: 0.225 G_Rec: 0.421 D_GP: 0.077 D_real: 0.737 D_fake: 0.751 
+(epoch: 181, iters: 3360, time: 0.064) G_GAN: -0.018 G_GAN_Feat: 1.023 G_ID: 0.130 G_Rec: 0.433 D_GP: 0.345 D_real: 0.386 D_fake: 1.018 
+(epoch: 181, iters: 3760, time: 0.064) G_GAN: 0.689 G_GAN_Feat: 0.946 G_ID: 0.165 G_Rec: 0.407 D_GP: 0.029 D_real: 1.247 D_fake: 0.325 
+(epoch: 181, iters: 4160, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.876 G_ID: 0.136 G_Rec: 0.333 D_GP: 0.104 D_real: 0.532 D_fake: 0.909 
+(epoch: 181, iters: 4560, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 1.035 G_ID: 0.212 G_Rec: 0.394 D_GP: 0.037 D_real: 0.778 D_fake: 0.665 
+(epoch: 181, iters: 4960, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.680 G_ID: 0.154 G_Rec: 0.300 D_GP: 0.023 D_real: 1.255 D_fake: 0.702 
+(epoch: 181, iters: 5360, time: 0.064) G_GAN: 0.868 G_GAN_Feat: 0.791 G_ID: 0.201 G_Rec: 0.410 D_GP: 0.018 D_real: 1.709 D_fake: 0.236 
+(epoch: 181, iters: 5760, time: 0.064) G_GAN: 0.101 G_GAN_Feat: 0.653 G_ID: 0.150 G_Rec: 0.338 D_GP: 0.028 D_real: 1.016 D_fake: 0.899 
+(epoch: 181, iters: 6160, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.799 G_ID: 0.217 G_Rec: 0.365 D_GP: 0.027 D_real: 1.302 D_fake: 0.464 
+(epoch: 181, iters: 6560, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.754 G_ID: 0.125 G_Rec: 0.343 D_GP: 0.036 D_real: 0.985 D_fake: 0.773 
+(epoch: 181, iters: 6960, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.846 G_ID: 0.214 G_Rec: 0.387 D_GP: 0.024 D_real: 1.245 D_fake: 0.499 
+(epoch: 181, iters: 7360, time: 0.064) G_GAN: 0.010 G_GAN_Feat: 0.619 G_ID: 0.161 G_Rec: 0.279 D_GP: 0.020 D_real: 0.977 D_fake: 0.990 
+(epoch: 181, iters: 7760, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.900 G_ID: 0.185 G_Rec: 0.374 D_GP: 0.069 D_real: 0.834 D_fake: 0.747 
+(epoch: 181, iters: 8160, time: 0.064) G_GAN: 0.625 G_GAN_Feat: 0.666 G_ID: 0.117 G_Rec: 0.292 D_GP: 0.028 D_real: 1.524 D_fake: 0.397 
+(epoch: 181, iters: 8560, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 1.080 G_ID: 0.196 G_Rec: 0.494 D_GP: 0.173 D_real: 0.622 D_fake: 0.709 
+(epoch: 182, iters: 352, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.778 G_ID: 0.160 G_Rec: 0.305 D_GP: 0.050 D_real: 0.714 D_fake: 0.887 
+(epoch: 182, iters: 752, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.943 G_ID: 0.178 G_Rec: 0.386 D_GP: 0.044 D_real: 0.938 D_fake: 0.641 
+(epoch: 182, iters: 1152, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.766 G_ID: 0.180 G_Rec: 0.279 D_GP: 0.028 D_real: 1.113 D_fake: 0.647 
+(epoch: 182, iters: 1552, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.730 G_ID: 0.229 G_Rec: 0.353 D_GP: 0.029 D_real: 1.126 D_fake: 0.684 
+(epoch: 182, iters: 1952, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.763 G_ID: 0.139 G_Rec: 0.333 D_GP: 0.076 D_real: 0.778 D_fake: 0.913 
+(epoch: 182, iters: 2352, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.967 G_ID: 0.178 G_Rec: 0.448 D_GP: 0.031 D_real: 0.879 D_fake: 0.634 
+(epoch: 182, iters: 2752, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.794 G_ID: 0.117 G_Rec: 0.349 D_GP: 0.025 D_real: 1.043 D_fake: 0.827 
+(epoch: 182, iters: 3152, time: 0.064) G_GAN: 0.467 G_GAN_Feat: 0.819 G_ID: 0.184 G_Rec: 0.361 D_GP: 0.023 D_real: 1.250 D_fake: 0.534 
+(epoch: 182, iters: 3552, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.691 G_ID: 0.155 G_Rec: 0.333 D_GP: 0.039 D_real: 1.091 D_fake: 0.776 
+(epoch: 182, iters: 3952, time: 0.064) G_GAN: 0.867 G_GAN_Feat: 1.025 G_ID: 0.173 G_Rec: 0.411 D_GP: 0.051 D_real: 1.355 D_fake: 0.237 
+(epoch: 182, iters: 4352, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.862 G_ID: 0.141 G_Rec: 0.323 D_GP: 0.065 D_real: 0.640 D_fake: 0.812 
+(epoch: 182, iters: 4752, time: 0.064) G_GAN: 0.874 G_GAN_Feat: 0.961 G_ID: 0.190 G_Rec: 0.419 D_GP: 0.028 D_real: 1.382 D_fake: 0.238 
+(epoch: 182, iters: 5152, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.688 G_ID: 0.152 G_Rec: 0.293 D_GP: 0.022 D_real: 1.409 D_fake: 0.558 
+(epoch: 182, iters: 5552, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 1.106 G_ID: 0.185 G_Rec: 0.438 D_GP: 0.033 D_real: 0.913 D_fake: 0.517 
+(epoch: 182, iters: 5952, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.911 G_ID: 0.138 G_Rec: 0.334 D_GP: 0.083 D_real: 0.710 D_fake: 0.744 
+(epoch: 182, iters: 6352, time: 0.064) G_GAN: 0.683 G_GAN_Feat: 0.856 G_ID: 0.190 G_Rec: 0.388 D_GP: 0.022 D_real: 1.485 D_fake: 0.339 
+(epoch: 182, iters: 6752, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.709 G_ID: 0.161 G_Rec: 0.314 D_GP: 0.029 D_real: 1.131 D_fake: 0.668 
+(epoch: 182, iters: 7152, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.946 G_ID: 0.211 G_Rec: 0.353 D_GP: 0.028 D_real: 1.072 D_fake: 0.803 
+(epoch: 182, iters: 7552, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.718 G_ID: 0.150 G_Rec: 0.320 D_GP: 0.032 D_real: 1.243 D_fake: 0.723 
+(epoch: 182, iters: 7952, time: 0.064) G_GAN: 0.602 G_GAN_Feat: 1.042 G_ID: 0.207 G_Rec: 0.436 D_GP: 0.047 D_real: 1.072 D_fake: 0.417 
+(epoch: 182, iters: 8352, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.608 G_ID: 0.131 G_Rec: 0.319 D_GP: 0.021 D_real: 1.326 D_fake: 0.674 
+(epoch: 183, iters: 144, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.818 G_ID: 0.174 G_Rec: 0.418 D_GP: 0.025 D_real: 1.190 D_fake: 0.584 
+(epoch: 183, iters: 544, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.679 G_ID: 0.147 G_Rec: 0.319 D_GP: 0.031 D_real: 1.037 D_fake: 0.827 
+(epoch: 183, iters: 944, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.863 G_ID: 0.186 G_Rec: 0.423 D_GP: 0.026 D_real: 1.052 D_fake: 0.593 
+(epoch: 183, iters: 1344, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.698 G_ID: 0.144 G_Rec: 0.294 D_GP: 0.056 D_real: 1.181 D_fake: 0.679 
+(epoch: 183, iters: 1744, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 1.034 G_ID: 0.214 G_Rec: 0.417 D_GP: 0.222 D_real: 0.324 D_fake: 0.760 
+(epoch: 183, iters: 2144, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.801 G_ID: 0.144 G_Rec: 0.328 D_GP: 0.048 D_real: 0.856 D_fake: 0.713 
+(epoch: 183, iters: 2544, time: 0.064) G_GAN: 0.592 G_GAN_Feat: 0.784 G_ID: 0.189 G_Rec: 0.392 D_GP: 0.022 D_real: 1.374 D_fake: 0.414 
+(epoch: 183, iters: 2944, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.769 G_ID: 0.144 G_Rec: 0.321 D_GP: 0.077 D_real: 0.926 D_fake: 0.644 
+(epoch: 183, iters: 3344, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.906 G_ID: 0.186 G_Rec: 0.380 D_GP: 0.092 D_real: 0.761 D_fake: 0.758 
+(epoch: 183, iters: 3744, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.704 G_ID: 0.144 G_Rec: 0.300 D_GP: 0.030 D_real: 1.136 D_fake: 0.657 
+(epoch: 183, iters: 4144, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.893 G_ID: 0.196 G_Rec: 0.385 D_GP: 0.041 D_real: 0.757 D_fake: 0.814 
+(epoch: 183, iters: 4544, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.818 G_ID: 0.133 G_Rec: 0.360 D_GP: 0.063 D_real: 0.918 D_fake: 0.771 
+(epoch: 183, iters: 4944, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.802 G_ID: 0.178 G_Rec: 0.376 D_GP: 0.031 D_real: 0.873 D_fake: 0.790 
+(epoch: 183, iters: 5344, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.765 G_ID: 0.116 G_Rec: 0.303 D_GP: 0.038 D_real: 1.133 D_fake: 0.693 
+(epoch: 183, iters: 5744, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.842 G_ID: 0.196 G_Rec: 0.392 D_GP: 0.028 D_real: 1.080 D_fake: 0.602 
+(epoch: 183, iters: 6144, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.882 G_ID: 0.140 G_Rec: 0.302 D_GP: 0.182 D_real: 0.450 D_fake: 0.803 
+(epoch: 183, iters: 6544, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 1.015 G_ID: 0.189 G_Rec: 0.461 D_GP: 0.082 D_real: 0.826 D_fake: 0.717 
+(epoch: 183, iters: 6944, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 0.663 G_ID: 0.139 G_Rec: 0.295 D_GP: 0.024 D_real: 1.310 D_fake: 0.585 
+(epoch: 183, iters: 7344, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 1.111 G_ID: 0.207 G_Rec: 0.402 D_GP: 0.034 D_real: 0.453 D_fake: 0.649 
+(epoch: 183, iters: 7744, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.665 G_ID: 0.133 G_Rec: 0.305 D_GP: 0.025 D_real: 1.173 D_fake: 0.762 
+(epoch: 183, iters: 8144, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.876 G_ID: 0.168 G_Rec: 0.405 D_GP: 0.027 D_real: 1.012 D_fake: 0.651 
+(epoch: 183, iters: 8544, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.763 G_ID: 0.148 G_Rec: 0.319 D_GP: 0.028 D_real: 1.080 D_fake: 0.838 
+(epoch: 184, iters: 336, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 1.025 G_ID: 0.226 G_Rec: 0.448 D_GP: 0.086 D_real: 0.798 D_fake: 0.721 
+(epoch: 184, iters: 736, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.872 G_ID: 0.147 G_Rec: 0.334 D_GP: 0.175 D_real: 0.732 D_fake: 0.840 
+(epoch: 184, iters: 1136, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.971 G_ID: 0.195 G_Rec: 0.390 D_GP: 0.084 D_real: 0.661 D_fake: 0.697 
+(epoch: 184, iters: 1536, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.776 G_ID: 0.133 G_Rec: 0.344 D_GP: 0.026 D_real: 1.175 D_fake: 0.652 
+(epoch: 184, iters: 1936, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.943 G_ID: 0.211 G_Rec: 0.403 D_GP: 0.027 D_real: 1.019 D_fake: 0.677 
+(epoch: 184, iters: 2336, time: 0.064) G_GAN: 0.006 G_GAN_Feat: 0.711 G_ID: 0.176 G_Rec: 0.352 D_GP: 0.037 D_real: 0.796 D_fake: 0.994 
+(epoch: 184, iters: 2736, time: 0.064) G_GAN: 0.628 G_GAN_Feat: 0.851 G_ID: 0.172 G_Rec: 0.372 D_GP: 0.028 D_real: 1.333 D_fake: 0.384 
+(epoch: 184, iters: 3136, time: 0.064) G_GAN: -0.093 G_GAN_Feat: 0.796 G_ID: 0.130 G_Rec: 0.330 D_GP: 0.025 D_real: 0.883 D_fake: 1.098 
+(epoch: 184, iters: 3536, time: 0.064) G_GAN: 0.903 G_GAN_Feat: 0.948 G_ID: 0.152 G_Rec: 0.399 D_GP: 0.026 D_real: 1.482 D_fake: 0.152 
+(epoch: 184, iters: 3936, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.912 G_ID: 0.151 G_Rec: 0.382 D_GP: 0.078 D_real: 1.024 D_fake: 0.714 
+(epoch: 184, iters: 4336, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.799 G_ID: 0.214 G_Rec: 0.402 D_GP: 0.021 D_real: 1.016 D_fake: 0.754 
+(epoch: 184, iters: 4736, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.766 G_ID: 0.138 G_Rec: 0.327 D_GP: 0.044 D_real: 0.879 D_fake: 0.797 
+(epoch: 184, iters: 5136, time: 0.064) G_GAN: 0.547 G_GAN_Feat: 0.933 G_ID: 0.186 G_Rec: 0.409 D_GP: 0.038 D_real: 1.036 D_fake: 0.457 
+(epoch: 184, iters: 5536, time: 0.064) G_GAN: 0.664 G_GAN_Feat: 0.848 G_ID: 0.107 G_Rec: 0.358 D_GP: 0.167 D_real: 1.265 D_fake: 0.366 
+(epoch: 184, iters: 5936, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 1.010 G_ID: 0.180 G_Rec: 0.433 D_GP: 0.104 D_real: 0.472 D_fake: 0.761 
+(epoch: 184, iters: 6336, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.819 G_ID: 0.136 G_Rec: 0.322 D_GP: 0.038 D_real: 0.944 D_fake: 0.738 
+(epoch: 184, iters: 6736, time: 0.064) G_GAN: 0.595 G_GAN_Feat: 1.113 G_ID: 0.198 G_Rec: 0.415 D_GP: 0.091 D_real: 0.572 D_fake: 0.414 
+(epoch: 184, iters: 7136, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.830 G_ID: 0.168 G_Rec: 0.345 D_GP: 0.112 D_real: 0.739 D_fake: 0.844 
+(epoch: 184, iters: 7536, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 1.130 G_ID: 0.165 G_Rec: 0.425 D_GP: 0.138 D_real: 0.556 D_fake: 0.652 
+(epoch: 184, iters: 7936, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.743 G_ID: 0.129 G_Rec: 0.301 D_GP: 0.022 D_real: 1.015 D_fake: 0.797 
+(epoch: 184, iters: 8336, time: 0.064) G_GAN: 0.548 G_GAN_Feat: 1.078 G_ID: 0.203 G_Rec: 0.454 D_GP: 0.031 D_real: 0.863 D_fake: 0.484 
+(epoch: 185, iters: 128, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.727 G_ID: 0.131 G_Rec: 0.293 D_GP: 0.022 D_real: 1.396 D_fake: 0.538 
+(epoch: 185, iters: 528, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.994 G_ID: 0.174 G_Rec: 0.414 D_GP: 0.040 D_real: 0.826 D_fake: 0.630 
+(epoch: 185, iters: 928, time: 0.064) G_GAN: 0.560 G_GAN_Feat: 0.813 G_ID: 0.143 G_Rec: 0.340 D_GP: 0.026 D_real: 1.346 D_fake: 0.544 
+(epoch: 185, iters: 1328, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 1.038 G_ID: 0.189 G_Rec: 0.418 D_GP: 0.024 D_real: 1.230 D_fake: 0.555 
+(epoch: 185, iters: 1728, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.688 G_ID: 0.135 G_Rec: 0.311 D_GP: 0.027 D_real: 1.035 D_fake: 0.786 
+(epoch: 185, iters: 2128, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 1.004 G_ID: 0.192 G_Rec: 0.401 D_GP: 0.133 D_real: 0.731 D_fake: 0.617 
+(epoch: 185, iters: 2528, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.715 G_ID: 0.130 G_Rec: 0.308 D_GP: 0.021 D_real: 1.111 D_fake: 0.751 
+(epoch: 185, iters: 2928, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 1.102 G_ID: 0.186 G_Rec: 0.451 D_GP: 0.035 D_real: 0.520 D_fake: 0.854 
+(epoch: 185, iters: 3328, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.686 G_ID: 0.148 G_Rec: 0.325 D_GP: 0.022 D_real: 1.199 D_fake: 0.785 
+(epoch: 185, iters: 3728, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 1.111 G_ID: 0.187 G_Rec: 0.442 D_GP: 0.086 D_real: 0.743 D_fake: 0.608 
+(epoch: 185, iters: 4128, time: 0.064) G_GAN: 0.487 G_GAN_Feat: 0.746 G_ID: 0.146 G_Rec: 0.328 D_GP: 0.021 D_real: 1.467 D_fake: 0.516 
+(epoch: 185, iters: 4528, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.818 G_ID: 0.182 G_Rec: 0.417 D_GP: 0.022 D_real: 1.043 D_fake: 0.731 
+(epoch: 185, iters: 4928, time: 0.064) G_GAN: -0.084 G_GAN_Feat: 0.776 G_ID: 0.157 G_Rec: 0.329 D_GP: 0.059 D_real: 0.769 D_fake: 1.084 
+(epoch: 185, iters: 5328, time: 0.064) G_GAN: 0.615 G_GAN_Feat: 0.827 G_ID: 0.176 G_Rec: 0.379 D_GP: 0.022 D_real: 1.353 D_fake: 0.398 
+(epoch: 185, iters: 5728, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.688 G_ID: 0.142 G_Rec: 0.313 D_GP: 0.027 D_real: 1.156 D_fake: 0.740 
+(epoch: 185, iters: 6128, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 1.114 G_ID: 0.200 G_Rec: 0.463 D_GP: 0.140 D_real: 0.476 D_fake: 0.485 
+(epoch: 185, iters: 6528, time: 0.064) G_GAN: -0.015 G_GAN_Feat: 0.866 G_ID: 0.142 G_Rec: 0.328 D_GP: 0.131 D_real: 0.393 D_fake: 1.015 
+(epoch: 185, iters: 6928, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.968 G_ID: 0.180 G_Rec: 0.443 D_GP: 0.027 D_real: 0.994 D_fake: 0.642 
+(epoch: 185, iters: 7328, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.745 G_ID: 0.120 G_Rec: 0.291 D_GP: 0.025 D_real: 0.983 D_fake: 0.821 
+(epoch: 185, iters: 7728, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.941 G_ID: 0.186 G_Rec: 0.404 D_GP: 0.027 D_real: 1.115 D_fake: 0.484 
+(epoch: 185, iters: 8128, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.876 G_ID: 0.137 G_Rec: 0.311 D_GP: 0.073 D_real: 0.979 D_fake: 0.518 
+(epoch: 185, iters: 8528, time: 0.064) G_GAN: 0.141 G_GAN_Feat: 0.929 G_ID: 0.209 G_Rec: 0.386 D_GP: 0.037 D_real: 0.877 D_fake: 0.859 
+(epoch: 186, iters: 320, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.764 G_ID: 0.137 G_Rec: 0.309 D_GP: 0.048 D_real: 1.169 D_fake: 0.689 
+(epoch: 186, iters: 720, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.949 G_ID: 0.218 G_Rec: 0.438 D_GP: 0.084 D_real: 0.808 D_fake: 0.800 
+(epoch: 186, iters: 1120, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.757 G_ID: 0.139 G_Rec: 0.317 D_GP: 0.027 D_real: 0.988 D_fake: 0.857 
+(epoch: 186, iters: 1520, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 0.898 G_ID: 0.157 G_Rec: 0.395 D_GP: 0.027 D_real: 1.188 D_fake: 0.452 
+(epoch: 186, iters: 1920, time: 0.064) G_GAN: -0.094 G_GAN_Feat: 0.783 G_ID: 0.160 G_Rec: 0.302 D_GP: 0.021 D_real: 0.792 D_fake: 1.094 
+(epoch: 186, iters: 2320, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 1.102 G_ID: 0.216 G_Rec: 0.406 D_GP: 0.564 D_real: 0.450 D_fake: 0.565 
+(epoch: 186, iters: 2720, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.638 G_ID: 0.139 G_Rec: 0.322 D_GP: 0.020 D_real: 1.257 D_fake: 0.704 
+(epoch: 186, iters: 3120, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 1.056 G_ID: 0.219 G_Rec: 0.432 D_GP: 0.117 D_real: 0.399 D_fake: 0.746 
+(epoch: 186, iters: 3520, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.787 G_ID: 0.130 G_Rec: 0.305 D_GP: 0.040 D_real: 1.016 D_fake: 0.748 
+(epoch: 186, iters: 3920, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 1.015 G_ID: 0.203 G_Rec: 0.417 D_GP: 0.050 D_real: 0.798 D_fake: 0.592 
+(epoch: 186, iters: 4320, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 1.039 G_ID: 0.134 G_Rec: 0.372 D_GP: 0.089 D_real: 0.425 D_fake: 0.778 
+(epoch: 186, iters: 4720, time: 0.064) G_GAN: 0.570 G_GAN_Feat: 0.802 G_ID: 0.171 G_Rec: 0.406 D_GP: 0.017 D_real: 1.396 D_fake: 0.437 
+(epoch: 186, iters: 5120, time: 0.064) G_GAN: 0.096 G_GAN_Feat: 0.720 G_ID: 0.148 G_Rec: 0.356 D_GP: 0.033 D_real: 0.936 D_fake: 0.904 
+(epoch: 186, iters: 5520, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.990 G_ID: 0.235 G_Rec: 0.413 D_GP: 0.032 D_real: 0.922 D_fake: 0.537 
+(epoch: 186, iters: 5920, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.840 G_ID: 0.130 G_Rec: 0.333 D_GP: 0.068 D_real: 0.865 D_fake: 0.832 
+(epoch: 186, iters: 6320, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.922 G_ID: 0.192 G_Rec: 0.416 D_GP: 0.033 D_real: 0.870 D_fake: 0.699 
+(epoch: 186, iters: 6720, time: 0.064) G_GAN: -0.011 G_GAN_Feat: 0.724 G_ID: 0.157 G_Rec: 0.315 D_GP: 0.027 D_real: 0.970 D_fake: 1.013 
+(epoch: 186, iters: 7120, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 1.017 G_ID: 0.185 G_Rec: 0.403 D_GP: 0.043 D_real: 0.970 D_fake: 0.677 
+(epoch: 186, iters: 7520, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.748 G_ID: 0.133 G_Rec: 0.303 D_GP: 0.018 D_real: 1.026 D_fake: 0.937 
+(epoch: 186, iters: 7920, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.905 G_ID: 0.219 G_Rec: 0.416 D_GP: 0.022 D_real: 1.125 D_fake: 0.604 
+(epoch: 186, iters: 8320, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.768 G_ID: 0.144 G_Rec: 0.316 D_GP: 0.038 D_real: 0.895 D_fake: 0.783 
+(epoch: 187, iters: 112, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.849 G_ID: 0.163 G_Rec: 0.400 D_GP: 0.024 D_real: 1.166 D_fake: 0.635 
+(epoch: 187, iters: 512, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.807 G_ID: 0.141 G_Rec: 0.346 D_GP: 0.025 D_real: 1.195 D_fake: 0.705 
+(epoch: 187, iters: 912, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.885 G_ID: 0.172 G_Rec: 0.407 D_GP: 0.024 D_real: 1.124 D_fake: 0.706 
+(epoch: 187, iters: 1312, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.705 G_ID: 0.138 G_Rec: 0.319 D_GP: 0.027 D_real: 1.029 D_fake: 0.828 
+(epoch: 187, iters: 1712, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.995 G_ID: 0.208 G_Rec: 0.413 D_GP: 0.027 D_real: 1.005 D_fake: 0.633 
+(epoch: 187, iters: 2112, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.718 G_ID: 0.122 G_Rec: 0.302 D_GP: 0.020 D_real: 1.023 D_fake: 0.847 
+(epoch: 187, iters: 2512, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.953 G_ID: 0.192 G_Rec: 0.424 D_GP: 0.027 D_real: 1.064 D_fake: 0.635 
+(epoch: 187, iters: 2912, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 1.027 G_ID: 0.140 G_Rec: 0.363 D_GP: 0.086 D_real: 0.948 D_fake: 0.807 
+(epoch: 187, iters: 3312, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.845 G_ID: 0.208 G_Rec: 0.352 D_GP: 0.026 D_real: 1.101 D_fake: 0.651 
+(epoch: 187, iters: 3712, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.709 G_ID: 0.165 G_Rec: 0.314 D_GP: 0.025 D_real: 1.249 D_fake: 0.699 
+(epoch: 187, iters: 4112, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.828 G_ID: 0.192 G_Rec: 0.383 D_GP: 0.023 D_real: 1.207 D_fake: 0.584 
+(epoch: 187, iters: 4512, time: 0.064) G_GAN: -0.122 G_GAN_Feat: 0.871 G_ID: 0.146 G_Rec: 0.326 D_GP: 0.164 D_real: 0.178 D_fake: 1.122 
+(epoch: 187, iters: 4912, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.847 G_ID: 0.181 G_Rec: 0.469 D_GP: 0.025 D_real: 0.883 D_fake: 0.883 
+(epoch: 187, iters: 5312, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.734 G_ID: 0.138 G_Rec: 0.300 D_GP: 0.033 D_real: 1.111 D_fake: 0.711 
+(epoch: 187, iters: 5712, time: 0.064) G_GAN: 0.923 G_GAN_Feat: 0.989 G_ID: 0.183 G_Rec: 0.423 D_GP: 0.028 D_real: 1.405 D_fake: 0.190 
+(epoch: 187, iters: 6112, time: 0.064) G_GAN: -0.082 G_GAN_Feat: 0.983 G_ID: 0.175 G_Rec: 0.340 D_GP: 0.118 D_real: 0.423 D_fake: 1.082 
+(epoch: 187, iters: 6512, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.768 G_ID: 0.183 G_Rec: 0.384 D_GP: 0.019 D_real: 1.195 D_fake: 0.579 
+(epoch: 187, iters: 6912, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.949 G_ID: 0.152 G_Rec: 0.333 D_GP: 0.172 D_real: 0.453 D_fake: 0.741 
+(epoch: 187, iters: 7312, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 1.122 G_ID: 0.184 G_Rec: 0.456 D_GP: 0.080 D_real: 0.598 D_fake: 0.488 
+(epoch: 187, iters: 7712, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.734 G_ID: 0.120 G_Rec: 0.285 D_GP: 0.023 D_real: 1.064 D_fake: 0.878 
+(epoch: 187, iters: 8112, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 1.076 G_ID: 0.179 G_Rec: 0.396 D_GP: 0.031 D_real: 0.829 D_fake: 0.492 
+(epoch: 187, iters: 8512, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.923 G_ID: 0.138 G_Rec: 0.333 D_GP: 0.051 D_real: 0.587 D_fake: 0.789 
+(epoch: 188, iters: 304, time: 0.064) G_GAN: 0.878 G_GAN_Feat: 1.021 G_ID: 0.163 G_Rec: 0.429 D_GP: 0.027 D_real: 1.388 D_fake: 0.213 
+(epoch: 188, iters: 704, time: 0.064) G_GAN: -0.213 G_GAN_Feat: 0.713 G_ID: 0.156 G_Rec: 0.291 D_GP: 0.025 D_real: 0.818 D_fake: 1.213 
+(epoch: 188, iters: 1104, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.971 G_ID: 0.191 G_Rec: 0.400 D_GP: 0.027 D_real: 0.830 D_fake: 0.734 
+(epoch: 188, iters: 1504, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.938 G_ID: 0.132 G_Rec: 0.327 D_GP: 0.101 D_real: 0.459 D_fake: 0.724 
+(epoch: 188, iters: 1904, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.790 G_ID: 0.186 G_Rec: 0.409 D_GP: 0.019 D_real: 1.235 D_fake: 0.586 
+(epoch: 188, iters: 2304, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.788 G_ID: 0.125 G_Rec: 0.305 D_GP: 0.031 D_real: 1.189 D_fake: 0.682 
+(epoch: 188, iters: 2704, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.850 G_ID: 0.213 G_Rec: 0.396 D_GP: 0.024 D_real: 1.032 D_fake: 0.717 
+(epoch: 188, iters: 3104, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.705 G_ID: 0.140 G_Rec: 0.330 D_GP: 0.022 D_real: 1.243 D_fake: 0.614 
+(epoch: 188, iters: 3504, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.824 G_ID: 0.194 G_Rec: 0.362 D_GP: 0.027 D_real: 1.000 D_fake: 0.707 
+(epoch: 188, iters: 3904, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.834 G_ID: 0.144 G_Rec: 0.317 D_GP: 0.110 D_real: 0.685 D_fake: 0.947 
+(epoch: 188, iters: 4304, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.893 G_ID: 0.196 G_Rec: 0.380 D_GP: 0.037 D_real: 0.926 D_fake: 0.631 
+(epoch: 188, iters: 4704, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.605 G_ID: 0.138 G_Rec: 0.288 D_GP: 0.023 D_real: 1.163 D_fake: 0.834 
+(epoch: 188, iters: 5104, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 1.058 G_ID: 0.187 G_Rec: 0.427 D_GP: 0.129 D_real: 0.467 D_fake: 0.789 
+(epoch: 188, iters: 5504, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.710 G_ID: 0.157 G_Rec: 0.333 D_GP: 0.023 D_real: 1.115 D_fake: 0.752 
+(epoch: 188, iters: 5904, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.947 G_ID: 0.181 G_Rec: 0.373 D_GP: 0.066 D_real: 0.675 D_fake: 0.722 
+(epoch: 188, iters: 6304, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.771 G_ID: 0.146 G_Rec: 0.292 D_GP: 0.031 D_real: 1.050 D_fake: 0.763 
+(epoch: 188, iters: 6704, time: 0.064) G_GAN: 0.558 G_GAN_Feat: 1.002 G_ID: 0.177 G_Rec: 0.398 D_GP: 0.035 D_real: 1.052 D_fake: 0.449 
+(epoch: 188, iters: 7104, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.808 G_ID: 0.147 G_Rec: 0.301 D_GP: 0.050 D_real: 0.912 D_fake: 0.740 
+(epoch: 188, iters: 7504, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.969 G_ID: 0.172 G_Rec: 0.383 D_GP: 0.025 D_real: 1.005 D_fake: 0.602 
+(epoch: 188, iters: 7904, time: 0.064) G_GAN: 0.462 G_GAN_Feat: 0.635 G_ID: 0.125 G_Rec: 0.318 D_GP: 0.020 D_real: 1.458 D_fake: 0.543 
+(epoch: 188, iters: 8304, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.882 G_ID: 0.207 G_Rec: 0.416 D_GP: 0.031 D_real: 0.777 D_fake: 0.854 
+(epoch: 189, iters: 96, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.719 G_ID: 0.131 G_Rec: 0.298 D_GP: 0.040 D_real: 0.980 D_fake: 0.809 
+(epoch: 189, iters: 496, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.860 G_ID: 0.199 G_Rec: 0.382 D_GP: 0.029 D_real: 1.188 D_fake: 0.501 
+(epoch: 189, iters: 896, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.733 G_ID: 0.116 G_Rec: 0.308 D_GP: 0.042 D_real: 0.996 D_fake: 0.725 
+(epoch: 189, iters: 1296, time: 0.064) G_GAN: 0.817 G_GAN_Feat: 1.011 G_ID: 0.197 G_Rec: 0.416 D_GP: 0.084 D_real: 1.229 D_fake: 0.455 
+(epoch: 189, iters: 1696, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.733 G_ID: 0.133 G_Rec: 0.360 D_GP: 0.023 D_real: 1.203 D_fake: 0.683 
+(epoch: 189, iters: 2096, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 1.021 G_ID: 0.186 G_Rec: 0.457 D_GP: 0.773 D_real: 0.586 D_fake: 0.707 
+(epoch: 189, iters: 2496, time: 0.064) G_GAN: 0.042 G_GAN_Feat: 0.615 G_ID: 0.167 G_Rec: 0.295 D_GP: 0.020 D_real: 0.936 D_fake: 0.958 
+(epoch: 189, iters: 2896, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 1.159 G_ID: 0.195 G_Rec: 0.457 D_GP: 0.404 D_real: 0.433 D_fake: 0.742 
+(epoch: 189, iters: 3296, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.834 G_ID: 0.159 G_Rec: 0.388 D_GP: 0.102 D_real: 0.814 D_fake: 0.751 
+(epoch: 189, iters: 3696, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.812 G_ID: 0.158 G_Rec: 0.398 D_GP: 0.027 D_real: 1.283 D_fake: 0.516 
+(epoch: 189, iters: 4096, time: 0.064) G_GAN: 0.110 G_GAN_Feat: 0.744 G_ID: 0.161 G_Rec: 0.299 D_GP: 0.032 D_real: 0.980 D_fake: 0.890 
+(epoch: 189, iters: 4496, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.857 G_ID: 0.190 G_Rec: 0.393 D_GP: 0.026 D_real: 1.184 D_fake: 0.499 
+(epoch: 189, iters: 4896, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.723 G_ID: 0.131 G_Rec: 0.323 D_GP: 0.023 D_real: 1.295 D_fake: 0.569 
+(epoch: 189, iters: 5296, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.942 G_ID: 0.187 G_Rec: 0.394 D_GP: 0.029 D_real: 1.054 D_fake: 0.623 
+(epoch: 189, iters: 5696, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.573 G_ID: 0.123 G_Rec: 0.293 D_GP: 0.026 D_real: 1.400 D_fake: 0.596 
+(epoch: 189, iters: 6096, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.919 G_ID: 0.183 G_Rec: 0.457 D_GP: 0.105 D_real: 0.725 D_fake: 0.712 
+(epoch: 189, iters: 6496, time: 0.064) G_GAN: -0.029 G_GAN_Feat: 0.661 G_ID: 0.127 G_Rec: 0.332 D_GP: 0.027 D_real: 0.880 D_fake: 1.029 
+(epoch: 189, iters: 6896, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.844 G_ID: 0.192 G_Rec: 0.340 D_GP: 0.037 D_real: 1.176 D_fake: 0.554 
+(epoch: 189, iters: 7296, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.774 G_ID: 0.177 G_Rec: 0.313 D_GP: 0.038 D_real: 1.083 D_fake: 0.690 
+(epoch: 189, iters: 7696, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.969 G_ID: 0.197 G_Rec: 0.378 D_GP: 0.056 D_real: 0.580 D_fake: 0.719 
+(epoch: 189, iters: 8096, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.714 G_ID: 0.172 G_Rec: 0.314 D_GP: 0.024 D_real: 1.172 D_fake: 0.773 
+(epoch: 189, iters: 8496, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 1.146 G_ID: 0.202 G_Rec: 0.462 D_GP: 0.063 D_real: 0.324 D_fake: 0.554 
+(epoch: 190, iters: 288, time: 0.064) G_GAN: 0.098 G_GAN_Feat: 0.563 G_ID: 0.149 G_Rec: 0.298 D_GP: 0.019 D_real: 1.038 D_fake: 0.902 
+(epoch: 190, iters: 688, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.760 G_ID: 0.181 G_Rec: 0.399 D_GP: 0.021 D_real: 1.152 D_fake: 0.569 
+(epoch: 190, iters: 1088, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.573 G_ID: 0.132 G_Rec: 0.302 D_GP: 0.020 D_real: 1.110 D_fake: 0.845 
+(epoch: 190, iters: 1488, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.807 G_ID: 0.200 G_Rec: 0.395 D_GP: 0.039 D_real: 1.115 D_fake: 0.598 
+(epoch: 190, iters: 1888, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.659 G_ID: 0.125 G_Rec: 0.319 D_GP: 0.034 D_real: 1.003 D_fake: 0.888 
+(epoch: 190, iters: 2288, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.786 G_ID: 0.182 G_Rec: 0.381 D_GP: 0.027 D_real: 0.986 D_fake: 0.776 
+(epoch: 190, iters: 2688, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.671 G_ID: 0.117 G_Rec: 0.301 D_GP: 0.053 D_real: 0.988 D_fake: 0.828 
+(epoch: 190, iters: 3088, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.803 G_ID: 0.208 G_Rec: 0.365 D_GP: 0.062 D_real: 0.823 D_fake: 0.816 
+(epoch: 190, iters: 3488, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.736 G_ID: 0.139 G_Rec: 0.303 D_GP: 0.077 D_real: 0.947 D_fake: 0.692 
+(epoch: 190, iters: 3888, time: 0.064) G_GAN: 0.660 G_GAN_Feat: 0.843 G_ID: 0.174 G_Rec: 0.380 D_GP: 0.025 D_real: 1.362 D_fake: 0.375 
+(epoch: 190, iters: 4288, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.708 G_ID: 0.125 G_Rec: 0.306 D_GP: 0.034 D_real: 1.266 D_fake: 0.603 
+(epoch: 190, iters: 4688, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.888 G_ID: 0.208 G_Rec: 0.372 D_GP: 0.032 D_real: 0.838 D_fake: 0.827 
+(epoch: 190, iters: 5088, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.938 G_ID: 0.151 G_Rec: 0.303 D_GP: 0.060 D_real: 0.536 D_fake: 0.808 
+(epoch: 190, iters: 5488, time: 0.064) G_GAN: 0.641 G_GAN_Feat: 0.881 G_ID: 0.190 G_Rec: 0.397 D_GP: 0.026 D_real: 1.291 D_fake: 0.373 
+(epoch: 190, iters: 5888, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 0.986 G_ID: 0.172 G_Rec: 0.336 D_GP: 0.079 D_real: 0.994 D_fake: 0.697 
+(epoch: 190, iters: 6288, time: 0.064) G_GAN: 0.885 G_GAN_Feat: 0.911 G_ID: 0.196 G_Rec: 0.406 D_GP: 0.029 D_real: 1.648 D_fake: 0.222 
+(epoch: 190, iters: 6688, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 1.004 G_ID: 0.140 G_Rec: 0.363 D_GP: 0.085 D_real: 0.770 D_fake: 0.485 
+(epoch: 190, iters: 7088, time: 0.064) G_GAN: 0.655 G_GAN_Feat: 0.926 G_ID: 0.176 G_Rec: 0.404 D_GP: 0.030 D_real: 1.265 D_fake: 0.354 
+(epoch: 190, iters: 7488, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.823 G_ID: 0.144 G_Rec: 0.311 D_GP: 0.064 D_real: 0.724 D_fake: 0.846 
+(epoch: 190, iters: 7888, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.848 G_ID: 0.206 G_Rec: 0.400 D_GP: 0.025 D_real: 1.144 D_fake: 0.557 
+(epoch: 190, iters: 8288, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.775 G_ID: 0.124 G_Rec: 0.337 D_GP: 0.040 D_real: 1.012 D_fake: 0.727 
+(epoch: 191, iters: 80, time: 0.064) G_GAN: 0.682 G_GAN_Feat: 1.216 G_ID: 0.200 G_Rec: 0.454 D_GP: 0.277 D_real: 0.460 D_fake: 0.374 
+(epoch: 191, iters: 480, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.695 G_ID: 0.138 G_Rec: 0.369 D_GP: 0.021 D_real: 1.166 D_fake: 0.711 
+(epoch: 191, iters: 880, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.843 G_ID: 0.185 G_Rec: 0.381 D_GP: 0.028 D_real: 1.138 D_fake: 0.556 
+(epoch: 191, iters: 1280, time: 0.064) G_GAN: 0.052 G_GAN_Feat: 0.824 G_ID: 0.142 G_Rec: 0.328 D_GP: 0.106 D_real: 0.468 D_fake: 0.948 
+(epoch: 191, iters: 1680, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.829 G_ID: 0.169 G_Rec: 0.377 D_GP: 0.022 D_real: 1.153 D_fake: 0.609 
+(epoch: 191, iters: 2080, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.831 G_ID: 0.136 G_Rec: 0.389 D_GP: 0.064 D_real: 0.936 D_fake: 0.765 
+(epoch: 191, iters: 2480, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.817 G_ID: 0.202 G_Rec: 0.390 D_GP: 0.029 D_real: 1.281 D_fake: 0.492 
+(epoch: 191, iters: 2880, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.895 G_ID: 0.144 G_Rec: 0.367 D_GP: 0.140 D_real: 0.548 D_fake: 0.655 
+(epoch: 191, iters: 3280, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 1.096 G_ID: 0.157 G_Rec: 0.375 D_GP: 0.047 D_real: 0.571 D_fake: 0.627 
+(epoch: 191, iters: 3680, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.826 G_ID: 0.131 G_Rec: 0.320 D_GP: 0.072 D_real: 1.161 D_fake: 0.672 
+(epoch: 191, iters: 4080, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.936 G_ID: 0.193 G_Rec: 0.396 D_GP: 0.031 D_real: 1.106 D_fake: 0.568 
+(epoch: 191, iters: 4480, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.794 G_ID: 0.124 G_Rec: 0.324 D_GP: 0.045 D_real: 0.640 D_fake: 0.915 
+(epoch: 191, iters: 4880, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.117 G_ID: 0.195 G_Rec: 0.454 D_GP: 2.094 D_real: 0.499 D_fake: 0.652 
+(epoch: 191, iters: 5280, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.601 G_ID: 0.137 G_Rec: 0.465 D_GP: 0.018 D_real: 1.230 D_fake: 0.753 
+(epoch: 191, iters: 5680, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.764 G_ID: 0.198 G_Rec: 0.405 D_GP: 0.019 D_real: 1.091 D_fake: 0.621 
+(epoch: 191, iters: 6080, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.693 G_ID: 0.140 G_Rec: 0.331 D_GP: 0.037 D_real: 0.940 D_fake: 0.829 
+(epoch: 191, iters: 6480, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 1.013 G_ID: 0.186 G_Rec: 0.404 D_GP: 0.093 D_real: 0.552 D_fake: 0.769 
+(epoch: 191, iters: 6880, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.746 G_ID: 0.143 G_Rec: 0.314 D_GP: 0.059 D_real: 0.927 D_fake: 0.715 
+(epoch: 191, iters: 7280, time: 0.064) G_GAN: 0.656 G_GAN_Feat: 0.981 G_ID: 0.205 G_Rec: 0.426 D_GP: 0.059 D_real: 1.085 D_fake: 0.430 
+(epoch: 191, iters: 7680, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.776 G_ID: 0.151 G_Rec: 0.351 D_GP: 0.028 D_real: 1.386 D_fake: 0.575 
+(epoch: 191, iters: 8080, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.950 G_ID: 0.203 G_Rec: 0.371 D_GP: 0.076 D_real: 0.729 D_fake: 0.908 
+(epoch: 191, iters: 8480, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.749 G_ID: 0.132 G_Rec: 0.310 D_GP: 0.040 D_real: 0.964 D_fake: 0.784 
+(epoch: 192, iters: 272, time: 0.064) G_GAN: 0.495 G_GAN_Feat: 0.823 G_ID: 0.192 G_Rec: 0.391 D_GP: 0.022 D_real: 1.333 D_fake: 0.508 
+(epoch: 192, iters: 672, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.803 G_ID: 0.146 G_Rec: 0.323 D_GP: 0.070 D_real: 1.019 D_fake: 0.593 
+(epoch: 192, iters: 1072, time: 0.064) G_GAN: 0.557 G_GAN_Feat: 0.959 G_ID: 0.182 G_Rec: 0.418 D_GP: 0.037 D_real: 1.019 D_fake: 0.448 
+(epoch: 192, iters: 1472, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.732 G_ID: 0.139 G_Rec: 0.336 D_GP: 0.040 D_real: 1.122 D_fake: 0.665 
+(epoch: 192, iters: 1872, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.811 G_ID: 0.188 G_Rec: 0.396 D_GP: 0.025 D_real: 1.161 D_fake: 0.517 
+(epoch: 192, iters: 2272, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.646 G_ID: 0.138 G_Rec: 0.285 D_GP: 0.028 D_real: 1.097 D_fake: 0.770 
+(epoch: 192, iters: 2672, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 1.044 G_ID: 0.196 G_Rec: 0.443 D_GP: 0.112 D_real: 0.568 D_fake: 0.654 
+(epoch: 192, iters: 3072, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.663 G_ID: 0.139 G_Rec: 0.330 D_GP: 0.042 D_real: 1.136 D_fake: 0.767 
+(epoch: 192, iters: 3472, time: 0.064) G_GAN: 0.700 G_GAN_Feat: 0.928 G_ID: 0.185 G_Rec: 0.415 D_GP: 0.036 D_real: 1.219 D_fake: 0.334 
+(epoch: 192, iters: 3872, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.757 G_ID: 0.144 G_Rec: 0.321 D_GP: 0.060 D_real: 1.014 D_fake: 0.715 
+(epoch: 192, iters: 4272, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 1.098 G_ID: 0.204 G_Rec: 0.450 D_GP: 0.202 D_real: 0.338 D_fake: 0.638 
+(epoch: 192, iters: 4672, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.683 G_ID: 0.126 G_Rec: 0.309 D_GP: 0.022 D_real: 1.166 D_fake: 0.744 
+(epoch: 192, iters: 5072, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.862 G_ID: 0.235 G_Rec: 0.392 D_GP: 0.030 D_real: 1.022 D_fake: 0.613 
+(epoch: 192, iters: 5472, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 0.721 G_ID: 0.128 G_Rec: 0.315 D_GP: 0.040 D_real: 1.260 D_fake: 0.559 
+(epoch: 192, iters: 5872, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.873 G_ID: 0.185 G_Rec: 0.384 D_GP: 0.024 D_real: 0.961 D_fake: 0.669 
+(epoch: 192, iters: 6272, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.777 G_ID: 0.139 G_Rec: 0.312 D_GP: 0.035 D_real: 1.029 D_fake: 0.739 
+(epoch: 192, iters: 6672, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.987 G_ID: 0.216 G_Rec: 0.429 D_GP: 0.054 D_real: 0.924 D_fake: 0.517 
+(epoch: 192, iters: 7072, time: 0.064) G_GAN: -0.049 G_GAN_Feat: 1.017 G_ID: 0.144 G_Rec: 0.431 D_GP: 0.291 D_real: 0.382 D_fake: 1.051 
+(epoch: 192, iters: 7472, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.869 G_ID: 0.200 G_Rec: 0.402 D_GP: 0.024 D_real: 1.069 D_fake: 0.733 
+(epoch: 192, iters: 7872, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.656 G_ID: 0.172 G_Rec: 0.281 D_GP: 0.027 D_real: 1.297 D_fake: 0.621 
+(epoch: 192, iters: 8272, time: 0.064) G_GAN: 0.540 G_GAN_Feat: 0.970 G_ID: 0.205 G_Rec: 0.434 D_GP: 0.037 D_real: 1.114 D_fake: 0.504 
+(epoch: 193, iters: 64, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.708 G_ID: 0.136 G_Rec: 0.291 D_GP: 0.025 D_real: 1.148 D_fake: 0.736 
+(epoch: 193, iters: 464, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.830 G_ID: 0.208 G_Rec: 0.350 D_GP: 0.026 D_real: 1.205 D_fake: 0.514 
+(epoch: 193, iters: 864, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 0.630 G_ID: 0.137 G_Rec: 0.307 D_GP: 0.020 D_real: 1.211 D_fake: 0.794 
+(epoch: 193, iters: 1264, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.794 G_ID: 0.195 G_Rec: 0.391 D_GP: 0.024 D_real: 1.067 D_fake: 0.689 
+(epoch: 193, iters: 1664, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.664 G_ID: 0.130 G_Rec: 0.312 D_GP: 0.029 D_real: 1.048 D_fake: 0.855 
+(epoch: 193, iters: 2064, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.869 G_ID: 0.180 G_Rec: 0.404 D_GP: 0.034 D_real: 0.943 D_fake: 0.634 
+(epoch: 193, iters: 2464, time: 0.064) G_GAN: -0.166 G_GAN_Feat: 0.827 G_ID: 0.157 G_Rec: 0.367 D_GP: 0.051 D_real: 0.430 D_fake: 1.168 
+(epoch: 193, iters: 2864, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.951 G_ID: 0.192 G_Rec: 0.383 D_GP: 0.057 D_real: 0.758 D_fake: 0.723 
+(epoch: 193, iters: 3264, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 1.028 G_ID: 0.157 G_Rec: 0.322 D_GP: 0.047 D_real: 0.404 D_fake: 0.711 
+(epoch: 193, iters: 3664, time: 0.064) G_GAN: 0.665 G_GAN_Feat: 0.891 G_ID: 0.192 G_Rec: 0.399 D_GP: 0.030 D_real: 1.254 D_fake: 0.347 
+(epoch: 193, iters: 4064, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.757 G_ID: 0.136 G_Rec: 0.333 D_GP: 0.035 D_real: 0.960 D_fake: 0.769 
+(epoch: 193, iters: 4464, time: 0.064) G_GAN: 0.729 G_GAN_Feat: 0.905 G_ID: 0.196 G_Rec: 0.387 D_GP: 0.027 D_real: 1.491 D_fake: 0.299 
+(epoch: 193, iters: 4864, time: 0.064) G_GAN: -0.100 G_GAN_Feat: 0.749 G_ID: 0.156 G_Rec: 0.331 D_GP: 0.060 D_real: 0.627 D_fake: 1.100 
+(epoch: 193, iters: 5264, time: 0.064) G_GAN: 0.562 G_GAN_Feat: 0.970 G_ID: 0.180 G_Rec: 0.382 D_GP: 0.096 D_real: 0.826 D_fake: 0.461 
+(epoch: 193, iters: 5664, time: 0.064) G_GAN: 0.022 G_GAN_Feat: 0.742 G_ID: 0.157 G_Rec: 0.329 D_GP: 0.032 D_real: 0.911 D_fake: 0.978 
+(epoch: 193, iters: 6064, time: 0.064) G_GAN: 0.579 G_GAN_Feat: 1.055 G_ID: 0.211 G_Rec: 0.417 D_GP: 0.067 D_real: 0.630 D_fake: 0.430 
+(epoch: 193, iters: 6464, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.966 G_ID: 0.160 G_Rec: 0.373 D_GP: 0.441 D_real: 0.368 D_fake: 0.857 
+(epoch: 193, iters: 6864, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 1.010 G_ID: 0.185 G_Rec: 0.420 D_GP: 0.074 D_real: 0.889 D_fake: 0.452 
+(epoch: 193, iters: 7264, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.702 G_ID: 0.141 G_Rec: 0.341 D_GP: 0.022 D_real: 1.289 D_fake: 0.649 
+(epoch: 193, iters: 7664, time: 0.064) G_GAN: 0.598 G_GAN_Feat: 0.860 G_ID: 0.165 G_Rec: 0.390 D_GP: 0.030 D_real: 1.335 D_fake: 0.420 
+(epoch: 193, iters: 8064, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 0.715 G_ID: 0.128 G_Rec: 0.298 D_GP: 0.027 D_real: 0.934 D_fake: 0.848 
+(epoch: 193, iters: 8464, time: 0.064) G_GAN: 0.663 G_GAN_Feat: 0.867 G_ID: 0.176 G_Rec: 0.401 D_GP: 0.023 D_real: 1.396 D_fake: 0.344 
+(epoch: 194, iters: 256, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.686 G_ID: 0.155 G_Rec: 0.296 D_GP: 0.025 D_real: 1.140 D_fake: 0.800 
+(epoch: 194, iters: 656, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.951 G_ID: 0.170 G_Rec: 0.381 D_GP: 0.088 D_real: 0.755 D_fake: 0.612 
+(epoch: 194, iters: 1056, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 0.798 G_ID: 0.154 G_Rec: 0.326 D_GP: 0.090 D_real: 1.117 D_fake: 0.455 
+(epoch: 194, iters: 1456, time: 0.064) G_GAN: 0.591 G_GAN_Feat: 0.961 G_ID: 0.176 G_Rec: 0.403 D_GP: 0.040 D_real: 1.165 D_fake: 0.424 
+(epoch: 194, iters: 1856, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.779 G_ID: 0.152 G_Rec: 0.323 D_GP: 0.071 D_real: 0.828 D_fake: 0.824 
+(epoch: 194, iters: 2256, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.821 G_ID: 0.188 G_Rec: 0.374 D_GP: 0.023 D_real: 1.087 D_fake: 0.620 
+(epoch: 194, iters: 2656, time: 0.064) G_GAN: 0.070 G_GAN_Feat: 0.754 G_ID: 0.135 G_Rec: 0.310 D_GP: 0.032 D_real: 0.790 D_fake: 0.930 
+(epoch: 194, iters: 3056, time: 0.064) G_GAN: 0.575 G_GAN_Feat: 0.967 G_ID: 0.170 G_Rec: 0.447 D_GP: 0.043 D_real: 1.133 D_fake: 0.438 
+(epoch: 194, iters: 3456, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.711 G_ID: 0.137 G_Rec: 0.307 D_GP: 0.029 D_real: 1.206 D_fake: 0.649 
+(epoch: 194, iters: 3856, time: 0.064) G_GAN: 0.805 G_GAN_Feat: 0.961 G_ID: 0.178 G_Rec: 0.384 D_GP: 0.034 D_real: 1.444 D_fake: 0.251 
+(epoch: 194, iters: 4256, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.832 G_ID: 0.155 G_Rec: 0.319 D_GP: 0.040 D_real: 0.771 D_fake: 0.897 
+(epoch: 194, iters: 4656, time: 0.064) G_GAN: 0.609 G_GAN_Feat: 0.844 G_ID: 0.168 G_Rec: 0.350 D_GP: 0.032 D_real: 1.291 D_fake: 0.407 
+(epoch: 194, iters: 5056, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.775 G_ID: 0.134 G_Rec: 0.298 D_GP: 0.038 D_real: 1.144 D_fake: 0.608 
+(epoch: 194, iters: 5456, time: 0.064) G_GAN: 0.588 G_GAN_Feat: 1.154 G_ID: 0.172 G_Rec: 0.401 D_GP: 0.068 D_real: 0.476 D_fake: 0.425 
+(epoch: 194, iters: 5856, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.891 G_ID: 0.137 G_Rec: 0.301 D_GP: 0.045 D_real: 0.978 D_fake: 0.572 
+(epoch: 194, iters: 6256, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.832 G_ID: 0.198 G_Rec: 0.421 D_GP: 0.025 D_real: 1.128 D_fake: 0.573 
+(epoch: 194, iters: 6656, time: 0.064) G_GAN: -0.079 G_GAN_Feat: 0.744 G_ID: 0.162 G_Rec: 0.333 D_GP: 0.050 D_real: 0.686 D_fake: 1.079 
+(epoch: 194, iters: 7056, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.916 G_ID: 0.191 G_Rec: 0.397 D_GP: 0.046 D_real: 0.839 D_fake: 0.678 
+(epoch: 194, iters: 7456, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.738 G_ID: 0.135 G_Rec: 0.311 D_GP: 0.053 D_real: 0.875 D_fake: 0.927 
+(epoch: 194, iters: 7856, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 1.008 G_ID: 0.189 G_Rec: 0.414 D_GP: 0.037 D_real: 0.682 D_fake: 0.659 
+(epoch: 194, iters: 8256, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.632 G_ID: 0.142 G_Rec: 0.268 D_GP: 0.026 D_real: 1.204 D_fake: 0.732 
+(epoch: 195, iters: 48, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 1.022 G_ID: 0.181 G_Rec: 0.404 D_GP: 0.086 D_real: 0.542 D_fake: 0.732 
+(epoch: 195, iters: 448, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 0.790 G_ID: 0.159 G_Rec: 0.312 D_GP: 0.034 D_real: 1.196 D_fake: 0.533 
+(epoch: 195, iters: 848, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 1.128 G_ID: 0.199 G_Rec: 0.418 D_GP: 0.084 D_real: 0.372 D_fake: 0.558 
+(epoch: 195, iters: 1248, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.783 G_ID: 0.165 G_Rec: 0.338 D_GP: 0.047 D_real: 1.064 D_fake: 0.645 
+(epoch: 195, iters: 1648, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.997 G_ID: 0.200 G_Rec: 0.398 D_GP: 0.074 D_real: 0.450 D_fake: 0.762 
+(epoch: 195, iters: 2048, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.667 G_ID: 0.143 G_Rec: 0.316 D_GP: 0.021 D_real: 1.393 D_fake: 0.574 
+(epoch: 195, iters: 2448, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.892 G_ID: 0.180 G_Rec: 0.418 D_GP: 0.024 D_real: 1.006 D_fake: 0.785 
+(epoch: 195, iters: 2848, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.746 G_ID: 0.137 G_Rec: 0.295 D_GP: 0.027 D_real: 1.059 D_fake: 0.808 
+(epoch: 195, iters: 3248, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 0.898 G_ID: 0.185 G_Rec: 0.431 D_GP: 0.022 D_real: 1.198 D_fake: 0.537 
+(epoch: 195, iters: 3648, time: 0.064) G_GAN: 0.020 G_GAN_Feat: 0.697 G_ID: 0.126 G_Rec: 0.288 D_GP: 0.025 D_real: 0.871 D_fake: 0.980 
+(epoch: 195, iters: 4048, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.941 G_ID: 0.180 G_Rec: 0.392 D_GP: 0.074 D_real: 0.814 D_fake: 0.559 
+(epoch: 195, iters: 4448, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.754 G_ID: 0.153 G_Rec: 0.311 D_GP: 0.028 D_real: 1.036 D_fake: 0.823 
+(epoch: 195, iters: 4848, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.935 G_ID: 0.178 G_Rec: 0.358 D_GP: 0.045 D_real: 0.848 D_fake: 0.577 
+(epoch: 195, iters: 5248, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.885 G_ID: 0.159 G_Rec: 0.341 D_GP: 0.028 D_real: 0.994 D_fake: 0.670 
+(epoch: 195, iters: 5648, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 1.153 G_ID: 0.166 G_Rec: 0.399 D_GP: 0.481 D_real: 0.379 D_fake: 0.768 
+(epoch: 195, iters: 6048, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.642 G_ID: 0.137 G_Rec: 0.350 D_GP: 0.020 D_real: 1.278 D_fake: 0.676 
+(epoch: 195, iters: 6448, time: 0.064) G_GAN: 0.712 G_GAN_Feat: 1.001 G_ID: 0.192 G_Rec: 0.450 D_GP: 0.054 D_real: 1.007 D_fake: 0.311 
+(epoch: 195, iters: 6848, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.909 G_ID: 0.140 G_Rec: 0.309 D_GP: 0.037 D_real: 0.472 D_fake: 0.861 
+(epoch: 195, iters: 7248, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.881 G_ID: 0.168 G_Rec: 0.422 D_GP: 0.033 D_real: 1.044 D_fake: 0.593 
+(epoch: 195, iters: 7648, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.907 G_ID: 0.147 G_Rec: 0.353 D_GP: 0.198 D_real: 0.858 D_fake: 0.716 
+(epoch: 195, iters: 8048, time: 0.064) G_GAN: 0.656 G_GAN_Feat: 0.871 G_ID: 0.198 G_Rec: 0.390 D_GP: 0.030 D_real: 1.399 D_fake: 0.380 
+(epoch: 195, iters: 8448, time: 0.064) G_GAN: 0.135 G_GAN_Feat: 0.703 G_ID: 0.140 G_Rec: 0.275 D_GP: 0.027 D_real: 0.976 D_fake: 0.865 
+(epoch: 196, iters: 240, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.827 G_ID: 0.195 G_Rec: 0.395 D_GP: 0.030 D_real: 1.175 D_fake: 0.651 
+(epoch: 196, iters: 640, time: 0.064) G_GAN: -0.215 G_GAN_Feat: 0.917 G_ID: 0.164 G_Rec: 0.363 D_GP: 0.057 D_real: 0.903 D_fake: 1.215 
+(epoch: 196, iters: 1040, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.923 G_ID: 0.182 G_Rec: 0.418 D_GP: 0.031 D_real: 1.123 D_fake: 0.596 
+(epoch: 196, iters: 1440, time: 0.064) G_GAN: -0.116 G_GAN_Feat: 0.741 G_ID: 0.131 G_Rec: 0.333 D_GP: 0.110 D_real: 0.654 D_fake: 1.116 
+(epoch: 196, iters: 1840, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.935 G_ID: 0.194 G_Rec: 0.400 D_GP: 0.031 D_real: 1.093 D_fake: 0.769 
+(epoch: 196, iters: 2240, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.818 G_ID: 0.118 G_Rec: 0.337 D_GP: 0.054 D_real: 1.179 D_fake: 0.697 
+(epoch: 196, iters: 2640, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.846 G_ID: 0.180 G_Rec: 0.429 D_GP: 0.026 D_real: 0.956 D_fake: 0.694 
+(epoch: 196, iters: 3040, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.633 G_ID: 0.137 G_Rec: 0.317 D_GP: 0.024 D_real: 1.079 D_fake: 0.842 
+(epoch: 196, iters: 3440, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 1.062 G_ID: 0.192 G_Rec: 0.460 D_GP: 0.167 D_real: 0.597 D_fake: 0.732 
+(epoch: 196, iters: 3840, time: 0.064) G_GAN: -0.087 G_GAN_Feat: 0.767 G_ID: 0.139 G_Rec: 0.333 D_GP: 0.044 D_real: 0.730 D_fake: 1.087 
+(epoch: 196, iters: 4240, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.780 G_ID: 0.154 G_Rec: 0.384 D_GP: 0.020 D_real: 1.366 D_fake: 0.514 
+(epoch: 196, iters: 4640, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.744 G_ID: 0.159 G_Rec: 0.316 D_GP: 0.027 D_real: 0.972 D_fake: 0.841 
+(epoch: 196, iters: 5040, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 0.893 G_ID: 0.179 G_Rec: 0.419 D_GP: 0.026 D_real: 1.285 D_fake: 0.517 
+(epoch: 196, iters: 5440, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.850 G_ID: 0.139 G_Rec: 0.341 D_GP: 0.057 D_real: 0.727 D_fake: 0.752 
+(epoch: 196, iters: 5840, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 1.166 G_ID: 0.192 G_Rec: 0.420 D_GP: 0.074 D_real: 0.659 D_fake: 0.616 
+(epoch: 196, iters: 6240, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.593 G_ID: 0.158 G_Rec: 0.289 D_GP: 0.018 D_real: 1.159 D_fake: 0.787 
+(epoch: 196, iters: 6640, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.857 G_ID: 0.173 G_Rec: 0.431 D_GP: 0.023 D_real: 0.989 D_fake: 0.726 
+(epoch: 196, iters: 7040, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 1.115 G_ID: 0.130 G_Rec: 0.378 D_GP: 0.054 D_real: 1.326 D_fake: 0.875 
+(epoch: 196, iters: 7440, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.805 G_ID: 0.184 G_Rec: 0.404 D_GP: 0.022 D_real: 0.936 D_fake: 0.879 
+(epoch: 196, iters: 7840, time: 0.064) G_GAN: -0.015 G_GAN_Feat: 0.700 G_ID: 0.139 G_Rec: 0.304 D_GP: 0.037 D_real: 0.829 D_fake: 1.016 
+(epoch: 196, iters: 8240, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.808 G_ID: 0.190 G_Rec: 0.358 D_GP: 0.035 D_real: 1.111 D_fake: 0.688 
+(epoch: 197, iters: 32, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.973 G_ID: 0.152 G_Rec: 0.323 D_GP: 0.126 D_real: 1.055 D_fake: 0.755 
+(epoch: 197, iters: 432, time: 0.064) G_GAN: 0.473 G_GAN_Feat: 0.844 G_ID: 0.221 G_Rec: 0.374 D_GP: 0.023 D_real: 1.237 D_fake: 0.534 
+(epoch: 197, iters: 832, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.814 G_ID: 0.151 G_Rec: 0.300 D_GP: 0.071 D_real: 0.671 D_fake: 0.805 
+(epoch: 197, iters: 1232, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.824 G_ID: 0.220 G_Rec: 0.410 D_GP: 0.024 D_real: 1.064 D_fake: 0.673 
+(epoch: 197, iters: 1632, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.668 G_ID: 0.116 G_Rec: 0.333 D_GP: 0.020 D_real: 0.973 D_fake: 0.938 
+(epoch: 197, iters: 2032, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.863 G_ID: 0.190 G_Rec: 0.388 D_GP: 0.046 D_real: 0.882 D_fake: 0.764 
+(epoch: 197, iters: 2432, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.827 G_ID: 0.146 G_Rec: 0.332 D_GP: 0.145 D_real: 0.507 D_fake: 0.977 
+(epoch: 197, iters: 2832, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.968 G_ID: 0.185 G_Rec: 0.427 D_GP: 0.033 D_real: 0.836 D_fake: 0.701 
+(epoch: 197, iters: 3232, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.655 G_ID: 0.129 G_Rec: 0.354 D_GP: 0.028 D_real: 1.331 D_fake: 0.563 
+(epoch: 197, iters: 3632, time: 0.064) G_GAN: 0.622 G_GAN_Feat: 1.025 G_ID: 0.206 G_Rec: 0.414 D_GP: 0.058 D_real: 0.803 D_fake: 0.402 
+(epoch: 197, iters: 4032, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.744 G_ID: 0.146 G_Rec: 0.308 D_GP: 0.025 D_real: 1.326 D_fake: 0.582 
+(epoch: 197, iters: 4432, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.924 G_ID: 0.184 G_Rec: 0.413 D_GP: 0.027 D_real: 0.996 D_fake: 0.684 
+(epoch: 197, iters: 4832, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.712 G_ID: 0.129 G_Rec: 0.312 D_GP: 0.027 D_real: 1.289 D_fake: 0.617 
+(epoch: 197, iters: 5232, time: 0.064) G_GAN: 0.691 G_GAN_Feat: 0.862 G_ID: 0.203 G_Rec: 0.380 D_GP: 0.027 D_real: 1.338 D_fake: 0.347 
+(epoch: 197, iters: 5632, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.677 G_ID: 0.168 G_Rec: 0.350 D_GP: 0.030 D_real: 1.035 D_fake: 0.844 
+(epoch: 197, iters: 6032, time: 0.064) G_GAN: 0.700 G_GAN_Feat: 0.786 G_ID: 0.179 G_Rec: 0.381 D_GP: 0.025 D_real: 1.423 D_fake: 0.349 
+(epoch: 197, iters: 6432, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.674 G_ID: 0.129 G_Rec: 0.314 D_GP: 0.020 D_real: 1.254 D_fake: 0.658 
+(epoch: 197, iters: 6832, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.787 G_ID: 0.211 G_Rec: 0.420 D_GP: 0.033 D_real: 0.888 D_fake: 0.866 
+(epoch: 197, iters: 7232, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.682 G_ID: 0.136 G_Rec: 0.305 D_GP: 0.043 D_real: 1.130 D_fake: 0.743 
+(epoch: 197, iters: 7632, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.959 G_ID: 0.187 G_Rec: 0.402 D_GP: 0.159 D_real: 0.802 D_fake: 0.598 
+(epoch: 197, iters: 8032, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.810 G_ID: 0.152 G_Rec: 0.321 D_GP: 0.043 D_real: 0.793 D_fake: 0.879 
+(epoch: 197, iters: 8432, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.962 G_ID: 0.238 G_Rec: 0.389 D_GP: 0.043 D_real: 0.778 D_fake: 0.605 
+(epoch: 198, iters: 224, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.866 G_ID: 0.128 G_Rec: 0.343 D_GP: 0.052 D_real: 0.672 D_fake: 0.921 
+(epoch: 198, iters: 624, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.873 G_ID: 0.184 G_Rec: 0.393 D_GP: 0.035 D_real: 0.705 D_fake: 0.964 
+(epoch: 198, iters: 1024, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.953 G_ID: 0.124 G_Rec: 0.365 D_GP: 0.980 D_real: 0.376 D_fake: 0.885 
+(epoch: 198, iters: 1424, time: 0.064) G_GAN: 0.545 G_GAN_Feat: 0.777 G_ID: 0.170 G_Rec: 0.363 D_GP: 0.021 D_real: 1.330 D_fake: 0.460 
+(epoch: 198, iters: 1824, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.633 G_ID: 0.121 G_Rec: 0.278 D_GP: 0.028 D_real: 1.307 D_fake: 0.601 
+(epoch: 198, iters: 2224, time: 0.064) G_GAN: 0.444 G_GAN_Feat: 0.824 G_ID: 0.182 G_Rec: 0.369 D_GP: 0.035 D_real: 1.177 D_fake: 0.557 
+(epoch: 198, iters: 2624, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 0.765 G_ID: 0.144 G_Rec: 0.304 D_GP: 0.098 D_real: 1.102 D_fake: 0.586 
+(epoch: 198, iters: 3024, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 1.047 G_ID: 0.176 G_Rec: 0.426 D_GP: 0.162 D_real: 0.896 D_fake: 0.634 
+(epoch: 198, iters: 3424, time: 0.064) G_GAN: -0.074 G_GAN_Feat: 0.803 G_ID: 0.121 G_Rec: 0.377 D_GP: 0.024 D_real: 0.800 D_fake: 1.074 
+(epoch: 198, iters: 3824, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 1.107 G_ID: 0.176 G_Rec: 0.391 D_GP: 0.246 D_real: 0.369 D_fake: 0.576 
+(epoch: 198, iters: 4224, time: 0.064) G_GAN: 0.133 G_GAN_Feat: 0.746 G_ID: 0.132 G_Rec: 0.314 D_GP: 0.029 D_real: 1.013 D_fake: 0.867 
+(epoch: 198, iters: 4624, time: 0.064) G_GAN: 0.563 G_GAN_Feat: 0.974 G_ID: 0.207 G_Rec: 0.408 D_GP: 0.031 D_real: 1.174 D_fake: 0.443 
+(epoch: 198, iters: 5024, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.671 G_ID: 0.130 G_Rec: 0.298 D_GP: 0.021 D_real: 1.173 D_fake: 0.761 
+(epoch: 198, iters: 5424, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.895 G_ID: 0.199 G_Rec: 0.437 D_GP: 0.080 D_real: 0.829 D_fake: 0.750 
+(epoch: 198, iters: 5824, time: 0.064) G_GAN: 0.060 G_GAN_Feat: 0.742 G_ID: 0.115 G_Rec: 0.305 D_GP: 0.052 D_real: 0.847 D_fake: 0.940 
+(epoch: 198, iters: 6224, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.807 G_ID: 0.161 G_Rec: 0.375 D_GP: 0.023 D_real: 1.242 D_fake: 0.489 
+(epoch: 198, iters: 6624, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.683 G_ID: 0.137 G_Rec: 0.305 D_GP: 0.023 D_real: 1.110 D_fake: 0.782 
+(epoch: 198, iters: 7024, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.869 G_ID: 0.191 G_Rec: 0.388 D_GP: 0.024 D_real: 1.021 D_fake: 0.733 
+(epoch: 198, iters: 7424, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.847 G_ID: 0.140 G_Rec: 0.297 D_GP: 0.050 D_real: 0.775 D_fake: 0.684 
+(epoch: 198, iters: 7824, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.768 G_ID: 0.190 G_Rec: 0.401 D_GP: 0.022 D_real: 1.049 D_fake: 0.735 
+(epoch: 198, iters: 8224, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.586 G_ID: 0.134 G_Rec: 0.313 D_GP: 0.021 D_real: 1.250 D_fake: 0.755 
+(epoch: 199, iters: 16, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.712 G_ID: 0.163 G_Rec: 0.360 D_GP: 0.022 D_real: 1.104 D_fake: 0.749 
+(epoch: 199, iters: 416, time: 0.064) G_GAN: 0.058 G_GAN_Feat: 0.619 G_ID: 0.198 G_Rec: 0.307 D_GP: 0.025 D_real: 1.056 D_fake: 0.942 
+(epoch: 199, iters: 816, time: 0.064) G_GAN: 0.608 G_GAN_Feat: 0.807 G_ID: 0.166 G_Rec: 0.416 D_GP: 0.021 D_real: 1.313 D_fake: 0.423 
+(epoch: 199, iters: 1216, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.747 G_ID: 0.117 G_Rec: 0.288 D_GP: 0.030 D_real: 1.139 D_fake: 0.779 
+(epoch: 199, iters: 1616, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.884 G_ID: 0.188 G_Rec: 0.433 D_GP: 0.029 D_real: 0.988 D_fake: 0.747 
+(epoch: 199, iters: 2016, time: 0.064) G_GAN: 0.146 G_GAN_Feat: 0.788 G_ID: 0.162 G_Rec: 0.307 D_GP: 0.049 D_real: 0.703 D_fake: 0.854 
+(epoch: 199, iters: 2416, time: 0.064) G_GAN: 0.462 G_GAN_Feat: 0.714 G_ID: 0.195 G_Rec: 0.364 D_GP: 0.021 D_real: 1.217 D_fake: 0.539 
+(epoch: 199, iters: 2816, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.628 G_ID: 0.136 G_Rec: 0.297 D_GP: 0.030 D_real: 1.049 D_fake: 0.947 
+(epoch: 199, iters: 3216, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.793 G_ID: 0.188 G_Rec: 0.400 D_GP: 0.027 D_real: 1.076 D_fake: 0.718 
+(epoch: 199, iters: 3616, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.767 G_ID: 0.143 G_Rec: 0.349 D_GP: 0.040 D_real: 1.246 D_fake: 0.568 
+(epoch: 199, iters: 4016, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.855 G_ID: 0.182 G_Rec: 0.385 D_GP: 0.029 D_real: 0.842 D_fake: 0.833 
+(epoch: 199, iters: 4416, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.717 G_ID: 0.136 G_Rec: 0.311 D_GP: 0.044 D_real: 1.147 D_fake: 0.720 
+(epoch: 199, iters: 4816, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 1.011 G_ID: 0.185 G_Rec: 0.422 D_GP: 0.052 D_real: 0.899 D_fake: 0.527 
+(epoch: 199, iters: 5216, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.730 G_ID: 0.128 G_Rec: 0.307 D_GP: 0.028 D_real: 1.075 D_fake: 0.751 
+(epoch: 199, iters: 5616, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.830 G_ID: 0.180 G_Rec: 0.400 D_GP: 0.020 D_real: 1.123 D_fake: 0.603 
+(epoch: 199, iters: 6016, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.602 G_ID: 0.133 G_Rec: 0.278 D_GP: 0.022 D_real: 1.254 D_fake: 0.688 
+(epoch: 199, iters: 6416, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.809 G_ID: 0.193 G_Rec: 0.396 D_GP: 0.036 D_real: 1.038 D_fake: 0.670 
+(epoch: 199, iters: 6816, time: 0.064) G_GAN: 0.133 G_GAN_Feat: 0.749 G_ID: 0.138 G_Rec: 0.305 D_GP: 0.054 D_real: 1.007 D_fake: 0.867 
+(epoch: 199, iters: 7216, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 0.837 G_ID: 0.205 G_Rec: 0.388 D_GP: 0.020 D_real: 1.287 D_fake: 0.456 
+(epoch: 199, iters: 7616, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.701 G_ID: 0.128 G_Rec: 0.308 D_GP: 0.038 D_real: 1.019 D_fake: 0.882 
+(epoch: 199, iters: 8016, time: 0.064) G_GAN: 0.615 G_GAN_Feat: 1.115 G_ID: 0.191 G_Rec: 0.451 D_GP: 0.090 D_real: 0.596 D_fake: 0.407 
+(epoch: 199, iters: 8416, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 0.685 G_ID: 0.140 G_Rec: 0.313 D_GP: 0.022 D_real: 1.228 D_fake: 0.709 
+(epoch: 200, iters: 208, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 1.043 G_ID: 0.197 G_Rec: 0.411 D_GP: 0.054 D_real: 0.665 D_fake: 0.438 
+(epoch: 200, iters: 608, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.598 G_ID: 0.137 G_Rec: 0.277 D_GP: 0.021 D_real: 1.199 D_fake: 0.764 
+(epoch: 200, iters: 1008, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.796 G_ID: 0.209 G_Rec: 0.388 D_GP: 0.027 D_real: 1.000 D_fake: 0.723 
+(epoch: 200, iters: 1408, time: 0.064) G_GAN: 0.014 G_GAN_Feat: 0.772 G_ID: 0.142 G_Rec: 0.338 D_GP: 0.053 D_real: 0.666 D_fake: 0.987 
+(epoch: 200, iters: 1808, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.997 G_ID: 0.189 G_Rec: 0.450 D_GP: 0.083 D_real: 0.664 D_fake: 0.575 
+(epoch: 200, iters: 2208, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.856 G_ID: 0.145 G_Rec: 0.312 D_GP: 0.061 D_real: 0.683 D_fake: 0.697 
+(epoch: 200, iters: 2608, time: 0.064) G_GAN: 0.444 G_GAN_Feat: 0.866 G_ID: 0.183 G_Rec: 0.398 D_GP: 0.026 D_real: 1.174 D_fake: 0.567 
+(epoch: 200, iters: 3008, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.693 G_ID: 0.126 G_Rec: 0.308 D_GP: 0.024 D_real: 0.956 D_fake: 0.918 
+(epoch: 200, iters: 3408, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.992 G_ID: 0.183 G_Rec: 0.435 D_GP: 0.072 D_real: 0.699 D_fake: 0.727 
+(epoch: 200, iters: 3808, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.734 G_ID: 0.148 G_Rec: 0.291 D_GP: 0.025 D_real: 1.273 D_fake: 0.595 
+(epoch: 200, iters: 4208, time: 0.064) G_GAN: 0.550 G_GAN_Feat: 0.900 G_ID: 0.188 G_Rec: 0.421 D_GP: 0.030 D_real: 1.239 D_fake: 0.465 
+(epoch: 200, iters: 4608, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 1.008 G_ID: 0.149 G_Rec: 0.322 D_GP: 0.214 D_real: 0.368 D_fake: 0.732 
+(epoch: 200, iters: 5008, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 0.969 G_ID: 0.193 G_Rec: 0.485 D_GP: 0.025 D_real: 1.216 D_fake: 0.453 
+(epoch: 200, iters: 5408, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.648 G_ID: 0.128 G_Rec: 0.310 D_GP: 0.026 D_real: 1.082 D_fake: 0.888 
+(epoch: 200, iters: 5808, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.784 G_ID: 0.189 G_Rec: 0.376 D_GP: 0.029 D_real: 1.253 D_fake: 0.567 
+(epoch: 200, iters: 6208, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.718 G_ID: 0.125 G_Rec: 0.323 D_GP: 0.038 D_real: 0.946 D_fake: 0.783 
+(epoch: 200, iters: 6608, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 1.081 G_ID: 0.190 G_Rec: 0.442 D_GP: 0.061 D_real: 0.627 D_fake: 0.594 
+(epoch: 200, iters: 7008, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.777 G_ID: 0.152 G_Rec: 0.339 D_GP: 0.031 D_real: 0.959 D_fake: 0.947 
+(epoch: 200, iters: 7408, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 0.910 G_ID: 0.166 G_Rec: 0.411 D_GP: 0.037 D_real: 1.197 D_fake: 0.493 
+(epoch: 200, iters: 7808, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.676 G_ID: 0.144 G_Rec: 0.283 D_GP: 0.025 D_real: 1.123 D_fake: 0.741 
+(epoch: 200, iters: 8208, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.860 G_ID: 0.184 G_Rec: 0.391 D_GP: 0.023 D_real: 1.351 D_fake: 0.394 
+(epoch: 200, iters: 8608, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.798 G_ID: 0.133 G_Rec: 0.322 D_GP: 0.026 D_real: 1.020 D_fake: 0.697 
+(epoch: 201, iters: 400, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.898 G_ID: 0.174 G_Rec: 0.368 D_GP: 0.033 D_real: 0.942 D_fake: 0.575 
+(epoch: 201, iters: 800, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.745 G_ID: 0.141 G_Rec: 0.308 D_GP: 0.029 D_real: 1.020 D_fake: 0.725 
+(epoch: 201, iters: 1200, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 1.131 G_ID: 0.170 G_Rec: 0.414 D_GP: 0.209 D_real: 0.425 D_fake: 0.489 
+(epoch: 201, iters: 1600, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.775 G_ID: 0.132 G_Rec: 0.301 D_GP: 0.027 D_real: 1.152 D_fake: 0.713 
+(epoch: 201, iters: 2000, time: 0.064) G_GAN: 0.602 G_GAN_Feat: 0.732 G_ID: 0.166 G_Rec: 0.387 D_GP: 0.020 D_real: 1.479 D_fake: 0.423 
+(epoch: 201, iters: 2400, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.649 G_ID: 0.147 G_Rec: 0.306 D_GP: 0.020 D_real: 1.026 D_fake: 0.882 
+(epoch: 201, iters: 2800, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.796 G_ID: 0.194 G_Rec: 0.391 D_GP: 0.027 D_real: 1.161 D_fake: 0.556 
+(epoch: 201, iters: 3200, time: 0.064) G_GAN: 0.089 G_GAN_Feat: 0.625 G_ID: 0.138 G_Rec: 0.281 D_GP: 0.025 D_real: 1.016 D_fake: 0.914 
+(epoch: 201, iters: 3600, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.970 G_ID: 0.165 G_Rec: 0.402 D_GP: 0.085 D_real: 0.571 D_fake: 0.756 
+(epoch: 201, iters: 4000, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.649 G_ID: 0.142 G_Rec: 0.268 D_GP: 0.028 D_real: 1.034 D_fake: 0.856 
+(epoch: 201, iters: 4400, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.977 G_ID: 0.199 G_Rec: 0.449 D_GP: 0.033 D_real: 0.995 D_fake: 0.535 
+(epoch: 201, iters: 4800, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.690 G_ID: 0.136 G_Rec: 0.296 D_GP: 0.026 D_real: 1.061 D_fake: 0.921 
+(epoch: 201, iters: 5200, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.950 G_ID: 0.179 G_Rec: 0.406 D_GP: 0.027 D_real: 1.081 D_fake: 0.486 
+(epoch: 201, iters: 5600, time: 0.064) G_GAN: 0.075 G_GAN_Feat: 0.920 G_ID: 0.141 G_Rec: 0.321 D_GP: 0.104 D_real: 0.559 D_fake: 0.925 
+(epoch: 201, iters: 6000, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 0.813 G_ID: 0.166 G_Rec: 0.369 D_GP: 0.020 D_real: 1.220 D_fake: 0.591 
+(epoch: 201, iters: 6400, time: 0.064) G_GAN: -0.013 G_GAN_Feat: 0.845 G_ID: 0.136 G_Rec: 0.302 D_GP: 0.116 D_real: 0.501 D_fake: 1.013 
+(epoch: 201, iters: 6800, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 0.863 G_ID: 0.172 G_Rec: 0.405 D_GP: 0.023 D_real: 1.341 D_fake: 0.430 
+(epoch: 201, iters: 7200, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.706 G_ID: 0.133 G_Rec: 0.293 D_GP: 0.025 D_real: 1.230 D_fake: 0.632 
+(epoch: 201, iters: 7600, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.857 G_ID: 0.193 G_Rec: 0.397 D_GP: 0.028 D_real: 1.075 D_fake: 0.550 
+(epoch: 201, iters: 8000, time: 0.064) G_GAN: 0.635 G_GAN_Feat: 0.964 G_ID: 0.126 G_Rec: 0.325 D_GP: 0.037 D_real: 1.354 D_fake: 0.528 
+(epoch: 201, iters: 8400, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.789 G_ID: 0.195 G_Rec: 0.401 D_GP: 0.026 D_real: 1.257 D_fake: 0.525 
+(epoch: 202, iters: 192, time: 0.064) G_GAN: 0.097 G_GAN_Feat: 0.551 G_ID: 0.139 G_Rec: 0.345 D_GP: 0.017 D_real: 1.048 D_fake: 0.903 
+(epoch: 202, iters: 592, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.679 G_ID: 0.176 G_Rec: 0.337 D_GP: 0.023 D_real: 1.172 D_fake: 0.672 
+(epoch: 202, iters: 992, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.602 G_ID: 0.154 G_Rec: 0.307 D_GP: 0.024 D_real: 1.023 D_fake: 0.845 
+(epoch: 202, iters: 1392, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.773 G_ID: 0.181 G_Rec: 0.382 D_GP: 0.026 D_real: 0.926 D_fake: 0.840 
+(epoch: 202, iters: 1792, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.617 G_ID: 0.126 G_Rec: 0.289 D_GP: 0.024 D_real: 1.005 D_fake: 0.859 
+(epoch: 202, iters: 2192, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.927 G_ID: 0.178 G_Rec: 0.429 D_GP: 0.116 D_real: 0.768 D_fake: 0.654 
+(epoch: 202, iters: 2592, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.637 G_ID: 0.133 G_Rec: 0.337 D_GP: 0.020 D_real: 1.242 D_fake: 0.704 
+(epoch: 202, iters: 2992, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.915 G_ID: 0.183 G_Rec: 0.474 D_GP: 0.026 D_real: 1.348 D_fake: 0.480 
+(epoch: 202, iters: 3392, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.593 G_ID: 0.137 G_Rec: 0.304 D_GP: 0.021 D_real: 1.242 D_fake: 0.717 
+(epoch: 202, iters: 3792, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.842 G_ID: 0.189 G_Rec: 0.380 D_GP: 0.044 D_real: 1.040 D_fake: 0.550 
+(epoch: 202, iters: 4192, time: 0.064) G_GAN: -0.026 G_GAN_Feat: 0.750 G_ID: 0.150 G_Rec: 0.312 D_GP: 0.069 D_real: 0.667 D_fake: 1.026 
+(epoch: 202, iters: 4592, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 1.014 G_ID: 0.183 G_Rec: 0.443 D_GP: 0.517 D_real: 0.659 D_fake: 0.592 
+(epoch: 202, iters: 4992, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.745 G_ID: 0.139 G_Rec: 0.335 D_GP: 0.036 D_real: 0.963 D_fake: 0.822 
+(epoch: 202, iters: 5392, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.866 G_ID: 0.177 G_Rec: 0.399 D_GP: 0.023 D_real: 1.024 D_fake: 0.672 
+(epoch: 202, iters: 5792, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.885 G_ID: 0.118 G_Rec: 0.324 D_GP: 0.453 D_real: 0.587 D_fake: 0.684 
+(epoch: 202, iters: 6192, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.859 G_ID: 0.192 G_Rec: 0.389 D_GP: 0.034 D_real: 1.080 D_fake: 0.596 
+(epoch: 202, iters: 6592, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.703 G_ID: 0.127 G_Rec: 0.286 D_GP: 0.040 D_real: 1.199 D_fake: 0.581 
+(epoch: 202, iters: 6992, time: 0.064) G_GAN: -0.206 G_GAN_Feat: 1.055 G_ID: 0.224 G_Rec: 0.423 D_GP: 0.113 D_real: 0.079 D_fake: 1.206 
+(epoch: 202, iters: 7392, time: 0.064) G_GAN: 0.067 G_GAN_Feat: 0.848 G_ID: 0.170 G_Rec: 0.308 D_GP: 0.030 D_real: 0.723 D_fake: 0.933 
+(epoch: 202, iters: 7792, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.840 G_ID: 0.172 G_Rec: 0.368 D_GP: 0.024 D_real: 1.134 D_fake: 0.628 
+(epoch: 202, iters: 8192, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.763 G_ID: 0.137 G_Rec: 0.316 D_GP: 0.045 D_real: 0.908 D_fake: 0.760 
+(epoch: 202, iters: 8592, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 1.020 G_ID: 0.174 G_Rec: 0.399 D_GP: 0.052 D_real: 0.968 D_fake: 0.727 
+(epoch: 203, iters: 384, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.914 G_ID: 0.131 G_Rec: 0.365 D_GP: 0.033 D_real: 0.498 D_fake: 0.810 
+(epoch: 203, iters: 784, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.923 G_ID: 0.153 G_Rec: 0.367 D_GP: 0.072 D_real: 0.959 D_fake: 0.498 
+(epoch: 203, iters: 1184, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.759 G_ID: 0.138 G_Rec: 0.328 D_GP: 0.031 D_real: 1.097 D_fake: 0.733 
+(epoch: 203, iters: 1584, time: 0.064) G_GAN: 0.693 G_GAN_Feat: 0.821 G_ID: 0.169 G_Rec: 0.370 D_GP: 0.022 D_real: 1.442 D_fake: 0.322 
+(epoch: 203, iters: 1984, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.647 G_ID: 0.138 G_Rec: 0.280 D_GP: 0.024 D_real: 1.229 D_fake: 0.660 
+(epoch: 203, iters: 2384, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.861 G_ID: 0.183 G_Rec: 0.387 D_GP: 0.022 D_real: 1.161 D_fake: 0.564 
+(epoch: 203, iters: 2784, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.850 G_ID: 0.119 G_Rec: 0.327 D_GP: 0.077 D_real: 0.649 D_fake: 0.659 
+(epoch: 203, iters: 3184, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 1.064 G_ID: 0.216 G_Rec: 0.419 D_GP: 0.048 D_real: 0.579 D_fake: 0.533 
+(epoch: 203, iters: 3584, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.699 G_ID: 0.116 G_Rec: 0.321 D_GP: 0.021 D_real: 1.356 D_fake: 0.550 
+(epoch: 203, iters: 3984, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.914 G_ID: 0.204 G_Rec: 0.436 D_GP: 0.033 D_real: 0.904 D_fake: 0.769 
+(epoch: 203, iters: 4384, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 0.640 G_ID: 0.127 G_Rec: 0.361 D_GP: 0.021 D_real: 1.214 D_fake: 0.710 
+(epoch: 203, iters: 4784, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.924 G_ID: 0.196 G_Rec: 0.445 D_GP: 0.052 D_real: 0.802 D_fake: 0.650 
+(epoch: 203, iters: 5184, time: 0.064) G_GAN: 0.045 G_GAN_Feat: 0.778 G_ID: 0.133 G_Rec: 0.291 D_GP: 0.065 D_real: 0.618 D_fake: 0.956 
+(epoch: 203, iters: 5584, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 1.057 G_ID: 0.197 G_Rec: 0.475 D_GP: 0.046 D_real: 0.753 D_fake: 0.526 
+(epoch: 203, iters: 5984, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.576 G_ID: 0.141 G_Rec: 0.314 D_GP: 0.018 D_real: 1.503 D_fake: 0.513 
+(epoch: 203, iters: 6384, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.814 G_ID: 0.221 G_Rec: 0.447 D_GP: 0.022 D_real: 1.048 D_fake: 0.636 
+(epoch: 203, iters: 6784, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.623 G_ID: 0.136 G_Rec: 0.312 D_GP: 0.022 D_real: 1.182 D_fake: 0.777 
+(epoch: 203, iters: 7184, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.697 G_ID: 0.176 G_Rec: 0.403 D_GP: 0.022 D_real: 1.184 D_fake: 0.551 
+(epoch: 203, iters: 7584, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.624 G_ID: 0.194 G_Rec: 0.319 D_GP: 0.027 D_real: 0.939 D_fake: 0.988 
+(epoch: 203, iters: 7984, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.788 G_ID: 0.192 G_Rec: 0.384 D_GP: 0.022 D_real: 1.114 D_fake: 0.728 
+(epoch: 203, iters: 8384, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.552 G_ID: 0.138 G_Rec: 0.285 D_GP: 0.020 D_real: 1.219 D_fake: 0.728 
+(epoch: 204, iters: 176, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.803 G_ID: 0.183 G_Rec: 0.399 D_GP: 0.026 D_real: 1.202 D_fake: 0.491 
+(epoch: 204, iters: 576, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.647 G_ID: 0.124 G_Rec: 0.306 D_GP: 0.045 D_real: 1.149 D_fake: 0.760 
+(epoch: 204, iters: 976, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.828 G_ID: 0.221 G_Rec: 0.402 D_GP: 0.023 D_real: 0.999 D_fake: 0.766 
+(epoch: 204, iters: 1376, time: 0.064) G_GAN: 0.124 G_GAN_Feat: 0.661 G_ID: 0.137 G_Rec: 0.313 D_GP: 0.042 D_real: 1.022 D_fake: 0.876 
+(epoch: 204, iters: 1776, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.783 G_ID: 0.239 G_Rec: 0.404 D_GP: 0.028 D_real: 1.106 D_fake: 0.671 
+(epoch: 204, iters: 2176, time: 0.064) G_GAN: 0.110 G_GAN_Feat: 0.705 G_ID: 0.144 G_Rec: 0.309 D_GP: 0.064 D_real: 1.002 D_fake: 0.890 
+(epoch: 204, iters: 2576, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.865 G_ID: 0.179 G_Rec: 0.405 D_GP: 0.035 D_real: 1.060 D_fake: 0.662 
+(epoch: 204, iters: 2976, time: 0.064) G_GAN: -0.006 G_GAN_Feat: 0.838 G_ID: 0.124 G_Rec: 0.364 D_GP: 0.108 D_real: 0.773 D_fake: 1.007 
+(epoch: 204, iters: 3376, time: 0.064) G_GAN: 0.656 G_GAN_Feat: 0.855 G_ID: 0.168 G_Rec: 0.418 D_GP: 0.030 D_real: 1.312 D_fake: 0.373 
+(epoch: 204, iters: 3776, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.668 G_ID: 0.144 G_Rec: 0.296 D_GP: 0.021 D_real: 1.192 D_fake: 0.737 
+(epoch: 204, iters: 4176, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.743 G_ID: 0.166 G_Rec: 0.358 D_GP: 0.026 D_real: 1.068 D_fake: 0.656 
+(epoch: 204, iters: 4576, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.689 G_ID: 0.126 G_Rec: 0.293 D_GP: 0.027 D_real: 1.165 D_fake: 0.636 
+(epoch: 204, iters: 4976, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.727 G_ID: 0.181 G_Rec: 0.371 D_GP: 0.023 D_real: 1.170 D_fake: 0.612 
+(epoch: 204, iters: 5376, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.639 G_ID: 0.122 G_Rec: 0.287 D_GP: 0.025 D_real: 1.289 D_fake: 0.631 
+(epoch: 204, iters: 5776, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.799 G_ID: 0.212 G_Rec: 0.412 D_GP: 0.025 D_real: 1.150 D_fake: 0.658 
+(epoch: 204, iters: 6176, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.734 G_ID: 0.141 G_Rec: 0.325 D_GP: 0.033 D_real: 0.883 D_fake: 0.927 
+(epoch: 204, iters: 6576, time: 0.064) G_GAN: 0.473 G_GAN_Feat: 0.809 G_ID: 0.194 G_Rec: 0.395 D_GP: 0.023 D_real: 1.271 D_fake: 0.530 
+(epoch: 204, iters: 6976, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 0.612 G_ID: 0.145 G_Rec: 0.269 D_GP: 0.025 D_real: 1.408 D_fake: 0.509 
+(epoch: 204, iters: 7376, time: 0.064) G_GAN: 0.694 G_GAN_Feat: 0.911 G_ID: 0.173 G_Rec: 0.465 D_GP: 0.028 D_real: 1.416 D_fake: 0.329 
+(epoch: 204, iters: 7776, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.802 G_ID: 0.143 G_Rec: 0.316 D_GP: 0.039 D_real: 1.153 D_fake: 0.564 
+(epoch: 204, iters: 8176, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 0.817 G_ID: 0.160 G_Rec: 0.395 D_GP: 0.020 D_real: 1.343 D_fake: 0.431 
+(epoch: 204, iters: 8576, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.657 G_ID: 0.141 G_Rec: 0.308 D_GP: 0.029 D_real: 1.057 D_fake: 0.798 
+(epoch: 205, iters: 368, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.954 G_ID: 0.154 G_Rec: 0.459 D_GP: 0.041 D_real: 0.876 D_fake: 0.624 
+(epoch: 205, iters: 768, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.764 G_ID: 0.123 G_Rec: 0.348 D_GP: 0.033 D_real: 1.044 D_fake: 0.730 
+(epoch: 205, iters: 1168, time: 0.064) G_GAN: 0.547 G_GAN_Feat: 1.158 G_ID: 0.173 G_Rec: 0.462 D_GP: 0.134 D_real: 1.037 D_fake: 0.471 
+(epoch: 205, iters: 1568, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.710 G_ID: 0.134 G_Rec: 0.296 D_GP: 0.029 D_real: 1.250 D_fake: 0.653 
+(epoch: 205, iters: 1968, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.951 G_ID: 0.154 G_Rec: 0.415 D_GP: 0.044 D_real: 0.848 D_fake: 0.591 
+(epoch: 205, iters: 2368, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.725 G_ID: 0.125 G_Rec: 0.317 D_GP: 0.037 D_real: 1.121 D_fake: 0.681 
+(epoch: 205, iters: 2768, time: 0.064) G_GAN: 0.865 G_GAN_Feat: 0.880 G_ID: 0.175 G_Rec: 0.375 D_GP: 0.029 D_real: 1.547 D_fake: 0.206 
+(epoch: 205, iters: 3168, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.771 G_ID: 0.124 G_Rec: 0.320 D_GP: 0.035 D_real: 1.136 D_fake: 0.662 
+(epoch: 205, iters: 3568, time: 0.064) G_GAN: 0.864 G_GAN_Feat: 1.269 G_ID: 0.172 G_Rec: 0.440 D_GP: 0.133 D_real: 0.624 D_fake: 0.254 
+(epoch: 205, iters: 3968, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.737 G_ID: 0.143 G_Rec: 0.327 D_GP: 0.023 D_real: 1.325 D_fake: 0.590 
+(epoch: 205, iters: 4368, time: 0.064) G_GAN: 0.444 G_GAN_Feat: 1.039 G_ID: 0.188 G_Rec: 0.402 D_GP: 0.028 D_real: 0.778 D_fake: 0.561 
+(epoch: 205, iters: 4768, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.617 G_ID: 0.117 G_Rec: 0.288 D_GP: 0.023 D_real: 1.318 D_fake: 0.619 
+(epoch: 205, iters: 5168, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.892 G_ID: 0.181 G_Rec: 0.456 D_GP: 0.035 D_real: 1.023 D_fake: 0.617 
+(epoch: 205, iters: 5568, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.676 G_ID: 0.131 G_Rec: 0.307 D_GP: 0.033 D_real: 1.099 D_fake: 0.718 
+(epoch: 205, iters: 5968, time: 0.064) G_GAN: 0.396 G_GAN_Feat: 0.856 G_ID: 0.167 G_Rec: 0.445 D_GP: 0.026 D_real: 1.003 D_fake: 0.608 
+(epoch: 205, iters: 6368, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.608 G_ID: 0.149 G_Rec: 0.270 D_GP: 0.023 D_real: 1.137 D_fake: 0.797 
+(epoch: 205, iters: 6768, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.003 G_ID: 0.175 G_Rec: 0.406 D_GP: 0.048 D_real: 0.760 D_fake: 0.652 
+(epoch: 205, iters: 7168, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.696 G_ID: 0.144 G_Rec: 0.280 D_GP: 0.022 D_real: 1.067 D_fake: 0.768 
+(epoch: 205, iters: 7568, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 1.145 G_ID: 0.173 G_Rec: 0.439 D_GP: 0.106 D_real: 0.507 D_fake: 0.526 
+(epoch: 205, iters: 7968, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.743 G_ID: 0.148 G_Rec: 0.317 D_GP: 0.020 D_real: 0.997 D_fake: 0.881 
+(epoch: 205, iters: 8368, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 1.257 G_ID: 0.207 G_Rec: 0.499 D_GP: 0.362 D_real: 0.349 D_fake: 0.502 
+(epoch: 206, iters: 160, time: 0.064) G_GAN: 0.098 G_GAN_Feat: 0.752 G_ID: 0.137 G_Rec: 0.374 D_GP: 0.035 D_real: 0.779 D_fake: 0.902 
+(epoch: 206, iters: 560, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.921 G_ID: 0.209 G_Rec: 0.427 D_GP: 0.024 D_real: 1.107 D_fake: 0.528 
+(epoch: 206, iters: 960, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.673 G_ID: 0.172 G_Rec: 0.279 D_GP: 0.029 D_real: 1.174 D_fake: 0.856 
+(epoch: 206, iters: 1360, time: 0.064) G_GAN: 0.462 G_GAN_Feat: 0.933 G_ID: 0.185 G_Rec: 0.365 D_GP: 0.030 D_real: 0.996 D_fake: 0.540 
+(epoch: 206, iters: 1760, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.764 G_ID: 0.131 G_Rec: 0.324 D_GP: 0.039 D_real: 0.962 D_fake: 0.781 
+(epoch: 206, iters: 2160, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.834 G_ID: 0.202 G_Rec: 0.415 D_GP: 0.027 D_real: 0.960 D_fake: 0.756 
+(epoch: 206, iters: 2560, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.704 G_ID: 0.172 G_Rec: 0.283 D_GP: 0.027 D_real: 1.186 D_fake: 0.679 
+(epoch: 206, iters: 2960, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.785 G_ID: 0.182 G_Rec: 0.324 D_GP: 0.022 D_real: 1.022 D_fake: 0.726 
+(epoch: 206, iters: 3360, time: 0.064) G_GAN: 0.650 G_GAN_Feat: 0.748 G_ID: 0.131 G_Rec: 0.333 D_GP: 0.024 D_real: 1.523 D_fake: 0.367 
+(epoch: 206, iters: 3760, time: 0.064) G_GAN: 0.556 G_GAN_Feat: 0.852 G_ID: 0.155 G_Rec: 0.394 D_GP: 0.026 D_real: 1.312 D_fake: 0.448 
+(epoch: 206, iters: 4160, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.577 G_ID: 0.171 G_Rec: 0.311 D_GP: 0.016 D_real: 1.214 D_fake: 0.729 
+(epoch: 206, iters: 4560, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.696 G_ID: 0.187 G_Rec: 0.332 D_GP: 0.021 D_real: 1.214 D_fake: 0.594 
+(epoch: 206, iters: 4960, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.667 G_ID: 0.147 G_Rec: 0.332 D_GP: 0.035 D_real: 1.052 D_fake: 0.799 
+(epoch: 206, iters: 5360, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 0.880 G_ID: 0.171 G_Rec: 0.378 D_GP: 0.028 D_real: 1.137 D_fake: 0.582 
+(epoch: 206, iters: 5760, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.873 G_ID: 0.165 G_Rec: 0.312 D_GP: 0.197 D_real: 0.482 D_fake: 0.818 
+(epoch: 206, iters: 6160, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.907 G_ID: 0.166 G_Rec: 0.389 D_GP: 0.038 D_real: 1.030 D_fake: 0.537 
+(epoch: 206, iters: 6560, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.640 G_ID: 0.123 G_Rec: 0.288 D_GP: 0.023 D_real: 1.384 D_fake: 0.547 
+(epoch: 206, iters: 6960, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 1.217 G_ID: 0.170 G_Rec: 0.410 D_GP: 0.488 D_real: 0.362 D_fake: 0.664 
+(epoch: 206, iters: 7360, time: 0.064) G_GAN: 0.146 G_GAN_Feat: 0.723 G_ID: 0.139 G_Rec: 0.284 D_GP: 0.026 D_real: 1.040 D_fake: 0.854 
+(epoch: 206, iters: 7760, time: 0.064) G_GAN: 0.604 G_GAN_Feat: 0.935 G_ID: 0.169 G_Rec: 0.392 D_GP: 0.043 D_real: 1.218 D_fake: 0.481 
+(epoch: 206, iters: 8160, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.893 G_ID: 0.165 G_Rec: 0.353 D_GP: 0.060 D_real: 0.652 D_fake: 0.756 
+(epoch: 206, iters: 8560, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.662 G_ID: 0.190 G_Rec: 0.368 D_GP: 0.018 D_real: 1.103 D_fake: 0.740 
+(epoch: 207, iters: 352, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.533 G_ID: 0.113 G_Rec: 0.289 D_GP: 0.016 D_real: 1.356 D_fake: 0.615 
+(epoch: 207, iters: 752, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.721 G_ID: 0.177 G_Rec: 0.422 D_GP: 0.017 D_real: 1.181 D_fake: 0.659 
+(epoch: 207, iters: 1152, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.529 G_ID: 0.175 G_Rec: 0.295 D_GP: 0.018 D_real: 1.017 D_fake: 0.959 
+(epoch: 207, iters: 1552, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.750 G_ID: 0.162 G_Rec: 0.452 D_GP: 0.022 D_real: 1.067 D_fake: 0.647 
+(epoch: 207, iters: 1952, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.561 G_ID: 0.149 G_Rec: 0.298 D_GP: 0.020 D_real: 1.011 D_fake: 0.938 
+(epoch: 207, iters: 2352, time: 0.064) G_GAN: 0.040 G_GAN_Feat: 0.753 G_ID: 0.163 G_Rec: 0.391 D_GP: 0.024 D_real: 0.751 D_fake: 0.960 
+(epoch: 207, iters: 2752, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.568 G_ID: 0.148 G_Rec: 0.315 D_GP: 0.020 D_real: 1.072 D_fake: 0.879 
+(epoch: 207, iters: 3152, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.772 G_ID: 0.191 G_Rec: 0.375 D_GP: 0.028 D_real: 0.918 D_fake: 0.850 
+(epoch: 207, iters: 3552, time: 0.064) G_GAN: -0.010 G_GAN_Feat: 0.667 G_ID: 0.128 G_Rec: 0.334 D_GP: 0.055 D_real: 0.905 D_fake: 1.010 
+(epoch: 207, iters: 3952, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.936 G_ID: 0.186 G_Rec: 0.417 D_GP: 0.050 D_real: 0.720 D_fake: 0.756 
+(epoch: 207, iters: 4352, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.621 G_ID: 0.145 G_Rec: 0.285 D_GP: 0.026 D_real: 1.088 D_fake: 0.855 
+(epoch: 207, iters: 4752, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.887 G_ID: 0.165 G_Rec: 0.398 D_GP: 0.049 D_real: 1.079 D_fake: 0.539 
+(epoch: 207, iters: 5152, time: 0.064) G_GAN: -0.056 G_GAN_Feat: 0.733 G_ID: 0.121 G_Rec: 0.313 D_GP: 0.083 D_real: 0.651 D_fake: 1.056 
+(epoch: 207, iters: 5552, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.872 G_ID: 0.198 G_Rec: 0.393 D_GP: 0.030 D_real: 0.907 D_fake: 0.762 
+(epoch: 207, iters: 5952, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.757 G_ID: 0.158 G_Rec: 0.334 D_GP: 0.030 D_real: 0.990 D_fake: 0.885 
+(epoch: 207, iters: 6352, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.825 G_ID: 0.162 G_Rec: 0.379 D_GP: 0.024 D_real: 1.218 D_fake: 0.509 
+(epoch: 207, iters: 6752, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.684 G_ID: 0.136 G_Rec: 0.317 D_GP: 0.030 D_real: 0.965 D_fake: 0.853 
+(epoch: 207, iters: 7152, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 1.046 G_ID: 0.165 G_Rec: 0.457 D_GP: 0.107 D_real: 0.657 D_fake: 0.654 
+(epoch: 207, iters: 7552, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.765 G_ID: 0.144 G_Rec: 0.324 D_GP: 0.041 D_real: 0.941 D_fake: 0.723 
+(epoch: 207, iters: 7952, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.769 G_ID: 0.205 G_Rec: 0.386 D_GP: 0.023 D_real: 1.103 D_fake: 0.687 
+(epoch: 207, iters: 8352, time: 0.064) G_GAN: 0.038 G_GAN_Feat: 0.779 G_ID: 0.152 G_Rec: 0.345 D_GP: 0.058 D_real: 0.823 D_fake: 0.962 
+(epoch: 208, iters: 144, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 1.182 G_ID: 0.170 G_Rec: 0.414 D_GP: 0.101 D_real: 0.458 D_fake: 0.602 
+(epoch: 208, iters: 544, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.742 G_ID: 0.139 G_Rec: 0.311 D_GP: 0.053 D_real: 0.832 D_fake: 0.787 
+(epoch: 208, iters: 944, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.963 G_ID: 0.193 G_Rec: 0.446 D_GP: 0.060 D_real: 0.924 D_fake: 0.622 
+(epoch: 208, iters: 1344, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.791 G_ID: 0.131 G_Rec: 0.352 D_GP: 0.034 D_real: 0.956 D_fake: 0.726 
+(epoch: 208, iters: 1744, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.951 G_ID: 0.193 G_Rec: 0.402 D_GP: 0.044 D_real: 0.589 D_fake: 0.801 
+(epoch: 208, iters: 2144, time: 0.064) G_GAN: -0.002 G_GAN_Feat: 0.654 G_ID: 0.154 G_Rec: 0.286 D_GP: 0.024 D_real: 0.962 D_fake: 1.002 
+(epoch: 208, iters: 2544, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.897 G_ID: 0.188 G_Rec: 0.379 D_GP: 0.023 D_real: 0.961 D_fake: 0.730 
+(epoch: 208, iters: 2944, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.856 G_ID: 0.119 G_Rec: 0.322 D_GP: 0.108 D_real: 1.013 D_fake: 0.714 
+(epoch: 208, iters: 3344, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.818 G_ID: 0.189 G_Rec: 0.354 D_GP: 0.028 D_real: 1.155 D_fake: 0.572 
+(epoch: 208, iters: 3744, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.812 G_ID: 0.163 G_Rec: 0.335 D_GP: 0.073 D_real: 0.726 D_fake: 0.947 
+(epoch: 208, iters: 4144, time: 0.064) G_GAN: 0.925 G_GAN_Feat: 1.102 G_ID: 0.203 G_Rec: 0.418 D_GP: 0.047 D_real: 1.338 D_fake: 0.455 
+(epoch: 208, iters: 4544, time: 0.064) G_GAN: -0.071 G_GAN_Feat: 0.645 G_ID: 0.152 G_Rec: 0.291 D_GP: 0.025 D_real: 0.862 D_fake: 1.071 
+(epoch: 208, iters: 4944, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.991 G_ID: 0.180 G_Rec: 0.414 D_GP: 0.057 D_real: 0.725 D_fake: 0.624 
+(epoch: 208, iters: 5344, time: 0.064) G_GAN: 0.591 G_GAN_Feat: 0.758 G_ID: 0.141 G_Rec: 0.311 D_GP: 0.035 D_real: 1.423 D_fake: 0.466 
+(epoch: 208, iters: 5744, time: 0.064) G_GAN: 0.204 G_GAN_Feat: 0.831 G_ID: 0.172 G_Rec: 0.400 D_GP: 0.025 D_real: 0.920 D_fake: 0.797 
+(epoch: 208, iters: 6144, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.816 G_ID: 0.189 G_Rec: 0.298 D_GP: 0.043 D_real: 0.740 D_fake: 0.875 
+(epoch: 208, iters: 6544, time: 0.064) G_GAN: 0.783 G_GAN_Feat: 1.110 G_ID: 0.203 G_Rec: 0.459 D_GP: 0.407 D_real: 0.805 D_fake: 0.329 
+(epoch: 208, iters: 6944, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.706 G_ID: 0.143 G_Rec: 0.295 D_GP: 0.034 D_real: 1.167 D_fake: 0.671 
+(epoch: 208, iters: 7344, time: 0.064) G_GAN: 0.529 G_GAN_Feat: 0.970 G_ID: 0.167 G_Rec: 0.411 D_GP: 0.080 D_real: 0.939 D_fake: 0.476 
+(epoch: 208, iters: 7744, time: 0.064) G_GAN: 0.037 G_GAN_Feat: 0.774 G_ID: 0.138 G_Rec: 0.351 D_GP: 0.067 D_real: 0.679 D_fake: 0.964 
+(epoch: 208, iters: 8144, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 0.892 G_ID: 0.154 G_Rec: 0.408 D_GP: 0.025 D_real: 1.096 D_fake: 0.519 
+(epoch: 208, iters: 8544, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.849 G_ID: 0.133 G_Rec: 0.319 D_GP: 0.029 D_real: 0.849 D_fake: 0.808 
+(epoch: 209, iters: 336, time: 0.064) G_GAN: 0.592 G_GAN_Feat: 0.878 G_ID: 0.180 G_Rec: 0.431 D_GP: 0.027 D_real: 1.293 D_fake: 0.429 
+(epoch: 209, iters: 736, time: 0.064) G_GAN: 0.068 G_GAN_Feat: 0.612 G_ID: 0.144 G_Rec: 0.312 D_GP: 0.023 D_real: 1.019 D_fake: 0.932 
+(epoch: 209, iters: 1136, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.853 G_ID: 0.190 G_Rec: 0.423 D_GP: 0.035 D_real: 0.870 D_fake: 0.804 
+(epoch: 209, iters: 1536, time: 0.064) G_GAN: -0.025 G_GAN_Feat: 0.722 G_ID: 0.156 G_Rec: 0.300 D_GP: 0.055 D_real: 0.671 D_fake: 1.026 
+(epoch: 209, iters: 1936, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.919 G_ID: 0.163 G_Rec: 0.387 D_GP: 0.279 D_real: 0.689 D_fake: 0.653 
+(epoch: 209, iters: 2336, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.696 G_ID: 0.118 G_Rec: 0.316 D_GP: 0.036 D_real: 0.968 D_fake: 0.814 
+(epoch: 209, iters: 2736, time: 0.064) G_GAN: 0.475 G_GAN_Feat: 0.778 G_ID: 0.192 G_Rec: 0.329 D_GP: 0.023 D_real: 1.316 D_fake: 0.535 
+(epoch: 209, iters: 3136, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.661 G_ID: 0.130 G_Rec: 0.279 D_GP: 0.023 D_real: 1.242 D_fake: 0.701 
+(epoch: 209, iters: 3536, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.780 G_ID: 0.179 G_Rec: 0.354 D_GP: 0.021 D_real: 1.033 D_fake: 0.887 
+(epoch: 209, iters: 3936, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.688 G_ID: 0.136 G_Rec: 0.344 D_GP: 0.025 D_real: 1.143 D_fake: 0.756 
+(epoch: 209, iters: 4336, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.891 G_ID: 0.173 G_Rec: 0.370 D_GP: 0.037 D_real: 0.937 D_fake: 0.719 
+(epoch: 209, iters: 4736, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.755 G_ID: 0.126 G_Rec: 0.287 D_GP: 0.049 D_real: 0.902 D_fake: 0.696 
+(epoch: 209, iters: 5136, time: 0.064) G_GAN: 0.554 G_GAN_Feat: 1.060 G_ID: 0.169 G_Rec: 0.438 D_GP: 0.059 D_real: 0.889 D_fake: 0.462 
+(epoch: 209, iters: 5536, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.713 G_ID: 0.135 G_Rec: 0.282 D_GP: 0.026 D_real: 1.240 D_fake: 0.640 
+(epoch: 209, iters: 5936, time: 0.064) G_GAN: 0.562 G_GAN_Feat: 0.965 G_ID: 0.156 G_Rec: 0.430 D_GP: 0.028 D_real: 1.194 D_fake: 0.446 
+(epoch: 209, iters: 6336, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.783 G_ID: 0.128 G_Rec: 0.317 D_GP: 0.027 D_real: 0.919 D_fake: 0.812 
+(epoch: 209, iters: 6736, time: 0.064) G_GAN: 0.470 G_GAN_Feat: 0.834 G_ID: 0.162 G_Rec: 0.368 D_GP: 0.022 D_real: 1.328 D_fake: 0.533 
+(epoch: 209, iters: 7136, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 0.749 G_ID: 0.134 G_Rec: 0.297 D_GP: 0.042 D_real: 0.863 D_fake: 0.988 
+(epoch: 209, iters: 7536, time: 0.064) G_GAN: 0.634 G_GAN_Feat: 0.911 G_ID: 0.155 G_Rec: 0.449 D_GP: 0.026 D_real: 1.351 D_fake: 0.378 
+(epoch: 209, iters: 7936, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.772 G_ID: 0.132 G_Rec: 0.385 D_GP: 0.132 D_real: 0.912 D_fake: 0.712 
+(epoch: 209, iters: 8336, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 1.038 G_ID: 0.222 G_Rec: 0.410 D_GP: 0.039 D_real: 0.653 D_fake: 0.590 
+(epoch: 210, iters: 128, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.967 G_ID: 0.128 G_Rec: 0.361 D_GP: 0.224 D_real: 0.536 D_fake: 0.897 
+(epoch: 210, iters: 528, time: 0.064) G_GAN: 0.654 G_GAN_Feat: 0.975 G_ID: 0.147 G_Rec: 0.394 D_GP: 0.026 D_real: 1.238 D_fake: 0.371 
+(epoch: 210, iters: 928, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.774 G_ID: 0.139 G_Rec: 0.314 D_GP: 0.025 D_real: 1.169 D_fake: 0.640 
+(epoch: 210, iters: 1328, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.933 G_ID: 0.179 G_Rec: 0.420 D_GP: 0.036 D_real: 1.015 D_fake: 0.540 
+(epoch: 210, iters: 1728, time: 0.064) G_GAN: 0.058 G_GAN_Feat: 0.723 G_ID: 0.157 G_Rec: 0.347 D_GP: 0.025 D_real: 0.936 D_fake: 0.942 
+(epoch: 210, iters: 2128, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.879 G_ID: 0.181 G_Rec: 0.395 D_GP: 0.056 D_real: 0.885 D_fake: 0.633 
+(epoch: 210, iters: 2528, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 0.698 G_ID: 0.141 G_Rec: 0.316 D_GP: 0.023 D_real: 1.378 D_fake: 0.491 
+(epoch: 210, iters: 2928, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 0.840 G_ID: 0.193 G_Rec: 0.426 D_GP: 0.021 D_real: 1.229 D_fake: 0.505 
+(epoch: 210, iters: 3328, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.683 G_ID: 0.132 G_Rec: 0.307 D_GP: 0.048 D_real: 1.212 D_fake: 0.596 
+(epoch: 210, iters: 3728, time: 0.064) G_GAN: 0.627 G_GAN_Feat: 0.919 G_ID: 0.180 G_Rec: 0.409 D_GP: 0.030 D_real: 1.349 D_fake: 0.401 
+(epoch: 210, iters: 4128, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.740 G_ID: 0.125 G_Rec: 0.285 D_GP: 0.027 D_real: 1.051 D_fake: 0.799 
+(epoch: 210, iters: 4528, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 1.167 G_ID: 0.228 G_Rec: 0.434 D_GP: 0.544 D_real: 0.306 D_fake: 0.823 
+(epoch: 210, iters: 4928, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.940 G_ID: 0.111 G_Rec: 0.318 D_GP: 0.057 D_real: 0.473 D_fake: 0.683 
+(epoch: 210, iters: 5328, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.759 G_ID: 0.172 G_Rec: 0.371 D_GP: 0.023 D_real: 1.277 D_fake: 0.515 
+(epoch: 210, iters: 5728, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.654 G_ID: 0.163 G_Rec: 0.330 D_GP: 0.025 D_real: 0.973 D_fake: 0.923 
+(epoch: 210, iters: 6128, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.801 G_ID: 0.209 G_Rec: 0.357 D_GP: 0.027 D_real: 0.874 D_fake: 0.833 
+(epoch: 210, iters: 6528, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.648 G_ID: 0.143 G_Rec: 0.339 D_GP: 0.021 D_real: 1.148 D_fake: 0.759 
+(epoch: 210, iters: 6928, time: 0.064) G_GAN: 0.008 G_GAN_Feat: 1.020 G_ID: 0.211 G_Rec: 0.417 D_GP: 0.212 D_real: 0.192 D_fake: 0.992 
+(epoch: 210, iters: 7328, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.642 G_ID: 0.158 G_Rec: 0.279 D_GP: 0.022 D_real: 1.237 D_fake: 0.693 
+(epoch: 210, iters: 7728, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.842 G_ID: 0.192 G_Rec: 0.398 D_GP: 0.025 D_real: 1.186 D_fake: 0.611 
+(epoch: 210, iters: 8128, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 0.820 G_ID: 0.142 G_Rec: 0.310 D_GP: 0.027 D_real: 0.997 D_fake: 0.673 
+(epoch: 210, iters: 8528, time: 0.064) G_GAN: 0.688 G_GAN_Feat: 0.952 G_ID: 0.165 G_Rec: 0.440 D_GP: 0.021 D_real: 1.403 D_fake: 0.348 
+(epoch: 211, iters: 320, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.594 G_ID: 0.142 G_Rec: 0.285 D_GP: 0.019 D_real: 1.238 D_fake: 0.692 
+(epoch: 211, iters: 720, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 1.014 G_ID: 0.181 G_Rec: 0.440 D_GP: 0.058 D_real: 0.849 D_fake: 0.538 
+(epoch: 211, iters: 1120, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.710 G_ID: 0.154 G_Rec: 0.304 D_GP: 0.023 D_real: 1.032 D_fake: 0.790 
+(epoch: 211, iters: 1520, time: 0.064) G_GAN: 0.601 G_GAN_Feat: 0.863 G_ID: 0.190 G_Rec: 0.406 D_GP: 0.027 D_real: 1.422 D_fake: 0.426 
+(epoch: 211, iters: 1920, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.744 G_ID: 0.142 G_Rec: 0.342 D_GP: 0.053 D_real: 0.993 D_fake: 0.751 
+(epoch: 211, iters: 2320, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.865 G_ID: 0.164 G_Rec: 0.363 D_GP: 0.028 D_real: 1.122 D_fake: 0.573 
+(epoch: 211, iters: 2720, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.675 G_ID: 0.130 G_Rec: 0.286 D_GP: 0.022 D_real: 1.065 D_fake: 0.873 
+(epoch: 211, iters: 3120, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.824 G_ID: 0.179 G_Rec: 0.401 D_GP: 0.019 D_real: 1.092 D_fake: 0.616 
+(epoch: 211, iters: 3520, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.710 G_ID: 0.133 G_Rec: 0.292 D_GP: 0.025 D_real: 1.300 D_fake: 0.576 
+(epoch: 211, iters: 3920, time: 0.064) G_GAN: 0.552 G_GAN_Feat: 1.065 G_ID: 0.183 G_Rec: 0.385 D_GP: 0.044 D_real: 0.547 D_fake: 0.451 
+(epoch: 211, iters: 4320, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.643 G_ID: 0.122 G_Rec: 0.280 D_GP: 0.024 D_real: 1.109 D_fake: 0.813 
+(epoch: 211, iters: 4720, time: 0.064) G_GAN: 0.521 G_GAN_Feat: 0.928 G_ID: 0.184 G_Rec: 0.393 D_GP: 0.060 D_real: 1.055 D_fake: 0.484 
+(epoch: 211, iters: 5120, time: 0.064) G_GAN: 0.469 G_GAN_Feat: 0.786 G_ID: 0.138 G_Rec: 0.315 D_GP: 0.035 D_real: 1.068 D_fake: 0.534 
+(epoch: 211, iters: 5520, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 1.029 G_ID: 0.175 G_Rec: 0.410 D_GP: 0.033 D_real: 0.757 D_fake: 0.512 
+(epoch: 211, iters: 5920, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.652 G_ID: 0.122 G_Rec: 0.284 D_GP: 0.021 D_real: 1.306 D_fake: 0.615 
+(epoch: 211, iters: 6320, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.905 G_ID: 0.192 G_Rec: 0.420 D_GP: 0.024 D_real: 1.024 D_fake: 0.634 
+(epoch: 211, iters: 6720, time: 0.064) G_GAN: 0.008 G_GAN_Feat: 0.754 G_ID: 0.141 G_Rec: 0.337 D_GP: 0.032 D_real: 0.830 D_fake: 0.992 
+(epoch: 211, iters: 7120, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.958 G_ID: 0.157 G_Rec: 0.424 D_GP: 0.047 D_real: 0.817 D_fake: 0.690 
+(epoch: 211, iters: 7520, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.884 G_ID: 0.138 G_Rec: 0.347 D_GP: 0.090 D_real: 0.571 D_fake: 0.797 
+(epoch: 211, iters: 7920, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.937 G_ID: 0.205 G_Rec: 0.392 D_GP: 0.029 D_real: 0.984 D_fake: 0.633 
+(epoch: 211, iters: 8320, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 0.718 G_ID: 0.140 G_Rec: 0.331 D_GP: 0.021 D_real: 1.085 D_fake: 0.863 
+(epoch: 212, iters: 112, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.814 G_ID: 0.165 G_Rec: 0.410 D_GP: 0.023 D_real: 1.093 D_fake: 0.634 
+(epoch: 212, iters: 512, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.608 G_ID: 0.139 G_Rec: 0.292 D_GP: 0.021 D_real: 1.160 D_fake: 0.863 
+(epoch: 212, iters: 912, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.787 G_ID: 0.147 G_Rec: 0.401 D_GP: 0.019 D_real: 1.219 D_fake: 0.576 
+(epoch: 212, iters: 1312, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.680 G_ID: 0.123 G_Rec: 0.310 D_GP: 0.025 D_real: 1.085 D_fake: 0.797 
+(epoch: 212, iters: 1712, time: 0.064) G_GAN: 0.747 G_GAN_Feat: 0.956 G_ID: 0.181 G_Rec: 0.427 D_GP: 0.025 D_real: 1.476 D_fake: 0.276 
+(epoch: 212, iters: 2112, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.733 G_ID: 0.128 G_Rec: 0.316 D_GP: 0.028 D_real: 1.220 D_fake: 0.657 
+(epoch: 212, iters: 2512, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.823 G_ID: 0.168 G_Rec: 0.378 D_GP: 0.025 D_real: 1.085 D_fake: 0.562 
+(epoch: 212, iters: 2912, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.688 G_ID: 0.122 G_Rec: 0.288 D_GP: 0.034 D_real: 1.048 D_fake: 0.745 
+(epoch: 212, iters: 3312, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.850 G_ID: 0.173 G_Rec: 0.395 D_GP: 0.026 D_real: 1.307 D_fake: 0.504 
+(epoch: 212, iters: 3712, time: 0.064) G_GAN: 0.038 G_GAN_Feat: 0.593 G_ID: 0.135 G_Rec: 0.289 D_GP: 0.019 D_real: 1.005 D_fake: 0.962 
+(epoch: 212, iters: 4112, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.821 G_ID: 0.185 G_Rec: 0.450 D_GP: 0.027 D_real: 0.909 D_fake: 0.779 
+(epoch: 212, iters: 4512, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.670 G_ID: 0.119 G_Rec: 0.307 D_GP: 0.072 D_real: 0.901 D_fake: 0.887 
+(epoch: 212, iters: 4912, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.852 G_ID: 0.185 G_Rec: 0.401 D_GP: 0.031 D_real: 1.119 D_fake: 0.573 
+(epoch: 212, iters: 5312, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.812 G_ID: 0.125 G_Rec: 0.324 D_GP: 0.056 D_real: 0.871 D_fake: 0.709 
+(epoch: 212, iters: 5712, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.930 G_ID: 0.166 G_Rec: 0.394 D_GP: 0.058 D_real: 0.764 D_fake: 0.885 
+(epoch: 212, iters: 6112, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.788 G_ID: 0.151 G_Rec: 0.316 D_GP: 0.064 D_real: 0.773 D_fake: 0.789 
+(epoch: 212, iters: 6512, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 0.889 G_ID: 0.187 G_Rec: 0.403 D_GP: 0.025 D_real: 1.197 D_fake: 0.493 
+(epoch: 212, iters: 6912, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.871 G_ID: 0.121 G_Rec: 0.308 D_GP: 0.102 D_real: 0.618 D_fake: 0.617 
+(epoch: 212, iters: 7312, time: 0.064) G_GAN: 0.669 G_GAN_Feat: 0.813 G_ID: 0.158 G_Rec: 0.460 D_GP: 0.019 D_real: 1.467 D_fake: 0.366 
+(epoch: 212, iters: 7712, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.624 G_ID: 0.133 G_Rec: 0.313 D_GP: 0.026 D_real: 1.094 D_fake: 0.831 
+(epoch: 212, iters: 8112, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.925 G_ID: 0.155 G_Rec: 0.409 D_GP: 0.064 D_real: 0.956 D_fake: 0.569 
+(epoch: 212, iters: 8512, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.757 G_ID: 0.144 G_Rec: 0.297 D_GP: 0.041 D_real: 1.132 D_fake: 0.597 
+(epoch: 213, iters: 304, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.830 G_ID: 0.183 G_Rec: 0.387 D_GP: 0.025 D_real: 1.154 D_fake: 0.548 
+(epoch: 213, iters: 704, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.732 G_ID: 0.122 G_Rec: 0.301 D_GP: 0.042 D_real: 1.067 D_fake: 0.728 
+(epoch: 213, iters: 1104, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.976 G_ID: 0.162 G_Rec: 0.447 D_GP: 0.025 D_real: 0.893 D_fake: 0.702 
+(epoch: 213, iters: 1504, time: 0.064) G_GAN: -0.027 G_GAN_Feat: 0.676 G_ID: 0.132 G_Rec: 0.298 D_GP: 0.022 D_real: 0.873 D_fake: 1.027 
+(epoch: 213, iters: 1904, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 1.053 G_ID: 0.194 G_Rec: 0.446 D_GP: 0.159 D_real: 0.492 D_fake: 0.630 
+(epoch: 213, iters: 2304, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.816 G_ID: 0.139 G_Rec: 0.350 D_GP: 0.048 D_real: 0.892 D_fake: 0.686 
+(epoch: 213, iters: 2704, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 0.784 G_ID: 0.179 G_Rec: 0.399 D_GP: 0.019 D_real: 1.392 D_fake: 0.372 
+(epoch: 213, iters: 3104, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.592 G_ID: 0.134 G_Rec: 0.312 D_GP: 0.018 D_real: 1.097 D_fake: 0.852 
+(epoch: 213, iters: 3504, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.710 G_ID: 0.166 G_Rec: 0.350 D_GP: 0.026 D_real: 1.213 D_fake: 0.669 
+(epoch: 213, iters: 3904, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.599 G_ID: 0.106 G_Rec: 0.291 D_GP: 0.028 D_real: 1.143 D_fake: 0.776 
+(epoch: 213, iters: 4304, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.818 G_ID: 0.174 G_Rec: 0.408 D_GP: 0.046 D_real: 1.057 D_fake: 0.657 
+(epoch: 213, iters: 4704, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.768 G_ID: 0.135 G_Rec: 0.327 D_GP: 0.027 D_real: 1.111 D_fake: 0.723 
+(epoch: 213, iters: 5104, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.854 G_ID: 0.180 G_Rec: 0.400 D_GP: 0.039 D_real: 1.175 D_fake: 0.490 
+(epoch: 213, iters: 5504, time: 0.064) G_GAN: 0.043 G_GAN_Feat: 0.741 G_ID: 0.124 G_Rec: 0.329 D_GP: 0.073 D_real: 0.856 D_fake: 0.957 
+(epoch: 213, iters: 5904, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.942 G_ID: 0.179 G_Rec: 0.399 D_GP: 0.139 D_real: 0.592 D_fake: 0.733 
+(epoch: 213, iters: 6304, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.830 G_ID: 0.158 G_Rec: 0.341 D_GP: 0.054 D_real: 0.912 D_fake: 0.694 
+(epoch: 213, iters: 6704, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.824 G_ID: 0.207 G_Rec: 0.355 D_GP: 0.026 D_real: 0.957 D_fake: 0.742 
+(epoch: 213, iters: 7104, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.704 G_ID: 0.125 G_Rec: 0.327 D_GP: 0.025 D_real: 1.228 D_fake: 0.674 
+(epoch: 213, iters: 7504, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.935 G_ID: 0.186 G_Rec: 0.386 D_GP: 0.043 D_real: 0.859 D_fake: 0.599 
+(epoch: 213, iters: 7904, time: 0.064) G_GAN: 0.555 G_GAN_Feat: 0.793 G_ID: 0.121 G_Rec: 0.318 D_GP: 0.077 D_real: 1.223 D_fake: 0.474 
+(epoch: 213, iters: 8304, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.852 G_ID: 0.156 G_Rec: 0.413 D_GP: 0.026 D_real: 0.943 D_fake: 0.777 
+(epoch: 214, iters: 96, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.713 G_ID: 0.131 G_Rec: 0.316 D_GP: 0.050 D_real: 1.050 D_fake: 0.775 
+(epoch: 214, iters: 496, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.974 G_ID: 0.168 G_Rec: 0.386 D_GP: 0.025 D_real: 1.043 D_fake: 0.672 
+(epoch: 214, iters: 896, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.809 G_ID: 0.136 G_Rec: 0.319 D_GP: 0.038 D_real: 0.840 D_fake: 0.787 
+(epoch: 214, iters: 1296, time: 0.064) G_GAN: 0.762 G_GAN_Feat: 0.903 G_ID: 0.190 G_Rec: 0.376 D_GP: 0.038 D_real: 1.428 D_fake: 0.347 
+(epoch: 214, iters: 1696, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.785 G_ID: 0.120 G_Rec: 0.278 D_GP: 0.070 D_real: 1.051 D_fake: 0.882 
+(epoch: 214, iters: 2096, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.995 G_ID: 0.171 G_Rec: 0.469 D_GP: 0.071 D_real: 0.617 D_fake: 0.864 
+(epoch: 214, iters: 2496, time: 0.064) G_GAN: 0.010 G_GAN_Feat: 0.848 G_ID: 0.161 G_Rec: 0.332 D_GP: 0.141 D_real: 0.417 D_fake: 0.991 
+(epoch: 214, iters: 2896, time: 0.064) G_GAN: 0.879 G_GAN_Feat: 0.807 G_ID: 0.166 G_Rec: 0.371 D_GP: 0.025 D_real: 1.612 D_fake: 0.220 
+(epoch: 214, iters: 3296, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.666 G_ID: 0.189 G_Rec: 0.288 D_GP: 0.025 D_real: 1.022 D_fake: 0.863 
+(epoch: 214, iters: 3696, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.951 G_ID: 0.198 G_Rec: 0.443 D_GP: 0.059 D_real: 1.032 D_fake: 0.647 
+(epoch: 214, iters: 4096, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.690 G_ID: 0.134 G_Rec: 0.269 D_GP: 0.023 D_real: 1.154 D_fake: 0.732 
+(epoch: 214, iters: 4496, time: 0.064) G_GAN: 0.722 G_GAN_Feat: 0.771 G_ID: 0.148 G_Rec: 0.349 D_GP: 0.026 D_real: 1.522 D_fake: 0.291 
+(epoch: 214, iters: 4896, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.899 G_ID: 0.128 G_Rec: 0.324 D_GP: 0.622 D_real: 0.450 D_fake: 0.767 
+(epoch: 214, iters: 5296, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.840 G_ID: 0.180 G_Rec: 0.382 D_GP: 0.028 D_real: 1.162 D_fake: 0.548 
+(epoch: 214, iters: 5696, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.781 G_ID: 0.144 G_Rec: 0.335 D_GP: 0.054 D_real: 0.885 D_fake: 0.694 
+(epoch: 214, iters: 6096, time: 0.064) G_GAN: 0.672 G_GAN_Feat: 0.883 G_ID: 0.171 G_Rec: 0.412 D_GP: 0.036 D_real: 1.309 D_fake: 0.364 
+(epoch: 214, iters: 6496, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.908 G_ID: 0.143 G_Rec: 0.341 D_GP: 0.092 D_real: 0.485 D_fake: 0.792 
+(epoch: 214, iters: 6896, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.828 G_ID: 0.184 G_Rec: 0.447 D_GP: 0.020 D_real: 1.188 D_fake: 0.584 
+(epoch: 214, iters: 7296, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.796 G_ID: 0.128 G_Rec: 0.318 D_GP: 0.043 D_real: 1.055 D_fake: 0.665 
+(epoch: 214, iters: 7696, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.804 G_ID: 0.161 G_Rec: 0.361 D_GP: 0.022 D_real: 1.236 D_fake: 0.583 
+(epoch: 214, iters: 8096, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.655 G_ID: 0.131 G_Rec: 0.283 D_GP: 0.024 D_real: 1.215 D_fake: 0.674 
+(epoch: 214, iters: 8496, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.957 G_ID: 0.171 G_Rec: 0.403 D_GP: 0.024 D_real: 1.106 D_fake: 0.473 
+(epoch: 215, iters: 288, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.610 G_ID: 0.129 G_Rec: 0.308 D_GP: 0.020 D_real: 0.972 D_fake: 0.959 
+(epoch: 215, iters: 688, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.835 G_ID: 0.168 G_Rec: 0.374 D_GP: 0.026 D_real: 0.958 D_fake: 0.755 
+(epoch: 215, iters: 1088, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.687 G_ID: 0.128 G_Rec: 0.300 D_GP: 0.025 D_real: 1.063 D_fake: 0.894 
+(epoch: 215, iters: 1488, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.950 G_ID: 0.160 G_Rec: 0.380 D_GP: 0.034 D_real: 0.982 D_fake: 0.489 
+(epoch: 215, iters: 1888, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.708 G_ID: 0.133 G_Rec: 0.281 D_GP: 0.030 D_real: 1.196 D_fake: 0.605 
+(epoch: 215, iters: 2288, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.986 G_ID: 0.199 G_Rec: 0.430 D_GP: 0.038 D_real: 0.963 D_fake: 0.534 
+(epoch: 215, iters: 2688, time: 0.064) G_GAN: -0.024 G_GAN_Feat: 0.772 G_ID: 0.119 G_Rec: 0.344 D_GP: 0.085 D_real: 0.585 D_fake: 1.024 
+(epoch: 215, iters: 3088, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 1.141 G_ID: 0.162 G_Rec: 0.438 D_GP: 0.505 D_real: 0.312 D_fake: 0.902 
+(epoch: 215, iters: 3488, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.636 G_ID: 0.151 G_Rec: 0.287 D_GP: 0.027 D_real: 1.272 D_fake: 0.661 
+(epoch: 215, iters: 3888, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 1.068 G_ID: 0.174 G_Rec: 0.476 D_GP: 0.104 D_real: 0.723 D_fake: 0.412 
+(epoch: 215, iters: 4288, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.654 G_ID: 0.128 G_Rec: 0.286 D_GP: 0.028 D_real: 1.042 D_fake: 0.872 
+(epoch: 215, iters: 4688, time: 0.064) G_GAN: 0.695 G_GAN_Feat: 0.927 G_ID: 0.171 G_Rec: 0.406 D_GP: 0.049 D_real: 1.248 D_fake: 0.323 
+(epoch: 215, iters: 5088, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.803 G_ID: 0.147 G_Rec: 0.312 D_GP: 0.076 D_real: 0.690 D_fake: 0.741 
+(epoch: 215, iters: 5488, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.843 G_ID: 0.190 G_Rec: 0.370 D_GP: 0.020 D_real: 1.005 D_fake: 0.802 
+(epoch: 215, iters: 5888, time: 0.064) G_GAN: 0.183 G_GAN_Feat: 0.626 G_ID: 0.120 G_Rec: 0.282 D_GP: 0.022 D_real: 1.093 D_fake: 0.817 
+(epoch: 215, iters: 6288, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.923 G_ID: 0.197 G_Rec: 0.431 D_GP: 0.082 D_real: 0.754 D_fake: 0.693 
+(epoch: 215, iters: 6688, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.835 G_ID: 0.156 G_Rec: 0.353 D_GP: 0.029 D_real: 0.710 D_fake: 0.921 
+(epoch: 215, iters: 7088, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 0.981 G_ID: 0.187 G_Rec: 0.418 D_GP: 0.040 D_real: 0.910 D_fake: 0.483 
+(epoch: 215, iters: 7488, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.857 G_ID: 0.136 G_Rec: 0.290 D_GP: 0.052 D_real: 0.609 D_fake: 0.843 
+(epoch: 215, iters: 7888, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.869 G_ID: 0.192 G_Rec: 0.388 D_GP: 0.020 D_real: 0.913 D_fake: 0.855 
+(epoch: 215, iters: 8288, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.738 G_ID: 0.135 G_Rec: 0.298 D_GP: 0.022 D_real: 1.140 D_fake: 0.746 
+(epoch: 216, iters: 80, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.889 G_ID: 0.169 G_Rec: 0.349 D_GP: 0.021 D_real: 0.768 D_fake: 0.853 
+(epoch: 216, iters: 480, time: 0.064) G_GAN: 0.020 G_GAN_Feat: 0.748 G_ID: 0.114 G_Rec: 0.316 D_GP: 0.028 D_real: 0.859 D_fake: 0.980 
+(epoch: 216, iters: 880, time: 0.064) G_GAN: 0.768 G_GAN_Feat: 1.086 G_ID: 0.179 G_Rec: 0.445 D_GP: 0.233 D_real: 0.697 D_fake: 0.405 
+(epoch: 216, iters: 1280, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.684 G_ID: 0.133 G_Rec: 0.344 D_GP: 0.027 D_real: 1.138 D_fake: 0.791 
+(epoch: 216, iters: 1680, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.852 G_ID: 0.165 G_Rec: 0.403 D_GP: 0.033 D_real: 0.939 D_fake: 0.769 
+(epoch: 216, iters: 2080, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.608 G_ID: 0.143 G_Rec: 0.278 D_GP: 0.020 D_real: 1.248 D_fake: 0.709 
+(epoch: 216, iters: 2480, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.885 G_ID: 0.162 G_Rec: 0.389 D_GP: 0.029 D_real: 1.069 D_fake: 0.615 
+(epoch: 216, iters: 2880, time: 0.064) G_GAN: -0.017 G_GAN_Feat: 0.648 G_ID: 0.133 G_Rec: 0.273 D_GP: 0.033 D_real: 0.867 D_fake: 1.017 
+(epoch: 216, iters: 3280, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 0.771 G_ID: 0.170 G_Rec: 0.391 D_GP: 0.022 D_real: 1.268 D_fake: 0.562 
+(epoch: 216, iters: 3680, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.580 G_ID: 0.173 G_Rec: 0.281 D_GP: 0.019 D_real: 1.041 D_fake: 0.952 
+(epoch: 216, iters: 4080, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.795 G_ID: 0.169 G_Rec: 0.388 D_GP: 0.022 D_real: 1.091 D_fake: 0.661 
+(epoch: 216, iters: 4480, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.668 G_ID: 0.143 G_Rec: 0.320 D_GP: 0.034 D_real: 1.115 D_fake: 0.714 
+(epoch: 216, iters: 4880, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.781 G_ID: 0.182 G_Rec: 0.365 D_GP: 0.030 D_real: 0.990 D_fake: 0.720 
+(epoch: 216, iters: 5280, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.645 G_ID: 0.129 G_Rec: 0.320 D_GP: 0.025 D_real: 1.024 D_fake: 0.831 
+(epoch: 216, iters: 5680, time: 0.064) G_GAN: 0.610 G_GAN_Feat: 0.873 G_ID: 0.171 G_Rec: 0.403 D_GP: 0.041 D_real: 1.213 D_fake: 0.421 
+(epoch: 216, iters: 6080, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.753 G_ID: 0.128 G_Rec: 0.303 D_GP: 0.048 D_real: 0.899 D_fake: 0.765 
+(epoch: 216, iters: 6480, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.894 G_ID: 0.180 G_Rec: 0.427 D_GP: 0.031 D_real: 0.879 D_fake: 0.765 
+(epoch: 216, iters: 6880, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.608 G_ID: 0.136 G_Rec: 0.257 D_GP: 0.027 D_real: 1.038 D_fake: 0.857 
+(epoch: 216, iters: 7280, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.941 G_ID: 0.170 G_Rec: 0.439 D_GP: 0.040 D_real: 0.794 D_fake: 0.698 
+(epoch: 216, iters: 7680, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.840 G_ID: 0.128 G_Rec: 0.319 D_GP: 0.088 D_real: 0.680 D_fake: 0.712 
+(epoch: 216, iters: 8080, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.807 G_ID: 0.157 G_Rec: 0.384 D_GP: 0.022 D_real: 1.261 D_fake: 0.588 
+(epoch: 216, iters: 8480, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.707 G_ID: 0.149 G_Rec: 0.294 D_GP: 0.043 D_real: 0.941 D_fake: 0.941 
+(epoch: 217, iters: 272, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.879 G_ID: 0.176 G_Rec: 0.406 D_GP: 0.076 D_real: 1.063 D_fake: 0.726 
+(epoch: 217, iters: 672, time: 0.064) G_GAN: 0.076 G_GAN_Feat: 0.719 G_ID: 0.122 G_Rec: 0.299 D_GP: 0.035 D_real: 0.935 D_fake: 0.924 
+(epoch: 217, iters: 1072, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.872 G_ID: 0.183 G_Rec: 0.367 D_GP: 0.030 D_real: 0.785 D_fake: 0.816 
+(epoch: 217, iters: 1472, time: 0.064) G_GAN: 0.044 G_GAN_Feat: 0.720 G_ID: 0.166 G_Rec: 0.296 D_GP: 0.022 D_real: 0.942 D_fake: 0.956 
+(epoch: 217, iters: 1872, time: 0.064) G_GAN: 0.832 G_GAN_Feat: 0.938 G_ID: 0.183 G_Rec: 0.426 D_GP: 0.024 D_real: 1.501 D_fake: 0.229 
+(epoch: 217, iters: 2272, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.704 G_ID: 0.120 G_Rec: 0.336 D_GP: 0.020 D_real: 0.933 D_fake: 0.898 
+(epoch: 217, iters: 2672, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.903 G_ID: 0.151 G_Rec: 0.395 D_GP: 0.047 D_real: 0.892 D_fake: 0.651 
+(epoch: 217, iters: 3072, time: 0.064) G_GAN: -0.176 G_GAN_Feat: 0.802 G_ID: 0.139 G_Rec: 0.308 D_GP: 0.061 D_real: 0.617 D_fake: 1.176 
+(epoch: 217, iters: 3472, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.852 G_ID: 0.201 G_Rec: 0.401 D_GP: 0.024 D_real: 1.210 D_fake: 0.596 
+(epoch: 217, iters: 3872, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.887 G_ID: 0.194 G_Rec: 0.336 D_GP: 0.110 D_real: 0.807 D_fake: 0.556 
+(epoch: 217, iters: 4272, time: 0.064) G_GAN: 0.669 G_GAN_Feat: 0.911 G_ID: 0.175 G_Rec: 0.409 D_GP: 0.091 D_real: 1.361 D_fake: 0.381 
+(epoch: 217, iters: 4672, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.742 G_ID: 0.168 G_Rec: 0.332 D_GP: 0.057 D_real: 0.836 D_fake: 0.938 
+(epoch: 217, iters: 5072, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.774 G_ID: 0.181 G_Rec: 0.395 D_GP: 0.022 D_real: 1.257 D_fake: 0.611 
+(epoch: 217, iters: 5472, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.618 G_ID: 0.148 G_Rec: 0.281 D_GP: 0.022 D_real: 1.129 D_fake: 0.788 
+(epoch: 217, iters: 5872, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.955 G_ID: 0.194 G_Rec: 0.411 D_GP: 0.037 D_real: 0.718 D_fake: 0.658 
+(epoch: 217, iters: 6272, time: 0.064) G_GAN: 0.050 G_GAN_Feat: 0.741 G_ID: 0.159 G_Rec: 0.322 D_GP: 0.025 D_real: 0.896 D_fake: 0.950 
+(epoch: 217, iters: 6672, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.918 G_ID: 0.172 G_Rec: 0.401 D_GP: 0.031 D_real: 1.166 D_fake: 0.492 
+(epoch: 217, iters: 7072, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.741 G_ID: 0.119 G_Rec: 0.302 D_GP: 0.048 D_real: 0.969 D_fake: 0.798 
+(epoch: 217, iters: 7472, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.830 G_ID: 0.212 G_Rec: 0.387 D_GP: 0.028 D_real: 1.066 D_fake: 0.682 
+(epoch: 217, iters: 7872, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.681 G_ID: 0.142 G_Rec: 0.318 D_GP: 0.022 D_real: 1.124 D_fake: 0.782 
+(epoch: 217, iters: 8272, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 1.053 G_ID: 0.183 G_Rec: 0.411 D_GP: 0.218 D_real: 0.554 D_fake: 0.583 
+(epoch: 218, iters: 64, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 0.600 G_ID: 0.135 G_Rec: 0.316 D_GP: 0.019 D_real: 1.186 D_fake: 0.793 
+(epoch: 218, iters: 464, time: 0.064) G_GAN: 0.620 G_GAN_Feat: 0.807 G_ID: 0.142 G_Rec: 0.398 D_GP: 0.019 D_real: 1.382 D_fake: 0.396 
+(epoch: 218, iters: 864, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.643 G_ID: 0.119 G_Rec: 0.311 D_GP: 0.028 D_real: 1.129 D_fake: 0.781 
+(epoch: 218, iters: 1264, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.801 G_ID: 0.219 G_Rec: 0.390 D_GP: 0.031 D_real: 1.148 D_fake: 0.608 
+(epoch: 218, iters: 1664, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.727 G_ID: 0.140 G_Rec: 0.308 D_GP: 0.033 D_real: 1.009 D_fake: 0.738 
+(epoch: 218, iters: 2064, time: 0.064) G_GAN: 0.697 G_GAN_Feat: 0.853 G_ID: 0.166 G_Rec: 0.402 D_GP: 0.025 D_real: 1.369 D_fake: 0.330 
+(epoch: 218, iters: 2464, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.725 G_ID: 0.151 G_Rec: 0.310 D_GP: 0.029 D_real: 1.161 D_fake: 0.682 
+(epoch: 218, iters: 2864, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.974 G_ID: 0.191 G_Rec: 0.417 D_GP: 0.028 D_real: 0.820 D_fake: 0.647 
+(epoch: 218, iters: 3264, time: 0.064) G_GAN: -0.050 G_GAN_Feat: 0.750 G_ID: 0.125 G_Rec: 0.299 D_GP: 0.026 D_real: 0.791 D_fake: 1.050 
+(epoch: 218, iters: 3664, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.806 G_ID: 0.212 G_Rec: 0.392 D_GP: 0.027 D_real: 1.033 D_fake: 0.801 
+(epoch: 218, iters: 4064, time: 0.064) G_GAN: 0.108 G_GAN_Feat: 0.661 G_ID: 0.132 G_Rec: 0.311 D_GP: 0.028 D_real: 1.012 D_fake: 0.893 
+(epoch: 218, iters: 4464, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.781 G_ID: 0.186 G_Rec: 0.370 D_GP: 0.020 D_real: 1.147 D_fake: 0.664 
+(epoch: 218, iters: 4864, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.610 G_ID: 0.136 G_Rec: 0.263 D_GP: 0.021 D_real: 1.173 D_fake: 0.745 
+(epoch: 218, iters: 5264, time: 0.064) G_GAN: 0.797 G_GAN_Feat: 1.289 G_ID: 0.185 G_Rec: 0.441 D_GP: 0.033 D_real: 1.469 D_fake: 0.291 
+(epoch: 218, iters: 5664, time: 0.064) G_GAN: 0.008 G_GAN_Feat: 0.646 G_ID: 0.141 G_Rec: 0.318 D_GP: 0.018 D_real: 0.958 D_fake: 0.992 
+(epoch: 218, iters: 6064, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.818 G_ID: 0.199 G_Rec: 0.394 D_GP: 0.030 D_real: 0.848 D_fake: 0.922 
+(epoch: 218, iters: 6464, time: 0.064) G_GAN: -0.068 G_GAN_Feat: 0.669 G_ID: 0.140 G_Rec: 0.327 D_GP: 0.028 D_real: 0.804 D_fake: 1.069 
+(epoch: 218, iters: 6864, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.935 G_ID: 0.177 G_Rec: 0.424 D_GP: 0.060 D_real: 0.922 D_fake: 0.589 
+(epoch: 218, iters: 7264, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.680 G_ID: 0.162 G_Rec: 0.271 D_GP: 0.024 D_real: 1.147 D_fake: 0.714 
+(epoch: 218, iters: 7664, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.938 G_ID: 0.183 G_Rec: 0.411 D_GP: 0.026 D_real: 1.200 D_fake: 0.575 
+(epoch: 218, iters: 8064, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.722 G_ID: 0.145 G_Rec: 0.325 D_GP: 0.037 D_real: 1.023 D_fake: 0.825 
+(epoch: 218, iters: 8464, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.876 G_ID: 0.163 G_Rec: 0.404 D_GP: 0.022 D_real: 1.065 D_fake: 0.617 
+(epoch: 219, iters: 256, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.846 G_ID: 0.152 G_Rec: 0.319 D_GP: 0.097 D_real: 0.847 D_fake: 0.639 
+(epoch: 219, iters: 656, time: 0.064) G_GAN: 0.644 G_GAN_Feat: 1.005 G_ID: 0.158 G_Rec: 0.409 D_GP: 0.037 D_real: 1.431 D_fake: 0.371 
+(epoch: 219, iters: 1056, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.675 G_ID: 0.152 G_Rec: 0.319 D_GP: 0.018 D_real: 1.131 D_fake: 0.779 
+(epoch: 219, iters: 1456, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.903 G_ID: 0.149 G_Rec: 0.382 D_GP: 0.046 D_real: 1.016 D_fake: 0.542 
+(epoch: 219, iters: 1856, time: 0.064) G_GAN: -0.294 G_GAN_Feat: 0.723 G_ID: 0.126 G_Rec: 0.299 D_GP: 0.042 D_real: 0.592 D_fake: 1.294 
+(epoch: 219, iters: 2256, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 1.016 G_ID: 0.183 G_Rec: 0.458 D_GP: 0.067 D_real: 0.621 D_fake: 0.614 
+(epoch: 219, iters: 2656, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.715 G_ID: 0.142 G_Rec: 0.313 D_GP: 0.029 D_real: 1.025 D_fake: 0.919 
+(epoch: 219, iters: 3056, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.916 G_ID: 0.159 G_Rec: 0.394 D_GP: 0.027 D_real: 1.070 D_fake: 0.654 
+(epoch: 219, iters: 3456, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.732 G_ID: 0.130 G_Rec: 0.295 D_GP: 0.038 D_real: 1.120 D_fake: 0.640 
+(epoch: 219, iters: 3856, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.899 G_ID: 0.163 G_Rec: 0.387 D_GP: 0.042 D_real: 0.922 D_fake: 0.771 
+(epoch: 219, iters: 4256, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.799 G_ID: 0.143 G_Rec: 0.326 D_GP: 0.040 D_real: 1.075 D_fake: 0.725 
+(epoch: 219, iters: 4656, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 1.211 G_ID: 0.200 G_Rec: 0.436 D_GP: 0.584 D_real: 0.359 D_fake: 0.577 
+(epoch: 219, iters: 5056, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.715 G_ID: 0.134 G_Rec: 0.290 D_GP: 0.028 D_real: 1.048 D_fake: 0.822 
+(epoch: 219, iters: 5456, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.884 G_ID: 0.196 G_Rec: 0.408 D_GP: 0.021 D_real: 1.036 D_fake: 0.637 
+(epoch: 219, iters: 5856, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.751 G_ID: 0.126 G_Rec: 0.293 D_GP: 0.025 D_real: 1.111 D_fake: 0.682 
+(epoch: 219, iters: 6256, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.905 G_ID: 0.167 G_Rec: 0.395 D_GP: 0.025 D_real: 1.149 D_fake: 0.572 
+(epoch: 219, iters: 6656, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.621 G_ID: 0.148 G_Rec: 0.275 D_GP: 0.023 D_real: 1.264 D_fake: 0.731 
+(epoch: 219, iters: 7056, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.858 G_ID: 0.194 G_Rec: 0.372 D_GP: 0.029 D_real: 0.947 D_fake: 0.763 
+(epoch: 219, iters: 7456, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.845 G_ID: 0.146 G_Rec: 0.355 D_GP: 0.077 D_real: 0.733 D_fake: 0.798 
+(epoch: 219, iters: 7856, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 1.020 G_ID: 0.204 G_Rec: 0.439 D_GP: 0.258 D_real: 0.634 D_fake: 0.591 
+(epoch: 219, iters: 8256, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.737 G_ID: 0.114 G_Rec: 0.282 D_GP: 0.035 D_real: 1.148 D_fake: 0.736 
+(epoch: 220, iters: 48, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.929 G_ID: 0.187 G_Rec: 0.410 D_GP: 0.048 D_real: 0.815 D_fake: 0.665 
+(epoch: 220, iters: 448, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.884 G_ID: 0.134 G_Rec: 0.330 D_GP: 0.062 D_real: 0.493 D_fake: 0.915 
+(epoch: 220, iters: 848, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 1.007 G_ID: 0.208 G_Rec: 0.390 D_GP: 0.043 D_real: 0.417 D_fake: 0.794 
+(epoch: 220, iters: 1248, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.695 G_ID: 0.171 G_Rec: 0.279 D_GP: 0.026 D_real: 1.197 D_fake: 0.746 
+(epoch: 220, iters: 1648, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.954 G_ID: 0.160 G_Rec: 0.423 D_GP: 0.041 D_real: 1.161 D_fake: 0.472 
+(epoch: 220, iters: 2048, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.739 G_ID: 0.134 G_Rec: 0.341 D_GP: 0.023 D_real: 1.026 D_fake: 0.821 
+(epoch: 220, iters: 2448, time: 0.064) G_GAN: 0.556 G_GAN_Feat: 0.940 G_ID: 0.180 G_Rec: 0.398 D_GP: 0.025 D_real: 1.193 D_fake: 0.449 
+(epoch: 220, iters: 2848, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.692 G_ID: 0.126 G_Rec: 0.307 D_GP: 0.050 D_real: 0.904 D_fake: 0.942 
+(epoch: 220, iters: 3248, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.749 G_ID: 0.175 G_Rec: 0.375 D_GP: 0.019 D_real: 1.188 D_fake: 0.631 
+(epoch: 220, iters: 3648, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.640 G_ID: 0.145 G_Rec: 0.326 D_GP: 0.028 D_real: 1.084 D_fake: 0.805 
+(epoch: 220, iters: 4048, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.799 G_ID: 0.194 G_Rec: 0.387 D_GP: 0.031 D_real: 1.038 D_fake: 0.676 
+(epoch: 220, iters: 4448, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.638 G_ID: 0.125 G_Rec: 0.263 D_GP: 0.039 D_real: 0.998 D_fake: 0.834 
+(epoch: 220, iters: 4848, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.932 G_ID: 0.177 G_Rec: 0.422 D_GP: 0.063 D_real: 0.866 D_fake: 0.683 
+(epoch: 220, iters: 5248, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.809 G_ID: 0.133 G_Rec: 0.324 D_GP: 0.082 D_real: 0.717 D_fake: 0.819 
+(epoch: 220, iters: 5648, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.996 G_ID: 0.190 G_Rec: 0.454 D_GP: 0.126 D_real: 0.699 D_fake: 0.658 
+(epoch: 220, iters: 6048, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.875 G_ID: 0.139 G_Rec: 0.342 D_GP: 0.028 D_real: 1.088 D_fake: 0.623 
+(epoch: 220, iters: 6448, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.832 G_ID: 0.205 G_Rec: 0.348 D_GP: 0.036 D_real: 1.212 D_fake: 0.562 
+(epoch: 220, iters: 6848, time: 0.064) G_GAN: -0.083 G_GAN_Feat: 0.882 G_ID: 0.195 G_Rec: 0.318 D_GP: 0.225 D_real: 0.294 D_fake: 1.084 
+(epoch: 220, iters: 7248, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 1.002 G_ID: 0.189 G_Rec: 0.388 D_GP: 0.031 D_real: 0.737 D_fake: 0.795 
+(epoch: 220, iters: 7648, time: 0.064) G_GAN: 0.768 G_GAN_Feat: 1.046 G_ID: 0.151 G_Rec: 0.345 D_GP: 0.057 D_real: 1.100 D_fake: 0.655 
+(epoch: 220, iters: 8048, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 0.997 G_ID: 0.165 G_Rec: 0.442 D_GP: 0.022 D_real: 1.171 D_fake: 0.558 
+(epoch: 220, iters: 8448, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.786 G_ID: 0.122 G_Rec: 0.334 D_GP: 0.084 D_real: 0.678 D_fake: 0.921 
+(epoch: 221, iters: 240, time: 0.064) G_GAN: 0.629 G_GAN_Feat: 0.899 G_ID: 0.164 G_Rec: 0.390 D_GP: 0.043 D_real: 1.293 D_fake: 0.383 
+(epoch: 221, iters: 640, time: 0.064) G_GAN: 0.165 G_GAN_Feat: 0.627 G_ID: 0.146 G_Rec: 0.309 D_GP: 0.023 D_real: 1.128 D_fake: 0.835 
+(epoch: 221, iters: 1040, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 0.825 G_ID: 0.162 G_Rec: 0.362 D_GP: 0.026 D_real: 1.243 D_fake: 0.480 
+(epoch: 221, iters: 1440, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.643 G_ID: 0.133 G_Rec: 0.277 D_GP: 0.020 D_real: 1.365 D_fake: 0.582 
+(epoch: 221, iters: 1840, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 1.092 G_ID: 0.159 G_Rec: 0.468 D_GP: 0.046 D_real: 0.557 D_fake: 0.580 
+(epoch: 221, iters: 2240, time: 0.064) G_GAN: -0.043 G_GAN_Feat: 0.687 G_ID: 0.161 G_Rec: 0.314 D_GP: 0.028 D_real: 0.801 D_fake: 1.044 
+(epoch: 221, iters: 2640, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.937 G_ID: 0.181 G_Rec: 0.439 D_GP: 0.033 D_real: 0.997 D_fake: 0.626 
+(epoch: 221, iters: 3040, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.750 G_ID: 0.139 G_Rec: 0.339 D_GP: 0.032 D_real: 1.172 D_fake: 0.686 
+(epoch: 221, iters: 3440, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.882 G_ID: 0.164 G_Rec: 0.396 D_GP: 0.023 D_real: 1.219 D_fake: 0.511 
+(epoch: 221, iters: 3840, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.642 G_ID: 0.142 G_Rec: 0.292 D_GP: 0.022 D_real: 1.221 D_fake: 0.717 
+(epoch: 221, iters: 4240, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.863 G_ID: 0.152 G_Rec: 0.368 D_GP: 0.024 D_real: 1.302 D_fake: 0.463 
+(epoch: 221, iters: 4640, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.537 G_ID: 0.130 G_Rec: 0.299 D_GP: 0.018 D_real: 1.211 D_fake: 0.782 
+(epoch: 221, iters: 5040, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.713 G_ID: 0.180 G_Rec: 0.393 D_GP: 0.020 D_real: 1.145 D_fake: 0.725 
+(epoch: 221, iters: 5440, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.569 G_ID: 0.136 G_Rec: 0.315 D_GP: 0.017 D_real: 1.119 D_fake: 0.829 
+(epoch: 221, iters: 5840, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.775 G_ID: 0.170 G_Rec: 0.455 D_GP: 0.022 D_real: 1.142 D_fake: 0.608 
+(epoch: 221, iters: 6240, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.589 G_ID: 0.156 G_Rec: 0.317 D_GP: 0.027 D_real: 1.019 D_fake: 0.885 
+(epoch: 221, iters: 6640, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.717 G_ID: 0.194 G_Rec: 0.381 D_GP: 0.026 D_real: 0.997 D_fake: 0.809 
+(epoch: 221, iters: 7040, time: 0.064) G_GAN: -0.034 G_GAN_Feat: 0.538 G_ID: 0.132 G_Rec: 0.306 D_GP: 0.022 D_real: 0.939 D_fake: 1.034 
+(epoch: 221, iters: 7440, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.725 G_ID: 0.177 G_Rec: 0.401 D_GP: 0.027 D_real: 0.860 D_fake: 0.889 
+(epoch: 221, iters: 7840, time: 0.064) G_GAN: 0.016 G_GAN_Feat: 0.627 G_ID: 0.175 G_Rec: 0.306 D_GP: 0.038 D_real: 0.883 D_fake: 0.984 
+(epoch: 221, iters: 8240, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.757 G_ID: 0.195 G_Rec: 0.365 D_GP: 0.037 D_real: 0.853 D_fake: 0.867 
+(epoch: 222, iters: 32, time: 0.064) G_GAN: 0.098 G_GAN_Feat: 0.610 G_ID: 0.119 G_Rec: 0.299 D_GP: 0.027 D_real: 1.126 D_fake: 0.905 
+(epoch: 222, iters: 432, time: 0.064) G_GAN: 0.120 G_GAN_Feat: 0.787 G_ID: 0.190 G_Rec: 0.408 D_GP: 0.025 D_real: 0.915 D_fake: 0.881 
+(epoch: 222, iters: 832, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 0.581 G_ID: 0.115 G_Rec: 0.268 D_GP: 0.026 D_real: 1.005 D_fake: 0.943 
+(epoch: 222, iters: 1232, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.740 G_ID: 0.163 G_Rec: 0.367 D_GP: 0.027 D_real: 1.248 D_fake: 0.580 
+(epoch: 222, iters: 1632, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.611 G_ID: 0.153 G_Rec: 0.308 D_GP: 0.039 D_real: 0.946 D_fake: 0.927 
+(epoch: 222, iters: 2032, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.801 G_ID: 0.170 G_Rec: 0.402 D_GP: 0.023 D_real: 1.066 D_fake: 0.718 
+(epoch: 222, iters: 2432, time: 0.064) G_GAN: 0.022 G_GAN_Feat: 0.689 G_ID: 0.129 G_Rec: 0.293 D_GP: 0.039 D_real: 0.831 D_fake: 0.978 
+(epoch: 222, iters: 2832, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.814 G_ID: 0.189 G_Rec: 0.372 D_GP: 0.037 D_real: 0.927 D_fake: 0.772 
+(epoch: 222, iters: 3232, time: 0.064) G_GAN: -0.181 G_GAN_Feat: 0.711 G_ID: 0.165 G_Rec: 0.336 D_GP: 0.035 D_real: 0.814 D_fake: 1.182 
+(epoch: 222, iters: 3632, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.790 G_ID: 0.180 G_Rec: 0.400 D_GP: 0.025 D_real: 1.082 D_fake: 0.656 
+(epoch: 222, iters: 4032, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.601 G_ID: 0.121 G_Rec: 0.288 D_GP: 0.023 D_real: 1.273 D_fake: 0.675 
+(epoch: 222, iters: 4432, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.831 G_ID: 0.173 G_Rec: 0.461 D_GP: 0.042 D_real: 1.090 D_fake: 0.543 
+(epoch: 222, iters: 4832, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.553 G_ID: 0.122 G_Rec: 0.261 D_GP: 0.022 D_real: 1.127 D_fake: 0.804 
+(epoch: 222, iters: 5232, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 1.118 G_ID: 0.182 G_Rec: 0.512 D_GP: 1.347 D_real: 0.623 D_fake: 0.578 
+(epoch: 222, iters: 5632, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.577 G_ID: 0.129 G_Rec: 0.279 D_GP: 0.021 D_real: 1.176 D_fake: 0.759 
+(epoch: 222, iters: 6032, time: 0.064) G_GAN: 0.504 G_GAN_Feat: 0.838 G_ID: 0.154 G_Rec: 0.382 D_GP: 0.041 D_real: 1.107 D_fake: 0.498 
+(epoch: 222, iters: 6432, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.650 G_ID: 0.128 G_Rec: 0.291 D_GP: 0.031 D_real: 1.142 D_fake: 0.795 
+(epoch: 222, iters: 6832, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.895 G_ID: 0.184 G_Rec: 0.432 D_GP: 0.053 D_real: 0.800 D_fake: 0.740 
+(epoch: 222, iters: 7232, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.618 G_ID: 0.140 G_Rec: 0.290 D_GP: 0.022 D_real: 1.065 D_fake: 0.803 
+(epoch: 222, iters: 7632, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.773 G_ID: 0.175 G_Rec: 0.380 D_GP: 0.026 D_real: 1.204 D_fake: 0.559 
+(epoch: 222, iters: 8032, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.591 G_ID: 0.120 G_Rec: 0.277 D_GP: 0.022 D_real: 1.326 D_fake: 0.635 
+(epoch: 222, iters: 8432, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.895 G_ID: 0.199 G_Rec: 0.370 D_GP: 0.062 D_real: 0.908 D_fake: 0.496 
+(epoch: 223, iters: 224, time: 0.064) G_GAN: 0.183 G_GAN_Feat: 0.676 G_ID: 0.141 G_Rec: 0.301 D_GP: 0.032 D_real: 0.958 D_fake: 0.817 
+(epoch: 223, iters: 624, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.817 G_ID: 0.179 G_Rec: 0.460 D_GP: 0.020 D_real: 1.241 D_fake: 0.570 
+(epoch: 223, iters: 1024, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.730 G_ID: 0.166 G_Rec: 0.315 D_GP: 0.035 D_real: 0.971 D_fake: 0.841 
+(epoch: 223, iters: 1424, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.811 G_ID: 0.169 G_Rec: 0.450 D_GP: 0.024 D_real: 1.189 D_fake: 0.506 
+(epoch: 223, iters: 1824, time: 0.064) G_GAN: -0.041 G_GAN_Feat: 0.662 G_ID: 0.156 G_Rec: 0.301 D_GP: 0.042 D_real: 0.776 D_fake: 1.041 
+(epoch: 223, iters: 2224, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.940 G_ID: 0.166 G_Rec: 0.431 D_GP: 0.035 D_real: 0.898 D_fake: 0.702 
+(epoch: 223, iters: 2624, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.731 G_ID: 0.126 G_Rec: 0.333 D_GP: 0.101 D_real: 0.770 D_fake: 0.921 
+(epoch: 223, iters: 3024, time: 0.064) G_GAN: 0.572 G_GAN_Feat: 0.841 G_ID: 0.170 G_Rec: 0.382 D_GP: 0.024 D_real: 1.228 D_fake: 0.452 
+(epoch: 223, iters: 3424, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.755 G_ID: 0.141 G_Rec: 0.324 D_GP: 0.058 D_real: 0.958 D_fake: 0.798 
+(epoch: 223, iters: 3824, time: 0.064) G_GAN: 0.568 G_GAN_Feat: 1.018 G_ID: 0.163 G_Rec: 0.425 D_GP: 0.157 D_real: 0.682 D_fake: 0.440 
+(epoch: 223, iters: 4224, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.702 G_ID: 0.144 G_Rec: 0.374 D_GP: 0.029 D_real: 1.098 D_fake: 0.829 
+(epoch: 223, iters: 4624, time: 0.064) G_GAN: 0.586 G_GAN_Feat: 0.838 G_ID: 0.168 G_Rec: 0.390 D_GP: 0.020 D_real: 1.330 D_fake: 0.419 
+(epoch: 223, iters: 5024, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.718 G_ID: 0.134 G_Rec: 0.301 D_GP: 0.057 D_real: 0.790 D_fake: 0.926 
+(epoch: 223, iters: 5424, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.883 G_ID: 0.194 G_Rec: 0.404 D_GP: 0.025 D_real: 1.245 D_fake: 0.440 
+(epoch: 223, iters: 5824, time: 0.064) G_GAN: 0.571 G_GAN_Feat: 0.788 G_ID: 0.136 G_Rec: 0.321 D_GP: 0.036 D_real: 1.409 D_fake: 0.539 
+(epoch: 223, iters: 6224, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.952 G_ID: 0.160 G_Rec: 0.373 D_GP: 0.161 D_real: 0.397 D_fake: 0.874 
+(epoch: 223, iters: 6624, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.729 G_ID: 0.126 G_Rec: 0.357 D_GP: 0.033 D_real: 1.132 D_fake: 0.729 
+(epoch: 223, iters: 7024, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.764 G_ID: 0.194 G_Rec: 0.421 D_GP: 0.020 D_real: 1.110 D_fake: 0.750 
+(epoch: 223, iters: 7424, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.836 G_ID: 0.134 G_Rec: 0.356 D_GP: 0.021 D_real: 0.933 D_fake: 0.907 
+(epoch: 223, iters: 7824, time: 0.064) G_GAN: 0.025 G_GAN_Feat: 0.752 G_ID: 0.163 G_Rec: 0.359 D_GP: 0.020 D_real: 0.878 D_fake: 0.978 
+(epoch: 223, iters: 8224, time: 0.064) G_GAN: 0.024 G_GAN_Feat: 0.654 G_ID: 0.138 G_Rec: 0.301 D_GP: 0.030 D_real: 0.956 D_fake: 0.976 
+(epoch: 224, iters: 16, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 1.031 G_ID: 0.151 G_Rec: 0.429 D_GP: 0.085 D_real: 0.633 D_fake: 0.595 
+(epoch: 224, iters: 416, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.587 G_ID: 0.124 G_Rec: 0.251 D_GP: 0.025 D_real: 1.350 D_fake: 0.614 
+(epoch: 224, iters: 816, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.698 G_ID: 0.172 G_Rec: 0.334 D_GP: 0.021 D_real: 1.019 D_fake: 0.759 
+(epoch: 224, iters: 1216, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.628 G_ID: 0.143 G_Rec: 0.295 D_GP: 0.024 D_real: 0.940 D_fake: 0.965 
+(epoch: 224, iters: 1616, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.831 G_ID: 0.170 G_Rec: 0.377 D_GP: 0.035 D_real: 0.826 D_fake: 0.886 
+(epoch: 224, iters: 2016, time: 0.064) G_GAN: -0.224 G_GAN_Feat: 0.790 G_ID: 0.118 G_Rec: 0.325 D_GP: 0.100 D_real: 0.361 D_fake: 1.224 
+(epoch: 224, iters: 2416, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.917 G_ID: 0.164 G_Rec: 0.399 D_GP: 0.088 D_real: 0.913 D_fake: 0.489 
+(epoch: 224, iters: 2816, time: 0.064) G_GAN: 0.068 G_GAN_Feat: 0.905 G_ID: 0.122 G_Rec: 0.328 D_GP: 0.085 D_real: 0.513 D_fake: 0.933 
+(epoch: 224, iters: 3216, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.926 G_ID: 0.195 G_Rec: 0.401 D_GP: 0.037 D_real: 1.107 D_fake: 0.684 
+(epoch: 224, iters: 3616, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.707 G_ID: 0.154 G_Rec: 0.300 D_GP: 0.034 D_real: 1.044 D_fake: 0.744 
+(epoch: 224, iters: 4016, time: 0.064) G_GAN: 0.694 G_GAN_Feat: 0.831 G_ID: 0.173 G_Rec: 0.394 D_GP: 0.023 D_real: 1.421 D_fake: 0.330 
+(epoch: 224, iters: 4416, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.743 G_ID: 0.130 G_Rec: 0.288 D_GP: 0.041 D_real: 1.111 D_fake: 0.605 
+(epoch: 224, iters: 4816, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.797 G_ID: 0.167 G_Rec: 0.366 D_GP: 0.025 D_real: 1.356 D_fake: 0.441 
+(epoch: 224, iters: 5216, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.682 G_ID: 0.133 G_Rec: 0.298 D_GP: 0.030 D_real: 1.239 D_fake: 0.652 
+(epoch: 224, iters: 5616, time: 0.064) G_GAN: 0.833 G_GAN_Feat: 0.994 G_ID: 0.173 G_Rec: 0.440 D_GP: 0.071 D_real: 1.141 D_fake: 0.389 
+(epoch: 224, iters: 6016, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.909 G_ID: 0.134 G_Rec: 0.354 D_GP: 0.140 D_real: 0.478 D_fake: 0.684 
+(epoch: 224, iters: 6416, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 1.206 G_ID: 0.179 G_Rec: 0.435 D_GP: 0.369 D_real: 0.270 D_fake: 0.611 
+(epoch: 224, iters: 6816, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.877 G_ID: 0.118 G_Rec: 0.308 D_GP: 0.050 D_real: 1.008 D_fake: 0.726 
+(epoch: 224, iters: 7216, time: 0.064) G_GAN: 0.607 G_GAN_Feat: 0.878 G_ID: 0.186 G_Rec: 0.412 D_GP: 0.020 D_real: 1.313 D_fake: 0.403 
+(epoch: 224, iters: 7616, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.643 G_ID: 0.152 G_Rec: 0.287 D_GP: 0.023 D_real: 1.190 D_fake: 0.752 
+(epoch: 224, iters: 8016, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 1.062 G_ID: 0.188 G_Rec: 0.451 D_GP: 0.315 D_real: 0.377 D_fake: 0.707 
+(epoch: 224, iters: 8416, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.744 G_ID: 0.127 G_Rec: 0.299 D_GP: 0.030 D_real: 1.035 D_fake: 0.730 
+(epoch: 225, iters: 208, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.946 G_ID: 0.174 G_Rec: 0.413 D_GP: 0.057 D_real: 0.889 D_fake: 0.661 
+(epoch: 225, iters: 608, time: 0.064) G_GAN: -0.008 G_GAN_Feat: 0.724 G_ID: 0.149 G_Rec: 0.306 D_GP: 0.021 D_real: 0.926 D_fake: 1.008 
+(epoch: 225, iters: 1008, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.847 G_ID: 0.162 G_Rec: 0.368 D_GP: 0.034 D_real: 1.212 D_fake: 0.495 
+(epoch: 225, iters: 1408, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.655 G_ID: 0.138 G_Rec: 0.294 D_GP: 0.020 D_real: 1.290 D_fake: 0.655 
+(epoch: 225, iters: 1808, time: 0.064) G_GAN: 0.104 G_GAN_Feat: 0.838 G_ID: 0.173 G_Rec: 0.373 D_GP: 0.021 D_real: 0.885 D_fake: 0.896 
+(epoch: 225, iters: 2208, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.876 G_ID: 0.156 G_Rec: 0.325 D_GP: 0.098 D_real: 0.539 D_fake: 0.618 
+(epoch: 225, iters: 2608, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.822 G_ID: 0.165 G_Rec: 0.411 D_GP: 0.024 D_real: 0.964 D_fake: 0.771 
+(epoch: 225, iters: 3008, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.631 G_ID: 0.121 G_Rec: 0.295 D_GP: 0.023 D_real: 1.177 D_fake: 0.783 
+(epoch: 225, iters: 3408, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.938 G_ID: 0.169 G_Rec: 0.432 D_GP: 0.036 D_real: 0.976 D_fake: 0.581 
+(epoch: 225, iters: 3808, time: 0.064) G_GAN: -0.226 G_GAN_Feat: 0.563 G_ID: 0.122 G_Rec: 0.317 D_GP: 0.016 D_real: 0.738 D_fake: 1.227 
+(epoch: 225, iters: 4208, time: 0.064) G_GAN: -0.061 G_GAN_Feat: 0.753 G_ID: 0.200 G_Rec: 0.384 D_GP: 0.021 D_real: 0.740 D_fake: 1.062 
+(epoch: 225, iters: 4608, time: 0.064) G_GAN: -0.231 G_GAN_Feat: 0.730 G_ID: 0.123 G_Rec: 0.355 D_GP: 0.041 D_real: 0.633 D_fake: 1.232 
+(epoch: 225, iters: 5008, time: 0.064) G_GAN: -0.018 G_GAN_Feat: 0.769 G_ID: 0.220 G_Rec: 0.374 D_GP: 0.026 D_real: 0.779 D_fake: 1.018 
+(epoch: 225, iters: 5408, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.621 G_ID: 0.127 G_Rec: 0.286 D_GP: 0.029 D_real: 0.937 D_fake: 0.987 
+(epoch: 225, iters: 5808, time: 0.064) G_GAN: 0.495 G_GAN_Feat: 0.782 G_ID: 0.159 G_Rec: 0.377 D_GP: 0.025 D_real: 1.241 D_fake: 0.520 
+(epoch: 225, iters: 6208, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.898 G_ID: 0.120 G_Rec: 0.335 D_GP: 0.159 D_real: 0.549 D_fake: 0.767 
+(epoch: 225, iters: 6608, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.890 G_ID: 0.190 G_Rec: 0.375 D_GP: 0.045 D_real: 0.858 D_fake: 0.672 
+(epoch: 225, iters: 7008, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.688 G_ID: 0.152 G_Rec: 0.365 D_GP: 0.020 D_real: 1.254 D_fake: 0.726 
+(epoch: 225, iters: 7408, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.819 G_ID: 0.168 G_Rec: 0.413 D_GP: 0.023 D_real: 1.159 D_fake: 0.623 
+(epoch: 225, iters: 7808, time: 0.064) G_GAN: -0.070 G_GAN_Feat: 0.702 G_ID: 0.143 G_Rec: 0.303 D_GP: 0.034 D_real: 0.831 D_fake: 1.070 
+(epoch: 225, iters: 8208, time: 0.064) G_GAN: 0.590 G_GAN_Feat: 0.835 G_ID: 0.165 G_Rec: 0.371 D_GP: 0.022 D_real: 1.268 D_fake: 0.417 
+(epoch: 225, iters: 8608, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.714 G_ID: 0.129 G_Rec: 0.290 D_GP: 0.032 D_real: 1.042 D_fake: 0.814 
+(epoch: 226, iters: 400, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.809 G_ID: 0.168 G_Rec: 0.412 D_GP: 0.024 D_real: 1.131 D_fake: 0.608 
+(epoch: 226, iters: 800, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.773 G_ID: 0.113 G_Rec: 0.314 D_GP: 0.100 D_real: 0.778 D_fake: 0.831 
+(epoch: 226, iters: 1200, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.873 G_ID: 0.211 G_Rec: 0.387 D_GP: 0.024 D_real: 1.095 D_fake: 0.575 
+(epoch: 226, iters: 1600, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.857 G_ID: 0.156 G_Rec: 0.319 D_GP: 0.159 D_real: 0.505 D_fake: 0.819 
+(epoch: 226, iters: 2000, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.812 G_ID: 0.224 G_Rec: 0.338 D_GP: 0.028 D_real: 1.146 D_fake: 0.565 
+(epoch: 226, iters: 2400, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.906 G_ID: 0.134 G_Rec: 0.317 D_GP: 0.032 D_real: 0.708 D_fake: 0.707 
+(epoch: 226, iters: 2800, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.717 G_ID: 0.178 G_Rec: 0.385 D_GP: 0.021 D_real: 1.179 D_fake: 0.679 
+(epoch: 226, iters: 3200, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.568 G_ID: 0.144 G_Rec: 0.310 D_GP: 0.019 D_real: 1.284 D_fake: 0.675 
+(epoch: 226, iters: 3600, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.701 G_ID: 0.172 G_Rec: 0.353 D_GP: 0.021 D_real: 1.209 D_fake: 0.662 
+(epoch: 226, iters: 4000, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.652 G_ID: 0.134 G_Rec: 0.322 D_GP: 0.028 D_real: 1.016 D_fake: 0.834 
+(epoch: 226, iters: 4400, time: 0.064) G_GAN: 0.072 G_GAN_Feat: 0.791 G_ID: 0.173 G_Rec: 0.391 D_GP: 0.037 D_real: 0.805 D_fake: 0.928 
+(epoch: 226, iters: 4800, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.617 G_ID: 0.138 G_Rec: 0.313 D_GP: 0.028 D_real: 1.018 D_fake: 0.909 
+(epoch: 226, iters: 5200, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.827 G_ID: 0.164 G_Rec: 0.396 D_GP: 0.033 D_real: 1.185 D_fake: 0.651 
+(epoch: 226, iters: 5600, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.611 G_ID: 0.120 G_Rec: 0.299 D_GP: 0.031 D_real: 1.165 D_fake: 0.763 
+(epoch: 226, iters: 6000, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.953 G_ID: 0.190 G_Rec: 0.438 D_GP: 0.049 D_real: 1.021 D_fake: 0.534 
+(epoch: 226, iters: 6400, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.703 G_ID: 0.142 G_Rec: 0.329 D_GP: 0.053 D_real: 0.948 D_fake: 0.812 
+(epoch: 226, iters: 6800, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.767 G_ID: 0.151 G_Rec: 0.381 D_GP: 0.025 D_real: 1.168 D_fake: 0.572 
+(epoch: 226, iters: 7200, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.644 G_ID: 0.136 G_Rec: 0.310 D_GP: 0.034 D_real: 1.257 D_fake: 0.687 
+(epoch: 226, iters: 7600, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.898 G_ID: 0.173 G_Rec: 0.402 D_GP: 0.038 D_real: 1.109 D_fake: 0.563 
+(epoch: 226, iters: 8000, time: 0.064) G_GAN: -0.020 G_GAN_Feat: 0.722 G_ID: 0.154 G_Rec: 0.305 D_GP: 0.039 D_real: 0.684 D_fake: 1.020 
+(epoch: 226, iters: 8400, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 1.019 G_ID: 0.180 G_Rec: 0.413 D_GP: 0.124 D_real: 0.449 D_fake: 0.799 
+(epoch: 227, iters: 192, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.639 G_ID: 0.130 G_Rec: 0.314 D_GP: 0.017 D_real: 1.214 D_fake: 0.769 
+(epoch: 227, iters: 592, time: 0.064) G_GAN: 0.480 G_GAN_Feat: 0.777 G_ID: 0.161 G_Rec: 0.400 D_GP: 0.020 D_real: 1.218 D_fake: 0.522 
+(epoch: 227, iters: 992, time: 0.064) G_GAN: 0.183 G_GAN_Feat: 0.706 G_ID: 0.131 G_Rec: 0.350 D_GP: 0.040 D_real: 0.977 D_fake: 0.817 
+(epoch: 227, iters: 1392, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.759 G_ID: 0.167 G_Rec: 0.384 D_GP: 0.029 D_real: 1.296 D_fake: 0.511 
+(epoch: 227, iters: 1792, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.729 G_ID: 0.130 G_Rec: 0.362 D_GP: 0.052 D_real: 1.129 D_fake: 0.749 
+(epoch: 227, iters: 2192, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.861 G_ID: 0.190 G_Rec: 0.446 D_GP: 0.029 D_real: 1.008 D_fake: 0.597 
+(epoch: 227, iters: 2592, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.632 G_ID: 0.114 G_Rec: 0.295 D_GP: 0.031 D_real: 1.181 D_fake: 0.722 
+(epoch: 227, iters: 2992, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 1.011 G_ID: 0.182 G_Rec: 0.420 D_GP: 0.088 D_real: 0.805 D_fake: 0.632 
+(epoch: 227, iters: 3392, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.804 G_ID: 0.127 G_Rec: 0.306 D_GP: 0.040 D_real: 0.695 D_fake: 0.915 
+(epoch: 227, iters: 3792, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.897 G_ID: 0.150 G_Rec: 0.416 D_GP: 0.023 D_real: 0.877 D_fake: 0.878 
+(epoch: 227, iters: 4192, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.695 G_ID: 0.139 G_Rec: 0.287 D_GP: 0.026 D_real: 1.176 D_fake: 0.729 
+(epoch: 227, iters: 4592, time: 0.064) G_GAN: 0.567 G_GAN_Feat: 1.122 G_ID: 0.228 G_Rec: 0.439 D_GP: 0.090 D_real: 0.423 D_fake: 0.489 
+(epoch: 227, iters: 4992, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 0.695 G_ID: 0.132 G_Rec: 0.331 D_GP: 0.031 D_real: 1.121 D_fake: 0.691 
+(epoch: 227, iters: 5392, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.869 G_ID: 0.174 G_Rec: 0.379 D_GP: 0.042 D_real: 0.942 D_fake: 0.646 
+(epoch: 227, iters: 5792, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.881 G_ID: 0.141 G_Rec: 0.322 D_GP: 0.067 D_real: 1.356 D_fake: 0.721 
+(epoch: 227, iters: 6192, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.854 G_ID: 0.167 G_Rec: 0.409 D_GP: 0.028 D_real: 0.962 D_fake: 0.653 
+(epoch: 227, iters: 6592, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.630 G_ID: 0.111 G_Rec: 0.302 D_GP: 0.031 D_real: 1.082 D_fake: 0.868 
+(epoch: 227, iters: 6992, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 1.008 G_ID: 0.164 G_Rec: 0.454 D_GP: 0.057 D_real: 0.789 D_fake: 0.638 
+(epoch: 227, iters: 7392, time: 0.064) G_GAN: 0.076 G_GAN_Feat: 0.681 G_ID: 0.129 G_Rec: 0.327 D_GP: 0.037 D_real: 1.011 D_fake: 0.925 
+(epoch: 227, iters: 7792, time: 0.064) G_GAN: 0.529 G_GAN_Feat: 0.942 G_ID: 0.147 G_Rec: 0.395 D_GP: 0.066 D_real: 0.956 D_fake: 0.478 
+(epoch: 227, iters: 8192, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.596 G_ID: 0.143 G_Rec: 0.252 D_GP: 0.022 D_real: 1.034 D_fake: 0.887 
+(epoch: 227, iters: 8592, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.824 G_ID: 0.169 G_Rec: 0.403 D_GP: 0.031 D_real: 1.077 D_fake: 0.610 
+(epoch: 228, iters: 384, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.652 G_ID: 0.153 G_Rec: 0.331 D_GP: 0.028 D_real: 1.130 D_fake: 0.813 
+(epoch: 228, iters: 784, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.885 G_ID: 0.158 G_Rec: 0.390 D_GP: 0.063 D_real: 0.848 D_fake: 0.832 
+(epoch: 228, iters: 1184, time: 0.064) G_GAN: 0.026 G_GAN_Feat: 0.741 G_ID: 0.130 G_Rec: 0.308 D_GP: 0.044 D_real: 0.797 D_fake: 0.974 
+(epoch: 228, iters: 1584, time: 0.064) G_GAN: 0.647 G_GAN_Feat: 1.097 G_ID: 0.160 G_Rec: 0.422 D_GP: 0.039 D_real: 1.221 D_fake: 0.408 
+(epoch: 228, iters: 1984, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.825 G_ID: 0.142 G_Rec: 0.313 D_GP: 0.124 D_real: 0.709 D_fake: 0.907 
+(epoch: 228, iters: 2384, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.851 G_ID: 0.198 G_Rec: 0.413 D_GP: 0.028 D_real: 1.116 D_fake: 0.570 
+(epoch: 228, iters: 2784, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.630 G_ID: 0.128 G_Rec: 0.273 D_GP: 0.026 D_real: 1.027 D_fake: 0.852 
+(epoch: 228, iters: 3184, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.801 G_ID: 0.163 G_Rec: 0.368 D_GP: 0.033 D_real: 1.074 D_fake: 0.671 
+(epoch: 228, iters: 3584, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 1.051 G_ID: 0.117 G_Rec: 0.328 D_GP: 0.169 D_real: 0.537 D_fake: 0.945 
+(epoch: 228, iters: 3984, time: 0.064) G_GAN: 0.720 G_GAN_Feat: 0.966 G_ID: 0.198 G_Rec: 0.409 D_GP: 0.036 D_real: 1.287 D_fake: 0.359 
+(epoch: 228, iters: 4384, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 1.016 G_ID: 0.126 G_Rec: 0.327 D_GP: 0.316 D_real: 0.385 D_fake: 0.792 
+(epoch: 228, iters: 4784, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.908 G_ID: 0.203 G_Rec: 0.372 D_GP: 0.050 D_real: 0.856 D_fake: 0.648 
+(epoch: 228, iters: 5184, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.720 G_ID: 0.155 G_Rec: 0.307 D_GP: 0.023 D_real: 1.047 D_fake: 0.838 
+(epoch: 228, iters: 5584, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.873 G_ID: 0.202 G_Rec: 0.398 D_GP: 0.026 D_real: 0.913 D_fake: 0.750 
+(epoch: 228, iters: 5984, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.699 G_ID: 0.147 G_Rec: 0.305 D_GP: 0.020 D_real: 0.993 D_fake: 0.917 
+(epoch: 228, iters: 6384, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.874 G_ID: 0.176 G_Rec: 0.353 D_GP: 0.062 D_real: 0.839 D_fake: 0.618 
+(epoch: 228, iters: 6784, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.600 G_ID: 0.123 G_Rec: 0.285 D_GP: 0.022 D_real: 1.159 D_fake: 0.775 
+(epoch: 228, iters: 7184, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.872 G_ID: 0.183 G_Rec: 0.365 D_GP: 0.026 D_real: 1.190 D_fake: 0.544 
+(epoch: 228, iters: 7584, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.892 G_ID: 0.141 G_Rec: 0.308 D_GP: 0.047 D_real: 0.616 D_fake: 0.801 
+(epoch: 228, iters: 7984, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 1.145 G_ID: 0.180 G_Rec: 0.421 D_GP: 0.243 D_real: 0.421 D_fake: 0.552 
+(epoch: 228, iters: 8384, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.842 G_ID: 0.126 G_Rec: 0.310 D_GP: 0.077 D_real: 0.751 D_fake: 0.695 
+(epoch: 229, iters: 176, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.838 G_ID: 0.144 G_Rec: 0.366 D_GP: 0.027 D_real: 1.103 D_fake: 0.669 
+(epoch: 229, iters: 576, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.779 G_ID: 0.116 G_Rec: 0.354 D_GP: 0.023 D_real: 1.099 D_fake: 0.725 
+(epoch: 229, iters: 976, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.904 G_ID: 0.162 G_Rec: 0.421 D_GP: 0.024 D_real: 1.211 D_fake: 0.581 
+(epoch: 229, iters: 1376, time: 0.064) G_GAN: -0.210 G_GAN_Feat: 0.656 G_ID: 0.143 G_Rec: 0.304 D_GP: 0.020 D_real: 0.745 D_fake: 1.210 
+(epoch: 229, iters: 1776, time: 0.064) G_GAN: -0.130 G_GAN_Feat: 0.750 G_ID: 0.170 G_Rec: 0.389 D_GP: 0.017 D_real: 0.675 D_fake: 1.131 
+(epoch: 229, iters: 2176, time: 0.064) G_GAN: -0.192 G_GAN_Feat: 0.564 G_ID: 0.112 G_Rec: 0.267 D_GP: 0.019 D_real: 0.803 D_fake: 1.192 
+(epoch: 229, iters: 2576, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.808 G_ID: 0.171 G_Rec: 0.386 D_GP: 0.030 D_real: 0.781 D_fake: 0.953 
+(epoch: 229, iters: 2976, time: 0.064) G_GAN: -0.275 G_GAN_Feat: 0.742 G_ID: 0.122 G_Rec: 0.326 D_GP: 0.060 D_real: 0.403 D_fake: 1.275 
+(epoch: 229, iters: 3376, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 0.971 G_ID: 0.166 G_Rec: 0.444 D_GP: 0.035 D_real: 1.150 D_fake: 0.550 
+(epoch: 229, iters: 3776, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.784 G_ID: 0.125 G_Rec: 0.315 D_GP: 0.038 D_real: 0.920 D_fake: 0.721 
+(epoch: 229, iters: 4176, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.969 G_ID: 0.184 G_Rec: 0.368 D_GP: 0.102 D_real: 0.348 D_fake: 0.882 
+(epoch: 229, iters: 4576, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.587 G_ID: 0.110 G_Rec: 0.295 D_GP: 0.020 D_real: 1.260 D_fake: 0.714 
+(epoch: 229, iters: 4976, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.718 G_ID: 0.158 G_Rec: 0.413 D_GP: 0.017 D_real: 1.136 D_fake: 0.676 
+(epoch: 229, iters: 5376, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.607 G_ID: 0.131 G_Rec: 0.330 D_GP: 0.022 D_real: 1.136 D_fake: 0.810 
+(epoch: 229, iters: 5776, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.695 G_ID: 0.181 G_Rec: 0.339 D_GP: 0.023 D_real: 0.937 D_fake: 0.850 
+(epoch: 229, iters: 6176, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.584 G_ID: 0.137 G_Rec: 0.342 D_GP: 0.026 D_real: 0.994 D_fake: 0.932 
+(epoch: 229, iters: 6576, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.827 G_ID: 0.167 G_Rec: 0.480 D_GP: 0.035 D_real: 0.750 D_fake: 0.878 
+(epoch: 229, iters: 6976, time: 0.064) G_GAN: 0.046 G_GAN_Feat: 0.589 G_ID: 0.162 G_Rec: 0.283 D_GP: 0.028 D_real: 0.998 D_fake: 0.954 
+(epoch: 229, iters: 7376, time: 0.064) G_GAN: 0.204 G_GAN_Feat: 0.816 G_ID: 0.189 G_Rec: 0.392 D_GP: 0.064 D_real: 0.777 D_fake: 0.799 
+(epoch: 229, iters: 7776, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.622 G_ID: 0.142 G_Rec: 0.314 D_GP: 0.036 D_real: 1.002 D_fake: 0.889 
+(epoch: 229, iters: 8176, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.844 G_ID: 0.173 G_Rec: 0.406 D_GP: 0.056 D_real: 0.785 D_fake: 0.736 
+(epoch: 229, iters: 8576, time: 0.064) G_GAN: 0.109 G_GAN_Feat: 0.591 G_ID: 0.136 G_Rec: 0.303 D_GP: 0.021 D_real: 1.033 D_fake: 0.891 
+(epoch: 230, iters: 368, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.805 G_ID: 0.155 G_Rec: 0.379 D_GP: 0.032 D_real: 0.862 D_fake: 0.830 
+(epoch: 230, iters: 768, time: 0.064) G_GAN: -0.028 G_GAN_Feat: 0.709 G_ID: 0.122 G_Rec: 0.328 D_GP: 0.029 D_real: 0.930 D_fake: 1.028 
+(epoch: 230, iters: 1168, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.825 G_ID: 0.166 G_Rec: 0.424 D_GP: 0.023 D_real: 1.162 D_fake: 0.566 
+(epoch: 230, iters: 1568, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.646 G_ID: 0.130 G_Rec: 0.449 D_GP: 0.025 D_real: 1.048 D_fake: 0.856 
+(epoch: 230, iters: 1968, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.804 G_ID: 0.171 G_Rec: 0.396 D_GP: 0.028 D_real: 1.142 D_fake: 0.631 
+(epoch: 230, iters: 2368, time: 0.064) G_GAN: -0.019 G_GAN_Feat: 0.718 G_ID: 0.145 G_Rec: 0.301 D_GP: 0.074 D_real: 0.715 D_fake: 1.019 
+(epoch: 230, iters: 2768, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.812 G_ID: 0.193 G_Rec: 0.386 D_GP: 0.029 D_real: 1.040 D_fake: 0.638 
+(epoch: 230, iters: 3168, time: 0.064) G_GAN: -0.105 G_GAN_Feat: 0.639 G_ID: 0.160 G_Rec: 0.325 D_GP: 0.022 D_real: 0.921 D_fake: 1.105 
+(epoch: 230, iters: 3568, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 0.842 G_ID: 0.169 G_Rec: 0.395 D_GP: 0.025 D_real: 0.842 D_fake: 0.901 
+(epoch: 230, iters: 3968, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.691 G_ID: 0.112 G_Rec: 0.303 D_GP: 0.035 D_real: 1.258 D_fake: 0.572 
+(epoch: 230, iters: 4368, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.722 G_ID: 0.166 G_Rec: 0.362 D_GP: 0.019 D_real: 1.082 D_fake: 0.762 
+(epoch: 230, iters: 4768, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.579 G_ID: 0.116 G_Rec: 0.295 D_GP: 0.020 D_real: 1.372 D_fake: 0.598 
+(epoch: 230, iters: 5168, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.794 G_ID: 0.184 G_Rec: 0.363 D_GP: 0.030 D_real: 1.169 D_fake: 0.548 
+(epoch: 230, iters: 5568, time: 0.064) G_GAN: 0.054 G_GAN_Feat: 0.679 G_ID: 0.137 G_Rec: 0.310 D_GP: 0.030 D_real: 1.036 D_fake: 0.946 
+(epoch: 230, iters: 5968, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.871 G_ID: 0.175 G_Rec: 0.380 D_GP: 0.036 D_real: 1.011 D_fake: 0.587 
+(epoch: 230, iters: 6368, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.674 G_ID: 0.113 G_Rec: 0.313 D_GP: 0.041 D_real: 1.029 D_fake: 0.829 
+(epoch: 230, iters: 6768, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.946 G_ID: 0.180 G_Rec: 0.452 D_GP: 0.041 D_real: 0.887 D_fake: 0.614 
+(epoch: 230, iters: 7168, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.857 G_ID: 0.138 G_Rec: 0.310 D_GP: 0.132 D_real: 0.931 D_fake: 0.536 
+(epoch: 230, iters: 7568, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.871 G_ID: 0.176 G_Rec: 0.479 D_GP: 0.019 D_real: 1.257 D_fake: 0.669 
+(epoch: 230, iters: 7968, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.622 G_ID: 0.129 G_Rec: 0.312 D_GP: 0.020 D_real: 0.984 D_fake: 0.916 
+(epoch: 230, iters: 8368, time: 0.064) G_GAN: -0.020 G_GAN_Feat: 0.805 G_ID: 0.172 G_Rec: 0.363 D_GP: 0.031 D_real: 0.738 D_fake: 1.020 
+(epoch: 231, iters: 160, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.620 G_ID: 0.114 G_Rec: 0.341 D_GP: 0.028 D_real: 1.038 D_fake: 0.839 
+(epoch: 231, iters: 560, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.808 G_ID: 0.154 G_Rec: 0.360 D_GP: 0.037 D_real: 1.091 D_fake: 0.610 
+(epoch: 231, iters: 960, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 0.696 G_ID: 0.138 G_Rec: 0.337 D_GP: 0.033 D_real: 1.008 D_fake: 0.849 
+(epoch: 231, iters: 1360, time: 0.064) G_GAN: 0.830 G_GAN_Feat: 0.911 G_ID: 0.166 G_Rec: 0.408 D_GP: 0.057 D_real: 1.468 D_fake: 0.211 
+(epoch: 231, iters: 1760, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.706 G_ID: 0.135 G_Rec: 0.334 D_GP: 0.025 D_real: 1.193 D_fake: 0.643 
+(epoch: 231, iters: 2160, time: 0.064) G_GAN: 0.473 G_GAN_Feat: 0.778 G_ID: 0.178 G_Rec: 0.379 D_GP: 0.021 D_real: 1.210 D_fake: 0.532 
+(epoch: 231, iters: 2560, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.677 G_ID: 0.138 G_Rec: 0.309 D_GP: 0.024 D_real: 0.919 D_fake: 0.938 
+(epoch: 231, iters: 2960, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.984 G_ID: 0.184 G_Rec: 0.424 D_GP: 0.039 D_real: 0.939 D_fake: 0.522 
+(epoch: 231, iters: 3360, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.662 G_ID: 0.130 G_Rec: 0.308 D_GP: 0.025 D_real: 1.110 D_fake: 0.808 
+(epoch: 231, iters: 3760, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.739 G_ID: 0.173 G_Rec: 0.351 D_GP: 0.032 D_real: 1.271 D_fake: 0.477 
+(epoch: 231, iters: 4160, time: 0.064) G_GAN: 0.025 G_GAN_Feat: 0.731 G_ID: 0.135 G_Rec: 0.308 D_GP: 0.049 D_real: 0.808 D_fake: 0.975 
+(epoch: 231, iters: 4560, time: 0.064) G_GAN: -0.008 G_GAN_Feat: 1.070 G_ID: 0.212 G_Rec: 0.413 D_GP: 0.069 D_real: 0.145 D_fake: 1.013 
+(epoch: 231, iters: 4960, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.715 G_ID: 0.141 G_Rec: 0.299 D_GP: 0.039 D_real: 0.924 D_fake: 0.826 
+(epoch: 231, iters: 5360, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.950 G_ID: 0.189 G_Rec: 0.415 D_GP: 0.057 D_real: 0.715 D_fake: 0.658 
+(epoch: 231, iters: 5760, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.784 G_ID: 0.108 G_Rec: 0.287 D_GP: 0.072 D_real: 0.905 D_fake: 0.779 
+(epoch: 231, iters: 6160, time: 0.064) G_GAN: 0.675 G_GAN_Feat: 0.865 G_ID: 0.193 G_Rec: 0.357 D_GP: 0.029 D_real: 1.363 D_fake: 0.337 
+(epoch: 231, iters: 6560, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.724 G_ID: 0.138 G_Rec: 0.299 D_GP: 0.042 D_real: 0.877 D_fake: 0.885 
+(epoch: 231, iters: 6960, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.921 G_ID: 0.179 G_Rec: 0.401 D_GP: 0.033 D_real: 0.998 D_fake: 0.640 
+(epoch: 231, iters: 7360, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.742 G_ID: 0.133 G_Rec: 0.305 D_GP: 0.026 D_real: 1.179 D_fake: 0.646 
+(epoch: 231, iters: 7760, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 0.729 G_ID: 0.187 G_Rec: 0.379 D_GP: 0.021 D_real: 1.303 D_fake: 0.593 
+(epoch: 231, iters: 8160, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.591 G_ID: 0.124 G_Rec: 0.307 D_GP: 0.018 D_real: 1.212 D_fake: 0.773 
+(epoch: 231, iters: 8560, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.798 G_ID: 0.193 G_Rec: 0.433 D_GP: 0.018 D_real: 1.063 D_fake: 0.697 
+(epoch: 232, iters: 352, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.558 G_ID: 0.132 G_Rec: 0.279 D_GP: 0.025 D_real: 1.052 D_fake: 0.898 
+(epoch: 232, iters: 752, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.818 G_ID: 0.174 G_Rec: 0.407 D_GP: 0.028 D_real: 0.989 D_fake: 0.686 
+(epoch: 232, iters: 1152, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.686 G_ID: 0.142 G_Rec: 0.324 D_GP: 0.035 D_real: 0.873 D_fake: 0.942 
+(epoch: 232, iters: 1552, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.871 G_ID: 0.193 G_Rec: 0.432 D_GP: 0.056 D_real: 0.855 D_fake: 0.729 
+(epoch: 232, iters: 1952, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.669 G_ID: 0.131 G_Rec: 0.291 D_GP: 0.044 D_real: 0.994 D_fake: 0.807 
+(epoch: 232, iters: 2352, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 0.896 G_ID: 0.214 G_Rec: 0.430 D_GP: 0.045 D_real: 0.888 D_fake: 0.711 
+(epoch: 232, iters: 2752, time: 0.064) G_GAN: 0.058 G_GAN_Feat: 0.667 G_ID: 0.132 G_Rec: 0.310 D_GP: 0.052 D_real: 0.937 D_fake: 0.942 
+(epoch: 232, iters: 3152, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.864 G_ID: 0.181 G_Rec: 0.436 D_GP: 0.022 D_real: 1.218 D_fake: 0.550 
+(epoch: 232, iters: 3552, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.656 G_ID: 0.153 G_Rec: 0.272 D_GP: 0.043 D_real: 1.119 D_fake: 0.736 
+(epoch: 232, iters: 3952, time: 0.064) G_GAN: 0.571 G_GAN_Feat: 0.900 G_ID: 0.191 G_Rec: 0.434 D_GP: 0.027 D_real: 1.199 D_fake: 0.445 
+(epoch: 232, iters: 4352, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.669 G_ID: 0.130 G_Rec: 0.300 D_GP: 0.026 D_real: 1.215 D_fake: 0.763 
+(epoch: 232, iters: 4752, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 0.914 G_ID: 0.172 G_Rec: 0.410 D_GP: 0.025 D_real: 1.063 D_fake: 0.587 
+(epoch: 232, iters: 5152, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.662 G_ID: 0.124 G_Rec: 0.275 D_GP: 0.042 D_real: 0.907 D_fake: 0.947 
+(epoch: 232, iters: 5552, time: 0.064) G_GAN: 0.530 G_GAN_Feat: 0.754 G_ID: 0.174 G_Rec: 0.381 D_GP: 0.020 D_real: 1.321 D_fake: 0.475 
+(epoch: 232, iters: 5952, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 0.666 G_ID: 0.165 G_Rec: 0.296 D_GP: 0.025 D_real: 1.130 D_fake: 0.757 
+(epoch: 232, iters: 6352, time: 0.064) G_GAN: 0.585 G_GAN_Feat: 0.785 G_ID: 0.178 G_Rec: 0.383 D_GP: 0.026 D_real: 1.300 D_fake: 0.437 
+(epoch: 232, iters: 6752, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 0.788 G_ID: 0.142 G_Rec: 0.333 D_GP: 0.058 D_real: 0.663 D_fake: 0.943 
+(epoch: 232, iters: 7152, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.913 G_ID: 0.177 G_Rec: 0.408 D_GP: 0.050 D_real: 0.960 D_fake: 0.437 
+(epoch: 232, iters: 7552, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.672 G_ID: 0.128 G_Rec: 0.315 D_GP: 0.024 D_real: 1.200 D_fake: 0.682 
+(epoch: 232, iters: 7952, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 0.982 G_ID: 0.179 G_Rec: 0.449 D_GP: 0.037 D_real: 1.156 D_fake: 0.413 
+(epoch: 232, iters: 8352, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.954 G_ID: 0.145 G_Rec: 0.296 D_GP: 0.080 D_real: 0.390 D_fake: 0.802 
+(epoch: 233, iters: 144, time: 0.064) G_GAN: 0.682 G_GAN_Feat: 1.001 G_ID: 0.170 G_Rec: 0.425 D_GP: 0.021 D_real: 1.414 D_fake: 0.394 
+(epoch: 233, iters: 544, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.631 G_ID: 0.139 G_Rec: 0.297 D_GP: 0.018 D_real: 1.122 D_fake: 0.819 
+(epoch: 233, iters: 944, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.848 G_ID: 0.179 G_Rec: 0.424 D_GP: 0.023 D_real: 1.001 D_fake: 0.733 
+(epoch: 233, iters: 1344, time: 0.064) G_GAN: 0.030 G_GAN_Feat: 0.598 G_ID: 0.149 G_Rec: 0.287 D_GP: 0.022 D_real: 0.992 D_fake: 0.970 
+(epoch: 233, iters: 1744, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.874 G_ID: 0.175 G_Rec: 0.402 D_GP: 0.045 D_real: 0.870 D_fake: 0.667 
+(epoch: 233, iters: 2144, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.721 G_ID: 0.169 G_Rec: 0.311 D_GP: 0.025 D_real: 1.206 D_fake: 0.693 
+(epoch: 233, iters: 2544, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 1.023 G_ID: 0.184 G_Rec: 0.421 D_GP: 0.167 D_real: 0.560 D_fake: 0.578 
+(epoch: 233, iters: 2944, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 0.673 G_ID: 0.130 G_Rec: 0.321 D_GP: 0.022 D_real: 0.973 D_fake: 0.919 
+(epoch: 233, iters: 3344, time: 0.064) G_GAN: 0.750 G_GAN_Feat: 0.787 G_ID: 0.178 G_Rec: 0.367 D_GP: 0.026 D_real: 1.502 D_fake: 0.315 
+(epoch: 233, iters: 3744, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.780 G_ID: 0.155 G_Rec: 0.324 D_GP: 0.038 D_real: 1.016 D_fake: 0.644 
+(epoch: 233, iters: 4144, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.061 G_ID: 0.167 G_Rec: 0.428 D_GP: 0.065 D_real: 0.470 D_fake: 0.652 
+(epoch: 233, iters: 4544, time: 0.064) G_GAN: -0.002 G_GAN_Feat: 0.683 G_ID: 0.131 G_Rec: 0.303 D_GP: 0.030 D_real: 0.947 D_fake: 1.002 
+(epoch: 233, iters: 4944, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.923 G_ID: 0.172 G_Rec: 0.402 D_GP: 0.039 D_real: 1.091 D_fake: 0.499 
+(epoch: 233, iters: 5344, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.646 G_ID: 0.145 G_Rec: 0.306 D_GP: 0.020 D_real: 1.375 D_fake: 0.632 
+(epoch: 233, iters: 5744, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.869 G_ID: 0.210 G_Rec: 0.430 D_GP: 0.021 D_real: 1.006 D_fake: 0.711 
+(epoch: 233, iters: 6144, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.648 G_ID: 0.133 G_Rec: 0.335 D_GP: 0.023 D_real: 0.997 D_fake: 0.939 
+(epoch: 233, iters: 6544, time: 0.064) G_GAN: 0.469 G_GAN_Feat: 0.771 G_ID: 0.148 G_Rec: 0.370 D_GP: 0.025 D_real: 1.252 D_fake: 0.553 
+(epoch: 233, iters: 6944, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.667 G_ID: 0.110 G_Rec: 0.289 D_GP: 0.036 D_real: 1.065 D_fake: 0.780 
+(epoch: 233, iters: 7344, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.904 G_ID: 0.172 G_Rec: 0.393 D_GP: 0.030 D_real: 1.111 D_fake: 0.577 
+(epoch: 233, iters: 7744, time: 0.064) G_GAN: -0.033 G_GAN_Feat: 0.935 G_ID: 0.131 G_Rec: 0.340 D_GP: 0.172 D_real: 0.608 D_fake: 1.034 
+(epoch: 233, iters: 8144, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.906 G_ID: 0.186 G_Rec: 0.375 D_GP: 0.034 D_real: 1.043 D_fake: 0.658 
+(epoch: 233, iters: 8544, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.656 G_ID: 0.123 G_Rec: 0.297 D_GP: 0.020 D_real: 1.205 D_fake: 0.737 
+(epoch: 234, iters: 336, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.953 G_ID: 0.164 G_Rec: 0.427 D_GP: 0.051 D_real: 0.704 D_fake: 0.671 
+(epoch: 234, iters: 736, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.670 G_ID: 0.115 G_Rec: 0.295 D_GP: 0.026 D_real: 1.217 D_fake: 0.683 
+(epoch: 234, iters: 1136, time: 0.064) G_GAN: 0.787 G_GAN_Feat: 0.919 G_ID: 0.163 G_Rec: 0.375 D_GP: 0.323 D_real: 1.141 D_fake: 0.281 
+(epoch: 234, iters: 1536, time: 0.064) G_GAN: -0.271 G_GAN_Feat: 0.589 G_ID: 0.121 G_Rec: 0.281 D_GP: 0.017 D_real: 0.694 D_fake: 1.271 
+(epoch: 234, iters: 1936, time: 0.064) G_GAN: -0.093 G_GAN_Feat: 0.774 G_ID: 0.182 G_Rec: 0.381 D_GP: 0.019 D_real: 0.740 D_fake: 1.094 
+(epoch: 234, iters: 2336, time: 0.064) G_GAN: -0.090 G_GAN_Feat: 0.605 G_ID: 0.127 G_Rec: 0.288 D_GP: 0.023 D_real: 0.831 D_fake: 1.090 
+(epoch: 234, iters: 2736, time: 0.064) G_GAN: -0.038 G_GAN_Feat: 0.816 G_ID: 0.161 G_Rec: 0.396 D_GP: 0.026 D_real: 0.711 D_fake: 1.038 
+(epoch: 234, iters: 3136, time: 0.064) G_GAN: -0.005 G_GAN_Feat: 0.645 G_ID: 0.129 G_Rec: 0.282 D_GP: 0.030 D_real: 0.887 D_fake: 1.007 
+(epoch: 234, iters: 3536, time: 0.064) G_GAN: 0.561 G_GAN_Feat: 0.933 G_ID: 0.160 G_Rec: 0.428 D_GP: 0.043 D_real: 1.107 D_fake: 0.445 
+(epoch: 234, iters: 3936, time: 0.064) G_GAN: -0.069 G_GAN_Feat: 0.788 G_ID: 0.111 G_Rec: 0.300 D_GP: 0.115 D_real: 0.518 D_fake: 1.069 
+(epoch: 234, iters: 4336, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 1.121 G_ID: 0.148 G_Rec: 0.413 D_GP: 0.209 D_real: 0.506 D_fake: 0.537 
+(epoch: 234, iters: 4736, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.674 G_ID: 0.139 G_Rec: 0.261 D_GP: 0.029 D_real: 1.061 D_fake: 0.830 
+(epoch: 234, iters: 5136, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.972 G_ID: 0.182 G_Rec: 0.392 D_GP: 0.071 D_real: 0.719 D_fake: 0.556 
+(epoch: 234, iters: 5536, time: 0.064) G_GAN: 0.564 G_GAN_Feat: 0.738 G_ID: 0.124 G_Rec: 0.285 D_GP: 0.025 D_real: 1.436 D_fake: 0.481 
+(epoch: 234, iters: 5936, time: 0.064) G_GAN: 0.557 G_GAN_Feat: 0.920 G_ID: 0.157 G_Rec: 0.400 D_GP: 0.032 D_real: 1.108 D_fake: 0.460 
+(epoch: 234, iters: 6336, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 0.992 G_ID: 0.125 G_Rec: 0.324 D_GP: 0.157 D_real: 0.590 D_fake: 0.488 
+(epoch: 234, iters: 6736, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.859 G_ID: 0.185 G_Rec: 0.413 D_GP: 0.050 D_real: 1.168 D_fake: 0.662 
+(epoch: 234, iters: 7136, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.731 G_ID: 0.115 G_Rec: 0.317 D_GP: 0.024 D_real: 1.142 D_fake: 0.713 
+(epoch: 234, iters: 7536, time: 0.064) G_GAN: 0.068 G_GAN_Feat: 0.877 G_ID: 0.165 G_Rec: 0.396 D_GP: 0.040 D_real: 0.797 D_fake: 0.933 
+(epoch: 234, iters: 7936, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.625 G_ID: 0.153 G_Rec: 0.298 D_GP: 0.024 D_real: 1.026 D_fake: 0.879 
+(epoch: 234, iters: 8336, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 0.821 G_ID: 0.179 G_Rec: 0.381 D_GP: 0.024 D_real: 1.254 D_fake: 0.567 
+(epoch: 235, iters: 128, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.670 G_ID: 0.128 G_Rec: 0.291 D_GP: 0.035 D_real: 0.978 D_fake: 0.886 
+(epoch: 235, iters: 528, time: 0.064) G_GAN: 0.754 G_GAN_Feat: 1.018 G_ID: 0.176 G_Rec: 0.402 D_GP: 0.059 D_real: 1.222 D_fake: 0.315 
+(epoch: 235, iters: 928, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.813 G_ID: 0.138 G_Rec: 0.318 D_GP: 0.032 D_real: 1.006 D_fake: 0.657 
+(epoch: 235, iters: 1328, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 1.059 G_ID: 0.200 G_Rec: 0.434 D_GP: 0.069 D_real: 0.537 D_fake: 0.528 
+(epoch: 235, iters: 1728, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.933 G_ID: 0.122 G_Rec: 0.335 D_GP: 0.035 D_real: 0.925 D_fake: 0.516 
+(epoch: 235, iters: 2128, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.848 G_ID: 0.168 G_Rec: 0.364 D_GP: 0.024 D_real: 1.089 D_fake: 0.608 
+(epoch: 235, iters: 2528, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.781 G_ID: 0.123 G_Rec: 0.331 D_GP: 0.050 D_real: 0.828 D_fake: 0.817 
+(epoch: 235, iters: 2928, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.894 G_ID: 0.178 G_Rec: 0.385 D_GP: 0.036 D_real: 1.134 D_fake: 0.548 
+(epoch: 235, iters: 3328, time: 0.064) G_GAN: 0.028 G_GAN_Feat: 0.790 G_ID: 0.156 G_Rec: 0.324 D_GP: 0.041 D_real: 0.996 D_fake: 0.973 
+(epoch: 235, iters: 3728, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 1.083 G_ID: 0.176 G_Rec: 0.412 D_GP: 0.100 D_real: 0.804 D_fake: 0.707 
+(epoch: 235, iters: 4128, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.663 G_ID: 0.131 G_Rec: 0.314 D_GP: 0.024 D_real: 1.352 D_fake: 0.628 
+(epoch: 235, iters: 4528, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.909 G_ID: 0.177 G_Rec: 0.410 D_GP: 0.024 D_real: 1.064 D_fake: 0.731 
+(epoch: 235, iters: 4928, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.670 G_ID: 0.114 G_Rec: 0.318 D_GP: 0.116 D_real: 0.825 D_fake: 0.927 
+(epoch: 235, iters: 5328, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.861 G_ID: 0.164 G_Rec: 0.420 D_GP: 0.022 D_real: 1.157 D_fake: 0.582 
+(epoch: 235, iters: 5728, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.674 G_ID: 0.154 G_Rec: 0.332 D_GP: 0.043 D_real: 0.939 D_fake: 0.888 
+(epoch: 235, iters: 6128, time: 0.064) G_GAN: -0.043 G_GAN_Feat: 1.048 G_ID: 0.172 G_Rec: 0.396 D_GP: 0.305 D_real: 0.430 D_fake: 1.043 
+(epoch: 235, iters: 6528, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.734 G_ID: 0.136 G_Rec: 0.288 D_GP: 0.039 D_real: 1.120 D_fake: 0.712 
+(epoch: 235, iters: 6928, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.834 G_ID: 0.177 G_Rec: 0.366 D_GP: 0.027 D_real: 1.115 D_fake: 0.649 
+(epoch: 235, iters: 7328, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.671 G_ID: 0.122 G_Rec: 0.296 D_GP: 0.033 D_real: 1.149 D_fake: 0.711 
+(epoch: 235, iters: 7728, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.858 G_ID: 0.170 G_Rec: 0.386 D_GP: 0.025 D_real: 0.891 D_fake: 0.783 
+(epoch: 235, iters: 8128, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.633 G_ID: 0.119 G_Rec: 0.284 D_GP: 0.022 D_real: 1.070 D_fake: 0.805 
+(epoch: 235, iters: 8528, time: 0.064) G_GAN: 0.434 G_GAN_Feat: 1.078 G_ID: 0.185 G_Rec: 0.436 D_GP: 0.153 D_real: 0.542 D_fake: 0.569 
+(epoch: 236, iters: 320, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.788 G_ID: 0.121 G_Rec: 0.287 D_GP: 0.049 D_real: 0.944 D_fake: 0.637 
+(epoch: 236, iters: 720, time: 0.064) G_GAN: 0.799 G_GAN_Feat: 0.943 G_ID: 0.186 G_Rec: 0.420 D_GP: 0.027 D_real: 1.425 D_fake: 0.246 
+(epoch: 236, iters: 1120, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.687 G_ID: 0.136 G_Rec: 0.289 D_GP: 0.024 D_real: 1.157 D_fake: 0.767 
+(epoch: 236, iters: 1520, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 1.033 G_ID: 0.174 G_Rec: 0.434 D_GP: 0.080 D_real: 0.516 D_fake: 0.604 
+(epoch: 236, iters: 1920, time: 0.064) G_GAN: 0.098 G_GAN_Feat: 0.709 G_ID: 0.154 G_Rec: 0.291 D_GP: 0.022 D_real: 1.042 D_fake: 0.902 
+(epoch: 236, iters: 2320, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 1.039 G_ID: 0.168 G_Rec: 0.405 D_GP: 0.026 D_real: 0.862 D_fake: 0.565 
+(epoch: 236, iters: 2720, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.734 G_ID: 0.139 G_Rec: 0.346 D_GP: 0.028 D_real: 1.057 D_fake: 0.824 
+(epoch: 236, iters: 3120, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 1.226 G_ID: 0.186 G_Rec: 0.498 D_GP: 0.044 D_real: 0.292 D_fake: 0.453 
+(epoch: 236, iters: 3520, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 1.003 G_ID: 0.138 G_Rec: 0.351 D_GP: 0.041 D_real: 0.410 D_fake: 0.764 
+(epoch: 236, iters: 3920, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.804 G_ID: 0.161 G_Rec: 0.411 D_GP: 0.020 D_real: 1.031 D_fake: 0.754 
+(epoch: 236, iters: 4320, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 0.691 G_ID: 0.144 G_Rec: 0.295 D_GP: 0.034 D_real: 0.877 D_fake: 0.919 
+(epoch: 236, iters: 4720, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.947 G_ID: 0.161 G_Rec: 0.420 D_GP: 0.033 D_real: 1.164 D_fake: 0.738 
+(epoch: 236, iters: 5120, time: 0.064) G_GAN: 0.141 G_GAN_Feat: 0.620 G_ID: 0.113 G_Rec: 0.302 D_GP: 0.019 D_real: 1.143 D_fake: 0.859 
+(epoch: 236, iters: 5520, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.862 G_ID: 0.154 G_Rec: 0.382 D_GP: 0.024 D_real: 1.158 D_fake: 0.594 
+(epoch: 236, iters: 5920, time: 0.064) G_GAN: 0.097 G_GAN_Feat: 0.882 G_ID: 0.123 G_Rec: 0.313 D_GP: 0.066 D_real: 0.480 D_fake: 0.903 
+(epoch: 236, iters: 6320, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.887 G_ID: 0.200 G_Rec: 0.407 D_GP: 0.023 D_real: 1.097 D_fake: 0.644 
+(epoch: 236, iters: 6720, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.780 G_ID: 0.126 G_Rec: 0.303 D_GP: 0.027 D_real: 1.135 D_fake: 0.648 
+(epoch: 236, iters: 7120, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 0.897 G_ID: 0.168 G_Rec: 0.405 D_GP: 0.029 D_real: 1.173 D_fake: 0.479 
+(epoch: 236, iters: 7520, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.721 G_ID: 0.118 G_Rec: 0.294 D_GP: 0.040 D_real: 1.154 D_fake: 0.679 
+(epoch: 236, iters: 7920, time: 0.064) G_GAN: 0.556 G_GAN_Feat: 0.865 G_ID: 0.192 G_Rec: 0.402 D_GP: 0.027 D_real: 1.231 D_fake: 0.451 
+(epoch: 236, iters: 8320, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.762 G_ID: 0.131 G_Rec: 0.315 D_GP: 0.023 D_real: 0.977 D_fake: 0.816 
+(epoch: 237, iters: 112, time: 0.064) G_GAN: 0.504 G_GAN_Feat: 0.941 G_ID: 0.189 G_Rec: 0.370 D_GP: 0.085 D_real: 0.743 D_fake: 0.500 
+(epoch: 237, iters: 512, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.961 G_ID: 0.116 G_Rec: 0.296 D_GP: 0.394 D_real: 0.523 D_fake: 0.705 
+(epoch: 237, iters: 912, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 1.026 G_ID: 0.158 G_Rec: 0.398 D_GP: 0.069 D_real: 0.564 D_fake: 0.690 
+(epoch: 237, iters: 1312, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.755 G_ID: 0.151 G_Rec: 0.292 D_GP: 0.031 D_real: 1.116 D_fake: 0.668 
+(epoch: 237, iters: 1712, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.728 G_ID: 0.157 G_Rec: 0.422 D_GP: 0.019 D_real: 1.298 D_fake: 0.550 
+(epoch: 237, iters: 2112, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.557 G_ID: 0.117 G_Rec: 0.269 D_GP: 0.019 D_real: 1.303 D_fake: 0.669 
+(epoch: 237, iters: 2512, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.953 G_ID: 0.166 G_Rec: 0.448 D_GP: 0.036 D_real: 0.949 D_fake: 0.663 
+(epoch: 237, iters: 2912, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.798 G_ID: 0.130 G_Rec: 0.307 D_GP: 0.026 D_real: 0.835 D_fake: 0.826 
+(epoch: 237, iters: 3312, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.774 G_ID: 0.188 G_Rec: 0.376 D_GP: 0.021 D_real: 1.032 D_fake: 0.784 
+(epoch: 237, iters: 3712, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.710 G_ID: 0.128 G_Rec: 0.308 D_GP: 0.064 D_real: 0.951 D_fake: 0.790 
+(epoch: 237, iters: 4112, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.876 G_ID: 0.150 G_Rec: 0.384 D_GP: 0.035 D_real: 1.041 D_fake: 0.593 
+(epoch: 237, iters: 4512, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.719 G_ID: 0.122 G_Rec: 0.301 D_GP: 0.024 D_real: 1.123 D_fake: 0.796 
+(epoch: 237, iters: 4912, time: 0.064) G_GAN: 0.610 G_GAN_Feat: 0.833 G_ID: 0.149 G_Rec: 0.446 D_GP: 0.020 D_real: 1.357 D_fake: 0.398 
+(epoch: 237, iters: 5312, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.619 G_ID: 0.106 G_Rec: 0.292 D_GP: 0.021 D_real: 1.336 D_fake: 0.714 
+(epoch: 237, iters: 5712, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.804 G_ID: 0.189 G_Rec: 0.396 D_GP: 0.024 D_real: 1.128 D_fake: 0.627 
+(epoch: 237, iters: 6112, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.615 G_ID: 0.137 G_Rec: 0.313 D_GP: 0.028 D_real: 1.141 D_fake: 0.780 
+(epoch: 237, iters: 6512, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.750 G_ID: 0.165 G_Rec: 0.358 D_GP: 0.030 D_real: 1.137 D_fake: 0.637 
+(epoch: 237, iters: 6912, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.648 G_ID: 0.143 G_Rec: 0.294 D_GP: 0.035 D_real: 0.930 D_fake: 0.916 
+(epoch: 237, iters: 7312, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.855 G_ID: 0.200 G_Rec: 0.395 D_GP: 0.104 D_real: 1.030 D_fake: 0.592 
+(epoch: 237, iters: 7712, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.705 G_ID: 0.135 G_Rec: 0.302 D_GP: 0.039 D_real: 0.921 D_fake: 0.836 
+(epoch: 237, iters: 8112, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.882 G_ID: 0.173 G_Rec: 0.377 D_GP: 0.098 D_real: 0.844 D_fake: 0.673 
+(epoch: 237, iters: 8512, time: 0.064) G_GAN: -0.053 G_GAN_Feat: 0.684 G_ID: 0.156 G_Rec: 0.292 D_GP: 0.033 D_real: 0.855 D_fake: 1.053 
+(epoch: 238, iters: 304, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.787 G_ID: 0.163 G_Rec: 0.345 D_GP: 0.036 D_real: 1.160 D_fake: 0.574 
+(epoch: 238, iters: 704, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.680 G_ID: 0.137 G_Rec: 0.289 D_GP: 0.026 D_real: 0.982 D_fake: 0.878 
+(epoch: 238, iters: 1104, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.810 G_ID: 0.186 G_Rec: 0.366 D_GP: 0.033 D_real: 1.053 D_fake: 0.721 
+(epoch: 238, iters: 1504, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.706 G_ID: 0.143 G_Rec: 0.324 D_GP: 0.027 D_real: 1.059 D_fake: 0.814 
+(epoch: 238, iters: 1904, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 1.064 G_ID: 0.162 G_Rec: 0.422 D_GP: 0.121 D_real: 0.578 D_fake: 0.472 
+(epoch: 238, iters: 2304, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.613 G_ID: 0.109 G_Rec: 0.284 D_GP: 0.019 D_real: 1.317 D_fake: 0.626 
+(epoch: 238, iters: 2704, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.811 G_ID: 0.173 G_Rec: 0.377 D_GP: 0.028 D_real: 1.151 D_fake: 0.553 
+(epoch: 238, iters: 3104, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.697 G_ID: 0.125 G_Rec: 0.296 D_GP: 0.059 D_real: 0.989 D_fake: 0.866 
+(epoch: 238, iters: 3504, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.784 G_ID: 0.160 G_Rec: 0.363 D_GP: 0.023 D_real: 1.157 D_fake: 0.641 
+(epoch: 238, iters: 3904, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.676 G_ID: 0.123 G_Rec: 0.296 D_GP: 0.028 D_real: 1.003 D_fake: 0.829 
+(epoch: 238, iters: 4304, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.819 G_ID: 0.199 G_Rec: 0.358 D_GP: 0.026 D_real: 1.089 D_fake: 0.661 
+(epoch: 238, iters: 4704, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.721 G_ID: 0.136 G_Rec: 0.298 D_GP: 0.041 D_real: 1.179 D_fake: 0.589 
+(epoch: 238, iters: 5104, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.765 G_ID: 0.154 G_Rec: 0.326 D_GP: 0.026 D_real: 1.267 D_fake: 0.540 
+(epoch: 238, iters: 5504, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.725 G_ID: 0.127 G_Rec: 0.302 D_GP: 0.022 D_real: 1.018 D_fake: 0.872 
+(epoch: 238, iters: 5904, time: 0.064) G_GAN: 0.567 G_GAN_Feat: 0.927 G_ID: 0.208 G_Rec: 0.402 D_GP: 0.031 D_real: 1.127 D_fake: 0.449 
+(epoch: 238, iters: 6304, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.842 G_ID: 0.122 G_Rec: 0.299 D_GP: 0.065 D_real: 0.705 D_fake: 0.604 
+(epoch: 238, iters: 6704, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.747 G_ID: 0.195 G_Rec: 0.399 D_GP: 0.019 D_real: 1.182 D_fake: 0.653 
+(epoch: 238, iters: 7104, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.657 G_ID: 0.179 G_Rec: 0.357 D_GP: 0.022 D_real: 1.079 D_fake: 0.876 
+(epoch: 238, iters: 7504, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.766 G_ID: 0.166 G_Rec: 0.375 D_GP: 0.022 D_real: 1.099 D_fake: 0.661 
+(epoch: 238, iters: 7904, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.575 G_ID: 0.126 G_Rec: 0.276 D_GP: 0.024 D_real: 1.004 D_fake: 0.931 
+(epoch: 238, iters: 8304, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.940 G_ID: 0.187 G_Rec: 0.441 D_GP: 0.044 D_real: 0.653 D_fake: 0.671 
+(epoch: 239, iters: 96, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.839 G_ID: 0.126 G_Rec: 0.311 D_GP: 0.216 D_real: 0.646 D_fake: 0.883 
+(epoch: 239, iters: 496, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.965 G_ID: 0.193 G_Rec: 0.367 D_GP: 0.079 D_real: 0.306 D_fake: 0.921 
+(epoch: 239, iters: 896, time: 0.064) G_GAN: 0.075 G_GAN_Feat: 0.640 G_ID: 0.140 G_Rec: 0.285 D_GP: 0.021 D_real: 1.012 D_fake: 0.925 
+(epoch: 239, iters: 1296, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.821 G_ID: 0.175 G_Rec: 0.405 D_GP: 0.032 D_real: 1.038 D_fake: 0.647 
+(epoch: 239, iters: 1696, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.657 G_ID: 0.127 G_Rec: 0.292 D_GP: 0.024 D_real: 0.908 D_fake: 0.977 
+(epoch: 239, iters: 2096, time: 0.064) G_GAN: 0.610 G_GAN_Feat: 0.848 G_ID: 0.174 G_Rec: 0.387 D_GP: 0.038 D_real: 1.152 D_fake: 0.425 
+(epoch: 239, iters: 2496, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.776 G_ID: 0.115 G_Rec: 0.320 D_GP: 0.042 D_real: 1.153 D_fake: 0.722 
+(epoch: 239, iters: 2896, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 1.128 G_ID: 0.176 G_Rec: 0.440 D_GP: 0.061 D_real: 0.416 D_fake: 0.601 
+(epoch: 239, iters: 3296, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.738 G_ID: 0.134 G_Rec: 0.300 D_GP: 0.056 D_real: 1.105 D_fake: 0.622 
+(epoch: 239, iters: 3696, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.826 G_ID: 0.215 G_Rec: 0.369 D_GP: 0.027 D_real: 1.034 D_fake: 0.803 
+(epoch: 239, iters: 4096, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.833 G_ID: 0.111 G_Rec: 0.311 D_GP: 0.031 D_real: 0.877 D_fake: 0.708 
+(epoch: 239, iters: 4496, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.832 G_ID: 0.158 G_Rec: 0.420 D_GP: 0.021 D_real: 0.842 D_fake: 0.947 
+(epoch: 239, iters: 4896, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.691 G_ID: 0.133 G_Rec: 0.307 D_GP: 0.022 D_real: 1.133 D_fake: 0.798 
+(epoch: 239, iters: 5296, time: 0.064) G_GAN: 0.540 G_GAN_Feat: 1.122 G_ID: 0.164 G_Rec: 0.465 D_GP: 0.192 D_real: 0.372 D_fake: 0.511 
+(epoch: 239, iters: 5696, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.896 G_ID: 0.127 G_Rec: 0.342 D_GP: 0.057 D_real: 0.528 D_fake: 0.769 
+(epoch: 239, iters: 6096, time: 0.064) G_GAN: 0.441 G_GAN_Feat: 0.885 G_ID: 0.167 G_Rec: 0.409 D_GP: 0.022 D_real: 1.182 D_fake: 0.565 
+(epoch: 239, iters: 6496, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.656 G_ID: 0.154 G_Rec: 0.307 D_GP: 0.019 D_real: 1.105 D_fake: 0.823 
+(epoch: 239, iters: 6896, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.769 G_ID: 0.174 G_Rec: 0.415 D_GP: 0.019 D_real: 1.141 D_fake: 0.632 
+(epoch: 239, iters: 7296, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.537 G_ID: 0.131 G_Rec: 0.285 D_GP: 0.016 D_real: 1.191 D_fake: 0.761 
+(epoch: 239, iters: 7696, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.781 G_ID: 0.184 G_Rec: 0.410 D_GP: 0.025 D_real: 1.233 D_fake: 0.542 
+(epoch: 239, iters: 8096, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.597 G_ID: 0.130 G_Rec: 0.288 D_GP: 0.024 D_real: 1.082 D_fake: 0.800 
+(epoch: 239, iters: 8496, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.775 G_ID: 0.172 G_Rec: 0.362 D_GP: 0.029 D_real: 1.124 D_fake: 0.641 
+(epoch: 240, iters: 288, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.614 G_ID: 0.123 G_Rec: 0.273 D_GP: 0.025 D_real: 1.116 D_fake: 0.818 
+(epoch: 240, iters: 688, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.943 G_ID: 0.175 G_Rec: 0.412 D_GP: 0.037 D_real: 0.933 D_fake: 0.574 
+(epoch: 240, iters: 1088, time: 0.064) G_GAN: 0.052 G_GAN_Feat: 0.732 G_ID: 0.144 G_Rec: 0.327 D_GP: 0.043 D_real: 0.801 D_fake: 0.948 
+(epoch: 240, iters: 1488, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.811 G_ID: 0.169 G_Rec: 0.355 D_GP: 0.022 D_real: 1.336 D_fake: 0.513 
+(epoch: 240, iters: 1888, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.710 G_ID: 0.146 G_Rec: 0.333 D_GP: 0.029 D_real: 0.975 D_fake: 0.819 
+(epoch: 240, iters: 2288, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 1.051 G_ID: 0.182 G_Rec: 0.429 D_GP: 0.155 D_real: 0.514 D_fake: 0.640 
+(epoch: 240, iters: 2688, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.722 G_ID: 0.126 G_Rec: 0.314 D_GP: 0.021 D_real: 1.128 D_fake: 0.710 
+(epoch: 240, iters: 3088, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.784 G_ID: 0.185 G_Rec: 0.372 D_GP: 0.034 D_real: 1.069 D_fake: 0.690 
+(epoch: 240, iters: 3488, time: 0.064) G_GAN: 0.070 G_GAN_Feat: 0.730 G_ID: 0.118 G_Rec: 0.271 D_GP: 0.042 D_real: 0.800 D_fake: 0.930 
+(epoch: 240, iters: 3888, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.882 G_ID: 0.205 G_Rec: 0.378 D_GP: 0.103 D_real: 0.809 D_fake: 0.548 
+(epoch: 240, iters: 4288, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.923 G_ID: 0.123 G_Rec: 0.320 D_GP: 0.129 D_real: 0.514 D_fake: 0.744 
+(epoch: 240, iters: 4688, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.845 G_ID: 0.168 G_Rec: 0.367 D_GP: 0.022 D_real: 1.207 D_fake: 0.573 
+(epoch: 240, iters: 5088, time: 0.064) G_GAN: -0.036 G_GAN_Feat: 0.680 G_ID: 0.146 G_Rec: 0.298 D_GP: 0.020 D_real: 0.913 D_fake: 1.036 
+(epoch: 240, iters: 5488, time: 0.064) G_GAN: 0.645 G_GAN_Feat: 0.819 G_ID: 0.163 G_Rec: 0.432 D_GP: 0.027 D_real: 1.316 D_fake: 0.366 
+(epoch: 240, iters: 5888, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.650 G_ID: 0.143 G_Rec: 0.291 D_GP: 0.021 D_real: 1.073 D_fake: 0.869 
+(epoch: 240, iters: 6288, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 0.931 G_ID: 0.153 G_Rec: 0.410 D_GP: 0.063 D_real: 0.977 D_fake: 0.544 
+(epoch: 240, iters: 6688, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.808 G_ID: 0.157 G_Rec: 0.337 D_GP: 0.083 D_real: 0.524 D_fake: 0.888 
+(epoch: 240, iters: 7088, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.950 G_ID: 0.175 G_Rec: 0.366 D_GP: 0.098 D_real: 0.522 D_fake: 0.612 
+(epoch: 240, iters: 7488, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.726 G_ID: 0.129 G_Rec: 0.298 D_GP: 0.026 D_real: 1.073 D_fake: 0.765 
+(epoch: 240, iters: 7888, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.927 G_ID: 0.185 G_Rec: 0.390 D_GP: 0.112 D_real: 0.798 D_fake: 0.762 
+(epoch: 240, iters: 8288, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.761 G_ID: 0.139 G_Rec: 0.309 D_GP: 0.025 D_real: 1.402 D_fake: 0.524 
+(epoch: 241, iters: 80, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 1.083 G_ID: 0.187 G_Rec: 0.426 D_GP: 0.943 D_real: 0.430 D_fake: 0.889 
+(epoch: 241, iters: 480, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.687 G_ID: 0.110 G_Rec: 0.290 D_GP: 0.021 D_real: 1.105 D_fake: 0.795 
+(epoch: 241, iters: 880, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.945 G_ID: 0.170 G_Rec: 0.410 D_GP: 0.023 D_real: 1.086 D_fake: 0.553 
+(epoch: 241, iters: 1280, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.827 G_ID: 0.147 G_Rec: 0.339 D_GP: 0.088 D_real: 0.568 D_fake: 0.814 
+(epoch: 241, iters: 1680, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.979 G_ID: 0.191 G_Rec: 0.378 D_GP: 0.036 D_real: 0.856 D_fake: 0.508 
+(epoch: 241, iters: 2080, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.733 G_ID: 0.134 G_Rec: 0.287 D_GP: 0.035 D_real: 1.049 D_fake: 0.733 
+(epoch: 241, iters: 2480, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.903 G_ID: 0.163 G_Rec: 0.407 D_GP: 0.104 D_real: 0.819 D_fake: 0.691 
+(epoch: 241, iters: 2880, time: 0.064) G_GAN: -0.060 G_GAN_Feat: 0.719 G_ID: 0.140 G_Rec: 0.297 D_GP: 0.032 D_real: 0.749 D_fake: 1.060 
+(epoch: 241, iters: 3280, time: 0.064) G_GAN: 0.814 G_GAN_Feat: 1.002 G_ID: 0.160 G_Rec: 0.441 D_GP: 0.039 D_real: 1.226 D_fake: 0.256 
+(epoch: 241, iters: 3680, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.845 G_ID: 0.124 G_Rec: 0.319 D_GP: 0.068 D_real: 0.784 D_fake: 0.824 
+(epoch: 241, iters: 4080, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.749 G_ID: 0.173 G_Rec: 0.368 D_GP: 0.018 D_real: 1.359 D_fake: 0.634 
+(epoch: 241, iters: 4480, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.619 G_ID: 0.124 G_Rec: 0.318 D_GP: 0.022 D_real: 1.187 D_fake: 0.774 
+(epoch: 241, iters: 4880, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.697 G_ID: 0.129 G_Rec: 0.346 D_GP: 0.015 D_real: 1.326 D_fake: 0.539 
+(epoch: 241, iters: 5280, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.649 G_ID: 0.126 G_Rec: 0.319 D_GP: 0.036 D_real: 1.064 D_fake: 0.839 
+(epoch: 241, iters: 5680, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.782 G_ID: 0.168 G_Rec: 0.380 D_GP: 0.024 D_real: 1.198 D_fake: 0.554 
+(epoch: 241, iters: 6080, time: 0.064) G_GAN: 0.049 G_GAN_Feat: 0.633 G_ID: 0.133 G_Rec: 0.288 D_GP: 0.044 D_real: 0.923 D_fake: 0.951 
+(epoch: 241, iters: 6480, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.732 G_ID: 0.172 G_Rec: 0.364 D_GP: 0.020 D_real: 1.175 D_fake: 0.631 
+(epoch: 241, iters: 6880, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.622 G_ID: 0.112 G_Rec: 0.306 D_GP: 0.039 D_real: 1.018 D_fake: 0.855 
+(epoch: 241, iters: 7280, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.924 G_ID: 0.163 G_Rec: 0.411 D_GP: 0.026 D_real: 1.018 D_fake: 0.610 
+(epoch: 241, iters: 7680, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.592 G_ID: 0.132 G_Rec: 0.263 D_GP: 0.022 D_real: 1.119 D_fake: 0.798 
+(epoch: 241, iters: 8080, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.872 G_ID: 0.154 G_Rec: 0.436 D_GP: 0.041 D_real: 1.032 D_fake: 0.658 
+(epoch: 241, iters: 8480, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.690 G_ID: 0.106 G_Rec: 0.293 D_GP: 0.128 D_real: 0.817 D_fake: 0.952 
+(epoch: 242, iters: 272, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.777 G_ID: 0.152 G_Rec: 0.378 D_GP: 0.022 D_real: 1.000 D_fake: 0.788 
+(epoch: 242, iters: 672, time: 0.064) G_GAN: 0.097 G_GAN_Feat: 0.635 G_ID: 0.107 G_Rec: 0.319 D_GP: 0.028 D_real: 1.095 D_fake: 0.903 
+(epoch: 242, iters: 1072, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.815 G_ID: 0.155 G_Rec: 0.391 D_GP: 0.027 D_real: 1.101 D_fake: 0.654 
+(epoch: 242, iters: 1472, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.818 G_ID: 0.118 G_Rec: 0.322 D_GP: 0.457 D_real: 0.715 D_fake: 0.886 
+(epoch: 242, iters: 1872, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.813 G_ID: 0.166 G_Rec: 0.360 D_GP: 0.023 D_real: 0.931 D_fake: 0.761 
+(epoch: 242, iters: 2272, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.683 G_ID: 0.125 G_Rec: 0.298 D_GP: 0.034 D_real: 1.034 D_fake: 0.761 
+(epoch: 242, iters: 2672, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.922 G_ID: 0.187 G_Rec: 0.425 D_GP: 0.028 D_real: 1.065 D_fake: 0.618 
+(epoch: 242, iters: 3072, time: 0.064) G_GAN: -0.087 G_GAN_Feat: 0.760 G_ID: 0.133 G_Rec: 0.301 D_GP: 0.102 D_real: 0.521 D_fake: 1.087 
+(epoch: 242, iters: 3472, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.756 G_ID: 0.174 G_Rec: 0.356 D_GP: 0.022 D_real: 1.235 D_fake: 0.606 
+(epoch: 242, iters: 3872, time: 0.064) G_GAN: 0.021 G_GAN_Feat: 0.707 G_ID: 0.130 G_Rec: 0.328 D_GP: 0.022 D_real: 0.913 D_fake: 0.979 
+(epoch: 242, iters: 4272, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.841 G_ID: 0.168 G_Rec: 0.409 D_GP: 0.026 D_real: 0.993 D_fake: 0.787 
+(epoch: 242, iters: 4672, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.769 G_ID: 0.139 G_Rec: 0.342 D_GP: 0.083 D_real: 0.739 D_fake: 0.896 
+(epoch: 242, iters: 5072, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.807 G_ID: 0.194 G_Rec: 0.403 D_GP: 0.024 D_real: 1.163 D_fake: 0.632 
+(epoch: 242, iters: 5472, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.597 G_ID: 0.130 G_Rec: 0.279 D_GP: 0.019 D_real: 1.165 D_fake: 0.778 
+(epoch: 242, iters: 5872, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.807 G_ID: 0.180 G_Rec: 0.379 D_GP: 0.026 D_real: 1.073 D_fake: 0.670 
+(epoch: 242, iters: 6272, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.734 G_ID: 0.132 G_Rec: 0.269 D_GP: 0.035 D_real: 1.028 D_fake: 0.736 
+(epoch: 242, iters: 6672, time: 0.064) G_GAN: 0.647 G_GAN_Feat: 1.153 G_ID: 0.170 G_Rec: 0.455 D_GP: 0.033 D_real: 0.958 D_fake: 0.402 
+(epoch: 242, iters: 7072, time: 0.064) G_GAN: -0.231 G_GAN_Feat: 0.743 G_ID: 0.126 G_Rec: 0.285 D_GP: 0.036 D_real: 0.517 D_fake: 1.231 
+(epoch: 242, iters: 7472, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.941 G_ID: 0.196 G_Rec: 0.418 D_GP: 0.059 D_real: 0.823 D_fake: 0.580 
+(epoch: 242, iters: 7872, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.571 G_ID: 0.116 G_Rec: 0.282 D_GP: 0.018 D_real: 1.270 D_fake: 0.713 
+(epoch: 242, iters: 8272, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.874 G_ID: 0.169 G_Rec: 0.432 D_GP: 0.034 D_real: 0.937 D_fake: 0.748 
+(epoch: 243, iters: 64, time: 0.064) G_GAN: 0.001 G_GAN_Feat: 0.671 G_ID: 0.140 G_Rec: 0.292 D_GP: 0.035 D_real: 0.863 D_fake: 0.999 
+(epoch: 243, iters: 464, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.936 G_ID: 0.186 G_Rec: 0.406 D_GP: 0.037 D_real: 0.855 D_fake: 0.601 
+(epoch: 243, iters: 864, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.641 G_ID: 0.129 G_Rec: 0.271 D_GP: 0.025 D_real: 1.405 D_fake: 0.484 
+(epoch: 243, iters: 1264, time: 0.064) G_GAN: 0.070 G_GAN_Feat: 0.857 G_ID: 0.177 G_Rec: 0.442 D_GP: 0.020 D_real: 0.765 D_fake: 0.930 
+(epoch: 243, iters: 1664, time: 0.064) G_GAN: -0.112 G_GAN_Feat: 0.659 G_ID: 0.137 G_Rec: 0.312 D_GP: 0.026 D_real: 0.765 D_fake: 1.112 
+(epoch: 243, iters: 2064, time: 0.064) G_GAN: -0.069 G_GAN_Feat: 0.976 G_ID: 0.185 G_Rec: 0.452 D_GP: 0.098 D_real: 0.404 D_fake: 1.069 
+(epoch: 243, iters: 2464, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.672 G_ID: 0.121 G_Rec: 0.283 D_GP: 0.021 D_real: 1.061 D_fake: 0.868 
+(epoch: 243, iters: 2864, time: 0.064) G_GAN: 0.561 G_GAN_Feat: 0.847 G_ID: 0.189 G_Rec: 0.376 D_GP: 0.033 D_real: 1.224 D_fake: 0.481 
+(epoch: 243, iters: 3264, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.690 G_ID: 0.107 G_Rec: 0.283 D_GP: 0.024 D_real: 1.189 D_fake: 0.699 
+(epoch: 243, iters: 3664, time: 0.064) G_GAN: 0.716 G_GAN_Feat: 0.934 G_ID: 0.177 G_Rec: 0.391 D_GP: 0.022 D_real: 1.452 D_fake: 0.374 
+(epoch: 243, iters: 4064, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.640 G_ID: 0.132 G_Rec: 0.290 D_GP: 0.023 D_real: 1.431 D_fake: 0.532 
+(epoch: 243, iters: 4464, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.980 G_ID: 0.164 G_Rec: 0.429 D_GP: 0.033 D_real: 1.093 D_fake: 0.407 
+(epoch: 243, iters: 4864, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.556 G_ID: 0.140 G_Rec: 0.287 D_GP: 0.015 D_real: 1.163 D_fake: 0.799 
+(epoch: 243, iters: 5264, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.722 G_ID: 0.187 G_Rec: 0.373 D_GP: 0.018 D_real: 1.174 D_fake: 0.630 
+(epoch: 243, iters: 5664, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.540 G_ID: 0.138 G_Rec: 0.278 D_GP: 0.016 D_real: 1.190 D_fake: 0.782 
+(epoch: 243, iters: 6064, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.768 G_ID: 0.163 G_Rec: 0.375 D_GP: 0.029 D_real: 0.988 D_fake: 0.831 
+(epoch: 243, iters: 6464, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.546 G_ID: 0.127 G_Rec: 0.265 D_GP: 0.022 D_real: 1.032 D_fake: 0.898 
+(epoch: 243, iters: 6864, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.868 G_ID: 0.150 G_Rec: 0.421 D_GP: 0.048 D_real: 0.824 D_fake: 0.761 
+(epoch: 243, iters: 7264, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.720 G_ID: 0.124 G_Rec: 0.325 D_GP: 0.043 D_real: 0.885 D_fake: 0.858 
+(epoch: 243, iters: 7664, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.909 G_ID: 0.173 G_Rec: 0.433 D_GP: 0.052 D_real: 1.008 D_fake: 0.617 
+(epoch: 243, iters: 8064, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.783 G_ID: 0.124 G_Rec: 0.326 D_GP: 0.073 D_real: 0.922 D_fake: 0.726 
+(epoch: 243, iters: 8464, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.801 G_ID: 0.175 G_Rec: 0.414 D_GP: 0.021 D_real: 1.079 D_fake: 0.631 
+(epoch: 244, iters: 256, time: 0.064) G_GAN: 0.110 G_GAN_Feat: 0.731 G_ID: 0.127 G_Rec: 0.312 D_GP: 0.077 D_real: 0.782 D_fake: 0.890 
+(epoch: 244, iters: 656, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.886 G_ID: 0.157 G_Rec: 0.392 D_GP: 0.043 D_real: 0.929 D_fake: 0.575 
+(epoch: 244, iters: 1056, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.691 G_ID: 0.133 G_Rec: 0.312 D_GP: 0.024 D_real: 1.269 D_fake: 0.609 
+(epoch: 244, iters: 1456, time: 0.064) G_GAN: 0.494 G_GAN_Feat: 0.868 G_ID: 0.183 G_Rec: 0.400 D_GP: 0.024 D_real: 1.116 D_fake: 0.507 
+(epoch: 244, iters: 1856, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 0.620 G_ID: 0.147 G_Rec: 0.310 D_GP: 0.024 D_real: 0.997 D_fake: 0.901 
+(epoch: 244, iters: 2256, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 0.755 G_ID: 0.162 G_Rec: 0.358 D_GP: 0.031 D_real: 1.262 D_fake: 0.523 
+(epoch: 244, iters: 2656, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.689 G_ID: 0.116 G_Rec: 0.309 D_GP: 0.039 D_real: 1.078 D_fake: 0.653 
+(epoch: 244, iters: 3056, time: 0.064) G_GAN: 0.671 G_GAN_Feat: 0.905 G_ID: 0.183 G_Rec: 0.411 D_GP: 0.039 D_real: 1.187 D_fake: 0.357 
+(epoch: 244, iters: 3456, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.651 G_ID: 0.108 G_Rec: 0.260 D_GP: 0.023 D_real: 1.212 D_fake: 0.740 
+(epoch: 244, iters: 3856, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 1.079 G_ID: 0.175 G_Rec: 0.422 D_GP: 0.046 D_real: 0.255 D_fake: 0.831 
+(epoch: 244, iters: 4256, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.689 G_ID: 0.164 G_Rec: 0.319 D_GP: 0.021 D_real: 0.931 D_fake: 0.913 
+(epoch: 244, iters: 4656, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.852 G_ID: 0.147 G_Rec: 0.396 D_GP: 0.028 D_real: 0.993 D_fake: 0.668 
+(epoch: 244, iters: 5056, time: 0.064) G_GAN: 0.016 G_GAN_Feat: 0.606 G_ID: 0.119 G_Rec: 0.266 D_GP: 0.023 D_real: 0.962 D_fake: 0.984 
+(epoch: 244, iters: 5456, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.919 G_ID: 0.163 G_Rec: 0.378 D_GP: 0.032 D_real: 1.177 D_fake: 0.556 
+(epoch: 244, iters: 5856, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.737 G_ID: 0.131 G_Rec: 0.329 D_GP: 0.025 D_real: 1.130 D_fake: 0.761 
+(epoch: 244, iters: 6256, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.891 G_ID: 0.186 G_Rec: 0.377 D_GP: 0.035 D_real: 1.017 D_fake: 0.586 
+(epoch: 244, iters: 6656, time: 0.064) G_GAN: 0.131 G_GAN_Feat: 0.687 G_ID: 0.132 G_Rec: 0.288 D_GP: 0.051 D_real: 0.873 D_fake: 0.869 
+(epoch: 244, iters: 7056, time: 0.064) G_GAN: 0.462 G_GAN_Feat: 0.934 G_ID: 0.160 G_Rec: 0.430 D_GP: 0.099 D_real: 0.856 D_fake: 0.544 
+(epoch: 244, iters: 7456, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.621 G_ID: 0.145 G_Rec: 0.287 D_GP: 0.023 D_real: 1.196 D_fake: 0.638 
+(epoch: 244, iters: 7856, time: 0.064) G_GAN: 0.767 G_GAN_Feat: 0.826 G_ID: 0.162 G_Rec: 0.388 D_GP: 0.023 D_real: 1.565 D_fake: 0.265 
+(epoch: 244, iters: 8256, time: 0.064) G_GAN: -0.168 G_GAN_Feat: 0.932 G_ID: 0.120 G_Rec: 0.308 D_GP: 0.740 D_real: 0.328 D_fake: 1.170 
+(epoch: 245, iters: 48, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.863 G_ID: 0.157 G_Rec: 0.379 D_GP: 0.024 D_real: 1.127 D_fake: 0.609 
+(epoch: 245, iters: 448, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.799 G_ID: 0.157 G_Rec: 0.366 D_GP: 0.056 D_real: 0.774 D_fake: 0.823 
+(epoch: 245, iters: 848, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 0.968 G_ID: 0.183 G_Rec: 0.405 D_GP: 0.052 D_real: 0.848 D_fake: 0.519 
+(epoch: 245, iters: 1248, time: 0.064) G_GAN: -0.267 G_GAN_Feat: 0.670 G_ID: 0.122 G_Rec: 0.370 D_GP: 0.025 D_real: 0.693 D_fake: 1.267 
+(epoch: 245, iters: 1648, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.903 G_ID: 0.156 G_Rec: 0.389 D_GP: 0.031 D_real: 1.088 D_fake: 0.606 
+(epoch: 245, iters: 2048, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 0.661 G_ID: 0.134 G_Rec: 0.276 D_GP: 0.025 D_real: 1.450 D_fake: 0.510 
+(epoch: 245, iters: 2448, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 1.027 G_ID: 0.173 G_Rec: 0.450 D_GP: 0.033 D_real: 0.854 D_fake: 0.677 
+(epoch: 245, iters: 2848, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.679 G_ID: 0.124 G_Rec: 0.314 D_GP: 0.022 D_real: 1.148 D_fake: 0.777 
+(epoch: 245, iters: 3248, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.822 G_ID: 0.167 G_Rec: 0.379 D_GP: 0.023 D_real: 1.182 D_fake: 0.597 
+(epoch: 245, iters: 3648, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.720 G_ID: 0.134 G_Rec: 0.311 D_GP: 0.026 D_real: 0.991 D_fake: 0.860 
+(epoch: 245, iters: 4048, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.812 G_ID: 0.188 G_Rec: 0.372 D_GP: 0.022 D_real: 1.026 D_fake: 0.737 
+(epoch: 245, iters: 4448, time: 0.064) G_GAN: -0.289 G_GAN_Feat: 0.867 G_ID: 0.134 G_Rec: 0.331 D_GP: 0.644 D_real: 0.332 D_fake: 1.289 
+(epoch: 245, iters: 4848, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.957 G_ID: 0.167 G_Rec: 0.411 D_GP: 0.057 D_real: 0.763 D_fake: 0.643 
+(epoch: 245, iters: 5248, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.691 G_ID: 0.115 G_Rec: 0.283 D_GP: 0.035 D_real: 1.135 D_fake: 0.674 
+(epoch: 245, iters: 5648, time: 0.064) G_GAN: 0.587 G_GAN_Feat: 0.883 G_ID: 0.171 G_Rec: 0.394 D_GP: 0.027 D_real: 1.333 D_fake: 0.462 
+(epoch: 245, iters: 6048, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.759 G_ID: 0.131 G_Rec: 0.300 D_GP: 0.031 D_real: 1.080 D_fake: 0.690 
+(epoch: 245, iters: 6448, time: 0.064) G_GAN: 0.846 G_GAN_Feat: 0.907 G_ID: 0.174 G_Rec: 0.404 D_GP: 0.024 D_real: 1.545 D_fake: 0.217 
+(epoch: 245, iters: 6848, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.599 G_ID: 0.119 G_Rec: 0.280 D_GP: 0.019 D_real: 1.132 D_fake: 0.765 
+(epoch: 245, iters: 7248, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.839 G_ID: 0.208 G_Rec: 0.400 D_GP: 0.026 D_real: 0.928 D_fake: 0.808 
+(epoch: 245, iters: 7648, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.809 G_ID: 0.120 G_Rec: 0.336 D_GP: 0.378 D_real: 0.736 D_fake: 0.821 
+(epoch: 245, iters: 8048, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 1.014 G_ID: 0.166 G_Rec: 0.450 D_GP: 0.090 D_real: 0.621 D_fake: 0.548 
+(epoch: 245, iters: 8448, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.872 G_ID: 0.126 G_Rec: 0.290 D_GP: 0.059 D_real: 0.510 D_fake: 0.785 
+(epoch: 246, iters: 240, time: 0.064) G_GAN: 0.641 G_GAN_Feat: 0.854 G_ID: 0.181 G_Rec: 0.403 D_GP: 0.021 D_real: 1.361 D_fake: 0.419 
+(epoch: 246, iters: 640, time: 0.064) G_GAN: 0.110 G_GAN_Feat: 0.758 G_ID: 0.121 G_Rec: 0.324 D_GP: 0.026 D_real: 0.901 D_fake: 0.890 
+(epoch: 246, iters: 1040, time: 0.064) G_GAN: 0.800 G_GAN_Feat: 1.047 G_ID: 0.160 G_Rec: 0.438 D_GP: 0.065 D_real: 0.601 D_fake: 0.321 
+(epoch: 246, iters: 1440, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 0.626 G_ID: 0.127 G_Rec: 0.311 D_GP: 0.017 D_real: 1.193 D_fake: 0.806 
+(epoch: 246, iters: 1840, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.857 G_ID: 0.175 G_Rec: 0.411 D_GP: 0.021 D_real: 1.018 D_fake: 0.741 
+(epoch: 246, iters: 2240, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.722 G_ID: 0.117 G_Rec: 0.318 D_GP: 0.029 D_real: 1.055 D_fake: 0.894 
+(epoch: 246, iters: 2640, time: 0.064) G_GAN: 0.751 G_GAN_Feat: 0.856 G_ID: 0.208 G_Rec: 0.384 D_GP: 0.025 D_real: 1.545 D_fake: 0.295 
+(epoch: 246, iters: 3040, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.780 G_ID: 0.118 G_Rec: 0.283 D_GP: 0.114 D_real: 0.746 D_fake: 0.702 
+(epoch: 246, iters: 3440, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.925 G_ID: 0.168 G_Rec: 0.463 D_GP: 0.022 D_real: 1.104 D_fake: 0.537 
+(epoch: 246, iters: 3840, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.638 G_ID: 0.122 G_Rec: 0.308 D_GP: 0.022 D_real: 1.241 D_fake: 0.740 
+(epoch: 246, iters: 4240, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.700 G_ID: 0.190 G_Rec: 0.346 D_GP: 0.018 D_real: 1.278 D_fake: 0.587 
+(epoch: 246, iters: 4640, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.557 G_ID: 0.129 G_Rec: 0.274 D_GP: 0.019 D_real: 1.178 D_fake: 0.774 
+(epoch: 246, iters: 5040, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.807 G_ID: 0.164 G_Rec: 0.381 D_GP: 0.030 D_real: 1.069 D_fake: 0.709 
+(epoch: 246, iters: 5440, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.707 G_ID: 0.116 G_Rec: 0.301 D_GP: 0.032 D_real: 1.005 D_fake: 0.844 
+(epoch: 246, iters: 5840, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.898 G_ID: 0.206 G_Rec: 0.389 D_GP: 0.026 D_real: 1.117 D_fake: 0.519 
+(epoch: 246, iters: 6240, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.600 G_ID: 0.159 G_Rec: 0.295 D_GP: 0.023 D_real: 1.166 D_fake: 0.808 
+(epoch: 246, iters: 6640, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.840 G_ID: 0.174 G_Rec: 0.360 D_GP: 0.029 D_real: 1.183 D_fake: 0.672 
+(epoch: 246, iters: 7040, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.702 G_ID: 0.106 G_Rec: 0.297 D_GP: 0.032 D_real: 1.136 D_fake: 0.672 
+(epoch: 246, iters: 7440, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.864 G_ID: 0.192 G_Rec: 0.358 D_GP: 0.055 D_real: 0.698 D_fake: 0.849 
+(epoch: 246, iters: 7840, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.752 G_ID: 0.123 G_Rec: 0.283 D_GP: 0.083 D_real: 0.917 D_fake: 0.735 
+(epoch: 246, iters: 8240, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.753 G_ID: 0.163 G_Rec: 0.356 D_GP: 0.023 D_real: 1.186 D_fake: 0.593 
+(epoch: 247, iters: 32, time: 0.064) G_GAN: 0.141 G_GAN_Feat: 0.875 G_ID: 0.144 G_Rec: 0.340 D_GP: 0.335 D_real: 0.523 D_fake: 0.861 
+(epoch: 247, iters: 432, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 1.140 G_ID: 0.184 G_Rec: 0.411 D_GP: 0.155 D_real: 0.488 D_fake: 0.475 
+(epoch: 247, iters: 832, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.586 G_ID: 0.154 G_Rec: 0.326 D_GP: 0.020 D_real: 1.250 D_fake: 0.717 
+(epoch: 247, iters: 1232, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.805 G_ID: 0.169 G_Rec: 0.387 D_GP: 0.020 D_real: 1.230 D_fake: 0.529 
+(epoch: 247, iters: 1632, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.611 G_ID: 0.109 G_Rec: 0.278 D_GP: 0.026 D_real: 1.059 D_fake: 0.840 
+(epoch: 247, iters: 2032, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.793 G_ID: 0.162 G_Rec: 0.385 D_GP: 0.024 D_real: 0.977 D_fake: 0.736 
+(epoch: 247, iters: 2432, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.610 G_ID: 0.158 G_Rec: 0.290 D_GP: 0.025 D_real: 1.096 D_fake: 0.776 
+(epoch: 247, iters: 2832, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.865 G_ID: 0.178 G_Rec: 0.384 D_GP: 0.042 D_real: 1.050 D_fake: 0.600 
+(epoch: 247, iters: 3232, time: 0.064) G_GAN: 0.096 G_GAN_Feat: 0.666 G_ID: 0.141 G_Rec: 0.294 D_GP: 0.028 D_real: 0.932 D_fake: 0.904 
+(epoch: 247, iters: 3632, time: 0.064) G_GAN: 0.651 G_GAN_Feat: 0.852 G_ID: 0.153 G_Rec: 0.375 D_GP: 0.027 D_real: 1.382 D_fake: 0.365 
+(epoch: 247, iters: 4032, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.747 G_ID: 0.115 G_Rec: 0.290 D_GP: 0.051 D_real: 1.002 D_fake: 0.755 
+(epoch: 247, iters: 4432, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 0.977 G_ID: 0.183 G_Rec: 0.440 D_GP: 0.044 D_real: 0.863 D_fake: 0.527 
+(epoch: 247, iters: 4832, time: 0.064) G_GAN: -0.109 G_GAN_Feat: 1.017 G_ID: 0.130 G_Rec: 0.356 D_GP: 3.370 D_real: 0.390 D_fake: 1.110 
+(epoch: 247, iters: 5232, time: 0.064) G_GAN: 0.629 G_GAN_Feat: 0.817 G_ID: 0.138 G_Rec: 0.421 D_GP: 0.028 D_real: 1.368 D_fake: 0.391 
+(epoch: 247, iters: 5632, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.664 G_ID: 0.130 G_Rec: 0.335 D_GP: 0.034 D_real: 1.040 D_fake: 0.847 
+(epoch: 247, iters: 6032, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.872 G_ID: 0.170 G_Rec: 0.448 D_GP: 0.046 D_real: 0.818 D_fake: 0.788 
+(epoch: 247, iters: 6432, time: 0.064) G_GAN: 0.025 G_GAN_Feat: 0.599 G_ID: 0.119 G_Rec: 0.303 D_GP: 0.047 D_real: 0.868 D_fake: 0.975 
+(epoch: 247, iters: 6832, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.806 G_ID: 0.154 G_Rec: 0.393 D_GP: 0.030 D_real: 1.026 D_fake: 0.725 
+(epoch: 247, iters: 7232, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.601 G_ID: 0.131 G_Rec: 0.289 D_GP: 0.022 D_real: 1.112 D_fake: 0.839 
+(epoch: 247, iters: 7632, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.842 G_ID: 0.185 G_Rec: 0.372 D_GP: 0.057 D_real: 0.741 D_fake: 0.813 
+(epoch: 247, iters: 8032, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.598 G_ID: 0.137 G_Rec: 0.304 D_GP: 0.020 D_real: 1.194 D_fake: 0.715 
+(epoch: 247, iters: 8432, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.999 G_ID: 0.183 G_Rec: 0.390 D_GP: 0.396 D_real: 0.516 D_fake: 0.698 
+(epoch: 248, iters: 224, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.637 G_ID: 0.120 G_Rec: 0.283 D_GP: 0.026 D_real: 1.126 D_fake: 0.794 
+(epoch: 248, iters: 624, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 0.959 G_ID: 0.159 G_Rec: 0.407 D_GP: 0.089 D_real: 0.940 D_fake: 0.496 
+(epoch: 248, iters: 1024, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.933 G_ID: 0.137 G_Rec: 0.337 D_GP: 0.128 D_real: 0.502 D_fake: 0.942 
+(epoch: 248, iters: 1424, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.824 G_ID: 0.189 G_Rec: 0.385 D_GP: 0.029 D_real: 1.000 D_fake: 0.763 
+(epoch: 248, iters: 1824, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.766 G_ID: 0.134 G_Rec: 0.311 D_GP: 0.146 D_real: 0.979 D_fake: 0.959 
+(epoch: 248, iters: 2224, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.869 G_ID: 0.170 G_Rec: 0.390 D_GP: 0.037 D_real: 1.117 D_fake: 0.497 
+(epoch: 248, iters: 2624, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.684 G_ID: 0.149 G_Rec: 0.277 D_GP: 0.025 D_real: 1.279 D_fake: 0.665 
+(epoch: 248, iters: 3024, time: 0.064) G_GAN: 0.596 G_GAN_Feat: 1.048 G_ID: 0.158 G_Rec: 0.456 D_GP: 0.101 D_real: 1.093 D_fake: 0.428 
+(epoch: 248, iters: 3424, time: 0.064) G_GAN: -0.177 G_GAN_Feat: 0.846 G_ID: 0.153 G_Rec: 0.308 D_GP: 0.086 D_real: 0.816 D_fake: 1.177 
+(epoch: 248, iters: 3824, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.853 G_ID: 0.177 G_Rec: 0.391 D_GP: 0.021 D_real: 1.107 D_fake: 0.613 
+(epoch: 248, iters: 4224, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.771 G_ID: 0.131 G_Rec: 0.303 D_GP: 0.037 D_real: 0.821 D_fake: 0.920 
+(epoch: 248, iters: 4624, time: 0.064) G_GAN: 0.667 G_GAN_Feat: 0.895 G_ID: 0.159 G_Rec: 0.408 D_GP: 0.023 D_real: 1.340 D_fake: 0.343 
+(epoch: 248, iters: 5024, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.645 G_ID: 0.124 G_Rec: 0.262 D_GP: 0.022 D_real: 1.147 D_fake: 0.766 
+(epoch: 248, iters: 5424, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.856 G_ID: 0.173 G_Rec: 0.390 D_GP: 0.034 D_real: 0.885 D_fake: 0.722 
+(epoch: 248, iters: 5824, time: 0.064) G_GAN: -0.004 G_GAN_Feat: 0.763 G_ID: 0.129 G_Rec: 0.313 D_GP: 0.048 D_real: 0.760 D_fake: 1.005 
+(epoch: 248, iters: 6224, time: 0.064) G_GAN: 0.535 G_GAN_Feat: 1.123 G_ID: 0.169 G_Rec: 0.400 D_GP: 0.134 D_real: 0.492 D_fake: 0.467 
+(epoch: 248, iters: 6624, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.647 G_ID: 0.142 G_Rec: 0.306 D_GP: 0.024 D_real: 1.228 D_fake: 0.681 
+(epoch: 248, iters: 7024, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.875 G_ID: 0.172 G_Rec: 0.377 D_GP: 0.025 D_real: 1.109 D_fake: 0.517 
+(epoch: 248, iters: 7424, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.829 G_ID: 0.133 G_Rec: 0.308 D_GP: 0.034 D_real: 0.841 D_fake: 0.785 
+(epoch: 248, iters: 7824, time: 0.064) G_GAN: 0.613 G_GAN_Feat: 0.946 G_ID: 0.162 G_Rec: 0.388 D_GP: 0.034 D_real: 1.143 D_fake: 0.409 
+(epoch: 248, iters: 8224, time: 0.064) G_GAN: -0.272 G_GAN_Feat: 0.981 G_ID: 0.135 G_Rec: 0.338 D_GP: 0.176 D_real: 0.135 D_fake: 1.272 
+(epoch: 249, iters: 16, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 0.888 G_ID: 0.175 G_Rec: 0.448 D_GP: 0.026 D_real: 1.283 D_fake: 0.463 
+(epoch: 249, iters: 416, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.651 G_ID: 0.116 G_Rec: 0.374 D_GP: 0.071 D_real: 1.020 D_fake: 0.801 
+(epoch: 249, iters: 816, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 0.793 G_ID: 0.143 G_Rec: 0.391 D_GP: 0.022 D_real: 1.195 D_fake: 0.488 
+(epoch: 249, iters: 1216, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.632 G_ID: 0.104 G_Rec: 0.289 D_GP: 0.023 D_real: 1.113 D_fake: 0.777 
+(epoch: 249, iters: 1616, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.827 G_ID: 0.161 G_Rec: 0.358 D_GP: 0.044 D_real: 1.003 D_fake: 0.712 
+(epoch: 249, iters: 2016, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.635 G_ID: 0.149 G_Rec: 0.296 D_GP: 0.022 D_real: 1.173 D_fake: 0.785 
+(epoch: 249, iters: 2416, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.706 G_ID: 0.171 G_Rec: 0.367 D_GP: 0.018 D_real: 1.122 D_fake: 0.846 
+(epoch: 249, iters: 2816, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.568 G_ID: 0.126 G_Rec: 0.317 D_GP: 0.021 D_real: 1.181 D_fake: 0.801 
+(epoch: 249, iters: 3216, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.725 G_ID: 0.162 G_Rec: 0.367 D_GP: 0.019 D_real: 1.098 D_fake: 0.686 
+(epoch: 249, iters: 3616, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 0.561 G_ID: 0.148 G_Rec: 0.259 D_GP: 0.029 D_real: 1.006 D_fake: 0.901 
+(epoch: 249, iters: 4016, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.741 G_ID: 0.164 G_Rec: 0.365 D_GP: 0.021 D_real: 1.301 D_fake: 0.491 
+(epoch: 249, iters: 4416, time: 0.064) G_GAN: -0.059 G_GAN_Feat: 0.562 G_ID: 0.143 G_Rec: 0.321 D_GP: 0.022 D_real: 0.850 D_fake: 1.059 
+(epoch: 249, iters: 4816, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.792 G_ID: 0.143 G_Rec: 0.387 D_GP: 0.024 D_real: 1.122 D_fake: 0.637 
+(epoch: 249, iters: 5216, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 0.648 G_ID: 0.156 G_Rec: 0.289 D_GP: 0.024 D_real: 1.206 D_fake: 0.757 
+(epoch: 249, iters: 5616, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.835 G_ID: 0.159 G_Rec: 0.398 D_GP: 0.024 D_real: 1.162 D_fake: 0.578 
+(epoch: 249, iters: 6016, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.719 G_ID: 0.136 G_Rec: 0.306 D_GP: 0.052 D_real: 0.858 D_fake: 0.867 
+(epoch: 249, iters: 6416, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 0.878 G_ID: 0.196 G_Rec: 0.414 D_GP: 0.025 D_real: 1.266 D_fake: 0.511 
+(epoch: 249, iters: 6816, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.752 G_ID: 0.134 G_Rec: 0.340 D_GP: 0.054 D_real: 0.975 D_fake: 0.710 
+(epoch: 249, iters: 7216, time: 0.064) G_GAN: 0.841 G_GAN_Feat: 0.894 G_ID: 0.236 G_Rec: 0.450 D_GP: 0.035 D_real: 1.402 D_fake: 0.276 
+(epoch: 249, iters: 7616, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.711 G_ID: 0.120 G_Rec: 0.325 D_GP: 0.039 D_real: 0.899 D_fake: 0.844 
+(epoch: 249, iters: 8016, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.865 G_ID: 0.189 G_Rec: 0.392 D_GP: 0.028 D_real: 0.954 D_fake: 0.764 
+(epoch: 249, iters: 8416, time: 0.064) G_GAN: 0.110 G_GAN_Feat: 0.820 G_ID: 0.140 G_Rec: 0.302 D_GP: 0.084 D_real: 0.597 D_fake: 0.894 
+(epoch: 250, iters: 208, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.926 G_ID: 0.163 G_Rec: 0.362 D_GP: 0.063 D_real: 0.723 D_fake: 0.730 
+(epoch: 250, iters: 608, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.612 G_ID: 0.150 G_Rec: 0.288 D_GP: 0.019 D_real: 1.068 D_fake: 0.908 
+(epoch: 250, iters: 1008, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.808 G_ID: 0.177 G_Rec: 0.382 D_GP: 0.033 D_real: 0.971 D_fake: 0.746 
+(epoch: 250, iters: 1408, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.691 G_ID: 0.121 G_Rec: 0.304 D_GP: 0.041 D_real: 0.967 D_fake: 0.910 
+(epoch: 250, iters: 1808, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.949 G_ID: 0.173 G_Rec: 0.476 D_GP: 0.031 D_real: 1.224 D_fake: 0.550 
+(epoch: 250, iters: 2208, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.710 G_ID: 0.123 G_Rec: 0.296 D_GP: 0.028 D_real: 1.008 D_fake: 0.875 
+(epoch: 250, iters: 2608, time: 0.064) G_GAN: 0.543 G_GAN_Feat: 0.763 G_ID: 0.146 G_Rec: 0.340 D_GP: 0.021 D_real: 1.310 D_fake: 0.470 
+(epoch: 250, iters: 3008, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.603 G_ID: 0.126 G_Rec: 0.260 D_GP: 0.022 D_real: 1.252 D_fake: 0.702 
+(epoch: 250, iters: 3408, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.939 G_ID: 0.194 G_Rec: 0.394 D_GP: 0.029 D_real: 0.774 D_fake: 0.878 
+(epoch: 250, iters: 3808, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.711 G_ID: 0.126 G_Rec: 0.310 D_GP: 0.033 D_real: 0.957 D_fake: 0.808 
+(epoch: 250, iters: 4208, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.788 G_ID: 0.179 G_Rec: 0.341 D_GP: 0.028 D_real: 1.112 D_fake: 0.661 
+(epoch: 250, iters: 4608, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.785 G_ID: 0.125 G_Rec: 0.313 D_GP: 0.044 D_real: 0.881 D_fake: 0.718 
+(epoch: 250, iters: 5008, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.888 G_ID: 0.203 G_Rec: 0.422 D_GP: 0.029 D_real: 1.167 D_fake: 0.542 
+(epoch: 250, iters: 5408, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 1.047 G_ID: 0.138 G_Rec: 0.352 D_GP: 0.948 D_real: 0.578 D_fake: 0.568 
+(epoch: 250, iters: 5808, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.826 G_ID: 0.178 G_Rec: 0.369 D_GP: 0.027 D_real: 1.170 D_fake: 0.555 
+(epoch: 250, iters: 6208, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.656 G_ID: 0.132 G_Rec: 0.291 D_GP: 0.027 D_real: 1.212 D_fake: 0.686 
+(epoch: 250, iters: 6608, time: 0.064) G_GAN: 0.701 G_GAN_Feat: 0.881 G_ID: 0.156 G_Rec: 0.441 D_GP: 0.029 D_real: 1.397 D_fake: 0.315 
+(epoch: 250, iters: 7008, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.569 G_ID: 0.130 G_Rec: 0.287 D_GP: 0.018 D_real: 1.049 D_fake: 0.918 
+(epoch: 250, iters: 7408, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.877 G_ID: 0.139 G_Rec: 0.462 D_GP: 0.032 D_real: 0.743 D_fake: 0.844 
+(epoch: 250, iters: 7808, time: 0.064) G_GAN: -0.004 G_GAN_Feat: 0.680 G_ID: 0.153 G_Rec: 0.326 D_GP: 0.027 D_real: 0.847 D_fake: 1.004 
+(epoch: 250, iters: 8208, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 0.867 G_ID: 0.211 G_Rec: 0.397 D_GP: 0.025 D_real: 0.913 D_fake: 0.712 
+(epoch: 250, iters: 8608, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.728 G_ID: 0.144 G_Rec: 0.294 D_GP: 0.037 D_real: 1.070 D_fake: 0.763 
+(epoch: 251, iters: 400, time: 0.064) G_GAN: 0.701 G_GAN_Feat: 0.823 G_ID: 0.168 G_Rec: 0.358 D_GP: 0.024 D_real: 1.507 D_fake: 0.319 
+(epoch: 251, iters: 800, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.560 G_ID: 0.137 G_Rec: 0.289 D_GP: 0.019 D_real: 1.241 D_fake: 0.745 
+(epoch: 251, iters: 1200, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 0.883 G_ID: 0.143 G_Rec: 0.415 D_GP: 0.031 D_real: 1.199 D_fake: 0.452 
+(epoch: 251, iters: 1600, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.586 G_ID: 0.123 G_Rec: 0.286 D_GP: 0.022 D_real: 1.041 D_fake: 0.918 
+(epoch: 251, iters: 2000, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 0.895 G_ID: 0.169 G_Rec: 0.401 D_GP: 0.030 D_real: 1.086 D_fake: 0.543 
+(epoch: 251, iters: 2400, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.705 G_ID: 0.120 G_Rec: 0.323 D_GP: 0.033 D_real: 1.065 D_fake: 0.714 
+(epoch: 251, iters: 2800, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.928 G_ID: 0.165 G_Rec: 0.401 D_GP: 0.176 D_real: 0.709 D_fake: 0.864 
+(epoch: 251, iters: 3200, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.751 G_ID: 0.120 G_Rec: 0.303 D_GP: 0.036 D_real: 0.965 D_fake: 0.742 
+(epoch: 251, iters: 3600, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 0.866 G_ID: 0.161 G_Rec: 0.404 D_GP: 0.025 D_real: 1.068 D_fake: 0.625 
+(epoch: 251, iters: 4000, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.797 G_ID: 0.128 G_Rec: 0.335 D_GP: 0.295 D_real: 0.631 D_fake: 0.887 
+(epoch: 251, iters: 4400, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.835 G_ID: 0.170 G_Rec: 0.378 D_GP: 0.030 D_real: 1.072 D_fake: 0.609 
+(epoch: 251, iters: 4800, time: 0.064) G_GAN: 0.120 G_GAN_Feat: 0.696 G_ID: 0.130 G_Rec: 0.289 D_GP: 0.030 D_real: 0.952 D_fake: 0.880 
+(epoch: 251, iters: 5200, time: 0.064) G_GAN: 0.750 G_GAN_Feat: 0.782 G_ID: 0.155 G_Rec: 0.347 D_GP: 0.022 D_real: 1.556 D_fake: 0.348 
+(epoch: 251, iters: 5600, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.680 G_ID: 0.140 G_Rec: 0.281 D_GP: 0.022 D_real: 1.230 D_fake: 0.656 
+(epoch: 251, iters: 6000, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.955 G_ID: 0.184 G_Rec: 0.387 D_GP: 0.027 D_real: 0.717 D_fake: 0.702 
+(epoch: 251, iters: 6400, time: 0.064) G_GAN: 0.063 G_GAN_Feat: 0.775 G_ID: 0.147 G_Rec: 0.325 D_GP: 0.070 D_real: 0.826 D_fake: 0.937 
+(epoch: 251, iters: 6800, time: 0.064) G_GAN: -0.008 G_GAN_Feat: 1.032 G_ID: 0.201 G_Rec: 0.402 D_GP: 0.378 D_real: 0.406 D_fake: 1.008 
+(epoch: 251, iters: 7200, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.696 G_ID: 0.150 G_Rec: 0.297 D_GP: 0.030 D_real: 1.046 D_fake: 0.838 
+(epoch: 251, iters: 7600, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 0.862 G_ID: 0.199 G_Rec: 0.412 D_GP: 0.025 D_real: 1.177 D_fake: 0.430 
+(epoch: 251, iters: 8000, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.646 G_ID: 0.132 G_Rec: 0.281 D_GP: 0.022 D_real: 1.250 D_fake: 0.685 
+(epoch: 251, iters: 8400, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.975 G_ID: 0.199 G_Rec: 0.418 D_GP: 0.118 D_real: 0.709 D_fake: 0.650 
+(epoch: 252, iters: 192, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.687 G_ID: 0.136 G_Rec: 0.284 D_GP: 0.025 D_real: 1.072 D_fake: 0.755 
+(epoch: 252, iters: 592, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 0.834 G_ID: 0.157 G_Rec: 0.387 D_GP: 0.022 D_real: 1.297 D_fake: 0.491 
+(epoch: 252, iters: 992, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.670 G_ID: 0.124 G_Rec: 0.251 D_GP: 0.026 D_real: 1.064 D_fake: 0.830 
+(epoch: 252, iters: 1392, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.782 G_ID: 0.176 G_Rec: 0.361 D_GP: 0.024 D_real: 1.070 D_fake: 0.724 
+(epoch: 252, iters: 1792, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.603 G_ID: 0.155 G_Rec: 0.254 D_GP: 0.028 D_real: 1.020 D_fake: 0.900 
+(epoch: 252, iters: 2192, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 0.981 G_ID: 0.155 G_Rec: 0.418 D_GP: 0.042 D_real: 0.917 D_fake: 0.430 
+(epoch: 252, iters: 2592, time: 0.064) G_GAN: -0.129 G_GAN_Feat: 0.894 G_ID: 0.132 G_Rec: 0.348 D_GP: 1.274 D_real: 0.261 D_fake: 1.129 
+(epoch: 252, iters: 2992, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.842 G_ID: 0.180 G_Rec: 0.371 D_GP: 0.030 D_real: 1.075 D_fake: 0.618 
+(epoch: 252, iters: 3392, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.706 G_ID: 0.126 G_Rec: 0.299 D_GP: 0.020 D_real: 1.357 D_fake: 0.583 
+(epoch: 252, iters: 3792, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 1.019 G_ID: 0.171 G_Rec: 0.438 D_GP: 0.056 D_real: 0.731 D_fake: 0.592 
+(epoch: 252, iters: 4192, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.896 G_ID: 0.113 G_Rec: 0.298 D_GP: 0.037 D_real: 0.733 D_fake: 0.711 
+(epoch: 252, iters: 4592, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.934 G_ID: 0.159 G_Rec: 0.384 D_GP: 0.023 D_real: 1.040 D_fake: 0.589 
+(epoch: 252, iters: 4992, time: 0.064) G_GAN: -0.270 G_GAN_Feat: 0.900 G_ID: 0.124 G_Rec: 0.360 D_GP: 0.214 D_real: 0.575 D_fake: 1.270 
+(epoch: 252, iters: 5392, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.876 G_ID: 0.176 G_Rec: 0.384 D_GP: 0.047 D_real: 0.913 D_fake: 0.660 
+(epoch: 252, iters: 5792, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.905 G_ID: 0.142 G_Rec: 0.330 D_GP: 0.174 D_real: 0.712 D_fake: 0.627 
+(epoch: 252, iters: 6192, time: 0.064) G_GAN: 0.605 G_GAN_Feat: 1.006 G_ID: 0.173 G_Rec: 0.407 D_GP: 0.031 D_real: 0.970 D_fake: 0.405 
+(epoch: 252, iters: 6592, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.696 G_ID: 0.123 G_Rec: 0.297 D_GP: 0.024 D_real: 1.104 D_fake: 0.849 
+(epoch: 252, iters: 6992, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 1.005 G_ID: 0.162 G_Rec: 0.421 D_GP: 0.078 D_real: 1.027 D_fake: 0.533 
+(epoch: 252, iters: 7392, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.994 G_ID: 0.139 G_Rec: 0.342 D_GP: 1.859 D_real: 0.348 D_fake: 0.718 
+(epoch: 252, iters: 7792, time: 0.064) G_GAN: 0.640 G_GAN_Feat: 0.752 G_ID: 0.161 G_Rec: 0.346 D_GP: 0.023 D_real: 1.411 D_fake: 0.425 
+(epoch: 252, iters: 8192, time: 0.064) G_GAN: 0.039 G_GAN_Feat: 0.658 G_ID: 0.113 G_Rec: 0.319 D_GP: 0.026 D_real: 0.901 D_fake: 0.961 
+(epoch: 252, iters: 8592, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 1.051 G_ID: 0.178 G_Rec: 0.452 D_GP: 0.072 D_real: 0.979 D_fake: 0.826 
+(epoch: 253, iters: 384, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.665 G_ID: 0.134 G_Rec: 0.326 D_GP: 0.045 D_real: 1.004 D_fake: 0.823 
+(epoch: 253, iters: 784, time: 0.064) G_GAN: 0.487 G_GAN_Feat: 0.953 G_ID: 0.149 G_Rec: 0.381 D_GP: 0.038 D_real: 0.968 D_fake: 0.517 
+(epoch: 253, iters: 1184, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.676 G_ID: 0.118 G_Rec: 0.285 D_GP: 0.019 D_real: 1.128 D_fake: 0.831 
+(epoch: 253, iters: 1584, time: 0.064) G_GAN: 0.669 G_GAN_Feat: 0.953 G_ID: 0.148 G_Rec: 0.419 D_GP: 0.029 D_real: 1.156 D_fake: 0.375 
+(epoch: 253, iters: 1984, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.829 G_ID: 0.120 G_Rec: 0.358 D_GP: 0.038 D_real: 1.042 D_fake: 0.675 
+(epoch: 253, iters: 2384, time: 0.064) G_GAN: 0.419 G_GAN_Feat: 1.011 G_ID: 0.144 G_Rec: 0.422 D_GP: 0.022 D_real: 0.963 D_fake: 0.582 
+(epoch: 253, iters: 2784, time: 0.064) G_GAN: -0.106 G_GAN_Feat: 0.732 G_ID: 0.145 G_Rec: 0.287 D_GP: 0.027 D_real: 0.689 D_fake: 1.106 
+(epoch: 253, iters: 3184, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.849 G_ID: 0.180 G_Rec: 0.409 D_GP: 0.022 D_real: 1.034 D_fake: 0.736 
+(epoch: 253, iters: 3584, time: 0.064) G_GAN: -0.003 G_GAN_Feat: 0.740 G_ID: 0.130 G_Rec: 0.319 D_GP: 0.032 D_real: 0.780 D_fake: 1.004 
+(epoch: 253, iters: 3984, time: 0.064) G_GAN: 0.708 G_GAN_Feat: 0.823 G_ID: 0.164 G_Rec: 0.369 D_GP: 0.021 D_real: 1.509 D_fake: 0.312 
+(epoch: 253, iters: 4384, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.644 G_ID: 0.130 G_Rec: 0.308 D_GP: 0.022 D_real: 1.219 D_fake: 0.702 
+(epoch: 253, iters: 4784, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.972 G_ID: 0.155 G_Rec: 0.415 D_GP: 0.084 D_real: 0.697 D_fake: 0.932 
+(epoch: 253, iters: 5184, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.891 G_ID: 0.141 G_Rec: 0.337 D_GP: 0.105 D_real: 1.200 D_fake: 0.657 
+(epoch: 253, iters: 5584, time: 0.064) G_GAN: 0.556 G_GAN_Feat: 0.963 G_ID: 0.184 G_Rec: 0.416 D_GP: 0.046 D_real: 0.880 D_fake: 0.455 
+(epoch: 253, iters: 5984, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.747 G_ID: 0.132 G_Rec: 0.302 D_GP: 0.021 D_real: 1.131 D_fake: 0.807 
+(epoch: 253, iters: 6384, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.765 G_ID: 0.145 G_Rec: 0.347 D_GP: 0.022 D_real: 1.061 D_fake: 0.823 
+(epoch: 253, iters: 6784, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.834 G_ID: 0.127 G_Rec: 0.324 D_GP: 0.045 D_real: 1.059 D_fake: 0.687 
+(epoch: 253, iters: 7184, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.875 G_ID: 0.161 G_Rec: 0.400 D_GP: 0.046 D_real: 0.741 D_fake: 0.832 
+(epoch: 253, iters: 7584, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.665 G_ID: 0.130 G_Rec: 0.317 D_GP: 0.023 D_real: 1.164 D_fake: 0.756 
+(epoch: 253, iters: 7984, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.873 G_ID: 0.165 G_Rec: 0.441 D_GP: 0.024 D_real: 1.042 D_fake: 0.688 
+(epoch: 253, iters: 8384, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.724 G_ID: 0.116 G_Rec: 0.306 D_GP: 0.039 D_real: 0.925 D_fake: 0.777 
+(epoch: 254, iters: 176, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.980 G_ID: 0.180 G_Rec: 0.387 D_GP: 0.035 D_real: 0.634 D_fake: 0.688 
+(epoch: 254, iters: 576, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.756 G_ID: 0.139 G_Rec: 0.311 D_GP: 0.030 D_real: 0.992 D_fake: 0.687 
+(epoch: 254, iters: 976, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.866 G_ID: 0.180 G_Rec: 0.332 D_GP: 0.027 D_real: 1.280 D_fake: 0.515 
+(epoch: 254, iters: 1376, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.729 G_ID: 0.117 G_Rec: 0.305 D_GP: 0.036 D_real: 1.297 D_fake: 0.485 
+(epoch: 254, iters: 1776, time: 0.064) G_GAN: 0.540 G_GAN_Feat: 0.801 G_ID: 0.195 G_Rec: 0.386 D_GP: 0.019 D_real: 1.445 D_fake: 0.480 
+(epoch: 254, iters: 2176, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.772 G_ID: 0.160 G_Rec: 0.333 D_GP: 0.039 D_real: 1.009 D_fake: 0.769 
+(epoch: 254, iters: 2576, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.821 G_ID: 0.150 G_Rec: 0.381 D_GP: 0.021 D_real: 1.024 D_fake: 0.739 
+(epoch: 254, iters: 2976, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.831 G_ID: 0.136 G_Rec: 0.332 D_GP: 0.074 D_real: 0.586 D_fake: 0.743 
+(epoch: 254, iters: 3376, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.909 G_ID: 0.169 G_Rec: 0.409 D_GP: 0.032 D_real: 1.176 D_fake: 0.497 
+(epoch: 254, iters: 3776, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.895 G_ID: 0.126 G_Rec: 0.315 D_GP: 0.125 D_real: 0.434 D_fake: 0.959 
+(epoch: 254, iters: 4176, time: 0.064) G_GAN: 0.877 G_GAN_Feat: 1.115 G_ID: 0.180 G_Rec: 0.425 D_GP: 0.098 D_real: 1.177 D_fake: 0.408 
+(epoch: 254, iters: 4576, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.701 G_ID: 0.128 G_Rec: 0.300 D_GP: 0.031 D_real: 1.119 D_fake: 0.768 
+(epoch: 254, iters: 4976, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.810 G_ID: 0.174 G_Rec: 0.407 D_GP: 0.020 D_real: 1.239 D_fake: 0.530 
+(epoch: 254, iters: 5376, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.621 G_ID: 0.119 G_Rec: 0.294 D_GP: 0.024 D_real: 1.268 D_fake: 0.729 
+(epoch: 254, iters: 5776, time: 0.064) G_GAN: 0.773 G_GAN_Feat: 1.024 G_ID: 0.177 G_Rec: 0.438 D_GP: 0.046 D_real: 1.067 D_fake: 0.368 
+(epoch: 254, iters: 6176, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.684 G_ID: 0.138 G_Rec: 0.344 D_GP: 0.018 D_real: 1.153 D_fake: 0.754 
+(epoch: 254, iters: 6576, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.784 G_ID: 0.146 G_Rec: 0.370 D_GP: 0.023 D_real: 1.307 D_fake: 0.464 
+(epoch: 254, iters: 6976, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.590 G_ID: 0.141 G_Rec: 0.340 D_GP: 0.020 D_real: 1.228 D_fake: 0.769 
+(epoch: 254, iters: 7376, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.793 G_ID: 0.168 G_Rec: 0.387 D_GP: 0.027 D_real: 1.169 D_fake: 0.546 
+(epoch: 254, iters: 7776, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.601 G_ID: 0.113 G_Rec: 0.289 D_GP: 0.024 D_real: 1.148 D_fake: 0.731 
+(epoch: 254, iters: 8176, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 0.815 G_ID: 0.162 G_Rec: 0.422 D_GP: 0.029 D_real: 1.164 D_fake: 0.527 
+(epoch: 254, iters: 8576, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.736 G_ID: 0.127 G_Rec: 0.306 D_GP: 0.026 D_real: 1.053 D_fake: 0.805 
+(epoch: 255, iters: 368, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.873 G_ID: 0.159 G_Rec: 0.364 D_GP: 0.029 D_real: 0.874 D_fake: 0.640 
+(epoch: 255, iters: 768, time: 0.064) G_GAN: 0.027 G_GAN_Feat: 0.662 G_ID: 0.170 G_Rec: 0.283 D_GP: 0.038 D_real: 0.812 D_fake: 0.974 
+(epoch: 255, iters: 1168, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.924 G_ID: 0.186 G_Rec: 0.393 D_GP: 0.068 D_real: 0.858 D_fake: 0.657 
+(epoch: 255, iters: 1568, time: 0.064) G_GAN: 0.060 G_GAN_Feat: 0.751 G_ID: 0.123 G_Rec: 0.306 D_GP: 0.071 D_real: 0.651 D_fake: 0.941 
+(epoch: 255, iters: 1968, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.905 G_ID: 0.179 G_Rec: 0.366 D_GP: 0.053 D_real: 0.744 D_fake: 0.700 
+(epoch: 255, iters: 2368, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 0.714 G_ID: 0.120 G_Rec: 0.300 D_GP: 0.028 D_real: 1.241 D_fake: 0.547 
+(epoch: 255, iters: 2768, time: 0.064) G_GAN: 0.742 G_GAN_Feat: 1.026 G_ID: 0.193 G_Rec: 0.520 D_GP: 0.081 D_real: 1.132 D_fake: 0.512 
+(epoch: 255, iters: 3168, time: 0.064) G_GAN: 0.076 G_GAN_Feat: 0.638 G_ID: 0.148 G_Rec: 0.276 D_GP: 0.021 D_real: 0.994 D_fake: 0.924 
+(epoch: 255, iters: 3568, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.865 G_ID: 0.168 G_Rec: 0.394 D_GP: 0.022 D_real: 1.102 D_fake: 0.566 
+(epoch: 255, iters: 3968, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.642 G_ID: 0.170 G_Rec: 0.272 D_GP: 0.021 D_real: 1.055 D_fake: 0.915 
+(epoch: 255, iters: 4368, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.998 G_ID: 0.182 G_Rec: 0.439 D_GP: 0.081 D_real: 0.579 D_fake: 0.745 
+(epoch: 255, iters: 4768, time: 0.064) G_GAN: 0.075 G_GAN_Feat: 0.612 G_ID: 0.132 G_Rec: 0.271 D_GP: 0.023 D_real: 1.116 D_fake: 0.925 
+(epoch: 255, iters: 5168, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.938 G_ID: 0.166 G_Rec: 0.427 D_GP: 0.046 D_real: 0.736 D_fake: 0.805 
+(epoch: 255, iters: 5568, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.692 G_ID: 0.117 G_Rec: 0.282 D_GP: 0.038 D_real: 0.896 D_fake: 0.898 
+(epoch: 255, iters: 5968, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 0.904 G_ID: 0.197 G_Rec: 0.378 D_GP: 0.026 D_real: 1.283 D_fake: 0.390 
+(epoch: 255, iters: 6368, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.748 G_ID: 0.157 G_Rec: 0.294 D_GP: 0.033 D_real: 1.039 D_fake: 0.751 
+(epoch: 255, iters: 6768, time: 0.064) G_GAN: 0.616 G_GAN_Feat: 0.956 G_ID: 0.176 G_Rec: 0.386 D_GP: 0.025 D_real: 1.307 D_fake: 0.387 
+(epoch: 255, iters: 7168, time: 0.064) G_GAN: 0.101 G_GAN_Feat: 0.959 G_ID: 0.106 G_Rec: 0.350 D_GP: 0.049 D_real: 0.576 D_fake: 0.900 
+(epoch: 255, iters: 7568, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.973 G_ID: 0.151 G_Rec: 0.413 D_GP: 0.033 D_real: 1.086 D_fake: 0.547 
+(epoch: 255, iters: 7968, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.940 G_ID: 0.131 G_Rec: 0.304 D_GP: 0.188 D_real: 0.388 D_fake: 0.768 
+(epoch: 255, iters: 8368, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 0.841 G_ID: 0.175 G_Rec: 0.402 D_GP: 0.022 D_real: 1.380 D_fake: 0.388 
+(epoch: 256, iters: 160, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.924 G_ID: 0.137 G_Rec: 0.323 D_GP: 0.034 D_real: 0.539 D_fake: 0.803 
+(epoch: 256, iters: 560, time: 0.064) G_GAN: 0.530 G_GAN_Feat: 0.837 G_ID: 0.155 G_Rec: 0.408 D_GP: 0.033 D_real: 1.351 D_fake: 0.499 
+(epoch: 256, iters: 960, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 0.561 G_ID: 0.125 G_Rec: 0.288 D_GP: 0.018 D_real: 0.976 D_fake: 0.988 
+(epoch: 256, iters: 1360, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.717 G_ID: 0.169 G_Rec: 0.383 D_GP: 0.021 D_real: 1.057 D_fake: 0.788 
+(epoch: 256, iters: 1760, time: 0.064) G_GAN: 0.070 G_GAN_Feat: 0.603 G_ID: 0.144 G_Rec: 0.347 D_GP: 0.018 D_real: 1.017 D_fake: 0.930 
+(epoch: 256, iters: 2160, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.853 G_ID: 0.147 G_Rec: 0.435 D_GP: 0.039 D_real: 0.944 D_fake: 0.822 
+(epoch: 256, iters: 2560, time: 0.064) G_GAN: 0.027 G_GAN_Feat: 0.653 G_ID: 0.138 G_Rec: 0.315 D_GP: 0.032 D_real: 0.856 D_fake: 0.973 
+(epoch: 256, iters: 2960, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.743 G_ID: 0.167 G_Rec: 0.346 D_GP: 0.026 D_real: 1.061 D_fake: 0.761 
+(epoch: 256, iters: 3360, time: 0.064) G_GAN: -0.107 G_GAN_Feat: 0.659 G_ID: 0.108 G_Rec: 0.315 D_GP: 0.042 D_real: 0.711 D_fake: 1.107 
+(epoch: 256, iters: 3760, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.899 G_ID: 0.172 G_Rec: 0.434 D_GP: 0.040 D_real: 0.755 D_fake: 0.847 
+(epoch: 256, iters: 4160, time: 0.064) G_GAN: 0.002 G_GAN_Feat: 0.687 G_ID: 0.121 G_Rec: 0.298 D_GP: 0.064 D_real: 0.812 D_fake: 0.998 
+(epoch: 256, iters: 4560, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.851 G_ID: 0.204 G_Rec: 0.395 D_GP: 0.034 D_real: 0.785 D_fake: 0.908 
+(epoch: 256, iters: 4960, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.605 G_ID: 0.129 G_Rec: 0.264 D_GP: 0.021 D_real: 1.341 D_fake: 0.572 
+(epoch: 256, iters: 5360, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.871 G_ID: 0.159 G_Rec: 0.359 D_GP: 0.028 D_real: 1.068 D_fake: 0.575 
+(epoch: 256, iters: 5760, time: 0.064) G_GAN: 0.027 G_GAN_Feat: 0.726 G_ID: 0.130 G_Rec: 0.313 D_GP: 0.026 D_real: 0.803 D_fake: 0.973 
+(epoch: 256, iters: 6160, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.903 G_ID: 0.176 G_Rec: 0.402 D_GP: 0.040 D_real: 0.989 D_fake: 0.542 
+(epoch: 256, iters: 6560, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.615 G_ID: 0.161 G_Rec: 0.303 D_GP: 0.022 D_real: 1.309 D_fake: 0.668 
+(epoch: 256, iters: 6960, time: 0.064) G_GAN: 0.444 G_GAN_Feat: 0.858 G_ID: 0.173 G_Rec: 0.397 D_GP: 0.033 D_real: 1.068 D_fake: 0.558 
+(epoch: 256, iters: 7360, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.574 G_ID: 0.140 G_Rec: 0.255 D_GP: 0.019 D_real: 1.418 D_fake: 0.654 
+(epoch: 256, iters: 7760, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.873 G_ID: 0.182 G_Rec: 0.415 D_GP: 0.028 D_real: 1.126 D_fake: 0.573 
+(epoch: 256, iters: 8160, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.758 G_ID: 0.134 G_Rec: 0.318 D_GP: 0.065 D_real: 0.909 D_fake: 0.725 
+(epoch: 256, iters: 8560, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.834 G_ID: 0.167 G_Rec: 0.367 D_GP: 0.028 D_real: 1.103 D_fake: 0.614 
+(epoch: 257, iters: 352, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.684 G_ID: 0.131 G_Rec: 0.275 D_GP: 0.021 D_real: 1.125 D_fake: 0.763 
+(epoch: 257, iters: 752, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 0.848 G_ID: 0.158 G_Rec: 0.375 D_GP: 0.023 D_real: 1.257 D_fake: 0.511 
+(epoch: 257, iters: 1152, time: 0.064) G_GAN: 0.407 G_GAN_Feat: 0.785 G_ID: 0.155 G_Rec: 0.354 D_GP: 0.043 D_real: 1.104 D_fake: 0.595 
+(epoch: 257, iters: 1552, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.999 G_ID: 0.177 G_Rec: 0.427 D_GP: 0.023 D_real: 0.785 D_fake: 0.692 
+(epoch: 257, iters: 1952, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.805 G_ID: 0.142 G_Rec: 0.297 D_GP: 0.066 D_real: 0.611 D_fake: 0.736 
+(epoch: 257, iters: 2352, time: 0.064) G_GAN: 0.720 G_GAN_Feat: 1.041 G_ID: 0.157 G_Rec: 0.412 D_GP: 0.033 D_real: 1.179 D_fake: 0.351 
+(epoch: 257, iters: 2752, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.761 G_ID: 0.129 G_Rec: 0.313 D_GP: 0.037 D_real: 0.887 D_fake: 0.860 
+(epoch: 257, iters: 3152, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.789 G_ID: 0.157 G_Rec: 0.376 D_GP: 0.017 D_real: 0.963 D_fake: 0.892 
+(epoch: 257, iters: 3552, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.644 G_ID: 0.116 G_Rec: 0.284 D_GP: 0.027 D_real: 1.049 D_fake: 0.852 
+(epoch: 257, iters: 3952, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.812 G_ID: 0.177 G_Rec: 0.384 D_GP: 0.024 D_real: 1.200 D_fake: 0.615 
+(epoch: 257, iters: 4352, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.728 G_ID: 0.109 G_Rec: 0.290 D_GP: 0.033 D_real: 1.247 D_fake: 0.572 
+(epoch: 257, iters: 4752, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 0.846 G_ID: 0.190 G_Rec: 0.391 D_GP: 0.034 D_real: 1.251 D_fake: 0.461 
+(epoch: 257, iters: 5152, time: 0.064) G_GAN: -0.062 G_GAN_Feat: 0.698 G_ID: 0.105 G_Rec: 0.296 D_GP: 0.030 D_real: 0.755 D_fake: 1.062 
+(epoch: 257, iters: 5552, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.921 G_ID: 0.170 G_Rec: 0.413 D_GP: 0.046 D_real: 0.697 D_fake: 0.785 
+(epoch: 257, iters: 5952, time: 0.064) G_GAN: -0.057 G_GAN_Feat: 0.835 G_ID: 0.132 G_Rec: 0.307 D_GP: 0.036 D_real: 1.008 D_fake: 1.060 
+(epoch: 257, iters: 6352, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.977 G_ID: 0.157 G_Rec: 0.424 D_GP: 0.039 D_real: 0.931 D_fake: 0.500 
+(epoch: 257, iters: 6752, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.646 G_ID: 0.131 G_Rec: 0.328 D_GP: 0.018 D_real: 1.129 D_fake: 0.858 
+(epoch: 257, iters: 7152, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 0.840 G_ID: 0.181 G_Rec: 0.407 D_GP: 0.021 D_real: 1.089 D_fake: 0.675 
+(epoch: 257, iters: 7552, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.678 G_ID: 0.113 G_Rec: 0.305 D_GP: 0.020 D_real: 1.227 D_fake: 0.691 
+(epoch: 257, iters: 7952, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.916 G_ID: 0.169 G_Rec: 0.404 D_GP: 0.047 D_real: 0.925 D_fake: 0.547 
+(epoch: 257, iters: 8352, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.763 G_ID: 0.123 G_Rec: 0.335 D_GP: 0.051 D_real: 0.914 D_fake: 0.853 
+(epoch: 258, iters: 144, time: 0.064) G_GAN: 0.988 G_GAN_Feat: 1.237 G_ID: 0.168 G_Rec: 0.407 D_GP: 0.028 D_real: 1.428 D_fake: 0.232 
+(epoch: 258, iters: 544, time: 0.064) G_GAN: -0.014 G_GAN_Feat: 0.724 G_ID: 0.148 G_Rec: 0.304 D_GP: 0.031 D_real: 0.816 D_fake: 1.014 
+(epoch: 258, iters: 944, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.978 G_ID: 0.170 G_Rec: 0.422 D_GP: 0.075 D_real: 0.904 D_fake: 0.577 
+(epoch: 258, iters: 1344, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.712 G_ID: 0.118 G_Rec: 0.307 D_GP: 0.020 D_real: 1.128 D_fake: 0.712 
+(epoch: 258, iters: 1744, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.939 G_ID: 0.179 G_Rec: 0.380 D_GP: 0.066 D_real: 0.840 D_fake: 0.522 
+(epoch: 258, iters: 2144, time: 0.064) G_GAN: -0.038 G_GAN_Feat: 0.841 G_ID: 0.138 G_Rec: 0.301 D_GP: 0.099 D_real: 0.392 D_fake: 1.038 
+(epoch: 258, iters: 2544, time: 0.064) G_GAN: 0.495 G_GAN_Feat: 0.882 G_ID: 0.144 G_Rec: 0.401 D_GP: 0.021 D_real: 1.239 D_fake: 0.507 
+(epoch: 258, iters: 2944, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.831 G_ID: 0.176 G_Rec: 0.314 D_GP: 0.035 D_real: 0.945 D_fake: 0.652 
+(epoch: 258, iters: 3344, time: 0.064) G_GAN: 0.743 G_GAN_Feat: 0.986 G_ID: 0.170 G_Rec: 0.395 D_GP: 0.066 D_real: 1.195 D_fake: 0.299 
+(epoch: 258, iters: 3744, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.793 G_ID: 0.124 G_Rec: 0.306 D_GP: 0.040 D_real: 1.041 D_fake: 0.615 
+(epoch: 258, iters: 4144, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.787 G_ID: 0.162 G_Rec: 0.385 D_GP: 0.019 D_real: 1.194 D_fake: 0.644 
+(epoch: 258, iters: 4544, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.629 G_ID: 0.125 G_Rec: 0.315 D_GP: 0.019 D_real: 1.087 D_fake: 0.889 
+(epoch: 258, iters: 4944, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.816 G_ID: 0.186 G_Rec: 0.388 D_GP: 0.024 D_real: 0.961 D_fake: 0.739 
+(epoch: 258, iters: 5344, time: 0.064) G_GAN: 0.052 G_GAN_Feat: 0.645 G_ID: 0.135 G_Rec: 0.297 D_GP: 0.024 D_real: 1.021 D_fake: 0.948 
+(epoch: 258, iters: 5744, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 0.960 G_ID: 0.161 G_Rec: 0.443 D_GP: 0.106 D_real: 0.957 D_fake: 0.558 
+(epoch: 258, iters: 6144, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.678 G_ID: 0.114 G_Rec: 0.319 D_GP: 0.026 D_real: 0.985 D_fake: 0.888 
+(epoch: 258, iters: 6544, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.788 G_ID: 0.169 G_Rec: 0.400 D_GP: 0.023 D_real: 1.094 D_fake: 0.645 
+(epoch: 258, iters: 6944, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.781 G_ID: 0.158 G_Rec: 0.318 D_GP: 0.105 D_real: 0.717 D_fake: 0.834 
+(epoch: 258, iters: 7344, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.819 G_ID: 0.157 G_Rec: 0.359 D_GP: 0.031 D_real: 1.080 D_fake: 0.647 
+(epoch: 258, iters: 7744, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.777 G_ID: 0.121 G_Rec: 0.316 D_GP: 0.025 D_real: 1.255 D_fake: 0.890 
+(epoch: 258, iters: 8144, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.777 G_ID: 0.167 G_Rec: 0.410 D_GP: 0.018 D_real: 1.153 D_fake: 0.638 
+(epoch: 258, iters: 8544, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.535 G_ID: 0.125 G_Rec: 0.270 D_GP: 0.019 D_real: 1.224 D_fake: 0.730 
+(epoch: 259, iters: 336, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 0.771 G_ID: 0.164 G_Rec: 0.410 D_GP: 0.025 D_real: 1.174 D_fake: 0.572 
+(epoch: 259, iters: 736, time: 0.064) G_GAN: 0.109 G_GAN_Feat: 0.629 G_ID: 0.131 G_Rec: 0.317 D_GP: 0.030 D_real: 1.038 D_fake: 0.892 
+(epoch: 259, iters: 1136, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.771 G_ID: 0.186 G_Rec: 0.357 D_GP: 0.035 D_real: 1.058 D_fake: 0.652 
+(epoch: 259, iters: 1536, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.673 G_ID: 0.126 G_Rec: 0.301 D_GP: 0.049 D_real: 0.893 D_fake: 0.931 
+(epoch: 259, iters: 1936, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.863 G_ID: 0.168 G_Rec: 0.383 D_GP: 0.024 D_real: 1.025 D_fake: 0.743 
+(epoch: 259, iters: 2336, time: 0.064) G_GAN: 0.101 G_GAN_Feat: 0.625 G_ID: 0.130 G_Rec: 0.254 D_GP: 0.033 D_real: 1.013 D_fake: 0.899 
+(epoch: 259, iters: 2736, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.753 G_ID: 0.172 G_Rec: 0.351 D_GP: 0.025 D_real: 1.052 D_fake: 0.783 
+(epoch: 259, iters: 3136, time: 0.064) G_GAN: 0.045 G_GAN_Feat: 0.705 G_ID: 0.135 G_Rec: 0.310 D_GP: 0.024 D_real: 0.908 D_fake: 0.955 
+(epoch: 259, iters: 3536, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.840 G_ID: 0.175 G_Rec: 0.368 D_GP: 0.030 D_real: 1.090 D_fake: 0.640 
+(epoch: 259, iters: 3936, time: 0.064) G_GAN: 0.133 G_GAN_Feat: 0.737 G_ID: 0.109 G_Rec: 0.312 D_GP: 0.059 D_real: 0.897 D_fake: 0.868 
+(epoch: 259, iters: 4336, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.934 G_ID: 0.151 G_Rec: 0.391 D_GP: 0.025 D_real: 1.088 D_fake: 0.587 
+(epoch: 259, iters: 4736, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.678 G_ID: 0.125 G_Rec: 0.312 D_GP: 0.026 D_real: 0.923 D_fake: 0.959 
+(epoch: 259, iters: 5136, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.770 G_ID: 0.160 G_Rec: 0.342 D_GP: 0.024 D_real: 1.168 D_fake: 0.608 
+(epoch: 259, iters: 5536, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.859 G_ID: 0.136 G_Rec: 0.323 D_GP: 0.055 D_real: 0.537 D_fake: 0.862 
+(epoch: 259, iters: 5936, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.958 G_ID: 0.161 G_Rec: 0.369 D_GP: 0.039 D_real: 0.743 D_fake: 0.678 
+(epoch: 259, iters: 6336, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.751 G_ID: 0.116 G_Rec: 0.332 D_GP: 0.036 D_real: 1.049 D_fake: 0.737 
+(epoch: 259, iters: 6736, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 1.081 G_ID: 0.173 G_Rec: 0.493 D_GP: 0.209 D_real: 0.368 D_fake: 0.777 
+(epoch: 259, iters: 7136, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.647 G_ID: 0.125 G_Rec: 0.277 D_GP: 0.018 D_real: 1.166 D_fake: 0.763 
+(epoch: 259, iters: 7536, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 0.885 G_ID: 0.161 G_Rec: 0.404 D_GP: 0.038 D_real: 1.173 D_fake: 0.539 
+(epoch: 259, iters: 7936, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.721 G_ID: 0.130 G_Rec: 0.302 D_GP: 0.029 D_real: 1.130 D_fake: 0.652 
+(epoch: 259, iters: 8336, time: 0.064) G_GAN: 0.852 G_GAN_Feat: 1.092 G_ID: 0.154 G_Rec: 0.452 D_GP: 0.084 D_real: 1.379 D_fake: 0.309 
+(epoch: 260, iters: 128, time: 0.064) G_GAN: -0.148 G_GAN_Feat: 0.699 G_ID: 0.131 G_Rec: 0.286 D_GP: 0.021 D_real: 0.904 D_fake: 1.149 
+(epoch: 260, iters: 528, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.842 G_ID: 0.154 G_Rec: 0.488 D_GP: 0.028 D_real: 1.133 D_fake: 0.502 
+(epoch: 260, iters: 928, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.911 G_ID: 0.146 G_Rec: 0.322 D_GP: 0.298 D_real: 0.357 D_fake: 0.768 
+(epoch: 260, iters: 1328, time: 0.064) G_GAN: 0.813 G_GAN_Feat: 1.025 G_ID: 0.159 G_Rec: 0.402 D_GP: 0.039 D_real: 1.064 D_fake: 0.234 
+(epoch: 260, iters: 1728, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.544 G_ID: 0.130 G_Rec: 0.256 D_GP: 0.019 D_real: 1.085 D_fake: 0.900 
+(epoch: 260, iters: 2128, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.784 G_ID: 0.194 G_Rec: 0.383 D_GP: 0.019 D_real: 1.239 D_fake: 0.578 
+(epoch: 260, iters: 2528, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.577 G_ID: 0.137 G_Rec: 0.286 D_GP: 0.020 D_real: 1.046 D_fake: 0.849 
+(epoch: 260, iters: 2928, time: 0.064) G_GAN: 0.519 G_GAN_Feat: 0.835 G_ID: 0.130 G_Rec: 0.403 D_GP: 0.030 D_real: 1.279 D_fake: 0.494 
+(epoch: 260, iters: 3328, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.568 G_ID: 0.125 G_Rec: 0.312 D_GP: 0.028 D_real: 1.052 D_fake: 0.881 
+(epoch: 260, iters: 3728, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.843 G_ID: 0.156 G_Rec: 0.418 D_GP: 0.031 D_real: 0.989 D_fake: 0.649 
+(epoch: 260, iters: 4128, time: 0.064) G_GAN: -0.029 G_GAN_Feat: 0.738 G_ID: 0.135 G_Rec: 0.304 D_GP: 0.066 D_real: 0.706 D_fake: 1.029 
+(epoch: 260, iters: 4528, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.890 G_ID: 0.144 G_Rec: 0.439 D_GP: 0.030 D_real: 1.088 D_fake: 0.491 
+(epoch: 260, iters: 4928, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.639 G_ID: 0.135 G_Rec: 0.266 D_GP: 0.026 D_real: 1.089 D_fake: 0.816 
+(epoch: 260, iters: 5328, time: 0.064) G_GAN: 0.558 G_GAN_Feat: 0.925 G_ID: 0.151 G_Rec: 0.404 D_GP: 0.031 D_real: 1.156 D_fake: 0.446 
+(epoch: 260, iters: 5728, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.673 G_ID: 0.113 G_Rec: 0.282 D_GP: 0.040 D_real: 1.025 D_fake: 0.841 
+(epoch: 260, iters: 6128, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 0.975 G_ID: 0.162 G_Rec: 0.368 D_GP: 0.101 D_real: 0.872 D_fake: 0.483 
+(epoch: 260, iters: 6528, time: 0.064) G_GAN: -0.072 G_GAN_Feat: 0.804 G_ID: 0.151 G_Rec: 0.341 D_GP: 0.188 D_real: 0.485 D_fake: 1.073 
+(epoch: 260, iters: 6928, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.912 G_ID: 0.165 G_Rec: 0.404 D_GP: 0.037 D_real: 1.088 D_fake: 0.572 
+(epoch: 260, iters: 7328, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.705 G_ID: 0.154 G_Rec: 0.299 D_GP: 0.029 D_real: 0.948 D_fake: 0.909 
+(epoch: 260, iters: 7728, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.952 G_ID: 0.173 G_Rec: 0.415 D_GP: 0.068 D_real: 0.863 D_fake: 0.621 
+(epoch: 260, iters: 8128, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.637 G_ID: 0.119 G_Rec: 0.265 D_GP: 0.022 D_real: 1.161 D_fake: 0.808 
+(epoch: 260, iters: 8528, time: 0.064) G_GAN: 0.560 G_GAN_Feat: 0.947 G_ID: 0.168 G_Rec: 0.430 D_GP: 0.032 D_real: 1.043 D_fake: 0.445 
+(epoch: 261, iters: 320, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.850 G_ID: 0.128 G_Rec: 0.289 D_GP: 0.033 D_real: 0.825 D_fake: 0.676 
+(epoch: 261, iters: 720, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.864 G_ID: 0.187 G_Rec: 0.379 D_GP: 0.029 D_real: 1.028 D_fake: 0.609 
+(epoch: 261, iters: 1120, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.677 G_ID: 0.125 G_Rec: 0.298 D_GP: 0.026 D_real: 1.143 D_fake: 0.779 
+(epoch: 261, iters: 1520, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.848 G_ID: 0.178 G_Rec: 0.403 D_GP: 0.022 D_real: 1.007 D_fake: 0.743 
+(epoch: 261, iters: 1920, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.864 G_ID: 0.123 G_Rec: 0.339 D_GP: 0.068 D_real: 0.935 D_fake: 0.723 
+(epoch: 261, iters: 2320, time: 0.064) G_GAN: 0.529 G_GAN_Feat: 0.919 G_ID: 0.168 G_Rec: 0.359 D_GP: 0.052 D_real: 0.871 D_fake: 0.476 
+(epoch: 261, iters: 2720, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.636 G_ID: 0.121 G_Rec: 0.311 D_GP: 0.019 D_real: 1.090 D_fake: 0.907 
+(epoch: 261, iters: 3120, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.816 G_ID: 0.172 G_Rec: 0.397 D_GP: 0.021 D_real: 1.130 D_fake: 0.666 
+(epoch: 261, iters: 3520, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.586 G_ID: 0.126 G_Rec: 0.289 D_GP: 0.018 D_real: 1.201 D_fake: 0.729 
+(epoch: 261, iters: 3920, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.728 G_ID: 0.169 G_Rec: 0.350 D_GP: 0.019 D_real: 0.980 D_fake: 0.820 
+(epoch: 261, iters: 4320, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.624 G_ID: 0.119 G_Rec: 0.301 D_GP: 0.024 D_real: 1.077 D_fake: 0.827 
+(epoch: 261, iters: 4720, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.848 G_ID: 0.152 G_Rec: 0.396 D_GP: 0.025 D_real: 1.189 D_fake: 0.502 
+(epoch: 261, iters: 5120, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.665 G_ID: 0.132 G_Rec: 0.288 D_GP: 0.021 D_real: 1.138 D_fake: 0.733 
+(epoch: 261, iters: 5520, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.948 G_ID: 0.162 G_Rec: 0.415 D_GP: 0.100 D_real: 0.753 D_fake: 0.570 
+(epoch: 261, iters: 5920, time: 0.064) G_GAN: 0.004 G_GAN_Feat: 0.837 G_ID: 0.147 G_Rec: 0.330 D_GP: 0.234 D_real: 0.456 D_fake: 0.996 
+(epoch: 261, iters: 6320, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.913 G_ID: 0.156 G_Rec: 0.425 D_GP: 0.021 D_real: 1.054 D_fake: 0.655 
+(epoch: 261, iters: 6720, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.709 G_ID: 0.107 G_Rec: 0.275 D_GP: 0.030 D_real: 0.886 D_fake: 0.856 
+(epoch: 261, iters: 7120, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 1.020 G_ID: 0.161 G_Rec: 0.442 D_GP: 0.032 D_real: 0.889 D_fake: 0.462 
+(epoch: 261, iters: 7520, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.657 G_ID: 0.124 G_Rec: 0.272 D_GP: 0.022 D_real: 1.227 D_fake: 0.683 
+(epoch: 261, iters: 7920, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 1.138 G_ID: 0.191 G_Rec: 0.450 D_GP: 0.039 D_real: 0.959 D_fake: 0.800 
+(epoch: 261, iters: 8320, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.613 G_ID: 0.117 G_Rec: 0.261 D_GP: 0.023 D_real: 1.207 D_fake: 0.800 
+(epoch: 262, iters: 112, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.878 G_ID: 0.169 G_Rec: 0.354 D_GP: 0.022 D_real: 1.063 D_fake: 0.629 
+(epoch: 262, iters: 512, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.867 G_ID: 0.121 G_Rec: 0.284 D_GP: 0.055 D_real: 0.545 D_fake: 0.837 
+(epoch: 262, iters: 912, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.897 G_ID: 0.180 G_Rec: 0.423 D_GP: 0.026 D_real: 1.007 D_fake: 0.675 
+(epoch: 262, iters: 1312, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.677 G_ID: 0.143 G_Rec: 0.308 D_GP: 0.038 D_real: 0.919 D_fake: 0.923 
+(epoch: 262, iters: 1712, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.886 G_ID: 0.158 G_Rec: 0.383 D_GP: 0.037 D_real: 0.931 D_fake: 0.632 
+(epoch: 262, iters: 2112, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.737 G_ID: 0.128 G_Rec: 0.291 D_GP: 0.032 D_real: 1.012 D_fake: 0.696 
+(epoch: 262, iters: 2512, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 0.918 G_ID: 0.164 G_Rec: 0.388 D_GP: 0.040 D_real: 1.170 D_fake: 0.408 
+(epoch: 262, iters: 2912, time: 0.064) G_GAN: 0.124 G_GAN_Feat: 0.789 G_ID: 0.150 G_Rec: 0.369 D_GP: 0.029 D_real: 0.816 D_fake: 0.876 
+(epoch: 262, iters: 3312, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 1.124 G_ID: 0.149 G_Rec: 0.413 D_GP: 0.028 D_real: 1.403 D_fake: 0.475 
+(epoch: 262, iters: 3712, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.677 G_ID: 0.151 G_Rec: 0.312 D_GP: 0.019 D_real: 0.985 D_fake: 0.964 
+(epoch: 262, iters: 4112, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.769 G_ID: 0.207 G_Rec: 0.331 D_GP: 0.033 D_real: 0.884 D_fake: 0.824 
+(epoch: 262, iters: 4512, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.668 G_ID: 0.121 G_Rec: 0.262 D_GP: 0.031 D_real: 1.094 D_fake: 0.764 
+(epoch: 262, iters: 4912, time: 0.064) G_GAN: 0.613 G_GAN_Feat: 0.970 G_ID: 0.149 G_Rec: 0.382 D_GP: 0.027 D_real: 1.334 D_fake: 0.402 
+(epoch: 262, iters: 5312, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.749 G_ID: 0.133 G_Rec: 0.274 D_GP: 0.025 D_real: 0.938 D_fake: 0.805 
+(epoch: 262, iters: 5712, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.915 G_ID: 0.167 G_Rec: 0.372 D_GP: 0.044 D_real: 0.823 D_fake: 0.731 
+(epoch: 262, iters: 6112, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.603 G_ID: 0.141 G_Rec: 0.280 D_GP: 0.021 D_real: 1.300 D_fake: 0.698 
+(epoch: 262, iters: 6512, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.894 G_ID: 0.194 G_Rec: 0.396 D_GP: 0.033 D_real: 0.859 D_fake: 0.792 
+(epoch: 262, iters: 6912, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.833 G_ID: 0.135 G_Rec: 0.324 D_GP: 0.022 D_real: 1.019 D_fake: 0.789 
+(epoch: 262, iters: 7312, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.839 G_ID: 0.193 G_Rec: 0.390 D_GP: 0.024 D_real: 1.083 D_fake: 0.694 
+(epoch: 262, iters: 7712, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.823 G_ID: 0.121 G_Rec: 0.296 D_GP: 0.070 D_real: 0.670 D_fake: 0.752 
+(epoch: 262, iters: 8112, time: 0.064) G_GAN: 0.596 G_GAN_Feat: 1.346 G_ID: 0.161 G_Rec: 0.447 D_GP: 0.109 D_real: 1.281 D_fake: 0.582 
+(epoch: 262, iters: 8512, time: 0.064) G_GAN: -0.011 G_GAN_Feat: 0.657 G_ID: 0.132 G_Rec: 0.287 D_GP: 0.021 D_real: 0.953 D_fake: 1.011 
+(epoch: 263, iters: 304, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.898 G_ID: 0.151 G_Rec: 0.398 D_GP: 0.032 D_real: 0.829 D_fake: 0.792 
+(epoch: 263, iters: 704, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.714 G_ID: 0.105 G_Rec: 0.281 D_GP: 0.027 D_real: 1.139 D_fake: 0.665 
+(epoch: 263, iters: 1104, time: 0.064) G_GAN: 0.597 G_GAN_Feat: 0.802 G_ID: 0.158 G_Rec: 0.417 D_GP: 0.021 D_real: 1.319 D_fake: 0.413 
+(epoch: 263, iters: 1504, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.614 G_ID: 0.110 G_Rec: 0.311 D_GP: 0.026 D_real: 1.047 D_fake: 0.872 
+(epoch: 263, iters: 1904, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.775 G_ID: 0.169 G_Rec: 0.385 D_GP: 0.032 D_real: 1.136 D_fake: 0.682 
+(epoch: 263, iters: 2304, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.578 G_ID: 0.123 G_Rec: 0.250 D_GP: 0.022 D_real: 1.079 D_fake: 0.872 
+(epoch: 263, iters: 2704, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.770 G_ID: 0.166 G_Rec: 0.384 D_GP: 0.025 D_real: 1.264 D_fake: 0.530 
+(epoch: 263, iters: 3104, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.575 G_ID: 0.123 G_Rec: 0.261 D_GP: 0.019 D_real: 1.229 D_fake: 0.723 
+(epoch: 263, iters: 3504, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.912 G_ID: 0.173 G_Rec: 0.417 D_GP: 0.049 D_real: 0.951 D_fake: 0.762 
+(epoch: 263, iters: 3904, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.614 G_ID: 0.124 G_Rec: 0.278 D_GP: 0.019 D_real: 1.216 D_fake: 0.769 
+(epoch: 263, iters: 4304, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.984 G_ID: 0.159 G_Rec: 0.426 D_GP: 0.142 D_real: 0.492 D_fake: 0.865 
+(epoch: 263, iters: 4704, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.695 G_ID: 0.130 G_Rec: 0.306 D_GP: 0.033 D_real: 1.065 D_fake: 0.768 
+(epoch: 263, iters: 5104, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.795 G_ID: 0.172 G_Rec: 0.358 D_GP: 0.022 D_real: 1.135 D_fake: 0.652 
+(epoch: 263, iters: 5504, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.676 G_ID: 0.120 G_Rec: 0.292 D_GP: 0.073 D_real: 0.927 D_fake: 0.832 
+(epoch: 263, iters: 5904, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.793 G_ID: 0.156 G_Rec: 0.330 D_GP: 0.028 D_real: 1.117 D_fake: 0.661 
+(epoch: 263, iters: 6304, time: 0.064) G_GAN: 0.016 G_GAN_Feat: 0.752 G_ID: 0.137 G_Rec: 0.300 D_GP: 0.052 D_real: 0.667 D_fake: 0.984 
+(epoch: 263, iters: 6704, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.889 G_ID: 0.174 G_Rec: 0.372 D_GP: 0.041 D_real: 0.895 D_fake: 0.752 
+(epoch: 263, iters: 7104, time: 0.064) G_GAN: -0.069 G_GAN_Feat: 0.759 G_ID: 0.152 G_Rec: 0.308 D_GP: 0.042 D_real: 0.666 D_fake: 1.070 
+(epoch: 263, iters: 7504, time: 0.064) G_GAN: 0.707 G_GAN_Feat: 0.918 G_ID: 0.157 G_Rec: 0.427 D_GP: 0.022 D_real: 1.333 D_fake: 0.333 
+(epoch: 263, iters: 7904, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.651 G_ID: 0.114 G_Rec: 0.276 D_GP: 0.029 D_real: 1.184 D_fake: 0.819 
+(epoch: 263, iters: 8304, time: 0.064) G_GAN: 0.629 G_GAN_Feat: 0.811 G_ID: 0.158 G_Rec: 0.367 D_GP: 0.024 D_real: 1.411 D_fake: 0.385 
+(epoch: 264, iters: 96, time: 0.064) G_GAN: 0.139 G_GAN_Feat: 0.649 G_ID: 0.141 G_Rec: 0.272 D_GP: 0.023 D_real: 1.111 D_fake: 0.862 
+(epoch: 264, iters: 496, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.754 G_ID: 0.164 G_Rec: 0.332 D_GP: 0.026 D_real: 1.235 D_fake: 0.622 
+(epoch: 264, iters: 896, time: 0.064) G_GAN: -0.013 G_GAN_Feat: 0.707 G_ID: 0.114 G_Rec: 0.308 D_GP: 0.051 D_real: 0.826 D_fake: 1.013 
+(epoch: 264, iters: 1296, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.832 G_ID: 0.152 G_Rec: 0.436 D_GP: 0.035 D_real: 1.044 D_fake: 0.650 
+(epoch: 264, iters: 1696, time: 0.064) G_GAN: 0.065 G_GAN_Feat: 0.720 G_ID: 0.120 G_Rec: 0.315 D_GP: 0.034 D_real: 0.828 D_fake: 0.935 
+(epoch: 264, iters: 2096, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.878 G_ID: 0.173 G_Rec: 0.412 D_GP: 0.045 D_real: 0.993 D_fake: 0.649 
+(epoch: 264, iters: 2496, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.726 G_ID: 0.114 G_Rec: 0.281 D_GP: 0.053 D_real: 1.063 D_fake: 0.674 
+(epoch: 264, iters: 2896, time: 0.064) G_GAN: 0.072 G_GAN_Feat: 0.955 G_ID: 0.173 G_Rec: 0.388 D_GP: 0.034 D_real: 0.984 D_fake: 0.936 
+(epoch: 264, iters: 3296, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.869 G_ID: 0.132 G_Rec: 0.362 D_GP: 0.061 D_real: 0.972 D_fake: 0.575 
+(epoch: 264, iters: 3696, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.827 G_ID: 0.176 G_Rec: 0.379 D_GP: 0.023 D_real: 1.225 D_fake: 0.567 
+(epoch: 264, iters: 4096, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.643 G_ID: 0.115 G_Rec: 0.273 D_GP: 0.024 D_real: 1.249 D_fake: 0.706 
+(epoch: 264, iters: 4496, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 1.000 G_ID: 0.154 G_Rec: 0.404 D_GP: 0.070 D_real: 0.906 D_fake: 0.515 
+(epoch: 264, iters: 4896, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.784 G_ID: 0.133 G_Rec: 0.318 D_GP: 0.048 D_real: 0.678 D_fake: 0.893 
+(epoch: 264, iters: 5296, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.831 G_ID: 0.159 G_Rec: 0.408 D_GP: 0.020 D_real: 1.141 D_fake: 0.603 
+(epoch: 264, iters: 5696, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.660 G_ID: 0.128 G_Rec: 0.285 D_GP: 0.030 D_real: 0.982 D_fake: 0.883 
+(epoch: 264, iters: 6096, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 0.900 G_ID: 0.191 G_Rec: 0.390 D_GP: 0.036 D_real: 1.168 D_fake: 0.540 
+(epoch: 264, iters: 6496, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.697 G_ID: 0.125 G_Rec: 0.267 D_GP: 0.033 D_real: 0.986 D_fake: 0.777 
+(epoch: 264, iters: 6896, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.994 G_ID: 0.169 G_Rec: 0.410 D_GP: 0.047 D_real: 0.749 D_fake: 0.599 
+(epoch: 264, iters: 7296, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.931 G_ID: 0.137 G_Rec: 0.331 D_GP: 0.496 D_real: 0.554 D_fake: 0.668 
+(epoch: 264, iters: 7696, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.933 G_ID: 0.168 G_Rec: 0.402 D_GP: 0.023 D_real: 1.188 D_fake: 0.440 
+(epoch: 264, iters: 8096, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.671 G_ID: 0.122 G_Rec: 0.309 D_GP: 0.022 D_real: 1.377 D_fake: 0.614 
+(epoch: 264, iters: 8496, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 1.106 G_ID: 0.151 G_Rec: 0.432 D_GP: 0.130 D_real: 0.797 D_fake: 0.492 
+(epoch: 265, iters: 288, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.716 G_ID: 0.134 G_Rec: 0.318 D_GP: 0.024 D_real: 1.207 D_fake: 0.758 
+(epoch: 265, iters: 688, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.835 G_ID: 0.156 G_Rec: 0.356 D_GP: 0.055 D_real: 1.123 D_fake: 0.543 
+(epoch: 265, iters: 1088, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.869 G_ID: 0.113 G_Rec: 0.293 D_GP: 0.147 D_real: 0.409 D_fake: 0.772 
+(epoch: 265, iters: 1488, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.951 G_ID: 0.157 G_Rec: 0.388 D_GP: 0.029 D_real: 0.985 D_fake: 0.519 
+(epoch: 265, iters: 1888, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.703 G_ID: 0.108 G_Rec: 0.296 D_GP: 0.026 D_real: 1.352 D_fake: 0.641 
+(epoch: 265, iters: 2288, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 1.019 G_ID: 0.183 G_Rec: 0.437 D_GP: 0.082 D_real: 0.808 D_fake: 0.584 
+(epoch: 265, iters: 2688, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.937 G_ID: 0.132 G_Rec: 0.322 D_GP: 0.063 D_real: 0.468 D_fake: 0.727 
+(epoch: 265, iters: 3088, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.796 G_ID: 0.165 G_Rec: 0.384 D_GP: 0.019 D_real: 1.231 D_fake: 0.527 
+(epoch: 265, iters: 3488, time: 0.064) G_GAN: -0.013 G_GAN_Feat: 0.647 G_ID: 0.122 G_Rec: 0.294 D_GP: 0.018 D_real: 0.928 D_fake: 1.013 
+(epoch: 265, iters: 3888, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.965 G_ID: 0.165 G_Rec: 0.406 D_GP: 0.039 D_real: 0.924 D_fake: 0.535 
+(epoch: 265, iters: 4288, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.729 G_ID: 0.111 G_Rec: 0.300 D_GP: 0.028 D_real: 1.190 D_fake: 0.608 
+(epoch: 265, iters: 4688, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.827 G_ID: 0.158 G_Rec: 0.349 D_GP: 0.022 D_real: 1.249 D_fake: 0.487 
+(epoch: 265, iters: 5088, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.624 G_ID: 0.140 G_Rec: 0.332 D_GP: 0.019 D_real: 1.245 D_fake: 0.723 
+(epoch: 265, iters: 5488, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 0.666 G_ID: 0.165 G_Rec: 0.350 D_GP: 0.020 D_real: 1.261 D_fake: 0.562 
+(epoch: 265, iters: 5888, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.576 G_ID: 0.108 G_Rec: 0.282 D_GP: 0.023 D_real: 1.131 D_fake: 0.821 
+(epoch: 265, iters: 6288, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.814 G_ID: 0.159 G_Rec: 0.435 D_GP: 0.034 D_real: 1.143 D_fake: 0.576 
+(epoch: 265, iters: 6688, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.716 G_ID: 0.119 G_Rec: 0.309 D_GP: 0.052 D_real: 0.839 D_fake: 0.927 
+(epoch: 265, iters: 7088, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 0.795 G_ID: 0.150 G_Rec: 0.387 D_GP: 0.022 D_real: 1.231 D_fake: 0.511 
+(epoch: 265, iters: 7488, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.669 G_ID: 0.144 G_Rec: 0.284 D_GP: 0.020 D_real: 1.141 D_fake: 0.737 
+(epoch: 265, iters: 7888, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.924 G_ID: 0.160 G_Rec: 0.387 D_GP: 0.046 D_real: 0.985 D_fake: 0.518 
+(epoch: 265, iters: 8288, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.713 G_ID: 0.107 G_Rec: 0.330 D_GP: 0.035 D_real: 1.045 D_fake: 0.689 
+(epoch: 266, iters: 80, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.962 G_ID: 0.173 G_Rec: 0.421 D_GP: 0.033 D_real: 0.747 D_fake: 0.752 
+(epoch: 266, iters: 480, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.847 G_ID: 0.151 G_Rec: 0.327 D_GP: 0.045 D_real: 0.475 D_fake: 0.900 
+(epoch: 266, iters: 880, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.823 G_ID: 0.163 G_Rec: 0.376 D_GP: 0.024 D_real: 1.130 D_fake: 0.754 
+(epoch: 266, iters: 1280, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 0.696 G_ID: 0.129 G_Rec: 0.298 D_GP: 0.031 D_real: 0.932 D_fake: 0.919 
+(epoch: 266, iters: 1680, time: 0.064) G_GAN: 0.943 G_GAN_Feat: 1.122 G_ID: 0.147 G_Rec: 0.440 D_GP: 0.070 D_real: 1.356 D_fake: 0.339 
+(epoch: 266, iters: 2080, time: 0.064) G_GAN: -0.157 G_GAN_Feat: 0.752 G_ID: 0.151 G_Rec: 0.313 D_GP: 0.052 D_real: 0.572 D_fake: 1.157 
+(epoch: 266, iters: 2480, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.851 G_ID: 0.171 G_Rec: 0.384 D_GP: 0.032 D_real: 0.933 D_fake: 0.737 
+(epoch: 266, iters: 2880, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.736 G_ID: 0.134 G_Rec: 0.287 D_GP: 0.051 D_real: 1.097 D_fake: 0.780 
+(epoch: 266, iters: 3280, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 0.771 G_ID: 0.182 G_Rec: 0.370 D_GP: 0.021 D_real: 1.093 D_fake: 0.709 
+(epoch: 266, iters: 3680, time: 0.064) G_GAN: -0.063 G_GAN_Feat: 0.687 G_ID: 0.148 G_Rec: 0.287 D_GP: 0.050 D_real: 0.798 D_fake: 1.063 
+(epoch: 266, iters: 4080, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.927 G_ID: 0.163 G_Rec: 0.418 D_GP: 0.034 D_real: 0.993 D_fake: 0.622 
+(epoch: 266, iters: 4480, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 1.043 G_ID: 0.116 G_Rec: 0.330 D_GP: 1.340 D_real: 0.541 D_fake: 0.691 
+(epoch: 266, iters: 4880, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.871 G_ID: 0.179 G_Rec: 0.382 D_GP: 0.023 D_real: 1.067 D_fake: 0.599 
+(epoch: 266, iters: 5280, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.876 G_ID: 0.147 G_Rec: 0.341 D_GP: 0.057 D_real: 0.762 D_fake: 0.738 
+(epoch: 266, iters: 5680, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 0.913 G_ID: 0.165 G_Rec: 0.389 D_GP: 0.027 D_real: 1.172 D_fake: 0.445 
+(epoch: 266, iters: 6080, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.658 G_ID: 0.103 G_Rec: 0.265 D_GP: 0.022 D_real: 1.311 D_fake: 0.614 
+(epoch: 266, iters: 6480, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.910 G_ID: 0.169 G_Rec: 0.411 D_GP: 0.028 D_real: 1.036 D_fake: 0.609 
+(epoch: 266, iters: 6880, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.779 G_ID: 0.119 G_Rec: 0.303 D_GP: 0.052 D_real: 1.251 D_fake: 0.491 
+(epoch: 266, iters: 7280, time: 0.064) G_GAN: 0.706 G_GAN_Feat: 0.852 G_ID: 0.161 G_Rec: 0.347 D_GP: 0.023 D_real: 1.561 D_fake: 0.317 
+(epoch: 266, iters: 7680, time: 0.064) G_GAN: 0.410 G_GAN_Feat: 0.681 G_ID: 0.126 G_Rec: 0.310 D_GP: 0.027 D_real: 1.349 D_fake: 0.600 
+(epoch: 266, iters: 8080, time: 0.064) G_GAN: 0.609 G_GAN_Feat: 1.046 G_ID: 0.167 G_Rec: 0.400 D_GP: 0.080 D_real: 0.564 D_fake: 0.404 
+(epoch: 266, iters: 8480, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.699 G_ID: 0.131 G_Rec: 0.292 D_GP: 0.026 D_real: 1.143 D_fake: 0.662 
+(epoch: 267, iters: 272, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 0.993 G_ID: 0.160 G_Rec: 0.413 D_GP: 0.029 D_real: 1.023 D_fake: 0.594 
+(epoch: 267, iters: 672, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.835 G_ID: 0.139 G_Rec: 0.296 D_GP: 0.112 D_real: 0.743 D_fake: 0.750 
+(epoch: 267, iters: 1072, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.797 G_ID: 0.199 G_Rec: 0.398 D_GP: 0.019 D_real: 1.091 D_fake: 0.662 
+(epoch: 267, iters: 1472, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.583 G_ID: 0.120 G_Rec: 0.316 D_GP: 0.020 D_real: 1.256 D_fake: 0.709 
+(epoch: 267, iters: 1872, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.951 G_ID: 0.208 G_Rec: 0.452 D_GP: 0.055 D_real: 0.878 D_fake: 0.671 
+(epoch: 267, iters: 2272, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.687 G_ID: 0.119 G_Rec: 0.294 D_GP: 0.033 D_real: 1.013 D_fake: 0.845 
+(epoch: 267, iters: 2672, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.797 G_ID: 0.193 G_Rec: 0.413 D_GP: 0.020 D_real: 1.101 D_fake: 0.622 
+(epoch: 267, iters: 3072, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.621 G_ID: 0.125 G_Rec: 0.298 D_GP: 0.028 D_real: 1.112 D_fake: 0.818 
+(epoch: 267, iters: 3472, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.845 G_ID: 0.156 G_Rec: 0.405 D_GP: 0.030 D_real: 1.119 D_fake: 0.600 
+(epoch: 267, iters: 3872, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.612 G_ID: 0.124 G_Rec: 0.286 D_GP: 0.024 D_real: 1.080 D_fake: 0.849 
+(epoch: 267, iters: 4272, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.865 G_ID: 0.186 G_Rec: 0.388 D_GP: 0.139 D_real: 0.852 D_fake: 0.772 
+(epoch: 267, iters: 4672, time: 0.064) G_GAN: 0.072 G_GAN_Feat: 0.713 G_ID: 0.132 G_Rec: 0.293 D_GP: 0.041 D_real: 0.812 D_fake: 0.928 
+(epoch: 267, iters: 5072, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.756 G_ID: 0.173 G_Rec: 0.335 D_GP: 0.020 D_real: 1.117 D_fake: 0.653 
+(epoch: 267, iters: 5472, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.814 G_ID: 0.132 G_Rec: 0.314 D_GP: 0.093 D_real: 1.083 D_fake: 0.662 
+(epoch: 267, iters: 5872, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 1.252 G_ID: 0.149 G_Rec: 0.427 D_GP: 0.091 D_real: 1.158 D_fake: 0.697 
+(epoch: 267, iters: 6272, time: 0.064) G_GAN: 0.043 G_GAN_Feat: 0.691 G_ID: 0.133 G_Rec: 0.296 D_GP: 0.031 D_real: 0.830 D_fake: 0.957 
+(epoch: 267, iters: 6672, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.951 G_ID: 0.153 G_Rec: 0.433 D_GP: 0.023 D_real: 1.093 D_fake: 0.716 
+(epoch: 267, iters: 7072, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.732 G_ID: 0.124 G_Rec: 0.296 D_GP: 0.040 D_real: 0.987 D_fake: 0.885 
+(epoch: 267, iters: 7472, time: 0.064) G_GAN: 0.572 G_GAN_Feat: 1.029 G_ID: 0.160 G_Rec: 0.432 D_GP: 0.039 D_real: 0.963 D_fake: 0.451 
+(epoch: 267, iters: 7872, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.747 G_ID: 0.169 G_Rec: 0.296 D_GP: 0.024 D_real: 1.145 D_fake: 0.682 
+(epoch: 267, iters: 8272, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.818 G_ID: 0.141 G_Rec: 0.403 D_GP: 0.018 D_real: 1.227 D_fake: 0.599 
+(epoch: 268, iters: 64, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.704 G_ID: 0.140 G_Rec: 0.306 D_GP: 0.027 D_real: 0.863 D_fake: 0.977 
+(epoch: 268, iters: 464, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.907 G_ID: 0.137 G_Rec: 0.403 D_GP: 0.033 D_real: 0.998 D_fake: 0.518 
+(epoch: 268, iters: 864, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.725 G_ID: 0.108 G_Rec: 0.295 D_GP: 0.104 D_real: 1.142 D_fake: 0.540 
+(epoch: 268, iters: 1264, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.990 G_ID: 0.141 G_Rec: 0.427 D_GP: 0.036 D_real: 1.046 D_fake: 0.518 
+(epoch: 268, iters: 1664, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 0.725 G_ID: 0.141 G_Rec: 0.306 D_GP: 0.021 D_real: 1.427 D_fake: 0.506 
+(epoch: 268, iters: 2064, time: 0.064) G_GAN: -0.010 G_GAN_Feat: 0.968 G_ID: 0.187 G_Rec: 0.393 D_GP: 0.051 D_real: 0.470 D_fake: 1.010 
+(epoch: 268, iters: 2464, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.728 G_ID: 0.143 G_Rec: 0.329 D_GP: 0.023 D_real: 1.242 D_fake: 0.638 
+(epoch: 268, iters: 2864, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.891 G_ID: 0.183 G_Rec: 0.363 D_GP: 0.020 D_real: 1.082 D_fake: 0.642 
+(epoch: 268, iters: 3264, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.854 G_ID: 0.096 G_Rec: 0.330 D_GP: 0.056 D_real: 0.877 D_fake: 0.730 
+(epoch: 268, iters: 3664, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.866 G_ID: 0.170 G_Rec: 0.366 D_GP: 0.047 D_real: 1.245 D_fake: 0.486 
+(epoch: 268, iters: 4064, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.571 G_ID: 0.115 G_Rec: 0.293 D_GP: 0.022 D_real: 1.112 D_fake: 0.853 
+(epoch: 268, iters: 4464, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.858 G_ID: 0.150 G_Rec: 0.416 D_GP: 0.034 D_real: 0.963 D_fake: 0.720 
+(epoch: 268, iters: 4864, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.628 G_ID: 0.118 G_Rec: 0.278 D_GP: 0.024 D_real: 1.194 D_fake: 0.744 
+(epoch: 268, iters: 5264, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.962 G_ID: 0.179 G_Rec: 0.433 D_GP: 0.157 D_real: 0.609 D_fake: 0.667 
+(epoch: 268, iters: 5664, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.689 G_ID: 0.130 G_Rec: 0.290 D_GP: 0.029 D_real: 0.908 D_fake: 0.968 
+(epoch: 268, iters: 6064, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.981 G_ID: 0.179 G_Rec: 0.410 D_GP: 0.033 D_real: 0.511 D_fake: 0.895 
+(epoch: 268, iters: 6464, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.711 G_ID: 0.121 G_Rec: 0.281 D_GP: 0.033 D_real: 1.063 D_fake: 0.909 
+(epoch: 268, iters: 6864, time: 0.064) G_GAN: 0.666 G_GAN_Feat: 0.945 G_ID: 0.186 G_Rec: 0.407 D_GP: 0.032 D_real: 1.256 D_fake: 0.359 
+(epoch: 268, iters: 7264, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.596 G_ID: 0.122 G_Rec: 0.299 D_GP: 0.018 D_real: 1.254 D_fake: 0.735 
+(epoch: 268, iters: 7664, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.714 G_ID: 0.153 G_Rec: 0.324 D_GP: 0.022 D_real: 1.134 D_fake: 0.697 
+(epoch: 268, iters: 8064, time: 0.064) G_GAN: 0.007 G_GAN_Feat: 0.641 G_ID: 0.131 G_Rec: 0.290 D_GP: 0.022 D_real: 0.921 D_fake: 0.993 
+(epoch: 268, iters: 8464, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.808 G_ID: 0.173 G_Rec: 0.388 D_GP: 0.023 D_real: 1.105 D_fake: 0.637 
+(epoch: 269, iters: 256, time: 0.064) G_GAN: -0.070 G_GAN_Feat: 0.762 G_ID: 0.188 G_Rec: 0.281 D_GP: 0.200 D_real: 0.384 D_fake: 1.070 
+(epoch: 269, iters: 656, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.937 G_ID: 0.167 G_Rec: 0.380 D_GP: 0.025 D_real: 0.939 D_fake: 0.688 
+(epoch: 269, iters: 1056, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.815 G_ID: 0.127 G_Rec: 0.334 D_GP: 0.028 D_real: 0.901 D_fake: 0.746 
+(epoch: 269, iters: 1456, time: 0.064) G_GAN: 0.139 G_GAN_Feat: 0.740 G_ID: 0.200 G_Rec: 0.391 D_GP: 0.021 D_real: 0.993 D_fake: 0.861 
+(epoch: 269, iters: 1856, time: 0.064) G_GAN: 0.131 G_GAN_Feat: 0.696 G_ID: 0.101 G_Rec: 0.302 D_GP: 0.027 D_real: 1.006 D_fake: 0.869 
+(epoch: 269, iters: 2256, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.838 G_ID: 0.187 G_Rec: 0.393 D_GP: 0.031 D_real: 1.056 D_fake: 0.631 
+(epoch: 269, iters: 2656, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.743 G_ID: 0.111 G_Rec: 0.295 D_GP: 0.034 D_real: 0.976 D_fake: 0.665 
+(epoch: 269, iters: 3056, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.851 G_ID: 0.166 G_Rec: 0.422 D_GP: 0.021 D_real: 1.096 D_fake: 0.643 
+(epoch: 269, iters: 3456, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.696 G_ID: 0.126 G_Rec: 0.274 D_GP: 0.035 D_real: 1.017 D_fake: 0.744 
+(epoch: 269, iters: 3856, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.788 G_ID: 0.153 G_Rec: 0.355 D_GP: 0.021 D_real: 1.205 D_fake: 0.616 
+(epoch: 269, iters: 4256, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.692 G_ID: 0.109 G_Rec: 0.284 D_GP: 0.020 D_real: 1.145 D_fake: 0.770 
+(epoch: 269, iters: 4656, time: 0.064) G_GAN: 0.579 G_GAN_Feat: 1.175 G_ID: 0.164 G_Rec: 0.436 D_GP: 1.962 D_real: 0.407 D_fake: 0.490 
+(epoch: 269, iters: 5056, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.603 G_ID: 0.123 G_Rec: 0.272 D_GP: 0.018 D_real: 1.202 D_fake: 0.750 
+(epoch: 269, iters: 5456, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.841 G_ID: 0.150 G_Rec: 0.395 D_GP: 0.023 D_real: 1.251 D_fake: 0.531 
+(epoch: 269, iters: 5856, time: 0.064) G_GAN: 0.040 G_GAN_Feat: 0.804 G_ID: 0.111 G_Rec: 0.315 D_GP: 0.049 D_real: 1.047 D_fake: 0.961 
+(epoch: 269, iters: 6256, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.834 G_ID: 0.162 G_Rec: 0.420 D_GP: 0.043 D_real: 1.018 D_fake: 0.609 
+(epoch: 269, iters: 6656, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.624 G_ID: 0.140 G_Rec: 0.260 D_GP: 0.027 D_real: 1.013 D_fake: 0.851 
+(epoch: 269, iters: 7056, time: 0.064) G_GAN: 0.017 G_GAN_Feat: 0.876 G_ID: 0.163 G_Rec: 0.424 D_GP: 0.025 D_real: 0.757 D_fake: 0.983 
+(epoch: 269, iters: 7456, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.740 G_ID: 0.140 G_Rec: 0.309 D_GP: 0.044 D_real: 0.883 D_fake: 0.825 
+(epoch: 269, iters: 7856, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.936 G_ID: 0.205 G_Rec: 0.423 D_GP: 0.031 D_real: 0.999 D_fake: 0.646 
+(epoch: 269, iters: 8256, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.674 G_ID: 0.124 G_Rec: 0.280 D_GP: 0.035 D_real: 1.054 D_fake: 0.738 
+(epoch: 270, iters: 48, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.825 G_ID: 0.168 G_Rec: 0.400 D_GP: 0.022 D_real: 1.070 D_fake: 0.640 
+(epoch: 270, iters: 448, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.712 G_ID: 0.109 G_Rec: 0.264 D_GP: 0.033 D_real: 0.888 D_fake: 0.845 
+(epoch: 270, iters: 848, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.867 G_ID: 0.163 G_Rec: 0.352 D_GP: 0.040 D_real: 1.048 D_fake: 0.640 
+(epoch: 270, iters: 1248, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.873 G_ID: 0.131 G_Rec: 0.316 D_GP: 0.065 D_real: 0.910 D_fake: 0.819 
+(epoch: 270, iters: 1648, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.971 G_ID: 0.161 G_Rec: 0.402 D_GP: 0.025 D_real: 0.989 D_fake: 0.774 
+(epoch: 270, iters: 2048, time: 0.064) G_GAN: -0.054 G_GAN_Feat: 0.688 G_ID: 0.162 G_Rec: 0.308 D_GP: 0.026 D_real: 0.812 D_fake: 1.054 
+(epoch: 270, iters: 2448, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.887 G_ID: 0.175 G_Rec: 0.409 D_GP: 0.032 D_real: 0.965 D_fake: 0.732 
+(epoch: 270, iters: 2848, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.774 G_ID: 0.122 G_Rec: 0.329 D_GP: 0.029 D_real: 0.793 D_fake: 0.855 
+(epoch: 270, iters: 3248, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.883 G_ID: 0.170 G_Rec: 0.383 D_GP: 0.081 D_real: 1.177 D_fake: 0.440 
+(epoch: 270, iters: 3648, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.739 G_ID: 0.137 G_Rec: 0.349 D_GP: 0.044 D_real: 0.785 D_fake: 0.882 
+(epoch: 270, iters: 4048, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 1.002 G_ID: 0.150 G_Rec: 0.389 D_GP: 0.052 D_real: 0.659 D_fake: 0.522 
+(epoch: 270, iters: 4448, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.683 G_ID: 0.136 G_Rec: 0.291 D_GP: 0.021 D_real: 1.157 D_fake: 0.721 
+(epoch: 270, iters: 4848, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.906 G_ID: 0.174 G_Rec: 0.381 D_GP: 0.028 D_real: 1.129 D_fake: 0.566 
+(epoch: 270, iters: 5248, time: 0.064) G_GAN: 0.131 G_GAN_Feat: 0.726 G_ID: 0.118 G_Rec: 0.313 D_GP: 0.050 D_real: 0.946 D_fake: 0.871 
+(epoch: 270, iters: 5648, time: 0.064) G_GAN: 0.555 G_GAN_Feat: 1.019 G_ID: 0.146 G_Rec: 0.439 D_GP: 0.069 D_real: 0.874 D_fake: 0.452 
+(epoch: 270, iters: 6048, time: 0.064) G_GAN: 0.528 G_GAN_Feat: 0.743 G_ID: 0.138 G_Rec: 0.304 D_GP: 0.024 D_real: 1.371 D_fake: 0.476 
+(epoch: 270, iters: 6448, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.828 G_ID: 0.159 G_Rec: 0.382 D_GP: 0.021 D_real: 1.247 D_fake: 0.608 
+(epoch: 270, iters: 6848, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.600 G_ID: 0.124 G_Rec: 0.268 D_GP: 0.019 D_real: 1.097 D_fake: 0.860 
+(epoch: 270, iters: 7248, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.866 G_ID: 0.163 G_Rec: 0.389 D_GP: 0.022 D_real: 1.147 D_fake: 0.627 
+(epoch: 270, iters: 7648, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.704 G_ID: 0.140 G_Rec: 0.302 D_GP: 0.032 D_real: 0.981 D_fake: 0.856 
+(epoch: 270, iters: 8048, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.907 G_ID: 0.182 G_Rec: 0.386 D_GP: 0.029 D_real: 0.888 D_fake: 0.800 
+(epoch: 270, iters: 8448, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.745 G_ID: 0.157 G_Rec: 0.308 D_GP: 0.060 D_real: 0.953 D_fake: 0.705 
+(epoch: 271, iters: 240, time: 0.064) G_GAN: 0.677 G_GAN_Feat: 0.908 G_ID: 0.149 G_Rec: 0.390 D_GP: 0.027 D_real: 1.356 D_fake: 0.335 
+(epoch: 271, iters: 640, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.797 G_ID: 0.121 G_Rec: 0.291 D_GP: 0.027 D_real: 0.761 D_fake: 0.828 
+(epoch: 271, iters: 1040, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 0.930 G_ID: 0.180 G_Rec: 0.394 D_GP: 0.027 D_real: 1.103 D_fake: 0.463 
+(epoch: 271, iters: 1440, time: 0.064) G_GAN: 0.120 G_GAN_Feat: 0.678 G_ID: 0.140 G_Rec: 0.279 D_GP: 0.021 D_real: 1.022 D_fake: 0.880 
+(epoch: 271, iters: 1840, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.820 G_ID: 0.156 G_Rec: 0.413 D_GP: 0.020 D_real: 1.126 D_fake: 0.549 
+(epoch: 271, iters: 2240, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.715 G_ID: 0.129 G_Rec: 0.314 D_GP: 0.044 D_real: 1.077 D_fake: 0.800 
+(epoch: 271, iters: 2640, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.857 G_ID: 0.165 G_Rec: 0.403 D_GP: 0.021 D_real: 1.187 D_fake: 0.498 
+(epoch: 271, iters: 3040, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.647 G_ID: 0.176 G_Rec: 0.281 D_GP: 0.022 D_real: 1.201 D_fake: 0.764 
+(epoch: 271, iters: 3440, time: 0.064) G_GAN: 0.552 G_GAN_Feat: 0.915 G_ID: 0.154 G_Rec: 0.371 D_GP: 0.023 D_real: 1.229 D_fake: 0.462 
+(epoch: 271, iters: 3840, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.887 G_ID: 0.129 G_Rec: 0.305 D_GP: 0.034 D_real: 0.439 D_fake: 0.837 
+(epoch: 271, iters: 4240, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.914 G_ID: 0.193 G_Rec: 0.432 D_GP: 0.024 D_real: 1.213 D_fake: 0.611 
+(epoch: 271, iters: 4640, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 0.615 G_ID: 0.144 G_Rec: 0.299 D_GP: 0.018 D_real: 1.124 D_fake: 0.838 
+(epoch: 271, iters: 5040, time: 0.064) G_GAN: 0.165 G_GAN_Feat: 0.809 G_ID: 0.190 G_Rec: 0.366 D_GP: 0.030 D_real: 0.969 D_fake: 0.835 
+(epoch: 271, iters: 5440, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.663 G_ID: 0.127 G_Rec: 0.297 D_GP: 0.032 D_real: 1.127 D_fake: 0.753 
+(epoch: 271, iters: 5840, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.833 G_ID: 0.174 G_Rec: 0.390 D_GP: 0.027 D_real: 0.959 D_fake: 0.719 
+(epoch: 271, iters: 6240, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.836 G_ID: 0.105 G_Rec: 0.322 D_GP: 0.058 D_real: 0.816 D_fake: 0.736 
+(epoch: 271, iters: 6640, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.930 G_ID: 0.151 G_Rec: 0.408 D_GP: 0.024 D_real: 1.076 D_fake: 0.783 
+(epoch: 271, iters: 7040, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.785 G_ID: 0.119 G_Rec: 0.325 D_GP: 0.043 D_real: 0.872 D_fake: 0.900 
+(epoch: 271, iters: 7440, time: 0.064) G_GAN: 0.571 G_GAN_Feat: 0.954 G_ID: 0.170 G_Rec: 0.349 D_GP: 0.035 D_real: 1.418 D_fake: 0.439 
+(epoch: 271, iters: 7840, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.725 G_ID: 0.128 G_Rec: 0.285 D_GP: 0.034 D_real: 0.977 D_fake: 0.828 
+(epoch: 271, iters: 8240, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.852 G_ID: 0.155 G_Rec: 0.357 D_GP: 0.023 D_real: 1.311 D_fake: 0.554 
+(epoch: 272, iters: 32, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.978 G_ID: 0.132 G_Rec: 0.341 D_GP: 0.108 D_real: 0.445 D_fake: 0.706 
+(epoch: 272, iters: 432, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.915 G_ID: 0.175 G_Rec: 0.397 D_GP: 0.027 D_real: 1.022 D_fake: 0.670 
+(epoch: 272, iters: 832, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.682 G_ID: 0.124 G_Rec: 0.336 D_GP: 0.028 D_real: 1.284 D_fake: 0.712 
+(epoch: 272, iters: 1232, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.776 G_ID: 0.174 G_Rec: 0.397 D_GP: 0.020 D_real: 1.082 D_fake: 0.700 
+(epoch: 272, iters: 1632, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.560 G_ID: 0.099 G_Rec: 0.254 D_GP: 0.021 D_real: 1.225 D_fake: 0.717 
+(epoch: 272, iters: 2032, time: 0.064) G_GAN: -0.015 G_GAN_Feat: 0.963 G_ID: 0.158 G_Rec: 0.443 D_GP: 0.180 D_real: 0.612 D_fake: 1.015 
+(epoch: 272, iters: 2432, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.633 G_ID: 0.141 G_Rec: 0.273 D_GP: 0.037 D_real: 1.110 D_fake: 0.739 
+(epoch: 272, iters: 2832, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.974 G_ID: 0.169 G_Rec: 0.400 D_GP: 0.028 D_real: 1.195 D_fake: 0.552 
+(epoch: 272, iters: 3232, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.756 G_ID: 0.115 G_Rec: 0.330 D_GP: 0.044 D_real: 0.871 D_fake: 0.952 
+(epoch: 272, iters: 3632, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.862 G_ID: 0.164 G_Rec: 0.386 D_GP: 0.020 D_real: 1.258 D_fake: 0.540 
+(epoch: 272, iters: 4032, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.758 G_ID: 0.121 G_Rec: 0.312 D_GP: 0.040 D_real: 0.977 D_fake: 0.755 
+(epoch: 272, iters: 4432, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 0.946 G_ID: 0.160 G_Rec: 0.374 D_GP: 0.064 D_real: 0.897 D_fake: 0.462 
+(epoch: 272, iters: 4832, time: 0.064) G_GAN: 0.663 G_GAN_Feat: 0.775 G_ID: 0.126 G_Rec: 0.281 D_GP: 0.112 D_real: 1.288 D_fake: 0.545 
+(epoch: 272, iters: 5232, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 1.027 G_ID: 0.160 G_Rec: 0.396 D_GP: 0.186 D_real: 0.459 D_fake: 0.561 
+(epoch: 272, iters: 5632, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.652 G_ID: 0.150 G_Rec: 0.307 D_GP: 0.019 D_real: 1.324 D_fake: 0.704 
+(epoch: 272, iters: 6032, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.780 G_ID: 0.180 G_Rec: 0.375 D_GP: 0.020 D_real: 1.271 D_fake: 0.546 
+(epoch: 272, iters: 6432, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.618 G_ID: 0.119 G_Rec: 0.297 D_GP: 0.022 D_real: 1.211 D_fake: 0.716 
+(epoch: 272, iters: 6832, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.735 G_ID: 0.173 G_Rec: 0.352 D_GP: 0.023 D_real: 1.333 D_fake: 0.512 
+(epoch: 272, iters: 7232, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.696 G_ID: 0.131 G_Rec: 0.328 D_GP: 0.034 D_real: 0.962 D_fake: 0.864 
+(epoch: 272, iters: 7632, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.974 G_ID: 0.169 G_Rec: 0.411 D_GP: 0.069 D_real: 0.892 D_fake: 0.660 
+(epoch: 272, iters: 8032, time: 0.064) G_GAN: 0.165 G_GAN_Feat: 0.622 G_ID: 0.111 G_Rec: 0.272 D_GP: 0.026 D_real: 1.077 D_fake: 0.835 
+(epoch: 272, iters: 8432, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.822 G_ID: 0.166 G_Rec: 0.373 D_GP: 0.037 D_real: 1.076 D_fake: 0.619 
+(epoch: 273, iters: 224, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.631 G_ID: 0.105 G_Rec: 0.329 D_GP: 0.035 D_real: 1.056 D_fake: 0.823 
+(epoch: 273, iters: 624, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.886 G_ID: 0.141 G_Rec: 0.392 D_GP: 0.036 D_real: 0.927 D_fake: 0.650 
+(epoch: 273, iters: 1024, time: 0.064) G_GAN: 0.109 G_GAN_Feat: 0.755 G_ID: 0.135 G_Rec: 0.358 D_GP: 0.053 D_real: 0.760 D_fake: 0.891 
+(epoch: 273, iters: 1424, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.811 G_ID: 0.175 G_Rec: 0.365 D_GP: 0.029 D_real: 1.064 D_fake: 0.663 
+(epoch: 273, iters: 1824, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.762 G_ID: 0.134 G_Rec: 0.341 D_GP: 0.047 D_real: 1.085 D_fake: 0.625 
+(epoch: 273, iters: 2224, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 1.014 G_ID: 0.157 G_Rec: 0.453 D_GP: 0.153 D_real: 0.456 D_fake: 0.748 
+(epoch: 273, iters: 2624, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.815 G_ID: 0.128 G_Rec: 0.296 D_GP: 0.028 D_real: 1.134 D_fake: 0.757 
+(epoch: 273, iters: 3024, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 0.934 G_ID: 0.172 G_Rec: 0.396 D_GP: 0.060 D_real: 0.954 D_fake: 0.458 
+(epoch: 273, iters: 3424, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.706 G_ID: 0.097 G_Rec: 0.277 D_GP: 0.027 D_real: 1.021 D_fake: 0.810 
+(epoch: 273, iters: 3824, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.825 G_ID: 0.172 G_Rec: 0.392 D_GP: 0.022 D_real: 1.116 D_fake: 0.681 
+(epoch: 273, iters: 4224, time: 0.064) G_GAN: 0.073 G_GAN_Feat: 0.622 G_ID: 0.128 G_Rec: 0.289 D_GP: 0.019 D_real: 1.069 D_fake: 0.927 
+(epoch: 273, iters: 4624, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.879 G_ID: 0.155 G_Rec: 0.445 D_GP: 0.037 D_real: 0.930 D_fake: 0.625 
+(epoch: 273, iters: 5024, time: 0.064) G_GAN: -0.040 G_GAN_Feat: 0.789 G_ID: 0.135 G_Rec: 0.318 D_GP: 0.121 D_real: 0.510 D_fake: 1.041 
+(epoch: 273, iters: 5424, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.822 G_ID: 0.176 G_Rec: 0.374 D_GP: 0.026 D_real: 1.134 D_fake: 0.555 
+(epoch: 273, iters: 5824, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.636 G_ID: 0.134 G_Rec: 0.285 D_GP: 0.028 D_real: 1.090 D_fake: 0.788 
+(epoch: 273, iters: 6224, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.798 G_ID: 0.168 G_Rec: 0.385 D_GP: 0.030 D_real: 1.098 D_fake: 0.669 
+(epoch: 273, iters: 6624, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 1.061 G_ID: 0.135 G_Rec: 0.371 D_GP: 1.437 D_real: 0.384 D_fake: 0.766 
+(epoch: 273, iters: 7024, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.859 G_ID: 0.161 G_Rec: 0.429 D_GP: 0.022 D_real: 1.153 D_fake: 0.659 
+(epoch: 273, iters: 7424, time: 0.064) G_GAN: -0.042 G_GAN_Feat: 0.580 G_ID: 0.123 G_Rec: 0.302 D_GP: 0.019 D_real: 0.897 D_fake: 1.042 
+(epoch: 273, iters: 7824, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.852 G_ID: 0.165 G_Rec: 0.409 D_GP: 0.036 D_real: 0.943 D_fake: 0.732 
+(epoch: 273, iters: 8224, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.636 G_ID: 0.118 G_Rec: 0.272 D_GP: 0.024 D_real: 1.041 D_fake: 0.870 
+(epoch: 274, iters: 16, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.906 G_ID: 0.155 G_Rec: 0.420 D_GP: 0.060 D_real: 1.038 D_fake: 0.571 
+(epoch: 274, iters: 416, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.669 G_ID: 0.112 G_Rec: 0.302 D_GP: 0.022 D_real: 1.259 D_fake: 0.704 
+(epoch: 274, iters: 816, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.978 G_ID: 0.161 G_Rec: 0.397 D_GP: 0.302 D_real: 0.393 D_fake: 0.721 
+(epoch: 274, iters: 1216, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.773 G_ID: 0.122 G_Rec: 0.296 D_GP: 0.081 D_real: 0.699 D_fake: 0.952 
+(epoch: 274, iters: 1616, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.709 G_ID: 0.158 G_Rec: 0.381 D_GP: 0.017 D_real: 1.104 D_fake: 0.647 
+(epoch: 274, iters: 2016, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.557 G_ID: 0.110 G_Rec: 0.313 D_GP: 0.016 D_real: 1.154 D_fake: 0.781 
+(epoch: 274, iters: 2416, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.661 G_ID: 0.168 G_Rec: 0.351 D_GP: 0.021 D_real: 1.044 D_fake: 0.749 
+(epoch: 274, iters: 2816, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.527 G_ID: 0.103 G_Rec: 0.266 D_GP: 0.018 D_real: 1.102 D_fake: 0.821 
+(epoch: 274, iters: 3216, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.706 G_ID: 0.182 G_Rec: 0.349 D_GP: 0.025 D_real: 1.154 D_fake: 0.648 
+(epoch: 274, iters: 3616, time: 0.064) G_GAN: -0.057 G_GAN_Feat: 0.721 G_ID: 0.119 G_Rec: 0.333 D_GP: 0.042 D_real: 0.737 D_fake: 1.058 
+(epoch: 274, iters: 4016, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.794 G_ID: 0.157 G_Rec: 0.404 D_GP: 0.029 D_real: 1.122 D_fake: 0.570 
+(epoch: 274, iters: 4416, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.641 G_ID: 0.140 G_Rec: 0.291 D_GP: 0.038 D_real: 1.020 D_fake: 0.852 
+(epoch: 274, iters: 4816, time: 0.064) G_GAN: 0.055 G_GAN_Feat: 0.832 G_ID: 0.169 G_Rec: 0.399 D_GP: 0.044 D_real: 0.713 D_fake: 0.945 
+(epoch: 274, iters: 5216, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.780 G_ID: 0.130 G_Rec: 0.360 D_GP: 0.064 D_real: 0.675 D_fake: 0.913 
+(epoch: 274, iters: 5616, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.779 G_ID: 0.218 G_Rec: 0.399 D_GP: 0.028 D_real: 1.010 D_fake: 0.715 
+(epoch: 274, iters: 6016, time: 0.064) G_GAN: 0.050 G_GAN_Feat: 0.715 G_ID: 0.130 G_Rec: 0.289 D_GP: 0.051 D_real: 0.846 D_fake: 0.950 
+(epoch: 274, iters: 6416, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.913 G_ID: 0.196 G_Rec: 0.396 D_GP: 0.050 D_real: 0.959 D_fake: 0.655 
+(epoch: 274, iters: 6816, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.599 G_ID: 0.115 G_Rec: 0.267 D_GP: 0.024 D_real: 1.105 D_fake: 0.825 
+(epoch: 274, iters: 7216, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.844 G_ID: 0.154 G_Rec: 0.400 D_GP: 0.021 D_real: 0.960 D_fake: 0.786 
+(epoch: 274, iters: 7616, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.609 G_ID: 0.127 G_Rec: 0.278 D_GP: 0.024 D_real: 1.057 D_fake: 0.797 
+(epoch: 274, iters: 8016, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.773 G_ID: 0.177 G_Rec: 0.361 D_GP: 0.029 D_real: 1.140 D_fake: 0.705 
+(epoch: 274, iters: 8416, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.673 G_ID: 0.117 G_Rec: 0.306 D_GP: 0.056 D_real: 1.071 D_fake: 0.732 
+(epoch: 275, iters: 208, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.720 G_ID: 0.172 G_Rec: 0.348 D_GP: 0.023 D_real: 1.172 D_fake: 0.658 
+(epoch: 275, iters: 608, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.738 G_ID: 0.113 G_Rec: 0.322 D_GP: 0.037 D_real: 1.037 D_fake: 0.774 
+(epoch: 275, iters: 1008, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.800 G_ID: 0.177 G_Rec: 0.359 D_GP: 0.032 D_real: 0.883 D_fake: 0.829 
+(epoch: 275, iters: 1408, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.705 G_ID: 0.117 G_Rec: 0.292 D_GP: 0.082 D_real: 0.957 D_fake: 0.808 
+(epoch: 275, iters: 1808, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.906 G_ID: 0.160 G_Rec: 0.384 D_GP: 0.064 D_real: 0.830 D_fake: 0.682 
+(epoch: 275, iters: 2208, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.746 G_ID: 0.131 G_Rec: 0.298 D_GP: 0.051 D_real: 1.119 D_fake: 0.552 
+(epoch: 275, iters: 2608, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 1.119 G_ID: 0.153 G_Rec: 0.424 D_GP: 0.226 D_real: 1.161 D_fake: 0.615 
+(epoch: 275, iters: 3008, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.718 G_ID: 0.157 G_Rec: 0.303 D_GP: 0.020 D_real: 1.024 D_fake: 0.821 
+(epoch: 275, iters: 3408, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.753 G_ID: 0.167 G_Rec: 0.335 D_GP: 0.035 D_real: 1.051 D_fake: 0.734 
+(epoch: 275, iters: 3808, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.665 G_ID: 0.131 G_Rec: 0.278 D_GP: 0.020 D_real: 1.241 D_fake: 0.687 
+(epoch: 275, iters: 4208, time: 0.064) G_GAN: 0.575 G_GAN_Feat: 0.837 G_ID: 0.144 G_Rec: 0.401 D_GP: 0.032 D_real: 1.266 D_fake: 0.439 
+(epoch: 275, iters: 4608, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.730 G_ID: 0.126 G_Rec: 0.293 D_GP: 0.035 D_real: 0.967 D_fake: 0.790 
+(epoch: 275, iters: 5008, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.936 G_ID: 0.157 G_Rec: 0.401 D_GP: 0.026 D_real: 1.042 D_fake: 0.499 
+(epoch: 275, iters: 5408, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.674 G_ID: 0.138 G_Rec: 0.276 D_GP: 0.023 D_real: 1.068 D_fake: 0.765 
+(epoch: 275, iters: 5808, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 1.014 G_ID: 0.181 G_Rec: 0.428 D_GP: 0.213 D_real: 0.539 D_fake: 0.702 
+(epoch: 275, iters: 6208, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.884 G_ID: 0.108 G_Rec: 0.301 D_GP: 0.836 D_real: 0.518 D_fake: 0.742 
+(epoch: 275, iters: 6608, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.767 G_ID: 0.173 G_Rec: 0.348 D_GP: 0.020 D_real: 1.097 D_fake: 0.747 
+(epoch: 275, iters: 7008, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.883 G_ID: 0.110 G_Rec: 0.301 D_GP: 0.041 D_real: 0.495 D_fake: 0.883 
+(epoch: 275, iters: 7408, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 1.080 G_ID: 0.144 G_Rec: 0.442 D_GP: 0.079 D_real: 0.562 D_fake: 0.458 
+(epoch: 275, iters: 7808, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.873 G_ID: 0.118 G_Rec: 0.318 D_GP: 0.031 D_real: 0.811 D_fake: 0.645 
+(epoch: 275, iters: 8208, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.832 G_ID: 0.166 G_Rec: 0.429 D_GP: 0.018 D_real: 1.228 D_fake: 0.558 
+(epoch: 275, iters: 8608, time: 0.064) G_GAN: -0.088 G_GAN_Feat: 0.584 G_ID: 0.147 G_Rec: 0.254 D_GP: 0.021 D_real: 0.836 D_fake: 1.088 
+(epoch: 276, iters: 400, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 0.962 G_ID: 0.170 G_Rec: 0.417 D_GP: 0.046 D_real: 1.069 D_fake: 0.418 
+(epoch: 276, iters: 800, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.687 G_ID: 0.120 G_Rec: 0.313 D_GP: 0.036 D_real: 1.207 D_fake: 0.627 
+(epoch: 276, iters: 1200, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 0.967 G_ID: 0.162 G_Rec: 0.431 D_GP: 0.025 D_real: 1.114 D_fake: 0.430 
+(epoch: 276, iters: 1600, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.695 G_ID: 0.158 G_Rec: 0.309 D_GP: 0.020 D_real: 1.165 D_fake: 0.724 
+(epoch: 276, iters: 2000, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.978 G_ID: 0.161 G_Rec: 0.402 D_GP: 0.062 D_real: 0.715 D_fake: 0.567 
+(epoch: 276, iters: 2400, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.663 G_ID: 0.115 G_Rec: 0.296 D_GP: 0.022 D_real: 1.178 D_fake: 0.914 
+(epoch: 276, iters: 2800, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.677 G_ID: 0.159 G_Rec: 0.327 D_GP: 0.020 D_real: 1.334 D_fake: 0.573 
+(epoch: 276, iters: 3200, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.572 G_ID: 0.126 G_Rec: 0.277 D_GP: 0.021 D_real: 1.285 D_fake: 0.679 
+(epoch: 276, iters: 3600, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.735 G_ID: 0.144 G_Rec: 0.340 D_GP: 0.022 D_real: 1.276 D_fake: 0.573 
+(epoch: 276, iters: 4000, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.611 G_ID: 0.132 G_Rec: 0.290 D_GP: 0.025 D_real: 1.097 D_fake: 0.789 
+(epoch: 276, iters: 4400, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.806 G_ID: 0.160 G_Rec: 0.386 D_GP: 0.023 D_real: 1.188 D_fake: 0.565 
+(epoch: 276, iters: 4800, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.718 G_ID: 0.132 G_Rec: 0.317 D_GP: 0.060 D_real: 0.803 D_fake: 0.909 
+(epoch: 276, iters: 5200, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.810 G_ID: 0.166 G_Rec: 0.400 D_GP: 0.024 D_real: 1.126 D_fake: 0.651 
+(epoch: 276, iters: 5600, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.678 G_ID: 0.117 G_Rec: 0.314 D_GP: 0.030 D_real: 0.922 D_fake: 0.936 
+(epoch: 276, iters: 6000, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.785 G_ID: 0.166 G_Rec: 0.375 D_GP: 0.028 D_real: 1.015 D_fake: 0.741 
+(epoch: 276, iters: 6400, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.642 G_ID: 0.113 G_Rec: 0.272 D_GP: 0.022 D_real: 1.048 D_fake: 0.840 
+(epoch: 276, iters: 6800, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.857 G_ID: 0.150 G_Rec: 0.375 D_GP: 0.028 D_real: 1.078 D_fake: 0.635 
+(epoch: 276, iters: 7200, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.577 G_ID: 0.119 G_Rec: 0.261 D_GP: 0.024 D_real: 1.133 D_fake: 0.818 
+(epoch: 276, iters: 7600, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.905 G_ID: 0.140 G_Rec: 0.397 D_GP: 0.036 D_real: 0.950 D_fake: 0.621 
+(epoch: 276, iters: 8000, time: 0.064) G_GAN: 0.037 G_GAN_Feat: 0.830 G_ID: 0.144 G_Rec: 0.339 D_GP: 0.083 D_real: 0.769 D_fake: 0.963 
+(epoch: 276, iters: 8400, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.859 G_ID: 0.176 G_Rec: 0.384 D_GP: 0.021 D_real: 0.939 D_fake: 0.751 
+(epoch: 277, iters: 192, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 0.704 G_ID: 0.116 G_Rec: 0.282 D_GP: 0.042 D_real: 1.137 D_fake: 0.686 
+(epoch: 277, iters: 592, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.853 G_ID: 0.157 G_Rec: 0.376 D_GP: 0.022 D_real: 1.127 D_fake: 0.621 
+(epoch: 277, iters: 992, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.742 G_ID: 0.147 G_Rec: 0.384 D_GP: 0.047 D_real: 0.957 D_fake: 0.802 
+(epoch: 277, iters: 1392, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.935 G_ID: 0.161 G_Rec: 0.408 D_GP: 0.036 D_real: 0.943 D_fake: 0.634 
+(epoch: 277, iters: 1792, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.684 G_ID: 0.125 G_Rec: 0.271 D_GP: 0.029 D_real: 1.092 D_fake: 0.729 
+(epoch: 277, iters: 2192, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.834 G_ID: 0.185 G_Rec: 0.366 D_GP: 0.021 D_real: 1.043 D_fake: 0.742 
+(epoch: 277, iters: 2592, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.902 G_ID: 0.118 G_Rec: 0.316 D_GP: 0.095 D_real: 0.582 D_fake: 0.764 
+(epoch: 277, iters: 2992, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.929 G_ID: 0.188 G_Rec: 0.380 D_GP: 0.047 D_real: 1.153 D_fake: 0.557 
+(epoch: 277, iters: 3392, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.631 G_ID: 0.114 G_Rec: 0.301 D_GP: 0.019 D_real: 1.244 D_fake: 0.747 
+(epoch: 277, iters: 3792, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.730 G_ID: 0.148 G_Rec: 0.354 D_GP: 0.022 D_real: 1.025 D_fake: 0.781 
+(epoch: 277, iters: 4192, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.719 G_ID: 0.135 G_Rec: 0.306 D_GP: 0.051 D_real: 0.914 D_fake: 0.914 
+(epoch: 277, iters: 4592, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.953 G_ID: 0.167 G_Rec: 0.395 D_GP: 0.055 D_real: 0.806 D_fake: 0.661 
+(epoch: 277, iters: 4992, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.800 G_ID: 0.122 G_Rec: 0.333 D_GP: 0.039 D_real: 1.110 D_fake: 0.630 
+(epoch: 277, iters: 5392, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.849 G_ID: 0.180 G_Rec: 0.338 D_GP: 0.053 D_real: 0.761 D_fake: 0.898 
+(epoch: 277, iters: 5792, time: 0.064) G_GAN: -0.208 G_GAN_Feat: 0.902 G_ID: 0.144 G_Rec: 0.336 D_GP: 0.130 D_real: 0.122 D_fake: 1.209 
+(epoch: 277, iters: 6192, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 0.976 G_ID: 0.173 G_Rec: 0.400 D_GP: 0.038 D_real: 1.188 D_fake: 0.382 
+(epoch: 277, iters: 6592, time: 0.064) G_GAN: -0.005 G_GAN_Feat: 0.747 G_ID: 0.127 G_Rec: 0.310 D_GP: 0.028 D_real: 0.881 D_fake: 1.005 
+(epoch: 277, iters: 6992, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.905 G_ID: 0.166 G_Rec: 0.397 D_GP: 0.022 D_real: 0.907 D_fake: 0.778 
+(epoch: 277, iters: 7392, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.722 G_ID: 0.117 G_Rec: 0.328 D_GP: 0.036 D_real: 1.295 D_fake: 0.612 
+(epoch: 277, iters: 7792, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 1.067 G_ID: 0.151 G_Rec: 0.418 D_GP: 0.046 D_real: 0.470 D_fake: 0.517 
+(epoch: 277, iters: 8192, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.709 G_ID: 0.116 G_Rec: 0.309 D_GP: 0.018 D_real: 1.195 D_fake: 0.746 
+(epoch: 277, iters: 8592, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.935 G_ID: 0.149 G_Rec: 0.389 D_GP: 0.039 D_real: 0.759 D_fake: 0.780 
+(epoch: 278, iters: 384, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.815 G_ID: 0.117 G_Rec: 0.314 D_GP: 0.107 D_real: 0.558 D_fake: 0.821 
+(epoch: 278, iters: 784, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.914 G_ID: 0.187 G_Rec: 0.405 D_GP: 0.032 D_real: 1.071 D_fake: 0.780 
+(epoch: 278, iters: 1184, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.626 G_ID: 0.125 G_Rec: 0.277 D_GP: 0.020 D_real: 1.053 D_fake: 0.909 
+(epoch: 278, iters: 1584, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.803 G_ID: 0.161 G_Rec: 0.361 D_GP: 0.021 D_real: 1.282 D_fake: 0.541 
+(epoch: 278, iters: 1984, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 1.031 G_ID: 0.121 G_Rec: 0.314 D_GP: 0.384 D_real: 0.402 D_fake: 0.753 
+(epoch: 278, iters: 2384, time: 0.064) G_GAN: -0.027 G_GAN_Feat: 1.030 G_ID: 0.178 G_Rec: 0.381 D_GP: 0.096 D_real: 0.196 D_fake: 1.027 
+(epoch: 278, iters: 2784, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.763 G_ID: 0.117 G_Rec: 0.291 D_GP: 0.035 D_real: 0.870 D_fake: 0.895 
+(epoch: 278, iters: 3184, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 1.005 G_ID: 0.151 G_Rec: 0.419 D_GP: 0.044 D_real: 0.834 D_fake: 0.585 
+(epoch: 278, iters: 3584, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.721 G_ID: 0.142 G_Rec: 0.293 D_GP: 0.025 D_real: 1.204 D_fake: 0.597 
+(epoch: 278, iters: 3984, time: 0.064) G_GAN: 0.563 G_GAN_Feat: 0.875 G_ID: 0.141 G_Rec: 0.369 D_GP: 0.025 D_real: 1.214 D_fake: 0.441 
+(epoch: 278, iters: 4384, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.696 G_ID: 0.151 G_Rec: 0.308 D_GP: 0.022 D_real: 1.246 D_fake: 0.615 
+(epoch: 278, iters: 4784, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.742 G_ID: 0.181 G_Rec: 0.391 D_GP: 0.018 D_real: 1.171 D_fake: 0.660 
+(epoch: 278, iters: 5184, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.606 G_ID: 0.107 G_Rec: 0.301 D_GP: 0.017 D_real: 1.112 D_fake: 0.889 
+(epoch: 278, iters: 5584, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.708 G_ID: 0.191 G_Rec: 0.358 D_GP: 0.019 D_real: 0.903 D_fake: 0.907 
+(epoch: 278, iters: 5984, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.518 G_ID: 0.115 G_Rec: 0.252 D_GP: 0.020 D_real: 1.100 D_fake: 0.872 
+(epoch: 278, iters: 6384, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.723 G_ID: 0.167 G_Rec: 0.344 D_GP: 0.024 D_real: 0.964 D_fake: 0.868 
+(epoch: 278, iters: 6784, time: 0.064) G_GAN: -0.101 G_GAN_Feat: 0.694 G_ID: 0.138 G_Rec: 0.324 D_GP: 0.040 D_real: 0.737 D_fake: 1.101 
+(epoch: 278, iters: 7184, time: 0.064) G_GAN: 0.480 G_GAN_Feat: 0.901 G_ID: 0.153 G_Rec: 0.384 D_GP: 0.037 D_real: 1.084 D_fake: 0.534 
+(epoch: 278, iters: 7584, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.622 G_ID: 0.116 G_Rec: 0.300 D_GP: 0.032 D_real: 1.219 D_fake: 0.702 
+(epoch: 278, iters: 7984, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 0.784 G_ID: 0.154 G_Rec: 0.362 D_GP: 0.028 D_real: 1.134 D_fake: 0.691 
+(epoch: 278, iters: 8384, time: 0.064) G_GAN: -0.182 G_GAN_Feat: 0.758 G_ID: 0.120 G_Rec: 0.314 D_GP: 0.048 D_real: 0.958 D_fake: 1.182 
+(epoch: 279, iters: 176, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.910 G_ID: 0.177 G_Rec: 0.380 D_GP: 0.117 D_real: 0.782 D_fake: 0.725 
+(epoch: 279, iters: 576, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.689 G_ID: 0.134 G_Rec: 0.299 D_GP: 0.039 D_real: 1.131 D_fake: 0.669 
+(epoch: 279, iters: 976, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.907 G_ID: 0.167 G_Rec: 0.437 D_GP: 0.126 D_real: 0.660 D_fake: 0.725 
+(epoch: 279, iters: 1376, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.852 G_ID: 0.124 G_Rec: 0.315 D_GP: 0.104 D_real: 0.675 D_fake: 0.754 
+(epoch: 279, iters: 1776, time: 0.064) G_GAN: 0.070 G_GAN_Feat: 0.781 G_ID: 0.167 G_Rec: 0.381 D_GP: 0.025 D_real: 0.922 D_fake: 0.930 
+(epoch: 279, iters: 2176, time: 0.064) G_GAN: -0.009 G_GAN_Feat: 0.930 G_ID: 0.122 G_Rec: 0.358 D_GP: 0.307 D_real: 0.308 D_fake: 1.010 
+(epoch: 279, iters: 2576, time: 0.064) G_GAN: 0.715 G_GAN_Feat: 1.205 G_ID: 0.163 G_Rec: 0.436 D_GP: 0.066 D_real: 1.284 D_fake: 0.405 
+(epoch: 279, iters: 2976, time: 0.064) G_GAN: 0.040 G_GAN_Feat: 0.623 G_ID: 0.128 G_Rec: 0.284 D_GP: 0.020 D_real: 0.974 D_fake: 0.962 
+(epoch: 279, iters: 3376, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.929 G_ID: 0.181 G_Rec: 0.392 D_GP: 0.080 D_real: 0.496 D_fake: 0.968 
+(epoch: 279, iters: 3776, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.813 G_ID: 0.118 G_Rec: 0.317 D_GP: 0.116 D_real: 0.442 D_fake: 0.874 
+(epoch: 279, iters: 4176, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.849 G_ID: 0.170 G_Rec: 0.405 D_GP: 0.030 D_real: 1.098 D_fake: 0.640 
+(epoch: 279, iters: 4576, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.656 G_ID: 0.140 G_Rec: 0.285 D_GP: 0.021 D_real: 1.162 D_fake: 0.719 
+(epoch: 279, iters: 4976, time: 0.064) G_GAN: 0.605 G_GAN_Feat: 0.806 G_ID: 0.156 G_Rec: 0.386 D_GP: 0.022 D_real: 1.365 D_fake: 0.399 
+(epoch: 279, iters: 5376, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.626 G_ID: 0.108 G_Rec: 0.279 D_GP: 0.028 D_real: 1.149 D_fake: 0.757 
+(epoch: 279, iters: 5776, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.940 G_ID: 0.193 G_Rec: 0.419 D_GP: 0.056 D_real: 0.611 D_fake: 0.772 
+(epoch: 279, iters: 6176, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.792 G_ID: 0.126 G_Rec: 0.314 D_GP: 0.075 D_real: 0.773 D_fake: 0.848 
+(epoch: 279, iters: 6576, time: 0.064) G_GAN: 0.553 G_GAN_Feat: 1.053 G_ID: 0.186 G_Rec: 0.406 D_GP: 0.160 D_real: 0.700 D_fake: 0.556 
+(epoch: 279, iters: 6976, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 0.705 G_ID: 0.124 G_Rec: 0.306 D_GP: 0.024 D_real: 1.382 D_fake: 0.615 
+(epoch: 279, iters: 7376, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.935 G_ID: 0.181 G_Rec: 0.362 D_GP: 0.032 D_real: 0.795 D_fake: 0.685 
+(epoch: 279, iters: 7776, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.707 G_ID: 0.110 G_Rec: 0.289 D_GP: 0.025 D_real: 1.170 D_fake: 0.633 
+(epoch: 279, iters: 8176, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 0.877 G_ID: 0.154 G_Rec: 0.389 D_GP: 0.021 D_real: 1.427 D_fake: 0.445 
+(epoch: 279, iters: 8576, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.717 G_ID: 0.135 G_Rec: 0.296 D_GP: 0.024 D_real: 1.182 D_fake: 0.770 
+(epoch: 280, iters: 368, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.890 G_ID: 0.132 G_Rec: 0.394 D_GP: 0.026 D_real: 1.073 D_fake: 0.620 
+(epoch: 280, iters: 768, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.737 G_ID: 0.131 G_Rec: 0.314 D_GP: 0.057 D_real: 0.818 D_fake: 0.869 
+(epoch: 280, iters: 1168, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.924 G_ID: 0.202 G_Rec: 0.383 D_GP: 0.078 D_real: 1.066 D_fake: 0.540 
+(epoch: 280, iters: 1568, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.666 G_ID: 0.136 G_Rec: 0.302 D_GP: 0.028 D_real: 1.303 D_fake: 0.633 
+(epoch: 280, iters: 1968, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.963 G_ID: 0.176 G_Rec: 0.441 D_GP: 0.207 D_real: 0.649 D_fake: 0.677 
+(epoch: 280, iters: 2368, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.787 G_ID: 0.145 G_Rec: 0.309 D_GP: 0.027 D_real: 0.871 D_fake: 0.886 
+(epoch: 280, iters: 2768, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.944 G_ID: 0.172 G_Rec: 0.430 D_GP: 0.044 D_real: 0.918 D_fake: 0.682 
+(epoch: 280, iters: 3168, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 1.021 G_ID: 0.132 G_Rec: 0.350 D_GP: 0.360 D_real: 0.416 D_fake: 0.722 
+(epoch: 280, iters: 3568, time: 0.064) G_GAN: -0.012 G_GAN_Feat: 1.043 G_ID: 0.173 G_Rec: 0.399 D_GP: 0.042 D_real: 0.545 D_fake: 1.012 
+(epoch: 280, iters: 3968, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.720 G_ID: 0.125 G_Rec: 0.272 D_GP: 0.028 D_real: 0.983 D_fake: 0.770 
+(epoch: 280, iters: 4368, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.747 G_ID: 0.183 G_Rec: 0.379 D_GP: 0.019 D_real: 1.070 D_fake: 0.703 
+(epoch: 280, iters: 4768, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.601 G_ID: 0.118 G_Rec: 0.269 D_GP: 0.032 D_real: 0.946 D_fake: 0.987 
+(epoch: 280, iters: 5168, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.733 G_ID: 0.151 G_Rec: 0.330 D_GP: 0.023 D_real: 1.099 D_fake: 0.750 
+(epoch: 280, iters: 5568, time: 0.064) G_GAN: -0.002 G_GAN_Feat: 0.633 G_ID: 0.126 G_Rec: 0.294 D_GP: 0.021 D_real: 0.962 D_fake: 1.002 
+(epoch: 280, iters: 5968, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.875 G_ID: 0.173 G_Rec: 0.416 D_GP: 0.067 D_real: 0.933 D_fake: 0.695 
+(epoch: 280, iters: 6368, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.771 G_ID: 0.130 G_Rec: 0.343 D_GP: 0.029 D_real: 1.005 D_fake: 0.747 
+(epoch: 280, iters: 6768, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.849 G_ID: 0.173 G_Rec: 0.393 D_GP: 0.026 D_real: 1.177 D_fake: 0.617 
+(epoch: 280, iters: 7168, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.581 G_ID: 0.132 G_Rec: 0.247 D_GP: 0.022 D_real: 1.126 D_fake: 0.840 
+(epoch: 280, iters: 7568, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.830 G_ID: 0.168 G_Rec: 0.349 D_GP: 0.038 D_real: 0.942 D_fake: 0.819 
+(epoch: 280, iters: 7968, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.743 G_ID: 0.128 G_Rec: 0.299 D_GP: 0.044 D_real: 0.975 D_fake: 0.731 
+(epoch: 280, iters: 8368, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 1.089 G_ID: 0.174 G_Rec: 0.432 D_GP: 0.177 D_real: 0.297 D_fake: 0.703 
+(epoch: 281, iters: 160, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.629 G_ID: 0.116 G_Rec: 0.275 D_GP: 0.022 D_real: 1.210 D_fake: 0.719 
+(epoch: 281, iters: 560, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.846 G_ID: 0.175 G_Rec: 0.347 D_GP: 0.029 D_real: 1.018 D_fake: 0.645 
+(epoch: 281, iters: 960, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.727 G_ID: 0.135 G_Rec: 0.274 D_GP: 0.029 D_real: 0.964 D_fake: 0.790 
+(epoch: 281, iters: 1360, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 0.923 G_ID: 0.180 G_Rec: 0.399 D_GP: 0.028 D_real: 1.236 D_fake: 0.429 
+(epoch: 281, iters: 1760, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.701 G_ID: 0.133 G_Rec: 0.282 D_GP: 0.022 D_real: 1.167 D_fake: 0.676 
+(epoch: 281, iters: 2160, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.786 G_ID: 0.189 G_Rec: 0.355 D_GP: 0.021 D_real: 1.024 D_fake: 0.783 
+(epoch: 281, iters: 2560, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.650 G_ID: 0.133 G_Rec: 0.310 D_GP: 0.022 D_real: 1.073 D_fake: 0.905 
+(epoch: 281, iters: 2960, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.846 G_ID: 0.148 G_Rec: 0.385 D_GP: 0.058 D_real: 0.975 D_fake: 0.743 
+(epoch: 281, iters: 3360, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.645 G_ID: 0.113 G_Rec: 0.274 D_GP: 0.031 D_real: 1.137 D_fake: 0.724 
+(epoch: 281, iters: 3760, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.939 G_ID: 0.178 G_Rec: 0.409 D_GP: 0.076 D_real: 0.793 D_fake: 0.754 
+(epoch: 281, iters: 4160, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.797 G_ID: 0.136 G_Rec: 0.283 D_GP: 0.048 D_real: 0.601 D_fake: 0.847 
+(epoch: 281, iters: 4560, time: 0.064) G_GAN: 0.844 G_GAN_Feat: 0.932 G_ID: 0.148 G_Rec: 0.406 D_GP: 0.025 D_real: 1.586 D_fake: 0.203 
+(epoch: 281, iters: 4960, time: 0.064) G_GAN: -0.065 G_GAN_Feat: 0.661 G_ID: 0.149 G_Rec: 0.333 D_GP: 0.019 D_real: 0.863 D_fake: 1.065 
+(epoch: 281, iters: 5360, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.871 G_ID: 0.163 G_Rec: 0.376 D_GP: 0.024 D_real: 0.871 D_fake: 0.812 
+(epoch: 281, iters: 5760, time: 0.064) G_GAN: -0.009 G_GAN_Feat: 0.716 G_ID: 0.149 G_Rec: 0.330 D_GP: 0.045 D_real: 0.756 D_fake: 1.009 
+(epoch: 281, iters: 6160, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.844 G_ID: 0.165 G_Rec: 0.365 D_GP: 0.029 D_real: 1.031 D_fake: 0.744 
+(epoch: 281, iters: 6560, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.753 G_ID: 0.117 G_Rec: 0.315 D_GP: 0.031 D_real: 0.969 D_fake: 0.782 
+(epoch: 281, iters: 6960, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.916 G_ID: 0.164 G_Rec: 0.416 D_GP: 0.035 D_real: 1.073 D_fake: 0.527 
+(epoch: 281, iters: 7360, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.797 G_ID: 0.114 G_Rec: 0.333 D_GP: 0.033 D_real: 0.977 D_fake: 0.662 
+(epoch: 281, iters: 7760, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.864 G_ID: 0.156 G_Rec: 0.386 D_GP: 0.022 D_real: 1.196 D_fake: 0.564 
+(epoch: 281, iters: 8160, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.642 G_ID: 0.134 G_Rec: 0.281 D_GP: 0.020 D_real: 1.241 D_fake: 0.664 
+(epoch: 281, iters: 8560, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.877 G_ID: 0.169 G_Rec: 0.413 D_GP: 0.023 D_real: 1.132 D_fake: 0.665 
+(epoch: 282, iters: 352, time: 0.064) G_GAN: 0.050 G_GAN_Feat: 0.645 G_ID: 0.129 G_Rec: 0.282 D_GP: 0.023 D_real: 0.969 D_fake: 0.950 
+(epoch: 282, iters: 752, time: 0.064) G_GAN: -0.065 G_GAN_Feat: 0.836 G_ID: 0.194 G_Rec: 0.367 D_GP: 0.026 D_real: 0.768 D_fake: 1.065 
+(epoch: 282, iters: 1152, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.740 G_ID: 0.122 G_Rec: 0.288 D_GP: 0.028 D_real: 1.044 D_fake: 0.656 
+(epoch: 282, iters: 1552, time: 0.064) G_GAN: 0.667 G_GAN_Feat: 1.217 G_ID: 0.175 G_Rec: 0.497 D_GP: 0.052 D_real: 0.630 D_fake: 0.377 
+(epoch: 282, iters: 1952, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.845 G_ID: 0.132 G_Rec: 0.297 D_GP: 0.111 D_real: 0.491 D_fake: 0.858 
+(epoch: 282, iters: 2352, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.972 G_ID: 0.149 G_Rec: 0.371 D_GP: 0.021 D_real: 1.132 D_fake: 0.490 
+(epoch: 282, iters: 2752, time: 0.064) G_GAN: 0.008 G_GAN_Feat: 0.689 G_ID: 0.125 G_Rec: 0.307 D_GP: 0.028 D_real: 0.902 D_fake: 0.992 
+(epoch: 282, iters: 3152, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.797 G_ID: 0.171 G_Rec: 0.371 D_GP: 0.021 D_real: 1.099 D_fake: 0.737 
+(epoch: 282, iters: 3552, time: 0.064) G_GAN: -0.077 G_GAN_Feat: 0.685 G_ID: 0.135 G_Rec: 0.283 D_GP: 0.035 D_real: 0.853 D_fake: 1.077 
+(epoch: 282, iters: 3952, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 1.006 G_ID: 0.165 G_Rec: 0.394 D_GP: 0.050 D_real: 0.737 D_fake: 0.665 
+(epoch: 282, iters: 4352, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.635 G_ID: 0.148 G_Rec: 0.289 D_GP: 0.022 D_real: 1.152 D_fake: 0.792 
+(epoch: 282, iters: 4752, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.835 G_ID: 0.163 G_Rec: 0.351 D_GP: 0.030 D_real: 1.164 D_fake: 0.649 
+(epoch: 282, iters: 5152, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.740 G_ID: 0.116 G_Rec: 0.287 D_GP: 0.033 D_real: 0.920 D_fake: 0.827 
+(epoch: 282, iters: 5552, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.941 G_ID: 0.194 G_Rec: 0.371 D_GP: 0.068 D_real: 1.108 D_fake: 0.584 
+(epoch: 282, iters: 5952, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.774 G_ID: 0.131 G_Rec: 0.294 D_GP: 0.045 D_real: 1.189 D_fake: 0.632 
+(epoch: 282, iters: 6352, time: 0.064) G_GAN: 0.569 G_GAN_Feat: 1.026 G_ID: 0.165 G_Rec: 0.397 D_GP: 0.036 D_real: 0.847 D_fake: 0.441 
+(epoch: 282, iters: 6752, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.671 G_ID: 0.131 G_Rec: 0.319 D_GP: 0.021 D_real: 1.026 D_fake: 0.854 
+(epoch: 282, iters: 7152, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.826 G_ID: 0.179 G_Rec: 0.397 D_GP: 0.029 D_real: 0.849 D_fake: 0.851 
+(epoch: 282, iters: 7552, time: 0.064) G_GAN: -0.072 G_GAN_Feat: 0.663 G_ID: 0.114 G_Rec: 0.304 D_GP: 0.054 D_real: 0.810 D_fake: 1.072 
+(epoch: 282, iters: 7952, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.838 G_ID: 0.172 G_Rec: 0.412 D_GP: 0.030 D_real: 1.019 D_fake: 0.724 
+(epoch: 282, iters: 8352, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.781 G_ID: 0.120 G_Rec: 0.331 D_GP: 0.053 D_real: 0.766 D_fake: 0.888 
+(epoch: 283, iters: 144, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.925 G_ID: 0.169 G_Rec: 0.399 D_GP: 0.031 D_real: 1.060 D_fake: 0.602 
+(epoch: 283, iters: 544, time: 0.064) G_GAN: 0.097 G_GAN_Feat: 0.815 G_ID: 0.148 G_Rec: 0.312 D_GP: 0.046 D_real: 0.758 D_fake: 0.903 
+(epoch: 283, iters: 944, time: 0.064) G_GAN: 0.473 G_GAN_Feat: 0.845 G_ID: 0.136 G_Rec: 0.382 D_GP: 0.029 D_real: 1.152 D_fake: 0.531 
+(epoch: 283, iters: 1344, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.789 G_ID: 0.133 G_Rec: 0.350 D_GP: 0.063 D_real: 0.911 D_fake: 0.866 
+(epoch: 283, iters: 1744, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 1.041 G_ID: 0.158 G_Rec: 0.451 D_GP: 0.040 D_real: 0.818 D_fake: 0.579 
+(epoch: 283, iters: 2144, time: 0.064) G_GAN: 0.131 G_GAN_Feat: 0.850 G_ID: 0.122 G_Rec: 0.291 D_GP: 0.031 D_real: 0.599 D_fake: 0.870 
+(epoch: 283, iters: 2544, time: 0.064) G_GAN: 0.636 G_GAN_Feat: 0.836 G_ID: 0.187 G_Rec: 0.366 D_GP: 0.022 D_real: 1.366 D_fake: 0.406 
+(epoch: 283, iters: 2944, time: 0.064) G_GAN: 0.039 G_GAN_Feat: 0.780 G_ID: 0.152 G_Rec: 0.287 D_GP: 0.026 D_real: 0.782 D_fake: 0.961 
+(epoch: 283, iters: 3344, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.847 G_ID: 0.161 G_Rec: 0.373 D_GP: 0.029 D_real: 1.100 D_fake: 0.615 
+(epoch: 283, iters: 3744, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.651 G_ID: 0.142 G_Rec: 0.326 D_GP: 0.019 D_real: 1.086 D_fake: 0.831 
+(epoch: 283, iters: 4144, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.935 G_ID: 0.154 G_Rec: 0.375 D_GP: 0.054 D_real: 0.614 D_fake: 0.801 
+(epoch: 283, iters: 4544, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.830 G_ID: 0.150 G_Rec: 0.320 D_GP: 0.039 D_real: 1.029 D_fake: 0.592 
+(epoch: 283, iters: 4944, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.813 G_ID: 0.178 G_Rec: 0.385 D_GP: 0.021 D_real: 1.201 D_fake: 0.637 
+(epoch: 283, iters: 5344, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.572 G_ID: 0.111 G_Rec: 0.288 D_GP: 0.018 D_real: 1.320 D_fake: 0.658 
+(epoch: 283, iters: 5744, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.851 G_ID: 0.166 G_Rec: 0.378 D_GP: 0.021 D_real: 1.217 D_fake: 0.605 
+(epoch: 283, iters: 6144, time: 0.064) G_GAN: 0.089 G_GAN_Feat: 0.696 G_ID: 0.110 G_Rec: 0.314 D_GP: 0.027 D_real: 0.909 D_fake: 0.911 
+(epoch: 283, iters: 6544, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.878 G_ID: 0.184 G_Rec: 0.409 D_GP: 0.024 D_real: 1.097 D_fake: 0.663 
+(epoch: 283, iters: 6944, time: 0.064) G_GAN: 0.071 G_GAN_Feat: 0.627 G_ID: 0.165 G_Rec: 0.306 D_GP: 0.019 D_real: 1.034 D_fake: 0.929 
+(epoch: 283, iters: 7344, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.820 G_ID: 0.160 G_Rec: 0.388 D_GP: 0.024 D_real: 1.075 D_fake: 0.651 
+(epoch: 283, iters: 7744, time: 0.064) G_GAN: 0.165 G_GAN_Feat: 0.682 G_ID: 0.134 G_Rec: 0.309 D_GP: 0.030 D_real: 1.040 D_fake: 0.835 
+(epoch: 283, iters: 8144, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.900 G_ID: 0.160 G_Rec: 0.391 D_GP: 0.028 D_real: 1.111 D_fake: 0.578 
+(epoch: 283, iters: 8544, time: 0.064) G_GAN: 0.051 G_GAN_Feat: 0.783 G_ID: 0.125 G_Rec: 0.296 D_GP: 0.036 D_real: 0.845 D_fake: 0.952 
+(epoch: 284, iters: 336, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.843 G_ID: 0.148 G_Rec: 0.376 D_GP: 0.025 D_real: 1.207 D_fake: 0.500 
+(epoch: 284, iters: 736, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.659 G_ID: 0.127 G_Rec: 0.272 D_GP: 0.020 D_real: 1.094 D_fake: 0.771 
+(epoch: 284, iters: 1136, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.895 G_ID: 0.169 G_Rec: 0.352 D_GP: 0.028 D_real: 1.216 D_fake: 0.632 
+(epoch: 284, iters: 1536, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.681 G_ID: 0.118 G_Rec: 0.281 D_GP: 0.022 D_real: 1.152 D_fake: 0.768 
+(epoch: 284, iters: 1936, time: 0.064) G_GAN: 0.528 G_GAN_Feat: 0.818 G_ID: 0.153 G_Rec: 0.369 D_GP: 0.022 D_real: 1.327 D_fake: 0.484 
+(epoch: 284, iters: 2336, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.585 G_ID: 0.121 G_Rec: 0.279 D_GP: 0.016 D_real: 1.087 D_fake: 0.846 
+(epoch: 284, iters: 2736, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.804 G_ID: 0.148 G_Rec: 0.374 D_GP: 0.025 D_real: 1.014 D_fake: 0.711 
+(epoch: 284, iters: 3136, time: 0.064) G_GAN: 0.050 G_GAN_Feat: 0.765 G_ID: 0.142 G_Rec: 0.305 D_GP: 0.038 D_real: 0.622 D_fake: 0.950 
+(epoch: 284, iters: 3536, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.886 G_ID: 0.156 G_Rec: 0.389 D_GP: 0.033 D_real: 0.929 D_fake: 0.687 
+(epoch: 284, iters: 3936, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.692 G_ID: 0.107 G_Rec: 0.266 D_GP: 0.024 D_real: 1.089 D_fake: 0.784 
+(epoch: 284, iters: 4336, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.856 G_ID: 0.164 G_Rec: 0.367 D_GP: 0.028 D_real: 0.990 D_fake: 0.786 
+(epoch: 284, iters: 4736, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.872 G_ID: 0.130 G_Rec: 0.353 D_GP: 0.154 D_real: 0.446 D_fake: 0.906 
+(epoch: 284, iters: 5136, time: 0.064) G_GAN: 0.548 G_GAN_Feat: 0.975 G_ID: 0.144 G_Rec: 0.396 D_GP: 0.026 D_real: 1.228 D_fake: 0.470 
+(epoch: 284, iters: 5536, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.905 G_ID: 0.176 G_Rec: 0.378 D_GP: 0.030 D_real: 0.699 D_fake: 0.806 
+(epoch: 284, iters: 5936, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.921 G_ID: 0.151 G_Rec: 0.397 D_GP: 0.030 D_real: 0.956 D_fake: 0.752 
+(epoch: 284, iters: 6336, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.715 G_ID: 0.104 G_Rec: 0.270 D_GP: 0.029 D_real: 1.010 D_fake: 0.775 
+(epoch: 284, iters: 6736, time: 0.064) G_GAN: 0.705 G_GAN_Feat: 0.962 G_ID: 0.161 G_Rec: 0.454 D_GP: 0.032 D_real: 1.379 D_fake: 0.400 
+(epoch: 284, iters: 7136, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.643 G_ID: 0.117 G_Rec: 0.254 D_GP: 0.025 D_real: 1.325 D_fake: 0.641 
+(epoch: 284, iters: 7536, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.940 G_ID: 0.150 G_Rec: 0.406 D_GP: 0.049 D_real: 1.188 D_fake: 0.595 
+(epoch: 284, iters: 7936, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.657 G_ID: 0.126 G_Rec: 0.287 D_GP: 0.021 D_real: 1.076 D_fake: 0.833 
+(epoch: 284, iters: 8336, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.891 G_ID: 0.147 G_Rec: 0.402 D_GP: 0.044 D_real: 1.131 D_fake: 0.524 
+(epoch: 285, iters: 128, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 0.786 G_ID: 0.138 G_Rec: 0.292 D_GP: 0.034 D_real: 1.082 D_fake: 0.591 
+(epoch: 285, iters: 528, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.905 G_ID: 0.161 G_Rec: 0.352 D_GP: 0.033 D_real: 1.030 D_fake: 0.501 
+(epoch: 285, iters: 928, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.856 G_ID: 0.116 G_Rec: 0.322 D_GP: 0.038 D_real: 0.746 D_fake: 0.642 
+(epoch: 285, iters: 1328, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.742 G_ID: 0.159 G_Rec: 0.474 D_GP: 0.019 D_real: 1.232 D_fake: 0.590 
+(epoch: 285, iters: 1728, time: 0.064) G_GAN: -0.022 G_GAN_Feat: 0.610 G_ID: 0.142 G_Rec: 0.282 D_GP: 0.020 D_real: 0.917 D_fake: 1.022 
+(epoch: 285, iters: 2128, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.806 G_ID: 0.147 G_Rec: 0.361 D_GP: 0.025 D_real: 1.203 D_fake: 0.565 
+(epoch: 285, iters: 2528, time: 0.064) G_GAN: -0.081 G_GAN_Feat: 0.669 G_ID: 0.127 G_Rec: 0.298 D_GP: 0.023 D_real: 0.833 D_fake: 1.081 
+(epoch: 285, iters: 2928, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.834 G_ID: 0.155 G_Rec: 0.379 D_GP: 0.026 D_real: 1.107 D_fake: 0.615 
+(epoch: 285, iters: 3328, time: 0.064) G_GAN: 0.028 G_GAN_Feat: 0.714 G_ID: 0.117 G_Rec: 0.297 D_GP: 0.037 D_real: 0.766 D_fake: 0.972 
+(epoch: 285, iters: 3728, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 1.061 G_ID: 0.163 G_Rec: 0.439 D_GP: 0.199 D_real: 0.637 D_fake: 0.566 
+(epoch: 285, iters: 4128, time: 0.064) G_GAN: 0.089 G_GAN_Feat: 0.717 G_ID: 0.106 G_Rec: 0.307 D_GP: 0.022 D_real: 0.927 D_fake: 0.911 
+(epoch: 285, iters: 4528, time: 0.064) G_GAN: 0.586 G_GAN_Feat: 0.910 G_ID: 0.179 G_Rec: 0.407 D_GP: 0.033 D_real: 1.094 D_fake: 0.456 
+(epoch: 285, iters: 4928, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.640 G_ID: 0.124 G_Rec: 0.311 D_GP: 0.019 D_real: 1.310 D_fake: 0.696 
+(epoch: 285, iters: 5328, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.814 G_ID: 0.174 G_Rec: 0.363 D_GP: 0.024 D_real: 1.216 D_fake: 0.605 
+(epoch: 285, iters: 5728, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.734 G_ID: 0.135 G_Rec: 0.296 D_GP: 0.048 D_real: 0.947 D_fake: 0.788 
+(epoch: 285, iters: 6128, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.919 G_ID: 0.160 G_Rec: 0.411 D_GP: 0.031 D_real: 0.907 D_fake: 0.612 
+(epoch: 285, iters: 6528, time: 0.064) G_GAN: -0.023 G_GAN_Feat: 0.705 G_ID: 0.110 G_Rec: 0.288 D_GP: 0.023 D_real: 0.835 D_fake: 1.023 
+(epoch: 285, iters: 6928, time: 0.064) G_GAN: 0.697 G_GAN_Feat: 0.866 G_ID: 0.156 G_Rec: 0.380 D_GP: 0.024 D_real: 1.479 D_fake: 0.318 
+(epoch: 285, iters: 7328, time: 0.064) G_GAN: -0.066 G_GAN_Feat: 0.614 G_ID: 0.149 G_Rec: 0.294 D_GP: 0.025 D_real: 1.015 D_fake: 1.066 
+(epoch: 285, iters: 7728, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.753 G_ID: 0.154 G_Rec: 0.397 D_GP: 0.033 D_real: 0.968 D_fake: 0.821 
+(epoch: 285, iters: 8128, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.604 G_ID: 0.125 G_Rec: 0.289 D_GP: 0.028 D_real: 1.009 D_fake: 0.897 
+(epoch: 285, iters: 8528, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.806 G_ID: 0.169 G_Rec: 0.382 D_GP: 0.029 D_real: 1.196 D_fake: 0.599 
+(epoch: 286, iters: 320, time: 0.064) G_GAN: 0.043 G_GAN_Feat: 0.733 G_ID: 0.114 G_Rec: 0.316 D_GP: 0.288 D_real: 0.617 D_fake: 0.958 
+(epoch: 286, iters: 720, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.851 G_ID: 0.153 G_Rec: 0.416 D_GP: 0.025 D_real: 0.967 D_fake: 0.670 
+(epoch: 286, iters: 1120, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.631 G_ID: 0.138 G_Rec: 0.284 D_GP: 0.019 D_real: 1.163 D_fake: 0.765 
+(epoch: 286, iters: 1520, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.790 G_ID: 0.168 G_Rec: 0.344 D_GP: 0.024 D_real: 1.114 D_fake: 0.628 
+(epoch: 286, iters: 1920, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.755 G_ID: 0.147 G_Rec: 0.337 D_GP: 0.022 D_real: 1.340 D_fake: 0.587 
+(epoch: 286, iters: 2320, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.642 G_ID: 0.146 G_Rec: 0.320 D_GP: 0.017 D_real: 1.143 D_fake: 0.666 
+(epoch: 286, iters: 2720, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.573 G_ID: 0.135 G_Rec: 0.288 D_GP: 0.018 D_real: 1.109 D_fake: 0.816 
+(epoch: 286, iters: 3120, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.709 G_ID: 0.153 G_Rec: 0.345 D_GP: 0.021 D_real: 1.091 D_fake: 0.650 
+(epoch: 286, iters: 3520, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.649 G_ID: 0.120 G_Rec: 0.314 D_GP: 0.034 D_real: 1.160 D_fake: 0.700 
+(epoch: 286, iters: 3920, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.765 G_ID: 0.158 G_Rec: 0.371 D_GP: 0.041 D_real: 1.029 D_fake: 0.765 
+(epoch: 286, iters: 4320, time: 0.064) G_GAN: 0.061 G_GAN_Feat: 0.622 G_ID: 0.133 G_Rec: 0.302 D_GP: 0.029 D_real: 0.917 D_fake: 0.939 
+(epoch: 286, iters: 4720, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.891 G_ID: 0.162 G_Rec: 0.386 D_GP: 0.072 D_real: 1.123 D_fake: 0.532 
+(epoch: 286, iters: 5120, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.685 G_ID: 0.116 G_Rec: 0.290 D_GP: 0.054 D_real: 0.887 D_fake: 0.917 
+(epoch: 286, iters: 5520, time: 0.064) G_GAN: 0.520 G_GAN_Feat: 0.881 G_ID: 0.161 G_Rec: 0.403 D_GP: 0.022 D_real: 1.201 D_fake: 0.487 
+(epoch: 286, iters: 5920, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.699 G_ID: 0.149 G_Rec: 0.304 D_GP: 0.082 D_real: 0.851 D_fake: 0.871 
+(epoch: 286, iters: 6320, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.888 G_ID: 0.155 G_Rec: 0.393 D_GP: 0.040 D_real: 1.004 D_fake: 0.544 
+(epoch: 286, iters: 6720, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.650 G_ID: 0.116 G_Rec: 0.295 D_GP: 0.020 D_real: 1.207 D_fake: 0.741 
+(epoch: 286, iters: 7120, time: 0.064) G_GAN: -0.019 G_GAN_Feat: 0.840 G_ID: 0.161 G_Rec: 0.412 D_GP: 0.036 D_real: 0.804 D_fake: 1.019 
+(epoch: 286, iters: 7520, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.636 G_ID: 0.148 G_Rec: 0.271 D_GP: 0.025 D_real: 0.991 D_fake: 0.918 
+(epoch: 286, iters: 7920, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.772 G_ID: 0.173 G_Rec: 0.340 D_GP: 0.022 D_real: 1.193 D_fake: 0.607 
+(epoch: 286, iters: 8320, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.729 G_ID: 0.120 G_Rec: 0.290 D_GP: 0.030 D_real: 1.037 D_fake: 0.885 
+(epoch: 287, iters: 112, time: 0.064) G_GAN: 0.706 G_GAN_Feat: 0.952 G_ID: 0.117 G_Rec: 0.402 D_GP: 0.032 D_real: 1.213 D_fake: 0.310 
+(epoch: 287, iters: 512, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.666 G_ID: 0.115 G_Rec: 0.290 D_GP: 0.018 D_real: 1.076 D_fake: 0.878 
+(epoch: 287, iters: 912, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.787 G_ID: 0.141 G_Rec: 0.360 D_GP: 0.022 D_real: 1.035 D_fake: 0.664 
+(epoch: 287, iters: 1312, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.891 G_ID: 0.130 G_Rec: 0.319 D_GP: 0.028 D_real: 1.391 D_fake: 0.676 
+(epoch: 287, iters: 1712, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 0.898 G_ID: 0.149 G_Rec: 0.387 D_GP: 0.022 D_real: 1.277 D_fake: 0.458 
+(epoch: 287, iters: 2112, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.691 G_ID: 0.117 G_Rec: 0.308 D_GP: 0.024 D_real: 1.151 D_fake: 0.726 
+(epoch: 287, iters: 2512, time: 0.064) G_GAN: 0.640 G_GAN_Feat: 0.993 G_ID: 0.164 G_Rec: 0.397 D_GP: 0.040 D_real: 1.085 D_fake: 0.451 
+(epoch: 287, iters: 2912, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.676 G_ID: 0.118 G_Rec: 0.316 D_GP: 0.018 D_real: 1.081 D_fake: 0.833 
+(epoch: 287, iters: 3312, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 0.838 G_ID: 0.159 G_Rec: 0.384 D_GP: 0.020 D_real: 1.274 D_fake: 0.464 
+(epoch: 287, iters: 3712, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.761 G_ID: 0.138 G_Rec: 0.378 D_GP: 0.077 D_real: 0.827 D_fake: 0.989 
+(epoch: 287, iters: 4112, time: 0.064) G_GAN: 0.778 G_GAN_Feat: 0.796 G_ID: 0.165 G_Rec: 0.376 D_GP: 0.021 D_real: 1.519 D_fake: 0.261 
+(epoch: 287, iters: 4512, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.629 G_ID: 0.128 G_Rec: 0.269 D_GP: 0.023 D_real: 1.353 D_fake: 0.589 
+(epoch: 287, iters: 4912, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.763 G_ID: 0.155 G_Rec: 0.350 D_GP: 0.023 D_real: 1.231 D_fake: 0.573 
+(epoch: 287, iters: 5312, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.720 G_ID: 0.124 G_Rec: 0.302 D_GP: 0.039 D_real: 0.870 D_fake: 0.894 
+(epoch: 287, iters: 5712, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 1.019 G_ID: 0.183 G_Rec: 0.437 D_GP: 0.142 D_real: 0.496 D_fake: 0.835 
+(epoch: 287, iters: 6112, time: 0.064) G_GAN: 0.156 G_GAN_Feat: 0.815 G_ID: 0.121 G_Rec: 0.324 D_GP: 0.099 D_real: 0.780 D_fake: 0.846 
+(epoch: 287, iters: 6512, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.939 G_ID: 0.164 G_Rec: 0.404 D_GP: 0.053 D_real: 0.624 D_fake: 0.684 
+(epoch: 287, iters: 6912, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.686 G_ID: 0.115 G_Rec: 0.302 D_GP: 0.016 D_real: 1.234 D_fake: 0.716 
+(epoch: 287, iters: 7312, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.956 G_ID: 0.165 G_Rec: 0.446 D_GP: 0.054 D_real: 1.078 D_fake: 0.537 
+(epoch: 287, iters: 7712, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.734 G_ID: 0.122 G_Rec: 0.319 D_GP: 0.044 D_real: 0.903 D_fake: 0.941 
+(epoch: 287, iters: 8112, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.882 G_ID: 0.178 G_Rec: 0.436 D_GP: 0.024 D_real: 1.082 D_fake: 0.799 
+(epoch: 287, iters: 8512, time: 0.064) G_GAN: 0.026 G_GAN_Feat: 0.701 G_ID: 0.164 G_Rec: 0.274 D_GP: 0.040 D_real: 0.820 D_fake: 0.974 
+(epoch: 288, iters: 304, time: 0.064) G_GAN: 0.520 G_GAN_Feat: 1.012 G_ID: 0.143 G_Rec: 0.482 D_GP: 0.054 D_real: 0.883 D_fake: 0.488 
+(epoch: 288, iters: 704, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.658 G_ID: 0.114 G_Rec: 0.269 D_GP: 0.027 D_real: 1.147 D_fake: 0.744 
+(epoch: 288, iters: 1104, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.947 G_ID: 0.142 G_Rec: 0.422 D_GP: 0.038 D_real: 0.610 D_fake: 0.799 
+(epoch: 288, iters: 1504, time: 0.064) G_GAN: 0.056 G_GAN_Feat: 0.598 G_ID: 0.151 G_Rec: 0.299 D_GP: 0.022 D_real: 1.030 D_fake: 0.944 
+(epoch: 288, iters: 1904, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.837 G_ID: 0.158 G_Rec: 0.402 D_GP: 0.027 D_real: 1.042 D_fake: 0.691 
+(epoch: 288, iters: 2304, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.703 G_ID: 0.146 G_Rec: 0.332 D_GP: 0.051 D_real: 1.002 D_fake: 0.805 
+(epoch: 288, iters: 2704, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.937 G_ID: 0.149 G_Rec: 0.431 D_GP: 0.050 D_real: 1.027 D_fake: 0.543 
+(epoch: 288, iters: 3104, time: 0.064) G_GAN: 0.005 G_GAN_Feat: 0.804 G_ID: 0.119 G_Rec: 0.333 D_GP: 0.198 D_real: 0.625 D_fake: 0.995 
+(epoch: 288, iters: 3504, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.985 G_ID: 0.156 G_Rec: 0.444 D_GP: 0.061 D_real: 0.870 D_fake: 0.606 
+(epoch: 288, iters: 3904, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.657 G_ID: 0.124 G_Rec: 0.297 D_GP: 0.031 D_real: 1.121 D_fake: 0.704 
+(epoch: 288, iters: 4304, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.921 G_ID: 0.155 G_Rec: 0.377 D_GP: 0.032 D_real: 1.022 D_fake: 0.535 
+(epoch: 288, iters: 4704, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.703 G_ID: 0.136 G_Rec: 0.295 D_GP: 0.039 D_real: 1.000 D_fake: 0.685 
+(epoch: 288, iters: 5104, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.819 G_ID: 0.165 G_Rec: 0.371 D_GP: 0.034 D_real: 1.008 D_fake: 0.678 
+(epoch: 288, iters: 5504, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.740 G_ID: 0.148 G_Rec: 0.369 D_GP: 0.035 D_real: 0.856 D_fake: 0.781 
+(epoch: 288, iters: 5904, time: 0.064) G_GAN: 0.594 G_GAN_Feat: 0.876 G_ID: 0.195 G_Rec: 0.389 D_GP: 0.023 D_real: 1.315 D_fake: 0.425 
+(epoch: 288, iters: 6304, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.666 G_ID: 0.130 G_Rec: 0.290 D_GP: 0.031 D_real: 1.092 D_fake: 0.785 
+(epoch: 288, iters: 6704, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 0.848 G_ID: 0.158 G_Rec: 0.365 D_GP: 0.030 D_real: 1.071 D_fake: 0.520 
+(epoch: 288, iters: 7104, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.869 G_ID: 0.134 G_Rec: 0.303 D_GP: 0.631 D_real: 0.472 D_fake: 0.707 
+(epoch: 288, iters: 7504, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 1.075 G_ID: 0.181 G_Rec: 0.457 D_GP: 0.277 D_real: 0.454 D_fake: 0.642 
+(epoch: 288, iters: 7904, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.791 G_ID: 0.142 G_Rec: 0.330 D_GP: 0.027 D_real: 1.010 D_fake: 0.753 
+(epoch: 288, iters: 8304, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.982 G_ID: 0.157 G_Rec: 0.434 D_GP: 0.024 D_real: 0.927 D_fake: 0.694 
+(epoch: 289, iters: 96, time: 0.064) G_GAN: -0.114 G_GAN_Feat: 0.674 G_ID: 0.142 G_Rec: 0.281 D_GP: 0.031 D_real: 0.832 D_fake: 1.114 
+(epoch: 289, iters: 496, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 1.056 G_ID: 0.172 G_Rec: 0.433 D_GP: 0.034 D_real: 1.343 D_fake: 0.586 
+(epoch: 289, iters: 896, time: 0.064) G_GAN: -0.011 G_GAN_Feat: 0.640 G_ID: 0.131 G_Rec: 0.282 D_GP: 0.032 D_real: 0.942 D_fake: 1.011 
+(epoch: 289, iters: 1296, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.780 G_ID: 0.173 G_Rec: 0.364 D_GP: 0.021 D_real: 1.103 D_fake: 0.693 
+(epoch: 289, iters: 1696, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.561 G_ID: 0.103 G_Rec: 0.282 D_GP: 0.021 D_real: 1.079 D_fake: 0.856 
+(epoch: 289, iters: 2096, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.737 G_ID: 0.173 G_Rec: 0.372 D_GP: 0.022 D_real: 1.067 D_fake: 0.729 
+(epoch: 289, iters: 2496, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.595 G_ID: 0.128 G_Rec: 0.274 D_GP: 0.021 D_real: 1.055 D_fake: 0.877 
+(epoch: 289, iters: 2896, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 0.757 G_ID: 0.170 G_Rec: 0.398 D_GP: 0.025 D_real: 1.126 D_fake: 0.626 
+(epoch: 289, iters: 3296, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.733 G_ID: 0.126 G_Rec: 0.308 D_GP: 0.035 D_real: 1.083 D_fake: 0.805 
+(epoch: 289, iters: 3696, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.851 G_ID: 0.150 G_Rec: 0.392 D_GP: 0.022 D_real: 1.197 D_fake: 0.545 
+(epoch: 289, iters: 4096, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.923 G_ID: 0.127 G_Rec: 0.318 D_GP: 0.084 D_real: 0.898 D_fake: 0.893 
+(epoch: 289, iters: 4496, time: 0.064) G_GAN: 0.819 G_GAN_Feat: 0.880 G_ID: 0.158 G_Rec: 0.353 D_GP: 0.049 D_real: 1.361 D_fake: 0.252 
+(epoch: 289, iters: 4896, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.857 G_ID: 0.128 G_Rec: 0.304 D_GP: 0.092 D_real: 0.628 D_fake: 0.704 
+(epoch: 289, iters: 5296, time: 0.064) G_GAN: 0.659 G_GAN_Feat: 0.862 G_ID: 0.137 G_Rec: 0.392 D_GP: 0.022 D_real: 1.377 D_fake: 0.357 
+(epoch: 289, iters: 5696, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.614 G_ID: 0.115 G_Rec: 0.264 D_GP: 0.020 D_real: 1.076 D_fake: 0.885 
+(epoch: 289, iters: 6096, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.845 G_ID: 0.158 G_Rec: 0.402 D_GP: 0.032 D_real: 1.067 D_fake: 0.669 
+(epoch: 289, iters: 6496, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.667 G_ID: 0.122 G_Rec: 0.276 D_GP: 0.026 D_real: 1.102 D_fake: 0.774 
+(epoch: 289, iters: 6896, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.911 G_ID: 0.177 G_Rec: 0.373 D_GP: 0.036 D_real: 0.836 D_fake: 0.751 
+(epoch: 289, iters: 7296, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.744 G_ID: 0.134 G_Rec: 0.280 D_GP: 0.029 D_real: 1.198 D_fake: 0.703 
+(epoch: 289, iters: 7696, time: 0.064) G_GAN: 0.637 G_GAN_Feat: 0.970 G_ID: 0.198 G_Rec: 0.400 D_GP: 0.038 D_real: 1.201 D_fake: 0.424 
+(epoch: 289, iters: 8096, time: 0.064) G_GAN: -0.038 G_GAN_Feat: 0.851 G_ID: 0.119 G_Rec: 0.318 D_GP: 0.038 D_real: 0.604 D_fake: 1.038 
+(epoch: 289, iters: 8496, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.986 G_ID: 0.175 G_Rec: 0.383 D_GP: 0.080 D_real: 0.623 D_fake: 0.567 
+(epoch: 290, iters: 288, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.648 G_ID: 0.126 G_Rec: 0.302 D_GP: 0.023 D_real: 1.065 D_fake: 0.893 
+(epoch: 290, iters: 688, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.999 G_ID: 0.145 G_Rec: 0.450 D_GP: 0.046 D_real: 0.994 D_fake: 0.648 
+(epoch: 290, iters: 1088, time: 0.064) G_GAN: 0.101 G_GAN_Feat: 0.634 G_ID: 0.122 G_Rec: 0.237 D_GP: 0.024 D_real: 1.106 D_fake: 0.899 
+(epoch: 290, iters: 1488, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.879 G_ID: 0.143 G_Rec: 0.425 D_GP: 0.022 D_real: 0.810 D_fake: 0.918 
+(epoch: 290, iters: 1888, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.684 G_ID: 0.119 G_Rec: 0.272 D_GP: 0.027 D_real: 1.121 D_fake: 0.828 
+(epoch: 290, iters: 2288, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 0.897 G_ID: 0.167 G_Rec: 0.424 D_GP: 0.034 D_real: 1.117 D_fake: 0.546 
+(epoch: 290, iters: 2688, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.636 G_ID: 0.116 G_Rec: 0.292 D_GP: 0.028 D_real: 1.224 D_fake: 0.681 
+(epoch: 290, iters: 3088, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 0.780 G_ID: 0.156 G_Rec: 0.357 D_GP: 0.024 D_real: 1.272 D_fake: 0.495 
+(epoch: 290, iters: 3488, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.777 G_ID: 0.121 G_Rec: 0.289 D_GP: 0.026 D_real: 1.354 D_fake: 0.546 
+(epoch: 290, iters: 3888, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 1.113 G_ID: 0.167 G_Rec: 0.448 D_GP: 0.335 D_real: 0.462 D_fake: 0.689 
+(epoch: 290, iters: 4288, time: 0.064) G_GAN: 0.341 G_GAN_Feat: 0.547 G_ID: 0.122 G_Rec: 0.272 D_GP: 0.017 D_real: 1.319 D_fake: 0.659 
+(epoch: 290, iters: 4688, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.836 G_ID: 0.166 G_Rec: 0.448 D_GP: 0.025 D_real: 1.093 D_fake: 0.589 
+(epoch: 290, iters: 5088, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.583 G_ID: 0.121 G_Rec: 0.276 D_GP: 0.021 D_real: 1.102 D_fake: 0.864 
+(epoch: 290, iters: 5488, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.795 G_ID: 0.151 G_Rec: 0.397 D_GP: 0.024 D_real: 1.025 D_fake: 0.762 
+(epoch: 290, iters: 5888, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 0.685 G_ID: 0.137 G_Rec: 0.302 D_GP: 0.043 D_real: 0.836 D_fake: 0.943 
+(epoch: 290, iters: 6288, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.829 G_ID: 0.153 G_Rec: 0.391 D_GP: 0.026 D_real: 1.071 D_fake: 0.648 
+(epoch: 290, iters: 6688, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.673 G_ID: 0.128 G_Rec: 0.309 D_GP: 0.038 D_real: 1.065 D_fake: 0.765 
+(epoch: 290, iters: 7088, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.781 G_ID: 0.146 G_Rec: 0.353 D_GP: 0.027 D_real: 1.094 D_fake: 0.714 
+(epoch: 290, iters: 7488, time: 0.064) G_GAN: 0.204 G_GAN_Feat: 0.682 G_ID: 0.128 G_Rec: 0.318 D_GP: 0.133 D_real: 0.873 D_fake: 0.797 
+(epoch: 290, iters: 7888, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.850 G_ID: 0.177 G_Rec: 0.363 D_GP: 0.035 D_real: 1.050 D_fake: 0.671 
+(epoch: 290, iters: 8288, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.637 G_ID: 0.120 G_Rec: 0.318 D_GP: 0.024 D_real: 1.207 D_fake: 0.747 
+(epoch: 291, iters: 80, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 1.008 G_ID: 0.138 G_Rec: 0.430 D_GP: 0.116 D_real: 0.556 D_fake: 0.683 
+(epoch: 291, iters: 480, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.664 G_ID: 0.097 G_Rec: 0.242 D_GP: 0.026 D_real: 1.169 D_fake: 0.773 
+(epoch: 291, iters: 880, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.756 G_ID: 0.147 G_Rec: 0.381 D_GP: 0.020 D_real: 1.281 D_fake: 0.541 
+(epoch: 291, iters: 1280, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.665 G_ID: 0.123 G_Rec: 0.301 D_GP: 0.037 D_real: 1.010 D_fake: 0.879 
+(epoch: 291, iters: 1680, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.728 G_ID: 0.168 G_Rec: 0.341 D_GP: 0.030 D_real: 0.985 D_fake: 0.818 
+(epoch: 291, iters: 2080, time: 0.064) G_GAN: 0.148 G_GAN_Feat: 0.857 G_ID: 0.138 G_Rec: 0.342 D_GP: 0.068 D_real: 0.698 D_fake: 0.852 
+(epoch: 291, iters: 2480, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.846 G_ID: 0.151 G_Rec: 0.419 D_GP: 0.022 D_real: 1.219 D_fake: 0.500 
+(epoch: 291, iters: 2880, time: 0.064) G_GAN: 0.108 G_GAN_Feat: 0.854 G_ID: 0.124 G_Rec: 0.327 D_GP: 0.234 D_real: 0.597 D_fake: 0.892 
+(epoch: 291, iters: 3280, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.878 G_ID: 0.150 G_Rec: 0.368 D_GP: 0.022 D_real: 1.126 D_fake: 0.581 
+(epoch: 291, iters: 3680, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.756 G_ID: 0.122 G_Rec: 0.299 D_GP: 0.091 D_real: 0.761 D_fake: 0.736 
+(epoch: 291, iters: 4080, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.867 G_ID: 0.170 G_Rec: 0.383 D_GP: 0.030 D_real: 1.002 D_fake: 0.790 
+(epoch: 291, iters: 4480, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.807 G_ID: 0.107 G_Rec: 0.312 D_GP: 0.054 D_real: 0.757 D_fake: 0.785 
+(epoch: 291, iters: 4880, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 1.092 G_ID: 0.162 G_Rec: 0.402 D_GP: 0.113 D_real: 0.423 D_fake: 0.660 
+(epoch: 291, iters: 5280, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.596 G_ID: 0.117 G_Rec: 0.245 D_GP: 0.017 D_real: 1.262 D_fake: 0.699 
+(epoch: 291, iters: 5680, time: 0.064) G_GAN: 0.509 G_GAN_Feat: 0.814 G_ID: 0.161 G_Rec: 0.402 D_GP: 0.020 D_real: 1.247 D_fake: 0.504 
+(epoch: 291, iters: 6080, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.631 G_ID: 0.127 G_Rec: 0.284 D_GP: 0.023 D_real: 1.107 D_fake: 0.846 
+(epoch: 291, iters: 6480, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.815 G_ID: 0.207 G_Rec: 0.343 D_GP: 0.027 D_real: 0.927 D_fake: 0.908 
+(epoch: 291, iters: 6880, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.726 G_ID: 0.113 G_Rec: 0.269 D_GP: 0.038 D_real: 1.199 D_fake: 0.632 
+(epoch: 291, iters: 7280, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.898 G_ID: 0.155 G_Rec: 0.404 D_GP: 0.027 D_real: 0.913 D_fake: 0.715 
+(epoch: 291, iters: 7680, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.724 G_ID: 0.107 G_Rec: 0.288 D_GP: 0.122 D_real: 0.895 D_fake: 0.776 
+(epoch: 291, iters: 8080, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 0.838 G_ID: 0.163 G_Rec: 0.400 D_GP: 0.019 D_real: 1.389 D_fake: 0.405 
+(epoch: 291, iters: 8480, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.739 G_ID: 0.116 G_Rec: 0.304 D_GP: 0.055 D_real: 1.102 D_fake: 0.812 
+(epoch: 292, iters: 272, time: 0.064) G_GAN: 0.580 G_GAN_Feat: 1.096 G_ID: 0.182 G_Rec: 0.414 D_GP: 0.101 D_real: 0.396 D_fake: 0.448 
+(epoch: 292, iters: 672, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 0.658 G_ID: 0.117 G_Rec: 0.259 D_GP: 0.021 D_real: 1.439 D_fake: 0.597 
+(epoch: 292, iters: 1072, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.797 G_ID: 0.182 G_Rec: 0.378 D_GP: 0.020 D_real: 1.375 D_fake: 0.475 
+(epoch: 292, iters: 1472, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.637 G_ID: 0.106 G_Rec: 0.277 D_GP: 0.019 D_real: 1.174 D_fake: 0.761 
+(epoch: 292, iters: 1872, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.916 G_ID: 0.170 G_Rec: 0.386 D_GP: 0.117 D_real: 0.739 D_fake: 0.791 
+(epoch: 292, iters: 2272, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.771 G_ID: 0.138 G_Rec: 0.313 D_GP: 0.047 D_real: 0.963 D_fake: 0.668 
+(epoch: 292, iters: 2672, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 0.984 G_ID: 0.149 G_Rec: 0.429 D_GP: 0.039 D_real: 1.132 D_fake: 0.529 
+(epoch: 292, iters: 3072, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.727 G_ID: 0.161 G_Rec: 0.269 D_GP: 0.024 D_real: 1.223 D_fake: 0.705 
+(epoch: 292, iters: 3472, time: 0.064) G_GAN: 0.042 G_GAN_Feat: 0.833 G_ID: 0.161 G_Rec: 0.365 D_GP: 0.022 D_real: 0.943 D_fake: 0.958 
+(epoch: 292, iters: 3872, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.645 G_ID: 0.116 G_Rec: 0.280 D_GP: 0.023 D_real: 1.059 D_fake: 0.854 
+(epoch: 292, iters: 4272, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.665 G_ID: 0.148 G_Rec: 0.330 D_GP: 0.019 D_real: 1.281 D_fake: 0.640 
+(epoch: 292, iters: 4672, time: 0.064) G_GAN: -0.000 G_GAN_Feat: 0.602 G_ID: 0.133 G_Rec: 0.294 D_GP: 0.024 D_real: 0.921 D_fake: 1.000 
+(epoch: 292, iters: 5072, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.753 G_ID: 0.165 G_Rec: 0.353 D_GP: 0.027 D_real: 0.981 D_fake: 0.812 
+(epoch: 292, iters: 5472, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.636 G_ID: 0.123 G_Rec: 0.296 D_GP: 0.027 D_real: 1.046 D_fake: 0.920 
+(epoch: 292, iters: 5872, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.837 G_ID: 0.177 G_Rec: 0.390 D_GP: 0.023 D_real: 0.813 D_fake: 0.832 
+(epoch: 292, iters: 6272, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.706 G_ID: 0.122 G_Rec: 0.358 D_GP: 0.029 D_real: 0.935 D_fake: 0.880 
+(epoch: 292, iters: 6672, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.780 G_ID: 0.150 G_Rec: 0.364 D_GP: 0.022 D_real: 1.202 D_fake: 0.580 
+(epoch: 292, iters: 7072, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.787 G_ID: 0.139 G_Rec: 0.308 D_GP: 0.039 D_real: 1.101 D_fake: 0.641 
+(epoch: 292, iters: 7472, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.888 G_ID: 0.141 G_Rec: 0.377 D_GP: 0.042 D_real: 1.021 D_fake: 0.499 
+(epoch: 292, iters: 7872, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 0.747 G_ID: 0.117 G_Rec: 0.288 D_GP: 0.041 D_real: 1.336 D_fake: 0.488 
+(epoch: 292, iters: 8272, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.849 G_ID: 0.157 G_Rec: 0.400 D_GP: 0.022 D_real: 1.208 D_fake: 0.577 
+(epoch: 293, iters: 64, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.659 G_ID: 0.140 G_Rec: 0.270 D_GP: 0.020 D_real: 1.233 D_fake: 0.692 
+(epoch: 293, iters: 464, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.961 G_ID: 0.157 G_Rec: 0.400 D_GP: 0.051 D_real: 0.732 D_fake: 0.709 
+(epoch: 293, iters: 864, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.747 G_ID: 0.147 G_Rec: 0.290 D_GP: 0.057 D_real: 1.042 D_fake: 0.603 
+(epoch: 293, iters: 1264, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.894 G_ID: 0.170 G_Rec: 0.395 D_GP: 0.029 D_real: 0.961 D_fake: 0.753 
+(epoch: 293, iters: 1664, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.625 G_ID: 0.119 G_Rec: 0.281 D_GP: 0.022 D_real: 1.175 D_fake: 0.753 
+(epoch: 293, iters: 2064, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.846 G_ID: 0.154 G_Rec: 0.365 D_GP: 0.032 D_real: 1.163 D_fake: 0.659 
+(epoch: 293, iters: 2464, time: 0.064) G_GAN: 0.021 G_GAN_Feat: 0.689 G_ID: 0.139 G_Rec: 0.297 D_GP: 0.062 D_real: 0.757 D_fake: 0.980 
+(epoch: 293, iters: 2864, time: 0.064) G_GAN: 0.537 G_GAN_Feat: 0.923 G_ID: 0.174 G_Rec: 0.382 D_GP: 0.038 D_real: 1.085 D_fake: 0.484 
+(epoch: 293, iters: 3264, time: 0.064) G_GAN: 0.104 G_GAN_Feat: 0.629 G_ID: 0.145 G_Rec: 0.252 D_GP: 0.022 D_real: 0.992 D_fake: 0.896 
+(epoch: 293, iters: 3664, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 1.114 G_ID: 0.158 G_Rec: 0.399 D_GP: 0.141 D_real: 0.351 D_fake: 0.701 
+(epoch: 293, iters: 4064, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.648 G_ID: 0.158 G_Rec: 0.262 D_GP: 0.026 D_real: 1.161 D_fake: 0.738 
+(epoch: 293, iters: 4464, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.895 G_ID: 0.157 G_Rec: 0.404 D_GP: 0.033 D_real: 0.913 D_fake: 0.746 
+(epoch: 293, iters: 4864, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.681 G_ID: 0.122 G_Rec: 0.269 D_GP: 0.030 D_real: 0.923 D_fake: 0.914 
+(epoch: 293, iters: 5264, time: 0.064) G_GAN: 0.603 G_GAN_Feat: 0.976 G_ID: 0.177 G_Rec: 0.398 D_GP: 0.095 D_real: 0.983 D_fake: 0.432 
+(epoch: 293, iters: 5664, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.870 G_ID: 0.125 G_Rec: 0.338 D_GP: 0.084 D_real: 0.491 D_fake: 0.735 
+(epoch: 293, iters: 6064, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.885 G_ID: 0.171 G_Rec: 0.376 D_GP: 0.028 D_real: 0.861 D_fake: 0.844 
+(epoch: 293, iters: 6464, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.831 G_ID: 0.113 G_Rec: 0.294 D_GP: 0.077 D_real: 0.616 D_fake: 0.730 
+(epoch: 293, iters: 6864, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 1.158 G_ID: 0.161 G_Rec: 0.452 D_GP: 0.279 D_real: 0.482 D_fake: 0.699 
+(epoch: 293, iters: 7264, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.835 G_ID: 0.129 G_Rec: 0.331 D_GP: 0.034 D_real: 0.876 D_fake: 0.999 
+(epoch: 293, iters: 7664, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.865 G_ID: 0.165 G_Rec: 0.378 D_GP: 0.023 D_real: 1.005 D_fake: 0.809 
+(epoch: 293, iters: 8064, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.779 G_ID: 0.126 G_Rec: 0.343 D_GP: 0.061 D_real: 0.809 D_fake: 0.895 
+(epoch: 293, iters: 8464, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.903 G_ID: 0.165 G_Rec: 0.409 D_GP: 0.048 D_real: 0.775 D_fake: 0.788 
+(epoch: 294, iters: 256, time: 0.064) G_GAN: -0.003 G_GAN_Feat: 0.734 G_ID: 0.134 G_Rec: 0.292 D_GP: 0.074 D_real: 0.689 D_fake: 1.003 
+(epoch: 294, iters: 656, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 0.864 G_ID: 0.173 G_Rec: 0.376 D_GP: 0.026 D_real: 1.269 D_fake: 0.463 
+(epoch: 294, iters: 1056, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.686 G_ID: 0.133 G_Rec: 0.284 D_GP: 0.025 D_real: 1.308 D_fake: 0.616 
+(epoch: 294, iters: 1456, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.922 G_ID: 0.144 G_Rec: 0.389 D_GP: 0.029 D_real: 1.086 D_fake: 0.504 
+(epoch: 294, iters: 1856, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.839 G_ID: 0.114 G_Rec: 0.287 D_GP: 0.026 D_real: 0.869 D_fake: 0.615 
+(epoch: 294, iters: 2256, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.838 G_ID: 0.156 G_Rec: 0.386 D_GP: 0.023 D_real: 1.044 D_fake: 0.743 
+(epoch: 294, iters: 2656, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.628 G_ID: 0.128 G_Rec: 0.268 D_GP: 0.022 D_real: 1.142 D_fake: 0.795 
+(epoch: 294, iters: 3056, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.847 G_ID: 0.176 G_Rec: 0.367 D_GP: 0.022 D_real: 1.216 D_fake: 0.648 
+(epoch: 294, iters: 3456, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.899 G_ID: 0.101 G_Rec: 0.315 D_GP: 0.065 D_real: 0.614 D_fake: 0.673 
+(epoch: 294, iters: 3856, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.869 G_ID: 0.159 G_Rec: 0.393 D_GP: 0.022 D_real: 1.132 D_fake: 0.609 
+(epoch: 294, iters: 4256, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.817 G_ID: 0.146 G_Rec: 0.292 D_GP: 0.039 D_real: 1.119 D_fake: 0.822 
+(epoch: 294, iters: 4656, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.937 G_ID: 0.153 G_Rec: 0.349 D_GP: 0.027 D_real: 0.826 D_fake: 0.615 
+(epoch: 294, iters: 5056, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.619 G_ID: 0.141 G_Rec: 0.282 D_GP: 0.020 D_real: 0.991 D_fake: 0.968 
+(epoch: 294, iters: 5456, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.953 G_ID: 0.156 G_Rec: 0.398 D_GP: 0.101 D_real: 0.696 D_fake: 0.698 
+(epoch: 294, iters: 5856, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.605 G_ID: 0.137 G_Rec: 0.285 D_GP: 0.018 D_real: 1.282 D_fake: 0.694 
+(epoch: 294, iters: 6256, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.861 G_ID: 0.144 G_Rec: 0.442 D_GP: 0.029 D_real: 1.215 D_fake: 0.491 
+(epoch: 294, iters: 6656, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.708 G_ID: 0.122 G_Rec: 0.298 D_GP: 0.021 D_real: 1.172 D_fake: 0.724 
+(epoch: 294, iters: 7056, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 0.903 G_ID: 0.158 G_Rec: 0.377 D_GP: 0.051 D_real: 1.135 D_fake: 0.501 
+(epoch: 294, iters: 7456, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.733 G_ID: 0.123 G_Rec: 0.279 D_GP: 0.040 D_real: 0.867 D_fake: 0.746 
+(epoch: 294, iters: 7856, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.967 G_ID: 0.166 G_Rec: 0.413 D_GP: 0.054 D_real: 0.682 D_fake: 0.629 
+(epoch: 294, iters: 8256, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.779 G_ID: 0.109 G_Rec: 0.288 D_GP: 0.050 D_real: 0.701 D_fake: 0.744 
+(epoch: 295, iters: 48, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.832 G_ID: 0.159 G_Rec: 0.364 D_GP: 0.030 D_real: 1.209 D_fake: 0.500 
+(epoch: 295, iters: 448, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.830 G_ID: 0.107 G_Rec: 0.308 D_GP: 0.039 D_real: 0.936 D_fake: 0.672 
+(epoch: 295, iters: 848, time: 0.064) G_GAN: 0.616 G_GAN_Feat: 1.167 G_ID: 0.160 G_Rec: 0.428 D_GP: 0.039 D_real: 0.372 D_fake: 0.415 
+(epoch: 295, iters: 1248, time: 0.064) G_GAN: -0.006 G_GAN_Feat: 0.603 G_ID: 0.121 G_Rec: 0.311 D_GP: 0.017 D_real: 0.978 D_fake: 1.006 
+(epoch: 295, iters: 1648, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.689 G_ID: 0.145 G_Rec: 0.356 D_GP: 0.019 D_real: 1.196 D_fake: 0.705 
+(epoch: 295, iters: 2048, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.555 G_ID: 0.106 G_Rec: 0.271 D_GP: 0.026 D_real: 1.185 D_fake: 0.781 
+(epoch: 295, iters: 2448, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.748 G_ID: 0.149 G_Rec: 0.401 D_GP: 0.027 D_real: 0.947 D_fake: 0.809 
+(epoch: 295, iters: 2848, time: 0.064) G_GAN: 0.108 G_GAN_Feat: 0.566 G_ID: 0.122 G_Rec: 0.256 D_GP: 0.027 D_real: 1.021 D_fake: 0.892 
+(epoch: 295, iters: 3248, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.755 G_ID: 0.165 G_Rec: 0.388 D_GP: 0.019 D_real: 1.156 D_fake: 0.676 
+(epoch: 295, iters: 3648, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.674 G_ID: 0.165 G_Rec: 0.296 D_GP: 0.023 D_real: 0.948 D_fake: 0.887 
+(epoch: 295, iters: 4048, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.792 G_ID: 0.166 G_Rec: 0.357 D_GP: 0.039 D_real: 1.166 D_fake: 0.581 
+(epoch: 295, iters: 4448, time: 0.064) G_GAN: 0.096 G_GAN_Feat: 0.744 G_ID: 0.105 G_Rec: 0.328 D_GP: 0.063 D_real: 0.826 D_fake: 0.904 
+(epoch: 295, iters: 4848, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.838 G_ID: 0.160 G_Rec: 0.384 D_GP: 0.045 D_real: 0.969 D_fake: 0.739 
+(epoch: 295, iters: 5248, time: 0.064) G_GAN: 0.104 G_GAN_Feat: 0.653 G_ID: 0.113 G_Rec: 0.277 D_GP: 0.025 D_real: 1.033 D_fake: 0.896 
+(epoch: 295, iters: 5648, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.823 G_ID: 0.150 G_Rec: 0.398 D_GP: 0.024 D_real: 1.215 D_fake: 0.543 
+(epoch: 295, iters: 6048, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.709 G_ID: 0.179 G_Rec: 0.300 D_GP: 0.048 D_real: 0.786 D_fake: 0.898 
+(epoch: 295, iters: 6448, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.894 G_ID: 0.155 G_Rec: 0.367 D_GP: 0.059 D_real: 0.724 D_fake: 0.724 
+(epoch: 295, iters: 6848, time: 0.064) G_GAN: 0.468 G_GAN_Feat: 0.702 G_ID: 0.122 G_Rec: 0.270 D_GP: 0.024 D_real: 1.284 D_fake: 0.532 
+(epoch: 295, iters: 7248, time: 0.064) G_GAN: 0.822 G_GAN_Feat: 1.100 G_ID: 0.174 G_Rec: 0.402 D_GP: 0.064 D_real: 1.073 D_fake: 0.390 
+(epoch: 295, iters: 7648, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.710 G_ID: 0.135 G_Rec: 0.299 D_GP: 0.025 D_real: 1.120 D_fake: 0.821 
+(epoch: 295, iters: 8048, time: 0.064) G_GAN: 0.640 G_GAN_Feat: 0.921 G_ID: 0.163 G_Rec: 0.414 D_GP: 0.037 D_real: 1.147 D_fake: 0.405 
+(epoch: 295, iters: 8448, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.611 G_ID: 0.117 G_Rec: 0.264 D_GP: 0.019 D_real: 1.106 D_fake: 0.834 
+(epoch: 296, iters: 240, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.943 G_ID: 0.156 G_Rec: 0.425 D_GP: 0.065 D_real: 0.959 D_fake: 0.510 
+(epoch: 296, iters: 640, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.893 G_ID: 0.135 G_Rec: 0.334 D_GP: 0.393 D_real: 0.603 D_fake: 0.903 
+(epoch: 296, iters: 1040, time: 0.064) G_GAN: 0.694 G_GAN_Feat: 0.800 G_ID: 0.148 G_Rec: 0.356 D_GP: 0.022 D_real: 1.492 D_fake: 0.320 
+(epoch: 296, iters: 1440, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.702 G_ID: 0.133 G_Rec: 0.322 D_GP: 0.020 D_real: 1.125 D_fake: 0.822 
+(epoch: 296, iters: 1840, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 0.829 G_ID: 0.151 G_Rec: 0.378 D_GP: 0.019 D_real: 1.317 D_fake: 0.527 
+(epoch: 296, iters: 2240, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.853 G_ID: 0.131 G_Rec: 0.337 D_GP: 0.100 D_real: 0.897 D_fake: 0.656 
+(epoch: 296, iters: 2640, time: 0.064) G_GAN: 0.273 G_GAN_Feat: 0.882 G_ID: 0.176 G_Rec: 0.356 D_GP: 0.037 D_real: 1.108 D_fake: 0.729 
+(epoch: 296, iters: 3040, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.631 G_ID: 0.126 G_Rec: 0.282 D_GP: 0.019 D_real: 1.369 D_fake: 0.558 
+(epoch: 296, iters: 3440, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.891 G_ID: 0.170 G_Rec: 0.362 D_GP: 0.030 D_real: 1.053 D_fake: 0.673 
+(epoch: 296, iters: 3840, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.792 G_ID: 0.126 G_Rec: 0.293 D_GP: 0.073 D_real: 1.019 D_fake: 0.688 
+(epoch: 296, iters: 4240, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.954 G_ID: 0.159 G_Rec: 0.383 D_GP: 0.046 D_real: 0.856 D_fake: 0.589 
+(epoch: 296, iters: 4640, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.863 G_ID: 0.123 G_Rec: 0.312 D_GP: 0.136 D_real: 0.586 D_fake: 0.738 
+(epoch: 296, iters: 5040, time: 0.064) G_GAN: 0.735 G_GAN_Feat: 1.073 G_ID: 0.172 G_Rec: 0.411 D_GP: 1.489 D_real: 0.654 D_fake: 0.317 
+(epoch: 296, iters: 5440, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.540 G_ID: 0.122 G_Rec: 0.257 D_GP: 0.017 D_real: 1.075 D_fake: 0.894 
+(epoch: 296, iters: 5840, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.824 G_ID: 0.157 G_Rec: 0.409 D_GP: 0.018 D_real: 1.060 D_fake: 0.650 
+(epoch: 296, iters: 6240, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.606 G_ID: 0.103 G_Rec: 0.280 D_GP: 0.019 D_real: 1.214 D_fake: 0.738 
+(epoch: 296, iters: 6640, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.765 G_ID: 0.178 G_Rec: 0.368 D_GP: 0.027 D_real: 1.060 D_fake: 0.804 
+(epoch: 296, iters: 7040, time: 0.064) G_GAN: 0.034 G_GAN_Feat: 0.583 G_ID: 0.140 G_Rec: 0.276 D_GP: 0.026 D_real: 0.937 D_fake: 0.966 
+(epoch: 296, iters: 7440, time: 0.064) G_GAN: 0.063 G_GAN_Feat: 0.780 G_ID: 0.184 G_Rec: 0.377 D_GP: 0.037 D_real: 0.791 D_fake: 0.937 
+(epoch: 296, iters: 7840, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.653 G_ID: 0.129 G_Rec: 0.293 D_GP: 0.035 D_real: 0.982 D_fake: 0.909 
+(epoch: 296, iters: 8240, time: 0.064) G_GAN: 0.139 G_GAN_Feat: 0.756 G_ID: 0.164 G_Rec: 0.370 D_GP: 0.023 D_real: 0.932 D_fake: 0.861 
+(epoch: 297, iters: 32, time: 0.064) G_GAN: 0.135 G_GAN_Feat: 0.675 G_ID: 0.128 G_Rec: 0.300 D_GP: 0.030 D_real: 1.053 D_fake: 0.865 
+(epoch: 297, iters: 432, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.854 G_ID: 0.163 G_Rec: 0.399 D_GP: 0.048 D_real: 1.058 D_fake: 0.585 
+(epoch: 297, iters: 832, time: 0.064) G_GAN: 0.052 G_GAN_Feat: 0.940 G_ID: 0.122 G_Rec: 0.344 D_GP: 0.022 D_real: 1.280 D_fake: 0.951 
+(epoch: 297, iters: 1232, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.787 G_ID: 0.177 G_Rec: 0.402 D_GP: 0.020 D_real: 0.934 D_fake: 0.821 
+(epoch: 297, iters: 1632, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.598 G_ID: 0.133 G_Rec: 0.291 D_GP: 0.019 D_real: 1.106 D_fake: 0.788 
+(epoch: 297, iters: 2032, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.796 G_ID: 0.143 G_Rec: 0.398 D_GP: 0.024 D_real: 1.171 D_fake: 0.609 
+(epoch: 297, iters: 2432, time: 0.064) G_GAN: 0.098 G_GAN_Feat: 0.631 G_ID: 0.153 G_Rec: 0.328 D_GP: 0.023 D_real: 1.040 D_fake: 0.902 
+(epoch: 297, iters: 2832, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.722 G_ID: 0.162 G_Rec: 0.315 D_GP: 0.035 D_real: 1.241 D_fake: 0.584 
+(epoch: 297, iters: 3232, time: 0.064) G_GAN: -0.030 G_GAN_Feat: 0.706 G_ID: 0.144 G_Rec: 0.287 D_GP: 0.052 D_real: 0.745 D_fake: 1.030 
+(epoch: 297, iters: 3632, time: 0.064) G_GAN: 0.319 G_GAN_Feat: 0.868 G_ID: 0.139 G_Rec: 0.389 D_GP: 0.046 D_real: 0.966 D_fake: 0.681 
+(epoch: 297, iters: 4032, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.621 G_ID: 0.105 G_Rec: 0.262 D_GP: 0.035 D_real: 1.236 D_fake: 0.672 
+(epoch: 297, iters: 4432, time: 0.064) G_GAN: -0.221 G_GAN_Feat: 1.105 G_ID: 0.201 G_Rec: 0.466 D_GP: 1.106 D_real: 0.257 D_fake: 1.221 
+(epoch: 297, iters: 4832, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.719 G_ID: 0.115 G_Rec: 0.311 D_GP: 0.027 D_real: 1.065 D_fake: 0.737 
+(epoch: 297, iters: 5232, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 0.813 G_ID: 0.166 G_Rec: 0.347 D_GP: 0.030 D_real: 1.297 D_fake: 0.466 
+(epoch: 297, iters: 5632, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.657 G_ID: 0.110 G_Rec: 0.293 D_GP: 0.026 D_real: 1.184 D_fake: 0.775 
+(epoch: 297, iters: 6032, time: 0.064) G_GAN: 0.644 G_GAN_Feat: 1.034 G_ID: 0.154 G_Rec: 0.439 D_GP: 0.077 D_real: 0.854 D_fake: 0.385 
+(epoch: 297, iters: 6432, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.687 G_ID: 0.128 G_Rec: 0.286 D_GP: 0.033 D_real: 1.019 D_fake: 0.868 
+(epoch: 297, iters: 6832, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.946 G_ID: 0.171 G_Rec: 0.377 D_GP: 0.148 D_real: 0.583 D_fake: 0.735 
+(epoch: 297, iters: 7232, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.736 G_ID: 0.131 G_Rec: 0.274 D_GP: 0.027 D_real: 1.053 D_fake: 0.735 
+(epoch: 297, iters: 7632, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.895 G_ID: 0.157 G_Rec: 0.419 D_GP: 0.030 D_real: 1.020 D_fake: 0.637 
+(epoch: 297, iters: 8032, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.621 G_ID: 0.116 G_Rec: 0.273 D_GP: 0.019 D_real: 1.220 D_fake: 0.711 
+(epoch: 297, iters: 8432, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 0.876 G_ID: 0.187 G_Rec: 0.407 D_GP: 0.034 D_real: 1.275 D_fake: 0.373 
+(epoch: 298, iters: 224, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.660 G_ID: 0.113 G_Rec: 0.290 D_GP: 0.029 D_real: 1.118 D_fake: 0.771 
+(epoch: 298, iters: 624, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.936 G_ID: 0.180 G_Rec: 0.386 D_GP: 0.102 D_real: 0.748 D_fake: 0.676 
+(epoch: 298, iters: 1024, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.774 G_ID: 0.103 G_Rec: 0.303 D_GP: 0.099 D_real: 0.800 D_fake: 0.723 
+(epoch: 298, iters: 1424, time: 0.064) G_GAN: 0.787 G_GAN_Feat: 0.846 G_ID: 0.135 G_Rec: 0.414 D_GP: 0.026 D_real: 1.396 D_fake: 0.270 
+(epoch: 298, iters: 1824, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.659 G_ID: 0.133 G_Rec: 0.293 D_GP: 0.035 D_real: 1.145 D_fake: 0.707 
+(epoch: 298, iters: 2224, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.890 G_ID: 0.139 G_Rec: 0.385 D_GP: 0.028 D_real: 1.049 D_fake: 0.545 
+(epoch: 298, iters: 2624, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.740 G_ID: 0.116 G_Rec: 0.276 D_GP: 0.027 D_real: 0.843 D_fake: 0.749 
+(epoch: 298, iters: 3024, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.852 G_ID: 0.163 G_Rec: 0.397 D_GP: 0.035 D_real: 0.771 D_fake: 0.931 
+(epoch: 298, iters: 3424, time: 0.064) G_GAN: -0.169 G_GAN_Feat: 0.733 G_ID: 0.123 G_Rec: 0.270 D_GP: 0.027 D_real: 0.654 D_fake: 1.169 
+(epoch: 298, iters: 3824, time: 0.064) G_GAN: 0.498 G_GAN_Feat: 0.928 G_ID: 0.160 G_Rec: 0.415 D_GP: 0.025 D_real: 1.107 D_fake: 0.507 
+(epoch: 298, iters: 4224, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.782 G_ID: 0.150 G_Rec: 0.310 D_GP: 0.030 D_real: 0.961 D_fake: 0.755 
+(epoch: 298, iters: 4624, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 0.878 G_ID: 0.147 G_Rec: 0.381 D_GP: 0.030 D_real: 1.163 D_fake: 0.521 
+(epoch: 298, iters: 5024, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.736 G_ID: 0.152 G_Rec: 0.356 D_GP: 0.026 D_real: 0.972 D_fake: 0.926 
+(epoch: 298, iters: 5424, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.830 G_ID: 0.192 G_Rec: 0.359 D_GP: 0.023 D_real: 0.834 D_fake: 0.864 
+(epoch: 298, iters: 5824, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.756 G_ID: 0.132 G_Rec: 0.311 D_GP: 0.048 D_real: 0.996 D_fake: 0.785 
+(epoch: 298, iters: 6224, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.901 G_ID: 0.158 G_Rec: 0.411 D_GP: 0.027 D_real: 1.076 D_fake: 0.590 
+(epoch: 298, iters: 6624, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.704 G_ID: 0.121 G_Rec: 0.255 D_GP: 0.039 D_real: 0.935 D_fake: 0.830 
+(epoch: 298, iters: 7024, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 1.070 G_ID: 0.169 G_Rec: 0.424 D_GP: 0.194 D_real: 0.442 D_fake: 0.817 
+(epoch: 298, iters: 7424, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.638 G_ID: 0.132 G_Rec: 0.300 D_GP: 0.021 D_real: 1.087 D_fake: 0.860 
+(epoch: 298, iters: 7824, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.918 G_ID: 0.177 G_Rec: 0.432 D_GP: 0.043 D_real: 0.733 D_fake: 0.864 
+(epoch: 298, iters: 8224, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 0.678 G_ID: 0.123 G_Rec: 0.274 D_GP: 0.043 D_real: 0.917 D_fake: 0.901 
+(epoch: 299, iters: 16, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.879 G_ID: 0.152 G_Rec: 0.349 D_GP: 0.031 D_real: 0.918 D_fake: 0.642 
+(epoch: 299, iters: 416, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.653 G_ID: 0.133 G_Rec: 0.271 D_GP: 0.029 D_real: 1.036 D_fake: 0.845 
+(epoch: 299, iters: 816, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.880 G_ID: 0.165 G_Rec: 0.372 D_GP: 0.025 D_real: 1.067 D_fake: 0.531 
+(epoch: 299, iters: 1216, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.704 G_ID: 0.105 G_Rec: 0.267 D_GP: 0.025 D_real: 0.974 D_fake: 0.810 
+(epoch: 299, iters: 1616, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 1.013 G_ID: 0.169 G_Rec: 0.374 D_GP: 0.041 D_real: 0.709 D_fake: 0.757 
+(epoch: 299, iters: 2016, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.706 G_ID: 0.119 G_Rec: 0.278 D_GP: 0.023 D_real: 1.099 D_fake: 0.764 
+(epoch: 299, iters: 2416, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.831 G_ID: 0.155 G_Rec: 0.376 D_GP: 0.024 D_real: 1.029 D_fake: 0.756 
+(epoch: 299, iters: 2816, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.702 G_ID: 0.140 G_Rec: 0.264 D_GP: 0.026 D_real: 1.128 D_fake: 0.752 
+(epoch: 299, iters: 3216, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.773 G_ID: 0.171 G_Rec: 0.362 D_GP: 0.020 D_real: 1.175 D_fake: 0.636 
+(epoch: 299, iters: 3616, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.726 G_ID: 0.128 G_Rec: 0.292 D_GP: 0.030 D_real: 0.876 D_fake: 0.968 
+(epoch: 299, iters: 4016, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.827 G_ID: 0.164 G_Rec: 0.365 D_GP: 0.025 D_real: 0.941 D_fake: 0.821 
+(epoch: 299, iters: 4416, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.704 G_ID: 0.109 G_Rec: 0.283 D_GP: 0.038 D_real: 1.090 D_fake: 0.730 
+(epoch: 299, iters: 4816, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.799 G_ID: 0.144 G_Rec: 0.363 D_GP: 0.021 D_real: 1.185 D_fake: 0.573 
+(epoch: 299, iters: 5216, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.975 G_ID: 0.121 G_Rec: 0.319 D_GP: 0.041 D_real: 1.169 D_fake: 0.855 
+(epoch: 299, iters: 5616, time: 0.064) G_GAN: 0.917 G_GAN_Feat: 1.094 G_ID: 0.165 G_Rec: 0.443 D_GP: 0.097 D_real: 1.350 D_fake: 0.368 
+(epoch: 299, iters: 6016, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.707 G_ID: 0.113 G_Rec: 0.280 D_GP: 0.040 D_real: 1.152 D_fake: 0.581 
+(epoch: 299, iters: 6416, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 1.107 G_ID: 0.188 G_Rec: 0.446 D_GP: 0.104 D_real: 0.365 D_fake: 0.632 
+(epoch: 299, iters: 6816, time: 0.064) G_GAN: -0.078 G_GAN_Feat: 0.605 G_ID: 0.135 G_Rec: 0.277 D_GP: 0.019 D_real: 0.902 D_fake: 1.078 
+(epoch: 299, iters: 7216, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.846 G_ID: 0.178 G_Rec: 0.400 D_GP: 0.025 D_real: 0.952 D_fake: 0.752 
+(epoch: 299, iters: 7616, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.658 G_ID: 0.112 G_Rec: 0.302 D_GP: 0.034 D_real: 0.946 D_fake: 0.883 
+(epoch: 299, iters: 8016, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.783 G_ID: 0.178 G_Rec: 0.371 D_GP: 0.021 D_real: 1.236 D_fake: 0.609 
+(epoch: 299, iters: 8416, time: 0.064) G_GAN: 0.104 G_GAN_Feat: 0.905 G_ID: 0.140 G_Rec: 0.337 D_GP: 0.115 D_real: 0.324 D_fake: 0.896 
+(epoch: 300, iters: 208, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.866 G_ID: 0.166 G_Rec: 0.389 D_GP: 0.027 D_real: 1.124 D_fake: 0.555 
+(epoch: 300, iters: 608, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.843 G_ID: 0.114 G_Rec: 0.327 D_GP: 0.066 D_real: 0.517 D_fake: 0.745 
+(epoch: 300, iters: 1008, time: 0.064) G_GAN: -0.031 G_GAN_Feat: 0.777 G_ID: 0.203 G_Rec: 0.357 D_GP: 0.019 D_real: 0.806 D_fake: 1.031 
+(epoch: 300, iters: 1408, time: 0.064) G_GAN: -0.008 G_GAN_Feat: 0.613 G_ID: 0.124 G_Rec: 0.269 D_GP: 0.018 D_real: 0.945 D_fake: 1.008 
+(epoch: 300, iters: 1808, time: 0.064) G_GAN: -0.030 G_GAN_Feat: 0.755 G_ID: 0.167 G_Rec: 0.371 D_GP: 0.019 D_real: 0.801 D_fake: 1.030 
+(epoch: 300, iters: 2208, time: 0.064) G_GAN: -0.094 G_GAN_Feat: 0.680 G_ID: 0.116 G_Rec: 0.300 D_GP: 0.027 D_real: 0.727 D_fake: 1.094 
+(epoch: 300, iters: 2608, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.863 G_ID: 0.152 G_Rec: 0.422 D_GP: 0.036 D_real: 1.144 D_fake: 0.576 
+(epoch: 300, iters: 3008, time: 0.064) G_GAN: 0.152 G_GAN_Feat: 0.712 G_ID: 0.151 G_Rec: 0.288 D_GP: 0.027 D_real: 0.906 D_fake: 0.850 
+(epoch: 300, iters: 3408, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.877 G_ID: 0.174 G_Rec: 0.389 D_GP: 0.038 D_real: 1.000 D_fake: 0.614 
+(epoch: 300, iters: 3808, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.705 G_ID: 0.112 G_Rec: 0.300 D_GP: 0.037 D_real: 1.135 D_fake: 0.703 
+(epoch: 300, iters: 4208, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.881 G_ID: 0.165 G_Rec: 0.393 D_GP: 0.031 D_real: 1.135 D_fake: 0.662 
+(epoch: 300, iters: 4608, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.764 G_ID: 0.123 G_Rec: 0.312 D_GP: 0.037 D_real: 1.222 D_fake: 0.491 
+(epoch: 300, iters: 5008, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.971 G_ID: 0.166 G_Rec: 0.410 D_GP: 0.069 D_real: 0.706 D_fake: 0.659 
+(epoch: 300, iters: 5408, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.705 G_ID: 0.124 G_Rec: 0.281 D_GP: 0.029 D_real: 1.175 D_fake: 0.661 
+(epoch: 300, iters: 5808, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.956 G_ID: 0.179 G_Rec: 0.404 D_GP: 0.042 D_real: 0.744 D_fake: 0.801 
+(epoch: 300, iters: 6208, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.815 G_ID: 0.131 G_Rec: 0.289 D_GP: 0.038 D_real: 1.044 D_fake: 0.547 
+(epoch: 300, iters: 6608, time: 0.064) G_GAN: 0.711 G_GAN_Feat: 0.986 G_ID: 0.155 G_Rec: 0.413 D_GP: 0.039 D_real: 1.024 D_fake: 0.330 
+(epoch: 300, iters: 7008, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.856 G_ID: 0.125 G_Rec: 0.291 D_GP: 0.047 D_real: 0.700 D_fake: 0.530 
+(epoch: 300, iters: 7408, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.805 G_ID: 0.174 G_Rec: 0.382 D_GP: 0.022 D_real: 1.228 D_fake: 0.499 
+(epoch: 300, iters: 7808, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.789 G_ID: 0.120 G_Rec: 0.311 D_GP: 0.116 D_real: 0.751 D_fake: 0.908 
+(epoch: 300, iters: 8208, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.813 G_ID: 0.138 G_Rec: 0.378 D_GP: 0.023 D_real: 0.958 D_fake: 0.819 
+(epoch: 300, iters: 8608, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.633 G_ID: 0.116 G_Rec: 0.282 D_GP: 0.023 D_real: 1.149 D_fake: 0.743 
+(epoch: 301, iters: 400, time: 0.064) G_GAN: 0.597 G_GAN_Feat: 0.908 G_ID: 0.144 G_Rec: 0.399 D_GP: 0.027 D_real: 1.318 D_fake: 0.410 
+(epoch: 301, iters: 800, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.757 G_ID: 0.117 G_Rec: 0.295 D_GP: 0.056 D_real: 0.995 D_fake: 0.868 
+(epoch: 301, iters: 1200, time: 0.064) G_GAN: 0.637 G_GAN_Feat: 0.881 G_ID: 0.156 G_Rec: 0.341 D_GP: 0.026 D_real: 1.305 D_fake: 0.388 
+(epoch: 301, iters: 1600, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 0.796 G_ID: 0.101 G_Rec: 0.305 D_GP: 0.025 D_real: 1.458 D_fake: 0.466 
+(epoch: 301, iters: 2000, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.902 G_ID: 0.145 G_Rec: 0.378 D_GP: 0.021 D_real: 1.092 D_fake: 0.656 
+(epoch: 301, iters: 2400, time: 0.064) G_GAN: 0.165 G_GAN_Feat: 0.648 G_ID: 0.114 G_Rec: 0.270 D_GP: 0.020 D_real: 1.098 D_fake: 0.835 
+(epoch: 301, iters: 2800, time: 0.064) G_GAN: 0.625 G_GAN_Feat: 0.888 G_ID: 0.176 G_Rec: 0.408 D_GP: 0.032 D_real: 1.455 D_fake: 0.386 
+(epoch: 301, iters: 3200, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.604 G_ID: 0.127 G_Rec: 0.280 D_GP: 0.024 D_real: 0.944 D_fake: 0.989 
+(epoch: 301, iters: 3600, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.833 G_ID: 0.186 G_Rec: 0.417 D_GP: 0.032 D_real: 1.038 D_fake: 0.714 
+(epoch: 301, iters: 4000, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.686 G_ID: 0.122 G_Rec: 0.294 D_GP: 0.040 D_real: 0.808 D_fake: 0.938 
+(epoch: 301, iters: 4400, time: 0.064) G_GAN: 0.704 G_GAN_Feat: 0.960 G_ID: 0.144 G_Rec: 0.380 D_GP: 0.207 D_real: 1.058 D_fake: 0.321 
+(epoch: 301, iters: 4800, time: 0.064) G_GAN: 0.007 G_GAN_Feat: 0.675 G_ID: 0.127 G_Rec: 0.292 D_GP: 0.020 D_real: 1.027 D_fake: 0.993 
+(epoch: 301, iters: 5200, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.846 G_ID: 0.189 G_Rec: 0.370 D_GP: 0.030 D_real: 0.925 D_fake: 0.767 
+(epoch: 301, iters: 5600, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 0.626 G_ID: 0.122 G_Rec: 0.276 D_GP: 0.018 D_real: 1.363 D_fake: 0.611 
+(epoch: 301, iters: 6000, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.888 G_ID: 0.176 G_Rec: 0.372 D_GP: 0.053 D_real: 0.866 D_fake: 0.622 
+(epoch: 301, iters: 6400, time: 0.064) G_GAN: 0.017 G_GAN_Feat: 0.602 G_ID: 0.120 G_Rec: 0.283 D_GP: 0.021 D_real: 1.024 D_fake: 0.983 
+(epoch: 301, iters: 6800, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.841 G_ID: 0.163 G_Rec: 0.375 D_GP: 0.045 D_real: 1.013 D_fake: 0.724 
+(epoch: 301, iters: 7200, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.717 G_ID: 0.110 G_Rec: 0.310 D_GP: 0.048 D_real: 0.875 D_fake: 0.910 
+(epoch: 301, iters: 7600, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.986 G_ID: 0.146 G_Rec: 0.419 D_GP: 0.029 D_real: 0.848 D_fake: 0.633 
+(epoch: 301, iters: 8000, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.801 G_ID: 0.123 G_Rec: 0.292 D_GP: 0.028 D_real: 0.751 D_fake: 0.834 
+(epoch: 301, iters: 8400, time: 0.064) G_GAN: 0.509 G_GAN_Feat: 0.844 G_ID: 0.170 G_Rec: 0.347 D_GP: 0.041 D_real: 1.292 D_fake: 0.503 
+(epoch: 302, iters: 192, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.812 G_ID: 0.124 G_Rec: 0.300 D_GP: 0.029 D_real: 0.897 D_fake: 0.748 
+(epoch: 302, iters: 592, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.915 G_ID: 0.142 G_Rec: 0.361 D_GP: 0.024 D_real: 0.788 D_fake: 0.744 
+(epoch: 302, iters: 992, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.716 G_ID: 0.113 G_Rec: 0.316 D_GP: 0.025 D_real: 1.279 D_fake: 0.637 
+(epoch: 302, iters: 1392, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 1.158 G_ID: 0.157 G_Rec: 0.414 D_GP: 0.553 D_real: 0.309 D_fake: 0.612 
+(epoch: 302, iters: 1792, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.810 G_ID: 0.113 G_Rec: 0.304 D_GP: 0.030 D_real: 0.881 D_fake: 0.678 
+(epoch: 302, iters: 2192, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.836 G_ID: 0.145 G_Rec: 0.326 D_GP: 0.024 D_real: 0.951 D_fake: 0.797 
+(epoch: 302, iters: 2592, time: 0.064) G_GAN: -0.098 G_GAN_Feat: 0.872 G_ID: 0.138 G_Rec: 0.315 D_GP: 0.022 D_real: 0.501 D_fake: 1.098 
+(epoch: 302, iters: 2992, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 1.042 G_ID: 0.171 G_Rec: 0.412 D_GP: 0.034 D_real: 0.442 D_fake: 0.733 
+(epoch: 302, iters: 3392, time: 0.064) G_GAN: 0.398 G_GAN_Feat: 0.567 G_ID: 0.128 G_Rec: 0.261 D_GP: 0.016 D_real: 1.372 D_fake: 0.604 
+(epoch: 302, iters: 3792, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 0.802 G_ID: 0.153 G_Rec: 0.355 D_GP: 0.022 D_real: 1.216 D_fake: 0.490 
+(epoch: 302, iters: 4192, time: 0.064) G_GAN: 0.135 G_GAN_Feat: 0.615 G_ID: 0.126 G_Rec: 0.285 D_GP: 0.023 D_real: 1.021 D_fake: 0.865 
+(epoch: 302, iters: 4592, time: 0.064) G_GAN: 0.515 G_GAN_Feat: 1.033 G_ID: 0.169 G_Rec: 0.415 D_GP: 0.100 D_real: 0.536 D_fake: 0.517 
+(epoch: 302, iters: 4992, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.697 G_ID: 0.129 G_Rec: 0.294 D_GP: 0.030 D_real: 1.134 D_fake: 0.720 
+(epoch: 302, iters: 5392, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.946 G_ID: 0.134 G_Rec: 0.409 D_GP: 0.038 D_real: 1.019 D_fake: 0.767 
+(epoch: 302, iters: 5792, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 1.010 G_ID: 0.138 G_Rec: 0.318 D_GP: 0.046 D_real: 1.284 D_fake: 0.610 
+(epoch: 302, iters: 6192, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.822 G_ID: 0.175 G_Rec: 0.357 D_GP: 0.027 D_real: 1.018 D_fake: 0.758 
+(epoch: 302, iters: 6592, time: 0.064) G_GAN: 0.232 G_GAN_Feat: 0.806 G_ID: 0.128 G_Rec: 0.321 D_GP: 0.039 D_real: 0.844 D_fake: 0.770 
+(epoch: 302, iters: 6992, time: 0.064) G_GAN: 1.149 G_GAN_Feat: 1.001 G_ID: 0.162 G_Rec: 0.452 D_GP: 0.104 D_real: 1.608 D_fake: 0.274 
+(epoch: 302, iters: 7392, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.625 G_ID: 0.155 G_Rec: 0.274 D_GP: 0.022 D_real: 0.962 D_fake: 0.915 
+(epoch: 302, iters: 7792, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.885 G_ID: 0.172 G_Rec: 0.461 D_GP: 0.028 D_real: 0.937 D_fake: 0.723 
+(epoch: 302, iters: 8192, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.660 G_ID: 0.110 G_Rec: 0.285 D_GP: 0.030 D_real: 0.966 D_fake: 0.842 
+(epoch: 302, iters: 8592, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 0.985 G_ID: 0.143 G_Rec: 0.421 D_GP: 0.047 D_real: 0.864 D_fake: 0.482 
+(epoch: 303, iters: 384, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.863 G_ID: 0.135 G_Rec: 0.306 D_GP: 0.048 D_real: 0.477 D_fake: 0.888 
+(epoch: 303, iters: 784, time: 0.064) G_GAN: 0.722 G_GAN_Feat: 1.021 G_ID: 0.191 G_Rec: 0.410 D_GP: 0.085 D_real: 0.898 D_fake: 0.535 
+(epoch: 303, iters: 1184, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.912 G_ID: 0.098 G_Rec: 0.309 D_GP: 0.253 D_real: 0.360 D_fake: 0.792 
+(epoch: 303, iters: 1584, time: 0.064) G_GAN: 0.628 G_GAN_Feat: 1.083 G_ID: 0.191 G_Rec: 0.435 D_GP: 0.042 D_real: 0.924 D_fake: 0.431 
+(epoch: 303, iters: 1984, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.968 G_ID: 0.127 G_Rec: 0.282 D_GP: 0.059 D_real: 0.499 D_fake: 0.606 
+(epoch: 303, iters: 2384, time: 0.064) G_GAN: 0.586 G_GAN_Feat: 0.988 G_ID: 0.167 G_Rec: 0.426 D_GP: 0.022 D_real: 1.358 D_fake: 0.428 
+(epoch: 303, iters: 2784, time: 0.064) G_GAN: -0.022 G_GAN_Feat: 0.799 G_ID: 0.153 G_Rec: 0.297 D_GP: 0.060 D_real: 0.605 D_fake: 1.022 
+(epoch: 303, iters: 3184, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.994 G_ID: 0.143 G_Rec: 0.404 D_GP: 0.163 D_real: 0.475 D_fake: 0.620 
+(epoch: 303, iters: 3584, time: 0.064) G_GAN: 0.997 G_GAN_Feat: 0.787 G_ID: 0.124 G_Rec: 0.331 D_GP: 0.366 D_real: 1.473 D_fake: 0.482 
+(epoch: 303, iters: 3984, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.778 G_ID: 0.140 G_Rec: 0.383 D_GP: 0.020 D_real: 1.194 D_fake: 0.680 
+(epoch: 303, iters: 4384, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.659 G_ID: 0.131 G_Rec: 0.304 D_GP: 0.025 D_real: 1.090 D_fake: 0.789 
+(epoch: 303, iters: 4784, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.825 G_ID: 0.166 G_Rec: 0.383 D_GP: 0.034 D_real: 1.029 D_fake: 0.698 
+(epoch: 303, iters: 5184, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 0.688 G_ID: 0.104 G_Rec: 0.315 D_GP: 0.037 D_real: 1.027 D_fake: 0.824 
+(epoch: 303, iters: 5584, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.793 G_ID: 0.148 G_Rec: 0.360 D_GP: 0.026 D_real: 1.094 D_fake: 0.752 
+(epoch: 303, iters: 5984, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.649 G_ID: 0.141 G_Rec: 0.296 D_GP: 0.030 D_real: 1.006 D_fake: 0.907 
+(epoch: 303, iters: 6384, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.893 G_ID: 0.173 G_Rec: 0.434 D_GP: 0.082 D_real: 0.708 D_fake: 0.826 
+(epoch: 303, iters: 6784, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.697 G_ID: 0.143 G_Rec: 0.304 D_GP: 0.026 D_real: 1.050 D_fake: 0.918 
+(epoch: 303, iters: 7184, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.817 G_ID: 0.150 G_Rec: 0.346 D_GP: 0.028 D_real: 1.106 D_fake: 0.678 
+(epoch: 303, iters: 7584, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.726 G_ID: 0.117 G_Rec: 0.317 D_GP: 0.061 D_real: 1.022 D_fake: 0.830 
+(epoch: 303, iters: 7984, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.819 G_ID: 0.146 G_Rec: 0.394 D_GP: 0.025 D_real: 1.256 D_fake: 0.522 
+(epoch: 303, iters: 8384, time: 0.064) G_GAN: 0.230 G_GAN_Feat: 0.777 G_ID: 0.131 G_Rec: 0.359 D_GP: 0.130 D_real: 0.780 D_fake: 0.771 
+(epoch: 304, iters: 176, time: 0.064) G_GAN: 0.668 G_GAN_Feat: 0.874 G_ID: 0.171 G_Rec: 0.375 D_GP: 0.054 D_real: 1.139 D_fake: 0.358 
+(epoch: 304, iters: 576, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.543 G_ID: 0.130 G_Rec: 0.292 D_GP: 0.018 D_real: 1.161 D_fake: 0.845 
+(epoch: 304, iters: 976, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.717 G_ID: 0.141 G_Rec: 0.346 D_GP: 0.018 D_real: 1.265 D_fake: 0.609 
+(epoch: 304, iters: 1376, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.591 G_ID: 0.122 G_Rec: 0.273 D_GP: 0.024 D_real: 1.087 D_fake: 0.801 
+(epoch: 304, iters: 1776, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.774 G_ID: 0.168 G_Rec: 0.371 D_GP: 0.029 D_real: 1.114 D_fake: 0.559 
+(epoch: 304, iters: 2176, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.691 G_ID: 0.118 G_Rec: 0.301 D_GP: 0.036 D_real: 1.089 D_fake: 0.751 
+(epoch: 304, iters: 2576, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.924 G_ID: 0.147 G_Rec: 0.399 D_GP: 0.106 D_real: 0.723 D_fake: 0.607 
+(epoch: 304, iters: 2976, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.685 G_ID: 0.100 G_Rec: 0.304 D_GP: 0.076 D_real: 1.088 D_fake: 0.697 
+(epoch: 304, iters: 3376, time: 0.064) G_GAN: 0.044 G_GAN_Feat: 0.862 G_ID: 0.183 G_Rec: 0.416 D_GP: 0.030 D_real: 0.736 D_fake: 0.956 
+(epoch: 304, iters: 3776, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.694 G_ID: 0.120 G_Rec: 0.295 D_GP: 0.029 D_real: 0.987 D_fake: 0.871 
+(epoch: 304, iters: 4176, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.816 G_ID: 0.166 G_Rec: 0.369 D_GP: 0.030 D_real: 1.090 D_fake: 0.624 
+(epoch: 304, iters: 4576, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.776 G_ID: 0.121 G_Rec: 0.289 D_GP: 0.088 D_real: 0.822 D_fake: 0.749 
+(epoch: 304, iters: 4976, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 1.402 G_ID: 0.166 G_Rec: 0.506 D_GP: 1.087 D_real: 0.459 D_fake: 0.607 
+(epoch: 304, iters: 5376, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.797 G_ID: 0.126 G_Rec: 0.319 D_GP: 0.049 D_real: 0.828 D_fake: 0.846 
+(epoch: 304, iters: 5776, time: 0.064) G_GAN: 0.601 G_GAN_Feat: 0.972 G_ID: 0.140 G_Rec: 0.421 D_GP: 0.103 D_real: 0.883 D_fake: 0.416 
+(epoch: 304, iters: 6176, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.766 G_ID: 0.117 G_Rec: 0.280 D_GP: 0.078 D_real: 0.666 D_fake: 0.923 
+(epoch: 304, iters: 6576, time: 0.064) G_GAN: 0.636 G_GAN_Feat: 1.085 G_ID: 0.152 G_Rec: 0.447 D_GP: 0.065 D_real: 0.721 D_fake: 0.383 
+(epoch: 304, iters: 6976, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.778 G_ID: 0.123 G_Rec: 0.286 D_GP: 0.031 D_real: 0.855 D_fake: 0.887 
+(epoch: 304, iters: 7376, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.989 G_ID: 0.157 G_Rec: 0.411 D_GP: 0.028 D_real: 1.061 D_fake: 0.494 
+(epoch: 304, iters: 7776, time: 0.064) G_GAN: 0.172 G_GAN_Feat: 0.776 G_ID: 0.135 G_Rec: 0.282 D_GP: 0.032 D_real: 0.778 D_fake: 0.829 
+(epoch: 304, iters: 8176, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.887 G_ID: 0.193 G_Rec: 0.375 D_GP: 0.026 D_real: 0.952 D_fake: 0.742 
+(epoch: 304, iters: 8576, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.901 G_ID: 0.136 G_Rec: 0.307 D_GP: 0.123 D_real: 0.443 D_fake: 0.726 
+(epoch: 305, iters: 368, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.782 G_ID: 0.172 G_Rec: 0.410 D_GP: 0.019 D_real: 1.223 D_fake: 0.614 
+(epoch: 305, iters: 768, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.554 G_ID: 0.104 G_Rec: 0.272 D_GP: 0.021 D_real: 1.220 D_fake: 0.809 
+(epoch: 305, iters: 1168, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.777 G_ID: 0.155 G_Rec: 0.363 D_GP: 0.029 D_real: 0.999 D_fake: 0.748 
+(epoch: 305, iters: 1568, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.550 G_ID: 0.103 G_Rec: 0.267 D_GP: 0.018 D_real: 1.164 D_fake: 0.851 
+(epoch: 305, iters: 1968, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 0.773 G_ID: 0.148 G_Rec: 0.359 D_GP: 0.020 D_real: 1.265 D_fake: 0.565 
+(epoch: 305, iters: 2368, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.677 G_ID: 0.146 G_Rec: 0.308 D_GP: 0.033 D_real: 0.906 D_fake: 0.906 
+(epoch: 305, iters: 2768, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.903 G_ID: 0.140 G_Rec: 0.392 D_GP: 0.037 D_real: 0.952 D_fake: 0.649 
+(epoch: 305, iters: 3168, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.742 G_ID: 0.094 G_Rec: 0.309 D_GP: 0.125 D_real: 0.799 D_fake: 0.850 
+(epoch: 305, iters: 3568, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.905 G_ID: 0.179 G_Rec: 0.405 D_GP: 0.024 D_real: 1.246 D_fake: 0.468 
+(epoch: 305, iters: 3968, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 0.719 G_ID: 0.145 G_Rec: 0.304 D_GP: 0.052 D_real: 1.034 D_fake: 0.693 
+(epoch: 305, iters: 4368, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 0.810 G_ID: 0.160 G_Rec: 0.383 D_GP: 0.025 D_real: 1.429 D_fake: 0.426 
+(epoch: 305, iters: 4768, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.641 G_ID: 0.139 G_Rec: 0.285 D_GP: 0.046 D_real: 0.862 D_fake: 0.968 
+(epoch: 305, iters: 5168, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.796 G_ID: 0.152 G_Rec: 0.382 D_GP: 0.023 D_real: 1.152 D_fake: 0.573 
+(epoch: 305, iters: 5568, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.908 G_ID: 0.153 G_Rec: 0.346 D_GP: 0.398 D_real: 0.576 D_fake: 0.665 
+(epoch: 305, iters: 5968, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.897 G_ID: 0.179 G_Rec: 0.414 D_GP: 0.025 D_real: 0.875 D_fake: 0.803 
+(epoch: 305, iters: 6368, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.727 G_ID: 0.119 G_Rec: 0.310 D_GP: 0.022 D_real: 1.144 D_fake: 0.752 
+(epoch: 305, iters: 6768, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.792 G_ID: 0.166 G_Rec: 0.351 D_GP: 0.021 D_real: 1.260 D_fake: 0.520 
+(epoch: 305, iters: 7168, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.787 G_ID: 0.118 G_Rec: 0.288 D_GP: 0.060 D_real: 0.486 D_fake: 0.915 
+(epoch: 305, iters: 7568, time: 0.064) G_GAN: 0.494 G_GAN_Feat: 0.839 G_ID: 0.168 G_Rec: 0.375 D_GP: 0.023 D_real: 1.232 D_fake: 0.520 
+(epoch: 305, iters: 7968, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.849 G_ID: 0.135 G_Rec: 0.325 D_GP: 0.070 D_real: 0.853 D_fake: 0.604 
+(epoch: 305, iters: 8368, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.919 G_ID: 0.172 G_Rec: 0.357 D_GP: 0.027 D_real: 1.199 D_fake: 0.570 
+(epoch: 306, iters: 160, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.733 G_ID: 0.109 G_Rec: 0.286 D_GP: 0.024 D_real: 0.988 D_fake: 0.840 
+(epoch: 306, iters: 560, time: 0.064) G_GAN: 0.990 G_GAN_Feat: 1.099 G_ID: 0.143 G_Rec: 0.454 D_GP: 0.024 D_real: 1.433 D_fake: 0.251 
+(epoch: 306, iters: 960, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 1.023 G_ID: 0.132 G_Rec: 0.323 D_GP: 0.629 D_real: 0.455 D_fake: 0.637 
+(epoch: 306, iters: 1360, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.856 G_ID: 0.150 G_Rec: 0.348 D_GP: 0.021 D_real: 1.042 D_fake: 0.651 
+(epoch: 306, iters: 1760, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.727 G_ID: 0.111 G_Rec: 0.285 D_GP: 0.027 D_real: 0.979 D_fake: 0.855 
+(epoch: 306, iters: 2160, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.756 G_ID: 0.188 G_Rec: 0.320 D_GP: 0.025 D_real: 1.103 D_fake: 0.754 
+(epoch: 306, iters: 2560, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.680 G_ID: 0.125 G_Rec: 0.288 D_GP: 0.023 D_real: 1.173 D_fake: 0.786 
+(epoch: 306, iters: 2960, time: 0.064) G_GAN: 0.578 G_GAN_Feat: 1.235 G_ID: 0.171 G_Rec: 0.429 D_GP: 0.068 D_real: 0.527 D_fake: 0.432 
+(epoch: 306, iters: 3360, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.535 G_ID: 0.129 G_Rec: 0.306 D_GP: 0.018 D_real: 1.198 D_fake: 0.808 
+(epoch: 306, iters: 3760, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.739 G_ID: 0.159 G_Rec: 0.388 D_GP: 0.019 D_real: 1.257 D_fake: 0.577 
+(epoch: 306, iters: 4160, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.558 G_ID: 0.125 G_Rec: 0.287 D_GP: 0.032 D_real: 1.108 D_fake: 0.814 
+(epoch: 306, iters: 4560, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.715 G_ID: 0.155 G_Rec: 0.378 D_GP: 0.022 D_real: 1.173 D_fake: 0.583 
+(epoch: 306, iters: 4960, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.557 G_ID: 0.120 G_Rec: 0.285 D_GP: 0.024 D_real: 1.260 D_fake: 0.711 
+(epoch: 306, iters: 5360, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.737 G_ID: 0.157 G_Rec: 0.373 D_GP: 0.024 D_real: 1.098 D_fake: 0.697 
+(epoch: 306, iters: 5760, time: 0.064) G_GAN: 0.060 G_GAN_Feat: 0.553 G_ID: 0.134 G_Rec: 0.266 D_GP: 0.024 D_real: 0.988 D_fake: 0.940 
+(epoch: 306, iters: 6160, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.808 G_ID: 0.199 G_Rec: 0.387 D_GP: 0.035 D_real: 0.996 D_fake: 0.636 
+(epoch: 306, iters: 6560, time: 0.064) G_GAN: 0.033 G_GAN_Feat: 0.587 G_ID: 0.114 G_Rec: 0.260 D_GP: 0.038 D_real: 0.913 D_fake: 0.967 
+(epoch: 306, iters: 6960, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.790 G_ID: 0.145 G_Rec: 0.389 D_GP: 0.046 D_real: 1.007 D_fake: 0.736 
+(epoch: 306, iters: 7360, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.676 G_ID: 0.137 G_Rec: 0.304 D_GP: 0.034 D_real: 1.017 D_fake: 0.879 
+(epoch: 306, iters: 7760, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.815 G_ID: 0.150 G_Rec: 0.375 D_GP: 0.030 D_real: 0.907 D_fake: 0.739 
+(epoch: 306, iters: 8160, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.658 G_ID: 0.119 G_Rec: 0.311 D_GP: 0.038 D_real: 1.142 D_fake: 0.691 
+(epoch: 306, iters: 8560, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.891 G_ID: 0.168 G_Rec: 0.378 D_GP: 0.037 D_real: 1.102 D_fake: 0.489 
+(epoch: 307, iters: 352, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.644 G_ID: 0.121 G_Rec: 0.317 D_GP: 0.024 D_real: 1.000 D_fake: 0.827 
+(epoch: 307, iters: 752, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 0.814 G_ID: 0.158 G_Rec: 0.382 D_GP: 0.024 D_real: 1.273 D_fake: 0.457 
+(epoch: 307, iters: 1152, time: 0.064) G_GAN: -0.025 G_GAN_Feat: 0.784 G_ID: 0.158 G_Rec: 0.334 D_GP: 0.182 D_real: 0.506 D_fake: 1.025 
+(epoch: 307, iters: 1552, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 1.010 G_ID: 0.153 G_Rec: 0.397 D_GP: 0.219 D_real: 0.335 D_fake: 0.780 
+(epoch: 307, iters: 1952, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.718 G_ID: 0.115 G_Rec: 0.260 D_GP: 0.070 D_real: 0.815 D_fake: 0.722 
+(epoch: 307, iters: 2352, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.888 G_ID: 0.160 G_Rec: 0.332 D_GP: 0.031 D_real: 0.985 D_fake: 0.781 
+(epoch: 307, iters: 2752, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.841 G_ID: 0.123 G_Rec: 0.307 D_GP: 0.024 D_real: 0.909 D_fake: 0.833 
+(epoch: 307, iters: 3152, time: 0.064) G_GAN: -0.090 G_GAN_Feat: 1.281 G_ID: 0.148 G_Rec: 0.450 D_GP: 0.051 D_real: 1.212 D_fake: 1.093 
+(epoch: 307, iters: 3552, time: 0.064) G_GAN: -0.280 G_GAN_Feat: 0.760 G_ID: 0.123 G_Rec: 0.318 D_GP: 0.020 D_real: 0.644 D_fake: 1.280 
+(epoch: 307, iters: 3952, time: 0.064) G_GAN: 0.097 G_GAN_Feat: 0.789 G_ID: 0.156 G_Rec: 0.359 D_GP: 0.021 D_real: 0.939 D_fake: 0.904 
+(epoch: 307, iters: 4352, time: 0.064) G_GAN: -0.034 G_GAN_Feat: 0.663 G_ID: 0.112 G_Rec: 0.308 D_GP: 0.023 D_real: 0.855 D_fake: 1.034 
+(epoch: 307, iters: 4752, time: 0.064) G_GAN: 0.052 G_GAN_Feat: 0.840 G_ID: 0.184 G_Rec: 0.342 D_GP: 0.038 D_real: 0.784 D_fake: 0.949 
+(epoch: 307, iters: 5152, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.875 G_ID: 0.135 G_Rec: 0.312 D_GP: 0.201 D_real: 0.469 D_fake: 0.901 
+(epoch: 307, iters: 5552, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.907 G_ID: 0.151 G_Rec: 0.430 D_GP: 0.043 D_real: 1.206 D_fake: 0.439 
+(epoch: 307, iters: 5952, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.614 G_ID: 0.119 G_Rec: 0.269 D_GP: 0.021 D_real: 1.308 D_fake: 0.661 
+(epoch: 307, iters: 6352, time: 0.064) G_GAN: 0.520 G_GAN_Feat: 1.062 G_ID: 0.150 G_Rec: 0.440 D_GP: 0.173 D_real: 0.592 D_fake: 0.494 
+(epoch: 307, iters: 6752, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.897 G_ID: 0.118 G_Rec: 0.318 D_GP: 0.024 D_real: 0.789 D_fake: 0.609 
+(epoch: 307, iters: 7152, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.965 G_ID: 0.160 G_Rec: 0.396 D_GP: 0.109 D_real: 0.674 D_fake: 0.754 
+(epoch: 307, iters: 7552, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.739 G_ID: 0.121 G_Rec: 0.311 D_GP: 0.023 D_real: 1.253 D_fake: 0.636 
+(epoch: 307, iters: 7952, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.959 G_ID: 0.161 G_Rec: 0.382 D_GP: 0.032 D_real: 0.937 D_fake: 0.572 
+(epoch: 307, iters: 8352, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.787 G_ID: 0.134 G_Rec: 0.268 D_GP: 0.032 D_real: 0.846 D_fake: 0.675 
+(epoch: 308, iters: 144, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.824 G_ID: 0.145 G_Rec: 0.374 D_GP: 0.021 D_real: 1.245 D_fake: 0.531 
+(epoch: 308, iters: 544, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.644 G_ID: 0.156 G_Rec: 0.303 D_GP: 0.018 D_real: 1.139 D_fake: 0.797 
+(epoch: 308, iters: 944, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.883 G_ID: 0.151 G_Rec: 0.382 D_GP: 0.026 D_real: 1.085 D_fake: 0.615 
+(epoch: 308, iters: 1344, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.620 G_ID: 0.115 G_Rec: 0.290 D_GP: 0.024 D_real: 1.269 D_fake: 0.642 
+(epoch: 308, iters: 1744, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.788 G_ID: 0.151 G_Rec: 0.351 D_GP: 0.031 D_real: 1.105 D_fake: 0.667 
+(epoch: 308, iters: 2144, time: 0.064) G_GAN: -0.406 G_GAN_Feat: 0.635 G_ID: 0.140 G_Rec: 0.316 D_GP: 0.020 D_real: 0.609 D_fake: 1.406 
+(epoch: 308, iters: 2544, time: 0.064) G_GAN: 0.065 G_GAN_Feat: 0.767 G_ID: 0.149 G_Rec: 0.355 D_GP: 0.021 D_real: 0.813 D_fake: 0.937 
+(epoch: 308, iters: 2944, time: 0.064) G_GAN: -0.096 G_GAN_Feat: 0.602 G_ID: 0.122 G_Rec: 0.267 D_GP: 0.022 D_real: 0.834 D_fake: 1.099 
+(epoch: 308, iters: 3344, time: 0.064) G_GAN: 0.010 G_GAN_Feat: 0.909 G_ID: 0.169 G_Rec: 0.399 D_GP: 0.049 D_real: 0.559 D_fake: 0.990 
+(epoch: 308, iters: 3744, time: 0.064) G_GAN: 0.058 G_GAN_Feat: 0.669 G_ID: 0.130 G_Rec: 0.279 D_GP: 0.037 D_real: 0.834 D_fake: 0.942 
+(epoch: 308, iters: 4144, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.871 G_ID: 0.135 G_Rec: 0.379 D_GP: 0.042 D_real: 0.999 D_fake: 0.489 
+(epoch: 308, iters: 4544, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.715 G_ID: 0.121 G_Rec: 0.283 D_GP: 0.037 D_real: 1.242 D_fake: 0.543 
+(epoch: 308, iters: 4944, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.850 G_ID: 0.169 G_Rec: 0.431 D_GP: 0.025 D_real: 1.101 D_fake: 0.711 
+(epoch: 308, iters: 5344, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.618 G_ID: 0.114 G_Rec: 0.275 D_GP: 0.021 D_real: 1.133 D_fake: 0.782 
+(epoch: 308, iters: 5744, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.873 G_ID: 0.157 G_Rec: 0.387 D_GP: 0.036 D_real: 0.952 D_fake: 0.710 
+(epoch: 308, iters: 6144, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.611 G_ID: 0.131 G_Rec: 0.264 D_GP: 0.025 D_real: 1.046 D_fake: 0.832 
+(epoch: 308, iters: 6544, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.873 G_ID: 0.163 G_Rec: 0.419 D_GP: 0.032 D_real: 1.177 D_fake: 0.511 
+(epoch: 308, iters: 6944, time: 0.064) G_GAN: 0.124 G_GAN_Feat: 0.627 G_ID: 0.114 G_Rec: 0.291 D_GP: 0.023 D_real: 1.027 D_fake: 0.876 
+(epoch: 308, iters: 7344, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.769 G_ID: 0.139 G_Rec: 0.358 D_GP: 0.024 D_real: 1.137 D_fake: 0.574 
+(epoch: 308, iters: 7744, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.578 G_ID: 0.121 G_Rec: 0.275 D_GP: 0.023 D_real: 1.028 D_fake: 0.893 
+(epoch: 308, iters: 8144, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 0.842 G_ID: 0.170 G_Rec: 0.369 D_GP: 0.033 D_real: 1.141 D_fake: 0.516 
+(epoch: 308, iters: 8544, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.652 G_ID: 0.119 G_Rec: 0.278 D_GP: 0.052 D_real: 0.941 D_fake: 0.863 
+(epoch: 309, iters: 336, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.971 G_ID: 0.161 G_Rec: 0.450 D_GP: 0.153 D_real: 0.790 D_fake: 0.522 
+(epoch: 309, iters: 736, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.703 G_ID: 0.124 G_Rec: 0.283 D_GP: 0.066 D_real: 0.799 D_fake: 0.889 
+(epoch: 309, iters: 1136, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 0.887 G_ID: 0.147 G_Rec: 0.392 D_GP: 0.040 D_real: 1.222 D_fake: 0.441 
+(epoch: 309, iters: 1536, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.714 G_ID: 0.119 G_Rec: 0.260 D_GP: 0.032 D_real: 1.204 D_fake: 0.624 
+(epoch: 309, iters: 1936, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.876 G_ID: 0.160 G_Rec: 0.384 D_GP: 0.025 D_real: 1.040 D_fake: 0.637 
+(epoch: 309, iters: 2336, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.861 G_ID: 0.128 G_Rec: 0.323 D_GP: 0.169 D_real: 0.598 D_fake: 0.817 
+(epoch: 309, iters: 2736, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 0.934 G_ID: 0.170 G_Rec: 0.404 D_GP: 0.039 D_real: 1.229 D_fake: 0.544 
+(epoch: 309, iters: 3136, time: 0.064) G_GAN: 0.633 G_GAN_Feat: 0.861 G_ID: 0.126 G_Rec: 0.310 D_GP: 0.215 D_real: 0.899 D_fake: 0.557 
+(epoch: 309, iters: 3536, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.935 G_ID: 0.148 G_Rec: 0.388 D_GP: 0.033 D_real: 1.126 D_fake: 0.603 
+(epoch: 309, iters: 3936, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.670 G_ID: 0.124 G_Rec: 0.292 D_GP: 0.025 D_real: 1.164 D_fake: 0.652 
+(epoch: 309, iters: 4336, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 1.086 G_ID: 0.158 G_Rec: 0.436 D_GP: 0.168 D_real: 0.509 D_fake: 0.552 
+(epoch: 309, iters: 4736, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.767 G_ID: 0.109 G_Rec: 0.297 D_GP: 0.039 D_real: 1.044 D_fake: 0.550 
+(epoch: 309, iters: 5136, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.850 G_ID: 0.146 G_Rec: 0.447 D_GP: 0.023 D_real: 1.177 D_fake: 0.643 
+(epoch: 309, iters: 5536, time: 0.064) G_GAN: -0.072 G_GAN_Feat: 0.664 G_ID: 0.107 G_Rec: 0.292 D_GP: 0.019 D_real: 0.816 D_fake: 1.072 
+(epoch: 309, iters: 5936, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.828 G_ID: 0.136 G_Rec: 0.364 D_GP: 0.025 D_real: 0.933 D_fake: 0.785 
+(epoch: 309, iters: 6336, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 0.654 G_ID: 0.126 G_Rec: 0.287 D_GP: 0.025 D_real: 0.965 D_fake: 0.919 
+(epoch: 309, iters: 6736, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.937 G_ID: 0.162 G_Rec: 0.386 D_GP: 0.028 D_real: 0.806 D_fake: 0.750 
+(epoch: 309, iters: 7136, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.771 G_ID: 0.121 G_Rec: 0.308 D_GP: 0.031 D_real: 0.920 D_fake: 0.687 
+(epoch: 309, iters: 7536, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.872 G_ID: 0.166 G_Rec: 0.358 D_GP: 0.023 D_real: 1.220 D_fake: 0.560 
+(epoch: 309, iters: 7936, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.616 G_ID: 0.156 G_Rec: 0.262 D_GP: 0.023 D_real: 1.053 D_fake: 0.808 
+(epoch: 309, iters: 8336, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 0.946 G_ID: 0.162 G_Rec: 0.359 D_GP: 0.053 D_real: 0.886 D_fake: 0.584 
+(epoch: 310, iters: 128, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.757 G_ID: 0.138 G_Rec: 0.303 D_GP: 0.053 D_real: 1.131 D_fake: 0.729 
+(epoch: 310, iters: 528, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 1.012 G_ID: 0.144 G_Rec: 0.418 D_GP: 0.135 D_real: 0.522 D_fake: 0.757 
+(epoch: 310, iters: 928, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.716 G_ID: 0.129 G_Rec: 0.290 D_GP: 0.030 D_real: 1.193 D_fake: 0.623 
+(epoch: 310, iters: 1328, time: 0.064) G_GAN: 0.797 G_GAN_Feat: 1.103 G_ID: 0.167 G_Rec: 0.428 D_GP: 0.343 D_real: 0.508 D_fake: 0.352 
+(epoch: 310, iters: 1728, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.703 G_ID: 0.139 G_Rec: 0.294 D_GP: 0.028 D_real: 1.247 D_fake: 0.661 
+(epoch: 310, iters: 2128, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.751 G_ID: 0.145 G_Rec: 0.313 D_GP: 0.022 D_real: 1.231 D_fake: 0.677 
+(epoch: 310, iters: 2528, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.806 G_ID: 0.115 G_Rec: 0.302 D_GP: 0.047 D_real: 1.154 D_fake: 0.686 
+(epoch: 310, iters: 2928, time: 0.064) G_GAN: 0.690 G_GAN_Feat: 0.983 G_ID: 0.153 G_Rec: 0.377 D_GP: 0.038 D_real: 1.078 D_fake: 0.367 
+(epoch: 310, iters: 3328, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.701 G_ID: 0.102 G_Rec: 0.265 D_GP: 0.032 D_real: 1.091 D_fake: 0.662 
+(epoch: 310, iters: 3728, time: 0.064) G_GAN: 0.851 G_GAN_Feat: 0.918 G_ID: 0.152 G_Rec: 0.408 D_GP: 0.068 D_real: 1.521 D_fake: 0.220 
+(epoch: 310, iters: 4128, time: 0.064) G_GAN: 0.110 G_GAN_Feat: 0.621 G_ID: 0.126 G_Rec: 0.267 D_GP: 0.022 D_real: 1.008 D_fake: 0.890 
+(epoch: 310, iters: 4528, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.883 G_ID: 0.177 G_Rec: 0.410 D_GP: 0.025 D_real: 1.013 D_fake: 0.646 
+(epoch: 310, iters: 4928, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.792 G_ID: 0.138 G_Rec: 0.292 D_GP: 0.067 D_real: 0.836 D_fake: 0.819 
+(epoch: 310, iters: 5328, time: 0.064) G_GAN: 0.727 G_GAN_Feat: 0.954 G_ID: 0.158 G_Rec: 0.402 D_GP: 0.021 D_real: 1.398 D_fake: 0.289 
+(epoch: 310, iters: 5728, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.703 G_ID: 0.123 G_Rec: 0.281 D_GP: 0.019 D_real: 1.095 D_fake: 0.784 
+(epoch: 310, iters: 6128, time: 0.064) G_GAN: 0.579 G_GAN_Feat: 0.977 G_ID: 0.178 G_Rec: 0.406 D_GP: 0.026 D_real: 1.051 D_fake: 0.425 
+(epoch: 310, iters: 6528, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.595 G_ID: 0.120 G_Rec: 0.289 D_GP: 0.020 D_real: 1.301 D_fake: 0.746 
+(epoch: 310, iters: 6928, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.847 G_ID: 0.165 G_Rec: 0.491 D_GP: 0.031 D_real: 1.105 D_fake: 0.583 
+(epoch: 310, iters: 7328, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.661 G_ID: 0.147 G_Rec: 0.297 D_GP: 0.029 D_real: 0.989 D_fake: 0.898 
+(epoch: 310, iters: 7728, time: 0.064) G_GAN: 0.310 G_GAN_Feat: 0.824 G_ID: 0.144 G_Rec: 0.391 D_GP: 0.032 D_real: 0.961 D_fake: 0.692 
+(epoch: 310, iters: 8128, time: 0.064) G_GAN: 0.183 G_GAN_Feat: 0.655 G_ID: 0.114 G_Rec: 0.298 D_GP: 0.023 D_real: 1.121 D_fake: 0.817 
+(epoch: 310, iters: 8528, time: 0.064) G_GAN: 0.567 G_GAN_Feat: 0.879 G_ID: 0.152 G_Rec: 0.399 D_GP: 0.039 D_real: 1.284 D_fake: 0.449 
+(epoch: 311, iters: 320, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.622 G_ID: 0.112 G_Rec: 0.289 D_GP: 0.019 D_real: 1.181 D_fake: 0.754 
+(epoch: 311, iters: 720, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.934 G_ID: 0.145 G_Rec: 0.375 D_GP: 0.085 D_real: 0.875 D_fake: 0.510 
+(epoch: 311, iters: 1120, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.826 G_ID: 0.129 G_Rec: 0.296 D_GP: 0.061 D_real: 0.463 D_fake: 0.889 
+(epoch: 311, iters: 1520, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.794 G_ID: 0.170 G_Rec: 0.386 D_GP: 0.017 D_real: 1.261 D_fake: 0.604 
+(epoch: 311, iters: 1920, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.662 G_ID: 0.110 G_Rec: 0.336 D_GP: 0.021 D_real: 1.123 D_fake: 0.814 
+(epoch: 311, iters: 2320, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.745 G_ID: 0.162 G_Rec: 0.362 D_GP: 0.019 D_real: 1.273 D_fake: 0.580 
+(epoch: 311, iters: 2720, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.577 G_ID: 0.116 G_Rec: 0.299 D_GP: 0.024 D_real: 1.156 D_fake: 0.789 
+(epoch: 311, iters: 3120, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.729 G_ID: 0.150 G_Rec: 0.337 D_GP: 0.023 D_real: 1.204 D_fake: 0.640 
+(epoch: 311, iters: 3520, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.700 G_ID: 0.136 G_Rec: 0.327 D_GP: 0.104 D_real: 0.881 D_fake: 0.989 
+(epoch: 311, iters: 3920, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.747 G_ID: 0.165 G_Rec: 0.346 D_GP: 0.024 D_real: 1.159 D_fake: 0.650 
+(epoch: 311, iters: 4320, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.597 G_ID: 0.130 G_Rec: 0.268 D_GP: 0.023 D_real: 1.122 D_fake: 0.826 
+(epoch: 311, iters: 4720, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.799 G_ID: 0.161 G_Rec: 0.370 D_GP: 0.029 D_real: 1.108 D_fake: 0.655 
+(epoch: 311, iters: 5120, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.755 G_ID: 0.119 G_Rec: 0.306 D_GP: 0.242 D_real: 0.525 D_fake: 0.936 
+(epoch: 311, iters: 5520, time: 0.064) G_GAN: 0.641 G_GAN_Feat: 0.854 G_ID: 0.161 G_Rec: 0.396 D_GP: 0.023 D_real: 1.384 D_fake: 0.369 
+(epoch: 311, iters: 5920, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.744 G_ID: 0.097 G_Rec: 0.315 D_GP: 0.100 D_real: 0.963 D_fake: 0.695 
+(epoch: 311, iters: 6320, time: 0.064) G_GAN: 0.470 G_GAN_Feat: 0.833 G_ID: 0.168 G_Rec: 0.360 D_GP: 0.025 D_real: 1.207 D_fake: 0.538 
+(epoch: 311, iters: 6720, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.764 G_ID: 0.109 G_Rec: 0.332 D_GP: 0.038 D_real: 0.977 D_fake: 0.737 
+(epoch: 311, iters: 7120, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.873 G_ID: 0.169 G_Rec: 0.425 D_GP: 0.051 D_real: 0.975 D_fake: 0.605 
+(epoch: 311, iters: 7520, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.832 G_ID: 0.113 G_Rec: 0.298 D_GP: 0.051 D_real: 0.835 D_fake: 0.797 
+(epoch: 311, iters: 7920, time: 0.064) G_GAN: 0.530 G_GAN_Feat: 0.946 G_ID: 0.156 G_Rec: 0.388 D_GP: 0.030 D_real: 1.207 D_fake: 0.475 
+(epoch: 311, iters: 8320, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.568 G_ID: 0.111 G_Rec: 0.241 D_GP: 0.018 D_real: 1.157 D_fake: 0.800 
+(epoch: 312, iters: 112, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.843 G_ID: 0.154 G_Rec: 0.371 D_GP: 0.037 D_real: 1.051 D_fake: 0.680 
+(epoch: 312, iters: 512, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.664 G_ID: 0.103 G_Rec: 0.282 D_GP: 0.059 D_real: 1.140 D_fake: 0.781 
+(epoch: 312, iters: 912, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 0.893 G_ID: 0.159 G_Rec: 0.391 D_GP: 0.022 D_real: 1.352 D_fake: 0.410 
+(epoch: 312, iters: 1312, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.630 G_ID: 0.113 G_Rec: 0.272 D_GP: 0.017 D_real: 1.369 D_fake: 0.545 
+(epoch: 312, iters: 1712, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 1.030 G_ID: 0.148 G_Rec: 0.419 D_GP: 0.151 D_real: 0.559 D_fake: 0.583 
+(epoch: 312, iters: 2112, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.733 G_ID: 0.111 G_Rec: 0.307 D_GP: 0.023 D_real: 0.941 D_fake: 0.870 
+(epoch: 312, iters: 2512, time: 0.064) G_GAN: 0.078 G_GAN_Feat: 0.752 G_ID: 0.170 G_Rec: 0.340 D_GP: 0.021 D_real: 0.984 D_fake: 0.922 
+(epoch: 312, iters: 2912, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.685 G_ID: 0.117 G_Rec: 0.282 D_GP: 0.046 D_real: 1.098 D_fake: 0.782 
+(epoch: 312, iters: 3312, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 1.071 G_ID: 0.159 G_Rec: 0.416 D_GP: 0.555 D_real: 0.662 D_fake: 0.538 
+(epoch: 312, iters: 3712, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.672 G_ID: 0.100 G_Rec: 0.274 D_GP: 0.023 D_real: 1.089 D_fake: 0.783 
+(epoch: 312, iters: 4112, time: 0.064) G_GAN: 0.675 G_GAN_Feat: 1.069 G_ID: 0.162 G_Rec: 0.439 D_GP: 0.181 D_real: 1.007 D_fake: 0.471 
+(epoch: 312, iters: 4512, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.846 G_ID: 0.135 G_Rec: 0.336 D_GP: 0.082 D_real: 0.558 D_fake: 0.787 
+(epoch: 312, iters: 4912, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.969 G_ID: 0.169 G_Rec: 0.381 D_GP: 0.063 D_real: 0.748 D_fake: 0.694 
+(epoch: 312, iters: 5312, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.709 G_ID: 0.151 G_Rec: 0.292 D_GP: 0.029 D_real: 1.101 D_fake: 0.691 
+(epoch: 312, iters: 5712, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.763 G_ID: 0.162 G_Rec: 0.427 D_GP: 0.020 D_real: 1.104 D_fake: 0.723 
+(epoch: 312, iters: 6112, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.503 G_ID: 0.109 G_Rec: 0.299 D_GP: 0.020 D_real: 1.057 D_fake: 0.887 
+(epoch: 312, iters: 6512, time: 0.064) G_GAN: -0.018 G_GAN_Feat: 0.677 G_ID: 0.152 G_Rec: 0.354 D_GP: 0.021 D_real: 0.821 D_fake: 1.018 
+(epoch: 312, iters: 6912, time: 0.064) G_GAN: 0.183 G_GAN_Feat: 0.505 G_ID: 0.128 G_Rec: 0.285 D_GP: 0.021 D_real: 1.135 D_fake: 0.817 
+(epoch: 312, iters: 7312, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.715 G_ID: 0.148 G_Rec: 0.373 D_GP: 0.021 D_real: 1.032 D_fake: 0.711 
+(epoch: 312, iters: 7712, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.545 G_ID: 0.113 G_Rec: 0.273 D_GP: 0.022 D_real: 1.037 D_fake: 0.909 
+(epoch: 312, iters: 8112, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.745 G_ID: 0.163 G_Rec: 0.370 D_GP: 0.026 D_real: 0.966 D_fake: 0.760 
+(epoch: 312, iters: 8512, time: 0.064) G_GAN: 0.065 G_GAN_Feat: 0.562 G_ID: 0.133 G_Rec: 0.267 D_GP: 0.028 D_real: 1.018 D_fake: 0.935 
+(epoch: 313, iters: 304, time: 0.064) G_GAN: -0.055 G_GAN_Feat: 0.804 G_ID: 0.166 G_Rec: 0.400 D_GP: 0.034 D_real: 0.668 D_fake: 1.055 
+(epoch: 313, iters: 704, time: 0.064) G_GAN: 0.098 G_GAN_Feat: 0.571 G_ID: 0.136 G_Rec: 0.261 D_GP: 0.022 D_real: 1.079 D_fake: 0.903 
+(epoch: 313, iters: 1104, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.863 G_ID: 0.182 G_Rec: 0.408 D_GP: 0.045 D_real: 0.797 D_fake: 0.848 
+(epoch: 313, iters: 1504, time: 0.064) G_GAN: 0.076 G_GAN_Feat: 0.663 G_ID: 0.125 G_Rec: 0.307 D_GP: 0.034 D_real: 0.860 D_fake: 0.924 
+(epoch: 313, iters: 1904, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.828 G_ID: 0.142 G_Rec: 0.398 D_GP: 0.022 D_real: 1.177 D_fake: 0.590 
+(epoch: 313, iters: 2304, time: 0.064) G_GAN: 0.024 G_GAN_Feat: 0.614 G_ID: 0.134 G_Rec: 0.276 D_GP: 0.036 D_real: 0.917 D_fake: 0.976 
+(epoch: 313, iters: 2704, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.737 G_ID: 0.149 G_Rec: 0.319 D_GP: 0.027 D_real: 1.089 D_fake: 0.747 
+(epoch: 313, iters: 3104, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.628 G_ID: 0.118 G_Rec: 0.267 D_GP: 0.021 D_real: 1.290 D_fake: 0.702 
+(epoch: 313, iters: 3504, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.852 G_ID: 0.157 G_Rec: 0.415 D_GP: 0.021 D_real: 1.215 D_fake: 0.552 
+(epoch: 313, iters: 3904, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.699 G_ID: 0.111 G_Rec: 0.280 D_GP: 0.057 D_real: 0.896 D_fake: 0.780 
+(epoch: 313, iters: 4304, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.934 G_ID: 0.155 G_Rec: 0.423 D_GP: 0.082 D_real: 0.957 D_fake: 0.504 
+(epoch: 313, iters: 4704, time: 0.064) G_GAN: -0.016 G_GAN_Feat: 0.755 G_ID: 0.123 G_Rec: 0.312 D_GP: 0.032 D_real: 1.108 D_fake: 1.017 
+(epoch: 313, iters: 5104, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.736 G_ID: 0.183 G_Rec: 0.355 D_GP: 0.018 D_real: 1.144 D_fake: 0.751 
+(epoch: 313, iters: 5504, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.668 G_ID: 0.118 G_Rec: 0.303 D_GP: 0.028 D_real: 1.147 D_fake: 0.732 
+(epoch: 313, iters: 5904, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.824 G_ID: 0.148 G_Rec: 0.387 D_GP: 0.028 D_real: 0.955 D_fake: 0.752 
+(epoch: 313, iters: 6304, time: 0.064) G_GAN: -0.050 G_GAN_Feat: 0.706 G_ID: 0.125 G_Rec: 0.312 D_GP: 0.043 D_real: 0.718 D_fake: 1.050 
+(epoch: 313, iters: 6704, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.758 G_ID: 0.147 G_Rec: 0.359 D_GP: 0.020 D_real: 1.141 D_fake: 0.694 
+(epoch: 313, iters: 7104, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.616 G_ID: 0.114 G_Rec: 0.264 D_GP: 0.027 D_real: 1.091 D_fake: 0.808 
+(epoch: 313, iters: 7504, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.866 G_ID: 0.168 G_Rec: 0.387 D_GP: 0.049 D_real: 0.821 D_fake: 0.702 
+(epoch: 313, iters: 7904, time: 0.064) G_GAN: 0.104 G_GAN_Feat: 0.696 G_ID: 0.133 G_Rec: 0.295 D_GP: 0.027 D_real: 0.957 D_fake: 0.896 
+(epoch: 313, iters: 8304, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.852 G_ID: 0.160 G_Rec: 0.374 D_GP: 0.030 D_real: 1.049 D_fake: 0.585 
+(epoch: 314, iters: 96, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.805 G_ID: 0.131 G_Rec: 0.331 D_GP: 0.049 D_real: 1.055 D_fake: 0.770 
+(epoch: 314, iters: 496, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 1.013 G_ID: 0.156 G_Rec: 0.415 D_GP: 0.091 D_real: 0.623 D_fake: 0.697 
+(epoch: 314, iters: 896, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.639 G_ID: 0.106 G_Rec: 0.259 D_GP: 0.029 D_real: 1.131 D_fake: 0.810 
+(epoch: 314, iters: 1296, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.999 G_ID: 0.152 G_Rec: 0.423 D_GP: 0.039 D_real: 0.806 D_fake: 0.699 
+(epoch: 314, iters: 1696, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.643 G_ID: 0.141 G_Rec: 0.296 D_GP: 0.021 D_real: 1.064 D_fake: 0.843 
+(epoch: 314, iters: 2096, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.931 G_ID: 0.153 G_Rec: 0.398 D_GP: 0.081 D_real: 0.893 D_fake: 0.665 
+(epoch: 314, iters: 2496, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.670 G_ID: 0.124 G_Rec: 0.289 D_GP: 0.025 D_real: 1.270 D_fake: 0.591 
+(epoch: 314, iters: 2896, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.866 G_ID: 0.140 G_Rec: 0.397 D_GP: 0.028 D_real: 1.030 D_fake: 0.706 
+(epoch: 314, iters: 3296, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.707 G_ID: 0.126 G_Rec: 0.303 D_GP: 0.041 D_real: 0.927 D_fake: 0.887 
+(epoch: 314, iters: 3696, time: 0.064) G_GAN: 0.133 G_GAN_Feat: 0.859 G_ID: 0.177 G_Rec: 0.435 D_GP: 0.026 D_real: 0.985 D_fake: 0.868 
+(epoch: 314, iters: 4096, time: 0.064) G_GAN: -0.030 G_GAN_Feat: 0.631 G_ID: 0.127 G_Rec: 0.280 D_GP: 0.037 D_real: 0.885 D_fake: 1.030 
+(epoch: 314, iters: 4496, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.785 G_ID: 0.168 G_Rec: 0.359 D_GP: 0.028 D_real: 0.959 D_fake: 0.765 
+(epoch: 314, iters: 4896, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.688 G_ID: 0.148 G_Rec: 0.285 D_GP: 0.090 D_real: 0.813 D_fake: 0.885 
+(epoch: 314, iters: 5296, time: 0.064) G_GAN: 0.108 G_GAN_Feat: 1.000 G_ID: 0.184 G_Rec: 0.419 D_GP: 0.046 D_real: 0.509 D_fake: 0.894 
+(epoch: 314, iters: 5696, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.610 G_ID: 0.110 G_Rec: 0.289 D_GP: 0.023 D_real: 1.169 D_fake: 0.813 
+(epoch: 314, iters: 6096, time: 0.064) G_GAN: 0.379 G_GAN_Feat: 0.901 G_ID: 0.171 G_Rec: 0.394 D_GP: 0.045 D_real: 0.865 D_fake: 0.622 
+(epoch: 314, iters: 6496, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.659 G_ID: 0.124 G_Rec: 0.307 D_GP: 0.026 D_real: 1.134 D_fake: 0.663 
+(epoch: 314, iters: 6896, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 1.048 G_ID: 0.163 G_Rec: 0.413 D_GP: 0.082 D_real: 0.329 D_fake: 0.637 
+(epoch: 314, iters: 7296, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.822 G_ID: 0.118 G_Rec: 0.311 D_GP: 0.025 D_real: 0.949 D_fake: 0.610 
+(epoch: 314, iters: 7696, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 0.809 G_ID: 0.159 G_Rec: 0.374 D_GP: 0.023 D_real: 1.229 D_fake: 0.604 
+(epoch: 314, iters: 8096, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.823 G_ID: 0.128 G_Rec: 0.335 D_GP: 0.082 D_real: 0.668 D_fake: 0.910 
+(epoch: 314, iters: 8496, time: 0.064) G_GAN: 0.469 G_GAN_Feat: 0.969 G_ID: 0.151 G_Rec: 0.403 D_GP: 0.088 D_real: 0.736 D_fake: 0.534 
+(epoch: 315, iters: 288, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.862 G_ID: 0.135 G_Rec: 0.315 D_GP: 0.063 D_real: 0.690 D_fake: 0.639 
+(epoch: 315, iters: 688, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.926 G_ID: 0.158 G_Rec: 0.422 D_GP: 0.020 D_real: 0.980 D_fake: 0.788 
+(epoch: 315, iters: 1088, time: 0.064) G_GAN: -0.014 G_GAN_Feat: 0.715 G_ID: 0.133 G_Rec: 0.386 D_GP: 0.021 D_real: 0.888 D_fake: 1.014 
+(epoch: 315, iters: 1488, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.779 G_ID: 0.159 G_Rec: 0.374 D_GP: 0.023 D_real: 0.946 D_fake: 0.893 
+(epoch: 315, iters: 1888, time: 0.064) G_GAN: -0.040 G_GAN_Feat: 0.623 G_ID: 0.121 G_Rec: 0.285 D_GP: 0.024 D_real: 0.879 D_fake: 1.040 
+(epoch: 315, iters: 2288, time: 0.064) G_GAN: 0.352 G_GAN_Feat: 0.816 G_ID: 0.137 G_Rec: 0.391 D_GP: 0.023 D_real: 1.120 D_fake: 0.661 
+(epoch: 315, iters: 2688, time: 0.064) G_GAN: -0.143 G_GAN_Feat: 0.667 G_ID: 0.121 G_Rec: 0.285 D_GP: 0.038 D_real: 0.736 D_fake: 1.143 
+(epoch: 315, iters: 3088, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.900 G_ID: 0.119 G_Rec: 0.396 D_GP: 0.034 D_real: 1.046 D_fake: 0.636 
+(epoch: 315, iters: 3488, time: 0.064) G_GAN: 0.704 G_GAN_Feat: 0.861 G_ID: 0.116 G_Rec: 0.343 D_GP: 0.047 D_real: 1.123 D_fake: 0.508 
+(epoch: 315, iters: 3888, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.865 G_ID: 0.173 G_Rec: 0.370 D_GP: 0.051 D_real: 0.907 D_fake: 0.800 
+(epoch: 315, iters: 4288, time: 0.064) G_GAN: 0.588 G_GAN_Feat: 0.824 G_ID: 0.115 G_Rec: 0.306 D_GP: 0.045 D_real: 1.416 D_fake: 0.468 
+(epoch: 315, iters: 4688, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 1.123 G_ID: 0.158 G_Rec: 0.468 D_GP: 0.215 D_real: 0.298 D_fake: 0.745 
+(epoch: 315, iters: 5088, time: 0.064) G_GAN: 0.146 G_GAN_Feat: 0.633 G_ID: 0.131 G_Rec: 0.271 D_GP: 0.023 D_real: 1.075 D_fake: 0.854 
+(epoch: 315, iters: 5488, time: 0.064) G_GAN: 0.637 G_GAN_Feat: 1.161 G_ID: 0.162 G_Rec: 0.480 D_GP: 0.208 D_real: 0.444 D_fake: 0.434 
+(epoch: 315, iters: 5888, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.712 G_ID: 0.131 G_Rec: 0.305 D_GP: 0.033 D_real: 0.880 D_fake: 0.885 
+(epoch: 315, iters: 6288, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 1.027 G_ID: 0.169 G_Rec: 0.394 D_GP: 0.276 D_real: 0.638 D_fake: 0.491 
+(epoch: 315, iters: 6688, time: 0.064) G_GAN: 0.594 G_GAN_Feat: 0.982 G_ID: 0.114 G_Rec: 0.289 D_GP: 0.037 D_real: 1.397 D_fake: 0.561 
+(epoch: 315, iters: 7088, time: 0.064) G_GAN: 0.631 G_GAN_Feat: 0.859 G_ID: 0.144 G_Rec: 0.354 D_GP: 0.030 D_real: 1.267 D_fake: 0.383 
+(epoch: 315, iters: 7488, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.900 G_ID: 0.113 G_Rec: 0.299 D_GP: 0.067 D_real: 0.379 D_fake: 0.659 
+(epoch: 315, iters: 7888, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 1.046 G_ID: 0.163 G_Rec: 0.419 D_GP: 0.043 D_real: 0.788 D_fake: 0.698 
+(epoch: 315, iters: 8288, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.747 G_ID: 0.118 G_Rec: 0.280 D_GP: 0.037 D_real: 0.957 D_fake: 0.782 
+(epoch: 316, iters: 80, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 1.271 G_ID: 0.167 G_Rec: 0.452 D_GP: 0.528 D_real: 0.312 D_fake: 0.612 
+(epoch: 316, iters: 480, time: 0.064) G_GAN: -0.151 G_GAN_Feat: 0.633 G_ID: 0.101 G_Rec: 0.319 D_GP: 0.021 D_real: 0.872 D_fake: 1.151 
+(epoch: 316, iters: 880, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.858 G_ID: 0.145 G_Rec: 0.408 D_GP: 0.018 D_real: 0.895 D_fake: 0.832 
+(epoch: 316, iters: 1280, time: 0.064) G_GAN: -0.111 G_GAN_Feat: 0.700 G_ID: 0.122 G_Rec: 0.327 D_GP: 0.026 D_real: 0.785 D_fake: 1.112 
+(epoch: 316, iters: 1680, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.760 G_ID: 0.147 G_Rec: 0.352 D_GP: 0.024 D_real: 0.893 D_fake: 0.832 
+(epoch: 316, iters: 2080, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 0.765 G_ID: 0.125 G_Rec: 0.306 D_GP: 0.082 D_real: 0.707 D_fake: 0.988 
+(epoch: 316, iters: 2480, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.985 G_ID: 0.137 G_Rec: 0.415 D_GP: 0.062 D_real: 0.720 D_fake: 0.598 
+(epoch: 316, iters: 2880, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.665 G_ID: 0.120 G_Rec: 0.306 D_GP: 0.029 D_real: 1.157 D_fake: 0.889 
+(epoch: 316, iters: 3280, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.816 G_ID: 0.149 G_Rec: 0.381 D_GP: 0.031 D_real: 1.118 D_fake: 0.613 
+(epoch: 316, iters: 3680, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.685 G_ID: 0.121 G_Rec: 0.319 D_GP: 0.041 D_real: 1.057 D_fake: 0.809 
+(epoch: 316, iters: 4080, time: 0.064) G_GAN: 0.581 G_GAN_Feat: 0.785 G_ID: 0.162 G_Rec: 0.344 D_GP: 0.041 D_real: 1.327 D_fake: 0.432 
+(epoch: 316, iters: 4480, time: 0.064) G_GAN: -0.057 G_GAN_Feat: 0.621 G_ID: 0.118 G_Rec: 0.271 D_GP: 0.023 D_real: 0.844 D_fake: 1.057 
+(epoch: 316, iters: 4880, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 0.953 G_ID: 0.168 G_Rec: 0.412 D_GP: 0.030 D_real: 1.118 D_fake: 0.547 
+(epoch: 316, iters: 5280, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.742 G_ID: 0.126 G_Rec: 0.290 D_GP: 0.023 D_real: 1.154 D_fake: 0.693 
+(epoch: 316, iters: 5680, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 1.048 G_ID: 0.154 G_Rec: 0.446 D_GP: 0.038 D_real: 1.003 D_fake: 0.574 
+(epoch: 316, iters: 6080, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.898 G_ID: 0.155 G_Rec: 0.315 D_GP: 0.122 D_real: 0.582 D_fake: 0.563 
+================ Training Loss (Tue Apr 21 12:46:57 2020) ================
+(epoch: 315, iters: 7488, time: 0.075) G_GAN: 0.436 G_GAN_Feat: 0.885 G_ID: 0.143 G_Rec: 0.375 D_GP: 0.027 D_real: 1.123 D_fake: 0.566 
+(epoch: 315, iters: 7888, time: 0.063) G_GAN: 0.172 G_GAN_Feat: 0.831 G_ID: 0.129 G_Rec: 0.312 D_GP: 0.032 D_real: 0.934 D_fake: 0.857 
+(epoch: 315, iters: 8288, time: 0.063) G_GAN: 0.629 G_GAN_Feat: 1.244 G_ID: 0.151 G_Rec: 0.425 D_GP: 0.072 D_real: 0.481 D_fake: 0.384 
+(epoch: 315, iters: 8688, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.816 G_ID: 0.118 G_Rec: 0.341 D_GP: 0.073 D_real: 0.723 D_fake: 0.699 
+(epoch: 315, iters: 9088, time: 0.063) G_GAN: 0.632 G_GAN_Feat: 0.942 G_ID: 0.132 G_Rec: 0.419 D_GP: 0.027 D_real: 1.228 D_fake: 0.406 
+(epoch: 315, iters: 9488, time: 0.063) G_GAN: 0.339 G_GAN_Feat: 0.769 G_ID: 0.094 G_Rec: 0.305 D_GP: 0.038 D_real: 1.035 D_fake: 0.667 
+(epoch: 315, iters: 9888, time: 0.063) G_GAN: 0.933 G_GAN_Feat: 1.035 G_ID: 0.140 G_Rec: 0.405 D_GP: 0.032 D_real: 1.385 D_fake: 0.148 
+(epoch: 315, iters: 10288, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.686 G_ID: 0.124 G_Rec: 0.289 D_GP: 0.023 D_real: 1.286 D_fake: 0.618 
+(epoch: 315, iters: 10688, time: 0.063) G_GAN: 0.751 G_GAN_Feat: 1.101 G_ID: 0.159 G_Rec: 0.440 D_GP: 0.062 D_real: 0.495 D_fake: 0.286 
+(epoch: 315, iters: 11088, time: 0.063) G_GAN: 0.415 G_GAN_Feat: 0.733 G_ID: 0.112 G_Rec: 0.283 D_GP: 0.029 D_real: 1.292 D_fake: 0.592 
+(epoch: 315, iters: 11488, time: 0.063) G_GAN: 0.606 G_GAN_Feat: 1.195 G_ID: 0.163 G_Rec: 0.421 D_GP: 0.142 D_real: 0.290 D_fake: 0.508 
+(epoch: 315, iters: 11888, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.732 G_ID: 0.102 G_Rec: 0.286 D_GP: 0.027 D_real: 1.314 D_fake: 0.580 
+(epoch: 315, iters: 12288, time: 0.064) G_GAN: 0.795 G_GAN_Feat: 1.130 G_ID: 0.150 G_Rec: 0.454 D_GP: 0.036 D_real: 1.068 D_fake: 0.261 
+(epoch: 315, iters: 12688, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.978 G_ID: 0.119 G_Rec: 0.361 D_GP: 0.087 D_real: 0.949 D_fake: 0.661 
+(epoch: 315, iters: 13088, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 1.163 G_ID: 0.145 G_Rec: 0.473 D_GP: 0.084 D_real: 0.491 D_fake: 0.524 
+(epoch: 315, iters: 13488, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.744 G_ID: 0.114 G_Rec: 0.312 D_GP: 0.033 D_real: 1.316 D_fake: 0.624 
+(epoch: 315, iters: 13888, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.893 G_ID: 0.142 G_Rec: 0.406 D_GP: 0.045 D_real: 0.997 D_fake: 0.561 
+(epoch: 315, iters: 14288, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.828 G_ID: 0.125 G_Rec: 0.318 D_GP: 0.166 D_real: 0.875 D_fake: 0.619 
+(epoch: 315, iters: 14688, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.946 G_ID: 0.135 G_Rec: 0.411 D_GP: 0.028 D_real: 1.180 D_fake: 0.469 
+(epoch: 315, iters: 15088, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.644 G_ID: 0.109 G_Rec: 0.272 D_GP: 0.026 D_real: 1.276 D_fake: 0.658 
+(epoch: 315, iters: 15488, time: 0.063) G_GAN: 0.653 G_GAN_Feat: 0.875 G_ID: 0.142 G_Rec: 0.405 D_GP: 0.030 D_real: 1.307 D_fake: 0.364 
+(epoch: 316, iters: 286, time: 0.063) G_GAN: 0.178 G_GAN_Feat: 0.858 G_ID: 0.099 G_Rec: 0.324 D_GP: 0.109 D_real: 0.509 D_fake: 0.824 
+(epoch: 316, iters: 686, time: 0.063) G_GAN: 0.728 G_GAN_Feat: 1.022 G_ID: 0.135 G_Rec: 0.447 D_GP: 0.034 D_real: 1.279 D_fake: 0.301 
+(epoch: 316, iters: 1086, time: 0.064) G_GAN: 0.131 G_GAN_Feat: 0.753 G_ID: 0.120 G_Rec: 0.326 D_GP: 0.030 D_real: 0.947 D_fake: 0.869 
+(epoch: 316, iters: 1486, time: 0.063) G_GAN: 0.516 G_GAN_Feat: 0.917 G_ID: 0.154 G_Rec: 0.416 D_GP: 0.042 D_real: 1.112 D_fake: 0.488 
+(epoch: 316, iters: 1886, time: 0.063) G_GAN: 0.396 G_GAN_Feat: 0.821 G_ID: 0.095 G_Rec: 0.286 D_GP: 0.057 D_real: 0.844 D_fake: 0.605 
+(epoch: 316, iters: 2286, time: 0.063) G_GAN: 0.320 G_GAN_Feat: 0.992 G_ID: 0.138 G_Rec: 0.433 D_GP: 0.033 D_real: 0.959 D_fake: 0.681 
+(epoch: 316, iters: 2686, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.833 G_ID: 0.110 G_Rec: 0.334 D_GP: 0.029 D_real: 1.181 D_fake: 0.578 
+(epoch: 316, iters: 3086, time: 0.063) G_GAN: 0.819 G_GAN_Feat: 1.221 G_ID: 0.150 G_Rec: 0.472 D_GP: 0.570 D_real: 0.536 D_fake: 0.269 
+(epoch: 316, iters: 3486, time: 0.063) G_GAN: 0.366 G_GAN_Feat: 0.850 G_ID: 0.106 G_Rec: 0.313 D_GP: 0.028 D_real: 0.922 D_fake: 0.635 
+(epoch: 316, iters: 3886, time: 0.063) G_GAN: 0.928 G_GAN_Feat: 1.102 G_ID: 0.154 G_Rec: 0.452 D_GP: 0.040 D_real: 1.486 D_fake: 0.242 
+(epoch: 316, iters: 4286, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.660 G_ID: 0.120 G_Rec: 0.299 D_GP: 0.025 D_real: 1.111 D_fake: 0.799 
+(epoch: 316, iters: 4686, time: 0.063) G_GAN: 0.411 G_GAN_Feat: 0.899 G_ID: 0.157 G_Rec: 0.403 D_GP: 0.034 D_real: 0.993 D_fake: 0.592 
+(epoch: 316, iters: 5086, time: 0.063) G_GAN: 0.332 G_GAN_Feat: 0.691 G_ID: 0.098 G_Rec: 0.289 D_GP: 0.027 D_real: 1.151 D_fake: 0.669 
+(epoch: 316, iters: 5486, time: 0.063) G_GAN: 0.598 G_GAN_Feat: 0.986 G_ID: 0.155 G_Rec: 0.445 D_GP: 0.056 D_real: 1.043 D_fake: 0.461 
+(epoch: 316, iters: 5886, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.755 G_ID: 0.102 G_Rec: 0.284 D_GP: 0.044 D_real: 0.985 D_fake: 0.686 
+(epoch: 316, iters: 6286, time: 0.063) G_GAN: 0.524 G_GAN_Feat: 0.996 G_ID: 0.134 G_Rec: 0.420 D_GP: 0.064 D_real: 0.750 D_fake: 0.479 
+(epoch: 316, iters: 6686, time: 0.063) G_GAN: 0.219 G_GAN_Feat: 0.752 G_ID: 0.105 G_Rec: 0.302 D_GP: 0.028 D_real: 0.955 D_fake: 0.782 
+(epoch: 316, iters: 7086, time: 0.063) G_GAN: 0.843 G_GAN_Feat: 0.939 G_ID: 0.124 G_Rec: 0.404 D_GP: 0.032 D_real: 1.306 D_fake: 0.231 
+(epoch: 316, iters: 7486, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.809 G_ID: 0.128 G_Rec: 0.305 D_GP: 0.063 D_real: 0.570 D_fake: 0.823 
+(epoch: 316, iters: 7886, time: 0.063) G_GAN: 0.548 G_GAN_Feat: 1.000 G_ID: 0.152 G_Rec: 0.410 D_GP: 0.178 D_real: 0.841 D_fake: 0.497 
+(epoch: 316, iters: 8286, time: 0.063) G_GAN: 0.303 G_GAN_Feat: 0.812 G_ID: 0.126 G_Rec: 0.293 D_GP: 0.045 D_real: 0.767 D_fake: 0.698 
+(epoch: 316, iters: 8686, time: 0.063) G_GAN: 0.602 G_GAN_Feat: 0.872 G_ID: 0.122 G_Rec: 0.364 D_GP: 0.030 D_real: 1.261 D_fake: 0.408 
+(epoch: 317, iters: 478, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.821 G_ID: 0.115 G_Rec: 0.311 D_GP: 0.033 D_real: 0.967 D_fake: 0.674 
+(epoch: 317, iters: 878, time: 0.063) G_GAN: 0.710 G_GAN_Feat: 1.150 G_ID: 0.122 G_Rec: 0.448 D_GP: 0.270 D_real: 0.414 D_fake: 0.327 
+(epoch: 317, iters: 1278, time: 0.063) G_GAN: 0.463 G_GAN_Feat: 0.963 G_ID: 0.116 G_Rec: 0.306 D_GP: 0.074 D_real: 0.394 D_fake: 0.559 
+(epoch: 317, iters: 1678, time: 0.063) G_GAN: 0.306 G_GAN_Feat: 0.960 G_ID: 0.137 G_Rec: 0.456 D_GP: 0.032 D_real: 1.053 D_fake: 0.695 
+(epoch: 317, iters: 2078, time: 0.064) G_GAN: 0.110 G_GAN_Feat: 0.768 G_ID: 0.145 G_Rec: 0.318 D_GP: 0.031 D_real: 0.970 D_fake: 0.891 
+(epoch: 317, iters: 2478, time: 0.063) G_GAN: 0.473 G_GAN_Feat: 1.213 G_ID: 0.145 G_Rec: 0.467 D_GP: 0.138 D_real: 0.291 D_fake: 0.534 
+(epoch: 317, iters: 2878, time: 0.063) G_GAN: 0.492 G_GAN_Feat: 0.886 G_ID: 0.112 G_Rec: 0.337 D_GP: 0.043 D_real: 1.042 D_fake: 0.563 
+(epoch: 317, iters: 3278, time: 0.063) G_GAN: 0.559 G_GAN_Feat: 1.035 G_ID: 0.111 G_Rec: 0.435 D_GP: 0.028 D_real: 1.002 D_fake: 0.468 
+(epoch: 317, iters: 3678, time: 0.064) G_GAN: -0.027 G_GAN_Feat: 0.831 G_ID: 0.131 G_Rec: 0.335 D_GP: 0.067 D_real: 0.549 D_fake: 1.028 
+(epoch: 317, iters: 4078, time: 0.063) G_GAN: 0.615 G_GAN_Feat: 0.968 G_ID: 0.124 G_Rec: 0.427 D_GP: 0.028 D_real: 1.118 D_fake: 0.400 
+(epoch: 317, iters: 4478, time: 0.063) G_GAN: 0.224 G_GAN_Feat: 0.738 G_ID: 0.106 G_Rec: 0.294 D_GP: 0.040 D_real: 0.949 D_fake: 0.776 
+(epoch: 317, iters: 4878, time: 0.063) G_GAN: 0.715 G_GAN_Feat: 0.935 G_ID: 0.119 G_Rec: 0.435 D_GP: 0.026 D_real: 1.450 D_fake: 0.307 
+(epoch: 317, iters: 5278, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.743 G_ID: 0.119 G_Rec: 0.302 D_GP: 0.032 D_real: 0.858 D_fake: 0.849 
+(epoch: 317, iters: 5678, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 1.023 G_ID: 0.124 G_Rec: 0.407 D_GP: 0.068 D_real: 0.721 D_fake: 0.573 
+(epoch: 317, iters: 6078, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.875 G_ID: 0.108 G_Rec: 0.316 D_GP: 0.064 D_real: 0.325 D_fake: 0.885 
+(epoch: 317, iters: 6478, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 1.193 G_ID: 0.138 G_Rec: 0.462 D_GP: 0.093 D_real: 0.214 D_fake: 0.532 
+(epoch: 317, iters: 6878, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.676 G_ID: 0.106 G_Rec: 0.297 D_GP: 0.023 D_real: 1.273 D_fake: 0.618 
+(epoch: 317, iters: 7278, time: 0.063) G_GAN: 0.526 G_GAN_Feat: 0.972 G_ID: 0.129 G_Rec: 0.431 D_GP: 0.037 D_real: 0.985 D_fake: 0.479 
+(epoch: 317, iters: 7678, time: 0.063) G_GAN: 0.338 G_GAN_Feat: 0.747 G_ID: 0.133 G_Rec: 0.289 D_GP: 0.037 D_real: 1.165 D_fake: 0.663 
+(epoch: 317, iters: 8078, time: 0.063) G_GAN: 0.738 G_GAN_Feat: 0.902 G_ID: 0.119 G_Rec: 0.400 D_GP: 0.030 D_real: 1.337 D_fake: 0.286 
+(epoch: 317, iters: 8478, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.769 G_ID: 0.132 G_Rec: 0.327 D_GP: 0.054 D_real: 0.908 D_fake: 0.918 
+(epoch: 318, iters: 270, time: 0.063) G_GAN: 0.506 G_GAN_Feat: 0.874 G_ID: 0.138 G_Rec: 0.409 D_GP: 0.034 D_real: 1.092 D_fake: 0.506 
+(epoch: 318, iters: 670, time: 0.063) G_GAN: 0.173 G_GAN_Feat: 0.904 G_ID: 0.121 G_Rec: 0.333 D_GP: 0.063 D_real: 0.853 D_fake: 0.845 
+(epoch: 318, iters: 1070, time: 0.063) G_GAN: 0.588 G_GAN_Feat: 0.811 G_ID: 0.128 G_Rec: 0.389 D_GP: 0.031 D_real: 1.325 D_fake: 0.423 
+(epoch: 318, iters: 1470, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.656 G_ID: 0.127 G_Rec: 0.297 D_GP: 0.027 D_real: 0.933 D_fake: 0.989 
+(epoch: 318, iters: 1870, time: 0.063) G_GAN: 0.528 G_GAN_Feat: 0.946 G_ID: 0.121 G_Rec: 0.468 D_GP: 0.033 D_real: 1.161 D_fake: 0.485 
+(epoch: 318, iters: 2270, time: 0.063) G_GAN: 0.187 G_GAN_Feat: 0.641 G_ID: 0.096 G_Rec: 0.292 D_GP: 0.028 D_real: 1.127 D_fake: 0.814 
+(epoch: 318, iters: 2670, time: 0.063) G_GAN: 0.557 G_GAN_Feat: 0.844 G_ID: 0.128 G_Rec: 0.396 D_GP: 0.033 D_real: 1.299 D_fake: 0.470 
+(epoch: 318, iters: 3070, time: 0.064) G_GAN: 0.032 G_GAN_Feat: 0.662 G_ID: 0.121 G_Rec: 0.302 D_GP: 0.036 D_real: 0.849 D_fake: 0.968 
+(epoch: 318, iters: 3470, time: 0.063) G_GAN: 0.214 G_GAN_Feat: 0.871 G_ID: 0.124 G_Rec: 0.410 D_GP: 0.066 D_real: 0.811 D_fake: 0.790 
+(epoch: 318, iters: 3870, time: 0.063) G_GAN: -0.087 G_GAN_Feat: 0.765 G_ID: 0.102 G_Rec: 0.324 D_GP: 0.055 D_real: 0.675 D_fake: 1.087 
+(epoch: 318, iters: 4270, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 1.108 G_ID: 0.132 G_Rec: 0.491 D_GP: 0.049 D_real: 0.874 D_fake: 0.829 
+(epoch: 318, iters: 4670, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.731 G_ID: 0.097 G_Rec: 0.331 D_GP: 0.043 D_real: 1.075 D_fake: 0.776 
+(epoch: 318, iters: 5070, time: 0.063) G_GAN: 0.409 G_GAN_Feat: 0.916 G_ID: 0.131 G_Rec: 0.414 D_GP: 0.046 D_real: 1.045 D_fake: 0.596 
+(epoch: 318, iters: 5470, time: 0.063) G_GAN: 0.054 G_GAN_Feat: 0.740 G_ID: 0.121 G_Rec: 0.338 D_GP: 0.037 D_real: 0.907 D_fake: 0.946 
+(epoch: 318, iters: 5870, time: 0.063) G_GAN: 0.628 G_GAN_Feat: 1.041 G_ID: 0.129 G_Rec: 0.479 D_GP: 0.062 D_real: 0.883 D_fake: 0.391 
+(epoch: 318, iters: 6270, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.896 G_ID: 0.117 G_Rec: 0.332 D_GP: 0.160 D_real: 0.557 D_fake: 0.761 
+(epoch: 318, iters: 6670, time: 0.063) G_GAN: 0.372 G_GAN_Feat: 0.878 G_ID: 0.147 G_Rec: 0.369 D_GP: 0.040 D_real: 0.925 D_fake: 0.628 
+(epoch: 318, iters: 7070, time: 0.063) G_GAN: 0.367 G_GAN_Feat: 0.770 G_ID: 0.107 G_Rec: 0.320 D_GP: 0.056 D_real: 1.046 D_fake: 0.640 
+(epoch: 318, iters: 7470, time: 0.063) G_GAN: 0.956 G_GAN_Feat: 0.974 G_ID: 0.126 G_Rec: 0.417 D_GP: 0.032 D_real: 1.510 D_fake: 0.164 
+(epoch: 318, iters: 7870, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.789 G_ID: 0.110 G_Rec: 0.308 D_GP: 0.057 D_real: 0.782 D_fake: 0.790 
+(epoch: 318, iters: 8270, time: 0.063) G_GAN: 0.369 G_GAN_Feat: 0.964 G_ID: 0.139 G_Rec: 0.426 D_GP: 0.032 D_real: 0.954 D_fake: 0.632 
+(epoch: 318, iters: 8670, time: 0.063) G_GAN: -0.080 G_GAN_Feat: 0.844 G_ID: 0.119 G_Rec: 0.316 D_GP: 0.130 D_real: 0.593 D_fake: 1.082 
+(epoch: 319, iters: 462, time: 0.063) G_GAN: 0.703 G_GAN_Feat: 0.989 G_ID: 0.140 G_Rec: 0.413 D_GP: 0.053 D_real: 1.029 D_fake: 0.329 
+(epoch: 319, iters: 862, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.944 G_ID: 0.102 G_Rec: 0.351 D_GP: 0.480 D_real: 0.456 D_fake: 0.739 
+(epoch: 319, iters: 1262, time: 0.063) G_GAN: 0.399 G_GAN_Feat: 1.009 G_ID: 0.143 G_Rec: 0.429 D_GP: 0.039 D_real: 0.882 D_fake: 0.618 
+(epoch: 319, iters: 1662, time: 0.063) G_GAN: -0.030 G_GAN_Feat: 0.719 G_ID: 0.119 G_Rec: 0.276 D_GP: 0.028 D_real: 0.882 D_fake: 1.030 
+(epoch: 319, iters: 2062, time: 0.063) G_GAN: 0.440 G_GAN_Feat: 1.050 G_ID: 0.152 G_Rec: 0.400 D_GP: 0.033 D_real: 0.495 D_fake: 0.561 
+(epoch: 319, iters: 2462, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.965 G_ID: 0.107 G_Rec: 0.307 D_GP: 0.102 D_real: 0.273 D_fake: 0.714 
+(epoch: 319, iters: 2862, time: 0.063) G_GAN: 0.485 G_GAN_Feat: 0.882 G_ID: 0.151 G_Rec: 0.475 D_GP: 0.028 D_real: 1.196 D_fake: 0.543 
+(epoch: 319, iters: 3262, time: 0.063) G_GAN: -0.078 G_GAN_Feat: 0.677 G_ID: 0.108 G_Rec: 0.305 D_GP: 0.027 D_real: 0.830 D_fake: 1.078 
+(epoch: 319, iters: 3662, time: 0.063) G_GAN: 0.284 G_GAN_Feat: 0.851 G_ID: 0.117 G_Rec: 0.418 D_GP: 0.028 D_real: 0.893 D_fake: 0.718 
+(epoch: 319, iters: 4062, time: 0.064) G_GAN: -0.106 G_GAN_Feat: 0.658 G_ID: 0.113 G_Rec: 0.315 D_GP: 0.028 D_real: 0.733 D_fake: 1.106 
+(epoch: 319, iters: 4462, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 0.861 G_ID: 0.123 G_Rec: 0.410 D_GP: 0.041 D_real: 0.828 D_fake: 0.811 
+(epoch: 319, iters: 4862, time: 0.063) G_GAN: -0.178 G_GAN_Feat: 0.735 G_ID: 0.132 G_Rec: 0.315 D_GP: 0.038 D_real: 0.624 D_fake: 1.178 
+(epoch: 319, iters: 5262, time: 0.063) G_GAN: 0.233 G_GAN_Feat: 0.828 G_ID: 0.147 G_Rec: 0.376 D_GP: 0.033 D_real: 0.913 D_fake: 0.768 
+(epoch: 319, iters: 5662, time: 0.063) G_GAN: 0.223 G_GAN_Feat: 0.757 G_ID: 0.107 G_Rec: 0.308 D_GP: 0.038 D_real: 0.955 D_fake: 0.778 
+(epoch: 319, iters: 6062, time: 0.063) G_GAN: 0.430 G_GAN_Feat: 0.954 G_ID: 0.142 G_Rec: 0.418 D_GP: 0.047 D_real: 0.920 D_fake: 0.571 
+(epoch: 319, iters: 6462, time: 0.063) G_GAN: 0.162 G_GAN_Feat: 0.722 G_ID: 0.120 G_Rec: 0.319 D_GP: 0.040 D_real: 1.021 D_fake: 0.838 
+(epoch: 319, iters: 6862, time: 0.064) G_GAN: 0.627 G_GAN_Feat: 0.936 G_ID: 0.130 G_Rec: 0.403 D_GP: 0.039 D_real: 1.063 D_fake: 0.381 
+(epoch: 319, iters: 7262, time: 0.063) G_GAN: 0.014 G_GAN_Feat: 0.815 G_ID: 0.103 G_Rec: 0.350 D_GP: 0.068 D_real: 0.587 D_fake: 0.987 
+(epoch: 319, iters: 7662, time: 0.063) G_GAN: 0.418 G_GAN_Feat: 0.989 G_ID: 0.138 G_Rec: 0.419 D_GP: 0.062 D_real: 0.914 D_fake: 0.585 
+(epoch: 319, iters: 8062, time: 0.063) G_GAN: 0.225 G_GAN_Feat: 0.781 G_ID: 0.103 G_Rec: 0.304 D_GP: 0.043 D_real: 0.857 D_fake: 0.776 
+(epoch: 319, iters: 8462, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 1.014 G_ID: 0.141 G_Rec: 0.466 D_GP: 0.062 D_real: 0.823 D_fake: 0.622 
+(epoch: 320, iters: 254, time: 0.063) G_GAN: 0.443 G_GAN_Feat: 0.874 G_ID: 0.102 G_Rec: 0.319 D_GP: 0.176 D_real: 0.753 D_fake: 0.593 
+(epoch: 320, iters: 654, time: 0.063) G_GAN: 0.678 G_GAN_Feat: 0.975 G_ID: 0.129 G_Rec: 0.460 D_GP: 0.034 D_real: 1.260 D_fake: 0.335 
+(epoch: 320, iters: 1054, time: 0.063) G_GAN: -0.069 G_GAN_Feat: 0.931 G_ID: 0.130 G_Rec: 0.331 D_GP: 0.040 D_real: 0.648 D_fake: 1.069 
+(epoch: 320, iters: 1454, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 1.001 G_ID: 0.111 G_Rec: 0.455 D_GP: 0.035 D_real: 0.944 D_fake: 0.736 
+(epoch: 320, iters: 1854, time: 0.063) G_GAN: 0.997 G_GAN_Feat: 0.909 G_ID: 0.113 G_Rec: 0.360 D_GP: 0.174 D_real: 1.446 D_fake: 0.361 
+(epoch: 320, iters: 2254, time: 0.063) G_GAN: 0.581 G_GAN_Feat: 1.215 G_ID: 0.147 G_Rec: 0.484 D_GP: 0.247 D_real: 0.512 D_fake: 0.443 
+(epoch: 320, iters: 2654, time: 0.063) G_GAN: 0.380 G_GAN_Feat: 0.813 G_ID: 0.108 G_Rec: 0.324 D_GP: 0.045 D_real: 0.884 D_fake: 0.623 
+(epoch: 320, iters: 3054, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.918 G_ID: 0.175 G_Rec: 0.438 D_GP: 0.031 D_real: 1.162 D_fake: 0.555 
+(epoch: 320, iters: 3454, time: 0.063) G_GAN: 0.216 G_GAN_Feat: 0.740 G_ID: 0.140 G_Rec: 0.343 D_GP: 0.031 D_real: 1.041 D_fake: 0.785 
+(epoch: 320, iters: 3854, time: 0.063) G_GAN: 0.404 G_GAN_Feat: 0.960 G_ID: 0.152 G_Rec: 0.415 D_GP: 0.045 D_real: 0.854 D_fake: 0.601 
+(epoch: 320, iters: 4254, time: 0.063) G_GAN: 0.185 G_GAN_Feat: 0.700 G_ID: 0.106 G_Rec: 0.305 D_GP: 0.028 D_real: 1.057 D_fake: 0.815 
+(epoch: 320, iters: 4654, time: 0.064) G_GAN: 0.511 G_GAN_Feat: 0.986 G_ID: 0.124 G_Rec: 0.426 D_GP: 0.032 D_real: 1.062 D_fake: 0.491 
+(epoch: 320, iters: 5054, time: 0.063) G_GAN: 0.322 G_GAN_Feat: 0.987 G_ID: 0.087 G_Rec: 0.325 D_GP: 1.654 D_real: 0.347 D_fake: 0.692 
+(epoch: 320, iters: 5454, time: 0.063) G_GAN: 0.445 G_GAN_Feat: 0.975 G_ID: 0.111 G_Rec: 0.417 D_GP: 0.034 D_real: 0.997 D_fake: 0.557 
+(epoch: 320, iters: 5854, time: 0.063) G_GAN: 0.008 G_GAN_Feat: 1.035 G_ID: 0.107 G_Rec: 0.354 D_GP: 0.436 D_real: 0.323 D_fake: 0.992 
+(epoch: 320, iters: 6254, time: 0.064) G_GAN: 0.661 G_GAN_Feat: 1.234 G_ID: 0.142 G_Rec: 0.504 D_GP: 0.178 D_real: 0.386 D_fake: 0.415 
+(epoch: 320, iters: 6654, time: 0.063) G_GAN: 0.478 G_GAN_Feat: 0.854 G_ID: 0.121 G_Rec: 0.340 D_GP: 0.038 D_real: 1.063 D_fake: 0.552 
+(epoch: 320, iters: 7054, time: 0.063) G_GAN: 0.490 G_GAN_Feat: 0.951 G_ID: 0.134 G_Rec: 0.484 D_GP: 0.027 D_real: 1.212 D_fake: 0.523 
+(epoch: 320, iters: 7454, time: 0.063) G_GAN: 0.059 G_GAN_Feat: 0.732 G_ID: 0.101 G_Rec: 0.313 D_GP: 0.031 D_real: 0.853 D_fake: 0.941 
+(epoch: 320, iters: 7854, time: 0.064) G_GAN: 0.608 G_GAN_Feat: 1.072 G_ID: 0.117 G_Rec: 0.454 D_GP: 0.143 D_real: 0.673 D_fake: 0.414 
+(epoch: 320, iters: 8254, time: 0.063) G_GAN: 0.337 G_GAN_Feat: 0.790 G_ID: 0.112 G_Rec: 0.335 D_GP: 0.050 D_real: 1.066 D_fake: 0.694 
+(epoch: 320, iters: 8654, time: 0.063) G_GAN: 0.557 G_GAN_Feat: 1.223 G_ID: 0.135 G_Rec: 0.496 D_GP: 0.137 D_real: 0.499 D_fake: 0.469 
+(epoch: 321, iters: 446, time: 0.063) G_GAN: 0.476 G_GAN_Feat: 0.743 G_ID: 0.106 G_Rec: 0.315 D_GP: 0.027 D_real: 1.314 D_fake: 0.530 
+(epoch: 321, iters: 846, time: 0.064) G_GAN: -0.190 G_GAN_Feat: 1.140 G_ID: 0.146 G_Rec: 0.483 D_GP: 0.029 D_real: 0.513 D_fake: 1.191 
+(epoch: 321, iters: 1246, time: 0.063) G_GAN: -0.088 G_GAN_Feat: 0.686 G_ID: 0.106 G_Rec: 0.342 D_GP: 0.024 D_real: 0.830 D_fake: 1.088 
+(epoch: 321, iters: 1646, time: 0.063) G_GAN: -0.018 G_GAN_Feat: 0.813 G_ID: 0.123 G_Rec: 0.393 D_GP: 0.030 D_real: 0.764 D_fake: 1.018 
+(epoch: 321, iters: 2046, time: 0.063) G_GAN: 0.077 G_GAN_Feat: 0.631 G_ID: 0.106 G_Rec: 0.291 D_GP: 0.034 D_real: 0.953 D_fake: 0.925 
+(epoch: 321, iters: 2446, time: 0.064) G_GAN: 0.128 G_GAN_Feat: 0.803 G_ID: 0.133 G_Rec: 0.389 D_GP: 0.032 D_real: 0.807 D_fake: 0.873 
+(epoch: 321, iters: 2846, time: 0.063) G_GAN: -0.111 G_GAN_Feat: 0.738 G_ID: 0.114 G_Rec: 0.366 D_GP: 0.059 D_real: 0.682 D_fake: 1.112 
+(epoch: 321, iters: 3246, time: 0.063) G_GAN: 0.573 G_GAN_Feat: 0.888 G_ID: 0.115 G_Rec: 0.441 D_GP: 0.039 D_real: 1.091 D_fake: 0.503 
+(epoch: 321, iters: 3646, time: 0.063) G_GAN: 0.023 G_GAN_Feat: 0.653 G_ID: 0.112 G_Rec: 0.304 D_GP: 0.040 D_real: 0.856 D_fake: 0.977 
+(epoch: 321, iters: 4046, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.944 G_ID: 0.135 G_Rec: 0.450 D_GP: 0.057 D_real: 0.986 D_fake: 0.575 
+(epoch: 321, iters: 4446, time: 0.063) G_GAN: -0.041 G_GAN_Feat: 0.759 G_ID: 0.116 G_Rec: 0.321 D_GP: 0.051 D_real: 0.626 D_fake: 1.045 
+(epoch: 321, iters: 4846, time: 0.063) G_GAN: 0.276 G_GAN_Feat: 0.901 G_ID: 0.121 G_Rec: 0.416 D_GP: 0.049 D_real: 0.908 D_fake: 0.725 
+(epoch: 321, iters: 5246, time: 0.063) G_GAN: 0.098 G_GAN_Feat: 0.669 G_ID: 0.094 G_Rec: 0.319 D_GP: 0.037 D_real: 0.971 D_fake: 0.903 
+(epoch: 321, iters: 5646, time: 0.064) G_GAN: 0.572 G_GAN_Feat: 0.960 G_ID: 0.122 G_Rec: 0.444 D_GP: 0.053 D_real: 1.109 D_fake: 0.480 
+(epoch: 321, iters: 6046, time: 0.063) G_GAN: 0.234 G_GAN_Feat: 0.670 G_ID: 0.112 G_Rec: 0.296 D_GP: 0.041 D_real: 1.148 D_fake: 0.768 
+(epoch: 321, iters: 6446, time: 0.063) G_GAN: 0.365 G_GAN_Feat: 0.892 G_ID: 0.131 G_Rec: 0.385 D_GP: 0.047 D_real: 0.975 D_fake: 0.639 
+(epoch: 321, iters: 6846, time: 0.063) G_GAN: 0.217 G_GAN_Feat: 0.688 G_ID: 0.109 G_Rec: 0.312 D_GP: 0.035 D_real: 1.165 D_fake: 0.783 
+(epoch: 321, iters: 7246, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.920 G_ID: 0.154 G_Rec: 0.449 D_GP: 0.037 D_real: 0.798 D_fake: 0.714 
+(epoch: 321, iters: 7646, time: 0.063) G_GAN: 0.144 G_GAN_Feat: 0.766 G_ID: 0.112 G_Rec: 0.304 D_GP: 0.051 D_real: 0.930 D_fake: 0.856 
+(epoch: 321, iters: 8046, time: 0.063) G_GAN: 0.322 G_GAN_Feat: 1.001 G_ID: 0.129 G_Rec: 0.429 D_GP: 0.055 D_real: 0.854 D_fake: 0.680 
+(epoch: 321, iters: 8446, time: 0.063) G_GAN: 0.262 G_GAN_Feat: 0.855 G_ID: 0.106 G_Rec: 0.314 D_GP: 0.082 D_real: 0.673 D_fake: 0.739 
+(epoch: 322, iters: 238, time: 0.064) G_GAN: 0.394 G_GAN_Feat: 1.217 G_ID: 0.152 G_Rec: 0.482 D_GP: 0.238 D_real: 0.429 D_fake: 0.616 
+(epoch: 322, iters: 638, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 0.740 G_ID: 0.112 G_Rec: 0.315 D_GP: 0.030 D_real: 1.056 D_fake: 0.810 
+(epoch: 322, iters: 1038, time: 0.063) G_GAN: 0.236 G_GAN_Feat: 0.947 G_ID: 0.128 G_Rec: 0.443 D_GP: 0.037 D_real: 0.878 D_fake: 0.765 
+(epoch: 322, iters: 1438, time: 0.063) G_GAN: 0.050 G_GAN_Feat: 0.723 G_ID: 0.105 G_Rec: 0.309 D_GP: 0.048 D_real: 0.793 D_fake: 0.950 
+(epoch: 322, iters: 1838, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 1.027 G_ID: 0.137 G_Rec: 0.406 D_GP: 0.059 D_real: 0.408 D_fake: 0.847 
+(epoch: 322, iters: 2238, time: 0.063) G_GAN: 0.455 G_GAN_Feat: 0.783 G_ID: 0.092 G_Rec: 0.315 D_GP: 0.039 D_real: 1.130 D_fake: 0.559 
+(epoch: 322, iters: 2638, time: 0.063) G_GAN: 0.643 G_GAN_Feat: 0.969 G_ID: 0.131 G_Rec: 0.452 D_GP: 0.031 D_real: 1.250 D_fake: 0.377 
+(epoch: 322, iters: 3038, time: 0.063) G_GAN: 0.232 G_GAN_Feat: 0.889 G_ID: 0.114 G_Rec: 0.354 D_GP: 0.041 D_real: 0.827 D_fake: 0.769 
+(epoch: 322, iters: 3438, time: 0.064) G_GAN: 0.029 G_GAN_Feat: 1.008 G_ID: 0.151 G_Rec: 0.465 D_GP: 0.057 D_real: 0.581 D_fake: 0.973 
+(epoch: 322, iters: 3838, time: 0.063) G_GAN: 0.091 G_GAN_Feat: 0.699 G_ID: 0.111 G_Rec: 0.286 D_GP: 0.030 D_real: 0.979 D_fake: 0.909 
+(epoch: 322, iters: 4238, time: 0.063) G_GAN: 0.682 G_GAN_Feat: 0.900 G_ID: 0.115 G_Rec: 0.471 D_GP: 0.029 D_real: 1.326 D_fake: 0.371 
+(epoch: 322, iters: 4638, time: 0.063) G_GAN: 0.193 G_GAN_Feat: 0.725 G_ID: 0.103 G_Rec: 0.305 D_GP: 0.033 D_real: 1.142 D_fake: 0.808 
+(epoch: 322, iters: 5038, time: 0.064) G_GAN: 0.606 G_GAN_Feat: 0.961 G_ID: 0.140 G_Rec: 0.462 D_GP: 0.033 D_real: 1.108 D_fake: 0.441 
+(epoch: 322, iters: 5438, time: 0.063) G_GAN: 0.423 G_GAN_Feat: 0.702 G_ID: 0.096 G_Rec: 0.295 D_GP: 0.029 D_real: 1.355 D_fake: 0.581 
+(epoch: 322, iters: 5838, time: 0.063) G_GAN: 0.427 G_GAN_Feat: 0.937 G_ID: 0.128 G_Rec: 0.393 D_GP: 0.039 D_real: 0.889 D_fake: 0.574 
+(epoch: 322, iters: 6238, time: 0.063) G_GAN: -0.181 G_GAN_Feat: 0.811 G_ID: 0.115 G_Rec: 0.365 D_GP: 0.128 D_real: 0.477 D_fake: 1.181 
+(epoch: 322, iters: 6638, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.995 G_ID: 0.160 G_Rec: 0.453 D_GP: 0.060 D_real: 0.668 D_fake: 0.895 
+(epoch: 322, iters: 7038, time: 0.063) G_GAN: 0.422 G_GAN_Feat: 0.812 G_ID: 0.105 G_Rec: 0.342 D_GP: 0.043 D_real: 1.037 D_fake: 0.579 
+(epoch: 322, iters: 7438, time: 0.063) G_GAN: 0.825 G_GAN_Feat: 0.981 G_ID: 0.130 G_Rec: 0.441 D_GP: 0.028 D_real: 1.389 D_fake: 0.236 
+(epoch: 322, iters: 7838, time: 0.063) G_GAN: 0.274 G_GAN_Feat: 0.795 G_ID: 0.098 G_Rec: 0.305 D_GP: 0.045 D_real: 1.194 D_fake: 0.727 
+(epoch: 322, iters: 8238, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 1.082 G_ID: 0.144 G_Rec: 0.439 D_GP: 0.062 D_real: 0.510 D_fake: 0.682 
+(epoch: 322, iters: 8638, time: 0.063) G_GAN: 0.242 G_GAN_Feat: 0.947 G_ID: 0.116 G_Rec: 0.318 D_GP: 0.112 D_real: 0.362 D_fake: 0.758 
+(epoch: 323, iters: 430, time: 0.063) G_GAN: 0.023 G_GAN_Feat: 1.024 G_ID: 0.152 G_Rec: 0.431 D_GP: 0.036 D_real: 0.841 D_fake: 0.980 
+(epoch: 323, iters: 830, time: 0.063) G_GAN: 0.309 G_GAN_Feat: 0.715 G_ID: 0.103 G_Rec: 0.321 D_GP: 0.032 D_real: 1.107 D_fake: 0.697 
+(epoch: 323, iters: 1230, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.916 G_ID: 0.118 G_Rec: 0.440 D_GP: 0.029 D_real: 1.076 D_fake: 0.554 
+(epoch: 323, iters: 1630, time: 0.063) G_GAN: 0.420 G_GAN_Feat: 0.898 G_ID: 0.112 G_Rec: 0.316 D_GP: 0.034 D_real: 0.737 D_fake: 0.591 
+(epoch: 323, iters: 2030, time: 0.063) G_GAN: 0.144 G_GAN_Feat: 1.214 G_ID: 0.142 G_Rec: 0.484 D_GP: 0.337 D_real: 0.288 D_fake: 0.856 
+(epoch: 323, iters: 2430, time: 0.063) G_GAN: 0.273 G_GAN_Feat: 0.684 G_ID: 0.113 G_Rec: 0.324 D_GP: 0.025 D_real: 1.173 D_fake: 0.727 
+(epoch: 323, iters: 2830, time: 0.064) G_GAN: 0.554 G_GAN_Feat: 0.946 G_ID: 0.123 G_Rec: 0.475 D_GP: 0.032 D_real: 1.147 D_fake: 0.463 
+(epoch: 323, iters: 3230, time: 0.063) G_GAN: 0.159 G_GAN_Feat: 0.670 G_ID: 0.095 G_Rec: 0.302 D_GP: 0.030 D_real: 0.986 D_fake: 0.841 
+(epoch: 323, iters: 3630, time: 0.063) G_GAN: 0.416 G_GAN_Feat: 1.023 G_ID: 0.121 G_Rec: 0.491 D_GP: 0.079 D_real: 0.794 D_fake: 0.594 
+(epoch: 323, iters: 4030, time: 0.063) G_GAN: 0.133 G_GAN_Feat: 0.771 G_ID: 0.108 G_Rec: 0.314 D_GP: 0.060 D_real: 0.713 D_fake: 0.867 
+(epoch: 323, iters: 4430, time: 0.064) G_GAN: 0.812 G_GAN_Feat: 0.952 G_ID: 0.131 G_Rec: 0.413 D_GP: 0.050 D_real: 1.408 D_fake: 0.267 
+(epoch: 323, iters: 4830, time: 0.063) G_GAN: 0.289 G_GAN_Feat: 0.717 G_ID: 0.094 G_Rec: 0.273 D_GP: 0.034 D_real: 1.131 D_fake: 0.711 
+(epoch: 323, iters: 5230, time: 0.063) G_GAN: 0.396 G_GAN_Feat: 1.105 G_ID: 0.120 G_Rec: 0.432 D_GP: 0.057 D_real: 0.553 D_fake: 0.606 
+(epoch: 323, iters: 5630, time: 0.063) G_GAN: -0.037 G_GAN_Feat: 0.738 G_ID: 0.115 G_Rec: 0.343 D_GP: 0.026 D_real: 0.888 D_fake: 1.039 
+(epoch: 323, iters: 6030, time: 0.064) G_GAN: 0.601 G_GAN_Feat: 0.833 G_ID: 0.121 G_Rec: 0.384 D_GP: 0.031 D_real: 1.268 D_fake: 0.415 
+(epoch: 323, iters: 6430, time: 0.063) G_GAN: 0.084 G_GAN_Feat: 0.708 G_ID: 0.110 G_Rec: 0.318 D_GP: 0.034 D_real: 0.986 D_fake: 0.916 
+(epoch: 323, iters: 6830, time: 0.063) G_GAN: 0.412 G_GAN_Feat: 0.950 G_ID: 0.138 G_Rec: 0.417 D_GP: 0.054 D_real: 0.910 D_fake: 0.595 
+(epoch: 323, iters: 7230, time: 0.063) G_GAN: 0.086 G_GAN_Feat: 0.686 G_ID: 0.104 G_Rec: 0.295 D_GP: 0.050 D_real: 0.891 D_fake: 0.914 
+(epoch: 323, iters: 7630, time: 0.064) G_GAN: 0.532 G_GAN_Feat: 0.988 G_ID: 0.146 G_Rec: 0.419 D_GP: 0.057 D_real: 0.904 D_fake: 0.477 
+(epoch: 323, iters: 8030, time: 0.063) G_GAN: 0.359 G_GAN_Feat: 0.760 G_ID: 0.099 G_Rec: 0.311 D_GP: 0.032 D_real: 1.181 D_fake: 0.642 
+(epoch: 323, iters: 8430, time: 0.063) G_GAN: 0.333 G_GAN_Feat: 0.950 G_ID: 0.130 G_Rec: 0.421 D_GP: 0.039 D_real: 0.905 D_fake: 0.668 
+(epoch: 324, iters: 222, time: 0.063) G_GAN: 0.507 G_GAN_Feat: 0.759 G_ID: 0.104 G_Rec: 0.308 D_GP: 0.032 D_real: 1.287 D_fake: 0.496 
+(epoch: 324, iters: 622, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 0.912 G_ID: 0.153 G_Rec: 0.417 D_GP: 0.029 D_real: 1.219 D_fake: 0.494 
+(epoch: 324, iters: 1022, time: 0.063) G_GAN: -0.085 G_GAN_Feat: 0.767 G_ID: 0.106 G_Rec: 0.330 D_GP: 0.061 D_real: 0.564 D_fake: 1.085 
+(epoch: 324, iters: 1422, time: 0.063) G_GAN: 0.293 G_GAN_Feat: 1.016 G_ID: 0.129 G_Rec: 0.450 D_GP: 0.056 D_real: 1.147 D_fake: 0.713 
+(epoch: 324, iters: 1822, time: 0.063) G_GAN: 0.129 G_GAN_Feat: 0.705 G_ID: 0.128 G_Rec: 0.299 D_GP: 0.035 D_real: 0.938 D_fake: 0.871 
+(epoch: 324, iters: 2222, time: 0.063) G_GAN: 0.484 G_GAN_Feat: 0.904 G_ID: 0.138 G_Rec: 0.419 D_GP: 0.034 D_real: 1.059 D_fake: 0.519 
+(epoch: 324, iters: 2622, time: 0.063) G_GAN: 0.438 G_GAN_Feat: 0.840 G_ID: 0.119 G_Rec: 0.332 D_GP: 0.077 D_real: 0.938 D_fake: 0.566 
+(epoch: 324, iters: 3022, time: 0.063) G_GAN: 0.367 G_GAN_Feat: 1.145 G_ID: 0.123 G_Rec: 0.464 D_GP: 0.910 D_real: 0.545 D_fake: 0.687 
+(epoch: 324, iters: 3422, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.736 G_ID: 0.082 G_Rec: 0.315 D_GP: 0.023 D_real: 1.002 D_fake: 0.913 
+(epoch: 324, iters: 3822, time: 0.063) G_GAN: 0.280 G_GAN_Feat: 0.874 G_ID: 0.121 G_Rec: 0.399 D_GP: 0.029 D_real: 0.971 D_fake: 0.721 
+(epoch: 324, iters: 4222, time: 0.063) G_GAN: -0.046 G_GAN_Feat: 0.760 G_ID: 0.109 G_Rec: 0.317 D_GP: 0.062 D_real: 0.731 D_fake: 1.046 
+(epoch: 324, iters: 4622, time: 0.063) G_GAN: 0.562 G_GAN_Feat: 0.928 G_ID: 0.117 G_Rec: 0.384 D_GP: 0.040 D_real: 1.144 D_fake: 0.459 
+(epoch: 324, iters: 5022, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.762 G_ID: 0.128 G_Rec: 0.310 D_GP: 0.117 D_real: 0.812 D_fake: 0.861 
+(epoch: 324, iters: 5422, time: 0.063) G_GAN: 0.559 G_GAN_Feat: 0.894 G_ID: 0.112 G_Rec: 0.392 D_GP: 0.035 D_real: 1.188 D_fake: 0.444 
+(epoch: 324, iters: 5822, time: 0.063) G_GAN: 0.090 G_GAN_Feat: 0.760 G_ID: 0.119 G_Rec: 0.334 D_GP: 0.028 D_real: 0.989 D_fake: 0.910 
+(epoch: 324, iters: 6222, time: 0.063) G_GAN: 0.475 G_GAN_Feat: 0.913 G_ID: 0.130 G_Rec: 0.448 D_GP: 0.028 D_real: 1.114 D_fake: 0.539 
+(epoch: 324, iters: 6622, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.671 G_ID: 0.105 G_Rec: 0.288 D_GP: 0.031 D_real: 1.041 D_fake: 0.862 
+(epoch: 324, iters: 7022, time: 0.063) G_GAN: 0.542 G_GAN_Feat: 0.921 G_ID: 0.138 G_Rec: 0.403 D_GP: 0.047 D_real: 1.120 D_fake: 0.463 
+(epoch: 324, iters: 7422, time: 0.063) G_GAN: 0.528 G_GAN_Feat: 0.802 G_ID: 0.100 G_Rec: 0.334 D_GP: 0.055 D_real: 1.200 D_fake: 0.494 
+(epoch: 324, iters: 7822, time: 0.063) G_GAN: 0.855 G_GAN_Feat: 1.003 G_ID: 0.145 G_Rec: 0.424 D_GP: 0.121 D_real: 1.269 D_fake: 0.303 
+(epoch: 324, iters: 8222, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.750 G_ID: 0.117 G_Rec: 0.311 D_GP: 0.031 D_real: 1.209 D_fake: 0.627 
+(epoch: 324, iters: 8622, time: 0.063) G_GAN: 0.396 G_GAN_Feat: 0.998 G_ID: 0.139 G_Rec: 0.440 D_GP: 0.030 D_real: 0.977 D_fake: 0.608 
+(epoch: 325, iters: 414, time: 0.063) G_GAN: -0.025 G_GAN_Feat: 0.742 G_ID: 0.092 G_Rec: 0.297 D_GP: 0.033 D_real: 0.780 D_fake: 1.025 
+(epoch: 325, iters: 814, time: 0.063) G_GAN: 0.696 G_GAN_Feat: 0.949 G_ID: 0.122 G_Rec: 0.435 D_GP: 0.041 D_real: 1.202 D_fake: 0.323 
+(epoch: 325, iters: 1214, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.756 G_ID: 0.100 G_Rec: 0.289 D_GP: 0.032 D_real: 1.253 D_fake: 0.486 
+(epoch: 325, iters: 1614, time: 0.063) G_GAN: 0.449 G_GAN_Feat: 1.050 G_ID: 0.128 G_Rec: 0.444 D_GP: 0.043 D_real: 0.862 D_fake: 0.555 
+(epoch: 325, iters: 2014, time: 0.063) G_GAN: 0.178 G_GAN_Feat: 1.134 G_ID: 0.113 G_Rec: 0.350 D_GP: 0.029 D_real: 1.547 D_fake: 0.835 
+(epoch: 325, iters: 2414, time: 0.063) G_GAN: 0.224 G_GAN_Feat: 0.788 G_ID: 0.141 G_Rec: 0.376 D_GP: 0.025 D_real: 1.054 D_fake: 0.779 
+(epoch: 325, iters: 2814, time: 0.064) G_GAN: -0.105 G_GAN_Feat: 0.661 G_ID: 0.106 G_Rec: 0.283 D_GP: 0.023 D_real: 0.806 D_fake: 1.106 
+(epoch: 325, iters: 3214, time: 0.063) G_GAN: 0.405 G_GAN_Feat: 0.924 G_ID: 0.130 G_Rec: 0.444 D_GP: 0.036 D_real: 1.056 D_fake: 0.624 
+(epoch: 325, iters: 3614, time: 0.063) G_GAN: -0.100 G_GAN_Feat: 0.715 G_ID: 0.129 G_Rec: 0.337 D_GP: 0.030 D_real: 0.651 D_fake: 1.100 
+(epoch: 325, iters: 4014, time: 0.063) G_GAN: 0.413 G_GAN_Feat: 0.923 G_ID: 0.123 G_Rec: 0.416 D_GP: 0.032 D_real: 1.038 D_fake: 0.589 
+(epoch: 325, iters: 4414, time: 0.064) G_GAN: 0.096 G_GAN_Feat: 0.697 G_ID: 0.115 G_Rec: 0.298 D_GP: 0.035 D_real: 0.861 D_fake: 0.904 
+(epoch: 325, iters: 4814, time: 0.063) G_GAN: 0.328 G_GAN_Feat: 0.896 G_ID: 0.143 G_Rec: 0.388 D_GP: 0.050 D_real: 0.910 D_fake: 0.673 
+(epoch: 325, iters: 5214, time: 0.063) G_GAN: 0.227 G_GAN_Feat: 0.854 G_ID: 0.104 G_Rec: 0.337 D_GP: 0.063 D_real: 0.694 D_fake: 0.773 
+(epoch: 325, iters: 5614, time: 0.063) G_GAN: 0.540 G_GAN_Feat: 0.969 G_ID: 0.130 G_Rec: 0.413 D_GP: 0.060 D_real: 0.936 D_fake: 0.466 
+(epoch: 325, iters: 6014, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.684 G_ID: 0.110 G_Rec: 0.296 D_GP: 0.028 D_real: 1.085 D_fake: 0.809 
+(epoch: 325, iters: 6414, time: 0.063) G_GAN: 0.452 G_GAN_Feat: 1.129 G_ID: 0.172 G_Rec: 0.501 D_GP: 0.130 D_real: 0.462 D_fake: 0.569 
+(epoch: 325, iters: 6814, time: 0.063) G_GAN: 0.252 G_GAN_Feat: 0.823 G_ID: 0.098 G_Rec: 0.307 D_GP: 0.048 D_real: 0.784 D_fake: 0.749 
+(epoch: 325, iters: 7214, time: 0.063) G_GAN: 0.278 G_GAN_Feat: 0.965 G_ID: 0.136 G_Rec: 0.431 D_GP: 0.035 D_real: 0.846 D_fake: 0.724 
+(epoch: 325, iters: 7614, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.807 G_ID: 0.130 G_Rec: 0.326 D_GP: 0.034 D_real: 0.883 D_fake: 0.745 
+(epoch: 325, iters: 8014, time: 0.063) G_GAN: 0.987 G_GAN_Feat: 1.171 G_ID: 0.135 G_Rec: 0.466 D_GP: 0.117 D_real: 0.844 D_fake: 0.141 
+(epoch: 325, iters: 8414, time: 0.063) G_GAN: 0.191 G_GAN_Feat: 0.797 G_ID: 0.114 G_Rec: 0.303 D_GP: 0.037 D_real: 1.001 D_fake: 0.818 
+(epoch: 326, iters: 206, time: 0.063) G_GAN: 0.378 G_GAN_Feat: 1.044 G_ID: 0.140 G_Rec: 0.476 D_GP: 0.038 D_real: 0.851 D_fake: 0.623 
+(epoch: 326, iters: 606, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.673 G_ID: 0.094 G_Rec: 0.273 D_GP: 0.031 D_real: 1.107 D_fake: 0.784 
+(epoch: 326, iters: 1006, time: 0.063) G_GAN: 0.723 G_GAN_Feat: 0.964 G_ID: 0.126 G_Rec: 0.429 D_GP: 0.047 D_real: 1.243 D_fake: 0.312 
+(epoch: 326, iters: 1406, time: 0.063) G_GAN: 0.349 G_GAN_Feat: 0.796 G_ID: 0.097 G_Rec: 0.300 D_GP: 0.046 D_real: 1.023 D_fake: 0.652 
+(epoch: 326, iters: 1806, time: 0.063) G_GAN: 0.673 G_GAN_Feat: 1.133 G_ID: 0.132 G_Rec: 0.436 D_GP: 0.106 D_real: 0.628 D_fake: 0.344 
+(epoch: 326, iters: 2206, time: 0.064) G_GAN: 0.176 G_GAN_Feat: 1.108 G_ID: 0.108 G_Rec: 0.367 D_GP: 0.129 D_real: 1.109 D_fake: 0.858 
+(epoch: 326, iters: 2606, time: 0.063) G_GAN: 0.499 G_GAN_Feat: 1.036 G_ID: 0.127 G_Rec: 0.450 D_GP: 0.039 D_real: 0.952 D_fake: 0.522 
+(epoch: 326, iters: 3006, time: 0.063) G_GAN: 0.157 G_GAN_Feat: 0.775 G_ID: 0.116 G_Rec: 0.310 D_GP: 0.045 D_real: 0.948 D_fake: 0.844 
+(epoch: 326, iters: 3406, time: 0.063) G_GAN: 0.756 G_GAN_Feat: 1.238 G_ID: 0.129 G_Rec: 0.464 D_GP: 0.052 D_real: 0.470 D_fake: 0.291 
+(epoch: 326, iters: 3806, time: 0.064) G_GAN: 0.068 G_GAN_Feat: 0.796 G_ID: 0.122 G_Rec: 0.346 D_GP: 0.039 D_real: 0.771 D_fake: 0.932 
+(epoch: 326, iters: 4206, time: 0.063) G_GAN: 0.642 G_GAN_Feat: 1.234 G_ID: 0.139 G_Rec: 0.483 D_GP: 0.076 D_real: 0.460 D_fake: 0.379 
+(epoch: 326, iters: 4606, time: 0.063) G_GAN: -0.075 G_GAN_Feat: 0.911 G_ID: 0.114 G_Rec: 0.346 D_GP: 0.071 D_real: 0.270 D_fake: 1.077 
+(epoch: 326, iters: 5006, time: 0.063) G_GAN: 0.778 G_GAN_Feat: 1.176 G_ID: 0.114 G_Rec: 0.455 D_GP: 0.044 D_real: 0.778 D_fake: 0.274 
+(epoch: 326, iters: 5406, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.736 G_ID: 0.127 G_Rec: 0.303 D_GP: 0.045 D_real: 1.082 D_fake: 0.795 
+(epoch: 326, iters: 5806, time: 0.063) G_GAN: 0.502 G_GAN_Feat: 1.104 G_ID: 0.119 G_Rec: 0.455 D_GP: 0.038 D_real: 0.982 D_fake: 0.503 
+(epoch: 326, iters: 6206, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.827 G_ID: 0.111 G_Rec: 0.349 D_GP: 0.038 D_real: 1.262 D_fake: 0.604 
+(epoch: 326, iters: 6606, time: 0.063) G_GAN: 0.201 G_GAN_Feat: 0.938 G_ID: 0.133 G_Rec: 0.385 D_GP: 0.036 D_real: 0.853 D_fake: 0.799 
+(epoch: 326, iters: 7006, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.828 G_ID: 0.083 G_Rec: 0.314 D_GP: 0.046 D_real: 0.894 D_fake: 0.756 
+(epoch: 326, iters: 7406, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 1.009 G_ID: 0.142 G_Rec: 0.453 D_GP: 0.032 D_real: 0.929 D_fake: 0.578 
+(epoch: 326, iters: 7806, time: 0.063) G_GAN: 0.236 G_GAN_Feat: 0.729 G_ID: 0.107 G_Rec: 0.335 D_GP: 0.026 D_real: 1.219 D_fake: 0.764 
+(epoch: 326, iters: 8206, time: 0.063) G_GAN: 0.654 G_GAN_Feat: 0.914 G_ID: 0.132 G_Rec: 0.446 D_GP: 0.030 D_real: 1.304 D_fake: 0.376 
+(epoch: 326, iters: 8606, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.721 G_ID: 0.089 G_Rec: 0.309 D_GP: 0.041 D_real: 1.120 D_fake: 0.717 
+(epoch: 327, iters: 398, time: 0.063) G_GAN: 0.639 G_GAN_Feat: 0.908 G_ID: 0.115 G_Rec: 0.421 D_GP: 0.046 D_real: 1.194 D_fake: 0.403 
+(epoch: 327, iters: 798, time: 0.063) G_GAN: 0.059 G_GAN_Feat: 0.650 G_ID: 0.107 G_Rec: 0.299 D_GP: 0.038 D_real: 0.928 D_fake: 0.941 
+(epoch: 327, iters: 1198, time: 0.063) G_GAN: 0.042 G_GAN_Feat: 0.985 G_ID: 0.155 G_Rec: 0.463 D_GP: 0.071 D_real: 0.489 D_fake: 0.958 
+(epoch: 327, iters: 1598, time: 0.064) G_GAN: 0.204 G_GAN_Feat: 0.726 G_ID: 0.127 G_Rec: 0.328 D_GP: 0.038 D_real: 0.961 D_fake: 0.796 
+(epoch: 327, iters: 1998, time: 0.063) G_GAN: 0.863 G_GAN_Feat: 0.893 G_ID: 0.115 G_Rec: 0.422 D_GP: 0.027 D_real: 1.579 D_fake: 0.191 
+(epoch: 327, iters: 2398, time: 0.063) G_GAN: 0.163 G_GAN_Feat: 0.693 G_ID: 0.102 G_Rec: 0.300 D_GP: 0.040 D_real: 1.025 D_fake: 0.837 
+(epoch: 327, iters: 2798, time: 0.063) G_GAN: 0.493 G_GAN_Feat: 1.035 G_ID: 0.127 G_Rec: 0.469 D_GP: 0.060 D_real: 0.759 D_fake: 0.520 
+(epoch: 327, iters: 3198, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.983 G_ID: 0.115 G_Rec: 0.355 D_GP: 0.368 D_real: 0.338 D_fake: 0.911 
+(epoch: 327, iters: 3598, time: 0.063) G_GAN: 0.377 G_GAN_Feat: 0.980 G_ID: 0.116 G_Rec: 0.421 D_GP: 0.045 D_real: 0.873 D_fake: 0.626 
+(epoch: 327, iters: 3998, time: 0.063) G_GAN: 0.214 G_GAN_Feat: 0.684 G_ID: 0.099 G_Rec: 0.283 D_GP: 0.033 D_real: 1.106 D_fake: 0.786 
+(epoch: 327, iters: 4398, time: 0.063) G_GAN: 0.627 G_GAN_Feat: 1.123 G_ID: 0.126 G_Rec: 0.447 D_GP: 0.079 D_real: 0.604 D_fake: 0.418 
+(epoch: 327, iters: 4798, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.894 G_ID: 0.103 G_Rec: 0.340 D_GP: 0.042 D_real: 0.792 D_fake: 0.596 
+(epoch: 327, iters: 5198, time: 0.063) G_GAN: 0.746 G_GAN_Feat: 1.153 G_ID: 0.151 G_Rec: 0.467 D_GP: 0.086 D_real: 0.612 D_fake: 0.314 
+(epoch: 327, iters: 5598, time: 0.063) G_GAN: 0.098 G_GAN_Feat: 0.657 G_ID: 0.105 G_Rec: 0.305 D_GP: 0.032 D_real: 1.047 D_fake: 0.902 
+(epoch: 327, iters: 5998, time: 0.063) G_GAN: 0.351 G_GAN_Feat: 0.857 G_ID: 0.139 G_Rec: 0.399 D_GP: 0.031 D_real: 1.009 D_fake: 0.651 
+(epoch: 327, iters: 6398, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.650 G_ID: 0.112 G_Rec: 0.316 D_GP: 0.029 D_real: 1.167 D_fake: 0.788 
+(epoch: 327, iters: 6798, time: 0.063) G_GAN: 0.394 G_GAN_Feat: 0.912 G_ID: 0.145 G_Rec: 0.443 D_GP: 0.059 D_real: 0.978 D_fake: 0.620 
+(epoch: 327, iters: 7198, time: 0.063) G_GAN: 0.090 G_GAN_Feat: 0.700 G_ID: 0.115 G_Rec: 0.309 D_GP: 0.039 D_real: 0.892 D_fake: 0.910 
+(epoch: 327, iters: 7598, time: 0.063) G_GAN: 0.386 G_GAN_Feat: 0.783 G_ID: 0.139 G_Rec: 0.356 D_GP: 0.030 D_real: 1.133 D_fake: 0.619 
+(epoch: 327, iters: 7998, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.763 G_ID: 0.114 G_Rec: 0.312 D_GP: 0.085 D_real: 0.989 D_fake: 0.730 
+(epoch: 327, iters: 8398, time: 0.063) G_GAN: 0.482 G_GAN_Feat: 0.838 G_ID: 0.119 G_Rec: 0.397 D_GP: 0.030 D_real: 1.196 D_fake: 0.540 
+(epoch: 328, iters: 190, time: 0.063) G_GAN: -0.165 G_GAN_Feat: 0.827 G_ID: 0.114 G_Rec: 0.330 D_GP: 0.081 D_real: 0.548 D_fake: 1.165 
+(epoch: 328, iters: 590, time: 0.063) G_GAN: 0.394 G_GAN_Feat: 0.889 G_ID: 0.127 G_Rec: 0.409 D_GP: 0.030 D_real: 1.072 D_fake: 0.609 
+(epoch: 328, iters: 990, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.754 G_ID: 0.100 G_Rec: 0.331 D_GP: 0.031 D_real: 0.896 D_fake: 0.926 
+(epoch: 328, iters: 1390, time: 0.063) G_GAN: 0.666 G_GAN_Feat: 1.010 G_ID: 0.117 G_Rec: 0.454 D_GP: 0.037 D_real: 1.062 D_fake: 0.376 
+(epoch: 328, iters: 1790, time: 0.063) G_GAN: 0.025 G_GAN_Feat: 0.840 G_ID: 0.112 G_Rec: 0.333 D_GP: 0.068 D_real: 0.535 D_fake: 0.975 
+(epoch: 328, iters: 2190, time: 0.063) G_GAN: 0.491 G_GAN_Feat: 0.917 G_ID: 0.123 G_Rec: 0.406 D_GP: 0.038 D_real: 1.099 D_fake: 0.519 
+(epoch: 328, iters: 2590, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.890 G_ID: 0.101 G_Rec: 0.342 D_GP: 1.264 D_real: 0.495 D_fake: 0.821 
+(epoch: 328, iters: 2990, time: 0.063) G_GAN: 0.127 G_GAN_Feat: 0.905 G_ID: 0.149 G_Rec: 0.406 D_GP: 0.054 D_real: 0.720 D_fake: 0.874 
+(epoch: 328, iters: 3390, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.767 G_ID: 0.115 G_Rec: 0.317 D_GP: 0.043 D_real: 0.865 D_fake: 0.815 
+(epoch: 328, iters: 3790, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.838 G_ID: 0.129 G_Rec: 0.398 D_GP: 0.028 D_real: 1.185 D_fake: 0.623 
+(epoch: 328, iters: 4190, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.695 G_ID: 0.104 G_Rec: 0.302 D_GP: 0.033 D_real: 1.005 D_fake: 0.813 
+(epoch: 328, iters: 4590, time: 0.063) G_GAN: 0.368 G_GAN_Feat: 0.980 G_ID: 0.166 G_Rec: 0.445 D_GP: 0.046 D_real: 0.950 D_fake: 0.637 
+(epoch: 328, iters: 4990, time: 0.063) G_GAN: -0.170 G_GAN_Feat: 0.887 G_ID: 0.100 G_Rec: 0.330 D_GP: 0.129 D_real: 0.251 D_fake: 1.170 
+(epoch: 328, iters: 5390, time: 0.063) G_GAN: 0.502 G_GAN_Feat: 0.976 G_ID: 0.129 G_Rec: 0.454 D_GP: 0.038 D_real: 1.013 D_fake: 0.501 
+(epoch: 328, iters: 5790, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.836 G_ID: 0.120 G_Rec: 0.333 D_GP: 0.043 D_real: 0.770 D_fake: 0.796 
+(epoch: 328, iters: 6190, time: 0.063) G_GAN: 0.801 G_GAN_Feat: 1.225 G_ID: 0.137 G_Rec: 0.487 D_GP: 0.095 D_real: 0.612 D_fake: 0.257 
+(epoch: 328, iters: 6590, time: 0.063) G_GAN: 0.402 G_GAN_Feat: 0.676 G_ID: 0.100 G_Rec: 0.334 D_GP: 0.028 D_real: 1.305 D_fake: 0.600 
+(epoch: 328, iters: 6990, time: 0.063) G_GAN: 0.414 G_GAN_Feat: 0.905 G_ID: 0.142 G_Rec: 0.443 D_GP: 0.034 D_real: 1.000 D_fake: 0.596 
+(epoch: 328, iters: 7390, time: 0.063) G_GAN: 0.010 G_GAN_Feat: 0.796 G_ID: 0.123 G_Rec: 0.349 D_GP: 0.056 D_real: 0.668 D_fake: 0.990 
+(epoch: 328, iters: 7790, time: 0.064) G_GAN: 0.389 G_GAN_Feat: 0.873 G_ID: 0.130 G_Rec: 0.376 D_GP: 0.095 D_real: 1.025 D_fake: 0.617 
+(epoch: 328, iters: 8190, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.678 G_ID: 0.104 G_Rec: 0.292 D_GP: 0.034 D_real: 1.240 D_fake: 0.560 
+(epoch: 328, iters: 8590, time: 0.064) G_GAN: 0.535 G_GAN_Feat: 1.042 G_ID: 0.144 G_Rec: 0.460 D_GP: 0.045 D_real: 0.907 D_fake: 0.486 
+(epoch: 329, iters: 382, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.741 G_ID: 0.114 G_Rec: 0.307 D_GP: 0.039 D_real: 0.762 D_fake: 0.895 
+(epoch: 329, iters: 782, time: 0.064) G_GAN: 0.658 G_GAN_Feat: 0.981 G_ID: 0.128 G_Rec: 0.440 D_GP: 0.085 D_real: 1.008 D_fake: 0.360 
+(epoch: 329, iters: 1182, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.944 G_ID: 0.117 G_Rec: 0.349 D_GP: 0.240 D_real: 0.373 D_fake: 0.867 
+(epoch: 329, iters: 1582, time: 0.064) G_GAN: 0.635 G_GAN_Feat: 1.044 G_ID: 0.140 G_Rec: 0.448 D_GP: 0.038 D_real: 1.023 D_fake: 0.383 
+(epoch: 329, iters: 1982, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.871 G_ID: 0.109 G_Rec: 0.327 D_GP: 0.057 D_real: 0.607 D_fake: 0.764 
+(epoch: 329, iters: 2382, time: 0.064) G_GAN: 0.495 G_GAN_Feat: 1.211 G_ID: 0.121 G_Rec: 0.465 D_GP: 1.065 D_real: 0.505 D_fake: 0.552 
+(epoch: 329, iters: 2782, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.657 G_ID: 0.095 G_Rec: 0.291 D_GP: 0.030 D_real: 0.971 D_fake: 0.887 
+(epoch: 329, iters: 3182, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 1.068 G_ID: 0.123 G_Rec: 0.499 D_GP: 0.043 D_real: 0.825 D_fake: 0.621 
+(epoch: 329, iters: 3582, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.771 G_ID: 0.110 G_Rec: 0.316 D_GP: 0.087 D_real: 0.868 D_fake: 0.740 
+(epoch: 329, iters: 3982, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 1.036 G_ID: 0.147 G_Rec: 0.455 D_GP: 0.092 D_real: 0.547 D_fake: 0.602 
+(epoch: 329, iters: 4382, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.850 G_ID: 0.099 G_Rec: 0.307 D_GP: 0.084 D_real: 0.489 D_fake: 0.908 
+(epoch: 329, iters: 4782, time: 0.064) G_GAN: 0.028 G_GAN_Feat: 1.079 G_ID: 0.141 G_Rec: 0.481 D_GP: 0.034 D_real: 0.498 D_fake: 0.972 
+(epoch: 329, iters: 5182, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.783 G_ID: 0.087 G_Rec: 0.304 D_GP: 0.045 D_real: 0.945 D_fake: 0.733 
+(epoch: 329, iters: 5582, time: 0.064) G_GAN: 0.684 G_GAN_Feat: 1.000 G_ID: 0.150 G_Rec: 0.477 D_GP: 0.033 D_real: 1.229 D_fake: 0.335 
+(epoch: 329, iters: 5982, time: 0.064) G_GAN: -0.092 G_GAN_Feat: 0.791 G_ID: 0.117 G_Rec: 0.326 D_GP: 0.063 D_real: 0.745 D_fake: 1.093 
+(epoch: 329, iters: 6382, time: 0.064) G_GAN: 0.860 G_GAN_Feat: 1.086 G_ID: 0.116 G_Rec: 0.457 D_GP: 0.101 D_real: 0.891 D_fake: 0.231 
+(epoch: 329, iters: 6782, time: 0.063) G_GAN: 0.421 G_GAN_Feat: 0.812 G_ID: 0.107 G_Rec: 0.331 D_GP: 0.143 D_real: 0.862 D_fake: 0.588 
+(epoch: 329, iters: 7182, time: 0.063) G_GAN: 0.442 G_GAN_Feat: 0.873 G_ID: 0.132 G_Rec: 0.433 D_GP: 0.029 D_real: 1.149 D_fake: 0.559 
+(epoch: 329, iters: 7582, time: 0.063) G_GAN: 0.065 G_GAN_Feat: 0.733 G_ID: 0.141 G_Rec: 0.320 D_GP: 0.034 D_real: 0.925 D_fake: 0.935 
+(epoch: 329, iters: 7982, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 1.000 G_ID: 0.139 G_Rec: 0.441 D_GP: 0.036 D_real: 1.183 D_fake: 0.549 
+(epoch: 329, iters: 8382, time: 0.063) G_GAN: 0.311 G_GAN_Feat: 0.716 G_ID: 0.132 G_Rec: 0.289 D_GP: 0.042 D_real: 1.194 D_fake: 0.690 
+(epoch: 330, iters: 174, time: 0.063) G_GAN: 0.700 G_GAN_Feat: 0.909 G_ID: 0.119 G_Rec: 0.445 D_GP: 0.029 D_real: 1.335 D_fake: 0.348 
+(epoch: 330, iters: 574, time: 0.063) G_GAN: 0.171 G_GAN_Feat: 0.773 G_ID: 0.108 G_Rec: 0.319 D_GP: 0.047 D_real: 0.782 D_fake: 0.830 
+(epoch: 330, iters: 974, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 1.135 G_ID: 0.137 G_Rec: 0.447 D_GP: 0.069 D_real: 0.445 D_fake: 0.600 
+(epoch: 330, iters: 1374, time: 0.063) G_GAN: 0.333 G_GAN_Feat: 0.925 G_ID: 0.094 G_Rec: 0.334 D_GP: 0.491 D_real: 0.397 D_fake: 0.703 
+(epoch: 330, iters: 1774, time: 0.063) G_GAN: 0.460 G_GAN_Feat: 0.912 G_ID: 0.129 G_Rec: 0.409 D_GP: 0.032 D_real: 1.188 D_fake: 0.555 
+(epoch: 330, iters: 2174, time: 0.063) G_GAN: 0.149 G_GAN_Feat: 0.795 G_ID: 0.118 G_Rec: 0.308 D_GP: 0.038 D_real: 0.809 D_fake: 0.853 
+(epoch: 330, iters: 2574, time: 0.064) G_GAN: 0.569 G_GAN_Feat: 1.046 G_ID: 0.153 G_Rec: 0.429 D_GP: 0.033 D_real: 0.729 D_fake: 0.434 
+(epoch: 330, iters: 2974, time: 0.063) G_GAN: 0.352 G_GAN_Feat: 0.786 G_ID: 0.119 G_Rec: 0.330 D_GP: 0.034 D_real: 1.166 D_fake: 0.656 
+(epoch: 330, iters: 3374, time: 0.063) G_GAN: 0.757 G_GAN_Feat: 1.016 G_ID: 0.168 G_Rec: 0.424 D_GP: 0.072 D_real: 1.066 D_fake: 0.299 
+(epoch: 330, iters: 3774, time: 0.063) G_GAN: 0.312 G_GAN_Feat: 0.777 G_ID: 0.119 G_Rec: 0.295 D_GP: 0.041 D_real: 1.080 D_fake: 0.699 
+(epoch: 330, iters: 4174, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.950 G_ID: 0.144 G_Rec: 0.410 D_GP: 0.031 D_real: 0.927 D_fake: 0.750 
+(epoch: 330, iters: 4574, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.850 G_ID: 0.111 G_Rec: 0.315 D_GP: 0.031 D_real: 0.882 D_fake: 0.752 
+(epoch: 330, iters: 4974, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 1.028 G_ID: 0.144 G_Rec: 0.447 D_GP: 0.036 D_real: 1.105 D_fake: 0.353 
+(epoch: 330, iters: 5374, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.760 G_ID: 0.096 G_Rec: 0.315 D_GP: 0.033 D_real: 0.911 D_fake: 0.844 
+(epoch: 330, iters: 5774, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 1.161 G_ID: 0.121 G_Rec: 0.484 D_GP: 0.115 D_real: 0.626 D_fake: 0.503 
+(epoch: 330, iters: 6174, time: 0.064) G_GAN: -0.057 G_GAN_Feat: 0.792 G_ID: 0.132 G_Rec: 0.325 D_GP: 0.047 D_real: 0.626 D_fake: 1.057 
+(epoch: 330, iters: 6574, time: 0.064) G_GAN: 0.770 G_GAN_Feat: 0.955 G_ID: 0.147 G_Rec: 0.414 D_GP: 0.033 D_real: 1.111 D_fake: 0.307 
+(epoch: 330, iters: 6974, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.703 G_ID: 0.111 G_Rec: 0.282 D_GP: 0.051 D_real: 1.009 D_fake: 0.812 
+(epoch: 330, iters: 7374, time: 0.064) G_GAN: 0.563 G_GAN_Feat: 1.023 G_ID: 0.140 G_Rec: 0.445 D_GP: 0.055 D_real: 0.960 D_fake: 0.445 
+(epoch: 330, iters: 7774, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.833 G_ID: 0.098 G_Rec: 0.322 D_GP: 0.043 D_real: 0.951 D_fake: 0.721 
+(epoch: 330, iters: 8174, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 1.101 G_ID: 0.131 G_Rec: 0.469 D_GP: 0.110 D_real: 0.911 D_fake: 1.004 
+(epoch: 330, iters: 8574, time: 0.064) G_GAN: -0.030 G_GAN_Feat: 0.650 G_ID: 0.118 G_Rec: 0.307 D_GP: 0.026 D_real: 0.956 D_fake: 1.031 
+(epoch: 331, iters: 366, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.840 G_ID: 0.134 G_Rec: 0.439 D_GP: 0.029 D_real: 1.108 D_fake: 0.619 
+(epoch: 331, iters: 766, time: 0.064) G_GAN: -0.016 G_GAN_Feat: 0.652 G_ID: 0.111 G_Rec: 0.292 D_GP: 0.035 D_real: 0.826 D_fake: 1.016 
+(epoch: 331, iters: 1166, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.911 G_ID: 0.114 G_Rec: 0.440 D_GP: 0.034 D_real: 0.856 D_fake: 0.813 
+(epoch: 331, iters: 1566, time: 0.064) G_GAN: -0.075 G_GAN_Feat: 0.660 G_ID: 0.115 G_Rec: 0.323 D_GP: 0.031 D_real: 0.776 D_fake: 1.075 
+(epoch: 331, iters: 1966, time: 0.064) G_GAN: 0.338 G_GAN_Feat: 0.845 G_ID: 0.129 G_Rec: 0.389 D_GP: 0.035 D_real: 0.949 D_fake: 0.662 
+(epoch: 331, iters: 2366, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.677 G_ID: 0.098 G_Rec: 0.288 D_GP: 0.036 D_real: 0.978 D_fake: 0.870 
+(epoch: 331, iters: 2766, time: 0.064) G_GAN: 0.501 G_GAN_Feat: 0.978 G_ID: 0.114 G_Rec: 0.471 D_GP: 0.050 D_real: 0.968 D_fake: 0.522 
+(epoch: 331, iters: 3166, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.696 G_ID: 0.108 G_Rec: 0.301 D_GP: 0.035 D_real: 0.977 D_fake: 0.906 
+(epoch: 331, iters: 3566, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 1.085 G_ID: 0.103 G_Rec: 0.477 D_GP: 0.085 D_real: 0.524 D_fake: 0.839 
+(epoch: 331, iters: 3966, time: 0.063) G_GAN: 0.072 G_GAN_Feat: 0.782 G_ID: 0.123 G_Rec: 0.333 D_GP: 0.073 D_real: 0.745 D_fake: 0.929 
+(epoch: 331, iters: 4366, time: 0.063) G_GAN: 0.541 G_GAN_Feat: 0.975 G_ID: 0.135 G_Rec: 0.419 D_GP: 0.066 D_real: 1.157 D_fake: 0.507 
+(epoch: 331, iters: 4766, time: 0.063) G_GAN: 0.233 G_GAN_Feat: 0.704 G_ID: 0.126 G_Rec: 0.314 D_GP: 0.029 D_real: 1.090 D_fake: 0.768 
+(epoch: 331, iters: 5166, time: 0.064) G_GAN: 0.594 G_GAN_Feat: 0.916 G_ID: 0.122 G_Rec: 0.431 D_GP: 0.050 D_real: 1.023 D_fake: 0.439 
+(epoch: 331, iters: 5566, time: 0.063) G_GAN: 0.147 G_GAN_Feat: 0.787 G_ID: 0.104 G_Rec: 0.346 D_GP: 0.084 D_real: 0.817 D_fake: 0.856 
+(epoch: 331, iters: 5966, time: 0.063) G_GAN: 0.659 G_GAN_Feat: 0.956 G_ID: 0.126 G_Rec: 0.410 D_GP: 0.054 D_real: 1.133 D_fake: 0.412 
+(epoch: 331, iters: 6366, time: 0.063) G_GAN: 0.058 G_GAN_Feat: 0.786 G_ID: 0.114 G_Rec: 0.315 D_GP: 0.043 D_real: 0.741 D_fake: 0.942 
+(epoch: 331, iters: 6766, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.929 G_ID: 0.119 G_Rec: 0.446 D_GP: 0.028 D_real: 0.755 D_fake: 0.875 
+(epoch: 331, iters: 7166, time: 0.063) G_GAN: 0.507 G_GAN_Feat: 0.860 G_ID: 0.112 G_Rec: 0.351 D_GP: 0.094 D_real: 0.923 D_fake: 0.509 
+(epoch: 331, iters: 7566, time: 0.063) G_GAN: 0.473 G_GAN_Feat: 0.863 G_ID: 0.113 G_Rec: 0.383 D_GP: 0.029 D_real: 1.120 D_fake: 0.533 
+(epoch: 331, iters: 7966, time: 0.063) G_GAN: 0.234 G_GAN_Feat: 0.865 G_ID: 0.105 G_Rec: 0.376 D_GP: 0.326 D_real: 0.721 D_fake: 0.804 
+(epoch: 331, iters: 8366, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.854 G_ID: 0.148 G_Rec: 0.370 D_GP: 0.032 D_real: 1.146 D_fake: 0.528 
+(epoch: 332, iters: 158, time: 0.063) G_GAN: 0.222 G_GAN_Feat: 0.725 G_ID: 0.107 G_Rec: 0.298 D_GP: 0.033 D_real: 1.077 D_fake: 0.778 
+(epoch: 332, iters: 558, time: 0.063) G_GAN: 0.270 G_GAN_Feat: 0.933 G_ID: 0.168 G_Rec: 0.405 D_GP: 0.033 D_real: 0.860 D_fake: 0.730 
+(epoch: 332, iters: 958, time: 0.063) G_GAN: 0.124 G_GAN_Feat: 0.822 G_ID: 0.119 G_Rec: 0.342 D_GP: 0.044 D_real: 0.781 D_fake: 0.876 
+(epoch: 332, iters: 1358, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.929 G_ID: 0.145 G_Rec: 0.413 D_GP: 0.050 D_real: 0.781 D_fake: 0.679 
+(epoch: 332, iters: 1758, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.841 G_ID: 0.090 G_Rec: 0.351 D_GP: 0.081 D_real: 0.849 D_fake: 0.818 
+(epoch: 332, iters: 2158, time: 0.063) G_GAN: 0.257 G_GAN_Feat: 1.195 G_ID: 0.167 G_Rec: 0.477 D_GP: 0.169 D_real: 0.174 D_fake: 0.755 
+(epoch: 332, iters: 2558, time: 0.063) G_GAN: 0.333 G_GAN_Feat: 0.909 G_ID: 0.108 G_Rec: 0.418 D_GP: 0.063 D_real: 0.640 D_fake: 0.668 
+(epoch: 332, iters: 2958, time: 0.064) G_GAN: 0.855 G_GAN_Feat: 0.971 G_ID: 0.128 G_Rec: 0.436 D_GP: 0.039 D_real: 1.453 D_fake: 0.215 
+(epoch: 332, iters: 3358, time: 0.063) G_GAN: 0.356 G_GAN_Feat: 0.715 G_ID: 0.123 G_Rec: 0.327 D_GP: 0.030 D_real: 1.160 D_fake: 0.646 
+(epoch: 332, iters: 3758, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 0.949 G_ID: 0.131 G_Rec: 0.445 D_GP: 0.040 D_real: 1.032 D_fake: 0.595 
+(epoch: 332, iters: 4158, time: 0.063) G_GAN: 0.087 G_GAN_Feat: 0.676 G_ID: 0.088 G_Rec: 0.301 D_GP: 0.038 D_real: 0.947 D_fake: 0.913 
+(epoch: 332, iters: 4558, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 1.157 G_ID: 0.147 G_Rec: 0.504 D_GP: 0.084 D_real: 0.497 D_fake: 0.624 
+(epoch: 332, iters: 4958, time: 0.063) G_GAN: 0.254 G_GAN_Feat: 0.779 G_ID: 0.097 G_Rec: 0.321 D_GP: 0.033 D_real: 0.966 D_fake: 0.746 
+(epoch: 332, iters: 5358, time: 0.063) G_GAN: 0.838 G_GAN_Feat: 1.120 G_ID: 0.134 G_Rec: 0.477 D_GP: 0.066 D_real: 0.966 D_fake: 0.236 
+(epoch: 332, iters: 5758, time: 0.063) G_GAN: 0.205 G_GAN_Feat: 0.791 G_ID: 0.115 G_Rec: 0.300 D_GP: 0.069 D_real: 0.807 D_fake: 0.796 
+(epoch: 332, iters: 6158, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 1.106 G_ID: 0.120 G_Rec: 0.477 D_GP: 0.135 D_real: 0.529 D_fake: 0.526 
+(epoch: 332, iters: 6558, time: 0.063) G_GAN: 0.223 G_GAN_Feat: 0.963 G_ID: 0.106 G_Rec: 0.330 D_GP: 0.469 D_real: 0.253 D_fake: 0.781 
+(epoch: 332, iters: 6958, time: 0.063) G_GAN: 0.268 G_GAN_Feat: 0.932 G_ID: 0.139 G_Rec: 0.442 D_GP: 0.031 D_real: 1.043 D_fake: 0.733 
+(epoch: 332, iters: 7358, time: 0.063) G_GAN: 0.187 G_GAN_Feat: 0.856 G_ID: 0.122 G_Rec: 0.321 D_GP: 0.035 D_real: 0.946 D_fake: 0.815 
+(epoch: 332, iters: 7758, time: 0.064) G_GAN: 0.654 G_GAN_Feat: 0.997 G_ID: 0.125 G_Rec: 0.453 D_GP: 0.029 D_real: 1.156 D_fake: 0.387 
+(epoch: 332, iters: 8158, time: 0.063) G_GAN: 0.462 G_GAN_Feat: 0.751 G_ID: 0.112 G_Rec: 0.297 D_GP: 0.034 D_real: 1.270 D_fake: 0.539 
+(epoch: 332, iters: 8558, time: 0.063) G_GAN: 0.849 G_GAN_Feat: 1.023 G_ID: 0.119 G_Rec: 0.490 D_GP: 0.031 D_real: 1.287 D_fake: 0.259 
+(epoch: 333, iters: 350, time: 0.063) G_GAN: 0.116 G_GAN_Feat: 0.766 G_ID: 0.114 G_Rec: 0.330 D_GP: 0.030 D_real: 0.936 D_fake: 0.884 
+(epoch: 333, iters: 750, time: 0.064) G_GAN: 0.696 G_GAN_Feat: 0.960 G_ID: 0.129 G_Rec: 0.415 D_GP: 0.030 D_real: 1.200 D_fake: 0.332 
+(epoch: 333, iters: 1150, time: 0.063) G_GAN: 0.248 G_GAN_Feat: 0.848 G_ID: 0.115 G_Rec: 0.297 D_GP: 0.051 D_real: 0.717 D_fake: 0.752 
+(epoch: 333, iters: 1550, time: 0.063) G_GAN: 0.403 G_GAN_Feat: 0.943 G_ID: 0.134 G_Rec: 0.414 D_GP: 0.043 D_real: 1.020 D_fake: 0.601 
+(epoch: 333, iters: 1950, time: 0.063) G_GAN: 0.665 G_GAN_Feat: 0.716 G_ID: 0.112 G_Rec: 0.352 D_GP: 0.046 D_real: 1.502 D_fake: 0.479 
+(epoch: 333, iters: 2350, time: 0.064) G_GAN: 0.606 G_GAN_Feat: 0.914 G_ID: 0.120 G_Rec: 0.447 D_GP: 0.030 D_real: 1.258 D_fake: 0.425 
+(epoch: 333, iters: 2750, time: 0.063) G_GAN: 0.105 G_GAN_Feat: 0.658 G_ID: 0.108 G_Rec: 0.297 D_GP: 0.031 D_real: 0.947 D_fake: 0.895 
+(epoch: 333, iters: 3150, time: 0.063) G_GAN: 0.485 G_GAN_Feat: 0.866 G_ID: 0.137 G_Rec: 0.411 D_GP: 0.041 D_real: 1.044 D_fake: 0.538 
+(epoch: 333, iters: 3550, time: 0.063) G_GAN: 0.587 G_GAN_Feat: 0.690 G_ID: 0.094 G_Rec: 0.299 D_GP: 0.029 D_real: 1.515 D_fake: 0.448 
+(epoch: 333, iters: 3950, time: 0.063) G_GAN: 0.266 G_GAN_Feat: 0.963 G_ID: 0.157 G_Rec: 0.436 D_GP: 0.065 D_real: 0.727 D_fake: 0.738 
+(epoch: 333, iters: 4350, time: 0.063) G_GAN: 0.247 G_GAN_Feat: 0.754 G_ID: 0.104 G_Rec: 0.332 D_GP: 0.036 D_real: 0.985 D_fake: 0.755 
+(epoch: 333, iters: 4750, time: 0.063) G_GAN: 0.599 G_GAN_Feat: 1.076 G_ID: 0.131 G_Rec: 0.451 D_GP: 0.239 D_real: 0.539 D_fake: 0.445 
+(epoch: 333, iters: 5150, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.718 G_ID: 0.095 G_Rec: 0.311 D_GP: 0.033 D_real: 1.185 D_fake: 0.756 
+(epoch: 333, iters: 5550, time: 0.063) G_GAN: 0.057 G_GAN_Feat: 1.135 G_ID: 0.122 G_Rec: 0.488 D_GP: 0.065 D_real: 0.804 D_fake: 0.946 
+(epoch: 333, iters: 5950, time: 0.063) G_GAN: 0.214 G_GAN_Feat: 0.763 G_ID: 0.134 G_Rec: 0.324 D_GP: 0.061 D_real: 0.818 D_fake: 0.789 
+(epoch: 333, iters: 6350, time: 0.063) G_GAN: 0.624 G_GAN_Feat: 0.972 G_ID: 0.137 G_Rec: 0.431 D_GP: 0.037 D_real: 1.092 D_fake: 0.416 
+(epoch: 333, iters: 6750, time: 0.064) G_GAN: 0.037 G_GAN_Feat: 0.747 G_ID: 0.099 G_Rec: 0.323 D_GP: 0.035 D_real: 0.965 D_fake: 0.964 
+(epoch: 333, iters: 7150, time: 0.063) G_GAN: 0.364 G_GAN_Feat: 0.953 G_ID: 0.133 G_Rec: 0.432 D_GP: 0.037 D_real: 1.023 D_fake: 0.636 
+(epoch: 333, iters: 7550, time: 0.063) G_GAN: 0.171 G_GAN_Feat: 0.736 G_ID: 0.132 G_Rec: 0.292 D_GP: 0.033 D_real: 1.048 D_fake: 0.829 
+(epoch: 333, iters: 7950, time: 0.063) G_GAN: 0.505 G_GAN_Feat: 1.155 G_ID: 0.133 G_Rec: 0.486 D_GP: 0.037 D_real: 0.825 D_fake: 0.504 
+(epoch: 333, iters: 8350, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.760 G_ID: 0.125 G_Rec: 0.324 D_GP: 0.031 D_real: 1.148 D_fake: 0.730 
+(epoch: 334, iters: 142, time: 0.063) G_GAN: 0.576 G_GAN_Feat: 1.002 G_ID: 0.120 G_Rec: 0.426 D_GP: 0.044 D_real: 1.150 D_fake: 0.461 
+(epoch: 334, iters: 542, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 0.891 G_ID: 0.115 G_Rec: 0.368 D_GP: 0.089 D_real: 0.927 D_fake: 0.656 
+(epoch: 334, iters: 942, time: 0.063) G_GAN: 0.347 G_GAN_Feat: 1.217 G_ID: 0.129 G_Rec: 0.505 D_GP: 0.486 D_real: 0.311 D_fake: 0.657 
+(epoch: 334, iters: 1342, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.880 G_ID: 0.096 G_Rec: 0.299 D_GP: 0.034 D_real: 1.101 D_fake: 0.565 
+(epoch: 334, iters: 1742, time: 0.063) G_GAN: 0.868 G_GAN_Feat: 1.241 G_ID: 0.127 G_Rec: 0.442 D_GP: 0.054 D_real: 0.620 D_fake: 0.188 
+(epoch: 334, iters: 2142, time: 0.063) G_GAN: 0.209 G_GAN_Feat: 1.024 G_ID: 0.112 G_Rec: 0.388 D_GP: 0.360 D_real: 0.089 D_fake: 0.895 
+(epoch: 334, iters: 2542, time: 0.063) G_GAN: 0.586 G_GAN_Feat: 1.002 G_ID: 0.133 G_Rec: 0.409 D_GP: 0.031 D_real: 1.127 D_fake: 0.422 
+(epoch: 334, iters: 2942, time: 0.064) G_GAN: 0.754 G_GAN_Feat: 0.897 G_ID: 0.105 G_Rec: 0.308 D_GP: 0.044 D_real: 1.163 D_fake: 0.366 
+(epoch: 334, iters: 3342, time: 0.063) G_GAN: 0.759 G_GAN_Feat: 1.186 G_ID: 0.116 G_Rec: 0.503 D_GP: 0.040 D_real: 1.337 D_fake: 0.338 
+(epoch: 334, iters: 3742, time: 0.063) G_GAN: 0.111 G_GAN_Feat: 0.654 G_ID: 0.116 G_Rec: 0.308 D_GP: 0.024 D_real: 1.099 D_fake: 0.890 
+(epoch: 334, iters: 4142, time: 0.063) G_GAN: 0.369 G_GAN_Feat: 0.835 G_ID: 0.113 G_Rec: 0.434 D_GP: 0.026 D_real: 1.162 D_fake: 0.636 
+(epoch: 334, iters: 4542, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.626 G_ID: 0.094 G_Rec: 0.299 D_GP: 0.027 D_real: 1.122 D_fake: 0.839 
+(epoch: 334, iters: 4942, time: 0.063) G_GAN: 0.325 G_GAN_Feat: 0.800 G_ID: 0.130 G_Rec: 0.376 D_GP: 0.029 D_real: 1.115 D_fake: 0.679 
+(epoch: 334, iters: 5342, time: 0.063) G_GAN: 0.041 G_GAN_Feat: 0.697 G_ID: 0.131 G_Rec: 0.314 D_GP: 0.031 D_real: 0.934 D_fake: 0.960 
+(epoch: 334, iters: 5742, time: 0.063) G_GAN: 0.338 G_GAN_Feat: 0.813 G_ID: 0.123 G_Rec: 0.391 D_GP: 0.027 D_real: 1.098 D_fake: 0.668 
+(epoch: 334, iters: 6142, time: 0.064) G_GAN: -0.011 G_GAN_Feat: 0.639 G_ID: 0.123 G_Rec: 0.275 D_GP: 0.034 D_real: 0.844 D_fake: 1.011 
+(epoch: 334, iters: 6542, time: 0.063) G_GAN: 0.311 G_GAN_Feat: 0.860 G_ID: 0.131 G_Rec: 0.395 D_GP: 0.042 D_real: 0.920 D_fake: 0.695 
+(epoch: 334, iters: 6942, time: 0.063) G_GAN: -0.163 G_GAN_Feat: 0.716 G_ID: 0.116 G_Rec: 0.307 D_GP: 0.038 D_real: 0.620 D_fake: 1.163 
+(epoch: 334, iters: 7342, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.866 G_ID: 0.115 G_Rec: 0.405 D_GP: 0.034 D_real: 1.123 D_fake: 0.569 
+(epoch: 334, iters: 7742, time: 0.064) G_GAN: -0.168 G_GAN_Feat: 0.731 G_ID: 0.105 G_Rec: 0.326 D_GP: 0.079 D_real: 0.681 D_fake: 1.168 
+(epoch: 334, iters: 8142, time: 0.063) G_GAN: 0.485 G_GAN_Feat: 0.965 G_ID: 0.146 G_Rec: 0.435 D_GP: 0.042 D_real: 0.906 D_fake: 0.536 
+(epoch: 334, iters: 8542, time: 0.063) G_GAN: 0.101 G_GAN_Feat: 0.813 G_ID: 0.118 G_Rec: 0.341 D_GP: 0.044 D_real: 0.799 D_fake: 0.900 
+(epoch: 335, iters: 334, time: 0.063) G_GAN: 0.159 G_GAN_Feat: 0.916 G_ID: 0.139 G_Rec: 0.424 D_GP: 0.037 D_real: 0.708 D_fake: 0.842 
+(epoch: 335, iters: 734, time: 0.064) G_GAN: -0.005 G_GAN_Feat: 0.740 G_ID: 0.096 G_Rec: 0.315 D_GP: 0.076 D_real: 0.742 D_fake: 1.006 
+(epoch: 335, iters: 1134, time: 0.063) G_GAN: 0.693 G_GAN_Feat: 0.946 G_ID: 0.126 G_Rec: 0.432 D_GP: 0.033 D_real: 1.308 D_fake: 0.357 
+(epoch: 335, iters: 1534, time: 0.063) G_GAN: 0.051 G_GAN_Feat: 0.682 G_ID: 0.093 G_Rec: 0.292 D_GP: 0.028 D_real: 0.918 D_fake: 0.949 
+(epoch: 335, iters: 1934, time: 0.063) G_GAN: 0.721 G_GAN_Feat: 0.993 G_ID: 0.127 G_Rec: 0.433 D_GP: 0.061 D_real: 1.185 D_fake: 0.337 
+(epoch: 335, iters: 2334, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.693 G_ID: 0.109 G_Rec: 0.304 D_GP: 0.030 D_real: 0.992 D_fake: 0.907 
+(epoch: 335, iters: 2734, time: 0.063) G_GAN: 0.353 G_GAN_Feat: 1.129 G_ID: 0.134 G_Rec: 0.509 D_GP: 0.360 D_real: 0.502 D_fake: 0.661 
+(epoch: 335, iters: 3134, time: 0.063) G_GAN: 0.390 G_GAN_Feat: 0.706 G_ID: 0.096 G_Rec: 0.315 D_GP: 0.026 D_real: 1.209 D_fake: 0.616 
+(epoch: 335, iters: 3534, time: 0.063) G_GAN: 0.576 G_GAN_Feat: 0.990 G_ID: 0.126 G_Rec: 0.450 D_GP: 0.035 D_real: 1.141 D_fake: 0.441 
+(epoch: 335, iters: 3934, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.714 G_ID: 0.107 G_Rec: 0.335 D_GP: 0.030 D_real: 1.204 D_fake: 0.713 
+(epoch: 335, iters: 4334, time: 0.063) G_GAN: 0.506 G_GAN_Feat: 0.934 G_ID: 0.131 G_Rec: 0.437 D_GP: 0.037 D_real: 1.121 D_fake: 0.519 
+(epoch: 335, iters: 4734, time: 0.063) G_GAN: -0.340 G_GAN_Feat: 0.774 G_ID: 0.120 G_Rec: 0.320 D_GP: 0.072 D_real: 0.497 D_fake: 1.340 
+(epoch: 335, iters: 5134, time: 0.063) G_GAN: 0.518 G_GAN_Feat: 1.083 G_ID: 0.126 G_Rec: 0.465 D_GP: 0.161 D_real: 0.739 D_fake: 0.515 
+(epoch: 335, iters: 5534, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.793 G_ID: 0.106 G_Rec: 0.305 D_GP: 0.059 D_real: 1.025 D_fake: 0.617 
+(epoch: 335, iters: 5934, time: 0.063) G_GAN: 0.493 G_GAN_Feat: 1.079 G_ID: 0.130 G_Rec: 0.473 D_GP: 0.099 D_real: 0.607 D_fake: 0.526 
+(epoch: 335, iters: 6334, time: 0.063) G_GAN: -0.003 G_GAN_Feat: 0.784 G_ID: 0.116 G_Rec: 0.334 D_GP: 0.032 D_real: 0.879 D_fake: 1.003 
+(epoch: 335, iters: 6734, time: 0.063) G_GAN: 0.446 G_GAN_Feat: 0.946 G_ID: 0.119 G_Rec: 0.463 D_GP: 0.032 D_real: 1.041 D_fake: 0.559 
+(epoch: 335, iters: 7134, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.805 G_ID: 0.111 G_Rec: 0.313 D_GP: 0.064 D_real: 0.814 D_fake: 0.750 
+(epoch: 335, iters: 7534, time: 0.063) G_GAN: 0.867 G_GAN_Feat: 0.998 G_ID: 0.120 G_Rec: 0.419 D_GP: 0.044 D_real: 1.353 D_fake: 0.238 
+(epoch: 335, iters: 7934, time: 0.063) G_GAN: 0.296 G_GAN_Feat: 0.790 G_ID: 0.113 G_Rec: 0.339 D_GP: 0.032 D_real: 1.165 D_fake: 0.705 
+(epoch: 335, iters: 8334, time: 0.063) G_GAN: 0.574 G_GAN_Feat: 0.822 G_ID: 0.108 G_Rec: 0.419 D_GP: 0.028 D_real: 1.264 D_fake: 0.438 
+(epoch: 336, iters: 126, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.744 G_ID: 0.107 G_Rec: 0.286 D_GP: 0.045 D_real: 1.081 D_fake: 0.697 
+(epoch: 336, iters: 526, time: 0.063) G_GAN: 0.342 G_GAN_Feat: 0.997 G_ID: 0.153 G_Rec: 0.441 D_GP: 0.034 D_real: 0.866 D_fake: 0.665 
+(epoch: 336, iters: 926, time: 0.063) G_GAN: 0.184 G_GAN_Feat: 0.738 G_ID: 0.110 G_Rec: 0.297 D_GP: 0.038 D_real: 0.954 D_fake: 0.818 
+(epoch: 336, iters: 1326, time: 0.064) G_GAN: 0.804 G_GAN_Feat: 1.140 G_ID: 0.125 G_Rec: 0.536 D_GP: 0.050 D_real: 0.986 D_fake: 0.300 
+(epoch: 336, iters: 1726, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.884 G_ID: 0.106 G_Rec: 0.330 D_GP: 0.076 D_real: 0.739 D_fake: 0.765 
+(epoch: 336, iters: 2126, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 1.021 G_ID: 0.127 G_Rec: 0.437 D_GP: 0.057 D_real: 0.524 D_fake: 0.677 
+(epoch: 336, iters: 2526, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.770 G_ID: 0.112 G_Rec: 0.354 D_GP: 0.033 D_real: 0.896 D_fake: 0.908 
+(epoch: 336, iters: 2926, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 1.097 G_ID: 0.138 G_Rec: 0.434 D_GP: 0.048 D_real: 0.464 D_fake: 0.667 
+(epoch: 336, iters: 3326, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.747 G_ID: 0.100 G_Rec: 0.306 D_GP: 0.058 D_real: 0.911 D_fake: 0.809 
+(epoch: 336, iters: 3726, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.977 G_ID: 0.147 G_Rec: 0.463 D_GP: 0.031 D_real: 0.967 D_fake: 0.614 
+(epoch: 336, iters: 4126, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 0.743 G_ID: 0.097 G_Rec: 0.296 D_GP: 0.034 D_real: 1.263 D_fake: 0.525 
+(epoch: 336, iters: 4526, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.978 G_ID: 0.142 G_Rec: 0.443 D_GP: 0.034 D_real: 1.044 D_fake: 0.631 
+(epoch: 336, iters: 4926, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.807 G_ID: 0.114 G_Rec: 0.343 D_GP: 0.033 D_real: 0.876 D_fake: 0.840 
+(epoch: 336, iters: 5326, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 1.205 G_ID: 0.112 G_Rec: 0.465 D_GP: 0.413 D_real: 0.271 D_fake: 0.490 
+(epoch: 336, iters: 5726, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.688 G_ID: 0.099 G_Rec: 0.292 D_GP: 0.028 D_real: 0.967 D_fake: 0.959 
+(epoch: 336, iters: 6126, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 0.955 G_ID: 0.138 G_Rec: 0.432 D_GP: 0.052 D_real: 0.951 D_fake: 0.498 
+(epoch: 336, iters: 6526, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.908 G_ID: 0.113 G_Rec: 0.330 D_GP: 0.068 D_real: 0.474 D_fake: 0.674 
+(epoch: 336, iters: 6926, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 0.961 G_ID: 0.120 G_Rec: 0.509 D_GP: 0.036 D_real: 1.132 D_fake: 0.495 
+(epoch: 336, iters: 7326, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.609 G_ID: 0.110 G_Rec: 0.280 D_GP: 0.031 D_real: 1.100 D_fake: 0.831 
+(epoch: 336, iters: 7726, time: 0.064) G_GAN: 0.252 G_GAN_Feat: 0.910 G_ID: 0.136 G_Rec: 0.445 D_GP: 0.040 D_real: 0.941 D_fake: 0.749 
+(epoch: 336, iters: 8126, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.766 G_ID: 0.098 G_Rec: 0.326 D_GP: 0.058 D_real: 0.950 D_fake: 0.768 
+(epoch: 336, iters: 8526, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 1.005 G_ID: 0.133 G_Rec: 0.478 D_GP: 0.104 D_real: 0.418 D_fake: 0.888 
+(epoch: 337, iters: 318, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 0.701 G_ID: 0.110 G_Rec: 0.296 D_GP: 0.030 D_real: 1.228 D_fake: 0.643 
+(epoch: 337, iters: 718, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.961 G_ID: 0.128 G_Rec: 0.408 D_GP: 0.061 D_real: 0.740 D_fake: 0.619 
+(epoch: 337, iters: 1118, time: 0.064) G_GAN: 0.225 G_GAN_Feat: 0.847 G_ID: 0.105 G_Rec: 0.315 D_GP: 0.055 D_real: 0.623 D_fake: 0.776 
+(epoch: 337, iters: 1518, time: 0.063) G_GAN: 0.728 G_GAN_Feat: 1.060 G_ID: 0.140 G_Rec: 0.440 D_GP: 0.042 D_real: 1.081 D_fake: 0.315 
+(epoch: 337, iters: 1918, time: 0.063) G_GAN: 0.040 G_GAN_Feat: 0.782 G_ID: 0.118 G_Rec: 0.323 D_GP: 0.067 D_real: 0.630 D_fake: 0.960 
+(epoch: 337, iters: 2318, time: 0.063) G_GAN: 0.492 G_GAN_Feat: 0.952 G_ID: 0.138 G_Rec: 0.436 D_GP: 0.034 D_real: 1.094 D_fake: 0.512 
+(epoch: 337, iters: 2718, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.666 G_ID: 0.091 G_Rec: 0.290 D_GP: 0.035 D_real: 1.191 D_fake: 0.752 
+(epoch: 337, iters: 3118, time: 0.063) G_GAN: 0.583 G_GAN_Feat: 1.027 G_ID: 0.127 G_Rec: 0.464 D_GP: 0.038 D_real: 1.026 D_fake: 0.438 
+(epoch: 337, iters: 3518, time: 0.063) G_GAN: 0.098 G_GAN_Feat: 0.826 G_ID: 0.108 G_Rec: 0.333 D_GP: 0.089 D_real: 0.524 D_fake: 0.903 
+(epoch: 337, iters: 3918, time: 0.063) G_GAN: 0.389 G_GAN_Feat: 0.921 G_ID: 0.130 G_Rec: 0.429 D_GP: 0.030 D_real: 1.079 D_fake: 0.615 
+(epoch: 337, iters: 4318, time: 0.064) G_GAN: -0.207 G_GAN_Feat: 0.656 G_ID: 0.103 G_Rec: 0.320 D_GP: 0.032 D_real: 0.799 D_fake: 1.207 
+(epoch: 337, iters: 4718, time: 0.063) G_GAN: 0.535 G_GAN_Feat: 0.866 G_ID: 0.129 G_Rec: 0.400 D_GP: 0.033 D_real: 1.269 D_fake: 0.483 
+(epoch: 337, iters: 5118, time: 0.063) G_GAN: 0.227 G_GAN_Feat: 0.665 G_ID: 0.102 G_Rec: 0.310 D_GP: 0.028 D_real: 1.105 D_fake: 0.773 
+(epoch: 337, iters: 5518, time: 0.063) G_GAN: 0.439 G_GAN_Feat: 0.963 G_ID: 0.117 G_Rec: 0.473 D_GP: 0.028 D_real: 1.169 D_fake: 0.565 
+(epoch: 337, iters: 5918, time: 0.064) G_GAN: -0.001 G_GAN_Feat: 0.714 G_ID: 0.130 G_Rec: 0.287 D_GP: 0.042 D_real: 0.845 D_fake: 1.001 
+(epoch: 337, iters: 6318, time: 0.063) G_GAN: 0.547 G_GAN_Feat: 1.089 G_ID: 0.142 G_Rec: 0.485 D_GP: 0.475 D_real: 0.858 D_fake: 0.543 
+(epoch: 337, iters: 6718, time: 0.063) G_GAN: 0.143 G_GAN_Feat: 0.700 G_ID: 0.107 G_Rec: 0.305 D_GP: 0.034 D_real: 1.085 D_fake: 0.857 
+(epoch: 337, iters: 7118, time: 0.063) G_GAN: 0.247 G_GAN_Feat: 0.899 G_ID: 0.133 G_Rec: 0.404 D_GP: 0.041 D_real: 0.856 D_fake: 0.754 
+(epoch: 337, iters: 7518, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.697 G_ID: 0.110 G_Rec: 0.285 D_GP: 0.031 D_real: 1.053 D_fake: 0.866 
+(epoch: 337, iters: 7918, time: 0.063) G_GAN: 0.427 G_GAN_Feat: 0.929 G_ID: 0.119 G_Rec: 0.410 D_GP: 0.035 D_real: 1.128 D_fake: 0.579 
+(epoch: 337, iters: 8318, time: 0.063) G_GAN: 0.341 G_GAN_Feat: 0.755 G_ID: 0.110 G_Rec: 0.319 D_GP: 0.048 D_real: 1.018 D_fake: 0.660 
+(epoch: 338, iters: 110, time: 0.063) G_GAN: 0.574 G_GAN_Feat: 1.122 G_ID: 0.140 G_Rec: 0.468 D_GP: 0.360 D_real: 0.549 D_fake: 0.451 
+(epoch: 338, iters: 510, time: 0.063) G_GAN: 0.213 G_GAN_Feat: 0.934 G_ID: 0.107 G_Rec: 0.365 D_GP: 0.377 D_real: 0.391 D_fake: 0.794 
+(epoch: 338, iters: 910, time: 0.063) G_GAN: 0.662 G_GAN_Feat: 1.012 G_ID: 0.153 G_Rec: 0.424 D_GP: 0.052 D_real: 0.964 D_fake: 0.364 
+(epoch: 338, iters: 1310, time: 0.064) G_GAN: 0.146 G_GAN_Feat: 0.988 G_ID: 0.120 G_Rec: 0.352 D_GP: 0.318 D_real: 0.380 D_fake: 0.857 
+(epoch: 338, iters: 1710, time: 0.064) G_GAN: 0.869 G_GAN_Feat: 1.009 G_ID: 0.128 G_Rec: 0.476 D_GP: 0.033 D_real: 1.549 D_fake: 0.188 
+(epoch: 338, iters: 2110, time: 0.063) G_GAN: 0.569 G_GAN_Feat: 0.825 G_ID: 0.102 G_Rec: 0.353 D_GP: 0.038 D_real: 1.388 D_fake: 0.444 
+(epoch: 338, iters: 2510, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.111 G_ID: 0.128 G_Rec: 0.483 D_GP: 0.139 D_real: 0.715 D_fake: 0.662 
+(epoch: 338, iters: 2910, time: 0.063) G_GAN: 0.158 G_GAN_Feat: 0.805 G_ID: 0.108 G_Rec: 0.336 D_GP: 0.063 D_real: 0.804 D_fake: 0.842 
+(epoch: 338, iters: 3310, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 1.006 G_ID: 0.147 G_Rec: 0.419 D_GP: 0.051 D_real: 0.725 D_fake: 0.660 
+(epoch: 338, iters: 3710, time: 0.063) G_GAN: 0.151 G_GAN_Feat: 0.841 G_ID: 0.104 G_Rec: 0.354 D_GP: 0.031 D_real: 0.950 D_fake: 0.849 
+(epoch: 338, iters: 4110, time: 0.063) G_GAN: 0.441 G_GAN_Feat: 1.120 G_ID: 0.127 G_Rec: 0.505 D_GP: 0.125 D_real: 0.294 D_fake: 0.586 
+(epoch: 338, iters: 4510, time: 0.063) G_GAN: -0.428 G_GAN_Feat: 0.842 G_ID: 0.113 G_Rec: 0.326 D_GP: 0.043 D_real: 0.431 D_fake: 1.428 
+(epoch: 338, iters: 4910, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.902 G_ID: 0.145 G_Rec: 0.416 D_GP: 0.034 D_real: 0.997 D_fake: 0.675 
+(epoch: 338, iters: 5310, time: 0.063) G_GAN: 0.200 G_GAN_Feat: 0.865 G_ID: 0.116 G_Rec: 0.339 D_GP: 0.078 D_real: 0.542 D_fake: 0.801 
+(epoch: 338, iters: 5710, time: 0.063) G_GAN: 0.303 G_GAN_Feat: 1.165 G_ID: 0.142 G_Rec: 0.487 D_GP: 0.244 D_real: 0.513 D_fake: 0.715 
+(epoch: 338, iters: 6110, time: 0.063) G_GAN: 0.200 G_GAN_Feat: 1.136 G_ID: 0.095 G_Rec: 0.384 D_GP: 1.795 D_real: 0.811 D_fake: 0.805 
+(epoch: 338, iters: 6510, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.893 G_ID: 0.118 G_Rec: 0.437 D_GP: 0.028 D_real: 0.854 D_fake: 0.845 
+(epoch: 338, iters: 6910, time: 0.063) G_GAN: -0.024 G_GAN_Feat: 0.812 G_ID: 0.102 G_Rec: 0.350 D_GP: 0.091 D_real: 0.736 D_fake: 1.024 
+(epoch: 338, iters: 7310, time: 0.063) G_GAN: 0.208 G_GAN_Feat: 0.863 G_ID: 0.128 G_Rec: 0.411 D_GP: 0.030 D_real: 0.891 D_fake: 0.795 
+(epoch: 338, iters: 7710, time: 0.063) G_GAN: -0.261 G_GAN_Feat: 0.752 G_ID: 0.101 G_Rec: 0.342 D_GP: 0.057 D_real: 0.498 D_fake: 1.261 
+(epoch: 338, iters: 8110, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.941 G_ID: 0.139 G_Rec: 0.394 D_GP: 0.081 D_real: 0.613 D_fake: 0.861 
+(epoch: 338, iters: 8510, time: 0.063) G_GAN: 0.009 G_GAN_Feat: 0.855 G_ID: 0.125 G_Rec: 0.340 D_GP: 0.108 D_real: 0.590 D_fake: 0.991 
+(epoch: 339, iters: 302, time: 0.063) G_GAN: 0.434 G_GAN_Feat: 1.076 G_ID: 0.132 G_Rec: 0.487 D_GP: 0.164 D_real: 0.507 D_fake: 0.578 
+(epoch: 339, iters: 702, time: 0.063) G_GAN: 0.326 G_GAN_Feat: 0.679 G_ID: 0.094 G_Rec: 0.323 D_GP: 0.023 D_real: 1.241 D_fake: 0.675 
+(epoch: 339, iters: 1102, time: 0.064) G_GAN: 0.639 G_GAN_Feat: 1.058 G_ID: 0.137 G_Rec: 0.482 D_GP: 0.099 D_real: 0.946 D_fake: 0.370 
+(epoch: 339, iters: 1502, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 0.805 G_ID: 0.122 G_Rec: 0.341 D_GP: 0.031 D_real: 1.074 D_fake: 0.758 
+(epoch: 339, iters: 1902, time: 0.063) G_GAN: 0.421 G_GAN_Feat: 1.063 G_ID: 0.128 G_Rec: 0.469 D_GP: 0.040 D_real: 0.611 D_fake: 0.580 
+(epoch: 339, iters: 2302, time: 0.063) G_GAN: 0.096 G_GAN_Feat: 0.728 G_ID: 0.117 G_Rec: 0.320 D_GP: 0.024 D_real: 0.984 D_fake: 0.905 
+(epoch: 339, iters: 2702, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 1.002 G_ID: 0.120 G_Rec: 0.449 D_GP: 0.050 D_real: 0.874 D_fake: 0.665 
+(epoch: 339, iters: 3102, time: 0.063) G_GAN: 0.035 G_GAN_Feat: 0.688 G_ID: 0.110 G_Rec: 0.284 D_GP: 0.031 D_real: 0.833 D_fake: 0.966 
+(epoch: 339, iters: 3502, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.946 G_ID: 0.157 G_Rec: 0.407 D_GP: 0.042 D_real: 0.709 D_fake: 0.798 
+(epoch: 339, iters: 3902, time: 0.063) G_GAN: 0.160 G_GAN_Feat: 0.742 G_ID: 0.097 G_Rec: 0.326 D_GP: 0.040 D_real: 1.037 D_fake: 0.840 
+(epoch: 339, iters: 4302, time: 0.064) G_GAN: 0.421 G_GAN_Feat: 1.027 G_ID: 0.130 G_Rec: 0.476 D_GP: 0.045 D_real: 0.762 D_fake: 0.595 
+(epoch: 339, iters: 4702, time: 0.063) G_GAN: 0.116 G_GAN_Feat: 0.839 G_ID: 0.112 G_Rec: 0.342 D_GP: 0.102 D_real: 0.642 D_fake: 0.885 
+(epoch: 339, iters: 5102, time: 0.064) G_GAN: 0.576 G_GAN_Feat: 0.967 G_ID: 0.130 G_Rec: 0.426 D_GP: 0.041 D_real: 1.199 D_fake: 0.445 
+(epoch: 339, iters: 5502, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.847 G_ID: 0.101 G_Rec: 0.354 D_GP: 0.032 D_real: 0.719 D_fake: 0.785 
+(epoch: 339, iters: 5902, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 1.017 G_ID: 0.122 G_Rec: 0.423 D_GP: 0.049 D_real: 0.785 D_fake: 0.465 
+(epoch: 339, iters: 6302, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.709 G_ID: 0.110 G_Rec: 0.315 D_GP: 0.027 D_real: 1.284 D_fake: 0.645 
+(epoch: 339, iters: 6702, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.884 G_ID: 0.134 G_Rec: 0.422 D_GP: 0.032 D_real: 0.907 D_fake: 0.748 
+(epoch: 339, iters: 7102, time: 0.064) G_GAN: -0.023 G_GAN_Feat: 0.748 G_ID: 0.117 G_Rec: 0.327 D_GP: 0.035 D_real: 0.835 D_fake: 1.023 
+(epoch: 339, iters: 7502, time: 0.064) G_GAN: 0.182 G_GAN_Feat: 0.970 G_ID: 0.160 G_Rec: 0.433 D_GP: 0.043 D_real: 0.586 D_fake: 0.819 
+(epoch: 339, iters: 7902, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.745 G_ID: 0.117 G_Rec: 0.308 D_GP: 0.082 D_real: 0.995 D_fake: 0.683 
+(epoch: 339, iters: 8302, time: 0.063) G_GAN: 0.724 G_GAN_Feat: 1.103 G_ID: 0.137 G_Rec: 0.486 D_GP: 0.108 D_real: 0.704 D_fake: 0.344 
+(epoch: 339, iters: 8702, time: 0.063) G_GAN: 0.311 G_GAN_Feat: 0.774 G_ID: 0.115 G_Rec: 0.303 D_GP: 0.034 D_real: 1.134 D_fake: 0.689 
+(epoch: 340, iters: 494, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 1.015 G_ID: 0.127 G_Rec: 0.425 D_GP: 0.080 D_real: 0.651 D_fake: 0.703 
+(epoch: 340, iters: 894, time: 0.063) G_GAN: 0.278 G_GAN_Feat: 0.783 G_ID: 0.105 G_Rec: 0.361 D_GP: 0.026 D_real: 1.100 D_fake: 0.726 
+(epoch: 340, iters: 1294, time: 0.064) G_GAN: 0.620 G_GAN_Feat: 1.071 G_ID: 0.138 G_Rec: 0.456 D_GP: 0.037 D_real: 1.223 D_fake: 0.420 
+(epoch: 340, iters: 1694, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.816 G_ID: 0.143 G_Rec: 0.325 D_GP: 0.062 D_real: 0.904 D_fake: 0.779 
+(epoch: 340, iters: 2094, time: 0.064) G_GAN: 0.800 G_GAN_Feat: 1.232 G_ID: 0.152 G_Rec: 0.484 D_GP: 0.238 D_real: 0.404 D_fake: 0.252 
+(epoch: 340, iters: 2494, time: 0.064) G_GAN: -0.021 G_GAN_Feat: 0.752 G_ID: 0.109 G_Rec: 0.335 D_GP: 0.038 D_real: 0.832 D_fake: 1.021 
+(epoch: 340, iters: 2894, time: 0.063) G_GAN: 0.449 G_GAN_Feat: 0.977 G_ID: 0.139 G_Rec: 0.463 D_GP: 0.046 D_real: 0.863 D_fake: 0.562 
+(epoch: 340, iters: 3294, time: 0.063) G_GAN: 0.220 G_GAN_Feat: 0.729 G_ID: 0.098 G_Rec: 0.306 D_GP: 0.033 D_real: 1.077 D_fake: 0.781 
+(epoch: 340, iters: 3694, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 1.164 G_ID: 0.142 G_Rec: 0.460 D_GP: 0.864 D_real: 0.274 D_fake: 0.506 
+(epoch: 340, iters: 4094, time: 0.063) G_GAN: 0.254 G_GAN_Feat: 0.700 G_ID: 0.092 G_Rec: 0.342 D_GP: 0.032 D_real: 1.202 D_fake: 0.747 
+(epoch: 340, iters: 4494, time: 0.063) G_GAN: 0.605 G_GAN_Feat: 1.060 G_ID: 0.122 G_Rec: 0.429 D_GP: 0.063 D_real: 0.794 D_fake: 0.440 
+(epoch: 340, iters: 4894, time: 0.063) G_GAN: 0.264 G_GAN_Feat: 0.703 G_ID: 0.116 G_Rec: 0.315 D_GP: 0.027 D_real: 1.192 D_fake: 0.827 
+(epoch: 340, iters: 5294, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.851 G_ID: 0.121 G_Rec: 0.426 D_GP: 0.026 D_real: 1.058 D_fake: 0.648 
+(epoch: 340, iters: 5694, time: 0.063) G_GAN: 0.220 G_GAN_Feat: 0.613 G_ID: 0.101 G_Rec: 0.309 D_GP: 0.026 D_real: 1.194 D_fake: 0.780 
+(epoch: 340, iters: 6094, time: 0.063) G_GAN: 0.456 G_GAN_Feat: 0.936 G_ID: 0.116 G_Rec: 0.472 D_GP: 0.040 D_real: 1.071 D_fake: 0.554 
+(epoch: 340, iters: 6494, time: 0.063) G_GAN: 0.134 G_GAN_Feat: 0.694 G_ID: 0.103 G_Rec: 0.331 D_GP: 0.046 D_real: 0.992 D_fake: 0.867 
+(epoch: 340, iters: 6894, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.791 G_ID: 0.114 G_Rec: 0.375 D_GP: 0.038 D_real: 0.977 D_fake: 0.735 
+(epoch: 340, iters: 7294, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.737 G_ID: 0.097 G_Rec: 0.370 D_GP: 0.061 D_real: 0.834 D_fake: 0.912 
+(epoch: 340, iters: 7694, time: 0.063) G_GAN: 0.310 G_GAN_Feat: 0.911 G_ID: 0.138 G_Rec: 0.429 D_GP: 0.047 D_real: 0.880 D_fake: 0.698 
+(epoch: 340, iters: 8094, time: 0.063) G_GAN: 0.001 G_GAN_Feat: 0.720 G_ID: 0.101 G_Rec: 0.327 D_GP: 0.068 D_real: 0.808 D_fake: 0.999 
+(epoch: 340, iters: 8494, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.880 G_ID: 0.111 G_Rec: 0.417 D_GP: 0.037 D_real: 0.995 D_fake: 0.591 
+(epoch: 341, iters: 286, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.687 G_ID: 0.099 G_Rec: 0.306 D_GP: 0.038 D_real: 1.007 D_fake: 0.884 
+(epoch: 341, iters: 686, time: 0.063) G_GAN: 0.112 G_GAN_Feat: 0.931 G_ID: 0.128 G_Rec: 0.450 D_GP: 0.049 D_real: 0.752 D_fake: 0.893 
+(epoch: 341, iters: 1086, time: 0.063) G_GAN: 0.079 G_GAN_Feat: 0.660 G_ID: 0.125 G_Rec: 0.280 D_GP: 0.031 D_real: 0.994 D_fake: 0.921 
+(epoch: 341, iters: 1486, time: 0.064) G_GAN: 0.594 G_GAN_Feat: 0.969 G_ID: 0.132 G_Rec: 0.425 D_GP: 0.066 D_real: 1.092 D_fake: 0.446 
+(epoch: 341, iters: 1886, time: 0.064) G_GAN: 0.063 G_GAN_Feat: 0.754 G_ID: 0.103 G_Rec: 0.321 D_GP: 0.042 D_real: 0.883 D_fake: 0.937 
+(epoch: 341, iters: 2286, time: 0.063) G_GAN: 0.118 G_GAN_Feat: 0.906 G_ID: 0.159 G_Rec: 0.385 D_GP: 0.048 D_real: 0.694 D_fake: 0.882 
+(epoch: 341, iters: 2686, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.773 G_ID: 0.099 G_Rec: 0.317 D_GP: 0.030 D_real: 0.859 D_fake: 0.964 
+(epoch: 341, iters: 3086, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 1.017 G_ID: 0.115 G_Rec: 0.425 D_GP: 0.059 D_real: 0.746 D_fake: 0.669 
+(epoch: 341, iters: 3486, time: 0.063) G_GAN: 0.059 G_GAN_Feat: 0.768 G_ID: 0.107 G_Rec: 0.333 D_GP: 0.038 D_real: 0.877 D_fake: 0.942 
+(epoch: 341, iters: 3886, time: 0.063) G_GAN: 0.293 G_GAN_Feat: 0.893 G_ID: 0.121 G_Rec: 0.378 D_GP: 0.041 D_real: 0.878 D_fake: 0.708 
+(epoch: 341, iters: 4286, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 0.795 G_ID: 0.093 G_Rec: 0.322 D_GP: 0.042 D_real: 0.851 D_fake: 0.989 
+(epoch: 341, iters: 4686, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 1.001 G_ID: 0.132 G_Rec: 0.492 D_GP: 0.058 D_real: 0.987 D_fake: 0.522 
+(epoch: 341, iters: 5086, time: 0.063) G_GAN: 0.151 G_GAN_Feat: 0.746 G_ID: 0.121 G_Rec: 0.310 D_GP: 0.030 D_real: 1.021 D_fake: 0.849 
+(epoch: 341, iters: 5486, time: 0.063) G_GAN: 0.286 G_GAN_Feat: 0.955 G_ID: 0.126 G_Rec: 0.435 D_GP: 0.033 D_real: 0.899 D_fake: 0.716 
+(epoch: 341, iters: 5886, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.905 G_ID: 0.112 G_Rec: 0.337 D_GP: 0.051 D_real: 0.483 D_fake: 0.952 
+(epoch: 341, iters: 6286, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.961 G_ID: 0.132 G_Rec: 0.402 D_GP: 0.058 D_real: 0.917 D_fake: 0.506 
+(epoch: 341, iters: 6686, time: 0.064) G_GAN: 0.004 G_GAN_Feat: 0.749 G_ID: 0.125 G_Rec: 0.321 D_GP: 0.026 D_real: 0.885 D_fake: 0.996 
+(epoch: 341, iters: 7086, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 1.061 G_ID: 0.128 G_Rec: 0.417 D_GP: 0.033 D_real: 0.874 D_fake: 0.625 
+(epoch: 341, iters: 7486, time: 0.063) G_GAN: 0.201 G_GAN_Feat: 0.861 G_ID: 0.111 G_Rec: 0.319 D_GP: 0.104 D_real: 0.550 D_fake: 0.799 
+(epoch: 341, iters: 7886, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.902 G_ID: 0.146 G_Rec: 0.447 D_GP: 0.029 D_real: 1.069 D_fake: 0.665 
+(epoch: 341, iters: 8286, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.730 G_ID: 0.089 G_Rec: 0.314 D_GP: 0.028 D_real: 1.144 D_fake: 0.761 
+(epoch: 341, iters: 8686, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.993 G_ID: 0.146 G_Rec: 0.453 D_GP: 0.033 D_real: 0.844 D_fake: 0.681 
+(epoch: 342, iters: 478, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.777 G_ID: 0.119 G_Rec: 0.310 D_GP: 0.038 D_real: 1.159 D_fake: 0.616 
+(epoch: 342, iters: 878, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.988 G_ID: 0.132 G_Rec: 0.440 D_GP: 0.063 D_real: 0.970 D_fake: 0.554 
+(epoch: 342, iters: 1278, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.845 G_ID: 0.112 G_Rec: 0.320 D_GP: 0.060 D_real: 0.614 D_fake: 0.880 
+(epoch: 342, iters: 1678, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.965 G_ID: 0.157 G_Rec: 0.431 D_GP: 0.029 D_real: 0.774 D_fake: 0.745 
+(epoch: 342, iters: 2078, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 0.789 G_ID: 0.100 G_Rec: 0.313 D_GP: 0.043 D_real: 1.070 D_fake: 0.633 
+(epoch: 342, iters: 2478, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 1.074 G_ID: 0.142 G_Rec: 0.462 D_GP: 0.079 D_real: 0.513 D_fake: 0.763 
+(epoch: 342, iters: 2878, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.788 G_ID: 0.110 G_Rec: 0.358 D_GP: 0.030 D_real: 1.062 D_fake: 0.792 
+(epoch: 342, iters: 3278, time: 0.064) G_GAN: 0.705 G_GAN_Feat: 0.880 G_ID: 0.129 G_Rec: 0.447 D_GP: 0.031 D_real: 1.268 D_fake: 0.397 
+(epoch: 342, iters: 3678, time: 0.063) G_GAN: 0.106 G_GAN_Feat: 0.689 G_ID: 0.099 G_Rec: 0.314 D_GP: 0.030 D_real: 0.979 D_fake: 0.894 
+(epoch: 342, iters: 4078, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.988 G_ID: 0.130 G_Rec: 0.478 D_GP: 0.037 D_real: 1.095 D_fake: 0.534 
+(epoch: 342, iters: 4478, time: 0.063) G_GAN: 0.314 G_GAN_Feat: 0.789 G_ID: 0.117 G_Rec: 0.356 D_GP: 0.053 D_real: 0.972 D_fake: 0.688 
+(epoch: 342, iters: 4878, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.960 G_ID: 0.146 G_Rec: 0.449 D_GP: 0.218 D_real: 0.874 D_fake: 0.746 
+(epoch: 342, iters: 5278, time: 0.064) G_GAN: 0.002 G_GAN_Feat: 0.728 G_ID: 0.096 G_Rec: 0.289 D_GP: 0.048 D_real: 0.849 D_fake: 0.998 
+(epoch: 342, iters: 5678, time: 0.063) G_GAN: 0.413 G_GAN_Feat: 1.042 G_ID: 0.134 G_Rec: 0.446 D_GP: 0.057 D_real: 0.943 D_fake: 0.604 
+(epoch: 342, iters: 6078, time: 0.063) G_GAN: 0.014 G_GAN_Feat: 0.830 G_ID: 0.096 G_Rec: 0.323 D_GP: 0.033 D_real: 0.770 D_fake: 0.987 
+(epoch: 342, iters: 6478, time: 0.063) G_GAN: 0.291 G_GAN_Feat: 1.018 G_ID: 0.124 G_Rec: 0.438 D_GP: 0.053 D_real: 0.724 D_fake: 0.710 
+(epoch: 342, iters: 6878, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.726 G_ID: 0.112 G_Rec: 0.295 D_GP: 0.030 D_real: 1.149 D_fake: 0.664 
+(epoch: 342, iters: 7278, time: 0.063) G_GAN: 0.410 G_GAN_Feat: 0.947 G_ID: 0.152 G_Rec: 0.396 D_GP: 0.035 D_real: 1.167 D_fake: 0.593 
+(epoch: 342, iters: 7678, time: 0.063) G_GAN: 0.137 G_GAN_Feat: 1.002 G_ID: 0.103 G_Rec: 0.405 D_GP: 0.245 D_real: 0.440 D_fake: 0.865 
+(epoch: 342, iters: 8078, time: 0.063) G_GAN: 0.448 G_GAN_Feat: 1.180 G_ID: 0.145 G_Rec: 0.448 D_GP: 0.067 D_real: 0.460 D_fake: 0.554 
+(epoch: 342, iters: 8478, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.967 G_ID: 0.125 G_Rec: 0.334 D_GP: 0.080 D_real: 0.643 D_fake: 0.714 
+(epoch: 343, iters: 270, time: 0.063) G_GAN: 0.520 G_GAN_Feat: 1.167 G_ID: 0.150 G_Rec: 0.447 D_GP: 0.032 D_real: 0.905 D_fake: 0.485 
+(epoch: 343, iters: 670, time: 0.063) G_GAN: 0.062 G_GAN_Feat: 0.700 G_ID: 0.110 G_Rec: 0.347 D_GP: 0.027 D_real: 0.964 D_fake: 0.938 
+(epoch: 343, iters: 1070, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.931 G_ID: 0.157 G_Rec: 0.435 D_GP: 0.036 D_real: 0.926 D_fake: 0.590 
+(epoch: 343, iters: 1470, time: 0.064) G_GAN: -0.107 G_GAN_Feat: 0.818 G_ID: 0.118 G_Rec: 0.361 D_GP: 0.121 D_real: 0.617 D_fake: 1.107 
+(epoch: 343, iters: 1870, time: 0.063) G_GAN: 0.242 G_GAN_Feat: 0.864 G_ID: 0.132 G_Rec: 0.372 D_GP: 0.043 D_real: 0.989 D_fake: 0.760 
+(epoch: 343, iters: 2270, time: 0.063) G_GAN: 0.386 G_GAN_Feat: 0.692 G_ID: 0.106 G_Rec: 0.302 D_GP: 0.036 D_real: 1.285 D_fake: 0.621 
+(epoch: 343, iters: 2670, time: 0.063) G_GAN: 0.560 G_GAN_Feat: 0.997 G_ID: 0.151 G_Rec: 0.486 D_GP: 0.070 D_real: 1.069 D_fake: 0.453 
+(epoch: 343, iters: 3070, time: 0.064) G_GAN: 0.214 G_GAN_Feat: 0.721 G_ID: 0.111 G_Rec: 0.287 D_GP: 0.042 D_real: 0.967 D_fake: 0.786 
+(epoch: 343, iters: 3470, time: 0.064) G_GAN: 0.796 G_GAN_Feat: 1.043 G_ID: 0.141 G_Rec: 0.429 D_GP: 0.094 D_real: 0.997 D_fake: 0.293 
+(epoch: 343, iters: 3870, time: 0.063) G_GAN: -0.083 G_GAN_Feat: 0.800 G_ID: 0.110 G_Rec: 0.312 D_GP: 0.044 D_real: 0.786 D_fake: 1.084 
+(epoch: 343, iters: 4270, time: 0.064) G_GAN: 0.692 G_GAN_Feat: 1.064 G_ID: 0.125 G_Rec: 0.458 D_GP: 0.039 D_real: 0.959 D_fake: 0.343 
+(epoch: 343, iters: 4670, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.943 G_ID: 0.114 G_Rec: 0.354 D_GP: 0.981 D_real: 0.401 D_fake: 0.739 
+(epoch: 343, iters: 5070, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 1.181 G_ID: 0.133 G_Rec: 0.500 D_GP: 0.155 D_real: 0.439 D_fake: 0.786 
+(epoch: 343, iters: 5470, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.724 G_ID: 0.115 G_Rec: 0.307 D_GP: 0.033 D_real: 1.126 D_fake: 0.738 
+(epoch: 343, iters: 5870, time: 0.064) G_GAN: 0.315 G_GAN_Feat: 1.066 G_ID: 0.127 G_Rec: 0.394 D_GP: 0.057 D_real: 0.386 D_fake: 0.723 
+(epoch: 343, iters: 6270, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.857 G_ID: 0.103 G_Rec: 0.349 D_GP: 0.035 D_real: 1.306 D_fake: 0.493 
+(epoch: 343, iters: 6670, time: 0.063) G_GAN: 0.105 G_GAN_Feat: 1.221 G_ID: 0.147 G_Rec: 0.490 D_GP: 0.172 D_real: 0.089 D_fake: 0.901 
+(epoch: 343, iters: 7070, time: 0.063) G_GAN: 0.022 G_GAN_Feat: 0.750 G_ID: 0.122 G_Rec: 0.289 D_GP: 0.043 D_real: 0.745 D_fake: 0.978 
+(epoch: 343, iters: 7470, time: 0.063) G_GAN: 0.286 G_GAN_Feat: 1.123 G_ID: 0.122 G_Rec: 0.434 D_GP: 0.093 D_real: 0.751 D_fake: 0.722 
+(epoch: 343, iters: 7870, time: 0.064) G_GAN: 0.586 G_GAN_Feat: 0.821 G_ID: 0.099 G_Rec: 0.334 D_GP: 0.046 D_real: 1.389 D_fake: 0.468 
+(epoch: 343, iters: 8270, time: 0.063) G_GAN: 0.401 G_GAN_Feat: 0.990 G_ID: 0.122 G_Rec: 0.428 D_GP: 0.029 D_real: 0.990 D_fake: 0.599 
+(epoch: 343, iters: 8670, time: 0.063) G_GAN: 0.786 G_GAN_Feat: 0.828 G_ID: 0.113 G_Rec: 0.330 D_GP: 0.037 D_real: 1.386 D_fake: 0.275 
+(epoch: 344, iters: 462, time: 0.063) G_GAN: 0.416 G_GAN_Feat: 0.961 G_ID: 0.136 G_Rec: 0.436 D_GP: 0.032 D_real: 1.005 D_fake: 0.587 
+(epoch: 344, iters: 862, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 0.836 G_ID: 0.097 G_Rec: 0.329 D_GP: 0.034 D_real: 1.131 D_fake: 0.585 
+(epoch: 344, iters: 1262, time: 0.063) G_GAN: 0.545 G_GAN_Feat: 0.873 G_ID: 0.132 G_Rec: 0.378 D_GP: 0.032 D_real: 1.331 D_fake: 0.459 
+(epoch: 344, iters: 1662, time: 0.063) G_GAN: 0.348 G_GAN_Feat: 0.764 G_ID: 0.123 G_Rec: 0.315 D_GP: 0.045 D_real: 1.153 D_fake: 0.658 
+(epoch: 344, iters: 2062, time: 0.064) G_GAN: 0.637 G_GAN_Feat: 0.885 G_ID: 0.146 G_Rec: 0.453 D_GP: 0.033 D_real: 1.288 D_fake: 0.374 
+(epoch: 344, iters: 2462, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.732 G_ID: 0.113 G_Rec: 0.318 D_GP: 0.044 D_real: 0.979 D_fake: 0.832 
+(epoch: 344, iters: 2862, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.980 G_ID: 0.149 G_Rec: 0.445 D_GP: 0.033 D_real: 1.053 D_fake: 0.641 
+(epoch: 344, iters: 3262, time: 0.063) G_GAN: 0.201 G_GAN_Feat: 0.681 G_ID: 0.102 G_Rec: 0.305 D_GP: 0.038 D_real: 1.129 D_fake: 0.799 
+(epoch: 344, iters: 3662, time: 0.063) G_GAN: 0.500 G_GAN_Feat: 0.838 G_ID: 0.130 G_Rec: 0.379 D_GP: 0.039 D_real: 1.179 D_fake: 0.510 
+(epoch: 344, iters: 4062, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.687 G_ID: 0.108 G_Rec: 0.302 D_GP: 0.038 D_real: 0.934 D_fake: 0.898 
+(epoch: 344, iters: 4462, time: 0.063) G_GAN: 0.623 G_GAN_Feat: 0.937 G_ID: 0.118 G_Rec: 0.413 D_GP: 0.032 D_real: 1.313 D_fake: 0.390 
+(epoch: 344, iters: 4862, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.706 G_ID: 0.111 G_Rec: 0.308 D_GP: 0.028 D_real: 1.040 D_fake: 0.762 
+(epoch: 344, iters: 5262, time: 0.063) G_GAN: 0.667 G_GAN_Feat: 0.998 G_ID: 0.130 G_Rec: 0.440 D_GP: 0.034 D_real: 1.393 D_fake: 0.354 
+(epoch: 344, iters: 5662, time: 0.064) G_GAN: -0.011 G_GAN_Feat: 0.892 G_ID: 0.100 G_Rec: 0.326 D_GP: 0.235 D_real: 0.322 D_fake: 1.012 
+(epoch: 344, iters: 6062, time: 0.063) G_GAN: 0.635 G_GAN_Feat: 1.193 G_ID: 0.127 G_Rec: 0.447 D_GP: 0.050 D_real: 0.682 D_fake: 0.446 
+(epoch: 344, iters: 6462, time: 0.063) G_GAN: 0.184 G_GAN_Feat: 0.736 G_ID: 0.093 G_Rec: 0.269 D_GP: 0.036 D_real: 0.927 D_fake: 0.816 
+(epoch: 344, iters: 6862, time: 0.063) G_GAN: 0.786 G_GAN_Feat: 1.003 G_ID: 0.128 G_Rec: 0.411 D_GP: 0.033 D_real: 1.091 D_fake: 0.247 
+(epoch: 344, iters: 7262, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 0.854 G_ID: 0.126 G_Rec: 0.316 D_GP: 0.035 D_real: 0.753 D_fake: 0.901 
+(epoch: 344, iters: 7662, time: 0.063) G_GAN: 0.648 G_GAN_Feat: 1.212 G_ID: 0.148 G_Rec: 0.488 D_GP: 0.044 D_real: 0.626 D_fake: 0.366 
+(epoch: 344, iters: 8062, time: 0.063) G_GAN: 0.194 G_GAN_Feat: 0.735 G_ID: 0.105 G_Rec: 0.327 D_GP: 0.026 D_real: 1.125 D_fake: 0.809 
+(epoch: 344, iters: 8462, time: 0.063) G_GAN: 0.723 G_GAN_Feat: 0.922 G_ID: 0.122 G_Rec: 0.434 D_GP: 0.031 D_real: 1.366 D_fake: 0.382 
+(epoch: 345, iters: 254, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.998 G_ID: 0.103 G_Rec: 0.341 D_GP: 0.153 D_real: 0.435 D_fake: 0.794 
+(epoch: 345, iters: 654, time: 0.063) G_GAN: 0.741 G_GAN_Feat: 0.936 G_ID: 0.115 G_Rec: 0.384 D_GP: 0.033 D_real: 1.320 D_fake: 0.281 
+(epoch: 345, iters: 1054, time: 0.063) G_GAN: 0.170 G_GAN_Feat: 0.817 G_ID: 0.105 G_Rec: 0.342 D_GP: 0.036 D_real: 0.928 D_fake: 0.832 
+(epoch: 345, iters: 1454, time: 0.063) G_GAN: 0.591 G_GAN_Feat: 1.013 G_ID: 0.114 G_Rec: 0.446 D_GP: 0.036 D_real: 1.055 D_fake: 0.418 
+(epoch: 345, iters: 1854, time: 0.064) G_GAN: 0.467 G_GAN_Feat: 1.051 G_ID: 0.101 G_Rec: 0.385 D_GP: 0.389 D_real: 0.475 D_fake: 0.625 
+(epoch: 345, iters: 2254, time: 0.063) G_GAN: 0.382 G_GAN_Feat: 0.923 G_ID: 0.119 G_Rec: 0.429 D_GP: 0.028 D_real: 1.078 D_fake: 0.621 
+(epoch: 345, iters: 2654, time: 0.063) G_GAN: 0.362 G_GAN_Feat: 0.843 G_ID: 0.097 G_Rec: 0.332 D_GP: 0.034 D_real: 1.169 D_fake: 0.655 
+(epoch: 345, iters: 3054, time: 0.064) G_GAN: 0.506 G_GAN_Feat: 0.946 G_ID: 0.133 G_Rec: 0.417 D_GP: 0.035 D_real: 1.050 D_fake: 0.500 
+(epoch: 345, iters: 3454, time: 0.064) G_GAN: 0.571 G_GAN_Feat: 0.910 G_ID: 0.106 G_Rec: 0.351 D_GP: 0.065 D_real: 0.935 D_fake: 0.529 
+(epoch: 345, iters: 3854, time: 0.064) G_GAN: 0.547 G_GAN_Feat: 1.176 G_ID: 0.152 G_Rec: 0.470 D_GP: 0.153 D_real: 0.404 D_fake: 0.468 
+(epoch: 345, iters: 4254, time: 0.064) G_GAN: 0.205 G_GAN_Feat: 0.858 G_ID: 0.103 G_Rec: 0.374 D_GP: 0.031 D_real: 0.671 D_fake: 0.796 
+(epoch: 345, iters: 4654, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 1.148 G_ID: 0.140 G_Rec: 0.509 D_GP: 0.205 D_real: 0.419 D_fake: 0.597 
+(epoch: 345, iters: 5054, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.824 G_ID: 0.094 G_Rec: 0.318 D_GP: 0.028 D_real: 1.014 D_fake: 0.816 
+(epoch: 345, iters: 5454, time: 0.064) G_GAN: 0.826 G_GAN_Feat: 0.964 G_ID: 0.118 G_Rec: 0.469 D_GP: 0.036 D_real: 1.351 D_fake: 0.336 
+(epoch: 345, iters: 5854, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.661 G_ID: 0.086 G_Rec: 0.288 D_GP: 0.029 D_real: 1.027 D_fake: 0.850 
+(epoch: 345, iters: 6254, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.896 G_ID: 0.125 G_Rec: 0.400 D_GP: 0.031 D_real: 0.997 D_fake: 0.725 
+(epoch: 345, iters: 6654, time: 0.064) G_GAN: 0.545 G_GAN_Feat: 0.924 G_ID: 0.097 G_Rec: 0.361 D_GP: 0.490 D_real: 0.963 D_fake: 0.472 
+(epoch: 345, iters: 7054, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 1.033 G_ID: 0.124 G_Rec: 0.469 D_GP: 0.093 D_real: 0.682 D_fake: 0.689 
+(epoch: 345, iters: 7454, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.764 G_ID: 0.100 G_Rec: 0.302 D_GP: 0.036 D_real: 0.936 D_fake: 0.822 
+(epoch: 345, iters: 7854, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 1.005 G_ID: 0.135 G_Rec: 0.443 D_GP: 0.041 D_real: 0.768 D_fake: 0.726 
+(epoch: 345, iters: 8254, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.835 G_ID: 0.101 G_Rec: 0.319 D_GP: 0.046 D_real: 0.783 D_fake: 0.917 
+(epoch: 345, iters: 8654, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 0.982 G_ID: 0.135 G_Rec: 0.444 D_GP: 0.033 D_real: 1.192 D_fake: 0.505 
+(epoch: 346, iters: 446, time: 0.064) G_GAN: 0.531 G_GAN_Feat: 1.044 G_ID: 0.104 G_Rec: 0.356 D_GP: 0.286 D_real: 0.468 D_fake: 0.536 
+(epoch: 346, iters: 846, time: 0.063) G_GAN: 0.501 G_GAN_Feat: 1.169 G_ID: 0.117 G_Rec: 0.465 D_GP: 0.037 D_real: 0.421 D_fake: 0.506 
+(epoch: 346, iters: 1246, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.772 G_ID: 0.117 G_Rec: 0.342 D_GP: 0.030 D_real: 0.969 D_fake: 0.878 
+(epoch: 346, iters: 1646, time: 0.063) G_GAN: 0.395 G_GAN_Feat: 0.933 G_ID: 0.122 G_Rec: 0.452 D_GP: 0.032 D_real: 1.008 D_fake: 0.614 
+(epoch: 346, iters: 2046, time: 0.063) G_GAN: -0.136 G_GAN_Feat: 0.649 G_ID: 0.115 G_Rec: 0.290 D_GP: 0.030 D_real: 0.815 D_fake: 1.136 
+(epoch: 346, iters: 2446, time: 0.063) G_GAN: 0.569 G_GAN_Feat: 0.990 G_ID: 0.116 G_Rec: 0.483 D_GP: 0.035 D_real: 0.963 D_fake: 0.477 
+(epoch: 346, iters: 2846, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.758 G_ID: 0.104 G_Rec: 0.375 D_GP: 0.027 D_real: 1.022 D_fake: 0.779 
+(epoch: 346, iters: 3246, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.909 G_ID: 0.120 G_Rec: 0.430 D_GP: 0.031 D_real: 1.138 D_fake: 0.570 
+(epoch: 346, iters: 3646, time: 0.063) G_GAN: -0.001 G_GAN_Feat: 0.734 G_ID: 0.147 G_Rec: 0.338 D_GP: 0.048 D_real: 0.836 D_fake: 1.001 
+(epoch: 346, iters: 4046, time: 0.064) G_GAN: 0.431 G_GAN_Feat: 1.103 G_ID: 0.131 G_Rec: 0.480 D_GP: 0.231 D_real: 0.482 D_fake: 0.582 
+(epoch: 346, iters: 4446, time: 0.064) G_GAN: 0.139 G_GAN_Feat: 0.744 G_ID: 0.120 G_Rec: 0.296 D_GP: 0.035 D_real: 1.025 D_fake: 0.870 
+(epoch: 346, iters: 4846, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 1.016 G_ID: 0.107 G_Rec: 0.426 D_GP: 0.042 D_real: 0.911 D_fake: 0.588 
+(epoch: 346, iters: 5246, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.886 G_ID: 0.119 G_Rec: 0.320 D_GP: 0.043 D_real: 0.555 D_fake: 0.941 
+(epoch: 346, iters: 5646, time: 0.063) G_GAN: 0.287 G_GAN_Feat: 1.013 G_ID: 0.110 G_Rec: 0.406 D_GP: 0.044 D_real: 0.614 D_fake: 0.713 
+(epoch: 346, iters: 6046, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.822 G_ID: 0.101 G_Rec: 0.317 D_GP: 0.049 D_real: 1.076 D_fake: 0.645 
+(epoch: 346, iters: 6446, time: 0.063) G_GAN: 0.664 G_GAN_Feat: 0.996 G_ID: 0.127 G_Rec: 0.460 D_GP: 0.031 D_real: 1.309 D_fake: 0.394 
+(epoch: 346, iters: 6846, time: 0.063) G_GAN: 0.270 G_GAN_Feat: 0.732 G_ID: 0.106 G_Rec: 0.292 D_GP: 0.028 D_real: 1.103 D_fake: 0.730 
+(epoch: 346, iters: 7246, time: 0.063) G_GAN: 0.538 G_GAN_Feat: 1.005 G_ID: 0.136 G_Rec: 0.453 D_GP: 0.037 D_real: 1.035 D_fake: 0.483 
+(epoch: 346, iters: 7646, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.793 G_ID: 0.122 G_Rec: 0.317 D_GP: 0.038 D_real: 0.967 D_fake: 0.906 
+(epoch: 346, iters: 8046, time: 0.063) G_GAN: 0.738 G_GAN_Feat: 0.954 G_ID: 0.121 G_Rec: 0.445 D_GP: 0.032 D_real: 1.399 D_fake: 0.350 
+(epoch: 346, iters: 8446, time: 0.063) G_GAN: 0.206 G_GAN_Feat: 0.782 G_ID: 0.116 G_Rec: 0.319 D_GP: 0.027 D_real: 1.058 D_fake: 0.794 
+(epoch: 347, iters: 238, time: 0.063) G_GAN: 0.578 G_GAN_Feat: 1.049 G_ID: 0.111 G_Rec: 0.488 D_GP: 0.032 D_real: 1.070 D_fake: 0.427 
+(epoch: 347, iters: 638, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.787 G_ID: 0.113 G_Rec: 0.316 D_GP: 0.031 D_real: 1.086 D_fake: 0.724 
+(epoch: 347, iters: 1038, time: 0.063) G_GAN: 0.453 G_GAN_Feat: 1.089 G_ID: 0.130 G_Rec: 0.420 D_GP: 0.052 D_real: 0.690 D_fake: 0.551 
+(epoch: 347, iters: 1438, time: 0.063) G_GAN: 0.682 G_GAN_Feat: 0.799 G_ID: 0.091 G_Rec: 0.345 D_GP: 0.035 D_real: 1.443 D_fake: 0.486 
+(epoch: 347, iters: 1838, time: 0.063) G_GAN: 0.334 G_GAN_Feat: 0.865 G_ID: 0.136 G_Rec: 0.420 D_GP: 0.031 D_real: 1.081 D_fake: 0.667 
+(epoch: 347, iters: 2238, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.696 G_ID: 0.108 G_Rec: 0.331 D_GP: 0.028 D_real: 1.086 D_fake: 0.810 
+(epoch: 347, iters: 2638, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.883 G_ID: 0.126 G_Rec: 0.400 D_GP: 0.035 D_real: 1.017 D_fake: 0.596 
+(epoch: 347, iters: 3038, time: 0.063) G_GAN: -0.197 G_GAN_Feat: 0.726 G_ID: 0.122 G_Rec: 0.347 D_GP: 0.039 D_real: 0.632 D_fake: 1.197 
+(epoch: 347, iters: 3438, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.885 G_ID: 0.122 G_Rec: 0.418 D_GP: 0.034 D_real: 1.079 D_fake: 0.592 
+(epoch: 347, iters: 3838, time: 0.064) G_GAN: -0.071 G_GAN_Feat: 0.782 G_ID: 0.111 G_Rec: 0.347 D_GP: 0.053 D_real: 0.609 D_fake: 1.071 
+(epoch: 347, iters: 4238, time: 0.063) G_GAN: 0.473 G_GAN_Feat: 0.984 G_ID: 0.126 G_Rec: 0.491 D_GP: 0.040 D_real: 1.144 D_fake: 0.546 
+(epoch: 347, iters: 4638, time: 0.063) G_GAN: 0.082 G_GAN_Feat: 0.667 G_ID: 0.107 G_Rec: 0.295 D_GP: 0.037 D_real: 0.958 D_fake: 0.918 
+(epoch: 347, iters: 5038, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.948 G_ID: 0.130 G_Rec: 0.403 D_GP: 0.056 D_real: 1.092 D_fake: 0.568 
+(epoch: 347, iters: 5438, time: 0.063) G_GAN: 0.107 G_GAN_Feat: 0.698 G_ID: 0.115 G_Rec: 0.325 D_GP: 0.035 D_real: 0.960 D_fake: 0.893 
+(epoch: 347, iters: 5838, time: 0.063) G_GAN: 0.189 G_GAN_Feat: 0.977 G_ID: 0.138 G_Rec: 0.466 D_GP: 0.082 D_real: 0.604 D_fake: 0.811 
+(epoch: 347, iters: 6238, time: 0.063) G_GAN: 0.014 G_GAN_Feat: 0.834 G_ID: 0.137 G_Rec: 0.343 D_GP: 0.042 D_real: 1.090 D_fake: 0.986 
+(epoch: 347, iters: 6638, time: 0.064) G_GAN: 0.623 G_GAN_Feat: 0.944 G_ID: 0.111 G_Rec: 0.435 D_GP: 0.072 D_real: 1.297 D_fake: 0.395 
+(epoch: 347, iters: 7038, time: 0.063) G_GAN: 0.351 G_GAN_Feat: 0.804 G_ID: 0.101 G_Rec: 0.369 D_GP: 0.075 D_real: 1.011 D_fake: 0.657 
+(epoch: 347, iters: 7438, time: 0.063) G_GAN: 0.546 G_GAN_Feat: 0.994 G_ID: 0.127 G_Rec: 0.454 D_GP: 0.036 D_real: 1.015 D_fake: 0.468 
+(epoch: 347, iters: 7838, time: 0.063) G_GAN: 0.221 G_GAN_Feat: 0.813 G_ID: 0.101 G_Rec: 0.328 D_GP: 0.037 D_real: 0.941 D_fake: 0.781 
+(epoch: 347, iters: 8238, time: 0.064) G_GAN: 0.628 G_GAN_Feat: 0.997 G_ID: 0.126 G_Rec: 0.450 D_GP: 0.033 D_real: 1.245 D_fake: 0.389 
+(epoch: 347, iters: 8638, time: 0.063) G_GAN: 0.077 G_GAN_Feat: 0.878 G_ID: 0.114 G_Rec: 0.350 D_GP: 0.067 D_real: 0.480 D_fake: 0.923 
+(epoch: 348, iters: 430, time: 0.063) G_GAN: 0.409 G_GAN_Feat: 0.967 G_ID: 0.113 G_Rec: 0.444 D_GP: 0.039 D_real: 0.905 D_fake: 0.591 
+(epoch: 348, iters: 830, time: 0.063) G_GAN: 0.387 G_GAN_Feat: 1.042 G_ID: 0.114 G_Rec: 0.362 D_GP: 0.087 D_real: 0.246 D_fake: 0.659 
+(epoch: 348, iters: 1230, time: 0.064) G_GAN: 0.622 G_GAN_Feat: 1.152 G_ID: 0.124 G_Rec: 0.474 D_GP: 0.089 D_real: 0.969 D_fake: 0.422 
+(epoch: 348, iters: 1630, time: 0.063) G_GAN: 0.435 G_GAN_Feat: 0.747 G_ID: 0.101 G_Rec: 0.305 D_GP: 0.044 D_real: 1.238 D_fake: 0.575 
+(epoch: 348, iters: 2030, time: 0.063) G_GAN: 0.451 G_GAN_Feat: 1.107 G_ID: 0.114 G_Rec: 0.471 D_GP: 0.046 D_real: 0.663 D_fake: 0.557 
+(epoch: 348, iters: 2430, time: 0.063) G_GAN: 0.165 G_GAN_Feat: 0.773 G_ID: 0.109 G_Rec: 0.308 D_GP: 0.033 D_real: 0.949 D_fake: 0.836 
+(epoch: 348, iters: 2830, time: 0.064) G_GAN: 0.652 G_GAN_Feat: 1.000 G_ID: 0.144 G_Rec: 0.423 D_GP: 0.034 D_real: 1.255 D_fake: 0.371 
+(epoch: 348, iters: 3230, time: 0.063) G_GAN: 0.078 G_GAN_Feat: 0.665 G_ID: 0.120 G_Rec: 0.287 D_GP: 0.032 D_real: 1.023 D_fake: 0.922 
+(epoch: 348, iters: 3630, time: 0.063) G_GAN: 0.294 G_GAN_Feat: 1.051 G_ID: 0.130 G_Rec: 0.474 D_GP: 0.053 D_real: 0.872 D_fake: 0.706 
+(epoch: 348, iters: 4030, time: 0.063) G_GAN: 0.200 G_GAN_Feat: 0.742 G_ID: 0.117 G_Rec: 0.311 D_GP: 0.029 D_real: 0.897 D_fake: 0.800 
+(epoch: 348, iters: 4430, time: 0.064) G_GAN: 0.682 G_GAN_Feat: 1.080 G_ID: 0.114 G_Rec: 0.477 D_GP: 0.052 D_real: 0.955 D_fake: 0.352 
+(epoch: 348, iters: 4830, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.796 G_ID: 0.103 G_Rec: 0.322 D_GP: 0.051 D_real: 1.133 D_fake: 0.853 
+(epoch: 348, iters: 5230, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 1.031 G_ID: 0.122 G_Rec: 0.434 D_GP: 0.034 D_real: 0.794 D_fake: 0.618 
+(epoch: 348, iters: 5630, time: 0.064) G_GAN: 1.125 G_GAN_Feat: 1.159 G_ID: 0.119 G_Rec: 0.408 D_GP: 0.116 D_real: 1.515 D_fake: 0.335 
+(epoch: 348, iters: 6030, time: 0.064) G_GAN: 0.015 G_GAN_Feat: 0.883 G_ID: 0.137 G_Rec: 0.438 D_GP: 0.028 D_real: 0.751 D_fake: 0.986 
+(epoch: 348, iters: 6430, time: 0.064) G_GAN: -0.104 G_GAN_Feat: 0.712 G_ID: 0.109 G_Rec: 0.313 D_GP: 0.032 D_real: 0.757 D_fake: 1.104 
+(epoch: 348, iters: 6830, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.948 G_ID: 0.125 G_Rec: 0.457 D_GP: 0.041 D_real: 0.940 D_fake: 0.697 
+(epoch: 348, iters: 7230, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.723 G_ID: 0.112 G_Rec: 0.298 D_GP: 0.041 D_real: 0.983 D_fake: 0.879 
+(epoch: 348, iters: 7630, time: 0.064) G_GAN: 1.004 G_GAN_Feat: 1.187 G_ID: 0.123 G_Rec: 0.544 D_GP: 0.085 D_real: 1.040 D_fake: 0.166 
+(epoch: 348, iters: 8030, time: 0.063) G_GAN: 0.400 G_GAN_Feat: 0.778 G_ID: 0.118 G_Rec: 0.339 D_GP: 0.032 D_real: 1.206 D_fake: 0.612 
+(epoch: 348, iters: 8430, time: 0.063) G_GAN: 0.656 G_GAN_Feat: 1.044 G_ID: 0.123 G_Rec: 0.434 D_GP: 0.054 D_real: 1.004 D_fake: 0.359 
+(epoch: 349, iters: 222, time: 0.063) G_GAN: 0.096 G_GAN_Feat: 0.956 G_ID: 0.112 G_Rec: 0.367 D_GP: 0.050 D_real: 0.365 D_fake: 0.905 
+(epoch: 349, iters: 622, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 1.153 G_ID: 0.148 G_Rec: 0.462 D_GP: 0.079 D_real: 0.372 D_fake: 0.650 
+(epoch: 349, iters: 1022, time: 0.063) G_GAN: 0.572 G_GAN_Feat: 0.951 G_ID: 0.095 G_Rec: 0.328 D_GP: 0.780 D_real: 0.589 D_fake: 0.461 
+(epoch: 349, iters: 1422, time: 0.063) G_GAN: 0.590 G_GAN_Feat: 1.076 G_ID: 0.127 G_Rec: 0.441 D_GP: 0.071 D_real: 0.633 D_fake: 0.432 
+(epoch: 349, iters: 1822, time: 0.063) G_GAN: 0.112 G_GAN_Feat: 0.844 G_ID: 0.100 G_Rec: 0.326 D_GP: 0.060 D_real: 0.744 D_fake: 0.889 
+(epoch: 349, iters: 2222, time: 0.064) G_GAN: 0.800 G_GAN_Feat: 1.086 G_ID: 0.120 G_Rec: 0.399 D_GP: 0.042 D_real: 1.006 D_fake: 0.230 
+(epoch: 349, iters: 2622, time: 0.063) G_GAN: 0.568 G_GAN_Feat: 1.078 G_ID: 0.115 G_Rec: 0.385 D_GP: 0.063 D_real: 1.012 D_fake: 0.615 
+(epoch: 349, iters: 3022, time: 0.063) G_GAN: 0.435 G_GAN_Feat: 1.076 G_ID: 0.136 G_Rec: 0.431 D_GP: 0.040 D_real: 0.577 D_fake: 0.568 
+(epoch: 349, iters: 3422, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.975 G_ID: 0.103 G_Rec: 0.337 D_GP: 0.030 D_real: 0.785 D_fake: 0.609 
+(epoch: 349, iters: 3822, time: 0.064) G_GAN: 0.916 G_GAN_Feat: 1.068 G_ID: 0.130 G_Rec: 0.415 D_GP: 0.037 D_real: 1.180 D_fake: 0.166 
+(epoch: 349, iters: 4222, time: 0.063) G_GAN: 0.097 G_GAN_Feat: 1.066 G_ID: 0.117 G_Rec: 0.364 D_GP: 0.325 D_real: 0.259 D_fake: 0.908 
+(epoch: 349, iters: 4622, time: 0.063) G_GAN: 0.694 G_GAN_Feat: 1.063 G_ID: 0.130 G_Rec: 0.599 D_GP: 0.037 D_real: 1.199 D_fake: 0.326 
+(epoch: 349, iters: 5022, time: 0.064) G_GAN: -0.028 G_GAN_Feat: 0.792 G_ID: 0.095 G_Rec: 0.303 D_GP: 0.061 D_real: 0.716 D_fake: 1.029 
+(epoch: 349, iters: 5422, time: 0.064) G_GAN: 0.589 G_GAN_Feat: 0.971 G_ID: 0.125 G_Rec: 0.477 D_GP: 0.041 D_real: 1.082 D_fake: 0.424 
+(epoch: 349, iters: 5822, time: 0.063) G_GAN: 0.536 G_GAN_Feat: 0.745 G_ID: 0.102 G_Rec: 0.367 D_GP: 0.040 D_real: 1.371 D_fake: 0.470 
+(epoch: 349, iters: 6222, time: 0.063) G_GAN: 0.552 G_GAN_Feat: 1.104 G_ID: 0.129 G_Rec: 0.438 D_GP: 0.042 D_real: 0.831 D_fake: 0.457 
+(epoch: 349, iters: 6622, time: 0.064) G_GAN: 0.250 G_GAN_Feat: 0.677 G_ID: 0.121 G_Rec: 0.327 D_GP: 0.027 D_real: 1.175 D_fake: 0.751 
+(epoch: 349, iters: 7022, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.862 G_ID: 0.115 G_Rec: 0.384 D_GP: 0.044 D_real: 1.094 D_fake: 0.633 
+(epoch: 349, iters: 7422, time: 0.063) G_GAN: 0.295 G_GAN_Feat: 0.788 G_ID: 0.127 G_Rec: 0.334 D_GP: 0.080 D_real: 0.967 D_fake: 0.708 
+(epoch: 349, iters: 7822, time: 0.063) G_GAN: 0.467 G_GAN_Feat: 1.029 G_ID: 0.113 G_Rec: 0.450 D_GP: 0.040 D_real: 1.003 D_fake: 0.546 
+(epoch: 349, iters: 8222, time: 0.063) G_GAN: 0.126 G_GAN_Feat: 0.795 G_ID: 0.090 G_Rec: 0.313 D_GP: 0.027 D_real: 1.063 D_fake: 0.874 
+(epoch: 349, iters: 8622, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 1.074 G_ID: 0.146 G_Rec: 0.471 D_GP: 0.094 D_real: 0.657 D_fake: 0.624 
+(epoch: 350, iters: 414, time: 0.063) G_GAN: 0.203 G_GAN_Feat: 0.933 G_ID: 0.111 G_Rec: 0.323 D_GP: 0.059 D_real: 1.159 D_fake: 0.800 
+(epoch: 350, iters: 814, time: 0.063) G_GAN: 0.876 G_GAN_Feat: 1.041 G_ID: 0.139 G_Rec: 0.436 D_GP: 0.037 D_real: 1.475 D_fake: 0.194 
+(epoch: 350, iters: 1214, time: 0.063) G_GAN: 0.002 G_GAN_Feat: 0.795 G_ID: 0.107 G_Rec: 0.310 D_GP: 0.025 D_real: 0.758 D_fake: 0.998 
+(epoch: 350, iters: 1614, time: 0.064) G_GAN: 0.620 G_GAN_Feat: 1.113 G_ID: 0.110 G_Rec: 0.408 D_GP: 0.068 D_real: 0.417 D_fake: 0.391 
+(epoch: 350, iters: 2014, time: 0.063) G_GAN: 0.178 G_GAN_Feat: 0.874 G_ID: 0.097 G_Rec: 0.332 D_GP: 0.028 D_real: 0.941 D_fake: 0.822 
+(epoch: 350, iters: 2414, time: 0.063) G_GAN: 1.007 G_GAN_Feat: 1.268 G_ID: 0.122 G_Rec: 0.507 D_GP: 0.182 D_real: 0.532 D_fake: 0.257 
+(epoch: 350, iters: 2814, time: 0.063) G_GAN: 0.044 G_GAN_Feat: 0.686 G_ID: 0.103 G_Rec: 0.346 D_GP: 0.025 D_real: 0.956 D_fake: 0.956 
+(epoch: 350, iters: 3214, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.794 G_ID: 0.133 G_Rec: 0.382 D_GP: 0.026 D_real: 1.222 D_fake: 0.610 
+(epoch: 350, iters: 3614, time: 0.063) G_GAN: 0.173 G_GAN_Feat: 0.650 G_ID: 0.107 G_Rec: 0.297 D_GP: 0.029 D_real: 1.071 D_fake: 0.828 
+(epoch: 350, iters: 4014, time: 0.063) G_GAN: -0.070 G_GAN_Feat: 0.874 G_ID: 0.177 G_Rec: 0.411 D_GP: 0.031 D_real: 0.646 D_fake: 1.071 
+(epoch: 350, iters: 4414, time: 0.063) G_GAN: 0.109 G_GAN_Feat: 0.678 G_ID: 0.096 G_Rec: 0.310 D_GP: 0.030 D_real: 0.964 D_fake: 0.892 
+(epoch: 350, iters: 4814, time: 0.064) G_GAN: 0.078 G_GAN_Feat: 0.936 G_ID: 0.127 G_Rec: 0.463 D_GP: 0.039 D_real: 0.691 D_fake: 0.923 
+(epoch: 350, iters: 5214, time: 0.063) G_GAN: 0.205 G_GAN_Feat: 0.703 G_ID: 0.101 G_Rec: 0.301 D_GP: 0.036 D_real: 1.060 D_fake: 0.802 
+(epoch: 350, iters: 5614, time: 0.063) G_GAN: 0.383 G_GAN_Feat: 0.966 G_ID: 0.128 G_Rec: 0.461 D_GP: 0.049 D_real: 0.814 D_fake: 0.634 
+(epoch: 350, iters: 6014, time: 0.063) G_GAN: 0.132 G_GAN_Feat: 0.753 G_ID: 0.097 G_Rec: 0.350 D_GP: 0.057 D_real: 0.796 D_fake: 0.868 
+(epoch: 350, iters: 6414, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.908 G_ID: 0.125 G_Rec: 0.412 D_GP: 0.052 D_real: 0.787 D_fake: 0.679 
+(epoch: 350, iters: 6814, time: 0.063) G_GAN: -0.100 G_GAN_Feat: 0.685 G_ID: 0.120 G_Rec: 0.301 D_GP: 0.034 D_real: 0.742 D_fake: 1.100 
+(epoch: 350, iters: 7214, time: 0.063) G_GAN: 0.579 G_GAN_Feat: 1.001 G_ID: 0.138 G_Rec: 0.458 D_GP: 0.082 D_real: 0.844 D_fake: 0.441 
+(epoch: 350, iters: 7614, time: 0.063) G_GAN: 0.053 G_GAN_Feat: 0.734 G_ID: 0.093 G_Rec: 0.343 D_GP: 0.030 D_real: 1.024 D_fake: 0.950 
+(epoch: 350, iters: 8014, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 0.997 G_ID: 0.126 G_Rec: 0.441 D_GP: 0.055 D_real: 0.590 D_fake: 0.734 
+(epoch: 350, iters: 8414, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.793 G_ID: 0.110 G_Rec: 0.356 D_GP: 0.064 D_real: 0.953 D_fake: 0.726 
+(epoch: 351, iters: 206, time: 0.064) G_GAN: 0.674 G_GAN_Feat: 1.022 G_ID: 0.133 G_Rec: 0.437 D_GP: 0.090 D_real: 0.877 D_fake: 0.376 
+(epoch: 351, iters: 606, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.811 G_ID: 0.101 G_Rec: 0.313 D_GP: 0.123 D_real: 0.690 D_fake: 0.814 
+(epoch: 351, iters: 1006, time: 0.064) G_GAN: 0.513 G_GAN_Feat: 1.072 G_ID: 0.134 G_Rec: 0.449 D_GP: 0.062 D_real: 0.933 D_fake: 0.493 
+(epoch: 351, iters: 1406, time: 0.064) G_GAN: 0.524 G_GAN_Feat: 0.703 G_ID: 0.093 G_Rec: 0.272 D_GP: 0.034 D_real: 1.402 D_fake: 0.488 
+(epoch: 351, iters: 1806, time: 0.064) G_GAN: 0.604 G_GAN_Feat: 1.152 G_ID: 0.131 G_Rec: 0.464 D_GP: 0.319 D_real: 0.411 D_fake: 0.439 
+(epoch: 351, iters: 2206, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.853 G_ID: 0.110 G_Rec: 0.322 D_GP: 0.338 D_real: 0.614 D_fake: 0.644 
+(epoch: 351, iters: 2606, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 1.051 G_ID: 0.120 G_Rec: 0.431 D_GP: 0.066 D_real: 0.847 D_fake: 0.509 
+(epoch: 351, iters: 3006, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 0.923 G_ID: 0.116 G_Rec: 0.352 D_GP: 0.103 D_real: 0.514 D_fake: 0.609 
+(epoch: 351, iters: 3406, time: 0.063) G_GAN: 0.568 G_GAN_Feat: 0.983 G_ID: 0.129 G_Rec: 0.456 D_GP: 0.042 D_real: 0.979 D_fake: 0.443 
+(epoch: 351, iters: 3806, time: 0.063) G_GAN: 0.353 G_GAN_Feat: 0.889 G_ID: 0.106 G_Rec: 0.305 D_GP: 0.049 D_real: 0.624 D_fake: 0.649 
+(epoch: 351, iters: 4206, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 1.053 G_ID: 0.137 G_Rec: 0.404 D_GP: 0.044 D_real: 0.564 D_fake: 0.763 
+(epoch: 351, iters: 4606, time: 0.064) G_GAN: 0.484 G_GAN_Feat: 0.749 G_ID: 0.102 G_Rec: 0.319 D_GP: 0.041 D_real: 1.449 D_fake: 0.523 
+(epoch: 351, iters: 5006, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 1.006 G_ID: 0.143 G_Rec: 0.434 D_GP: 0.080 D_real: 0.634 D_fake: 0.561 
+(epoch: 351, iters: 5406, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.987 G_ID: 0.114 G_Rec: 0.358 D_GP: 0.356 D_real: 0.402 D_fake: 0.651 
+(epoch: 351, iters: 5806, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 0.974 G_ID: 0.115 G_Rec: 0.461 D_GP: 0.032 D_real: 0.848 D_fake: 0.807 
+(epoch: 351, iters: 6206, time: 0.064) G_GAN: 0.046 G_GAN_Feat: 0.652 G_ID: 0.106 G_Rec: 0.313 D_GP: 0.032 D_real: 0.879 D_fake: 0.954 
+(epoch: 351, iters: 6606, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.983 G_ID: 0.126 G_Rec: 0.466 D_GP: 0.048 D_real: 1.015 D_fake: 0.598 
+(epoch: 351, iters: 7006, time: 0.063) G_GAN: 0.213 G_GAN_Feat: 0.791 G_ID: 0.100 G_Rec: 0.348 D_GP: 0.080 D_real: 0.799 D_fake: 0.787 
+(epoch: 351, iters: 7406, time: 0.063) G_GAN: 0.357 G_GAN_Feat: 0.950 G_ID: 0.131 G_Rec: 0.436 D_GP: 0.048 D_real: 0.914 D_fake: 0.660 
+(epoch: 351, iters: 7806, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.872 G_ID: 0.108 G_Rec: 0.363 D_GP: 0.063 D_real: 0.709 D_fake: 0.833 
+(epoch: 351, iters: 8206, time: 0.063) G_GAN: 0.594 G_GAN_Feat: 1.010 G_ID: 0.120 G_Rec: 0.490 D_GP: 0.048 D_real: 1.162 D_fake: 0.436 
+(epoch: 351, iters: 8606, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.739 G_ID: 0.104 G_Rec: 0.331 D_GP: 0.043 D_real: 0.863 D_fake: 0.871 
+(epoch: 352, iters: 398, time: 0.063) G_GAN: 0.590 G_GAN_Feat: 0.852 G_ID: 0.113 G_Rec: 0.378 D_GP: 0.031 D_real: 1.353 D_fake: 0.442 
+(epoch: 352, iters: 798, time: 0.063) G_GAN: 0.011 G_GAN_Feat: 0.805 G_ID: 0.097 G_Rec: 0.338 D_GP: 0.037 D_real: 0.797 D_fake: 0.989 
+(epoch: 352, iters: 1198, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 0.992 G_ID: 0.132 G_Rec: 0.453 D_GP: 0.045 D_real: 0.720 D_fake: 0.641 
+(epoch: 352, iters: 1598, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.850 G_ID: 0.105 G_Rec: 0.319 D_GP: 0.161 D_real: 0.482 D_fake: 0.783 
+(epoch: 352, iters: 1998, time: 0.063) G_GAN: 0.700 G_GAN_Feat: 1.200 G_ID: 0.112 G_Rec: 0.445 D_GP: 0.089 D_real: 0.561 D_fake: 0.329 
+(epoch: 352, iters: 2398, time: 0.063) G_GAN: 0.284 G_GAN_Feat: 0.721 G_ID: 0.092 G_Rec: 0.283 D_GP: 0.031 D_real: 1.123 D_fake: 0.716 
+(epoch: 352, iters: 2798, time: 0.063) G_GAN: 0.577 G_GAN_Feat: 0.984 G_ID: 0.121 G_Rec: 0.423 D_GP: 0.036 D_real: 1.187 D_fake: 0.430 
+(epoch: 352, iters: 3198, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 0.928 G_ID: 0.105 G_Rec: 0.331 D_GP: 0.044 D_real: 1.014 D_fake: 0.489 
+(epoch: 352, iters: 3598, time: 0.064) G_GAN: 0.583 G_GAN_Feat: 1.057 G_ID: 0.112 G_Rec: 0.426 D_GP: 0.040 D_real: 1.040 D_fake: 0.435 
+(epoch: 352, iters: 3998, time: 0.064) G_GAN: -0.089 G_GAN_Feat: 0.886 G_ID: 0.102 G_Rec: 0.373 D_GP: 0.156 D_real: 0.558 D_fake: 1.090 
+(epoch: 352, iters: 4398, time: 0.064) G_GAN: 0.268 G_GAN_Feat: 0.944 G_ID: 0.120 G_Rec: 0.424 D_GP: 0.032 D_real: 0.911 D_fake: 0.733 
+(epoch: 352, iters: 4798, time: 0.064) G_GAN: -0.094 G_GAN_Feat: 0.710 G_ID: 0.115 G_Rec: 0.299 D_GP: 0.032 D_real: 0.875 D_fake: 1.094 
+(epoch: 352, iters: 5198, time: 0.064) G_GAN: 0.549 G_GAN_Feat: 1.031 G_ID: 0.126 G_Rec: 0.420 D_GP: 0.044 D_real: 0.980 D_fake: 0.461 
+(epoch: 352, iters: 5598, time: 0.064) G_GAN: 0.384 G_GAN_Feat: 0.902 G_ID: 0.119 G_Rec: 0.319 D_GP: 0.037 D_real: 0.874 D_fake: 0.629 
+(epoch: 352, iters: 5998, time: 0.064) G_GAN: 0.673 G_GAN_Feat: 1.119 G_ID: 0.113 G_Rec: 0.429 D_GP: 0.064 D_real: 0.576 D_fake: 0.352 
+(epoch: 352, iters: 6398, time: 0.064) G_GAN: 0.040 G_GAN_Feat: 0.681 G_ID: 0.103 G_Rec: 0.299 D_GP: 0.027 D_real: 0.967 D_fake: 0.960 
+(epoch: 352, iters: 6798, time: 0.064) G_GAN: 0.661 G_GAN_Feat: 1.068 G_ID: 0.112 G_Rec: 0.454 D_GP: 0.039 D_real: 1.037 D_fake: 0.368 
+(epoch: 352, iters: 7198, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.828 G_ID: 0.122 G_Rec: 0.320 D_GP: 0.036 D_real: 0.806 D_fake: 0.897 
+(epoch: 352, iters: 7598, time: 0.064) G_GAN: 0.588 G_GAN_Feat: 1.122 G_ID: 0.128 G_Rec: 0.467 D_GP: 0.053 D_real: 0.980 D_fake: 0.449 
+(epoch: 352, iters: 7998, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.899 G_ID: 0.129 G_Rec: 0.327 D_GP: 0.322 D_real: 0.703 D_fake: 0.838 
+(epoch: 352, iters: 8398, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.954 G_ID: 0.125 G_Rec: 0.393 D_GP: 0.053 D_real: 1.097 D_fake: 0.498 
+(epoch: 353, iters: 190, time: 0.064) G_GAN: 0.053 G_GAN_Feat: 0.761 G_ID: 0.123 G_Rec: 0.327 D_GP: 0.028 D_real: 0.987 D_fake: 0.947 
+(epoch: 353, iters: 590, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.948 G_ID: 0.121 G_Rec: 0.437 D_GP: 0.035 D_real: 1.053 D_fake: 0.565 
+(epoch: 353, iters: 990, time: 0.064) G_GAN: 0.380 G_GAN_Feat: 0.761 G_ID: 0.097 G_Rec: 0.307 D_GP: 0.032 D_real: 1.239 D_fake: 0.621 
+(epoch: 353, iters: 1390, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.877 G_ID: 0.130 G_Rec: 0.440 D_GP: 0.032 D_real: 1.200 D_fake: 0.628 
+(epoch: 353, iters: 1790, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.716 G_ID: 0.104 G_Rec: 0.295 D_GP: 0.031 D_real: 1.149 D_fake: 0.766 
+(epoch: 353, iters: 2190, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.894 G_ID: 0.138 G_Rec: 0.408 D_GP: 0.035 D_real: 0.967 D_fake: 0.552 
+(epoch: 353, iters: 2590, time: 0.064) G_GAN: 0.081 G_GAN_Feat: 0.754 G_ID: 0.114 G_Rec: 0.372 D_GP: 0.030 D_real: 0.968 D_fake: 0.919 
+(epoch: 353, iters: 2990, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 1.039 G_ID: 0.130 G_Rec: 0.439 D_GP: 0.044 D_real: 0.722 D_fake: 0.636 
+(epoch: 353, iters: 3390, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.970 G_ID: 0.100 G_Rec: 0.345 D_GP: 0.185 D_real: 0.327 D_fake: 0.692 
+(epoch: 353, iters: 3790, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 1.251 G_ID: 0.146 G_Rec: 0.461 D_GP: 0.065 D_real: 0.202 D_fake: 0.691 
+(epoch: 353, iters: 4190, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.741 G_ID: 0.084 G_Rec: 0.341 D_GP: 0.030 D_real: 1.238 D_fake: 0.736 
+(epoch: 353, iters: 4590, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.884 G_ID: 0.134 G_Rec: 0.440 D_GP: 0.031 D_real: 1.074 D_fake: 0.664 
+(epoch: 353, iters: 4990, time: 0.064) G_GAN: 0.014 G_GAN_Feat: 0.656 G_ID: 0.109 G_Rec: 0.303 D_GP: 0.029 D_real: 0.903 D_fake: 0.987 
+(epoch: 353, iters: 5390, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.840 G_ID: 0.119 G_Rec: 0.388 D_GP: 0.034 D_real: 0.958 D_fake: 0.663 
+(epoch: 353, iters: 5790, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.565 G_ID: 0.081 G_Rec: 0.269 D_GP: 0.028 D_real: 1.081 D_fake: 0.908 
+(epoch: 353, iters: 6190, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.801 G_ID: 0.132 G_Rec: 0.380 D_GP: 0.035 D_real: 1.142 D_fake: 0.596 
+(epoch: 353, iters: 6590, time: 0.064) G_GAN: -0.069 G_GAN_Feat: 0.719 G_ID: 0.099 G_Rec: 0.366 D_GP: 0.045 D_real: 0.805 D_fake: 1.069 
+(epoch: 353, iters: 6990, time: 0.064) G_GAN: -0.044 G_GAN_Feat: 0.840 G_ID: 0.129 G_Rec: 0.405 D_GP: 0.039 D_real: 0.591 D_fake: 1.044 
+(epoch: 353, iters: 7390, time: 0.064) G_GAN: -0.122 G_GAN_Feat: 0.687 G_ID: 0.110 G_Rec: 0.321 D_GP: 0.039 D_real: 0.709 D_fake: 1.122 
+(epoch: 353, iters: 7790, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 0.943 G_ID: 0.121 G_Rec: 0.493 D_GP: 0.042 D_real: 1.152 D_fake: 0.546 
+(epoch: 353, iters: 8190, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.677 G_ID: 0.106 G_Rec: 0.314 D_GP: 0.048 D_real: 0.854 D_fake: 0.907 
+(epoch: 353, iters: 8590, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.896 G_ID: 0.130 G_Rec: 0.415 D_GP: 0.060 D_real: 0.749 D_fake: 0.845 
+(epoch: 354, iters: 382, time: 0.064) G_GAN: -0.078 G_GAN_Feat: 0.752 G_ID: 0.123 G_Rec: 0.339 D_GP: 0.052 D_real: 0.759 D_fake: 1.079 
+(epoch: 354, iters: 782, time: 0.064) G_GAN: -0.084 G_GAN_Feat: 1.019 G_ID: 0.131 G_Rec: 0.483 D_GP: 0.052 D_real: 0.392 D_fake: 1.084 
+(epoch: 354, iters: 1182, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.687 G_ID: 0.086 G_Rec: 0.314 D_GP: 0.074 D_real: 0.805 D_fake: 0.907 
+(epoch: 354, iters: 1582, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.878 G_ID: 0.131 G_Rec: 0.403 D_GP: 0.040 D_real: 1.055 D_fake: 0.581 
+(epoch: 354, iters: 1982, time: 0.064) G_GAN: 0.209 G_GAN_Feat: 0.679 G_ID: 0.099 G_Rec: 0.286 D_GP: 0.045 D_real: 1.021 D_fake: 0.794 
+(epoch: 354, iters: 2382, time: 0.064) G_GAN: 0.201 G_GAN_Feat: 0.916 G_ID: 0.131 G_Rec: 0.425 D_GP: 0.064 D_real: 0.724 D_fake: 0.799 
+(epoch: 354, iters: 2782, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.785 G_ID: 0.102 G_Rec: 0.343 D_GP: 0.050 D_real: 0.763 D_fake: 0.923 
+(epoch: 354, iters: 3182, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.901 G_ID: 0.118 G_Rec: 0.440 D_GP: 0.042 D_real: 0.914 D_fake: 0.715 
+(epoch: 354, iters: 3582, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.812 G_ID: 0.109 G_Rec: 0.333 D_GP: 0.061 D_real: 0.823 D_fake: 0.841 
+(epoch: 354, iters: 3982, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 0.955 G_ID: 0.129 G_Rec: 0.426 D_GP: 0.047 D_real: 0.730 D_fake: 0.943 
+(epoch: 354, iters: 4382, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.869 G_ID: 0.102 G_Rec: 0.341 D_GP: 0.089 D_real: 1.064 D_fake: 0.720 
+(epoch: 354, iters: 4782, time: 0.064) G_GAN: 0.499 G_GAN_Feat: 1.049 G_ID: 0.127 G_Rec: 0.462 D_GP: 0.427 D_real: 0.629 D_fake: 0.511 
+(epoch: 354, iters: 5182, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.888 G_ID: 0.101 G_Rec: 0.351 D_GP: 0.087 D_real: 0.514 D_fake: 0.879 
+(epoch: 354, iters: 5582, time: 0.064) G_GAN: 0.627 G_GAN_Feat: 0.963 G_ID: 0.115 G_Rec: 0.432 D_GP: 0.066 D_real: 1.143 D_fake: 0.395 
+(epoch: 354, iters: 5982, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.824 G_ID: 0.113 G_Rec: 0.326 D_GP: 0.095 D_real: 0.520 D_fake: 0.919 
+(epoch: 354, iters: 6382, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.904 G_ID: 0.113 G_Rec: 0.425 D_GP: 0.041 D_real: 1.068 D_fake: 0.601 
+(epoch: 354, iters: 6782, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.758 G_ID: 0.104 G_Rec: 0.319 D_GP: 0.045 D_real: 0.910 D_fake: 0.893 
+(epoch: 354, iters: 7182, time: 0.064) G_GAN: 0.733 G_GAN_Feat: 0.912 G_ID: 0.118 G_Rec: 0.433 D_GP: 0.032 D_real: 1.412 D_fake: 0.318 
+(epoch: 354, iters: 7582, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.801 G_ID: 0.109 G_Rec: 0.402 D_GP: 0.041 D_real: 0.934 D_fake: 0.644 
+(epoch: 354, iters: 7982, time: 0.063) G_GAN: 0.196 G_GAN_Feat: 1.052 G_ID: 0.140 G_Rec: 0.454 D_GP: 0.157 D_real: 0.269 D_fake: 0.814 
+(epoch: 354, iters: 8382, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.855 G_ID: 0.107 G_Rec: 0.318 D_GP: 0.064 D_real: 0.526 D_fake: 0.862 
+(epoch: 355, iters: 174, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 1.020 G_ID: 0.115 G_Rec: 0.431 D_GP: 0.040 D_real: 0.749 D_fake: 0.547 
+(epoch: 355, iters: 574, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.894 G_ID: 0.115 G_Rec: 0.364 D_GP: 0.083 D_real: 0.536 D_fake: 0.882 
+(epoch: 355, iters: 974, time: 0.064) G_GAN: 0.604 G_GAN_Feat: 0.922 G_ID: 0.114 G_Rec: 0.399 D_GP: 0.035 D_real: 1.320 D_fake: 0.464 
+(epoch: 355, iters: 1374, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.777 G_ID: 0.104 G_Rec: 0.349 D_GP: 0.033 D_real: 0.984 D_fake: 0.765 
+(epoch: 355, iters: 1774, time: 0.064) G_GAN: 0.476 G_GAN_Feat: 0.936 G_ID: 0.115 G_Rec: 0.420 D_GP: 0.037 D_real: 1.120 D_fake: 0.525 
+(epoch: 355, iters: 2174, time: 0.063) G_GAN: 0.548 G_GAN_Feat: 0.835 G_ID: 0.092 G_Rec: 0.326 D_GP: 0.044 D_real: 1.094 D_fake: 0.481 
+(epoch: 355, iters: 2574, time: 0.064) G_GAN: 0.627 G_GAN_Feat: 0.888 G_ID: 0.118 G_Rec: 0.417 D_GP: 0.027 D_real: 1.321 D_fake: 0.387 
+(epoch: 355, iters: 2974, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.729 G_ID: 0.093 G_Rec: 0.287 D_GP: 0.038 D_real: 0.968 D_fake: 0.866 
+(epoch: 355, iters: 3374, time: 0.064) G_GAN: 0.782 G_GAN_Feat: 0.875 G_ID: 0.111 G_Rec: 0.372 D_GP: 0.046 D_real: 1.342 D_fake: 0.286 
+(epoch: 355, iters: 3774, time: 0.064) G_GAN: 0.702 G_GAN_Feat: 0.899 G_ID: 0.090 G_Rec: 0.304 D_GP: 0.039 D_real: 1.479 D_fake: 0.356 
+(epoch: 355, iters: 4174, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 1.080 G_ID: 0.117 G_Rec: 0.487 D_GP: 0.047 D_real: 0.728 D_fake: 0.705 
+(epoch: 355, iters: 4574, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.823 G_ID: 0.096 G_Rec: 0.340 D_GP: 0.049 D_real: 0.924 D_fake: 0.704 
+(epoch: 355, iters: 4974, time: 0.063) G_GAN: 0.400 G_GAN_Feat: 0.919 G_ID: 0.142 G_Rec: 0.444 D_GP: 0.034 D_real: 1.013 D_fake: 0.600 
+(epoch: 355, iters: 5374, time: 0.063) G_GAN: 0.327 G_GAN_Feat: 0.751 G_ID: 0.084 G_Rec: 0.359 D_GP: 0.029 D_real: 1.102 D_fake: 0.673 
+(epoch: 355, iters: 5774, time: 0.064) G_GAN: 1.076 G_GAN_Feat: 1.116 G_ID: 0.107 G_Rec: 0.439 D_GP: 0.064 D_real: 1.194 D_fake: 0.163 
+(epoch: 355, iters: 6174, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 0.803 G_ID: 0.120 G_Rec: 0.341 D_GP: 0.032 D_real: 1.384 D_fake: 0.446 
+(epoch: 355, iters: 6574, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 1.060 G_ID: 0.130 G_Rec: 0.497 D_GP: 0.042 D_real: 0.945 D_fake: 0.525 
+(epoch: 355, iters: 6974, time: 0.064) G_GAN: 0.520 G_GAN_Feat: 0.895 G_ID: 0.108 G_Rec: 0.354 D_GP: 0.061 D_real: 1.020 D_fake: 0.544 
+(epoch: 355, iters: 7374, time: 0.064) G_GAN: 0.686 G_GAN_Feat: 1.369 G_ID: 0.125 G_Rec: 0.501 D_GP: 0.126 D_real: 1.111 D_fake: 0.403 
+(epoch: 355, iters: 7774, time: 0.064) G_GAN: -0.080 G_GAN_Feat: 0.691 G_ID: 0.128 G_Rec: 0.331 D_GP: 0.027 D_real: 0.827 D_fake: 1.080 
+(epoch: 355, iters: 8174, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 1.034 G_ID: 0.145 G_Rec: 0.486 D_GP: 0.037 D_real: 0.767 D_fake: 0.778 
+(epoch: 355, iters: 8574, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.724 G_ID: 0.101 G_Rec: 0.349 D_GP: 0.064 D_real: 0.955 D_fake: 0.808 
+(epoch: 356, iters: 366, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.930 G_ID: 0.141 G_Rec: 0.465 D_GP: 0.043 D_real: 0.840 D_fake: 0.670 
+(epoch: 356, iters: 766, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.775 G_ID: 0.127 G_Rec: 0.317 D_GP: 0.033 D_real: 1.151 D_fake: 0.756 
+(epoch: 356, iters: 1166, time: 0.064) G_GAN: 0.595 G_GAN_Feat: 1.021 G_ID: 0.124 G_Rec: 0.406 D_GP: 0.073 D_real: 0.691 D_fake: 0.421 
+(epoch: 356, iters: 1566, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.910 G_ID: 0.117 G_Rec: 0.318 D_GP: 0.057 D_real: 0.500 D_fake: 0.564 
+(epoch: 356, iters: 1966, time: 0.064) G_GAN: 0.751 G_GAN_Feat: 0.939 G_ID: 0.124 G_Rec: 0.432 D_GP: 0.033 D_real: 1.369 D_fake: 0.316 
+(epoch: 356, iters: 2366, time: 0.064) G_GAN: 0.006 G_GAN_Feat: 0.723 G_ID: 0.099 G_Rec: 0.316 D_GP: 0.029 D_real: 0.997 D_fake: 0.994 
+(epoch: 356, iters: 2766, time: 0.064) G_GAN: 0.496 G_GAN_Feat: 0.977 G_ID: 0.130 G_Rec: 0.449 D_GP: 0.037 D_real: 0.990 D_fake: 0.546 
+(epoch: 356, iters: 3166, time: 0.064) G_GAN: 0.027 G_GAN_Feat: 0.853 G_ID: 0.099 G_Rec: 0.360 D_GP: 0.077 D_real: 0.673 D_fake: 0.975 
+(epoch: 356, iters: 3566, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.975 G_ID: 0.119 G_Rec: 0.433 D_GP: 0.056 D_real: 0.682 D_fake: 0.754 
+(epoch: 356, iters: 3966, time: 0.064) G_GAN: 0.147 G_GAN_Feat: 0.693 G_ID: 0.112 G_Rec: 0.293 D_GP: 0.039 D_real: 1.032 D_fake: 0.853 
+(epoch: 356, iters: 4366, time: 0.064) G_GAN: 0.636 G_GAN_Feat: 1.074 G_ID: 0.135 G_Rec: 0.500 D_GP: 0.071 D_real: 0.858 D_fake: 0.389 
+(epoch: 356, iters: 4766, time: 0.064) G_GAN: 0.567 G_GAN_Feat: 0.973 G_ID: 0.125 G_Rec: 0.340 D_GP: 0.628 D_real: 0.482 D_fake: 0.486 
+(epoch: 356, iters: 5166, time: 0.064) G_GAN: 0.874 G_GAN_Feat: 1.138 G_ID: 0.118 G_Rec: 0.448 D_GP: 0.137 D_real: 0.637 D_fake: 0.242 
+(epoch: 356, iters: 5566, time: 0.064) G_GAN: -0.077 G_GAN_Feat: 0.819 G_ID: 0.117 G_Rec: 0.314 D_GP: 0.039 D_real: 0.682 D_fake: 1.077 
+(epoch: 356, iters: 5966, time: 0.064) G_GAN: 0.430 G_GAN_Feat: 0.986 G_ID: 0.151 G_Rec: 0.440 D_GP: 0.034 D_real: 1.162 D_fake: 0.575 
+(epoch: 356, iters: 6366, time: 0.064) G_GAN: -0.030 G_GAN_Feat: 0.676 G_ID: 0.135 G_Rec: 0.299 D_GP: 0.030 D_real: 0.837 D_fake: 1.030 
+(epoch: 356, iters: 6766, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.931 G_ID: 0.133 G_Rec: 0.426 D_GP: 0.033 D_real: 0.930 D_fake: 0.711 
+(epoch: 356, iters: 7166, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.771 G_ID: 0.091 G_Rec: 0.328 D_GP: 0.129 D_real: 0.941 D_fake: 0.742 
+(epoch: 356, iters: 7566, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.997 G_ID: 0.143 G_Rec: 0.449 D_GP: 0.267 D_real: 0.515 D_fake: 0.760 
+(epoch: 356, iters: 7966, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.782 G_ID: 0.095 G_Rec: 0.346 D_GP: 0.030 D_real: 0.940 D_fake: 0.793 
+(epoch: 356, iters: 8366, time: 0.064) G_GAN: 0.654 G_GAN_Feat: 0.956 G_ID: 0.124 G_Rec: 0.412 D_GP: 0.039 D_real: 1.110 D_fake: 0.365 
+(epoch: 357, iters: 158, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.669 G_ID: 0.104 G_Rec: 0.303 D_GP: 0.026 D_real: 1.118 D_fake: 0.874 
+(epoch: 357, iters: 558, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 0.891 G_ID: 0.138 G_Rec: 0.440 D_GP: 0.036 D_real: 0.957 D_fake: 0.760 
+(epoch: 357, iters: 958, time: 0.064) G_GAN: -0.155 G_GAN_Feat: 0.710 G_ID: 0.111 G_Rec: 0.310 D_GP: 0.049 D_real: 0.617 D_fake: 1.155 
+(epoch: 357, iters: 1358, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.967 G_ID: 0.131 G_Rec: 0.449 D_GP: 0.053 D_real: 0.527 D_fake: 0.922 
+(epoch: 357, iters: 1758, time: 0.064) G_GAN: -0.001 G_GAN_Feat: 0.799 G_ID: 0.107 G_Rec: 0.331 D_GP: 0.046 D_real: 0.721 D_fake: 1.001 
+(epoch: 357, iters: 2158, time: 0.064) G_GAN: 0.797 G_GAN_Feat: 0.924 G_ID: 0.111 G_Rec: 0.426 D_GP: 0.035 D_real: 1.349 D_fake: 0.349 
+(epoch: 357, iters: 2558, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.819 G_ID: 0.098 G_Rec: 0.332 D_GP: 0.067 D_real: 1.027 D_fake: 0.741 
+(epoch: 357, iters: 2958, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 1.086 G_ID: 0.113 G_Rec: 0.450 D_GP: 0.178 D_real: 0.455 D_fake: 0.681 
+(epoch: 357, iters: 3358, time: 0.064) G_GAN: -0.191 G_GAN_Feat: 0.898 G_ID: 0.112 G_Rec: 0.335 D_GP: 0.292 D_real: 0.262 D_fake: 1.191 
+(epoch: 357, iters: 3758, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.995 G_ID: 0.142 G_Rec: 0.471 D_GP: 0.033 D_real: 0.798 D_fake: 0.725 
+(epoch: 357, iters: 4158, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.804 G_ID: 0.095 G_Rec: 0.372 D_GP: 0.083 D_real: 0.661 D_fake: 0.915 
+(epoch: 357, iters: 4558, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.984 G_ID: 0.134 G_Rec: 0.388 D_GP: 0.044 D_real: 0.735 D_fake: 0.889 
+(epoch: 357, iters: 4958, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.973 G_ID: 0.100 G_Rec: 0.359 D_GP: 0.098 D_real: 0.627 D_fake: 0.560 
+(epoch: 357, iters: 5358, time: 0.064) G_GAN: 0.657 G_GAN_Feat: 0.933 G_ID: 0.121 G_Rec: 0.380 D_GP: 0.036 D_real: 1.303 D_fake: 0.371 
+(epoch: 357, iters: 5758, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.819 G_ID: 0.131 G_Rec: 0.328 D_GP: 0.051 D_real: 1.174 D_fake: 0.632 
+(epoch: 357, iters: 6158, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.950 G_ID: 0.114 G_Rec: 0.456 D_GP: 0.034 D_real: 1.010 D_fake: 0.555 
+(epoch: 357, iters: 6558, time: 0.063) G_GAN: 0.419 G_GAN_Feat: 0.753 G_ID: 0.093 G_Rec: 0.311 D_GP: 0.033 D_real: 1.235 D_fake: 0.589 
+(epoch: 357, iters: 6958, time: 0.063) G_GAN: 0.365 G_GAN_Feat: 0.952 G_ID: 0.103 G_Rec: 0.430 D_GP: 0.035 D_real: 1.049 D_fake: 0.636 
+(epoch: 357, iters: 7358, time: 0.063) G_GAN: 0.137 G_GAN_Feat: 0.842 G_ID: 0.089 G_Rec: 0.303 D_GP: 0.028 D_real: 0.846 D_fake: 0.863 
+(epoch: 357, iters: 7758, time: 0.064) G_GAN: 0.694 G_GAN_Feat: 0.974 G_ID: 0.114 G_Rec: 0.474 D_GP: 0.030 D_real: 1.328 D_fake: 0.344 
+(epoch: 357, iters: 8158, time: 0.063) G_GAN: 0.130 G_GAN_Feat: 0.815 G_ID: 0.093 G_Rec: 0.347 D_GP: 0.049 D_real: 0.842 D_fake: 0.872 
+(epoch: 357, iters: 8558, time: 0.063) G_GAN: 0.442 G_GAN_Feat: 1.233 G_ID: 0.125 G_Rec: 0.512 D_GP: 0.220 D_real: 0.406 D_fake: 0.570 
+(epoch: 358, iters: 350, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.826 G_ID: 0.094 G_Rec: 0.304 D_GP: 0.045 D_real: 0.951 D_fake: 0.638 
+(epoch: 358, iters: 750, time: 0.064) G_GAN: 0.651 G_GAN_Feat: 1.036 G_ID: 0.137 G_Rec: 0.399 D_GP: 0.045 D_real: 0.954 D_fake: 0.377 
+(epoch: 358, iters: 1150, time: 0.064) G_GAN: 0.099 G_GAN_Feat: 0.837 G_ID: 0.091 G_Rec: 0.332 D_GP: 0.040 D_real: 0.849 D_fake: 0.901 
+(epoch: 358, iters: 1550, time: 0.063) G_GAN: 0.453 G_GAN_Feat: 0.894 G_ID: 0.148 G_Rec: 0.437 D_GP: 0.030 D_real: 1.066 D_fake: 0.551 
+(epoch: 358, iters: 1950, time: 0.063) G_GAN: 0.186 G_GAN_Feat: 0.902 G_ID: 0.109 G_Rec: 0.321 D_GP: 0.099 D_real: 0.385 D_fake: 0.830 
+(epoch: 358, iters: 2350, time: 0.064) G_GAN: -0.043 G_GAN_Feat: 1.263 G_ID: 0.139 G_Rec: 0.490 D_GP: 0.124 D_real: 0.712 D_fake: 1.044 
+(epoch: 358, iters: 2750, time: 0.063) G_GAN: 0.125 G_GAN_Feat: 0.720 G_ID: 0.140 G_Rec: 0.307 D_GP: 0.030 D_real: 0.956 D_fake: 0.875 
+(epoch: 358, iters: 3150, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.933 G_ID: 0.149 G_Rec: 0.440 D_GP: 0.030 D_real: 1.101 D_fake: 0.620 
+(epoch: 358, iters: 3550, time: 0.063) G_GAN: 0.285 G_GAN_Feat: 0.835 G_ID: 0.103 G_Rec: 0.340 D_GP: 0.052 D_real: 0.777 D_fake: 0.723 
+(epoch: 358, iters: 3950, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.890 G_ID: 0.124 G_Rec: 0.384 D_GP: 0.039 D_real: 1.159 D_fake: 0.542 
+(epoch: 358, iters: 4350, time: 0.063) G_GAN: 0.437 G_GAN_Feat: 0.636 G_ID: 0.091 G_Rec: 0.270 D_GP: 0.029 D_real: 1.364 D_fake: 0.564 
+(epoch: 358, iters: 4750, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.946 G_ID: 0.131 G_Rec: 0.423 D_GP: 0.035 D_real: 0.938 D_fake: 0.533 
+(epoch: 358, iters: 5150, time: 0.063) G_GAN: 0.074 G_GAN_Feat: 0.768 G_ID: 0.092 G_Rec: 0.319 D_GP: 0.050 D_real: 0.695 D_fake: 0.926 
+(epoch: 358, iters: 5550, time: 0.064) G_GAN: 0.600 G_GAN_Feat: 1.044 G_ID: 0.124 G_Rec: 0.473 D_GP: 0.064 D_real: 1.018 D_fake: 0.460 
+(epoch: 358, iters: 5950, time: 0.063) G_GAN: 0.438 G_GAN_Feat: 0.775 G_ID: 0.102 G_Rec: 0.340 D_GP: 0.055 D_real: 1.218 D_fake: 0.568 
+(epoch: 358, iters: 6350, time: 0.063) G_GAN: 0.585 G_GAN_Feat: 1.227 G_ID: 0.122 G_Rec: 0.446 D_GP: 0.178 D_real: 0.519 D_fake: 0.437 
+(epoch: 358, iters: 6750, time: 0.063) G_GAN: 0.333 G_GAN_Feat: 0.869 G_ID: 0.114 G_Rec: 0.330 D_GP: 0.043 D_real: 0.894 D_fake: 0.668 
+(epoch: 358, iters: 7150, time: 0.064) G_GAN: 0.841 G_GAN_Feat: 1.128 G_ID: 0.130 G_Rec: 0.405 D_GP: 0.412 D_real: 0.743 D_fake: 0.202 
+(epoch: 358, iters: 7550, time: 0.063) G_GAN: 0.307 G_GAN_Feat: 0.800 G_ID: 0.102 G_Rec: 0.332 D_GP: 0.034 D_real: 1.208 D_fake: 0.700 
+(epoch: 358, iters: 7950, time: 0.063) G_GAN: 0.435 G_GAN_Feat: 1.150 G_ID: 0.131 G_Rec: 0.455 D_GP: 0.165 D_real: 0.302 D_fake: 0.575 
+(epoch: 358, iters: 8350, time: 0.063) G_GAN: 0.039 G_GAN_Feat: 0.663 G_ID: 0.102 G_Rec: 0.321 D_GP: 0.028 D_real: 0.952 D_fake: 0.961 
+(epoch: 359, iters: 142, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.947 G_ID: 0.116 G_Rec: 0.436 D_GP: 0.035 D_real: 1.080 D_fake: 0.654 
+(epoch: 359, iters: 542, time: 0.063) G_GAN: 0.088 G_GAN_Feat: 0.643 G_ID: 0.092 G_Rec: 0.314 D_GP: 0.028 D_real: 0.993 D_fake: 0.912 
+(epoch: 359, iters: 942, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.850 G_ID: 0.123 G_Rec: 0.386 D_GP: 0.032 D_real: 1.042 D_fake: 0.686 
+(epoch: 359, iters: 1342, time: 0.063) G_GAN: 0.090 G_GAN_Feat: 0.645 G_ID: 0.101 G_Rec: 0.298 D_GP: 0.029 D_real: 1.045 D_fake: 0.910 
+(epoch: 359, iters: 1742, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.874 G_ID: 0.138 G_Rec: 0.402 D_GP: 0.044 D_real: 0.930 D_fake: 0.676 
+(epoch: 359, iters: 2142, time: 0.063) G_GAN: 0.308 G_GAN_Feat: 0.705 G_ID: 0.098 G_Rec: 0.286 D_GP: 0.040 D_real: 1.116 D_fake: 0.694 
+(epoch: 359, iters: 2542, time: 0.063) G_GAN: 0.518 G_GAN_Feat: 1.013 G_ID: 0.107 G_Rec: 0.474 D_GP: 0.064 D_real: 1.022 D_fake: 0.530 
+(epoch: 359, iters: 2942, time: 0.063) G_GAN: 0.290 G_GAN_Feat: 0.721 G_ID: 0.122 G_Rec: 0.325 D_GP: 0.030 D_real: 1.142 D_fake: 0.710 
+(epoch: 359, iters: 3342, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 1.005 G_ID: 0.109 G_Rec: 0.478 D_GP: 0.042 D_real: 1.121 D_fake: 0.492 
+(epoch: 359, iters: 3742, time: 0.063) G_GAN: 0.160 G_GAN_Feat: 0.654 G_ID: 0.097 G_Rec: 0.294 D_GP: 0.029 D_real: 1.006 D_fake: 0.840 
+(epoch: 359, iters: 4142, time: 0.063) G_GAN: 0.237 G_GAN_Feat: 0.920 G_ID: 0.136 G_Rec: 0.434 D_GP: 0.044 D_real: 0.860 D_fake: 0.764 
+(epoch: 359, iters: 4542, time: 0.063) G_GAN: 0.307 G_GAN_Feat: 0.728 G_ID: 0.104 G_Rec: 0.299 D_GP: 0.034 D_real: 1.081 D_fake: 0.694 
+(epoch: 359, iters: 4942, time: 0.064) G_GAN: 0.604 G_GAN_Feat: 1.026 G_ID: 0.128 G_Rec: 0.464 D_GP: 0.060 D_real: 1.031 D_fake: 0.407 
+(epoch: 359, iters: 5342, time: 0.063) G_GAN: 0.188 G_GAN_Feat: 0.752 G_ID: 0.101 G_Rec: 0.308 D_GP: 0.037 D_real: 0.986 D_fake: 0.812 
+(epoch: 359, iters: 5742, time: 0.063) G_GAN: 0.011 G_GAN_Feat: 1.137 G_ID: 0.123 G_Rec: 0.469 D_GP: 0.041 D_real: 0.471 D_fake: 0.989 
+(epoch: 359, iters: 6142, time: 0.063) G_GAN: 0.192 G_GAN_Feat: 0.876 G_ID: 0.108 G_Rec: 0.340 D_GP: 0.151 D_real: 0.493 D_fake: 0.808 
+(epoch: 359, iters: 6542, time: 0.064) G_GAN: 0.712 G_GAN_Feat: 0.903 G_ID: 0.133 G_Rec: 0.448 D_GP: 0.032 D_real: 1.365 D_fake: 0.303 
+(epoch: 359, iters: 6942, time: 0.063) G_GAN: 0.232 G_GAN_Feat: 0.751 G_ID: 0.121 G_Rec: 0.300 D_GP: 0.038 D_real: 1.101 D_fake: 0.769 
+(epoch: 359, iters: 7342, time: 0.063) G_GAN: 0.180 G_GAN_Feat: 0.987 G_ID: 0.140 G_Rec: 0.454 D_GP: 0.029 D_real: 0.816 D_fake: 0.821 
+(epoch: 359, iters: 7742, time: 0.063) G_GAN: -0.106 G_GAN_Feat: 0.741 G_ID: 0.107 G_Rec: 0.326 D_GP: 0.029 D_real: 0.783 D_fake: 1.106 
+(epoch: 359, iters: 8142, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.936 G_ID: 0.104 G_Rec: 0.432 D_GP: 0.030 D_real: 0.931 D_fake: 0.591 
+(epoch: 359, iters: 8542, time: 0.063) G_GAN: 0.102 G_GAN_Feat: 0.791 G_ID: 0.090 G_Rec: 0.335 D_GP: 0.058 D_real: 0.792 D_fake: 0.899 
+(epoch: 360, iters: 334, time: 0.063) G_GAN: 0.321 G_GAN_Feat: 0.936 G_ID: 0.134 G_Rec: 0.420 D_GP: 0.036 D_real: 0.981 D_fake: 0.679 
+(epoch: 360, iters: 734, time: 0.063) G_GAN: 0.270 G_GAN_Feat: 0.736 G_ID: 0.091 G_Rec: 0.314 D_GP: 0.035 D_real: 1.148 D_fake: 0.731 
+(epoch: 360, iters: 1134, time: 0.064) G_GAN: 0.738 G_GAN_Feat: 0.923 G_ID: 0.122 G_Rec: 0.511 D_GP: 0.029 D_real: 1.408 D_fake: 0.299 
+(epoch: 360, iters: 1534, time: 0.063) G_GAN: 0.306 G_GAN_Feat: 0.800 G_ID: 0.104 G_Rec: 0.296 D_GP: 0.060 D_real: 0.792 D_fake: 0.739 
+(epoch: 360, iters: 1934, time: 0.063) G_GAN: 0.393 G_GAN_Feat: 1.121 G_ID: 0.137 G_Rec: 0.440 D_GP: 0.094 D_real: 0.257 D_fake: 0.616 
+(epoch: 360, iters: 2334, time: 0.063) G_GAN: 0.285 G_GAN_Feat: 0.762 G_ID: 0.109 G_Rec: 0.304 D_GP: 0.032 D_real: 1.089 D_fake: 0.715 
+(epoch: 360, iters: 2734, time: 0.064) G_GAN: 0.566 G_GAN_Feat: 1.007 G_ID: 0.121 G_Rec: 0.464 D_GP: 0.034 D_real: 1.033 D_fake: 0.442 
+(epoch: 360, iters: 3134, time: 0.063) G_GAN: 0.043 G_GAN_Feat: 0.675 G_ID: 0.108 G_Rec: 0.253 D_GP: 0.035 D_real: 1.017 D_fake: 0.957 
+(epoch: 360, iters: 3534, time: 0.063) G_GAN: 0.385 G_GAN_Feat: 0.889 G_ID: 0.133 G_Rec: 0.417 D_GP: 0.029 D_real: 1.045 D_fake: 0.626 
+(epoch: 360, iters: 3934, time: 0.063) G_GAN: 0.408 G_GAN_Feat: 0.967 G_ID: 0.118 G_Rec: 0.356 D_GP: 0.101 D_real: 0.394 D_fake: 0.647 
+(epoch: 360, iters: 4334, time: 0.064) G_GAN: 0.549 G_GAN_Feat: 1.021 G_ID: 0.129 G_Rec: 0.441 D_GP: 0.041 D_real: 0.993 D_fake: 0.466 
+(epoch: 360, iters: 4734, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.722 G_ID: 0.101 G_Rec: 0.310 D_GP: 0.029 D_real: 1.012 D_fake: 0.763 
+(epoch: 360, iters: 5134, time: 0.063) G_GAN: 0.571 G_GAN_Feat: 0.920 G_ID: 0.119 G_Rec: 0.441 D_GP: 0.029 D_real: 1.321 D_fake: 0.456 
+(epoch: 360, iters: 5534, time: 0.063) G_GAN: -0.041 G_GAN_Feat: 0.743 G_ID: 0.121 G_Rec: 0.333 D_GP: 0.036 D_real: 0.824 D_fake: 1.041 
+(epoch: 360, iters: 5934, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.876 G_ID: 0.114 G_Rec: 0.413 D_GP: 0.033 D_real: 1.052 D_fake: 0.596 
+(epoch: 360, iters: 6334, time: 0.063) G_GAN: 0.144 G_GAN_Feat: 0.718 G_ID: 0.114 G_Rec: 0.330 D_GP: 0.042 D_real: 0.910 D_fake: 0.856 
+(epoch: 360, iters: 6734, time: 0.063) G_GAN: 0.576 G_GAN_Feat: 0.943 G_ID: 0.134 G_Rec: 0.422 D_GP: 0.033 D_real: 1.098 D_fake: 0.462 
+(epoch: 360, iters: 7134, time: 0.063) G_GAN: 0.161 G_GAN_Feat: 0.725 G_ID: 0.096 G_Rec: 0.328 D_GP: 0.029 D_real: 1.029 D_fake: 0.839 
+(epoch: 360, iters: 7534, time: 0.064) G_GAN: 0.809 G_GAN_Feat: 0.981 G_ID: 0.120 G_Rec: 0.455 D_GP: 0.035 D_real: 1.350 D_fake: 0.247 
+(epoch: 360, iters: 7934, time: 0.063) G_GAN: 0.299 G_GAN_Feat: 0.752 G_ID: 0.104 G_Rec: 0.314 D_GP: 0.028 D_real: 1.078 D_fake: 0.701 
+(epoch: 360, iters: 8334, time: 0.063) G_GAN: 0.601 G_GAN_Feat: 1.238 G_ID: 0.151 G_Rec: 0.509 D_GP: 0.079 D_real: 0.232 D_fake: 0.438 
+(epoch: 361, iters: 126, time: 0.063) G_GAN: 0.532 G_GAN_Feat: 0.971 G_ID: 0.099 G_Rec: 0.339 D_GP: 0.913 D_real: 0.581 D_fake: 0.588 
+(epoch: 361, iters: 526, time: 0.063) G_GAN: 0.307 G_GAN_Feat: 0.998 G_ID: 0.152 G_Rec: 0.470 D_GP: 0.035 D_real: 0.896 D_fake: 0.695 
+(epoch: 361, iters: 926, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.950 G_ID: 0.102 G_Rec: 0.332 D_GP: 0.169 D_real: 0.470 D_fake: 0.674 
+(epoch: 361, iters: 1326, time: 0.063) G_GAN: 0.647 G_GAN_Feat: 1.090 G_ID: 0.123 G_Rec: 0.420 D_GP: 0.092 D_real: 0.496 D_fake: 0.372 
+(epoch: 361, iters: 1726, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.656 G_ID: 0.098 G_Rec: 0.272 D_GP: 0.035 D_real: 1.198 D_fake: 0.744 
+(epoch: 361, iters: 2126, time: 0.063) G_GAN: 0.335 G_GAN_Feat: 0.855 G_ID: 0.128 G_Rec: 0.409 D_GP: 0.033 D_real: 0.990 D_fake: 0.669 
+(epoch: 361, iters: 2526, time: 0.063) G_GAN: 0.199 G_GAN_Feat: 0.760 G_ID: 0.107 G_Rec: 0.311 D_GP: 0.033 D_real: 0.930 D_fake: 0.801 
+(epoch: 361, iters: 2926, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 1.049 G_ID: 0.128 G_Rec: 0.452 D_GP: 0.073 D_real: 0.831 D_fake: 0.575 
+(epoch: 361, iters: 3326, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.724 G_ID: 0.101 G_Rec: 0.298 D_GP: 0.032 D_real: 0.891 D_fake: 0.850 
+(epoch: 361, iters: 3726, time: 0.063) G_GAN: 0.141 G_GAN_Feat: 0.917 G_ID: 0.137 G_Rec: 0.445 D_GP: 0.036 D_real: 0.765 D_fake: 0.861 
+(epoch: 361, iters: 4126, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.791 G_ID: 0.111 G_Rec: 0.295 D_GP: 0.037 D_real: 0.745 D_fake: 0.989 
+(epoch: 361, iters: 4526, time: 0.064) G_GAN: 0.799 G_GAN_Feat: 1.035 G_ID: 0.132 G_Rec: 0.482 D_GP: 0.038 D_real: 1.266 D_fake: 0.268 
+(epoch: 361, iters: 4926, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.731 G_ID: 0.102 G_Rec: 0.312 D_GP: 0.035 D_real: 1.114 D_fake: 0.711 
+(epoch: 361, iters: 5326, time: 0.063) G_GAN: 0.563 G_GAN_Feat: 0.977 G_ID: 0.124 G_Rec: 0.425 D_GP: 0.073 D_real: 0.789 D_fake: 0.459 
+(epoch: 361, iters: 5726, time: 0.063) G_GAN: 0.338 G_GAN_Feat: 0.805 G_ID: 0.103 G_Rec: 0.326 D_GP: 0.038 D_real: 1.253 D_fake: 0.667 
+(epoch: 361, iters: 6126, time: 0.063) G_GAN: 0.703 G_GAN_Feat: 0.860 G_ID: 0.115 G_Rec: 0.417 D_GP: 0.027 D_real: 1.415 D_fake: 0.352 
+(epoch: 361, iters: 6526, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.717 G_ID: 0.109 G_Rec: 0.317 D_GP: 0.036 D_real: 0.918 D_fake: 0.918 
+(epoch: 361, iters: 6926, time: 0.063) G_GAN: 0.579 G_GAN_Feat: 0.863 G_ID: 0.124 G_Rec: 0.403 D_GP: 0.027 D_real: 1.267 D_fake: 0.454 
+(epoch: 361, iters: 7326, time: 0.063) G_GAN: 0.263 G_GAN_Feat: 0.743 G_ID: 0.088 G_Rec: 0.301 D_GP: 0.034 D_real: 1.117 D_fake: 0.738 
+(epoch: 361, iters: 7726, time: 0.063) G_GAN: 0.402 G_GAN_Feat: 1.013 G_ID: 0.127 G_Rec: 0.442 D_GP: 0.045 D_real: 0.817 D_fake: 0.599 
+(epoch: 361, iters: 8126, time: 0.064) G_GAN: 0.395 G_GAN_Feat: 0.785 G_ID: 0.094 G_Rec: 0.336 D_GP: 0.054 D_real: 0.984 D_fake: 0.606 
+(epoch: 361, iters: 8526, time: 0.063) G_GAN: 0.437 G_GAN_Feat: 0.877 G_ID: 0.121 G_Rec: 0.413 D_GP: 0.030 D_real: 1.180 D_fake: 0.567 
+(epoch: 362, iters: 318, time: 0.063) G_GAN: 0.106 G_GAN_Feat: 0.818 G_ID: 0.112 G_Rec: 0.341 D_GP: 0.087 D_real: 0.752 D_fake: 0.895 
+(epoch: 362, iters: 718, time: 0.063) G_GAN: 0.281 G_GAN_Feat: 0.934 G_ID: 0.120 G_Rec: 0.398 D_GP: 0.037 D_real: 0.765 D_fake: 0.721 
+(epoch: 362, iters: 1118, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.768 G_ID: 0.101 G_Rec: 0.438 D_GP: 0.029 D_real: 0.843 D_fake: 0.882 
+(epoch: 362, iters: 1518, time: 0.063) G_GAN: 0.700 G_GAN_Feat: 1.002 G_ID: 0.119 G_Rec: 0.428 D_GP: 0.036 D_real: 1.297 D_fake: 0.313 
+(epoch: 362, iters: 1918, time: 0.063) G_GAN: 0.436 G_GAN_Feat: 0.817 G_ID: 0.096 G_Rec: 0.309 D_GP: 0.037 D_real: 1.201 D_fake: 0.568 
+(epoch: 362, iters: 2318, time: 0.063) G_GAN: 0.418 G_GAN_Feat: 1.088 G_ID: 0.120 G_Rec: 0.465 D_GP: 0.037 D_real: 0.942 D_fake: 0.585 
+(epoch: 362, iters: 2718, time: 0.064) G_GAN: -0.110 G_GAN_Feat: 0.872 G_ID: 0.116 G_Rec: 0.375 D_GP: 0.036 D_real: 0.666 D_fake: 1.110 
+(epoch: 362, iters: 3118, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 1.029 G_ID: 0.138 G_Rec: 0.438 D_GP: 0.088 D_real: 0.735 D_fake: 0.481 
+(epoch: 362, iters: 3518, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.923 G_ID: 0.123 G_Rec: 0.320 D_GP: 0.067 D_real: 0.220 D_fake: 0.870 
+(epoch: 362, iters: 3918, time: 0.063) G_GAN: 0.240 G_GAN_Feat: 0.837 G_ID: 0.132 G_Rec: 0.469 D_GP: 0.031 D_real: 1.006 D_fake: 0.761 
+(epoch: 362, iters: 4318, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.602 G_ID: 0.099 G_Rec: 0.313 D_GP: 0.029 D_real: 0.983 D_fake: 0.915 
+(epoch: 362, iters: 4718, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.792 G_ID: 0.128 G_Rec: 0.405 D_GP: 0.031 D_real: 1.200 D_fake: 0.574 
+(epoch: 362, iters: 5118, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.655 G_ID: 0.103 G_Rec: 0.315 D_GP: 0.038 D_real: 1.137 D_fake: 0.740 
+(epoch: 362, iters: 5518, time: 0.063) G_GAN: 0.388 G_GAN_Feat: 0.875 G_ID: 0.120 G_Rec: 0.460 D_GP: 0.042 D_real: 1.037 D_fake: 0.616 
+(epoch: 362, iters: 5918, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.599 G_ID: 0.105 G_Rec: 0.285 D_GP: 0.033 D_real: 1.144 D_fake: 0.784 
+(epoch: 362, iters: 6318, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.930 G_ID: 0.124 G_Rec: 0.497 D_GP: 0.062 D_real: 0.930 D_fake: 0.628 
+(epoch: 362, iters: 6718, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.693 G_ID: 0.104 G_Rec: 0.325 D_GP: 0.040 D_real: 0.873 D_fake: 0.964 
+(epoch: 362, iters: 7118, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.792 G_ID: 0.125 G_Rec: 0.408 D_GP: 0.039 D_real: 0.936 D_fake: 0.771 
+(epoch: 362, iters: 7518, time: 0.064) G_GAN: 0.097 G_GAN_Feat: 0.611 G_ID: 0.093 G_Rec: 0.288 D_GP: 0.029 D_real: 1.002 D_fake: 0.903 
+(epoch: 362, iters: 7918, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.936 G_ID: 0.126 G_Rec: 0.468 D_GP: 0.068 D_real: 0.694 D_fake: 0.795 
+(epoch: 362, iters: 8318, time: 0.063) G_GAN: 0.037 G_GAN_Feat: 0.656 G_ID: 0.132 G_Rec: 0.285 D_GP: 0.042 D_real: 0.886 D_fake: 0.963 
+(epoch: 363, iters: 110, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 0.924 G_ID: 0.141 G_Rec: 0.419 D_GP: 0.043 D_real: 1.128 D_fake: 0.436 
+(epoch: 363, iters: 510, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.691 G_ID: 0.099 G_Rec: 0.304 D_GP: 0.042 D_real: 1.124 D_fake: 0.698 
+(epoch: 363, iters: 910, time: 0.064) G_GAN: 0.550 G_GAN_Feat: 0.924 G_ID: 0.115 G_Rec: 0.434 D_GP: 0.036 D_real: 1.082 D_fake: 0.475 
+(epoch: 363, iters: 1310, time: 0.063) G_GAN: 0.210 G_GAN_Feat: 0.736 G_ID: 0.094 G_Rec: 0.302 D_GP: 0.052 D_real: 0.890 D_fake: 0.791 
+(epoch: 363, iters: 1710, time: 0.064) G_GAN: 0.539 G_GAN_Feat: 0.905 G_ID: 0.126 G_Rec: 0.407 D_GP: 0.073 D_real: 1.059 D_fake: 0.467 
+(epoch: 363, iters: 2110, time: 0.064) G_GAN: 0.008 G_GAN_Feat: 0.782 G_ID: 0.130 G_Rec: 0.321 D_GP: 0.064 D_real: 0.658 D_fake: 0.992 
+(epoch: 363, iters: 2510, time: 0.063) G_GAN: 0.545 G_GAN_Feat: 0.967 G_ID: 0.136 G_Rec: 0.466 D_GP: 0.064 D_real: 0.945 D_fake: 0.480 
+(epoch: 363, iters: 2910, time: 0.064) G_GAN: -0.059 G_GAN_Feat: 0.778 G_ID: 0.118 G_Rec: 0.353 D_GP: 0.037 D_real: 0.941 D_fake: 1.060 
+(epoch: 363, iters: 3310, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.916 G_ID: 0.132 G_Rec: 0.446 D_GP: 0.086 D_real: 0.875 D_fake: 0.632 
+(epoch: 363, iters: 3710, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.801 G_ID: 0.101 G_Rec: 0.316 D_GP: 0.096 D_real: 0.797 D_fake: 0.719 
+(epoch: 363, iters: 4110, time: 0.063) G_GAN: 0.582 G_GAN_Feat: 0.959 G_ID: 0.137 G_Rec: 0.391 D_GP: 0.046 D_real: 1.212 D_fake: 0.434 
+(epoch: 363, iters: 4510, time: 0.063) G_GAN: 0.351 G_GAN_Feat: 0.841 G_ID: 0.099 G_Rec: 0.362 D_GP: 0.211 D_real: 0.564 D_fake: 0.657 
+(epoch: 363, iters: 4910, time: 0.064) G_GAN: 0.564 G_GAN_Feat: 0.929 G_ID: 0.113 G_Rec: 0.449 D_GP: 0.034 D_real: 1.226 D_fake: 0.450 
+(epoch: 363, iters: 5310, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.786 G_ID: 0.104 G_Rec: 0.315 D_GP: 0.047 D_real: 1.017 D_fake: 0.545 
+(epoch: 363, iters: 5710, time: 0.063) G_GAN: 0.403 G_GAN_Feat: 0.882 G_ID: 0.119 G_Rec: 0.391 D_GP: 0.032 D_real: 1.205 D_fake: 0.598 
+(epoch: 363, iters: 6110, time: 0.063) G_GAN: 0.239 G_GAN_Feat: 0.797 G_ID: 0.127 G_Rec: 0.318 D_GP: 0.049 D_real: 0.994 D_fake: 0.762 
+(epoch: 363, iters: 6510, time: 0.064) G_GAN: 0.653 G_GAN_Feat: 1.099 G_ID: 0.147 G_Rec: 0.451 D_GP: 0.059 D_real: 0.671 D_fake: 0.362 
+(epoch: 363, iters: 6910, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 0.858 G_ID: 0.109 G_Rec: 0.320 D_GP: 0.128 D_real: 0.603 D_fake: 0.811 
+(epoch: 363, iters: 7310, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 1.085 G_ID: 0.140 G_Rec: 0.469 D_GP: 0.260 D_real: 0.494 D_fake: 0.890 
+(epoch: 363, iters: 7710, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.747 G_ID: 0.103 G_Rec: 0.322 D_GP: 0.046 D_real: 1.019 D_fake: 0.740 
+(epoch: 363, iters: 8110, time: 0.064) G_GAN: 0.006 G_GAN_Feat: 0.997 G_ID: 0.147 G_Rec: 0.397 D_GP: 0.067 D_real: 0.446 D_fake: 0.994 
+(epoch: 363, iters: 8510, time: 0.064) G_GAN: -0.213 G_GAN_Feat: 0.849 G_ID: 0.096 G_Rec: 0.335 D_GP: 0.181 D_real: 0.239 D_fake: 1.214 
+(epoch: 364, iters: 302, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.884 G_ID: 0.119 G_Rec: 0.390 D_GP: 0.034 D_real: 1.105 D_fake: 0.489 
+(epoch: 364, iters: 702, time: 0.063) G_GAN: 0.446 G_GAN_Feat: 1.005 G_ID: 0.113 G_Rec: 0.364 D_GP: 0.467 D_real: 0.555 D_fake: 0.563 
+(epoch: 364, iters: 1102, time: 0.064) G_GAN: 0.769 G_GAN_Feat: 1.082 G_ID: 0.150 G_Rec: 0.503 D_GP: 0.055 D_real: 1.026 D_fake: 0.267 
+(epoch: 364, iters: 1502, time: 0.064) G_GAN: -0.060 G_GAN_Feat: 0.964 G_ID: 0.112 G_Rec: 0.360 D_GP: 0.330 D_real: 0.188 D_fake: 1.063 
+(epoch: 364, iters: 1902, time: 0.063) G_GAN: 0.926 G_GAN_Feat: 1.001 G_ID: 0.134 G_Rec: 0.458 D_GP: 0.032 D_real: 1.391 D_fake: 0.169 
+(epoch: 364, iters: 2302, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.925 G_ID: 0.104 G_Rec: 0.358 D_GP: 0.056 D_real: 0.491 D_fake: 0.836 
+(epoch: 364, iters: 2702, time: 0.064) G_GAN: 0.751 G_GAN_Feat: 0.992 G_ID: 0.121 G_Rec: 0.414 D_GP: 0.031 D_real: 1.239 D_fake: 0.316 
+(epoch: 364, iters: 3102, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.643 G_ID: 0.121 G_Rec: 0.303 D_GP: 0.027 D_real: 0.997 D_fake: 0.898 
+(epoch: 364, iters: 3502, time: 0.063) G_GAN: 0.166 G_GAN_Feat: 0.851 G_ID: 0.143 G_Rec: 0.439 D_GP: 0.031 D_real: 0.891 D_fake: 0.834 
+(epoch: 364, iters: 3902, time: 0.063) G_GAN: -0.066 G_GAN_Feat: 0.688 G_ID: 0.115 G_Rec: 0.311 D_GP: 0.043 D_real: 0.748 D_fake: 1.066 
+(epoch: 364, iters: 4302, time: 0.063) G_GAN: 0.118 G_GAN_Feat: 0.900 G_ID: 0.132 G_Rec: 0.407 D_GP: 0.045 D_real: 0.693 D_fake: 0.882 
+(epoch: 364, iters: 4702, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.740 G_ID: 0.109 G_Rec: 0.339 D_GP: 0.139 D_real: 0.736 D_fake: 0.914 
+(epoch: 364, iters: 5102, time: 0.063) G_GAN: 0.531 G_GAN_Feat: 0.994 G_ID: 0.122 G_Rec: 0.470 D_GP: 0.196 D_real: 0.872 D_fake: 0.506 
+(epoch: 364, iters: 5502, time: 0.063) G_GAN: 0.160 G_GAN_Feat: 0.619 G_ID: 0.101 G_Rec: 0.295 D_GP: 0.029 D_real: 1.091 D_fake: 0.840 
+(epoch: 364, iters: 5902, time: 0.063) G_GAN: 0.755 G_GAN_Feat: 0.999 G_ID: 0.138 G_Rec: 0.445 D_GP: 0.054 D_real: 1.217 D_fake: 0.363 
+(epoch: 364, iters: 6302, time: 0.064) G_GAN: -0.082 G_GAN_Feat: 0.737 G_ID: 0.099 G_Rec: 0.355 D_GP: 0.070 D_real: 0.669 D_fake: 1.082 
+(epoch: 364, iters: 6702, time: 0.063) G_GAN: 0.343 G_GAN_Feat: 1.039 G_ID: 0.111 G_Rec: 0.485 D_GP: 0.052 D_real: 0.756 D_fake: 0.662 
+(epoch: 364, iters: 7102, time: 0.063) G_GAN: -0.126 G_GAN_Feat: 0.705 G_ID: 0.100 G_Rec: 0.292 D_GP: 0.049 D_real: 0.673 D_fake: 1.126 
+(epoch: 364, iters: 7502, time: 0.063) G_GAN: 0.284 G_GAN_Feat: 1.019 G_ID: 0.115 G_Rec: 0.474 D_GP: 0.054 D_real: 0.766 D_fake: 0.719 
+(epoch: 364, iters: 7902, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.714 G_ID: 0.105 G_Rec: 0.284 D_GP: 0.032 D_real: 1.019 D_fake: 0.840 
+(epoch: 364, iters: 8302, time: 0.063) G_GAN: 0.438 G_GAN_Feat: 0.848 G_ID: 0.118 G_Rec: 0.367 D_GP: 0.037 D_real: 1.118 D_fake: 0.566 
+(epoch: 364, iters: 8702, time: 0.064) G_GAN: 0.067 G_GAN_Feat: 0.773 G_ID: 0.128 G_Rec: 0.359 D_GP: 0.029 D_real: 0.966 D_fake: 0.934 
+(epoch: 365, iters: 494, time: 0.063) G_GAN: 0.509 G_GAN_Feat: 1.055 G_ID: 0.112 G_Rec: 0.458 D_GP: 0.054 D_real: 0.800 D_fake: 0.497 
+(epoch: 365, iters: 894, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.757 G_ID: 0.105 G_Rec: 0.299 D_GP: 0.032 D_real: 1.001 D_fake: 0.737 
+(epoch: 365, iters: 1294, time: 0.063) G_GAN: 0.588 G_GAN_Feat: 0.956 G_ID: 0.105 G_Rec: 0.400 D_GP: 0.031 D_real: 1.112 D_fake: 0.445 
+(epoch: 365, iters: 1694, time: 0.063) G_GAN: 0.259 G_GAN_Feat: 0.840 G_ID: 0.090 G_Rec: 0.322 D_GP: 0.112 D_real: 0.752 D_fake: 0.747 
+(epoch: 365, iters: 2094, time: 0.063) G_GAN: 0.500 G_GAN_Feat: 1.062 G_ID: 0.115 G_Rec: 0.421 D_GP: 0.041 D_real: 0.798 D_fake: 0.505 
+(epoch: 365, iters: 2494, time: 0.064) G_GAN: 0.885 G_GAN_Feat: 1.002 G_ID: 0.095 G_Rec: 0.333 D_GP: 0.066 D_real: 0.980 D_fake: 0.205 
+(epoch: 365, iters: 2894, time: 0.064) G_GAN: 0.603 G_GAN_Feat: 1.064 G_ID: 0.120 G_Rec: 0.419 D_GP: 0.030 D_real: 0.811 D_fake: 0.409 
+(epoch: 365, iters: 3294, time: 0.063) G_GAN: 0.386 G_GAN_Feat: 0.930 G_ID: 0.101 G_Rec: 0.313 D_GP: 0.042 D_real: 0.556 D_fake: 0.666 
+(epoch: 365, iters: 3694, time: 0.063) G_GAN: 0.775 G_GAN_Feat: 1.091 G_ID: 0.124 G_Rec: 0.497 D_GP: 0.112 D_real: 0.631 D_fake: 0.295 
+(epoch: 365, iters: 4094, time: 0.064) G_GAN: 0.022 G_GAN_Feat: 0.940 G_ID: 0.100 G_Rec: 0.327 D_GP: 0.050 D_real: 0.232 D_fake: 0.978 
+(epoch: 365, iters: 4494, time: 0.063) G_GAN: 0.545 G_GAN_Feat: 1.259 G_ID: 0.124 G_Rec: 0.486 D_GP: 0.040 D_real: 1.136 D_fake: 0.535 
+(epoch: 365, iters: 4894, time: 0.063) G_GAN: 0.322 G_GAN_Feat: 0.714 G_ID: 0.098 G_Rec: 0.317 D_GP: 0.026 D_real: 1.278 D_fake: 0.683 
+(epoch: 365, iters: 5294, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 0.942 G_ID: 0.124 G_Rec: 0.426 D_GP: 0.034 D_real: 1.034 D_fake: 0.578 
+(epoch: 365, iters: 5694, time: 0.063) G_GAN: 0.676 G_GAN_Feat: 0.782 G_ID: 0.101 G_Rec: 0.324 D_GP: 0.090 D_real: 1.270 D_fake: 0.343 
+(epoch: 365, iters: 6094, time: 0.063) G_GAN: 0.489 G_GAN_Feat: 0.826 G_ID: 0.131 G_Rec: 0.392 D_GP: 0.030 D_real: 1.214 D_fake: 0.518 
+(epoch: 365, iters: 6494, time: 0.063) G_GAN: 0.225 G_GAN_Feat: 0.771 G_ID: 0.109 G_Rec: 0.316 D_GP: 0.037 D_real: 0.979 D_fake: 0.776 
+(epoch: 365, iters: 6894, time: 0.064) G_GAN: 0.497 G_GAN_Feat: 1.249 G_ID: 0.118 G_Rec: 0.518 D_GP: 0.050 D_real: 1.182 D_fake: 0.544 
+(epoch: 365, iters: 7294, time: 0.063) G_GAN: 0.288 G_GAN_Feat: 0.749 G_ID: 0.126 G_Rec: 0.325 D_GP: 0.030 D_real: 1.152 D_fake: 0.718 
+(epoch: 365, iters: 7694, time: 0.063) G_GAN: 0.605 G_GAN_Feat: 0.875 G_ID: 0.131 G_Rec: 0.408 D_GP: 0.041 D_real: 1.180 D_fake: 0.469 
+(epoch: 365, iters: 8094, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 0.737 G_ID: 0.095 G_Rec: 0.327 D_GP: 0.036 D_real: 0.904 D_fake: 0.783 
+(epoch: 365, iters: 8494, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.894 G_ID: 0.135 G_Rec: 0.434 D_GP: 0.030 D_real: 1.183 D_fake: 0.550 
+(epoch: 366, iters: 286, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.859 G_ID: 0.111 G_Rec: 0.346 D_GP: 0.034 D_real: 0.859 D_fake: 0.765 
+(epoch: 366, iters: 686, time: 0.064) G_GAN: 0.733 G_GAN_Feat: 1.119 G_ID: 0.145 G_Rec: 0.493 D_GP: 0.047 D_real: 0.870 D_fake: 0.319 
+(epoch: 366, iters: 1086, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 1.082 G_ID: 0.126 G_Rec: 0.347 D_GP: 0.073 D_real: 0.088 D_fake: 0.964 
+(epoch: 366, iters: 1486, time: 0.064) G_GAN: 0.448 G_GAN_Feat: 0.867 G_ID: 0.115 G_Rec: 0.419 D_GP: 0.027 D_real: 1.125 D_fake: 0.555 
+(epoch: 366, iters: 1886, time: 0.063) G_GAN: 0.022 G_GAN_Feat: 0.734 G_ID: 0.087 G_Rec: 0.303 D_GP: 0.030 D_real: 0.909 D_fake: 0.978 
+(epoch: 366, iters: 2286, time: 0.063) G_GAN: 0.221 G_GAN_Feat: 0.883 G_ID: 0.130 G_Rec: 0.402 D_GP: 0.037 D_real: 0.930 D_fake: 0.780 
+(epoch: 366, iters: 2686, time: 0.063) G_GAN: 0.140 G_GAN_Feat: 0.735 G_ID: 0.095 G_Rec: 0.284 D_GP: 0.047 D_real: 0.787 D_fake: 0.860 
+(epoch: 366, iters: 3086, time: 0.064) G_GAN: 0.591 G_GAN_Feat: 1.020 G_ID: 0.123 G_Rec: 0.430 D_GP: 0.036 D_real: 0.925 D_fake: 0.425 
+(epoch: 366, iters: 3486, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.908 G_ID: 0.098 G_Rec: 0.334 D_GP: 0.086 D_real: 0.517 D_fake: 0.728 
+(epoch: 366, iters: 3886, time: 0.063) G_GAN: 0.567 G_GAN_Feat: 1.193 G_ID: 0.137 G_Rec: 0.446 D_GP: 0.131 D_real: 0.259 D_fake: 0.488 
+(epoch: 366, iters: 4286, time: 0.063) G_GAN: 0.363 G_GAN_Feat: 0.856 G_ID: 0.116 G_Rec: 0.312 D_GP: 0.052 D_real: 0.966 D_fake: 0.643 
+(epoch: 366, iters: 4686, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.979 G_ID: 0.135 G_Rec: 0.476 D_GP: 0.037 D_real: 0.964 D_fake: 0.603 
+(epoch: 366, iters: 5086, time: 0.063) G_GAN: 0.189 G_GAN_Feat: 0.731 G_ID: 0.096 G_Rec: 0.327 D_GP: 0.030 D_real: 1.018 D_fake: 0.816 
+(epoch: 366, iters: 5486, time: 0.063) G_GAN: 0.574 G_GAN_Feat: 1.018 G_ID: 0.128 G_Rec: 0.485 D_GP: 0.096 D_real: 0.896 D_fake: 0.474 
+(epoch: 366, iters: 5886, time: 0.063) G_GAN: 0.194 G_GAN_Feat: 0.736 G_ID: 0.101 G_Rec: 0.301 D_GP: 0.036 D_real: 1.037 D_fake: 0.807 
+(epoch: 366, iters: 6286, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.866 G_ID: 0.137 G_Rec: 0.380 D_GP: 0.036 D_real: 1.001 D_fake: 0.719 
+(epoch: 366, iters: 6686, time: 0.063) G_GAN: 0.028 G_GAN_Feat: 0.957 G_ID: 0.115 G_Rec: 0.360 D_GP: 0.432 D_real: 0.184 D_fake: 0.979 
+(epoch: 366, iters: 7086, time: 0.063) G_GAN: 0.662 G_GAN_Feat: 1.098 G_ID: 0.126 G_Rec: 0.477 D_GP: 0.049 D_real: 0.907 D_fake: 0.377 
+(epoch: 366, iters: 7486, time: 0.063) G_GAN: 0.226 G_GAN_Feat: 0.915 G_ID: 0.097 G_Rec: 0.342 D_GP: 0.072 D_real: 0.502 D_fake: 0.775 
+(epoch: 366, iters: 7886, time: 0.064) G_GAN: 0.358 G_GAN_Feat: 1.164 G_ID: 0.122 G_Rec: 0.437 D_GP: 0.089 D_real: 0.491 D_fake: 0.647 
+(epoch: 366, iters: 8286, time: 0.063) G_GAN: 0.336 G_GAN_Feat: 0.846 G_ID: 0.106 G_Rec: 0.307 D_GP: 0.062 D_real: 0.725 D_fake: 0.664 
+(epoch: 366, iters: 8686, time: 0.063) G_GAN: 0.511 G_GAN_Feat: 1.022 G_ID: 0.141 G_Rec: 0.462 D_GP: 0.038 D_real: 1.033 D_fake: 0.502 
+(epoch: 367, iters: 478, time: 0.063) G_GAN: 0.251 G_GAN_Feat: 0.778 G_ID: 0.098 G_Rec: 0.320 D_GP: 0.043 D_real: 1.115 D_fake: 0.749 
+(epoch: 367, iters: 878, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.926 G_ID: 0.136 G_Rec: 0.453 D_GP: 0.037 D_real: 0.964 D_fake: 0.683 
+(epoch: 367, iters: 1278, time: 0.063) G_GAN: 0.274 G_GAN_Feat: 0.811 G_ID: 0.105 G_Rec: 0.313 D_GP: 0.039 D_real: 0.893 D_fake: 0.727 
+(epoch: 367, iters: 1678, time: 0.063) G_GAN: 0.975 G_GAN_Feat: 1.169 G_ID: 0.105 G_Rec: 0.464 D_GP: 0.324 D_real: 0.414 D_fake: 0.193 
+(epoch: 367, iters: 2078, time: 0.063) G_GAN: 0.457 G_GAN_Feat: 0.946 G_ID: 0.104 G_Rec: 0.342 D_GP: 0.063 D_real: 0.967 D_fake: 0.615 
+(epoch: 367, iters: 2478, time: 0.064) G_GAN: 0.701 G_GAN_Feat: 1.117 G_ID: 0.125 G_Rec: 0.460 D_GP: 0.101 D_real: 0.711 D_fake: 0.335 
+(epoch: 367, iters: 2878, time: 0.063) G_GAN: -0.062 G_GAN_Feat: 0.864 G_ID: 0.115 G_Rec: 0.342 D_GP: 0.029 D_real: 0.781 D_fake: 1.062 
+(epoch: 367, iters: 3278, time: 0.063) G_GAN: 0.897 G_GAN_Feat: 1.111 G_ID: 0.105 G_Rec: 0.464 D_GP: 0.033 D_real: 1.112 D_fake: 0.171 
+(epoch: 367, iters: 3678, time: 0.063) G_GAN: -0.066 G_GAN_Feat: 0.731 G_ID: 0.120 G_Rec: 0.311 D_GP: 0.028 D_real: 0.872 D_fake: 1.066 
+(epoch: 367, iters: 4078, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 1.128 G_ID: 0.121 G_Rec: 0.470 D_GP: 0.069 D_real: 0.731 D_fake: 0.603 
+(epoch: 367, iters: 4478, time: 0.064) G_GAN: -0.045 G_GAN_Feat: 0.846 G_ID: 0.103 G_Rec: 0.332 D_GP: 0.118 D_real: 0.501 D_fake: 1.045 
+(epoch: 367, iters: 4878, time: 0.064) G_GAN: 0.936 G_GAN_Feat: 1.109 G_ID: 0.125 G_Rec: 0.480 D_GP: 0.105 D_real: 1.054 D_fake: 0.214 
+(epoch: 367, iters: 5278, time: 0.063) G_GAN: 0.287 G_GAN_Feat: 0.756 G_ID: 0.102 G_Rec: 0.315 D_GP: 0.036 D_real: 1.115 D_fake: 0.714 
+(epoch: 367, iters: 5678, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 0.937 G_ID: 0.120 G_Rec: 0.397 D_GP: 0.042 D_real: 1.211 D_fake: 0.431 
+(epoch: 367, iters: 6078, time: 0.063) G_GAN: 0.159 G_GAN_Feat: 0.890 G_ID: 0.107 G_Rec: 0.304 D_GP: 0.033 D_real: 0.558 D_fake: 0.841 
+(epoch: 367, iters: 6478, time: 0.063) G_GAN: 0.785 G_GAN_Feat: 1.078 G_ID: 0.144 G_Rec: 0.484 D_GP: 0.032 D_real: 0.998 D_fake: 0.346 
+(epoch: 367, iters: 6878, time: 0.063) G_GAN: 0.381 G_GAN_Feat: 1.062 G_ID: 0.094 G_Rec: 0.369 D_GP: 0.032 D_real: 1.135 D_fake: 0.687 
+(epoch: 367, iters: 7278, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.871 G_ID: 0.136 G_Rec: 0.442 D_GP: 0.027 D_real: 1.085 D_fake: 0.681 
+(epoch: 367, iters: 7678, time: 0.063) G_GAN: 0.111 G_GAN_Feat: 0.620 G_ID: 0.081 G_Rec: 0.308 D_GP: 0.026 D_real: 1.092 D_fake: 0.890 
+(epoch: 367, iters: 8078, time: 0.063) G_GAN: 0.398 G_GAN_Feat: 0.808 G_ID: 0.113 G_Rec: 0.416 D_GP: 0.028 D_real: 1.203 D_fake: 0.612 
+(epoch: 367, iters: 8478, time: 0.063) G_GAN: 0.049 G_GAN_Feat: 0.614 G_ID: 0.090 G_Rec: 0.293 D_GP: 0.033 D_real: 0.979 D_fake: 0.951 
+(epoch: 368, iters: 270, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.826 G_ID: 0.121 G_Rec: 0.467 D_GP: 0.038 D_real: 0.992 D_fake: 0.719 
+(epoch: 368, iters: 670, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.623 G_ID: 0.103 G_Rec: 0.291 D_GP: 0.037 D_real: 0.927 D_fake: 0.901 
+(epoch: 368, iters: 1070, time: 0.063) G_GAN: 0.089 G_GAN_Feat: 0.759 G_ID: 0.110 G_Rec: 0.366 D_GP: 0.038 D_real: 0.797 D_fake: 0.911 
+(epoch: 368, iters: 1470, time: 0.063) G_GAN: -0.104 G_GAN_Feat: 0.648 G_ID: 0.105 G_Rec: 0.331 D_GP: 0.035 D_real: 0.796 D_fake: 1.104 
+(epoch: 368, iters: 1870, time: 0.064) G_GAN: 0.029 G_GAN_Feat: 0.853 G_ID: 0.127 G_Rec: 0.412 D_GP: 0.046 D_real: 0.694 D_fake: 0.971 
+(epoch: 368, iters: 2270, time: 0.063) G_GAN: -0.177 G_GAN_Feat: 0.678 G_ID: 0.111 G_Rec: 0.320 D_GP: 0.050 D_real: 0.673 D_fake: 1.177 
+(epoch: 368, iters: 2670, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.817 G_ID: 0.111 G_Rec: 0.405 D_GP: 0.041 D_real: 1.053 D_fake: 0.646 
+(epoch: 368, iters: 3070, time: 0.063) G_GAN: 0.005 G_GAN_Feat: 0.697 G_ID: 0.096 G_Rec: 0.319 D_GP: 0.039 D_real: 0.903 D_fake: 0.995 
+(epoch: 368, iters: 3470, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.857 G_ID: 0.134 G_Rec: 0.381 D_GP: 0.044 D_real: 1.030 D_fake: 0.697 
+(epoch: 368, iters: 3870, time: 0.063) G_GAN: 0.322 G_GAN_Feat: 0.704 G_ID: 0.105 G_Rec: 0.309 D_GP: 0.036 D_real: 1.160 D_fake: 0.679 
+(epoch: 368, iters: 4270, time: 0.063) G_GAN: 0.112 G_GAN_Feat: 0.833 G_ID: 0.124 G_Rec: 0.373 D_GP: 0.043 D_real: 0.771 D_fake: 0.889 
+(epoch: 368, iters: 4670, time: 0.063) G_GAN: 0.196 G_GAN_Feat: 0.783 G_ID: 0.101 G_Rec: 0.301 D_GP: 0.107 D_real: 0.769 D_fake: 0.804 
+(epoch: 368, iters: 5070, time: 0.064) G_GAN: 0.622 G_GAN_Feat: 0.901 G_ID: 0.108 G_Rec: 0.378 D_GP: 0.047 D_real: 1.204 D_fake: 0.388 
+(epoch: 368, iters: 5470, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.659 G_ID: 0.094 G_Rec: 0.303 D_GP: 0.031 D_real: 1.007 D_fake: 0.913 
+(epoch: 368, iters: 5870, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.890 G_ID: 0.119 G_Rec: 0.432 D_GP: 0.034 D_real: 0.996 D_fake: 0.662 
+(epoch: 368, iters: 6270, time: 0.064) G_GAN: -0.100 G_GAN_Feat: 0.721 G_ID: 0.145 G_Rec: 0.321 D_GP: 0.047 D_real: 0.722 D_fake: 1.100 
+(epoch: 368, iters: 6670, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 1.023 G_ID: 0.145 G_Rec: 0.473 D_GP: 0.103 D_real: 0.430 D_fake: 0.886 
+(epoch: 368, iters: 7070, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.702 G_ID: 0.104 G_Rec: 0.302 D_GP: 0.038 D_real: 0.869 D_fake: 0.871 
+(epoch: 368, iters: 7470, time: 0.064) G_GAN: 0.678 G_GAN_Feat: 0.968 G_ID: 0.126 G_Rec: 0.474 D_GP: 0.035 D_real: 1.217 D_fake: 0.351 
+(epoch: 368, iters: 7870, time: 0.064) G_GAN: 0.112 G_GAN_Feat: 0.785 G_ID: 0.121 G_Rec: 0.354 D_GP: 0.050 D_real: 0.817 D_fake: 0.888 
+(epoch: 368, iters: 8270, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.883 G_ID: 0.129 G_Rec: 0.379 D_GP: 0.041 D_real: 1.061 D_fake: 0.624 
+(epoch: 368, iters: 8670, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.771 G_ID: 0.104 G_Rec: 0.354 D_GP: 0.033 D_real: 1.103 D_fake: 0.695 
+(epoch: 369, iters: 462, time: 0.064) G_GAN: 0.568 G_GAN_Feat: 0.923 G_ID: 0.131 G_Rec: 0.388 D_GP: 0.048 D_real: 1.243 D_fake: 0.439 
+(epoch: 369, iters: 862, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.668 G_ID: 0.103 G_Rec: 0.292 D_GP: 0.029 D_real: 1.245 D_fake: 0.692 
+(epoch: 369, iters: 1262, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.958 G_ID: 0.122 G_Rec: 0.434 D_GP: 0.042 D_real: 1.006 D_fake: 0.595 
+(epoch: 369, iters: 1662, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.788 G_ID: 0.117 G_Rec: 0.311 D_GP: 0.063 D_real: 0.659 D_fake: 0.910 
+(epoch: 369, iters: 2062, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 1.080 G_ID: 0.138 G_Rec: 0.490 D_GP: 0.051 D_real: 0.743 D_fake: 0.551 
+(epoch: 369, iters: 2462, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 0.839 G_ID: 0.111 G_Rec: 0.332 D_GP: 0.042 D_real: 1.314 D_fake: 0.531 
+(epoch: 369, iters: 2862, time: 0.064) G_GAN: 0.874 G_GAN_Feat: 0.868 G_ID: 0.104 G_Rec: 0.433 D_GP: 0.028 D_real: 1.570 D_fake: 0.324 
+(epoch: 369, iters: 3262, time: 0.064) G_GAN: -0.026 G_GAN_Feat: 0.637 G_ID: 0.119 G_Rec: 0.292 D_GP: 0.029 D_real: 0.899 D_fake: 1.026 
+(epoch: 369, iters: 3662, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.845 G_ID: 0.120 G_Rec: 0.406 D_GP: 0.036 D_real: 0.959 D_fake: 0.634 
+(epoch: 369, iters: 4062, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.626 G_ID: 0.113 G_Rec: 0.278 D_GP: 0.035 D_real: 1.015 D_fake: 0.871 
+(epoch: 369, iters: 4462, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.919 G_ID: 0.156 G_Rec: 0.440 D_GP: 0.037 D_real: 0.745 D_fake: 0.814 
+(epoch: 369, iters: 4862, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.724 G_ID: 0.099 G_Rec: 0.300 D_GP: 0.047 D_real: 0.871 D_fake: 0.881 
+(epoch: 369, iters: 5262, time: 0.064) G_GAN: 0.564 G_GAN_Feat: 0.938 G_ID: 0.131 G_Rec: 0.427 D_GP: 0.074 D_real: 1.047 D_fake: 0.463 
+(epoch: 369, iters: 5662, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.805 G_ID: 0.092 G_Rec: 0.331 D_GP: 0.045 D_real: 0.951 D_fake: 0.742 
+(epoch: 369, iters: 6062, time: 0.064) G_GAN: 0.617 G_GAN_Feat: 0.992 G_ID: 0.128 G_Rec: 0.442 D_GP: 0.041 D_real: 1.215 D_fake: 0.391 
+(epoch: 369, iters: 6462, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.725 G_ID: 0.092 G_Rec: 0.287 D_GP: 0.032 D_real: 1.147 D_fake: 0.742 
+(epoch: 369, iters: 6862, time: 0.064) G_GAN: 0.649 G_GAN_Feat: 1.054 G_ID: 0.111 G_Rec: 0.451 D_GP: 0.053 D_real: 0.942 D_fake: 0.371 
+(epoch: 369, iters: 7262, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.871 G_ID: 0.100 G_Rec: 0.337 D_GP: 0.070 D_real: 0.704 D_fake: 0.694 
+(epoch: 369, iters: 7662, time: 0.064) G_GAN: 0.485 G_GAN_Feat: 0.960 G_ID: 0.131 G_Rec: 0.452 D_GP: 0.052 D_real: 1.012 D_fake: 0.555 
+(epoch: 369, iters: 8062, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.717 G_ID: 0.116 G_Rec: 0.317 D_GP: 0.036 D_real: 1.043 D_fake: 0.773 
+(epoch: 369, iters: 8462, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 1.100 G_ID: 0.150 G_Rec: 0.489 D_GP: 0.061 D_real: 0.652 D_fake: 0.657 
+(epoch: 370, iters: 254, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.808 G_ID: 0.095 G_Rec: 0.303 D_GP: 0.034 D_real: 1.031 D_fake: 0.615 
+(epoch: 370, iters: 654, time: 0.064) G_GAN: 1.037 G_GAN_Feat: 1.029 G_ID: 0.112 G_Rec: 0.478 D_GP: 0.102 D_real: 1.488 D_fake: 0.246 
+(epoch: 370, iters: 1054, time: 0.064) G_GAN: 0.005 G_GAN_Feat: 0.754 G_ID: 0.112 G_Rec: 0.322 D_GP: 0.034 D_real: 0.766 D_fake: 0.995 
+(epoch: 370, iters: 1454, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 1.027 G_ID: 0.142 G_Rec: 0.487 D_GP: 0.068 D_real: 0.460 D_fake: 0.945 
+(epoch: 370, iters: 1854, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.681 G_ID: 0.088 G_Rec: 0.290 D_GP: 0.033 D_real: 1.019 D_fake: 0.895 
+(epoch: 370, iters: 2254, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.941 G_ID: 0.124 G_Rec: 0.424 D_GP: 0.031 D_real: 1.013 D_fake: 0.668 
+(epoch: 370, iters: 2654, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.769 G_ID: 0.091 G_Rec: 0.311 D_GP: 0.037 D_real: 0.960 D_fake: 0.751 
+(epoch: 370, iters: 3054, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 1.105 G_ID: 0.135 G_Rec: 0.496 D_GP: 0.095 D_real: 0.658 D_fake: 0.536 
+(epoch: 370, iters: 3454, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.817 G_ID: 0.105 G_Rec: 0.313 D_GP: 0.050 D_real: 0.792 D_fake: 0.866 
+(epoch: 370, iters: 3854, time: 0.064) G_GAN: 0.806 G_GAN_Feat: 1.058 G_ID: 0.119 G_Rec: 0.459 D_GP: 0.067 D_real: 1.067 D_fake: 0.290 
+(epoch: 370, iters: 4254, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.838 G_ID: 0.098 G_Rec: 0.311 D_GP: 0.041 D_real: 0.946 D_fake: 0.671 
+(epoch: 370, iters: 4654, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 1.115 G_ID: 0.161 G_Rec: 0.454 D_GP: 0.138 D_real: 0.322 D_fake: 0.552 
+(epoch: 370, iters: 5054, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.832 G_ID: 0.101 G_Rec: 0.321 D_GP: 0.053 D_real: 0.942 D_fake: 0.772 
+(epoch: 370, iters: 5454, time: 0.064) G_GAN: 1.058 G_GAN_Feat: 1.194 G_ID: 0.128 G_Rec: 0.543 D_GP: 0.156 D_real: 0.601 D_fake: 0.117 
+(epoch: 370, iters: 5854, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.802 G_ID: 0.087 G_Rec: 0.315 D_GP: 0.069 D_real: 0.930 D_fake: 0.839 
+(epoch: 370, iters: 6254, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 1.024 G_ID: 0.124 G_Rec: 0.462 D_GP: 0.034 D_real: 0.878 D_fake: 0.624 
+(epoch: 370, iters: 6654, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.836 G_ID: 0.100 G_Rec: 0.335 D_GP: 0.037 D_real: 0.878 D_fake: 0.722 
+(epoch: 370, iters: 7054, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 1.035 G_ID: 0.119 G_Rec: 0.474 D_GP: 0.032 D_real: 0.988 D_fake: 0.575 
+(epoch: 370, iters: 7454, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.772 G_ID: 0.123 G_Rec: 0.291 D_GP: 0.032 D_real: 1.138 D_fake: 0.647 
+(epoch: 370, iters: 7854, time: 0.064) G_GAN: 0.029 G_GAN_Feat: 1.009 G_ID: 0.118 G_Rec: 0.458 D_GP: 0.047 D_real: 0.539 D_fake: 0.971 
+(epoch: 370, iters: 8254, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.895 G_ID: 0.117 G_Rec: 0.348 D_GP: 0.136 D_real: 0.632 D_fake: 0.666 
+(epoch: 370, iters: 8654, time: 0.064) G_GAN: 0.714 G_GAN_Feat: 1.005 G_ID: 0.126 G_Rec: 0.453 D_GP: 0.036 D_real: 1.389 D_fake: 0.315 
+(epoch: 371, iters: 446, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.814 G_ID: 0.102 G_Rec: 0.316 D_GP: 0.040 D_real: 0.931 D_fake: 0.786 
+(epoch: 371, iters: 846, time: 0.064) G_GAN: 0.809 G_GAN_Feat: 1.174 G_ID: 0.122 G_Rec: 0.483 D_GP: 0.061 D_real: 0.634 D_fake: 0.237 
+(epoch: 371, iters: 1246, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.960 G_ID: 0.098 G_Rec: 0.377 D_GP: 0.103 D_real: 0.679 D_fake: 0.770 
+(epoch: 371, iters: 1646, time: 0.064) G_GAN: 0.855 G_GAN_Feat: 1.034 G_ID: 0.133 G_Rec: 0.466 D_GP: 0.042 D_real: 1.432 D_fake: 0.207 
+(epoch: 371, iters: 2046, time: 0.064) G_GAN: -0.022 G_GAN_Feat: 0.752 G_ID: 0.109 G_Rec: 0.363 D_GP: 0.031 D_real: 0.790 D_fake: 1.022 
+(epoch: 371, iters: 2446, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 1.056 G_ID: 0.114 G_Rec: 0.432 D_GP: 0.054 D_real: 0.614 D_fake: 0.633 
+(epoch: 371, iters: 2846, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.725 G_ID: 0.102 G_Rec: 0.315 D_GP: 0.030 D_real: 0.954 D_fake: 0.916 
+(epoch: 371, iters: 3246, time: 0.064) G_GAN: 0.567 G_GAN_Feat: 0.952 G_ID: 0.127 G_Rec: 0.417 D_GP: 0.046 D_real: 1.104 D_fake: 0.476 
+(epoch: 371, iters: 3646, time: 0.064) G_GAN: -0.083 G_GAN_Feat: 0.861 G_ID: 0.111 G_Rec: 0.324 D_GP: 0.134 D_real: 0.353 D_fake: 1.091 
+(epoch: 371, iters: 4046, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.953 G_ID: 0.126 G_Rec: 0.449 D_GP: 0.055 D_real: 1.046 D_fake: 0.566 
+(epoch: 371, iters: 4446, time: 0.064) G_GAN: 0.029 G_GAN_Feat: 0.836 G_ID: 0.106 G_Rec: 0.347 D_GP: 0.102 D_real: 0.622 D_fake: 0.972 
+(epoch: 371, iters: 4846, time: 0.064) G_GAN: -0.088 G_GAN_Feat: 1.010 G_ID: 0.129 G_Rec: 0.433 D_GP: 1.688 D_real: 0.349 D_fake: 1.090 
+(epoch: 371, iters: 5246, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.629 G_ID: 0.088 G_Rec: 0.290 D_GP: 0.026 D_real: 1.169 D_fake: 0.773 
+(epoch: 371, iters: 5646, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 0.897 G_ID: 0.111 G_Rec: 0.454 D_GP: 0.036 D_real: 1.101 D_fake: 0.556 
+(epoch: 371, iters: 6046, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.681 G_ID: 0.098 G_Rec: 0.279 D_GP: 0.037 D_real: 0.896 D_fake: 0.912 
+(epoch: 371, iters: 6446, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 0.937 G_ID: 0.144 G_Rec: 0.439 D_GP: 0.050 D_real: 0.975 D_fake: 0.644 
+(epoch: 371, iters: 6846, time: 0.064) G_GAN: -0.067 G_GAN_Feat: 0.694 G_ID: 0.096 G_Rec: 0.305 D_GP: 0.038 D_real: 0.757 D_fake: 1.067 
+(epoch: 371, iters: 7246, time: 0.064) G_GAN: 0.551 G_GAN_Feat: 0.986 G_ID: 0.124 G_Rec: 0.453 D_GP: 0.052 D_real: 0.939 D_fake: 0.471 
+(epoch: 371, iters: 7646, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.776 G_ID: 0.153 G_Rec: 0.360 D_GP: 0.070 D_real: 1.038 D_fake: 0.665 
+(epoch: 371, iters: 8046, time: 0.064) G_GAN: 0.683 G_GAN_Feat: 1.005 G_ID: 0.134 G_Rec: 0.481 D_GP: 0.045 D_real: 1.072 D_fake: 0.351 
+(epoch: 371, iters: 8446, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 1.036 G_ID: 0.116 G_Rec: 0.381 D_GP: 0.080 D_real: 0.819 D_fake: 0.520 
+(epoch: 372, iters: 238, time: 0.064) G_GAN: 0.614 G_GAN_Feat: 0.998 G_ID: 0.118 G_Rec: 0.431 D_GP: 0.047 D_real: 0.910 D_fake: 0.407 
+(epoch: 372, iters: 638, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.866 G_ID: 0.110 G_Rec: 0.336 D_GP: 0.072 D_real: 0.753 D_fake: 0.612 
+(epoch: 372, iters: 1038, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 1.147 G_ID: 0.113 G_Rec: 0.451 D_GP: 0.100 D_real: 0.310 D_fake: 0.693 
+(epoch: 372, iters: 1438, time: 0.064) G_GAN: 0.652 G_GAN_Feat: 0.761 G_ID: 0.114 G_Rec: 0.310 D_GP: 0.077 D_real: 1.337 D_fake: 0.546 
+(epoch: 372, iters: 1838, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.891 G_ID: 0.118 G_Rec: 0.398 D_GP: 0.030 D_real: 1.038 D_fake: 0.666 
+(epoch: 372, iters: 2238, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.708 G_ID: 0.097 G_Rec: 0.309 D_GP: 0.033 D_real: 1.121 D_fake: 0.802 
+(epoch: 372, iters: 2638, time: 0.064) G_GAN: 0.101 G_GAN_Feat: 1.085 G_ID: 0.143 G_Rec: 0.461 D_GP: 0.109 D_real: 0.353 D_fake: 0.907 
+(epoch: 372, iters: 3038, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 0.983 G_ID: 0.104 G_Rec: 0.342 D_GP: 0.039 D_real: 1.480 D_fake: 0.773 
+(epoch: 372, iters: 3438, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 0.929 G_ID: 0.132 G_Rec: 0.432 D_GP: 0.032 D_real: 1.298 D_fake: 0.389 
+(epoch: 372, iters: 3838, time: 0.064) G_GAN: 0.508 G_GAN_Feat: 0.772 G_ID: 0.110 G_Rec: 0.295 D_GP: 0.036 D_real: 1.337 D_fake: 0.513 
+(epoch: 372, iters: 4238, time: 0.064) G_GAN: 0.709 G_GAN_Feat: 1.208 G_ID: 0.119 G_Rec: 0.502 D_GP: 0.648 D_real: 0.494 D_fake: 0.361 
+(epoch: 372, iters: 4638, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.722 G_ID: 0.102 G_Rec: 0.309 D_GP: 0.028 D_real: 1.052 D_fake: 0.858 
+(epoch: 372, iters: 5038, time: 0.064) G_GAN: 0.636 G_GAN_Feat: 0.847 G_ID: 0.129 G_Rec: 0.404 D_GP: 0.031 D_real: 1.377 D_fake: 0.371 
+(epoch: 372, iters: 5438, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.629 G_ID: 0.092 G_Rec: 0.285 D_GP: 0.028 D_real: 1.058 D_fake: 0.915 
+(epoch: 372, iters: 5838, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.929 G_ID: 0.140 G_Rec: 0.426 D_GP: 0.038 D_real: 0.878 D_fake: 0.727 
+(epoch: 372, iters: 6238, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.710 G_ID: 0.088 G_Rec: 0.295 D_GP: 0.083 D_real: 0.892 D_fake: 0.787 
+(epoch: 372, iters: 6638, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 0.876 G_ID: 0.140 G_Rec: 0.440 D_GP: 0.033 D_real: 1.105 D_fake: 0.665 
+(epoch: 372, iters: 7038, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.760 G_ID: 0.125 G_Rec: 0.328 D_GP: 0.038 D_real: 0.951 D_fake: 0.817 
+(epoch: 372, iters: 7438, time: 0.064) G_GAN: 0.622 G_GAN_Feat: 0.845 G_ID: 0.119 G_Rec: 0.372 D_GP: 0.032 D_real: 1.287 D_fake: 0.414 
+(epoch: 372, iters: 7838, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.805 G_ID: 0.102 G_Rec: 0.357 D_GP: 0.051 D_real: 1.086 D_fake: 0.642 
+(epoch: 372, iters: 8238, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 1.218 G_ID: 0.121 G_Rec: 0.444 D_GP: 0.053 D_real: 1.501 D_fake: 0.799 
+(epoch: 372, iters: 8638, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.721 G_ID: 0.100 G_Rec: 0.309 D_GP: 0.035 D_real: 1.013 D_fake: 0.840 
+(epoch: 373, iters: 430, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 1.126 G_ID: 0.128 G_Rec: 0.490 D_GP: 0.144 D_real: 0.415 D_fake: 0.713 
+(epoch: 373, iters: 830, time: 0.064) G_GAN: 0.520 G_GAN_Feat: 0.723 G_ID: 0.101 G_Rec: 0.305 D_GP: 0.035 D_real: 1.402 D_fake: 0.488 
+(epoch: 373, iters: 1230, time: 0.064) G_GAN: 0.732 G_GAN_Feat: 1.019 G_ID: 0.113 G_Rec: 0.409 D_GP: 0.061 D_real: 1.046 D_fake: 0.286 
+(epoch: 373, iters: 1630, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.915 G_ID: 0.110 G_Rec: 0.336 D_GP: 0.117 D_real: 0.319 D_fake: 0.805 
+(epoch: 373, iters: 2030, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 1.053 G_ID: 0.138 G_Rec: 0.477 D_GP: 0.062 D_real: 0.581 D_fake: 0.667 
+(epoch: 373, iters: 2430, time: 0.064) G_GAN: 0.199 G_GAN_Feat: 0.752 G_ID: 0.103 G_Rec: 0.307 D_GP: 0.029 D_real: 1.061 D_fake: 0.801 
+(epoch: 373, iters: 2830, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 1.005 G_ID: 0.123 G_Rec: 0.503 D_GP: 0.039 D_real: 0.821 D_fake: 0.835 
+(epoch: 373, iters: 3230, time: 0.064) G_GAN: 0.204 G_GAN_Feat: 0.686 G_ID: 0.099 G_Rec: 0.300 D_GP: 0.032 D_real: 1.114 D_fake: 0.796 
+(epoch: 373, iters: 3630, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.902 G_ID: 0.113 G_Rec: 0.382 D_GP: 0.035 D_real: 0.956 D_fake: 0.703 
+(epoch: 373, iters: 4030, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.814 G_ID: 0.105 G_Rec: 0.323 D_GP: 0.062 D_real: 0.898 D_fake: 0.874 
+(epoch: 373, iters: 4430, time: 0.064) G_GAN: 0.444 G_GAN_Feat: 1.233 G_ID: 0.123 G_Rec: 0.471 D_GP: 0.054 D_real: 0.320 D_fake: 0.560 
+(epoch: 373, iters: 4830, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.649 G_ID: 0.107 G_Rec: 0.312 D_GP: 0.029 D_real: 1.108 D_fake: 0.850 
+(epoch: 373, iters: 5230, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.874 G_ID: 0.137 G_Rec: 0.404 D_GP: 0.032 D_real: 0.981 D_fake: 0.738 
+(epoch: 373, iters: 5630, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.609 G_ID: 0.100 G_Rec: 0.276 D_GP: 0.029 D_real: 1.197 D_fake: 0.704 
+(epoch: 373, iters: 6030, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.918 G_ID: 0.120 G_Rec: 0.446 D_GP: 0.035 D_real: 1.105 D_fake: 0.571 
+(epoch: 373, iters: 6430, time: 0.064) G_GAN: -0.168 G_GAN_Feat: 0.629 G_ID: 0.102 G_Rec: 0.272 D_GP: 0.033 D_real: 0.731 D_fake: 1.168 
+(epoch: 373, iters: 6830, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 0.814 G_ID: 0.106 G_Rec: 0.379 D_GP: 0.033 D_real: 1.166 D_fake: 0.576 
+(epoch: 373, iters: 7230, time: 0.064) G_GAN: -0.168 G_GAN_Feat: 0.755 G_ID: 0.131 G_Rec: 0.329 D_GP: 0.065 D_real: 0.546 D_fake: 1.168 
+(epoch: 373, iters: 7630, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.901 G_ID: 0.118 G_Rec: 0.406 D_GP: 0.048 D_real: 0.821 D_fake: 0.701 
+(epoch: 373, iters: 8030, time: 0.064) G_GAN: 0.133 G_GAN_Feat: 0.771 G_ID: 0.107 G_Rec: 0.306 D_GP: 0.032 D_real: 0.885 D_fake: 0.867 
+(epoch: 373, iters: 8430, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.984 G_ID: 0.123 G_Rec: 0.439 D_GP: 0.062 D_real: 1.066 D_fake: 0.491 
+(epoch: 374, iters: 222, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.740 G_ID: 0.111 G_Rec: 0.320 D_GP: 0.043 D_real: 1.077 D_fake: 0.714 
+(epoch: 374, iters: 622, time: 0.064) G_GAN: 0.744 G_GAN_Feat: 1.133 G_ID: 0.117 G_Rec: 0.498 D_GP: 0.737 D_real: 0.619 D_fake: 0.330 
+(epoch: 374, iters: 1022, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.687 G_ID: 0.108 G_Rec: 0.291 D_GP: 0.030 D_real: 1.099 D_fake: 0.771 
+(epoch: 374, iters: 1422, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 1.022 G_ID: 0.131 G_Rec: 0.402 D_GP: 0.084 D_real: 0.360 D_fake: 0.782 
+(epoch: 374, iters: 1822, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.789 G_ID: 0.101 G_Rec: 0.302 D_GP: 0.044 D_real: 0.885 D_fake: 0.786 
+(epoch: 374, iters: 2222, time: 0.064) G_GAN: 0.620 G_GAN_Feat: 0.970 G_ID: 0.124 G_Rec: 0.402 D_GP: 0.053 D_real: 1.145 D_fake: 0.412 
+(epoch: 374, iters: 2622, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.860 G_ID: 0.103 G_Rec: 0.351 D_GP: 0.040 D_real: 0.906 D_fake: 0.826 
+(epoch: 374, iters: 3022, time: 0.063) G_GAN: 0.945 G_GAN_Feat: 1.029 G_ID: 0.117 G_Rec: 0.449 D_GP: 0.048 D_real: 1.238 D_fake: 0.163 
+(epoch: 374, iters: 3422, time: 0.063) G_GAN: -0.120 G_GAN_Feat: 0.982 G_ID: 0.096 G_Rec: 0.351 D_GP: 0.244 D_real: 0.289 D_fake: 1.120 
+(epoch: 374, iters: 3822, time: 0.063) G_GAN: 0.394 G_GAN_Feat: 0.964 G_ID: 0.136 G_Rec: 0.417 D_GP: 0.034 D_real: 0.888 D_fake: 0.620 
+(epoch: 374, iters: 4222, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.796 G_ID: 0.107 G_Rec: 0.331 D_GP: 0.030 D_real: 1.087 D_fake: 0.643 
+(epoch: 374, iters: 4622, time: 0.063) G_GAN: 0.762 G_GAN_Feat: 1.119 G_ID: 0.118 G_Rec: 0.426 D_GP: 0.078 D_real: 0.706 D_fake: 0.384 
+(epoch: 374, iters: 5022, time: 0.063) G_GAN: -0.127 G_GAN_Feat: 0.835 G_ID: 0.096 G_Rec: 0.288 D_GP: 0.037 D_real: 0.579 D_fake: 1.127 
+(epoch: 374, iters: 5422, time: 0.063) G_GAN: 0.432 G_GAN_Feat: 1.009 G_ID: 0.124 G_Rec: 0.458 D_GP: 0.037 D_real: 1.002 D_fake: 0.571 
+(epoch: 374, iters: 5822, time: 0.064) G_GAN: -0.164 G_GAN_Feat: 0.827 G_ID: 0.105 G_Rec: 0.333 D_GP: 0.068 D_real: 0.406 D_fake: 1.164 
+(epoch: 374, iters: 6222, time: 0.063) G_GAN: 0.672 G_GAN_Feat: 1.031 G_ID: 0.137 G_Rec: 0.454 D_GP: 0.051 D_real: 0.930 D_fake: 0.346 
+(epoch: 374, iters: 6622, time: 0.063) G_GAN: 0.926 G_GAN_Feat: 1.004 G_ID: 0.110 G_Rec: 0.358 D_GP: 0.071 D_real: 1.320 D_fake: 0.332 
+(epoch: 374, iters: 7022, time: 0.063) G_GAN: 0.550 G_GAN_Feat: 0.982 G_ID: 0.123 G_Rec: 0.463 D_GP: 0.030 D_real: 1.274 D_fake: 0.472 
+(epoch: 374, iters: 7422, time: 0.063) G_GAN: -0.049 G_GAN_Feat: 0.708 G_ID: 0.110 G_Rec: 0.305 D_GP: 0.028 D_real: 0.876 D_fake: 1.049 
+(epoch: 374, iters: 7822, time: 0.063) G_GAN: 0.423 G_GAN_Feat: 0.901 G_ID: 0.125 G_Rec: 0.455 D_GP: 0.040 D_real: 1.187 D_fake: 0.593 
+(epoch: 374, iters: 8222, time: 0.063) G_GAN: 0.021 G_GAN_Feat: 0.693 G_ID: 0.096 G_Rec: 0.305 D_GP: 0.035 D_real: 0.837 D_fake: 0.979 
+(epoch: 374, iters: 8622, time: 0.064) G_GAN: -0.011 G_GAN_Feat: 0.821 G_ID: 0.132 G_Rec: 0.401 D_GP: 0.027 D_real: 0.896 D_fake: 1.011 
+(epoch: 375, iters: 414, time: 0.063) G_GAN: 0.324 G_GAN_Feat: 0.692 G_ID: 0.093 G_Rec: 0.307 D_GP: 0.026 D_real: 1.257 D_fake: 0.678 
+(epoch: 375, iters: 814, time: 0.063) G_GAN: 0.409 G_GAN_Feat: 0.871 G_ID: 0.122 G_Rec: 0.392 D_GP: 0.036 D_real: 1.105 D_fake: 0.596 
+(epoch: 375, iters: 1214, time: 0.063) G_GAN: 0.249 G_GAN_Feat: 0.723 G_ID: 0.094 G_Rec: 0.304 D_GP: 0.041 D_real: 1.083 D_fake: 0.751 
+(epoch: 375, iters: 1614, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.942 G_ID: 0.112 G_Rec: 0.459 D_GP: 0.051 D_real: 1.066 D_fake: 0.567 
+(epoch: 375, iters: 2014, time: 0.063) G_GAN: -0.091 G_GAN_Feat: 0.792 G_ID: 0.109 G_Rec: 0.331 D_GP: 0.046 D_real: 0.641 D_fake: 1.091 
+(epoch: 375, iters: 2414, time: 0.063) G_GAN: 0.191 G_GAN_Feat: 1.021 G_ID: 0.124 G_Rec: 0.498 D_GP: 0.050 D_real: 0.795 D_fake: 0.810 
+(epoch: 375, iters: 2814, time: 0.063) G_GAN: 0.139 G_GAN_Feat: 0.816 G_ID: 0.107 G_Rec: 0.336 D_GP: 0.076 D_real: 0.667 D_fake: 0.861 
+(epoch: 375, iters: 3214, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 0.995 G_ID: 0.130 G_Rec: 0.424 D_GP: 0.056 D_real: 0.948 D_fake: 0.518 
+(epoch: 375, iters: 3614, time: 0.063) G_GAN: 0.475 G_GAN_Feat: 0.909 G_ID: 0.109 G_Rec: 0.340 D_GP: 0.180 D_real: 0.776 D_fake: 0.570 
+(epoch: 375, iters: 4014, time: 0.063) G_GAN: 0.971 G_GAN_Feat: 1.253 G_ID: 0.115 G_Rec: 0.500 D_GP: 0.036 D_real: 1.680 D_fake: 0.171 
+(epoch: 375, iters: 4414, time: 0.063) G_GAN: 0.145 G_GAN_Feat: 0.807 G_ID: 0.111 G_Rec: 0.348 D_GP: 0.056 D_real: 0.945 D_fake: 0.856 
+(epoch: 375, iters: 4814, time: 0.064) G_GAN: 0.697 G_GAN_Feat: 0.999 G_ID: 0.105 G_Rec: 0.434 D_GP: 0.080 D_real: 1.050 D_fake: 0.343 
+(epoch: 375, iters: 5214, time: 0.063) G_GAN: 0.282 G_GAN_Feat: 0.814 G_ID: 0.102 G_Rec: 0.308 D_GP: 0.057 D_real: 0.991 D_fake: 0.752 
+(epoch: 375, iters: 5614, time: 0.063) G_GAN: 0.684 G_GAN_Feat: 1.123 G_ID: 0.131 G_Rec: 0.425 D_GP: 0.052 D_real: 0.537 D_fake: 0.348 
+(epoch: 375, iters: 6014, time: 0.063) G_GAN: 0.226 G_GAN_Feat: 0.936 G_ID: 0.100 G_Rec: 0.326 D_GP: 0.167 D_real: 0.544 D_fake: 0.774 
+(epoch: 375, iters: 6414, time: 0.064) G_GAN: 0.788 G_GAN_Feat: 1.075 G_ID: 0.128 G_Rec: 0.433 D_GP: 0.056 D_real: 0.720 D_fake: 0.253 
+(epoch: 375, iters: 6814, time: 0.063) G_GAN: 0.492 G_GAN_Feat: 0.934 G_ID: 0.102 G_Rec: 0.360 D_GP: 0.055 D_real: 0.893 D_fake: 0.527 
+(epoch: 375, iters: 7214, time: 0.063) G_GAN: 0.422 G_GAN_Feat: 0.842 G_ID: 0.124 G_Rec: 0.404 D_GP: 0.034 D_real: 1.109 D_fake: 0.582 
+(epoch: 375, iters: 7614, time: 0.063) G_GAN: 0.142 G_GAN_Feat: 0.662 G_ID: 0.101 G_Rec: 0.285 D_GP: 0.032 D_real: 1.007 D_fake: 0.858 
+(epoch: 375, iters: 8014, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 1.007 G_ID: 0.127 G_Rec: 0.471 D_GP: 0.048 D_real: 0.739 D_fake: 0.679 
+(epoch: 375, iters: 8414, time: 0.063) G_GAN: 0.096 G_GAN_Feat: 0.854 G_ID: 0.111 G_Rec: 0.384 D_GP: 0.086 D_real: 0.783 D_fake: 0.905 
+(epoch: 376, iters: 206, time: 0.063) G_GAN: 0.524 G_GAN_Feat: 0.903 G_ID: 0.120 G_Rec: 0.429 D_GP: 0.031 D_real: 1.184 D_fake: 0.482 
+(epoch: 376, iters: 606, time: 0.063) G_GAN: 0.147 G_GAN_Feat: 0.882 G_ID: 0.107 G_Rec: 0.360 D_GP: 0.347 D_real: 0.499 D_fake: 0.868 
+(epoch: 376, iters: 1006, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.964 G_ID: 0.125 G_Rec: 0.435 D_GP: 0.058 D_real: 0.949 D_fake: 0.581 
+(epoch: 376, iters: 1406, time: 0.063) G_GAN: 0.299 G_GAN_Feat: 0.852 G_ID: 0.102 G_Rec: 0.361 D_GP: 0.097 D_real: 0.882 D_fake: 0.707 
+(epoch: 376, iters: 1806, time: 0.063) G_GAN: 0.505 G_GAN_Feat: 1.016 G_ID: 0.176 G_Rec: 0.493 D_GP: 0.051 D_real: 1.059 D_fake: 0.498 
+(epoch: 376, iters: 2206, time: 0.063) G_GAN: 0.355 G_GAN_Feat: 0.734 G_ID: 0.095 G_Rec: 0.314 D_GP: 0.033 D_real: 1.184 D_fake: 0.649 
+(epoch: 376, iters: 2606, time: 0.064) G_GAN: 0.640 G_GAN_Feat: 1.011 G_ID: 0.121 G_Rec: 0.437 D_GP: 0.039 D_real: 1.019 D_fake: 0.393 
+(epoch: 376, iters: 3006, time: 0.064) G_GAN: 0.644 G_GAN_Feat: 0.768 G_ID: 0.097 G_Rec: 0.332 D_GP: 0.036 D_real: 1.551 D_fake: 0.370 
+(epoch: 376, iters: 3406, time: 0.064) G_GAN: 0.758 G_GAN_Feat: 0.934 G_ID: 0.108 G_Rec: 0.391 D_GP: 0.039 D_real: 1.409 D_fake: 0.277 
+(epoch: 376, iters: 3806, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.741 G_ID: 0.116 G_Rec: 0.272 D_GP: 0.038 D_real: 1.223 D_fake: 0.607 
+(epoch: 376, iters: 4206, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 1.187 G_ID: 0.134 G_Rec: 0.424 D_GP: 0.035 D_real: 1.157 D_fake: 0.710 
+(epoch: 376, iters: 4606, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.617 G_ID: 0.108 G_Rec: 0.271 D_GP: 0.028 D_real: 1.108 D_fake: 0.913 
+(epoch: 376, iters: 5006, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.827 G_ID: 0.114 G_Rec: 0.462 D_GP: 0.030 D_real: 1.082 D_fake: 0.651 
+(epoch: 376, iters: 5406, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.678 G_ID: 0.097 G_Rec: 0.314 D_GP: 0.038 D_real: 1.061 D_fake: 0.820 
+(epoch: 376, iters: 5806, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.969 G_ID: 0.121 G_Rec: 0.482 D_GP: 0.055 D_real: 0.796 D_fake: 0.715 
+(epoch: 376, iters: 6206, time: 0.064) G_GAN: 0.116 G_GAN_Feat: 0.816 G_ID: 0.108 G_Rec: 0.360 D_GP: 0.114 D_real: 0.726 D_fake: 0.885 
+(epoch: 376, iters: 6606, time: 0.064) G_GAN: 0.562 G_GAN_Feat: 0.985 G_ID: 0.120 G_Rec: 0.416 D_GP: 0.034 D_real: 1.028 D_fake: 0.453 
+(epoch: 376, iters: 7006, time: 0.064) G_GAN: 0.326 G_GAN_Feat: 0.686 G_ID: 0.086 G_Rec: 0.296 D_GP: 0.035 D_real: 1.220 D_fake: 0.674 
+(epoch: 376, iters: 7406, time: 0.064) G_GAN: 0.472 G_GAN_Feat: 0.991 G_ID: 0.120 G_Rec: 0.453 D_GP: 0.063 D_real: 0.844 D_fake: 0.534 
+(epoch: 376, iters: 7806, time: 0.064) G_GAN: 0.219 G_GAN_Feat: 0.782 G_ID: 0.103 G_Rec: 0.313 D_GP: 0.043 D_real: 0.961 D_fake: 0.781 
+(epoch: 376, iters: 8206, time: 0.064) G_GAN: 0.507 G_GAN_Feat: 0.937 G_ID: 0.128 G_Rec: 0.410 D_GP: 0.035 D_real: 1.150 D_fake: 0.498 
+(epoch: 376, iters: 8606, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.761 G_ID: 0.087 G_Rec: 0.315 D_GP: 0.033 D_real: 1.050 D_fake: 0.833 
+(epoch: 377, iters: 398, time: 0.064) G_GAN: 0.580 G_GAN_Feat: 1.013 G_ID: 0.119 G_Rec: 0.452 D_GP: 0.036 D_real: 1.035 D_fake: 0.431 
+(epoch: 377, iters: 798, time: 0.063) G_GAN: -0.068 G_GAN_Feat: 0.941 G_ID: 0.113 G_Rec: 0.337 D_GP: 0.578 D_real: 0.206 D_fake: 1.079 
+(epoch: 377, iters: 1198, time: 0.063) G_GAN: 0.450 G_GAN_Feat: 1.001 G_ID: 0.109 G_Rec: 0.428 D_GP: 0.049 D_real: 0.853 D_fake: 0.561 
+(epoch: 377, iters: 1598, time: 0.063) G_GAN: 0.109 G_GAN_Feat: 0.707 G_ID: 0.119 G_Rec: 0.361 D_GP: 0.029 D_real: 0.924 D_fake: 0.891 
+(epoch: 377, iters: 1998, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 1.086 G_ID: 0.120 G_Rec: 0.450 D_GP: 0.042 D_real: 0.861 D_fake: 0.493 
+(epoch: 377, iters: 2398, time: 0.063) G_GAN: 0.332 G_GAN_Feat: 1.021 G_ID: 0.093 G_Rec: 0.349 D_GP: 0.107 D_real: 0.479 D_fake: 0.677 
+(epoch: 377, iters: 2798, time: 0.063) G_GAN: 0.529 G_GAN_Feat: 0.967 G_ID: 0.133 G_Rec: 0.441 D_GP: 0.034 D_real: 1.093 D_fake: 0.488 
+(epoch: 377, iters: 3198, time: 0.063) G_GAN: 0.109 G_GAN_Feat: 0.697 G_ID: 0.122 G_Rec: 0.313 D_GP: 0.038 D_real: 0.996 D_fake: 0.891 
+(epoch: 377, iters: 3598, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.931 G_ID: 0.111 G_Rec: 0.429 D_GP: 0.033 D_real: 0.976 D_fake: 0.665 
+(epoch: 377, iters: 3998, time: 0.063) G_GAN: 0.143 G_GAN_Feat: 0.666 G_ID: 0.102 G_Rec: 0.288 D_GP: 0.042 D_real: 0.971 D_fake: 0.857 
+(epoch: 377, iters: 4398, time: 0.063) G_GAN: 0.231 G_GAN_Feat: 0.977 G_ID: 0.142 G_Rec: 0.457 D_GP: 0.040 D_real: 1.041 D_fake: 0.776 
+(epoch: 377, iters: 4798, time: 0.063) G_GAN: 0.159 G_GAN_Feat: 0.723 G_ID: 0.090 G_Rec: 0.305 D_GP: 0.031 D_real: 1.039 D_fake: 0.841 
+(epoch: 377, iters: 5198, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.953 G_ID: 0.130 G_Rec: 0.425 D_GP: 0.063 D_real: 0.880 D_fake: 0.592 
+(epoch: 377, iters: 5598, time: 0.063) G_GAN: 0.118 G_GAN_Feat: 0.852 G_ID: 0.107 G_Rec: 0.325 D_GP: 0.059 D_real: 0.560 D_fake: 0.882 
+(epoch: 377, iters: 5998, time: 0.063) G_GAN: 0.316 G_GAN_Feat: 1.014 G_ID: 0.109 G_Rec: 0.480 D_GP: 0.047 D_real: 0.888 D_fake: 0.685 
+(epoch: 377, iters: 6398, time: 0.063) G_GAN: 0.209 G_GAN_Feat: 0.857 G_ID: 0.103 G_Rec: 0.330 D_GP: 0.037 D_real: 1.006 D_fake: 0.791 
+(epoch: 377, iters: 6798, time: 0.064) G_GAN: 0.996 G_GAN_Feat: 1.008 G_ID: 0.120 G_Rec: 0.434 D_GP: 0.035 D_real: 1.554 D_fake: 0.115 
+(epoch: 377, iters: 7198, time: 0.063) G_GAN: 0.458 G_GAN_Feat: 0.798 G_ID: 0.096 G_Rec: 0.319 D_GP: 0.055 D_real: 1.445 D_fake: 0.544 
+(epoch: 377, iters: 7598, time: 0.063) G_GAN: 0.283 G_GAN_Feat: 0.995 G_ID: 0.148 G_Rec: 0.442 D_GP: 0.033 D_real: 0.901 D_fake: 0.719 
+(epoch: 377, iters: 7998, time: 0.063) G_GAN: -0.124 G_GAN_Feat: 0.867 G_ID: 0.107 G_Rec: 0.321 D_GP: 0.048 D_real: 0.538 D_fake: 1.124 
+(epoch: 377, iters: 8398, time: 0.064) G_GAN: 0.679 G_GAN_Feat: 1.026 G_ID: 0.119 G_Rec: 0.440 D_GP: 0.052 D_real: 1.110 D_fake: 0.354 
+(epoch: 378, iters: 190, time: 0.063) G_GAN: 0.271 G_GAN_Feat: 0.804 G_ID: 0.114 G_Rec: 0.310 D_GP: 0.041 D_real: 1.022 D_fake: 0.731 
+(epoch: 378, iters: 590, time: 0.063) G_GAN: 0.509 G_GAN_Feat: 0.922 G_ID: 0.115 G_Rec: 0.456 D_GP: 0.026 D_real: 1.230 D_fake: 0.497 
+(epoch: 378, iters: 990, time: 0.063) G_GAN: 0.104 G_GAN_Feat: 0.793 G_ID: 0.107 G_Rec: 0.335 D_GP: 0.042 D_real: 0.755 D_fake: 0.896 
+(epoch: 378, iters: 1390, time: 0.064) G_GAN: 0.678 G_GAN_Feat: 1.002 G_ID: 0.126 G_Rec: 0.440 D_GP: 0.036 D_real: 0.979 D_fake: 0.346 
+(epoch: 378, iters: 1790, time: 0.063) G_GAN: 0.418 G_GAN_Feat: 1.007 G_ID: 0.114 G_Rec: 0.356 D_GP: 0.416 D_real: 0.314 D_fake: 0.712 
+(epoch: 378, iters: 2190, time: 0.063) G_GAN: 0.416 G_GAN_Feat: 0.858 G_ID: 0.130 G_Rec: 0.392 D_GP: 0.036 D_real: 1.179 D_fake: 0.589 
+(epoch: 378, iters: 2590, time: 0.063) G_GAN: 0.108 G_GAN_Feat: 0.692 G_ID: 0.102 G_Rec: 0.293 D_GP: 0.028 D_real: 1.064 D_fake: 0.893 
+(epoch: 378, iters: 2990, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 1.003 G_ID: 0.135 G_Rec: 0.453 D_GP: 0.040 D_real: 0.892 D_fake: 0.764 
+(epoch: 378, iters: 3390, time: 0.063) G_GAN: 0.133 G_GAN_Feat: 0.701 G_ID: 0.105 G_Rec: 0.296 D_GP: 0.040 D_real: 1.023 D_fake: 0.870 
+(epoch: 378, iters: 3790, time: 0.063) G_GAN: 0.125 G_GAN_Feat: 1.040 G_ID: 0.135 G_Rec: 0.462 D_GP: 0.071 D_real: 0.438 D_fake: 0.875 
+(epoch: 378, iters: 4190, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.756 G_ID: 0.110 G_Rec: 0.333 D_GP: 0.034 D_real: 0.988 D_fake: 0.847 
+(epoch: 378, iters: 4590, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 0.985 G_ID: 0.122 G_Rec: 0.484 D_GP: 0.042 D_real: 1.262 D_fake: 0.509 
+(epoch: 378, iters: 4990, time: 0.063) G_GAN: 0.042 G_GAN_Feat: 0.886 G_ID: 0.110 G_Rec: 0.335 D_GP: 0.052 D_real: 0.405 D_fake: 0.958 
+(epoch: 378, iters: 5390, time: 0.063) G_GAN: 0.713 G_GAN_Feat: 0.999 G_ID: 0.124 G_Rec: 0.424 D_GP: 0.037 D_real: 1.180 D_fake: 0.309 
+(epoch: 378, iters: 5790, time: 0.063) G_GAN: 0.237 G_GAN_Feat: 0.836 G_ID: 0.100 G_Rec: 0.341 D_GP: 0.034 D_real: 1.046 D_fake: 0.769 
+(epoch: 378, iters: 6190, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.900 G_ID: 0.129 G_Rec: 0.423 D_GP: 0.028 D_real: 1.110 D_fake: 0.669 
+(epoch: 378, iters: 6590, time: 0.063) G_GAN: 0.119 G_GAN_Feat: 0.726 G_ID: 0.100 G_Rec: 0.293 D_GP: 0.031 D_real: 0.944 D_fake: 0.881 
+(epoch: 378, iters: 6990, time: 0.063) G_GAN: 0.388 G_GAN_Feat: 1.031 G_ID: 0.140 G_Rec: 0.496 D_GP: 0.095 D_real: 0.838 D_fake: 0.624 
+(epoch: 378, iters: 7390, time: 0.063) G_GAN: 0.069 G_GAN_Feat: 0.747 G_ID: 0.098 G_Rec: 0.308 D_GP: 0.040 D_real: 0.933 D_fake: 0.932 
+(epoch: 378, iters: 7790, time: 0.064) G_GAN: 0.494 G_GAN_Feat: 0.926 G_ID: 0.121 G_Rec: 0.415 D_GP: 0.038 D_real: 1.042 D_fake: 0.527 
+(epoch: 378, iters: 8190, time: 0.063) G_GAN: -0.035 G_GAN_Feat: 0.767 G_ID: 0.095 G_Rec: 0.326 D_GP: 0.047 D_real: 0.728 D_fake: 1.035 
+(epoch: 378, iters: 8590, time: 0.063) G_GAN: 0.530 G_GAN_Feat: 1.029 G_ID: 0.121 G_Rec: 0.456 D_GP: 0.045 D_real: 0.833 D_fake: 0.489 
+(epoch: 379, iters: 382, time: 0.063) G_GAN: 0.220 G_GAN_Feat: 0.673 G_ID: 0.105 G_Rec: 0.269 D_GP: 0.034 D_real: 1.092 D_fake: 0.780 
+(epoch: 379, iters: 782, time: 0.064) G_GAN: 0.665 G_GAN_Feat: 0.956 G_ID: 0.112 G_Rec: 0.494 D_GP: 0.037 D_real: 1.285 D_fake: 0.352 
+(epoch: 379, iters: 1182, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.869 G_ID: 0.079 G_Rec: 0.308 D_GP: 0.124 D_real: 0.730 D_fake: 0.526 
+(epoch: 379, iters: 1582, time: 0.063) G_GAN: 0.491 G_GAN_Feat: 1.218 G_ID: 0.138 G_Rec: 0.557 D_GP: 0.043 D_real: 0.987 D_fake: 0.541 
+(epoch: 379, iters: 1982, time: 0.063) G_GAN: -0.050 G_GAN_Feat: 0.709 G_ID: 0.110 G_Rec: 0.305 D_GP: 0.030 D_real: 0.824 D_fake: 1.050 
+(epoch: 379, iters: 2382, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 1.035 G_ID: 0.140 G_Rec: 0.467 D_GP: 0.120 D_real: 0.530 D_fake: 0.548 
+(epoch: 379, iters: 2782, time: 0.063) G_GAN: 0.432 G_GAN_Feat: 0.827 G_ID: 0.089 G_Rec: 0.315 D_GP: 0.059 D_real: 0.933 D_fake: 0.573 
+(epoch: 379, iters: 3182, time: 0.063) G_GAN: 0.682 G_GAN_Feat: 0.995 G_ID: 0.132 G_Rec: 0.414 D_GP: 0.047 D_real: 1.255 D_fake: 0.343 
+(epoch: 379, iters: 3582, time: 0.063) G_GAN: 0.102 G_GAN_Feat: 0.805 G_ID: 0.109 G_Rec: 0.329 D_GP: 0.062 D_real: 0.748 D_fake: 0.898 
+(epoch: 379, iters: 3982, time: 0.063) G_GAN: 1.118 G_GAN_Feat: 0.951 G_ID: 0.158 G_Rec: 0.421 D_GP: 0.035 D_real: 1.666 D_fake: 0.106 
+(epoch: 379, iters: 4382, time: 0.063) G_GAN: 0.336 G_GAN_Feat: 0.734 G_ID: 0.100 G_Rec: 0.292 D_GP: 0.035 D_real: 1.114 D_fake: 0.667 
+(epoch: 379, iters: 4782, time: 0.063) G_GAN: 0.678 G_GAN_Feat: 1.050 G_ID: 0.124 G_Rec: 0.495 D_GP: 0.036 D_real: 1.211 D_fake: 0.367 
+(epoch: 379, iters: 5182, time: 0.064) G_GAN: 0.071 G_GAN_Feat: 0.879 G_ID: 0.106 G_Rec: 0.342 D_GP: 0.116 D_real: 0.667 D_fake: 0.929 
+(epoch: 379, iters: 5582, time: 0.063) G_GAN: 0.606 G_GAN_Feat: 1.127 G_ID: 0.114 G_Rec: 0.473 D_GP: 0.079 D_real: 0.714 D_fake: 0.409 
+(epoch: 379, iters: 5982, time: 0.063) G_GAN: -0.109 G_GAN_Feat: 0.785 G_ID: 0.128 G_Rec: 0.347 D_GP: 0.035 D_real: 0.797 D_fake: 1.109 
+(epoch: 379, iters: 6382, time: 0.063) G_GAN: 0.673 G_GAN_Feat: 0.990 G_ID: 0.106 G_Rec: 0.440 D_GP: 0.033 D_real: 1.273 D_fake: 0.359 
+(epoch: 379, iters: 6782, time: 0.064) G_GAN: 0.234 G_GAN_Feat: 0.791 G_ID: 0.109 G_Rec: 0.340 D_GP: 0.037 D_real: 0.899 D_fake: 0.767 
+(epoch: 379, iters: 7182, time: 0.063) G_GAN: 0.559 G_GAN_Feat: 0.873 G_ID: 0.128 G_Rec: 0.395 D_GP: 0.029 D_real: 1.240 D_fake: 0.456 
+(epoch: 379, iters: 7582, time: 0.063) G_GAN: 0.158 G_GAN_Feat: 0.806 G_ID: 0.125 G_Rec: 0.340 D_GP: 0.036 D_real: 0.762 D_fake: 0.843 
+(epoch: 379, iters: 7982, time: 0.063) G_GAN: 0.330 G_GAN_Feat: 0.906 G_ID: 0.123 G_Rec: 0.414 D_GP: 0.034 D_real: 1.036 D_fake: 0.673 
+(epoch: 379, iters: 8382, time: 0.064) G_GAN: -0.235 G_GAN_Feat: 0.697 G_ID: 0.100 G_Rec: 0.290 D_GP: 0.033 D_real: 0.648 D_fake: 1.235 
+(epoch: 380, iters: 174, time: 0.063) G_GAN: 0.348 G_GAN_Feat: 1.080 G_ID: 0.118 G_Rec: 0.491 D_GP: 0.050 D_real: 0.647 D_fake: 0.668 
+(epoch: 380, iters: 574, time: 0.063) G_GAN: 0.077 G_GAN_Feat: 0.714 G_ID: 0.098 G_Rec: 0.299 D_GP: 0.043 D_real: 0.873 D_fake: 0.923 
+(epoch: 380, iters: 974, time: 0.063) G_GAN: 0.488 G_GAN_Feat: 1.011 G_ID: 0.109 G_Rec: 0.450 D_GP: 0.037 D_real: 1.028 D_fake: 0.517 
+(epoch: 380, iters: 1374, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.790 G_ID: 0.098 G_Rec: 0.307 D_GP: 0.034 D_real: 1.179 D_fake: 0.662 
+(epoch: 380, iters: 1774, time: 0.063) G_GAN: 0.900 G_GAN_Feat: 1.237 G_ID: 0.123 G_Rec: 0.463 D_GP: 0.072 D_real: 1.352 D_fake: 0.254 
+(epoch: 380, iters: 2174, time: 0.063) G_GAN: 0.166 G_GAN_Feat: 0.740 G_ID: 0.113 G_Rec: 0.299 D_GP: 0.029 D_real: 1.000 D_fake: 0.835 
+(epoch: 380, iters: 2574, time: 0.063) G_GAN: 0.189 G_GAN_Feat: 1.083 G_ID: 0.128 G_Rec: 0.431 D_GP: 0.049 D_real: 0.467 D_fake: 0.811 
+(epoch: 380, iters: 2974, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.804 G_ID: 0.098 G_Rec: 0.312 D_GP: 0.034 D_real: 0.809 D_fake: 0.893 
+(epoch: 380, iters: 3374, time: 0.063) G_GAN: 0.732 G_GAN_Feat: 1.098 G_ID: 0.116 G_Rec: 0.452 D_GP: 0.054 D_real: 1.093 D_fake: 0.308 
+(epoch: 380, iters: 3774, time: 0.063) G_GAN: 0.307 G_GAN_Feat: 0.907 G_ID: 0.094 G_Rec: 0.311 D_GP: 0.057 D_real: 0.625 D_fake: 0.695 
+(epoch: 380, iters: 4174, time: 0.063) G_GAN: 0.562 G_GAN_Feat: 0.845 G_ID: 0.112 G_Rec: 0.401 D_GP: 0.032 D_real: 1.270 D_fake: 0.459 
+(epoch: 380, iters: 4574, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.729 G_ID: 0.092 G_Rec: 0.325 D_GP: 0.038 D_real: 1.182 D_fake: 0.669 
+(epoch: 380, iters: 4974, time: 0.063) G_GAN: 0.533 G_GAN_Feat: 0.948 G_ID: 0.127 G_Rec: 0.440 D_GP: 0.039 D_real: 1.158 D_fake: 0.497 
+(epoch: 380, iters: 5374, time: 0.063) G_GAN: 0.262 G_GAN_Feat: 0.797 G_ID: 0.105 G_Rec: 0.336 D_GP: 0.082 D_real: 0.832 D_fake: 0.743 
+(epoch: 380, iters: 5774, time: 0.063) G_GAN: 0.558 G_GAN_Feat: 0.912 G_ID: 0.116 G_Rec: 0.417 D_GP: 0.033 D_real: 1.073 D_fake: 0.450 
+(epoch: 380, iters: 6174, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.848 G_ID: 0.117 G_Rec: 0.323 D_GP: 0.046 D_real: 0.883 D_fake: 0.858 
+(epoch: 380, iters: 6574, time: 0.063) G_GAN: 0.431 G_GAN_Feat: 1.079 G_ID: 0.109 G_Rec: 0.453 D_GP: 0.044 D_real: 0.637 D_fake: 0.572 
+(epoch: 380, iters: 6974, time: 0.063) G_GAN: -0.222 G_GAN_Feat: 0.888 G_ID: 0.114 G_Rec: 0.352 D_GP: 0.051 D_real: 0.412 D_fake: 1.222 
+(epoch: 380, iters: 7374, time: 0.063) G_GAN: 0.697 G_GAN_Feat: 1.016 G_ID: 0.120 G_Rec: 0.476 D_GP: 0.035 D_real: 1.323 D_fake: 0.325 
+(epoch: 380, iters: 7774, time: 0.064) G_GAN: 0.181 G_GAN_Feat: 0.734 G_ID: 0.099 G_Rec: 0.318 D_GP: 0.041 D_real: 1.026 D_fake: 0.819 
+(epoch: 380, iters: 8174, time: 0.063) G_GAN: 0.499 G_GAN_Feat: 1.039 G_ID: 0.123 G_Rec: 0.477 D_GP: 0.062 D_real: 0.778 D_fake: 0.511 
+(epoch: 380, iters: 8574, time: 0.063) G_GAN: -0.057 G_GAN_Feat: 0.987 G_ID: 0.123 G_Rec: 0.349 D_GP: 0.219 D_real: 0.392 D_fake: 1.060 
+(epoch: 381, iters: 366, time: 0.063) G_GAN: 0.834 G_GAN_Feat: 1.283 G_ID: 0.130 G_Rec: 0.520 D_GP: 0.636 D_real: 0.404 D_fake: 0.315 
+(epoch: 381, iters: 766, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.849 G_ID: 0.127 G_Rec: 0.300 D_GP: 0.037 D_real: 0.891 D_fake: 0.764 
+(epoch: 381, iters: 1166, time: 0.063) G_GAN: 0.305 G_GAN_Feat: 0.927 G_ID: 0.120 G_Rec: 0.481 D_GP: 0.037 D_real: 1.025 D_fake: 0.699 
+(epoch: 381, iters: 1566, time: 0.063) G_GAN: 0.275 G_GAN_Feat: 0.756 G_ID: 0.103 G_Rec: 0.331 D_GP: 0.036 D_real: 1.124 D_fake: 0.725 
+(epoch: 381, iters: 1966, time: 0.063) G_GAN: 0.739 G_GAN_Feat: 0.943 G_ID: 0.122 G_Rec: 0.440 D_GP: 0.044 D_real: 1.320 D_fake: 0.386 
+(epoch: 381, iters: 2366, time: 0.064) G_GAN: -0.055 G_GAN_Feat: 0.799 G_ID: 0.109 G_Rec: 0.354 D_GP: 0.097 D_real: 0.610 D_fake: 1.056 
+(epoch: 381, iters: 2766, time: 0.063) G_GAN: 0.211 G_GAN_Feat: 0.932 G_ID: 0.121 G_Rec: 0.438 D_GP: 0.056 D_real: 0.735 D_fake: 0.790 
+(epoch: 381, iters: 3166, time: 0.063) G_GAN: 0.306 G_GAN_Feat: 0.739 G_ID: 0.106 G_Rec: 0.318 D_GP: 0.037 D_real: 1.220 D_fake: 0.696 
+(epoch: 381, iters: 3566, time: 0.063) G_GAN: 0.494 G_GAN_Feat: 1.105 G_ID: 0.117 G_Rec: 0.486 D_GP: 0.093 D_real: 0.469 D_fake: 0.507 
+(epoch: 381, iters: 3966, time: 0.064) G_GAN: 0.175 G_GAN_Feat: 0.909 G_ID: 0.107 G_Rec: 0.328 D_GP: 0.293 D_real: 0.483 D_fake: 0.838 
+(epoch: 381, iters: 4366, time: 0.063) G_GAN: 0.532 G_GAN_Feat: 1.052 G_ID: 0.130 G_Rec: 0.412 D_GP: 0.042 D_real: 0.846 D_fake: 0.477 
+(epoch: 381, iters: 4766, time: 0.063) G_GAN: 0.082 G_GAN_Feat: 0.791 G_ID: 0.121 G_Rec: 0.329 D_GP: 0.034 D_real: 0.911 D_fake: 0.919 
+(epoch: 381, iters: 5166, time: 0.063) G_GAN: 0.472 G_GAN_Feat: 1.171 G_ID: 0.138 G_Rec: 0.478 D_GP: 0.153 D_real: 0.294 D_fake: 0.536 
+(epoch: 381, iters: 5566, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.737 G_ID: 0.094 G_Rec: 0.312 D_GP: 0.036 D_real: 1.039 D_fake: 0.757 
+(epoch: 381, iters: 5966, time: 0.063) G_GAN: 0.540 G_GAN_Feat: 1.054 G_ID: 0.129 G_Rec: 0.432 D_GP: 0.132 D_real: 0.610 D_fake: 0.471 
+(epoch: 381, iters: 6366, time: 0.063) G_GAN: -0.027 G_GAN_Feat: 0.871 G_ID: 0.103 G_Rec: 0.319 D_GP: 0.036 D_real: 0.352 D_fake: 1.027 
+(epoch: 381, iters: 6766, time: 0.063) G_GAN: 0.611 G_GAN_Feat: 1.153 G_ID: 0.126 G_Rec: 0.501 D_GP: 0.375 D_real: 0.829 D_fake: 0.518 
+(epoch: 381, iters: 7166, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.836 G_ID: 0.119 G_Rec: 0.318 D_GP: 0.035 D_real: 0.982 D_fake: 0.637 
+(epoch: 381, iters: 7566, time: 0.063) G_GAN: 0.196 G_GAN_Feat: 0.985 G_ID: 0.128 G_Rec: 0.457 D_GP: 0.033 D_real: 0.862 D_fake: 0.804 
+(epoch: 381, iters: 7966, time: 0.063) G_GAN: 0.314 G_GAN_Feat: 0.958 G_ID: 0.095 G_Rec: 0.354 D_GP: 0.125 D_real: 0.595 D_fake: 0.691 
+(epoch: 381, iters: 8366, time: 0.063) G_GAN: 0.580 G_GAN_Feat: 0.867 G_ID: 0.112 G_Rec: 0.436 D_GP: 0.029 D_real: 1.406 D_fake: 0.429 
+(epoch: 382, iters: 158, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.707 G_ID: 0.093 G_Rec: 0.321 D_GP: 0.033 D_real: 1.192 D_fake: 0.763 
+(epoch: 382, iters: 558, time: 0.063) G_GAN: 0.439 G_GAN_Feat: 0.937 G_ID: 0.122 G_Rec: 0.447 D_GP: 0.031 D_real: 1.038 D_fake: 0.563 
+(epoch: 382, iters: 958, time: 0.063) G_GAN: -0.036 G_GAN_Feat: 0.679 G_ID: 0.090 G_Rec: 0.303 D_GP: 0.031 D_real: 0.880 D_fake: 1.036 
+(epoch: 382, iters: 1358, time: 0.063) G_GAN: 0.281 G_GAN_Feat: 0.899 G_ID: 0.144 G_Rec: 0.402 D_GP: 0.035 D_real: 0.967 D_fake: 0.719 
+(epoch: 382, iters: 1758, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.821 G_ID: 0.112 G_Rec: 0.349 D_GP: 0.029 D_real: 1.019 D_fake: 0.858 
+(epoch: 382, iters: 2158, time: 0.063) G_GAN: 0.208 G_GAN_Feat: 0.903 G_ID: 0.129 G_Rec: 0.423 D_GP: 0.047 D_real: 0.767 D_fake: 0.794 
+(epoch: 382, iters: 2558, time: 0.063) G_GAN: 0.063 G_GAN_Feat: 0.738 G_ID: 0.110 G_Rec: 0.306 D_GP: 0.033 D_real: 0.894 D_fake: 0.937 
+(epoch: 382, iters: 2958, time: 0.063) G_GAN: 0.350 G_GAN_Feat: 1.111 G_ID: 0.133 G_Rec: 0.444 D_GP: 0.104 D_real: 0.349 D_fake: 0.655 
+(epoch: 382, iters: 3358, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.693 G_ID: 0.130 G_Rec: 0.299 D_GP: 0.032 D_real: 0.960 D_fake: 0.910 
+(epoch: 382, iters: 3758, time: 0.063) G_GAN: 0.471 G_GAN_Feat: 1.006 G_ID: 0.112 G_Rec: 0.425 D_GP: 0.033 D_real: 1.011 D_fake: 0.530 
+(epoch: 382, iters: 4158, time: 0.063) G_GAN: 0.465 G_GAN_Feat: 0.686 G_ID: 0.094 G_Rec: 0.282 D_GP: 0.035 D_real: 1.435 D_fake: 0.543 
+(epoch: 382, iters: 4558, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.945 G_ID: 0.108 G_Rec: 0.411 D_GP: 0.030 D_real: 1.102 D_fake: 0.536 
+(epoch: 382, iters: 4958, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.886 G_ID: 0.126 G_Rec: 0.360 D_GP: 0.211 D_real: 0.679 D_fake: 0.747 
+(epoch: 382, iters: 5358, time: 0.063) G_GAN: 0.276 G_GAN_Feat: 0.998 G_ID: 0.123 G_Rec: 0.412 D_GP: 0.103 D_real: 0.624 D_fake: 0.724 
+(epoch: 382, iters: 5758, time: 0.063) G_GAN: 0.279 G_GAN_Feat: 0.759 G_ID: 0.102 G_Rec: 0.328 D_GP: 0.033 D_real: 1.151 D_fake: 0.722 
+(epoch: 382, iters: 6158, time: 0.063) G_GAN: 0.677 G_GAN_Feat: 1.040 G_ID: 0.114 G_Rec: 0.444 D_GP: 0.108 D_real: 1.105 D_fake: 0.374 
+(epoch: 382, iters: 6558, time: 0.064) G_GAN: 0.671 G_GAN_Feat: 0.819 G_ID: 0.101 G_Rec: 0.323 D_GP: 0.037 D_real: 1.556 D_fake: 0.409 
+(epoch: 382, iters: 6958, time: 0.063) G_GAN: 0.099 G_GAN_Feat: 1.060 G_ID: 0.121 G_Rec: 0.435 D_GP: 0.114 D_real: 0.348 D_fake: 0.901 
+(epoch: 382, iters: 7358, time: 0.063) G_GAN: 0.510 G_GAN_Feat: 0.928 G_ID: 0.095 G_Rec: 0.346 D_GP: 0.086 D_real: 0.633 D_fake: 0.497 
+(epoch: 382, iters: 7758, time: 0.063) G_GAN: 0.271 G_GAN_Feat: 0.993 G_ID: 0.131 G_Rec: 0.450 D_GP: 0.036 D_real: 0.900 D_fake: 0.730 
+(epoch: 382, iters: 8158, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 0.729 G_ID: 0.106 G_Rec: 0.281 D_GP: 0.034 D_real: 1.220 D_fake: 0.544 
+(epoch: 382, iters: 8558, time: 0.063) G_GAN: 0.811 G_GAN_Feat: 1.118 G_ID: 0.151 G_Rec: 0.459 D_GP: 0.160 D_real: 0.918 D_fake: 0.397 
+(epoch: 383, iters: 350, time: 0.063) G_GAN: -0.408 G_GAN_Feat: 0.774 G_ID: 0.118 G_Rec: 0.354 D_GP: 0.031 D_real: 0.676 D_fake: 1.408 
+(epoch: 383, iters: 750, time: 0.063) G_GAN: 0.278 G_GAN_Feat: 0.903 G_ID: 0.151 G_Rec: 0.418 D_GP: 0.034 D_real: 0.895 D_fake: 0.724 
+(epoch: 383, iters: 1150, time: 0.064) G_GAN: 0.079 G_GAN_Feat: 0.771 G_ID: 0.093 G_Rec: 0.339 D_GP: 0.035 D_real: 0.832 D_fake: 0.922 
+(epoch: 383, iters: 1550, time: 0.063) G_GAN: 0.210 G_GAN_Feat: 0.997 G_ID: 0.127 G_Rec: 0.409 D_GP: 0.051 D_real: 0.752 D_fake: 0.794 
+(epoch: 383, iters: 1950, time: 0.063) G_GAN: 0.321 G_GAN_Feat: 0.944 G_ID: 0.104 G_Rec: 0.317 D_GP: 0.396 D_real: 0.503 D_fake: 0.679 
+(epoch: 383, iters: 2350, time: 0.063) G_GAN: 0.672 G_GAN_Feat: 0.886 G_ID: 0.105 G_Rec: 0.372 D_GP: 0.029 D_real: 1.361 D_fake: 0.352 
+(epoch: 383, iters: 2750, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.858 G_ID: 0.091 G_Rec: 0.355 D_GP: 0.032 D_real: 0.881 D_fake: 0.721 
+(epoch: 383, iters: 3150, time: 0.063) G_GAN: 0.469 G_GAN_Feat: 0.974 G_ID: 0.140 G_Rec: 0.455 D_GP: 0.041 D_real: 1.018 D_fake: 0.539 
+(epoch: 383, iters: 3550, time: 0.063) G_GAN: 0.077 G_GAN_Feat: 0.757 G_ID: 0.106 G_Rec: 0.301 D_GP: 0.042 D_real: 0.791 D_fake: 0.923 
+(epoch: 383, iters: 3950, time: 0.063) G_GAN: 0.409 G_GAN_Feat: 1.034 G_ID: 0.133 G_Rec: 0.466 D_GP: 0.052 D_real: 0.830 D_fake: 0.598 
+(epoch: 383, iters: 4350, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.896 G_ID: 0.109 G_Rec: 0.298 D_GP: 0.067 D_real: 0.588 D_fake: 0.683 
+(epoch: 383, iters: 4750, time: 0.063) G_GAN: 0.298 G_GAN_Feat: 1.149 G_ID: 0.129 G_Rec: 0.422 D_GP: 0.042 D_real: 0.473 D_fake: 0.704 
+(epoch: 383, iters: 5150, time: 0.063) G_GAN: 0.314 G_GAN_Feat: 0.794 G_ID: 0.109 G_Rec: 0.315 D_GP: 0.033 D_real: 1.096 D_fake: 0.687 
+(epoch: 383, iters: 5550, time: 0.063) G_GAN: 0.314 G_GAN_Feat: 0.903 G_ID: 0.138 G_Rec: 0.423 D_GP: 0.030 D_real: 1.050 D_fake: 0.695 
+(epoch: 383, iters: 5950, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.833 G_ID: 0.088 G_Rec: 0.355 D_GP: 0.040 D_real: 0.871 D_fake: 0.875 
+(epoch: 383, iters: 6350, time: 0.063) G_GAN: 0.212 G_GAN_Feat: 1.000 G_ID: 0.133 G_Rec: 0.471 D_GP: 0.037 D_real: 0.728 D_fake: 0.788 
+(epoch: 383, iters: 6750, time: 0.063) G_GAN: 0.143 G_GAN_Feat: 0.722 G_ID: 0.096 G_Rec: 0.297 D_GP: 0.031 D_real: 1.047 D_fake: 0.857 
+(epoch: 383, iters: 7150, time: 0.063) G_GAN: 0.351 G_GAN_Feat: 1.014 G_ID: 0.120 G_Rec: 0.435 D_GP: 0.040 D_real: 0.851 D_fake: 0.652 
+(epoch: 383, iters: 7550, time: 0.064) G_GAN: -0.022 G_GAN_Feat: 0.791 G_ID: 0.103 G_Rec: 0.312 D_GP: 0.035 D_real: 0.904 D_fake: 1.022 
+(epoch: 383, iters: 7950, time: 0.063) G_GAN: 0.423 G_GAN_Feat: 1.052 G_ID: 0.126 G_Rec: 0.426 D_GP: 0.152 D_real: 0.500 D_fake: 0.582 
+(epoch: 383, iters: 8350, time: 0.064) G_GAN: 0.555 G_GAN_Feat: 0.932 G_ID: 0.116 G_Rec: 0.359 D_GP: 0.179 D_real: 0.663 D_fake: 0.507 
+(epoch: 384, iters: 142, time: 0.064) G_GAN: 0.610 G_GAN_Feat: 0.938 G_ID: 0.131 G_Rec: 0.421 D_GP: 0.030 D_real: 1.207 D_fake: 0.443 
+(epoch: 384, iters: 542, time: 0.064) G_GAN: 0.594 G_GAN_Feat: 1.078 G_ID: 0.100 G_Rec: 0.374 D_GP: 0.038 D_real: 1.363 D_fake: 0.510 
+(epoch: 384, iters: 942, time: 0.064) G_GAN: 0.763 G_GAN_Feat: 1.143 G_ID: 0.122 G_Rec: 0.455 D_GP: 0.082 D_real: 0.677 D_fake: 0.275 
+(epoch: 384, iters: 1342, time: 0.064) G_GAN: -0.047 G_GAN_Feat: 0.845 G_ID: 0.119 G_Rec: 0.349 D_GP: 0.036 D_real: 0.728 D_fake: 1.047 
+(epoch: 384, iters: 1742, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.950 G_ID: 0.121 G_Rec: 0.420 D_GP: 0.044 D_real: 0.939 D_fake: 0.590 
+(epoch: 384, iters: 2142, time: 0.063) G_GAN: 0.195 G_GAN_Feat: 0.758 G_ID: 0.101 G_Rec: 0.291 D_GP: 0.045 D_real: 0.941 D_fake: 0.807 
+(epoch: 384, iters: 2542, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 1.161 G_ID: 0.130 G_Rec: 0.464 D_GP: 0.325 D_real: 0.152 D_fake: 0.642 
+(epoch: 384, iters: 2942, time: 0.063) G_GAN: 0.583 G_GAN_Feat: 1.005 G_ID: 0.113 G_Rec: 0.365 D_GP: 0.286 D_real: 0.439 D_fake: 0.560 
+(epoch: 384, iters: 3342, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 1.238 G_ID: 0.119 G_Rec: 0.465 D_GP: 0.045 D_real: 0.323 D_fake: 0.705 
+(epoch: 384, iters: 3742, time: 0.063) G_GAN: 0.094 G_GAN_Feat: 0.891 G_ID: 0.109 G_Rec: 0.349 D_GP: 0.039 D_real: 0.695 D_fake: 0.907 
+(epoch: 384, iters: 4142, time: 0.063) G_GAN: 0.508 G_GAN_Feat: 1.031 G_ID: 0.129 G_Rec: 0.491 D_GP: 0.031 D_real: 1.070 D_fake: 0.494 
+(epoch: 384, iters: 4542, time: 0.063) G_GAN: 0.715 G_GAN_Feat: 1.099 G_ID: 0.114 G_Rec: 0.406 D_GP: 0.136 D_real: 0.492 D_fake: 0.441 
+(epoch: 384, iters: 4942, time: 0.064) G_GAN: 0.842 G_GAN_Feat: 1.189 G_ID: 0.120 G_Rec: 0.469 D_GP: 0.047 D_real: 1.175 D_fake: 0.232 
+(epoch: 384, iters: 5342, time: 0.063) G_GAN: 0.300 G_GAN_Feat: 1.004 G_ID: 0.111 G_Rec: 0.365 D_GP: 0.094 D_real: 0.881 D_fake: 0.707 
+(epoch: 384, iters: 5742, time: 0.063) G_GAN: 0.393 G_GAN_Feat: 1.075 G_ID: 0.137 G_Rec: 0.479 D_GP: 0.050 D_real: 0.727 D_fake: 0.608 
+(epoch: 384, iters: 6142, time: 0.064) G_GAN: 0.218 G_GAN_Feat: 1.001 G_ID: 0.096 G_Rec: 0.408 D_GP: 0.270 D_real: 0.387 D_fake: 0.786 
+(epoch: 384, iters: 6542, time: 0.064) G_GAN: 0.865 G_GAN_Feat: 1.090 G_ID: 0.131 G_Rec: 0.464 D_GP: 0.038 D_real: 0.927 D_fake: 0.181 
+(epoch: 384, iters: 6942, time: 0.063) G_GAN: 0.149 G_GAN_Feat: 0.701 G_ID: 0.106 G_Rec: 0.314 D_GP: 0.024 D_real: 1.086 D_fake: 0.851 
+(epoch: 384, iters: 7342, time: 0.063) G_GAN: 0.177 G_GAN_Feat: 1.059 G_ID: 0.126 G_Rec: 0.467 D_GP: 0.040 D_real: 0.658 D_fake: 0.823 
+(epoch: 384, iters: 7742, time: 0.063) G_GAN: 0.203 G_GAN_Feat: 0.704 G_ID: 0.124 G_Rec: 0.309 D_GP: 0.031 D_real: 1.053 D_fake: 0.798 
+(epoch: 384, iters: 8142, time: 0.064) G_GAN: 0.786 G_GAN_Feat: 1.094 G_ID: 0.114 G_Rec: 0.488 D_GP: 0.078 D_real: 1.361 D_fake: 0.268 
+(epoch: 384, iters: 8542, time: 0.063) G_GAN: 0.058 G_GAN_Feat: 0.718 G_ID: 0.121 G_Rec: 0.338 D_GP: 0.033 D_real: 0.832 D_fake: 0.942 
+(epoch: 385, iters: 334, time: 0.063) G_GAN: 0.068 G_GAN_Feat: 0.882 G_ID: 0.135 G_Rec: 0.401 D_GP: 0.038 D_real: 0.681 D_fake: 0.932 
+(epoch: 385, iters: 734, time: 0.063) G_GAN: 0.022 G_GAN_Feat: 0.742 G_ID: 0.101 G_Rec: 0.323 D_GP: 0.035 D_real: 0.848 D_fake: 0.979 
+(epoch: 385, iters: 1134, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 0.975 G_ID: 0.129 G_Rec: 0.463 D_GP: 0.030 D_real: 1.176 D_fake: 0.542 
+(epoch: 385, iters: 1534, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 0.764 G_ID: 0.099 G_Rec: 0.348 D_GP: 0.031 D_real: 0.992 D_fake: 0.808 
+(epoch: 385, iters: 1934, time: 0.063) G_GAN: 0.512 G_GAN_Feat: 0.860 G_ID: 0.122 G_Rec: 0.404 D_GP: 0.037 D_real: 1.220 D_fake: 0.521 
+(epoch: 385, iters: 2334, time: 0.063) G_GAN: 0.125 G_GAN_Feat: 0.713 G_ID: 0.121 G_Rec: 0.316 D_GP: 0.039 D_real: 1.010 D_fake: 0.875 
+(epoch: 385, iters: 2734, time: 0.064) G_GAN: 0.056 G_GAN_Feat: 1.003 G_ID: 0.144 G_Rec: 0.458 D_GP: 0.060 D_real: 0.421 D_fake: 0.944 
+(epoch: 385, iters: 3134, time: 0.063) G_GAN: -0.093 G_GAN_Feat: 0.903 G_ID: 0.128 G_Rec: 0.342 D_GP: 0.294 D_real: 0.302 D_fake: 1.093 
+(epoch: 385, iters: 3534, time: 0.063) G_GAN: 0.422 G_GAN_Feat: 1.075 G_ID: 0.118 G_Rec: 0.473 D_GP: 0.111 D_real: 0.505 D_fake: 0.591 
+(epoch: 385, iters: 3934, time: 0.063) G_GAN: 0.761 G_GAN_Feat: 0.754 G_ID: 0.092 G_Rec: 0.313 D_GP: 0.034 D_real: 1.682 D_fake: 0.274 
+(epoch: 385, iters: 4334, time: 0.064) G_GAN: 0.827 G_GAN_Feat: 0.991 G_ID: 0.124 G_Rec: 0.407 D_GP: 0.045 D_real: 1.404 D_fake: 0.219 
+(epoch: 385, iters: 4734, time: 0.063) G_GAN: 0.421 G_GAN_Feat: 0.935 G_ID: 0.098 G_Rec: 0.312 D_GP: 0.044 D_real: 1.214 D_fake: 0.629 
+(epoch: 385, iters: 5134, time: 0.063) G_GAN: 0.460 G_GAN_Feat: 0.983 G_ID: 0.157 G_Rec: 0.413 D_GP: 0.043 D_real: 1.117 D_fake: 0.541 
+(epoch: 385, iters: 5534, time: 0.063) G_GAN: 0.448 G_GAN_Feat: 0.798 G_ID: 0.101 G_Rec: 0.312 D_GP: 0.032 D_real: 1.372 D_fake: 0.557 
+(epoch: 385, iters: 5934, time: 0.064) G_GAN: 0.711 G_GAN_Feat: 1.036 G_ID: 0.124 G_Rec: 0.459 D_GP: 0.055 D_real: 1.056 D_fake: 0.319 
+(epoch: 385, iters: 6334, time: 0.063) G_GAN: 0.328 G_GAN_Feat: 0.771 G_ID: 0.099 G_Rec: 0.352 D_GP: 0.059 D_real: 1.122 D_fake: 0.697 
+(epoch: 385, iters: 6734, time: 0.063) G_GAN: 0.552 G_GAN_Feat: 0.911 G_ID: 0.132 G_Rec: 0.441 D_GP: 0.040 D_real: 1.096 D_fake: 0.480 
+(epoch: 385, iters: 7134, time: 0.063) G_GAN: 0.053 G_GAN_Feat: 0.687 G_ID: 0.104 G_Rec: 0.328 D_GP: 0.032 D_real: 0.915 D_fake: 0.947 
+(epoch: 385, iters: 7534, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.882 G_ID: 0.123 G_Rec: 0.425 D_GP: 0.031 D_real: 1.022 D_fake: 0.672 
+(epoch: 385, iters: 7934, time: 0.063) G_GAN: 0.195 G_GAN_Feat: 0.723 G_ID: 0.124 G_Rec: 0.301 D_GP: 0.042 D_real: 0.877 D_fake: 0.805 
+(epoch: 385, iters: 8334, time: 0.063) G_GAN: 0.519 G_GAN_Feat: 0.927 G_ID: 0.124 G_Rec: 0.435 D_GP: 0.029 D_real: 1.099 D_fake: 0.500 
+(epoch: 386, iters: 126, time: 0.063) G_GAN: 0.039 G_GAN_Feat: 0.844 G_ID: 0.106 G_Rec: 0.337 D_GP: 0.043 D_real: 0.698 D_fake: 0.961 
+(epoch: 386, iters: 526, time: 0.064) G_GAN: 0.810 G_GAN_Feat: 0.900 G_ID: 0.106 G_Rec: 0.410 D_GP: 0.033 D_real: 1.514 D_fake: 0.233 
+(epoch: 386, iters: 926, time: 0.063) G_GAN: 0.454 G_GAN_Feat: 0.740 G_ID: 0.095 G_Rec: 0.294 D_GP: 0.030 D_real: 1.297 D_fake: 0.550 
+(epoch: 386, iters: 1326, time: 0.063) G_GAN: 0.686 G_GAN_Feat: 1.033 G_ID: 0.120 G_Rec: 0.412 D_GP: 0.076 D_real: 0.909 D_fake: 0.345 
+(epoch: 386, iters: 1726, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.744 G_ID: 0.107 G_Rec: 0.340 D_GP: 0.037 D_real: 1.011 D_fake: 0.860 
+(epoch: 386, iters: 2126, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.888 G_ID: 0.140 G_Rec: 0.432 D_GP: 0.033 D_real: 0.847 D_fake: 0.875 
+(epoch: 386, iters: 2526, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.642 G_ID: 0.095 G_Rec: 0.259 D_GP: 0.027 D_real: 1.010 D_fake: 0.923 
+(epoch: 386, iters: 2926, time: 0.064) G_GAN: 0.535 G_GAN_Feat: 0.994 G_ID: 0.127 G_Rec: 0.468 D_GP: 0.032 D_real: 1.127 D_fake: 0.475 
+(epoch: 386, iters: 3326, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.852 G_ID: 0.118 G_Rec: 0.332 D_GP: 0.061 D_real: 0.565 D_fake: 0.959 
+(epoch: 386, iters: 3726, time: 0.064) G_GAN: 0.618 G_GAN_Feat: 0.924 G_ID: 0.131 G_Rec: 0.392 D_GP: 0.041 D_real: 1.098 D_fake: 0.391 
+(epoch: 386, iters: 4126, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.794 G_ID: 0.096 G_Rec: 0.294 D_GP: 0.035 D_real: 1.002 D_fake: 0.666 
+(epoch: 386, iters: 4526, time: 0.064) G_GAN: 0.527 G_GAN_Feat: 0.934 G_ID: 0.133 G_Rec: 0.454 D_GP: 0.036 D_real: 1.240 D_fake: 0.492 
+(epoch: 386, iters: 4926, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.805 G_ID: 0.101 G_Rec: 0.366 D_GP: 0.039 D_real: 1.032 D_fake: 0.792 
+(epoch: 386, iters: 5326, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.916 G_ID: 0.113 G_Rec: 0.441 D_GP: 0.034 D_real: 0.960 D_fake: 0.628 
+(epoch: 386, iters: 5726, time: 0.064) G_GAN: 0.028 G_GAN_Feat: 0.723 G_ID: 0.107 G_Rec: 0.319 D_GP: 0.035 D_real: 0.877 D_fake: 0.972 
+(epoch: 386, iters: 6126, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.919 G_ID: 0.120 G_Rec: 0.418 D_GP: 0.053 D_real: 0.913 D_fake: 0.696 
+(epoch: 386, iters: 6526, time: 0.064) G_GAN: 0.056 G_GAN_Feat: 0.915 G_ID: 0.115 G_Rec: 0.365 D_GP: 0.045 D_real: 0.589 D_fake: 0.944 
+(epoch: 386, iters: 6926, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.943 G_ID: 0.126 G_Rec: 0.402 D_GP: 0.036 D_real: 0.903 D_fake: 0.657 
+(epoch: 386, iters: 7326, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.724 G_ID: 0.105 G_Rec: 0.287 D_GP: 0.033 D_real: 1.294 D_fake: 0.560 
+(epoch: 386, iters: 7726, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 1.071 G_ID: 0.118 G_Rec: 0.434 D_GP: 0.092 D_real: 0.590 D_fake: 0.442 
+(epoch: 386, iters: 8126, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.935 G_ID: 0.097 G_Rec: 0.346 D_GP: 0.078 D_real: 0.771 D_fake: 0.722 
+(epoch: 386, iters: 8526, time: 0.064) G_GAN: 0.659 G_GAN_Feat: 1.151 G_ID: 0.135 G_Rec: 0.517 D_GP: 0.093 D_real: 1.027 D_fake: 0.544 
+(epoch: 387, iters: 318, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.900 G_ID: 0.106 G_Rec: 0.339 D_GP: 0.142 D_real: 0.651 D_fake: 0.626 
+(epoch: 387, iters: 718, time: 0.064) G_GAN: 0.257 G_GAN_Feat: 1.173 G_ID: 0.163 G_Rec: 0.487 D_GP: 0.046 D_real: 0.545 D_fake: 0.745 
+(epoch: 387, iters: 1118, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.771 G_ID: 0.107 G_Rec: 0.315 D_GP: 0.037 D_real: 1.160 D_fake: 0.723 
+(epoch: 387, iters: 1518, time: 0.064) G_GAN: 0.717 G_GAN_Feat: 1.098 G_ID: 0.142 G_Rec: 0.439 D_GP: 0.043 D_real: 0.950 D_fake: 0.331 
+(epoch: 387, iters: 1918, time: 0.064) G_GAN: -0.143 G_GAN_Feat: 0.997 G_ID: 0.110 G_Rec: 0.322 D_GP: 0.040 D_real: 0.462 D_fake: 1.143 
+(epoch: 387, iters: 2318, time: 0.064) G_GAN: 0.827 G_GAN_Feat: 1.036 G_ID: 0.116 G_Rec: 0.462 D_GP: 0.054 D_real: 1.362 D_fake: 0.298 
+(epoch: 387, iters: 2718, time: 0.064) G_GAN: 0.143 G_GAN_Feat: 0.855 G_ID: 0.109 G_Rec: 0.371 D_GP: 0.043 D_real: 0.939 D_fake: 0.857 
+(epoch: 387, iters: 3118, time: 0.064) G_GAN: 0.714 G_GAN_Feat: 0.975 G_ID: 0.122 G_Rec: 0.404 D_GP: 0.048 D_real: 1.273 D_fake: 0.330 
+(epoch: 387, iters: 3518, time: 0.063) G_GAN: 0.742 G_GAN_Feat: 0.980 G_ID: 0.115 G_Rec: 0.335 D_GP: 0.100 D_real: 0.525 D_fake: 0.506 
+(epoch: 387, iters: 3918, time: 0.063) G_GAN: 1.195 G_GAN_Feat: 1.289 G_ID: 0.116 G_Rec: 0.489 D_GP: 0.251 D_real: 0.618 D_fake: 0.130 
+(epoch: 387, iters: 4318, time: 0.063) G_GAN: 0.363 G_GAN_Feat: 0.888 G_ID: 0.107 G_Rec: 0.310 D_GP: 0.040 D_real: 0.801 D_fake: 0.680 
+(epoch: 387, iters: 4718, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 0.952 G_ID: 0.125 G_Rec: 0.467 D_GP: 0.028 D_real: 1.109 D_fake: 0.557 
+(epoch: 387, iters: 5118, time: 0.063) G_GAN: 0.095 G_GAN_Feat: 0.660 G_ID: 0.131 G_Rec: 0.306 D_GP: 0.029 D_real: 1.019 D_fake: 0.905 
+(epoch: 387, iters: 5518, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 0.837 G_ID: 0.132 G_Rec: 0.381 D_GP: 0.033 D_real: 1.077 D_fake: 0.581 
+(epoch: 387, iters: 5918, time: 0.063) G_GAN: -0.076 G_GAN_Feat: 0.703 G_ID: 0.085 G_Rec: 0.308 D_GP: 0.031 D_real: 0.800 D_fake: 1.076 
+(epoch: 387, iters: 6318, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.969 G_ID: 0.118 G_Rec: 0.489 D_GP: 0.069 D_real: 1.032 D_fake: 0.557 
+(epoch: 387, iters: 6718, time: 0.063) G_GAN: -0.000 G_GAN_Feat: 0.713 G_ID: 0.169 G_Rec: 0.314 D_GP: 0.036 D_real: 0.768 D_fake: 1.000 
+(epoch: 387, iters: 7118, time: 0.063) G_GAN: 0.388 G_GAN_Feat: 1.100 G_ID: 0.119 G_Rec: 0.498 D_GP: 0.127 D_real: 0.493 D_fake: 0.635 
+(epoch: 387, iters: 7518, time: 0.063) G_GAN: 0.078 G_GAN_Feat: 0.717 G_ID: 0.126 G_Rec: 0.291 D_GP: 0.042 D_real: 0.743 D_fake: 0.922 
+(epoch: 387, iters: 7918, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.975 G_ID: 0.127 G_Rec: 0.425 D_GP: 0.052 D_real: 0.771 D_fake: 0.606 
+(epoch: 387, iters: 8318, time: 0.063) G_GAN: -0.091 G_GAN_Feat: 0.861 G_ID: 0.101 G_Rec: 0.354 D_GP: 0.036 D_real: 1.012 D_fake: 1.108 
+(epoch: 388, iters: 110, time: 0.063) G_GAN: 0.626 G_GAN_Feat: 0.885 G_ID: 0.114 G_Rec: 0.426 D_GP: 0.030 D_real: 1.340 D_fake: 0.402 
+(epoch: 388, iters: 510, time: 0.063) G_GAN: 0.215 G_GAN_Feat: 0.813 G_ID: 0.106 G_Rec: 0.301 D_GP: 0.096 D_real: 0.787 D_fake: 0.786 
+(epoch: 388, iters: 910, time: 0.064) G_GAN: 0.829 G_GAN_Feat: 1.056 G_ID: 0.110 G_Rec: 0.476 D_GP: 0.040 D_real: 1.196 D_fake: 0.223 
+(epoch: 388, iters: 1310, time: 0.064) G_GAN: 0.400 G_GAN_Feat: 0.802 G_ID: 0.113 G_Rec: 0.337 D_GP: 0.098 D_real: 1.095 D_fake: 0.603 
+(epoch: 388, iters: 1710, time: 0.064) G_GAN: 0.591 G_GAN_Feat: 1.053 G_ID: 0.125 G_Rec: 0.417 D_GP: 0.047 D_real: 0.802 D_fake: 0.415 
+(epoch: 388, iters: 2110, time: 0.063) G_GAN: 0.577 G_GAN_Feat: 0.833 G_ID: 0.153 G_Rec: 0.307 D_GP: 0.045 D_real: 1.149 D_fake: 0.429 
+(epoch: 388, iters: 2510, time: 0.064) G_GAN: -0.090 G_GAN_Feat: 0.868 G_ID: 0.121 G_Rec: 0.389 D_GP: 0.032 D_real: 0.843 D_fake: 1.090 
+(epoch: 388, iters: 2910, time: 0.063) G_GAN: 0.432 G_GAN_Feat: 0.851 G_ID: 0.104 G_Rec: 0.291 D_GP: 0.091 D_real: 0.836 D_fake: 0.569 
+(epoch: 388, iters: 3310, time: 0.063) G_GAN: 0.609 G_GAN_Feat: 1.048 G_ID: 0.143 G_Rec: 0.455 D_GP: 0.040 D_real: 1.187 D_fake: 0.401 
+(epoch: 388, iters: 3710, time: 0.063) G_GAN: 0.225 G_GAN_Feat: 0.757 G_ID: 0.111 G_Rec: 0.317 D_GP: 0.034 D_real: 1.087 D_fake: 0.776 
+(epoch: 388, iters: 4110, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.984 G_ID: 0.142 G_Rec: 0.460 D_GP: 0.038 D_real: 0.786 D_fake: 0.830 
+(epoch: 388, iters: 4510, time: 0.063) G_GAN: 0.171 G_GAN_Feat: 0.755 G_ID: 0.122 G_Rec: 0.308 D_GP: 0.031 D_real: 1.091 D_fake: 0.829 
+(epoch: 388, iters: 4910, time: 0.063) G_GAN: 0.327 G_GAN_Feat: 1.299 G_ID: 0.138 G_Rec: 0.499 D_GP: 0.331 D_real: 0.862 D_fake: 0.688 
+(epoch: 388, iters: 5310, time: 0.063) G_GAN: 0.132 G_GAN_Feat: 0.752 G_ID: 0.112 G_Rec: 0.317 D_GP: 0.030 D_real: 0.940 D_fake: 0.868 
+(epoch: 388, iters: 5710, time: 0.064) G_GAN: 0.545 G_GAN_Feat: 0.916 G_ID: 0.110 G_Rec: 0.421 D_GP: 0.038 D_real: 1.076 D_fake: 0.476 
+(epoch: 388, iters: 6110, time: 0.063) G_GAN: 0.375 G_GAN_Feat: 0.797 G_ID: 0.107 G_Rec: 0.321 D_GP: 0.046 D_real: 0.986 D_fake: 0.633 
+(epoch: 388, iters: 6510, time: 0.064) G_GAN: 0.823 G_GAN_Feat: 0.982 G_ID: 0.126 G_Rec: 0.390 D_GP: 0.063 D_real: 1.372 D_fake: 0.264 
+(epoch: 388, iters: 6910, time: 0.064) G_GAN: -0.004 G_GAN_Feat: 0.936 G_ID: 0.101 G_Rec: 0.365 D_GP: 0.053 D_real: 0.953 D_fake: 1.004 
+(epoch: 388, iters: 7310, time: 0.063) G_GAN: 0.623 G_GAN_Feat: 0.958 G_ID: 0.143 G_Rec: 0.417 D_GP: 0.047 D_real: 1.262 D_fake: 0.415 
+(epoch: 388, iters: 7710, time: 0.063) G_GAN: 0.322 G_GAN_Feat: 0.749 G_ID: 0.110 G_Rec: 0.281 D_GP: 0.034 D_real: 0.967 D_fake: 0.680 
+(epoch: 388, iters: 8110, time: 0.063) G_GAN: 0.520 G_GAN_Feat: 1.062 G_ID: 0.119 G_Rec: 0.418 D_GP: 0.089 D_real: 0.859 D_fake: 0.526 
+(epoch: 388, iters: 8510, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.842 G_ID: 0.096 G_Rec: 0.310 D_GP: 0.062 D_real: 1.039 D_fake: 0.545 
+(epoch: 389, iters: 302, time: 0.063) G_GAN: 0.570 G_GAN_Feat: 1.065 G_ID: 0.118 G_Rec: 0.475 D_GP: 0.035 D_real: 1.066 D_fake: 0.439 
+(epoch: 389, iters: 702, time: 0.063) G_GAN: 0.260 G_GAN_Feat: 0.894 G_ID: 0.080 G_Rec: 0.324 D_GP: 1.041 D_real: 0.477 D_fake: 0.744 
+(epoch: 389, iters: 1102, time: 0.063) G_GAN: 0.577 G_GAN_Feat: 1.008 G_ID: 0.107 G_Rec: 0.467 D_GP: 0.031 D_real: 1.118 D_fake: 0.471 
+(epoch: 389, iters: 1502, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.739 G_ID: 0.092 G_Rec: 0.297 D_GP: 0.031 D_real: 1.361 D_fake: 0.444 
+(epoch: 389, iters: 1902, time: 0.063) G_GAN: 0.276 G_GAN_Feat: 1.119 G_ID: 0.136 G_Rec: 0.478 D_GP: 0.031 D_real: 0.711 D_fake: 0.725 
+(epoch: 389, iters: 2302, time: 0.063) G_GAN: 0.747 G_GAN_Feat: 1.047 G_ID: 0.112 G_Rec: 0.401 D_GP: 0.152 D_real: 0.524 D_fake: 0.551 
+(epoch: 389, iters: 2702, time: 0.063) G_GAN: 0.541 G_GAN_Feat: 0.960 G_ID: 0.122 G_Rec: 0.434 D_GP: 0.030 D_real: 1.275 D_fake: 0.463 
+(epoch: 389, iters: 3102, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.979 G_ID: 0.096 G_Rec: 0.349 D_GP: 0.044 D_real: 0.545 D_fake: 0.863 
+(epoch: 389, iters: 3502, time: 0.063) G_GAN: 0.895 G_GAN_Feat: 1.142 G_ID: 0.133 G_Rec: 0.458 D_GP: 0.210 D_real: 0.843 D_fake: 0.207 
+(epoch: 389, iters: 3902, time: 0.063) G_GAN: 0.102 G_GAN_Feat: 0.793 G_ID: 0.108 G_Rec: 0.339 D_GP: 0.033 D_real: 0.947 D_fake: 0.898 
+(epoch: 389, iters: 4302, time: 0.063) G_GAN: 0.779 G_GAN_Feat: 0.995 G_ID: 0.115 G_Rec: 0.442 D_GP: 0.036 D_real: 1.339 D_fake: 0.250 
+(epoch: 389, iters: 4702, time: 0.064) G_GAN: 0.586 G_GAN_Feat: 0.887 G_ID: 0.088 G_Rec: 0.323 D_GP: 0.046 D_real: 0.820 D_fake: 0.540 
+(epoch: 389, iters: 5102, time: 0.063) G_GAN: 0.443 G_GAN_Feat: 1.160 G_ID: 0.132 G_Rec: 0.465 D_GP: 0.043 D_real: 1.019 D_fake: 0.563 
+(epoch: 389, iters: 5502, time: 0.063) G_GAN: -0.071 G_GAN_Feat: 0.880 G_ID: 0.115 G_Rec: 0.328 D_GP: 0.039 D_real: 0.465 D_fake: 1.072 
+(epoch: 389, iters: 5902, time: 0.063) G_GAN: 0.477 G_GAN_Feat: 1.022 G_ID: 0.114 G_Rec: 0.461 D_GP: 0.047 D_real: 0.988 D_fake: 0.531 
+(epoch: 389, iters: 6302, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 0.806 G_ID: 0.095 G_Rec: 0.318 D_GP: 0.044 D_real: 0.992 D_fake: 0.793 
+(epoch: 389, iters: 6702, time: 0.063) G_GAN: 0.635 G_GAN_Feat: 0.942 G_ID: 0.121 G_Rec: 0.434 D_GP: 0.030 D_real: 1.401 D_fake: 0.387 
+(epoch: 389, iters: 7102, time: 0.063) G_GAN: 0.207 G_GAN_Feat: 0.857 G_ID: 0.088 G_Rec: 0.298 D_GP: 0.030 D_real: 0.741 D_fake: 0.794 
+(epoch: 389, iters: 7502, time: 0.063) G_GAN: 0.394 G_GAN_Feat: 1.119 G_ID: 0.136 G_Rec: 0.466 D_GP: 0.047 D_real: 0.597 D_fake: 0.610 
+(epoch: 389, iters: 7902, time: 0.064) G_GAN: -0.389 G_GAN_Feat: 0.762 G_ID: 0.097 G_Rec: 0.343 D_GP: 0.036 D_real: 0.634 D_fake: 1.389 
+(epoch: 389, iters: 8302, time: 0.063) G_GAN: 0.487 G_GAN_Feat: 0.962 G_ID: 0.132 G_Rec: 0.420 D_GP: 0.033 D_real: 1.037 D_fake: 0.525 
+(epoch: 389, iters: 8702, time: 0.063) G_GAN: 0.264 G_GAN_Feat: 0.757 G_ID: 0.122 G_Rec: 0.322 D_GP: 0.033 D_real: 1.114 D_fake: 0.736 
+(epoch: 390, iters: 494, time: 0.063) G_GAN: 0.454 G_GAN_Feat: 0.989 G_ID: 0.124 G_Rec: 0.422 D_GP: 0.042 D_real: 0.942 D_fake: 0.562 
+(epoch: 390, iters: 894, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.776 G_ID: 0.105 G_Rec: 0.313 D_GP: 0.041 D_real: 1.364 D_fake: 0.715 
+(epoch: 390, iters: 1294, time: 0.063) G_GAN: 0.560 G_GAN_Feat: 0.886 G_ID: 0.113 G_Rec: 0.407 D_GP: 0.037 D_real: 1.276 D_fake: 0.443 
+(epoch: 390, iters: 1694, time: 0.063) G_GAN: 0.313 G_GAN_Feat: 0.729 G_ID: 0.097 G_Rec: 0.304 D_GP: 0.032 D_real: 1.216 D_fake: 0.688 
+(epoch: 390, iters: 2094, time: 0.063) G_GAN: 0.529 G_GAN_Feat: 0.991 G_ID: 0.116 G_Rec: 0.423 D_GP: 0.038 D_real: 1.180 D_fake: 0.492 
+(epoch: 390, iters: 2494, time: 0.064) G_GAN: -0.001 G_GAN_Feat: 0.920 G_ID: 0.119 G_Rec: 0.332 D_GP: 0.050 D_real: 0.349 D_fake: 1.001 
+(epoch: 390, iters: 2894, time: 0.063) G_GAN: 0.561 G_GAN_Feat: 0.954 G_ID: 0.115 G_Rec: 0.440 D_GP: 0.030 D_real: 1.256 D_fake: 0.455 
+(epoch: 390, iters: 3294, time: 0.063) G_GAN: -0.004 G_GAN_Feat: 0.741 G_ID: 0.104 G_Rec: 0.347 D_GP: 0.051 D_real: 0.781 D_fake: 1.004 
+(epoch: 390, iters: 3694, time: 0.063) G_GAN: 0.614 G_GAN_Feat: 1.079 G_ID: 0.143 G_Rec: 0.464 D_GP: 0.156 D_real: 0.806 D_fake: 0.398 
+(epoch: 390, iters: 4094, time: 0.064) G_GAN: -0.120 G_GAN_Feat: 0.943 G_ID: 0.135 G_Rec: 0.332 D_GP: 0.063 D_real: 0.188 D_fake: 1.120 
+(epoch: 390, iters: 4494, time: 0.063) G_GAN: 0.461 G_GAN_Feat: 1.048 G_ID: 0.135 G_Rec: 0.387 D_GP: 0.046 D_real: 0.813 D_fake: 0.540 
+(epoch: 390, iters: 4894, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.999 G_ID: 0.113 G_Rec: 0.362 D_GP: 0.065 D_real: 0.383 D_fake: 0.730 
+(epoch: 390, iters: 5294, time: 0.063) G_GAN: 0.293 G_GAN_Feat: 1.224 G_ID: 0.136 G_Rec: 0.469 D_GP: 0.049 D_real: 0.461 D_fake: 0.709 
+(epoch: 390, iters: 5694, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.872 G_ID: 0.114 G_Rec: 0.346 D_GP: 0.151 D_real: 0.535 D_fake: 0.889 
+(epoch: 390, iters: 6094, time: 0.063) G_GAN: 0.630 G_GAN_Feat: 1.109 G_ID: 0.138 G_Rec: 0.401 D_GP: 0.047 D_real: 0.760 D_fake: 0.414 
+(epoch: 390, iters: 6494, time: 0.063) G_GAN: 0.428 G_GAN_Feat: 0.863 G_ID: 0.099 G_Rec: 0.300 D_GP: 0.042 D_real: 0.790 D_fake: 0.592 
+(epoch: 390, iters: 6894, time: 0.063) G_GAN: 0.386 G_GAN_Feat: 0.965 G_ID: 0.130 G_Rec: 0.427 D_GP: 0.033 D_real: 1.018 D_fake: 0.615 
+(epoch: 390, iters: 7294, time: 0.064) G_GAN: 0.412 G_GAN_Feat: 0.800 G_ID: 0.094 G_Rec: 0.305 D_GP: 0.039 D_real: 1.158 D_fake: 0.589 
+(epoch: 390, iters: 7694, time: 0.063) G_GAN: 0.726 G_GAN_Feat: 0.972 G_ID: 0.143 G_Rec: 0.434 D_GP: 0.038 D_real: 1.293 D_fake: 0.296 
+(epoch: 390, iters: 8094, time: 0.063) G_GAN: 0.488 G_GAN_Feat: 1.095 G_ID: 0.104 G_Rec: 0.381 D_GP: 0.058 D_real: 1.474 D_fake: 0.548 
+(epoch: 390, iters: 8494, time: 0.063) G_GAN: 0.383 G_GAN_Feat: 0.988 G_ID: 0.127 G_Rec: 0.471 D_GP: 0.031 D_real: 1.039 D_fake: 0.652 
+(epoch: 391, iters: 286, time: 0.064) G_GAN: -0.046 G_GAN_Feat: 0.711 G_ID: 0.103 G_Rec: 0.281 D_GP: 0.034 D_real: 0.930 D_fake: 1.046 
+(epoch: 391, iters: 686, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 1.056 G_ID: 0.121 G_Rec: 0.448 D_GP: 0.038 D_real: 0.784 D_fake: 0.578 
+(epoch: 391, iters: 1086, time: 0.063) G_GAN: 0.347 G_GAN_Feat: 0.907 G_ID: 0.104 G_Rec: 0.313 D_GP: 0.037 D_real: 0.582 D_fake: 0.657 
+(epoch: 391, iters: 1486, time: 0.064) G_GAN: 0.820 G_GAN_Feat: 1.011 G_ID: 0.124 G_Rec: 0.425 D_GP: 0.070 D_real: 1.651 D_fake: 0.238 
+(epoch: 391, iters: 1886, time: 0.064) G_GAN: 0.180 G_GAN_Feat: 0.692 G_ID: 0.092 G_Rec: 0.309 D_GP: 0.028 D_real: 1.100 D_fake: 0.820 
+(epoch: 391, iters: 2286, time: 0.064) G_GAN: 0.453 G_GAN_Feat: 0.875 G_ID: 0.122 G_Rec: 0.410 D_GP: 0.033 D_real: 0.998 D_fake: 0.552 
+(epoch: 391, iters: 2686, time: 0.064) G_GAN: -0.302 G_GAN_Feat: 0.735 G_ID: 0.092 G_Rec: 0.306 D_GP: 0.035 D_real: 0.602 D_fake: 1.302 
+(epoch: 391, iters: 3086, time: 0.064) G_GAN: 0.774 G_GAN_Feat: 1.079 G_ID: 0.116 G_Rec: 0.479 D_GP: 0.112 D_real: 1.048 D_fake: 0.369 
+(epoch: 391, iters: 3486, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.808 G_ID: 0.099 G_Rec: 0.326 D_GP: 0.047 D_real: 0.754 D_fake: 0.906 
+(epoch: 391, iters: 3886, time: 0.063) G_GAN: 0.433 G_GAN_Feat: 1.054 G_ID: 0.118 G_Rec: 0.451 D_GP: 0.053 D_real: 0.892 D_fake: 0.575 
+(epoch: 391, iters: 4286, time: 0.063) G_GAN: 0.156 G_GAN_Feat: 0.895 G_ID: 0.129 G_Rec: 0.372 D_GP: 0.051 D_real: 0.766 D_fake: 0.845 
+(epoch: 391, iters: 4686, time: 0.063) G_GAN: 0.663 G_GAN_Feat: 1.049 G_ID: 0.125 G_Rec: 0.469 D_GP: 0.052 D_real: 0.930 D_fake: 0.363 
+(epoch: 391, iters: 5086, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 0.876 G_ID: 0.083 G_Rec: 0.332 D_GP: 0.050 D_real: 1.201 D_fake: 0.553 
+(epoch: 391, iters: 5486, time: 0.063) G_GAN: 0.746 G_GAN_Feat: 1.114 G_ID: 0.121 G_Rec: 0.475 D_GP: 0.053 D_real: 0.878 D_fake: 0.340 
+(epoch: 391, iters: 5886, time: 0.063) G_GAN: 0.160 G_GAN_Feat: 0.972 G_ID: 0.092 G_Rec: 0.315 D_GP: 0.112 D_real: 0.285 D_fake: 0.840 
+(epoch: 391, iters: 6286, time: 0.063) G_GAN: 0.653 G_GAN_Feat: 1.321 G_ID: 0.115 G_Rec: 0.475 D_GP: 0.172 D_real: 0.254 D_fake: 0.382 
+(epoch: 391, iters: 6686, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.935 G_ID: 0.099 G_Rec: 0.322 D_GP: 0.045 D_real: 0.490 D_fake: 0.626 
+(epoch: 391, iters: 7086, time: 0.063) G_GAN: 0.364 G_GAN_Feat: 1.025 G_ID: 0.133 G_Rec: 0.432 D_GP: 0.045 D_real: 0.711 D_fake: 0.637 
+(epoch: 391, iters: 7486, time: 0.063) G_GAN: 0.224 G_GAN_Feat: 0.940 G_ID: 0.103 G_Rec: 0.301 D_GP: 0.079 D_real: 0.324 D_fake: 0.781 
+(epoch: 391, iters: 7886, time: 0.063) G_GAN: 0.474 G_GAN_Feat: 1.043 G_ID: 0.147 G_Rec: 0.490 D_GP: 0.033 D_real: 1.014 D_fake: 0.530 
+(epoch: 391, iters: 8286, time: 0.064) G_GAN: -0.029 G_GAN_Feat: 0.753 G_ID: 0.109 G_Rec: 0.324 D_GP: 0.030 D_real: 0.851 D_fake: 1.029 
+(epoch: 391, iters: 8686, time: 0.063) G_GAN: 0.147 G_GAN_Feat: 1.043 G_ID: 0.109 G_Rec: 0.448 D_GP: 0.084 D_real: 0.460 D_fake: 0.855 
+(epoch: 392, iters: 478, time: 0.063) G_GAN: 0.234 G_GAN_Feat: 0.951 G_ID: 0.123 G_Rec: 0.324 D_GP: 0.211 D_real: 0.372 D_fake: 0.774 
+(epoch: 392, iters: 878, time: 0.063) G_GAN: 0.851 G_GAN_Feat: 1.320 G_ID: 0.121 G_Rec: 0.544 D_GP: 0.060 D_real: 0.427 D_fake: 0.212 
+(epoch: 392, iters: 1278, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.879 G_ID: 0.091 G_Rec: 0.288 D_GP: 0.077 D_real: 0.680 D_fake: 0.580 
+(epoch: 392, iters: 1678, time: 0.063) G_GAN: 0.485 G_GAN_Feat: 1.001 G_ID: 0.124 G_Rec: 0.506 D_GP: 0.035 D_real: 1.173 D_fake: 0.523 
+(epoch: 392, iters: 2078, time: 0.063) G_GAN: 0.093 G_GAN_Feat: 0.717 G_ID: 0.097 G_Rec: 0.302 D_GP: 0.040 D_real: 0.995 D_fake: 0.908 
+(epoch: 392, iters: 2478, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 1.116 G_ID: 0.129 G_Rec: 0.436 D_GP: 0.227 D_real: 0.388 D_fake: 0.584 
+(epoch: 392, iters: 2878, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.852 G_ID: 0.108 G_Rec: 0.399 D_GP: 0.069 D_real: 0.851 D_fake: 0.619 
+(epoch: 392, iters: 3278, time: 0.063) G_GAN: 0.342 G_GAN_Feat: 0.932 G_ID: 0.106 G_Rec: 0.441 D_GP: 0.032 D_real: 1.056 D_fake: 0.664 
+(epoch: 392, iters: 3678, time: 0.063) G_GAN: 0.218 G_GAN_Feat: 0.825 G_ID: 0.106 G_Rec: 0.323 D_GP: 0.056 D_real: 0.845 D_fake: 0.782 
+(epoch: 392, iters: 4078, time: 0.063) G_GAN: 0.466 G_GAN_Feat: 1.183 G_ID: 0.134 G_Rec: 0.473 D_GP: 0.042 D_real: 1.184 D_fake: 0.591 
+(epoch: 392, iters: 4478, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 0.676 G_ID: 0.099 G_Rec: 0.304 D_GP: 0.025 D_real: 1.125 D_fake: 0.794 
+(epoch: 392, iters: 4878, time: 0.063) G_GAN: 0.271 G_GAN_Feat: 0.916 G_ID: 0.110 G_Rec: 0.475 D_GP: 0.036 D_real: 0.841 D_fake: 0.730 
+(epoch: 392, iters: 5278, time: 0.063) G_GAN: -0.082 G_GAN_Feat: 0.677 G_ID: 0.093 G_Rec: 0.309 D_GP: 0.033 D_real: 0.793 D_fake: 1.082 
+(epoch: 392, iters: 5678, time: 0.063) G_GAN: 0.108 G_GAN_Feat: 0.953 G_ID: 0.118 G_Rec: 0.461 D_GP: 0.090 D_real: 0.672 D_fake: 0.894 
+(epoch: 392, iters: 6078, time: 0.064) G_GAN: 0.061 G_GAN_Feat: 0.658 G_ID: 0.121 G_Rec: 0.298 D_GP: 0.031 D_real: 0.975 D_fake: 0.939 
+(epoch: 392, iters: 6478, time: 0.063) G_GAN: 0.324 G_GAN_Feat: 0.862 G_ID: 0.104 G_Rec: 0.399 D_GP: 0.033 D_real: 0.991 D_fake: 0.677 
+(epoch: 392, iters: 6878, time: 0.063) G_GAN: -0.030 G_GAN_Feat: 0.850 G_ID: 0.126 G_Rec: 0.356 D_GP: 0.143 D_real: 0.549 D_fake: 1.030 
+(epoch: 392, iters: 7278, time: 0.063) G_GAN: 0.469 G_GAN_Feat: 1.013 G_ID: 0.113 G_Rec: 0.424 D_GP: 0.081 D_real: 0.721 D_fake: 0.551 
+(epoch: 392, iters: 7678, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.717 G_ID: 0.093 G_Rec: 0.307 D_GP: 0.042 D_real: 1.076 D_fake: 0.776 
+(epoch: 392, iters: 8078, time: 0.063) G_GAN: 0.283 G_GAN_Feat: 0.921 G_ID: 0.136 G_Rec: 0.408 D_GP: 0.039 D_real: 0.964 D_fake: 0.725 
+(epoch: 392, iters: 8478, time: 0.063) G_GAN: 0.333 G_GAN_Feat: 0.859 G_ID: 0.117 G_Rec: 0.326 D_GP: 0.082 D_real: 0.849 D_fake: 0.692 
+(epoch: 393, iters: 270, time: 0.063) G_GAN: 0.475 G_GAN_Feat: 0.919 G_ID: 0.122 G_Rec: 0.448 D_GP: 0.032 D_real: 1.162 D_fake: 0.562 
+(epoch: 393, iters: 670, time: 0.064) G_GAN: 0.093 G_GAN_Feat: 0.652 G_ID: 0.111 G_Rec: 0.292 D_GP: 0.029 D_real: 1.013 D_fake: 0.908 
+(epoch: 393, iters: 1070, time: 0.063) G_GAN: 0.227 G_GAN_Feat: 0.857 G_ID: 0.103 G_Rec: 0.411 D_GP: 0.039 D_real: 0.877 D_fake: 0.774 
+(epoch: 393, iters: 1470, time: 0.063) G_GAN: 0.014 G_GAN_Feat: 0.768 G_ID: 0.096 G_Rec: 0.336 D_GP: 0.044 D_real: 0.810 D_fake: 0.986 
+(epoch: 393, iters: 1870, time: 0.063) G_GAN: 0.801 G_GAN_Feat: 1.007 G_ID: 0.107 G_Rec: 0.416 D_GP: 0.045 D_real: 1.190 D_fake: 0.318 
+(epoch: 393, iters: 2270, time: 0.063) G_GAN: 0.265 G_GAN_Feat: 0.850 G_ID: 0.115 G_Rec: 0.342 D_GP: 0.095 D_real: 0.947 D_fake: 0.757 
+(epoch: 393, iters: 2670, time: 0.063) G_GAN: 0.606 G_GAN_Feat: 1.033 G_ID: 0.114 G_Rec: 0.435 D_GP: 0.042 D_real: 1.239 D_fake: 0.401 
+(epoch: 393, iters: 3070, time: 0.063) G_GAN: 0.368 G_GAN_Feat: 0.896 G_ID: 0.098 G_Rec: 0.321 D_GP: 0.205 D_real: 0.605 D_fake: 0.642 
+(epoch: 393, iters: 3470, time: 0.064) G_GAN: 0.311 G_GAN_Feat: 1.272 G_ID: 0.137 G_Rec: 0.470 D_GP: 0.116 D_real: 0.304 D_fake: 0.690 
+(epoch: 393, iters: 3870, time: 0.063) G_GAN: 0.436 G_GAN_Feat: 0.881 G_ID: 0.102 G_Rec: 0.322 D_GP: 0.049 D_real: 0.909 D_fake: 0.568 
+(epoch: 393, iters: 4270, time: 0.063) G_GAN: 0.791 G_GAN_Feat: 1.346 G_ID: 0.139 G_Rec: 0.506 D_GP: 0.073 D_real: 0.442 D_fake: 0.261 
+(epoch: 393, iters: 4670, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.683 G_ID: 0.090 G_Rec: 0.365 D_GP: 0.026 D_real: 1.157 D_fake: 0.702 
+(epoch: 393, iters: 5070, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.828 G_ID: 0.111 G_Rec: 0.395 D_GP: 0.033 D_real: 1.198 D_fake: 0.582 
+(epoch: 393, iters: 5470, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.619 G_ID: 0.109 G_Rec: 0.284 D_GP: 0.029 D_real: 1.104 D_fake: 0.817 
+(epoch: 393, iters: 5870, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.868 G_ID: 0.121 G_Rec: 0.444 D_GP: 0.030 D_real: 1.101 D_fake: 0.589 
+(epoch: 393, iters: 6270, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.703 G_ID: 0.084 G_Rec: 0.325 D_GP: 0.046 D_real: 1.000 D_fake: 0.866 
+(epoch: 393, iters: 6670, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.859 G_ID: 0.110 G_Rec: 0.406 D_GP: 0.034 D_real: 1.208 D_fake: 0.514 
+(epoch: 393, iters: 7070, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.652 G_ID: 0.099 G_Rec: 0.310 D_GP: 0.032 D_real: 1.298 D_fake: 0.688 
+(epoch: 393, iters: 7470, time: 0.064) G_GAN: 0.467 G_GAN_Feat: 0.867 G_ID: 0.126 G_Rec: 0.429 D_GP: 0.041 D_real: 1.012 D_fake: 0.554 
+(epoch: 393, iters: 7870, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.638 G_ID: 0.098 G_Rec: 0.289 D_GP: 0.043 D_real: 1.122 D_fake: 0.756 
+(epoch: 393, iters: 8270, time: 0.064) G_GAN: 0.482 G_GAN_Feat: 0.888 G_ID: 0.135 G_Rec: 0.448 D_GP: 0.037 D_real: 1.133 D_fake: 0.536 
+(epoch: 393, iters: 8670, time: 0.064) G_GAN: -0.030 G_GAN_Feat: 0.780 G_ID: 0.134 G_Rec: 0.366 D_GP: 0.072 D_real: 0.669 D_fake: 1.030 
+(epoch: 394, iters: 462, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.892 G_ID: 0.120 G_Rec: 0.431 D_GP: 0.039 D_real: 1.097 D_fake: 0.586 
+(epoch: 394, iters: 862, time: 0.064) G_GAN: -0.153 G_GAN_Feat: 0.675 G_ID: 0.103 G_Rec: 0.309 D_GP: 0.040 D_real: 0.729 D_fake: 1.153 
+(epoch: 394, iters: 1262, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.934 G_ID: 0.112 G_Rec: 0.446 D_GP: 0.049 D_real: 0.943 D_fake: 0.553 
+(epoch: 394, iters: 1662, time: 0.064) G_GAN: 0.015 G_GAN_Feat: 0.685 G_ID: 0.096 G_Rec: 0.321 D_GP: 0.037 D_real: 0.879 D_fake: 0.987 
+(epoch: 394, iters: 2062, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.899 G_ID: 0.119 G_Rec: 0.422 D_GP: 0.060 D_real: 0.895 D_fake: 0.666 
+(epoch: 394, iters: 2462, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.729 G_ID: 0.106 G_Rec: 0.296 D_GP: 0.082 D_real: 0.750 D_fake: 0.843 
+(epoch: 394, iters: 2862, time: 0.064) G_GAN: 0.605 G_GAN_Feat: 0.880 G_ID: 0.103 G_Rec: 0.409 D_GP: 0.038 D_real: 1.254 D_fake: 0.429 
+(epoch: 394, iters: 3262, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.740 G_ID: 0.117 G_Rec: 0.325 D_GP: 0.043 D_real: 0.845 D_fake: 0.798 
+(epoch: 394, iters: 3662, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 0.940 G_ID: 0.126 G_Rec: 0.409 D_GP: 0.128 D_real: 0.796 D_fake: 0.509 
+(epoch: 394, iters: 4062, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.727 G_ID: 0.120 G_Rec: 0.333 D_GP: 0.045 D_real: 0.953 D_fake: 0.805 
+(epoch: 394, iters: 4462, time: 0.064) G_GAN: 0.570 G_GAN_Feat: 0.970 G_ID: 0.109 G_Rec: 0.499 D_GP: 0.069 D_real: 1.076 D_fake: 0.444 
+(epoch: 394, iters: 4862, time: 0.063) G_GAN: 0.380 G_GAN_Feat: 0.707 G_ID: 0.112 G_Rec: 0.285 D_GP: 0.045 D_real: 1.170 D_fake: 0.623 
+(epoch: 394, iters: 5262, time: 0.063) G_GAN: 0.275 G_GAN_Feat: 0.930 G_ID: 0.130 G_Rec: 0.439 D_GP: 0.054 D_real: 0.841 D_fake: 0.725 
+(epoch: 394, iters: 5662, time: 0.063) G_GAN: 0.246 G_GAN_Feat: 0.741 G_ID: 0.103 G_Rec: 0.303 D_GP: 0.051 D_real: 1.085 D_fake: 0.754 
+(epoch: 394, iters: 6062, time: 0.064) G_GAN: 0.755 G_GAN_Feat: 1.090 G_ID: 0.119 G_Rec: 0.488 D_GP: 0.137 D_real: 0.802 D_fake: 0.334 
+(epoch: 394, iters: 6462, time: 0.063) G_GAN: -0.105 G_GAN_Feat: 0.731 G_ID: 0.107 G_Rec: 0.348 D_GP: 0.041 D_real: 0.852 D_fake: 1.105 
+(epoch: 394, iters: 6862, time: 0.063) G_GAN: 0.452 G_GAN_Feat: 0.871 G_ID: 0.116 G_Rec: 0.456 D_GP: 0.027 D_real: 1.262 D_fake: 0.562 
+(epoch: 394, iters: 7262, time: 0.063) G_GAN: 0.064 G_GAN_Feat: 0.635 G_ID: 0.083 G_Rec: 0.278 D_GP: 0.030 D_real: 1.014 D_fake: 0.936 
+(epoch: 394, iters: 7662, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.861 G_ID: 0.107 G_Rec: 0.423 D_GP: 0.033 D_real: 1.003 D_fake: 0.720 
+(epoch: 394, iters: 8062, time: 0.063) G_GAN: 0.094 G_GAN_Feat: 0.648 G_ID: 0.108 G_Rec: 0.283 D_GP: 0.038 D_real: 0.983 D_fake: 0.907 
+(epoch: 394, iters: 8462, time: 0.063) G_GAN: 0.214 G_GAN_Feat: 0.925 G_ID: 0.130 G_Rec: 0.438 D_GP: 0.052 D_real: 0.854 D_fake: 0.789 
+(epoch: 395, iters: 254, time: 0.063) G_GAN: 0.112 G_GAN_Feat: 0.635 G_ID: 0.101 G_Rec: 0.291 D_GP: 0.044 D_real: 0.979 D_fake: 0.888 
+(epoch: 395, iters: 654, time: 0.064) G_GAN: 0.043 G_GAN_Feat: 0.887 G_ID: 0.142 G_Rec: 0.418 D_GP: 0.046 D_real: 0.668 D_fake: 0.957 
+(epoch: 395, iters: 1054, time: 0.063) G_GAN: 0.186 G_GAN_Feat: 0.717 G_ID: 0.083 G_Rec: 0.309 D_GP: 0.114 D_real: 0.865 D_fake: 0.815 
+(epoch: 395, iters: 1454, time: 0.063) G_GAN: 0.800 G_GAN_Feat: 0.999 G_ID: 0.117 G_Rec: 0.470 D_GP: 0.060 D_real: 1.170 D_fake: 0.273 
+(epoch: 395, iters: 1854, time: 0.063) G_GAN: 0.346 G_GAN_Feat: 0.699 G_ID: 0.117 G_Rec: 0.281 D_GP: 0.042 D_real: 1.103 D_fake: 0.654 
+(epoch: 395, iters: 2254, time: 0.064) G_GAN: 0.445 G_GAN_Feat: 1.001 G_ID: 0.128 G_Rec: 0.476 D_GP: 0.040 D_real: 0.866 D_fake: 0.559 
+(epoch: 395, iters: 2654, time: 0.063) G_GAN: 0.204 G_GAN_Feat: 0.723 G_ID: 0.100 G_Rec: 0.325 D_GP: 0.033 D_real: 0.986 D_fake: 0.796 
+(epoch: 395, iters: 3054, time: 0.063) G_GAN: 0.551 G_GAN_Feat: 0.946 G_ID: 0.115 G_Rec: 0.446 D_GP: 0.042 D_real: 1.329 D_fake: 0.456 
+(epoch: 395, iters: 3454, time: 0.063) G_GAN: 0.204 G_GAN_Feat: 0.669 G_ID: 0.103 G_Rec: 0.290 D_GP: 0.031 D_real: 1.090 D_fake: 0.796 
+(epoch: 395, iters: 3854, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.896 G_ID: 0.135 G_Rec: 0.400 D_GP: 0.035 D_real: 0.898 D_fake: 0.625 
+(epoch: 395, iters: 4254, time: 0.063) G_GAN: 0.453 G_GAN_Feat: 0.869 G_ID: 0.109 G_Rec: 0.329 D_GP: 0.070 D_real: 0.853 D_fake: 0.552 
+(epoch: 395, iters: 4654, time: 0.063) G_GAN: 0.340 G_GAN_Feat: 0.993 G_ID: 0.121 G_Rec: 0.427 D_GP: 0.035 D_real: 0.857 D_fake: 0.664 
+(epoch: 395, iters: 5054, time: 0.063) G_GAN: 0.559 G_GAN_Feat: 0.747 G_ID: 0.098 G_Rec: 0.290 D_GP: 0.034 D_real: 1.281 D_fake: 0.445 
+(epoch: 395, iters: 5454, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 1.023 G_ID: 0.131 G_Rec: 0.481 D_GP: 0.041 D_real: 0.650 D_fake: 0.855 
+(epoch: 395, iters: 5854, time: 0.063) G_GAN: 0.361 G_GAN_Feat: 0.714 G_ID: 0.105 G_Rec: 0.307 D_GP: 0.046 D_real: 1.127 D_fake: 0.643 
+(epoch: 395, iters: 6254, time: 0.063) G_GAN: 0.288 G_GAN_Feat: 1.067 G_ID: 0.126 G_Rec: 0.494 D_GP: 0.115 D_real: 0.441 D_fake: 0.714 
+(epoch: 395, iters: 6654, time: 0.063) G_GAN: 0.189 G_GAN_Feat: 0.754 G_ID: 0.088 G_Rec: 0.292 D_GP: 0.049 D_real: 0.857 D_fake: 0.811 
+(epoch: 395, iters: 7054, time: 0.064) G_GAN: 0.911 G_GAN_Feat: 0.999 G_ID: 0.134 G_Rec: 0.448 D_GP: 0.151 D_real: 1.397 D_fake: 0.206 
+(epoch: 395, iters: 7454, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.927 G_ID: 0.106 G_Rec: 0.338 D_GP: 0.184 D_real: 0.399 D_fake: 0.817 
+(epoch: 395, iters: 7854, time: 0.063) G_GAN: 0.492 G_GAN_Feat: 1.107 G_ID: 0.128 G_Rec: 0.441 D_GP: 0.163 D_real: 0.533 D_fake: 0.514 
+(epoch: 395, iters: 8254, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 0.936 G_ID: 0.113 G_Rec: 0.336 D_GP: 0.123 D_real: 0.477 D_fake: 0.814 
+(epoch: 395, iters: 8654, time: 0.064) G_GAN: 0.779 G_GAN_Feat: 1.140 G_ID: 0.129 G_Rec: 0.466 D_GP: 0.459 D_real: 0.863 D_fake: 0.400 
+(epoch: 396, iters: 446, time: 0.063) G_GAN: 0.327 G_GAN_Feat: 0.922 G_ID: 0.113 G_Rec: 0.348 D_GP: 0.072 D_real: 0.465 D_fake: 0.749 
+(epoch: 396, iters: 846, time: 0.063) G_GAN: 0.266 G_GAN_Feat: 1.186 G_ID: 0.126 G_Rec: 0.457 D_GP: 0.364 D_real: 0.209 D_fake: 0.746 
+(epoch: 396, iters: 1246, time: 0.063) G_GAN: 0.160 G_GAN_Feat: 0.859 G_ID: 0.106 G_Rec: 0.382 D_GP: 0.050 D_real: 0.964 D_fake: 0.851 
+(epoch: 396, iters: 1646, time: 0.064) G_GAN: 0.465 G_GAN_Feat: 0.991 G_ID: 0.112 G_Rec: 0.444 D_GP: 0.035 D_real: 0.924 D_fake: 0.539 
+(epoch: 396, iters: 2046, time: 0.063) G_GAN: 0.291 G_GAN_Feat: 0.714 G_ID: 0.086 G_Rec: 0.269 D_GP: 0.036 D_real: 1.198 D_fake: 0.711 
+(epoch: 396, iters: 2446, time: 0.063) G_GAN: 0.686 G_GAN_Feat: 0.929 G_ID: 0.129 G_Rec: 0.418 D_GP: 0.035 D_real: 1.287 D_fake: 0.368 
+(epoch: 396, iters: 2846, time: 0.063) G_GAN: 0.249 G_GAN_Feat: 0.716 G_ID: 0.096 G_Rec: 0.288 D_GP: 0.029 D_real: 1.135 D_fake: 0.752 
+(epoch: 396, iters: 3246, time: 0.064) G_GAN: 0.381 G_GAN_Feat: 0.999 G_ID: 0.122 G_Rec: 0.386 D_GP: 0.055 D_real: 0.776 D_fake: 0.620 
+(epoch: 396, iters: 3646, time: 0.063) G_GAN: 0.288 G_GAN_Feat: 0.744 G_ID: 0.110 G_Rec: 0.290 D_GP: 0.040 D_real: 1.132 D_fake: 0.712 
+(epoch: 396, iters: 4046, time: 0.063) G_GAN: 0.768 G_GAN_Feat: 1.230 G_ID: 0.120 G_Rec: 0.440 D_GP: 0.116 D_real: 0.589 D_fake: 0.298 
+(epoch: 396, iters: 4446, time: 0.063) G_GAN: 0.517 G_GAN_Feat: 0.772 G_ID: 0.095 G_Rec: 0.311 D_GP: 0.033 D_real: 1.348 D_fake: 0.488 
+(epoch: 396, iters: 4846, time: 0.064) G_GAN: 0.599 G_GAN_Feat: 0.958 G_ID: 0.106 G_Rec: 0.463 D_GP: 0.040 D_real: 1.338 D_fake: 0.405 
+(epoch: 396, iters: 5246, time: 0.063) G_GAN: 0.499 G_GAN_Feat: 0.777 G_ID: 0.102 G_Rec: 0.302 D_GP: 0.044 D_real: 1.269 D_fake: 0.522 
+(epoch: 396, iters: 5646, time: 0.063) G_GAN: 0.647 G_GAN_Feat: 1.050 G_ID: 0.122 G_Rec: 0.467 D_GP: 0.044 D_real: 1.031 D_fake: 0.368 
+(epoch: 396, iters: 6046, time: 0.063) G_GAN: 0.481 G_GAN_Feat: 1.134 G_ID: 0.096 G_Rec: 0.347 D_GP: 0.625 D_real: 1.176 D_fake: 0.585 
+(epoch: 396, iters: 6446, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.908 G_ID: 0.114 G_Rec: 0.447 D_GP: 0.027 D_real: 0.849 D_fake: 0.918 
+(epoch: 396, iters: 6846, time: 0.063) G_GAN: -0.146 G_GAN_Feat: 0.684 G_ID: 0.090 G_Rec: 0.309 D_GP: 0.028 D_real: 0.775 D_fake: 1.146 
+(epoch: 396, iters: 7246, time: 0.063) G_GAN: 0.062 G_GAN_Feat: 0.980 G_ID: 0.116 G_Rec: 0.474 D_GP: 0.037 D_real: 0.670 D_fake: 0.939 
+(epoch: 396, iters: 7646, time: 0.063) G_GAN: -0.055 G_GAN_Feat: 0.762 G_ID: 0.092 G_Rec: 0.312 D_GP: 0.039 D_real: 0.791 D_fake: 1.055 
+(epoch: 396, iters: 8046, time: 0.064) G_GAN: -0.025 G_GAN_Feat: 1.147 G_ID: 0.115 G_Rec: 0.465 D_GP: 0.401 D_real: 0.233 D_fake: 1.026 
+(epoch: 396, iters: 8446, time: 0.063) G_GAN: 0.034 G_GAN_Feat: 0.757 G_ID: 0.101 G_Rec: 0.300 D_GP: 0.056 D_real: 0.822 D_fake: 0.966 
+(epoch: 397, iters: 238, time: 0.063) G_GAN: 0.258 G_GAN_Feat: 1.038 G_ID: 0.128 G_Rec: 0.427 D_GP: 0.096 D_real: 0.515 D_fake: 0.742 
+(epoch: 397, iters: 638, time: 0.063) G_GAN: 0.152 G_GAN_Feat: 0.839 G_ID: 0.100 G_Rec: 0.347 D_GP: 0.035 D_real: 0.865 D_fake: 0.848 
+(epoch: 397, iters: 1038, time: 0.064) G_GAN: 0.664 G_GAN_Feat: 1.157 G_ID: 0.117 G_Rec: 0.441 D_GP: 0.051 D_real: 0.793 D_fake: 0.393 
+(epoch: 397, iters: 1438, time: 0.063) G_GAN: 0.247 G_GAN_Feat: 0.812 G_ID: 0.094 G_Rec: 0.334 D_GP: 0.063 D_real: 0.957 D_fake: 0.754 
+(epoch: 397, iters: 1838, time: 0.063) G_GAN: 0.679 G_GAN_Feat: 0.887 G_ID: 0.130 G_Rec: 0.447 D_GP: 0.039 D_real: 1.282 D_fake: 0.357 
+(epoch: 397, iters: 2238, time: 0.063) G_GAN: 0.049 G_GAN_Feat: 0.795 G_ID: 0.096 G_Rec: 0.327 D_GP: 0.054 D_real: 0.840 D_fake: 0.951 
+(epoch: 397, iters: 2638, time: 0.064) G_GAN: 0.467 G_GAN_Feat: 1.017 G_ID: 0.134 G_Rec: 0.455 D_GP: 0.057 D_real: 0.869 D_fake: 0.564 
+(epoch: 397, iters: 3038, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.933 G_ID: 0.117 G_Rec: 0.355 D_GP: 0.101 D_real: 0.678 D_fake: 0.764 
+(epoch: 397, iters: 3438, time: 0.063) G_GAN: 0.580 G_GAN_Feat: 1.003 G_ID: 0.139 G_Rec: 0.458 D_GP: 0.047 D_real: 1.116 D_fake: 0.434 
+(epoch: 397, iters: 3838, time: 0.063) G_GAN: 0.399 G_GAN_Feat: 0.749 G_ID: 0.093 G_Rec: 0.282 D_GP: 0.037 D_real: 1.222 D_fake: 0.604 
+(epoch: 397, iters: 4238, time: 0.064) G_GAN: 0.920 G_GAN_Feat: 0.983 G_ID: 0.130 G_Rec: 0.446 D_GP: 0.053 D_real: 1.527 D_fake: 0.224 
+(epoch: 397, iters: 4638, time: 0.063) G_GAN: 0.371 G_GAN_Feat: 0.661 G_ID: 0.084 G_Rec: 0.279 D_GP: 0.029 D_real: 1.244 D_fake: 0.633 
+(epoch: 397, iters: 5038, time: 0.063) G_GAN: 0.137 G_GAN_Feat: 0.994 G_ID: 0.121 G_Rec: 0.446 D_GP: 0.043 D_real: 0.507 D_fake: 0.863 
+(epoch: 397, iters: 5438, time: 0.063) G_GAN: 0.168 G_GAN_Feat: 0.895 G_ID: 0.095 G_Rec: 0.328 D_GP: 0.095 D_real: 0.396 D_fake: 0.837 
+(epoch: 397, iters: 5838, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 0.951 G_ID: 0.115 G_Rec: 0.418 D_GP: 0.031 D_real: 1.277 D_fake: 0.357 
+(epoch: 397, iters: 6238, time: 0.063) G_GAN: 0.360 G_GAN_Feat: 0.877 G_ID: 0.093 G_Rec: 0.331 D_GP: 0.074 D_real: 0.613 D_fake: 0.691 
+(epoch: 397, iters: 6638, time: 0.063) G_GAN: 0.375 G_GAN_Feat: 0.921 G_ID: 0.118 G_Rec: 0.427 D_GP: 0.034 D_real: 1.017 D_fake: 0.627 
+(epoch: 397, iters: 7038, time: 0.063) G_GAN: 0.519 G_GAN_Feat: 0.729 G_ID: 0.101 G_Rec: 0.355 D_GP: 0.030 D_real: 1.350 D_fake: 0.496 
+(epoch: 397, iters: 7438, time: 0.063) G_GAN: 0.733 G_GAN_Feat: 1.051 G_ID: 0.118 G_Rec: 0.448 D_GP: 0.068 D_real: 1.002 D_fake: 0.305 
+(epoch: 397, iters: 7838, time: 0.063) G_GAN: 0.233 G_GAN_Feat: 0.801 G_ID: 0.115 G_Rec: 0.321 D_GP: 0.041 D_real: 0.803 D_fake: 0.771 
+(epoch: 397, iters: 8238, time: 0.063) G_GAN: 0.234 G_GAN_Feat: 0.973 G_ID: 0.139 G_Rec: 0.462 D_GP: 0.065 D_real: 0.908 D_fake: 0.767 
+(epoch: 397, iters: 8638, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.740 G_ID: 0.093 G_Rec: 0.316 D_GP: 0.057 D_real: 1.092 D_fake: 0.850 
+(epoch: 398, iters: 430, time: 0.063) G_GAN: 0.269 G_GAN_Feat: 0.981 G_ID: 0.108 G_Rec: 0.437 D_GP: 0.063 D_real: 0.891 D_fake: 0.732 
+(epoch: 398, iters: 830, time: 0.063) G_GAN: 0.181 G_GAN_Feat: 0.699 G_ID: 0.093 G_Rec: 0.295 D_GP: 0.042 D_real: 1.148 D_fake: 0.821 
+(epoch: 398, iters: 1230, time: 0.063) G_GAN: 0.347 G_GAN_Feat: 0.984 G_ID: 0.121 G_Rec: 0.459 D_GP: 0.067 D_real: 0.886 D_fake: 0.660 
+(epoch: 398, iters: 1630, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.816 G_ID: 0.104 G_Rec: 0.349 D_GP: 0.069 D_real: 0.700 D_fake: 0.987 
+(epoch: 398, iters: 2030, time: 0.063) G_GAN: 0.285 G_GAN_Feat: 0.923 G_ID: 0.125 G_Rec: 0.427 D_GP: 0.054 D_real: 0.858 D_fake: 0.725 
+(epoch: 398, iters: 2430, time: 0.063) G_GAN: 0.157 G_GAN_Feat: 0.736 G_ID: 0.092 G_Rec: 0.330 D_GP: 0.048 D_real: 0.984 D_fake: 0.844 
+(epoch: 398, iters: 2830, time: 0.063) G_GAN: 0.218 G_GAN_Feat: 0.933 G_ID: 0.123 G_Rec: 0.417 D_GP: 0.102 D_real: 0.787 D_fake: 0.783 
+(epoch: 398, iters: 3230, time: 0.064) G_GAN: 0.071 G_GAN_Feat: 0.703 G_ID: 0.106 G_Rec: 0.304 D_GP: 0.039 D_real: 0.881 D_fake: 0.929 
+(epoch: 398, iters: 3630, time: 0.063) G_GAN: 0.533 G_GAN_Feat: 0.993 G_ID: 0.124 G_Rec: 0.466 D_GP: 0.050 D_real: 1.028 D_fake: 0.486 
+(epoch: 398, iters: 4030, time: 0.063) G_GAN: 0.147 G_GAN_Feat: 0.774 G_ID: 0.119 G_Rec: 0.337 D_GP: 0.043 D_real: 0.986 D_fake: 0.854 
+(epoch: 398, iters: 4430, time: 0.063) G_GAN: 0.409 G_GAN_Feat: 1.007 G_ID: 0.102 G_Rec: 0.428 D_GP: 0.048 D_real: 1.181 D_fake: 0.608 
+(epoch: 398, iters: 4830, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.772 G_ID: 0.106 G_Rec: 0.305 D_GP: 0.052 D_real: 0.863 D_fake: 0.879 
+(epoch: 398, iters: 5230, time: 0.063) G_GAN: 0.209 G_GAN_Feat: 0.949 G_ID: 0.125 G_Rec: 0.420 D_GP: 0.051 D_real: 0.804 D_fake: 0.794 
+(epoch: 398, iters: 5630, time: 0.063) G_GAN: 0.212 G_GAN_Feat: 0.736 G_ID: 0.117 G_Rec: 0.296 D_GP: 0.036 D_real: 1.050 D_fake: 0.790 
+(epoch: 398, iters: 6030, time: 0.063) G_GAN: 0.381 G_GAN_Feat: 0.915 G_ID: 0.101 G_Rec: 0.438 D_GP: 0.038 D_real: 0.969 D_fake: 0.620 
+(epoch: 398, iters: 6430, time: 0.064) G_GAN: 0.014 G_GAN_Feat: 0.805 G_ID: 0.110 G_Rec: 0.336 D_GP: 0.075 D_real: 0.789 D_fake: 0.986 
+(epoch: 398, iters: 6830, time: 0.063) G_GAN: 0.463 G_GAN_Feat: 0.970 G_ID: 0.108 G_Rec: 0.443 D_GP: 0.041 D_real: 0.900 D_fake: 0.548 
+(epoch: 398, iters: 7230, time: 0.063) G_GAN: 0.070 G_GAN_Feat: 0.793 G_ID: 0.094 G_Rec: 0.321 D_GP: 0.052 D_real: 0.658 D_fake: 0.930 
+(epoch: 398, iters: 7630, time: 0.063) G_GAN: 0.663 G_GAN_Feat: 1.020 G_ID: 0.105 G_Rec: 0.431 D_GP: 0.036 D_real: 1.196 D_fake: 0.399 
+(epoch: 398, iters: 8030, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.803 G_ID: 0.099 G_Rec: 0.310 D_GP: 0.049 D_real: 0.801 D_fake: 0.845 
+(epoch: 398, iters: 8430, time: 0.063) G_GAN: 0.538 G_GAN_Feat: 1.030 G_ID: 0.115 G_Rec: 0.437 D_GP: 0.330 D_real: 0.752 D_fake: 0.475 
+(epoch: 399, iters: 222, time: 0.063) G_GAN: 0.085 G_GAN_Feat: 0.759 G_ID: 0.097 G_Rec: 0.306 D_GP: 0.050 D_real: 0.793 D_fake: 0.915 
+(epoch: 399, iters: 622, time: 0.063) G_GAN: 0.490 G_GAN_Feat: 0.933 G_ID: 0.129 G_Rec: 0.412 D_GP: 0.037 D_real: 1.099 D_fake: 0.513 
+(epoch: 399, iters: 1022, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.867 G_ID: 0.105 G_Rec: 0.334 D_GP: 0.121 D_real: 0.831 D_fake: 0.632 
+(epoch: 399, iters: 1422, time: 0.063) G_GAN: 0.406 G_GAN_Feat: 1.030 G_ID: 0.119 G_Rec: 0.432 D_GP: 0.043 D_real: 0.869 D_fake: 0.597 
+(epoch: 399, iters: 1822, time: 0.063) G_GAN: -0.107 G_GAN_Feat: 0.755 G_ID: 0.101 G_Rec: 0.294 D_GP: 0.035 D_real: 0.694 D_fake: 1.107 
+(epoch: 399, iters: 2222, time: 0.063) G_GAN: 0.379 G_GAN_Feat: 0.967 G_ID: 0.129 G_Rec: 0.457 D_GP: 0.035 D_real: 1.036 D_fake: 0.622 
+(epoch: 399, iters: 2622, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.793 G_ID: 0.095 G_Rec: 0.302 D_GP: 0.034 D_real: 1.299 D_fake: 0.653 
+(epoch: 399, iters: 3022, time: 0.063) G_GAN: 0.218 G_GAN_Feat: 0.920 G_ID: 0.123 G_Rec: 0.399 D_GP: 0.042 D_real: 0.828 D_fake: 0.784 
+(epoch: 399, iters: 3422, time: 0.063) G_GAN: 0.296 G_GAN_Feat: 0.803 G_ID: 0.091 G_Rec: 0.307 D_GP: 0.051 D_real: 0.973 D_fake: 0.704 
+(epoch: 399, iters: 3822, time: 0.063) G_GAN: 0.898 G_GAN_Feat: 1.011 G_ID: 0.123 G_Rec: 0.436 D_GP: 0.045 D_real: 1.359 D_fake: 0.213 
+(epoch: 399, iters: 4222, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.802 G_ID: 0.102 G_Rec: 0.318 D_GP: 0.039 D_real: 1.048 D_fake: 0.683 
+(epoch: 399, iters: 4622, time: 0.063) G_GAN: 0.558 G_GAN_Feat: 1.073 G_ID: 0.123 G_Rec: 0.452 D_GP: 0.117 D_real: 0.694 D_fake: 0.451 
+(epoch: 399, iters: 5022, time: 0.063) G_GAN: 0.602 G_GAN_Feat: 0.828 G_ID: 0.092 G_Rec: 0.272 D_GP: 0.038 D_real: 1.167 D_fake: 0.412 
+(epoch: 399, iters: 5422, time: 0.063) G_GAN: 0.619 G_GAN_Feat: 0.975 G_ID: 0.138 G_Rec: 0.433 D_GP: 0.033 D_real: 1.271 D_fake: 0.390 
+(epoch: 399, iters: 5822, time: 0.064) G_GAN: 0.387 G_GAN_Feat: 0.896 G_ID: 0.103 G_Rec: 0.335 D_GP: 0.063 D_real: 0.795 D_fake: 0.748 
+(epoch: 399, iters: 6222, time: 0.063) G_GAN: 0.607 G_GAN_Feat: 1.048 G_ID: 0.114 G_Rec: 0.435 D_GP: 0.040 D_real: 1.089 D_fake: 0.408 
+(epoch: 399, iters: 6622, time: 0.063) G_GAN: 0.261 G_GAN_Feat: 1.030 G_ID: 0.108 G_Rec: 0.365 D_GP: 0.031 D_real: 1.354 D_fake: 0.775 
+(epoch: 399, iters: 7022, time: 0.063) G_GAN: 0.156 G_GAN_Feat: 0.807 G_ID: 0.136 G_Rec: 0.420 D_GP: 0.026 D_real: 0.921 D_fake: 0.844 
+(epoch: 399, iters: 7422, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.672 G_ID: 0.100 G_Rec: 0.362 D_GP: 0.025 D_real: 0.986 D_fake: 0.917 
+(epoch: 399, iters: 7822, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.843 G_ID: 0.122 G_Rec: 0.430 D_GP: 0.030 D_real: 0.895 D_fake: 0.763 
+(epoch: 399, iters: 8222, time: 0.063) G_GAN: -0.026 G_GAN_Feat: 0.635 G_ID: 0.102 G_Rec: 0.319 D_GP: 0.035 D_real: 0.844 D_fake: 1.026 
+(epoch: 399, iters: 8622, time: 0.063) G_GAN: -0.030 G_GAN_Feat: 0.879 G_ID: 0.156 G_Rec: 0.442 D_GP: 0.055 D_real: 0.561 D_fake: 1.031 
+(epoch: 400, iters: 414, time: 0.064) G_GAN: -0.273 G_GAN_Feat: 0.859 G_ID: 0.118 G_Rec: 0.355 D_GP: 0.091 D_real: 0.374 D_fake: 1.273 
+(epoch: 400, iters: 814, time: 0.063) G_GAN: 0.541 G_GAN_Feat: 0.848 G_ID: 0.106 G_Rec: 0.404 D_GP: 0.036 D_real: 1.260 D_fake: 0.514 
+(epoch: 400, iters: 1214, time: 0.063) G_GAN: 0.054 G_GAN_Feat: 0.699 G_ID: 0.104 G_Rec: 0.296 D_GP: 0.037 D_real: 0.966 D_fake: 0.946 
+(epoch: 400, iters: 1614, time: 0.063) G_GAN: 0.619 G_GAN_Feat: 1.016 G_ID: 0.120 G_Rec: 0.440 D_GP: 0.042 D_real: 1.027 D_fake: 0.417 
+(epoch: 400, iters: 2014, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.592 G_ID: 0.092 G_Rec: 0.273 D_GP: 0.027 D_real: 1.076 D_fake: 0.869 
+(epoch: 400, iters: 2414, time: 0.063) G_GAN: 0.347 G_GAN_Feat: 0.873 G_ID: 0.105 G_Rec: 0.445 D_GP: 0.036 D_real: 1.002 D_fake: 0.668 
+(epoch: 400, iters: 2814, time: 0.063) G_GAN: -0.021 G_GAN_Feat: 0.617 G_ID: 0.091 G_Rec: 0.305 D_GP: 0.032 D_real: 0.796 D_fake: 1.021 
+(epoch: 400, iters: 3214, time: 0.063) G_GAN: 0.339 G_GAN_Feat: 0.872 G_ID: 0.127 G_Rec: 0.411 D_GP: 0.077 D_real: 0.914 D_fake: 0.687 
+(epoch: 400, iters: 3614, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.662 G_ID: 0.093 G_Rec: 0.351 D_GP: 0.035 D_real: 0.986 D_fake: 0.901 
+(epoch: 400, iters: 4014, time: 0.063) G_GAN: 0.268 G_GAN_Feat: 1.007 G_ID: 0.118 G_Rec: 0.459 D_GP: 0.107 D_real: 0.775 D_fake: 0.742 
+(epoch: 400, iters: 4414, time: 0.063) G_GAN: -0.157 G_GAN_Feat: 0.657 G_ID: 0.104 G_Rec: 0.320 D_GP: 0.029 D_real: 0.694 D_fake: 1.157 
+(epoch: 400, iters: 4814, time: 0.063) G_GAN: 0.149 G_GAN_Feat: 1.035 G_ID: 0.137 G_Rec: 0.466 D_GP: 0.056 D_real: 0.547 D_fake: 0.854 
+(epoch: 400, iters: 5214, time: 0.064) G_GAN: 0.367 G_GAN_Feat: 0.641 G_ID: 0.093 G_Rec: 0.262 D_GP: 0.033 D_real: 1.224 D_fake: 0.641 
+(epoch: 400, iters: 5614, time: 0.063) G_GAN: 0.336 G_GAN_Feat: 0.999 G_ID: 0.132 G_Rec: 0.429 D_GP: 0.155 D_real: 0.761 D_fake: 0.672 
+(epoch: 400, iters: 6014, time: 0.063) G_GAN: 0.041 G_GAN_Feat: 0.836 G_ID: 0.117 G_Rec: 0.343 D_GP: 0.096 D_real: 0.682 D_fake: 0.959 
+(epoch: 400, iters: 6414, time: 0.063) G_GAN: 0.282 G_GAN_Feat: 1.012 G_ID: 0.121 G_Rec: 0.426 D_GP: 0.075 D_real: 0.553 D_fake: 0.719 
+(epoch: 400, iters: 6814, time: 0.064) G_GAN: 0.058 G_GAN_Feat: 0.775 G_ID: 0.086 G_Rec: 0.291 D_GP: 0.040 D_real: 0.743 D_fake: 0.942 
+(epoch: 400, iters: 7214, time: 0.063) G_GAN: 0.519 G_GAN_Feat: 0.868 G_ID: 0.134 G_Rec: 0.438 D_GP: 0.029 D_real: 1.245 D_fake: 0.509 
+(epoch: 400, iters: 7614, time: 0.063) G_GAN: 0.317 G_GAN_Feat: 0.679 G_ID: 0.097 G_Rec: 0.310 D_GP: 0.030 D_real: 1.207 D_fake: 0.683 
+(epoch: 400, iters: 8014, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 0.842 G_ID: 0.121 G_Rec: 0.381 D_GP: 0.035 D_real: 1.034 D_fake: 0.581 
+(epoch: 400, iters: 8414, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.687 G_ID: 0.122 G_Rec: 0.285 D_GP: 0.037 D_real: 1.101 D_fake: 0.755 
+(epoch: 401, iters: 206, time: 0.064) G_GAN: 0.659 G_GAN_Feat: 0.965 G_ID: 0.101 G_Rec: 0.435 D_GP: 0.032 D_real: 1.337 D_fake: 0.349 
+(epoch: 401, iters: 606, time: 0.063) G_GAN: 0.376 G_GAN_Feat: 0.890 G_ID: 0.108 G_Rec: 0.363 D_GP: 0.115 D_real: 0.576 D_fake: 0.650 
+(epoch: 401, iters: 1006, time: 0.063) G_GAN: 0.886 G_GAN_Feat: 1.085 G_ID: 0.130 G_Rec: 0.426 D_GP: 0.146 D_real: 0.897 D_fake: 0.349 
+(epoch: 401, iters: 1406, time: 0.064) G_GAN: 0.307 G_GAN_Feat: 0.744 G_ID: 0.114 G_Rec: 0.308 D_GP: 0.028 D_real: 1.186 D_fake: 0.694 
+(epoch: 401, iters: 1806, time: 0.063) G_GAN: 0.896 G_GAN_Feat: 1.130 G_ID: 0.112 G_Rec: 0.478 D_GP: 0.129 D_real: 0.765 D_fake: 0.211 
+(epoch: 401, iters: 2206, time: 0.063) G_GAN: 0.226 G_GAN_Feat: 1.001 G_ID: 0.097 G_Rec: 0.338 D_GP: 0.809 D_real: 0.295 D_fake: 0.779 
+(epoch: 401, iters: 2606, time: 0.063) G_GAN: 0.119 G_GAN_Feat: 0.951 G_ID: 0.134 G_Rec: 0.439 D_GP: 0.060 D_real: 0.643 D_fake: 0.881 
+(epoch: 401, iters: 3006, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.765 G_ID: 0.102 G_Rec: 0.324 D_GP: 0.032 D_real: 1.131 D_fake: 0.761 
+(epoch: 401, iters: 3406, time: 0.063) G_GAN: 0.476 G_GAN_Feat: 0.955 G_ID: 0.117 G_Rec: 0.412 D_GP: 0.040 D_real: 1.047 D_fake: 0.531 
+(epoch: 401, iters: 3806, time: 0.063) G_GAN: 0.271 G_GAN_Feat: 0.725 G_ID: 0.101 G_Rec: 0.297 D_GP: 0.036 D_real: 1.106 D_fake: 0.730 
+(epoch: 401, iters: 4206, time: 0.063) G_GAN: 0.541 G_GAN_Feat: 0.917 G_ID: 0.127 G_Rec: 0.450 D_GP: 0.033 D_real: 1.237 D_fake: 0.470 
+(epoch: 401, iters: 4606, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.811 G_ID: 0.098 G_Rec: 0.318 D_GP: 0.037 D_real: 0.871 D_fake: 0.802 
+(epoch: 401, iters: 5006, time: 0.063) G_GAN: 0.710 G_GAN_Feat: 0.976 G_ID: 0.123 G_Rec: 0.385 D_GP: 0.046 D_real: 1.122 D_fake: 0.308 
+(epoch: 401, iters: 5406, time: 0.063) G_GAN: 0.168 G_GAN_Feat: 0.722 G_ID: 0.116 G_Rec: 0.301 D_GP: 0.038 D_real: 1.045 D_fake: 0.836 
+(epoch: 401, iters: 5806, time: 0.063) G_GAN: 0.307 G_GAN_Feat: 1.036 G_ID: 0.149 G_Rec: 0.472 D_GP: 0.285 D_real: 0.616 D_fake: 0.702 
+(epoch: 401, iters: 6206, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.738 G_ID: 0.099 G_Rec: 0.327 D_GP: 0.038 D_real: 1.207 D_fake: 0.596 
+(epoch: 401, iters: 6606, time: 0.063) G_GAN: 0.672 G_GAN_Feat: 1.126 G_ID: 0.122 G_Rec: 0.483 D_GP: 0.154 D_real: 0.500 D_fake: 0.373 
+(epoch: 401, iters: 7006, time: 0.063) G_GAN: 0.284 G_GAN_Feat: 0.795 G_ID: 0.108 G_Rec: 0.344 D_GP: 0.038 D_real: 1.101 D_fake: 0.716 
+(epoch: 401, iters: 7406, time: 0.063) G_GAN: 0.673 G_GAN_Feat: 0.899 G_ID: 0.116 G_Rec: 0.390 D_GP: 0.041 D_real: 1.323 D_fake: 0.365 
+(epoch: 401, iters: 7806, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.879 G_ID: 0.088 G_Rec: 0.352 D_GP: 0.054 D_real: 0.818 D_fake: 0.638 
+(epoch: 401, iters: 8206, time: 0.063) G_GAN: 0.626 G_GAN_Feat: 1.027 G_ID: 0.121 G_Rec: 0.434 D_GP: 0.032 D_real: 1.125 D_fake: 0.425 
+(epoch: 401, iters: 8606, time: 0.063) G_GAN: -0.029 G_GAN_Feat: 0.964 G_ID: 0.110 G_Rec: 0.351 D_GP: 0.214 D_real: 0.426 D_fake: 1.029 
+(epoch: 402, iters: 398, time: 0.063) G_GAN: 0.343 G_GAN_Feat: 0.909 G_ID: 0.130 G_Rec: 0.429 D_GP: 0.030 D_real: 1.018 D_fake: 0.660 
+(epoch: 402, iters: 798, time: 0.064) G_GAN: 0.090 G_GAN_Feat: 0.684 G_ID: 0.105 G_Rec: 0.311 D_GP: 0.027 D_real: 1.031 D_fake: 0.910 
+(epoch: 402, iters: 1198, time: 0.063) G_GAN: 0.425 G_GAN_Feat: 0.966 G_ID: 0.119 G_Rec: 0.457 D_GP: 0.045 D_real: 0.892 D_fake: 0.610 
+(epoch: 402, iters: 1598, time: 0.063) G_GAN: 0.100 G_GAN_Feat: 0.681 G_ID: 0.091 G_Rec: 0.303 D_GP: 0.037 D_real: 1.020 D_fake: 0.900 
+(epoch: 402, iters: 1998, time: 0.063) G_GAN: 0.323 G_GAN_Feat: 0.868 G_ID: 0.110 G_Rec: 0.435 D_GP: 0.041 D_real: 1.002 D_fake: 0.692 
+(epoch: 402, iters: 2398, time: 0.064) G_GAN: 0.047 G_GAN_Feat: 0.731 G_ID: 0.098 G_Rec: 0.306 D_GP: 0.041 D_real: 0.830 D_fake: 0.953 
+(epoch: 402, iters: 2798, time: 0.063) G_GAN: 0.556 G_GAN_Feat: 0.961 G_ID: 0.108 G_Rec: 0.476 D_GP: 0.036 D_real: 1.260 D_fake: 0.484 
+(epoch: 402, iters: 3198, time: 0.063) G_GAN: 0.004 G_GAN_Feat: 0.928 G_ID: 0.118 G_Rec: 0.387 D_GP: 0.556 D_real: 0.441 D_fake: 0.997 
+(epoch: 402, iters: 3598, time: 0.063) G_GAN: 0.412 G_GAN_Feat: 1.053 G_ID: 0.104 G_Rec: 0.500 D_GP: 0.040 D_real: 0.857 D_fake: 0.592 
+(epoch: 402, iters: 3998, time: 0.063) G_GAN: 0.405 G_GAN_Feat: 0.700 G_ID: 0.091 G_Rec: 0.316 D_GP: 0.049 D_real: 1.268 D_fake: 0.604 
+(epoch: 402, iters: 4398, time: 0.063) G_GAN: 0.469 G_GAN_Feat: 0.978 G_ID: 0.114 G_Rec: 0.417 D_GP: 0.113 D_real: 0.904 D_fake: 0.538 
+(epoch: 402, iters: 4798, time: 0.063) G_GAN: 0.224 G_GAN_Feat: 0.775 G_ID: 0.103 G_Rec: 0.317 D_GP: 0.088 D_real: 0.867 D_fake: 0.777 
+(epoch: 402, iters: 5198, time: 0.064) G_GAN: 0.730 G_GAN_Feat: 0.993 G_ID: 0.135 G_Rec: 0.469 D_GP: 0.041 D_real: 1.173 D_fake: 0.303 
+(epoch: 402, iters: 5598, time: 0.063) G_GAN: 0.145 G_GAN_Feat: 0.745 G_ID: 0.122 G_Rec: 0.313 D_GP: 0.036 D_real: 0.932 D_fake: 0.855 
+(epoch: 402, iters: 5998, time: 0.063) G_GAN: 0.696 G_GAN_Feat: 1.016 G_ID: 0.118 G_Rec: 0.440 D_GP: 0.545 D_real: 0.854 D_fake: 0.346 
+(epoch: 402, iters: 6398, time: 0.063) G_GAN: 0.220 G_GAN_Feat: 0.739 G_ID: 0.111 G_Rec: 0.299 D_GP: 0.038 D_real: 1.044 D_fake: 0.780 
+(epoch: 402, iters: 6798, time: 0.064) G_GAN: 0.557 G_GAN_Feat: 1.016 G_ID: 0.127 G_Rec: 0.466 D_GP: 0.047 D_real: 1.023 D_fake: 0.450 
+(epoch: 402, iters: 7198, time: 0.063) G_GAN: 0.416 G_GAN_Feat: 0.855 G_ID: 0.097 G_Rec: 0.314 D_GP: 0.080 D_real: 0.793 D_fake: 0.591 
+(epoch: 402, iters: 7598, time: 0.063) G_GAN: 0.257 G_GAN_Feat: 1.197 G_ID: 0.124 G_Rec: 0.510 D_GP: 0.255 D_real: 0.240 D_fake: 0.749 
+(epoch: 402, iters: 7998, time: 0.063) G_GAN: 0.298 G_GAN_Feat: 0.858 G_ID: 0.119 G_Rec: 0.356 D_GP: 0.049 D_real: 0.969 D_fake: 0.712 
+(epoch: 402, iters: 8398, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 1.039 G_ID: 0.113 G_Rec: 0.420 D_GP: 0.050 D_real: 0.581 D_fake: 0.580 
+(epoch: 403, iters: 190, time: 0.063) G_GAN: 0.673 G_GAN_Feat: 1.016 G_ID: 0.110 G_Rec: 0.318 D_GP: 0.484 D_real: 0.566 D_fake: 0.386 
+(epoch: 403, iters: 590, time: 0.063) G_GAN: 0.966 G_GAN_Feat: 1.084 G_ID: 0.120 G_Rec: 0.471 D_GP: 0.039 D_real: 1.289 D_fake: 0.181 
+(epoch: 403, iters: 990, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.978 G_ID: 0.114 G_Rec: 0.355 D_GP: 0.394 D_real: 0.284 D_fake: 0.833 
+(epoch: 403, iters: 1390, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 0.890 G_ID: 0.124 G_Rec: 0.413 D_GP: 0.029 D_real: 1.097 D_fake: 0.677 
+(epoch: 403, iters: 1790, time: 0.063) G_GAN: -0.012 G_GAN_Feat: 0.878 G_ID: 0.110 G_Rec: 0.331 D_GP: 0.129 D_real: 0.382 D_fake: 1.012 
+(epoch: 403, iters: 2190, time: 0.063) G_GAN: 0.717 G_GAN_Feat: 0.987 G_ID: 0.110 G_Rec: 0.395 D_GP: 0.032 D_real: 1.191 D_fake: 0.305 
+(epoch: 403, iters: 2590, time: 0.063) G_GAN: 0.192 G_GAN_Feat: 0.807 G_ID: 0.120 G_Rec: 0.332 D_GP: 0.066 D_real: 0.864 D_fake: 0.808 
+(epoch: 403, iters: 2990, time: 0.064) G_GAN: 0.605 G_GAN_Feat: 0.936 G_ID: 0.139 G_Rec: 0.412 D_GP: 0.034 D_real: 1.316 D_fake: 0.423 
+(epoch: 403, iters: 3390, time: 0.063) G_GAN: 0.147 G_GAN_Feat: 0.854 G_ID: 0.090 G_Rec: 0.319 D_GP: 0.032 D_real: 0.913 D_fake: 0.853 
+(epoch: 403, iters: 3790, time: 0.063) G_GAN: 0.417 G_GAN_Feat: 0.914 G_ID: 0.127 G_Rec: 0.453 D_GP: 0.036 D_real: 1.209 D_fake: 0.586 
+(epoch: 403, iters: 4190, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 0.693 G_ID: 0.104 G_Rec: 0.295 D_GP: 0.043 D_real: 0.814 D_fake: 0.934 
+(epoch: 403, iters: 4590, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.997 G_ID: 0.115 G_Rec: 0.443 D_GP: 0.057 D_real: 0.892 D_fake: 0.511 
+(epoch: 403, iters: 4990, time: 0.063) G_GAN: 0.289 G_GAN_Feat: 0.838 G_ID: 0.104 G_Rec: 0.323 D_GP: 0.034 D_real: 0.873 D_fake: 0.712 
+(epoch: 403, iters: 5390, time: 0.063) G_GAN: 0.451 G_GAN_Feat: 0.986 G_ID: 0.106 G_Rec: 0.464 D_GP: 0.038 D_real: 1.075 D_fake: 0.552 
+(epoch: 403, iters: 5790, time: 0.063) G_GAN: 0.285 G_GAN_Feat: 0.781 G_ID: 0.096 G_Rec: 0.324 D_GP: 0.110 D_real: 0.864 D_fake: 0.715 
+(epoch: 403, iters: 6190, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 1.015 G_ID: 0.139 G_Rec: 0.448 D_GP: 0.067 D_real: 0.864 D_fake: 0.639 
+(epoch: 403, iters: 6590, time: 0.063) G_GAN: 0.309 G_GAN_Feat: 0.798 G_ID: 0.095 G_Rec: 0.299 D_GP: 0.083 D_real: 0.856 D_fake: 0.693 
+(epoch: 403, iters: 6990, time: 0.063) G_GAN: 0.700 G_GAN_Feat: 1.163 G_ID: 0.126 G_Rec: 0.425 D_GP: 0.740 D_real: 0.589 D_fake: 0.325 
+(epoch: 403, iters: 7390, time: 0.063) G_GAN: 0.180 G_GAN_Feat: 0.836 G_ID: 0.096 G_Rec: 0.301 D_GP: 0.049 D_real: 0.689 D_fake: 0.821 
+(epoch: 403, iters: 7790, time: 0.064) G_GAN: 0.538 G_GAN_Feat: 0.981 G_ID: 0.118 G_Rec: 0.431 D_GP: 0.039 D_real: 1.048 D_fake: 0.472 
+(epoch: 403, iters: 8190, time: 0.063) G_GAN: 0.230 G_GAN_Feat: 0.822 G_ID: 0.096 G_Rec: 0.320 D_GP: 0.039 D_real: 0.981 D_fake: 0.770 
+(epoch: 403, iters: 8590, time: 0.063) G_GAN: 0.654 G_GAN_Feat: 1.029 G_ID: 0.108 G_Rec: 0.440 D_GP: 0.035 D_real: 1.161 D_fake: 0.383 
+(epoch: 404, iters: 382, time: 0.063) G_GAN: 0.260 G_GAN_Feat: 0.879 G_ID: 0.111 G_Rec: 0.336 D_GP: 0.096 D_real: 0.367 D_fake: 0.744 
+(epoch: 404, iters: 782, time: 0.064) G_GAN: 0.584 G_GAN_Feat: 1.254 G_ID: 0.126 G_Rec: 0.533 D_GP: 1.268 D_real: 0.302 D_fake: 0.489 
+(epoch: 404, iters: 1182, time: 0.063) G_GAN: -0.237 G_GAN_Feat: 0.670 G_ID: 0.115 G_Rec: 0.296 D_GP: 0.025 D_real: 0.681 D_fake: 1.237 
+(epoch: 404, iters: 1582, time: 0.063) G_GAN: 0.061 G_GAN_Feat: 0.958 G_ID: 0.115 G_Rec: 0.479 D_GP: 0.024 D_real: 0.840 D_fake: 0.940 
+(epoch: 404, iters: 1982, time: 0.063) G_GAN: -0.116 G_GAN_Feat: 0.698 G_ID: 0.094 G_Rec: 0.327 D_GP: 0.031 D_real: 0.756 D_fake: 1.117 
+(epoch: 404, iters: 2382, time: 0.064) G_GAN: 0.057 G_GAN_Feat: 0.819 G_ID: 0.111 G_Rec: 0.382 D_GP: 0.035 D_real: 0.706 D_fake: 0.946 
+(epoch: 404, iters: 2782, time: 0.063) G_GAN: -0.041 G_GAN_Feat: 0.669 G_ID: 0.089 G_Rec: 0.308 D_GP: 0.034 D_real: 0.838 D_fake: 1.041 
+(epoch: 404, iters: 3182, time: 0.063) G_GAN: -0.019 G_GAN_Feat: 0.943 G_ID: 0.118 G_Rec: 0.418 D_GP: 0.055 D_real: 0.532 D_fake: 1.019 
+(epoch: 404, iters: 3582, time: 0.063) G_GAN: 0.201 G_GAN_Feat: 0.742 G_ID: 0.096 G_Rec: 0.314 D_GP: 0.057 D_real: 0.930 D_fake: 0.804 
+(epoch: 404, iters: 3982, time: 0.064) G_GAN: 0.533 G_GAN_Feat: 0.965 G_ID: 0.114 G_Rec: 0.365 D_GP: 0.043 D_real: 0.979 D_fake: 0.475 
+(epoch: 404, iters: 4382, time: 0.063) G_GAN: 0.168 G_GAN_Feat: 0.722 G_ID: 0.091 G_Rec: 0.276 D_GP: 0.041 D_real: 0.907 D_fake: 0.832 
+(epoch: 404, iters: 4782, time: 0.063) G_GAN: 0.668 G_GAN_Feat: 0.991 G_ID: 0.103 G_Rec: 0.421 D_GP: 0.036 D_real: 1.075 D_fake: 0.356 
+(epoch: 404, iters: 5182, time: 0.063) G_GAN: 0.660 G_GAN_Feat: 0.853 G_ID: 0.095 G_Rec: 0.341 D_GP: 0.039 D_real: 1.451 D_fake: 0.355 
+(epoch: 404, iters: 5582, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 1.081 G_ID: 0.119 G_Rec: 0.443 D_GP: 0.045 D_real: 0.760 D_fake: 0.842 
+(epoch: 404, iters: 5982, time: 0.063) G_GAN: 0.229 G_GAN_Feat: 0.853 G_ID: 0.107 G_Rec: 0.303 D_GP: 0.044 D_real: 0.528 D_fake: 0.771 
+(epoch: 404, iters: 6382, time: 0.063) G_GAN: 0.335 G_GAN_Feat: 0.928 G_ID: 0.120 G_Rec: 0.445 D_GP: 0.031 D_real: 1.058 D_fake: 0.671 
+(epoch: 404, iters: 6782, time: 0.063) G_GAN: 0.216 G_GAN_Feat: 0.712 G_ID: 0.116 G_Rec: 0.301 D_GP: 0.034 D_real: 1.054 D_fake: 0.786 
+(epoch: 404, iters: 7182, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 1.107 G_ID: 0.114 G_Rec: 0.518 D_GP: 0.127 D_real: 0.702 D_fake: 0.555 
+(epoch: 404, iters: 7582, time: 0.063) G_GAN: 0.299 G_GAN_Feat: 0.768 G_ID: 0.103 G_Rec: 0.335 D_GP: 0.037 D_real: 0.998 D_fake: 0.704 
+(epoch: 404, iters: 7982, time: 0.063) G_GAN: 0.429 G_GAN_Feat: 1.163 G_ID: 0.111 G_Rec: 0.505 D_GP: 0.433 D_real: 1.237 D_fake: 0.809 
+(epoch: 404, iters: 8382, time: 0.063) G_GAN: -0.106 G_GAN_Feat: 0.821 G_ID: 0.109 G_Rec: 0.347 D_GP: 0.032 D_real: 0.647 D_fake: 1.106 
+(epoch: 405, iters: 174, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 1.016 G_ID: 0.133 G_Rec: 0.422 D_GP: 0.037 D_real: 0.772 D_fake: 0.727 
+(epoch: 405, iters: 574, time: 0.063) G_GAN: 0.284 G_GAN_Feat: 0.791 G_ID: 0.115 G_Rec: 0.298 D_GP: 0.065 D_real: 0.915 D_fake: 0.717 
+(epoch: 405, iters: 974, time: 0.063) G_GAN: 0.379 G_GAN_Feat: 0.911 G_ID: 0.121 G_Rec: 0.432 D_GP: 0.032 D_real: 1.046 D_fake: 0.623 
+(epoch: 405, iters: 1374, time: 0.063) G_GAN: 0.063 G_GAN_Feat: 0.824 G_ID: 0.112 G_Rec: 0.338 D_GP: 0.077 D_real: 0.713 D_fake: 0.937 
+(epoch: 405, iters: 1774, time: 0.064) G_GAN: 0.522 G_GAN_Feat: 1.061 G_ID: 0.110 G_Rec: 0.424 D_GP: 0.050 D_real: 0.735 D_fake: 0.479 
+(epoch: 405, iters: 2174, time: 0.063) G_GAN: 0.209 G_GAN_Feat: 0.966 G_ID: 0.095 G_Rec: 0.367 D_GP: 0.642 D_real: 0.271 D_fake: 0.804 
+(epoch: 405, iters: 2574, time: 0.063) G_GAN: 0.399 G_GAN_Feat: 0.969 G_ID: 0.112 G_Rec: 0.423 D_GP: 0.041 D_real: 1.064 D_fake: 0.601 
+(epoch: 405, iters: 2974, time: 0.063) G_GAN: 0.169 G_GAN_Feat: 1.022 G_ID: 0.114 G_Rec: 0.359 D_GP: 3.856 D_real: 0.381 D_fake: 0.835 
+(epoch: 405, iters: 3374, time: 0.064) G_GAN: 0.422 G_GAN_Feat: 0.859 G_ID: 0.110 G_Rec: 0.434 D_GP: 0.030 D_real: 1.175 D_fake: 0.581 
+(epoch: 405, iters: 3774, time: 0.063) G_GAN: 0.059 G_GAN_Feat: 0.730 G_ID: 0.091 G_Rec: 0.354 D_GP: 0.052 D_real: 0.824 D_fake: 0.941 
+(epoch: 405, iters: 4174, time: 0.063) G_GAN: 0.282 G_GAN_Feat: 0.919 G_ID: 0.113 G_Rec: 0.447 D_GP: 0.034 D_real: 0.945 D_fake: 0.719 
+(epoch: 405, iters: 4574, time: 0.063) G_GAN: 0.042 G_GAN_Feat: 0.723 G_ID: 0.092 G_Rec: 0.311 D_GP: 0.051 D_real: 0.813 D_fake: 0.958 
+(epoch: 405, iters: 4974, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 0.895 G_ID: 0.118 G_Rec: 0.391 D_GP: 0.063 D_real: 0.983 D_fake: 0.589 
+(epoch: 405, iters: 5374, time: 0.063) G_GAN: -0.120 G_GAN_Feat: 0.748 G_ID: 0.122 G_Rec: 0.331 D_GP: 0.063 D_real: 0.551 D_fake: 1.120 
+(epoch: 405, iters: 5774, time: 0.063) G_GAN: 0.506 G_GAN_Feat: 0.837 G_ID: 0.138 G_Rec: 0.375 D_GP: 0.028 D_real: 1.275 D_fake: 0.505 
+(epoch: 405, iters: 6174, time: 0.063) G_GAN: -0.044 G_GAN_Feat: 0.754 G_ID: 0.111 G_Rec: 0.334 D_GP: 0.049 D_real: 0.625 D_fake: 1.044 
+(epoch: 405, iters: 6574, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.956 G_ID: 0.113 G_Rec: 0.460 D_GP: 0.029 D_real: 1.049 D_fake: 0.578 
+(epoch: 405, iters: 6974, time: 0.063) G_GAN: -0.126 G_GAN_Feat: 0.842 G_ID: 0.098 G_Rec: 0.359 D_GP: 0.255 D_real: 0.419 D_fake: 1.127 
+(epoch: 405, iters: 7374, time: 0.063) G_GAN: 0.559 G_GAN_Feat: 0.956 G_ID: 0.122 G_Rec: 0.520 D_GP: 0.029 D_real: 1.104 D_fake: 0.445 
+(epoch: 405, iters: 7774, time: 0.063) G_GAN: 0.437 G_GAN_Feat: 0.713 G_ID: 0.093 G_Rec: 0.292 D_GP: 0.040 D_real: 1.203 D_fake: 0.571 
+(epoch: 405, iters: 8174, time: 0.064) G_GAN: 0.488 G_GAN_Feat: 1.115 G_ID: 0.132 G_Rec: 0.491 D_GP: 0.115 D_real: 0.488 D_fake: 0.519 
+(epoch: 405, iters: 8574, time: 0.063) G_GAN: 0.278 G_GAN_Feat: 0.712 G_ID: 0.102 G_Rec: 0.289 D_GP: 0.035 D_real: 1.048 D_fake: 0.722 
+(epoch: 406, iters: 366, time: 0.063) G_GAN: 0.518 G_GAN_Feat: 1.114 G_ID: 0.122 G_Rec: 0.471 D_GP: 0.219 D_real: 0.439 D_fake: 0.510 
+(epoch: 406, iters: 766, time: 0.063) G_GAN: 0.320 G_GAN_Feat: 0.854 G_ID: 0.112 G_Rec: 0.361 D_GP: 0.205 D_real: 0.985 D_fake: 0.817 
+(epoch: 406, iters: 1166, time: 0.064) G_GAN: 0.009 G_GAN_Feat: 0.925 G_ID: 0.142 G_Rec: 0.480 D_GP: 0.033 D_real: 0.681 D_fake: 0.991 
+(epoch: 406, iters: 1566, time: 0.063) G_GAN: 0.070 G_GAN_Feat: 0.784 G_ID: 0.099 G_Rec: 0.311 D_GP: 0.054 D_real: 0.882 D_fake: 0.930 
+(epoch: 406, iters: 1966, time: 0.063) G_GAN: 0.452 G_GAN_Feat: 1.114 G_ID: 0.127 G_Rec: 0.507 D_GP: 0.080 D_real: 0.363 D_fake: 0.553 
+(epoch: 406, iters: 2366, time: 0.063) G_GAN: 0.236 G_GAN_Feat: 1.011 G_ID: 0.097 G_Rec: 0.365 D_GP: 0.888 D_real: 0.374 D_fake: 0.767 
+(epoch: 406, iters: 2766, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.961 G_ID: 0.151 G_Rec: 0.417 D_GP: 0.038 D_real: 1.036 D_fake: 0.544 
+(epoch: 406, iters: 3166, time: 0.063) G_GAN: 0.314 G_GAN_Feat: 0.816 G_ID: 0.094 G_Rec: 0.325 D_GP: 0.046 D_real: 1.074 D_fake: 0.688 
+(epoch: 406, iters: 3566, time: 0.063) G_GAN: 0.793 G_GAN_Feat: 1.001 G_ID: 0.113 G_Rec: 0.469 D_GP: 0.041 D_real: 1.345 D_fake: 0.276 
+(epoch: 406, iters: 3966, time: 0.063) G_GAN: 0.233 G_GAN_Feat: 0.862 G_ID: 0.101 G_Rec: 0.310 D_GP: 0.065 D_real: 0.650 D_fake: 0.772 
+(epoch: 406, iters: 4366, time: 0.064) G_GAN: 0.700 G_GAN_Feat: 1.133 G_ID: 0.112 G_Rec: 0.445 D_GP: 0.035 D_real: 1.184 D_fake: 0.349 
+(epoch: 406, iters: 4766, time: 0.063) G_GAN: 0.157 G_GAN_Feat: 0.999 G_ID: 0.092 G_Rec: 0.337 D_GP: 0.189 D_real: 0.511 D_fake: 0.857 
+(epoch: 406, iters: 5166, time: 0.063) G_GAN: 0.414 G_GAN_Feat: 1.284 G_ID: 0.150 G_Rec: 0.451 D_GP: 0.153 D_real: 0.139 D_fake: 0.616 
+(epoch: 406, iters: 5566, time: 0.063) G_GAN: 0.049 G_GAN_Feat: 0.975 G_ID: 0.141 G_Rec: 0.357 D_GP: 0.552 D_real: 0.383 D_fake: 0.954 
+(epoch: 406, iters: 5966, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.919 G_ID: 0.131 G_Rec: 0.426 D_GP: 0.031 D_real: 1.048 D_fake: 0.592 
+(epoch: 406, iters: 6366, time: 0.063) G_GAN: 0.220 G_GAN_Feat: 0.841 G_ID: 0.112 G_Rec: 0.351 D_GP: 0.033 D_real: 1.014 D_fake: 0.781 
+(epoch: 406, iters: 6766, time: 0.063) G_GAN: 0.220 G_GAN_Feat: 1.107 G_ID: 0.123 G_Rec: 0.464 D_GP: 0.100 D_real: 0.260 D_fake: 0.785 
+(epoch: 406, iters: 7166, time: 0.063) G_GAN: 0.162 G_GAN_Feat: 0.720 G_ID: 0.123 G_Rec: 0.309 D_GP: 0.031 D_real: 1.111 D_fake: 0.867 
+(epoch: 406, iters: 7566, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 0.841 G_ID: 0.140 G_Rec: 0.415 D_GP: 0.026 D_real: 0.957 D_fake: 0.798 
+(epoch: 406, iters: 7966, time: 0.063) G_GAN: 0.167 G_GAN_Feat: 0.778 G_ID: 0.118 G_Rec: 0.340 D_GP: 0.031 D_real: 0.987 D_fake: 0.834 
+(epoch: 406, iters: 8366, time: 0.063) G_GAN: 0.391 G_GAN_Feat: 0.937 G_ID: 0.127 G_Rec: 0.455 D_GP: 0.031 D_real: 1.052 D_fake: 0.623 
+(epoch: 407, iters: 158, time: 0.063) G_GAN: 0.022 G_GAN_Feat: 0.704 G_ID: 0.091 G_Rec: 0.314 D_GP: 0.032 D_real: 0.925 D_fake: 0.978 
+(epoch: 407, iters: 558, time: 0.063) G_GAN: 0.108 G_GAN_Feat: 0.899 G_ID: 0.134 G_Rec: 0.416 D_GP: 0.055 D_real: 0.709 D_fake: 0.894 
+(epoch: 407, iters: 958, time: 0.064) G_GAN: -0.131 G_GAN_Feat: 0.720 G_ID: 0.098 G_Rec: 0.379 D_GP: 0.040 D_real: 0.647 D_fake: 1.131 
+(epoch: 407, iters: 1358, time: 0.064) G_GAN: 0.478 G_GAN_Feat: 1.076 G_ID: 0.110 G_Rec: 0.486 D_GP: 0.049 D_real: 0.895 D_fake: 0.529 
+(epoch: 407, iters: 1758, time: 0.064) G_GAN: 0.036 G_GAN_Feat: 0.812 G_ID: 0.101 G_Rec: 0.316 D_GP: 0.121 D_real: 0.638 D_fake: 0.964 
+(epoch: 407, iters: 2158, time: 0.063) G_GAN: 0.347 G_GAN_Feat: 1.045 G_ID: 0.118 G_Rec: 0.449 D_GP: 0.046 D_real: 0.864 D_fake: 0.655 
+(epoch: 407, iters: 2558, time: 0.063) G_GAN: 0.401 G_GAN_Feat: 0.832 G_ID: 0.095 G_Rec: 0.305 D_GP: 0.039 D_real: 1.033 D_fake: 0.600 
+(epoch: 407, iters: 2958, time: 0.063) G_GAN: 0.661 G_GAN_Feat: 1.216 G_ID: 0.115 G_Rec: 0.454 D_GP: 0.067 D_real: 0.678 D_fake: 0.373 
+(epoch: 407, iters: 3358, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 0.723 G_ID: 0.082 G_Rec: 0.269 D_GP: 0.034 D_real: 1.668 D_fake: 0.495 
+(epoch: 407, iters: 3758, time: 0.063) G_GAN: 0.827 G_GAN_Feat: 1.122 G_ID: 0.115 G_Rec: 0.435 D_GP: 0.046 D_real: 0.592 D_fake: 0.226 
+(epoch: 407, iters: 4158, time: 0.063) G_GAN: -0.015 G_GAN_Feat: 0.690 G_ID: 0.105 G_Rec: 0.339 D_GP: 0.045 D_real: 0.925 D_fake: 1.015 
+(epoch: 407, iters: 4558, time: 0.063) G_GAN: 0.074 G_GAN_Feat: 0.769 G_ID: 0.111 G_Rec: 0.403 D_GP: 0.035 D_real: 0.894 D_fake: 0.927 
+(epoch: 407, iters: 4958, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.591 G_ID: 0.097 G_Rec: 0.292 D_GP: 0.035 D_real: 1.077 D_fake: 0.839 
+(epoch: 407, iters: 5358, time: 0.063) G_GAN: 0.189 G_GAN_Feat: 0.801 G_ID: 0.140 G_Rec: 0.407 D_GP: 0.038 D_real: 0.828 D_fake: 0.813 
+(epoch: 407, iters: 5758, time: 0.063) G_GAN: 0.036 G_GAN_Feat: 0.625 G_ID: 0.093 G_Rec: 0.317 D_GP: 0.038 D_real: 0.998 D_fake: 0.964 
+(epoch: 407, iters: 6158, time: 0.063) G_GAN: 0.340 G_GAN_Feat: 0.804 G_ID: 0.118 G_Rec: 0.397 D_GP: 0.038 D_real: 0.976 D_fake: 0.669 
+(epoch: 407, iters: 6558, time: 0.064) G_GAN: -0.125 G_GAN_Feat: 0.682 G_ID: 0.097 G_Rec: 0.347 D_GP: 0.049 D_real: 0.750 D_fake: 1.125 
+(epoch: 407, iters: 6958, time: 0.063) G_GAN: 0.210 G_GAN_Feat: 0.882 G_ID: 0.128 G_Rec: 0.466 D_GP: 0.047 D_real: 0.742 D_fake: 0.795 
+(epoch: 407, iters: 7358, time: 0.063) G_GAN: -0.012 G_GAN_Feat: 0.711 G_ID: 0.097 G_Rec: 0.344 D_GP: 0.050 D_real: 0.809 D_fake: 1.012 
+(epoch: 407, iters: 7758, time: 0.063) G_GAN: 0.079 G_GAN_Feat: 0.787 G_ID: 0.118 G_Rec: 0.377 D_GP: 0.037 D_real: 0.736 D_fake: 0.921 
+(epoch: 407, iters: 8158, time: 0.064) G_GAN: -0.139 G_GAN_Feat: 0.577 G_ID: 0.105 G_Rec: 0.272 D_GP: 0.037 D_real: 0.848 D_fake: 1.139 
+(epoch: 407, iters: 8558, time: 0.063) G_GAN: 0.155 G_GAN_Feat: 0.850 G_ID: 0.116 G_Rec: 0.410 D_GP: 0.059 D_real: 0.850 D_fake: 0.847 
+(epoch: 408, iters: 350, time: 0.063) G_GAN: -0.262 G_GAN_Feat: 0.665 G_ID: 0.099 G_Rec: 0.313 D_GP: 0.043 D_real: 0.597 D_fake: 1.262 
+(epoch: 408, iters: 750, time: 0.063) G_GAN: -0.075 G_GAN_Feat: 0.848 G_ID: 0.137 G_Rec: 0.415 D_GP: 0.044 D_real: 0.579 D_fake: 1.075 
+(epoch: 408, iters: 1150, time: 0.064) G_GAN: -0.060 G_GAN_Feat: 0.675 G_ID: 0.107 G_Rec: 0.331 D_GP: 0.058 D_real: 0.714 D_fake: 1.063 
+(epoch: 408, iters: 1550, time: 0.063) G_GAN: 0.243 G_GAN_Feat: 0.934 G_ID: 0.113 G_Rec: 0.472 D_GP: 0.058 D_real: 0.837 D_fake: 0.760 
+(epoch: 408, iters: 1950, time: 0.063) G_GAN: 0.062 G_GAN_Feat: 0.600 G_ID: 0.094 G_Rec: 0.307 D_GP: 0.037 D_real: 0.901 D_fake: 0.939 
+(epoch: 408, iters: 2350, time: 0.063) G_GAN: 0.369 G_GAN_Feat: 0.792 G_ID: 0.115 G_Rec: 0.385 D_GP: 0.036 D_real: 0.982 D_fake: 0.645 
+(epoch: 408, iters: 2750, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.621 G_ID: 0.106 G_Rec: 0.281 D_GP: 0.034 D_real: 1.064 D_fake: 0.815 
+(epoch: 408, iters: 3150, time: 0.063) G_GAN: 0.315 G_GAN_Feat: 0.888 G_ID: 0.119 G_Rec: 0.442 D_GP: 0.040 D_real: 0.941 D_fake: 0.696 
+(epoch: 408, iters: 3550, time: 0.063) G_GAN: 0.052 G_GAN_Feat: 0.659 G_ID: 0.098 G_Rec: 0.302 D_GP: 0.033 D_real: 0.897 D_fake: 0.949 
+(epoch: 408, iters: 3950, time: 0.063) G_GAN: 0.267 G_GAN_Feat: 0.819 G_ID: 0.105 G_Rec: 0.376 D_GP: 0.039 D_real: 1.016 D_fake: 0.734 
+(epoch: 408, iters: 4350, time: 0.064) G_GAN: -0.021 G_GAN_Feat: 0.747 G_ID: 0.107 G_Rec: 0.326 D_GP: 0.053 D_real: 0.673 D_fake: 1.021 
+(epoch: 408, iters: 4750, time: 0.063) G_GAN: 0.383 G_GAN_Feat: 0.955 G_ID: 0.125 G_Rec: 0.420 D_GP: 0.079 D_real: 0.867 D_fake: 0.623 
+(epoch: 408, iters: 5150, time: 0.063) G_GAN: 0.126 G_GAN_Feat: 0.723 G_ID: 0.111 G_Rec: 0.328 D_GP: 0.039 D_real: 0.959 D_fake: 0.874 
+(epoch: 408, iters: 5550, time: 0.063) G_GAN: 0.444 G_GAN_Feat: 0.932 G_ID: 0.123 G_Rec: 0.418 D_GP: 0.061 D_real: 0.959 D_fake: 0.568 
+(epoch: 408, iters: 5950, time: 0.064) G_GAN: -0.063 G_GAN_Feat: 0.692 G_ID: 0.111 G_Rec: 0.337 D_GP: 0.036 D_real: 0.814 D_fake: 1.063 
+(epoch: 408, iters: 6350, time: 0.063) G_GAN: 0.505 G_GAN_Feat: 1.121 G_ID: 0.108 G_Rec: 0.507 D_GP: 0.142 D_real: 0.570 D_fake: 0.523 
+(epoch: 408, iters: 6750, time: 0.063) G_GAN: 0.335 G_GAN_Feat: 0.704 G_ID: 0.109 G_Rec: 0.300 D_GP: 0.043 D_real: 1.149 D_fake: 0.666 
+(epoch: 408, iters: 7150, time: 0.063) G_GAN: 0.112 G_GAN_Feat: 1.008 G_ID: 0.127 G_Rec: 0.488 D_GP: 0.052 D_real: 0.536 D_fake: 0.888 
+(epoch: 408, iters: 7550, time: 0.064) G_GAN: 0.055 G_GAN_Feat: 0.691 G_ID: 0.102 G_Rec: 0.301 D_GP: 0.035 D_real: 0.944 D_fake: 0.946 
+(epoch: 408, iters: 7950, time: 0.063) G_GAN: 0.384 G_GAN_Feat: 1.007 G_ID: 0.113 G_Rec: 0.436 D_GP: 0.057 D_real: 0.757 D_fake: 0.622 
+(epoch: 408, iters: 8350, time: 0.063) G_GAN: 0.227 G_GAN_Feat: 0.747 G_ID: 0.104 G_Rec: 0.328 D_GP: 0.039 D_real: 1.041 D_fake: 0.774 
+(epoch: 409, iters: 142, time: 0.063) G_GAN: 0.317 G_GAN_Feat: 0.911 G_ID: 0.142 G_Rec: 0.403 D_GP: 0.045 D_real: 0.796 D_fake: 0.683 
+(epoch: 409, iters: 542, time: 0.064) G_GAN: 0.260 G_GAN_Feat: 0.748 G_ID: 0.114 G_Rec: 0.330 D_GP: 0.072 D_real: 0.869 D_fake: 0.741 
+(epoch: 409, iters: 942, time: 0.063) G_GAN: 0.229 G_GAN_Feat: 0.981 G_ID: 0.120 G_Rec: 0.449 D_GP: 0.041 D_real: 0.845 D_fake: 0.773 
+(epoch: 409, iters: 1342, time: 0.063) G_GAN: -0.051 G_GAN_Feat: 0.819 G_ID: 0.122 G_Rec: 0.320 D_GP: 0.082 D_real: 0.321 D_fake: 1.051 
+(epoch: 409, iters: 1742, time: 0.063) G_GAN: 0.478 G_GAN_Feat: 1.079 G_ID: 0.111 G_Rec: 0.491 D_GP: 0.083 D_real: 0.568 D_fake: 0.531 
+(epoch: 409, iters: 2142, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.638 G_ID: 0.100 G_Rec: 0.291 D_GP: 0.027 D_real: 1.080 D_fake: 0.826 
+(epoch: 409, iters: 2542, time: 0.063) G_GAN: 0.597 G_GAN_Feat: 0.953 G_ID: 0.114 G_Rec: 0.447 D_GP: 0.030 D_real: 1.158 D_fake: 0.421 
+(epoch: 409, iters: 2942, time: 0.063) G_GAN: 0.233 G_GAN_Feat: 0.711 G_ID: 0.092 G_Rec: 0.332 D_GP: 0.036 D_real: 1.113 D_fake: 0.768 
+(epoch: 409, iters: 3342, time: 0.063) G_GAN: 0.394 G_GAN_Feat: 1.051 G_ID: 0.123 G_Rec: 0.481 D_GP: 0.113 D_real: 0.662 D_fake: 0.615 
+(epoch: 409, iters: 3742, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.668 G_ID: 0.097 G_Rec: 0.336 D_GP: 0.031 D_real: 1.306 D_fake: 0.638 
+(epoch: 409, iters: 4142, time: 0.063) G_GAN: 0.344 G_GAN_Feat: 0.995 G_ID: 0.119 G_Rec: 0.449 D_GP: 0.054 D_real: 0.705 D_fake: 0.657 
+(epoch: 409, iters: 4542, time: 0.063) G_GAN: 0.080 G_GAN_Feat: 0.760 G_ID: 0.108 G_Rec: 0.334 D_GP: 0.039 D_real: 0.830 D_fake: 0.920 
+(epoch: 409, iters: 4942, time: 0.063) G_GAN: 0.752 G_GAN_Feat: 0.959 G_ID: 0.122 G_Rec: 0.452 D_GP: 0.038 D_real: 1.221 D_fake: 0.288 
+(epoch: 409, iters: 5342, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.730 G_ID: 0.111 G_Rec: 0.352 D_GP: 0.028 D_real: 1.187 D_fake: 0.661 
+(epoch: 409, iters: 5742, time: 0.063) G_GAN: 0.426 G_GAN_Feat: 0.880 G_ID: 0.122 G_Rec: 0.405 D_GP: 0.037 D_real: 1.111 D_fake: 0.577 
+(epoch: 409, iters: 6142, time: 0.063) G_GAN: 0.528 G_GAN_Feat: 0.724 G_ID: 0.091 G_Rec: 0.297 D_GP: 0.031 D_real: 1.371 D_fake: 0.473 
+(epoch: 409, iters: 6542, time: 0.063) G_GAN: 0.531 G_GAN_Feat: 0.948 G_ID: 0.125 G_Rec: 0.446 D_GP: 0.033 D_real: 1.142 D_fake: 0.473 
+(epoch: 409, iters: 6942, time: 0.064) G_GAN: -0.000 G_GAN_Feat: 0.672 G_ID: 0.099 G_Rec: 0.267 D_GP: 0.031 D_real: 0.951 D_fake: 1.000 
+(epoch: 409, iters: 7342, time: 0.063) G_GAN: 0.388 G_GAN_Feat: 0.916 G_ID: 0.127 G_Rec: 0.461 D_GP: 0.034 D_real: 1.159 D_fake: 0.614 
+(epoch: 409, iters: 7742, time: 0.063) G_GAN: 0.312 G_GAN_Feat: 0.743 G_ID: 0.116 G_Rec: 0.323 D_GP: 0.036 D_real: 1.089 D_fake: 0.689 
+(epoch: 409, iters: 8142, time: 0.063) G_GAN: 0.443 G_GAN_Feat: 0.913 G_ID: 0.113 G_Rec: 0.412 D_GP: 0.038 D_real: 0.973 D_fake: 0.567 
+(epoch: 409, iters: 8542, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.826 G_ID: 0.102 G_Rec: 0.327 D_GP: 0.094 D_real: 0.729 D_fake: 0.752 
+(epoch: 410, iters: 334, time: 0.063) G_GAN: 0.660 G_GAN_Feat: 1.062 G_ID: 0.119 G_Rec: 0.460 D_GP: 0.050 D_real: 0.799 D_fake: 0.363 
+(epoch: 410, iters: 734, time: 0.063) G_GAN: 0.089 G_GAN_Feat: 0.859 G_ID: 0.089 G_Rec: 0.339 D_GP: 0.089 D_real: 0.314 D_fake: 0.911 
+(epoch: 410, iters: 1134, time: 0.063) G_GAN: 0.655 G_GAN_Feat: 1.004 G_ID: 0.121 G_Rec: 0.456 D_GP: 0.032 D_real: 1.113 D_fake: 0.363 
+(epoch: 410, iters: 1534, time: 0.064) G_GAN: 0.002 G_GAN_Feat: 0.756 G_ID: 0.103 G_Rec: 0.306 D_GP: 0.028 D_real: 0.938 D_fake: 0.998 
+(epoch: 410, iters: 1934, time: 0.063) G_GAN: 0.223 G_GAN_Feat: 1.045 G_ID: 0.109 G_Rec: 0.471 D_GP: 0.078 D_real: 0.697 D_fake: 0.779 
+(epoch: 410, iters: 2334, time: 0.063) G_GAN: 0.208 G_GAN_Feat: 0.702 G_ID: 0.096 G_Rec: 0.280 D_GP: 0.038 D_real: 1.034 D_fake: 0.793 
+(epoch: 410, iters: 2734, time: 0.063) G_GAN: 0.343 G_GAN_Feat: 1.007 G_ID: 0.138 G_Rec: 0.433 D_GP: 0.044 D_real: 0.740 D_fake: 0.657 
+(epoch: 410, iters: 3134, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.774 G_ID: 0.093 G_Rec: 0.321 D_GP: 0.033 D_real: 1.050 D_fake: 0.725 
+(epoch: 410, iters: 3534, time: 0.063) G_GAN: 0.458 G_GAN_Feat: 0.873 G_ID: 0.121 G_Rec: 0.374 D_GP: 0.031 D_real: 1.148 D_fake: 0.551 
+(epoch: 410, iters: 3934, time: 0.063) G_GAN: 0.045 G_GAN_Feat: 0.878 G_ID: 0.116 G_Rec: 0.361 D_GP: 0.123 D_real: 0.600 D_fake: 0.955 
+(epoch: 410, iters: 4334, time: 0.063) G_GAN: 0.319 G_GAN_Feat: 0.919 G_ID: 0.119 G_Rec: 0.472 D_GP: 0.030 D_real: 0.974 D_fake: 0.684 
+(epoch: 410, iters: 4734, time: 0.064) G_GAN: 0.054 G_GAN_Feat: 0.819 G_ID: 0.111 G_Rec: 0.311 D_GP: 0.031 D_real: 0.719 D_fake: 0.946 
+(epoch: 410, iters: 5134, time: 0.063) G_GAN: -0.186 G_GAN_Feat: 0.991 G_ID: 0.133 G_Rec: 0.428 D_GP: 0.166 D_real: 0.168 D_fake: 1.187 
+(epoch: 410, iters: 5534, time: 0.063) G_GAN: 0.602 G_GAN_Feat: 0.739 G_ID: 0.089 G_Rec: 0.305 D_GP: 0.035 D_real: 1.351 D_fake: 0.405 
+(epoch: 410, iters: 5934, time: 0.063) G_GAN: 0.377 G_GAN_Feat: 0.939 G_ID: 0.117 G_Rec: 0.430 D_GP: 0.032 D_real: 1.083 D_fake: 0.624 
+(epoch: 410, iters: 6334, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.766 G_ID: 0.107 G_Rec: 0.335 D_GP: 0.032 D_real: 0.950 D_fake: 0.863 
+(epoch: 410, iters: 6734, time: 0.063) G_GAN: 0.564 G_GAN_Feat: 0.966 G_ID: 0.123 G_Rec: 0.479 D_GP: 0.030 D_real: 1.167 D_fake: 0.440 
+(epoch: 410, iters: 7134, time: 0.063) G_GAN: 0.490 G_GAN_Feat: 0.841 G_ID: 0.100 G_Rec: 0.347 D_GP: 0.053 D_real: 1.020 D_fake: 0.530 
+(epoch: 410, iters: 7534, time: 0.063) G_GAN: 0.884 G_GAN_Feat: 0.980 G_ID: 0.110 G_Rec: 0.425 D_GP: 0.050 D_real: 1.330 D_fake: 0.165 
+(epoch: 410, iters: 7934, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.786 G_ID: 0.125 G_Rec: 0.307 D_GP: 0.034 D_real: 0.893 D_fake: 0.895 
+(epoch: 410, iters: 8334, time: 0.063) G_GAN: 0.883 G_GAN_Feat: 1.061 G_ID: 0.122 G_Rec: 0.444 D_GP: 0.062 D_real: 1.184 D_fake: 0.285 
+(epoch: 411, iters: 126, time: 0.063) G_GAN: 0.219 G_GAN_Feat: 0.720 G_ID: 0.102 G_Rec: 0.335 D_GP: 0.029 D_real: 1.052 D_fake: 0.781 
+(epoch: 411, iters: 526, time: 0.063) G_GAN: 0.244 G_GAN_Feat: 0.960 G_ID: 0.124 G_Rec: 0.409 D_GP: 0.050 D_real: 0.746 D_fake: 0.756 
+(epoch: 411, iters: 926, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.950 G_ID: 0.119 G_Rec: 0.335 D_GP: 0.549 D_real: 0.266 D_fake: 0.923 
+(epoch: 411, iters: 1326, time: 0.063) G_GAN: 0.676 G_GAN_Feat: 1.017 G_ID: 0.126 G_Rec: 0.431 D_GP: 0.035 D_real: 1.182 D_fake: 0.343 
+(epoch: 411, iters: 1726, time: 0.063) G_GAN: 0.516 G_GAN_Feat: 0.868 G_ID: 0.100 G_Rec: 0.337 D_GP: 0.052 D_real: 0.934 D_fake: 0.492 
+(epoch: 411, iters: 2126, time: 0.063) G_GAN: 0.577 G_GAN_Feat: 0.977 G_ID: 0.140 G_Rec: 0.400 D_GP: 0.029 D_real: 1.085 D_fake: 0.453 
+(epoch: 411, iters: 2526, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.882 G_ID: 0.099 G_Rec: 0.357 D_GP: 0.141 D_real: 0.775 D_fake: 0.719 
+(epoch: 411, iters: 2926, time: 0.063) G_GAN: 0.953 G_GAN_Feat: 0.979 G_ID: 0.122 G_Rec: 0.442 D_GP: 0.034 D_real: 1.602 D_fake: 0.213 
+(epoch: 411, iters: 3326, time: 0.063) G_GAN: 0.126 G_GAN_Feat: 0.808 G_ID: 0.101 G_Rec: 0.284 D_GP: 0.047 D_real: 0.862 D_fake: 0.874 
+(epoch: 411, iters: 3726, time: 0.063) G_GAN: 0.314 G_GAN_Feat: 1.035 G_ID: 0.122 G_Rec: 0.456 D_GP: 0.073 D_real: 0.788 D_fake: 0.687 
+(epoch: 411, iters: 4126, time: 0.064) G_GAN: 0.258 G_GAN_Feat: 0.726 G_ID: 0.103 G_Rec: 0.303 D_GP: 0.034 D_real: 1.001 D_fake: 0.742 
+(epoch: 411, iters: 4526, time: 0.063) G_GAN: 0.704 G_GAN_Feat: 1.181 G_ID: 0.120 G_Rec: 0.476 D_GP: 0.988 D_real: 0.790 D_fake: 0.342 
+(epoch: 411, iters: 4926, time: 0.063) G_GAN: 0.309 G_GAN_Feat: 0.798 G_ID: 0.096 G_Rec: 0.309 D_GP: 0.059 D_real: 1.044 D_fake: 0.691 
+(epoch: 411, iters: 5326, time: 0.063) G_GAN: 0.730 G_GAN_Feat: 0.916 G_ID: 0.119 G_Rec: 0.411 D_GP: 0.060 D_real: 1.339 D_fake: 0.389 
+(epoch: 411, iters: 5726, time: 0.063) G_GAN: 0.072 G_GAN_Feat: 0.833 G_ID: 0.112 G_Rec: 0.348 D_GP: 0.045 D_real: 0.792 D_fake: 0.929 
+(epoch: 411, iters: 6126, time: 0.063) G_GAN: 0.423 G_GAN_Feat: 0.929 G_ID: 0.113 G_Rec: 0.425 D_GP: 0.030 D_real: 1.054 D_fake: 0.579 
+(epoch: 411, iters: 6526, time: 0.063) G_GAN: 0.583 G_GAN_Feat: 0.791 G_ID: 0.086 G_Rec: 0.315 D_GP: 0.032 D_real: 1.330 D_fake: 0.436 
+(epoch: 411, iters: 6926, time: 0.064) G_GAN: 0.628 G_GAN_Feat: 1.150 G_ID: 0.115 G_Rec: 0.454 D_GP: 0.049 D_real: 0.920 D_fake: 0.391 
+(epoch: 411, iters: 7326, time: 0.063) G_GAN: 0.583 G_GAN_Feat: 1.013 G_ID: 0.104 G_Rec: 0.344 D_GP: 0.059 D_real: 0.516 D_fake: 0.433 
+(epoch: 411, iters: 7726, time: 0.063) G_GAN: 0.871 G_GAN_Feat: 1.070 G_ID: 0.115 G_Rec: 0.492 D_GP: 0.040 D_real: 1.262 D_fake: 0.186 
+(epoch: 411, iters: 8126, time: 0.063) G_GAN: 0.713 G_GAN_Feat: 0.851 G_ID: 0.099 G_Rec: 0.329 D_GP: 0.060 D_real: 1.275 D_fake: 0.475 
+(epoch: 411, iters: 8526, time: 0.064) G_GAN: 0.016 G_GAN_Feat: 0.992 G_ID: 0.134 G_Rec: 0.439 D_GP: 0.044 D_real: 0.649 D_fake: 0.984 
+(epoch: 412, iters: 318, time: 0.063) G_GAN: 0.521 G_GAN_Feat: 0.756 G_ID: 0.094 G_Rec: 0.285 D_GP: 0.038 D_real: 1.254 D_fake: 0.486 
+(epoch: 412, iters: 718, time: 0.063) G_GAN: 0.288 G_GAN_Feat: 1.164 G_ID: 0.129 G_Rec: 0.439 D_GP: 0.048 D_real: 0.369 D_fake: 0.713 
+(epoch: 412, iters: 1118, time: 0.063) G_GAN: 0.376 G_GAN_Feat: 0.930 G_ID: 0.106 G_Rec: 0.329 D_GP: 0.270 D_real: 0.511 D_fake: 0.627 
+(epoch: 412, iters: 1518, time: 0.064) G_GAN: 0.801 G_GAN_Feat: 0.990 G_ID: 0.107 G_Rec: 0.451 D_GP: 0.032 D_real: 1.392 D_fake: 0.233 
+(epoch: 412, iters: 1918, time: 0.063) G_GAN: 0.402 G_GAN_Feat: 0.721 G_ID: 0.089 G_Rec: 0.287 D_GP: 0.028 D_real: 1.310 D_fake: 0.603 
+(epoch: 412, iters: 2318, time: 0.063) G_GAN: 0.314 G_GAN_Feat: 0.835 G_ID: 0.119 G_Rec: 0.376 D_GP: 0.026 D_real: 1.118 D_fake: 0.687 
+(epoch: 412, iters: 2718, time: 0.063) G_GAN: 0.215 G_GAN_Feat: 0.779 G_ID: 0.097 G_Rec: 0.319 D_GP: 0.028 D_real: 1.114 D_fake: 0.787 
+(epoch: 412, iters: 3118, time: 0.064) G_GAN: 0.793 G_GAN_Feat: 1.228 G_ID: 0.121 G_Rec: 0.473 D_GP: 0.138 D_real: 0.674 D_fake: 0.387 
+(epoch: 412, iters: 3518, time: 0.063) G_GAN: 0.186 G_GAN_Feat: 0.765 G_ID: 0.090 G_Rec: 0.292 D_GP: 0.035 D_real: 1.077 D_fake: 0.814 
+(epoch: 412, iters: 3918, time: 0.063) G_GAN: 0.136 G_GAN_Feat: 0.943 G_ID: 0.123 G_Rec: 0.394 D_GP: 0.037 D_real: 0.800 D_fake: 0.865 
+(epoch: 412, iters: 4318, time: 0.063) G_GAN: 0.082 G_GAN_Feat: 0.785 G_ID: 0.111 G_Rec: 0.305 D_GP: 0.038 D_real: 0.867 D_fake: 0.918 
+(epoch: 412, iters: 4718, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 1.002 G_ID: 0.112 G_Rec: 0.470 D_GP: 0.067 D_real: 0.909 D_fake: 0.546 
+(epoch: 412, iters: 5118, time: 0.063) G_GAN: 0.291 G_GAN_Feat: 0.800 G_ID: 0.077 G_Rec: 0.296 D_GP: 0.080 D_real: 0.936 D_fake: 0.711 
+(epoch: 412, iters: 5518, time: 0.063) G_GAN: 0.509 G_GAN_Feat: 1.028 G_ID: 0.118 G_Rec: 0.422 D_GP: 0.057 D_real: 0.934 D_fake: 0.499 
+(epoch: 412, iters: 5918, time: 0.063) G_GAN: 0.263 G_GAN_Feat: 0.835 G_ID: 0.093 G_Rec: 0.310 D_GP: 0.074 D_real: 0.691 D_fake: 0.737 
+(epoch: 412, iters: 6318, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 1.043 G_ID: 0.136 G_Rec: 0.504 D_GP: 0.031 D_real: 0.984 D_fake: 0.518 
+(epoch: 412, iters: 6718, time: 0.063) G_GAN: 0.358 G_GAN_Feat: 0.841 G_ID: 0.097 G_Rec: 0.323 D_GP: 0.071 D_real: 0.847 D_fake: 0.645 
+(epoch: 412, iters: 7118, time: 0.063) G_GAN: 0.593 G_GAN_Feat: 1.006 G_ID: 0.116 G_Rec: 0.422 D_GP: 0.044 D_real: 0.990 D_fake: 0.417 
+(epoch: 412, iters: 7518, time: 0.063) G_GAN: 0.260 G_GAN_Feat: 0.673 G_ID: 0.091 G_Rec: 0.295 D_GP: 0.028 D_real: 1.173 D_fake: 0.742 
+(epoch: 412, iters: 7918, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.930 G_ID: 0.128 G_Rec: 0.423 D_GP: 0.039 D_real: 0.956 D_fake: 0.689 
+(epoch: 412, iters: 8318, time: 0.063) G_GAN: -0.061 G_GAN_Feat: 0.720 G_ID: 0.107 G_Rec: 0.313 D_GP: 0.036 D_real: 0.770 D_fake: 1.061 
+(epoch: 413, iters: 110, time: 0.063) G_GAN: 0.760 G_GAN_Feat: 0.921 G_ID: 0.100 G_Rec: 0.441 D_GP: 0.033 D_real: 1.294 D_fake: 0.353 
+(epoch: 413, iters: 510, time: 0.063) G_GAN: 0.127 G_GAN_Feat: 0.741 G_ID: 0.089 G_Rec: 0.298 D_GP: 0.035 D_real: 1.099 D_fake: 0.873 
+(epoch: 413, iters: 910, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.988 G_ID: 0.121 G_Rec: 0.439 D_GP: 0.113 D_real: 0.776 D_fake: 0.577 
+(epoch: 413, iters: 1310, time: 0.063) G_GAN: 0.216 G_GAN_Feat: 0.763 G_ID: 0.105 G_Rec: 0.303 D_GP: 0.035 D_real: 1.060 D_fake: 0.786 
+(epoch: 413, iters: 1710, time: 0.063) G_GAN: 0.591 G_GAN_Feat: 0.984 G_ID: 0.117 G_Rec: 0.460 D_GP: 0.030 D_real: 1.197 D_fake: 0.419 
+(epoch: 413, iters: 2110, time: 0.063) G_GAN: 0.389 G_GAN_Feat: 0.701 G_ID: 0.099 G_Rec: 0.306 D_GP: 0.026 D_real: 1.297 D_fake: 0.612 
+(epoch: 413, iters: 2510, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.917 G_ID: 0.112 G_Rec: 0.421 D_GP: 0.033 D_real: 0.888 D_fake: 0.723 
+(epoch: 413, iters: 2910, time: 0.063) G_GAN: 0.459 G_GAN_Feat: 0.815 G_ID: 0.105 G_Rec: 0.319 D_GP: 0.055 D_real: 0.987 D_fake: 0.561 
+(epoch: 413, iters: 3310, time: 0.063) G_GAN: 0.905 G_GAN_Feat: 1.078 G_ID: 0.103 G_Rec: 0.456 D_GP: 0.052 D_real: 0.999 D_fake: 0.170 
+(epoch: 413, iters: 3710, time: 0.063) G_GAN: 0.545 G_GAN_Feat: 0.879 G_ID: 0.104 G_Rec: 0.339 D_GP: 0.059 D_real: 1.197 D_fake: 0.574 
+(epoch: 413, iters: 4110, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 1.059 G_ID: 0.124 G_Rec: 0.438 D_GP: 0.040 D_real: 0.625 D_fake: 0.554 
+(epoch: 413, iters: 4510, time: 0.063) G_GAN: 0.027 G_GAN_Feat: 0.771 G_ID: 0.121 G_Rec: 0.313 D_GP: 0.034 D_real: 0.843 D_fake: 0.973 
+(epoch: 413, iters: 4910, time: 0.063) G_GAN: 0.698 G_GAN_Feat: 1.031 G_ID: 0.111 G_Rec: 0.436 D_GP: 0.068 D_real: 0.776 D_fake: 0.335 
+(epoch: 413, iters: 5310, time: 0.063) G_GAN: -0.034 G_GAN_Feat: 0.890 G_ID: 0.103 G_Rec: 0.343 D_GP: 0.035 D_real: 0.696 D_fake: 1.034 
+(epoch: 413, iters: 5710, time: 0.064) G_GAN: 0.285 G_GAN_Feat: 0.998 G_ID: 0.132 G_Rec: 0.448 D_GP: 0.038 D_real: 0.729 D_fake: 0.716 
+(epoch: 413, iters: 6110, time: 0.063) G_GAN: 0.369 G_GAN_Feat: 0.935 G_ID: 0.107 G_Rec: 0.314 D_GP: 0.089 D_real: 0.591 D_fake: 0.632 
+(epoch: 413, iters: 6510, time: 0.063) G_GAN: 0.397 G_GAN_Feat: 0.837 G_ID: 0.102 G_Rec: 0.418 D_GP: 0.033 D_real: 1.171 D_fake: 0.604 
+(epoch: 413, iters: 6910, time: 0.063) G_GAN: 0.168 G_GAN_Feat: 0.693 G_ID: 0.103 G_Rec: 0.313 D_GP: 0.029 D_real: 1.082 D_fake: 0.832 
+(epoch: 413, iters: 7310, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 1.013 G_ID: 0.115 G_Rec: 0.486 D_GP: 0.032 D_real: 0.890 D_fake: 0.571 
+(epoch: 413, iters: 7710, time: 0.063) G_GAN: -0.017 G_GAN_Feat: 0.677 G_ID: 0.102 G_Rec: 0.317 D_GP: 0.033 D_real: 0.868 D_fake: 1.017 
+(epoch: 413, iters: 8110, time: 0.063) G_GAN: 0.217 G_GAN_Feat: 0.888 G_ID: 0.126 G_Rec: 0.416 D_GP: 0.037 D_real: 0.894 D_fake: 0.789 
+(epoch: 413, iters: 8510, time: 0.063) G_GAN: 0.073 G_GAN_Feat: 0.745 G_ID: 0.090 G_Rec: 0.309 D_GP: 0.039 D_real: 0.858 D_fake: 0.927 
+(epoch: 414, iters: 302, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 0.982 G_ID: 0.145 G_Rec: 0.430 D_GP: 0.065 D_real: 0.835 D_fake: 0.564 
+(epoch: 414, iters: 702, time: 0.063) G_GAN: 0.006 G_GAN_Feat: 0.746 G_ID: 0.106 G_Rec: 0.333 D_GP: 0.056 D_real: 0.618 D_fake: 0.994 
+(epoch: 414, iters: 1102, time: 0.063) G_GAN: 0.445 G_GAN_Feat: 0.948 G_ID: 0.124 G_Rec: 0.455 D_GP: 0.040 D_real: 1.024 D_fake: 0.557 
+(epoch: 414, iters: 1502, time: 0.063) G_GAN: -0.012 G_GAN_Feat: 0.836 G_ID: 0.124 G_Rec: 0.346 D_GP: 0.040 D_real: 0.551 D_fake: 1.012 
+(epoch: 414, iters: 1902, time: 0.064) G_GAN: 0.576 G_GAN_Feat: 1.049 G_ID: 0.106 G_Rec: 0.421 D_GP: 0.066 D_real: 0.728 D_fake: 0.431 
+(epoch: 414, iters: 2302, time: 0.063) G_GAN: 0.505 G_GAN_Feat: 0.803 G_ID: 0.139 G_Rec: 0.336 D_GP: 0.034 D_real: 1.241 D_fake: 0.513 
+(epoch: 414, iters: 2702, time: 0.063) G_GAN: 0.412 G_GAN_Feat: 0.950 G_ID: 0.113 G_Rec: 0.440 D_GP: 0.034 D_real: 0.955 D_fake: 0.596 
+(epoch: 414, iters: 3102, time: 0.063) G_GAN: 0.339 G_GAN_Feat: 0.835 G_ID: 0.105 G_Rec: 0.326 D_GP: 0.032 D_real: 1.328 D_fake: 0.667 
+(epoch: 414, iters: 3502, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.859 G_ID: 0.117 G_Rec: 0.434 D_GP: 0.028 D_real: 1.172 D_fake: 0.566 
+(epoch: 414, iters: 3902, time: 0.063) G_GAN: 0.215 G_GAN_Feat: 0.716 G_ID: 0.092 G_Rec: 0.332 D_GP: 0.040 D_real: 1.043 D_fake: 0.785 
+(epoch: 414, iters: 4302, time: 0.063) G_GAN: 0.554 G_GAN_Feat: 0.913 G_ID: 0.132 G_Rec: 0.418 D_GP: 0.047 D_real: 1.152 D_fake: 0.456 
+(epoch: 414, iters: 4702, time: 0.063) G_GAN: 0.156 G_GAN_Feat: 0.737 G_ID: 0.090 G_Rec: 0.309 D_GP: 0.044 D_real: 0.932 D_fake: 0.844 
+(epoch: 414, iters: 5102, time: 0.064) G_GAN: 0.085 G_GAN_Feat: 0.924 G_ID: 0.151 G_Rec: 0.409 D_GP: 0.036 D_real: 0.767 D_fake: 0.915 
+(epoch: 414, iters: 5502, time: 0.063) G_GAN: 0.084 G_GAN_Feat: 0.805 G_ID: 0.113 G_Rec: 0.352 D_GP: 0.065 D_real: 0.771 D_fake: 0.917 
+(epoch: 414, iters: 5902, time: 0.063) G_GAN: 0.699 G_GAN_Feat: 1.030 G_ID: 0.123 G_Rec: 0.416 D_GP: 0.162 D_real: 0.967 D_fake: 0.354 
+(epoch: 414, iters: 6302, time: 0.063) G_GAN: 0.168 G_GAN_Feat: 0.778 G_ID: 0.099 G_Rec: 0.316 D_GP: 0.057 D_real: 0.788 D_fake: 0.832 
+(epoch: 414, iters: 6702, time: 0.064) G_GAN: 0.534 G_GAN_Feat: 0.966 G_ID: 0.114 G_Rec: 0.427 D_GP: 0.032 D_real: 1.099 D_fake: 0.471 
+(epoch: 414, iters: 7102, time: 0.063) G_GAN: 0.617 G_GAN_Feat: 0.818 G_ID: 0.095 G_Rec: 0.323 D_GP: 0.047 D_real: 1.413 D_fake: 0.505 
+(epoch: 414, iters: 7502, time: 0.063) G_GAN: 0.629 G_GAN_Feat: 0.989 G_ID: 0.117 G_Rec: 0.432 D_GP: 0.056 D_real: 1.287 D_fake: 0.387 
+(epoch: 414, iters: 7902, time: 0.063) G_GAN: 0.103 G_GAN_Feat: 0.841 G_ID: 0.106 G_Rec: 0.335 D_GP: 0.052 D_real: 0.604 D_fake: 0.897 
+(epoch: 414, iters: 8302, time: 0.064) G_GAN: 0.685 G_GAN_Feat: 1.036 G_ID: 0.127 G_Rec: 0.447 D_GP: 0.036 D_real: 1.007 D_fake: 0.337 
+(epoch: 414, iters: 8702, time: 0.063) G_GAN: 0.148 G_GAN_Feat: 0.859 G_ID: 0.092 G_Rec: 0.340 D_GP: 0.038 D_real: 0.745 D_fake: 0.857 
+(epoch: 415, iters: 494, time: 0.063) G_GAN: 0.652 G_GAN_Feat: 1.149 G_ID: 0.149 G_Rec: 0.477 D_GP: 0.045 D_real: 1.011 D_fake: 0.390 
+(epoch: 415, iters: 894, time: 0.063) G_GAN: 0.214 G_GAN_Feat: 0.651 G_ID: 0.100 G_Rec: 0.316 D_GP: 0.030 D_real: 1.127 D_fake: 0.787 
+(epoch: 415, iters: 1294, time: 0.064) G_GAN: 0.458 G_GAN_Feat: 0.895 G_ID: 0.120 G_Rec: 0.479 D_GP: 0.031 D_real: 1.231 D_fake: 0.562 
+(epoch: 415, iters: 1694, time: 0.063) G_GAN: 0.077 G_GAN_Feat: 0.692 G_ID: 0.092 G_Rec: 0.316 D_GP: 0.028 D_real: 1.041 D_fake: 0.924 
+(epoch: 415, iters: 2094, time: 0.063) G_GAN: 0.485 G_GAN_Feat: 0.872 G_ID: 0.101 G_Rec: 0.437 D_GP: 0.033 D_real: 1.140 D_fake: 0.528 
+(epoch: 415, iters: 2494, time: 0.063) G_GAN: -0.188 G_GAN_Feat: 0.651 G_ID: 0.089 G_Rec: 0.300 D_GP: 0.037 D_real: 0.769 D_fake: 1.188 
+(epoch: 415, iters: 2894, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.896 G_ID: 0.106 G_Rec: 0.450 D_GP: 0.037 D_real: 0.839 D_fake: 0.773 
+(epoch: 415, iters: 3294, time: 0.063) G_GAN: -0.046 G_GAN_Feat: 0.643 G_ID: 0.100 G_Rec: 0.292 D_GP: 0.044 D_real: 0.793 D_fake: 1.046 
+(epoch: 415, iters: 3694, time: 0.063) G_GAN: 0.464 G_GAN_Feat: 0.906 G_ID: 0.114 G_Rec: 0.452 D_GP: 0.041 D_real: 1.050 D_fake: 0.588 
+(epoch: 415, iters: 4094, time: 0.063) G_GAN: -0.055 G_GAN_Feat: 0.742 G_ID: 0.101 G_Rec: 0.357 D_GP: 0.064 D_real: 0.668 D_fake: 1.055 
+(epoch: 415, iters: 4494, time: 0.064) G_GAN: 0.917 G_GAN_Feat: 0.910 G_ID: 0.120 G_Rec: 0.448 D_GP: 0.041 D_real: 1.518 D_fake: 0.410 
+(epoch: 415, iters: 4894, time: 0.063) G_GAN: -0.131 G_GAN_Feat: 0.710 G_ID: 0.093 G_Rec: 0.324 D_GP: 0.048 D_real: 0.621 D_fake: 1.131 
+(epoch: 415, iters: 5294, time: 0.063) G_GAN: 0.199 G_GAN_Feat: 0.910 G_ID: 0.110 G_Rec: 0.469 D_GP: 0.036 D_real: 0.802 D_fake: 0.801 
+(epoch: 415, iters: 5694, time: 0.063) G_GAN: -0.059 G_GAN_Feat: 0.736 G_ID: 0.101 G_Rec: 0.320 D_GP: 0.054 D_real: 0.745 D_fake: 1.060 
+(epoch: 415, iters: 6094, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.887 G_ID: 0.117 G_Rec: 0.407 D_GP: 0.048 D_real: 0.731 D_fake: 0.775 
+(epoch: 415, iters: 6494, time: 0.063) G_GAN: 0.241 G_GAN_Feat: 0.711 G_ID: 0.101 G_Rec: 0.298 D_GP: 0.037 D_real: 1.056 D_fake: 0.760 
+(epoch: 415, iters: 6894, time: 0.063) G_GAN: 0.417 G_GAN_Feat: 0.965 G_ID: 0.119 G_Rec: 0.431 D_GP: 0.056 D_real: 0.869 D_fake: 0.596 
+(epoch: 415, iters: 7294, time: 0.063) G_GAN: -0.007 G_GAN_Feat: 0.838 G_ID: 0.100 G_Rec: 0.337 D_GP: 0.797 D_real: 0.414 D_fake: 1.008 
+(epoch: 415, iters: 7694, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 1.013 G_ID: 0.117 G_Rec: 0.481 D_GP: 0.081 D_real: 0.942 D_fake: 0.500 
+(epoch: 415, iters: 8094, time: 0.063) G_GAN: 0.041 G_GAN_Feat: 0.730 G_ID: 0.105 G_Rec: 0.303 D_GP: 0.048 D_real: 0.781 D_fake: 0.959 
+(epoch: 415, iters: 8494, time: 0.063) G_GAN: 0.305 G_GAN_Feat: 0.946 G_ID: 0.130 G_Rec: 0.461 D_GP: 0.050 D_real: 0.885 D_fake: 0.695 
+(epoch: 416, iters: 286, time: 0.063) G_GAN: 0.517 G_GAN_Feat: 0.833 G_ID: 0.101 G_Rec: 0.352 D_GP: 0.056 D_real: 1.210 D_fake: 0.496 
+(epoch: 416, iters: 686, time: 0.064) G_GAN: 0.725 G_GAN_Feat: 0.912 G_ID: 0.111 G_Rec: 0.449 D_GP: 0.033 D_real: 1.306 D_fake: 0.294 
+(epoch: 416, iters: 1086, time: 0.063) G_GAN: 0.153 G_GAN_Feat: 0.864 G_ID: 0.097 G_Rec: 0.358 D_GP: 0.140 D_real: 0.351 D_fake: 0.852 
+(epoch: 416, iters: 1486, time: 0.063) G_GAN: 0.852 G_GAN_Feat: 0.936 G_ID: 0.099 G_Rec: 0.401 D_GP: 0.036 D_real: 1.466 D_fake: 0.202 
+(epoch: 416, iters: 1886, time: 0.063) G_GAN: 0.008 G_GAN_Feat: 0.603 G_ID: 0.100 G_Rec: 0.306 D_GP: 0.028 D_real: 0.990 D_fake: 0.992 
+(epoch: 416, iters: 2286, time: 0.063) G_GAN: 0.275 G_GAN_Feat: 0.792 G_ID: 0.130 G_Rec: 0.406 D_GP: 0.026 D_real: 1.134 D_fake: 0.727 
+(epoch: 416, iters: 2686, time: 0.063) G_GAN: 0.062 G_GAN_Feat: 0.656 G_ID: 0.089 G_Rec: 0.300 D_GP: 0.029 D_real: 0.924 D_fake: 0.938 
+(epoch: 416, iters: 3086, time: 0.063) G_GAN: 0.228 G_GAN_Feat: 0.917 G_ID: 0.121 G_Rec: 0.432 D_GP: 0.057 D_real: 0.926 D_fake: 0.779 
+(epoch: 416, iters: 3486, time: 0.064) G_GAN: -0.010 G_GAN_Feat: 0.791 G_ID: 0.093 G_Rec: 0.327 D_GP: 0.092 D_real: 0.562 D_fake: 1.010 
+(epoch: 416, iters: 3886, time: 0.063) G_GAN: 0.653 G_GAN_Feat: 0.989 G_ID: 0.115 G_Rec: 0.481 D_GP: 0.038 D_real: 1.109 D_fake: 0.381 
+(epoch: 416, iters: 4286, time: 0.063) G_GAN: 0.080 G_GAN_Feat: 0.725 G_ID: 0.094 G_Rec: 0.302 D_GP: 0.051 D_real: 0.879 D_fake: 0.920 
+(epoch: 416, iters: 4686, time: 0.063) G_GAN: 0.567 G_GAN_Feat: 0.840 G_ID: 0.111 G_Rec: 0.421 D_GP: 0.036 D_real: 1.199 D_fake: 0.438 
+(epoch: 416, iters: 5086, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.719 G_ID: 0.094 G_Rec: 0.306 D_GP: 0.044 D_real: 1.262 D_fake: 0.501 
+(epoch: 416, iters: 5486, time: 0.063) G_GAN: 0.429 G_GAN_Feat: 0.967 G_ID: 0.117 G_Rec: 0.464 D_GP: 0.048 D_real: 0.954 D_fake: 0.575 
+(epoch: 416, iters: 5886, time: 0.063) G_GAN: -0.007 G_GAN_Feat: 0.799 G_ID: 0.102 G_Rec: 0.358 D_GP: 0.036 D_real: 0.751 D_fake: 1.007 
+(epoch: 416, iters: 6286, time: 0.063) G_GAN: 0.484 G_GAN_Feat: 0.900 G_ID: 0.137 G_Rec: 0.411 D_GP: 0.043 D_real: 1.019 D_fake: 0.521 
+(epoch: 416, iters: 6686, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.788 G_ID: 0.089 G_Rec: 0.321 D_GP: 0.045 D_real: 0.953 D_fake: 0.692 
+(epoch: 416, iters: 7086, time: 0.063) G_GAN: 0.537 G_GAN_Feat: 1.008 G_ID: 0.128 G_Rec: 0.418 D_GP: 0.071 D_real: 0.856 D_fake: 0.474 
+(epoch: 416, iters: 7486, time: 0.063) G_GAN: 0.425 G_GAN_Feat: 0.851 G_ID: 0.091 G_Rec: 0.313 D_GP: 0.050 D_real: 0.874 D_fake: 0.576 
+(epoch: 416, iters: 7886, time: 0.063) G_GAN: 0.410 G_GAN_Feat: 1.245 G_ID: 0.130 G_Rec: 0.468 D_GP: 0.065 D_real: 0.885 D_fake: 0.613 
+(epoch: 416, iters: 8286, time: 0.064) G_GAN: -0.103 G_GAN_Feat: 0.689 G_ID: 0.097 G_Rec: 0.318 D_GP: 0.028 D_real: 0.861 D_fake: 1.103 
+(epoch: 416, iters: 8686, time: 0.063) G_GAN: 0.139 G_GAN_Feat: 0.872 G_ID: 0.112 G_Rec: 0.425 D_GP: 0.029 D_real: 0.788 D_fake: 0.863 
+(epoch: 417, iters: 478, time: 0.063) G_GAN: -0.050 G_GAN_Feat: 0.666 G_ID: 0.122 G_Rec: 0.289 D_GP: 0.036 D_real: 0.826 D_fake: 1.050 
+(epoch: 417, iters: 878, time: 0.063) G_GAN: 0.395 G_GAN_Feat: 0.916 G_ID: 0.110 G_Rec: 0.451 D_GP: 0.043 D_real: 0.902 D_fake: 0.628 
+(epoch: 417, iters: 1278, time: 0.064) G_GAN: -0.024 G_GAN_Feat: 0.668 G_ID: 0.097 G_Rec: 0.308 D_GP: 0.039 D_real: 0.854 D_fake: 1.024 
+(epoch: 417, iters: 1678, time: 0.063) G_GAN: 0.534 G_GAN_Feat: 0.942 G_ID: 0.114 G_Rec: 0.478 D_GP: 0.035 D_real: 1.074 D_fake: 0.507 
+(epoch: 417, iters: 2078, time: 0.063) G_GAN: -0.031 G_GAN_Feat: 0.671 G_ID: 0.109 G_Rec: 0.325 D_GP: 0.029 D_real: 0.864 D_fake: 1.031 
+(epoch: 417, iters: 2478, time: 0.063) G_GAN: 0.150 G_GAN_Feat: 0.942 G_ID: 0.127 G_Rec: 0.477 D_GP: 0.042 D_real: 0.674 D_fake: 0.852 
+(epoch: 417, iters: 2878, time: 0.064) G_GAN: 0.002 G_GAN_Feat: 0.649 G_ID: 0.112 G_Rec: 0.282 D_GP: 0.031 D_real: 0.964 D_fake: 0.999 
+(epoch: 417, iters: 3278, time: 0.063) G_GAN: 0.501 G_GAN_Feat: 0.949 G_ID: 0.154 G_Rec: 0.452 D_GP: 0.046 D_real: 0.993 D_fake: 0.523 
+(epoch: 417, iters: 3678, time: 0.063) G_GAN: 0.271 G_GAN_Feat: 0.776 G_ID: 0.095 G_Rec: 0.334 D_GP: 0.051 D_real: 1.045 D_fake: 0.732 
+(epoch: 417, iters: 4078, time: 0.063) G_GAN: 0.543 G_GAN_Feat: 0.860 G_ID: 0.113 G_Rec: 0.385 D_GP: 0.032 D_real: 1.276 D_fake: 0.470 
+(epoch: 417, iters: 4478, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.762 G_ID: 0.087 G_Rec: 0.305 D_GP: 0.045 D_real: 0.932 D_fake: 0.775 
+(epoch: 417, iters: 4878, time: 0.063) G_GAN: 0.496 G_GAN_Feat: 0.930 G_ID: 0.116 G_Rec: 0.440 D_GP: 0.035 D_real: 1.145 D_fake: 0.512 
+(epoch: 417, iters: 5278, time: 0.063) G_GAN: 0.216 G_GAN_Feat: 0.733 G_ID: 0.093 G_Rec: 0.304 D_GP: 0.040 D_real: 1.056 D_fake: 0.786 
+(epoch: 417, iters: 5678, time: 0.063) G_GAN: 0.486 G_GAN_Feat: 0.925 G_ID: 0.113 G_Rec: 0.448 D_GP: 0.063 D_real: 0.930 D_fake: 0.530 
+(epoch: 417, iters: 6078, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.789 G_ID: 0.088 G_Rec: 0.307 D_GP: 0.064 D_real: 0.879 D_fake: 0.637 
+(epoch: 417, iters: 6478, time: 0.063) G_GAN: 0.549 G_GAN_Feat: 0.875 G_ID: 0.125 G_Rec: 0.391 D_GP: 0.031 D_real: 1.153 D_fake: 0.467 
+(epoch: 417, iters: 6878, time: 0.063) G_GAN: -0.040 G_GAN_Feat: 0.815 G_ID: 0.113 G_Rec: 0.340 D_GP: 0.098 D_real: 0.464 D_fake: 1.040 
+(epoch: 417, iters: 7278, time: 0.063) G_GAN: 0.528 G_GAN_Feat: 1.117 G_ID: 0.108 G_Rec: 0.492 D_GP: 0.110 D_real: 0.744 D_fake: 0.487 
+(epoch: 417, iters: 7678, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.960 G_ID: 0.115 G_Rec: 0.356 D_GP: 0.054 D_real: 0.811 D_fake: 0.870 
+(epoch: 417, iters: 8078, time: 0.063) G_GAN: 0.703 G_GAN_Feat: 0.977 G_ID: 0.110 G_Rec: 0.430 D_GP: 0.033 D_real: 1.298 D_fake: 0.333 
+(epoch: 417, iters: 8478, time: 0.063) G_GAN: -0.024 G_GAN_Feat: 0.938 G_ID: 0.102 G_Rec: 0.322 D_GP: 3.707 D_real: 0.330 D_fake: 1.025 
+(epoch: 418, iters: 270, time: 0.063) G_GAN: 0.360 G_GAN_Feat: 0.882 G_ID: 0.122 G_Rec: 0.441 D_GP: 0.029 D_real: 1.077 D_fake: 0.640 
+(epoch: 418, iters: 670, time: 0.064) G_GAN: -0.062 G_GAN_Feat: 0.643 G_ID: 0.140 G_Rec: 0.328 D_GP: 0.028 D_real: 0.808 D_fake: 1.062 
+(epoch: 418, iters: 1070, time: 0.063) G_GAN: 0.241 G_GAN_Feat: 0.837 G_ID: 0.116 G_Rec: 0.409 D_GP: 0.035 D_real: 0.984 D_fake: 0.760 
+(epoch: 418, iters: 1470, time: 0.063) G_GAN: -0.007 G_GAN_Feat: 0.603 G_ID: 0.104 G_Rec: 0.285 D_GP: 0.034 D_real: 0.902 D_fake: 1.007 
+(epoch: 418, iters: 1870, time: 0.063) G_GAN: 0.316 G_GAN_Feat: 0.810 G_ID: 0.126 G_Rec: 0.416 D_GP: 0.038 D_real: 1.006 D_fake: 0.698 
+(epoch: 418, iters: 2270, time: 0.064) G_GAN: -0.061 G_GAN_Feat: 0.698 G_ID: 0.101 G_Rec: 0.323 D_GP: 0.054 D_real: 0.768 D_fake: 1.061 
+(epoch: 418, iters: 2670, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 0.876 G_ID: 0.121 G_Rec: 0.433 D_GP: 0.046 D_real: 0.971 D_fake: 0.763 
+(epoch: 418, iters: 3070, time: 0.063) G_GAN: -0.143 G_GAN_Feat: 0.669 G_ID: 0.078 G_Rec: 0.312 D_GP: 0.049 D_real: 0.664 D_fake: 1.143 
+(epoch: 418, iters: 3470, time: 0.063) G_GAN: 0.296 G_GAN_Feat: 0.928 G_ID: 0.120 G_Rec: 0.462 D_GP: 0.044 D_real: 0.926 D_fake: 0.713 
+(epoch: 418, iters: 3870, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.676 G_ID: 0.110 G_Rec: 0.290 D_GP: 0.037 D_real: 1.156 D_fake: 0.762 
+(epoch: 418, iters: 4270, time: 0.063) G_GAN: 0.485 G_GAN_Feat: 0.961 G_ID: 0.107 G_Rec: 0.457 D_GP: 0.042 D_real: 1.220 D_fake: 0.540 
+(epoch: 418, iters: 4670, time: 0.063) G_GAN: 0.526 G_GAN_Feat: 0.783 G_ID: 0.119 G_Rec: 0.343 D_GP: 0.033 D_real: 1.416 D_fake: 0.481 
+(epoch: 418, iters: 5070, time: 0.063) G_GAN: 0.316 G_GAN_Feat: 0.952 G_ID: 0.120 G_Rec: 0.439 D_GP: 0.082 D_real: 0.763 D_fake: 0.691 
+(epoch: 418, iters: 5470, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.669 G_ID: 0.096 G_Rec: 0.298 D_GP: 0.040 D_real: 1.127 D_fake: 0.761 
+(epoch: 418, iters: 5870, time: 0.063) G_GAN: 0.342 G_GAN_Feat: 1.010 G_ID: 0.126 G_Rec: 0.472 D_GP: 0.052 D_real: 0.840 D_fake: 0.659 
+(epoch: 418, iters: 6270, time: 0.063) G_GAN: 0.094 G_GAN_Feat: 0.656 G_ID: 0.099 G_Rec: 0.302 D_GP: 0.028 D_real: 1.014 D_fake: 0.909 
+(epoch: 418, iters: 6670, time: 0.063) G_GAN: 0.448 G_GAN_Feat: 1.090 G_ID: 0.124 G_Rec: 0.464 D_GP: 0.085 D_real: 0.463 D_fake: 0.557 
+(epoch: 418, iters: 7070, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.743 G_ID: 0.091 G_Rec: 0.311 D_GP: 0.065 D_real: 1.264 D_fake: 0.587 
+(epoch: 418, iters: 7470, time: 0.063) G_GAN: 0.487 G_GAN_Feat: 0.970 G_ID: 0.118 G_Rec: 0.455 D_GP: 0.091 D_real: 1.075 D_fake: 0.523 
+(epoch: 418, iters: 7870, time: 0.063) G_GAN: 0.056 G_GAN_Feat: 0.660 G_ID: 0.110 G_Rec: 0.374 D_GP: 0.035 D_real: 0.955 D_fake: 0.944 
+(epoch: 418, iters: 8270, time: 0.063) G_GAN: 0.142 G_GAN_Feat: 0.943 G_ID: 0.111 G_Rec: 0.450 D_GP: 0.050 D_real: 0.777 D_fake: 0.858 
+(epoch: 418, iters: 8670, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.760 G_ID: 0.107 G_Rec: 0.318 D_GP: 0.034 D_real: 1.026 D_fake: 0.738 
+(epoch: 419, iters: 462, time: 0.063) G_GAN: 0.383 G_GAN_Feat: 0.891 G_ID: 0.124 G_Rec: 0.419 D_GP: 0.031 D_real: 0.951 D_fake: 0.618 
+(epoch: 419, iters: 862, time: 0.063) G_GAN: 0.431 G_GAN_Feat: 0.656 G_ID: 0.100 G_Rec: 0.270 D_GP: 0.025 D_real: 1.312 D_fake: 0.571 
+(epoch: 419, iters: 1262, time: 0.063) G_GAN: 0.541 G_GAN_Feat: 0.906 G_ID: 0.104 G_Rec: 0.408 D_GP: 0.034 D_real: 1.124 D_fake: 0.467 
+(epoch: 419, iters: 1662, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.739 G_ID: 0.105 G_Rec: 0.281 D_GP: 0.051 D_real: 1.160 D_fake: 0.728 
+(epoch: 419, iters: 2062, time: 0.063) G_GAN: 0.835 G_GAN_Feat: 0.984 G_ID: 0.114 G_Rec: 0.470 D_GP: 0.037 D_real: 1.401 D_fake: 0.250 
+(epoch: 419, iters: 2462, time: 0.063) G_GAN: 0.089 G_GAN_Feat: 0.718 G_ID: 0.096 G_Rec: 0.328 D_GP: 0.036 D_real: 0.977 D_fake: 0.911 
+(epoch: 419, iters: 2862, time: 0.063) G_GAN: 0.512 G_GAN_Feat: 0.891 G_ID: 0.101 G_Rec: 0.391 D_GP: 0.038 D_real: 1.121 D_fake: 0.507 
+(epoch: 419, iters: 3262, time: 0.064) G_GAN: 0.195 G_GAN_Feat: 0.746 G_ID: 0.096 G_Rec: 0.320 D_GP: 0.042 D_real: 1.021 D_fake: 0.805 
+(epoch: 419, iters: 3662, time: 0.063) G_GAN: 0.500 G_GAN_Feat: 0.911 G_ID: 0.112 G_Rec: 0.422 D_GP: 0.031 D_real: 1.218 D_fake: 0.512 
+(epoch: 419, iters: 4062, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.710 G_ID: 0.122 G_Rec: 0.290 D_GP: 0.032 D_real: 1.060 D_fake: 0.784 
+(epoch: 419, iters: 4462, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.914 G_ID: 0.126 G_Rec: 0.392 D_GP: 0.037 D_real: 1.051 D_fake: 0.529 
+(epoch: 419, iters: 4862, time: 0.064) G_GAN: -0.112 G_GAN_Feat: 0.785 G_ID: 0.120 G_Rec: 0.325 D_GP: 0.042 D_real: 0.600 D_fake: 1.112 
+(epoch: 419, iters: 5262, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.892 G_ID: 0.119 G_Rec: 0.376 D_GP: 0.030 D_real: 0.707 D_fake: 0.866 
+(epoch: 419, iters: 5662, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.732 G_ID: 0.103 G_Rec: 0.294 D_GP: 0.053 D_real: 1.033 D_fake: 0.675 
+(epoch: 419, iters: 6062, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 1.059 G_ID: 0.131 G_Rec: 0.459 D_GP: 0.062 D_real: 0.647 D_fake: 0.569 
+(epoch: 419, iters: 6462, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.812 G_ID: 0.105 G_Rec: 0.322 D_GP: 0.046 D_real: 0.995 D_fake: 0.756 
+(epoch: 419, iters: 6862, time: 0.064) G_GAN: 0.800 G_GAN_Feat: 1.035 G_ID: 0.132 G_Rec: 0.437 D_GP: 0.064 D_real: 0.818 D_fake: 0.255 
+(epoch: 419, iters: 7262, time: 0.064) G_GAN: -0.239 G_GAN_Feat: 0.832 G_ID: 0.100 G_Rec: 0.321 D_GP: 0.048 D_real: 0.546 D_fake: 1.240 
+(epoch: 419, iters: 7662, time: 0.064) G_GAN: 0.429 G_GAN_Feat: 1.029 G_ID: 0.127 G_Rec: 0.433 D_GP: 0.034 D_real: 0.831 D_fake: 0.572 
+(epoch: 419, iters: 8062, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.716 G_ID: 0.099 G_Rec: 0.316 D_GP: 0.039 D_real: 1.180 D_fake: 0.775 
+(epoch: 419, iters: 8462, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.951 G_ID: 0.114 G_Rec: 0.431 D_GP: 0.029 D_real: 0.974 D_fake: 0.708 
+(epoch: 420, iters: 254, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.723 G_ID: 0.106 G_Rec: 0.274 D_GP: 0.036 D_real: 1.177 D_fake: 0.654 
+(epoch: 420, iters: 654, time: 0.064) G_GAN: 0.510 G_GAN_Feat: 0.924 G_ID: 0.124 G_Rec: 0.430 D_GP: 0.034 D_real: 1.078 D_fake: 0.509 
+(epoch: 420, iters: 1054, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.793 G_ID: 0.096 G_Rec: 0.313 D_GP: 0.057 D_real: 0.953 D_fake: 0.651 
+(epoch: 420, iters: 1454, time: 0.063) G_GAN: 0.533 G_GAN_Feat: 0.929 G_ID: 0.117 G_Rec: 0.486 D_GP: 0.027 D_real: 1.179 D_fake: 0.473 
+(epoch: 420, iters: 1854, time: 0.063) G_GAN: 0.153 G_GAN_Feat: 0.796 G_ID: 0.105 G_Rec: 0.314 D_GP: 0.097 D_real: 0.840 D_fake: 0.848 
+(epoch: 420, iters: 2254, time: 0.063) G_GAN: 0.617 G_GAN_Feat: 0.916 G_ID: 0.112 G_Rec: 0.423 D_GP: 0.034 D_real: 1.198 D_fake: 0.395 
+(epoch: 420, iters: 2654, time: 0.064) G_GAN: 0.436 G_GAN_Feat: 0.801 G_ID: 0.087 G_Rec: 0.310 D_GP: 0.051 D_real: 1.149 D_fake: 0.572 
+(epoch: 420, iters: 3054, time: 0.063) G_GAN: 0.931 G_GAN_Feat: 1.168 G_ID: 0.113 G_Rec: 0.467 D_GP: 0.057 D_real: 0.877 D_fake: 0.237 
+(epoch: 420, iters: 3454, time: 0.063) G_GAN: 0.242 G_GAN_Feat: 0.901 G_ID: 0.110 G_Rec: 0.322 D_GP: 0.077 D_real: 0.493 D_fake: 0.761 
+(epoch: 420, iters: 3854, time: 0.063) G_GAN: 0.454 G_GAN_Feat: 1.039 G_ID: 0.125 G_Rec: 0.499 D_GP: 0.030 D_real: 1.089 D_fake: 0.558 
+(epoch: 420, iters: 4254, time: 0.064) G_GAN: 0.365 G_GAN_Feat: 0.752 G_ID: 0.095 G_Rec: 0.283 D_GP: 0.037 D_real: 1.136 D_fake: 0.637 
+(epoch: 420, iters: 4654, time: 0.063) G_GAN: 1.416 G_GAN_Feat: 1.097 G_ID: 0.132 G_Rec: 0.460 D_GP: 0.121 D_real: 1.590 D_fake: 0.148 
+(epoch: 420, iters: 5054, time: 0.063) G_GAN: 0.045 G_GAN_Feat: 0.731 G_ID: 0.104 G_Rec: 0.308 D_GP: 0.030 D_real: 0.996 D_fake: 0.955 
+(epoch: 420, iters: 5454, time: 0.063) G_GAN: 0.241 G_GAN_Feat: 0.929 G_ID: 0.116 G_Rec: 0.418 D_GP: 0.046 D_real: 0.877 D_fake: 0.762 
+(epoch: 420, iters: 5854, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.882 G_ID: 0.102 G_Rec: 0.336 D_GP: 0.083 D_real: 0.611 D_fake: 0.728 
+(epoch: 420, iters: 6254, time: 0.063) G_GAN: 0.442 G_GAN_Feat: 0.936 G_ID: 0.138 G_Rec: 0.457 D_GP: 0.035 D_real: 1.051 D_fake: 0.561 
+(epoch: 420, iters: 6654, time: 0.063) G_GAN: 0.237 G_GAN_Feat: 0.701 G_ID: 0.086 G_Rec: 0.307 D_GP: 0.047 D_real: 1.140 D_fake: 0.763 
+(epoch: 420, iters: 7054, time: 0.063) G_GAN: 0.643 G_GAN_Feat: 0.952 G_ID: 0.137 G_Rec: 0.463 D_GP: 0.038 D_real: 1.136 D_fake: 0.383 
+(epoch: 420, iters: 7454, time: 0.063) G_GAN: 0.407 G_GAN_Feat: 0.643 G_ID: 0.090 G_Rec: 0.271 D_GP: 0.043 D_real: 1.285 D_fake: 0.594 
+(epoch: 420, iters: 7854, time: 0.063) G_GAN: 0.480 G_GAN_Feat: 0.925 G_ID: 0.126 G_Rec: 0.391 D_GP: 0.043 D_real: 1.072 D_fake: 0.529 
+(epoch: 420, iters: 8254, time: 0.063) G_GAN: 0.503 G_GAN_Feat: 0.933 G_ID: 0.098 G_Rec: 0.353 D_GP: 0.282 D_real: 0.645 D_fake: 0.526 
+(epoch: 420, iters: 8654, time: 0.064) G_GAN: 0.643 G_GAN_Feat: 1.010 G_ID: 0.116 G_Rec: 0.439 D_GP: 0.037 D_real: 1.175 D_fake: 0.371 
+(epoch: 421, iters: 446, time: 0.063) G_GAN: 0.376 G_GAN_Feat: 0.771 G_ID: 0.122 G_Rec: 0.327 D_GP: 0.037 D_real: 1.211 D_fake: 0.640 
+(epoch: 421, iters: 846, time: 0.063) G_GAN: 0.577 G_GAN_Feat: 1.018 G_ID: 0.120 G_Rec: 0.499 D_GP: 0.135 D_real: 0.932 D_fake: 0.431 
+(epoch: 421, iters: 1246, time: 0.063) G_GAN: 0.283 G_GAN_Feat: 0.789 G_ID: 0.098 G_Rec: 0.318 D_GP: 0.051 D_real: 0.818 D_fake: 0.718 
+(epoch: 421, iters: 1646, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 1.042 G_ID: 0.131 G_Rec: 0.459 D_GP: 0.065 D_real: 0.620 D_fake: 0.563 
+(epoch: 421, iters: 2046, time: 0.063) G_GAN: 0.112 G_GAN_Feat: 0.854 G_ID: 0.130 G_Rec: 0.334 D_GP: 0.044 D_real: 0.822 D_fake: 0.889 
+(epoch: 421, iters: 2446, time: 0.063) G_GAN: 0.676 G_GAN_Feat: 1.279 G_ID: 0.128 G_Rec: 0.467 D_GP: 0.047 D_real: 0.531 D_fake: 0.345 
+(epoch: 421, iters: 2846, time: 0.063) G_GAN: 0.250 G_GAN_Feat: 0.842 G_ID: 0.119 G_Rec: 0.339 D_GP: 0.032 D_real: 1.092 D_fake: 0.751 
+(epoch: 421, iters: 3246, time: 0.064) G_GAN: 0.535 G_GAN_Feat: 0.991 G_ID: 0.122 G_Rec: 0.458 D_GP: 0.034 D_real: 1.111 D_fake: 0.491 
+(epoch: 421, iters: 3646, time: 0.063) G_GAN: -0.159 G_GAN_Feat: 0.712 G_ID: 0.095 G_Rec: 0.301 D_GP: 0.040 D_real: 0.669 D_fake: 1.159 
+(epoch: 421, iters: 4046, time: 0.063) G_GAN: 0.416 G_GAN_Feat: 0.971 G_ID: 0.136 G_Rec: 0.484 D_GP: 0.033 D_real: 1.027 D_fake: 0.590 
+(epoch: 421, iters: 4446, time: 0.063) G_GAN: 0.431 G_GAN_Feat: 0.990 G_ID: 0.098 G_Rec: 0.338 D_GP: 0.057 D_real: 0.657 D_fake: 0.574 
+(epoch: 421, iters: 4846, time: 0.064) G_GAN: 1.212 G_GAN_Feat: 1.140 G_ID: 0.105 G_Rec: 0.446 D_GP: 0.179 D_real: 1.433 D_fake: 0.236 
+(epoch: 421, iters: 5246, time: 0.063) G_GAN: 0.358 G_GAN_Feat: 1.009 G_ID: 0.097 G_Rec: 0.334 D_GP: 0.644 D_real: 0.382 D_fake: 0.708 
+(epoch: 421, iters: 5646, time: 0.063) G_GAN: 0.298 G_GAN_Feat: 1.038 G_ID: 0.133 G_Rec: 0.468 D_GP: 0.072 D_real: 0.572 D_fake: 0.706 
+(epoch: 421, iters: 6046, time: 0.063) G_GAN: 0.347 G_GAN_Feat: 0.963 G_ID: 0.097 G_Rec: 0.349 D_GP: 0.078 D_real: 0.379 D_fake: 0.665 
+(epoch: 421, iters: 6446, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 1.223 G_ID: 0.165 G_Rec: 0.464 D_GP: 0.069 D_real: 0.773 D_fake: 0.711 
+(epoch: 421, iters: 6846, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 0.975 G_ID: 0.099 G_Rec: 0.364 D_GP: 0.072 D_real: 0.331 D_fake: 0.812 
+(epoch: 421, iters: 7246, time: 0.063) G_GAN: 0.490 G_GAN_Feat: 1.242 G_ID: 0.153 G_Rec: 0.485 D_GP: 0.060 D_real: 0.315 D_fake: 0.515 
+(epoch: 421, iters: 7646, time: 0.063) G_GAN: 0.534 G_GAN_Feat: 0.817 G_ID: 0.087 G_Rec: 0.348 D_GP: 0.037 D_real: 1.109 D_fake: 0.480 
+(epoch: 421, iters: 8046, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.802 G_ID: 0.120 G_Rec: 0.422 D_GP: 0.026 D_real: 1.051 D_fake: 0.759 
+(epoch: 421, iters: 8446, time: 0.063) G_GAN: 0.155 G_GAN_Feat: 0.579 G_ID: 0.093 G_Rec: 0.284 D_GP: 0.026 D_real: 1.107 D_fake: 0.845 
+(epoch: 422, iters: 238, time: 0.063) G_GAN: 0.459 G_GAN_Feat: 0.847 G_ID: 0.120 G_Rec: 0.413 D_GP: 0.030 D_real: 1.177 D_fake: 0.547 
+(epoch: 422, iters: 638, time: 0.063) G_GAN: 0.253 G_GAN_Feat: 0.612 G_ID: 0.095 G_Rec: 0.344 D_GP: 0.030 D_real: 1.178 D_fake: 0.748 
+(epoch: 422, iters: 1038, time: 0.064) G_GAN: 0.447 G_GAN_Feat: 0.880 G_ID: 0.113 G_Rec: 0.422 D_GP: 0.047 D_real: 1.069 D_fake: 0.566 
+(epoch: 422, iters: 1438, time: 0.063) G_GAN: 0.181 G_GAN_Feat: 0.617 G_ID: 0.096 G_Rec: 0.287 D_GP: 0.035 D_real: 1.117 D_fake: 0.820 
+(epoch: 422, iters: 1838, time: 0.063) G_GAN: 0.516 G_GAN_Feat: 0.887 G_ID: 0.101 G_Rec: 0.411 D_GP: 0.039 D_real: 1.112 D_fake: 0.517 
+(epoch: 422, iters: 2238, time: 0.063) G_GAN: -0.082 G_GAN_Feat: 0.618 G_ID: 0.095 G_Rec: 0.293 D_GP: 0.030 D_real: 0.766 D_fake: 1.082 
+(epoch: 422, iters: 2638, time: 0.064) G_GAN: 0.416 G_GAN_Feat: 0.882 G_ID: 0.112 G_Rec: 0.432 D_GP: 0.037 D_real: 1.122 D_fake: 0.599 
+(epoch: 422, iters: 3038, time: 0.063) G_GAN: -0.025 G_GAN_Feat: 0.657 G_ID: 0.114 G_Rec: 0.302 D_GP: 0.035 D_real: 0.796 D_fake: 1.025 
+(epoch: 422, iters: 3438, time: 0.063) G_GAN: 0.311 G_GAN_Feat: 0.906 G_ID: 0.107 G_Rec: 0.426 D_GP: 0.036 D_real: 0.897 D_fake: 0.696 
+(epoch: 422, iters: 3838, time: 0.063) G_GAN: 0.108 G_GAN_Feat: 0.708 G_ID: 0.101 G_Rec: 0.341 D_GP: 0.038 D_real: 0.949 D_fake: 0.894 
+(epoch: 422, iters: 4238, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.882 G_ID: 0.127 G_Rec: 0.446 D_GP: 0.034 D_real: 0.903 D_fake: 0.784 
+(epoch: 422, iters: 4638, time: 0.063) G_GAN: -0.196 G_GAN_Feat: 0.767 G_ID: 0.119 G_Rec: 0.364 D_GP: 0.067 D_real: 0.471 D_fake: 1.196 
+(epoch: 422, iters: 5038, time: 0.063) G_GAN: 0.376 G_GAN_Feat: 0.966 G_ID: 0.126 G_Rec: 0.490 D_GP: 0.039 D_real: 0.916 D_fake: 0.633 
+(epoch: 422, iters: 5438, time: 0.063) G_GAN: 0.125 G_GAN_Feat: 0.697 G_ID: 0.116 G_Rec: 0.307 D_GP: 0.041 D_real: 0.892 D_fake: 0.875 
+(epoch: 422, iters: 5838, time: 0.064) G_GAN: 0.228 G_GAN_Feat: 0.907 G_ID: 0.115 G_Rec: 0.456 D_GP: 0.036 D_real: 0.861 D_fake: 0.773 
+(epoch: 422, iters: 6238, time: 0.063) G_GAN: 0.227 G_GAN_Feat: 0.691 G_ID: 0.100 G_Rec: 0.302 D_GP: 0.031 D_real: 1.094 D_fake: 0.774 
+(epoch: 422, iters: 6638, time: 0.063) G_GAN: 0.458 G_GAN_Feat: 0.966 G_ID: 0.112 G_Rec: 0.439 D_GP: 0.055 D_real: 1.055 D_fake: 0.544 
+(epoch: 422, iters: 7038, time: 0.063) G_GAN: 0.202 G_GAN_Feat: 0.831 G_ID: 0.102 G_Rec: 0.333 D_GP: 0.126 D_real: 0.808 D_fake: 0.800 
+(epoch: 422, iters: 7438, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.916 G_ID: 0.128 G_Rec: 0.422 D_GP: 0.031 D_real: 1.007 D_fake: 0.650 
+(epoch: 422, iters: 7838, time: 0.063) G_GAN: 0.234 G_GAN_Feat: 0.604 G_ID: 0.096 G_Rec: 0.291 D_GP: 0.027 D_real: 1.219 D_fake: 0.766 
+(epoch: 422, iters: 8238, time: 0.063) G_GAN: 0.485 G_GAN_Feat: 0.915 G_ID: 0.118 G_Rec: 0.425 D_GP: 0.032 D_real: 1.266 D_fake: 0.520 
+(epoch: 422, iters: 8638, time: 0.063) G_GAN: 0.208 G_GAN_Feat: 0.704 G_ID: 0.102 G_Rec: 0.320 D_GP: 0.035 D_real: 1.066 D_fake: 0.792 
+(epoch: 423, iters: 430, time: 0.064) G_GAN: 0.622 G_GAN_Feat: 0.925 G_ID: 0.109 G_Rec: 0.424 D_GP: 0.050 D_real: 1.197 D_fake: 0.395 
+(epoch: 423, iters: 830, time: 0.063) G_GAN: -0.040 G_GAN_Feat: 0.766 G_ID: 0.108 G_Rec: 0.358 D_GP: 0.062 D_real: 0.821 D_fake: 1.040 
+(epoch: 423, iters: 1230, time: 0.063) G_GAN: 0.518 G_GAN_Feat: 0.987 G_ID: 0.112 G_Rec: 0.484 D_GP: 0.068 D_real: 0.872 D_fake: 0.509 
+(epoch: 423, iters: 1630, time: 0.063) G_GAN: 0.139 G_GAN_Feat: 0.756 G_ID: 0.099 G_Rec: 0.295 D_GP: 0.051 D_real: 0.779 D_fake: 0.861 
+(epoch: 423, iters: 2030, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.910 G_ID: 0.136 G_Rec: 0.413 D_GP: 0.058 D_real: 0.891 D_fake: 0.585 
+(epoch: 423, iters: 2430, time: 0.063) G_GAN: 0.436 G_GAN_Feat: 0.783 G_ID: 0.108 G_Rec: 0.310 D_GP: 0.067 D_real: 0.959 D_fake: 0.575 
+(epoch: 423, iters: 2830, time: 0.063) G_GAN: 0.418 G_GAN_Feat: 0.910 G_ID: 0.105 G_Rec: 0.394 D_GP: 0.036 D_real: 1.052 D_fake: 0.583 
+(epoch: 423, iters: 3230, time: 0.063) G_GAN: 0.246 G_GAN_Feat: 0.730 G_ID: 0.083 G_Rec: 0.304 D_GP: 0.038 D_real: 1.088 D_fake: 0.754 
+(epoch: 423, iters: 3630, time: 0.064) G_GAN: 0.671 G_GAN_Feat: 0.981 G_ID: 0.108 G_Rec: 0.429 D_GP: 0.126 D_real: 0.951 D_fake: 0.370 
+(epoch: 423, iters: 4030, time: 0.063) G_GAN: 0.148 G_GAN_Feat: 0.776 G_ID: 0.108 G_Rec: 0.307 D_GP: 0.054 D_real: 0.771 D_fake: 0.852 
+(epoch: 423, iters: 4430, time: 0.063) G_GAN: 0.315 G_GAN_Feat: 1.010 G_ID: 0.113 G_Rec: 0.494 D_GP: 0.043 D_real: 0.852 D_fake: 0.700 
+(epoch: 423, iters: 4830, time: 0.063) G_GAN: 0.199 G_GAN_Feat: 0.780 G_ID: 0.086 G_Rec: 0.315 D_GP: 0.067 D_real: 0.911 D_fake: 0.803 
+(epoch: 423, iters: 5230, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.978 G_ID: 0.123 G_Rec: 0.443 D_GP: 0.046 D_real: 0.670 D_fake: 0.829 
+(epoch: 423, iters: 5630, time: 0.064) G_GAN: -0.109 G_GAN_Feat: 0.899 G_ID: 0.092 G_Rec: 0.373 D_GP: 0.155 D_real: 0.205 D_fake: 1.111 
+(epoch: 423, iters: 6030, time: 0.063) G_GAN: 0.274 G_GAN_Feat: 0.974 G_ID: 0.135 G_Rec: 0.424 D_GP: 0.040 D_real: 0.683 D_fake: 0.731 
+(epoch: 423, iters: 6430, time: 0.063) G_GAN: 0.108 G_GAN_Feat: 0.861 G_ID: 0.101 G_Rec: 0.357 D_GP: 0.094 D_real: 0.569 D_fake: 0.892 
+(epoch: 423, iters: 6830, time: 0.064) G_GAN: 0.629 G_GAN_Feat: 0.995 G_ID: 0.116 G_Rec: 0.490 D_GP: 0.052 D_real: 0.995 D_fake: 0.402 
+(epoch: 423, iters: 7230, time: 0.063) G_GAN: 0.221 G_GAN_Feat: 0.752 G_ID: 0.097 G_Rec: 0.340 D_GP: 0.034 D_real: 1.074 D_fake: 0.779 
+(epoch: 423, iters: 7630, time: 0.063) G_GAN: 0.546 G_GAN_Feat: 0.962 G_ID: 0.114 G_Rec: 0.430 D_GP: 0.043 D_real: 1.177 D_fake: 0.500 
+(epoch: 423, iters: 8030, time: 0.063) G_GAN: 0.442 G_GAN_Feat: 0.835 G_ID: 0.091 G_Rec: 0.315 D_GP: 0.054 D_real: 1.045 D_fake: 0.562 
+(epoch: 423, iters: 8430, time: 0.064) G_GAN: 0.382 G_GAN_Feat: 0.997 G_ID: 0.104 G_Rec: 0.529 D_GP: 0.036 D_real: 0.774 D_fake: 0.622 
+(epoch: 424, iters: 222, time: 0.063) G_GAN: 0.245 G_GAN_Feat: 0.826 G_ID: 0.111 G_Rec: 0.297 D_GP: 0.050 D_real: 0.698 D_fake: 0.761 
+(epoch: 424, iters: 622, time: 0.063) G_GAN: 0.446 G_GAN_Feat: 0.869 G_ID: 0.114 G_Rec: 0.415 D_GP: 0.026 D_real: 1.185 D_fake: 0.557 
+(epoch: 424, iters: 1022, time: 0.063) G_GAN: 0.259 G_GAN_Feat: 0.692 G_ID: 0.103 G_Rec: 0.305 D_GP: 0.032 D_real: 1.113 D_fake: 0.742 
+(epoch: 424, iters: 1422, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 1.008 G_ID: 0.111 G_Rec: 0.450 D_GP: 0.050 D_real: 0.725 D_fake: 0.684 
+(epoch: 424, iters: 1822, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.781 G_ID: 0.093 G_Rec: 0.301 D_GP: 0.045 D_real: 0.999 D_fake: 0.532 
+(epoch: 424, iters: 2222, time: 0.063) G_GAN: 0.962 G_GAN_Feat: 1.097 G_ID: 0.120 G_Rec: 0.462 D_GP: 0.139 D_real: 0.944 D_fake: 0.201 
+(epoch: 424, iters: 2622, time: 0.063) G_GAN: 0.223 G_GAN_Feat: 0.715 G_ID: 0.093 G_Rec: 0.304 D_GP: 0.036 D_real: 1.119 D_fake: 0.780 
+(epoch: 424, iters: 3022, time: 0.064) G_GAN: 0.512 G_GAN_Feat: 0.880 G_ID: 0.133 G_Rec: 0.382 D_GP: 0.029 D_real: 1.202 D_fake: 0.524 
+(epoch: 424, iters: 3422, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.907 G_ID: 0.105 G_Rec: 0.342 D_GP: 0.136 D_real: 0.499 D_fake: 0.588 
+(epoch: 424, iters: 3822, time: 0.063) G_GAN: 0.869 G_GAN_Feat: 1.011 G_ID: 0.131 G_Rec: 0.454 D_GP: 0.045 D_real: 1.296 D_fake: 0.209 
+(epoch: 424, iters: 4222, time: 0.063) G_GAN: 0.123 G_GAN_Feat: 0.700 G_ID: 0.089 G_Rec: 0.309 D_GP: 0.025 D_real: 1.073 D_fake: 0.877 
+(epoch: 424, iters: 4622, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.960 G_ID: 0.103 G_Rec: 0.437 D_GP: 0.031 D_real: 0.849 D_fake: 0.765 
+(epoch: 424, iters: 5022, time: 0.063) G_GAN: -0.190 G_GAN_Feat: 0.747 G_ID: 0.108 G_Rec: 0.297 D_GP: 0.033 D_real: 0.817 D_fake: 1.191 
+(epoch: 424, iters: 5422, time: 0.063) G_GAN: 0.808 G_GAN_Feat: 1.071 G_ID: 0.105 G_Rec: 0.485 D_GP: 0.034 D_real: 0.946 D_fake: 0.274 
+(epoch: 424, iters: 5822, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 0.788 G_ID: 0.101 G_Rec: 0.313 D_GP: 0.040 D_real: 0.954 D_fake: 0.811 
+(epoch: 424, iters: 6222, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 1.124 G_ID: 0.131 G_Rec: 0.444 D_GP: 0.042 D_real: 0.984 D_fake: 0.761 
+(epoch: 424, iters: 6622, time: 0.063) G_GAN: -0.164 G_GAN_Feat: 0.752 G_ID: 0.121 G_Rec: 0.325 D_GP: 0.031 D_real: 0.752 D_fake: 1.164 
+(epoch: 424, iters: 7022, time: 0.063) G_GAN: 0.260 G_GAN_Feat: 0.974 G_ID: 0.113 G_Rec: 0.457 D_GP: 0.035 D_real: 0.852 D_fake: 0.742 
+(epoch: 424, iters: 7422, time: 0.063) G_GAN: 0.350 G_GAN_Feat: 0.750 G_ID: 0.090 G_Rec: 0.313 D_GP: 0.035 D_real: 1.176 D_fake: 0.652 
+(epoch: 424, iters: 7822, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.935 G_ID: 0.110 G_Rec: 0.419 D_GP: 0.027 D_real: 0.901 D_fake: 0.784 
+(epoch: 424, iters: 8222, time: 0.063) G_GAN: -0.044 G_GAN_Feat: 0.764 G_ID: 0.100 G_Rec: 0.322 D_GP: 0.034 D_real: 0.742 D_fake: 1.045 
+(epoch: 424, iters: 8622, time: 0.063) G_GAN: 0.254 G_GAN_Feat: 1.003 G_ID: 0.111 G_Rec: 0.425 D_GP: 0.147 D_real: 0.652 D_fake: 0.747 
+(epoch: 425, iters: 414, time: 0.063) G_GAN: 0.214 G_GAN_Feat: 0.683 G_ID: 0.123 G_Rec: 0.278 D_GP: 0.031 D_real: 1.165 D_fake: 0.786 
+(epoch: 425, iters: 814, time: 0.064) G_GAN: 0.849 G_GAN_Feat: 1.068 G_ID: 0.107 G_Rec: 0.485 D_GP: 0.111 D_real: 1.138 D_fake: 0.230 
+(epoch: 425, iters: 1214, time: 0.063) G_GAN: 0.504 G_GAN_Feat: 0.728 G_ID: 0.096 G_Rec: 0.310 D_GP: 0.031 D_real: 1.430 D_fake: 0.504 
+(epoch: 425, iters: 1614, time: 0.063) G_GAN: 0.214 G_GAN_Feat: 0.852 G_ID: 0.134 G_Rec: 0.354 D_GP: 0.038 D_real: 0.920 D_fake: 0.786 
+(epoch: 425, iters: 2014, time: 0.063) G_GAN: 0.030 G_GAN_Feat: 0.739 G_ID: 0.128 G_Rec: 0.299 D_GP: 0.043 D_real: 0.943 D_fake: 0.970 
+(epoch: 425, iters: 2414, time: 0.064) G_GAN: 0.696 G_GAN_Feat: 0.861 G_ID: 0.132 G_Rec: 0.419 D_GP: 0.034 D_real: 1.407 D_fake: 0.330 
+(epoch: 425, iters: 2814, time: 0.063) G_GAN: 0.143 G_GAN_Feat: 0.644 G_ID: 0.105 G_Rec: 0.287 D_GP: 0.030 D_real: 1.033 D_fake: 0.857 
+(epoch: 425, iters: 3214, time: 0.063) G_GAN: 0.397 G_GAN_Feat: 0.875 G_ID: 0.120 G_Rec: 0.434 D_GP: 0.037 D_real: 1.033 D_fake: 0.615 
+(epoch: 425, iters: 3614, time: 0.063) G_GAN: 0.035 G_GAN_Feat: 0.696 G_ID: 0.107 G_Rec: 0.318 D_GP: 0.044 D_real: 0.885 D_fake: 0.965 
+(epoch: 425, iters: 4014, time: 0.063) G_GAN: 0.194 G_GAN_Feat: 0.910 G_ID: 0.111 G_Rec: 0.406 D_GP: 0.057 D_real: 0.761 D_fake: 0.807 
+(epoch: 425, iters: 4414, time: 0.063) G_GAN: 0.018 G_GAN_Feat: 0.732 G_ID: 0.093 G_Rec: 0.321 D_GP: 0.056 D_real: 0.805 D_fake: 0.982 
+(epoch: 425, iters: 4814, time: 0.063) G_GAN: 0.223 G_GAN_Feat: 0.843 G_ID: 0.129 G_Rec: 0.387 D_GP: 0.032 D_real: 0.990 D_fake: 0.777 
+(epoch: 425, iters: 5214, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.819 G_ID: 0.092 G_Rec: 0.360 D_GP: 0.084 D_real: 0.753 D_fake: 0.834 
+(epoch: 425, iters: 5614, time: 0.063) G_GAN: 0.247 G_GAN_Feat: 0.939 G_ID: 0.113 G_Rec: 0.449 D_GP: 0.038 D_real: 0.906 D_fake: 0.754 
+(epoch: 425, iters: 6014, time: 0.063) G_GAN: 0.247 G_GAN_Feat: 0.686 G_ID: 0.099 G_Rec: 0.283 D_GP: 0.052 D_real: 1.038 D_fake: 0.753 
+(epoch: 425, iters: 6414, time: 0.063) G_GAN: 0.917 G_GAN_Feat: 0.970 G_ID: 0.102 G_Rec: 0.435 D_GP: 0.038 D_real: 1.505 D_fake: 0.173 
+(epoch: 425, iters: 6814, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.766 G_ID: 0.110 G_Rec: 0.304 D_GP: 0.033 D_real: 0.848 D_fake: 0.865 
+(epoch: 425, iters: 7214, time: 0.063) G_GAN: 0.529 G_GAN_Feat: 0.988 G_ID: 0.115 G_Rec: 0.441 D_GP: 0.033 D_real: 1.092 D_fake: 0.474 
+(epoch: 425, iters: 7614, time: 0.063) G_GAN: 0.290 G_GAN_Feat: 0.806 G_ID: 0.112 G_Rec: 0.312 D_GP: 0.046 D_real: 0.861 D_fake: 0.731 
+(epoch: 425, iters: 8014, time: 0.063) G_GAN: 0.491 G_GAN_Feat: 0.907 G_ID: 0.145 G_Rec: 0.474 D_GP: 0.034 D_real: 1.101 D_fake: 0.515 
+(epoch: 425, iters: 8414, time: 0.064) G_GAN: 0.271 G_GAN_Feat: 0.690 G_ID: 0.113 G_Rec: 0.333 D_GP: 0.031 D_real: 1.146 D_fake: 0.731 
+(epoch: 426, iters: 206, time: 0.063) G_GAN: 0.746 G_GAN_Feat: 0.903 G_ID: 0.114 G_Rec: 0.446 D_GP: 0.035 D_real: 1.331 D_fake: 0.345 
+(epoch: 426, iters: 606, time: 0.063) G_GAN: 0.028 G_GAN_Feat: 0.817 G_ID: 0.095 G_Rec: 0.351 D_GP: 0.061 D_real: 0.648 D_fake: 0.972 
+(epoch: 426, iters: 1006, time: 0.063) G_GAN: 0.474 G_GAN_Feat: 0.867 G_ID: 0.122 G_Rec: 0.392 D_GP: 0.036 D_real: 1.146 D_fake: 0.545 
+(epoch: 426, iters: 1406, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.803 G_ID: 0.104 G_Rec: 0.373 D_GP: 0.042 D_real: 0.956 D_fake: 0.818 
+(epoch: 426, iters: 1806, time: 0.063) G_GAN: 0.716 G_GAN_Feat: 1.045 G_ID: 0.106 G_Rec: 0.486 D_GP: 0.033 D_real: 1.416 D_fake: 0.305 
+(epoch: 426, iters: 2206, time: 0.063) G_GAN: 0.216 G_GAN_Feat: 0.706 G_ID: 0.088 G_Rec: 0.267 D_GP: 0.039 D_real: 1.048 D_fake: 0.784 
+(epoch: 426, iters: 2606, time: 0.063) G_GAN: 0.434 G_GAN_Feat: 0.916 G_ID: 0.100 G_Rec: 0.440 D_GP: 0.027 D_real: 1.137 D_fake: 0.572 
+(epoch: 426, iters: 3006, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.774 G_ID: 0.090 G_Rec: 0.298 D_GP: 0.032 D_real: 1.020 D_fake: 0.752 
+(epoch: 426, iters: 3406, time: 0.063) G_GAN: 0.445 G_GAN_Feat: 0.951 G_ID: 0.137 G_Rec: 0.397 D_GP: 0.039 D_real: 1.055 D_fake: 0.574 
+(epoch: 426, iters: 3806, time: 0.063) G_GAN: -0.328 G_GAN_Feat: 1.137 G_ID: 0.119 G_Rec: 0.375 D_GP: 0.220 D_real: 0.927 D_fake: 1.328 
+(epoch: 426, iters: 4206, time: 0.063) G_GAN: 0.348 G_GAN_Feat: 0.880 G_ID: 0.126 G_Rec: 0.462 D_GP: 0.024 D_real: 1.076 D_fake: 0.658 
+(epoch: 426, iters: 4606, time: 0.064) G_GAN: -0.098 G_GAN_Feat: 0.696 G_ID: 0.092 G_Rec: 0.311 D_GP: 0.037 D_real: 0.810 D_fake: 1.098 
+(epoch: 426, iters: 5006, time: 0.063) G_GAN: 0.276 G_GAN_Feat: 0.955 G_ID: 0.129 G_Rec: 0.436 D_GP: 0.063 D_real: 0.751 D_fake: 0.726 
+(epoch: 426, iters: 5406, time: 0.063) G_GAN: 0.359 G_GAN_Feat: 0.823 G_ID: 0.103 G_Rec: 0.343 D_GP: 0.044 D_real: 0.845 D_fake: 0.645 
+(epoch: 426, iters: 5806, time: 0.063) G_GAN: 0.337 G_GAN_Feat: 0.897 G_ID: 0.114 G_Rec: 0.414 D_GP: 0.035 D_real: 0.999 D_fake: 0.668 
+(epoch: 426, iters: 6206, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.717 G_ID: 0.092 G_Rec: 0.314 D_GP: 0.033 D_real: 0.986 D_fake: 0.874 
+(epoch: 426, iters: 6606, time: 0.063) G_GAN: 0.324 G_GAN_Feat: 1.064 G_ID: 0.125 G_Rec: 0.438 D_GP: 0.063 D_real: 0.718 D_fake: 0.677 
+(epoch: 426, iters: 7006, time: 0.063) G_GAN: 0.179 G_GAN_Feat: 0.767 G_ID: 0.090 G_Rec: 0.319 D_GP: 0.060 D_real: 0.827 D_fake: 0.821 
+(epoch: 426, iters: 7406, time: 0.063) G_GAN: 0.669 G_GAN_Feat: 1.028 G_ID: 0.111 G_Rec: 0.441 D_GP: 0.049 D_real: 0.932 D_fake: 0.347 
+(epoch: 426, iters: 7806, time: 0.064) G_GAN: -0.125 G_GAN_Feat: 0.771 G_ID: 0.090 G_Rec: 0.330 D_GP: 0.068 D_real: 0.626 D_fake: 1.125 
+(epoch: 426, iters: 8206, time: 0.063) G_GAN: 0.573 G_GAN_Feat: 1.013 G_ID: 0.106 G_Rec: 0.491 D_GP: 0.030 D_real: 0.967 D_fake: 0.461 
+(epoch: 426, iters: 8606, time: 0.063) G_GAN: 0.355 G_GAN_Feat: 0.839 G_ID: 0.108 G_Rec: 0.310 D_GP: 0.067 D_real: 0.961 D_fake: 0.647 
+(epoch: 427, iters: 398, time: 0.063) G_GAN: 0.832 G_GAN_Feat: 1.136 G_ID: 0.117 G_Rec: 0.452 D_GP: 0.113 D_real: 0.678 D_fake: 0.328 
+(epoch: 427, iters: 798, time: 0.064) G_GAN: -0.039 G_GAN_Feat: 0.724 G_ID: 0.119 G_Rec: 0.315 D_GP: 0.034 D_real: 0.835 D_fake: 1.040 
+(epoch: 427, iters: 1198, time: 0.064) G_GAN: 0.630 G_GAN_Feat: 0.965 G_ID: 0.117 G_Rec: 0.454 D_GP: 0.036 D_real: 1.197 D_fake: 0.387 
+(epoch: 427, iters: 1598, time: 0.063) G_GAN: -0.024 G_GAN_Feat: 0.826 G_ID: 0.089 G_Rec: 0.344 D_GP: 0.076 D_real: 0.561 D_fake: 1.025 
+(epoch: 427, iters: 1998, time: 0.063) G_GAN: 1.528 G_GAN_Feat: 1.058 G_ID: 0.116 G_Rec: 0.505 D_GP: 0.165 D_real: 1.850 D_fake: 0.025 
+(epoch: 427, iters: 2398, time: 0.064) G_GAN: 0.119 G_GAN_Feat: 0.718 G_ID: 0.099 G_Rec: 0.309 D_GP: 0.040 D_real: 0.904 D_fake: 0.881 
+(epoch: 427, iters: 2798, time: 0.063) G_GAN: 0.768 G_GAN_Feat: 0.973 G_ID: 0.124 G_Rec: 0.433 D_GP: 0.134 D_real: 1.051 D_fake: 0.351 
+(epoch: 427, iters: 3198, time: 0.063) G_GAN: 0.173 G_GAN_Feat: 0.837 G_ID: 0.100 G_Rec: 0.338 D_GP: 0.047 D_real: 0.698 D_fake: 0.828 
+(epoch: 427, iters: 3598, time: 0.063) G_GAN: 0.965 G_GAN_Feat: 1.248 G_ID: 0.135 G_Rec: 0.495 D_GP: 0.084 D_real: 0.494 D_fake: 0.150 
+(epoch: 427, iters: 3998, time: 0.064) G_GAN: 0.425 G_GAN_Feat: 0.954 G_ID: 0.099 G_Rec: 0.346 D_GP: 0.055 D_real: 0.729 D_fake: 0.605 
+(epoch: 427, iters: 4398, time: 0.063) G_GAN: 0.846 G_GAN_Feat: 0.996 G_ID: 0.110 G_Rec: 0.381 D_GP: 0.038 D_real: 1.394 D_fake: 0.211 
+(epoch: 427, iters: 4798, time: 0.063) G_GAN: 0.285 G_GAN_Feat: 0.765 G_ID: 0.116 G_Rec: 0.339 D_GP: 0.033 D_real: 1.064 D_fake: 0.716 
+(epoch: 427, iters: 5198, time: 0.063) G_GAN: 0.209 G_GAN_Feat: 0.924 G_ID: 0.136 G_Rec: 0.463 D_GP: 0.031 D_real: 0.901 D_fake: 0.791 
+(epoch: 427, iters: 5598, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.859 G_ID: 0.091 G_Rec: 0.344 D_GP: 0.352 D_real: 0.583 D_fake: 0.897 
+(epoch: 427, iters: 5998, time: 0.063) G_GAN: 0.187 G_GAN_Feat: 0.964 G_ID: 0.124 G_Rec: 0.421 D_GP: 0.038 D_real: 0.852 D_fake: 0.815 
+(epoch: 427, iters: 6398, time: 0.063) G_GAN: 0.252 G_GAN_Feat: 0.693 G_ID: 0.116 G_Rec: 0.295 D_GP: 0.027 D_real: 1.105 D_fake: 0.748 
+(epoch: 427, iters: 6798, time: 0.063) G_GAN: 0.672 G_GAN_Feat: 0.931 G_ID: 0.119 G_Rec: 0.434 D_GP: 0.032 D_real: 1.382 D_fake: 0.343 
+(epoch: 427, iters: 7198, time: 0.064) G_GAN: 0.078 G_GAN_Feat: 0.805 G_ID: 0.114 G_Rec: 0.356 D_GP: 0.107 D_real: 0.756 D_fake: 0.924 
+(epoch: 427, iters: 7598, time: 0.063) G_GAN: 0.397 G_GAN_Feat: 1.204 G_ID: 0.115 G_Rec: 0.467 D_GP: 0.090 D_real: 0.229 D_fake: 0.606 
+(epoch: 427, iters: 7998, time: 0.063) G_GAN: 0.124 G_GAN_Feat: 0.740 G_ID: 0.091 G_Rec: 0.306 D_GP: 0.029 D_real: 1.053 D_fake: 0.877 
+(epoch: 427, iters: 8398, time: 0.063) G_GAN: 0.544 G_GAN_Feat: 0.955 G_ID: 0.122 G_Rec: 0.417 D_GP: 0.042 D_real: 1.097 D_fake: 0.490 
+(epoch: 428, iters: 190, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.761 G_ID: 0.097 G_Rec: 0.299 D_GP: 0.052 D_real: 1.226 D_fake: 0.656 
+(epoch: 428, iters: 590, time: 0.063) G_GAN: 0.763 G_GAN_Feat: 1.187 G_ID: 0.134 G_Rec: 0.524 D_GP: 0.151 D_real: 0.419 D_fake: 0.306 
+(epoch: 428, iters: 990, time: 0.063) G_GAN: 0.535 G_GAN_Feat: 0.816 G_ID: 0.109 G_Rec: 0.315 D_GP: 0.060 D_real: 1.268 D_fake: 0.509 
+(epoch: 428, iters: 1390, time: 0.063) G_GAN: 0.498 G_GAN_Feat: 1.129 G_ID: 0.146 G_Rec: 0.450 D_GP: 0.091 D_real: 0.368 D_fake: 0.504 
+(epoch: 428, iters: 1790, time: 0.064) G_GAN: 0.865 G_GAN_Feat: 0.956 G_ID: 0.109 G_Rec: 0.338 D_GP: 0.050 D_real: 0.883 D_fake: 0.204 
+(epoch: 428, iters: 2190, time: 0.063) G_GAN: 0.955 G_GAN_Feat: 1.040 G_ID: 0.103 G_Rec: 0.433 D_GP: 0.078 D_real: 1.454 D_fake: 0.224 
+(epoch: 428, iters: 2590, time: 0.063) G_GAN: 0.107 G_GAN_Feat: 0.649 G_ID: 0.094 G_Rec: 0.263 D_GP: 0.026 D_real: 1.068 D_fake: 0.893 
+(epoch: 428, iters: 2990, time: 0.063) G_GAN: 0.514 G_GAN_Feat: 1.101 G_ID: 0.125 G_Rec: 0.488 D_GP: 0.090 D_real: 0.563 D_fake: 0.500 
+(epoch: 428, iters: 3390, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 0.897 G_ID: 0.093 G_Rec: 0.300 D_GP: 0.143 D_real: 0.531 D_fake: 0.561 
+(epoch: 428, iters: 3790, time: 0.064) G_GAN: 0.662 G_GAN_Feat: 1.053 G_ID: 0.118 G_Rec: 0.433 D_GP: 0.052 D_real: 0.966 D_fake: 0.374 
+(epoch: 428, iters: 4190, time: 0.064) G_GAN: 0.328 G_GAN_Feat: 0.817 G_ID: 0.090 G_Rec: 0.318 D_GP: 0.049 D_real: 0.826 D_fake: 0.674 
+(epoch: 428, iters: 4590, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 1.190 G_ID: 0.144 G_Rec: 0.439 D_GP: 0.044 D_real: 1.077 D_fake: 0.837 
+(epoch: 428, iters: 4990, time: 0.064) G_GAN: 0.240 G_GAN_Feat: 0.610 G_ID: 0.097 G_Rec: 0.297 D_GP: 0.022 D_real: 1.201 D_fake: 0.760 
+(epoch: 428, iters: 5390, time: 0.063) G_GAN: 0.533 G_GAN_Feat: 0.913 G_ID: 0.105 G_Rec: 0.429 D_GP: 0.031 D_real: 1.190 D_fake: 0.491 
+(epoch: 428, iters: 5790, time: 0.063) G_GAN: 0.191 G_GAN_Feat: 0.693 G_ID: 0.092 G_Rec: 0.304 D_GP: 0.037 D_real: 1.011 D_fake: 0.809 
+(epoch: 428, iters: 6190, time: 0.063) G_GAN: 0.236 G_GAN_Feat: 0.887 G_ID: 0.123 G_Rec: 0.450 D_GP: 0.039 D_real: 0.760 D_fake: 0.765 
+(epoch: 428, iters: 6590, time: 0.064) G_GAN: 0.035 G_GAN_Feat: 0.763 G_ID: 0.106 G_Rec: 0.327 D_GP: 0.061 D_real: 0.926 D_fake: 0.966 
+(epoch: 428, iters: 6990, time: 0.063) G_GAN: 0.642 G_GAN_Feat: 0.908 G_ID: 0.112 G_Rec: 0.438 D_GP: 0.040 D_real: 1.195 D_fake: 0.402 
+(epoch: 428, iters: 7390, time: 0.063) G_GAN: 0.058 G_GAN_Feat: 0.812 G_ID: 0.086 G_Rec: 0.346 D_GP: 0.146 D_real: 0.801 D_fake: 0.942 
+(epoch: 428, iters: 7790, time: 0.063) G_GAN: 0.409 G_GAN_Feat: 0.963 G_ID: 0.114 G_Rec: 0.406 D_GP: 0.044 D_real: 0.891 D_fake: 0.595 
+(epoch: 428, iters: 8190, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.786 G_ID: 0.094 G_Rec: 0.320 D_GP: 0.068 D_real: 0.978 D_fake: 0.666 
+(epoch: 428, iters: 8590, time: 0.063) G_GAN: 0.313 G_GAN_Feat: 1.035 G_ID: 0.144 G_Rec: 0.417 D_GP: 0.060 D_real: 0.576 D_fake: 0.688 
+(epoch: 429, iters: 382, time: 0.063) G_GAN: 0.439 G_GAN_Feat: 0.815 G_ID: 0.099 G_Rec: 0.334 D_GP: 0.061 D_real: 0.999 D_fake: 0.563 
+(epoch: 429, iters: 782, time: 0.063) G_GAN: 0.384 G_GAN_Feat: 0.975 G_ID: 0.124 G_Rec: 0.411 D_GP: 0.043 D_real: 0.904 D_fake: 0.618 
+(epoch: 429, iters: 1182, time: 0.064) G_GAN: -0.109 G_GAN_Feat: 0.890 G_ID: 0.100 G_Rec: 0.349 D_GP: 0.141 D_real: 0.510 D_fake: 1.109 
+(epoch: 429, iters: 1582, time: 0.063) G_GAN: 0.679 G_GAN_Feat: 1.033 G_ID: 0.098 G_Rec: 0.417 D_GP: 0.050 D_real: 1.031 D_fake: 0.338 
+(epoch: 429, iters: 1982, time: 0.063) G_GAN: 0.089 G_GAN_Feat: 0.731 G_ID: 0.096 G_Rec: 0.300 D_GP: 0.041 D_real: 0.952 D_fake: 0.911 
+(epoch: 429, iters: 2382, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 1.041 G_ID: 0.116 G_Rec: 0.454 D_GP: 0.092 D_real: 0.644 D_fake: 0.640 
+(epoch: 429, iters: 2782, time: 0.064) G_GAN: 0.401 G_GAN_Feat: 0.755 G_ID: 0.093 G_Rec: 0.297 D_GP: 0.046 D_real: 1.242 D_fake: 0.691 
+(epoch: 429, iters: 3182, time: 0.064) G_GAN: 1.030 G_GAN_Feat: 1.183 G_ID: 0.110 G_Rec: 0.479 D_GP: 0.110 D_real: 0.924 D_fake: 0.184 
+(epoch: 429, iters: 3582, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.811 G_ID: 0.100 G_Rec: 0.332 D_GP: 0.032 D_real: 1.019 D_fake: 0.834 
+(epoch: 429, iters: 3982, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 1.111 G_ID: 0.125 G_Rec: 0.464 D_GP: 0.074 D_real: 0.684 D_fake: 0.644 
+(epoch: 429, iters: 4382, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.730 G_ID: 0.092 G_Rec: 0.302 D_GP: 0.032 D_real: 1.438 D_fake: 0.520 
+(epoch: 429, iters: 4782, time: 0.064) G_GAN: 0.617 G_GAN_Feat: 1.035 G_ID: 0.108 G_Rec: 0.475 D_GP: 0.057 D_real: 1.071 D_fake: 0.395 
+(epoch: 429, iters: 5182, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.754 G_ID: 0.091 G_Rec: 0.310 D_GP: 0.051 D_real: 0.995 D_fake: 0.629 
+(epoch: 429, iters: 5582, time: 0.064) G_GAN: 0.701 G_GAN_Feat: 1.055 G_ID: 0.111 G_Rec: 0.410 D_GP: 0.108 D_real: 0.908 D_fake: 0.418 
+(epoch: 429, iters: 5982, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.887 G_ID: 0.111 G_Rec: 0.314 D_GP: 0.039 D_real: 0.859 D_fake: 0.836 
+(epoch: 429, iters: 6382, time: 0.064) G_GAN: 0.688 G_GAN_Feat: 1.203 G_ID: 0.116 G_Rec: 0.487 D_GP: 0.063 D_real: 0.342 D_fake: 0.336 
+(epoch: 429, iters: 6782, time: 0.064) G_GAN: 0.097 G_GAN_Feat: 0.901 G_ID: 0.108 G_Rec: 0.309 D_GP: 0.037 D_real: 0.597 D_fake: 0.904 
+(epoch: 429, iters: 7182, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.797 G_ID: 0.112 G_Rec: 0.398 D_GP: 0.027 D_real: 1.157 D_fake: 0.611 
+(epoch: 429, iters: 7582, time: 0.064) G_GAN: 0.207 G_GAN_Feat: 0.664 G_ID: 0.115 G_Rec: 0.347 D_GP: 0.026 D_real: 1.120 D_fake: 0.796 
+(epoch: 429, iters: 7982, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.899 G_ID: 0.121 G_Rec: 0.429 D_GP: 0.030 D_real: 1.035 D_fake: 0.663 
+(epoch: 429, iters: 8382, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.641 G_ID: 0.094 G_Rec: 0.296 D_GP: 0.028 D_real: 1.071 D_fake: 0.863 
+(epoch: 430, iters: 174, time: 0.064) G_GAN: 0.308 G_GAN_Feat: 0.855 G_ID: 0.128 G_Rec: 0.408 D_GP: 0.032 D_real: 0.930 D_fake: 0.692 
+(epoch: 430, iters: 574, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.709 G_ID: 0.120 G_Rec: 0.343 D_GP: 0.035 D_real: 0.857 D_fake: 0.897 
+(epoch: 430, iters: 974, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.863 G_ID: 0.124 G_Rec: 0.426 D_GP: 0.038 D_real: 0.944 D_fake: 0.673 
+(epoch: 430, iters: 1374, time: 0.064) G_GAN: -0.065 G_GAN_Feat: 0.762 G_ID: 0.116 G_Rec: 0.357 D_GP: 0.072 D_real: 0.683 D_fake: 1.065 
+(epoch: 430, iters: 1774, time: 0.064) G_GAN: 0.233 G_GAN_Feat: 0.918 G_ID: 0.110 G_Rec: 0.416 D_GP: 0.054 D_real: 0.786 D_fake: 0.769 
+(epoch: 430, iters: 2174, time: 0.063) G_GAN: 0.160 G_GAN_Feat: 0.729 G_ID: 0.095 G_Rec: 0.314 D_GP: 0.042 D_real: 0.932 D_fake: 0.841 
+(epoch: 430, iters: 2574, time: 0.063) G_GAN: 0.215 G_GAN_Feat: 0.874 G_ID: 0.124 G_Rec: 0.403 D_GP: 0.040 D_real: 0.808 D_fake: 0.785 
+(epoch: 430, iters: 2974, time: 0.063) G_GAN: 0.228 G_GAN_Feat: 0.825 G_ID: 0.123 G_Rec: 0.374 D_GP: 0.064 D_real: 0.933 D_fake: 0.780 
+(epoch: 430, iters: 3374, time: 0.064) G_GAN: 0.582 G_GAN_Feat: 0.940 G_ID: 0.125 G_Rec: 0.442 D_GP: 0.033 D_real: 1.160 D_fake: 0.425 
+(epoch: 430, iters: 3774, time: 0.064) G_GAN: 0.300 G_GAN_Feat: 0.734 G_ID: 0.094 G_Rec: 0.300 D_GP: 0.048 D_real: 1.053 D_fake: 0.701 
+(epoch: 430, iters: 4174, time: 0.064) G_GAN: 0.701 G_GAN_Feat: 1.006 G_ID: 0.116 G_Rec: 0.446 D_GP: 0.049 D_real: 1.091 D_fake: 0.328 
+(epoch: 430, iters: 4574, time: 0.064) G_GAN: 0.103 G_GAN_Feat: 0.745 G_ID: 0.092 G_Rec: 0.304 D_GP: 0.046 D_real: 0.928 D_fake: 0.897 
+(epoch: 430, iters: 4974, time: 0.064) G_GAN: 0.591 G_GAN_Feat: 0.989 G_ID: 0.104 G_Rec: 0.468 D_GP: 0.032 D_real: 1.195 D_fake: 0.427 
+(epoch: 430, iters: 5374, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 0.769 G_ID: 0.106 G_Rec: 0.311 D_GP: 0.035 D_real: 1.108 D_fake: 0.683 
+(epoch: 430, iters: 5774, time: 0.064) G_GAN: 0.663 G_GAN_Feat: 1.013 G_ID: 0.118 G_Rec: 0.450 D_GP: 0.039 D_real: 1.114 D_fake: 0.349 
+(epoch: 430, iters: 6174, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.654 G_ID: 0.102 G_Rec: 0.302 D_GP: 0.033 D_real: 1.209 D_fake: 0.718 
+(epoch: 430, iters: 6574, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.906 G_ID: 0.129 G_Rec: 0.443 D_GP: 0.048 D_real: 0.925 D_fake: 0.676 
+(epoch: 430, iters: 6974, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.715 G_ID: 0.088 G_Rec: 0.301 D_GP: 0.037 D_real: 1.183 D_fake: 0.669 
+(epoch: 430, iters: 7374, time: 0.064) G_GAN: 0.487 G_GAN_Feat: 0.978 G_ID: 0.149 G_Rec: 0.449 D_GP: 0.063 D_real: 0.984 D_fake: 0.525 
+(epoch: 430, iters: 7774, time: 0.064) G_GAN: -0.058 G_GAN_Feat: 0.732 G_ID: 0.119 G_Rec: 0.291 D_GP: 0.046 D_real: 0.743 D_fake: 1.058 
+(epoch: 430, iters: 8174, time: 0.064) G_GAN: 0.579 G_GAN_Feat: 0.944 G_ID: 0.110 G_Rec: 0.432 D_GP: 0.029 D_real: 1.274 D_fake: 0.427 
+(epoch: 430, iters: 8574, time: 0.064) G_GAN: 0.269 G_GAN_Feat: 0.729 G_ID: 0.106 G_Rec: 0.319 D_GP: 0.034 D_real: 1.220 D_fake: 0.737 
+(epoch: 431, iters: 366, time: 0.064) G_GAN: 0.463 G_GAN_Feat: 0.983 G_ID: 0.112 G_Rec: 0.459 D_GP: 0.043 D_real: 0.979 D_fake: 0.542 
+(epoch: 431, iters: 766, time: 0.064) G_GAN: 0.118 G_GAN_Feat: 0.723 G_ID: 0.106 G_Rec: 0.299 D_GP: 0.044 D_real: 0.905 D_fake: 0.886 
+(epoch: 431, iters: 1166, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.938 G_ID: 0.115 G_Rec: 0.427 D_GP: 0.042 D_real: 0.951 D_fake: 0.577 
+(epoch: 431, iters: 1566, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.709 G_ID: 0.093 G_Rec: 0.282 D_GP: 0.035 D_real: 1.155 D_fake: 0.756 
+(epoch: 431, iters: 1966, time: 0.063) G_GAN: 0.336 G_GAN_Feat: 1.187 G_ID: 0.115 G_Rec: 0.508 D_GP: 0.118 D_real: 0.372 D_fake: 0.678 
+(epoch: 431, iters: 2366, time: 0.063) G_GAN: 0.290 G_GAN_Feat: 0.789 G_ID: 0.094 G_Rec: 0.301 D_GP: 0.040 D_real: 0.850 D_fake: 0.711 
+(epoch: 431, iters: 2766, time: 0.064) G_GAN: 0.704 G_GAN_Feat: 1.024 G_ID: 0.122 G_Rec: 0.447 D_GP: 0.035 D_real: 1.315 D_fake: 0.312 
+(epoch: 431, iters: 3166, time: 0.063) G_GAN: 0.444 G_GAN_Feat: 1.087 G_ID: 0.112 G_Rec: 0.364 D_GP: 0.141 D_real: 0.416 D_fake: 0.563 
+(epoch: 431, iters: 3566, time: 0.063) G_GAN: 0.630 G_GAN_Feat: 0.856 G_ID: 0.113 G_Rec: 0.399 D_GP: 0.037 D_real: 1.315 D_fake: 0.427 
+(epoch: 431, iters: 3966, time: 0.063) G_GAN: 0.029 G_GAN_Feat: 0.631 G_ID: 0.100 G_Rec: 0.301 D_GP: 0.026 D_real: 0.988 D_fake: 0.971 
+(epoch: 431, iters: 4366, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.886 G_ID: 0.120 G_Rec: 0.426 D_GP: 0.030 D_real: 0.829 D_fake: 0.782 
+(epoch: 431, iters: 4766, time: 0.063) G_GAN: 0.024 G_GAN_Feat: 0.708 G_ID: 0.079 G_Rec: 0.326 D_GP: 0.031 D_real: 0.934 D_fake: 0.976 
+(epoch: 431, iters: 5166, time: 0.063) G_GAN: 0.489 G_GAN_Feat: 1.008 G_ID: 0.112 G_Rec: 0.487 D_GP: 0.056 D_real: 1.053 D_fake: 0.559 
+(epoch: 431, iters: 5566, time: 0.063) G_GAN: -0.026 G_GAN_Feat: 0.664 G_ID: 0.104 G_Rec: 0.310 D_GP: 0.029 D_real: 0.949 D_fake: 1.027 
+(epoch: 431, iters: 5966, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 1.018 G_ID: 0.118 G_Rec: 0.466 D_GP: 0.049 D_real: 0.548 D_fake: 0.880 
+(epoch: 431, iters: 6366, time: 0.063) G_GAN: -0.160 G_GAN_Feat: 0.732 G_ID: 0.109 G_Rec: 0.323 D_GP: 0.065 D_real: 0.672 D_fake: 1.161 
+(epoch: 431, iters: 6766, time: 0.063) G_GAN: 0.249 G_GAN_Feat: 1.001 G_ID: 0.110 G_Rec: 0.463 D_GP: 0.043 D_real: 0.666 D_fake: 0.754 
+(epoch: 431, iters: 7166, time: 0.063) G_GAN: 0.063 G_GAN_Feat: 0.794 G_ID: 0.115 G_Rec: 0.321 D_GP: 0.039 D_real: 1.049 D_fake: 0.937 
+(epoch: 431, iters: 7566, time: 0.064) G_GAN: 0.637 G_GAN_Feat: 0.936 G_ID: 0.123 G_Rec: 0.442 D_GP: 0.045 D_real: 1.095 D_fake: 0.407 
+(epoch: 431, iters: 7966, time: 0.063) G_GAN: 0.097 G_GAN_Feat: 0.675 G_ID: 0.098 G_Rec: 0.311 D_GP: 0.044 D_real: 0.978 D_fake: 0.904 
+(epoch: 431, iters: 8366, time: 0.063) G_GAN: 0.365 G_GAN_Feat: 0.995 G_ID: 0.111 G_Rec: 0.459 D_GP: 0.049 D_real: 0.908 D_fake: 0.639 
+(epoch: 432, iters: 158, time: 0.063) G_GAN: 0.126 G_GAN_Feat: 0.829 G_ID: 0.106 G_Rec: 0.332 D_GP: 0.044 D_real: 1.008 D_fake: 0.877 
+(epoch: 432, iters: 558, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.953 G_ID: 0.117 G_Rec: 0.435 D_GP: 0.038 D_real: 0.876 D_fake: 0.644 
+(epoch: 432, iters: 958, time: 0.063) G_GAN: -0.059 G_GAN_Feat: 0.771 G_ID: 0.095 G_Rec: 0.322 D_GP: 0.048 D_real: 0.695 D_fake: 1.059 
+(epoch: 432, iters: 1358, time: 0.063) G_GAN: 0.301 G_GAN_Feat: 0.988 G_ID: 0.153 G_Rec: 0.458 D_GP: 0.036 D_real: 0.833 D_fake: 0.700 
+(epoch: 432, iters: 1758, time: 0.063) G_GAN: 0.457 G_GAN_Feat: 0.892 G_ID: 0.093 G_Rec: 0.330 D_GP: 0.086 D_real: 1.296 D_fake: 0.554 
+(epoch: 432, iters: 2158, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 1.000 G_ID: 0.124 G_Rec: 0.438 D_GP: 0.049 D_real: 0.816 D_fake: 0.654 
+(epoch: 432, iters: 2558, time: 0.063) G_GAN: 0.175 G_GAN_Feat: 0.983 G_ID: 0.107 G_Rec: 0.344 D_GP: 0.423 D_real: 0.265 D_fake: 0.835 
+(epoch: 432, iters: 2958, time: 0.063) G_GAN: 0.573 G_GAN_Feat: 1.078 G_ID: 0.125 G_Rec: 0.458 D_GP: 0.052 D_real: 0.807 D_fake: 0.438 
+(epoch: 432, iters: 3358, time: 0.063) G_GAN: 0.141 G_GAN_Feat: 0.870 G_ID: 0.094 G_Rec: 0.327 D_GP: 0.166 D_real: 0.335 D_fake: 0.860 
+(epoch: 432, iters: 3758, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.974 G_ID: 0.104 G_Rec: 0.439 D_GP: 0.037 D_real: 1.158 D_fake: 0.554 
+(epoch: 432, iters: 4158, time: 0.063) G_GAN: -0.037 G_GAN_Feat: 0.736 G_ID: 0.095 G_Rec: 0.333 D_GP: 0.064 D_real: 0.693 D_fake: 1.038 
+(epoch: 432, iters: 4558, time: 0.063) G_GAN: 0.379 G_GAN_Feat: 1.041 G_ID: 0.116 G_Rec: 0.478 D_GP: 0.043 D_real: 1.084 D_fake: 0.627 
+(epoch: 432, iters: 4958, time: 0.063) G_GAN: 0.154 G_GAN_Feat: 0.792 G_ID: 0.107 G_Rec: 0.325 D_GP: 0.034 D_real: 0.869 D_fake: 0.846 
+(epoch: 432, iters: 5358, time: 0.064) G_GAN: 0.700 G_GAN_Feat: 1.027 G_ID: 0.132 G_Rec: 0.445 D_GP: 0.059 D_real: 1.139 D_fake: 0.439 
+(epoch: 432, iters: 5758, time: 0.063) G_GAN: 0.214 G_GAN_Feat: 0.873 G_ID: 0.122 G_Rec: 0.347 D_GP: 0.045 D_real: 0.901 D_fake: 0.787 
+(epoch: 432, iters: 6158, time: 0.063) G_GAN: 0.515 G_GAN_Feat: 1.031 G_ID: 0.115 G_Rec: 0.437 D_GP: 0.055 D_real: 0.803 D_fake: 0.496 
+(epoch: 432, iters: 6558, time: 0.063) G_GAN: 0.425 G_GAN_Feat: 0.889 G_ID: 0.116 G_Rec: 0.300 D_GP: 0.048 D_real: 0.663 D_fake: 0.584 
+(epoch: 432, iters: 6958, time: 0.064) G_GAN: 0.558 G_GAN_Feat: 1.172 G_ID: 0.117 G_Rec: 0.455 D_GP: 0.051 D_real: 0.372 D_fake: 0.449 
+(epoch: 432, iters: 7358, time: 0.063) G_GAN: 0.149 G_GAN_Feat: 0.861 G_ID: 0.125 G_Rec: 0.335 D_GP: 0.043 D_real: 0.925 D_fake: 0.851 
+(epoch: 432, iters: 7758, time: 0.063) G_GAN: 0.714 G_GAN_Feat: 1.009 G_ID: 0.105 G_Rec: 0.433 D_GP: 0.038 D_real: 1.143 D_fake: 0.343 
+(epoch: 432, iters: 8158, time: 0.063) G_GAN: -0.154 G_GAN_Feat: 0.777 G_ID: 0.099 G_Rec: 0.282 D_GP: 0.030 D_real: 0.689 D_fake: 1.154 
+(epoch: 432, iters: 8558, time: 0.064) G_GAN: 0.532 G_GAN_Feat: 1.028 G_ID: 0.119 G_Rec: 0.398 D_GP: 0.038 D_real: 0.971 D_fake: 0.470 
+(epoch: 433, iters: 350, time: 0.063) G_GAN: 0.295 G_GAN_Feat: 0.753 G_ID: 0.123 G_Rec: 0.320 D_GP: 0.031 D_real: 1.233 D_fake: 0.706 
+(epoch: 433, iters: 750, time: 0.063) G_GAN: 0.369 G_GAN_Feat: 0.935 G_ID: 0.118 G_Rec: 0.399 D_GP: 0.028 D_real: 1.035 D_fake: 0.636 
+(epoch: 433, iters: 1150, time: 0.063) G_GAN: 0.124 G_GAN_Feat: 0.759 G_ID: 0.094 G_Rec: 0.314 D_GP: 0.029 D_real: 1.058 D_fake: 0.876 
+(epoch: 433, iters: 1550, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.930 G_ID: 0.107 G_Rec: 0.413 D_GP: 0.037 D_real: 0.951 D_fake: 0.724 
+(epoch: 433, iters: 1950, time: 0.063) G_GAN: 0.062 G_GAN_Feat: 0.788 G_ID: 0.090 G_Rec: 0.295 D_GP: 0.031 D_real: 0.918 D_fake: 0.938 
+(epoch: 433, iters: 2350, time: 0.063) G_GAN: 0.850 G_GAN_Feat: 1.063 G_ID: 0.124 G_Rec: 0.412 D_GP: 0.072 D_real: 0.975 D_fake: 0.243 
+(epoch: 433, iters: 2750, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.904 G_ID: 0.105 G_Rec: 0.324 D_GP: 0.046 D_real: 0.540 D_fake: 0.869 
+(epoch: 433, iters: 3150, time: 0.064) G_GAN: 0.814 G_GAN_Feat: 1.386 G_ID: 0.128 G_Rec: 0.517 D_GP: 0.047 D_real: 1.185 D_fake: 0.283 
+(epoch: 433, iters: 3550, time: 0.063) G_GAN: 0.189 G_GAN_Feat: 0.754 G_ID: 0.107 G_Rec: 0.307 D_GP: 0.034 D_real: 1.044 D_fake: 0.813 
+(epoch: 433, iters: 3950, time: 0.063) G_GAN: 0.921 G_GAN_Feat: 0.891 G_ID: 0.115 G_Rec: 0.446 D_GP: 0.034 D_real: 1.563 D_fake: 0.161 
+(epoch: 433, iters: 4350, time: 0.064) G_GAN: 0.150 G_GAN_Feat: 0.617 G_ID: 0.101 G_Rec: 0.273 D_GP: 0.027 D_real: 1.115 D_fake: 0.850 
+(epoch: 433, iters: 4750, time: 0.064) G_GAN: 0.262 G_GAN_Feat: 0.897 G_ID: 0.122 G_Rec: 0.492 D_GP: 0.041 D_real: 0.855 D_fake: 0.739 
+(epoch: 433, iters: 5150, time: 0.063) G_GAN: 0.089 G_GAN_Feat: 0.628 G_ID: 0.092 G_Rec: 0.296 D_GP: 0.032 D_real: 0.993 D_fake: 0.911 
+(epoch: 433, iters: 5550, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 0.849 G_ID: 0.111 G_Rec: 0.404 D_GP: 0.038 D_real: 1.104 D_fake: 0.585 
+(epoch: 433, iters: 5950, time: 0.064) G_GAN: -0.160 G_GAN_Feat: 0.635 G_ID: 0.106 G_Rec: 0.272 D_GP: 0.037 D_real: 0.740 D_fake: 1.160 
+(epoch: 433, iters: 6350, time: 0.064) G_GAN: 0.479 G_GAN_Feat: 0.934 G_ID: 0.118 G_Rec: 0.450 D_GP: 0.041 D_real: 1.089 D_fake: 0.529 
+(epoch: 433, iters: 6750, time: 0.063) G_GAN: 0.060 G_GAN_Feat: 0.655 G_ID: 0.101 G_Rec: 0.331 D_GP: 0.034 D_real: 0.924 D_fake: 0.940 
+(epoch: 433, iters: 7150, time: 0.063) G_GAN: 0.561 G_GAN_Feat: 0.967 G_ID: 0.114 G_Rec: 0.440 D_GP: 0.063 D_real: 0.890 D_fake: 0.459 
+(epoch: 433, iters: 7550, time: 0.063) G_GAN: 0.252 G_GAN_Feat: 0.770 G_ID: 0.107 G_Rec: 0.313 D_GP: 0.162 D_real: 0.708 D_fake: 0.757 
+(epoch: 433, iters: 7950, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.975 G_ID: 0.123 G_Rec: 0.416 D_GP: 0.046 D_real: 0.748 D_fake: 0.772 
+(epoch: 433, iters: 8350, time: 0.063) G_GAN: 0.149 G_GAN_Feat: 0.794 G_ID: 0.102 G_Rec: 0.333 D_GP: 0.044 D_real: 1.012 D_fake: 0.854 
+(epoch: 434, iters: 142, time: 0.063) G_GAN: 0.396 G_GAN_Feat: 0.971 G_ID: 0.131 G_Rec: 0.436 D_GP: 0.030 D_real: 1.037 D_fake: 0.610 
+(epoch: 434, iters: 542, time: 0.063) G_GAN: 0.264 G_GAN_Feat: 0.782 G_ID: 0.099 G_Rec: 0.309 D_GP: 0.040 D_real: 1.000 D_fake: 0.737 
+(epoch: 434, iters: 942, time: 0.064) G_GAN: 0.856 G_GAN_Feat: 0.977 G_ID: 0.106 G_Rec: 0.401 D_GP: 0.050 D_real: 1.326 D_fake: 0.184 
+(epoch: 434, iters: 1342, time: 0.063) G_GAN: 0.493 G_GAN_Feat: 0.798 G_ID: 0.085 G_Rec: 0.269 D_GP: 0.039 D_real: 0.919 D_fake: 0.513 
+(epoch: 434, iters: 1742, time: 0.063) G_GAN: 0.482 G_GAN_Feat: 1.127 G_ID: 0.124 G_Rec: 0.443 D_GP: 0.042 D_real: 0.246 D_fake: 0.526 
+(epoch: 434, iters: 2142, time: 0.063) G_GAN: 0.546 G_GAN_Feat: 0.823 G_ID: 0.110 G_Rec: 0.315 D_GP: 0.043 D_real: 1.387 D_fake: 0.493 
+(epoch: 434, iters: 2542, time: 0.064) G_GAN: 0.710 G_GAN_Feat: 1.067 G_ID: 0.120 G_Rec: 0.448 D_GP: 0.048 D_real: 1.075 D_fake: 0.317 
+(epoch: 434, iters: 2942, time: 0.064) G_GAN: 0.373 G_GAN_Feat: 0.797 G_ID: 0.107 G_Rec: 0.316 D_GP: 0.031 D_real: 1.072 D_fake: 0.632 
+(epoch: 434, iters: 3342, time: 0.063) G_GAN: 0.560 G_GAN_Feat: 0.952 G_ID: 0.125 G_Rec: 0.418 D_GP: 0.039 D_real: 1.220 D_fake: 0.490 
+(epoch: 434, iters: 3742, time: 0.063) G_GAN: -0.041 G_GAN_Feat: 0.772 G_ID: 0.093 G_Rec: 0.311 D_GP: 0.038 D_real: 0.881 D_fake: 1.041 
+(epoch: 434, iters: 4142, time: 0.064) G_GAN: 0.706 G_GAN_Feat: 1.127 G_ID: 0.104 G_Rec: 0.459 D_GP: 0.031 D_real: 1.344 D_fake: 0.347 
+(epoch: 434, iters: 4542, time: 0.063) G_GAN: 0.023 G_GAN_Feat: 0.774 G_ID: 0.110 G_Rec: 0.324 D_GP: 0.031 D_real: 0.925 D_fake: 0.978 
+(epoch: 434, iters: 4942, time: 0.063) G_GAN: 0.318 G_GAN_Feat: 0.935 G_ID: 0.122 G_Rec: 0.413 D_GP: 0.038 D_real: 0.914 D_fake: 0.684 
+(epoch: 434, iters: 5342, time: 0.063) G_GAN: 0.271 G_GAN_Feat: 0.786 G_ID: 0.108 G_Rec: 0.321 D_GP: 0.036 D_real: 1.025 D_fake: 0.729 
+(epoch: 434, iters: 5742, time: 0.063) G_GAN: 0.715 G_GAN_Feat: 1.093 G_ID: 0.117 G_Rec: 0.467 D_GP: 0.044 D_real: 1.114 D_fake: 0.339 
+(epoch: 434, iters: 6142, time: 0.063) G_GAN: 0.120 G_GAN_Feat: 0.740 G_ID: 0.087 G_Rec: 0.313 D_GP: 0.036 D_real: 0.924 D_fake: 0.880 
+(epoch: 434, iters: 6542, time: 0.063) G_GAN: 0.589 G_GAN_Feat: 1.213 G_ID: 0.126 G_Rec: 0.507 D_GP: 0.281 D_real: 0.396 D_fake: 0.436 
+(epoch: 434, iters: 6942, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 0.827 G_ID: 0.107 G_Rec: 0.321 D_GP: 0.040 D_real: 1.162 D_fake: 0.669 
+(epoch: 434, iters: 7342, time: 0.063) G_GAN: 0.857 G_GAN_Feat: 1.037 G_ID: 0.115 G_Rec: 0.425 D_GP: 0.054 D_real: 1.160 D_fake: 0.197 
+(epoch: 434, iters: 7742, time: 0.063) G_GAN: 0.656 G_GAN_Feat: 0.959 G_ID: 0.094 G_Rec: 0.338 D_GP: 0.093 D_real: 0.511 D_fake: 0.558 
+(epoch: 434, iters: 8142, time: 0.063) G_GAN: 0.565 G_GAN_Feat: 1.003 G_ID: 0.114 G_Rec: 0.440 D_GP: 0.034 D_real: 1.090 D_fake: 0.462 
+(epoch: 434, iters: 8542, time: 0.064) G_GAN: 0.223 G_GAN_Feat: 0.862 G_ID: 0.108 G_Rec: 0.300 D_GP: 0.042 D_real: 0.603 D_fake: 0.778 
+(epoch: 435, iters: 334, time: 0.063) G_GAN: 0.535 G_GAN_Feat: 1.015 G_ID: 0.114 G_Rec: 0.425 D_GP: 0.069 D_real: 0.719 D_fake: 0.485 
+(epoch: 435, iters: 734, time: 0.063) G_GAN: 0.480 G_GAN_Feat: 0.957 G_ID: 0.108 G_Rec: 0.331 D_GP: 0.037 D_real: 0.981 D_fake: 0.527 
+(epoch: 435, iters: 1134, time: 0.063) G_GAN: 0.606 G_GAN_Feat: 0.961 G_ID: 0.127 G_Rec: 0.416 D_GP: 0.040 D_real: 1.243 D_fake: 0.427 
+(epoch: 435, iters: 1534, time: 0.064) G_GAN: 0.026 G_GAN_Feat: 0.761 G_ID: 0.112 G_Rec: 0.349 D_GP: 0.035 D_real: 0.878 D_fake: 0.974 
+(epoch: 435, iters: 1934, time: 0.063) G_GAN: 0.188 G_GAN_Feat: 0.964 G_ID: 0.111 G_Rec: 0.445 D_GP: 0.040 D_real: 0.841 D_fake: 0.812 
+(epoch: 435, iters: 2334, time: 0.063) G_GAN: 0.173 G_GAN_Feat: 0.755 G_ID: 0.092 G_Rec: 0.320 D_GP: 0.034 D_real: 0.984 D_fake: 0.827 
+(epoch: 435, iters: 2734, time: 0.063) G_GAN: 0.573 G_GAN_Feat: 0.968 G_ID: 0.110 G_Rec: 0.505 D_GP: 0.033 D_real: 1.073 D_fake: 0.457 
+(epoch: 435, iters: 3134, time: 0.064) G_GAN: -0.100 G_GAN_Feat: 0.697 G_ID: 0.108 G_Rec: 0.310 D_GP: 0.027 D_real: 0.868 D_fake: 1.100 
+(epoch: 435, iters: 3534, time: 0.063) G_GAN: 0.448 G_GAN_Feat: 1.001 G_ID: 0.116 G_Rec: 0.509 D_GP: 0.035 D_real: 1.011 D_fake: 0.554 
+(epoch: 435, iters: 3934, time: 0.063) G_GAN: 0.410 G_GAN_Feat: 0.896 G_ID: 0.092 G_Rec: 0.351 D_GP: 0.197 D_real: 0.739 D_fake: 0.592 
+(epoch: 435, iters: 4334, time: 0.063) G_GAN: 0.658 G_GAN_Feat: 0.910 G_ID: 0.107 G_Rec: 0.385 D_GP: 0.043 D_real: 1.278 D_fake: 0.360 
+(epoch: 435, iters: 4734, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 0.826 G_ID: 0.096 G_Rec: 0.333 D_GP: 0.039 D_real: 1.444 D_fake: 0.560 
+(epoch: 435, iters: 5134, time: 0.063) G_GAN: 0.548 G_GAN_Feat: 1.077 G_ID: 0.106 G_Rec: 0.449 D_GP: 0.040 D_real: 0.765 D_fake: 0.465 
+(epoch: 435, iters: 5534, time: 0.063) G_GAN: 0.241 G_GAN_Feat: 0.723 G_ID: 0.083 G_Rec: 0.302 D_GP: 0.034 D_real: 1.140 D_fake: 0.760 
+(epoch: 435, iters: 5934, time: 0.063) G_GAN: 0.504 G_GAN_Feat: 0.899 G_ID: 0.121 G_Rec: 0.405 D_GP: 0.037 D_real: 1.064 D_fake: 0.512 
+(epoch: 435, iters: 6334, time: 0.064) G_GAN: 0.043 G_GAN_Feat: 0.746 G_ID: 0.097 G_Rec: 0.317 D_GP: 0.040 D_real: 0.832 D_fake: 0.957 
+(epoch: 435, iters: 6734, time: 0.063) G_GAN: 0.581 G_GAN_Feat: 1.014 G_ID: 0.115 G_Rec: 0.442 D_GP: 0.124 D_real: 0.903 D_fake: 0.460 
+(epoch: 435, iters: 7134, time: 0.063) G_GAN: 0.436 G_GAN_Feat: 0.849 G_ID: 0.097 G_Rec: 0.339 D_GP: 0.042 D_real: 0.996 D_fake: 0.576 
+(epoch: 435, iters: 7534, time: 0.063) G_GAN: 0.320 G_GAN_Feat: 0.941 G_ID: 0.124 G_Rec: 0.427 D_GP: 0.036 D_real: 0.958 D_fake: 0.680 
+(epoch: 435, iters: 7934, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.825 G_ID: 0.101 G_Rec: 0.303 D_GP: 0.066 D_real: 0.634 D_fake: 0.753 
+(epoch: 435, iters: 8334, time: 0.063) G_GAN: 0.159 G_GAN_Feat: 0.903 G_ID: 0.135 G_Rec: 0.507 D_GP: 0.032 D_real: 0.994 D_fake: 0.842 
+(epoch: 436, iters: 126, time: 0.063) G_GAN: 0.085 G_GAN_Feat: 0.645 G_ID: 0.096 G_Rec: 0.307 D_GP: 0.025 D_real: 1.012 D_fake: 0.917 
+(epoch: 436, iters: 526, time: 0.063) G_GAN: 0.388 G_GAN_Feat: 0.830 G_ID: 0.118 G_Rec: 0.423 D_GP: 0.031 D_real: 1.165 D_fake: 0.619 
+(epoch: 436, iters: 926, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.718 G_ID: 0.100 G_Rec: 0.320 D_GP: 0.039 D_real: 0.881 D_fake: 0.879 
+(epoch: 436, iters: 1326, time: 0.063) G_GAN: 0.225 G_GAN_Feat: 0.986 G_ID: 0.120 G_Rec: 0.454 D_GP: 0.073 D_real: 0.667 D_fake: 0.784 
+(epoch: 436, iters: 1726, time: 0.063) G_GAN: -0.241 G_GAN_Feat: 0.707 G_ID: 0.101 G_Rec: 0.307 D_GP: 0.038 D_real: 0.706 D_fake: 1.241 
+(epoch: 436, iters: 2126, time: 0.063) G_GAN: 0.487 G_GAN_Feat: 0.929 G_ID: 0.106 G_Rec: 0.422 D_GP: 0.049 D_real: 1.062 D_fake: 0.535 
+(epoch: 436, iters: 2526, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.647 G_ID: 0.090 G_Rec: 0.289 D_GP: 0.037 D_real: 1.150 D_fake: 0.781 
+(epoch: 436, iters: 2926, time: 0.063) G_GAN: 0.249 G_GAN_Feat: 0.926 G_ID: 0.118 G_Rec: 0.453 D_GP: 0.033 D_real: 0.925 D_fake: 0.755 
+(epoch: 436, iters: 3326, time: 0.063) G_GAN: -0.070 G_GAN_Feat: 0.826 G_ID: 0.111 G_Rec: 0.355 D_GP: 0.110 D_real: 0.385 D_fake: 1.070 
+(epoch: 436, iters: 3726, time: 0.063) G_GAN: 0.460 G_GAN_Feat: 0.925 G_ID: 0.111 G_Rec: 0.443 D_GP: 0.034 D_real: 1.107 D_fake: 0.545 
+(epoch: 436, iters: 4126, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.690 G_ID: 0.091 G_Rec: 0.322 D_GP: 0.029 D_real: 1.058 D_fake: 0.862 
+(epoch: 436, iters: 4526, time: 0.063) G_GAN: 0.352 G_GAN_Feat: 0.974 G_ID: 0.117 G_Rec: 0.476 D_GP: 0.038 D_real: 1.013 D_fake: 0.648 
+(epoch: 436, iters: 4926, time: 0.063) G_GAN: 0.307 G_GAN_Feat: 0.744 G_ID: 0.102 G_Rec: 0.378 D_GP: 0.039 D_real: 1.125 D_fake: 0.696 
+(epoch: 436, iters: 5326, time: 0.063) G_GAN: 0.712 G_GAN_Feat: 0.929 G_ID: 0.102 G_Rec: 0.428 D_GP: 0.038 D_real: 1.351 D_fake: 0.316 
+(epoch: 436, iters: 5726, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.788 G_ID: 0.115 G_Rec: 0.310 D_GP: 0.061 D_real: 1.031 D_fake: 0.594 
+(epoch: 436, iters: 6126, time: 0.063) G_GAN: 0.570 G_GAN_Feat: 0.983 G_ID: 0.113 G_Rec: 0.421 D_GP: 0.043 D_real: 1.022 D_fake: 0.441 
+(epoch: 436, iters: 6526, time: 0.063) G_GAN: 0.299 G_GAN_Feat: 0.709 G_ID: 0.089 G_Rec: 0.279 D_GP: 0.035 D_real: 1.136 D_fake: 0.701 
+(epoch: 436, iters: 6926, time: 0.063) G_GAN: 0.890 G_GAN_Feat: 0.996 G_ID: 0.126 G_Rec: 0.410 D_GP: 0.061 D_real: 1.257 D_fake: 0.184 
+(epoch: 436, iters: 7326, time: 0.064) G_GAN: 0.019 G_GAN_Feat: 0.862 G_ID: 0.093 G_Rec: 0.343 D_GP: 0.038 D_real: 0.656 D_fake: 0.981 
+(epoch: 436, iters: 7726, time: 0.063) G_GAN: 0.533 G_GAN_Feat: 1.205 G_ID: 0.117 G_Rec: 0.537 D_GP: 0.438 D_real: 0.445 D_fake: 0.482 
+(epoch: 436, iters: 8126, time: 0.063) G_GAN: 0.456 G_GAN_Feat: 0.919 G_ID: 0.092 G_Rec: 0.369 D_GP: 0.126 D_real: 0.656 D_fake: 0.555 
+(epoch: 436, iters: 8526, time: 0.063) G_GAN: 1.024 G_GAN_Feat: 0.951 G_ID: 0.114 G_Rec: 0.440 D_GP: 0.059 D_real: 1.508 D_fake: 0.107 
+(epoch: 437, iters: 318, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.960 G_ID: 0.097 G_Rec: 0.347 D_GP: 0.331 D_real: 0.378 D_fake: 0.775 
+(epoch: 437, iters: 718, time: 0.063) G_GAN: 0.675 G_GAN_Feat: 1.061 G_ID: 0.117 G_Rec: 0.402 D_GP: 0.037 D_real: 0.872 D_fake: 0.335 
+(epoch: 437, iters: 1118, time: 0.063) G_GAN: 0.124 G_GAN_Feat: 0.918 G_ID: 0.111 G_Rec: 0.334 D_GP: 0.058 D_real: 0.281 D_fake: 0.879 
+(epoch: 437, iters: 1518, time: 0.063) G_GAN: 0.913 G_GAN_Feat: 1.362 G_ID: 0.117 G_Rec: 0.511 D_GP: 0.077 D_real: 1.543 D_fake: 0.255 
+(epoch: 437, iters: 1918, time: 0.064) G_GAN: -0.207 G_GAN_Feat: 0.609 G_ID: 0.095 G_Rec: 0.312 D_GP: 0.026 D_real: 0.823 D_fake: 1.207 
+(epoch: 437, iters: 2318, time: 0.063) G_GAN: 0.515 G_GAN_Feat: 0.893 G_ID: 0.111 G_Rec: 0.451 D_GP: 0.026 D_real: 1.140 D_fake: 0.526 
+(epoch: 437, iters: 2718, time: 0.063) G_GAN: 0.043 G_GAN_Feat: 0.664 G_ID: 0.102 G_Rec: 0.328 D_GP: 0.033 D_real: 0.954 D_fake: 0.957 
+(epoch: 437, iters: 3118, time: 0.063) G_GAN: 0.078 G_GAN_Feat: 0.890 G_ID: 0.106 G_Rec: 0.439 D_GP: 0.048 D_real: 0.757 D_fake: 0.923 
+(epoch: 437, iters: 3518, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.619 G_ID: 0.092 G_Rec: 0.276 D_GP: 0.031 D_real: 0.989 D_fake: 0.990 
+(epoch: 437, iters: 3918, time: 0.063) G_GAN: 0.320 G_GAN_Feat: 0.874 G_ID: 0.113 G_Rec: 0.418 D_GP: 0.035 D_real: 1.012 D_fake: 0.691 
+(epoch: 437, iters: 4318, time: 0.063) G_GAN: 0.185 G_GAN_Feat: 0.697 G_ID: 0.093 G_Rec: 0.302 D_GP: 0.033 D_real: 1.074 D_fake: 0.817 
+(epoch: 437, iters: 4718, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 0.882 G_ID: 0.124 G_Rec: 0.376 D_GP: 0.041 D_real: 1.007 D_fake: 0.635 
+(epoch: 437, iters: 5118, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.805 G_ID: 0.118 G_Rec: 0.334 D_GP: 0.037 D_real: 0.942 D_fake: 0.851 
+(epoch: 437, iters: 5518, time: 0.063) G_GAN: 0.488 G_GAN_Feat: 0.849 G_ID: 0.118 G_Rec: 0.406 D_GP: 0.030 D_real: 1.282 D_fake: 0.515 
+(epoch: 437, iters: 5918, time: 0.063) G_GAN: 0.309 G_GAN_Feat: 0.647 G_ID: 0.087 G_Rec: 0.298 D_GP: 0.029 D_real: 1.234 D_fake: 0.692 
+(epoch: 437, iters: 6318, time: 0.063) G_GAN: 0.384 G_GAN_Feat: 0.879 G_ID: 0.117 G_Rec: 0.405 D_GP: 0.040 D_real: 1.017 D_fake: 0.622 
+(epoch: 437, iters: 6718, time: 0.064) G_GAN: 0.104 G_GAN_Feat: 0.620 G_ID: 0.093 G_Rec: 0.264 D_GP: 0.032 D_real: 1.029 D_fake: 0.896 
+(epoch: 437, iters: 7118, time: 0.063) G_GAN: 0.067 G_GAN_Feat: 0.987 G_ID: 0.116 G_Rec: 0.438 D_GP: 0.083 D_real: 0.571 D_fake: 0.934 
+(epoch: 437, iters: 7518, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.741 G_ID: 0.115 G_Rec: 0.314 D_GP: 0.045 D_real: 0.955 D_fake: 0.818 
+(epoch: 437, iters: 7918, time: 0.063) G_GAN: 0.639 G_GAN_Feat: 0.950 G_ID: 0.125 G_Rec: 0.445 D_GP: 0.032 D_real: 1.199 D_fake: 0.394 
+(epoch: 437, iters: 8318, time: 0.064) G_GAN: 0.213 G_GAN_Feat: 0.797 G_ID: 0.144 G_Rec: 0.314 D_GP: 0.037 D_real: 0.850 D_fake: 0.791 
+(epoch: 438, iters: 110, time: 0.063) G_GAN: 0.306 G_GAN_Feat: 0.929 G_ID: 0.119 G_Rec: 0.420 D_GP: 0.037 D_real: 1.014 D_fake: 0.694 
+(epoch: 438, iters: 510, time: 0.063) G_GAN: 0.132 G_GAN_Feat: 0.660 G_ID: 0.097 G_Rec: 0.287 D_GP: 0.029 D_real: 1.031 D_fake: 0.868 
+(epoch: 438, iters: 910, time: 0.063) G_GAN: 0.457 G_GAN_Feat: 0.853 G_ID: 0.135 G_Rec: 0.394 D_GP: 0.042 D_real: 1.140 D_fake: 0.557 
+(epoch: 438, iters: 1310, time: 0.064) G_GAN: 0.244 G_GAN_Feat: 0.778 G_ID: 0.083 G_Rec: 0.332 D_GP: 0.059 D_real: 1.072 D_fake: 0.756 
+(epoch: 438, iters: 1710, time: 0.063) G_GAN: 0.200 G_GAN_Feat: 1.002 G_ID: 0.100 G_Rec: 0.424 D_GP: 0.099 D_real: 0.523 D_fake: 0.803 
+(epoch: 438, iters: 2110, time: 0.063) G_GAN: 0.284 G_GAN_Feat: 0.764 G_ID: 0.101 G_Rec: 0.316 D_GP: 0.031 D_real: 1.075 D_fake: 0.716 
+(epoch: 438, iters: 2510, time: 0.063) G_GAN: 0.508 G_GAN_Feat: 0.976 G_ID: 0.112 G_Rec: 0.415 D_GP: 0.034 D_real: 1.106 D_fake: 0.497 
+(epoch: 438, iters: 2910, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.846 G_ID: 0.102 G_Rec: 0.334 D_GP: 0.044 D_real: 0.776 D_fake: 0.795 
+(epoch: 438, iters: 3310, time: 0.063) G_GAN: 0.524 G_GAN_Feat: 1.100 G_ID: 0.107 G_Rec: 0.420 D_GP: 0.127 D_real: 0.373 D_fake: 0.487 
+(epoch: 438, iters: 3710, time: 0.063) G_GAN: 0.332 G_GAN_Feat: 0.827 G_ID: 0.094 G_Rec: 0.301 D_GP: 0.032 D_real: 0.844 D_fake: 0.676 
+(epoch: 438, iters: 4110, time: 0.063) G_GAN: 0.495 G_GAN_Feat: 1.074 G_ID: 0.107 G_Rec: 0.399 D_GP: 0.061 D_real: 0.577 D_fake: 0.516 
+(epoch: 438, iters: 4510, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.746 G_ID: 0.089 G_Rec: 0.324 D_GP: 0.031 D_real: 0.968 D_fake: 0.900 
+(epoch: 438, iters: 4910, time: 0.063) G_GAN: 0.494 G_GAN_Feat: 1.052 G_ID: 0.115 G_Rec: 0.486 D_GP: 0.044 D_real: 0.939 D_fake: 0.546 
+(epoch: 438, iters: 5310, time: 0.063) G_GAN: 0.140 G_GAN_Feat: 0.855 G_ID: 0.098 G_Rec: 0.343 D_GP: 0.163 D_real: 0.651 D_fake: 0.860 
+(epoch: 438, iters: 5710, time: 0.063) G_GAN: 0.595 G_GAN_Feat: 1.098 G_ID: 0.126 G_Rec: 0.490 D_GP: 0.085 D_real: 0.756 D_fake: 0.423 
+(epoch: 438, iters: 6110, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.933 G_ID: 0.098 G_Rec: 0.338 D_GP: 0.191 D_real: 0.423 D_fake: 0.932 
+(epoch: 438, iters: 6510, time: 0.063) G_GAN: 0.268 G_GAN_Feat: 0.861 G_ID: 0.107 G_Rec: 0.419 D_GP: 0.031 D_real: 1.021 D_fake: 0.733 
+(epoch: 438, iters: 6910, time: 0.063) G_GAN: -0.022 G_GAN_Feat: 0.658 G_ID: 0.110 G_Rec: 0.300 D_GP: 0.029 D_real: 0.877 D_fake: 1.022 
+(epoch: 438, iters: 7310, time: 0.063) G_GAN: 0.106 G_GAN_Feat: 0.856 G_ID: 0.124 G_Rec: 0.429 D_GP: 0.035 D_real: 0.925 D_fake: 0.897 
+(epoch: 438, iters: 7710, time: 0.064) G_GAN: 0.052 G_GAN_Feat: 0.709 G_ID: 0.098 G_Rec: 0.359 D_GP: 0.053 D_real: 0.930 D_fake: 0.950 
+(epoch: 438, iters: 8110, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.920 G_ID: 0.130 G_Rec: 0.474 D_GP: 0.043 D_real: 0.728 D_fake: 0.818 
+(epoch: 438, iters: 8510, time: 0.063) G_GAN: -0.089 G_GAN_Feat: 0.627 G_ID: 0.120 G_Rec: 0.309 D_GP: 0.030 D_real: 0.789 D_fake: 1.089 
+(epoch: 439, iters: 302, time: 0.063) G_GAN: 0.365 G_GAN_Feat: 0.870 G_ID: 0.118 G_Rec: 0.423 D_GP: 0.042 D_real: 1.014 D_fake: 0.648 
+(epoch: 439, iters: 702, time: 0.064) G_GAN: -0.253 G_GAN_Feat: 0.731 G_ID: 0.096 G_Rec: 0.336 D_GP: 0.046 D_real: 0.570 D_fake: 1.253 
+(epoch: 439, iters: 1102, time: 0.063) G_GAN: 0.374 G_GAN_Feat: 0.919 G_ID: 0.132 G_Rec: 0.454 D_GP: 0.052 D_real: 0.924 D_fake: 0.640 
+(epoch: 439, iters: 1502, time: 0.063) G_GAN: 0.011 G_GAN_Feat: 0.680 G_ID: 0.083 G_Rec: 0.308 D_GP: 0.048 D_real: 0.865 D_fake: 0.989 
+(epoch: 439, iters: 1902, time: 0.063) G_GAN: 0.419 G_GAN_Feat: 0.873 G_ID: 0.126 G_Rec: 0.421 D_GP: 0.032 D_real: 0.981 D_fake: 0.593 
+(epoch: 439, iters: 2302, time: 0.063) G_GAN: 0.080 G_GAN_Feat: 0.687 G_ID: 0.114 G_Rec: 0.298 D_GP: 0.038 D_real: 0.903 D_fake: 0.920 
+(epoch: 439, iters: 2702, time: 0.063) G_GAN: 0.376 G_GAN_Feat: 0.948 G_ID: 0.137 G_Rec: 0.437 D_GP: 0.050 D_real: 0.969 D_fake: 0.633 
+(epoch: 439, iters: 3102, time: 0.063) G_GAN: 0.314 G_GAN_Feat: 0.647 G_ID: 0.089 G_Rec: 0.275 D_GP: 0.035 D_real: 1.233 D_fake: 0.687 
+(epoch: 439, iters: 3502, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.959 G_ID: 0.125 G_Rec: 0.449 D_GP: 0.059 D_real: 0.833 D_fake: 0.661 
+(epoch: 439, iters: 3902, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 0.737 G_ID: 0.090 G_Rec: 0.301 D_GP: 0.063 D_real: 0.994 D_fake: 0.640 
+(epoch: 439, iters: 4302, time: 0.063) G_GAN: 0.400 G_GAN_Feat: 0.911 G_ID: 0.123 G_Rec: 0.410 D_GP: 0.037 D_real: 1.001 D_fake: 0.605 
+(epoch: 439, iters: 4702, time: 0.063) G_GAN: 0.074 G_GAN_Feat: 0.691 G_ID: 0.094 G_Rec: 0.314 D_GP: 0.025 D_real: 0.999 D_fake: 0.926 
+(epoch: 439, iters: 5102, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 0.976 G_ID: 0.108 G_Rec: 0.446 D_GP: 0.052 D_real: 0.905 D_fake: 0.675 
+(epoch: 439, iters: 5502, time: 0.063) G_GAN: 0.121 G_GAN_Feat: 0.667 G_ID: 0.099 G_Rec: 0.281 D_GP: 0.036 D_real: 1.013 D_fake: 0.879 
+(epoch: 439, iters: 5902, time: 0.063) G_GAN: 0.575 G_GAN_Feat: 0.925 G_ID: 0.113 G_Rec: 0.402 D_GP: 0.061 D_real: 1.159 D_fake: 0.465 
+(epoch: 439, iters: 6302, time: 0.063) G_GAN: 0.133 G_GAN_Feat: 0.766 G_ID: 0.094 G_Rec: 0.297 D_GP: 0.044 D_real: 0.897 D_fake: 0.867 
+(epoch: 439, iters: 6702, time: 0.064) G_GAN: 0.602 G_GAN_Feat: 1.025 G_ID: 0.134 G_Rec: 0.469 D_GP: 0.038 D_real: 1.087 D_fake: 0.422 
+(epoch: 439, iters: 7102, time: 0.063) G_GAN: -0.103 G_GAN_Feat: 0.795 G_ID: 0.097 G_Rec: 0.321 D_GP: 0.049 D_real: 0.567 D_fake: 1.103 
+(epoch: 439, iters: 7502, time: 0.063) G_GAN: 0.327 G_GAN_Feat: 1.029 G_ID: 0.111 G_Rec: 0.463 D_GP: 0.100 D_real: 0.797 D_fake: 0.674 
+(epoch: 439, iters: 7902, time: 0.063) G_GAN: 0.163 G_GAN_Feat: 0.696 G_ID: 0.092 G_Rec: 0.267 D_GP: 0.051 D_real: 0.950 D_fake: 0.837 
+(epoch: 439, iters: 8302, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 1.052 G_ID: 0.125 G_Rec: 0.460 D_GP: 0.051 D_real: 0.908 D_fake: 0.441 
+(epoch: 439, iters: 8702, time: 0.063) G_GAN: -0.022 G_GAN_Feat: 0.828 G_ID: 0.103 G_Rec: 0.342 D_GP: 0.039 D_real: 0.675 D_fake: 1.022 
+(epoch: 440, iters: 494, time: 0.063) G_GAN: 0.201 G_GAN_Feat: 1.013 G_ID: 0.138 G_Rec: 0.460 D_GP: 0.086 D_real: 0.597 D_fake: 0.801 
+(epoch: 440, iters: 894, time: 0.063) G_GAN: 0.149 G_GAN_Feat: 0.757 G_ID: 0.093 G_Rec: 0.328 D_GP: 0.041 D_real: 0.920 D_fake: 0.851 
+(epoch: 440, iters: 1294, time: 0.064) G_GAN: 0.396 G_GAN_Feat: 1.011 G_ID: 0.114 G_Rec: 0.448 D_GP: 0.041 D_real: 0.734 D_fake: 0.604 
+(epoch: 440, iters: 1694, time: 0.063) G_GAN: 0.189 G_GAN_Feat: 0.779 G_ID: 0.108 G_Rec: 0.332 D_GP: 0.032 D_real: 1.048 D_fake: 0.811 
+(epoch: 440, iters: 2094, time: 0.063) G_GAN: 0.289 G_GAN_Feat: 1.100 G_ID: 0.120 G_Rec: 0.480 D_GP: 0.347 D_real: 0.258 D_fake: 0.716 
+(epoch: 440, iters: 2494, time: 0.063) G_GAN: 0.099 G_GAN_Feat: 0.882 G_ID: 0.103 G_Rec: 0.347 D_GP: 0.073 D_real: 0.295 D_fake: 0.901 
+(epoch: 440, iters: 2894, time: 0.064) G_GAN: 0.504 G_GAN_Feat: 0.984 G_ID: 0.104 G_Rec: 0.447 D_GP: 0.038 D_real: 1.062 D_fake: 0.506 
+(epoch: 440, iters: 3294, time: 0.063) G_GAN: 0.367 G_GAN_Feat: 0.860 G_ID: 0.093 G_Rec: 0.364 D_GP: 0.036 D_real: 1.078 D_fake: 0.635 
+(epoch: 440, iters: 3694, time: 0.063) G_GAN: 0.767 G_GAN_Feat: 1.168 G_ID: 0.123 G_Rec: 0.461 D_GP: 0.132 D_real: 0.501 D_fake: 0.284 
+(epoch: 440, iters: 4094, time: 0.063) G_GAN: 0.289 G_GAN_Feat: 0.749 G_ID: 0.097 G_Rec: 0.311 D_GP: 0.033 D_real: 1.287 D_fake: 0.716 
+(epoch: 440, iters: 4494, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 0.874 G_ID: 0.135 G_Rec: 0.417 D_GP: 0.028 D_real: 1.158 D_fake: 0.562 
+(epoch: 440, iters: 4894, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 0.726 G_ID: 0.091 G_Rec: 0.317 D_GP: 0.048 D_real: 0.992 D_fake: 0.810 
+(epoch: 440, iters: 5294, time: 0.063) G_GAN: 0.326 G_GAN_Feat: 0.939 G_ID: 0.122 G_Rec: 0.452 D_GP: 0.054 D_real: 0.902 D_fake: 0.677 
+(epoch: 440, iters: 5694, time: 0.063) G_GAN: 0.146 G_GAN_Feat: 0.703 G_ID: 0.091 G_Rec: 0.323 D_GP: 0.031 D_real: 0.960 D_fake: 0.855 
+(epoch: 440, iters: 6094, time: 0.064) G_GAN: 0.526 G_GAN_Feat: 0.981 G_ID: 0.109 G_Rec: 0.473 D_GP: 0.028 D_real: 1.081 D_fake: 0.484 
+(epoch: 440, iters: 6494, time: 0.063) G_GAN: -0.093 G_GAN_Feat: 0.730 G_ID: 0.104 G_Rec: 0.330 D_GP: 0.064 D_real: 0.667 D_fake: 1.093 
+(epoch: 440, iters: 6894, time: 0.063) G_GAN: 0.470 G_GAN_Feat: 1.038 G_ID: 0.131 G_Rec: 0.448 D_GP: 0.096 D_real: 0.605 D_fake: 0.545 
+(epoch: 440, iters: 7294, time: 0.063) G_GAN: 0.203 G_GAN_Feat: 0.779 G_ID: 0.095 G_Rec: 0.299 D_GP: 0.040 D_real: 0.914 D_fake: 0.797 
+(epoch: 440, iters: 7694, time: 0.064) G_GAN: 0.409 G_GAN_Feat: 0.907 G_ID: 0.112 G_Rec: 0.413 D_GP: 0.034 D_real: 1.054 D_fake: 0.592 
+(epoch: 440, iters: 8094, time: 0.063) G_GAN: 0.125 G_GAN_Feat: 0.714 G_ID: 0.090 G_Rec: 0.273 D_GP: 0.032 D_real: 1.004 D_fake: 0.875 
+(epoch: 440, iters: 8494, time: 0.063) G_GAN: 0.477 G_GAN_Feat: 0.932 G_ID: 0.164 G_Rec: 0.418 D_GP: 0.035 D_real: 1.011 D_fake: 0.524 
+(epoch: 441, iters: 286, time: 0.063) G_GAN: 0.486 G_GAN_Feat: 0.802 G_ID: 0.102 G_Rec: 0.335 D_GP: 0.046 D_real: 1.235 D_fake: 0.523 
+(epoch: 441, iters: 686, time: 0.064) G_GAN: 0.856 G_GAN_Feat: 1.025 G_ID: 0.133 G_Rec: 0.467 D_GP: 0.034 D_real: 1.165 D_fake: 0.227 
+(epoch: 441, iters: 1086, time: 0.063) G_GAN: 0.232 G_GAN_Feat: 0.836 G_ID: 0.123 G_Rec: 0.326 D_GP: 0.063 D_real: 0.820 D_fake: 0.779 
+(epoch: 441, iters: 1486, time: 0.063) G_GAN: 0.412 G_GAN_Feat: 0.947 G_ID: 0.124 G_Rec: 0.424 D_GP: 0.030 D_real: 1.010 D_fake: 0.626 
+(epoch: 441, iters: 1886, time: 0.063) G_GAN: 0.036 G_GAN_Feat: 0.775 G_ID: 0.087 G_Rec: 0.314 D_GP: 0.034 D_real: 0.811 D_fake: 0.968 
+(epoch: 441, iters: 2286, time: 0.064) G_GAN: 0.392 G_GAN_Feat: 0.996 G_ID: 0.125 G_Rec: 0.439 D_GP: 0.078 D_real: 0.839 D_fake: 0.614 
+(epoch: 441, iters: 2686, time: 0.063) G_GAN: -0.070 G_GAN_Feat: 0.710 G_ID: 0.104 G_Rec: 0.280 D_GP: 0.028 D_real: 0.831 D_fake: 1.070 
+(epoch: 441, iters: 3086, time: 0.063) G_GAN: 0.618 G_GAN_Feat: 1.185 G_ID: 0.128 G_Rec: 0.455 D_GP: 0.060 D_real: 0.390 D_fake: 0.427 
+(epoch: 441, iters: 3486, time: 0.063) G_GAN: 0.542 G_GAN_Feat: 0.791 G_ID: 0.101 G_Rec: 0.304 D_GP: 0.063 D_real: 1.164 D_fake: 0.470 
+(epoch: 441, iters: 3886, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 1.193 G_ID: 0.121 G_Rec: 0.445 D_GP: 0.147 D_real: 0.259 D_fake: 0.651 
+(epoch: 441, iters: 4286, time: 0.063) G_GAN: 0.513 G_GAN_Feat: 0.783 G_ID: 0.106 G_Rec: 0.300 D_GP: 0.041 D_real: 1.226 D_fake: 0.494 
+(epoch: 441, iters: 4686, time: 0.063) G_GAN: 0.529 G_GAN_Feat: 0.914 G_ID: 0.105 G_Rec: 0.442 D_GP: 0.029 D_real: 1.225 D_fake: 0.520 
+(epoch: 441, iters: 5086, time: 0.063) G_GAN: -0.083 G_GAN_Feat: 0.699 G_ID: 0.102 G_Rec: 0.295 D_GP: 0.030 D_real: 0.819 D_fake: 1.083 
+(epoch: 441, iters: 5486, time: 0.064) G_GAN: 0.056 G_GAN_Feat: 0.905 G_ID: 0.119 G_Rec: 0.443 D_GP: 0.038 D_real: 0.719 D_fake: 0.945 
+(epoch: 441, iters: 5886, time: 0.063) G_GAN: -0.166 G_GAN_Feat: 0.712 G_ID: 0.108 G_Rec: 0.314 D_GP: 0.036 D_real: 0.707 D_fake: 1.166 
+(epoch: 441, iters: 6286, time: 0.063) G_GAN: -0.010 G_GAN_Feat: 0.992 G_ID: 0.134 G_Rec: 0.464 D_GP: 0.109 D_real: 0.509 D_fake: 1.010 
+(epoch: 441, iters: 6686, time: 0.063) G_GAN: -0.204 G_GAN_Feat: 0.697 G_ID: 0.096 G_Rec: 0.315 D_GP: 0.043 D_real: 0.666 D_fake: 1.204 
+(epoch: 441, iters: 7086, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 1.031 G_ID: 0.115 G_Rec: 0.475 D_GP: 0.069 D_real: 0.796 D_fake: 0.688 
+(epoch: 441, iters: 7486, time: 0.063) G_GAN: -0.298 G_GAN_Feat: 0.911 G_ID: 0.114 G_Rec: 0.371 D_GP: 0.127 D_real: 0.204 D_fake: 1.298 
+(epoch: 441, iters: 7886, time: 0.063) G_GAN: 0.466 G_GAN_Feat: 0.986 G_ID: 0.121 G_Rec: 0.480 D_GP: 0.073 D_real: 0.839 D_fake: 0.574 
+(epoch: 441, iters: 8286, time: 0.063) G_GAN: 0.230 G_GAN_Feat: 0.698 G_ID: 0.100 G_Rec: 0.276 D_GP: 0.042 D_real: 1.094 D_fake: 0.772 
+(epoch: 441, iters: 8686, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.994 G_ID: 0.118 G_Rec: 0.492 D_GP: 0.038 D_real: 1.025 D_fake: 0.579 
+(epoch: 442, iters: 478, time: 0.063) G_GAN: 0.271 G_GAN_Feat: 0.813 G_ID: 0.110 G_Rec: 0.320 D_GP: 0.111 D_real: 0.778 D_fake: 0.735 
+(epoch: 442, iters: 878, time: 0.063) G_GAN: 0.926 G_GAN_Feat: 0.994 G_ID: 0.114 G_Rec: 0.452 D_GP: 0.047 D_real: 1.296 D_fake: 0.203 
+(epoch: 442, iters: 1278, time: 0.063) G_GAN: 0.022 G_GAN_Feat: 0.718 G_ID: 0.116 G_Rec: 0.366 D_GP: 0.028 D_real: 0.975 D_fake: 0.978 
+(epoch: 442, iters: 1678, time: 0.064) G_GAN: 0.342 G_GAN_Feat: 0.888 G_ID: 0.118 G_Rec: 0.430 D_GP: 0.026 D_real: 1.124 D_fake: 0.660 
+(epoch: 442, iters: 2078, time: 0.063) G_GAN: 0.111 G_GAN_Feat: 0.676 G_ID: 0.106 G_Rec: 0.324 D_GP: 0.030 D_real: 1.039 D_fake: 0.890 
+(epoch: 442, iters: 2478, time: 0.063) G_GAN: 0.161 G_GAN_Feat: 0.838 G_ID: 0.106 G_Rec: 0.384 D_GP: 0.036 D_real: 0.958 D_fake: 0.839 
+(epoch: 442, iters: 2878, time: 0.063) G_GAN: 0.236 G_GAN_Feat: 0.637 G_ID: 0.095 G_Rec: 0.260 D_GP: 0.032 D_real: 1.133 D_fake: 0.764 
+(epoch: 442, iters: 3278, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.880 G_ID: 0.143 G_Rec: 0.392 D_GP: 0.032 D_real: 0.883 D_fake: 0.840 
+(epoch: 442, iters: 3678, time: 0.063) G_GAN: -0.039 G_GAN_Feat: 0.803 G_ID: 0.097 G_Rec: 0.347 D_GP: 0.073 D_real: 0.627 D_fake: 1.039 
+(epoch: 442, iters: 4078, time: 0.063) G_GAN: 0.537 G_GAN_Feat: 0.981 G_ID: 0.114 G_Rec: 0.430 D_GP: 0.044 D_real: 1.037 D_fake: 0.470 
+(epoch: 442, iters: 4478, time: 0.063) G_GAN: 0.335 G_GAN_Feat: 0.735 G_ID: 0.079 G_Rec: 0.298 D_GP: 0.033 D_real: 1.169 D_fake: 0.665 
+(epoch: 442, iters: 4878, time: 0.064) G_GAN: 0.628 G_GAN_Feat: 1.020 G_ID: 0.101 G_Rec: 0.420 D_GP: 0.094 D_real: 0.998 D_fake: 0.396 
+(epoch: 442, iters: 5278, time: 0.063) G_GAN: 0.062 G_GAN_Feat: 0.736 G_ID: 0.109 G_Rec: 0.294 D_GP: 0.031 D_real: 0.874 D_fake: 0.938 
+(epoch: 442, iters: 5678, time: 0.063) G_GAN: 0.503 G_GAN_Feat: 1.020 G_ID: 0.128 G_Rec: 0.431 D_GP: 0.053 D_real: 0.955 D_fake: 0.512 
+(epoch: 442, iters: 6078, time: 0.063) G_GAN: 0.208 G_GAN_Feat: 0.759 G_ID: 0.086 G_Rec: 0.303 D_GP: 0.047 D_real: 0.900 D_fake: 0.792 
+(epoch: 442, iters: 6478, time: 0.064) G_GAN: 0.646 G_GAN_Feat: 0.893 G_ID: 0.122 G_Rec: 0.366 D_GP: 0.032 D_real: 1.351 D_fake: 0.370 
+(epoch: 442, iters: 6878, time: 0.063) G_GAN: 0.409 G_GAN_Feat: 0.970 G_ID: 0.121 G_Rec: 0.353 D_GP: 0.050 D_real: 1.071 D_fake: 0.657 
+(epoch: 442, iters: 7278, time: 0.063) G_GAN: 0.676 G_GAN_Feat: 1.024 G_ID: 0.104 G_Rec: 0.455 D_GP: 0.039 D_real: 1.304 D_fake: 0.455 
+(epoch: 442, iters: 7678, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.804 G_ID: 0.094 G_Rec: 0.317 D_GP: 0.043 D_real: 1.062 D_fake: 0.762 
+(epoch: 442, iters: 8078, time: 0.064) G_GAN: 0.735 G_GAN_Feat: 1.119 G_ID: 0.130 G_Rec: 0.508 D_GP: 0.046 D_real: 0.930 D_fake: 0.403 
+(epoch: 442, iters: 8478, time: 0.063) G_GAN: 0.104 G_GAN_Feat: 0.774 G_ID: 0.091 G_Rec: 0.296 D_GP: 0.028 D_real: 0.984 D_fake: 0.896 
+(epoch: 443, iters: 270, time: 0.063) G_GAN: 0.572 G_GAN_Feat: 1.013 G_ID: 0.120 G_Rec: 0.452 D_GP: 0.036 D_real: 1.103 D_fake: 0.433 
+(epoch: 443, iters: 670, time: 0.063) G_GAN: 0.204 G_GAN_Feat: 1.287 G_ID: 0.130 G_Rec: 0.430 D_GP: 0.212 D_real: 0.909 D_fake: 0.833 
+(epoch: 443, iters: 1070, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.883 G_ID: 0.140 G_Rec: 0.457 D_GP: 0.028 D_real: 0.964 D_fake: 0.843 
+(epoch: 443, iters: 1470, time: 0.063) G_GAN: -0.003 G_GAN_Feat: 0.691 G_ID: 0.107 G_Rec: 0.304 D_GP: 0.029 D_real: 0.837 D_fake: 1.003 
+(epoch: 443, iters: 1870, time: 0.063) G_GAN: 0.197 G_GAN_Feat: 0.978 G_ID: 0.115 G_Rec: 0.488 D_GP: 0.038 D_real: 0.740 D_fake: 0.810 
+(epoch: 443, iters: 2270, time: 0.063) G_GAN: 0.021 G_GAN_Feat: 0.721 G_ID: 0.103 G_Rec: 0.287 D_GP: 0.037 D_real: 0.836 D_fake: 0.979 
+(epoch: 443, iters: 2670, time: 0.064) G_GAN: 0.509 G_GAN_Feat: 0.969 G_ID: 0.124 G_Rec: 0.402 D_GP: 0.041 D_real: 1.108 D_fake: 0.533 
+(epoch: 443, iters: 3070, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.850 G_ID: 0.091 G_Rec: 0.299 D_GP: 0.037 D_real: 0.791 D_fake: 0.817 
+(epoch: 443, iters: 3470, time: 0.063) G_GAN: 0.689 G_GAN_Feat: 0.967 G_ID: 0.099 G_Rec: 0.413 D_GP: 0.038 D_real: 1.382 D_fake: 0.331 
+(epoch: 443, iters: 3870, time: 0.063) G_GAN: 0.182 G_GAN_Feat: 0.845 G_ID: 0.111 G_Rec: 0.305 D_GP: 0.039 D_real: 0.832 D_fake: 0.819 
+(epoch: 443, iters: 4270, time: 0.064) G_GAN: 0.892 G_GAN_Feat: 1.015 G_ID: 0.114 G_Rec: 0.432 D_GP: 0.043 D_real: 1.315 D_fake: 0.179 
+(epoch: 443, iters: 4670, time: 0.063) G_GAN: 0.237 G_GAN_Feat: 0.616 G_ID: 0.094 G_Rec: 0.306 D_GP: 0.026 D_real: 1.130 D_fake: 0.763 
+(epoch: 443, iters: 5070, time: 0.063) G_GAN: -0.049 G_GAN_Feat: 0.902 G_ID: 0.121 G_Rec: 0.447 D_GP: 0.050 D_real: 0.605 D_fake: 1.049 
+(epoch: 443, iters: 5470, time: 0.063) G_GAN: -0.037 G_GAN_Feat: 0.687 G_ID: 0.113 G_Rec: 0.326 D_GP: 0.045 D_real: 0.834 D_fake: 1.037 
+(epoch: 443, iters: 5870, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.924 G_ID: 0.139 G_Rec: 0.484 D_GP: 0.043 D_real: 0.834 D_fake: 0.706 
+(epoch: 443, iters: 6270, time: 0.063) G_GAN: 0.159 G_GAN_Feat: 0.698 G_ID: 0.096 G_Rec: 0.304 D_GP: 0.068 D_real: 0.953 D_fake: 0.843 
+(epoch: 443, iters: 6670, time: 0.063) G_GAN: 0.435 G_GAN_Feat: 1.017 G_ID: 0.133 G_Rec: 0.477 D_GP: 0.153 D_real: 0.558 D_fake: 0.580 
+(epoch: 443, iters: 7070, time: 0.063) G_GAN: 0.335 G_GAN_Feat: 0.759 G_ID: 0.089 G_Rec: 0.321 D_GP: 0.046 D_real: 1.280 D_fake: 0.669 
+(epoch: 443, iters: 7470, time: 0.063) G_GAN: 0.476 G_GAN_Feat: 0.892 G_ID: 0.129 G_Rec: 0.380 D_GP: 0.041 D_real: 1.070 D_fake: 0.536 
+(epoch: 443, iters: 7870, time: 0.063) G_GAN: 0.444 G_GAN_Feat: 0.751 G_ID: 0.100 G_Rec: 0.320 D_GP: 0.048 D_real: 1.253 D_fake: 0.571 
+(epoch: 443, iters: 8270, time: 0.063) G_GAN: 0.129 G_GAN_Feat: 0.941 G_ID: 0.123 G_Rec: 0.433 D_GP: 0.040 D_real: 0.771 D_fake: 0.871 
+(epoch: 443, iters: 8670, time: 0.064) G_GAN: -0.131 G_GAN_Feat: 0.714 G_ID: 0.113 G_Rec: 0.297 D_GP: 0.045 D_real: 0.809 D_fake: 1.131 
+(epoch: 444, iters: 462, time: 0.063) G_GAN: 0.182 G_GAN_Feat: 0.929 G_ID: 0.131 G_Rec: 0.434 D_GP: 0.030 D_real: 0.882 D_fake: 0.818 
+(epoch: 444, iters: 862, time: 0.063) G_GAN: 0.296 G_GAN_Feat: 0.913 G_ID: 0.105 G_Rec: 0.351 D_GP: 0.211 D_real: 0.403 D_fake: 0.731 
+(epoch: 444, iters: 1262, time: 0.063) G_GAN: 0.215 G_GAN_Feat: 1.019 G_ID: 0.146 G_Rec: 0.442 D_GP: 0.044 D_real: 0.497 D_fake: 0.788 
+(epoch: 444, iters: 1662, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 1.010 G_ID: 0.112 G_Rec: 0.361 D_GP: 0.205 D_real: 0.282 D_fake: 0.771 
+(epoch: 444, iters: 2062, time: 0.063) G_GAN: 0.571 G_GAN_Feat: 0.932 G_ID: 0.119 G_Rec: 0.378 D_GP: 0.033 D_real: 1.087 D_fake: 0.469 
+(epoch: 444, iters: 2462, time: 0.063) G_GAN: 0.047 G_GAN_Feat: 0.899 G_ID: 0.105 G_Rec: 0.328 D_GP: 0.107 D_real: 0.378 D_fake: 0.960 
+(epoch: 444, iters: 2862, time: 0.063) G_GAN: 0.388 G_GAN_Feat: 1.069 G_ID: 0.137 G_Rec: 0.389 D_GP: 0.044 D_real: 0.844 D_fake: 0.614 
+(epoch: 444, iters: 3262, time: 0.064) G_GAN: 0.135 G_GAN_Feat: 0.780 G_ID: 0.096 G_Rec: 0.318 D_GP: 0.036 D_real: 0.867 D_fake: 0.865 
+(epoch: 444, iters: 3662, time: 0.064) G_GAN: 0.589 G_GAN_Feat: 1.128 G_ID: 0.117 G_Rec: 0.497 D_GP: 0.094 D_real: 0.765 D_fake: 0.423 
+(epoch: 444, iters: 4062, time: 0.064) G_GAN: -0.105 G_GAN_Feat: 0.879 G_ID: 0.095 G_Rec: 0.339 D_GP: 0.422 D_real: 0.351 D_fake: 1.105 
+(epoch: 444, iters: 4462, time: 0.064) G_GAN: 0.247 G_GAN_Feat: 1.028 G_ID: 0.133 G_Rec: 0.412 D_GP: 0.036 D_real: 1.095 D_fake: 0.755 
+(epoch: 444, iters: 4862, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 0.772 G_ID: 0.095 G_Rec: 0.291 D_GP: 0.029 D_real: 1.238 D_fake: 0.542 
+(epoch: 444, iters: 5262, time: 0.064) G_GAN: 0.366 G_GAN_Feat: 0.903 G_ID: 0.104 G_Rec: 0.381 D_GP: 0.035 D_real: 0.980 D_fake: 0.635 
+(epoch: 444, iters: 5662, time: 0.063) G_GAN: 0.263 G_GAN_Feat: 0.828 G_ID: 0.095 G_Rec: 0.293 D_GP: 0.033 D_real: 0.815 D_fake: 0.738 
+(epoch: 444, iters: 6062, time: 0.064) G_GAN: 0.323 G_GAN_Feat: 0.961 G_ID: 0.114 G_Rec: 0.459 D_GP: 0.034 D_real: 0.949 D_fake: 0.678 
+(epoch: 444, iters: 6462, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.758 G_ID: 0.095 G_Rec: 0.337 D_GP: 0.032 D_real: 1.246 D_fake: 0.665 
+(epoch: 444, iters: 6862, time: 0.064) G_GAN: 0.075 G_GAN_Feat: 0.891 G_ID: 0.117 G_Rec: 0.420 D_GP: 0.033 D_real: 0.702 D_fake: 0.925 
+(epoch: 444, iters: 7262, time: 0.064) G_GAN: -0.182 G_GAN_Feat: 0.697 G_ID: 0.123 G_Rec: 0.315 D_GP: 0.033 D_real: 0.702 D_fake: 1.182 
+(epoch: 444, iters: 7662, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.857 G_ID: 0.115 G_Rec: 0.397 D_GP: 0.034 D_real: 0.929 D_fake: 0.839 
+(epoch: 444, iters: 8062, time: 0.064) G_GAN: 0.097 G_GAN_Feat: 0.758 G_ID: 0.100 G_Rec: 0.346 D_GP: 0.050 D_real: 0.865 D_fake: 0.903 
+(epoch: 444, iters: 8462, time: 0.064) G_GAN: 0.405 G_GAN_Feat: 0.816 G_ID: 0.125 G_Rec: 0.374 D_GP: 0.034 D_real: 1.152 D_fake: 0.602 
+(epoch: 445, iters: 254, time: 0.064) G_GAN: -0.104 G_GAN_Feat: 0.699 G_ID: 0.100 G_Rec: 0.314 D_GP: 0.031 D_real: 0.790 D_fake: 1.104 
+(epoch: 445, iters: 654, time: 0.064) G_GAN: 0.202 G_GAN_Feat: 1.007 G_ID: 0.119 G_Rec: 0.458 D_GP: 0.054 D_real: 0.712 D_fake: 0.799 
+(epoch: 445, iters: 1054, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.752 G_ID: 0.089 G_Rec: 0.300 D_GP: 0.042 D_real: 1.175 D_fake: 0.606 
+(epoch: 445, iters: 1454, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 0.935 G_ID: 0.114 G_Rec: 0.424 D_GP: 0.039 D_real: 1.195 D_fake: 0.379 
+(epoch: 445, iters: 1854, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 0.744 G_ID: 0.104 G_Rec: 0.338 D_GP: 0.034 D_real: 1.378 D_fake: 0.517 
+(epoch: 445, iters: 2254, time: 0.063) G_GAN: 0.552 G_GAN_Feat: 0.845 G_ID: 0.103 G_Rec: 0.460 D_GP: 0.028 D_real: 1.274 D_fake: 0.466 
+(epoch: 445, iters: 2654, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.618 G_ID: 0.087 G_Rec: 0.285 D_GP: 0.030 D_real: 1.124 D_fake: 0.747 
+(epoch: 445, iters: 3054, time: 0.064) G_GAN: 0.027 G_GAN_Feat: 0.930 G_ID: 0.121 G_Rec: 0.449 D_GP: 0.043 D_real: 0.703 D_fake: 0.973 
+(epoch: 445, iters: 3454, time: 0.063) G_GAN: -0.039 G_GAN_Feat: 0.744 G_ID: 0.106 G_Rec: 0.309 D_GP: 0.043 D_real: 0.784 D_fake: 1.039 
+(epoch: 445, iters: 3854, time: 0.063) G_GAN: 0.545 G_GAN_Feat: 0.957 G_ID: 0.110 G_Rec: 0.433 D_GP: 0.052 D_real: 1.070 D_fake: 0.473 
+(epoch: 445, iters: 4254, time: 0.064) G_GAN: -0.029 G_GAN_Feat: 0.816 G_ID: 0.110 G_Rec: 0.338 D_GP: 0.047 D_real: 0.793 D_fake: 1.031 
+(epoch: 445, iters: 4654, time: 0.063) G_GAN: 0.570 G_GAN_Feat: 1.041 G_ID: 0.115 G_Rec: 0.410 D_GP: 0.172 D_real: 0.685 D_fake: 0.440 
+(epoch: 445, iters: 5054, time: 0.063) G_GAN: 0.110 G_GAN_Feat: 0.760 G_ID: 0.120 G_Rec: 0.300 D_GP: 0.037 D_real: 0.854 D_fake: 0.890 
+(epoch: 445, iters: 5454, time: 0.063) G_GAN: 0.251 G_GAN_Feat: 1.000 G_ID: 0.107 G_Rec: 0.413 D_GP: 0.038 D_real: 0.799 D_fake: 0.749 
+(epoch: 445, iters: 5854, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.858 G_ID: 0.094 G_Rec: 0.327 D_GP: 0.038 D_real: 1.058 D_fake: 0.630 
+(epoch: 445, iters: 6254, time: 0.063) G_GAN: 0.483 G_GAN_Feat: 0.967 G_ID: 0.128 G_Rec: 0.416 D_GP: 0.036 D_real: 1.037 D_fake: 0.523 
+(epoch: 445, iters: 6654, time: 0.063) G_GAN: 0.350 G_GAN_Feat: 1.045 G_ID: 0.126 G_Rec: 0.354 D_GP: 0.211 D_real: 0.277 D_fake: 0.725 
+(epoch: 445, iters: 7054, time: 0.063) G_GAN: 0.526 G_GAN_Feat: 1.251 G_ID: 0.124 G_Rec: 0.502 D_GP: 0.359 D_real: 0.528 D_fake: 0.499 
+(epoch: 445, iters: 7454, time: 0.064) G_GAN: 0.327 G_GAN_Feat: 0.947 G_ID: 0.101 G_Rec: 0.401 D_GP: 0.045 D_real: 1.064 D_fake: 0.699 
+(epoch: 445, iters: 7854, time: 0.063) G_GAN: 0.266 G_GAN_Feat: 0.860 G_ID: 0.104 G_Rec: 0.439 D_GP: 0.027 D_real: 0.987 D_fake: 0.738 
+(epoch: 445, iters: 8254, time: 0.063) G_GAN: 0.121 G_GAN_Feat: 0.667 G_ID: 0.082 G_Rec: 0.298 D_GP: 0.029 D_real: 1.063 D_fake: 0.879 
+(epoch: 445, iters: 8654, time: 0.063) G_GAN: 0.166 G_GAN_Feat: 0.874 G_ID: 0.126 G_Rec: 0.411 D_GP: 0.038 D_real: 0.785 D_fake: 0.834 
+(epoch: 446, iters: 446, time: 0.064) G_GAN: -0.054 G_GAN_Feat: 0.684 G_ID: 0.084 G_Rec: 0.349 D_GP: 0.055 D_real: 0.846 D_fake: 1.054 
+(epoch: 446, iters: 846, time: 0.063) G_GAN: 0.390 G_GAN_Feat: 0.909 G_ID: 0.114 G_Rec: 0.439 D_GP: 0.033 D_real: 0.940 D_fake: 0.621 
+(epoch: 446, iters: 1246, time: 0.063) G_GAN: 0.207 G_GAN_Feat: 0.735 G_ID: 0.100 G_Rec: 0.320 D_GP: 0.039 D_real: 1.041 D_fake: 0.794 
+(epoch: 446, iters: 1646, time: 0.063) G_GAN: 0.307 G_GAN_Feat: 0.994 G_ID: 0.112 G_Rec: 0.427 D_GP: 0.037 D_real: 0.852 D_fake: 0.693 
+(epoch: 446, iters: 2046, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.705 G_ID: 0.102 G_Rec: 0.299 D_GP: 0.040 D_real: 1.180 D_fake: 0.691 
+(epoch: 446, iters: 2446, time: 0.063) G_GAN: 0.486 G_GAN_Feat: 0.985 G_ID: 0.130 G_Rec: 0.439 D_GP: 0.037 D_real: 1.112 D_fake: 0.553 
+(epoch: 446, iters: 2846, time: 0.063) G_GAN: 0.337 G_GAN_Feat: 0.749 G_ID: 0.098 G_Rec: 0.336 D_GP: 0.033 D_real: 1.214 D_fake: 0.666 
+(epoch: 446, iters: 3246, time: 0.063) G_GAN: 0.335 G_GAN_Feat: 0.843 G_ID: 0.109 G_Rec: 0.430 D_GP: 0.029 D_real: 1.067 D_fake: 0.666 
+(epoch: 446, iters: 3646, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.735 G_ID: 0.099 G_Rec: 0.324 D_GP: 0.033 D_real: 1.176 D_fake: 0.635 
+(epoch: 446, iters: 4046, time: 0.063) G_GAN: 0.318 G_GAN_Feat: 0.924 G_ID: 0.129 G_Rec: 0.395 D_GP: 0.061 D_real: 0.957 D_fake: 0.685 
+(epoch: 446, iters: 4446, time: 0.063) G_GAN: 0.371 G_GAN_Feat: 0.838 G_ID: 0.105 G_Rec: 0.319 D_GP: 0.084 D_real: 0.656 D_fake: 0.643 
+(epoch: 446, iters: 4846, time: 0.063) G_GAN: 0.593 G_GAN_Feat: 1.064 G_ID: 0.106 G_Rec: 0.439 D_GP: 0.061 D_real: 0.848 D_fake: 0.419 
+(epoch: 446, iters: 5246, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.718 G_ID: 0.095 G_Rec: 0.283 D_GP: 0.028 D_real: 1.153 D_fake: 0.717 
+(epoch: 446, iters: 5646, time: 0.064) G_GAN: 0.459 G_GAN_Feat: 1.025 G_ID: 0.123 G_Rec: 0.412 D_GP: 0.044 D_real: 0.914 D_fake: 0.547 
+(epoch: 446, iters: 6046, time: 0.064) G_GAN: 0.643 G_GAN_Feat: 0.789 G_ID: 0.107 G_Rec: 0.321 D_GP: 0.040 D_real: 1.457 D_fake: 0.385 
+(epoch: 446, iters: 6446, time: 0.063) G_GAN: 0.482 G_GAN_Feat: 1.030 G_ID: 0.130 G_Rec: 0.408 D_GP: 0.056 D_real: 0.687 D_fake: 0.537 
+(epoch: 446, iters: 6846, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.872 G_ID: 0.110 G_Rec: 0.302 D_GP: 0.039 D_real: 0.525 D_fake: 0.747 
+(epoch: 446, iters: 7246, time: 0.063) G_GAN: 0.258 G_GAN_Feat: 0.917 G_ID: 0.118 G_Rec: 0.443 D_GP: 0.034 D_real: 1.034 D_fake: 0.753 
+(epoch: 446, iters: 7646, time: 0.063) G_GAN: 0.103 G_GAN_Feat: 0.674 G_ID: 0.087 G_Rec: 0.301 D_GP: 0.040 D_real: 1.018 D_fake: 0.897 
+(epoch: 446, iters: 8046, time: 0.063) G_GAN: 0.222 G_GAN_Feat: 1.004 G_ID: 0.117 G_Rec: 0.502 D_GP: 0.076 D_real: 0.801 D_fake: 0.783 
+(epoch: 446, iters: 8446, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.660 G_ID: 0.093 G_Rec: 0.296 D_GP: 0.039 D_real: 0.869 D_fake: 0.989 
+(epoch: 447, iters: 238, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.907 G_ID: 0.106 G_Rec: 0.454 D_GP: 0.034 D_real: 0.918 D_fake: 0.661 
+(epoch: 447, iters: 638, time: 0.063) G_GAN: 0.145 G_GAN_Feat: 0.700 G_ID: 0.091 G_Rec: 0.315 D_GP: 0.033 D_real: 1.045 D_fake: 0.858 
+(epoch: 447, iters: 1038, time: 0.063) G_GAN: 0.569 G_GAN_Feat: 0.886 G_ID: 0.119 G_Rec: 0.400 D_GP: 0.032 D_real: 1.147 D_fake: 0.480 
+(epoch: 447, iters: 1438, time: 0.064) G_GAN: 0.095 G_GAN_Feat: 0.723 G_ID: 0.091 G_Rec: 0.310 D_GP: 0.054 D_real: 0.870 D_fake: 0.905 
+(epoch: 447, iters: 1838, time: 0.063) G_GAN: 0.288 G_GAN_Feat: 0.897 G_ID: 0.128 G_Rec: 0.401 D_GP: 0.036 D_real: 0.882 D_fake: 0.713 
+(epoch: 447, iters: 2238, time: 0.063) G_GAN: 0.316 G_GAN_Feat: 0.772 G_ID: 0.105 G_Rec: 0.336 D_GP: 0.039 D_real: 1.020 D_fake: 0.690 
+(epoch: 447, iters: 2638, time: 0.063) G_GAN: 0.382 G_GAN_Feat: 1.035 G_ID: 0.110 G_Rec: 0.478 D_GP: 0.067 D_real: 0.851 D_fake: 0.626 
+(epoch: 447, iters: 3038, time: 0.064) G_GAN: 0.217 G_GAN_Feat: 0.768 G_ID: 0.101 G_Rec: 0.333 D_GP: 0.043 D_real: 1.119 D_fake: 0.783 
+(epoch: 447, iters: 3438, time: 0.063) G_GAN: 0.507 G_GAN_Feat: 1.064 G_ID: 0.116 G_Rec: 0.455 D_GP: 0.131 D_real: 0.866 D_fake: 0.503 
+(epoch: 447, iters: 3838, time: 0.064) G_GAN: 0.243 G_GAN_Feat: 0.753 G_ID: 0.100 G_Rec: 0.329 D_GP: 0.054 D_real: 0.989 D_fake: 0.757 
+(epoch: 447, iters: 4238, time: 0.063) G_GAN: 0.411 G_GAN_Feat: 0.953 G_ID: 0.138 G_Rec: 0.429 D_GP: 0.039 D_real: 0.934 D_fake: 0.593 
+(epoch: 447, iters: 4638, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.686 G_ID: 0.107 G_Rec: 0.292 D_GP: 0.032 D_real: 1.129 D_fake: 0.759 
+(epoch: 447, iters: 5038, time: 0.064) G_GAN: 0.337 G_GAN_Feat: 0.948 G_ID: 0.113 G_Rec: 0.402 D_GP: 0.051 D_real: 0.770 D_fake: 0.664 
+(epoch: 447, iters: 5438, time: 0.063) G_GAN: 0.244 G_GAN_Feat: 0.685 G_ID: 0.102 G_Rec: 0.326 D_GP: 0.032 D_real: 1.147 D_fake: 0.756 
+(epoch: 447, iters: 5838, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.983 G_ID: 0.116 G_Rec: 0.477 D_GP: 0.037 D_real: 0.687 D_fake: 0.762 
+(epoch: 447, iters: 6238, time: 0.064) G_GAN: -0.070 G_GAN_Feat: 0.797 G_ID: 0.115 G_Rec: 0.314 D_GP: 0.044 D_real: 0.622 D_fake: 1.070 
+(epoch: 447, iters: 6638, time: 0.064) G_GAN: 0.688 G_GAN_Feat: 0.983 G_ID: 0.121 G_Rec: 0.443 D_GP: 0.040 D_real: 1.216 D_fake: 0.381 
+(epoch: 447, iters: 7038, time: 0.063) G_GAN: 0.177 G_GAN_Feat: 0.805 G_ID: 0.106 G_Rec: 0.344 D_GP: 0.041 D_real: 0.834 D_fake: 0.824 
+(epoch: 447, iters: 7438, time: 0.063) G_GAN: 0.363 G_GAN_Feat: 1.012 G_ID: 0.137 G_Rec: 0.423 D_GP: 0.050 D_real: 0.842 D_fake: 0.638 
+(epoch: 447, iters: 7838, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.695 G_ID: 0.097 G_Rec: 0.309 D_GP: 0.036 D_real: 1.084 D_fake: 0.749 
+(epoch: 447, iters: 8238, time: 0.063) G_GAN: 0.520 G_GAN_Feat: 1.038 G_ID: 0.112 G_Rec: 0.443 D_GP: 0.040 D_real: 0.914 D_fake: 0.485 
+(epoch: 447, iters: 8638, time: 0.063) G_GAN: 0.306 G_GAN_Feat: 0.766 G_ID: 0.107 G_Rec: 0.313 D_GP: 0.039 D_real: 1.013 D_fake: 0.695 
+(epoch: 448, iters: 430, time: 0.063) G_GAN: 0.564 G_GAN_Feat: 0.913 G_ID: 0.130 G_Rec: 0.411 D_GP: 0.037 D_real: 1.244 D_fake: 0.453 
+(epoch: 448, iters: 830, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.882 G_ID: 0.098 G_Rec: 0.346 D_GP: 0.137 D_real: 0.365 D_fake: 0.921 
+(epoch: 448, iters: 1230, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.994 G_ID: 0.114 G_Rec: 0.433 D_GP: 0.035 D_real: 0.927 D_fake: 0.521 
+(epoch: 448, iters: 1630, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.836 G_ID: 0.121 G_Rec: 0.351 D_GP: 0.054 D_real: 0.541 D_fake: 0.878 
+(epoch: 448, iters: 2030, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 1.002 G_ID: 0.116 G_Rec: 0.420 D_GP: 0.117 D_real: 0.525 D_fake: 0.649 
+(epoch: 448, iters: 2430, time: 0.064) G_GAN: -0.086 G_GAN_Feat: 0.836 G_ID: 0.083 G_Rec: 0.319 D_GP: 0.247 D_real: 0.370 D_fake: 1.091 
+(epoch: 448, iters: 2830, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 1.047 G_ID: 0.097 G_Rec: 0.456 D_GP: 0.040 D_real: 1.098 D_fake: 0.394 
+(epoch: 448, iters: 3230, time: 0.064) G_GAN: 0.135 G_GAN_Feat: 0.865 G_ID: 0.090 G_Rec: 0.332 D_GP: 0.187 D_real: 0.507 D_fake: 0.866 
+(epoch: 448, iters: 3630, time: 0.064) G_GAN: 0.457 G_GAN_Feat: 1.024 G_ID: 0.128 G_Rec: 0.434 D_GP: 0.052 D_real: 0.790 D_fake: 0.552 
+(epoch: 448, iters: 4030, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.848 G_ID: 0.112 G_Rec: 0.305 D_GP: 0.079 D_real: 0.350 D_fake: 0.994 
+(epoch: 448, iters: 4430, time: 0.064) G_GAN: 0.359 G_GAN_Feat: 0.994 G_ID: 0.121 G_Rec: 0.455 D_GP: 0.036 D_real: 1.001 D_fake: 0.643 
+(epoch: 448, iters: 4830, time: 0.064) G_GAN: -0.046 G_GAN_Feat: 0.799 G_ID: 0.092 G_Rec: 0.333 D_GP: 0.061 D_real: 0.792 D_fake: 1.046 
+(epoch: 448, iters: 5230, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 1.029 G_ID: 0.133 G_Rec: 0.434 D_GP: 0.180 D_real: 0.273 D_fake: 0.800 
+(epoch: 448, iters: 5630, time: 0.063) G_GAN: 0.113 G_GAN_Feat: 0.747 G_ID: 0.090 G_Rec: 0.296 D_GP: 0.033 D_real: 0.792 D_fake: 0.888 
+(epoch: 448, iters: 6030, time: 0.063) G_GAN: 0.386 G_GAN_Feat: 1.100 G_ID: 0.131 G_Rec: 0.485 D_GP: 0.043 D_real: 0.940 D_fake: 0.619 
+(epoch: 448, iters: 6430, time: 0.063) G_GAN: 0.388 G_GAN_Feat: 0.757 G_ID: 0.096 G_Rec: 0.315 D_GP: 0.030 D_real: 1.162 D_fake: 0.614 
+(epoch: 448, iters: 6830, time: 0.064) G_GAN: 0.956 G_GAN_Feat: 1.229 G_ID: 0.121 G_Rec: 0.461 D_GP: 0.071 D_real: 0.469 D_fake: 0.193 
+(epoch: 448, iters: 7230, time: 0.063) G_GAN: 0.158 G_GAN_Feat: 0.747 G_ID: 0.090 G_Rec: 0.329 D_GP: 0.029 D_real: 1.080 D_fake: 0.842 
+(epoch: 448, iters: 7630, time: 0.063) G_GAN: 0.050 G_GAN_Feat: 0.968 G_ID: 0.115 G_Rec: 0.429 D_GP: 0.036 D_real: 0.755 D_fake: 0.950 
+(epoch: 448, iters: 8030, time: 0.063) G_GAN: -0.038 G_GAN_Feat: 0.858 G_ID: 0.110 G_Rec: 0.316 D_GP: 0.045 D_real: 0.657 D_fake: 1.038 
+(epoch: 448, iters: 8430, time: 0.064) G_GAN: 0.674 G_GAN_Feat: 0.941 G_ID: 0.135 G_Rec: 0.354 D_GP: 0.041 D_real: 1.109 D_fake: 0.343 
+(epoch: 449, iters: 222, time: 0.064) G_GAN: 0.652 G_GAN_Feat: 0.769 G_ID: 0.097 G_Rec: 0.328 D_GP: 0.045 D_real: 1.480 D_fake: 0.379 
+(epoch: 449, iters: 622, time: 0.064) G_GAN: 0.516 G_GAN_Feat: 0.947 G_ID: 0.118 G_Rec: 0.439 D_GP: 0.033 D_real: 1.008 D_fake: 0.499 
+(epoch: 449, iters: 1022, time: 0.064) G_GAN: 0.301 G_GAN_Feat: 0.730 G_ID: 0.116 G_Rec: 0.295 D_GP: 0.033 D_real: 1.142 D_fake: 0.699 
+(epoch: 449, iters: 1422, time: 0.064) G_GAN: 0.771 G_GAN_Feat: 1.042 G_ID: 0.119 G_Rec: 0.461 D_GP: 0.048 D_real: 1.159 D_fake: 0.274 
+(epoch: 449, iters: 1822, time: 0.063) G_GAN: 0.289 G_GAN_Feat: 0.806 G_ID: 0.105 G_Rec: 0.322 D_GP: 0.067 D_real: 0.844 D_fake: 0.716 
+(epoch: 449, iters: 2222, time: 0.063) G_GAN: 0.686 G_GAN_Feat: 1.162 G_ID: 0.123 G_Rec: 0.435 D_GP: 0.748 D_real: 0.494 D_fake: 0.343 
+(epoch: 449, iters: 2622, time: 0.063) G_GAN: 0.168 G_GAN_Feat: 0.943 G_ID: 0.096 G_Rec: 0.339 D_GP: 0.766 D_real: 0.371 D_fake: 0.838 
+(epoch: 449, iters: 3022, time: 0.064) G_GAN: 0.605 G_GAN_Feat: 1.038 G_ID: 0.145 G_Rec: 0.445 D_GP: 0.035 D_real: 0.957 D_fake: 0.427 
+(epoch: 449, iters: 3422, time: 0.063) G_GAN: 0.429 G_GAN_Feat: 0.733 G_ID: 0.106 G_Rec: 0.268 D_GP: 0.035 D_real: 1.241 D_fake: 0.571 
+(epoch: 449, iters: 3822, time: 0.063) G_GAN: 0.192 G_GAN_Feat: 0.930 G_ID: 0.131 G_Rec: 0.483 D_GP: 0.031 D_real: 0.835 D_fake: 0.810 
+(epoch: 449, iters: 4222, time: 0.063) G_GAN: -0.063 G_GAN_Feat: 0.752 G_ID: 0.090 G_Rec: 0.297 D_GP: 0.055 D_real: 0.632 D_fake: 1.063 
+(epoch: 449, iters: 4622, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.955 G_ID: 0.138 G_Rec: 0.417 D_GP: 0.038 D_real: 0.948 D_fake: 0.651 
+(epoch: 449, iters: 5022, time: 0.063) G_GAN: 0.054 G_GAN_Feat: 0.879 G_ID: 0.107 G_Rec: 0.336 D_GP: 0.130 D_real: 0.340 D_fake: 0.947 
+(epoch: 449, iters: 5422, time: 0.063) G_GAN: 0.565 G_GAN_Feat: 1.059 G_ID: 0.110 G_Rec: 0.487 D_GP: 0.044 D_real: 0.784 D_fake: 0.445 
+(epoch: 449, iters: 5822, time: 0.063) G_GAN: -0.012 G_GAN_Feat: 0.763 G_ID: 0.111 G_Rec: 0.287 D_GP: 0.034 D_real: 0.789 D_fake: 1.014 
+(epoch: 449, iters: 6222, time: 0.064) G_GAN: 0.638 G_GAN_Feat: 0.947 G_ID: 0.123 G_Rec: 0.449 D_GP: 0.042 D_real: 1.353 D_fake: 0.392 
+(epoch: 449, iters: 6622, time: 0.063) G_GAN: 0.179 G_GAN_Feat: 0.682 G_ID: 0.099 G_Rec: 0.275 D_GP: 0.028 D_real: 1.141 D_fake: 0.821 
+(epoch: 449, iters: 7022, time: 0.063) G_GAN: 0.325 G_GAN_Feat: 0.852 G_ID: 0.117 G_Rec: 0.359 D_GP: 0.033 D_real: 1.079 D_fake: 0.676 
+(epoch: 449, iters: 7422, time: 0.063) G_GAN: 0.218 G_GAN_Feat: 0.738 G_ID: 0.093 G_Rec: 0.304 D_GP: 0.031 D_real: 1.124 D_fake: 0.783 
+(epoch: 449, iters: 7822, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 1.022 G_ID: 0.131 G_Rec: 0.465 D_GP: 0.067 D_real: 0.752 D_fake: 0.658 
+(epoch: 449, iters: 8222, time: 0.063) G_GAN: 0.242 G_GAN_Feat: 0.866 G_ID: 0.113 G_Rec: 0.377 D_GP: 0.223 D_real: 0.752 D_fake: 0.765 
+(epoch: 449, iters: 8622, time: 0.063) G_GAN: 0.498 G_GAN_Feat: 0.936 G_ID: 0.130 G_Rec: 0.425 D_GP: 0.033 D_real: 1.152 D_fake: 0.507 
+(epoch: 450, iters: 414, time: 0.063) G_GAN: -0.158 G_GAN_Feat: 0.748 G_ID: 0.116 G_Rec: 0.315 D_GP: 0.038 D_real: 0.656 D_fake: 1.158 
+(epoch: 450, iters: 814, time: 0.064) G_GAN: 0.616 G_GAN_Feat: 0.930 G_ID: 0.129 G_Rec: 0.445 D_GP: 0.033 D_real: 1.230 D_fake: 0.417 
+(epoch: 450, iters: 1214, time: 0.063) G_GAN: 0.158 G_GAN_Feat: 0.851 G_ID: 0.134 G_Rec: 0.330 D_GP: 0.053 D_real: 0.673 D_fake: 0.842 
+(epoch: 450, iters: 1614, time: 0.063) G_GAN: 0.694 G_GAN_Feat: 1.133 G_ID: 0.103 G_Rec: 0.454 D_GP: 0.079 D_real: 0.648 D_fake: 0.329 
+(epoch: 450, iters: 2014, time: 0.063) G_GAN: 0.423 G_GAN_Feat: 0.873 G_ID: 0.098 G_Rec: 0.328 D_GP: 0.036 D_real: 0.937 D_fake: 0.581 
+(epoch: 450, iters: 2414, time: 0.064) G_GAN: 0.716 G_GAN_Feat: 0.886 G_ID: 0.110 G_Rec: 0.394 D_GP: 0.080 D_real: 1.279 D_fake: 0.397 
+(epoch: 450, iters: 2814, time: 0.063) G_GAN: 0.068 G_GAN_Feat: 0.742 G_ID: 0.091 G_Rec: 0.306 D_GP: 0.032 D_real: 0.938 D_fake: 0.932 
+(epoch: 450, iters: 3214, time: 0.063) G_GAN: 0.392 G_GAN_Feat: 0.967 G_ID: 0.156 G_Rec: 0.421 D_GP: 0.037 D_real: 0.964 D_fake: 0.610 
+(epoch: 450, iters: 3614, time: 0.063) G_GAN: 0.256 G_GAN_Feat: 0.720 G_ID: 0.103 G_Rec: 0.288 D_GP: 0.033 D_real: 1.177 D_fake: 0.745 
+(epoch: 450, iters: 4014, time: 0.064) G_GAN: 0.544 G_GAN_Feat: 0.978 G_ID: 0.111 G_Rec: 0.449 D_GP: 0.036 D_real: 1.233 D_fake: 0.460 
+(epoch: 450, iters: 4414, time: 0.063) G_GAN: 0.430 G_GAN_Feat: 0.716 G_ID: 0.079 G_Rec: 0.272 D_GP: 0.025 D_real: 1.304 D_fake: 0.571 
+(epoch: 450, iters: 4814, time: 0.063) G_GAN: 0.833 G_GAN_Feat: 1.063 G_ID: 0.128 G_Rec: 0.403 D_GP: 0.048 D_real: 1.121 D_fake: 0.203 
+(epoch: 450, iters: 5214, time: 0.063) G_GAN: 0.312 G_GAN_Feat: 0.900 G_ID: 0.091 G_Rec: 0.327 D_GP: 0.040 D_real: 0.600 D_fake: 0.688 
+(epoch: 450, iters: 5614, time: 0.064) G_GAN: 0.812 G_GAN_Feat: 0.911 G_ID: 0.109 G_Rec: 0.419 D_GP: 0.027 D_real: 1.513 D_fake: 0.279 
+(epoch: 450, iters: 6014, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.788 G_ID: 0.087 G_Rec: 0.324 D_GP: 0.039 D_real: 1.079 D_fake: 0.651 
+(epoch: 450, iters: 6414, time: 0.063) G_GAN: 0.649 G_GAN_Feat: 1.042 G_ID: 0.109 G_Rec: 0.460 D_GP: 0.044 D_real: 1.218 D_fake: 0.378 
+(epoch: 450, iters: 6814, time: 0.063) G_GAN: 0.420 G_GAN_Feat: 0.689 G_ID: 0.092 G_Rec: 0.281 D_GP: 0.027 D_real: 1.272 D_fake: 0.581 
+(epoch: 450, iters: 7214, time: 0.064) G_GAN: 0.712 G_GAN_Feat: 0.958 G_ID: 0.127 G_Rec: 0.388 D_GP: 0.069 D_real: 1.002 D_fake: 0.306 
+(epoch: 450, iters: 7614, time: 0.063) G_GAN: 0.281 G_GAN_Feat: 0.785 G_ID: 0.096 G_Rec: 0.300 D_GP: 0.030 D_real: 0.946 D_fake: 0.719 
+(epoch: 450, iters: 8014, time: 0.063) G_GAN: 0.675 G_GAN_Feat: 1.119 G_ID: 0.123 G_Rec: 0.420 D_GP: 0.059 D_real: 0.604 D_fake: 0.356 
+(epoch: 450, iters: 8414, time: 0.063) G_GAN: 0.547 G_GAN_Feat: 0.956 G_ID: 0.101 G_Rec: 0.337 D_GP: 0.091 D_real: 0.629 D_fake: 0.522 
+(epoch: 451, iters: 206, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.867 G_ID: 0.131 G_Rec: 0.411 D_GP: 0.031 D_real: 1.075 D_fake: 0.647 
+(epoch: 451, iters: 606, time: 0.063) G_GAN: 0.222 G_GAN_Feat: 0.659 G_ID: 0.096 G_Rec: 0.280 D_GP: 0.026 D_real: 1.208 D_fake: 0.778 
+(epoch: 451, iters: 1006, time: 0.063) G_GAN: 0.358 G_GAN_Feat: 0.981 G_ID: 0.113 G_Rec: 0.443 D_GP: 0.037 D_real: 0.979 D_fake: 0.649 
+(epoch: 451, iters: 1406, time: 0.063) G_GAN: -0.209 G_GAN_Feat: 0.712 G_ID: 0.118 G_Rec: 0.359 D_GP: 0.032 D_real: 0.731 D_fake: 1.209 
+(epoch: 451, iters: 1806, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.884 G_ID: 0.122 G_Rec: 0.396 D_GP: 0.043 D_real: 0.769 D_fake: 0.871 
+(epoch: 451, iters: 2206, time: 0.063) G_GAN: -0.086 G_GAN_Feat: 0.755 G_ID: 0.121 G_Rec: 0.321 D_GP: 0.042 D_real: 0.565 D_fake: 1.086 
+(epoch: 451, iters: 2606, time: 0.063) G_GAN: 0.088 G_GAN_Feat: 0.970 G_ID: 0.117 G_Rec: 0.430 D_GP: 0.053 D_real: 0.581 D_fake: 0.912 
+(epoch: 451, iters: 3006, time: 0.063) G_GAN: 0.248 G_GAN_Feat: 0.791 G_ID: 0.082 G_Rec: 0.300 D_GP: 0.080 D_real: 0.794 D_fake: 0.757 
+(epoch: 451, iters: 3406, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.997 G_ID: 0.113 G_Rec: 0.452 D_GP: 0.035 D_real: 0.984 D_fake: 0.637 
+(epoch: 451, iters: 3806, time: 0.063) G_GAN: 0.264 G_GAN_Feat: 0.723 G_ID: 0.098 G_Rec: 0.317 D_GP: 0.038 D_real: 1.151 D_fake: 0.736 
+(epoch: 451, iters: 4206, time: 0.063) G_GAN: 0.463 G_GAN_Feat: 0.956 G_ID: 0.117 G_Rec: 0.417 D_GP: 0.054 D_real: 0.938 D_fake: 0.546 
+(epoch: 451, iters: 4606, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.763 G_ID: 0.089 G_Rec: 0.308 D_GP: 0.064 D_real: 0.858 D_fake: 0.747 
+(epoch: 451, iters: 5006, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 1.044 G_ID: 0.112 G_Rec: 0.455 D_GP: 0.097 D_real: 0.599 D_fake: 0.508 
+(epoch: 451, iters: 5406, time: 0.063) G_GAN: 0.298 G_GAN_Feat: 0.988 G_ID: 0.113 G_Rec: 0.388 D_GP: 0.149 D_real: 0.320 D_fake: 0.723 
+(epoch: 451, iters: 5806, time: 0.063) G_GAN: 0.432 G_GAN_Feat: 1.177 G_ID: 0.115 G_Rec: 0.471 D_GP: 0.080 D_real: 0.422 D_fake: 0.568 
+(epoch: 451, iters: 6206, time: 0.063) G_GAN: 0.072 G_GAN_Feat: 0.942 G_ID: 0.126 G_Rec: 0.347 D_GP: 0.141 D_real: 0.234 D_fake: 0.932 
+(epoch: 451, iters: 6606, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 1.001 G_ID: 0.147 G_Rec: 0.453 D_GP: 0.052 D_real: 0.673 D_fake: 0.724 
+(epoch: 451, iters: 7006, time: 0.063) G_GAN: 0.078 G_GAN_Feat: 0.776 G_ID: 0.107 G_Rec: 0.287 D_GP: 0.065 D_real: 0.748 D_fake: 0.924 
+(epoch: 451, iters: 7406, time: 0.063) G_GAN: 0.609 G_GAN_Feat: 1.211 G_ID: 0.109 G_Rec: 0.473 D_GP: 0.049 D_real: 0.603 D_fake: 0.407 
+(epoch: 451, iters: 7806, time: 0.063) G_GAN: 0.249 G_GAN_Feat: 0.723 G_ID: 0.090 G_Rec: 0.291 D_GP: 0.029 D_real: 1.132 D_fake: 0.751 
+(epoch: 451, iters: 8206, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 1.003 G_ID: 0.129 G_Rec: 0.440 D_GP: 0.047 D_real: 0.866 D_fake: 0.631 
+(epoch: 451, iters: 8606, time: 0.063) G_GAN: 0.166 G_GAN_Feat: 0.902 G_ID: 0.093 G_Rec: 0.358 D_GP: 0.186 D_real: 0.555 D_fake: 0.841 
+(epoch: 452, iters: 398, time: 0.063) G_GAN: 0.602 G_GAN_Feat: 1.172 G_ID: 0.126 G_Rec: 0.459 D_GP: 0.073 D_real: 0.292 D_fake: 0.405 
+(epoch: 452, iters: 798, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.907 G_ID: 0.092 G_Rec: 0.324 D_GP: 0.067 D_real: 0.709 D_fake: 0.525 
+(epoch: 452, iters: 1198, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 1.089 G_ID: 0.129 G_Rec: 0.465 D_GP: 0.040 D_real: 0.608 D_fake: 0.552 
+(epoch: 452, iters: 1598, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 0.735 G_ID: 0.090 G_Rec: 0.290 D_GP: 0.027 D_real: 1.056 D_fake: 0.810 
+(epoch: 452, iters: 1998, time: 0.064) G_GAN: 0.679 G_GAN_Feat: 1.083 G_ID: 0.120 G_Rec: 0.455 D_GP: 0.048 D_real: 0.846 D_fake: 0.354 
+(epoch: 452, iters: 2398, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 1.104 G_ID: 0.095 G_Rec: 0.352 D_GP: 0.224 D_real: 0.415 D_fake: 0.645 
+(epoch: 452, iters: 2798, time: 0.064) G_GAN: 1.045 G_GAN_Feat: 1.166 G_ID: 0.110 G_Rec: 0.442 D_GP: 0.091 D_real: 0.887 D_fake: 0.111 
+(epoch: 452, iters: 3198, time: 0.064) G_GAN: 0.092 G_GAN_Feat: 0.839 G_ID: 0.087 G_Rec: 0.293 D_GP: 0.044 D_real: 0.645 D_fake: 0.908 
+(epoch: 452, iters: 3598, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 1.020 G_ID: 0.119 G_Rec: 0.408 D_GP: 0.049 D_real: 0.658 D_fake: 0.446 
+(epoch: 452, iters: 3998, time: 0.063) G_GAN: 0.492 G_GAN_Feat: 0.776 G_ID: 0.092 G_Rec: 0.283 D_GP: 0.041 D_real: 1.099 D_fake: 0.524 
+(epoch: 452, iters: 4398, time: 0.064) G_GAN: 0.237 G_GAN_Feat: 0.926 G_ID: 0.113 G_Rec: 0.431 D_GP: 0.029 D_real: 0.896 D_fake: 0.766 
+(epoch: 452, iters: 4798, time: 0.063) G_GAN: 0.169 G_GAN_Feat: 0.741 G_ID: 0.088 G_Rec: 0.302 D_GP: 0.099 D_real: 0.970 D_fake: 0.832 
+(epoch: 452, iters: 5198, time: 0.063) G_GAN: 0.505 G_GAN_Feat: 1.003 G_ID: 0.129 G_Rec: 0.408 D_GP: 0.039 D_real: 0.927 D_fake: 0.503 
+(epoch: 452, iters: 5598, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.738 G_ID: 0.089 G_Rec: 0.310 D_GP: 0.033 D_real: 1.339 D_fake: 0.643 
+(epoch: 452, iters: 5998, time: 0.064) G_GAN: 0.640 G_GAN_Feat: 1.038 G_ID: 0.116 G_Rec: 0.415 D_GP: 0.064 D_real: 0.529 D_fake: 0.375 
+(epoch: 452, iters: 6398, time: 0.063) G_GAN: 0.081 G_GAN_Feat: 0.708 G_ID: 0.096 G_Rec: 0.326 D_GP: 0.034 D_real: 0.868 D_fake: 0.919 
+(epoch: 452, iters: 6798, time: 0.063) G_GAN: 0.439 G_GAN_Feat: 1.017 G_ID: 0.125 G_Rec: 0.432 D_GP: 0.085 D_real: 0.741 D_fake: 0.584 
+(epoch: 452, iters: 7198, time: 0.063) G_GAN: -0.148 G_GAN_Feat: 0.798 G_ID: 0.095 G_Rec: 0.326 D_GP: 0.037 D_real: 0.724 D_fake: 1.148 
+(epoch: 452, iters: 7598, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 0.943 G_ID: 0.135 G_Rec: 0.414 D_GP: 0.046 D_real: 0.796 D_fake: 0.809 
+(epoch: 452, iters: 7998, time: 0.063) G_GAN: -0.047 G_GAN_Feat: 0.732 G_ID: 0.081 G_Rec: 0.291 D_GP: 0.055 D_real: 0.840 D_fake: 1.047 
+(epoch: 452, iters: 8398, time: 0.063) G_GAN: 0.517 G_GAN_Feat: 1.036 G_ID: 0.116 G_Rec: 0.458 D_GP: 0.045 D_real: 0.936 D_fake: 0.498 
+(epoch: 453, iters: 190, time: 0.063) G_GAN: 0.296 G_GAN_Feat: 0.772 G_ID: 0.096 G_Rec: 0.301 D_GP: 0.038 D_real: 1.136 D_fake: 0.705 
+(epoch: 453, iters: 590, time: 0.063) G_GAN: 0.379 G_GAN_Feat: 0.931 G_ID: 0.116 G_Rec: 0.424 D_GP: 0.033 D_real: 1.096 D_fake: 0.621 
+(epoch: 453, iters: 990, time: 0.063) G_GAN: 0.298 G_GAN_Feat: 0.858 G_ID: 0.090 G_Rec: 0.335 D_GP: 0.059 D_real: 0.833 D_fake: 0.702 
+(epoch: 453, iters: 1390, time: 0.063) G_GAN: 0.452 G_GAN_Feat: 1.078 G_ID: 0.098 G_Rec: 0.470 D_GP: 0.051 D_real: 0.769 D_fake: 0.551 
+(epoch: 453, iters: 1790, time: 0.064) G_GAN: 0.283 G_GAN_Feat: 0.894 G_ID: 0.107 G_Rec: 0.334 D_GP: 0.037 D_real: 0.750 D_fake: 0.717 
+(epoch: 453, iters: 2190, time: 0.063) G_GAN: 0.861 G_GAN_Feat: 1.103 G_ID: 0.122 G_Rec: 0.478 D_GP: 0.076 D_real: 0.714 D_fake: 0.233 
+(epoch: 453, iters: 2590, time: 0.063) G_GAN: 0.131 G_GAN_Feat: 0.720 G_ID: 0.084 G_Rec: 0.306 D_GP: 0.032 D_real: 1.057 D_fake: 0.869 
+(epoch: 453, iters: 2990, time: 0.063) G_GAN: 0.432 G_GAN_Feat: 0.929 G_ID: 0.124 G_Rec: 0.449 D_GP: 0.041 D_real: 1.066 D_fake: 0.587 
+(epoch: 453, iters: 3390, time: 0.064) G_GAN: -0.082 G_GAN_Feat: 0.625 G_ID: 0.100 G_Rec: 0.276 D_GP: 0.032 D_real: 0.764 D_fake: 1.082 
+(epoch: 453, iters: 3790, time: 0.063) G_GAN: 0.197 G_GAN_Feat: 0.922 G_ID: 0.110 G_Rec: 0.412 D_GP: 0.034 D_real: 0.823 D_fake: 0.804 
+(epoch: 453, iters: 4190, time: 0.063) G_GAN: 0.038 G_GAN_Feat: 0.755 G_ID: 0.103 G_Rec: 0.318 D_GP: 0.052 D_real: 0.818 D_fake: 0.962 
+(epoch: 453, iters: 4590, time: 0.063) G_GAN: 0.228 G_GAN_Feat: 1.014 G_ID: 0.115 G_Rec: 0.478 D_GP: 0.043 D_real: 0.722 D_fake: 0.774 
+(epoch: 453, iters: 4990, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.714 G_ID: 0.080 G_Rec: 0.292 D_GP: 0.037 D_real: 1.091 D_fake: 0.724 
+(epoch: 453, iters: 5390, time: 0.063) G_GAN: 0.237 G_GAN_Feat: 0.907 G_ID: 0.121 G_Rec: 0.407 D_GP: 0.028 D_real: 1.087 D_fake: 0.763 
+(epoch: 453, iters: 5790, time: 0.063) G_GAN: 0.182 G_GAN_Feat: 0.728 G_ID: 0.107 G_Rec: 0.281 D_GP: 0.033 D_real: 1.101 D_fake: 0.818 
+(epoch: 453, iters: 6190, time: 0.063) G_GAN: 0.377 G_GAN_Feat: 0.995 G_ID: 0.125 G_Rec: 0.427 D_GP: 0.072 D_real: 0.793 D_fake: 0.625 
+(epoch: 453, iters: 6590, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.859 G_ID: 0.104 G_Rec: 0.330 D_GP: 0.064 D_real: 0.850 D_fake: 0.735 
+(epoch: 453, iters: 6990, time: 0.063) G_GAN: 0.563 G_GAN_Feat: 0.970 G_ID: 0.120 G_Rec: 0.445 D_GP: 0.032 D_real: 1.151 D_fake: 0.448 
+(epoch: 453, iters: 7390, time: 0.063) G_GAN: 0.157 G_GAN_Feat: 0.774 G_ID: 0.106 G_Rec: 0.317 D_GP: 0.033 D_real: 0.907 D_fake: 0.843 
+(epoch: 453, iters: 7790, time: 0.063) G_GAN: 0.657 G_GAN_Feat: 1.036 G_ID: 0.121 G_Rec: 0.465 D_GP: 0.037 D_real: 1.157 D_fake: 0.355 
+(epoch: 453, iters: 8190, time: 0.064) G_GAN: 0.255 G_GAN_Feat: 0.875 G_ID: 0.116 G_Rec: 0.350 D_GP: 0.075 D_real: 0.639 D_fake: 0.752 
+(epoch: 453, iters: 8590, time: 0.063) G_GAN: 0.750 G_GAN_Feat: 1.182 G_ID: 0.122 G_Rec: 0.476 D_GP: 0.073 D_real: 0.651 D_fake: 0.274 
+(epoch: 454, iters: 382, time: 0.063) G_GAN: -0.017 G_GAN_Feat: 0.883 G_ID: 0.110 G_Rec: 0.339 D_GP: 0.039 D_real: 0.375 D_fake: 1.017 
+(epoch: 454, iters: 782, time: 0.063) G_GAN: 0.324 G_GAN_Feat: 0.948 G_ID: 0.124 G_Rec: 0.389 D_GP: 0.037 D_real: 0.900 D_fake: 0.677 
+(epoch: 454, iters: 1182, time: 0.064) G_GAN: 0.349 G_GAN_Feat: 0.737 G_ID: 0.078 G_Rec: 0.326 D_GP: 0.029 D_real: 1.211 D_fake: 0.651 
+(epoch: 454, iters: 1582, time: 0.063) G_GAN: 0.629 G_GAN_Feat: 0.850 G_ID: 0.110 G_Rec: 0.383 D_GP: 0.042 D_real: 1.296 D_fake: 0.403 
+(epoch: 454, iters: 1982, time: 0.063) G_GAN: 0.090 G_GAN_Feat: 0.751 G_ID: 0.090 G_Rec: 0.330 D_GP: 0.036 D_real: 0.881 D_fake: 0.910 
+(epoch: 454, iters: 2382, time: 0.063) G_GAN: 0.545 G_GAN_Feat: 1.022 G_ID: 0.110 G_Rec: 0.410 D_GP: 0.035 D_real: 1.092 D_fake: 0.464 
+(epoch: 454, iters: 2782, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 0.697 G_ID: 0.079 G_Rec: 0.293 D_GP: 0.032 D_real: 0.953 D_fake: 0.894 
+(epoch: 454, iters: 3182, time: 0.063) G_GAN: 0.353 G_GAN_Feat: 1.042 G_ID: 0.111 G_Rec: 0.464 D_GP: 0.055 D_real: 0.930 D_fake: 0.649 
+(epoch: 454, iters: 3582, time: 0.063) G_GAN: 0.527 G_GAN_Feat: 0.751 G_ID: 0.095 G_Rec: 0.330 D_GP: 0.037 D_real: 1.406 D_fake: 0.477 
+(epoch: 454, iters: 3982, time: 0.063) G_GAN: 0.523 G_GAN_Feat: 0.894 G_ID: 0.132 G_Rec: 0.396 D_GP: 0.034 D_real: 1.143 D_fake: 0.487 
+(epoch: 454, iters: 4382, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.690 G_ID: 0.112 G_Rec: 0.341 D_GP: 0.031 D_real: 1.279 D_fake: 0.642 
+(epoch: 454, iters: 4782, time: 0.063) G_GAN: 0.362 G_GAN_Feat: 1.059 G_ID: 0.115 G_Rec: 0.472 D_GP: 0.090 D_real: 0.698 D_fake: 0.647 
+(epoch: 454, iters: 5182, time: 0.063) G_GAN: 0.344 G_GAN_Feat: 0.886 G_ID: 0.102 G_Rec: 0.299 D_GP: 0.072 D_real: 0.590 D_fake: 0.658 
+(epoch: 454, iters: 5582, time: 0.063) G_GAN: 0.421 G_GAN_Feat: 1.151 G_ID: 0.125 G_Rec: 0.471 D_GP: 0.672 D_real: 0.344 D_fake: 0.590 
+(epoch: 454, iters: 5982, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.925 G_ID: 0.085 G_Rec: 0.321 D_GP: 0.132 D_real: 0.324 D_fake: 0.679 
+(epoch: 454, iters: 6382, time: 0.063) G_GAN: 0.697 G_GAN_Feat: 1.006 G_ID: 0.119 G_Rec: 0.424 D_GP: 0.035 D_real: 1.197 D_fake: 0.316 
+(epoch: 454, iters: 6782, time: 0.063) G_GAN: 0.118 G_GAN_Feat: 0.817 G_ID: 0.094 G_Rec: 0.330 D_GP: 0.044 D_real: 0.796 D_fake: 0.882 
+(epoch: 454, iters: 7182, time: 0.063) G_GAN: 0.426 G_GAN_Feat: 0.936 G_ID: 0.126 G_Rec: 0.407 D_GP: 0.036 D_real: 1.076 D_fake: 0.582 
+(epoch: 454, iters: 7582, time: 0.064) G_GAN: 0.154 G_GAN_Feat: 0.821 G_ID: 0.100 G_Rec: 0.333 D_GP: 0.075 D_real: 0.720 D_fake: 0.847 
+(epoch: 454, iters: 7982, time: 0.063) G_GAN: 0.548 G_GAN_Feat: 1.156 G_ID: 0.119 G_Rec: 0.480 D_GP: 0.067 D_real: 0.442 D_fake: 0.466 
+(epoch: 454, iters: 8382, time: 0.063) G_GAN: 0.097 G_GAN_Feat: 0.708 G_ID: 0.095 G_Rec: 0.294 D_GP: 0.027 D_real: 0.977 D_fake: 0.903 
+(epoch: 455, iters: 174, time: 0.063) G_GAN: 0.863 G_GAN_Feat: 1.181 G_ID: 0.123 G_Rec: 0.457 D_GP: 0.091 D_real: 0.645 D_fake: 0.376 
+(epoch: 455, iters: 574, time: 0.064) G_GAN: 0.145 G_GAN_Feat: 0.784 G_ID: 0.110 G_Rec: 0.319 D_GP: 0.035 D_real: 0.965 D_fake: 0.855 
+(epoch: 455, iters: 974, time: 0.063) G_GAN: 0.609 G_GAN_Feat: 1.105 G_ID: 0.120 G_Rec: 0.447 D_GP: 0.051 D_real: 0.950 D_fake: 0.409 
+(epoch: 455, iters: 1374, time: 0.063) G_GAN: 0.118 G_GAN_Feat: 0.754 G_ID: 0.093 G_Rec: 0.320 D_GP: 0.036 D_real: 0.996 D_fake: 0.882 
+(epoch: 455, iters: 1774, time: 0.063) G_GAN: 0.689 G_GAN_Feat: 1.051 G_ID: 0.103 G_Rec: 0.489 D_GP: 0.036 D_real: 1.127 D_fake: 0.371 
+(epoch: 455, iters: 2174, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.833 G_ID: 0.088 G_Rec: 0.353 D_GP: 0.096 D_real: 0.808 D_fake: 0.690 
+(epoch: 455, iters: 2574, time: 0.063) G_GAN: 0.431 G_GAN_Feat: 1.051 G_ID: 0.136 G_Rec: 0.475 D_GP: 0.050 D_real: 0.759 D_fake: 0.582 
+(epoch: 455, iters: 2974, time: 0.063) G_GAN: 0.246 G_GAN_Feat: 1.074 G_ID: 0.095 G_Rec: 0.340 D_GP: 0.213 D_real: 0.301 D_fake: 0.756 
+(epoch: 455, iters: 3374, time: 0.063) G_GAN: 1.050 G_GAN_Feat: 1.150 G_ID: 0.128 G_Rec: 0.406 D_GP: 0.095 D_real: 0.853 D_fake: 0.234 
+(epoch: 455, iters: 3774, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 0.869 G_ID: 0.101 G_Rec: 0.327 D_GP: 0.050 D_real: 0.801 D_fake: 0.621 
+(epoch: 455, iters: 4174, time: 0.063) G_GAN: 0.664 G_GAN_Feat: 1.026 G_ID: 0.123 G_Rec: 0.479 D_GP: 0.035 D_real: 1.279 D_fake: 0.400 
+(epoch: 455, iters: 4574, time: 0.063) G_GAN: -0.041 G_GAN_Feat: 0.749 G_ID: 0.106 G_Rec: 0.321 D_GP: 0.039 D_real: 0.749 D_fake: 1.041 
+(epoch: 455, iters: 4974, time: 0.063) G_GAN: 0.718 G_GAN_Feat: 0.996 G_ID: 0.106 G_Rec: 0.422 D_GP: 0.054 D_real: 1.200 D_fake: 0.310 
+(epoch: 455, iters: 5374, time: 0.064) G_GAN: 0.572 G_GAN_Feat: 0.809 G_ID: 0.099 G_Rec: 0.303 D_GP: 0.087 D_real: 1.164 D_fake: 0.437 
+(epoch: 455, iters: 5774, time: 0.063) G_GAN: 0.816 G_GAN_Feat: 1.038 G_ID: 0.125 G_Rec: 0.427 D_GP: 0.043 D_real: 1.259 D_fake: 0.313 
+(epoch: 455, iters: 6174, time: 0.063) G_GAN: 0.465 G_GAN_Feat: 0.772 G_ID: 0.102 G_Rec: 0.297 D_GP: 0.036 D_real: 1.273 D_fake: 0.544 
+(epoch: 455, iters: 6574, time: 0.063) G_GAN: 0.454 G_GAN_Feat: 1.150 G_ID: 0.127 G_Rec: 0.468 D_GP: 0.066 D_real: 0.543 D_fake: 0.559 
+(epoch: 455, iters: 6974, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.778 G_ID: 0.100 G_Rec: 0.345 D_GP: 0.034 D_real: 1.052 D_fake: 0.764 
+(epoch: 455, iters: 7374, time: 0.063) G_GAN: 0.141 G_GAN_Feat: 0.897 G_ID: 0.134 G_Rec: 0.440 D_GP: 0.041 D_real: 0.834 D_fake: 0.860 
+(epoch: 455, iters: 7774, time: 0.063) G_GAN: 0.167 G_GAN_Feat: 0.723 G_ID: 0.092 G_Rec: 0.317 D_GP: 0.037 D_real: 1.072 D_fake: 0.834 
+(epoch: 455, iters: 8174, time: 0.063) G_GAN: 0.369 G_GAN_Feat: 0.922 G_ID: 0.104 G_Rec: 0.433 D_GP: 0.069 D_real: 1.028 D_fake: 0.636 
+(epoch: 455, iters: 8574, time: 0.064) G_GAN: -0.033 G_GAN_Feat: 0.754 G_ID: 0.118 G_Rec: 0.329 D_GP: 0.042 D_real: 0.747 D_fake: 1.033 
+(epoch: 456, iters: 366, time: 0.063) G_GAN: 0.385 G_GAN_Feat: 0.931 G_ID: 0.116 G_Rec: 0.423 D_GP: 0.045 D_real: 0.900 D_fake: 0.621 
+(epoch: 456, iters: 766, time: 0.063) G_GAN: 0.242 G_GAN_Feat: 0.645 G_ID: 0.107 G_Rec: 0.299 D_GP: 0.028 D_real: 1.126 D_fake: 0.759 
+(epoch: 456, iters: 1166, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.893 G_ID: 0.122 G_Rec: 0.402 D_GP: 0.048 D_real: 1.078 D_fake: 0.528 
+(epoch: 456, iters: 1566, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.769 G_ID: 0.091 G_Rec: 0.316 D_GP: 0.060 D_real: 1.007 D_fake: 0.764 
+(epoch: 456, iters: 1966, time: 0.063) G_GAN: 0.512 G_GAN_Feat: 0.925 G_ID: 0.115 G_Rec: 0.402 D_GP: 0.028 D_real: 1.152 D_fake: 0.492 
+(epoch: 456, iters: 2366, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 0.930 G_ID: 0.087 G_Rec: 0.347 D_GP: 0.162 D_real: 0.510 D_fake: 0.660 
+(epoch: 456, iters: 2766, time: 0.064) G_GAN: 0.141 G_GAN_Feat: 1.048 G_ID: 0.135 G_Rec: 0.461 D_GP: 0.036 D_real: 0.868 D_fake: 0.861 
+(epoch: 456, iters: 3166, time: 0.064) G_GAN: -0.067 G_GAN_Feat: 0.737 G_ID: 0.124 G_Rec: 0.342 D_GP: 0.029 D_real: 0.840 D_fake: 1.067 
+(epoch: 456, iters: 3566, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.890 G_ID: 0.122 G_Rec: 0.419 D_GP: 0.033 D_real: 0.838 D_fake: 0.835 
+(epoch: 456, iters: 3966, time: 0.063) G_GAN: -0.153 G_GAN_Feat: 0.728 G_ID: 0.102 G_Rec: 0.327 D_GP: 0.035 D_real: 0.704 D_fake: 1.153 
+(epoch: 456, iters: 4366, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.937 G_ID: 0.123 G_Rec: 0.426 D_GP: 0.040 D_real: 0.889 D_fake: 0.708 
+(epoch: 456, iters: 4766, time: 0.064) G_GAN: 0.130 G_GAN_Feat: 0.641 G_ID: 0.097 G_Rec: 0.269 D_GP: 0.035 D_real: 1.053 D_fake: 0.872 
+(epoch: 456, iters: 5166, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.991 G_ID: 0.113 G_Rec: 0.459 D_GP: 0.060 D_real: 0.566 D_fake: 0.808 
+(epoch: 456, iters: 5566, time: 0.064) G_GAN: -0.027 G_GAN_Feat: 0.670 G_ID: 0.107 G_Rec: 0.287 D_GP: 0.041 D_real: 0.813 D_fake: 1.030 
+(epoch: 456, iters: 5966, time: 0.064) G_GAN: 0.361 G_GAN_Feat: 1.002 G_ID: 0.118 G_Rec: 0.459 D_GP: 0.052 D_real: 0.750 D_fake: 0.648 
+(epoch: 456, iters: 6366, time: 0.064) G_GAN: -0.178 G_GAN_Feat: 0.789 G_ID: 0.105 G_Rec: 0.347 D_GP: 0.070 D_real: 0.464 D_fake: 1.178 
+(epoch: 456, iters: 6766, time: 0.064) G_GAN: 0.456 G_GAN_Feat: 1.039 G_ID: 0.120 G_Rec: 0.415 D_GP: 0.063 D_real: 0.743 D_fake: 0.549 
+(epoch: 456, iters: 7166, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.827 G_ID: 0.085 G_Rec: 0.331 D_GP: 0.047 D_real: 0.866 D_fake: 0.758 
+(epoch: 456, iters: 7566, time: 0.064) G_GAN: 0.609 G_GAN_Feat: 1.169 G_ID: 0.138 G_Rec: 0.479 D_GP: 0.073 D_real: 0.324 D_fake: 0.442 
+(epoch: 456, iters: 7966, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.762 G_ID: 0.088 G_Rec: 0.304 D_GP: 0.036 D_real: 1.181 D_fake: 0.725 
+(epoch: 456, iters: 8366, time: 0.064) G_GAN: 0.854 G_GAN_Feat: 1.106 G_ID: 0.115 G_Rec: 0.503 D_GP: 0.055 D_real: 1.121 D_fake: 0.221 
+(epoch: 457, iters: 158, time: 0.064) G_GAN: 0.499 G_GAN_Feat: 0.947 G_ID: 0.099 G_Rec: 0.333 D_GP: 0.046 D_real: 0.573 D_fake: 0.520 
+(epoch: 457, iters: 558, time: 0.064) G_GAN: 0.251 G_GAN_Feat: 0.904 G_ID: 0.104 G_Rec: 0.449 D_GP: 0.030 D_real: 1.006 D_fake: 0.751 
+(epoch: 457, iters: 958, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.680 G_ID: 0.098 G_Rec: 0.300 D_GP: 0.029 D_real: 0.943 D_fake: 0.923 
+(epoch: 457, iters: 1358, time: 0.063) G_GAN: 0.477 G_GAN_Feat: 0.965 G_ID: 0.122 G_Rec: 0.417 D_GP: 0.039 D_real: 1.033 D_fake: 0.526 
+(epoch: 457, iters: 1758, time: 0.063) G_GAN: 0.095 G_GAN_Feat: 0.656 G_ID: 0.093 G_Rec: 0.267 D_GP: 0.038 D_real: 0.967 D_fake: 0.905 
+(epoch: 457, iters: 2158, time: 0.063) G_GAN: 0.476 G_GAN_Feat: 0.987 G_ID: 0.112 G_Rec: 0.444 D_GP: 0.037 D_real: 1.024 D_fake: 0.526 
+(epoch: 457, iters: 2558, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.775 G_ID: 0.099 G_Rec: 0.279 D_GP: 0.066 D_real: 0.730 D_fake: 0.889 
+(epoch: 457, iters: 2958, time: 0.063) G_GAN: 0.647 G_GAN_Feat: 1.095 G_ID: 0.113 G_Rec: 0.470 D_GP: 0.048 D_real: 0.950 D_fake: 0.482 
+(epoch: 457, iters: 3358, time: 0.063) G_GAN: 0.339 G_GAN_Feat: 0.671 G_ID: 0.124 G_Rec: 0.335 D_GP: 0.044 D_real: 1.275 D_fake: 0.663 
+(epoch: 457, iters: 3758, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 0.809 G_ID: 0.112 G_Rec: 0.391 D_GP: 0.029 D_real: 1.123 D_fake: 0.531 
+(epoch: 457, iters: 4158, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.714 G_ID: 0.107 G_Rec: 0.319 D_GP: 0.040 D_real: 1.097 D_fake: 0.781 
+(epoch: 457, iters: 4558, time: 0.063) G_GAN: 0.359 G_GAN_Feat: 0.822 G_ID: 0.117 G_Rec: 0.398 D_GP: 0.029 D_real: 1.077 D_fake: 0.653 
+(epoch: 457, iters: 4958, time: 0.063) G_GAN: -0.047 G_GAN_Feat: 0.658 G_ID: 0.096 G_Rec: 0.318 D_GP: 0.034 D_real: 0.871 D_fake: 1.047 
+(epoch: 457, iters: 5358, time: 0.063) G_GAN: 0.081 G_GAN_Feat: 0.856 G_ID: 0.114 G_Rec: 0.426 D_GP: 0.033 D_real: 0.738 D_fake: 0.919 
+(epoch: 457, iters: 5758, time: 0.063) G_GAN: 0.073 G_GAN_Feat: 0.686 G_ID: 0.104 G_Rec: 0.331 D_GP: 0.040 D_real: 0.937 D_fake: 0.927 
+(epoch: 457, iters: 6158, time: 0.063) G_GAN: 0.311 G_GAN_Feat: 0.846 G_ID: 0.100 G_Rec: 0.413 D_GP: 0.037 D_real: 1.017 D_fake: 0.704 
+(epoch: 457, iters: 6558, time: 0.063) G_GAN: 0.022 G_GAN_Feat: 0.650 G_ID: 0.090 G_Rec: 0.294 D_GP: 0.046 D_real: 0.926 D_fake: 0.979 
+(epoch: 457, iters: 6958, time: 0.064) G_GAN: 0.158 G_GAN_Feat: 0.869 G_ID: 0.110 G_Rec: 0.409 D_GP: 0.037 D_real: 0.820 D_fake: 0.844 
+(epoch: 457, iters: 7358, time: 0.063) G_GAN: 0.157 G_GAN_Feat: 0.674 G_ID: 0.091 G_Rec: 0.340 D_GP: 0.035 D_real: 0.927 D_fake: 0.843 
+(epoch: 457, iters: 7758, time: 0.063) G_GAN: 0.348 G_GAN_Feat: 0.911 G_ID: 0.110 G_Rec: 0.476 D_GP: 0.036 D_real: 0.974 D_fake: 0.658 
+(epoch: 457, iters: 8158, time: 0.063) G_GAN: 0.068 G_GAN_Feat: 0.639 G_ID: 0.102 G_Rec: 0.275 D_GP: 0.036 D_real: 0.946 D_fake: 0.932 
+(epoch: 457, iters: 8558, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 1.028 G_ID: 0.113 G_Rec: 0.471 D_GP: 0.098 D_real: 0.804 D_fake: 0.568 
+(epoch: 458, iters: 350, time: 0.063) G_GAN: 0.230 G_GAN_Feat: 0.670 G_ID: 0.084 G_Rec: 0.287 D_GP: 0.042 D_real: 1.092 D_fake: 0.774 
+(epoch: 458, iters: 750, time: 0.063) G_GAN: 0.168 G_GAN_Feat: 0.983 G_ID: 0.120 G_Rec: 0.467 D_GP: 0.060 D_real: 0.685 D_fake: 0.835 
+(epoch: 458, iters: 1150, time: 0.063) G_GAN: 0.274 G_GAN_Feat: 0.748 G_ID: 0.107 G_Rec: 0.307 D_GP: 0.084 D_real: 0.882 D_fake: 0.729 
+(epoch: 458, iters: 1550, time: 0.064) G_GAN: 0.294 G_GAN_Feat: 0.882 G_ID: 0.145 G_Rec: 0.418 D_GP: 0.044 D_real: 0.884 D_fake: 0.706 
+(epoch: 458, iters: 1950, time: 0.063) G_GAN: 0.148 G_GAN_Feat: 0.734 G_ID: 0.084 G_Rec: 0.309 D_GP: 0.040 D_real: 0.940 D_fake: 0.852 
+(epoch: 458, iters: 2350, time: 0.063) G_GAN: 0.276 G_GAN_Feat: 0.915 G_ID: 0.127 G_Rec: 0.417 D_GP: 0.087 D_real: 0.818 D_fake: 0.724 
+(epoch: 458, iters: 2750, time: 0.063) G_GAN: 0.389 G_GAN_Feat: 0.765 G_ID: 0.099 G_Rec: 0.322 D_GP: 0.039 D_real: 1.165 D_fake: 0.626 
+(epoch: 458, iters: 3150, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.945 G_ID: 0.139 G_Rec: 0.403 D_GP: 0.042 D_real: 0.988 D_fake: 0.551 
+(epoch: 458, iters: 3550, time: 0.063) G_GAN: 0.121 G_GAN_Feat: 0.781 G_ID: 0.104 G_Rec: 0.332 D_GP: 0.051 D_real: 0.799 D_fake: 0.879 
+(epoch: 458, iters: 3950, time: 0.063) G_GAN: 0.793 G_GAN_Feat: 0.967 G_ID: 0.111 G_Rec: 0.405 D_GP: 0.047 D_real: 1.485 D_fake: 0.252 
+(epoch: 458, iters: 4350, time: 0.063) G_GAN: 0.019 G_GAN_Feat: 0.848 G_ID: 0.080 G_Rec: 0.346 D_GP: 0.092 D_real: 0.393 D_fake: 0.981 
+(epoch: 458, iters: 4750, time: 0.064) G_GAN: 0.330 G_GAN_Feat: 0.970 G_ID: 0.116 G_Rec: 0.432 D_GP: 0.044 D_real: 0.788 D_fake: 0.671 
+(epoch: 458, iters: 5150, time: 0.063) G_GAN: 0.421 G_GAN_Feat: 0.765 G_ID: 0.103 G_Rec: 0.300 D_GP: 0.032 D_real: 1.137 D_fake: 0.583 
+(epoch: 458, iters: 5550, time: 0.063) G_GAN: 0.478 G_GAN_Feat: 0.969 G_ID: 0.128 G_Rec: 0.423 D_GP: 0.037 D_real: 1.105 D_fake: 0.527 
+(epoch: 458, iters: 5950, time: 0.063) G_GAN: 0.031 G_GAN_Feat: 0.880 G_ID: 0.109 G_Rec: 0.335 D_GP: 0.084 D_real: 0.419 D_fake: 0.969 
+(epoch: 458, iters: 6350, time: 0.064) G_GAN: 0.452 G_GAN_Feat: 0.937 G_ID: 0.096 G_Rec: 0.407 D_GP: 0.052 D_real: 0.967 D_fake: 0.550 
+(epoch: 458, iters: 6750, time: 0.063) G_GAN: -0.005 G_GAN_Feat: 0.782 G_ID: 0.104 G_Rec: 0.324 D_GP: 0.042 D_real: 0.570 D_fake: 1.005 
+(epoch: 458, iters: 7150, time: 0.063) G_GAN: 0.398 G_GAN_Feat: 0.889 G_ID: 0.113 G_Rec: 0.429 D_GP: 0.035 D_real: 1.120 D_fake: 0.604 
+(epoch: 458, iters: 7550, time: 0.063) G_GAN: 0.258 G_GAN_Feat: 0.688 G_ID: 0.090 G_Rec: 0.325 D_GP: 0.055 D_real: 1.068 D_fake: 0.743 
+(epoch: 458, iters: 7950, time: 0.064) G_GAN: 0.634 G_GAN_Feat: 1.037 G_ID: 0.114 G_Rec: 0.429 D_GP: 0.158 D_real: 0.790 D_fake: 0.406 
+(epoch: 458, iters: 8350, time: 0.063) G_GAN: 0.177 G_GAN_Feat: 0.756 G_ID: 0.102 G_Rec: 0.308 D_GP: 0.053 D_real: 0.938 D_fake: 0.827 
+(epoch: 459, iters: 142, time: 0.063) G_GAN: 0.084 G_GAN_Feat: 0.985 G_ID: 0.112 G_Rec: 0.461 D_GP: 0.036 D_real: 0.612 D_fake: 0.916 
+(epoch: 459, iters: 542, time: 0.063) G_GAN: -0.042 G_GAN_Feat: 0.692 G_ID: 0.099 G_Rec: 0.289 D_GP: 0.054 D_real: 0.763 D_fake: 1.044 
+(epoch: 459, iters: 942, time: 0.064) G_GAN: 0.446 G_GAN_Feat: 1.067 G_ID: 0.145 G_Rec: 0.457 D_GP: 0.054 D_real: 0.789 D_fake: 0.562 
+(epoch: 459, iters: 1342, time: 0.063) G_GAN: -0.025 G_GAN_Feat: 0.892 G_ID: 0.121 G_Rec: 0.332 D_GP: 0.685 D_real: 0.179 D_fake: 1.031 
+(epoch: 459, iters: 1742, time: 0.063) G_GAN: 0.356 G_GAN_Feat: 1.064 G_ID: 0.116 G_Rec: 0.447 D_GP: 0.082 D_real: 0.566 D_fake: 0.645 
+(epoch: 459, iters: 2142, time: 0.063) G_GAN: 0.234 G_GAN_Feat: 0.766 G_ID: 0.083 G_Rec: 0.297 D_GP: 0.033 D_real: 1.021 D_fake: 0.766 
+(epoch: 459, iters: 2542, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.899 G_ID: 0.121 G_Rec: 0.394 D_GP: 0.045 D_real: 1.255 D_fake: 0.533 
+(epoch: 459, iters: 2942, time: 0.063) G_GAN: 0.272 G_GAN_Feat: 0.719 G_ID: 0.104 G_Rec: 0.307 D_GP: 0.032 D_real: 1.065 D_fake: 0.731 
+(epoch: 459, iters: 3342, time: 0.063) G_GAN: 0.585 G_GAN_Feat: 0.982 G_ID: 0.108 G_Rec: 0.395 D_GP: 0.042 D_real: 1.080 D_fake: 0.424 
+(epoch: 459, iters: 3742, time: 0.063) G_GAN: 0.128 G_GAN_Feat: 0.821 G_ID: 0.125 G_Rec: 0.278 D_GP: 0.082 D_real: 0.591 D_fake: 0.874 
+(epoch: 459, iters: 4142, time: 0.064) G_GAN: 0.196 G_GAN_Feat: 1.077 G_ID: 0.108 G_Rec: 0.452 D_GP: 0.131 D_real: 0.584 D_fake: 0.808 
+(epoch: 459, iters: 4542, time: 0.063) G_GAN: 0.244 G_GAN_Feat: 0.753 G_ID: 0.111 G_Rec: 0.300 D_GP: 0.035 D_real: 0.976 D_fake: 0.757 
+(epoch: 459, iters: 4942, time: 0.063) G_GAN: 0.505 G_GAN_Feat: 0.990 G_ID: 0.115 G_Rec: 0.421 D_GP: 0.097 D_real: 0.747 D_fake: 0.511 
+(epoch: 459, iters: 5342, time: 0.063) G_GAN: -0.010 G_GAN_Feat: 0.857 G_ID: 0.114 G_Rec: 0.308 D_GP: 0.051 D_real: 0.528 D_fake: 1.010 
+(epoch: 459, iters: 5742, time: 0.064) G_GAN: 0.601 G_GAN_Feat: 1.082 G_ID: 0.128 G_Rec: 0.402 D_GP: 0.041 D_real: 0.629 D_fake: 0.408 
+(epoch: 459, iters: 6142, time: 0.063) G_GAN: 0.597 G_GAN_Feat: 0.867 G_ID: 0.102 G_Rec: 0.331 D_GP: 0.041 D_real: 1.389 D_fake: 0.427 
+(epoch: 459, iters: 6542, time: 0.063) G_GAN: 0.226 G_GAN_Feat: 0.995 G_ID: 0.119 G_Rec: 0.430 D_GP: 0.182 D_real: 0.505 D_fake: 0.775 
+(epoch: 459, iters: 6942, time: 0.063) G_GAN: 0.099 G_GAN_Feat: 0.759 G_ID: 0.091 G_Rec: 0.299 D_GP: 0.033 D_real: 0.924 D_fake: 0.901 
+(epoch: 459, iters: 7342, time: 0.064) G_GAN: 0.471 G_GAN_Feat: 1.064 G_ID: 0.122 G_Rec: 0.480 D_GP: 0.039 D_real: 0.941 D_fake: 0.536 
+(epoch: 459, iters: 7742, time: 0.063) G_GAN: 0.620 G_GAN_Feat: 0.914 G_ID: 0.094 G_Rec: 0.312 D_GP: 0.057 D_real: 0.685 D_fake: 0.402 
+(epoch: 459, iters: 8142, time: 0.063) G_GAN: 0.346 G_GAN_Feat: 0.931 G_ID: 0.126 G_Rec: 0.443 D_GP: 0.033 D_real: 0.985 D_fake: 0.654 
+(epoch: 459, iters: 8542, time: 0.063) G_GAN: 0.134 G_GAN_Feat: 0.795 G_ID: 0.098 G_Rec: 0.336 D_GP: 0.078 D_real: 0.803 D_fake: 0.867 
+(epoch: 460, iters: 334, time: 0.064) G_GAN: 0.411 G_GAN_Feat: 0.947 G_ID: 0.108 G_Rec: 0.434 D_GP: 0.033 D_real: 1.018 D_fake: 0.592 
+(epoch: 460, iters: 734, time: 0.063) G_GAN: 0.200 G_GAN_Feat: 0.787 G_ID: 0.112 G_Rec: 0.303 D_GP: 0.048 D_real: 0.959 D_fake: 0.802 
+(epoch: 460, iters: 1134, time: 0.063) G_GAN: 0.459 G_GAN_Feat: 1.056 G_ID: 0.122 G_Rec: 0.449 D_GP: 0.033 D_real: 0.979 D_fake: 0.544 
+(epoch: 460, iters: 1534, time: 0.063) G_GAN: 0.069 G_GAN_Feat: 0.795 G_ID: 0.095 G_Rec: 0.333 D_GP: 0.045 D_real: 0.849 D_fake: 0.931 
+(epoch: 460, iters: 1934, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.998 G_ID: 0.122 G_Rec: 0.424 D_GP: 0.037 D_real: 0.957 D_fake: 0.505 
+(epoch: 460, iters: 2334, time: 0.063) G_GAN: 0.367 G_GAN_Feat: 0.874 G_ID: 0.099 G_Rec: 0.376 D_GP: 0.034 D_real: 0.953 D_fake: 0.633 
+(epoch: 460, iters: 2734, time: 0.063) G_GAN: 0.706 G_GAN_Feat: 1.097 G_ID: 0.132 G_Rec: 0.437 D_GP: 0.128 D_real: 0.548 D_fake: 0.329 
+(epoch: 460, iters: 3134, time: 0.063) G_GAN: 0.053 G_GAN_Feat: 0.748 G_ID: 0.109 G_Rec: 0.358 D_GP: 0.032 D_real: 0.958 D_fake: 0.947 
+(epoch: 460, iters: 3534, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.884 G_ID: 0.122 G_Rec: 0.411 D_GP: 0.033 D_real: 1.043 D_fake: 0.754 
+(epoch: 460, iters: 3934, time: 0.063) G_GAN: 0.022 G_GAN_Feat: 0.683 G_ID: 0.115 G_Rec: 0.279 D_GP: 0.035 D_real: 0.883 D_fake: 0.978 
+(epoch: 460, iters: 4334, time: 0.063) G_GAN: 0.286 G_GAN_Feat: 0.889 G_ID: 0.135 G_Rec: 0.397 D_GP: 0.036 D_real: 0.932 D_fake: 0.717 
+(epoch: 460, iters: 4734, time: 0.063) G_GAN: -0.009 G_GAN_Feat: 0.784 G_ID: 0.100 G_Rec: 0.338 D_GP: 0.057 D_real: 0.760 D_fake: 1.009 
+(epoch: 460, iters: 5134, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.950 G_ID: 0.117 G_Rec: 0.423 D_GP: 0.034 D_real: 1.086 D_fake: 0.579 
+(epoch: 460, iters: 5534, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.739 G_ID: 0.095 G_Rec: 0.286 D_GP: 0.036 D_real: 1.142 D_fake: 0.702 
+(epoch: 460, iters: 5934, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.988 G_ID: 0.112 G_Rec: 0.454 D_GP: 0.033 D_real: 0.945 D_fake: 0.693 
+(epoch: 460, iters: 6334, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.849 G_ID: 0.098 G_Rec: 0.307 D_GP: 0.050 D_real: 0.632 D_fake: 0.721 
+(epoch: 460, iters: 6734, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 1.172 G_ID: 0.123 G_Rec: 0.479 D_GP: 0.072 D_real: 0.183 D_fake: 0.810 
+(epoch: 460, iters: 7134, time: 0.064) G_GAN: 0.248 G_GAN_Feat: 0.741 G_ID: 0.091 G_Rec: 0.310 D_GP: 0.033 D_real: 1.206 D_fake: 0.754 
+(epoch: 460, iters: 7534, time: 0.064) G_GAN: 0.292 G_GAN_Feat: 0.993 G_ID: 0.128 G_Rec: 0.430 D_GP: 0.039 D_real: 0.755 D_fake: 0.708 
+(epoch: 460, iters: 7934, time: 0.064) G_GAN: 0.423 G_GAN_Feat: 0.868 G_ID: 0.097 G_Rec: 0.303 D_GP: 0.068 D_real: 0.775 D_fake: 0.583 
+(epoch: 460, iters: 8334, time: 0.064) G_GAN: 0.745 G_GAN_Feat: 1.277 G_ID: 0.109 G_Rec: 0.506 D_GP: 0.106 D_real: 0.942 D_fake: 0.375 
+(epoch: 461, iters: 126, time: 0.064) G_GAN: 0.372 G_GAN_Feat: 0.967 G_ID: 0.101 G_Rec: 0.366 D_GP: 0.227 D_real: 0.323 D_fake: 0.696 
+(epoch: 461, iters: 526, time: 0.063) G_GAN: 0.256 G_GAN_Feat: 1.001 G_ID: 0.149 G_Rec: 0.449 D_GP: 0.038 D_real: 0.911 D_fake: 0.745 
+(epoch: 461, iters: 926, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.836 G_ID: 0.099 G_Rec: 0.326 D_GP: 0.042 D_real: 0.797 D_fake: 0.792 
+(epoch: 461, iters: 1326, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.948 G_ID: 0.115 G_Rec: 0.388 D_GP: 0.035 D_real: 1.173 D_fake: 0.546 
+(epoch: 461, iters: 1726, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.812 G_ID: 0.127 G_Rec: 0.357 D_GP: 0.039 D_real: 1.075 D_fake: 0.827 
+(epoch: 461, iters: 2126, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.871 G_ID: 0.104 G_Rec: 0.419 D_GP: 0.030 D_real: 1.061 D_fake: 0.645 
+(epoch: 461, iters: 2526, time: 0.063) G_GAN: 0.067 G_GAN_Feat: 0.656 G_ID: 0.110 G_Rec: 0.294 D_GP: 0.033 D_real: 1.012 D_fake: 0.934 
+(epoch: 461, iters: 2926, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 0.882 G_ID: 0.115 G_Rec: 0.410 D_GP: 0.045 D_real: 0.996 D_fake: 0.672 
+(epoch: 461, iters: 3326, time: 0.064) G_GAN: 0.109 G_GAN_Feat: 0.728 G_ID: 0.081 G_Rec: 0.321 D_GP: 0.035 D_real: 0.928 D_fake: 0.892 
+(epoch: 461, iters: 3726, time: 0.063) G_GAN: 0.317 G_GAN_Feat: 0.959 G_ID: 0.106 G_Rec: 0.434 D_GP: 0.038 D_real: 0.980 D_fake: 0.689 
+(epoch: 461, iters: 4126, time: 0.064) G_GAN: -0.106 G_GAN_Feat: 0.697 G_ID: 0.109 G_Rec: 0.297 D_GP: 0.041 D_real: 0.785 D_fake: 1.106 
+(epoch: 461, iters: 4526, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 0.873 G_ID: 0.121 G_Rec: 0.404 D_GP: 0.040 D_real: 0.831 D_fake: 0.790 
+(epoch: 461, iters: 4926, time: 0.063) G_GAN: 0.216 G_GAN_Feat: 0.761 G_ID: 0.111 G_Rec: 0.331 D_GP: 0.036 D_real: 0.980 D_fake: 0.785 
+(epoch: 461, iters: 5326, time: 0.064) G_GAN: 0.633 G_GAN_Feat: 0.916 G_ID: 0.102 G_Rec: 0.403 D_GP: 0.043 D_real: 1.341 D_fake: 0.405 
+(epoch: 461, iters: 5726, time: 0.064) G_GAN: 0.565 G_GAN_Feat: 0.679 G_ID: 0.101 G_Rec: 0.293 D_GP: 0.030 D_real: 1.464 D_fake: 0.462 
+(epoch: 461, iters: 6126, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 1.004 G_ID: 0.118 G_Rec: 0.455 D_GP: 0.052 D_real: 0.813 D_fake: 0.662 
+(epoch: 461, iters: 6526, time: 0.064) G_GAN: 0.048 G_GAN_Feat: 0.791 G_ID: 0.082 G_Rec: 0.378 D_GP: 0.044 D_real: 0.954 D_fake: 0.953 
+(epoch: 461, iters: 6926, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.033 G_ID: 0.113 G_Rec: 0.453 D_GP: 0.045 D_real: 0.798 D_fake: 0.652 
+(epoch: 461, iters: 7326, time: 0.064) G_GAN: 0.149 G_GAN_Feat: 0.743 G_ID: 0.094 G_Rec: 0.296 D_GP: 0.044 D_real: 0.931 D_fake: 0.851 
+(epoch: 461, iters: 7726, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 1.151 G_ID: 0.112 G_Rec: 0.496 D_GP: 0.209 D_real: 0.413 D_fake: 0.565 
+(epoch: 461, iters: 8126, time: 0.064) G_GAN: 0.466 G_GAN_Feat: 0.858 G_ID: 0.097 G_Rec: 0.305 D_GP: 0.040 D_real: 1.133 D_fake: 0.541 
+(epoch: 461, iters: 8526, time: 0.064) G_GAN: 0.543 G_GAN_Feat: 1.046 G_ID: 0.114 G_Rec: 0.458 D_GP: 0.065 D_real: 0.949 D_fake: 0.523 
+(epoch: 462, iters: 318, time: 0.064) G_GAN: 0.018 G_GAN_Feat: 0.733 G_ID: 0.102 G_Rec: 0.330 D_GP: 0.037 D_real: 0.960 D_fake: 0.982 
+(epoch: 462, iters: 718, time: 0.064) G_GAN: 0.684 G_GAN_Feat: 0.891 G_ID: 0.104 G_Rec: 0.420 D_GP: 0.030 D_real: 1.405 D_fake: 0.349 
+(epoch: 462, iters: 1118, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.798 G_ID: 0.109 G_Rec: 0.311 D_GP: 0.055 D_real: 0.697 D_fake: 0.923 
+(epoch: 462, iters: 1518, time: 0.064) G_GAN: 0.859 G_GAN_Feat: 0.964 G_ID: 0.106 G_Rec: 0.447 D_GP: 0.039 D_real: 1.615 D_fake: 0.239 
+(epoch: 462, iters: 1918, time: 0.064) G_GAN: -0.037 G_GAN_Feat: 0.780 G_ID: 0.107 G_Rec: 0.330 D_GP: 0.031 D_real: 0.821 D_fake: 1.037 
+(epoch: 462, iters: 2318, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.871 G_ID: 0.116 G_Rec: 0.387 D_GP: 0.029 D_real: 0.749 D_fake: 0.914 
+(epoch: 462, iters: 2718, time: 0.064) G_GAN: 0.123 G_GAN_Feat: 0.641 G_ID: 0.104 G_Rec: 0.280 D_GP: 0.033 D_real: 1.030 D_fake: 0.877 
+(epoch: 462, iters: 3118, time: 0.064) G_GAN: 0.133 G_GAN_Feat: 0.920 G_ID: 0.126 G_Rec: 0.399 D_GP: 0.050 D_real: 0.685 D_fake: 0.867 
+(epoch: 462, iters: 3518, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.650 G_ID: 0.096 G_Rec: 0.293 D_GP: 0.032 D_real: 1.146 D_fake: 0.745 
+(epoch: 462, iters: 3918, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 1.026 G_ID: 0.124 G_Rec: 0.467 D_GP: 0.072 D_real: 0.726 D_fake: 0.594 
+(epoch: 462, iters: 4318, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.897 G_ID: 0.108 G_Rec: 0.368 D_GP: 0.071 D_real: 0.727 D_fake: 0.817 
+(epoch: 462, iters: 4718, time: 0.064) G_GAN: 0.607 G_GAN_Feat: 0.920 G_ID: 0.114 G_Rec: 0.390 D_GP: 0.037 D_real: 1.238 D_fake: 0.415 
+(epoch: 462, iters: 5118, time: 0.064) G_GAN: 0.005 G_GAN_Feat: 0.821 G_ID: 0.099 G_Rec: 0.323 D_GP: 0.043 D_real: 0.719 D_fake: 0.995 
+(epoch: 462, iters: 5518, time: 0.064) G_GAN: 0.523 G_GAN_Feat: 1.201 G_ID: 0.128 G_Rec: 0.477 D_GP: 0.188 D_real: 0.300 D_fake: 0.491 
+(epoch: 462, iters: 5918, time: 0.064) G_GAN: 0.026 G_GAN_Feat: 1.074 G_ID: 0.096 G_Rec: 0.339 D_GP: 1.703 D_real: 0.660 D_fake: 0.996 
+(epoch: 462, iters: 6318, time: 0.064) G_GAN: 0.573 G_GAN_Feat: 1.032 G_ID: 0.109 G_Rec: 0.476 D_GP: 0.034 D_real: 1.135 D_fake: 0.451 
+(epoch: 462, iters: 6718, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.700 G_ID: 0.101 G_Rec: 0.305 D_GP: 0.040 D_real: 1.139 D_fake: 0.794 
+(epoch: 462, iters: 7118, time: 0.064) G_GAN: 0.491 G_GAN_Feat: 1.024 G_ID: 0.127 G_Rec: 0.389 D_GP: 0.050 D_real: 1.141 D_fake: 0.559 
+(epoch: 462, iters: 7518, time: 0.064) G_GAN: 0.570 G_GAN_Feat: 0.687 G_ID: 0.101 G_Rec: 0.281 D_GP: 0.031 D_real: 1.424 D_fake: 0.440 
+(epoch: 462, iters: 7918, time: 0.064) G_GAN: 0.354 G_GAN_Feat: 1.115 G_ID: 0.127 G_Rec: 0.415 D_GP: 0.047 D_real: 0.976 D_fake: 0.647 
+(epoch: 462, iters: 8318, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.910 G_ID: 0.103 G_Rec: 0.326 D_GP: 0.059 D_real: 0.395 D_fake: 0.695 
+(epoch: 463, iters: 110, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 1.282 G_ID: 0.116 G_Rec: 0.497 D_GP: 0.174 D_real: 0.351 D_fake: 0.513 
+(epoch: 463, iters: 510, time: 0.064) G_GAN: 0.363 G_GAN_Feat: 0.946 G_ID: 0.098 G_Rec: 0.336 D_GP: 0.092 D_real: 0.584 D_fake: 0.638 
+(epoch: 463, iters: 910, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.964 G_ID: 0.112 G_Rec: 0.459 D_GP: 0.030 D_real: 1.104 D_fake: 0.572 
+(epoch: 463, iters: 1310, time: 0.064) G_GAN: 0.242 G_GAN_Feat: 0.671 G_ID: 0.095 G_Rec: 0.291 D_GP: 0.029 D_real: 1.191 D_fake: 0.758 
+(epoch: 463, iters: 1710, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.855 G_ID: 0.123 G_Rec: 0.418 D_GP: 0.037 D_real: 0.796 D_fake: 0.909 
+(epoch: 463, iters: 2110, time: 0.064) G_GAN: -0.052 G_GAN_Feat: 0.719 G_ID: 0.095 G_Rec: 0.344 D_GP: 0.036 D_real: 0.802 D_fake: 1.052 
+(epoch: 463, iters: 2510, time: 0.063) G_GAN: 0.283 G_GAN_Feat: 0.883 G_ID: 0.115 G_Rec: 0.405 D_GP: 0.034 D_real: 1.023 D_fake: 0.730 
+(epoch: 463, iters: 2910, time: 0.064) G_GAN: 0.236 G_GAN_Feat: 0.670 G_ID: 0.093 G_Rec: 0.308 D_GP: 0.035 D_real: 1.105 D_fake: 0.768 
+(epoch: 463, iters: 3310, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.918 G_ID: 0.113 G_Rec: 0.428 D_GP: 0.033 D_real: 0.677 D_fake: 0.886 
+(epoch: 463, iters: 3710, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.699 G_ID: 0.088 G_Rec: 0.311 D_GP: 0.050 D_real: 0.926 D_fake: 0.890 
+(epoch: 463, iters: 4110, time: 0.064) G_GAN: 0.329 G_GAN_Feat: 1.008 G_ID: 0.112 G_Rec: 0.465 D_GP: 0.057 D_real: 0.596 D_fake: 0.693 
+(epoch: 463, iters: 4510, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.763 G_ID: 0.100 G_Rec: 0.285 D_GP: 0.044 D_real: 1.197 D_fake: 0.765 
+(epoch: 463, iters: 4910, time: 0.064) G_GAN: 0.677 G_GAN_Feat: 0.970 G_ID: 0.128 G_Rec: 0.452 D_GP: 0.046 D_real: 1.199 D_fake: 0.342 
+(epoch: 463, iters: 5310, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.753 G_ID: 0.089 G_Rec: 0.305 D_GP: 0.045 D_real: 1.012 D_fake: 0.670 
+(epoch: 463, iters: 5710, time: 0.064) G_GAN: 0.440 G_GAN_Feat: 0.911 G_ID: 0.118 G_Rec: 0.423 D_GP: 0.031 D_real: 1.213 D_fake: 0.564 
+(epoch: 463, iters: 6110, time: 0.064) G_GAN: 0.287 G_GAN_Feat: 0.722 G_ID: 0.092 G_Rec: 0.295 D_GP: 0.032 D_real: 1.158 D_fake: 0.714 
+(epoch: 463, iters: 6510, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.894 G_ID: 0.107 G_Rec: 0.400 D_GP: 0.032 D_real: 1.054 D_fake: 0.575 
+(epoch: 463, iters: 6910, time: 0.064) G_GAN: 0.253 G_GAN_Feat: 0.860 G_ID: 0.111 G_Rec: 0.317 D_GP: 0.152 D_real: 0.574 D_fake: 0.750 
+(epoch: 463, iters: 7310, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 1.070 G_ID: 0.130 G_Rec: 0.530 D_GP: 0.704 D_real: 0.307 D_fake: 0.700 
+(epoch: 463, iters: 7710, time: 0.064) G_GAN: 0.427 G_GAN_Feat: 0.818 G_ID: 0.096 G_Rec: 0.321 D_GP: 0.049 D_real: 1.063 D_fake: 0.578 
+(epoch: 463, iters: 8110, time: 0.063) G_GAN: 0.463 G_GAN_Feat: 0.946 G_ID: 0.110 G_Rec: 0.469 D_GP: 0.035 D_real: 1.056 D_fake: 0.554 
+(epoch: 463, iters: 8510, time: 0.064) G_GAN: 0.045 G_GAN_Feat: 0.585 G_ID: 0.098 G_Rec: 0.279 D_GP: 0.027 D_real: 0.997 D_fake: 0.955 
+(epoch: 464, iters: 302, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.968 G_ID: 0.113 G_Rec: 0.465 D_GP: 0.036 D_real: 0.712 D_fake: 0.786 
+(epoch: 464, iters: 702, time: 0.064) G_GAN: -0.066 G_GAN_Feat: 0.719 G_ID: 0.104 G_Rec: 0.325 D_GP: 0.050 D_real: 0.769 D_fake: 1.067 
+(epoch: 464, iters: 1102, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.910 G_ID: 0.111 G_Rec: 0.439 D_GP: 0.065 D_real: 1.015 D_fake: 0.569 
+(epoch: 464, iters: 1502, time: 0.064) G_GAN: -0.025 G_GAN_Feat: 0.697 G_ID: 0.117 G_Rec: 0.294 D_GP: 0.037 D_real: 0.847 D_fake: 1.025 
+(epoch: 464, iters: 1902, time: 0.064) G_GAN: 0.385 G_GAN_Feat: 0.911 G_ID: 0.113 G_Rec: 0.428 D_GP: 0.036 D_real: 0.980 D_fake: 0.629 
+(epoch: 464, iters: 2302, time: 0.064) G_GAN: 0.125 G_GAN_Feat: 0.656 G_ID: 0.091 G_Rec: 0.277 D_GP: 0.030 D_real: 0.997 D_fake: 0.875 
+(epoch: 464, iters: 2702, time: 0.064) G_GAN: 0.344 G_GAN_Feat: 1.050 G_ID: 0.117 G_Rec: 0.495 D_GP: 0.079 D_real: 0.628 D_fake: 0.670 
+(epoch: 464, iters: 3102, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.699 G_ID: 0.090 G_Rec: 0.307 D_GP: 0.038 D_real: 1.065 D_fake: 0.829 
+(epoch: 464, iters: 3502, time: 0.064) G_GAN: 0.675 G_GAN_Feat: 1.026 G_ID: 0.120 G_Rec: 0.399 D_GP: 0.042 D_real: 1.078 D_fake: 0.380 
+(epoch: 464, iters: 3902, time: 0.064) G_GAN: 0.246 G_GAN_Feat: 0.763 G_ID: 0.097 G_Rec: 0.360 D_GP: 0.036 D_real: 1.061 D_fake: 0.754 
+(epoch: 464, iters: 4302, time: 0.064) G_GAN: 0.559 G_GAN_Feat: 0.990 G_ID: 0.115 G_Rec: 0.442 D_GP: 0.056 D_real: 0.813 D_fake: 0.452 
+(epoch: 464, iters: 4702, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 0.795 G_ID: 0.103 G_Rec: 0.329 D_GP: 0.046 D_real: 1.120 D_fake: 0.597 
+(epoch: 464, iters: 5102, time: 0.064) G_GAN: 0.819 G_GAN_Feat: 1.049 G_ID: 0.104 G_Rec: 0.471 D_GP: 0.061 D_real: 1.081 D_fake: 0.274 
+(epoch: 464, iters: 5502, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.798 G_ID: 0.099 G_Rec: 0.332 D_GP: 0.032 D_real: 0.825 D_fake: 0.914 
+(epoch: 464, iters: 5902, time: 0.064) G_GAN: -0.233 G_GAN_Feat: 1.176 G_ID: 0.115 G_Rec: 0.527 D_GP: 0.066 D_real: 0.815 D_fake: 1.234 
+(epoch: 464, iters: 6302, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.687 G_ID: 0.096 G_Rec: 0.303 D_GP: 0.024 D_real: 1.057 D_fake: 0.909 
+(epoch: 464, iters: 6702, time: 0.064) G_GAN: 0.167 G_GAN_Feat: 0.953 G_ID: 0.113 G_Rec: 0.423 D_GP: 0.030 D_real: 0.796 D_fake: 0.838 
+(epoch: 464, iters: 7102, time: 0.064) G_GAN: -0.184 G_GAN_Feat: 0.746 G_ID: 0.094 G_Rec: 0.340 D_GP: 0.044 D_real: 0.651 D_fake: 1.184 
+(epoch: 464, iters: 7502, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 1.011 G_ID: 0.119 G_Rec: 0.475 D_GP: 0.048 D_real: 0.742 D_fake: 0.788 
+(epoch: 464, iters: 7902, time: 0.064) G_GAN: -0.217 G_GAN_Feat: 0.773 G_ID: 0.096 G_Rec: 0.323 D_GP: 0.062 D_real: 0.553 D_fake: 1.217 
+(epoch: 464, iters: 8302, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.930 G_ID: 0.116 G_Rec: 0.380 D_GP: 0.054 D_real: 1.021 D_fake: 0.598 
+(epoch: 464, iters: 8702, time: 0.064) G_GAN: 0.153 G_GAN_Feat: 0.879 G_ID: 0.091 G_Rec: 0.321 D_GP: 0.533 D_real: 0.395 D_fake: 0.847 
+(epoch: 465, iters: 494, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 1.027 G_ID: 0.124 G_Rec: 0.475 D_GP: 0.036 D_real: 1.316 D_fake: 0.486 
+(epoch: 465, iters: 894, time: 0.064) G_GAN: 0.249 G_GAN_Feat: 0.679 G_ID: 0.081 G_Rec: 0.272 D_GP: 0.028 D_real: 1.153 D_fake: 0.752 
+(epoch: 465, iters: 1294, time: 0.064) G_GAN: 0.590 G_GAN_Feat: 0.950 G_ID: 0.119 G_Rec: 0.441 D_GP: 0.040 D_real: 1.252 D_fake: 0.417 
+(epoch: 465, iters: 1694, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.958 G_ID: 0.088 G_Rec: 0.318 D_GP: 0.120 D_real: 0.620 D_fake: 0.518 
+(epoch: 465, iters: 2094, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.933 G_ID: 0.126 G_Rec: 0.451 D_GP: 0.041 D_real: 1.009 D_fake: 0.747 
+(epoch: 465, iters: 2494, time: 0.064) G_GAN: 0.027 G_GAN_Feat: 0.731 G_ID: 0.106 G_Rec: 0.317 D_GP: 0.035 D_real: 0.906 D_fake: 0.973 
+(epoch: 465, iters: 2894, time: 0.064) G_GAN: 0.399 G_GAN_Feat: 0.848 G_ID: 0.098 G_Rec: 0.424 D_GP: 0.027 D_real: 1.119 D_fake: 0.607 
+(epoch: 465, iters: 3294, time: 0.064) G_GAN: 0.129 G_GAN_Feat: 0.657 G_ID: 0.104 G_Rec: 0.303 D_GP: 0.035 D_real: 1.013 D_fake: 0.872 
+(epoch: 465, iters: 3694, time: 0.064) G_GAN: 0.339 G_GAN_Feat: 0.893 G_ID: 0.107 G_Rec: 0.451 D_GP: 0.032 D_real: 1.003 D_fake: 0.675 
+(epoch: 465, iters: 4094, time: 0.064) G_GAN: -0.059 G_GAN_Feat: 0.668 G_ID: 0.095 G_Rec: 0.324 D_GP: 0.030 D_real: 0.847 D_fake: 1.059 
+(epoch: 465, iters: 4494, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.913 G_ID: 0.108 G_Rec: 0.446 D_GP: 0.039 D_real: 1.077 D_fake: 0.602 
+(epoch: 465, iters: 4894, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.728 G_ID: 0.090 G_Rec: 0.325 D_GP: 0.044 D_real: 0.948 D_fake: 0.887 
+(epoch: 465, iters: 5294, time: 0.064) G_GAN: 0.325 G_GAN_Feat: 0.939 G_ID: 0.108 G_Rec: 0.455 D_GP: 0.035 D_real: 0.926 D_fake: 0.679 
+(epoch: 465, iters: 5694, time: 0.064) G_GAN: 0.105 G_GAN_Feat: 0.707 G_ID: 0.095 G_Rec: 0.301 D_GP: 0.041 D_real: 0.949 D_fake: 0.896 
+(epoch: 465, iters: 6094, time: 0.064) G_GAN: 0.210 G_GAN_Feat: 1.010 G_ID: 0.119 G_Rec: 0.448 D_GP: 0.057 D_real: 0.789 D_fake: 0.791 
+(epoch: 465, iters: 6494, time: 0.064) G_GAN: -0.049 G_GAN_Feat: 0.721 G_ID: 0.103 G_Rec: 0.277 D_GP: 0.043 D_real: 0.714 D_fake: 1.049 
+(epoch: 465, iters: 6894, time: 0.063) G_GAN: 0.194 G_GAN_Feat: 0.879 G_ID: 0.129 G_Rec: 0.364 D_GP: 0.040 D_real: 0.973 D_fake: 0.809 
+(epoch: 465, iters: 7294, time: 0.064) G_GAN: 0.054 G_GAN_Feat: 0.791 G_ID: 0.104 G_Rec: 0.323 D_GP: 0.062 D_real: 0.615 D_fake: 0.946 
+(epoch: 465, iters: 7694, time: 0.064) G_GAN: 0.762 G_GAN_Feat: 1.002 G_ID: 0.117 G_Rec: 0.428 D_GP: 0.043 D_real: 1.193 D_fake: 0.301 
+(epoch: 465, iters: 8094, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.845 G_ID: 0.103 G_Rec: 0.340 D_GP: 0.063 D_real: 0.945 D_fake: 0.603 
+(epoch: 465, iters: 8494, time: 0.064) G_GAN: 0.658 G_GAN_Feat: 1.031 G_ID: 0.110 G_Rec: 0.467 D_GP: 0.049 D_real: 1.049 D_fake: 0.373 
+(epoch: 466, iters: 286, time: 0.064) G_GAN: 0.288 G_GAN_Feat: 0.846 G_ID: 0.103 G_Rec: 0.332 D_GP: 0.033 D_real: 0.916 D_fake: 0.719 
+(epoch: 466, iters: 686, time: 0.064) G_GAN: 0.449 G_GAN_Feat: 1.186 G_ID: 0.097 G_Rec: 0.467 D_GP: 0.136 D_real: 0.450 D_fake: 0.561 
+(epoch: 466, iters: 1086, time: 0.064) G_GAN: 0.355 G_GAN_Feat: 0.972 G_ID: 0.105 G_Rec: 0.356 D_GP: 0.075 D_real: 0.494 D_fake: 0.648 
+(epoch: 466, iters: 1486, time: 0.064) G_GAN: 0.954 G_GAN_Feat: 1.159 G_ID: 0.130 G_Rec: 0.465 D_GP: 0.124 D_real: 0.724 D_fake: 0.204 
+(epoch: 466, iters: 1886, time: 0.063) G_GAN: -0.078 G_GAN_Feat: 0.836 G_ID: 0.106 G_Rec: 0.359 D_GP: 0.037 D_real: 0.605 D_fake: 1.078 
+(epoch: 466, iters: 2286, time: 0.064) G_GAN: 0.768 G_GAN_Feat: 1.033 G_ID: 0.120 G_Rec: 0.451 D_GP: 0.042 D_real: 1.474 D_fake: 0.288 
+(epoch: 466, iters: 2686, time: 0.064) G_GAN: 0.166 G_GAN_Feat: 0.748 G_ID: 0.099 G_Rec: 0.276 D_GP: 0.029 D_real: 0.998 D_fake: 0.834 
+(epoch: 466, iters: 3086, time: 0.063) G_GAN: 0.986 G_GAN_Feat: 1.179 G_ID: 0.125 G_Rec: 0.449 D_GP: 0.056 D_real: 1.087 D_fake: 0.195 
+(epoch: 466, iters: 3486, time: 0.063) G_GAN: 0.270 G_GAN_Feat: 0.735 G_ID: 0.083 G_Rec: 0.329 D_GP: 0.031 D_real: 1.142 D_fake: 0.731 
+(epoch: 466, iters: 3886, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.933 G_ID: 0.113 G_Rec: 0.475 D_GP: 0.028 D_real: 1.063 D_fake: 0.611 
+(epoch: 466, iters: 4286, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.648 G_ID: 0.107 G_Rec: 0.289 D_GP: 0.028 D_real: 1.025 D_fake: 0.860 
+(epoch: 466, iters: 4686, time: 0.063) G_GAN: 0.333 G_GAN_Feat: 0.922 G_ID: 0.104 G_Rec: 0.441 D_GP: 0.042 D_real: 0.845 D_fake: 0.680 
+(epoch: 466, iters: 5086, time: 0.063) G_GAN: 0.124 G_GAN_Feat: 0.686 G_ID: 0.081 G_Rec: 0.315 D_GP: 0.059 D_real: 0.903 D_fake: 0.878 
+(epoch: 466, iters: 5486, time: 0.063) G_GAN: 0.549 G_GAN_Feat: 0.920 G_ID: 0.117 G_Rec: 0.464 D_GP: 0.038 D_real: 1.165 D_fake: 0.487 
+(epoch: 466, iters: 5886, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.697 G_ID: 0.088 G_Rec: 0.294 D_GP: 0.039 D_real: 0.978 D_fake: 0.841 
+(epoch: 466, iters: 6286, time: 0.063) G_GAN: 0.422 G_GAN_Feat: 0.953 G_ID: 0.110 G_Rec: 0.414 D_GP: 0.036 D_real: 1.113 D_fake: 0.581 
+(epoch: 466, iters: 6686, time: 0.063) G_GAN: 0.218 G_GAN_Feat: 0.770 G_ID: 0.084 G_Rec: 0.318 D_GP: 0.034 D_real: 1.007 D_fake: 0.783 
+(epoch: 466, iters: 7086, time: 0.063) G_GAN: 0.472 G_GAN_Feat: 0.926 G_ID: 0.110 G_Rec: 0.414 D_GP: 0.036 D_real: 1.159 D_fake: 0.529 
+(epoch: 466, iters: 7486, time: 0.064) G_GAN: 0.138 G_GAN_Feat: 0.813 G_ID: 0.095 G_Rec: 0.309 D_GP: 0.039 D_real: 1.155 D_fake: 0.863 
+(epoch: 466, iters: 7886, time: 0.063) G_GAN: 0.597 G_GAN_Feat: 1.018 G_ID: 0.110 G_Rec: 0.433 D_GP: 0.051 D_real: 1.090 D_fake: 0.423 
+(epoch: 466, iters: 8286, time: 0.063) G_GAN: 0.369 G_GAN_Feat: 0.756 G_ID: 0.091 G_Rec: 0.295 D_GP: 0.030 D_real: 1.263 D_fake: 0.656 
+(epoch: 466, iters: 8686, time: 0.064) G_GAN: 0.636 G_GAN_Feat: 1.090 G_ID: 0.127 G_Rec: 0.446 D_GP: 0.047 D_real: 0.926 D_fake: 0.373 
+(epoch: 467, iters: 478, time: 0.063) G_GAN: 0.696 G_GAN_Feat: 0.719 G_ID: 0.084 G_Rec: 0.258 D_GP: 0.031 D_real: 1.548 D_fake: 0.316 
+(epoch: 467, iters: 878, time: 0.063) G_GAN: 0.449 G_GAN_Feat: 0.965 G_ID: 0.112 G_Rec: 0.465 D_GP: 0.030 D_real: 1.055 D_fake: 0.557 
+(epoch: 467, iters: 1278, time: 0.063) G_GAN: 0.267 G_GAN_Feat: 0.808 G_ID: 0.113 G_Rec: 0.320 D_GP: 0.034 D_real: 0.969 D_fake: 0.733 
+(epoch: 467, iters: 1678, time: 0.064) G_GAN: 0.545 G_GAN_Feat: 1.066 G_ID: 0.110 G_Rec: 0.472 D_GP: 0.032 D_real: 1.127 D_fake: 0.460 
+(epoch: 467, iters: 2078, time: 0.063) G_GAN: 0.309 G_GAN_Feat: 0.740 G_ID: 0.087 G_Rec: 0.287 D_GP: 0.035 D_real: 1.276 D_fake: 0.691 
+(epoch: 467, iters: 2478, time: 0.064) G_GAN: 0.608 G_GAN_Feat: 0.952 G_ID: 0.127 G_Rec: 0.412 D_GP: 0.034 D_real: 1.346 D_fake: 0.415 
+(epoch: 467, iters: 2878, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.965 G_ID: 0.119 G_Rec: 0.340 D_GP: 0.149 D_real: 0.378 D_fake: 0.864 
+(epoch: 467, iters: 3278, time: 0.064) G_GAN: 0.820 G_GAN_Feat: 0.962 G_ID: 0.122 G_Rec: 0.427 D_GP: 0.034 D_real: 1.334 D_fake: 0.272 
+(epoch: 467, iters: 3678, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 1.008 G_ID: 0.108 G_Rec: 0.349 D_GP: 0.056 D_real: 0.330 D_fake: 0.719 
+(epoch: 467, iters: 4078, time: 0.064) G_GAN: 0.589 G_GAN_Feat: 1.235 G_ID: 0.112 G_Rec: 0.426 D_GP: 0.035 D_real: 0.378 D_fake: 0.425 
+(epoch: 467, iters: 4478, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.914 G_ID: 0.105 G_Rec: 0.361 D_GP: 0.076 D_real: 0.767 D_fake: 0.790 
+(epoch: 467, iters: 4878, time: 0.064) G_GAN: 0.561 G_GAN_Feat: 0.953 G_ID: 0.106 G_Rec: 0.437 D_GP: 0.030 D_real: 1.169 D_fake: 0.463 
+(epoch: 467, iters: 5278, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.589 G_ID: 0.093 G_Rec: 0.265 D_GP: 0.024 D_real: 1.313 D_fake: 0.624 
+(epoch: 467, iters: 5678, time: 0.063) G_GAN: 0.441 G_GAN_Feat: 0.872 G_ID: 0.114 G_Rec: 0.396 D_GP: 0.030 D_real: 1.129 D_fake: 0.566 
+(epoch: 467, iters: 6078, time: 0.063) G_GAN: 0.272 G_GAN_Feat: 0.639 G_ID: 0.100 G_Rec: 0.290 D_GP: 0.027 D_real: 1.209 D_fake: 0.731 
+(epoch: 467, iters: 6478, time: 0.064) G_GAN: 0.226 G_GAN_Feat: 0.926 G_ID: 0.107 G_Rec: 0.442 D_GP: 0.040 D_real: 0.769 D_fake: 0.778 
+(epoch: 467, iters: 6878, time: 0.064) G_GAN: 0.018 G_GAN_Feat: 0.687 G_ID: 0.086 G_Rec: 0.275 D_GP: 0.037 D_real: 0.900 D_fake: 0.982 
+(epoch: 467, iters: 7278, time: 0.064) G_GAN: 0.678 G_GAN_Feat: 0.975 G_ID: 0.112 G_Rec: 0.453 D_GP: 0.035 D_real: 1.317 D_fake: 0.360 
+(epoch: 467, iters: 7678, time: 0.064) G_GAN: -0.001 G_GAN_Feat: 0.796 G_ID: 0.157 G_Rec: 0.318 D_GP: 0.034 D_real: 0.829 D_fake: 1.001 
+(epoch: 467, iters: 8078, time: 0.064) G_GAN: 0.356 G_GAN_Feat: 0.930 G_ID: 0.115 G_Rec: 0.421 D_GP: 0.061 D_real: 0.845 D_fake: 0.649 
+(epoch: 467, iters: 8478, time: 0.064) G_GAN: 0.437 G_GAN_Feat: 0.781 G_ID: 0.082 G_Rec: 0.294 D_GP: 0.072 D_real: 0.888 D_fake: 0.565 
+(epoch: 468, iters: 270, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 0.993 G_ID: 0.107 G_Rec: 0.485 D_GP: 0.032 D_real: 1.116 D_fake: 0.551 
+(epoch: 468, iters: 670, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.918 G_ID: 0.103 G_Rec: 0.334 D_GP: 0.095 D_real: 0.377 D_fake: 0.808 
+(epoch: 468, iters: 1070, time: 0.064) G_GAN: 0.980 G_GAN_Feat: 1.116 G_ID: 0.111 G_Rec: 0.498 D_GP: 0.096 D_real: 0.955 D_fake: 0.154 
+(epoch: 468, iters: 1470, time: 0.064) G_GAN: -0.035 G_GAN_Feat: 0.903 G_ID: 0.114 G_Rec: 0.369 D_GP: 0.137 D_real: 0.414 D_fake: 1.035 
+(epoch: 468, iters: 1870, time: 0.064) G_GAN: 0.687 G_GAN_Feat: 1.186 G_ID: 0.108 G_Rec: 0.451 D_GP: 0.103 D_real: 1.061 D_fake: 0.382 
+(epoch: 468, iters: 2270, time: 0.064) G_GAN: 0.284 G_GAN_Feat: 0.732 G_ID: 0.100 G_Rec: 0.283 D_GP: 0.034 D_real: 1.210 D_fake: 0.717 
+(epoch: 468, iters: 2670, time: 0.064) G_GAN: 0.873 G_GAN_Feat: 1.053 G_ID: 0.111 G_Rec: 0.494 D_GP: 0.035 D_real: 1.337 D_fake: 0.215 
+(epoch: 468, iters: 3070, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.840 G_ID: 0.102 G_Rec: 0.315 D_GP: 0.111 D_real: 0.995 D_fake: 0.723 
+(epoch: 468, iters: 3470, time: 0.063) G_GAN: 0.398 G_GAN_Feat: 1.071 G_ID: 0.124 G_Rec: 0.456 D_GP: 0.039 D_real: 0.818 D_fake: 0.603 
+(epoch: 468, iters: 3870, time: 0.064) G_GAN: -0.048 G_GAN_Feat: 0.727 G_ID: 0.098 G_Rec: 0.315 D_GP: 0.034 D_real: 0.784 D_fake: 1.048 
+(epoch: 468, iters: 4270, time: 0.064) G_GAN: 0.281 G_GAN_Feat: 0.937 G_ID: 0.118 G_Rec: 0.434 D_GP: 0.036 D_real: 0.971 D_fake: 0.719 
+(epoch: 468, iters: 4670, time: 0.063) G_GAN: 0.175 G_GAN_Feat: 0.715 G_ID: 0.090 G_Rec: 0.294 D_GP: 0.031 D_real: 1.027 D_fake: 0.825 
+(epoch: 468, iters: 5070, time: 0.063) G_GAN: 0.499 G_GAN_Feat: 1.058 G_ID: 0.116 G_Rec: 0.469 D_GP: 0.049 D_real: 0.843 D_fake: 0.508 
+(epoch: 468, iters: 5470, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.740 G_ID: 0.107 G_Rec: 0.287 D_GP: 0.057 D_real: 0.840 D_fake: 0.827 
+(epoch: 468, iters: 5870, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.838 G_ID: 0.115 G_Rec: 0.434 D_GP: 0.026 D_real: 1.266 D_fake: 0.514 
+(epoch: 468, iters: 6270, time: 0.063) G_GAN: 0.289 G_GAN_Feat: 0.651 G_ID: 0.093 G_Rec: 0.299 D_GP: 0.027 D_real: 1.181 D_fake: 0.715 
+(epoch: 468, iters: 6670, time: 0.064) G_GAN: 0.646 G_GAN_Feat: 0.922 G_ID: 0.110 G_Rec: 0.476 D_GP: 0.032 D_real: 1.282 D_fake: 0.405 
+(epoch: 468, iters: 7070, time: 0.063) G_GAN: 0.191 G_GAN_Feat: 0.720 G_ID: 0.101 G_Rec: 0.359 D_GP: 0.045 D_real: 1.023 D_fake: 0.811 
+(epoch: 468, iters: 7470, time: 0.064) G_GAN: 0.393 G_GAN_Feat: 0.949 G_ID: 0.101 G_Rec: 0.446 D_GP: 0.039 D_real: 1.074 D_fake: 0.609 
+(epoch: 468, iters: 7870, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.745 G_ID: 0.080 G_Rec: 0.354 D_GP: 0.039 D_real: 0.917 D_fake: 0.864 
+(epoch: 468, iters: 8270, time: 0.064) G_GAN: 0.435 G_GAN_Feat: 0.941 G_ID: 0.137 G_Rec: 0.417 D_GP: 0.036 D_real: 1.034 D_fake: 0.566 
+(epoch: 468, iters: 8670, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.829 G_ID: 0.097 G_Rec: 0.324 D_GP: 0.085 D_real: 0.845 D_fake: 0.777 
+(epoch: 469, iters: 462, time: 0.064) G_GAN: 0.561 G_GAN_Feat: 0.969 G_ID: 0.116 G_Rec: 0.484 D_GP: 0.031 D_real: 1.136 D_fake: 0.447 
+(epoch: 469, iters: 862, time: 0.064) G_GAN: 0.157 G_GAN_Feat: 0.785 G_ID: 0.132 G_Rec: 0.308 D_GP: 0.049 D_real: 0.839 D_fake: 0.844 
+(epoch: 469, iters: 1262, time: 0.064) G_GAN: 0.756 G_GAN_Feat: 1.061 G_ID: 0.116 G_Rec: 0.423 D_GP: 0.048 D_real: 1.180 D_fake: 0.360 
+(epoch: 469, iters: 1662, time: 0.064) G_GAN: 0.066 G_GAN_Feat: 0.656 G_ID: 0.125 G_Rec: 0.338 D_GP: 0.028 D_real: 0.969 D_fake: 0.934 
+(epoch: 469, iters: 2062, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 0.786 G_ID: 0.104 G_Rec: 0.373 D_GP: 0.031 D_real: 1.027 D_fake: 0.726 
+(epoch: 469, iters: 2462, time: 0.064) G_GAN: -0.207 G_GAN_Feat: 0.616 G_ID: 0.093 G_Rec: 0.294 D_GP: 0.030 D_real: 0.755 D_fake: 1.207 
+(epoch: 469, iters: 2862, time: 0.064) G_GAN: 0.316 G_GAN_Feat: 0.830 G_ID: 0.118 G_Rec: 0.443 D_GP: 0.029 D_real: 0.944 D_fake: 0.705 
+(epoch: 469, iters: 3262, time: 0.064) G_GAN: -0.113 G_GAN_Feat: 0.640 G_ID: 0.097 G_Rec: 0.302 D_GP: 0.029 D_real: 0.750 D_fake: 1.113 
+(epoch: 469, iters: 3662, time: 0.064) G_GAN: 0.321 G_GAN_Feat: 0.868 G_ID: 0.112 G_Rec: 0.431 D_GP: 0.039 D_real: 0.962 D_fake: 0.698 
+(epoch: 469, iters: 4062, time: 0.064) G_GAN: 0.051 G_GAN_Feat: 0.630 G_ID: 0.097 G_Rec: 0.285 D_GP: 0.040 D_real: 0.864 D_fake: 0.949 
+(epoch: 469, iters: 4462, time: 0.063) G_GAN: 0.351 G_GAN_Feat: 0.838 G_ID: 0.113 G_Rec: 0.387 D_GP: 0.036 D_real: 1.030 D_fake: 0.659 
+(epoch: 469, iters: 4862, time: 0.064) G_GAN: -0.032 G_GAN_Feat: 0.689 G_ID: 0.118 G_Rec: 0.309 D_GP: 0.047 D_real: 0.757 D_fake: 1.032 
+(epoch: 469, iters: 5262, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.974 G_ID: 0.118 G_Rec: 0.455 D_GP: 0.050 D_real: 0.687 D_fake: 0.840 
+(epoch: 469, iters: 5662, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.684 G_ID: 0.101 G_Rec: 0.322 D_GP: 0.037 D_real: 0.970 D_fake: 0.826 
+(epoch: 469, iters: 6062, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.954 G_ID: 0.125 G_Rec: 0.466 D_GP: 0.064 D_real: 0.788 D_fake: 0.841 
+(epoch: 469, iters: 6462, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.799 G_ID: 0.099 G_Rec: 0.349 D_GP: 0.056 D_real: 0.848 D_fake: 0.778 
+(epoch: 469, iters: 6862, time: 0.064) G_GAN: 0.331 G_GAN_Feat: 0.888 G_ID: 0.130 G_Rec: 0.423 D_GP: 0.035 D_real: 0.931 D_fake: 0.673 
+(epoch: 469, iters: 7262, time: 0.064) G_GAN: 0.220 G_GAN_Feat: 0.709 G_ID: 0.096 G_Rec: 0.317 D_GP: 0.034 D_real: 1.105 D_fake: 0.780 
+(epoch: 469, iters: 7662, time: 0.064) G_GAN: 0.562 G_GAN_Feat: 0.946 G_ID: 0.105 G_Rec: 0.426 D_GP: 0.037 D_real: 1.113 D_fake: 0.448 
+(epoch: 469, iters: 8062, time: 0.064) G_GAN: -0.186 G_GAN_Feat: 0.749 G_ID: 0.096 G_Rec: 0.334 D_GP: 0.056 D_real: 0.795 D_fake: 1.186 
+(epoch: 469, iters: 8462, time: 0.064) G_GAN: 0.293 G_GAN_Feat: 0.953 G_ID: 0.112 G_Rec: 0.443 D_GP: 0.077 D_real: 0.840 D_fake: 0.710 
+(epoch: 470, iters: 254, time: 0.064) G_GAN: 0.179 G_GAN_Feat: 0.706 G_ID: 0.084 G_Rec: 0.290 D_GP: 0.034 D_real: 1.144 D_fake: 0.821 
+(epoch: 470, iters: 654, time: 0.064) G_GAN: 0.335 G_GAN_Feat: 1.077 G_ID: 0.135 G_Rec: 0.487 D_GP: 0.185 D_real: 0.306 D_fake: 0.666 
+(epoch: 470, iters: 1054, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.731 G_ID: 0.108 G_Rec: 0.359 D_GP: 0.028 D_real: 1.080 D_fake: 0.832 
+(epoch: 470, iters: 1454, time: 0.064) G_GAN: 0.235 G_GAN_Feat: 0.827 G_ID: 0.127 G_Rec: 0.389 D_GP: 0.033 D_real: 0.892 D_fake: 0.765 
+(epoch: 470, iters: 1854, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.706 G_ID: 0.106 G_Rec: 0.298 D_GP: 0.036 D_real: 0.861 D_fake: 0.926 
+(epoch: 470, iters: 2254, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.962 G_ID: 0.117 G_Rec: 0.445 D_GP: 0.041 D_real: 0.798 D_fake: 0.893 
+(epoch: 470, iters: 2654, time: 0.064) G_GAN: 0.108 G_GAN_Feat: 0.750 G_ID: 0.093 G_Rec: 0.330 D_GP: 0.109 D_real: 0.850 D_fake: 0.892 
+(epoch: 470, iters: 3054, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.905 G_ID: 0.142 G_Rec: 0.399 D_GP: 0.051 D_real: 0.837 D_fake: 0.698 
+(epoch: 470, iters: 3454, time: 0.064) G_GAN: 0.169 G_GAN_Feat: 0.679 G_ID: 0.090 G_Rec: 0.281 D_GP: 0.030 D_real: 1.079 D_fake: 0.832 
+(epoch: 470, iters: 3854, time: 0.064) G_GAN: 0.486 G_GAN_Feat: 0.883 G_ID: 0.119 G_Rec: 0.405 D_GP: 0.038 D_real: 1.088 D_fake: 0.526 
+(epoch: 470, iters: 4254, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.951 G_ID: 0.121 G_Rec: 0.337 D_GP: 0.483 D_real: 0.217 D_fake: 0.990 
+(epoch: 470, iters: 4654, time: 0.064) G_GAN: 0.684 G_GAN_Feat: 1.056 G_ID: 0.127 G_Rec: 0.463 D_GP: 0.093 D_real: 0.454 D_fake: 0.416 
+(epoch: 470, iters: 5054, time: 0.064) G_GAN: 0.238 G_GAN_Feat: 0.628 G_ID: 0.106 G_Rec: 0.323 D_GP: 0.025 D_real: 1.168 D_fake: 0.768 
+(epoch: 470, iters: 5454, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.825 G_ID: 0.128 G_Rec: 0.418 D_GP: 0.028 D_real: 1.213 D_fake: 0.546 
+(epoch: 470, iters: 5854, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.607 G_ID: 0.093 G_Rec: 0.291 D_GP: 0.024 D_real: 1.102 D_fake: 0.845 
+(epoch: 470, iters: 6254, time: 0.064) G_GAN: 0.264 G_GAN_Feat: 0.797 G_ID: 0.125 G_Rec: 0.390 D_GP: 0.025 D_real: 1.041 D_fake: 0.736 
+(epoch: 470, iters: 6654, time: 0.064) G_GAN: 0.086 G_GAN_Feat: 0.633 G_ID: 0.101 G_Rec: 0.293 D_GP: 0.029 D_real: 1.050 D_fake: 0.914 
+(epoch: 470, iters: 7054, time: 0.064) G_GAN: 0.525 G_GAN_Feat: 0.882 G_ID: 0.098 G_Rec: 0.431 D_GP: 0.035 D_real: 1.153 D_fake: 0.491 
+(epoch: 470, iters: 7454, time: 0.064) G_GAN: 0.134 G_GAN_Feat: 0.639 G_ID: 0.087 G_Rec: 0.292 D_GP: 0.039 D_real: 1.008 D_fake: 0.866 
+(epoch: 470, iters: 7854, time: 0.064) G_GAN: 0.185 G_GAN_Feat: 0.850 G_ID: 0.127 G_Rec: 0.406 D_GP: 0.043 D_real: 0.858 D_fake: 0.815 
+(epoch: 470, iters: 8254, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.641 G_ID: 0.108 G_Rec: 0.290 D_GP: 0.035 D_real: 0.954 D_fake: 0.909 
+(epoch: 470, iters: 8654, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.888 G_ID: 0.118 G_Rec: 0.439 D_GP: 0.039 D_real: 0.792 D_fake: 0.791 
+(epoch: 471, iters: 446, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.674 G_ID: 0.097 G_Rec: 0.293 D_GP: 0.036 D_real: 1.029 D_fake: 0.809 
+(epoch: 471, iters: 846, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.890 G_ID: 0.117 G_Rec: 0.425 D_GP: 0.038 D_real: 0.973 D_fake: 0.639 
+(epoch: 471, iters: 1246, time: 0.064) G_GAN: 0.047 G_GAN_Feat: 0.695 G_ID: 0.082 G_Rec: 0.296 D_GP: 0.082 D_real: 0.851 D_fake: 0.953 
+(epoch: 471, iters: 1646, time: 0.064) G_GAN: 0.194 G_GAN_Feat: 0.880 G_ID: 0.125 G_Rec: 0.387 D_GP: 0.043 D_real: 0.857 D_fake: 0.807 
+(epoch: 471, iters: 2046, time: 0.064) G_GAN: -0.042 G_GAN_Feat: 0.850 G_ID: 0.101 G_Rec: 0.337 D_GP: 0.070 D_real: 0.844 D_fake: 1.043 
+(epoch: 471, iters: 2446, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.881 G_ID: 0.138 G_Rec: 0.415 D_GP: 0.032 D_real: 0.828 D_fake: 0.864 
+(epoch: 471, iters: 2846, time: 0.064) G_GAN: 0.002 G_GAN_Feat: 0.679 G_ID: 0.101 G_Rec: 0.284 D_GP: 0.044 D_real: 0.818 D_fake: 0.998 
+(epoch: 471, iters: 3246, time: 0.063) G_GAN: 0.486 G_GAN_Feat: 0.881 G_ID: 0.109 G_Rec: 0.389 D_GP: 0.037 D_real: 1.185 D_fake: 0.533 
+(epoch: 471, iters: 3646, time: 0.064) G_GAN: 0.203 G_GAN_Feat: 0.654 G_ID: 0.090 G_Rec: 0.274 D_GP: 0.031 D_real: 1.106 D_fake: 0.797 
+(epoch: 471, iters: 4046, time: 0.064) G_GAN: 0.608 G_GAN_Feat: 0.870 G_ID: 0.103 G_Rec: 0.389 D_GP: 0.032 D_real: 1.370 D_fake: 0.404 
+(epoch: 471, iters: 4446, time: 0.063) G_GAN: -0.100 G_GAN_Feat: 0.846 G_ID: 0.096 G_Rec: 0.362 D_GP: 0.193 D_real: 0.666 D_fake: 1.102 
+(epoch: 471, iters: 4846, time: 0.064) G_GAN: 0.390 G_GAN_Feat: 0.910 G_ID: 0.120 G_Rec: 0.451 D_GP: 0.031 D_real: 1.006 D_fake: 0.621 
+(epoch: 471, iters: 5246, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.641 G_ID: 0.078 G_Rec: 0.274 D_GP: 0.030 D_real: 1.097 D_fake: 0.807 
+(epoch: 471, iters: 5646, time: 0.064) G_GAN: 0.691 G_GAN_Feat: 0.931 G_ID: 0.110 G_Rec: 0.442 D_GP: 0.032 D_real: 1.248 D_fake: 0.345 
+(epoch: 471, iters: 6046, time: 0.063) G_GAN: 0.224 G_GAN_Feat: 0.678 G_ID: 0.094 G_Rec: 0.290 D_GP: 0.028 D_real: 1.074 D_fake: 0.776 
+(epoch: 471, iters: 6446, time: 0.063) G_GAN: 0.530 G_GAN_Feat: 0.897 G_ID: 0.106 G_Rec: 0.408 D_GP: 0.031 D_real: 1.215 D_fake: 0.476 
+(epoch: 471, iters: 6846, time: 0.064) G_GAN: 0.282 G_GAN_Feat: 0.781 G_ID: 0.090 G_Rec: 0.315 D_GP: 0.046 D_real: 1.059 D_fake: 0.719 
+(epoch: 471, iters: 7246, time: 0.064) G_GAN: 0.102 G_GAN_Feat: 0.924 G_ID: 0.154 G_Rec: 0.430 D_GP: 0.033 D_real: 0.782 D_fake: 0.898 
+(epoch: 471, iters: 7646, time: 0.063) G_GAN: 0.281 G_GAN_Feat: 0.702 G_ID: 0.115 G_Rec: 0.301 D_GP: 0.034 D_real: 1.153 D_fake: 0.725 
+(epoch: 471, iters: 8046, time: 0.064) G_GAN: 0.514 G_GAN_Feat: 0.921 G_ID: 0.116 G_Rec: 0.441 D_GP: 0.037 D_real: 1.176 D_fake: 0.499 
+(epoch: 471, iters: 8446, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.664 G_ID: 0.095 G_Rec: 0.294 D_GP: 0.035 D_real: 1.006 D_fake: 0.839 
+(epoch: 472, iters: 238, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 1.018 G_ID: 0.125 G_Rec: 0.448 D_GP: 0.070 D_real: 0.821 D_fake: 0.724 
+(epoch: 472, iters: 638, time: 0.063) G_GAN: 0.085 G_GAN_Feat: 0.650 G_ID: 0.102 G_Rec: 0.268 D_GP: 0.037 D_real: 1.019 D_fake: 0.915 
+(epoch: 472, iters: 1038, time: 0.063) G_GAN: 0.523 G_GAN_Feat: 0.970 G_ID: 0.138 G_Rec: 0.409 D_GP: 0.037 D_real: 1.050 D_fake: 0.487 
+(epoch: 472, iters: 1438, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.788 G_ID: 0.109 G_Rec: 0.334 D_GP: 0.044 D_real: 0.972 D_fake: 0.742 
+(epoch: 472, iters: 1838, time: 0.063) G_GAN: 0.354 G_GAN_Feat: 1.044 G_ID: 0.136 G_Rec: 0.422 D_GP: 0.151 D_real: 0.395 D_fake: 0.649 
+(epoch: 472, iters: 2238, time: 0.063) G_GAN: 0.050 G_GAN_Feat: 0.755 G_ID: 0.109 G_Rec: 0.330 D_GP: 0.032 D_real: 0.973 D_fake: 0.951 
+(epoch: 472, iters: 2638, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.996 G_ID: 0.134 G_Rec: 0.494 D_GP: 0.048 D_real: 0.701 D_fake: 0.737 
+(epoch: 472, iters: 3038, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.779 G_ID: 0.098 G_Rec: 0.329 D_GP: 0.058 D_real: 0.786 D_fake: 0.960 
+(epoch: 472, iters: 3438, time: 0.063) G_GAN: 0.734 G_GAN_Feat: 1.099 G_ID: 0.112 G_Rec: 0.464 D_GP: 0.039 D_real: 1.296 D_fake: 0.306 
+(epoch: 472, iters: 3838, time: 0.063) G_GAN: -0.092 G_GAN_Feat: 0.850 G_ID: 0.091 G_Rec: 0.347 D_GP: 0.063 D_real: 0.405 D_fake: 1.092 
+(epoch: 472, iters: 4238, time: 0.063) G_GAN: 0.788 G_GAN_Feat: 0.992 G_ID: 0.115 G_Rec: 0.433 D_GP: 0.057 D_real: 1.053 D_fake: 0.268 
+(epoch: 472, iters: 4638, time: 0.064) G_GAN: 0.082 G_GAN_Feat: 0.783 G_ID: 0.097 G_Rec: 0.337 D_GP: 0.044 D_real: 0.774 D_fake: 0.918 
+(epoch: 472, iters: 5038, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.969 G_ID: 0.135 G_Rec: 0.450 D_GP: 0.041 D_real: 0.893 D_fake: 0.640 
+(epoch: 472, iters: 5438, time: 0.063) G_GAN: 0.405 G_GAN_Feat: 0.804 G_ID: 0.114 G_Rec: 0.314 D_GP: 0.036 D_real: 1.112 D_fake: 0.596 
+(epoch: 472, iters: 5838, time: 0.064) G_GAN: 0.604 G_GAN_Feat: 1.121 G_ID: 0.110 G_Rec: 0.424 D_GP: 0.041 D_real: 0.642 D_fake: 0.406 
+(epoch: 472, iters: 6238, time: 0.064) G_GAN: 0.018 G_GAN_Feat: 0.699 G_ID: 0.106 G_Rec: 0.334 D_GP: 0.030 D_real: 0.934 D_fake: 0.984 
+(epoch: 472, iters: 6638, time: 0.064) G_GAN: 0.126 G_GAN_Feat: 0.937 G_ID: 0.119 G_Rec: 0.417 D_GP: 0.046 D_real: 0.677 D_fake: 0.878 
+(epoch: 472, iters: 7038, time: 0.063) G_GAN: -0.155 G_GAN_Feat: 0.699 G_ID: 0.100 G_Rec: 0.289 D_GP: 0.033 D_real: 0.838 D_fake: 1.155 
+(epoch: 472, iters: 7438, time: 0.064) G_GAN: 0.309 G_GAN_Feat: 0.914 G_ID: 0.109 G_Rec: 0.387 D_GP: 0.034 D_real: 1.107 D_fake: 0.692 
+(epoch: 472, iters: 7838, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.756 G_ID: 0.107 G_Rec: 0.294 D_GP: 0.044 D_real: 1.012 D_fake: 0.839 
+(epoch: 472, iters: 8238, time: 0.063) G_GAN: 0.682 G_GAN_Feat: 1.063 G_ID: 0.115 G_Rec: 0.476 D_GP: 0.062 D_real: 0.994 D_fake: 0.348 
+(epoch: 472, iters: 8638, time: 0.063) G_GAN: 0.484 G_GAN_Feat: 0.855 G_ID: 0.110 G_Rec: 0.310 D_GP: 0.122 D_real: 0.763 D_fake: 0.530 
+(epoch: 473, iters: 430, time: 0.063) G_GAN: 0.739 G_GAN_Feat: 1.186 G_ID: 0.104 G_Rec: 0.479 D_GP: 0.040 D_real: 0.778 D_fake: 0.317 
+(epoch: 473, iters: 830, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 0.868 G_ID: 0.103 G_Rec: 0.309 D_GP: 0.092 D_real: 0.787 D_fake: 0.647 
+(epoch: 473, iters: 1230, time: 0.063) G_GAN: 0.447 G_GAN_Feat: 0.934 G_ID: 0.111 G_Rec: 0.472 D_GP: 0.031 D_real: 1.143 D_fake: 0.567 
+(epoch: 473, iters: 1630, time: 0.064) G_GAN: 0.193 G_GAN_Feat: 0.662 G_ID: 0.100 G_Rec: 0.302 D_GP: 0.026 D_real: 1.151 D_fake: 0.808 
+(epoch: 473, iters: 2030, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 0.880 G_ID: 0.104 G_Rec: 0.419 D_GP: 0.033 D_real: 1.145 D_fake: 0.628 
+(epoch: 473, iters: 2430, time: 0.064) G_GAN: 0.211 G_GAN_Feat: 0.703 G_ID: 0.105 G_Rec: 0.318 D_GP: 0.040 D_real: 0.983 D_fake: 0.790 
+(epoch: 473, iters: 2830, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.922 G_ID: 0.120 G_Rec: 0.463 D_GP: 0.049 D_real: 0.808 D_fake: 0.726 
+(epoch: 473, iters: 3230, time: 0.064) G_GAN: 0.215 G_GAN_Feat: 0.671 G_ID: 0.085 G_Rec: 0.322 D_GP: 0.040 D_real: 1.131 D_fake: 0.786 
+(epoch: 473, iters: 3630, time: 0.063) G_GAN: 0.372 G_GAN_Feat: 0.973 G_ID: 0.106 G_Rec: 0.433 D_GP: 0.052 D_real: 0.906 D_fake: 0.639 
+(epoch: 473, iters: 4030, time: 0.064) G_GAN: 0.047 G_GAN_Feat: 0.662 G_ID: 0.099 G_Rec: 0.292 D_GP: 0.039 D_real: 0.934 D_fake: 0.953 
+(epoch: 473, iters: 4430, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 1.006 G_ID: 0.119 G_Rec: 0.473 D_GP: 0.074 D_real: 0.939 D_fake: 0.661 
+(epoch: 473, iters: 4830, time: 0.064) G_GAN: 0.100 G_GAN_Feat: 0.702 G_ID: 0.103 G_Rec: 0.290 D_GP: 0.040 D_real: 0.895 D_fake: 0.900 
+(epoch: 473, iters: 5230, time: 0.064) G_GAN: 0.320 G_GAN_Feat: 0.951 G_ID: 0.122 G_Rec: 0.410 D_GP: 0.054 D_real: 0.901 D_fake: 0.689 
+(epoch: 473, iters: 5630, time: 0.064) G_GAN: 0.239 G_GAN_Feat: 0.716 G_ID: 0.085 G_Rec: 0.285 D_GP: 0.050 D_real: 0.993 D_fake: 0.762 
+(epoch: 473, iters: 6030, time: 0.064) G_GAN: 0.415 G_GAN_Feat: 1.015 G_ID: 0.108 G_Rec: 0.421 D_GP: 0.064 D_real: 0.855 D_fake: 0.592 
+(epoch: 473, iters: 6430, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.768 G_ID: 0.109 G_Rec: 0.302 D_GP: 0.048 D_real: 0.963 D_fake: 0.830 
+(epoch: 473, iters: 6830, time: 0.063) G_GAN: 0.101 G_GAN_Feat: 1.208 G_ID: 0.109 G_Rec: 0.494 D_GP: 4.292 D_real: 0.437 D_fake: 0.930 
+(epoch: 473, iters: 7230, time: 0.064) G_GAN: 0.197 G_GAN_Feat: 0.626 G_ID: 0.098 G_Rec: 0.286 D_GP: 0.028 D_real: 1.154 D_fake: 0.804 
+(epoch: 473, iters: 7630, time: 0.064) G_GAN: 0.115 G_GAN_Feat: 0.793 G_ID: 0.118 G_Rec: 0.391 D_GP: 0.036 D_real: 0.812 D_fake: 0.886 
+(epoch: 473, iters: 8030, time: 0.064) G_GAN: 0.056 G_GAN_Feat: 0.636 G_ID: 0.086 G_Rec: 0.300 D_GP: 0.027 D_real: 1.040 D_fake: 0.944 
+(epoch: 473, iters: 8430, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.873 G_ID: 0.101 G_Rec: 0.427 D_GP: 0.054 D_real: 0.569 D_fake: 0.941 
+(epoch: 474, iters: 222, time: 0.064) G_GAN: -0.104 G_GAN_Feat: 0.715 G_ID: 0.094 G_Rec: 0.315 D_GP: 0.072 D_real: 0.684 D_fake: 1.105 
+(epoch: 474, iters: 622, time: 0.064) G_GAN: 0.132 G_GAN_Feat: 0.896 G_ID: 0.113 G_Rec: 0.417 D_GP: 0.036 D_real: 0.687 D_fake: 0.868 
+(epoch: 474, iters: 1022, time: 0.064) G_GAN: 0.096 G_GAN_Feat: 0.706 G_ID: 0.097 G_Rec: 0.321 D_GP: 0.041 D_real: 0.850 D_fake: 0.904 
+(epoch: 474, iters: 1422, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.901 G_ID: 0.104 G_Rec: 0.418 D_GP: 0.048 D_real: 0.759 D_fake: 0.738 
+(epoch: 474, iters: 1822, time: 0.064) G_GAN: -0.061 G_GAN_Feat: 0.790 G_ID: 0.121 G_Rec: 0.322 D_GP: 0.067 D_real: 0.579 D_fake: 1.061 
+(epoch: 474, iters: 2222, time: 0.064) G_GAN: 0.613 G_GAN_Feat: 0.973 G_ID: 0.122 G_Rec: 0.467 D_GP: 0.039 D_real: 1.159 D_fake: 0.412 
+(epoch: 474, iters: 2622, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.689 G_ID: 0.123 G_Rec: 0.272 D_GP: 0.043 D_real: 1.072 D_fake: 0.739 
+(epoch: 474, iters: 3022, time: 0.063) G_GAN: 0.549 G_GAN_Feat: 0.944 G_ID: 0.139 G_Rec: 0.442 D_GP: 0.036 D_real: 1.018 D_fake: 0.459 
+(epoch: 474, iters: 3422, time: 0.064) G_GAN: 0.413 G_GAN_Feat: 0.739 G_ID: 0.090 G_Rec: 0.323 D_GP: 0.042 D_real: 1.121 D_fake: 0.591 
+(epoch: 474, iters: 3822, time: 0.063) G_GAN: 0.345 G_GAN_Feat: 1.057 G_ID: 0.120 G_Rec: 0.506 D_GP: 0.042 D_real: 1.081 D_fake: 0.679 
+(epoch: 474, iters: 4222, time: 0.064) G_GAN: -0.026 G_GAN_Feat: 0.686 G_ID: 0.103 G_Rec: 0.324 D_GP: 0.031 D_real: 0.919 D_fake: 1.026 
+(epoch: 474, iters: 4622, time: 0.063) G_GAN: -0.059 G_GAN_Feat: 0.939 G_ID: 0.138 G_Rec: 0.451 D_GP: 0.050 D_real: 0.665 D_fake: 1.059 
+(epoch: 474, iters: 5022, time: 0.064) G_GAN: -0.027 G_GAN_Feat: 0.731 G_ID: 0.097 G_Rec: 0.325 D_GP: 0.065 D_real: 0.716 D_fake: 1.028 
+(epoch: 474, iters: 5422, time: 0.063) G_GAN: 0.069 G_GAN_Feat: 0.822 G_ID: 0.162 G_Rec: 0.370 D_GP: 0.040 D_real: 0.668 D_fake: 0.931 
+(epoch: 474, iters: 5822, time: 0.063) G_GAN: 0.012 G_GAN_Feat: 0.730 G_ID: 0.091 G_Rec: 0.303 D_GP: 0.042 D_real: 0.923 D_fake: 0.988 
+(epoch: 474, iters: 6222, time: 0.064) G_GAN: 0.529 G_GAN_Feat: 0.952 G_ID: 0.107 G_Rec: 0.407 D_GP: 0.046 D_real: 1.174 D_fake: 0.517 
+(epoch: 474, iters: 6622, time: 0.064) G_GAN: 0.245 G_GAN_Feat: 0.792 G_ID: 0.107 G_Rec: 0.346 D_GP: 0.050 D_real: 0.996 D_fake: 0.762 
+(epoch: 474, iters: 7022, time: 0.064) G_GAN: 0.503 G_GAN_Feat: 0.892 G_ID: 0.103 G_Rec: 0.417 D_GP: 0.033 D_real: 1.050 D_fake: 0.510 
+(epoch: 474, iters: 7422, time: 0.064) G_GAN: -0.088 G_GAN_Feat: 0.853 G_ID: 0.105 G_Rec: 0.355 D_GP: 0.105 D_real: 0.372 D_fake: 1.088 
+(epoch: 474, iters: 7822, time: 0.064) G_GAN: 0.464 G_GAN_Feat: 0.983 G_ID: 0.111 G_Rec: 0.403 D_GP: 0.037 D_real: 0.882 D_fake: 0.549 
+(epoch: 474, iters: 8222, time: 0.064) G_GAN: 0.049 G_GAN_Feat: 0.976 G_ID: 0.097 G_Rec: 0.365 D_GP: 1.100 D_real: 0.371 D_fake: 0.955 
+(epoch: 474, iters: 8622, time: 0.064) G_GAN: 0.490 G_GAN_Feat: 0.956 G_ID: 0.108 G_Rec: 0.498 D_GP: 0.028 D_real: 1.058 D_fake: 0.549 
+(epoch: 475, iters: 414, time: 0.064) G_GAN: -0.010 G_GAN_Feat: 0.752 G_ID: 0.092 G_Rec: 0.296 D_GP: 0.042 D_real: 0.700 D_fake: 1.010 
+(epoch: 475, iters: 814, time: 0.064) G_GAN: -0.160 G_GAN_Feat: 0.994 G_ID: 0.125 G_Rec: 0.433 D_GP: 0.146 D_real: 0.351 D_fake: 1.161 
+(epoch: 475, iters: 1214, time: 0.064) G_GAN: 0.160 G_GAN_Feat: 0.847 G_ID: 0.088 G_Rec: 0.363 D_GP: 0.132 D_real: 0.580 D_fake: 0.840 
+(epoch: 475, iters: 1614, time: 0.063) G_GAN: 0.579 G_GAN_Feat: 0.999 G_ID: 0.120 G_Rec: 0.427 D_GP: 0.039 D_real: 0.931 D_fake: 0.443 
+(epoch: 475, iters: 2014, time: 0.063) G_GAN: 0.226 G_GAN_Feat: 0.771 G_ID: 0.095 G_Rec: 0.284 D_GP: 0.033 D_real: 0.853 D_fake: 0.774 
+(epoch: 475, iters: 2414, time: 0.064) G_GAN: 0.774 G_GAN_Feat: 1.023 G_ID: 0.136 G_Rec: 0.419 D_GP: 0.054 D_real: 1.159 D_fake: 0.287 
+(epoch: 475, iters: 2814, time: 0.064) G_GAN: 0.142 G_GAN_Feat: 0.998 G_ID: 0.117 G_Rec: 0.367 D_GP: 0.685 D_real: 0.275 D_fake: 0.942 
+(epoch: 475, iters: 3214, time: 0.063) G_GAN: 1.014 G_GAN_Feat: 1.042 G_ID: 0.127 G_Rec: 0.445 D_GP: 0.074 D_real: 1.303 D_fake: 0.113 
+(epoch: 475, iters: 3614, time: 0.064) G_GAN: 0.037 G_GAN_Feat: 0.692 G_ID: 0.104 G_Rec: 0.303 D_GP: 0.032 D_real: 0.972 D_fake: 0.963 
+(epoch: 475, iters: 4014, time: 0.063) G_GAN: -0.029 G_GAN_Feat: 0.915 G_ID: 0.143 G_Rec: 0.426 D_GP: 0.036 D_real: 0.667 D_fake: 1.031 
+(epoch: 475, iters: 4414, time: 0.064) G_GAN: -0.022 G_GAN_Feat: 0.717 G_ID: 0.088 G_Rec: 0.319 D_GP: 0.069 D_real: 0.819 D_fake: 1.022 
+(epoch: 475, iters: 4814, time: 0.063) G_GAN: -0.041 G_GAN_Feat: 0.963 G_ID: 0.117 G_Rec: 0.445 D_GP: 0.047 D_real: 0.593 D_fake: 1.041 
+(epoch: 475, iters: 5214, time: 0.063) G_GAN: 0.008 G_GAN_Feat: 0.894 G_ID: 0.104 G_Rec: 0.342 D_GP: 0.059 D_real: 0.496 D_fake: 0.992 
+(epoch: 475, iters: 5614, time: 0.064) G_GAN: 0.333 G_GAN_Feat: 0.928 G_ID: 0.157 G_Rec: 0.416 D_GP: 0.039 D_real: 0.965 D_fake: 0.671 
+(epoch: 475, iters: 6014, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.691 G_ID: 0.091 G_Rec: 0.276 D_GP: 0.037 D_real: 1.232 D_fake: 0.634 
+(epoch: 475, iters: 6414, time: 0.064) G_GAN: 0.748 G_GAN_Feat: 0.953 G_ID: 0.117 G_Rec: 0.425 D_GP: 0.031 D_real: 1.278 D_fake: 0.301 
+(epoch: 475, iters: 6814, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.710 G_ID: 0.088 G_Rec: 0.291 D_GP: 0.029 D_real: 1.176 D_fake: 0.720 
+(epoch: 475, iters: 7214, time: 0.064) G_GAN: 0.528 G_GAN_Feat: 1.020 G_ID: 0.112 G_Rec: 0.437 D_GP: 0.060 D_real: 1.020 D_fake: 0.478 
+(epoch: 475, iters: 7614, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.804 G_ID: 0.103 G_Rec: 0.343 D_GP: 0.041 D_real: 1.021 D_fake: 0.713 
+(epoch: 475, iters: 8014, time: 0.064) G_GAN: 0.588 G_GAN_Feat: 0.969 G_ID: 0.097 G_Rec: 0.462 D_GP: 0.032 D_real: 1.213 D_fake: 0.441 
+(epoch: 475, iters: 8414, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.712 G_ID: 0.112 G_Rec: 0.286 D_GP: 0.030 D_real: 0.978 D_fake: 0.941 
+(epoch: 476, iters: 206, time: 0.064) G_GAN: 0.474 G_GAN_Feat: 0.994 G_ID: 0.132 G_Rec: 0.419 D_GP: 0.039 D_real: 0.910 D_fake: 0.546 
+(epoch: 476, iters: 606, time: 0.064) G_GAN: 0.227 G_GAN_Feat: 0.657 G_ID: 0.093 G_Rec: 0.277 D_GP: 0.027 D_real: 1.176 D_fake: 0.773 
+(epoch: 476, iters: 1006, time: 0.064) G_GAN: 0.493 G_GAN_Feat: 1.029 G_ID: 0.110 G_Rec: 0.496 D_GP: 0.037 D_real: 1.066 D_fake: 0.526 
+(epoch: 476, iters: 1406, time: 0.064) G_GAN: 0.164 G_GAN_Feat: 0.651 G_ID: 0.098 G_Rec: 0.273 D_GP: 0.029 D_real: 1.062 D_fake: 0.837 
+(epoch: 476, iters: 1806, time: 0.064) G_GAN: 0.334 G_GAN_Feat: 1.023 G_ID: 0.110 G_Rec: 0.479 D_GP: 0.068 D_real: 0.710 D_fake: 0.667 
+(epoch: 476, iters: 2206, time: 0.064) G_GAN: -0.134 G_GAN_Feat: 0.749 G_ID: 0.080 G_Rec: 0.309 D_GP: 0.194 D_real: 0.504 D_fake: 1.134 
+(epoch: 476, iters: 2606, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 1.013 G_ID: 0.110 G_Rec: 0.445 D_GP: 0.033 D_real: 0.822 D_fake: 0.726 
+(epoch: 476, iters: 3006, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.667 G_ID: 0.095 G_Rec: 0.263 D_GP: 0.033 D_real: 0.995 D_fake: 0.912 
+(epoch: 476, iters: 3406, time: 0.064) G_GAN: 0.894 G_GAN_Feat: 0.981 G_ID: 0.109 G_Rec: 0.435 D_GP: 0.041 D_real: 1.557 D_fake: 0.272 
+(epoch: 476, iters: 3806, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.736 G_ID: 0.097 G_Rec: 0.299 D_GP: 0.040 D_real: 0.865 D_fake: 0.941 
+(epoch: 476, iters: 4206, time: 0.064) G_GAN: 0.434 G_GAN_Feat: 0.950 G_ID: 0.129 G_Rec: 0.424 D_GP: 0.037 D_real: 1.120 D_fake: 0.569 
+(epoch: 476, iters: 4606, time: 0.064) G_GAN: 0.259 G_GAN_Feat: 0.748 G_ID: 0.095 G_Rec: 0.280 D_GP: 0.039 D_real: 1.056 D_fake: 0.742 
+(epoch: 476, iters: 5006, time: 0.064) G_GAN: 0.785 G_GAN_Feat: 1.064 G_ID: 0.101 G_Rec: 0.460 D_GP: 0.061 D_real: 0.905 D_fake: 0.254 
+(epoch: 476, iters: 5406, time: 0.064) G_GAN: 0.396 G_GAN_Feat: 0.910 G_ID: 0.108 G_Rec: 0.359 D_GP: 0.168 D_real: 0.714 D_fake: 0.718 
+(epoch: 476, iters: 5806, time: 0.064) G_GAN: 0.058 G_GAN_Feat: 0.866 G_ID: 0.127 G_Rec: 0.396 D_GP: 0.030 D_real: 0.844 D_fake: 0.943 
+(epoch: 476, iters: 6206, time: 0.064) G_GAN: -0.141 G_GAN_Feat: 0.754 G_ID: 0.095 G_Rec: 0.321 D_GP: 0.047 D_real: 0.594 D_fake: 1.141 
+(epoch: 476, iters: 6606, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 1.067 G_ID: 0.117 G_Rec: 0.478 D_GP: 0.101 D_real: 0.482 D_fake: 0.701 
+(epoch: 476, iters: 7006, time: 0.064) G_GAN: 0.624 G_GAN_Feat: 0.870 G_ID: 0.088 G_Rec: 0.349 D_GP: 0.045 D_real: 1.102 D_fake: 0.549 
+(epoch: 476, iters: 7406, time: 0.064) G_GAN: 0.455 G_GAN_Feat: 0.998 G_ID: 0.116 G_Rec: 0.427 D_GP: 0.052 D_real: 1.289 D_fake: 0.560 
+(epoch: 476, iters: 7806, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 0.734 G_ID: 0.079 G_Rec: 0.328 D_GP: 0.036 D_real: 1.199 D_fake: 0.709 
+(epoch: 476, iters: 8206, time: 0.064) G_GAN: 0.451 G_GAN_Feat: 0.987 G_ID: 0.113 G_Rec: 0.424 D_GP: 0.041 D_real: 0.956 D_fake: 0.556 
+(epoch: 476, iters: 8606, time: 0.064) G_GAN: 0.348 G_GAN_Feat: 0.824 G_ID: 0.088 G_Rec: 0.317 D_GP: 0.034 D_real: 1.082 D_fake: 0.653 
+(epoch: 477, iters: 398, time: 0.064) G_GAN: 0.628 G_GAN_Feat: 0.951 G_ID: 0.125 G_Rec: 0.446 D_GP: 0.043 D_real: 1.211 D_fake: 0.386 
+(epoch: 477, iters: 798, time: 0.064) G_GAN: 0.067 G_GAN_Feat: 0.814 G_ID: 0.089 G_Rec: 0.335 D_GP: 0.085 D_real: 0.826 D_fake: 0.933 
+(epoch: 477, iters: 1198, time: 0.064) G_GAN: 0.619 G_GAN_Feat: 0.953 G_ID: 0.101 G_Rec: 0.401 D_GP: 0.035 D_real: 1.220 D_fake: 0.425 
+(epoch: 477, iters: 1598, time: 0.064) G_GAN: 0.279 G_GAN_Feat: 0.849 G_ID: 0.083 G_Rec: 0.333 D_GP: 0.035 D_real: 0.679 D_fake: 0.721 
+(epoch: 477, iters: 1998, time: 0.064) G_GAN: 0.635 G_GAN_Feat: 1.021 G_ID: 0.106 G_Rec: 0.462 D_GP: 0.035 D_real: 1.171 D_fake: 0.433 
+(epoch: 477, iters: 2398, time: 0.064) G_GAN: 0.477 G_GAN_Feat: 0.871 G_ID: 0.096 G_Rec: 0.301 D_GP: 0.057 D_real: 0.953 D_fake: 0.533 
+(epoch: 477, iters: 2798, time: 0.064) G_GAN: 0.494 G_GAN_Feat: 1.052 G_ID: 0.104 G_Rec: 0.452 D_GP: 0.056 D_real: 0.860 D_fake: 0.525 
+(epoch: 477, iters: 3198, time: 0.064) G_GAN: 0.495 G_GAN_Feat: 1.013 G_ID: 0.091 G_Rec: 0.359 D_GP: 0.897 D_real: 0.578 D_fake: 0.563 
+(epoch: 477, iters: 3598, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.988 G_ID: 0.105 G_Rec: 0.461 D_GP: 0.038 D_real: 0.829 D_fake: 0.648 
+(epoch: 477, iters: 3998, time: 0.064) G_GAN: 0.071 G_GAN_Feat: 0.901 G_ID: 0.103 G_Rec: 0.334 D_GP: 0.050 D_real: 0.768 D_fake: 0.934 
+(epoch: 477, iters: 4398, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.945 G_ID: 0.136 G_Rec: 0.403 D_GP: 0.035 D_real: 0.959 D_fake: 0.746 
+(epoch: 477, iters: 4798, time: 0.064) G_GAN: 0.041 G_GAN_Feat: 0.685 G_ID: 0.098 G_Rec: 0.306 D_GP: 0.038 D_real: 0.868 D_fake: 0.959 
+(epoch: 477, iters: 5198, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 1.078 G_ID: 0.120 G_Rec: 0.441 D_GP: 0.041 D_real: 0.604 D_fake: 0.677 
+(epoch: 477, iters: 5598, time: 0.064) G_GAN: 0.303 G_GAN_Feat: 0.908 G_ID: 0.092 G_Rec: 0.343 D_GP: 0.074 D_real: 1.086 D_fake: 0.700 
+(epoch: 477, iters: 5998, time: 0.064) G_GAN: 1.046 G_GAN_Feat: 1.147 G_ID: 0.108 G_Rec: 0.477 D_GP: 0.071 D_real: 0.990 D_fake: 0.167 
+(epoch: 477, iters: 6398, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.694 G_ID: 0.091 G_Rec: 0.294 D_GP: 0.032 D_real: 1.116 D_fake: 0.849 
+(epoch: 477, iters: 6798, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.921 G_ID: 0.131 G_Rec: 0.424 D_GP: 0.042 D_real: 0.641 D_fake: 0.842 
+(epoch: 477, iters: 7198, time: 0.064) G_GAN: -0.195 G_GAN_Feat: 0.824 G_ID: 0.105 G_Rec: 0.328 D_GP: 0.088 D_real: 0.569 D_fake: 1.195 
+(epoch: 477, iters: 7598, time: 0.064) G_GAN: 0.832 G_GAN_Feat: 0.941 G_ID: 0.127 G_Rec: 0.392 D_GP: 0.036 D_real: 1.493 D_fake: 0.251 
+(epoch: 477, iters: 7998, time: 0.063) G_GAN: 0.318 G_GAN_Feat: 0.835 G_ID: 0.100 G_Rec: 0.379 D_GP: 0.053 D_real: 1.157 D_fake: 0.683 
+(epoch: 477, iters: 8398, time: 0.064) G_GAN: 0.460 G_GAN_Feat: 1.017 G_ID: 0.110 G_Rec: 0.403 D_GP: 0.099 D_real: 0.593 D_fake: 0.542 
+(epoch: 478, iters: 190, time: 0.063) G_GAN: 0.175 G_GAN_Feat: 0.900 G_ID: 0.091 G_Rec: 0.313 D_GP: 0.034 D_real: 0.844 D_fake: 0.825 
+(epoch: 478, iters: 590, time: 0.064) G_GAN: 0.417 G_GAN_Feat: 0.862 G_ID: 0.121 G_Rec: 0.451 D_GP: 0.027 D_real: 1.156 D_fake: 0.587 
+(epoch: 478, iters: 990, time: 0.063) G_GAN: 0.221 G_GAN_Feat: 0.646 G_ID: 0.077 G_Rec: 0.311 D_GP: 0.023 D_real: 1.148 D_fake: 0.779 
+(epoch: 478, iters: 1390, time: 0.064) G_GAN: 0.280 G_GAN_Feat: 0.891 G_ID: 0.110 G_Rec: 0.462 D_GP: 0.035 D_real: 0.836 D_fake: 0.724 
+(epoch: 478, iters: 1790, time: 0.063) G_GAN: 0.144 G_GAN_Feat: 0.603 G_ID: 0.105 G_Rec: 0.288 D_GP: 0.029 D_real: 1.013 D_fake: 0.856 
+(epoch: 478, iters: 2190, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.908 G_ID: 0.132 G_Rec: 0.453 D_GP: 0.047 D_real: 0.744 D_fake: 0.833 
+(epoch: 478, iters: 2590, time: 0.063) G_GAN: 0.027 G_GAN_Feat: 0.690 G_ID: 0.104 G_Rec: 0.328 D_GP: 0.043 D_real: 0.839 D_fake: 0.973 
+(epoch: 478, iters: 2990, time: 0.063) G_GAN: 0.339 G_GAN_Feat: 0.927 G_ID: 0.115 G_Rec: 0.470 D_GP: 0.039 D_real: 0.924 D_fake: 0.678 
+(epoch: 478, iters: 3390, time: 0.064) G_GAN: 0.006 G_GAN_Feat: 0.732 G_ID: 0.107 G_Rec: 0.366 D_GP: 0.042 D_real: 0.788 D_fake: 0.995 
+(epoch: 478, iters: 3790, time: 0.064) G_GAN: 0.113 G_GAN_Feat: 0.905 G_ID: 0.134 G_Rec: 0.465 D_GP: 0.034 D_real: 0.654 D_fake: 0.888 
+(epoch: 478, iters: 4190, time: 0.064) G_GAN: 0.084 G_GAN_Feat: 0.678 G_ID: 0.095 G_Rec: 0.313 D_GP: 0.043 D_real: 0.887 D_fake: 0.916 
+(epoch: 478, iters: 4590, time: 0.064) G_GAN: 0.140 G_GAN_Feat: 0.877 G_ID: 0.112 G_Rec: 0.402 D_GP: 0.040 D_real: 0.863 D_fake: 0.867 
+(epoch: 478, iters: 4990, time: 0.063) G_GAN: -0.141 G_GAN_Feat: 0.734 G_ID: 0.097 G_Rec: 0.334 D_GP: 0.058 D_real: 0.583 D_fake: 1.141 
+(epoch: 478, iters: 5390, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.907 G_ID: 0.118 G_Rec: 0.439 D_GP: 0.065 D_real: 0.777 D_fake: 0.849 
+(epoch: 478, iters: 5790, time: 0.064) G_GAN: 0.064 G_GAN_Feat: 0.753 G_ID: 0.093 G_Rec: 0.312 D_GP: 0.085 D_real: 0.747 D_fake: 0.937 
+(epoch: 478, iters: 6190, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.909 G_ID: 0.104 G_Rec: 0.415 D_GP: 0.048 D_real: 1.077 D_fake: 0.603 
+(epoch: 478, iters: 6590, time: 0.064) G_GAN: 0.170 G_GAN_Feat: 0.654 G_ID: 0.095 G_Rec: 0.289 D_GP: 0.033 D_real: 0.999 D_fake: 0.830 
+(epoch: 478, iters: 6990, time: 0.064) G_GAN: 0.375 G_GAN_Feat: 0.900 G_ID: 0.105 G_Rec: 0.383 D_GP: 0.039 D_real: 1.194 D_fake: 0.632 
+(epoch: 478, iters: 7390, time: 0.064) G_GAN: 0.091 G_GAN_Feat: 0.694 G_ID: 0.120 G_Rec: 0.326 D_GP: 0.030 D_real: 0.955 D_fake: 0.909 
+(epoch: 478, iters: 7790, time: 0.063) G_GAN: 0.382 G_GAN_Feat: 0.933 G_ID: 0.116 G_Rec: 0.455 D_GP: 0.043 D_real: 0.927 D_fake: 0.626 
+(epoch: 478, iters: 8190, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.648 G_ID: 0.104 G_Rec: 0.279 D_GP: 0.037 D_real: 1.049 D_fake: 0.784 
+(epoch: 478, iters: 8590, time: 0.064) G_GAN: 0.072 G_GAN_Feat: 0.884 G_ID: 0.123 G_Rec: 0.402 D_GP: 0.049 D_real: 0.704 D_fake: 0.928 
+(epoch: 479, iters: 382, time: 0.064) G_GAN: 0.144 G_GAN_Feat: 0.725 G_ID: 0.093 G_Rec: 0.307 D_GP: 0.044 D_real: 1.020 D_fake: 0.857 
+(epoch: 479, iters: 782, time: 0.064) G_GAN: 0.636 G_GAN_Feat: 0.957 G_ID: 0.108 G_Rec: 0.434 D_GP: 0.043 D_real: 1.126 D_fake: 0.402 
+(epoch: 479, iters: 1182, time: 0.064) G_GAN: 0.111 G_GAN_Feat: 0.762 G_ID: 0.094 G_Rec: 0.321 D_GP: 0.053 D_real: 0.803 D_fake: 0.889 
+(epoch: 479, iters: 1582, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 1.079 G_ID: 0.119 G_Rec: 0.484 D_GP: 0.640 D_real: 0.501 D_fake: 0.702 
+(epoch: 479, iters: 1982, time: 0.064) G_GAN: 0.206 G_GAN_Feat: 0.743 G_ID: 0.105 G_Rec: 0.294 D_GP: 0.035 D_real: 1.122 D_fake: 0.794 
+(epoch: 479, iters: 2382, time: 0.064) G_GAN: 0.274 G_GAN_Feat: 0.822 G_ID: 0.126 G_Rec: 0.373 D_GP: 0.032 D_real: 1.088 D_fake: 0.728 
+(epoch: 479, iters: 2782, time: 0.064) G_GAN: 0.212 G_GAN_Feat: 0.740 G_ID: 0.089 G_Rec: 0.324 D_GP: 0.036 D_real: 1.008 D_fake: 0.788 
+(epoch: 479, iters: 3182, time: 0.064) G_GAN: 0.834 G_GAN_Feat: 1.053 G_ID: 0.135 G_Rec: 0.481 D_GP: 0.050 D_real: 1.405 D_fake: 0.280 
+(epoch: 479, iters: 3582, time: 0.063) G_GAN: 0.076 G_GAN_Feat: 0.869 G_ID: 0.091 G_Rec: 0.323 D_GP: 0.128 D_real: 0.548 D_fake: 0.924 
+(epoch: 479, iters: 3982, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 1.002 G_ID: 0.115 G_Rec: 0.486 D_GP: 0.064 D_real: 0.902 D_fake: 0.562 
+(epoch: 479, iters: 4382, time: 0.064) G_GAN: -0.057 G_GAN_Feat: 0.723 G_ID: 0.109 G_Rec: 0.309 D_GP: 0.034 D_real: 0.904 D_fake: 1.057 
+(epoch: 479, iters: 4782, time: 0.064) G_GAN: 0.593 G_GAN_Feat: 0.903 G_ID: 0.122 G_Rec: 0.391 D_GP: 0.037 D_real: 1.132 D_fake: 0.425 
+(epoch: 479, iters: 5182, time: 0.064) G_GAN: 0.186 G_GAN_Feat: 0.709 G_ID: 0.101 G_Rec: 0.288 D_GP: 0.030 D_real: 1.079 D_fake: 0.814 
+(epoch: 479, iters: 5582, time: 0.064) G_GAN: 0.633 G_GAN_Feat: 1.028 G_ID: 0.099 G_Rec: 0.447 D_GP: 0.088 D_real: 0.876 D_fake: 0.389 
+(epoch: 479, iters: 5982, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.713 G_ID: 0.102 G_Rec: 0.296 D_GP: 0.032 D_real: 0.973 D_fake: 0.883 
+(epoch: 479, iters: 6382, time: 0.064) G_GAN: 0.567 G_GAN_Feat: 0.957 G_ID: 0.128 G_Rec: 0.442 D_GP: 0.035 D_real: 1.079 D_fake: 0.458 
+(epoch: 479, iters: 6782, time: 0.064) G_GAN: 0.314 G_GAN_Feat: 0.727 G_ID: 0.104 G_Rec: 0.301 D_GP: 0.034 D_real: 1.237 D_fake: 0.687 
+(epoch: 479, iters: 7182, time: 0.064) G_GAN: 0.492 G_GAN_Feat: 0.980 G_ID: 0.131 G_Rec: 0.400 D_GP: 0.036 D_real: 1.112 D_fake: 0.530 
+(epoch: 479, iters: 7582, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.734 G_ID: 0.089 G_Rec: 0.288 D_GP: 0.034 D_real: 1.112 D_fake: 0.714 
+(epoch: 479, iters: 7982, time: 0.064) G_GAN: 0.978 G_GAN_Feat: 1.002 G_ID: 0.114 G_Rec: 0.432 D_GP: 0.040 D_real: 1.573 D_fake: 0.190 
+(epoch: 479, iters: 8382, time: 0.064) G_GAN: 0.275 G_GAN_Feat: 0.698 G_ID: 0.092 G_Rec: 0.277 D_GP: 0.032 D_real: 1.152 D_fake: 0.725 
+(epoch: 480, iters: 174, time: 0.064) G_GAN: 1.056 G_GAN_Feat: 1.016 G_ID: 0.125 G_Rec: 0.426 D_GP: 0.062 D_real: 1.402 D_fake: 0.373 
+(epoch: 480, iters: 574, time: 0.063) G_GAN: 0.076 G_GAN_Feat: 0.655 G_ID: 0.100 G_Rec: 0.270 D_GP: 0.032 D_real: 0.996 D_fake: 0.925 
+(epoch: 480, iters: 974, time: 0.064) G_GAN: 0.629 G_GAN_Feat: 1.076 G_ID: 0.108 G_Rec: 0.462 D_GP: 0.094 D_real: 0.732 D_fake: 0.416 
+(epoch: 480, iters: 1374, time: 0.064) G_GAN: 0.063 G_GAN_Feat: 0.693 G_ID: 0.114 G_Rec: 0.264 D_GP: 0.033 D_real: 0.940 D_fake: 0.937 
+(epoch: 480, iters: 1774, time: 0.064) G_GAN: -0.048 G_GAN_Feat: 1.182 G_ID: 0.140 G_Rec: 0.483 D_GP: 0.070 D_real: 0.102 D_fake: 1.048 
+(epoch: 480, iters: 2174, time: 0.064) G_GAN: 0.012 G_GAN_Feat: 0.779 G_ID: 0.093 G_Rec: 0.314 D_GP: 0.041 D_real: 0.700 D_fake: 0.988 
+(epoch: 480, iters: 2574, time: 0.064) G_GAN: 0.289 G_GAN_Feat: 0.966 G_ID: 0.106 G_Rec: 0.432 D_GP: 0.032 D_real: 0.893 D_fake: 0.712 
+(epoch: 480, iters: 2974, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.799 G_ID: 0.102 G_Rec: 0.320 D_GP: 0.044 D_real: 1.123 D_fake: 0.734 
+(epoch: 480, iters: 3374, time: 0.063) G_GAN: 0.727 G_GAN_Feat: 1.080 G_ID: 0.113 G_Rec: 0.434 D_GP: 0.041 D_real: 0.927 D_fake: 0.374 
+(epoch: 480, iters: 3774, time: 0.064) G_GAN: 0.291 G_GAN_Feat: 1.055 G_ID: 0.086 G_Rec: 0.333 D_GP: 0.116 D_real: 0.496 D_fake: 0.711 
+(epoch: 480, iters: 4174, time: 0.064) G_GAN: 0.640 G_GAN_Feat: 1.003 G_ID: 0.118 G_Rec: 0.459 D_GP: 0.036 D_real: 1.256 D_fake: 0.440 
+(epoch: 480, iters: 4574, time: 0.064) G_GAN: 0.094 G_GAN_Feat: 0.647 G_ID: 0.104 G_Rec: 0.285 D_GP: 0.028 D_real: 1.025 D_fake: 0.906 
+(epoch: 480, iters: 4974, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 0.891 G_ID: 0.097 G_Rec: 0.430 D_GP: 0.032 D_real: 1.090 D_fake: 0.573 
+(epoch: 480, iters: 5374, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.696 G_ID: 0.098 G_Rec: 0.293 D_GP: 0.034 D_real: 0.864 D_fake: 0.987 
+(epoch: 480, iters: 5774, time: 0.063) G_GAN: 0.187 G_GAN_Feat: 0.865 G_ID: 0.110 G_Rec: 0.406 D_GP: 0.047 D_real: 0.789 D_fake: 0.819 
+(epoch: 480, iters: 6174, time: 0.063) G_GAN: 0.116 G_GAN_Feat: 0.700 G_ID: 0.094 G_Rec: 0.305 D_GP: 0.051 D_real: 0.942 D_fake: 0.887 
+(epoch: 480, iters: 6574, time: 0.064) G_GAN: 0.075 G_GAN_Feat: 0.969 G_ID: 0.112 G_Rec: 0.444 D_GP: 0.048 D_real: 0.639 D_fake: 0.925 
+(epoch: 480, iters: 6974, time: 0.064) G_GAN: -0.141 G_GAN_Feat: 0.622 G_ID: 0.075 G_Rec: 0.249 D_GP: 0.032 D_real: 0.868 D_fake: 1.141 
+(epoch: 480, iters: 7374, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.864 G_ID: 0.132 G_Rec: 0.373 D_GP: 0.039 D_real: 0.795 D_fake: 0.826 
+(epoch: 480, iters: 7774, time: 0.063) G_GAN: -0.057 G_GAN_Feat: 0.659 G_ID: 0.085 G_Rec: 0.265 D_GP: 0.044 D_real: 0.933 D_fake: 1.057 
+(epoch: 480, iters: 8174, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 0.956 G_ID: 0.123 G_Rec: 0.415 D_GP: 0.038 D_real: 0.850 D_fake: 0.812 
+(epoch: 480, iters: 8574, time: 0.064) G_GAN: 0.155 G_GAN_Feat: 0.717 G_ID: 0.099 G_Rec: 0.315 D_GP: 0.036 D_real: 0.968 D_fake: 0.845 
+(epoch: 481, iters: 366, time: 0.063) G_GAN: 0.430 G_GAN_Feat: 1.047 G_ID: 0.122 G_Rec: 0.476 D_GP: 0.077 D_real: 0.760 D_fake: 0.580 
+(epoch: 481, iters: 766, time: 0.063) G_GAN: -0.508 G_GAN_Feat: 1.083 G_ID: 0.102 G_Rec: 0.384 D_GP: 0.050 D_real: 1.081 D_fake: 1.508 
+(epoch: 481, iters: 1166, time: 0.063) G_GAN: 0.226 G_GAN_Feat: 0.832 G_ID: 0.116 G_Rec: 0.417 D_GP: 0.023 D_real: 1.010 D_fake: 0.787 
+(epoch: 481, iters: 1566, time: 0.064) G_GAN: -0.196 G_GAN_Feat: 0.636 G_ID: 0.099 G_Rec: 0.285 D_GP: 0.023 D_real: 0.769 D_fake: 1.200 
+(epoch: 481, iters: 1966, time: 0.064) G_GAN: 0.187 G_GAN_Feat: 0.886 G_ID: 0.119 G_Rec: 0.438 D_GP: 0.029 D_real: 0.850 D_fake: 0.822 
+(epoch: 481, iters: 2366, time: 0.064) G_GAN: -0.035 G_GAN_Feat: 0.664 G_ID: 0.087 G_Rec: 0.295 D_GP: 0.031 D_real: 0.847 D_fake: 1.036 
+(epoch: 481, iters: 2766, time: 0.064) G_GAN: -0.042 G_GAN_Feat: 0.894 G_ID: 0.129 G_Rec: 0.436 D_GP: 0.055 D_real: 0.587 D_fake: 1.043 
+(epoch: 481, iters: 3166, time: 0.064) G_GAN: 0.171 G_GAN_Feat: 0.697 G_ID: 0.113 G_Rec: 0.301 D_GP: 0.036 D_real: 0.995 D_fake: 0.831 
+(epoch: 481, iters: 3566, time: 0.063) G_GAN: 0.561 G_GAN_Feat: 0.896 G_ID: 0.099 G_Rec: 0.396 D_GP: 0.035 D_real: 1.235 D_fake: 0.476 
+(epoch: 481, iters: 3966, time: 0.063) G_GAN: 0.014 G_GAN_Feat: 0.802 G_ID: 0.094 G_Rec: 0.315 D_GP: 0.099 D_real: 0.626 D_fake: 0.986 
+(epoch: 481, iters: 4366, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.961 G_ID: 0.118 G_Rec: 0.443 D_GP: 0.036 D_real: 0.932 D_fake: 0.712 
+(epoch: 481, iters: 4766, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.858 G_ID: 0.107 G_Rec: 0.327 D_GP: 0.039 D_real: 0.758 D_fake: 0.784 
+(epoch: 481, iters: 5166, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 0.982 G_ID: 0.103 G_Rec: 0.424 D_GP: 0.042 D_real: 0.808 D_fake: 0.631 
+(epoch: 481, iters: 5566, time: 0.064) G_GAN: 0.192 G_GAN_Feat: 0.813 G_ID: 0.099 G_Rec: 0.312 D_GP: 0.045 D_real: 0.877 D_fake: 0.809 
+(epoch: 481, iters: 5966, time: 0.063) G_GAN: 0.324 G_GAN_Feat: 0.924 G_ID: 0.109 G_Rec: 0.392 D_GP: 0.030 D_real: 1.027 D_fake: 0.683 
+(epoch: 481, iters: 6366, time: 0.064) G_GAN: -0.083 G_GAN_Feat: 0.799 G_ID: 0.109 G_Rec: 0.327 D_GP: 0.040 D_real: 0.577 D_fake: 1.083 
+(epoch: 481, iters: 6766, time: 0.064) G_GAN: 0.632 G_GAN_Feat: 0.999 G_ID: 0.106 G_Rec: 0.458 D_GP: 0.037 D_real: 1.214 D_fake: 0.378 
+(epoch: 481, iters: 7166, time: 0.064) G_GAN: 0.443 G_GAN_Feat: 0.801 G_ID: 0.091 G_Rec: 0.340 D_GP: 0.033 D_real: 1.134 D_fake: 0.561 
+(epoch: 481, iters: 7566, time: 0.063) G_GAN: 0.720 G_GAN_Feat: 0.985 G_ID: 0.101 G_Rec: 0.431 D_GP: 0.077 D_real: 1.300 D_fake: 0.341 
+(epoch: 481, iters: 7966, time: 0.064) G_GAN: 0.174 G_GAN_Feat: 0.844 G_ID: 0.087 G_Rec: 0.350 D_GP: 0.046 D_real: 0.826 D_fake: 0.827 
+(epoch: 481, iters: 8366, time: 0.064) G_GAN: 0.542 G_GAN_Feat: 0.983 G_ID: 0.106 G_Rec: 0.407 D_GP: 0.056 D_real: 1.018 D_fake: 0.490 
+(epoch: 482, iters: 158, time: 0.063) G_GAN: 0.245 G_GAN_Feat: 0.928 G_ID: 0.099 G_Rec: 0.333 D_GP: 0.040 D_real: 0.558 D_fake: 0.755 
+(epoch: 482, iters: 558, time: 0.064) G_GAN: 0.159 G_GAN_Feat: 0.929 G_ID: 0.131 G_Rec: 0.444 D_GP: 0.037 D_real: 0.777 D_fake: 0.841 
+(epoch: 482, iters: 958, time: 0.064) G_GAN: 0.277 G_GAN_Feat: 0.917 G_ID: 0.081 G_Rec: 0.324 D_GP: 0.064 D_real: 0.845 D_fake: 0.723 
+(epoch: 482, iters: 1358, time: 0.064) G_GAN: 0.713 G_GAN_Feat: 0.996 G_ID: 0.108 G_Rec: 0.377 D_GP: 0.030 D_real: 1.085 D_fake: 0.403 
+(epoch: 482, iters: 1758, time: 0.063) G_GAN: -0.066 G_GAN_Feat: 0.778 G_ID: 0.091 G_Rec: 0.322 D_GP: 0.029 D_real: 0.826 D_fake: 1.067 
+(epoch: 482, iters: 2158, time: 0.063) G_GAN: 0.162 G_GAN_Feat: 0.896 G_ID: 0.135 G_Rec: 0.408 D_GP: 0.028 D_real: 0.741 D_fake: 0.843 
+(epoch: 482, iters: 2558, time: 0.064) G_GAN: 0.136 G_GAN_Feat: 0.784 G_ID: 0.110 G_Rec: 0.327 D_GP: 0.075 D_real: 0.768 D_fake: 0.865 
+(epoch: 482, iters: 2958, time: 0.063) G_GAN: 0.565 G_GAN_Feat: 1.084 G_ID: 0.117 G_Rec: 0.426 D_GP: 0.065 D_real: 0.499 D_fake: 0.446 
+(epoch: 482, iters: 3358, time: 0.063) G_GAN: 0.127 G_GAN_Feat: 0.733 G_ID: 0.099 G_Rec: 0.334 D_GP: 0.039 D_real: 0.983 D_fake: 0.874 
+(epoch: 482, iters: 3758, time: 0.064) G_GAN: 0.574 G_GAN_Feat: 0.939 G_ID: 0.112 G_Rec: 0.443 D_GP: 0.033 D_real: 1.190 D_fake: 0.435 
+(epoch: 482, iters: 4158, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.982 G_ID: 0.090 G_Rec: 0.349 D_GP: 0.574 D_real: 0.486 D_fake: 0.658 
+(epoch: 482, iters: 4558, time: 0.064) G_GAN: 0.575 G_GAN_Feat: 1.140 G_ID: 0.120 G_Rec: 0.436 D_GP: 0.055 D_real: 0.383 D_fake: 0.443 
+(epoch: 482, iters: 4958, time: 0.064) G_GAN: 0.502 G_GAN_Feat: 0.981 G_ID: 0.086 G_Rec: 0.338 D_GP: 0.100 D_real: 0.524 D_fake: 0.517 
+(epoch: 482, iters: 5358, time: 0.064) G_GAN: 0.388 G_GAN_Feat: 0.965 G_ID: 0.109 G_Rec: 0.436 D_GP: 0.031 D_real: 1.010 D_fake: 0.647 
+(epoch: 482, iters: 5758, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.871 G_ID: 0.097 G_Rec: 0.313 D_GP: 0.072 D_real: 0.597 D_fake: 0.710 
+(epoch: 482, iters: 6158, time: 0.063) G_GAN: 0.556 G_GAN_Feat: 1.191 G_ID: 0.094 G_Rec: 0.452 D_GP: 0.321 D_real: 0.371 D_fake: 0.565 
+(epoch: 482, iters: 6558, time: 0.063) G_GAN: -0.132 G_GAN_Feat: 0.774 G_ID: 0.083 G_Rec: 0.326 D_GP: 0.030 D_real: 0.790 D_fake: 1.132 
+(epoch: 482, iters: 6958, time: 0.063) G_GAN: 0.481 G_GAN_Feat: 1.156 G_ID: 0.129 G_Rec: 0.440 D_GP: 0.098 D_real: 0.508 D_fake: 0.530 
+(epoch: 482, iters: 7358, time: 0.064) G_GAN: 0.286 G_GAN_Feat: 0.898 G_ID: 0.104 G_Rec: 0.321 D_GP: 0.063 D_real: 0.309 D_fake: 0.717 
+(epoch: 482, iters: 7758, time: 0.064) G_GAN: 0.579 G_GAN_Feat: 0.982 G_ID: 0.129 G_Rec: 0.449 D_GP: 0.034 D_real: 1.322 D_fake: 0.458 
+(epoch: 482, iters: 8158, time: 0.063) G_GAN: 0.054 G_GAN_Feat: 0.773 G_ID: 0.092 G_Rec: 0.301 D_GP: 0.058 D_real: 0.795 D_fake: 0.947 
+(epoch: 482, iters: 8558, time: 0.063) G_GAN: 0.516 G_GAN_Feat: 1.008 G_ID: 0.107 G_Rec: 0.475 D_GP: 0.038 D_real: 0.851 D_fake: 0.494 
+(epoch: 483, iters: 350, time: 0.064) G_GAN: 0.518 G_GAN_Feat: 0.911 G_ID: 0.099 G_Rec: 0.342 D_GP: 0.336 D_real: 0.545 D_fake: 0.610 
+(epoch: 483, iters: 750, time: 0.063) G_GAN: 0.633 G_GAN_Feat: 1.062 G_ID: 0.107 G_Rec: 0.447 D_GP: 0.068 D_real: 0.763 D_fake: 0.378 
+(epoch: 483, iters: 1150, time: 0.063) G_GAN: 0.256 G_GAN_Feat: 0.809 G_ID: 0.099 G_Rec: 0.318 D_GP: 0.033 D_real: 0.848 D_fake: 0.755 
+(epoch: 483, iters: 1550, time: 0.063) G_GAN: 0.841 G_GAN_Feat: 1.119 G_ID: 0.126 G_Rec: 0.473 D_GP: 0.085 D_real: 1.125 D_fake: 0.287 
+(epoch: 483, iters: 1950, time: 0.064) G_GAN: 0.313 G_GAN_Feat: 0.758 G_ID: 0.098 G_Rec: 0.334 D_GP: 0.037 D_real: 1.112 D_fake: 0.690 
+(epoch: 483, iters: 2350, time: 0.063) G_GAN: 0.268 G_GAN_Feat: 0.991 G_ID: 0.115 G_Rec: 0.441 D_GP: 0.033 D_real: 0.780 D_fake: 0.732 
+(epoch: 483, iters: 2750, time: 0.063) G_GAN: 0.279 G_GAN_Feat: 0.995 G_ID: 0.099 G_Rec: 0.339 D_GP: 0.167 D_real: 0.318 D_fake: 0.733 
+(epoch: 483, iters: 3150, time: 0.063) G_GAN: 0.422 G_GAN_Feat: 0.890 G_ID: 0.112 G_Rec: 0.435 D_GP: 0.027 D_real: 1.118 D_fake: 0.585 
+(epoch: 483, iters: 3550, time: 0.064) G_GAN: -0.070 G_GAN_Feat: 0.683 G_ID: 0.123 G_Rec: 0.294 D_GP: 0.033 D_real: 0.814 D_fake: 1.070 
+(epoch: 483, iters: 3950, time: 0.063) G_GAN: 0.283 G_GAN_Feat: 0.896 G_ID: 0.111 G_Rec: 0.387 D_GP: 0.034 D_real: 0.960 D_fake: 0.718 
+(epoch: 483, iters: 4350, time: 0.063) G_GAN: 0.296 G_GAN_Feat: 0.810 G_ID: 0.123 G_Rec: 0.343 D_GP: 0.036 D_real: 1.063 D_fake: 0.712 
+(epoch: 483, iters: 4750, time: 0.063) G_GAN: 0.685 G_GAN_Feat: 1.125 G_ID: 0.121 G_Rec: 0.509 D_GP: 0.039 D_real: 0.996 D_fake: 0.385 
+(epoch: 483, iters: 5150, time: 0.064) G_GAN: 0.216 G_GAN_Feat: 0.796 G_ID: 0.094 G_Rec: 0.293 D_GP: 0.034 D_real: 0.968 D_fake: 0.785 
+(epoch: 483, iters: 5550, time: 0.063) G_GAN: 0.567 G_GAN_Feat: 1.157 G_ID: 0.126 G_Rec: 0.483 D_GP: 0.047 D_real: 1.049 D_fake: 0.456 
+(epoch: 483, iters: 5950, time: 0.063) G_GAN: 0.286 G_GAN_Feat: 0.835 G_ID: 0.102 G_Rec: 0.312 D_GP: 0.033 D_real: 0.790 D_fake: 0.715 
+(epoch: 483, iters: 6350, time: 0.063) G_GAN: 0.584 G_GAN_Feat: 1.101 G_ID: 0.114 G_Rec: 0.442 D_GP: 0.965 D_real: 0.698 D_fake: 0.461 
+(epoch: 483, iters: 6750, time: 0.064) G_GAN: 0.024 G_GAN_Feat: 0.772 G_ID: 0.114 G_Rec: 0.313 D_GP: 0.044 D_real: 0.789 D_fake: 0.976 
+(epoch: 483, iters: 7150, time: 0.063) G_GAN: 0.390 G_GAN_Feat: 1.128 G_ID: 0.109 G_Rec: 0.486 D_GP: 0.034 D_real: 0.680 D_fake: 0.611 
+(epoch: 483, iters: 7550, time: 0.063) G_GAN: 0.277 G_GAN_Feat: 0.770 G_ID: 0.115 G_Rec: 0.299 D_GP: 0.031 D_real: 1.072 D_fake: 0.724 
+(epoch: 483, iters: 7950, time: 0.063) G_GAN: 0.304 G_GAN_Feat: 1.008 G_ID: 0.116 G_Rec: 0.433 D_GP: 0.032 D_real: 0.924 D_fake: 0.697 
+(epoch: 483, iters: 8350, time: 0.064) G_GAN: 0.370 G_GAN_Feat: 0.742 G_ID: 0.094 G_Rec: 0.276 D_GP: 0.031 D_real: 1.173 D_fake: 0.632 
+(epoch: 484, iters: 142, time: 0.063) G_GAN: 0.475 G_GAN_Feat: 1.036 G_ID: 0.119 G_Rec: 0.441 D_GP: 0.036 D_real: 1.073 D_fake: 0.530 
+(epoch: 484, iters: 542, time: 0.063) G_GAN: 0.262 G_GAN_Feat: 0.734 G_ID: 0.093 G_Rec: 0.327 D_GP: 0.029 D_real: 1.174 D_fake: 0.738 
+(epoch: 484, iters: 942, time: 0.063) G_GAN: 0.164 G_GAN_Feat: 0.857 G_ID: 0.136 G_Rec: 0.380 D_GP: 0.033 D_real: 0.829 D_fake: 0.837 
+(epoch: 484, iters: 1342, time: 0.064) G_GAN: 0.062 G_GAN_Feat: 0.804 G_ID: 0.101 G_Rec: 0.351 D_GP: 0.037 D_real: 0.829 D_fake: 0.938 
+(epoch: 484, iters: 1742, time: 0.063) G_GAN: 0.489 G_GAN_Feat: 0.941 G_ID: 0.105 G_Rec: 0.541 D_GP: 0.030 D_real: 1.072 D_fake: 0.531 
+(epoch: 484, iters: 2142, time: 0.063) G_GAN: -0.288 G_GAN_Feat: 0.771 G_ID: 0.109 G_Rec: 0.336 D_GP: 0.046 D_real: 0.487 D_fake: 1.288 
+(epoch: 484, iters: 2542, time: 0.063) G_GAN: 0.380 G_GAN_Feat: 1.020 G_ID: 0.105 G_Rec: 0.454 D_GP: 0.048 D_real: 0.879 D_fake: 0.624 
+(epoch: 484, iters: 2942, time: 0.064) G_GAN: 0.298 G_GAN_Feat: 0.732 G_ID: 0.097 G_Rec: 0.291 D_GP: 0.039 D_real: 1.136 D_fake: 0.704 
+(epoch: 484, iters: 3342, time: 0.063) G_GAN: 0.833 G_GAN_Feat: 0.974 G_ID: 0.105 G_Rec: 0.387 D_GP: 0.039 D_real: 1.487 D_fake: 0.212 
+(epoch: 484, iters: 3742, time: 0.063) G_GAN: 0.204 G_GAN_Feat: 0.832 G_ID: 0.105 G_Rec: 0.337 D_GP: 0.149 D_real: 0.566 D_fake: 0.801 
+(epoch: 484, iters: 4142, time: 0.063) G_GAN: 0.526 G_GAN_Feat: 1.038 G_ID: 0.123 G_Rec: 0.467 D_GP: 0.044 D_real: 0.914 D_fake: 0.477 
+(epoch: 484, iters: 4542, time: 0.064) G_GAN: 0.438 G_GAN_Feat: 0.851 G_ID: 0.099 G_Rec: 0.318 D_GP: 0.042 D_real: 1.235 D_fake: 0.564 
+(epoch: 484, iters: 4942, time: 0.063) G_GAN: 0.526 G_GAN_Feat: 1.047 G_ID: 0.106 G_Rec: 0.476 D_GP: 0.037 D_real: 1.114 D_fake: 0.493 
+(epoch: 484, iters: 5342, time: 0.063) G_GAN: 0.021 G_GAN_Feat: 0.700 G_ID: 0.097 G_Rec: 0.285 D_GP: 0.034 D_real: 0.937 D_fake: 0.979 
+(epoch: 484, iters: 5742, time: 0.063) G_GAN: 0.415 G_GAN_Feat: 1.058 G_ID: 0.126 G_Rec: 0.443 D_GP: 0.044 D_real: 0.701 D_fake: 0.591 
+(epoch: 484, iters: 6142, time: 0.064) G_GAN: 0.254 G_GAN_Feat: 0.965 G_ID: 0.095 G_Rec: 0.375 D_GP: 0.113 D_real: 0.468 D_fake: 0.746 
+(epoch: 484, iters: 6542, time: 0.063) G_GAN: 0.287 G_GAN_Feat: 1.123 G_ID: 0.114 G_Rec: 0.436 D_GP: 0.038 D_real: 0.670 D_fake: 0.714 
+(epoch: 484, iters: 6942, time: 0.063) G_GAN: 0.349 G_GAN_Feat: 0.910 G_ID: 0.096 G_Rec: 0.336 D_GP: 0.058 D_real: 1.329 D_fake: 0.655 
+(epoch: 484, iters: 7342, time: 0.063) G_GAN: 0.200 G_GAN_Feat: 1.109 G_ID: 0.126 G_Rec: 0.436 D_GP: 0.033 D_real: 0.498 D_fake: 0.801 
+(epoch: 484, iters: 7742, time: 0.064) G_GAN: 0.241 G_GAN_Feat: 0.711 G_ID: 0.086 G_Rec: 0.276 D_GP: 0.033 D_real: 1.178 D_fake: 0.759 
+(epoch: 484, iters: 8142, time: 0.063) G_GAN: 0.655 G_GAN_Feat: 1.234 G_ID: 0.114 G_Rec: 0.494 D_GP: 0.249 D_real: 0.552 D_fake: 0.373 
+(epoch: 484, iters: 8542, time: 0.063) G_GAN: 0.357 G_GAN_Feat: 1.039 G_ID: 0.112 G_Rec: 0.355 D_GP: 0.123 D_real: 0.236 D_fake: 0.687 
+(epoch: 485, iters: 334, time: 0.063) G_GAN: 0.423 G_GAN_Feat: 0.947 G_ID: 0.115 G_Rec: 0.434 D_GP: 0.029 D_real: 1.091 D_fake: 0.583 
+(epoch: 485, iters: 734, time: 0.064) G_GAN: 0.184 G_GAN_Feat: 0.643 G_ID: 0.096 G_Rec: 0.307 D_GP: 0.027 D_real: 1.116 D_fake: 0.817 
+(epoch: 485, iters: 1134, time: 0.063) G_GAN: -0.054 G_GAN_Feat: 0.883 G_ID: 0.146 G_Rec: 0.441 D_GP: 0.033 D_real: 0.768 D_fake: 1.054 
+(epoch: 485, iters: 1534, time: 0.063) G_GAN: 0.278 G_GAN_Feat: 0.699 G_ID: 0.092 G_Rec: 0.302 D_GP: 0.034 D_real: 1.211 D_fake: 0.724 
+(epoch: 485, iters: 1934, time: 0.063) G_GAN: 0.292 G_GAN_Feat: 0.978 G_ID: 0.122 G_Rec: 0.443 D_GP: 0.049 D_real: 0.854 D_fake: 0.713 
+(epoch: 485, iters: 2334, time: 0.063) G_GAN: 0.252 G_GAN_Feat: 0.627 G_ID: 0.093 G_Rec: 0.275 D_GP: 0.027 D_real: 1.178 D_fake: 0.749 
+(epoch: 485, iters: 2734, time: 0.063) G_GAN: 0.301 G_GAN_Feat: 0.907 G_ID: 0.117 G_Rec: 0.432 D_GP: 0.033 D_real: 0.895 D_fake: 0.707 
+(epoch: 485, iters: 3134, time: 0.063) G_GAN: 0.103 G_GAN_Feat: 0.724 G_ID: 0.104 G_Rec: 0.313 D_GP: 0.043 D_real: 0.925 D_fake: 0.897 
+(epoch: 485, iters: 3534, time: 0.064) G_GAN: 0.324 G_GAN_Feat: 0.956 G_ID: 0.131 G_Rec: 0.457 D_GP: 0.042 D_real: 0.861 D_fake: 0.688 
+(epoch: 485, iters: 3934, time: 0.063) G_GAN: 0.065 G_GAN_Feat: 0.697 G_ID: 0.101 G_Rec: 0.305 D_GP: 0.029 D_real: 1.052 D_fake: 0.935 
+(epoch: 485, iters: 4334, time: 0.063) G_GAN: 0.389 G_GAN_Feat: 1.010 G_ID: 0.105 G_Rec: 0.460 D_GP: 0.051 D_real: 0.819 D_fake: 0.616 
+(epoch: 485, iters: 4734, time: 0.063) G_GAN: 0.133 G_GAN_Feat: 0.737 G_ID: 0.092 G_Rec: 0.298 D_GP: 0.032 D_real: 1.025 D_fake: 0.867 
+(epoch: 485, iters: 5134, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 0.991 G_ID: 0.131 G_Rec: 0.423 D_GP: 0.065 D_real: 0.784 D_fake: 0.631 
+(epoch: 485, iters: 5534, time: 0.063) G_GAN: 0.113 G_GAN_Feat: 0.711 G_ID: 0.102 G_Rec: 0.295 D_GP: 0.029 D_real: 1.008 D_fake: 0.887 
+(epoch: 485, iters: 5934, time: 0.063) G_GAN: 0.548 G_GAN_Feat: 1.038 G_ID: 0.106 G_Rec: 0.463 D_GP: 0.049 D_real: 0.918 D_fake: 0.464 
+(epoch: 485, iters: 6334, time: 0.063) G_GAN: 0.138 G_GAN_Feat: 0.741 G_ID: 0.108 G_Rec: 0.312 D_GP: 0.033 D_real: 0.887 D_fake: 0.862 
+(epoch: 485, iters: 6734, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.839 G_ID: 0.110 G_Rec: 0.382 D_GP: 0.031 D_real: 1.213 D_fake: 0.604 
+(epoch: 485, iters: 7134, time: 0.063) G_GAN: 0.323 G_GAN_Feat: 0.660 G_ID: 0.088 G_Rec: 0.301 D_GP: 0.034 D_real: 1.221 D_fake: 0.678 
+(epoch: 485, iters: 7534, time: 0.063) G_GAN: 0.427 G_GAN_Feat: 0.996 G_ID: 0.115 G_Rec: 0.434 D_GP: 0.085 D_real: 0.778 D_fake: 0.575 
+(epoch: 485, iters: 7934, time: 0.063) G_GAN: 0.180 G_GAN_Feat: 0.841 G_ID: 0.116 G_Rec: 0.413 D_GP: 0.056 D_real: 0.939 D_fake: 0.822 
+(epoch: 485, iters: 8334, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.975 G_ID: 0.136 G_Rec: 0.427 D_GP: 0.037 D_real: 1.199 D_fake: 0.555 
+(epoch: 486, iters: 126, time: 0.063) G_GAN: 0.425 G_GAN_Feat: 0.649 G_ID: 0.087 G_Rec: 0.323 D_GP: 0.029 D_real: 1.351 D_fake: 0.575 
+(epoch: 486, iters: 526, time: 0.063) G_GAN: 0.652 G_GAN_Feat: 0.893 G_ID: 0.104 G_Rec: 0.437 D_GP: 0.033 D_real: 1.373 D_fake: 0.393 
+(epoch: 486, iters: 926, time: 0.063) G_GAN: 0.224 G_GAN_Feat: 0.724 G_ID: 0.105 G_Rec: 0.323 D_GP: 0.036 D_real: 0.998 D_fake: 0.777 
+(epoch: 486, iters: 1326, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.897 G_ID: 0.121 G_Rec: 0.389 D_GP: 0.043 D_real: 0.941 D_fake: 0.689 
+(epoch: 486, iters: 1726, time: 0.063) G_GAN: -0.064 G_GAN_Feat: 0.784 G_ID: 0.108 G_Rec: 0.300 D_GP: 0.070 D_real: 0.560 D_fake: 1.064 
+(epoch: 486, iters: 2126, time: 0.063) G_GAN: 0.496 G_GAN_Feat: 1.028 G_ID: 0.107 G_Rec: 0.415 D_GP: 0.040 D_real: 0.883 D_fake: 0.507 
+(epoch: 486, iters: 2526, time: 0.063) G_GAN: 0.200 G_GAN_Feat: 0.855 G_ID: 0.117 G_Rec: 0.343 D_GP: 0.057 D_real: 1.034 D_fake: 0.811 
+(epoch: 486, iters: 2926, time: 0.064) G_GAN: 0.697 G_GAN_Feat: 0.937 G_ID: 0.100 G_Rec: 0.383 D_GP: 0.034 D_real: 1.359 D_fake: 0.329 
+(epoch: 486, iters: 3326, time: 0.063) G_GAN: 0.181 G_GAN_Feat: 0.942 G_ID: 0.099 G_Rec: 0.338 D_GP: 0.226 D_real: 0.142 D_fake: 1.005 
+(epoch: 486, iters: 3726, time: 0.063) G_GAN: 0.814 G_GAN_Feat: 1.116 G_ID: 0.102 G_Rec: 0.440 D_GP: 0.133 D_real: 0.816 D_fake: 0.253 
+(epoch: 486, iters: 4126, time: 0.063) G_GAN: 0.036 G_GAN_Feat: 0.999 G_ID: 0.108 G_Rec: 0.383 D_GP: 0.539 D_real: 0.214 D_fake: 0.971 
+(epoch: 486, iters: 4526, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 1.030 G_ID: 0.121 G_Rec: 0.471 D_GP: 0.036 D_real: 1.188 D_fake: 0.567 
+(epoch: 486, iters: 4926, time: 0.063) G_GAN: 0.145 G_GAN_Feat: 0.708 G_ID: 0.099 G_Rec: 0.320 D_GP: 0.031 D_real: 0.925 D_fake: 0.859 
+(epoch: 486, iters: 5326, time: 0.063) G_GAN: 0.442 G_GAN_Feat: 0.922 G_ID: 0.125 G_Rec: 0.438 D_GP: 0.034 D_real: 1.109 D_fake: 0.584 
+(epoch: 486, iters: 5726, time: 0.063) G_GAN: 0.213 G_GAN_Feat: 0.843 G_ID: 0.101 G_Rec: 0.364 D_GP: 0.038 D_real: 0.732 D_fake: 0.787 
+(epoch: 486, iters: 6126, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 1.057 G_ID: 0.110 G_Rec: 0.403 D_GP: 0.041 D_real: 0.848 D_fake: 0.503 
+(epoch: 486, iters: 6526, time: 0.063) G_GAN: 0.641 G_GAN_Feat: 0.813 G_ID: 0.100 G_Rec: 0.301 D_GP: 0.036 D_real: 1.368 D_fake: 0.372 
+(epoch: 486, iters: 6926, time: 0.063) G_GAN: 0.341 G_GAN_Feat: 1.255 G_ID: 0.114 G_Rec: 0.485 D_GP: 0.885 D_real: 0.329 D_fake: 0.661 
+(epoch: 486, iters: 7326, time: 0.063) G_GAN: 0.110 G_GAN_Feat: 0.766 G_ID: 0.078 G_Rec: 0.309 D_GP: 0.032 D_real: 0.798 D_fake: 0.890 
+(epoch: 486, iters: 7726, time: 0.064) G_GAN: 0.737 G_GAN_Feat: 1.005 G_ID: 0.114 G_Rec: 0.427 D_GP: 0.059 D_real: 1.191 D_fake: 0.290 
+(epoch: 486, iters: 8126, time: 0.063) G_GAN: 0.492 G_GAN_Feat: 0.938 G_ID: 0.110 G_Rec: 0.341 D_GP: 0.041 D_real: 0.883 D_fake: 0.516 
+(epoch: 486, iters: 8526, time: 0.063) G_GAN: 0.545 G_GAN_Feat: 0.945 G_ID: 0.147 G_Rec: 0.455 D_GP: 0.034 D_real: 1.256 D_fake: 0.469 
+(epoch: 487, iters: 318, time: 0.063) G_GAN: 0.374 G_GAN_Feat: 0.731 G_ID: 0.106 G_Rec: 0.297 D_GP: 0.040 D_real: 1.270 D_fake: 0.627 
+(epoch: 487, iters: 718, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 1.037 G_ID: 0.100 G_Rec: 0.424 D_GP: 0.040 D_real: 0.618 D_fake: 0.649 
+(epoch: 487, iters: 1118, time: 0.063) G_GAN: 0.226 G_GAN_Feat: 0.649 G_ID: 0.082 G_Rec: 0.285 D_GP: 0.031 D_real: 1.199 D_fake: 0.776 
+(epoch: 487, iters: 1518, time: 0.063) G_GAN: 0.459 G_GAN_Feat: 0.875 G_ID: 0.113 G_Rec: 0.407 D_GP: 0.032 D_real: 1.110 D_fake: 0.568 
+(epoch: 487, iters: 1918, time: 0.063) G_GAN: -0.023 G_GAN_Feat: 0.732 G_ID: 0.086 G_Rec: 0.321 D_GP: 0.042 D_real: 0.785 D_fake: 1.023 
+(epoch: 487, iters: 2318, time: 0.064) G_GAN: 0.183 G_GAN_Feat: 0.917 G_ID: 0.150 G_Rec: 0.374 D_GP: 0.043 D_real: 0.740 D_fake: 0.821 
+(epoch: 487, iters: 2718, time: 0.063) G_GAN: 0.270 G_GAN_Feat: 0.739 G_ID: 0.106 G_Rec: 0.312 D_GP: 0.040 D_real: 1.162 D_fake: 0.734 
+(epoch: 487, iters: 3118, time: 0.063) G_GAN: 0.478 G_GAN_Feat: 1.032 G_ID: 0.123 G_Rec: 0.467 D_GP: 0.041 D_real: 1.008 D_fake: 0.534 
+(epoch: 487, iters: 3518, time: 0.063) G_GAN: 0.430 G_GAN_Feat: 0.746 G_ID: 0.093 G_Rec: 0.316 D_GP: 0.030 D_real: 1.426 D_fake: 0.573 
+(epoch: 487, iters: 3918, time: 0.064) G_GAN: 0.267 G_GAN_Feat: 1.059 G_ID: 0.109 G_Rec: 0.444 D_GP: 0.042 D_real: 0.619 D_fake: 0.735 
+(epoch: 487, iters: 4318, time: 0.063) G_GAN: 0.688 G_GAN_Feat: 0.883 G_ID: 0.092 G_Rec: 0.325 D_GP: 0.056 D_real: 1.289 D_fake: 0.570 
+(epoch: 487, iters: 4718, time: 0.063) G_GAN: 0.674 G_GAN_Feat: 1.163 G_ID: 0.124 G_Rec: 0.468 D_GP: 0.093 D_real: 0.458 D_fake: 0.363 
+(epoch: 487, iters: 5118, time: 0.063) G_GAN: -0.061 G_GAN_Feat: 0.788 G_ID: 0.100 G_Rec: 0.279 D_GP: 0.032 D_real: 0.618 D_fake: 1.061 
+(epoch: 487, iters: 5518, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.904 G_ID: 0.122 G_Rec: 0.422 D_GP: 0.029 D_real: 0.963 D_fake: 0.730 
+(epoch: 487, iters: 5918, time: 0.063) G_GAN: 0.190 G_GAN_Feat: 0.729 G_ID: 0.083 G_Rec: 0.285 D_GP: 0.039 D_real: 0.964 D_fake: 0.810 
+(epoch: 487, iters: 6318, time: 0.063) G_GAN: 0.443 G_GAN_Feat: 0.989 G_ID: 0.106 G_Rec: 0.381 D_GP: 0.040 D_real: 1.167 D_fake: 0.562 
+(epoch: 487, iters: 6718, time: 0.063) G_GAN: 0.198 G_GAN_Feat: 0.735 G_ID: 0.102 G_Rec: 0.304 D_GP: 0.030 D_real: 1.115 D_fake: 0.802 
+(epoch: 487, iters: 7118, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.972 G_ID: 0.108 G_Rec: 0.432 D_GP: 0.044 D_real: 0.973 D_fake: 0.591 
+(epoch: 487, iters: 7518, time: 0.063) G_GAN: 0.321 G_GAN_Feat: 0.786 G_ID: 0.092 G_Rec: 0.345 D_GP: 0.038 D_real: 0.938 D_fake: 0.681 
+(epoch: 487, iters: 7918, time: 0.063) G_GAN: 0.629 G_GAN_Feat: 0.940 G_ID: 0.109 G_Rec: 0.421 D_GP: 0.031 D_real: 1.276 D_fake: 0.398 
+(epoch: 487, iters: 8318, time: 0.063) G_GAN: 0.423 G_GAN_Feat: 0.709 G_ID: 0.082 G_Rec: 0.295 D_GP: 0.033 D_real: 1.379 D_fake: 0.580 
+(epoch: 488, iters: 110, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 0.991 G_ID: 0.094 G_Rec: 0.455 D_GP: 0.036 D_real: 1.045 D_fake: 0.593 
+(epoch: 488, iters: 510, time: 0.063) G_GAN: 0.042 G_GAN_Feat: 0.728 G_ID: 0.108 G_Rec: 0.315 D_GP: 0.034 D_real: 0.865 D_fake: 0.959 
+(epoch: 488, iters: 910, time: 0.063) G_GAN: 0.275 G_GAN_Feat: 0.932 G_ID: 0.117 G_Rec: 0.424 D_GP: 0.041 D_real: 0.884 D_fake: 0.728 
+(epoch: 488, iters: 1310, time: 0.063) G_GAN: 0.150 G_GAN_Feat: 0.690 G_ID: 0.122 G_Rec: 0.293 D_GP: 0.031 D_real: 0.965 D_fake: 0.850 
+(epoch: 488, iters: 1710, time: 0.064) G_GAN: 0.336 G_GAN_Feat: 1.097 G_ID: 0.128 G_Rec: 0.476 D_GP: 0.379 D_real: 0.408 D_fake: 0.666 
+(epoch: 488, iters: 2110, time: 0.063) G_GAN: 0.067 G_GAN_Feat: 0.755 G_ID: 0.107 G_Rec: 0.315 D_GP: 0.041 D_real: 0.810 D_fake: 0.933 
+(epoch: 488, iters: 2510, time: 0.063) G_GAN: 0.666 G_GAN_Feat: 1.091 G_ID: 0.117 G_Rec: 0.466 D_GP: 0.058 D_real: 0.756 D_fake: 0.358 
+(epoch: 488, iters: 2910, time: 0.063) G_GAN: 0.176 G_GAN_Feat: 0.744 G_ID: 0.108 G_Rec: 0.271 D_GP: 0.037 D_real: 0.864 D_fake: 0.824 
+(epoch: 488, iters: 3310, time: 0.064) G_GAN: 0.938 G_GAN_Feat: 1.050 G_ID: 0.123 G_Rec: 0.509 D_GP: 0.031 D_real: 1.410 D_fake: 0.277 
+(epoch: 488, iters: 3710, time: 0.063) G_GAN: 0.187 G_GAN_Feat: 0.741 G_ID: 0.086 G_Rec: 0.277 D_GP: 0.037 D_real: 1.124 D_fake: 0.814 
+(epoch: 488, iters: 4110, time: 0.063) G_GAN: 0.662 G_GAN_Feat: 1.027 G_ID: 0.114 G_Rec: 0.433 D_GP: 0.030 D_real: 1.183 D_fake: 0.361 
+(epoch: 488, iters: 4510, time: 0.063) G_GAN: 0.435 G_GAN_Feat: 0.827 G_ID: 0.096 G_Rec: 0.310 D_GP: 0.041 D_real: 1.158 D_fake: 0.608 
+(epoch: 488, iters: 4910, time: 0.064) G_GAN: 0.598 G_GAN_Feat: 1.177 G_ID: 0.129 G_Rec: 0.469 D_GP: 0.111 D_real: 0.482 D_fake: 0.508 
+(epoch: 488, iters: 5310, time: 0.063) G_GAN: 0.160 G_GAN_Feat: 0.905 G_ID: 0.110 G_Rec: 0.364 D_GP: 0.100 D_real: 0.511 D_fake: 0.843 
+(epoch: 488, iters: 5710, time: 0.063) G_GAN: 0.596 G_GAN_Feat: 1.242 G_ID: 0.127 G_Rec: 0.465 D_GP: 1.181 D_real: 0.366 D_fake: 0.467 
+(epoch: 488, iters: 6110, time: 0.063) G_GAN: 0.145 G_GAN_Feat: 0.829 G_ID: 0.096 G_Rec: 0.320 D_GP: 0.038 D_real: 0.802 D_fake: 0.856 
+(epoch: 488, iters: 6510, time: 0.064) G_GAN: 0.781 G_GAN_Feat: 0.982 G_ID: 0.112 G_Rec: 0.422 D_GP: 0.039 D_real: 1.312 D_fake: 0.270 
+(epoch: 488, iters: 6910, time: 0.063) G_GAN: 0.060 G_GAN_Feat: 1.035 G_ID: 0.108 G_Rec: 0.389 D_GP: 0.047 D_real: 0.897 D_fake: 0.959 
+(epoch: 488, iters: 7310, time: 0.063) G_GAN: 0.301 G_GAN_Feat: 1.058 G_ID: 0.124 G_Rec: 0.455 D_GP: 0.030 D_real: 0.950 D_fake: 0.705 
+(epoch: 488, iters: 7710, time: 0.063) G_GAN: -0.017 G_GAN_Feat: 0.760 G_ID: 0.109 G_Rec: 0.312 D_GP: 0.042 D_real: 0.741 D_fake: 1.017 
+(epoch: 488, iters: 8110, time: 0.064) G_GAN: 0.609 G_GAN_Feat: 0.978 G_ID: 0.119 G_Rec: 0.410 D_GP: 0.037 D_real: 1.072 D_fake: 0.407 
+(epoch: 488, iters: 8510, time: 0.063) G_GAN: 0.562 G_GAN_Feat: 0.834 G_ID: 0.105 G_Rec: 0.321 D_GP: 0.072 D_real: 1.383 D_fake: 0.523 
+(epoch: 489, iters: 302, time: 0.063) G_GAN: 0.295 G_GAN_Feat: 1.036 G_ID: 0.116 G_Rec: 0.433 D_GP: 0.052 D_real: 0.309 D_fake: 0.736 
+(epoch: 489, iters: 702, time: 0.063) G_GAN: 0.340 G_GAN_Feat: 0.765 G_ID: 0.110 G_Rec: 0.294 D_GP: 0.031 D_real: 1.113 D_fake: 0.661 
+(epoch: 489, iters: 1102, time: 0.064) G_GAN: 0.546 G_GAN_Feat: 1.124 G_ID: 0.102 G_Rec: 0.441 D_GP: 0.036 D_real: 0.540 D_fake: 0.464 
+(epoch: 489, iters: 1502, time: 0.063) G_GAN: -0.085 G_GAN_Feat: 0.768 G_ID: 0.116 G_Rec: 0.349 D_GP: 0.033 D_real: 0.800 D_fake: 1.086 
+(epoch: 489, iters: 1902, time: 0.063) G_GAN: 0.549 G_GAN_Feat: 0.912 G_ID: 0.106 G_Rec: 0.418 D_GP: 0.028 D_real: 1.235 D_fake: 0.470 
+(epoch: 489, iters: 2302, time: 0.063) G_GAN: 0.070 G_GAN_Feat: 0.735 G_ID: 0.115 G_Rec: 0.321 D_GP: 0.039 D_real: 0.981 D_fake: 0.931 
+(epoch: 489, iters: 2702, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.889 G_ID: 0.097 G_Rec: 0.423 D_GP: 0.031 D_real: 1.095 D_fake: 0.624 
+(epoch: 489, iters: 3102, time: 0.063) G_GAN: 0.262 G_GAN_Feat: 0.710 G_ID: 0.090 G_Rec: 0.299 D_GP: 0.033 D_real: 1.117 D_fake: 0.739 
+(epoch: 489, iters: 3502, time: 0.063) G_GAN: 0.189 G_GAN_Feat: 1.008 G_ID: 0.119 G_Rec: 0.473 D_GP: 0.067 D_real: 0.711 D_fake: 0.813 
+(epoch: 489, iters: 3902, time: 0.063) G_GAN: -0.144 G_GAN_Feat: 0.781 G_ID: 0.114 G_Rec: 0.346 D_GP: 0.034 D_real: 0.651 D_fake: 1.144 
+(epoch: 489, iters: 4302, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 0.923 G_ID: 0.122 G_Rec: 0.415 D_GP: 0.034 D_real: 1.032 D_fake: 0.593 
+(epoch: 489, iters: 4702, time: 0.063) G_GAN: 0.230 G_GAN_Feat: 0.796 G_ID: 0.093 G_Rec: 0.327 D_GP: 0.041 D_real: 0.959 D_fake: 0.771 
+(epoch: 489, iters: 5102, time: 0.063) G_GAN: 0.271 G_GAN_Feat: 1.069 G_ID: 0.115 G_Rec: 0.477 D_GP: 0.084 D_real: 0.604 D_fake: 0.730 
+(epoch: 489, iters: 5502, time: 0.063) G_GAN: 0.196 G_GAN_Feat: 0.762 G_ID: 0.090 G_Rec: 0.289 D_GP: 0.040 D_real: 0.770 D_fake: 0.804 
+(epoch: 489, iters: 5902, time: 0.064) G_GAN: 0.740 G_GAN_Feat: 1.083 G_ID: 0.120 G_Rec: 0.481 D_GP: 0.051 D_real: 0.814 D_fake: 0.331 
+(epoch: 489, iters: 6302, time: 0.063) G_GAN: 0.219 G_GAN_Feat: 0.814 G_ID: 0.089 G_Rec: 0.298 D_GP: 0.035 D_real: 0.939 D_fake: 0.781 
+(epoch: 489, iters: 6702, time: 0.063) G_GAN: 0.518 G_GAN_Feat: 1.055 G_ID: 0.108 G_Rec: 0.408 D_GP: 0.070 D_real: 0.610 D_fake: 0.492 
+(epoch: 489, iters: 7102, time: 0.063) G_GAN: 0.169 G_GAN_Feat: 0.868 G_ID: 0.093 G_Rec: 0.289 D_GP: 0.035 D_real: 0.645 D_fake: 0.831 
+(epoch: 489, iters: 7502, time: 0.063) G_GAN: 0.237 G_GAN_Feat: 0.786 G_ID: 0.122 G_Rec: 0.392 D_GP: 0.028 D_real: 1.042 D_fake: 0.763 
+(epoch: 489, iters: 7902, time: 0.063) G_GAN: 0.020 G_GAN_Feat: 0.646 G_ID: 0.101 G_Rec: 0.307 D_GP: 0.031 D_real: 0.896 D_fake: 0.981 
+(epoch: 489, iters: 8302, time: 0.063) G_GAN: 0.362 G_GAN_Feat: 0.808 G_ID: 0.115 G_Rec: 0.396 D_GP: 0.032 D_real: 1.128 D_fake: 0.640 
+(epoch: 489, iters: 8702, time: 0.064) G_GAN: -0.050 G_GAN_Feat: 0.632 G_ID: 0.086 G_Rec: 0.272 D_GP: 0.032 D_real: 0.869 D_fake: 1.050 
+(epoch: 490, iters: 494, time: 0.063) G_GAN: 0.242 G_GAN_Feat: 0.869 G_ID: 0.130 G_Rec: 0.412 D_GP: 0.044 D_real: 0.739 D_fake: 0.760 
+(epoch: 490, iters: 894, time: 0.063) G_GAN: 0.038 G_GAN_Feat: 0.692 G_ID: 0.092 G_Rec: 0.313 D_GP: 0.038 D_real: 0.988 D_fake: 0.962 
+(epoch: 490, iters: 1294, time: 0.063) G_GAN: 0.514 G_GAN_Feat: 0.975 G_ID: 0.114 G_Rec: 0.446 D_GP: 0.040 D_real: 1.014 D_fake: 0.513 
+(epoch: 490, iters: 1694, time: 0.064) G_GAN: 0.224 G_GAN_Feat: 0.706 G_ID: 0.082 G_Rec: 0.323 D_GP: 0.049 D_real: 1.063 D_fake: 0.778 
+(epoch: 490, iters: 2094, time: 0.063) G_GAN: 0.347 G_GAN_Feat: 0.981 G_ID: 0.129 G_Rec: 0.453 D_GP: 0.053 D_real: 0.739 D_fake: 0.657 
+(epoch: 490, iters: 2494, time: 0.063) G_GAN: 0.309 G_GAN_Feat: 0.745 G_ID: 0.109 G_Rec: 0.313 D_GP: 0.058 D_real: 0.988 D_fake: 0.692 
+(epoch: 490, iters: 2894, time: 0.063) G_GAN: 0.637 G_GAN_Feat: 0.925 G_ID: 0.117 G_Rec: 0.414 D_GP: 0.037 D_real: 1.333 D_fake: 0.379 
+(epoch: 490, iters: 3294, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.861 G_ID: 0.109 G_Rec: 0.336 D_GP: 0.126 D_real: 0.293 D_fake: 0.922 
+(epoch: 490, iters: 3694, time: 0.063) G_GAN: 0.398 G_GAN_Feat: 0.984 G_ID: 0.113 G_Rec: 0.389 D_GP: 0.037 D_real: 1.004 D_fake: 0.614 
+(epoch: 490, iters: 4094, time: 0.063) G_GAN: 0.390 G_GAN_Feat: 0.753 G_ID: 0.086 G_Rec: 0.304 D_GP: 0.039 D_real: 1.216 D_fake: 0.611 
+(epoch: 490, iters: 4494, time: 0.063) G_GAN: 0.730 G_GAN_Feat: 1.030 G_ID: 0.120 G_Rec: 0.444 D_GP: 0.043 D_real: 1.261 D_fake: 0.332 
+(epoch: 490, iters: 4894, time: 0.064) G_GAN: 0.371 G_GAN_Feat: 0.763 G_ID: 0.099 G_Rec: 0.301 D_GP: 0.043 D_real: 1.116 D_fake: 0.630 
+(epoch: 490, iters: 5294, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.941 G_ID: 0.120 G_Rec: 0.461 D_GP: 0.025 D_real: 1.019 D_fake: 0.762 
+(epoch: 490, iters: 5694, time: 0.063) G_GAN: 0.148 G_GAN_Feat: 0.716 G_ID: 0.114 G_Rec: 0.300 D_GP: 0.035 D_real: 0.957 D_fake: 0.852 
+(epoch: 490, iters: 6094, time: 0.063) G_GAN: 0.377 G_GAN_Feat: 0.915 G_ID: 0.104 G_Rec: 0.408 D_GP: 0.037 D_real: 1.091 D_fake: 0.624 
+(epoch: 490, iters: 6494, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.814 G_ID: 0.096 G_Rec: 0.316 D_GP: 0.092 D_real: 1.053 D_fake: 0.649 
+(epoch: 490, iters: 6894, time: 0.063) G_GAN: 0.503 G_GAN_Feat: 1.103 G_ID: 0.123 G_Rec: 0.451 D_GP: 0.084 D_real: 0.506 D_fake: 0.501 
+(epoch: 490, iters: 7294, time: 0.063) G_GAN: 0.414 G_GAN_Feat: 0.848 G_ID: 0.098 G_Rec: 0.320 D_GP: 0.076 D_real: 0.754 D_fake: 0.617 
+(epoch: 490, iters: 7694, time: 0.063) G_GAN: 0.463 G_GAN_Feat: 1.001 G_ID: 0.142 G_Rec: 0.428 D_GP: 0.062 D_real: 0.831 D_fake: 0.549 
+(epoch: 490, iters: 8094, time: 0.064) G_GAN: 0.317 G_GAN_Feat: 0.827 G_ID: 0.107 G_Rec: 0.313 D_GP: 0.052 D_real: 0.745 D_fake: 0.689 
+(epoch: 490, iters: 8494, time: 0.063) G_GAN: 0.461 G_GAN_Feat: 0.945 G_ID: 0.118 G_Rec: 0.433 D_GP: 0.028 D_real: 1.237 D_fake: 0.542 
+(epoch: 491, iters: 286, time: 0.063) G_GAN: 0.125 G_GAN_Feat: 0.699 G_ID: 0.081 G_Rec: 0.294 D_GP: 0.034 D_real: 0.984 D_fake: 0.875 
+(epoch: 491, iters: 686, time: 0.063) G_GAN: 0.302 G_GAN_Feat: 0.950 G_ID: 0.115 G_Rec: 0.416 D_GP: 0.040 D_real: 0.913 D_fake: 0.707 
+(epoch: 491, iters: 1086, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.839 G_ID: 0.088 G_Rec: 0.357 D_GP: 0.059 D_real: 0.849 D_fake: 0.781 
+(epoch: 491, iters: 1486, time: 0.063) G_GAN: 0.794 G_GAN_Feat: 1.043 G_ID: 0.125 G_Rec: 0.426 D_GP: 0.053 D_real: 1.051 D_fake: 0.279 
+(epoch: 491, iters: 1886, time: 0.063) G_GAN: 0.106 G_GAN_Feat: 0.948 G_ID: 0.083 G_Rec: 0.347 D_GP: 0.360 D_real: 0.236 D_fake: 0.910 
+(epoch: 491, iters: 2286, time: 0.063) G_GAN: 0.696 G_GAN_Feat: 1.123 G_ID: 0.116 G_Rec: 0.448 D_GP: 0.053 D_real: 0.997 D_fake: 0.363 
+(epoch: 491, iters: 2686, time: 0.064) G_GAN: 0.299 G_GAN_Feat: 0.962 G_ID: 0.092 G_Rec: 0.351 D_GP: 0.208 D_real: 0.488 D_fake: 0.704 
+(epoch: 491, iters: 3086, time: 0.063) G_GAN: 0.466 G_GAN_Feat: 0.963 G_ID: 0.104 G_Rec: 0.453 D_GP: 0.031 D_real: 1.006 D_fake: 0.564 
+(epoch: 491, iters: 3486, time: 0.063) G_GAN: 0.252 G_GAN_Feat: 0.738 G_ID: 0.118 G_Rec: 0.295 D_GP: 0.039 D_real: 1.039 D_fake: 0.748 
+(epoch: 491, iters: 3886, time: 0.063) G_GAN: 0.375 G_GAN_Feat: 1.025 G_ID: 0.153 G_Rec: 0.421 D_GP: 0.058 D_real: 0.633 D_fake: 0.638 
+(epoch: 491, iters: 4286, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.903 G_ID: 0.107 G_Rec: 0.336 D_GP: 0.272 D_real: 0.302 D_fake: 0.881 
+(epoch: 491, iters: 4686, time: 0.063) G_GAN: 0.488 G_GAN_Feat: 0.977 G_ID: 0.128 G_Rec: 0.462 D_GP: 0.042 D_real: 1.142 D_fake: 0.516 
+(epoch: 491, iters: 5086, time: 0.063) G_GAN: 0.217 G_GAN_Feat: 0.686 G_ID: 0.087 G_Rec: 0.312 D_GP: 0.034 D_real: 1.060 D_fake: 0.784 
+(epoch: 491, iters: 5486, time: 0.063) G_GAN: 0.280 G_GAN_Feat: 1.025 G_ID: 0.122 G_Rec: 0.472 D_GP: 0.068 D_real: 0.730 D_fake: 0.722 
+(epoch: 491, iters: 5886, time: 0.064) G_GAN: 0.010 G_GAN_Feat: 0.797 G_ID: 0.090 G_Rec: 0.339 D_GP: 0.040 D_real: 0.761 D_fake: 0.990 
+(epoch: 491, iters: 6286, time: 0.063) G_GAN: 0.430 G_GAN_Feat: 0.891 G_ID: 0.155 G_Rec: 0.410 D_GP: 0.036 D_real: 1.041 D_fake: 0.580 
+(epoch: 491, iters: 6686, time: 0.063) G_GAN: 0.008 G_GAN_Feat: 0.814 G_ID: 0.101 G_Rec: 0.345 D_GP: 0.044 D_real: 0.611 D_fake: 0.992 
+(epoch: 491, iters: 7086, time: 0.063) G_GAN: 0.459 G_GAN_Feat: 1.068 G_ID: 0.122 G_Rec: 0.468 D_GP: 0.215 D_real: 0.515 D_fake: 0.548 
+(epoch: 491, iters: 7486, time: 0.064) G_GAN: 0.161 G_GAN_Feat: 0.840 G_ID: 0.099 G_Rec: 0.372 D_GP: 0.060 D_real: 0.654 D_fake: 0.840 
+(epoch: 491, iters: 7886, time: 0.063) G_GAN: 0.373 G_GAN_Feat: 1.348 G_ID: 0.117 G_Rec: 0.484 D_GP: 0.061 D_real: 1.202 D_fake: 0.647 
+(epoch: 491, iters: 8286, time: 0.063) G_GAN: 0.002 G_GAN_Feat: 0.649 G_ID: 0.088 G_Rec: 0.305 D_GP: 0.024 D_real: 0.994 D_fake: 0.998 
+(epoch: 491, iters: 8686, time: 0.063) G_GAN: 0.321 G_GAN_Feat: 0.925 G_ID: 0.121 G_Rec: 0.458 D_GP: 0.032 D_real: 0.933 D_fake: 0.688 
+(epoch: 492, iters: 478, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.741 G_ID: 0.081 G_Rec: 0.337 D_GP: 0.042 D_real: 1.025 D_fake: 0.854 
+(epoch: 492, iters: 878, time: 0.063) G_GAN: 0.076 G_GAN_Feat: 1.001 G_ID: 0.126 G_Rec: 0.489 D_GP: 0.072 D_real: 0.599 D_fake: 0.925 
+(epoch: 492, iters: 1278, time: 0.063) G_GAN: 0.011 G_GAN_Feat: 0.725 G_ID: 0.086 G_Rec: 0.306 D_GP: 0.060 D_real: 0.835 D_fake: 0.989 
+(epoch: 492, iters: 1678, time: 0.063) G_GAN: 0.361 G_GAN_Feat: 0.902 G_ID: 0.106 G_Rec: 0.404 D_GP: 0.040 D_real: 0.947 D_fake: 0.647 
+(epoch: 492, iters: 2078, time: 0.064) G_GAN: 0.173 G_GAN_Feat: 0.791 G_ID: 0.094 G_Rec: 0.306 D_GP: 0.050 D_real: 0.865 D_fake: 0.827 
+(epoch: 492, iters: 2478, time: 0.063) G_GAN: 0.652 G_GAN_Feat: 0.963 G_ID: 0.105 G_Rec: 0.426 D_GP: 0.035 D_real: 1.286 D_fake: 0.380 
+(epoch: 492, iters: 2878, time: 0.063) G_GAN: 0.323 G_GAN_Feat: 0.844 G_ID: 0.099 G_Rec: 0.333 D_GP: 0.078 D_real: 0.729 D_fake: 0.677 
+(epoch: 492, iters: 3278, time: 0.063) G_GAN: 0.064 G_GAN_Feat: 0.949 G_ID: 0.129 G_Rec: 0.453 D_GP: 0.037 D_real: 0.893 D_fake: 0.937 
+(epoch: 492, iters: 3678, time: 0.064) G_GAN: -0.023 G_GAN_Feat: 0.700 G_ID: 0.111 G_Rec: 0.287 D_GP: 0.029 D_real: 0.859 D_fake: 1.023 
+(epoch: 492, iters: 4078, time: 0.063) G_GAN: 0.362 G_GAN_Feat: 1.028 G_ID: 0.121 G_Rec: 0.431 D_GP: 0.084 D_real: 0.717 D_fake: 0.646 
+(epoch: 492, iters: 4478, time: 0.063) G_GAN: 0.342 G_GAN_Feat: 0.648 G_ID: 0.103 G_Rec: 0.269 D_GP: 0.033 D_real: 1.262 D_fake: 0.659 
+(epoch: 492, iters: 4878, time: 0.063) G_GAN: 0.398 G_GAN_Feat: 1.145 G_ID: 0.128 G_Rec: 0.435 D_GP: 0.770 D_real: 0.119 D_fake: 0.711 
+(epoch: 492, iters: 5278, time: 0.064) G_GAN: 0.013 G_GAN_Feat: 0.884 G_ID: 0.093 G_Rec: 0.342 D_GP: 0.239 D_real: 0.311 D_fake: 0.988 
+(epoch: 492, iters: 5678, time: 0.063) G_GAN: 0.536 G_GAN_Feat: 1.070 G_ID: 0.115 G_Rec: 0.489 D_GP: 0.034 D_real: 1.039 D_fake: 0.471 
+(epoch: 492, iters: 6078, time: 0.063) G_GAN: 0.028 G_GAN_Feat: 0.897 G_ID: 0.129 G_Rec: 0.304 D_GP: 0.033 D_real: 0.447 D_fake: 0.972 
+(epoch: 492, iters: 6478, time: 0.063) G_GAN: 0.418 G_GAN_Feat: 1.356 G_ID: 0.125 G_Rec: 0.462 D_GP: 0.105 D_real: 0.071 D_fake: 0.602 
+(epoch: 492, iters: 6878, time: 0.064) G_GAN: 0.720 G_GAN_Feat: 0.858 G_ID: 0.095 G_Rec: 0.332 D_GP: 0.065 D_real: 1.349 D_fake: 0.654 
+(epoch: 492, iters: 7278, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 1.224 G_ID: 0.109 G_Rec: 0.466 D_GP: 0.076 D_real: 0.135 D_fake: 0.766 
+(epoch: 492, iters: 7678, time: 0.063) G_GAN: 0.496 G_GAN_Feat: 0.838 G_ID: 0.091 G_Rec: 0.318 D_GP: 0.057 D_real: 1.113 D_fake: 0.510 
+(epoch: 492, iters: 8078, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 1.084 G_ID: 0.123 G_Rec: 0.559 D_GP: 0.035 D_real: 0.979 D_fake: 0.632 
+(epoch: 492, iters: 8478, time: 0.064) G_GAN: 0.261 G_GAN_Feat: 0.794 G_ID: 0.115 G_Rec: 0.327 D_GP: 0.030 D_real: 1.123 D_fake: 0.740 
+(epoch: 493, iters: 270, time: 0.063) G_GAN: 0.690 G_GAN_Feat: 1.012 G_ID: 0.114 G_Rec: 0.438 D_GP: 0.034 D_real: 1.082 D_fake: 0.378 
+(epoch: 493, iters: 670, time: 0.063) G_GAN: 0.064 G_GAN_Feat: 0.807 G_ID: 0.084 G_Rec: 0.331 D_GP: 0.078 D_real: 0.733 D_fake: 0.936 
+(epoch: 493, iters: 1070, time: 0.063) G_GAN: 0.579 G_GAN_Feat: 1.063 G_ID: 0.138 G_Rec: 0.465 D_GP: 0.050 D_real: 0.843 D_fake: 0.428 
+(epoch: 493, iters: 1470, time: 0.064) G_GAN: 0.360 G_GAN_Feat: 0.828 G_ID: 0.104 G_Rec: 0.323 D_GP: 0.081 D_real: 0.831 D_fake: 0.640 
+(epoch: 493, iters: 1870, time: 0.063) G_GAN: 0.697 G_GAN_Feat: 1.058 G_ID: 0.114 G_Rec: 0.442 D_GP: 0.065 D_real: 1.052 D_fake: 0.345 
+(epoch: 493, iters: 2270, time: 0.063) G_GAN: 0.498 G_GAN_Feat: 0.922 G_ID: 0.124 G_Rec: 0.307 D_GP: 0.059 D_real: 0.951 D_fake: 0.681 
+(epoch: 493, iters: 2670, time: 0.063) G_GAN: 0.858 G_GAN_Feat: 0.988 G_ID: 0.110 G_Rec: 0.440 D_GP: 0.031 D_real: 1.446 D_fake: 0.206 
+(epoch: 493, iters: 3070, time: 0.064) G_GAN: 0.483 G_GAN_Feat: 0.887 G_ID: 0.105 G_Rec: 0.309 D_GP: 0.050 D_real: 1.212 D_fake: 0.524 
+(epoch: 493, iters: 3470, time: 0.063) G_GAN: 0.985 G_GAN_Feat: 1.131 G_ID: 0.115 G_Rec: 0.472 D_GP: 0.039 D_real: 1.577 D_fake: 0.206 
+(epoch: 493, iters: 3870, time: 0.063) G_GAN: 0.329 G_GAN_Feat: 0.840 G_ID: 0.099 G_Rec: 0.323 D_GP: 0.030 D_real: 1.109 D_fake: 0.671 
+(epoch: 493, iters: 4270, time: 0.063) G_GAN: 0.387 G_GAN_Feat: 1.070 G_ID: 0.126 G_Rec: 0.509 D_GP: 0.032 D_real: 1.034 D_fake: 0.622 
+(epoch: 493, iters: 4670, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.709 G_ID: 0.100 G_Rec: 0.287 D_GP: 0.035 D_real: 1.169 D_fake: 0.742 
+(epoch: 493, iters: 5070, time: 0.063) G_GAN: 0.360 G_GAN_Feat: 0.968 G_ID: 0.109 G_Rec: 0.419 D_GP: 0.079 D_real: 0.768 D_fake: 0.642 
+(epoch: 493, iters: 5470, time: 0.063) G_GAN: 0.345 G_GAN_Feat: 0.813 G_ID: 0.096 G_Rec: 0.292 D_GP: 0.127 D_real: 0.784 D_fake: 0.656 
+(epoch: 493, iters: 5870, time: 0.063) G_GAN: 0.804 G_GAN_Feat: 0.976 G_ID: 0.120 G_Rec: 0.395 D_GP: 0.041 D_real: 1.347 D_fake: 0.254 
+(epoch: 493, iters: 6270, time: 0.064) G_GAN: 0.426 G_GAN_Feat: 0.729 G_ID: 0.094 G_Rec: 0.311 D_GP: 0.032 D_real: 1.359 D_fake: 0.577 
+(epoch: 493, iters: 6670, time: 0.063) G_GAN: 0.724 G_GAN_Feat: 1.152 G_ID: 0.149 G_Rec: 0.497 D_GP: 0.037 D_real: 1.048 D_fake: 0.376 
+(epoch: 493, iters: 7070, time: 0.063) G_GAN: 0.531 G_GAN_Feat: 0.787 G_ID: 0.109 G_Rec: 0.283 D_GP: 0.038 D_real: 1.233 D_fake: 0.553 
+(epoch: 493, iters: 7470, time: 0.063) G_GAN: 0.397 G_GAN_Feat: 0.843 G_ID: 0.123 G_Rec: 0.405 D_GP: 0.031 D_real: 1.164 D_fake: 0.611 
+(epoch: 493, iters: 7870, time: 0.064) G_GAN: 0.151 G_GAN_Feat: 0.740 G_ID: 0.095 G_Rec: 0.330 D_GP: 0.033 D_real: 1.025 D_fake: 0.849 
+(epoch: 493, iters: 8270, time: 0.063) G_GAN: 0.207 G_GAN_Feat: 0.884 G_ID: 0.121 G_Rec: 0.413 D_GP: 0.032 D_real: 0.985 D_fake: 0.794 
+(epoch: 493, iters: 8670, time: 0.063) G_GAN: -0.003 G_GAN_Feat: 0.803 G_ID: 0.099 G_Rec: 0.325 D_GP: 0.081 D_real: 0.617 D_fake: 1.003 
+(epoch: 494, iters: 462, time: 0.063) G_GAN: 0.633 G_GAN_Feat: 1.021 G_ID: 0.107 G_Rec: 0.481 D_GP: 0.035 D_real: 1.196 D_fake: 0.404 
+(epoch: 494, iters: 862, time: 0.064) G_GAN: 0.189 G_GAN_Feat: 0.656 G_ID: 0.102 G_Rec: 0.284 D_GP: 0.030 D_real: 1.124 D_fake: 0.811 
+(epoch: 494, iters: 1262, time: 0.063) G_GAN: 0.209 G_GAN_Feat: 0.950 G_ID: 0.121 G_Rec: 0.437 D_GP: 0.034 D_real: 0.859 D_fake: 0.792 
+(epoch: 494, iters: 1662, time: 0.063) G_GAN: 0.082 G_GAN_Feat: 0.779 G_ID: 0.107 G_Rec: 0.318 D_GP: 0.063 D_real: 0.683 D_fake: 0.921 
+(epoch: 494, iters: 2062, time: 0.063) G_GAN: 0.532 G_GAN_Feat: 1.008 G_ID: 0.144 G_Rec: 0.460 D_GP: 0.053 D_real: 1.003 D_fake: 0.486 
+(epoch: 494, iters: 2462, time: 0.064) G_GAN: 0.270 G_GAN_Feat: 0.771 G_ID: 0.094 G_Rec: 0.305 D_GP: 0.029 D_real: 1.060 D_fake: 0.731 
+(epoch: 494, iters: 2862, time: 0.063) G_GAN: 0.478 G_GAN_Feat: 1.028 G_ID: 0.117 G_Rec: 0.445 D_GP: 0.050 D_real: 0.687 D_fake: 0.538 
+(epoch: 494, iters: 3262, time: 0.063) G_GAN: -0.159 G_GAN_Feat: 0.765 G_ID: 0.096 G_Rec: 0.293 D_GP: 0.103 D_real: 0.571 D_fake: 1.159 
+(epoch: 494, iters: 3662, time: 0.063) G_GAN: 0.641 G_GAN_Feat: 1.048 G_ID: 0.127 G_Rec: 0.481 D_GP: 0.053 D_real: 1.055 D_fake: 0.384 
+(epoch: 494, iters: 4062, time: 0.063) G_GAN: 0.313 G_GAN_Feat: 0.643 G_ID: 0.086 G_Rec: 0.326 D_GP: 0.025 D_real: 1.242 D_fake: 0.688 
+(epoch: 494, iters: 4462, time: 0.063) G_GAN: 0.579 G_GAN_Feat: 0.904 G_ID: 0.117 G_Rec: 0.448 D_GP: 0.028 D_real: 1.324 D_fake: 0.429 
+(epoch: 494, iters: 4862, time: 0.063) G_GAN: 0.245 G_GAN_Feat: 0.660 G_ID: 0.102 G_Rec: 0.276 D_GP: 0.028 D_real: 1.190 D_fake: 0.755 
+(epoch: 494, iters: 5262, time: 0.064) G_GAN: 0.404 G_GAN_Feat: 0.951 G_ID: 0.112 G_Rec: 0.419 D_GP: 0.054 D_real: 0.988 D_fake: 0.600 
+(epoch: 494, iters: 5662, time: 0.063) G_GAN: 0.296 G_GAN_Feat: 0.733 G_ID: 0.089 G_Rec: 0.290 D_GP: 0.045 D_real: 1.120 D_fake: 0.705 
+(epoch: 494, iters: 6062, time: 0.063) G_GAN: 0.505 G_GAN_Feat: 0.954 G_ID: 0.125 G_Rec: 0.435 D_GP: 0.036 D_real: 1.129 D_fake: 0.503 
+(epoch: 494, iters: 6462, time: 0.063) G_GAN: 0.437 G_GAN_Feat: 0.744 G_ID: 0.085 G_Rec: 0.309 D_GP: 0.038 D_real: 1.212 D_fake: 0.565 
+(epoch: 494, iters: 6862, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.941 G_ID: 0.108 G_Rec: 0.429 D_GP: 0.035 D_real: 1.113 D_fake: 0.541 
+(epoch: 494, iters: 7262, time: 0.063) G_GAN: 0.290 G_GAN_Feat: 0.720 G_ID: 0.092 G_Rec: 0.310 D_GP: 0.040 D_real: 1.078 D_fake: 0.711 
+(epoch: 494, iters: 7662, time: 0.063) G_GAN: 0.308 G_GAN_Feat: 0.973 G_ID: 0.119 G_Rec: 0.400 D_GP: 0.056 D_real: 0.795 D_fake: 0.693 
+(epoch: 494, iters: 8062, time: 0.063) G_GAN: 0.304 G_GAN_Feat: 0.771 G_ID: 0.092 G_Rec: 0.304 D_GP: 0.057 D_real: 0.987 D_fake: 0.703 
+(epoch: 494, iters: 8462, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 1.012 G_ID: 0.119 G_Rec: 0.475 D_GP: 0.031 D_real: 0.870 D_fake: 0.638 
+(epoch: 495, iters: 254, time: 0.063) G_GAN: 0.114 G_GAN_Feat: 0.757 G_ID: 0.097 G_Rec: 0.282 D_GP: 0.028 D_real: 0.919 D_fake: 0.886 
+(epoch: 495, iters: 654, time: 0.063) G_GAN: 0.613 G_GAN_Feat: 1.002 G_ID: 0.105 G_Rec: 0.414 D_GP: 0.034 D_real: 1.305 D_fake: 0.410 
+(epoch: 495, iters: 1054, time: 0.063) G_GAN: 0.263 G_GAN_Feat: 0.864 G_ID: 0.087 G_Rec: 0.315 D_GP: 0.052 D_real: 0.577 D_fake: 0.739 
+(epoch: 495, iters: 1454, time: 0.064) G_GAN: 0.439 G_GAN_Feat: 1.079 G_ID: 0.114 G_Rec: 0.471 D_GP: 0.044 D_real: 0.980 D_fake: 0.569 
+(epoch: 495, iters: 1854, time: 0.063) G_GAN: 0.358 G_GAN_Feat: 0.839 G_ID: 0.100 G_Rec: 0.314 D_GP: 0.096 D_real: 0.701 D_fake: 0.646 
+(epoch: 495, iters: 2254, time: 0.063) G_GAN: 0.430 G_GAN_Feat: 0.928 G_ID: 0.106 G_Rec: 0.414 D_GP: 0.033 D_real: 1.122 D_fake: 0.580 
+(epoch: 495, iters: 2654, time: 0.063) G_GAN: 0.339 G_GAN_Feat: 0.761 G_ID: 0.111 G_Rec: 0.323 D_GP: 0.036 D_real: 1.138 D_fake: 0.662 
+(epoch: 495, iters: 3054, time: 0.064) G_GAN: 0.557 G_GAN_Feat: 1.147 G_ID: 0.122 G_Rec: 0.458 D_GP: 0.262 D_real: 0.395 D_fake: 0.465 
+(epoch: 495, iters: 3454, time: 0.063) G_GAN: 0.141 G_GAN_Feat: 0.946 G_ID: 0.090 G_Rec: 0.308 D_GP: 0.055 D_real: 0.222 D_fake: 0.862 
+(epoch: 495, iters: 3854, time: 0.063) G_GAN: 0.448 G_GAN_Feat: 0.881 G_ID: 0.118 G_Rec: 0.441 D_GP: 0.029 D_real: 1.198 D_fake: 0.559 
+(epoch: 495, iters: 4254, time: 0.063) G_GAN: 0.143 G_GAN_Feat: 0.644 G_ID: 0.101 G_Rec: 0.324 D_GP: 0.028 D_real: 1.057 D_fake: 0.857 
+(epoch: 495, iters: 4654, time: 0.064) G_GAN: 0.505 G_GAN_Feat: 0.861 G_ID: 0.112 G_Rec: 0.436 D_GP: 0.034 D_real: 1.042 D_fake: 0.512 
+(epoch: 495, iters: 5054, time: 0.063) G_GAN: 0.197 G_GAN_Feat: 0.666 G_ID: 0.104 G_Rec: 0.304 D_GP: 0.033 D_real: 1.058 D_fake: 0.803 
+(epoch: 495, iters: 5454, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.893 G_ID: 0.117 G_Rec: 0.424 D_GP: 0.042 D_real: 1.062 D_fake: 0.644 
+(epoch: 495, iters: 5854, time: 0.064) G_GAN: 0.127 G_GAN_Feat: 0.686 G_ID: 0.104 G_Rec: 0.314 D_GP: 0.034 D_real: 0.967 D_fake: 0.873 
+(epoch: 495, iters: 6254, time: 0.064) G_GAN: 0.124 G_GAN_Feat: 0.861 G_ID: 0.118 G_Rec: 0.403 D_GP: 0.038 D_real: 0.899 D_fake: 0.876 
+(epoch: 495, iters: 6654, time: 0.064) G_GAN: -0.079 G_GAN_Feat: 0.752 G_ID: 0.104 G_Rec: 0.341 D_GP: 0.046 D_real: 0.662 D_fake: 1.079 
+(epoch: 495, iters: 7054, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.914 G_ID: 0.124 G_Rec: 0.420 D_GP: 0.048 D_real: 0.907 D_fake: 0.670 
+(epoch: 495, iters: 7454, time: 0.064) G_GAN: -0.182 G_GAN_Feat: 0.740 G_ID: 0.108 G_Rec: 0.319 D_GP: 0.039 D_real: 0.605 D_fake: 1.182 
+(epoch: 495, iters: 7854, time: 0.064) G_GAN: 0.418 G_GAN_Feat: 0.975 G_ID: 0.115 G_Rec: 0.454 D_GP: 0.063 D_real: 0.789 D_fake: 0.615 
+(epoch: 495, iters: 8254, time: 0.064) G_GAN: -0.126 G_GAN_Feat: 0.756 G_ID: 0.099 G_Rec: 0.355 D_GP: 0.108 D_real: 0.550 D_fake: 1.126 
+(epoch: 495, iters: 8654, time: 0.064) G_GAN: 0.318 G_GAN_Feat: 1.027 G_ID: 0.113 G_Rec: 0.448 D_GP: 0.089 D_real: 0.641 D_fake: 0.691 
+(epoch: 496, iters: 446, time: 0.064) G_GAN: 0.122 G_GAN_Feat: 0.726 G_ID: 0.112 G_Rec: 0.306 D_GP: 0.049 D_real: 0.906 D_fake: 0.878 
+(epoch: 496, iters: 846, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 0.955 G_ID: 0.113 G_Rec: 0.398 D_GP: 0.050 D_real: 1.036 D_fake: 0.508 
+(epoch: 496, iters: 1246, time: 0.064) G_GAN: 0.035 G_GAN_Feat: 0.739 G_ID: 0.095 G_Rec: 0.308 D_GP: 0.053 D_real: 0.950 D_fake: 0.965 
+(epoch: 496, iters: 1646, time: 0.064) G_GAN: 0.481 G_GAN_Feat: 0.865 G_ID: 0.107 G_Rec: 0.391 D_GP: 0.036 D_real: 1.251 D_fake: 0.524 
+(epoch: 496, iters: 2046, time: 0.064) G_GAN: 0.107 G_GAN_Feat: 0.845 G_ID: 0.100 G_Rec: 0.357 D_GP: 0.077 D_real: 0.487 D_fake: 0.894 
+(epoch: 496, iters: 2446, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 1.054 G_ID: 0.140 G_Rec: 0.488 D_GP: 0.110 D_real: 0.452 D_fake: 0.681 
+(epoch: 496, iters: 2846, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.773 G_ID: 0.099 G_Rec: 0.313 D_GP: 0.074 D_real: 1.015 D_fake: 0.792 
+(epoch: 496, iters: 3246, time: 0.063) G_GAN: 0.624 G_GAN_Feat: 0.936 G_ID: 0.116 G_Rec: 0.441 D_GP: 0.070 D_real: 1.160 D_fake: 0.401 
+(epoch: 496, iters: 3646, time: 0.063) G_GAN: 0.282 G_GAN_Feat: 0.796 G_ID: 0.092 G_Rec: 0.327 D_GP: 0.050 D_real: 0.868 D_fake: 0.720 
+(epoch: 496, iters: 4046, time: 0.064) G_GAN: 0.402 G_GAN_Feat: 0.964 G_ID: 0.132 G_Rec: 0.407 D_GP: 0.050 D_real: 0.837 D_fake: 0.599 
+(epoch: 496, iters: 4446, time: 0.064) G_GAN: 0.040 G_GAN_Feat: 0.899 G_ID: 0.093 G_Rec: 0.333 D_GP: 0.117 D_real: 0.413 D_fake: 0.961 
+(epoch: 496, iters: 4846, time: 0.064) G_GAN: 0.369 G_GAN_Feat: 0.930 G_ID: 0.109 G_Rec: 0.450 D_GP: 0.026 D_real: 1.146 D_fake: 0.635 
+(epoch: 496, iters: 5246, time: 0.064) G_GAN: 0.059 G_GAN_Feat: 0.708 G_ID: 0.095 G_Rec: 0.314 D_GP: 0.031 D_real: 0.904 D_fake: 0.941 
+(epoch: 496, iters: 5646, time: 0.064) G_GAN: 0.607 G_GAN_Feat: 0.944 G_ID: 0.109 G_Rec: 0.506 D_GP: 0.046 D_real: 1.106 D_fake: 0.431 
+(epoch: 496, iters: 6046, time: 0.063) G_GAN: -0.131 G_GAN_Feat: 0.826 G_ID: 0.105 G_Rec: 0.325 D_GP: 0.115 D_real: 0.415 D_fake: 1.132 
+(epoch: 496, iters: 6446, time: 0.063) G_GAN: 0.561 G_GAN_Feat: 0.956 G_ID: 0.103 G_Rec: 0.416 D_GP: 0.041 D_real: 1.115 D_fake: 0.448 
+(epoch: 496, iters: 6846, time: 0.063) G_GAN: 0.166 G_GAN_Feat: 0.739 G_ID: 0.106 G_Rec: 0.309 D_GP: 0.036 D_real: 1.014 D_fake: 0.835 
+(epoch: 496, iters: 7246, time: 0.064) G_GAN: 0.489 G_GAN_Feat: 0.932 G_ID: 0.117 G_Rec: 0.396 D_GP: 0.039 D_real: 1.177 D_fake: 0.517 
+(epoch: 496, iters: 7646, time: 0.063) G_GAN: 0.297 G_GAN_Feat: 0.802 G_ID: 0.097 G_Rec: 0.291 D_GP: 0.039 D_real: 1.088 D_fake: 0.703 
+(epoch: 496, iters: 8046, time: 0.063) G_GAN: 0.501 G_GAN_Feat: 1.032 G_ID: 0.129 G_Rec: 0.479 D_GP: 0.078 D_real: 0.903 D_fake: 0.520 
+(epoch: 496, iters: 8446, time: 0.063) G_GAN: 0.098 G_GAN_Feat: 0.862 G_ID: 0.109 G_Rec: 0.325 D_GP: 0.057 D_real: 0.582 D_fake: 0.902 
+(epoch: 497, iters: 238, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.937 G_ID: 0.123 G_Rec: 0.379 D_GP: 0.068 D_real: 1.017 D_fake: 0.583 
+(epoch: 497, iters: 638, time: 0.063) G_GAN: 0.251 G_GAN_Feat: 0.721 G_ID: 0.093 G_Rec: 0.314 D_GP: 0.035 D_real: 1.019 D_fake: 0.749 
+(epoch: 497, iters: 1038, time: 0.063) G_GAN: 0.638 G_GAN_Feat: 0.963 G_ID: 0.112 G_Rec: 0.428 D_GP: 0.038 D_real: 1.276 D_fake: 0.384 
+(epoch: 497, iters: 1438, time: 0.063) G_GAN: 0.373 G_GAN_Feat: 0.717 G_ID: 0.101 G_Rec: 0.289 D_GP: 0.032 D_real: 1.278 D_fake: 0.628 
+(epoch: 497, iters: 1838, time: 0.064) G_GAN: 0.575 G_GAN_Feat: 1.022 G_ID: 0.119 G_Rec: 0.459 D_GP: 0.096 D_real: 1.108 D_fake: 0.442 
+(epoch: 497, iters: 2238, time: 0.063) G_GAN: 0.196 G_GAN_Feat: 0.802 G_ID: 0.095 G_Rec: 0.295 D_GP: 0.035 D_real: 0.827 D_fake: 0.807 
+(epoch: 497, iters: 2638, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 1.019 G_ID: 0.120 G_Rec: 0.403 D_GP: 0.039 D_real: 0.622 D_fake: 0.639 
+(epoch: 497, iters: 3038, time: 0.063) G_GAN: 0.088 G_GAN_Feat: 0.783 G_ID: 0.100 G_Rec: 0.289 D_GP: 0.032 D_real: 0.900 D_fake: 0.912 
+(epoch: 497, iters: 3438, time: 0.064) G_GAN: 0.858 G_GAN_Feat: 1.025 G_ID: 0.113 G_Rec: 0.430 D_GP: 0.056 D_real: 1.212 D_fake: 0.209 
+(epoch: 497, iters: 3838, time: 0.063) G_GAN: -0.051 G_GAN_Feat: 0.969 G_ID: 0.098 G_Rec: 0.371 D_GP: 0.205 D_real: 0.223 D_fake: 1.052 
+(epoch: 497, iters: 4238, time: 0.063) G_GAN: 0.387 G_GAN_Feat: 0.927 G_ID: 0.126 G_Rec: 0.408 D_GP: 0.036 D_real: 1.054 D_fake: 0.646 
+(epoch: 497, iters: 4638, time: 0.063) G_GAN: 0.384 G_GAN_Feat: 0.772 G_ID: 0.088 G_Rec: 0.287 D_GP: 0.037 D_real: 1.199 D_fake: 0.618 
+(epoch: 497, iters: 5038, time: 0.064) G_GAN: 0.278 G_GAN_Feat: 1.063 G_ID: 0.119 G_Rec: 0.461 D_GP: 0.036 D_real: 0.829 D_fake: 0.723 
+(epoch: 497, iters: 5438, time: 0.063) G_GAN: 0.103 G_GAN_Feat: 0.841 G_ID: 0.098 G_Rec: 0.305 D_GP: 0.058 D_real: 0.611 D_fake: 0.898 
+(epoch: 497, iters: 5838, time: 0.063) G_GAN: 0.773 G_GAN_Feat: 1.073 G_ID: 0.114 G_Rec: 0.428 D_GP: 0.037 D_real: 0.833 D_fake: 0.252 
+(epoch: 497, iters: 6238, time: 0.063) G_GAN: 0.376 G_GAN_Feat: 0.630 G_ID: 0.097 G_Rec: 0.290 D_GP: 0.025 D_real: 1.344 D_fake: 0.625 
+(epoch: 497, iters: 6638, time: 0.064) G_GAN: 0.424 G_GAN_Feat: 0.845 G_ID: 0.109 G_Rec: 0.383 D_GP: 0.031 D_real: 1.157 D_fake: 0.578 
+(epoch: 497, iters: 7038, time: 0.063) G_GAN: 0.139 G_GAN_Feat: 0.785 G_ID: 0.092 G_Rec: 0.348 D_GP: 0.050 D_real: 0.946 D_fake: 0.864 
+(epoch: 497, iters: 7438, time: 0.063) G_GAN: 0.635 G_GAN_Feat: 1.005 G_ID: 0.108 G_Rec: 0.429 D_GP: 0.043 D_real: 1.141 D_fake: 0.383 
+(epoch: 497, iters: 7838, time: 0.063) G_GAN: 0.278 G_GAN_Feat: 0.764 G_ID: 0.082 G_Rec: 0.316 D_GP: 0.045 D_real: 0.981 D_fake: 0.723 
+(epoch: 497, iters: 8238, time: 0.064) G_GAN: 0.656 G_GAN_Feat: 1.051 G_ID: 0.108 G_Rec: 0.462 D_GP: 0.048 D_real: 1.188 D_fake: 0.386 
+(epoch: 497, iters: 8638, time: 0.063) G_GAN: 0.452 G_GAN_Feat: 0.668 G_ID: 0.092 G_Rec: 0.281 D_GP: 0.027 D_real: 1.371 D_fake: 0.548 
+(epoch: 498, iters: 430, time: 0.063) G_GAN: 0.531 G_GAN_Feat: 1.017 G_ID: 0.110 G_Rec: 0.456 D_GP: 0.062 D_real: 1.048 D_fake: 0.478 
+(epoch: 498, iters: 830, time: 0.063) G_GAN: 0.148 G_GAN_Feat: 0.787 G_ID: 0.091 G_Rec: 0.325 D_GP: 0.096 D_real: 0.922 D_fake: 0.853 
+(epoch: 498, iters: 1230, time: 0.064) G_GAN: 0.929 G_GAN_Feat: 0.970 G_ID: 0.110 G_Rec: 0.405 D_GP: 0.038 D_real: 1.544 D_fake: 0.147 
+(epoch: 498, iters: 1630, time: 0.063) G_GAN: 0.108 G_GAN_Feat: 0.795 G_ID: 0.104 G_Rec: 0.323 D_GP: 0.032 D_real: 0.693 D_fake: 0.892 
+(epoch: 498, iters: 2030, time: 0.063) G_GAN: 0.594 G_GAN_Feat: 0.988 G_ID: 0.124 G_Rec: 0.416 D_GP: 0.040 D_real: 1.075 D_fake: 0.431 
+(epoch: 498, iters: 2430, time: 0.063) G_GAN: 0.154 G_GAN_Feat: 0.781 G_ID: 0.093 G_Rec: 0.296 D_GP: 0.034 D_real: 0.928 D_fake: 0.846 
+(epoch: 498, iters: 2830, time: 0.064) G_GAN: 0.353 G_GAN_Feat: 1.035 G_ID: 0.107 G_Rec: 0.412 D_GP: 0.035 D_real: 0.649 D_fake: 0.665 
+(epoch: 498, iters: 3230, time: 0.063) G_GAN: 0.020 G_GAN_Feat: 0.748 G_ID: 0.097 G_Rec: 0.286 D_GP: 0.032 D_real: 0.796 D_fake: 0.980 
+(epoch: 498, iters: 3630, time: 0.063) G_GAN: 0.804 G_GAN_Feat: 0.951 G_ID: 0.134 G_Rec: 0.441 D_GP: 0.042 D_real: 1.472 D_fake: 0.289 
+(epoch: 498, iters: 4030, time: 0.063) G_GAN: 0.121 G_GAN_Feat: 0.777 G_ID: 0.090 G_Rec: 0.340 D_GP: 0.029 D_real: 1.153 D_fake: 0.880 
+(epoch: 498, iters: 4430, time: 0.064) G_GAN: 0.305 G_GAN_Feat: 0.821 G_ID: 0.112 G_Rec: 0.392 D_GP: 0.027 D_real: 1.129 D_fake: 0.701 
+(epoch: 498, iters: 4830, time: 0.063) G_GAN: 0.007 G_GAN_Feat: 0.619 G_ID: 0.085 G_Rec: 0.288 D_GP: 0.025 D_real: 0.934 D_fake: 0.994 
+(epoch: 498, iters: 5230, time: 0.063) G_GAN: 0.253 G_GAN_Feat: 0.852 G_ID: 0.106 G_Rec: 0.404 D_GP: 0.037 D_real: 0.918 D_fake: 0.752 
+(epoch: 498, iters: 5630, time: 0.063) G_GAN: -0.196 G_GAN_Feat: 0.791 G_ID: 0.096 G_Rec: 0.358 D_GP: 0.033 D_real: 0.628 D_fake: 1.196 
+(epoch: 498, iters: 6030, time: 0.064) G_GAN: 0.025 G_GAN_Feat: 0.829 G_ID: 0.127 G_Rec: 0.387 D_GP: 0.041 D_real: 0.575 D_fake: 0.976 
+(epoch: 498, iters: 6430, time: 0.063) G_GAN: -0.163 G_GAN_Feat: 0.611 G_ID: 0.090 G_Rec: 0.290 D_GP: 0.029 D_real: 0.699 D_fake: 1.163 
+(epoch: 498, iters: 6830, time: 0.063) G_GAN: 0.274 G_GAN_Feat: 0.871 G_ID: 0.113 G_Rec: 0.425 D_GP: 0.035 D_real: 0.926 D_fake: 0.748 
+(epoch: 498, iters: 7230, time: 0.063) G_GAN: -0.364 G_GAN_Feat: 0.713 G_ID: 0.109 G_Rec: 0.316 D_GP: 0.040 D_real: 0.488 D_fake: 1.364 
+(epoch: 498, iters: 7630, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.945 G_ID: 0.113 G_Rec: 0.440 D_GP: 0.039 D_real: 0.986 D_fake: 0.632 
+(epoch: 498, iters: 8030, time: 0.063) G_GAN: -0.089 G_GAN_Feat: 0.759 G_ID: 0.101 G_Rec: 0.311 D_GP: 0.097 D_real: 0.585 D_fake: 1.089 
+(epoch: 498, iters: 8430, time: 0.063) G_GAN: 0.519 G_GAN_Feat: 1.044 G_ID: 0.116 G_Rec: 0.465 D_GP: 0.040 D_real: 1.035 D_fake: 0.497 
+(epoch: 499, iters: 222, time: 0.063) G_GAN: 0.299 G_GAN_Feat: 0.680 G_ID: 0.090 G_Rec: 0.295 D_GP: 0.042 D_real: 1.174 D_fake: 0.701 
+(epoch: 499, iters: 622, time: 0.063) G_GAN: 0.548 G_GAN_Feat: 0.915 G_ID: 0.117 G_Rec: 0.393 D_GP: 0.032 D_real: 1.210 D_fake: 0.454 
+(epoch: 499, iters: 1022, time: 0.063) G_GAN: 0.248 G_GAN_Feat: 0.741 G_ID: 0.111 G_Rec: 0.291 D_GP: 0.030 D_real: 1.092 D_fake: 0.752 
+(epoch: 499, iters: 1422, time: 0.063) G_GAN: 0.504 G_GAN_Feat: 1.030 G_ID: 0.114 G_Rec: 0.441 D_GP: 0.036 D_real: 0.938 D_fake: 0.500 
+(epoch: 499, iters: 1822, time: 0.064) G_GAN: -0.153 G_GAN_Feat: 0.760 G_ID: 0.097 G_Rec: 0.311 D_GP: 0.033 D_real: 0.845 D_fake: 1.153 
+(epoch: 499, iters: 2222, time: 0.063) G_GAN: 0.336 G_GAN_Feat: 1.036 G_ID: 0.107 G_Rec: 0.467 D_GP: 0.057 D_real: 0.848 D_fake: 0.670 
+(epoch: 499, iters: 2622, time: 0.063) G_GAN: 0.070 G_GAN_Feat: 0.780 G_ID: 0.111 G_Rec: 0.341 D_GP: 0.047 D_real: 0.834 D_fake: 0.933 
+(epoch: 499, iters: 3022, time: 0.063) G_GAN: 0.282 G_GAN_Feat: 0.934 G_ID: 0.126 G_Rec: 0.427 D_GP: 0.037 D_real: 0.857 D_fake: 0.722 
+(epoch: 499, iters: 3422, time: 0.064) G_GAN: 0.117 G_GAN_Feat: 0.787 G_ID: 0.098 G_Rec: 0.313 D_GP: 0.067 D_real: 0.788 D_fake: 0.884 
+(epoch: 499, iters: 3822, time: 0.063) G_GAN: 0.532 G_GAN_Feat: 1.041 G_ID: 0.123 G_Rec: 0.456 D_GP: 0.060 D_real: 0.887 D_fake: 0.497 
+(epoch: 499, iters: 4222, time: 0.063) G_GAN: -0.197 G_GAN_Feat: 0.811 G_ID: 0.115 G_Rec: 0.306 D_GP: 0.077 D_real: 0.553 D_fake: 1.198 
+(epoch: 499, iters: 4622, time: 0.063) G_GAN: 0.461 G_GAN_Feat: 1.094 G_ID: 0.108 G_Rec: 0.457 D_GP: 0.063 D_real: 0.883 D_fake: 0.547 
+(epoch: 499, iters: 5022, time: 0.064) G_GAN: 0.297 G_GAN_Feat: 0.827 G_ID: 0.087 G_Rec: 0.304 D_GP: 0.060 D_real: 0.839 D_fake: 0.753 
+(epoch: 499, iters: 5422, time: 0.063) G_GAN: 0.360 G_GAN_Feat: 0.838 G_ID: 0.108 G_Rec: 0.385 D_GP: 0.033 D_real: 1.073 D_fake: 0.641 
+(epoch: 499, iters: 5822, time: 0.063) G_GAN: 0.037 G_GAN_Feat: 0.665 G_ID: 0.097 G_Rec: 0.294 D_GP: 0.032 D_real: 0.985 D_fake: 0.963 
+(epoch: 499, iters: 6222, time: 0.063) G_GAN: 0.364 G_GAN_Feat: 0.912 G_ID: 0.113 G_Rec: 0.421 D_GP: 0.049 D_real: 1.049 D_fake: 0.645 
+(epoch: 499, iters: 6622, time: 0.064) G_GAN: 0.208 G_GAN_Feat: 0.676 G_ID: 0.080 G_Rec: 0.297 D_GP: 0.032 D_real: 1.083 D_fake: 0.795 
+(epoch: 499, iters: 7022, time: 0.063) G_GAN: 0.386 G_GAN_Feat: 0.902 G_ID: 0.103 G_Rec: 0.435 D_GP: 0.029 D_real: 1.020 D_fake: 0.628 
+(epoch: 499, iters: 7422, time: 0.063) G_GAN: 0.111 G_GAN_Feat: 0.708 G_ID: 0.103 G_Rec: 0.309 D_GP: 0.033 D_real: 0.997 D_fake: 0.889 
+(epoch: 499, iters: 7822, time: 0.063) G_GAN: 0.301 G_GAN_Feat: 0.860 G_ID: 0.115 G_Rec: 0.415 D_GP: 0.031 D_real: 1.046 D_fake: 0.703 
+(epoch: 499, iters: 8222, time: 0.064) G_GAN: 0.222 G_GAN_Feat: 0.732 G_ID: 0.085 G_Rec: 0.313 D_GP: 0.035 D_real: 1.033 D_fake: 0.780 
+(epoch: 499, iters: 8622, time: 0.063) G_GAN: 0.985 G_GAN_Feat: 1.019 G_ID: 0.104 G_Rec: 0.466 D_GP: 0.045 D_real: 1.403 D_fake: 0.288 
+(epoch: 500, iters: 414, time: 0.063) G_GAN: 0.171 G_GAN_Feat: 0.739 G_ID: 0.095 G_Rec: 0.315 D_GP: 0.038 D_real: 1.006 D_fake: 0.829 
+(epoch: 500, iters: 814, time: 0.063) G_GAN: 0.383 G_GAN_Feat: 1.007 G_ID: 0.121 G_Rec: 0.411 D_GP: 0.043 D_real: 0.834 D_fake: 0.620 
+(epoch: 500, iters: 1214, time: 0.064) G_GAN: 0.332 G_GAN_Feat: 0.775 G_ID: 0.091 G_Rec: 0.286 D_GP: 0.039 D_real: 1.113 D_fake: 0.683 
+(epoch: 500, iters: 1614, time: 0.063) G_GAN: 0.360 G_GAN_Feat: 0.888 G_ID: 0.114 G_Rec: 0.383 D_GP: 0.031 D_real: 1.095 D_fake: 0.641 
+(epoch: 500, iters: 2014, time: 0.063) G_GAN: 0.261 G_GAN_Feat: 0.677 G_ID: 0.100 G_Rec: 0.289 D_GP: 0.030 D_real: 1.114 D_fake: 0.740 
+(epoch: 500, iters: 2414, time: 0.063) G_GAN: 0.539 G_GAN_Feat: 0.982 G_ID: 0.122 G_Rec: 0.390 D_GP: 0.039 D_real: 0.984 D_fake: 0.468 
+(epoch: 500, iters: 2814, time: 0.064) G_GAN: 0.168 G_GAN_Feat: 0.898 G_ID: 0.123 G_Rec: 0.306 D_GP: 0.064 D_real: 0.607 D_fake: 0.832 
+(epoch: 500, iters: 3214, time: 0.063) G_GAN: 0.628 G_GAN_Feat: 1.152 G_ID: 0.115 G_Rec: 0.439 D_GP: 1.355 D_real: 0.337 D_fake: 0.444 
+(epoch: 500, iters: 3614, time: 0.063) G_GAN: 0.118 G_GAN_Feat: 0.673 G_ID: 0.082 G_Rec: 0.286 D_GP: 0.026 D_real: 1.173 D_fake: 0.882 
+(epoch: 500, iters: 4014, time: 0.063) G_GAN: 0.391 G_GAN_Feat: 0.989 G_ID: 0.113 G_Rec: 0.427 D_GP: 0.035 D_real: 0.859 D_fake: 0.609 
+(epoch: 500, iters: 4414, time: 0.064) G_GAN: 0.345 G_GAN_Feat: 0.765 G_ID: 0.081 G_Rec: 0.291 D_GP: 0.038 D_real: 1.280 D_fake: 0.669 
+(epoch: 500, iters: 4814, time: 0.063) G_GAN: 0.602 G_GAN_Feat: 0.998 G_ID: 0.116 G_Rec: 0.490 D_GP: 0.039 D_real: 1.107 D_fake: 0.426 
+(epoch: 500, iters: 5214, time: 0.063) G_GAN: 0.256 G_GAN_Feat: 0.785 G_ID: 0.092 G_Rec: 0.288 D_GP: 0.032 D_real: 1.012 D_fake: 0.745 
+(epoch: 500, iters: 5614, time: 0.063) G_GAN: 0.795 G_GAN_Feat: 1.131 G_ID: 0.114 G_Rec: 0.525 D_GP: 0.038 D_real: 1.323 D_fake: 0.262 
+(epoch: 500, iters: 6014, time: 0.064) G_GAN: 0.114 G_GAN_Feat: 0.826 G_ID: 0.098 G_Rec: 0.294 D_GP: 0.045 D_real: 0.476 D_fake: 0.886 
+(epoch: 500, iters: 6414, time: 0.063) G_GAN: 0.318 G_GAN_Feat: 1.043 G_ID: 0.098 G_Rec: 0.414 D_GP: 0.044 D_real: 0.891 D_fake: 0.683 
+(epoch: 500, iters: 6814, time: 0.063) G_GAN: 0.361 G_GAN_Feat: 0.713 G_ID: 0.090 G_Rec: 0.266 D_GP: 0.029 D_real: 1.273 D_fake: 0.708 
+(epoch: 500, iters: 7214, time: 0.063) G_GAN: 0.339 G_GAN_Feat: 0.934 G_ID: 0.119 G_Rec: 0.413 D_GP: 0.033 D_real: 0.988 D_fake: 0.675 
+(epoch: 500, iters: 7614, time: 0.064) G_GAN: 0.074 G_GAN_Feat: 0.669 G_ID: 0.093 G_Rec: 0.281 D_GP: 0.029 D_real: 0.989 D_fake: 0.926 
+(epoch: 500, iters: 8014, time: 0.063) G_GAN: 0.255 G_GAN_Feat: 1.055 G_ID: 0.108 G_Rec: 0.509 D_GP: 0.033 D_real: 0.635 D_fake: 0.759 
+(epoch: 500, iters: 8414, time: 0.063) G_GAN: 0.030 G_GAN_Feat: 0.729 G_ID: 0.099 G_Rec: 0.300 D_GP: 0.045 D_real: 0.925 D_fake: 0.970 
+(epoch: 501, iters: 206, time: 0.063) G_GAN: 0.113 G_GAN_Feat: 0.980 G_ID: 0.114 G_Rec: 0.404 D_GP: 0.092 D_real: 0.449 D_fake: 0.888 
+(epoch: 501, iters: 606, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.883 G_ID: 0.097 G_Rec: 0.323 D_GP: 0.220 D_real: 0.596 D_fake: 0.634 
+(epoch: 501, iters: 1006, time: 0.063) G_GAN: 0.712 G_GAN_Feat: 1.246 G_ID: 0.111 G_Rec: 0.519 D_GP: 0.156 D_real: 0.793 D_fake: 0.348 
+(epoch: 501, iters: 1406, time: 0.063) G_GAN: 0.450 G_GAN_Feat: 0.717 G_ID: 0.092 G_Rec: 0.317 D_GP: 0.033 D_real: 1.344 D_fake: 0.553 
+(epoch: 501, iters: 1806, time: 0.063) G_GAN: 0.337 G_GAN_Feat: 1.019 G_ID: 0.126 G_Rec: 0.448 D_GP: 0.090 D_real: 0.788 D_fake: 0.664 
+(epoch: 501, iters: 2206, time: 0.064) G_GAN: 0.432 G_GAN_Feat: 0.708 G_ID: 0.109 G_Rec: 0.281 D_GP: 0.028 D_real: 1.368 D_fake: 0.568 
+(epoch: 501, iters: 2606, time: 0.063) G_GAN: 0.466 G_GAN_Feat: 0.974 G_ID: 0.101 G_Rec: 0.430 D_GP: 0.032 D_real: 1.161 D_fake: 0.545 
+(epoch: 501, iters: 3006, time: 0.063) G_GAN: 0.157 G_GAN_Feat: 0.870 G_ID: 0.092 G_Rec: 0.286 D_GP: 0.384 D_real: 0.369 D_fake: 0.844 
+(epoch: 501, iters: 3406, time: 0.063) G_GAN: 0.775 G_GAN_Feat: 1.242 G_ID: 0.117 G_Rec: 0.517 D_GP: 0.137 D_real: 0.297 D_fake: 0.293 
+(epoch: 501, iters: 3806, time: 0.064) G_GAN: 0.541 G_GAN_Feat: 0.984 G_ID: 0.096 G_Rec: 0.355 D_GP: 0.417 D_real: 0.294 D_fake: 0.619 
+(epoch: 501, iters: 4206, time: 0.063) G_GAN: 0.789 G_GAN_Feat: 1.115 G_ID: 0.106 G_Rec: 0.471 D_GP: 0.054 D_real: 0.917 D_fake: 0.291 
+(epoch: 501, iters: 4606, time: 0.063) G_GAN: 0.022 G_GAN_Feat: 0.695 G_ID: 0.085 G_Rec: 0.306 D_GP: 0.028 D_real: 1.006 D_fake: 0.978 
+(epoch: 501, iters: 5006, time: 0.063) G_GAN: 0.368 G_GAN_Feat: 0.945 G_ID: 0.118 G_Rec: 0.431 D_GP: 0.029 D_real: 1.019 D_fake: 0.643 
+(epoch: 501, iters: 5406, time: 0.064) G_GAN: 0.087 G_GAN_Feat: 0.666 G_ID: 0.086 G_Rec: 0.283 D_GP: 0.029 D_real: 0.998 D_fake: 0.913 
+(epoch: 501, iters: 5806, time: 0.063) G_GAN: 0.217 G_GAN_Feat: 0.904 G_ID: 0.114 G_Rec: 0.410 D_GP: 0.035 D_real: 0.974 D_fake: 0.787 
+(epoch: 501, iters: 6206, time: 0.063) G_GAN: -0.161 G_GAN_Feat: 0.765 G_ID: 0.095 G_Rec: 0.301 D_GP: 0.054 D_real: 0.613 D_fake: 1.161 
+(epoch: 501, iters: 6606, time: 0.063) G_GAN: 0.103 G_GAN_Feat: 0.941 G_ID: 0.116 G_Rec: 0.459 D_GP: 0.049 D_real: 0.656 D_fake: 0.897 
+(epoch: 501, iters: 7006, time: 0.064) G_GAN: 0.080 G_GAN_Feat: 0.715 G_ID: 0.087 G_Rec: 0.298 D_GP: 0.037 D_real: 0.919 D_fake: 0.920 
+(epoch: 501, iters: 7406, time: 0.063) G_GAN: 0.542 G_GAN_Feat: 1.144 G_ID: 0.112 G_Rec: 0.481 D_GP: 0.170 D_real: 0.589 D_fake: 0.483 
+(epoch: 501, iters: 7806, time: 0.063) G_GAN: 0.464 G_GAN_Feat: 0.791 G_ID: 0.091 G_Rec: 0.350 D_GP: 0.041 D_real: 1.248 D_fake: 0.537 
+(epoch: 501, iters: 8206, time: 0.063) G_GAN: 0.478 G_GAN_Feat: 1.042 G_ID: 0.123 G_Rec: 0.441 D_GP: 0.158 D_real: 0.644 D_fake: 0.529 
+(epoch: 501, iters: 8606, time: 0.064) G_GAN: 0.304 G_GAN_Feat: 0.824 G_ID: 0.094 G_Rec: 0.305 D_GP: 0.132 D_real: 0.726 D_fake: 0.701 
+(epoch: 502, iters: 398, time: 0.063) G_GAN: 0.542 G_GAN_Feat: 0.906 G_ID: 0.110 G_Rec: 0.379 D_GP: 0.036 D_real: 1.171 D_fake: 0.468 
+(epoch: 502, iters: 798, time: 0.063) G_GAN: 0.527 G_GAN_Feat: 0.782 G_ID: 0.084 G_Rec: 0.312 D_GP: 0.046 D_real: 1.259 D_fake: 0.480 
+(epoch: 502, iters: 1198, time: 0.063) G_GAN: 0.584 G_GAN_Feat: 1.168 G_ID: 0.130 G_Rec: 0.484 D_GP: 0.095 D_real: 0.449 D_fake: 0.440 
+(epoch: 502, iters: 1598, time: 0.064) G_GAN: 0.266 G_GAN_Feat: 0.828 G_ID: 0.095 G_Rec: 0.302 D_GP: 0.136 D_real: 0.642 D_fake: 0.737 
+(epoch: 502, iters: 1998, time: 0.063) G_GAN: 0.749 G_GAN_Feat: 1.213 G_ID: 0.103 G_Rec: 0.490 D_GP: 0.068 D_real: 0.525 D_fake: 0.380 
+(epoch: 502, iters: 2398, time: 0.063) G_GAN: 0.004 G_GAN_Feat: 1.121 G_ID: 0.113 G_Rec: 0.406 D_GP: 2.753 D_real: 0.450 D_fake: 0.999 
+(epoch: 502, iters: 2798, time: 0.063) G_GAN: 0.236 G_GAN_Feat: 0.953 G_ID: 0.105 G_Rec: 0.497 D_GP: 0.030 D_real: 1.049 D_fake: 0.765 
+(epoch: 502, iters: 3198, time: 0.064) G_GAN: 0.231 G_GAN_Feat: 0.668 G_ID: 0.086 G_Rec: 0.307 D_GP: 0.027 D_real: 1.150 D_fake: 0.769 
+(epoch: 502, iters: 3598, time: 0.063) G_GAN: 0.209 G_GAN_Feat: 0.731 G_ID: 0.110 G_Rec: 0.349 D_GP: 0.028 D_real: 1.011 D_fake: 0.792 
+(epoch: 502, iters: 3998, time: 0.063) G_GAN: 0.236 G_GAN_Feat: 0.677 G_ID: 0.096 G_Rec: 0.302 D_GP: 0.053 D_real: 1.105 D_fake: 0.765 
+(epoch: 502, iters: 4398, time: 0.063) G_GAN: 0.370 G_GAN_Feat: 0.890 G_ID: 0.115 G_Rec: 0.420 D_GP: 0.035 D_real: 1.054 D_fake: 0.631 
+(epoch: 502, iters: 4798, time: 0.064) G_GAN: 0.256 G_GAN_Feat: 0.724 G_ID: 0.093 G_Rec: 0.331 D_GP: 0.040 D_real: 1.130 D_fake: 0.745 
+(epoch: 502, iters: 5198, time: 0.063) G_GAN: 0.225 G_GAN_Feat: 0.963 G_ID: 0.120 G_Rec: 0.461 D_GP: 0.045 D_real: 0.759 D_fake: 0.776 
+(epoch: 502, iters: 5598, time: 0.063) G_GAN: 0.055 G_GAN_Feat: 0.794 G_ID: 0.094 G_Rec: 0.391 D_GP: 0.122 D_real: 0.620 D_fake: 0.946 
+(epoch: 502, iters: 5998, time: 0.063) G_GAN: 0.283 G_GAN_Feat: 0.928 G_ID: 0.128 G_Rec: 0.422 D_GP: 0.044 D_real: 0.833 D_fake: 0.717 
+(epoch: 502, iters: 6398, time: 0.064) G_GAN: 0.674 G_GAN_Feat: 0.693 G_ID: 0.097 G_Rec: 0.404 D_GP: 0.027 D_real: 1.561 D_fake: 0.359 
+(epoch: 502, iters: 6798, time: 0.063) G_GAN: 0.447 G_GAN_Feat: 0.836 G_ID: 0.113 G_Rec: 0.393 D_GP: 0.030 D_real: 1.152 D_fake: 0.556 
+(epoch: 502, iters: 7198, time: 0.063) G_GAN: 0.124 G_GAN_Feat: 0.747 G_ID: 0.089 G_Rec: 0.330 D_GP: 0.039 D_real: 0.922 D_fake: 0.876 
+(epoch: 502, iters: 7598, time: 0.063) G_GAN: 0.384 G_GAN_Feat: 0.932 G_ID: 0.115 G_Rec: 0.459 D_GP: 0.037 D_real: 1.066 D_fake: 0.638 
+(epoch: 502, iters: 7998, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.738 G_ID: 0.084 G_Rec: 0.314 D_GP: 0.062 D_real: 0.871 D_fake: 0.989 
+(epoch: 502, iters: 8398, time: 0.063) G_GAN: 0.293 G_GAN_Feat: 0.989 G_ID: 0.114 G_Rec: 0.441 D_GP: 0.041 D_real: 0.886 D_fake: 0.707 
+(epoch: 503, iters: 190, time: 0.063) G_GAN: 0.209 G_GAN_Feat: 0.743 G_ID: 0.102 G_Rec: 0.309 D_GP: 0.078 D_real: 0.910 D_fake: 0.792 
+(epoch: 503, iters: 590, time: 0.063) G_GAN: 0.504 G_GAN_Feat: 0.956 G_ID: 0.115 G_Rec: 0.394 D_GP: 0.069 D_real: 0.995 D_fake: 0.516 
+(epoch: 503, iters: 990, time: 0.064) G_GAN: 0.121 G_GAN_Feat: 0.865 G_ID: 0.109 G_Rec: 0.341 D_GP: 0.043 D_real: 0.775 D_fake: 0.880 
+(epoch: 503, iters: 1390, time: 0.063) G_GAN: 0.396 G_GAN_Feat: 0.929 G_ID: 0.102 G_Rec: 0.459 D_GP: 0.028 D_real: 1.196 D_fake: 0.605 
+(epoch: 503, iters: 1790, time: 0.063) G_GAN: 0.161 G_GAN_Feat: 0.796 G_ID: 0.100 G_Rec: 0.321 D_GP: 0.043 D_real: 0.967 D_fake: 0.839 
+(epoch: 503, iters: 2190, time: 0.063) G_GAN: 0.511 G_GAN_Feat: 0.980 G_ID: 0.111 G_Rec: 0.457 D_GP: 0.041 D_real: 1.062 D_fake: 0.510 
+(epoch: 503, iters: 2590, time: 0.064) G_GAN: 0.191 G_GAN_Feat: 0.726 G_ID: 0.095 G_Rec: 0.306 D_GP: 0.035 D_real: 0.982 D_fake: 0.809 
+(epoch: 503, iters: 2990, time: 0.063) G_GAN: 0.081 G_GAN_Feat: 0.936 G_ID: 0.114 G_Rec: 0.428 D_GP: 0.047 D_real: 0.648 D_fake: 0.919 
+(epoch: 503, iters: 3390, time: 0.063) G_GAN: 0.044 G_GAN_Feat: 0.737 G_ID: 0.100 G_Rec: 0.290 D_GP: 0.046 D_real: 0.909 D_fake: 0.956 
+(epoch: 503, iters: 3790, time: 0.063) G_GAN: 0.628 G_GAN_Feat: 0.912 G_ID: 0.106 G_Rec: 0.376 D_GP: 0.034 D_real: 1.443 D_fake: 0.382 
+(epoch: 503, iters: 4190, time: 0.064) G_GAN: 0.077 G_GAN_Feat: 0.830 G_ID: 0.125 G_Rec: 0.399 D_GP: 0.042 D_real: 1.127 D_fake: 0.936 
+(epoch: 503, iters: 4590, time: 0.063) G_GAN: 0.337 G_GAN_Feat: 0.909 G_ID: 0.118 G_Rec: 0.466 D_GP: 0.024 D_real: 1.117 D_fake: 0.667 
+(epoch: 503, iters: 4990, time: 0.063) G_GAN: -0.047 G_GAN_Feat: 0.708 G_ID: 0.105 G_Rec: 0.345 D_GP: 0.027 D_real: 0.900 D_fake: 1.047 
+(epoch: 503, iters: 5390, time: 0.063) G_GAN: 0.444 G_GAN_Feat: 0.813 G_ID: 0.111 G_Rec: 0.393 D_GP: 0.030 D_real: 1.195 D_fake: 0.560 
+(epoch: 503, iters: 5790, time: 0.063) G_GAN: 0.141 G_GAN_Feat: 0.662 G_ID: 0.090 G_Rec: 0.353 D_GP: 0.028 D_real: 1.028 D_fake: 0.860 
+(epoch: 503, iters: 6190, time: 0.063) G_GAN: 0.284 G_GAN_Feat: 0.890 G_ID: 0.127 G_Rec: 0.458 D_GP: 0.040 D_real: 0.920 D_fake: 0.718 
+(epoch: 503, iters: 6590, time: 0.063) G_GAN: 0.055 G_GAN_Feat: 0.642 G_ID: 0.094 G_Rec: 0.310 D_GP: 0.045 D_real: 0.896 D_fake: 0.945 
+(epoch: 503, iters: 6990, time: 0.064) G_GAN: 0.177 G_GAN_Feat: 0.914 G_ID: 0.131 G_Rec: 0.441 D_GP: 0.054 D_real: 0.769 D_fake: 0.829 
+(epoch: 503, iters: 7390, time: 0.063) G_GAN: 0.215 G_GAN_Feat: 0.658 G_ID: 0.096 G_Rec: 0.312 D_GP: 0.039 D_real: 1.161 D_fake: 0.786 
+(epoch: 503, iters: 7790, time: 0.063) G_GAN: 0.304 G_GAN_Feat: 0.885 G_ID: 0.124 G_Rec: 0.424 D_GP: 0.044 D_real: 1.026 D_fake: 0.698 
+(epoch: 503, iters: 8190, time: 0.063) G_GAN: 0.052 G_GAN_Feat: 0.668 G_ID: 0.094 G_Rec: 0.300 D_GP: 0.040 D_real: 0.933 D_fake: 0.948 
+(epoch: 503, iters: 8590, time: 0.064) G_GAN: 0.351 G_GAN_Feat: 0.867 G_ID: 0.100 G_Rec: 0.416 D_GP: 0.037 D_real: 1.056 D_fake: 0.656 
+(epoch: 504, iters: 382, time: 0.063) G_GAN: 0.186 G_GAN_Feat: 0.670 G_ID: 0.082 G_Rec: 0.302 D_GP: 0.064 D_real: 1.061 D_fake: 0.815 
+(epoch: 504, iters: 782, time: 0.063) G_GAN: 0.170 G_GAN_Feat: 0.904 G_ID: 0.113 G_Rec: 0.443 D_GP: 0.040 D_real: 0.699 D_fake: 0.830 
+(epoch: 504, iters: 1182, time: 0.063) G_GAN: 0.007 G_GAN_Feat: 0.719 G_ID: 0.094 G_Rec: 0.310 D_GP: 0.046 D_real: 0.737 D_fake: 0.993 
+(epoch: 504, iters: 1582, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.888 G_ID: 0.128 G_Rec: 0.417 D_GP: 0.047 D_real: 0.774 D_fake: 0.688 
+(epoch: 504, iters: 1982, time: 0.063) G_GAN: 0.040 G_GAN_Feat: 0.696 G_ID: 0.083 G_Rec: 0.296 D_GP: 0.042 D_real: 0.817 D_fake: 0.960 
+(epoch: 504, iters: 2382, time: 0.063) G_GAN: 0.320 G_GAN_Feat: 0.961 G_ID: 0.101 G_Rec: 0.462 D_GP: 0.046 D_real: 0.896 D_fake: 0.684 
+(epoch: 504, iters: 2782, time: 0.063) G_GAN: 0.155 G_GAN_Feat: 0.760 G_ID: 0.095 G_Rec: 0.339 D_GP: 0.069 D_real: 0.873 D_fake: 0.846 
+(epoch: 504, iters: 3182, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.819 G_ID: 0.113 G_Rec: 0.367 D_GP: 0.035 D_real: 1.043 D_fake: 0.661 
+(epoch: 504, iters: 3582, time: 0.063) G_GAN: 0.255 G_GAN_Feat: 0.715 G_ID: 0.106 G_Rec: 0.309 D_GP: 0.045 D_real: 1.079 D_fake: 0.747 
+(epoch: 504, iters: 3982, time: 0.063) G_GAN: 0.646 G_GAN_Feat: 1.013 G_ID: 0.102 G_Rec: 0.434 D_GP: 0.059 D_real: 1.049 D_fake: 0.418 
+(epoch: 504, iters: 4382, time: 0.063) G_GAN: 0.145 G_GAN_Feat: 0.818 G_ID: 0.104 G_Rec: 0.366 D_GP: 0.092 D_real: 0.712 D_fake: 0.855 
+(epoch: 504, iters: 4782, time: 0.064) G_GAN: 0.368 G_GAN_Feat: 0.986 G_ID: 0.131 G_Rec: 0.465 D_GP: 0.042 D_real: 0.797 D_fake: 0.636 
+(epoch: 504, iters: 5182, time: 0.063) G_GAN: 0.072 G_GAN_Feat: 0.792 G_ID: 0.119 G_Rec: 0.318 D_GP: 0.211 D_real: 0.656 D_fake: 0.930 
+(epoch: 504, iters: 5582, time: 0.063) G_GAN: 0.442 G_GAN_Feat: 1.085 G_ID: 0.116 G_Rec: 0.502 D_GP: 0.375 D_real: 0.667 D_fake: 0.583 
+(epoch: 504, iters: 5982, time: 0.063) G_GAN: -0.199 G_GAN_Feat: 0.921 G_ID: 0.118 G_Rec: 0.393 D_GP: 0.352 D_real: 0.425 D_fake: 1.200 
+(epoch: 504, iters: 6382, time: 0.064) G_GAN: 0.500 G_GAN_Feat: 0.966 G_ID: 0.115 G_Rec: 0.422 D_GP: 0.041 D_real: 1.195 D_fake: 0.513 
+(epoch: 504, iters: 6782, time: 0.063) G_GAN: -0.129 G_GAN_Feat: 0.770 G_ID: 0.095 G_Rec: 0.314 D_GP: 0.044 D_real: 0.610 D_fake: 1.129 
+(epoch: 504, iters: 7182, time: 0.063) G_GAN: 0.541 G_GAN_Feat: 0.944 G_ID: 0.105 G_Rec: 0.392 D_GP: 0.038 D_real: 1.195 D_fake: 0.464 
+(epoch: 504, iters: 7582, time: 0.063) G_GAN: 0.340 G_GAN_Feat: 0.798 G_ID: 0.088 G_Rec: 0.287 D_GP: 0.055 D_real: 0.928 D_fake: 0.661 
+(epoch: 504, iters: 7982, time: 0.064) G_GAN: 0.295 G_GAN_Feat: 0.977 G_ID: 0.121 G_Rec: 0.445 D_GP: 0.032 D_real: 0.904 D_fake: 0.711 
+(epoch: 504, iters: 8382, time: 0.063) G_GAN: 0.167 G_GAN_Feat: 0.743 G_ID: 0.092 G_Rec: 0.301 D_GP: 0.032 D_real: 0.951 D_fake: 0.834 
+(epoch: 505, iters: 174, time: 0.063) G_GAN: 1.143 G_GAN_Feat: 1.145 G_ID: 0.114 G_Rec: 0.448 D_GP: 0.061 D_real: 1.485 D_fake: 0.122 
+(epoch: 505, iters: 574, time: 0.063) G_GAN: 0.635 G_GAN_Feat: 0.892 G_ID: 0.086 G_Rec: 0.324 D_GP: 0.054 D_real: 0.752 D_fake: 0.416 
+(epoch: 505, iters: 974, time: 0.064) G_GAN: 0.343 G_GAN_Feat: 0.953 G_ID: 0.118 G_Rec: 0.447 D_GP: 0.033 D_real: 0.986 D_fake: 0.663 
+(epoch: 505, iters: 1374, time: 0.063) G_GAN: 0.195 G_GAN_Feat: 0.657 G_ID: 0.099 G_Rec: 0.288 D_GP: 0.030 D_real: 1.102 D_fake: 0.807 
+(epoch: 505, iters: 1774, time: 0.063) G_GAN: 0.148 G_GAN_Feat: 0.861 G_ID: 0.115 G_Rec: 0.425 D_GP: 0.031 D_real: 0.859 D_fake: 0.854 
+(epoch: 505, iters: 2174, time: 0.063) G_GAN: 0.073 G_GAN_Feat: 0.665 G_ID: 0.079 G_Rec: 0.304 D_GP: 0.030 D_real: 0.933 D_fake: 0.927 
+(epoch: 505, iters: 2574, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.879 G_ID: 0.119 G_Rec: 0.423 D_GP: 0.044 D_real: 0.950 D_fake: 0.692 
+(epoch: 505, iters: 2974, time: 0.063) G_GAN: -0.209 G_GAN_Feat: 0.681 G_ID: 0.113 G_Rec: 0.296 D_GP: 0.036 D_real: 0.665 D_fake: 1.209 
+(epoch: 505, iters: 3374, time: 0.063) G_GAN: 0.400 G_GAN_Feat: 0.934 G_ID: 0.109 G_Rec: 0.510 D_GP: 0.050 D_real: 0.884 D_fake: 0.614 
+(epoch: 505, iters: 3774, time: 0.063) G_GAN: 0.057 G_GAN_Feat: 0.751 G_ID: 0.101 G_Rec: 0.314 D_GP: 0.070 D_real: 0.801 D_fake: 0.943 
+(epoch: 505, iters: 4174, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.994 G_ID: 0.117 G_Rec: 0.450 D_GP: 0.092 D_real: 0.639 D_fake: 0.783 
+(epoch: 505, iters: 4574, time: 0.063) G_GAN: 0.064 G_GAN_Feat: 0.789 G_ID: 0.095 G_Rec: 0.338 D_GP: 0.048 D_real: 0.967 D_fake: 0.937 
+(epoch: 505, iters: 4974, time: 0.063) G_GAN: 0.454 G_GAN_Feat: 1.077 G_ID: 0.142 G_Rec: 0.472 D_GP: 0.301 D_real: 0.618 D_fake: 0.560 
+(epoch: 505, iters: 5374, time: 0.063) G_GAN: 0.252 G_GAN_Feat: 0.741 G_ID: 0.129 G_Rec: 0.297 D_GP: 0.093 D_real: 0.849 D_fake: 0.748 
+(epoch: 505, iters: 5774, time: 0.064) G_GAN: 0.378 G_GAN_Feat: 1.036 G_ID: 0.102 G_Rec: 0.470 D_GP: 0.045 D_real: 0.780 D_fake: 0.623 
+(epoch: 505, iters: 6174, time: 0.063) G_GAN: 0.615 G_GAN_Feat: 0.721 G_ID: 0.104 G_Rec: 0.295 D_GP: 0.039 D_real: 1.398 D_fake: 0.402 
+(epoch: 505, iters: 6574, time: 0.063) G_GAN: 0.728 G_GAN_Feat: 1.079 G_ID: 0.122 G_Rec: 0.526 D_GP: 0.059 D_real: 0.996 D_fake: 0.300 
+(epoch: 505, iters: 6974, time: 0.063) G_GAN: 0.244 G_GAN_Feat: 0.881 G_ID: 0.112 G_Rec: 0.342 D_GP: 0.032 D_real: 0.967 D_fake: 0.757 
+(epoch: 505, iters: 7374, time: 0.064) G_GAN: 0.163 G_GAN_Feat: 1.016 G_ID: 0.125 G_Rec: 0.465 D_GP: 0.038 D_real: 0.864 D_fake: 0.837 
+(epoch: 505, iters: 7774, time: 0.063) G_GAN: 0.051 G_GAN_Feat: 0.673 G_ID: 0.088 G_Rec: 0.307 D_GP: 0.028 D_real: 0.976 D_fake: 0.949 
+(epoch: 505, iters: 8174, time: 0.063) G_GAN: 0.155 G_GAN_Feat: 0.972 G_ID: 0.120 G_Rec: 0.447 D_GP: 0.042 D_real: 0.808 D_fake: 0.847 
+(epoch: 505, iters: 8574, time: 0.063) G_GAN: 0.005 G_GAN_Feat: 0.781 G_ID: 0.115 G_Rec: 0.313 D_GP: 0.050 D_real: 0.696 D_fake: 0.996 
+(epoch: 506, iters: 366, time: 0.064) G_GAN: 0.391 G_GAN_Feat: 0.960 G_ID: 0.132 G_Rec: 0.413 D_GP: 0.045 D_real: 0.940 D_fake: 0.613 
+(epoch: 506, iters: 766, time: 0.063) G_GAN: 0.102 G_GAN_Feat: 0.786 G_ID: 0.125 G_Rec: 0.326 D_GP: 0.042 D_real: 0.806 D_fake: 0.898 
+(epoch: 506, iters: 1166, time: 0.063) G_GAN: 0.674 G_GAN_Feat: 1.018 G_ID: 0.110 G_Rec: 0.461 D_GP: 0.062 D_real: 1.021 D_fake: 0.344 
+(epoch: 506, iters: 1566, time: 0.063) G_GAN: 0.207 G_GAN_Feat: 0.811 G_ID: 0.103 G_Rec: 0.298 D_GP: 0.038 D_real: 0.811 D_fake: 0.794 
+(epoch: 506, iters: 1966, time: 0.064) G_GAN: 0.805 G_GAN_Feat: 1.186 G_ID: 0.106 G_Rec: 0.465 D_GP: 0.052 D_real: 0.656 D_fake: 0.268 
+(epoch: 506, iters: 2366, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 0.927 G_ID: 0.109 G_Rec: 0.315 D_GP: 0.048 D_real: 0.563 D_fake: 0.602 
+(epoch: 506, iters: 2766, time: 0.063) G_GAN: 0.317 G_GAN_Feat: 0.904 G_ID: 0.110 G_Rec: 0.464 D_GP: 0.031 D_real: 1.081 D_fake: 0.688 
+(epoch: 506, iters: 3166, time: 0.063) G_GAN: 0.145 G_GAN_Feat: 0.663 G_ID: 0.098 G_Rec: 0.310 D_GP: 0.028 D_real: 1.092 D_fake: 0.855 
+(epoch: 506, iters: 3566, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.802 G_ID: 0.117 G_Rec: 0.384 D_GP: 0.028 D_real: 0.946 D_fake: 0.781 
+(epoch: 506, iters: 3966, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.682 G_ID: 0.092 G_Rec: 0.313 D_GP: 0.039 D_real: 1.059 D_fake: 0.818 
+(epoch: 506, iters: 4366, time: 0.063) G_GAN: 0.153 G_GAN_Feat: 0.863 G_ID: 0.118 G_Rec: 0.405 D_GP: 0.043 D_real: 0.887 D_fake: 0.847 
+(epoch: 506, iters: 4766, time: 0.063) G_GAN: -0.088 G_GAN_Feat: 0.762 G_ID: 0.125 G_Rec: 0.361 D_GP: 0.040 D_real: 0.755 D_fake: 1.088 
+(epoch: 506, iters: 5166, time: 0.064) G_GAN: 0.347 G_GAN_Feat: 0.874 G_ID: 0.112 G_Rec: 0.425 D_GP: 0.036 D_real: 1.007 D_fake: 0.662 
+(epoch: 506, iters: 5566, time: 0.063) G_GAN: -0.049 G_GAN_Feat: 0.676 G_ID: 0.100 G_Rec: 0.336 D_GP: 0.030 D_real: 0.821 D_fake: 1.049 
+(epoch: 506, iters: 5966, time: 0.063) G_GAN: 0.121 G_GAN_Feat: 0.856 G_ID: 0.122 G_Rec: 0.400 D_GP: 0.034 D_real: 0.761 D_fake: 0.880 
+(epoch: 506, iters: 6366, time: 0.063) G_GAN: 0.063 G_GAN_Feat: 0.645 G_ID: 0.087 G_Rec: 0.302 D_GP: 0.042 D_real: 0.925 D_fake: 0.937 
+(epoch: 506, iters: 6766, time: 0.064) G_GAN: -0.033 G_GAN_Feat: 0.941 G_ID: 0.133 G_Rec: 0.445 D_GP: 0.042 D_real: 0.580 D_fake: 1.033 
+(epoch: 506, iters: 7166, time: 0.063) G_GAN: -0.125 G_GAN_Feat: 0.662 G_ID: 0.117 G_Rec: 0.287 D_GP: 0.037 D_real: 0.767 D_fake: 1.125 
+(epoch: 506, iters: 7566, time: 0.063) G_GAN: 0.372 G_GAN_Feat: 0.875 G_ID: 0.113 G_Rec: 0.422 D_GP: 0.036 D_real: 1.148 D_fake: 0.636 
+(epoch: 506, iters: 7966, time: 0.063) G_GAN: -0.113 G_GAN_Feat: 0.703 G_ID: 0.103 G_Rec: 0.302 D_GP: 0.040 D_real: 0.720 D_fake: 1.113 
+(epoch: 506, iters: 8366, time: 0.064) G_GAN: 0.374 G_GAN_Feat: 0.877 G_ID: 0.101 G_Rec: 0.393 D_GP: 0.034 D_real: 1.064 D_fake: 0.633 
+(epoch: 507, iters: 158, time: 0.063) G_GAN: 0.378 G_GAN_Feat: 0.712 G_ID: 0.111 G_Rec: 0.323 D_GP: 0.046 D_real: 1.184 D_fake: 0.636 
+(epoch: 507, iters: 558, time: 0.063) G_GAN: 0.140 G_GAN_Feat: 0.935 G_ID: 0.123 G_Rec: 0.453 D_GP: 0.070 D_real: 0.605 D_fake: 0.862 
+(epoch: 507, iters: 958, time: 0.063) G_GAN: -0.099 G_GAN_Feat: 0.710 G_ID: 0.082 G_Rec: 0.298 D_GP: 0.050 D_real: 0.710 D_fake: 1.099 
+(epoch: 507, iters: 1358, time: 0.064) G_GAN: 0.376 G_GAN_Feat: 0.929 G_ID: 0.113 G_Rec: 0.452 D_GP: 0.040 D_real: 1.001 D_fake: 0.639 
+(epoch: 507, iters: 1758, time: 0.063) G_GAN: 0.137 G_GAN_Feat: 0.771 G_ID: 0.100 G_Rec: 0.344 D_GP: 0.057 D_real: 0.822 D_fake: 0.864 
+(epoch: 507, iters: 2158, time: 0.063) G_GAN: 0.268 G_GAN_Feat: 0.876 G_ID: 0.094 G_Rec: 0.394 D_GP: 0.056 D_real: 0.984 D_fake: 0.735 
+(epoch: 507, iters: 2558, time: 0.063) G_GAN: 0.082 G_GAN_Feat: 0.672 G_ID: 0.088 G_Rec: 0.287 D_GP: 0.039 D_real: 1.087 D_fake: 0.919 
+(epoch: 507, iters: 2958, time: 0.064) G_GAN: 0.461 G_GAN_Feat: 0.905 G_ID: 0.106 G_Rec: 0.442 D_GP: 0.032 D_real: 1.172 D_fake: 0.545 
+(epoch: 507, iters: 3358, time: 0.063) G_GAN: 0.315 G_GAN_Feat: 0.673 G_ID: 0.092 G_Rec: 0.294 D_GP: 0.031 D_real: 1.172 D_fake: 0.687 
+(epoch: 507, iters: 3758, time: 0.063) G_GAN: 0.255 G_GAN_Feat: 1.055 G_ID: 0.108 G_Rec: 0.476 D_GP: 0.108 D_real: 0.580 D_fake: 0.747 
+(epoch: 507, iters: 4158, time: 0.063) G_GAN: 0.087 G_GAN_Feat: 0.690 G_ID: 0.098 G_Rec: 0.320 D_GP: 0.044 D_real: 0.865 D_fake: 0.913 
+(epoch: 507, iters: 4558, time: 0.064) G_GAN: 0.770 G_GAN_Feat: 0.973 G_ID: 0.112 G_Rec: 0.478 D_GP: 0.049 D_real: 1.291 D_fake: 0.313 
+(epoch: 507, iters: 4958, time: 0.063) G_GAN: 0.078 G_GAN_Feat: 0.727 G_ID: 0.104 G_Rec: 0.328 D_GP: 0.050 D_real: 0.848 D_fake: 0.922 
+(epoch: 507, iters: 5358, time: 0.063) G_GAN: 0.333 G_GAN_Feat: 0.970 G_ID: 0.114 G_Rec: 0.482 D_GP: 0.105 D_real: 0.653 D_fake: 0.672 
+(epoch: 507, iters: 5758, time: 0.063) G_GAN: 0.196 G_GAN_Feat: 0.864 G_ID: 0.106 G_Rec: 0.360 D_GP: 0.070 D_real: 0.605 D_fake: 0.806 
+(epoch: 507, iters: 6158, time: 0.064) G_GAN: 0.414 G_GAN_Feat: 1.021 G_ID: 0.118 G_Rec: 0.464 D_GP: 0.114 D_real: 0.697 D_fake: 0.594 
+(epoch: 507, iters: 6558, time: 0.063) G_GAN: 0.234 G_GAN_Feat: 0.842 G_ID: 0.109 G_Rec: 0.344 D_GP: 0.119 D_real: 0.647 D_fake: 0.776 
+(epoch: 507, iters: 6958, time: 0.063) G_GAN: 0.025 G_GAN_Feat: 1.013 G_ID: 0.106 G_Rec: 0.484 D_GP: 0.069 D_real: 0.541 D_fake: 0.976 
+(epoch: 507, iters: 7358, time: 0.063) G_GAN: 0.103 G_GAN_Feat: 0.677 G_ID: 0.089 G_Rec: 0.315 D_GP: 0.031 D_real: 1.017 D_fake: 0.897 
+(epoch: 507, iters: 7758, time: 0.064) G_GAN: 0.428 G_GAN_Feat: 0.946 G_ID: 0.111 G_Rec: 0.462 D_GP: 0.038 D_real: 1.011 D_fake: 0.584 
+(epoch: 507, iters: 8158, time: 0.063) G_GAN: 0.013 G_GAN_Feat: 0.815 G_ID: 0.093 G_Rec: 0.348 D_GP: 0.129 D_real: 0.421 D_fake: 0.989 
+(epoch: 507, iters: 8558, time: 0.063) G_GAN: 0.356 G_GAN_Feat: 1.043 G_ID: 0.120 G_Rec: 0.456 D_GP: 0.211 D_real: 0.767 D_fake: 0.656 
+(epoch: 508, iters: 350, time: 0.063) G_GAN: 0.061 G_GAN_Feat: 0.674 G_ID: 0.088 G_Rec: 0.275 D_GP: 0.036 D_real: 0.906 D_fake: 0.939 
+(epoch: 508, iters: 750, time: 0.064) G_GAN: 0.408 G_GAN_Feat: 1.028 G_ID: 0.122 G_Rec: 0.464 D_GP: 0.046 D_real: 0.826 D_fake: 0.596 
+(epoch: 508, iters: 1150, time: 0.063) G_GAN: -0.104 G_GAN_Feat: 0.895 G_ID: 0.105 G_Rec: 0.354 D_GP: 0.749 D_real: 0.223 D_fake: 1.105 
+(epoch: 508, iters: 1550, time: 0.063) G_GAN: 0.324 G_GAN_Feat: 0.918 G_ID: 0.117 G_Rec: 0.421 D_GP: 0.043 D_real: 0.893 D_fake: 0.680 
+(epoch: 508, iters: 1950, time: 0.063) G_GAN: 0.297 G_GAN_Feat: 0.770 G_ID: 0.088 G_Rec: 0.313 D_GP: 0.105 D_real: 0.818 D_fake: 0.707 
+(epoch: 508, iters: 2350, time: 0.063) G_GAN: 0.470 G_GAN_Feat: 1.096 G_ID: 0.091 G_Rec: 0.518 D_GP: 0.027 D_real: 1.159 D_fake: 0.544 
+(epoch: 508, iters: 2750, time: 0.063) G_GAN: 0.098 G_GAN_Feat: 0.716 G_ID: 0.100 G_Rec: 0.321 D_GP: 0.027 D_real: 0.924 D_fake: 0.902 
+(epoch: 508, iters: 3150, time: 0.063) G_GAN: 0.265 G_GAN_Feat: 0.884 G_ID: 0.110 G_Rec: 0.419 D_GP: 0.034 D_real: 0.975 D_fake: 0.737 
+(epoch: 508, iters: 3550, time: 0.064) G_GAN: 0.011 G_GAN_Feat: 0.737 G_ID: 0.083 G_Rec: 0.355 D_GP: 0.057 D_real: 0.825 D_fake: 0.990 
+(epoch: 508, iters: 3950, time: 0.063) G_GAN: 0.238 G_GAN_Feat: 0.832 G_ID: 0.116 G_Rec: 0.382 D_GP: 0.036 D_real: 0.974 D_fake: 0.771 
+(epoch: 508, iters: 4350, time: 0.063) G_GAN: 0.097 G_GAN_Feat: 0.687 G_ID: 0.097 G_Rec: 0.297 D_GP: 0.049 D_real: 0.948 D_fake: 0.904 
+(epoch: 508, iters: 4750, time: 0.063) G_GAN: 0.125 G_GAN_Feat: 0.922 G_ID: 0.109 G_Rec: 0.423 D_GP: 0.056 D_real: 0.718 D_fake: 0.876 
+(epoch: 508, iters: 5150, time: 0.064) G_GAN: -0.177 G_GAN_Feat: 0.728 G_ID: 0.080 G_Rec: 0.307 D_GP: 0.052 D_real: 0.608 D_fake: 1.177 
+(epoch: 508, iters: 5550, time: 0.063) G_GAN: 0.449 G_GAN_Feat: 1.037 G_ID: 0.105 G_Rec: 0.475 D_GP: 0.174 D_real: 0.660 D_fake: 0.559 
+(epoch: 508, iters: 5950, time: 0.063) G_GAN: -0.175 G_GAN_Feat: 0.804 G_ID: 0.115 G_Rec: 0.355 D_GP: 0.115 D_real: 0.477 D_fake: 1.177 
+(epoch: 508, iters: 6350, time: 0.063) G_GAN: 0.719 G_GAN_Feat: 1.063 G_ID: 0.121 G_Rec: 0.463 D_GP: 0.045 D_real: 1.108 D_fake: 0.315 
+(epoch: 508, iters: 6750, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.783 G_ID: 0.082 G_Rec: 0.295 D_GP: 0.049 D_real: 1.122 D_fake: 0.555 
+(epoch: 508, iters: 7150, time: 0.063) G_GAN: 0.197 G_GAN_Feat: 0.959 G_ID: 0.129 G_Rec: 0.408 D_GP: 0.044 D_real: 0.859 D_fake: 0.804 
+(epoch: 508, iters: 7550, time: 0.063) G_GAN: 0.042 G_GAN_Feat: 0.784 G_ID: 0.115 G_Rec: 0.320 D_GP: 0.032 D_real: 0.951 D_fake: 0.958 
+(epoch: 508, iters: 7950, time: 0.063) G_GAN: 0.124 G_GAN_Feat: 0.930 G_ID: 0.116 G_Rec: 0.453 D_GP: 0.044 D_real: 0.667 D_fake: 0.878 
+(epoch: 508, iters: 8350, time: 0.064) G_GAN: 0.088 G_GAN_Feat: 0.710 G_ID: 0.078 G_Rec: 0.284 D_GP: 0.068 D_real: 0.736 D_fake: 0.912 
+(epoch: 509, iters: 142, time: 0.063) G_GAN: 0.557 G_GAN_Feat: 0.879 G_ID: 0.105 G_Rec: 0.373 D_GP: 0.035 D_real: 1.236 D_fake: 0.477 
+(epoch: 509, iters: 542, time: 0.063) G_GAN: 0.122 G_GAN_Feat: 0.737 G_ID: 0.081 G_Rec: 0.287 D_GP: 0.034 D_real: 0.931 D_fake: 0.878 
+(epoch: 509, iters: 942, time: 0.063) G_GAN: 0.535 G_GAN_Feat: 1.165 G_ID: 0.124 G_Rec: 0.517 D_GP: 0.080 D_real: 0.495 D_fake: 0.479 
+(epoch: 509, iters: 1342, time: 0.064) G_GAN: 0.272 G_GAN_Feat: 0.874 G_ID: 0.092 G_Rec: 0.337 D_GP: 0.056 D_real: 0.586 D_fake: 0.755 
+(epoch: 509, iters: 1742, time: 0.063) G_GAN: 0.374 G_GAN_Feat: 1.080 G_ID: 0.122 G_Rec: 0.452 D_GP: 0.054 D_real: 0.792 D_fake: 0.633 
+(epoch: 509, iters: 2142, time: 0.063) G_GAN: -0.009 G_GAN_Feat: 0.881 G_ID: 0.124 G_Rec: 0.360 D_GP: 0.048 D_real: 0.659 D_fake: 1.010 
+(epoch: 509, iters: 2542, time: 0.063) G_GAN: 0.440 G_GAN_Feat: 0.965 G_ID: 0.133 G_Rec: 0.410 D_GP: 0.043 D_real: 0.843 D_fake: 0.564 
+(epoch: 509, iters: 2942, time: 0.064) G_GAN: 0.054 G_GAN_Feat: 0.763 G_ID: 0.117 G_Rec: 0.333 D_GP: 0.037 D_real: 0.991 D_fake: 0.946 
+(epoch: 509, iters: 3342, time: 0.063) G_GAN: 0.180 G_GAN_Feat: 1.005 G_ID: 0.109 G_Rec: 0.493 D_GP: 0.033 D_real: 0.894 D_fake: 0.820 
+(epoch: 509, iters: 3742, time: 0.063) G_GAN: 0.043 G_GAN_Feat: 0.609 G_ID: 0.088 G_Rec: 0.262 D_GP: 0.027 D_real: 1.061 D_fake: 0.957 
+(epoch: 509, iters: 4142, time: 0.063) G_GAN: 0.455 G_GAN_Feat: 0.919 G_ID: 0.111 G_Rec: 0.424 D_GP: 0.035 D_real: 1.048 D_fake: 0.582 
+(epoch: 509, iters: 4542, time: 0.064) G_GAN: -0.082 G_GAN_Feat: 0.739 G_ID: 0.117 G_Rec: 0.303 D_GP: 0.045 D_real: 0.670 D_fake: 1.082 
+(epoch: 509, iters: 4942, time: 0.063) G_GAN: 0.525 G_GAN_Feat: 0.927 G_ID: 0.108 G_Rec: 0.388 D_GP: 0.055 D_real: 1.084 D_fake: 0.486 
+(epoch: 509, iters: 5342, time: 0.063) G_GAN: 0.185 G_GAN_Feat: 0.719 G_ID: 0.099 G_Rec: 0.302 D_GP: 0.036 D_real: 1.106 D_fake: 0.815 
+(epoch: 509, iters: 5742, time: 0.063) G_GAN: 0.427 G_GAN_Feat: 0.861 G_ID: 0.118 G_Rec: 0.388 D_GP: 0.043 D_real: 1.092 D_fake: 0.588 
+(epoch: 509, iters: 6142, time: 0.064) G_GAN: 0.306 G_GAN_Feat: 0.789 G_ID: 0.093 G_Rec: 0.333 D_GP: 0.037 D_real: 0.968 D_fake: 0.696 
+(epoch: 509, iters: 6542, time: 0.063) G_GAN: 0.535 G_GAN_Feat: 0.920 G_ID: 0.106 G_Rec: 0.382 D_GP: 0.037 D_real: 1.199 D_fake: 0.488 
+(epoch: 509, iters: 6942, time: 0.063) G_GAN: -0.160 G_GAN_Feat: 0.746 G_ID: 0.106 G_Rec: 0.339 D_GP: 0.032 D_real: 0.792 D_fake: 1.160 
+(epoch: 509, iters: 7342, time: 0.063) G_GAN: 0.237 G_GAN_Feat: 0.917 G_ID: 0.106 G_Rec: 0.392 D_GP: 0.031 D_real: 0.897 D_fake: 0.766 
+(epoch: 509, iters: 7742, time: 0.064) G_GAN: 0.058 G_GAN_Feat: 0.802 G_ID: 0.085 G_Rec: 0.331 D_GP: 0.039 D_real: 0.813 D_fake: 0.942 
+(epoch: 509, iters: 8142, time: 0.063) G_GAN: 0.292 G_GAN_Feat: 0.990 G_ID: 0.110 G_Rec: 0.408 D_GP: 0.036 D_real: 0.917 D_fake: 0.709 
+(epoch: 509, iters: 8542, time: 0.063) G_GAN: 0.275 G_GAN_Feat: 0.859 G_ID: 0.087 G_Rec: 0.311 D_GP: 0.086 D_real: 0.740 D_fake: 0.728 
+(epoch: 510, iters: 334, time: 0.063) G_GAN: 0.535 G_GAN_Feat: 1.059 G_ID: 0.116 G_Rec: 0.440 D_GP: 0.118 D_real: 0.552 D_fake: 0.487 
+(epoch: 510, iters: 734, time: 0.064) G_GAN: 0.377 G_GAN_Feat: 0.979 G_ID: 0.102 G_Rec: 0.325 D_GP: 0.042 D_real: 0.845 D_fake: 0.626 
+(epoch: 510, iters: 1134, time: 0.063) G_GAN: 0.563 G_GAN_Feat: 1.039 G_ID: 0.108 G_Rec: 0.443 D_GP: 0.057 D_real: 1.132 D_fake: 0.529 
+(epoch: 510, iters: 1534, time: 0.063) G_GAN: 0.246 G_GAN_Feat: 0.763 G_ID: 0.133 G_Rec: 0.298 D_GP: 0.048 D_real: 1.056 D_fake: 0.755 
+(epoch: 510, iters: 1934, time: 0.063) G_GAN: 0.268 G_GAN_Feat: 1.093 G_ID: 0.115 G_Rec: 0.430 D_GP: 0.469 D_real: 0.389 D_fake: 0.737 
+(epoch: 510, iters: 2334, time: 0.064) G_GAN: 0.660 G_GAN_Feat: 0.713 G_ID: 0.080 G_Rec: 0.283 D_GP: 0.055 D_real: 1.518 D_fake: 0.474 
+(epoch: 510, iters: 2734, time: 0.063) G_GAN: 0.428 G_GAN_Feat: 1.022 G_ID: 0.124 G_Rec: 0.455 D_GP: 0.060 D_real: 0.942 D_fake: 0.578 
+(epoch: 510, iters: 3134, time: 0.063) G_GAN: 0.060 G_GAN_Feat: 0.752 G_ID: 0.096 G_Rec: 0.318 D_GP: 0.053 D_real: 0.801 D_fake: 0.940 
+(epoch: 510, iters: 3534, time: 0.063) G_GAN: 0.319 G_GAN_Feat: 0.899 G_ID: 0.108 G_Rec: 0.408 D_GP: 0.040 D_real: 1.024 D_fake: 0.688 
+(epoch: 510, iters: 3934, time: 0.064) G_GAN: -0.123 G_GAN_Feat: 0.822 G_ID: 0.092 G_Rec: 0.331 D_GP: 0.077 D_real: 0.883 D_fake: 1.123 
+(epoch: 510, iters: 4334, time: 0.063) G_GAN: 0.568 G_GAN_Feat: 1.035 G_ID: 0.107 G_Rec: 0.481 D_GP: 0.051 D_real: 1.038 D_fake: 0.443 
+(epoch: 510, iters: 4734, time: 0.063) G_GAN: 0.424 G_GAN_Feat: 0.824 G_ID: 0.132 G_Rec: 0.335 D_GP: 0.071 D_real: 0.961 D_fake: 0.616 
+(epoch: 510, iters: 5134, time: 0.063) G_GAN: 0.454 G_GAN_Feat: 1.032 G_ID: 0.113 G_Rec: 0.473 D_GP: 0.046 D_real: 1.147 D_fake: 0.562 
+(epoch: 510, iters: 5534, time: 0.064) G_GAN: 0.265 G_GAN_Feat: 0.789 G_ID: 0.095 G_Rec: 0.290 D_GP: 0.048 D_real: 0.893 D_fake: 0.735 
+(epoch: 510, iters: 5934, time: 0.063) G_GAN: 0.409 G_GAN_Feat: 0.970 G_ID: 0.113 G_Rec: 0.431 D_GP: 0.052 D_real: 0.892 D_fake: 0.596 
+(epoch: 510, iters: 6334, time: 0.063) G_GAN: 0.296 G_GAN_Feat: 0.881 G_ID: 0.100 G_Rec: 0.342 D_GP: 0.043 D_real: 0.756 D_fake: 0.705 
+(epoch: 510, iters: 6734, time: 0.063) G_GAN: 0.581 G_GAN_Feat: 1.077 G_ID: 0.109 G_Rec: 0.466 D_GP: 0.036 D_real: 0.832 D_fake: 0.478 
+(epoch: 510, iters: 7134, time: 0.064) G_GAN: 0.296 G_GAN_Feat: 0.856 G_ID: 0.091 G_Rec: 0.311 D_GP: 0.037 D_real: 1.121 D_fake: 0.707 
+(epoch: 510, iters: 7534, time: 0.063) G_GAN: 0.575 G_GAN_Feat: 1.111 G_ID: 0.131 G_Rec: 0.456 D_GP: 0.047 D_real: 0.686 D_fake: 0.433 
+(epoch: 510, iters: 7934, time: 0.063) G_GAN: -0.049 G_GAN_Feat: 0.859 G_ID: 0.092 G_Rec: 0.328 D_GP: 0.102 D_real: 0.526 D_fake: 1.049 
+(epoch: 510, iters: 8334, time: 0.063) G_GAN: 0.311 G_GAN_Feat: 1.026 G_ID: 0.139 G_Rec: 0.476 D_GP: 0.033 D_real: 0.868 D_fake: 0.689 
+(epoch: 511, iters: 126, time: 0.064) G_GAN: 0.433 G_GAN_Feat: 0.768 G_ID: 0.087 G_Rec: 0.293 D_GP: 0.032 D_real: 1.156 D_fake: 0.569 
+(epoch: 511, iters: 526, time: 0.063) G_GAN: 0.920 G_GAN_Feat: 1.157 G_ID: 0.109 G_Rec: 0.474 D_GP: 0.135 D_real: 0.765 D_fake: 0.183 
+(epoch: 511, iters: 926, time: 0.063) G_GAN: 0.307 G_GAN_Feat: 0.825 G_ID: 0.081 G_Rec: 0.327 D_GP: 0.032 D_real: 1.176 D_fake: 0.697 
+(epoch: 511, iters: 1326, time: 0.063) G_GAN: 0.636 G_GAN_Feat: 1.069 G_ID: 0.096 G_Rec: 0.474 D_GP: 0.041 D_real: 0.835 D_fake: 0.399 
+(epoch: 511, iters: 1726, time: 0.064) G_GAN: 0.007 G_GAN_Feat: 0.858 G_ID: 0.116 G_Rec: 0.318 D_GP: 0.105 D_real: 0.379 D_fake: 0.993 
+(epoch: 511, iters: 2126, time: 0.063) G_GAN: 0.835 G_GAN_Feat: 1.212 G_ID: 0.094 G_Rec: 0.474 D_GP: 0.368 D_real: 0.539 D_fake: 0.241 
+(epoch: 511, iters: 2526, time: 0.063) G_GAN: 0.326 G_GAN_Feat: 0.900 G_ID: 0.096 G_Rec: 0.332 D_GP: 0.058 D_real: 1.151 D_fake: 0.723 
+(epoch: 511, iters: 2926, time: 0.063) G_GAN: 0.419 G_GAN_Feat: 0.994 G_ID: 0.105 G_Rec: 0.435 D_GP: 0.035 D_real: 1.095 D_fake: 0.586 
+(epoch: 511, iters: 3326, time: 0.064) G_GAN: 0.386 G_GAN_Feat: 0.716 G_ID: 0.095 G_Rec: 0.317 D_GP: 0.031 D_real: 1.255 D_fake: 0.631 
+(epoch: 511, iters: 3726, time: 0.063) G_GAN: 0.256 G_GAN_Feat: 0.916 G_ID: 0.119 G_Rec: 0.451 D_GP: 0.031 D_real: 0.934 D_fake: 0.750 
+(epoch: 511, iters: 4126, time: 0.063) G_GAN: 0.079 G_GAN_Feat: 0.778 G_ID: 0.091 G_Rec: 0.335 D_GP: 0.053 D_real: 0.905 D_fake: 0.922 
+(epoch: 511, iters: 4526, time: 0.063) G_GAN: 0.268 G_GAN_Feat: 0.891 G_ID: 0.122 G_Rec: 0.396 D_GP: 0.037 D_real: 0.814 D_fake: 0.735 
+(epoch: 511, iters: 4926, time: 0.064) G_GAN: 0.383 G_GAN_Feat: 0.708 G_ID: 0.089 G_Rec: 0.283 D_GP: 0.033 D_real: 1.299 D_fake: 0.619 
+(epoch: 511, iters: 5326, time: 0.063) G_GAN: 0.567 G_GAN_Feat: 0.962 G_ID: 0.108 G_Rec: 0.446 D_GP: 0.042 D_real: 1.231 D_fake: 0.446 
+(epoch: 511, iters: 5726, time: 0.063) G_GAN: 0.540 G_GAN_Feat: 0.845 G_ID: 0.087 G_Rec: 0.311 D_GP: 0.054 D_real: 1.103 D_fake: 0.471 
+(epoch: 511, iters: 6126, time: 0.063) G_GAN: 0.910 G_GAN_Feat: 1.030 G_ID: 0.105 G_Rec: 0.460 D_GP: 0.040 D_real: 1.410 D_fake: 0.219 
+(epoch: 511, iters: 6526, time: 0.064) G_GAN: 0.340 G_GAN_Feat: 0.992 G_ID: 0.129 G_Rec: 0.368 D_GP: 0.041 D_real: 1.246 D_fake: 0.721 
+(epoch: 511, iters: 6926, time: 0.063) G_GAN: 0.425 G_GAN_Feat: 1.031 G_ID: 0.113 G_Rec: 0.482 D_GP: 0.038 D_real: 1.062 D_fake: 0.578 
+(epoch: 511, iters: 7326, time: 0.063) G_GAN: 0.376 G_GAN_Feat: 0.962 G_ID: 0.113 G_Rec: 0.342 D_GP: 0.079 D_real: 0.381 D_fake: 0.639 
+(epoch: 511, iters: 7726, time: 0.063) G_GAN: 0.692 G_GAN_Feat: 1.029 G_ID: 0.112 G_Rec: 0.431 D_GP: 0.048 D_real: 1.165 D_fake: 0.464 
+(epoch: 511, iters: 8126, time: 0.064) G_GAN: -0.064 G_GAN_Feat: 0.736 G_ID: 0.101 G_Rec: 0.323 D_GP: 0.037 D_real: 0.752 D_fake: 1.064 
+(epoch: 511, iters: 8526, time: 0.063) G_GAN: 0.653 G_GAN_Feat: 1.027 G_ID: 0.119 G_Rec: 0.451 D_GP: 0.081 D_real: 0.903 D_fake: 0.383 
+(epoch: 512, iters: 318, time: 0.063) G_GAN: 0.213 G_GAN_Feat: 0.815 G_ID: 0.104 G_Rec: 0.321 D_GP: 0.077 D_real: 0.640 D_fake: 0.791 
+(epoch: 512, iters: 718, time: 0.063) G_GAN: 0.289 G_GAN_Feat: 1.149 G_ID: 0.115 G_Rec: 0.487 D_GP: 0.250 D_real: 0.380 D_fake: 0.723 
+(epoch: 512, iters: 1118, time: 0.064) G_GAN: 0.586 G_GAN_Feat: 0.848 G_ID: 0.114 G_Rec: 0.323 D_GP: 0.051 D_real: 1.523 D_fake: 0.500 
+(epoch: 512, iters: 1518, time: 0.063) G_GAN: 0.237 G_GAN_Feat: 0.987 G_ID: 0.125 G_Rec: 0.465 D_GP: 0.031 D_real: 0.878 D_fake: 0.763 
+(epoch: 512, iters: 1918, time: 0.063) G_GAN: 0.068 G_GAN_Feat: 0.728 G_ID: 0.107 G_Rec: 0.316 D_GP: 0.048 D_real: 0.876 D_fake: 0.932 
+(epoch: 512, iters: 2318, time: 0.063) G_GAN: 0.479 G_GAN_Feat: 1.017 G_ID: 0.112 G_Rec: 0.459 D_GP: 0.049 D_real: 0.926 D_fake: 0.536 
+(epoch: 512, iters: 2718, time: 0.064) G_GAN: 0.450 G_GAN_Feat: 0.670 G_ID: 0.089 G_Rec: 0.315 D_GP: 0.028 D_real: 1.411 D_fake: 0.553 
+(epoch: 512, iters: 3118, time: 0.063) G_GAN: 0.404 G_GAN_Feat: 0.966 G_ID: 0.100 G_Rec: 0.480 D_GP: 0.031 D_real: 0.973 D_fake: 0.606 
+(epoch: 512, iters: 3518, time: 0.063) G_GAN: 0.053 G_GAN_Feat: 0.719 G_ID: 0.129 G_Rec: 0.320 D_GP: 0.035 D_real: 0.904 D_fake: 0.947 
+(epoch: 512, iters: 3918, time: 0.063) G_GAN: 0.363 G_GAN_Feat: 1.017 G_ID: 0.118 G_Rec: 0.467 D_GP: 0.050 D_real: 0.792 D_fake: 0.647 
+(epoch: 512, iters: 4318, time: 0.064) G_GAN: 0.137 G_GAN_Feat: 0.728 G_ID: 0.092 G_Rec: 0.310 D_GP: 0.041 D_real: 0.919 D_fake: 0.863 
+(epoch: 512, iters: 4718, time: 0.063) G_GAN: 0.239 G_GAN_Feat: 1.038 G_ID: 0.108 G_Rec: 0.457 D_GP: 0.047 D_real: 0.610 D_fake: 0.762 
+(epoch: 512, iters: 5118, time: 0.063) G_GAN: 0.277 G_GAN_Feat: 0.831 G_ID: 0.101 G_Rec: 0.324 D_GP: 0.044 D_real: 0.863 D_fake: 0.723 
+(epoch: 512, iters: 5518, time: 0.063) G_GAN: 0.503 G_GAN_Feat: 1.035 G_ID: 0.106 G_Rec: 0.450 D_GP: 0.047 D_real: 0.729 D_fake: 0.507 
+(epoch: 512, iters: 5918, time: 0.064) G_GAN: 0.357 G_GAN_Feat: 0.807 G_ID: 0.085 G_Rec: 0.332 D_GP: 0.031 D_real: 1.457 D_fake: 0.646 
+(epoch: 512, iters: 6318, time: 0.063) G_GAN: 0.538 G_GAN_Feat: 0.932 G_ID: 0.112 G_Rec: 0.425 D_GP: 0.038 D_real: 1.234 D_fake: 0.474 
+(epoch: 512, iters: 6718, time: 0.063) G_GAN: 0.216 G_GAN_Feat: 0.687 G_ID: 0.093 G_Rec: 0.279 D_GP: 0.030 D_real: 1.152 D_fake: 0.784 
+(epoch: 512, iters: 7118, time: 0.063) G_GAN: 0.322 G_GAN_Feat: 1.023 G_ID: 0.117 G_Rec: 0.420 D_GP: 0.036 D_real: 0.695 D_fake: 0.679 
+(epoch: 512, iters: 7518, time: 0.063) G_GAN: 0.417 G_GAN_Feat: 0.753 G_ID: 0.096 G_Rec: 0.317 D_GP: 0.045 D_real: 1.223 D_fake: 0.589 
+(epoch: 512, iters: 7918, time: 0.063) G_GAN: 0.315 G_GAN_Feat: 0.985 G_ID: 0.111 G_Rec: 0.424 D_GP: 0.056 D_real: 0.878 D_fake: 0.686 
+(epoch: 512, iters: 8318, time: 0.063) G_GAN: 0.206 G_GAN_Feat: 0.862 G_ID: 0.099 G_Rec: 0.320 D_GP: 0.072 D_real: 0.566 D_fake: 0.795 
+(epoch: 513, iters: 110, time: 0.064) G_GAN: 0.312 G_GAN_Feat: 0.854 G_ID: 0.120 G_Rec: 0.376 D_GP: 0.036 D_real: 1.096 D_fake: 0.691 
+(epoch: 513, iters: 510, time: 0.063) G_GAN: 0.084 G_GAN_Feat: 0.671 G_ID: 0.108 G_Rec: 0.297 D_GP: 0.033 D_real: 0.933 D_fake: 0.916 
+(epoch: 513, iters: 910, time: 0.063) G_GAN: 0.185 G_GAN_Feat: 0.945 G_ID: 0.109 G_Rec: 0.418 D_GP: 0.037 D_real: 0.801 D_fake: 0.817 
+(epoch: 513, iters: 1310, time: 0.063) G_GAN: 0.006 G_GAN_Feat: 0.772 G_ID: 0.095 G_Rec: 0.292 D_GP: 0.065 D_real: 0.779 D_fake: 0.995 
+(epoch: 513, iters: 1710, time: 0.064) G_GAN: 0.188 G_GAN_Feat: 1.056 G_ID: 0.112 G_Rec: 0.461 D_GP: 0.040 D_real: 0.625 D_fake: 0.813 
+(epoch: 513, iters: 2110, time: 0.063) G_GAN: 0.435 G_GAN_Feat: 0.723 G_ID: 0.096 G_Rec: 0.318 D_GP: 0.030 D_real: 1.341 D_fake: 0.565 
+(epoch: 513, iters: 2510, time: 0.063) G_GAN: 0.829 G_GAN_Feat: 0.984 G_ID: 0.109 G_Rec: 0.408 D_GP: 0.044 D_real: 1.353 D_fake: 0.254 
+(epoch: 513, iters: 2910, time: 0.063) G_GAN: 0.498 G_GAN_Feat: 0.728 G_ID: 0.098 G_Rec: 0.309 D_GP: 0.029 D_real: 1.454 D_fake: 0.513 
+(epoch: 513, iters: 3310, time: 0.064) G_GAN: 0.051 G_GAN_Feat: 0.915 G_ID: 0.125 G_Rec: 0.412 D_GP: 0.030 D_real: 0.816 D_fake: 0.949 
+(epoch: 513, iters: 3710, time: 0.063) G_GAN: 0.071 G_GAN_Feat: 0.663 G_ID: 0.093 G_Rec: 0.293 D_GP: 0.028 D_real: 0.992 D_fake: 0.931 
+(epoch: 513, iters: 4110, time: 0.063) G_GAN: 0.520 G_GAN_Feat: 0.853 G_ID: 0.110 G_Rec: 0.395 D_GP: 0.030 D_real: 1.205 D_fake: 0.503 
+(epoch: 513, iters: 4510, time: 0.063) G_GAN: 0.234 G_GAN_Feat: 0.748 G_ID: 0.093 G_Rec: 0.310 D_GP: 0.031 D_real: 1.145 D_fake: 0.767 
+(epoch: 513, iters: 4910, time: 0.064) G_GAN: 0.083 G_GAN_Feat: 0.950 G_ID: 0.135 G_Rec: 0.411 D_GP: 0.048 D_real: 0.689 D_fake: 0.917 
+(epoch: 513, iters: 5310, time: 0.063) G_GAN: 0.154 G_GAN_Feat: 0.734 G_ID: 0.091 G_Rec: 0.291 D_GP: 0.031 D_real: 0.981 D_fake: 0.846 
+(epoch: 513, iters: 5710, time: 0.063) G_GAN: 0.067 G_GAN_Feat: 1.060 G_ID: 0.117 G_Rec: 0.467 D_GP: 0.037 D_real: 0.798 D_fake: 0.933 
+(epoch: 513, iters: 6110, time: 0.063) G_GAN: 0.331 G_GAN_Feat: 0.942 G_ID: 0.079 G_Rec: 0.314 D_GP: 0.049 D_real: 0.739 D_fake: 0.670 
+(epoch: 513, iters: 6510, time: 0.064) G_GAN: 0.106 G_GAN_Feat: 1.089 G_ID: 0.105 G_Rec: 0.452 D_GP: 0.070 D_real: 0.484 D_fake: 0.899 
+(epoch: 513, iters: 6910, time: 0.063) G_GAN: 0.070 G_GAN_Feat: 0.783 G_ID: 0.083 G_Rec: 0.307 D_GP: 0.037 D_real: 0.883 D_fake: 0.930 
+(epoch: 513, iters: 7310, time: 0.063) G_GAN: 0.732 G_GAN_Feat: 1.016 G_ID: 0.106 G_Rec: 0.442 D_GP: 0.033 D_real: 1.248 D_fake: 0.303 
+(epoch: 513, iters: 7710, time: 0.063) G_GAN: 0.360 G_GAN_Feat: 0.865 G_ID: 0.086 G_Rec: 0.299 D_GP: 0.039 D_real: 0.920 D_fake: 0.641 
+(epoch: 513, iters: 8110, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.908 G_ID: 0.099 G_Rec: 0.403 D_GP: 0.027 D_real: 1.179 D_fake: 0.600 
+(epoch: 513, iters: 8510, time: 0.063) G_GAN: 0.236 G_GAN_Feat: 0.691 G_ID: 0.090 G_Rec: 0.307 D_GP: 0.031 D_real: 1.161 D_fake: 0.768 
+(epoch: 514, iters: 302, time: 0.063) G_GAN: 0.212 G_GAN_Feat: 1.016 G_ID: 0.123 G_Rec: 0.456 D_GP: 0.042 D_real: 0.677 D_fake: 0.788 
+(epoch: 514, iters: 702, time: 0.063) G_GAN: -0.032 G_GAN_Feat: 0.778 G_ID: 0.106 G_Rec: 0.309 D_GP: 0.049 D_real: 0.684 D_fake: 1.032 
+(epoch: 514, iters: 1102, time: 0.064) G_GAN: 0.322 G_GAN_Feat: 0.960 G_ID: 0.111 G_Rec: 0.421 D_GP: 0.055 D_real: 0.924 D_fake: 0.681 
+(epoch: 514, iters: 1502, time: 0.063) G_GAN: 0.113 G_GAN_Feat: 0.793 G_ID: 0.099 G_Rec: 0.330 D_GP: 0.054 D_real: 0.896 D_fake: 0.887 
+(epoch: 514, iters: 1902, time: 0.063) G_GAN: 0.371 G_GAN_Feat: 0.982 G_ID: 0.116 G_Rec: 0.448 D_GP: 0.050 D_real: 0.918 D_fake: 0.638 
+(epoch: 514, iters: 2302, time: 0.063) G_GAN: -0.108 G_GAN_Feat: 0.734 G_ID: 0.103 G_Rec: 0.299 D_GP: 0.044 D_real: 0.650 D_fake: 1.108 
+(epoch: 514, iters: 2702, time: 0.064) G_GAN: 0.442 G_GAN_Feat: 1.023 G_ID: 0.114 G_Rec: 0.450 D_GP: 0.048 D_real: 0.895 D_fake: 0.562 
+(epoch: 514, iters: 3102, time: 0.063) G_GAN: 0.083 G_GAN_Feat: 0.919 G_ID: 0.119 G_Rec: 0.324 D_GP: 0.255 D_real: 0.203 D_fake: 0.917 
+(epoch: 514, iters: 3502, time: 0.063) G_GAN: 0.659 G_GAN_Feat: 1.133 G_ID: 0.138 G_Rec: 0.501 D_GP: 0.117 D_real: 0.868 D_fake: 0.398 
+(epoch: 514, iters: 3902, time: 0.063) G_GAN: 0.400 G_GAN_Feat: 0.819 G_ID: 0.097 G_Rec: 0.304 D_GP: 0.057 D_real: 0.742 D_fake: 0.631 
+(epoch: 514, iters: 4302, time: 0.064) G_GAN: 0.454 G_GAN_Feat: 1.027 G_ID: 0.125 G_Rec: 0.436 D_GP: 0.039 D_real: 0.861 D_fake: 0.550 
+(epoch: 514, iters: 4702, time: 0.063) G_GAN: 0.356 G_GAN_Feat: 0.921 G_ID: 0.117 G_Rec: 0.313 D_GP: 0.253 D_real: 0.452 D_fake: 0.647 
+(epoch: 514, iters: 5102, time: 0.063) G_GAN: 0.310 G_GAN_Feat: 1.100 G_ID: 0.120 G_Rec: 0.433 D_GP: 0.039 D_real: 0.575 D_fake: 0.691 
+(epoch: 514, iters: 5502, time: 0.063) G_GAN: 0.225 G_GAN_Feat: 0.931 G_ID: 0.104 G_Rec: 0.319 D_GP: 0.070 D_real: 0.403 D_fake: 0.796 
+(epoch: 514, iters: 5902, time: 0.064) G_GAN: 0.364 G_GAN_Feat: 0.854 G_ID: 0.098 G_Rec: 0.456 D_GP: 0.027 D_real: 1.116 D_fake: 0.638 
+(epoch: 514, iters: 6302, time: 0.063) G_GAN: 0.126 G_GAN_Feat: 0.589 G_ID: 0.104 G_Rec: 0.277 D_GP: 0.029 D_real: 0.998 D_fake: 0.874 
+(epoch: 514, iters: 6702, time: 0.063) G_GAN: 0.275 G_GAN_Feat: 0.863 G_ID: 0.121 G_Rec: 0.433 D_GP: 0.041 D_real: 0.932 D_fake: 0.725 
+(epoch: 514, iters: 7102, time: 0.063) G_GAN: 0.137 G_GAN_Feat: 0.575 G_ID: 0.091 G_Rec: 0.285 D_GP: 0.029 D_real: 1.071 D_fake: 0.863 
+(epoch: 514, iters: 7502, time: 0.064) G_GAN: 0.124 G_GAN_Feat: 0.812 G_ID: 0.110 G_Rec: 0.403 D_GP: 0.033 D_real: 0.924 D_fake: 0.876 
+(epoch: 514, iters: 7902, time: 0.063) G_GAN: -0.118 G_GAN_Feat: 0.670 G_ID: 0.098 G_Rec: 0.342 D_GP: 0.037 D_real: 0.823 D_fake: 1.118 
+(epoch: 514, iters: 8302, time: 0.063) G_GAN: 0.202 G_GAN_Feat: 0.835 G_ID: 0.123 G_Rec: 0.411 D_GP: 0.038 D_real: 0.882 D_fake: 0.803 
+(epoch: 514, iters: 8702, time: 0.063) G_GAN: 0.071 G_GAN_Feat: 0.649 G_ID: 0.088 G_Rec: 0.341 D_GP: 0.032 D_real: 0.941 D_fake: 0.930 
+(epoch: 515, iters: 494, time: 0.064) G_GAN: 0.028 G_GAN_Feat: 0.901 G_ID: 0.116 G_Rec: 0.435 D_GP: 0.066 D_real: 0.686 D_fake: 0.972 
+(epoch: 515, iters: 894, time: 0.063) G_GAN: 0.085 G_GAN_Feat: 0.591 G_ID: 0.082 G_Rec: 0.287 D_GP: 0.028 D_real: 1.019 D_fake: 0.915 
+(epoch: 515, iters: 1294, time: 0.063) G_GAN: 0.096 G_GAN_Feat: 0.902 G_ID: 0.102 G_Rec: 0.444 D_GP: 0.042 D_real: 0.708 D_fake: 0.909 
+(epoch: 515, iters: 1694, time: 0.063) G_GAN: -0.418 G_GAN_Feat: 0.742 G_ID: 0.112 G_Rec: 0.362 D_GP: 0.062 D_real: 0.463 D_fake: 1.418 
+(epoch: 515, iters: 2094, time: 0.064) G_GAN: 0.276 G_GAN_Feat: 0.802 G_ID: 0.114 G_Rec: 0.390 D_GP: 0.035 D_real: 0.890 D_fake: 0.735 
+(epoch: 515, iters: 2494, time: 0.063) G_GAN: 0.012 G_GAN_Feat: 0.657 G_ID: 0.089 G_Rec: 0.298 D_GP: 0.039 D_real: 0.872 D_fake: 0.988 
+(epoch: 515, iters: 2894, time: 0.063) G_GAN: 0.002 G_GAN_Feat: 0.922 G_ID: 0.104 G_Rec: 0.444 D_GP: 0.037 D_real: 0.588 D_fake: 0.999 
+(epoch: 515, iters: 3294, time: 0.063) G_GAN: -0.062 G_GAN_Feat: 0.730 G_ID: 0.106 G_Rec: 0.353 D_GP: 0.057 D_real: 0.669 D_fake: 1.064 
+(epoch: 515, iters: 3694, time: 0.064) G_GAN: 0.198 G_GAN_Feat: 0.898 G_ID: 0.123 G_Rec: 0.430 D_GP: 0.039 D_real: 0.903 D_fake: 0.805 
+(epoch: 515, iters: 4094, time: 0.063) G_GAN: -0.121 G_GAN_Feat: 0.683 G_ID: 0.084 G_Rec: 0.302 D_GP: 0.047 D_real: 0.725 D_fake: 1.121 
+(epoch: 515, iters: 4494, time: 0.063) G_GAN: 0.244 G_GAN_Feat: 0.858 G_ID: 0.118 G_Rec: 0.414 D_GP: 0.034 D_real: 0.884 D_fake: 0.757 
+(epoch: 515, iters: 4894, time: 0.063) G_GAN: 0.029 G_GAN_Feat: 0.759 G_ID: 0.099 G_Rec: 0.317 D_GP: 0.066 D_real: 0.742 D_fake: 0.971 
+(epoch: 515, iters: 5294, time: 0.064) G_GAN: 0.350 G_GAN_Feat: 0.912 G_ID: 0.107 G_Rec: 0.411 D_GP: 0.042 D_real: 0.922 D_fake: 0.657 
+(epoch: 515, iters: 5694, time: 0.063) G_GAN: 0.333 G_GAN_Feat: 0.636 G_ID: 0.092 G_Rec: 0.279 D_GP: 0.050 D_real: 1.208 D_fake: 0.668 
+(epoch: 515, iters: 6094, time: 0.063) G_GAN: 0.287 G_GAN_Feat: 0.916 G_ID: 0.112 G_Rec: 0.407 D_GP: 0.064 D_real: 0.843 D_fake: 0.715 
+(epoch: 515, iters: 6494, time: 0.063) G_GAN: 0.338 G_GAN_Feat: 0.671 G_ID: 0.103 G_Rec: 0.282 D_GP: 0.036 D_real: 1.256 D_fake: 0.665 
+(epoch: 515, iters: 6894, time: 0.064) G_GAN: 0.406 G_GAN_Feat: 0.909 G_ID: 0.117 G_Rec: 0.419 D_GP: 0.042 D_real: 1.068 D_fake: 0.595 
+(epoch: 515, iters: 7294, time: 0.063) G_GAN: 0.125 G_GAN_Feat: 0.742 G_ID: 0.093 G_Rec: 0.405 D_GP: 0.064 D_real: 0.872 D_fake: 0.875 
+(epoch: 515, iters: 7694, time: 0.063) G_GAN: 0.302 G_GAN_Feat: 0.981 G_ID: 0.136 G_Rec: 0.440 D_GP: 0.047 D_real: 0.739 D_fake: 0.702 
+(epoch: 515, iters: 8094, time: 0.063) G_GAN: 0.118 G_GAN_Feat: 0.744 G_ID: 0.110 G_Rec: 0.313 D_GP: 0.075 D_real: 0.759 D_fake: 0.884 
+(epoch: 515, iters: 8494, time: 0.064) G_GAN: 0.362 G_GAN_Feat: 0.991 G_ID: 0.124 G_Rec: 0.434 D_GP: 0.085 D_real: 0.613 D_fake: 0.642 
+(epoch: 516, iters: 286, time: 0.063) G_GAN: 0.356 G_GAN_Feat: 0.693 G_ID: 0.092 G_Rec: 0.281 D_GP: 0.040 D_real: 1.304 D_fake: 0.645 
+(epoch: 516, iters: 686, time: 0.063) G_GAN: 0.720 G_GAN_Feat: 0.910 G_ID: 0.109 G_Rec: 0.382 D_GP: 0.038 D_real: 1.330 D_fake: 0.337 
+(epoch: 516, iters: 1086, time: 0.063) G_GAN: 0.129 G_GAN_Feat: 0.752 G_ID: 0.088 G_Rec: 0.290 D_GP: 0.067 D_real: 0.815 D_fake: 0.872 
+(epoch: 516, iters: 1486, time: 0.064) G_GAN: 0.611 G_GAN_Feat: 0.915 G_ID: 0.105 G_Rec: 0.413 D_GP: 0.037 D_real: 1.208 D_fake: 0.457 
+(epoch: 516, iters: 1886, time: 0.063) G_GAN: 0.100 G_GAN_Feat: 0.781 G_ID: 0.113 G_Rec: 0.315 D_GP: 0.045 D_real: 0.765 D_fake: 0.900 
+(epoch: 516, iters: 2286, time: 0.063) G_GAN: 0.301 G_GAN_Feat: 0.842 G_ID: 0.122 G_Rec: 0.403 D_GP: 0.025 D_real: 1.161 D_fake: 0.707 
+(epoch: 516, iters: 2686, time: 0.063) G_GAN: -0.025 G_GAN_Feat: 0.611 G_ID: 0.092 G_Rec: 0.298 D_GP: 0.025 D_real: 0.957 D_fake: 1.025 
+(epoch: 516, iters: 3086, time: 0.064) G_GAN: 0.221 G_GAN_Feat: 0.900 G_ID: 0.123 G_Rec: 0.437 D_GP: 0.037 D_real: 0.809 D_fake: 0.782 
+(epoch: 516, iters: 3486, time: 0.063) G_GAN: -0.204 G_GAN_Feat: 0.693 G_ID: 0.085 G_Rec: 0.321 D_GP: 0.041 D_real: 0.647 D_fake: 1.204 
+(epoch: 516, iters: 3886, time: 0.063) G_GAN: 0.284 G_GAN_Feat: 0.892 G_ID: 0.105 G_Rec: 0.419 D_GP: 0.036 D_real: 1.018 D_fake: 0.721 
+(epoch: 516, iters: 4286, time: 0.063) G_GAN: -0.084 G_GAN_Feat: 0.729 G_ID: 0.108 G_Rec: 0.345 D_GP: 0.050 D_real: 0.687 D_fake: 1.084 
+(epoch: 516, iters: 4686, time: 0.064) G_GAN: 0.397 G_GAN_Feat: 0.870 G_ID: 0.108 G_Rec: 0.410 D_GP: 0.034 D_real: 1.061 D_fake: 0.621 
+(epoch: 516, iters: 5086, time: 0.063) G_GAN: -0.043 G_GAN_Feat: 0.757 G_ID: 0.096 G_Rec: 0.326 D_GP: 0.038 D_real: 0.823 D_fake: 1.043 
+(epoch: 516, iters: 5486, time: 0.063) G_GAN: 0.451 G_GAN_Feat: 0.986 G_ID: 0.113 G_Rec: 0.460 D_GP: 0.062 D_real: 0.789 D_fake: 0.562 
+(epoch: 516, iters: 5886, time: 0.063) G_GAN: 0.139 G_GAN_Feat: 0.687 G_ID: 0.085 G_Rec: 0.300 D_GP: 0.034 D_real: 1.086 D_fake: 0.862 
+(epoch: 516, iters: 6286, time: 0.064) G_GAN: 0.263 G_GAN_Feat: 0.959 G_ID: 0.108 G_Rec: 0.436 D_GP: 0.048 D_real: 0.801 D_fake: 0.737 
+(epoch: 516, iters: 6686, time: 0.063) G_GAN: -0.054 G_GAN_Feat: 0.849 G_ID: 0.089 G_Rec: 0.370 D_GP: 0.276 D_real: 0.285 D_fake: 1.054 
+(epoch: 516, iters: 7086, time: 0.063) G_GAN: 0.435 G_GAN_Feat: 0.927 G_ID: 0.118 G_Rec: 0.390 D_GP: 0.036 D_real: 1.121 D_fake: 0.565 
+(epoch: 516, iters: 7486, time: 0.063) G_GAN: 0.119 G_GAN_Feat: 0.837 G_ID: 0.089 G_Rec: 0.332 D_GP: 0.354 D_real: 0.484 D_fake: 0.881 
+(epoch: 516, iters: 7886, time: 0.064) G_GAN: 0.580 G_GAN_Feat: 1.018 G_ID: 0.139 G_Rec: 0.456 D_GP: 0.071 D_real: 0.866 D_fake: 0.430 
+(epoch: 516, iters: 8286, time: 0.063) G_GAN: 0.163 G_GAN_Feat: 0.959 G_ID: 0.103 G_Rec: 0.381 D_GP: 0.566 D_real: 0.369 D_fake: 0.841 
+(epoch: 516, iters: 8686, time: 0.063) G_GAN: 0.593 G_GAN_Feat: 1.053 G_ID: 0.117 G_Rec: 0.512 D_GP: 0.052 D_real: 1.083 D_fake: 0.511 
+(epoch: 517, iters: 478, time: 0.063) G_GAN: 0.446 G_GAN_Feat: 0.821 G_ID: 0.096 G_Rec: 0.316 D_GP: 0.060 D_real: 0.968 D_fake: 0.600 
+(epoch: 517, iters: 878, time: 0.064) G_GAN: 0.660 G_GAN_Feat: 0.916 G_ID: 0.092 G_Rec: 0.450 D_GP: 0.029 D_real: 1.410 D_fake: 0.357 
+(epoch: 517, iters: 1278, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.680 G_ID: 0.078 G_Rec: 0.309 D_GP: 0.028 D_real: 1.094 D_fake: 0.817 
+(epoch: 517, iters: 1678, time: 0.063) G_GAN: 0.359 G_GAN_Feat: 0.890 G_ID: 0.108 G_Rec: 0.400 D_GP: 0.032 D_real: 1.141 D_fake: 0.643 
+(epoch: 517, iters: 2078, time: 0.063) G_GAN: 0.249 G_GAN_Feat: 0.651 G_ID: 0.090 G_Rec: 0.294 D_GP: 0.028 D_real: 1.132 D_fake: 0.751 
+(epoch: 517, iters: 2478, time: 0.064) G_GAN: 0.346 G_GAN_Feat: 0.826 G_ID: 0.099 G_Rec: 0.387 D_GP: 0.033 D_real: 1.102 D_fake: 0.654 
+(epoch: 517, iters: 2878, time: 0.063) G_GAN: 0.124 G_GAN_Feat: 0.695 G_ID: 0.086 G_Rec: 0.308 D_GP: 0.044 D_real: 0.914 D_fake: 0.878 
+(epoch: 517, iters: 3278, time: 0.063) G_GAN: 0.420 G_GAN_Feat: 0.883 G_ID: 0.109 G_Rec: 0.403 D_GP: 0.039 D_real: 1.117 D_fake: 0.582 
+(epoch: 517, iters: 3678, time: 0.063) G_GAN: 0.147 G_GAN_Feat: 0.649 G_ID: 0.087 G_Rec: 0.284 D_GP: 0.035 D_real: 1.010 D_fake: 0.853 
+(epoch: 517, iters: 4078, time: 0.063) G_GAN: 0.373 G_GAN_Feat: 0.916 G_ID: 0.100 G_Rec: 0.446 D_GP: 0.034 D_real: 1.105 D_fake: 0.633 
+(epoch: 517, iters: 4478, time: 0.063) G_GAN: 0.132 G_GAN_Feat: 0.702 G_ID: 0.091 G_Rec: 0.304 D_GP: 0.041 D_real: 0.948 D_fake: 0.870 
+(epoch: 517, iters: 4878, time: 0.063) G_GAN: 0.534 G_GAN_Feat: 0.939 G_ID: 0.118 G_Rec: 0.418 D_GP: 0.050 D_real: 1.174 D_fake: 0.496 
+(epoch: 517, iters: 5278, time: 0.064) G_GAN: 0.178 G_GAN_Feat: 0.763 G_ID: 0.085 G_Rec: 0.286 D_GP: 0.038 D_real: 0.952 D_fake: 0.823 
+(epoch: 517, iters: 5678, time: 0.063) G_GAN: 0.335 G_GAN_Feat: 1.057 G_ID: 0.112 G_Rec: 0.454 D_GP: 0.064 D_real: 0.698 D_fake: 0.669 
+(epoch: 517, iters: 6078, time: 0.063) G_GAN: 0.127 G_GAN_Feat: 0.824 G_ID: 0.105 G_Rec: 0.363 D_GP: 0.244 D_real: 0.693 D_fake: 0.885 
+(epoch: 517, iters: 6478, time: 0.063) G_GAN: 0.260 G_GAN_Feat: 1.073 G_ID: 0.116 G_Rec: 0.489 D_GP: 0.157 D_real: 0.398 D_fake: 0.741 
+(epoch: 517, iters: 6878, time: 0.064) G_GAN: 0.069 G_GAN_Feat: 0.689 G_ID: 0.088 G_Rec: 0.286 D_GP: 0.035 D_real: 0.903 D_fake: 0.931 
+(epoch: 517, iters: 7278, time: 0.063) G_GAN: 0.569 G_GAN_Feat: 0.872 G_ID: 0.118 G_Rec: 0.417 D_GP: 0.032 D_real: 1.194 D_fake: 0.450 
+(epoch: 517, iters: 7678, time: 0.063) G_GAN: 0.166 G_GAN_Feat: 0.793 G_ID: 0.095 G_Rec: 0.294 D_GP: 0.049 D_real: 0.779 D_fake: 0.834 
+(epoch: 517, iters: 8078, time: 0.063) G_GAN: 0.362 G_GAN_Feat: 0.931 G_ID: 0.107 G_Rec: 0.444 D_GP: 0.029 D_real: 1.042 D_fake: 0.639 
+(epoch: 517, iters: 8478, time: 0.064) G_GAN: 0.302 G_GAN_Feat: 0.798 G_ID: 0.104 G_Rec: 0.337 D_GP: 0.062 D_real: 0.988 D_fake: 0.699 
+(epoch: 518, iters: 270, time: 0.063) G_GAN: 0.765 G_GAN_Feat: 0.913 G_ID: 0.120 G_Rec: 0.416 D_GP: 0.036 D_real: 1.386 D_fake: 0.312 
+(epoch: 518, iters: 670, time: 0.063) G_GAN: 0.358 G_GAN_Feat: 0.852 G_ID: 0.129 G_Rec: 0.335 D_GP: 0.116 D_real: 0.780 D_fake: 0.715 
+(epoch: 518, iters: 1070, time: 0.063) G_GAN: 0.431 G_GAN_Feat: 1.048 G_ID: 0.116 G_Rec: 0.422 D_GP: 0.041 D_real: 0.726 D_fake: 0.573 
+(epoch: 518, iters: 1470, time: 0.064) G_GAN: 0.200 G_GAN_Feat: 0.829 G_ID: 0.106 G_Rec: 0.293 D_GP: 0.073 D_real: 0.574 D_fake: 0.802 
+(epoch: 518, iters: 1870, time: 0.063) G_GAN: 0.074 G_GAN_Feat: 0.915 G_ID: 0.130 G_Rec: 0.419 D_GP: 0.032 D_real: 0.800 D_fake: 0.927 
+(epoch: 518, iters: 2270, time: 0.063) G_GAN: 0.422 G_GAN_Feat: 0.835 G_ID: 0.086 G_Rec: 0.334 D_GP: 0.073 D_real: 0.676 D_fake: 0.597 
+(epoch: 518, iters: 2670, time: 0.063) G_GAN: 0.484 G_GAN_Feat: 1.032 G_ID: 0.103 G_Rec: 0.447 D_GP: 0.035 D_real: 0.969 D_fake: 0.525 
+(epoch: 518, iters: 3070, time: 0.064) G_GAN: 0.403 G_GAN_Feat: 0.802 G_ID: 0.093 G_Rec: 0.307 D_GP: 0.036 D_real: 1.041 D_fake: 0.603 
+(epoch: 518, iters: 3470, time: 0.063) G_GAN: 0.611 G_GAN_Feat: 1.158 G_ID: 0.107 G_Rec: 0.455 D_GP: 0.122 D_real: 0.511 D_fake: 0.407 
+(epoch: 518, iters: 3870, time: 0.063) G_GAN: 0.049 G_GAN_Feat: 0.874 G_ID: 0.098 G_Rec: 0.322 D_GP: 0.047 D_real: 0.553 D_fake: 0.951 
+(epoch: 518, iters: 4270, time: 0.063) G_GAN: 0.559 G_GAN_Feat: 0.893 G_ID: 0.108 G_Rec: 0.415 D_GP: 0.037 D_real: 1.297 D_fake: 0.469 
+(epoch: 518, iters: 4670, time: 0.064) G_GAN: -0.035 G_GAN_Feat: 0.726 G_ID: 0.106 G_Rec: 0.330 D_GP: 0.046 D_real: 0.846 D_fake: 1.035 
+(epoch: 518, iters: 5070, time: 0.063) G_GAN: 0.163 G_GAN_Feat: 0.944 G_ID: 0.107 G_Rec: 0.457 D_GP: 0.048 D_real: 0.771 D_fake: 0.838 
+(epoch: 518, iters: 5470, time: 0.063) G_GAN: -0.036 G_GAN_Feat: 0.723 G_ID: 0.091 G_Rec: 0.318 D_GP: 0.040 D_real: 0.904 D_fake: 1.036 
+(epoch: 518, iters: 5870, time: 0.063) G_GAN: 0.400 G_GAN_Feat: 0.936 G_ID: 0.107 G_Rec: 0.413 D_GP: 0.040 D_real: 0.958 D_fake: 0.604 
+(epoch: 518, iters: 6270, time: 0.064) G_GAN: 0.190 G_GAN_Feat: 0.654 G_ID: 0.095 G_Rec: 0.266 D_GP: 0.029 D_real: 1.066 D_fake: 0.810 
+(epoch: 518, iters: 6670, time: 0.063) G_GAN: 0.356 G_GAN_Feat: 0.933 G_ID: 0.118 G_Rec: 0.431 D_GP: 0.032 D_real: 0.905 D_fake: 0.644 
+(epoch: 518, iters: 7070, time: 0.063) G_GAN: 0.416 G_GAN_Feat: 0.782 G_ID: 0.095 G_Rec: 0.300 D_GP: 0.044 D_real: 1.109 D_fake: 0.588 
+(epoch: 518, iters: 7470, time: 0.063) G_GAN: 0.812 G_GAN_Feat: 1.093 G_ID: 0.111 G_Rec: 0.467 D_GP: 0.101 D_real: 1.049 D_fake: 0.316 
+(epoch: 518, iters: 7870, time: 0.064) G_GAN: 0.517 G_GAN_Feat: 0.924 G_ID: 0.097 G_Rec: 0.370 D_GP: 1.121 D_real: 0.810 D_fake: 0.511 
+(epoch: 518, iters: 8270, time: 0.063) G_GAN: 0.311 G_GAN_Feat: 0.954 G_ID: 0.120 G_Rec: 0.464 D_GP: 0.029 D_real: 0.942 D_fake: 0.690 
+(epoch: 518, iters: 8670, time: 0.063) G_GAN: 0.223 G_GAN_Feat: 0.665 G_ID: 0.097 G_Rec: 0.293 D_GP: 0.026 D_real: 1.128 D_fake: 0.778 
+(epoch: 519, iters: 462, time: 0.063) G_GAN: 0.269 G_GAN_Feat: 0.942 G_ID: 0.100 G_Rec: 0.403 D_GP: 0.045 D_real: 0.932 D_fake: 0.731 
+(epoch: 519, iters: 862, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.719 G_ID: 0.096 G_Rec: 0.274 D_GP: 0.032 D_real: 1.080 D_fake: 0.772 
+(epoch: 519, iters: 1262, time: 0.063) G_GAN: 0.521 G_GAN_Feat: 0.953 G_ID: 0.099 G_Rec: 0.454 D_GP: 0.046 D_real: 1.063 D_fake: 0.486 
+(epoch: 519, iters: 1662, time: 0.063) G_GAN: 0.493 G_GAN_Feat: 0.717 G_ID: 0.094 G_Rec: 0.293 D_GP: 0.029 D_real: 1.397 D_fake: 0.512 
+(epoch: 519, iters: 2062, time: 0.063) G_GAN: 0.554 G_GAN_Feat: 0.929 G_ID: 0.139 G_Rec: 0.394 D_GP: 0.035 D_real: 1.066 D_fake: 0.453 
+(epoch: 519, iters: 2462, time: 0.064) G_GAN: 0.229 G_GAN_Feat: 0.803 G_ID: 0.090 G_Rec: 0.316 D_GP: 0.040 D_real: 0.930 D_fake: 0.773 
+(epoch: 519, iters: 2862, time: 0.063) G_GAN: 0.438 G_GAN_Feat: 0.982 G_ID: 0.124 G_Rec: 0.421 D_GP: 0.033 D_real: 1.037 D_fake: 0.601 
+(epoch: 519, iters: 3262, time: 0.063) G_GAN: 0.046 G_GAN_Feat: 0.880 G_ID: 0.100 G_Rec: 0.368 D_GP: 0.046 D_real: 0.724 D_fake: 0.955 
+(epoch: 519, iters: 3662, time: 0.063) G_GAN: 0.197 G_GAN_Feat: 0.973 G_ID: 0.123 G_Rec: 0.445 D_GP: 0.060 D_real: 0.703 D_fake: 0.803 
+(epoch: 519, iters: 4062, time: 0.064) G_GAN: 0.162 G_GAN_Feat: 0.694 G_ID: 0.101 G_Rec: 0.291 D_GP: 0.037 D_real: 1.088 D_fake: 0.838 
+(epoch: 519, iters: 4462, time: 0.063) G_GAN: 0.743 G_GAN_Feat: 1.089 G_ID: 0.100 G_Rec: 0.470 D_GP: 0.155 D_real: 0.759 D_fake: 0.312 
+(epoch: 519, iters: 4862, time: 0.063) G_GAN: 0.018 G_GAN_Feat: 0.770 G_ID: 0.113 G_Rec: 0.329 D_GP: 0.031 D_real: 0.829 D_fake: 0.982 
+(epoch: 519, iters: 5262, time: 0.063) G_GAN: 0.506 G_GAN_Feat: 0.995 G_ID: 0.106 G_Rec: 0.445 D_GP: 0.030 D_real: 1.144 D_fake: 0.495 
+(epoch: 519, iters: 5662, time: 0.064) G_GAN: 0.420 G_GAN_Feat: 0.729 G_ID: 0.080 G_Rec: 0.296 D_GP: 0.037 D_real: 1.238 D_fake: 0.586 
+(epoch: 519, iters: 6062, time: 0.063) G_GAN: 0.781 G_GAN_Feat: 1.083 G_ID: 0.102 G_Rec: 0.471 D_GP: 0.042 D_real: 1.125 D_fake: 0.250 
+(epoch: 519, iters: 6462, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.864 G_ID: 0.108 G_Rec: 0.334 D_GP: 0.052 D_real: 0.838 D_fake: 0.827 
+(epoch: 519, iters: 6862, time: 0.063) G_GAN: 0.782 G_GAN_Feat: 1.019 G_ID: 0.120 G_Rec: 0.433 D_GP: 0.047 D_real: 1.197 D_fake: 0.267 
+(epoch: 519, iters: 7262, time: 0.064) G_GAN: 0.290 G_GAN_Feat: 0.803 G_ID: 0.088 G_Rec: 0.334 D_GP: 0.038 D_real: 1.170 D_fake: 0.717 
+(epoch: 519, iters: 7662, time: 0.063) G_GAN: 0.183 G_GAN_Feat: 0.998 G_ID: 0.141 G_Rec: 0.473 D_GP: 0.044 D_real: 0.620 D_fake: 0.822 
+(epoch: 519, iters: 8062, time: 0.063) G_GAN: 0.102 G_GAN_Feat: 0.680 G_ID: 0.098 G_Rec: 0.283 D_GP: 0.032 D_real: 1.063 D_fake: 0.898 
+(epoch: 519, iters: 8462, time: 0.063) G_GAN: 0.655 G_GAN_Feat: 0.900 G_ID: 0.109 G_Rec: 0.429 D_GP: 0.041 D_real: 1.281 D_fake: 0.431 
+(epoch: 520, iters: 254, time: 0.064) G_GAN: -0.002 G_GAN_Feat: 0.753 G_ID: 0.097 G_Rec: 0.321 D_GP: 0.034 D_real: 0.779 D_fake: 1.003 
diff --git a/mlops/mlflow/registry/Swimswap/checkpoints/people/opt.txt b/mlops/mlflow/registry/Swimswap/checkpoints/people/opt.txt
new file mode 100644
index 0000000..3cc4ed4
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/checkpoints/people/opt.txt
@@ -0,0 +1,72 @@
+------------ Options -------------
+batchSize: 8
+beta1: 0.5
+checkpoints_dir: ./checkpoints
+continue_train: False
+data_type: 32
+dataroot: ./datasets/cityscapes/
+debug: False
+display_freq: 99
+display_winsize: 512
+feat_num: 3
+fineSize: 512
+fp16: False
+gan_mode: hinge
+gpu_ids: [0]
+image_size: 224
+input_nc: 3
+instance_feat: False
+isTrain: True
+label_feat: False
+label_nc: 0
+lambda_GP: 10.0
+lambda_feat: 10.0
+lambda_id: 20.0
+lambda_rec: 10.0
+latent_size: 512
+loadSize: 1024
+load_features: False
+load_pretrain: 
+local_rank: 0
+lr: 0.0002
+max_dataset_size: inf
+model: pix2pixHD
+nThreads: 2
+n_blocks_global: 6
+n_blocks_local: 3
+n_clusters: 10
+n_downsample_E: 4
+n_downsample_global: 3
+n_layers_D: 4
+n_local_enhancers: 1
+name: people
+ndf: 64
+nef: 16
+netG: global
+ngf: 64
+niter: 10000
+niter_decay: 10000
+niter_fix_global: 0
+no_flip: False
+no_ganFeat_loss: False
+no_html: False
+no_instance: False
+no_vgg_loss: False
+norm: batch
+norm_G: spectralspadesyncbatch3x3
+num_D: 2
+output_nc: 3
+phase: train
+pool_size: 0
+print_freq: 100
+resize_or_crop: scale_width
+save_epoch_freq: 10000
+save_latest_freq: 10000
+semantic_nc: 3
+serial_batches: False
+tf_log: False
+times_G: 1
+use_dropout: False
+verbose: False
+which_epoch: latest
+-------------- End ----------------
diff --git a/mlops/mlflow/registry/Swimswap/insightface_func/__init__.py b/mlops/mlflow/registry/Swimswap/insightface_func/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mlops/mlflow/registry/Swimswap/insightface_func/face_detect_crop_multi.py b/mlops/mlflow/registry/Swimswap/insightface_func/face_detect_crop_multi.py
new file mode 100644
index 0000000..d75c36b
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/insightface_func/face_detect_crop_multi.py
@@ -0,0 +1,100 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 16:45:41
+Description: 
+'''
+from __future__ import division
+import collections
+import numpy as np
+import glob
+import os
+import os.path as osp
+import cv2
+from insightface.model_zoo import model_zoo
+from insightface_func.utils import face_align_ffhqandnewarc as face_align
+
+__all__ = ['Face_detect_crop', 'Face']
+
+Face = collections.namedtuple('Face', [
+    'bbox', 'kps', 'det_score', 'embedding', 'gender', 'age',
+    'embedding_norm', 'normed_embedding',
+    'landmark'
+])
+
+Face.__new__.__defaults__ = (None, ) * len(Face._fields)
+
+
+class Face_detect_crop:
+    def __init__(self, name, root='~/.insightface_func/models'):
+        self.models = {}
+        root = os.path.expanduser(root)
+        onnx_files = glob.glob(osp.join(root, name, '*.onnx'))
+        onnx_files = sorted(onnx_files)
+        for onnx_file in onnx_files:
+            if onnx_file.find('_selfgen_')>0:
+                #print('ignore:', onnx_file)
+                continue
+            model = model_zoo.get_model(onnx_file)
+            if model.taskname not in self.models:
+                print('find model:', onnx_file, model.taskname)
+                self.models[model.taskname] = model
+            else:
+                print('duplicated model task type, ignore:', onnx_file, model.taskname)
+                del model
+        assert 'detection' in self.models
+        self.det_model = self.models['detection']
+
+
+    def prepare(self, ctx_id, det_thresh=0.5, det_size=(640, 640), mode ='None'):
+        self.det_thresh = det_thresh
+        self.mode = mode
+        assert det_size is not None
+        print('set det-size:', det_size)
+        self.det_size = det_size
+        for taskname, model in self.models.items():
+            if taskname=='detection':
+                model.prepare(ctx_id, input_size=det_size)
+            else:
+                model.prepare(ctx_id)
+
+    def get(self, img, crop_size, max_num=0):
+        bboxes, kpss = self.det_model.detect(img,
+                                             threshold=self.det_thresh,
+                                             max_num=max_num,
+                                             metric='default')
+        if bboxes.shape[0] == 0:
+            return None
+        ret = []
+        # for i in range(bboxes.shape[0]):
+        #     bbox = bboxes[i, 0:4]
+        #     det_score = bboxes[i, 4]
+        #     kps = None
+        #     if kpss is not None:
+        #         kps = kpss[i]
+        #     M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') 
+        #     align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+        align_img_list = []
+        M_list = []
+        for i in range(bboxes.shape[0]):
+            kps = None
+            if kpss is not None:
+                kps = kpss[i]
+            M, _ = face_align.estimate_norm(kps, crop_size, mode = self.mode) 
+            align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+            align_img_list.append(align_img)
+            M_list.append(M)
+
+        # det_score = bboxes[..., 4]
+
+        # best_index = np.argmax(det_score)
+
+        # kps = None
+        # if kpss is not None:
+        #     kps = kpss[best_index]
+        # M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') 
+        # align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+        
+        return align_img_list, M_list
diff --git a/mlops/mlflow/registry/Swimswap/insightface_func/face_detect_crop_single.py b/mlops/mlflow/registry/Swimswap/insightface_func/face_detect_crop_single.py
new file mode 100644
index 0000000..f3f648f
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/insightface_func/face_detect_crop_single.py
@@ -0,0 +1,97 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 16:46:04
+Description: 
+'''
+from __future__ import division
+import collections
+import numpy as np
+import glob
+import os
+import os.path as osp
+import cv2
+from insightface.model_zoo import model_zoo
+from insightface_func.utils import face_align_ffhqandnewarc as face_align
+
+__all__ = ['Face_detect_crop', 'Face']
+
+Face = collections.namedtuple('Face', [
+    'bbox', 'kps', 'det_score', 'embedding', 'gender', 'age',
+    'embedding_norm', 'normed_embedding',
+    'landmark'
+])
+
+Face.__new__.__defaults__ = (None, ) * len(Face._fields)
+
+
+class Face_detect_crop:
+    def __init__(self, name, root='~/.insightface_func/models'):
+        self.models = {}
+        root = os.path.expanduser(root)
+        onnx_files = glob.glob(osp.join(root, name, '*.onnx'))
+        onnx_files = sorted(onnx_files)
+        for onnx_file in onnx_files:
+            if onnx_file.find('_selfgen_')>0:
+                #print('ignore:', onnx_file)
+                continue
+            model = model_zoo.get_model(onnx_file)
+            if model.taskname not in self.models:
+                print('find model:', onnx_file, model.taskname)
+                self.models[model.taskname] = model
+            else:
+                print('duplicated model task type, ignore:', onnx_file, model.taskname)
+                del model
+        assert 'detection' in self.models
+        self.det_model = self.models['detection']
+
+
+    def prepare(self, ctx_id, det_thresh=0.5, det_size=(640, 640), mode ='None'):
+        self.det_thresh = det_thresh
+        self.mode = mode
+        assert det_size is not None
+        print('set det-size:', det_size)
+        self.det_size = det_size
+        for taskname, model in self.models.items():
+            if taskname=='detection':
+                model.prepare(ctx_id, input_size=det_size)
+            else:
+                model.prepare(ctx_id)
+
+    def get(self, img, crop_size, max_num=0):
+        bboxes, kpss = self.det_model.detect(img,
+                                             threshold=self.det_thresh,
+                                             max_num=max_num,
+                                             metric='default')
+        if bboxes.shape[0] == 0:
+            return None
+        # ret = []
+        # for i in range(bboxes.shape[0]):
+        #     bbox = bboxes[i, 0:4]
+        #     det_score = bboxes[i, 4]
+        #     kps = None
+        #     if kpss is not None:
+        #         kps = kpss[i]
+        #     M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') 
+        #     align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+        # for i in range(bboxes.shape[0]):
+        #     kps = None
+        #     if kpss is not None:
+        #         kps = kpss[i]
+        #     M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') 
+        #     align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+
+        det_score = bboxes[..., 4]
+
+        # select the face with the hightest detection score
+        best_index = np.argmax(det_score)
+
+        kps = None
+        if kpss is not None:
+            kps = kpss[best_index]
+        M, _ = face_align.estimate_norm(kps, crop_size, mode = self.mode) 
+        align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+        
+        return [align_img], [M]
diff --git a/mlops/mlflow/registry/Swimswap/insightface_func/utils/face_align_ffhqandnewarc.py b/mlops/mlflow/registry/Swimswap/insightface_func/utils/face_align_ffhqandnewarc.py
new file mode 100644
index 0000000..2f5aeb7
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/insightface_func/utils/face_align_ffhqandnewarc.py
@@ -0,0 +1,159 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-15 19:42:42
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-15 20:01:47
+Description: 
+'''
+
+import cv2
+import numpy as np
+from skimage import transform as trans
+
+src1 = np.array([[51.642, 50.115], [57.617, 49.990], [35.740, 69.007],
+                 [51.157, 89.050], [57.025, 89.702]],
+                dtype=np.float32)
+#<--left
+src2 = np.array([[45.031, 50.118], [65.568, 50.872], [39.677, 68.111],
+                 [45.177, 86.190], [64.246, 86.758]],
+                dtype=np.float32)
+
+#---frontal
+src3 = np.array([[39.730, 51.138], [72.270, 51.138], [56.000, 68.493],
+                 [42.463, 87.010], [69.537, 87.010]],
+                dtype=np.float32)
+
+#-->right
+src4 = np.array([[46.845, 50.872], [67.382, 50.118], [72.737, 68.111],
+                 [48.167, 86.758], [67.236, 86.190]],
+                dtype=np.float32)
+
+#-->right profile
+src5 = np.array([[54.796, 49.990], [60.771, 50.115], [76.673, 69.007],
+                 [55.388, 89.702], [61.257, 89.050]],
+                dtype=np.float32)
+
+src = np.array([src1, src2, src3, src4, src5])
+src_map = src
+
+ffhq_src = np.array([[192.98138, 239.94708], [318.90277, 240.1936], [256.63416, 314.01935],
+                                           [201.26117, 371.41043], [313.08905, 371.15118]])
+ffhq_src = np.expand_dims(ffhq_src, axis=0)
+
+# arcface_src = np.array(
+#     [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366],
+#      [41.5493, 92.3655], [70.7299, 92.2041]],
+#     dtype=np.float32)
+
+# arcface_src = np.expand_dims(arcface_src, axis=0)
+
+# In[66]:
+
+
+# lmk is prediction; src is template
+def estimate_norm(lmk, image_size=112, mode='ffhq'):
+    assert lmk.shape == (5, 2)
+    tform = trans.SimilarityTransform()
+    lmk_tran = np.insert(lmk, 2, values=np.ones(5), axis=1)
+    min_M = []
+    min_index = []
+    min_error = float('inf')
+    if mode == 'ffhq':
+        # assert image_size == 112
+        src = ffhq_src * image_size / 512
+    else:
+        src = src_map * image_size / 112
+    for i in np.arange(src.shape[0]):
+        tform.estimate(lmk, src[i])
+        M = tform.params[0:2, :]
+        results = np.dot(M, lmk_tran.T)
+        results = results.T
+        error = np.sum(np.sqrt(np.sum((results - src[i])**2, axis=1)))
+        #         print(error)
+        if error < min_error:
+            min_error = error
+            min_M = M
+            min_index = i
+    return min_M, min_index
+
+
+def norm_crop(img, landmark, image_size=112, mode='ffhq'):
+    if mode == 'Both':
+        M_None, _ = estimate_norm(landmark, image_size, mode = 'newarc')
+        M_ffhq, _ = estimate_norm(landmark, image_size, mode='ffhq')
+        warped_None = cv2.warpAffine(img, M_None, (image_size, image_size), borderValue=0.0)
+        warped_ffhq = cv2.warpAffine(img, M_ffhq, (image_size, image_size), borderValue=0.0)
+        return warped_ffhq, warped_None
+    else:
+        M, pose_index = estimate_norm(landmark, image_size, mode)
+        warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
+        return warped
+
+def square_crop(im, S):
+    if im.shape[0] > im.shape[1]:
+        height = S
+        width = int(float(im.shape[1]) / im.shape[0] * S)
+        scale = float(S) / im.shape[0]
+    else:
+        width = S
+        height = int(float(im.shape[0]) / im.shape[1] * S)
+        scale = float(S) / im.shape[1]
+    resized_im = cv2.resize(im, (width, height))
+    det_im = np.zeros((S, S, 3), dtype=np.uint8)
+    det_im[:resized_im.shape[0], :resized_im.shape[1], :] = resized_im
+    return det_im, scale
+
+
+def transform(data, center, output_size, scale, rotation):
+    scale_ratio = scale
+    rot = float(rotation) * np.pi / 180.0
+    #translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio)
+    t1 = trans.SimilarityTransform(scale=scale_ratio)
+    cx = center[0] * scale_ratio
+    cy = center[1] * scale_ratio
+    t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy))
+    t3 = trans.SimilarityTransform(rotation=rot)
+    t4 = trans.SimilarityTransform(translation=(output_size / 2,
+                                                output_size / 2))
+    t = t1 + t2 + t3 + t4
+    M = t.params[0:2]
+    cropped = cv2.warpAffine(data,
+                             M, (output_size, output_size),
+                             borderValue=0.0)
+    return cropped, M
+
+
+def trans_points2d(pts, M):
+    new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
+    for i in range(pts.shape[0]):
+        pt = pts[i]
+        new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
+        new_pt = np.dot(M, new_pt)
+        #print('new_pt', new_pt.shape, new_pt)
+        new_pts[i] = new_pt[0:2]
+
+    return new_pts
+
+
+def trans_points3d(pts, M):
+    scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1])
+    #print(scale)
+    new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
+    for i in range(pts.shape[0]):
+        pt = pts[i]
+        new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
+        new_pt = np.dot(M, new_pt)
+        #print('new_pt', new_pt.shape, new_pt)
+        new_pts[i][0:2] = new_pt[0:2]
+        new_pts[i][2] = pts[i][2] * scale
+
+    return new_pts
+
+
+def trans_points(pts, M):
+    if pts.shape[1] == 2:
+        return trans_points2d(pts, M)
+    else:
+        return trans_points3d(pts, M)
+
diff --git a/mlops/mlflow/registry/Swimswap/model_registry.py b/mlops/mlflow/registry/Swimswap/model_registry.py
new file mode 100644
index 0000000..e70d709
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/model_registry.py
@@ -0,0 +1,66 @@
+import cv2
+import torch
+import fractions
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+from torchvision import transforms
+from models.models import create_model
+from options.test_options import TestOptions
+# from insightface_func.face_detect_crop_single import Face_detect_crop
+# from util.gifswap import gif_swap
+import os
+
+import mlflow
+
+os.environ["MLFLOW_S3_ENDPOINT_URL"] = ""
+os.environ["MLFLOW_TRACKING_URI"] = ""
+os.environ["AWS_ACCESS_KEY_ID"] = ""
+os.environ["AWS_SECRET_ACCESS_KEY"] = ""
+mlflow.set_experiment("Swimswap")
+
+
+def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
+
+transformer = transforms.Compose([
+        transforms.ToTensor(),
+        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+transformer_Arcface = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+# detransformer = transforms.Compose([
+#         transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
+#         transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
+#     ])
+
+def main():
+    opt = TestOptions().parse()
+
+    start_epoch, epoch_iter = 1, 0
+    crop_size = opt.crop_size
+
+    torch.nn.Module.dump_patches = True
+    if crop_size == 512:
+        opt.which_epoch = 550000
+        opt.name = '512'
+        mode = 'ffhq'
+    else:
+        mode = 'None'
+    model = create_model(opt)
+    model.eval()
+
+
+    with mlflow.start_run():
+        mlflow.pytorch.log_model(
+            pytorch_model=model,
+            artifact_path = "swimswap_pytorch",
+            # signature= signature,
+            # input_example = input_sample,
+            # pip_requirements = "rec.txt"
+        )
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/mlops/mlflow/registry/Swimswap/models/__init__.py b/mlops/mlflow/registry/Swimswap/models/__init__.py
new file mode 100644
index 0000000..289de91
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/models/__init__.py
@@ -0,0 +1,4 @@
+from .arcface_models import ArcMarginModel
+from .arcface_models import ResNet
+from .arcface_models import IRBlock
+from .arcface_models import SEBlock
\ No newline at end of file
diff --git a/mlops/mlflow/registry/Swimswap/models/arcface_models.py b/mlops/mlflow/registry/Swimswap/models/arcface_models.py
new file mode 100644
index 0000000..39a6ac5
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/models/arcface_models.py
@@ -0,0 +1,163 @@
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn import Parameter
+from .config import device, num_classes
+
+
+
+class SEBlock(nn.Module):
+    def __init__(self, channel, reduction=16):
+        super(SEBlock, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction),
+            nn.PReLU(),
+            nn.Linear(channel // reduction, channel),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1, 1)
+        return x * y
+
+
+class IRBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
+        super(IRBlock, self).__init__()
+        self.bn0 = nn.BatchNorm2d(inplanes)
+        self.conv1 = conv3x3(inplanes, inplanes)
+        self.bn1 = nn.BatchNorm2d(inplanes)
+        self.prelu = nn.PReLU()
+        self.conv2 = conv3x3(inplanes, planes, stride)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+        self.use_se = use_se
+        if self.use_se:
+            self.se = SEBlock(planes)
+
+    def forward(self, x):
+        residual = x
+        out = self.bn0(x)
+        out = self.conv1(out)
+        out = self.bn1(out)
+        out = self.prelu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        if self.use_se:
+            out = self.se(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.prelu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, use_se=True):
+        self.inplanes = 64
+        self.use_se = use_se
+        super(ResNet, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.prelu = nn.PReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.bn2 = nn.BatchNorm2d(512)
+        self.dropout = nn.Dropout()
+        self.fc = nn.Linear(512 * 7 * 7, 512)
+        self.bn3 = nn.BatchNorm1d(512)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.xavier_normal_(m.weight)
+            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.xavier_normal_(m.weight)
+                nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
+        self.inplanes = planes
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, use_se=self.use_se))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.prelu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.bn2(x)
+        x = self.dropout(x)
+        # feature = x
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        x = self.bn3(x)
+
+        return x
+
+
+class ArcMarginModel(nn.Module):
+    def __init__(self, args):
+        super(ArcMarginModel, self).__init__()
+
+        self.weight = Parameter(torch.FloatTensor(num_classes, args.emb_size))
+        nn.init.xavier_uniform_(self.weight)
+
+        self.easy_margin = args.easy_margin
+        self.m = args.margin_m
+        self.s = args.margin_s
+
+        self.cos_m = math.cos(self.m)
+        self.sin_m = math.sin(self.m)
+        self.th = math.cos(math.pi - self.m)
+        self.mm = math.sin(math.pi - self.m) * self.m
+
+    def forward(self, input, label):
+        x = F.normalize(input)
+        W = F.normalize(self.weight)
+        cosine = F.linear(x, W)
+        sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
+        phi = cosine * self.cos_m - sine * self.sin_m  # cos(theta + m)
+        if self.easy_margin:
+            phi = torch.where(cosine > 0, phi, cosine)
+        else:
+            phi = torch.where(cosine > self.th, phi, cosine - self.mm)
+        one_hot = torch.zeros(cosine.size(), device=device)
+        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
+        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
+        output *= self.s
+        return output
\ No newline at end of file
diff --git a/mlops/mlflow/registry/Swimswap/models/base_model.py b/mlops/mlflow/registry/Swimswap/models/base_model.py
new file mode 100644
index 0000000..0a6474a
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/models/base_model.py
@@ -0,0 +1,138 @@
+import os
+import torch
+import sys
+
+class BaseModel(torch.nn.Module):
+    def name(self):
+        return 'BaseModel'
+
+    def initialize(self, opt):
+        self.opt = opt
+        self.gpu_ids = opt.gpu_ids
+        self.isTrain = opt.isTrain
+        self.Tensor = torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor
+        self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)
+
+    def set_input(self, input):
+        self.input = input
+
+    def forward(self):
+        pass
+
+    # used in test time, no backprop
+    def test(self):
+        pass
+
+    def get_image_paths(self):
+        pass
+
+    def optimize_parameters(self):
+        pass
+
+    def get_current_visuals(self):
+        return self.input
+
+    def get_current_errors(self):
+        return {}
+
+    def save(self, label):
+        pass
+    
+    # helper saving function that can be used by subclasses
+    def save_network(self, network, network_label, epoch_label, gpu_ids=None):
+        save_filename = '{}_net_{}.pth'.format(epoch_label, network_label)
+        save_path = os.path.join(self.save_dir, save_filename)
+        torch.save(network.cpu().state_dict(), save_path)
+        if torch.cuda.is_available():
+            network.cuda()
+
+    def save_optim(self, network, network_label, epoch_label, gpu_ids=None):
+        save_filename = '{}_optim_{}.pth'.format(epoch_label, network_label)
+        save_path = os.path.join(self.save_dir, save_filename)
+        torch.save(network.state_dict(), save_path)
+
+
+    # helper loading function that can be used by subclasses
+    def load_network(self, network, network_label, epoch_label, save_dir=''):        
+        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
+        if not save_dir:
+            save_dir = self.save_dir
+        save_path = os.path.join(save_dir, save_filename)        
+        if not os.path.isfile(save_path):
+            print('%s not exists yet!' % save_path)
+            if network_label == 'G':
+                raise('Generator must exist!')
+        else:
+            #network.load_state_dict(torch.load(save_path))
+            try:
+                network.load_state_dict(torch.load(save_path))
+            except:   
+                pretrained_dict = torch.load(save_path)                
+                model_dict = network.state_dict()
+                try:
+                    pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}                    
+                    network.load_state_dict(pretrained_dict)
+                    if self.opt.verbose:
+                        print('Pretrained network %s has excessive layers; Only loading layers that are used' % network_label)
+                except:
+                    print('Pretrained network %s has fewer layers; The following are not initialized:' % network_label)
+                    for k, v in pretrained_dict.items():                      
+                        if v.size() == model_dict[k].size():
+                            model_dict[k] = v
+
+                    if sys.version_info >= (3,0):
+                        not_initialized = set()
+                    else:
+                        from sets import Set
+                        not_initialized = Set()                    
+
+                    for k, v in model_dict.items():
+                        if k not in pretrained_dict or v.size() != pretrained_dict[k].size():
+                            not_initialized.add(k.split('.')[0])
+                    
+                    print(sorted(not_initialized))
+                    network.load_state_dict(model_dict)
+
+    # helper loading function that can be used by subclasses
+    def load_optim(self, network, network_label, epoch_label, save_dir=''):        
+        save_filename = '%s_optim_%s.pth' % (epoch_label, network_label)
+        if not save_dir:
+            save_dir = self.save_dir
+        save_path = os.path.join(save_dir, save_filename)        
+        if not os.path.isfile(save_path):
+            print('%s not exists yet!' % save_path)
+            if network_label == 'G':
+                raise('Generator must exist!')
+        else:
+            #network.load_state_dict(torch.load(save_path))
+            try:
+                network.load_state_dict(torch.load(save_path, map_location=torch.device("cpu")))
+            except:   
+                pretrained_dict = torch.load(save_path, map_location=torch.device("cpu"))                
+                model_dict = network.state_dict()
+                try:
+                    pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}                    
+                    network.load_state_dict(pretrained_dict)
+                    if self.opt.verbose:
+                        print('Pretrained network %s has excessive layers; Only loading layers that are used' % network_label)
+                except:
+                    print('Pretrained network %s has fewer layers; The following are not initialized:' % network_label)
+                    for k, v in pretrained_dict.items():                      
+                        if v.size() == model_dict[k].size():
+                            model_dict[k] = v
+
+                    if sys.version_info >= (3,0):
+                        not_initialized = set()
+                    else:
+                        from sets import Set
+                        not_initialized = Set()                    
+
+                    for k, v in model_dict.items():
+                        if k not in pretrained_dict or v.size() != pretrained_dict[k].size():
+                            not_initialized.add(k.split('.')[0])
+                    
+                    print(sorted(not_initialized))
+                    network.load_state_dict(model_dict)                  
+
+    def update_learning_rate():
+        pass
diff --git a/mlops/mlflow/registry/Swimswap/models/config.py b/mlops/mlflow/registry/Swimswap/models/config.py
new file mode 100644
index 0000000..eb83edb
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/models/config.py
@@ -0,0 +1,28 @@
+import os
+
+import torch
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # sets device for model and PyTorch tensors
+
+# Model parameters
+image_w = 112
+image_h = 112
+channel = 3
+emb_size = 512
+
+# Training parameters
+num_workers = 1  # for data-loading; right now, only 1 works with h5py
+grad_clip = 5.  # clip gradients at an absolute value of
+print_freq = 100  # print training/validation stats  every __ batches
+checkpoint = None  # path to checkpoint, None if none
+
+# Data parameters
+num_classes = 93431
+num_samples = 5179510
+DATA_DIR = 'data'
+# faces_ms1m_folder = 'data/faces_ms1m_112x112'
+faces_ms1m_folder = 'data/ms1m-retinaface-t1'
+path_imgidx = os.path.join(faces_ms1m_folder, 'train.idx')
+path_imgrec = os.path.join(faces_ms1m_folder, 'train.rec')
+IMG_DIR = 'data/images'
+pickle_file = 'data/faces_ms1m_112x112.pickle'
diff --git a/mlops/mlflow/registry/Swimswap/models/fs_model.py b/mlops/mlflow/registry/Swimswap/models/fs_model.py
new file mode 100644
index 0000000..95ac877
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/models/fs_model.py
@@ -0,0 +1,242 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import os
+from torch.autograd import Variable
+from .base_model import BaseModel
+from . import networks
+
+class SpecificNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(SpecificNorm, self).__init__()
+        self.mean = np.array([0.485, 0.456, 0.406])
+        self.mean = torch.from_numpy(self.mean).float().cuda()
+        self.mean = self.mean.view([1, 3, 1, 1])
+
+        self.std = np.array([0.229, 0.224, 0.225])
+        self.std = torch.from_numpy(self.std).float().cuda()
+        self.std = self.std.view([1, 3, 1, 1])
+
+    def forward(self, x):
+        mean = self.mean.expand([1, 3, x.shape[2], x.shape[3]])
+        std = self.std.expand([1, 3, x.shape[2], x.shape[3]])
+
+        x = (x - mean) / std
+
+        return x
+
+class fsModel(BaseModel):
+    def name(self):
+        return 'fsModel'
+
+    def init_loss_filter(self, use_gan_feat_loss, use_vgg_loss):
+        flags = (True, use_gan_feat_loss, use_vgg_loss, True, True, True, True, True)
+
+        def loss_filter(g_gan, g_gan_feat, g_vgg, g_id, g_rec, g_mask, d_real, d_fake):
+            return [l for (l, f) in zip((g_gan, g_gan_feat, g_vgg, g_id, g_rec, g_mask, d_real, d_fake), flags) if f]
+
+        return loss_filter
+
+    def initialize(self, opt):
+        BaseModel.initialize(self, opt)
+        if opt.resize_or_crop != 'none' or not opt.isTrain:  # when training at full res this causes OOM
+            torch.backends.cudnn.benchmark = True
+        self.isTrain = opt.isTrain
+
+        device = torch.device("cuda:0")
+
+        if opt.crop_size == 224:
+            from .fs_networks import Generator_Adain_Upsample, Discriminator
+        elif opt.crop_size == 512:
+            from .fs_networks_512 import Generator_Adain_Upsample, Discriminator
+
+        # Generator network
+        self.netG = Generator_Adain_Upsample(input_nc=3, output_nc=3, latent_size=512, n_blocks=9, deep=False)
+        self.netG.to(device)
+
+        # Id network
+        netArc_checkpoint = opt.Arc_path
+        netArc_checkpoint = torch.load(netArc_checkpoint, map_location=torch.device("cpu"))
+        self.netArc = netArc_checkpoint
+        self.netArc = self.netArc.to(device)
+        self.netArc.eval()
+
+        if not self.isTrain:
+            pretrained_path = '' if not self.isTrain else opt.load_pretrain
+            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)
+            return
+
+        # Discriminator network
+        if opt.gan_mode == 'original':
+            use_sigmoid = True
+        else:
+            use_sigmoid = False
+        self.netD1 = Discriminator(input_nc=3, use_sigmoid=use_sigmoid)
+        self.netD2 = Discriminator(input_nc=3, use_sigmoid=use_sigmoid)
+        self.netD1.to(device)
+        self.netD2.to(device)
+
+        #
+        self.spNorm =SpecificNorm()
+        self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
+
+        # load networks
+        if opt.continue_train or opt.load_pretrain:
+            pretrained_path = '' if not self.isTrain else opt.load_pretrain
+            # print (pretrained_path)
+            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)
+            self.load_network(self.netD1, 'D1', opt.which_epoch, pretrained_path)
+            self.load_network(self.netD2, 'D2', opt.which_epoch, pretrained_path)
+
+
+
+        if self.isTrain:
+            # define loss functions
+            self.loss_filter = self.init_loss_filter(not opt.no_ganFeat_loss, not opt.no_vgg_loss)
+
+            self.criterionGAN = networks.GANLoss(opt.gan_mode, tensor=self.Tensor, opt=self.opt)
+            self.criterionFeat = nn.L1Loss()
+            self.criterionRec = nn.L1Loss()
+
+            # Names so we can breakout loss
+            self.loss_names = self.loss_filter('G_GAN', 'G_GAN_Feat', 'G_VGG', 'G_ID', 'G_Rec', 'D_GP',
+                                               'D_real', 'D_fake')
+
+           # initialize optimizers
+
+            # optimizer G
+            params = list(self.netG.parameters())
+            self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+
+            # optimizer D
+            params = list(self.netD1.parameters()) + list(self.netD2.parameters())
+            self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+
+    def _gradinet_penalty_D(self, netD, img_att, img_fake):
+        # interpolate sample
+        bs = img_fake.shape[0]
+        alpha = torch.rand(bs, 1, 1, 1).expand_as(img_fake).cuda()
+        interpolated = Variable(alpha * img_att + (1 - alpha) * img_fake, requires_grad=True)
+        pred_interpolated = netD.forward(interpolated)
+        pred_interpolated = pred_interpolated[-1]
+
+        # compute gradients
+        grad = torch.autograd.grad(outputs=pred_interpolated,
+                                   inputs=interpolated,
+                                   grad_outputs=torch.ones(pred_interpolated.size()).cuda(),
+                                   retain_graph=True,
+                                   create_graph=True,
+                                   only_inputs=True)[0]
+
+        # penalize gradients
+        grad = grad.view(grad.size(0), -1)
+        grad_l2norm = torch.sqrt(torch.sum(grad ** 2, dim=1))
+        loss_d_gp = torch.mean((grad_l2norm - 1) ** 2)
+
+        return loss_d_gp
+
+    def cosin_metric(self, x1, x2):
+        #return np.dot(x1, x2) / (np.linalg.norm(x1) * np.linalg.norm(x2))
+        return torch.sum(x1 * x2, dim=1) / (torch.norm(x1, dim=1) * torch.norm(x2, dim=1))
+
+    def forward(self, img_id, img_att, latent_id, latent_att, for_G=False):
+        loss_D_fake, loss_D_real, loss_D_GP = 0, 0, 0
+        loss_G_GAN, loss_G_GAN_Feat, loss_G_VGG, loss_G_ID, loss_G_Rec = 0,0,0,0,0
+
+        img_fake = self.netG.forward(img_att, latent_id)
+        if not self.isTrain:
+            return img_fake
+        img_fake_downsample = self.downsample(img_fake)
+        img_att_downsample = self.downsample(img_att)
+
+
+
+        # D_Fake
+        fea1_fake = self.netD1.forward(img_fake.detach())
+        fea2_fake = self.netD2.forward(img_fake_downsample.detach())
+        pred_fake = [fea1_fake, fea2_fake]
+        loss_D_fake = self.criterionGAN(pred_fake, False, for_discriminator=True)
+
+
+        # D_Feal
+        fea1_real = self.netD1.forward(img_att)
+        fea2_real = self.netD2.forward(img_att_downsample)
+        pred_real = [fea1_real, fea2_real]
+        fea_real = [fea1_real, fea2_real]
+        loss_D_real = self.criterionGAN(pred_real, True, for_discriminator=True)
+        #print('=====================D_Real========================')
+
+        # D_GP
+
+        loss_D_GP = 0
+
+        # G_GAN
+        fea1_fake = self.netD1.forward(img_fake)
+        fea2_fake = self.netD2.forward(img_fake_downsample)
+        #pred_fake = [fea1_fake[-1], fea2_fake[-1]]
+        pred_fake = [fea1_fake, fea2_fake]
+        fea_fake = [fea1_fake, fea2_fake]
+        loss_G_GAN = self.criterionGAN(pred_fake, True, for_discriminator=False)
+
+        # GAN feature matching loss
+        n_layers_D = 4
+        num_D = 2
+        if not self.opt.no_ganFeat_loss:
+            feat_weights = 4.0 / (n_layers_D + 1)
+            D_weights = 1.0 / num_D
+            for i in range(num_D):
+                for j in range(0, len(fea_fake[i]) - 1):
+                    loss_G_GAN_Feat += D_weights * feat_weights * \
+                                       self.criterionFeat(fea_fake[i][j],
+                                                          fea_real[i][j].detach()) * self.opt.lambda_feat
+
+
+        #G_ID
+        img_fake_down = F.interpolate(img_fake, size=(112,112))
+        img_fake_down = self.spNorm(img_fake_down)
+        latent_fake = self.netArc(img_fake_down)
+        loss_G_ID = (1 - self.cosin_metric(latent_fake, latent_id))
+        #print('=====================G_ID========================')
+        #print(loss_G_ID)
+
+        #G_Rec
+        loss_G_Rec = self.criterionRec(img_fake, img_att) * self.opt.lambda_rec
+
+        # Only return the fake_B image if necessary to save BW
+        return [self.loss_filter(loss_G_GAN, loss_G_GAN_Feat, loss_G_VGG, loss_G_ID, loss_G_Rec, loss_D_GP, loss_D_real, loss_D_fake),
+                img_fake]
+
+
+    def save(self, which_epoch):
+        self.save_network(self.netG, 'G', which_epoch, self.gpu_ids)
+        self.save_network(self.netD1, 'D1', which_epoch, self.gpu_ids)
+        self.save_network(self.netD2, 'D2', which_epoch, self.gpu_ids)
+        '''if self.gen_features:
+            self.save_network(self.netE, 'E', which_epoch, self.gpu_ids)'''
+
+    def update_fixed_params(self):
+        # after fixing the global generator for a number of iterations, also start finetuning it
+        params = list(self.netG.parameters())
+        if self.gen_features:
+            params += list(self.netE.parameters())
+        self.optimizer_G = torch.optim.Adam(params, lr=self.opt.lr, betas=(self.opt.beta1, 0.999))
+        if self.opt.verbose:
+            print('------------ Now also finetuning global generator -----------')
+
+    def update_learning_rate(self):
+        lrd = self.opt.lr / self.opt.niter_decay
+        lr = self.old_lr - lrd
+        for param_group in self.optimizer_D.param_groups:
+            param_group['lr'] = lr
+        for param_group in self.optimizer_G.param_groups:
+            param_group['lr'] = lr
+        if self.opt.verbose:
+            print('update learning rate: %f -> %f' % (self.old_lr, lr))
+        self.old_lr = lr
+
+
diff --git a/mlops/mlflow/registry/Swimswap/models/fs_networks.py b/mlops/mlflow/registry/Swimswap/models/fs_networks.py
new file mode 100644
index 0000000..227f6fa
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/models/fs_networks.py
@@ -0,0 +1,215 @@
+"""
+Copyright (C) 2019 NVIDIA Corporation.  All rights reserved.
+Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+"""
+
+import torch
+import torch.nn as nn
+
+
+class InstanceNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(InstanceNorm, self).__init__()
+        self.epsilon = epsilon
+
+    def forward(self, x):
+        x   = x - torch.mean(x, (2, 3), True)
+        tmp = torch.mul(x, x) # or x ** 2
+        tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
+        return x * tmp
+
+class ApplyStyle(nn.Module):
+    """
+        @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
+    """
+    def __init__(self, latent_size, channels):
+        super(ApplyStyle, self).__init__()
+        self.linear = nn.Linear(latent_size, channels * 2)
+
+    def forward(self, x, latent):
+        style = self.linear(latent)  # style => [batch_size, n_channels*2]
+        shape = [-1, 2, x.size(1), 1, 1]
+        style = style.view(shape)    # [batch_size, 2, n_channels, ...]
+        #x = x * (style[:, 0] + 1.) + style[:, 1]
+        x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1
+        return x
+
+class ResnetBlock_Adain(nn.Module):
+    def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
+        super(ResnetBlock_Adain, self).__init__()
+
+        p = 0
+        conv1 = []
+        if padding_type == 'reflect':
+            conv1 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv1 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()]
+        self.conv1 = nn.Sequential(*conv1)
+        self.style1 = ApplyStyle(latent_size, dim)
+        self.act1 = activation
+
+        p = 0
+        conv2 = []
+        if padding_type == 'reflect':
+            conv2 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv2 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
+        self.conv2 = nn.Sequential(*conv2)
+        self.style2 = ApplyStyle(latent_size, dim)
+
+
+    def forward(self, x, dlatents_in_slice):
+        y = self.conv1(x)
+        y = self.style1(y, dlatents_in_slice)
+        y = self.act1(y)
+        y = self.conv2(y)
+        y = self.style2(y, dlatents_in_slice)
+        out = x + y
+        return out
+
+
+
+class Generator_Adain_Upsample(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, n_blocks=6, deep=False,
+                 norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_Upsample, self).__init__()
+        activation = nn.ReLU(True)
+        self.deep = deep
+
+        self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 64, kernel_size=7, padding=0),
+                                         norm_layer(64), activation)
+        ### downsample
+        self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(128), activation)
+        self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(256), activation)
+        self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(512), activation)
+        if self.deep:
+            self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
+                                       norm_layer(512), activation)
+
+        ### resnet blocks
+        BN = []
+        for i in range(n_blocks):
+            BN += [
+                ResnetBlock_Adain(512, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        if self.deep:
+            self.up4 = nn.Sequential(
+                nn.Upsample(scale_factor=2, mode='bilinear'),
+                nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
+                nn.BatchNorm2d(512), activation
+            )
+        self.up3 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(256), activation
+        )
+        self.up2 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(128), activation
+        )
+        self.up1 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(64), activation
+        )
+        self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, kernel_size=7, padding=0),
+                                        nn.Tanh())
+
+    def forward(self, input, dlatents):
+        x = input  # 3*224*224
+
+        skip1 = self.first_layer(x)
+        skip2 = self.down1(skip1)
+        skip3 = self.down2(skip2)
+        if self.deep:
+            skip4 = self.down3(skip3)
+            x = self.down4(skip4)
+        else:
+            x = self.down3(skip3)
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+
+        if self.deep:
+            x = self.up4(x)
+        x = self.up3(x)
+        x = self.up2(x)
+        x = self.up1(x)
+        x = self.last_layer(x)
+        x = (x + 1) / 2
+
+        return x
+
+class Discriminator(nn.Module):
+    def __init__(self, input_nc, norm_layer=nn.BatchNorm2d, use_sigmoid=False):
+        super(Discriminator, self).__init__()
+
+        kw = 4
+        padw = 1
+        self.down1 = nn.Sequential(
+            nn.Conv2d(input_nc, 64, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)
+        )
+        self.down2 = nn.Sequential(
+            nn.Conv2d(64, 128, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(128), nn.LeakyReLU(0.2, True)
+        )
+        self.down3 = nn.Sequential(
+            nn.Conv2d(128, 256, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(256), nn.LeakyReLU(0.2, True)
+        )
+        self.down4 = nn.Sequential(
+            nn.Conv2d(256, 512, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(512), nn.LeakyReLU(0.2, True)
+        )
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(512, 512, kernel_size=kw, stride=1, padding=padw),
+            norm_layer(512),
+            nn.LeakyReLU(0.2, True)
+        )
+
+        if use_sigmoid:
+            self.conv2 = nn.Sequential(
+                nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw), nn.Sigmoid()
+            )
+        else:
+            self.conv2 = nn.Sequential(
+                nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw)
+            )
+
+    def forward(self, input):
+        out = []
+        x = self.down1(input)
+        out.append(x)
+        x = self.down2(x)
+        out.append(x)
+        x = self.down3(x)
+        out.append(x)
+        x = self.down4(x)
+        out.append(x)
+        x = self.conv1(x)
+        out.append(x)
+        x = self.conv2(x)
+        out.append(x)
+        
+        return out
\ No newline at end of file
diff --git a/mlops/mlflow/registry/Swimswap/models/fs_networks_512.py b/mlops/mlflow/registry/Swimswap/models/fs_networks_512.py
new file mode 100644
index 0000000..1b9f9c8
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/models/fs_networks_512.py
@@ -0,0 +1,232 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 16:55:48
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 16:58:06
+Description: 
+'''
+"""
+Copyright (C) 2019 NVIDIA Corporation.  All rights reserved.
+Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+"""
+
+import torch
+import torch.nn as nn
+
+
+class InstanceNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(InstanceNorm, self).__init__()
+        self.epsilon = epsilon
+
+    def forward(self, x):
+        x   = x - torch.mean(x, (2, 3), True)
+        tmp = torch.mul(x, x) # or x ** 2
+        tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
+        return x * tmp
+
+class ApplyStyle(nn.Module):
+    """
+        @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
+    """
+    def __init__(self, latent_size, channels):
+        super(ApplyStyle, self).__init__()
+        self.linear = nn.Linear(latent_size, channels * 2)
+
+    def forward(self, x, latent):
+        style = self.linear(latent)  # style => [batch_size, n_channels*2]
+        shape = [-1, 2, x.size(1), 1, 1]
+        style = style.view(shape)    # [batch_size, 2, n_channels, ...]
+        #x = x * (style[:, 0] + 1.) + style[:, 1]
+        x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1
+        return x
+
+class ResnetBlock_Adain(nn.Module):
+    def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
+        super(ResnetBlock_Adain, self).__init__()
+
+        p = 0
+        conv1 = []
+        if padding_type == 'reflect':
+            conv1 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv1 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()]
+        self.conv1 = nn.Sequential(*conv1)
+        self.style1 = ApplyStyle(latent_size, dim)
+        self.act1 = activation
+
+        p = 0
+        conv2 = []
+        if padding_type == 'reflect':
+            conv2 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv2 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
+        self.conv2 = nn.Sequential(*conv2)
+        self.style2 = ApplyStyle(latent_size, dim)
+
+
+    def forward(self, x, dlatents_in_slice):
+        y = self.conv1(x)
+        y = self.style1(y, dlatents_in_slice)
+        y = self.act1(y)
+        y = self.conv2(y)
+        y = self.style2(y, dlatents_in_slice)
+        out = x + y
+        return out
+
+
+
+class Generator_Adain_Upsample(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, n_blocks=6, deep=False,
+                 norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_Upsample, self).__init__()
+        activation = nn.ReLU(True)
+        self.deep = deep
+
+        self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 32, kernel_size=7, padding=0),
+                                         norm_layer(32), activation)
+        ### downsample
+        self.down0 = nn.Sequential(nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(64), activation)
+        self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(128), activation)
+        self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(256), activation)
+        self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(512), activation)
+        if self.deep:
+            self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
+                                       norm_layer(512), activation)
+
+        ### resnet blocks
+        BN = []
+        for i in range(n_blocks):
+            BN += [
+                ResnetBlock_Adain(512, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        if self.deep:
+            self.up4 = nn.Sequential(
+                nn.Upsample(scale_factor=2, mode='bilinear'),
+                nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
+                nn.BatchNorm2d(512), activation
+            )
+        self.up3 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(256), activation
+        )
+        self.up2 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(128), activation
+        )
+        self.up1 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(64), activation
+        )
+        self.up0 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(32), activation
+        )
+        self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(32, output_nc, kernel_size=7, padding=0),
+                                        nn.Tanh())
+
+    def forward(self, input, dlatents):
+        x = input  # 3*224*224
+
+        skip0 = self.first_layer(x)
+        skip1 = self.down0(skip0)
+        skip2 = self.down1(skip1)
+        skip3 = self.down2(skip2)
+        if self.deep:
+            skip4 = self.down3(skip3)
+            x = self.down4(skip4)
+        else:
+            x = self.down3(skip3)
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+
+        if self.deep:
+            x = self.up4(x)
+        x = self.up3(x)
+        x = self.up2(x)
+        x = self.up1(x)
+        x = self.up0(x)
+        x = self.last_layer(x)
+        x = (x + 1) / 2
+
+        return x
+
+class Discriminator(nn.Module):
+    def __init__(self, input_nc, norm_layer=nn.BatchNorm2d, use_sigmoid=False):
+        super(Discriminator, self).__init__()
+
+        kw = 4
+        padw = 1
+        self.down1 = nn.Sequential(
+            nn.Conv2d(input_nc, 64, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)
+        )
+        self.down2 = nn.Sequential(
+            nn.Conv2d(64, 128, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(128), nn.LeakyReLU(0.2, True)
+        )
+        self.down3 = nn.Sequential(
+            nn.Conv2d(128, 256, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(256), nn.LeakyReLU(0.2, True)
+        )
+        self.down4 = nn.Sequential(
+            nn.Conv2d(256, 512, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(512), nn.LeakyReLU(0.2, True)
+        )
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(512, 512, kernel_size=kw, stride=1, padding=padw),
+            norm_layer(512),
+            nn.LeakyReLU(0.2, True)
+        )
+
+        if use_sigmoid:
+            self.conv2 = nn.Sequential(
+                nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw), nn.Sigmoid()
+            )
+        else:
+            self.conv2 = nn.Sequential(
+                nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw)
+            )
+
+    def forward(self, input):
+        out = []
+        x = self.down1(input)
+        out.append(x)
+        x = self.down2(x)
+        out.append(x)
+        x = self.down3(x)
+        out.append(x)
+        x = self.down4(x)
+        out.append(x)
+        x = self.conv1(x)
+        out.append(x)
+        x = self.conv2(x)
+        out.append(x)
+        
+        return out
diff --git a/mlops/mlflow/registry/Swimswap/models/fs_networks_fix.py b/mlops/mlflow/registry/Swimswap/models/fs_networks_fix.py
new file mode 100644
index 0000000..c7b0525
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/models/fs_networks_fix.py
@@ -0,0 +1,172 @@
+"""
+Copyright (C) 2019 NVIDIA Corporation.  All rights reserved.
+Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+"""
+
+import torch
+import torch.nn as nn
+
+
+class InstanceNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(InstanceNorm, self).__init__()
+        self.epsilon = epsilon
+
+    def forward(self, x):
+        x   = x - torch.mean(x, (2, 3), True)
+        tmp = torch.mul(x, x) # or x ** 2
+        tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
+        return x * tmp
+
+class ApplyStyle(nn.Module):
+    """
+        @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
+    """
+    def __init__(self, latent_size, channels):
+        super(ApplyStyle, self).__init__()
+        self.linear = nn.Linear(latent_size, channels * 2)
+
+    def forward(self, x, latent):
+        style = self.linear(latent)  # style => [batch_size, n_channels*2]
+        shape = [-1, 2, x.size(1), 1, 1]
+        style = style.view(shape)    # [batch_size, 2, n_channels, ...]
+        #x = x * (style[:, 0] + 1.) + style[:, 1]
+        x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1
+        return x
+
+class ResnetBlock_Adain(nn.Module):
+    def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
+        super(ResnetBlock_Adain, self).__init__()
+
+        p = 0
+        conv1 = []
+        if padding_type == 'reflect':
+            conv1 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv1 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()]
+        self.conv1 = nn.Sequential(*conv1)
+        self.style1 = ApplyStyle(latent_size, dim)
+        self.act1 = activation
+
+        p = 0
+        conv2 = []
+        if padding_type == 'reflect':
+            conv2 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv2 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
+        self.conv2 = nn.Sequential(*conv2)
+        self.style2 = ApplyStyle(latent_size, dim)
+
+
+    def forward(self, x, dlatents_in_slice):
+        y = self.conv1(x)
+        y = self.style1(y, dlatents_in_slice)
+        y = self.act1(y)
+        y = self.conv2(y)
+        y = self.style2(y, dlatents_in_slice)
+        out = x + y
+        return out
+
+
+
+class Generator_Adain_Upsample(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, n_blocks=6, deep=False,
+                 norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_Upsample, self).__init__()
+
+        activation = nn.ReLU(True)
+        
+        self.deep = deep
+        
+        self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 64, kernel_size=7, padding=0),
+                                         norm_layer(64), activation)
+        ### downsample
+        self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(128), activation)
+        self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(256), activation)
+        self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(512), activation)
+                                   
+        if self.deep:
+            self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
+                                       norm_layer(512), activation)
+
+        ### resnet blocks
+        BN = []
+        for i in range(n_blocks):
+            BN += [
+                ResnetBlock_Adain(512, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        if self.deep:
+            self.up4 = nn.Sequential(
+                nn.Upsample(scale_factor=2, mode='bilinear',align_corners=False),
+                nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
+                nn.BatchNorm2d(512), activation
+            )
+        self.up3 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear',align_corners=False),
+            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(256), activation
+        )
+        self.up2 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear',align_corners=False),
+            nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(128), activation
+        )
+        self.up1 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear',align_corners=False),
+            nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(64), activation
+        )
+        self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, kernel_size=7, padding=0))
+
+    def forward(self, input, dlatents):
+        x = input  # 3*224*224
+
+        skip1 = self.first_layer(x)
+        skip2 = self.down1(skip1)
+        skip3 = self.down2(skip2)
+        if self.deep:
+            skip4 = self.down3(skip3)
+            x = self.down4(skip4)
+        else:
+            x = self.down3(skip3)
+        bot = []
+        bot.append(x)
+        features = []
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+            bot.append(x)
+
+        if self.deep:
+            x = self.up4(x)
+            features.append(x)
+        x = self.up3(x)
+        features.append(x)
+        x = self.up2(x)
+        features.append(x)
+        x = self.up1(x)
+        features.append(x)
+        x = self.last_layer(x)
+        # x = (x + 1) / 2
+
+        # return x, bot, features, dlatents
+        return x
\ No newline at end of file
diff --git a/mlops/mlflow/registry/Swimswap/models/models.py b/mlops/mlflow/registry/Swimswap/models/models.py
new file mode 100644
index 0000000..bf9e84e
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/models/models.py
@@ -0,0 +1,177 @@
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn import Parameter
+from .config import device, num_classes
+
+
+def create_model(opt):
+    #from .pix2pixHD_model import Pix2PixHDModel, InferenceModel
+    from .fs_model import fsModel
+    model = fsModel()
+
+    model.initialize(opt)
+    if opt.verbose:
+        print("model [%s] was created" % (model.name()))
+
+    if opt.isTrain and len(opt.gpu_ids) and not opt.fp16:
+        model = torch.nn.DataParallel(model, device_ids=opt.gpu_ids)
+
+    return model
+
+
+
+class SEBlock(nn.Module):
+    def __init__(self, channel, reduction=16):
+        super(SEBlock, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction),
+            nn.PReLU(),
+            nn.Linear(channel // reduction, channel),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1, 1)
+        return x * y
+
+
+class IRBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
+        super(IRBlock, self).__init__()
+        self.bn0 = nn.BatchNorm2d(inplanes)
+        self.conv1 = conv3x3(inplanes, inplanes)
+        self.bn1 = nn.BatchNorm2d(inplanes)
+        self.prelu = nn.PReLU()
+        self.conv2 = conv3x3(inplanes, planes, stride)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+        self.use_se = use_se
+        if self.use_se:
+            self.se = SEBlock(planes)
+
+    def forward(self, x):
+        residual = x
+        out = self.bn0(x)
+        out = self.conv1(out)
+        out = self.bn1(out)
+        out = self.prelu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        if self.use_se:
+            out = self.se(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.prelu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, use_se=True):
+        self.inplanes = 64
+        self.use_se = use_se
+        super(ResNet, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.prelu = nn.PReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.bn2 = nn.BatchNorm2d(512)
+        self.dropout = nn.Dropout()
+        self.fc = nn.Linear(512 * 7 * 7, 512)
+        self.bn3 = nn.BatchNorm1d(512)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.xavier_normal_(m.weight)
+            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.xavier_normal_(m.weight)
+                nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
+        self.inplanes = planes
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, use_se=self.use_se))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.prelu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.bn2(x)
+        x = self.dropout(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        x = self.bn3(x)
+
+        return x
+
+
+class ArcMarginModel(nn.Module):
+    def __init__(self, args):
+        super(ArcMarginModel, self).__init__()
+
+        self.weight = Parameter(torch.FloatTensor(num_classes, args.emb_size))
+        nn.init.xavier_uniform_(self.weight)
+
+        self.easy_margin = args.easy_margin
+        self.m = args.margin_m
+        self.s = args.margin_s
+
+        self.cos_m = math.cos(self.m)
+        self.sin_m = math.sin(self.m)
+        self.th = math.cos(math.pi - self.m)
+        self.mm = math.sin(math.pi - self.m) * self.m
+
+    def forward(self, input, label):
+        x = F.normalize(input)
+        W = F.normalize(self.weight)
+        cosine = F.linear(x, W)
+        sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
+        phi = cosine * self.cos_m - sine * self.sin_m  # cos(theta + m)
+        if self.easy_margin:
+            phi = torch.where(cosine > 0, phi, cosine)
+        else:
+            phi = torch.where(cosine > self.th, phi, cosine - self.mm)
+        one_hot = torch.zeros(cosine.size(), device=device)
+        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
+        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
+        output *= self.s
+        return output
diff --git a/mlops/mlflow/registry/Swimswap/models/networks.py b/mlops/mlflow/registry/Swimswap/models/networks.py
new file mode 100644
index 0000000..3c7eb4d
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/models/networks.py
@@ -0,0 +1,845 @@
+import torch
+import torch.nn as nn
+import functools
+from torch.autograd import Variable
+import numpy as np
+from torchvision import transforms
+import torch.nn.functional as F
+
+###############################################################################
+# Functions
+###############################################################################
+def weights_init(m):
+    classname = m.__class__.__name__
+    if classname.find('Conv') != -1:
+        m.weight.data.normal_(0.0, 0.02)
+    elif classname.find('BatchNorm2d') != -1:
+        m.weight.data.normal_(1.0, 0.02)
+        m.bias.data.fill_(0)
+
+def get_norm_layer(norm_type='instance'):
+    if norm_type == 'batch':
+        norm_layer = functools.partial(nn.BatchNorm2d, affine=True)
+    elif norm_type == 'instance':
+        norm_layer = functools.partial(nn.InstanceNorm2d, affine=False)
+    else:
+        raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
+    return norm_layer
+
+def define_G(input_nc, output_nc, ngf, netG, n_downsample_global=3, n_blocks_global=9, n_local_enhancers=1, 
+             n_blocks_local=3, norm='instance', gpu_ids=[]):    
+    norm_layer = get_norm_layer(norm_type=norm)     
+    if netG == 'global':    
+        netG = GlobalGenerator(input_nc, output_nc, ngf, n_downsample_global, n_blocks_global, norm_layer)       
+    elif netG == 'local':        
+        netG = LocalEnhancer(input_nc, output_nc, ngf, n_downsample_global, n_blocks_global, 
+                                  n_local_enhancers, n_blocks_local, norm_layer)
+    elif netG == 'encoder':
+        netG = Encoder(input_nc, output_nc, ngf, n_downsample_global, norm_layer)
+    else:
+        raise('generator not implemented!')
+    print(netG)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())   
+        netG.cuda(gpu_ids[0])
+    netG.apply(weights_init)
+    return netG
+
+def define_G_Adain(input_nc, output_nc, latent_size, ngf, netG, n_downsample_global=2, n_blocks_global=4, norm='instance', gpu_ids=[]):
+    norm_layer = get_norm_layer(norm_type=norm)
+    netG = Generator_Adain(input_nc, output_nc, latent_size, ngf, n_downsample_global, n_blocks_global, norm_layer)
+    print(netG)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        netG.cuda(gpu_ids[0])
+    netG.apply(weights_init)
+    return netG
+
+def define_G_Adain_Mask(input_nc, output_nc, latent_size, ngf, netG, n_downsample_global=2, n_blocks_global=4, norm='instance', gpu_ids=[]):
+    norm_layer = get_norm_layer(norm_type=norm)
+    netG = Generator_Adain_Mask(input_nc, output_nc, latent_size, ngf, n_downsample_global, n_blocks_global, norm_layer)
+    print(netG)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        netG.cuda(gpu_ids[0])
+    netG.apply(weights_init)
+    return netG
+
+def define_G_Adain_Upsample(input_nc, output_nc, latent_size, ngf, netG, n_downsample_global=2, n_blocks_global=4, norm='instance', gpu_ids=[]):
+    norm_layer = get_norm_layer(norm_type=norm)
+    netG = Generator_Adain_Upsample(input_nc, output_nc, latent_size, ngf, n_downsample_global, n_blocks_global, norm_layer)
+    print(netG)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        netG.cuda(gpu_ids[0])
+    netG.apply(weights_init)
+    return netG
+
+def define_G_Adain_2(input_nc, output_nc, latent_size, ngf, netG, n_downsample_global=2, n_blocks_global=4, norm='instance', gpu_ids=[]):
+    norm_layer = get_norm_layer(norm_type=norm)
+    netG = Generator_Adain_2(input_nc, output_nc, latent_size, ngf, n_downsample_global, n_blocks_global, norm_layer)
+    print(netG)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        netG.cuda(gpu_ids[0])
+    netG.apply(weights_init)
+    return netG
+
+def define_D(input_nc, ndf, n_layers_D, norm='instance', use_sigmoid=False, num_D=1, getIntermFeat=False, gpu_ids=[]):        
+    norm_layer = get_norm_layer(norm_type=norm)   
+    netD = MultiscaleDiscriminator(input_nc, ndf, n_layers_D, norm_layer, use_sigmoid, num_D, getIntermFeat)   
+    print(netD)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        netD.cuda(gpu_ids[0])
+    netD.apply(weights_init)
+    return netD
+
+def print_network(net):
+    if isinstance(net, list):
+        net = net[0]
+    num_params = 0
+    for param in net.parameters():
+        num_params += param.numel()
+    print(net)
+    print('Total number of parameters: %d' % num_params)
+
+##############################################################################
+# Losses
+##############################################################################
+class GANLoss(nn.Module):
+    def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0,
+                 tensor=torch.FloatTensor, opt=None):
+        super(GANLoss, self).__init__()
+        self.real_label = target_real_label
+        self.fake_label = target_fake_label
+        self.real_label_tensor = None
+        self.fake_label_tensor = None
+        self.zero_tensor = None
+        self.Tensor = tensor
+        self.gan_mode = gan_mode
+        self.opt = opt
+        if gan_mode == 'ls':
+            pass
+        elif gan_mode == 'original':
+            pass
+        elif gan_mode == 'w':
+            pass
+        elif gan_mode == 'hinge':
+            pass
+        else:
+            raise ValueError('Unexpected gan_mode {}'.format(gan_mode))
+
+    def get_target_tensor(self, input, target_is_real):
+        if target_is_real:
+            if self.real_label_tensor is None:
+                self.real_label_tensor = self.Tensor(1).fill_(self.real_label)
+                self.real_label_tensor.requires_grad_(False)
+            return self.real_label_tensor.expand_as(input)
+        else:
+            if self.fake_label_tensor is None:
+                self.fake_label_tensor = self.Tensor(1).fill_(self.fake_label)
+                self.fake_label_tensor.requires_grad_(False)
+            return self.fake_label_tensor.expand_as(input)
+
+    def get_zero_tensor(self, input):
+        if self.zero_tensor is None:
+            self.zero_tensor = self.Tensor(1).fill_(0)
+            self.zero_tensor.requires_grad_(False)
+        return self.zero_tensor.expand_as(input)
+
+    def loss(self, input, target_is_real, for_discriminator=True):
+        if self.gan_mode == 'original':  # cross entropy loss
+            target_tensor = self.get_target_tensor(input, target_is_real)
+            loss = F.binary_cross_entropy_with_logits(input, target_tensor)
+            return loss
+        elif self.gan_mode == 'ls':
+            target_tensor = self.get_target_tensor(input, target_is_real)
+            return F.mse_loss(input, target_tensor)
+        elif self.gan_mode == 'hinge':
+            if for_discriminator:
+                if target_is_real:
+                    minval = torch.min(input - 1, self.get_zero_tensor(input))
+                    loss = -torch.mean(minval)
+                else:
+                    minval = torch.min(-input - 1, self.get_zero_tensor(input))
+                    loss = -torch.mean(minval)
+            else:
+                assert target_is_real, "The generator's hinge loss must be aiming for real"
+                loss = -torch.mean(input)
+            return loss
+        else:
+            # wgan
+            if target_is_real:
+                return -input.mean()
+            else:
+                return input.mean()
+
+    def __call__(self, input, target_is_real, for_discriminator=True):
+        # computing loss is a bit complicated because |input| may not be
+        # a tensor, but list of tensors in case of multiscale discriminator
+        if isinstance(input, list):
+            loss = 0
+            for pred_i in input:
+                if isinstance(pred_i, list):
+                    pred_i = pred_i[-1]
+                loss_tensor = self.loss(pred_i, target_is_real, for_discriminator)
+                bs = 1 if len(loss_tensor.size()) == 0 else loss_tensor.size(0)
+                new_loss = torch.mean(loss_tensor.view(bs, -1), dim=1)
+                loss += new_loss
+            return loss / len(input)
+        else:
+            return self.loss(input, target_is_real, for_discriminator)
+
+class VGGLoss(nn.Module):
+    def __init__(self, gpu_ids):
+        super(VGGLoss, self).__init__()        
+        self.vgg = Vgg19().cuda()
+        self.criterion = nn.L1Loss()
+        self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]        
+
+    def forward(self, x, y):              
+        x_vgg, y_vgg = self.vgg(x), self.vgg(y)
+        loss = 0
+        for i in range(len(x_vgg)):
+            loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())        
+        return loss
+
+##############################################################################
+# Generator
+##############################################################################
+class LocalEnhancer(nn.Module):
+    def __init__(self, input_nc, output_nc, ngf=32, n_downsample_global=3, n_blocks_global=9, 
+                 n_local_enhancers=1, n_blocks_local=3, norm_layer=nn.BatchNorm2d, padding_type='reflect'):        
+        super(LocalEnhancer, self).__init__()
+        self.n_local_enhancers = n_local_enhancers
+        
+        ###### global generator model #####           
+        ngf_global = ngf * (2**n_local_enhancers)
+        model_global = GlobalGenerator(input_nc, output_nc, ngf_global, n_downsample_global, n_blocks_global, norm_layer).model        
+        model_global = [model_global[i] for i in range(len(model_global)-3)] # get rid of final convolution layers        
+        self.model = nn.Sequential(*model_global)                
+
+        ###### local enhancer layers #####
+        for n in range(1, n_local_enhancers+1):
+            ### downsample            
+            ngf_global = ngf * (2**(n_local_enhancers-n))
+            model_downsample = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf_global, kernel_size=7, padding=0), 
+                                norm_layer(ngf_global), nn.ReLU(True),
+                                nn.Conv2d(ngf_global, ngf_global * 2, kernel_size=3, stride=2, padding=1), 
+                                norm_layer(ngf_global * 2), nn.ReLU(True)]
+            ### residual blocks
+            model_upsample = []
+            for i in range(n_blocks_local):
+                model_upsample += [ResnetBlock(ngf_global * 2, padding_type=padding_type, norm_layer=norm_layer)]
+
+            ### upsample
+            model_upsample += [nn.ConvTranspose2d(ngf_global * 2, ngf_global, kernel_size=3, stride=2, padding=1, output_padding=1), 
+                               norm_layer(ngf_global), nn.ReLU(True)]      
+
+            ### final convolution
+            if n == n_local_enhancers:                
+                model_upsample += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]                       
+            
+            setattr(self, 'model'+str(n)+'_1', nn.Sequential(*model_downsample))
+            setattr(self, 'model'+str(n)+'_2', nn.Sequential(*model_upsample))                  
+        
+        self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
+
+    def forward(self, input): 
+        ### create input pyramid
+        input_downsampled = [input]
+        for i in range(self.n_local_enhancers):
+            input_downsampled.append(self.downsample(input_downsampled[-1]))
+
+        ### output at coarest level
+        output_prev = self.model(input_downsampled[-1])        
+        ### build up one layer at a time
+        for n_local_enhancers in range(1, self.n_local_enhancers+1):
+            model_downsample = getattr(self, 'model'+str(n_local_enhancers)+'_1')
+            model_upsample = getattr(self, 'model'+str(n_local_enhancers)+'_2')            
+            input_i = input_downsampled[self.n_local_enhancers-n_local_enhancers]            
+            output_prev = model_upsample(model_downsample(input_i) + output_prev)
+        return output_prev
+
+class GlobalGenerator(nn.Module):
+    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, 
+                 padding_type='reflect'):
+        assert(n_blocks >= 0)
+        super(GlobalGenerator, self).__init__()        
+        activation = nn.ReLU(True)        
+
+        model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2**i
+            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), activation]
+
+        ### resnet blocks
+        mult = 2**n_downsampling
+        for i in range(n_blocks):
+            model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer)]
+        
+        ### upsample         
+        for i in range(n_downsampling):
+            mult = 2**(n_downsampling - i)
+            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, output_padding=1),
+                       norm_layer(int(ngf * mult / 2)), activation]
+        model += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]        
+        self.model = nn.Sequential(*model)
+            
+    def forward(self, input):
+        return self.model(input)             
+        
+# Define a resnet block
+class ResnetBlock(nn.Module):
+    def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False):
+        super(ResnetBlock, self).__init__()
+        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout)
+
+    def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout):
+        conv_block = []
+        p = 0
+        if padding_type == 'reflect':
+            conv_block += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv_block += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+
+        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p),
+                       norm_layer(dim),
+                       activation]
+        if use_dropout:
+            conv_block += [nn.Dropout(0.5)]
+
+        p = 0
+        if padding_type == 'reflect':
+            conv_block += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv_block += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p),
+                       norm_layer(dim)]
+
+        return nn.Sequential(*conv_block)
+
+    def forward(self, x):
+        out = x + self.conv_block(x)
+        return out
+
+class InstanceNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(InstanceNorm, self).__init__()
+        self.epsilon = epsilon
+
+    def forward(self, x):
+        x   = x - torch.mean(x, (2, 3), True)
+        tmp = torch.mul(x, x) # or x ** 2
+        tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
+        return x * tmp
+
+class SpecificNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(SpecificNorm, self).__init__()
+        self.mean = np.array([0.485, 0.456, 0.406])
+        self.mean = torch.from_numpy(self.mean).float().cuda()
+        self.mean = self.mean.view([1, 3, 1, 1])
+
+        self.std = np.array([0.229, 0.224, 0.225])
+        self.std = torch.from_numpy(self.std).float().cuda()
+        self.std = self.std.view([1, 3, 1, 1])
+
+    def forward(self, x):
+        mean = self.mean.expand([1, 3, x.shape[2], x.shape[3]])
+        std = self.std.expand([1, 3, x.shape[2], x.shape[3]])
+
+        x = (x - mean) / std
+
+        return x
+
+class ApplyStyle(nn.Module):
+    """
+        @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
+    """
+    def __init__(self, latent_size, channels):
+        super(ApplyStyle, self).__init__()
+        self.linear = nn.Linear(latent_size, channels * 2)
+
+    def forward(self, x, latent):
+        style = self.linear(latent)  # style => [batch_size, n_channels*2]
+        shape = [-1, 2, x.size(1), 1, 1]
+        style = style.view(shape)    # [batch_size, 2, n_channels, ...]
+        x = x * (style[:, 0] + 1.) + style[:, 1]
+        return x
+
+class ResnetBlock_Adain(nn.Module):
+    def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
+        super(ResnetBlock_Adain, self).__init__()
+
+        p = 0
+        conv1 = []
+        if padding_type == 'reflect':
+            conv1 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv1 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()]
+        self.conv1 = nn.Sequential(*conv1)
+        self.style1 = ApplyStyle(latent_size, dim)
+        self.act1 = activation
+
+        p = 0
+        conv2 = []
+        if padding_type == 'reflect':
+            conv2 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv2 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
+        self.conv2 = nn.Sequential(*conv2)
+        self.style2 = ApplyStyle(latent_size, dim)
+
+
+    def forward(self, x, dlatents_in_slice):
+        y = self.conv1(x)
+        y = self.style1(y, dlatents_in_slice)
+        y = self.act1(y)
+        y = self.conv2(y)
+        y = self.style2(y, dlatents_in_slice)
+        out = x + y
+        return out
+
+class UpBlock_Adain(nn.Module):
+    def __init__(self, dim_in, dim_out, latent_size, padding_type, activation=nn.ReLU(True)):
+        super(UpBlock_Adain, self).__init__()
+
+        p = 0
+        conv1 = [nn.Upsample(scale_factor=2, mode='bilinear')]
+        if padding_type == 'reflect':
+            conv1 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv1 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv1 += [nn.Conv2d(dim_in, dim_out, kernel_size=3, padding = p), InstanceNorm()]
+        self.conv1 = nn.Sequential(*conv1)
+        self.style1 = ApplyStyle(latent_size, dim_out)
+        self.act1 = activation
+
+
+    def forward(self, x, dlatents_in_slice):
+        y = self.conv1(x)
+        y = self.style1(y, dlatents_in_slice)
+        y = self.act1(y)
+        return y
+
+class Encoder(nn.Module):
+    def __init__(self, input_nc, output_nc, ngf=32, n_downsampling=4, norm_layer=nn.BatchNorm2d):
+        super(Encoder, self).__init__()        
+        self.output_nc = output_nc        
+
+        model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), 
+                 norm_layer(ngf), nn.ReLU(True)]             
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2**i
+            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), nn.ReLU(True)]
+
+        ### upsample         
+        for i in range(n_downsampling):
+            mult = 2**(n_downsampling - i)
+            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, output_padding=1),
+                       norm_layer(int(ngf * mult / 2)), nn.ReLU(True)]        
+
+        model += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]
+        self.model = nn.Sequential(*model) 
+
+    def forward(self, input, inst):
+        outputs = self.model(input)
+
+        # instance-wise average pooling
+        outputs_mean = outputs.clone()
+        inst_list = np.unique(inst.cpu().numpy().astype(int))        
+        for i in inst_list:
+            for b in range(input.size()[0]):
+                indices = (inst[b:b+1] == int(i)).nonzero() # n x 4            
+                for j in range(self.output_nc):
+                    output_ins = outputs[indices[:,0] + b, indices[:,1] + j, indices[:,2], indices[:,3]]                    
+                    mean_feat = torch.mean(output_ins).expand_as(output_ins)                                        
+                    outputs_mean[indices[:,0] + b, indices[:,1] + j, indices[:,2], indices[:,3]] = mean_feat                       
+        return outputs_mean
+
+
+class Generator_Adain(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, ngf=64, n_downsampling=2, n_blocks=4, norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain, self).__init__()
+        activation = nn.ReLU(True)
+
+        Enc = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2 ** i
+            Enc += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), activation]
+        self.Encoder = nn.Sequential(*Enc)
+
+        ### resnet blocks
+        BN = []
+        mult = 2 ** n_downsampling
+        for i in range(n_blocks):
+            BN += [ResnetBlock_Adain(ngf*mult, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+        '''self.ResBlockAdain1 = ResnetBlock_Adain(ngf * mult, latent_size=latent_size, padding_type=padding_type,
+                                                activation=activation)
+        self.ResBlockAdain2 = ResnetBlock_Adain(ngf * mult, latent_size=latent_size, padding_type=padding_type,
+                                                activation=activation)
+        self.ResBlockAdain3 = ResnetBlock_Adain(ngf * mult, latent_size=latent_size, padding_type=padding_type,
+                                                activation=activation)
+        self.ResBlockAdain4 = ResnetBlock_Adain(ngf * mult, latent_size=latent_size, padding_type=padding_type,
+                                                activation=activation)'''
+
+        ### upsample
+        Dec = []
+        for i in range(n_downsampling):
+            mult = 2 ** (n_downsampling - i)
+            Dec += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1,
+                                         output_padding=1),
+                      norm_layer(int(ngf * mult / 2)), activation]
+        Dec += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]
+
+        self.Decoder = nn.Sequential(*Dec)
+        #self.model = nn.Sequential(*model)
+        self.spNorm = SpecificNorm()
+
+    def forward(self, input, dlatents):
+        x = input
+        x = self.Encoder(x)
+
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+        '''x = self.ResBlockAdain1(x, dlatents)
+        x = self.ResBlockAdain2(x, dlatents)
+        x = self.ResBlockAdain3(x, dlatents)
+        x = self.ResBlockAdain4(x, dlatents)'''
+
+        x = self.Decoder(x)
+
+        x = (x + 1) / 2
+        x = self.spNorm(x)
+
+        return x
+
+class Generator_Adain_Mask(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, ngf=64, n_downsampling=2, n_blocks=4, norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_Mask, self).__init__()
+        activation = nn.ReLU(True)
+
+        Enc = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2 ** i
+            Enc += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), activation]
+        self.Encoder = nn.Sequential(*Enc)
+
+        ### resnet blocks
+        BN = []
+        mult = 2 ** n_downsampling
+        for i in range(n_blocks):
+            BN += [ResnetBlock_Adain(ngf*mult, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        ### upsample
+        Dec = []
+        for i in range(n_downsampling):
+            mult = 2 ** (n_downsampling - i)
+            Dec += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1,
+                                         output_padding=1),
+                      norm_layer(int(ngf * mult / 2)), activation]
+        Fake_out = [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]
+        Mast_out = [nn.ReflectionPad2d(3), nn.Conv2d(ngf, 1, kernel_size=7, padding=0), nn.Sigmoid()]
+
+        self.Decoder = nn.Sequential(*Dec)
+        #self.model = nn.Sequential(*model)
+        self.spNorm = SpecificNorm()
+        self.Fake_out = nn.Sequential(*Fake_out)
+        self.Mask_out = nn.Sequential(*Mast_out)
+
+    def forward(self, input, dlatents):
+        x = input
+        x = self.Encoder(x)
+
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+
+        x = self.Decoder(x)
+
+        fake_out = self.Fake_out(x)
+        mask_out = self.Mask_out(x)
+
+        fake_out = (fake_out + 1) / 2
+        fake_out = self.spNorm(fake_out)
+
+        generated = fake_out * mask_out + input * (1-mask_out)
+
+        return generated, mask_out
+
+class Generator_Adain_Upsample(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, ngf=64, n_downsampling=2, n_blocks=4, norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_Upsample, self).__init__()
+        activation = nn.ReLU(True)
+
+        Enc = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2 ** i
+            Enc += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), activation]
+        self.Encoder = nn.Sequential(*Enc)
+
+        ### resnet blocks
+        BN = []
+        mult = 2 ** n_downsampling
+        for i in range(n_blocks):
+            BN += [ResnetBlock_Adain(ngf*mult, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        ### upsample
+        Dec = []
+        for i in range(n_downsampling):
+            mult = 2 ** (n_downsampling - i)
+            '''Dec += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1,
+                                         output_padding=1),
+                      norm_layer(int(ngf * mult / 2)), activation]'''
+            Dec += [nn.Upsample(scale_factor=2, mode='bilinear'),
+                    nn.Conv2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=1, padding=1),
+                    norm_layer(int(ngf * mult / 2)), activation]
+        Dec += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]
+
+        self.Decoder = nn.Sequential(*Dec)
+        self.spNorm = SpecificNorm()
+
+    def forward(self, input, dlatents):
+        x = input
+        x = self.Encoder(x)
+
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+
+        x = self.Decoder(x)
+
+        x = (x + 1) / 2
+        x = self.spNorm(x)
+
+        return x
+
+class Generator_Adain_2(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, ngf=64, n_downsampling=2, n_blocks=4, norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_2, self).__init__()
+        activation = nn.ReLU(True)
+
+        Enc = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2 ** i
+            Enc += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), activation]
+        self.Encoder = nn.Sequential(*Enc)
+
+        ### resnet blocks
+        BN = []
+        mult = 2 ** n_downsampling
+        for i in range(n_blocks):
+            BN += [ResnetBlock_Adain(ngf*mult, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        ### upsample
+        Dec = []
+        for i in range(n_downsampling):
+            mult = 2 ** (n_downsampling - i)
+            Dec += [UpBlock_Adain(dim_in=ngf * mult, dim_out=int(ngf * mult / 2), latent_size=latent_size, padding_type=padding_type)]
+        layer_out = [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]
+
+        self.Decoder = nn.Sequential(*Dec)
+        #self.model = nn.Sequential(*model)
+        self.spNorm = SpecificNorm()
+        self.layer_out = nn.Sequential(*layer_out)
+
+    def forward(self, input, dlatents):
+        x = input
+        x = self.Encoder(x)
+
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+
+        for i in range(len(self.Decoder)):
+            x = self.Decoder[i](x, dlatents)
+
+        x = self.layer_out(x)
+
+        x = (x + 1) / 2
+        x = self.spNorm(x)
+
+        return x
+
+class MultiscaleDiscriminator(nn.Module):
+    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, 
+                 use_sigmoid=False, num_D=3, getIntermFeat=False):
+        super(MultiscaleDiscriminator, self).__init__()
+        self.num_D = num_D
+        self.n_layers = n_layers
+        self.getIntermFeat = getIntermFeat
+     
+        for i in range(num_D):
+            netD = NLayerDiscriminator(input_nc, ndf, n_layers, norm_layer, use_sigmoid, getIntermFeat)
+            if getIntermFeat:                                
+                for j in range(n_layers+2):
+                    setattr(self, 'scale'+str(i)+'_layer'+str(j), getattr(netD, 'model'+str(j)))                                   
+            else:
+                setattr(self, 'layer'+str(i), netD.model)
+
+        self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
+
+    def singleD_forward(self, model, input):
+        if self.getIntermFeat:
+            result = [input]
+            for i in range(len(model)):
+                result.append(model[i](result[-1]))
+            return result[1:]
+        else:
+            return [model(input)]
+
+    def forward(self, input):        
+        num_D = self.num_D
+        result = []
+        input_downsampled = input
+        for i in range(num_D):
+            if self.getIntermFeat:
+                model = [getattr(self, 'scale'+str(num_D-1-i)+'_layer'+str(j)) for j in range(self.n_layers+2)]
+            else:
+                model = getattr(self, 'layer'+str(num_D-1-i))
+            result.append(self.singleD_forward(model, input_downsampled))
+            if i != (num_D-1):
+                input_downsampled = self.downsample(input_downsampled)
+        return result
+        
+# Defines the PatchGAN discriminator with the specified arguments.
+class NLayerDiscriminator(nn.Module):
+    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, getIntermFeat=False):
+        super(NLayerDiscriminator, self).__init__()
+        self.getIntermFeat = getIntermFeat
+        self.n_layers = n_layers
+
+        kw = 4
+        padw = 1
+        sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]]
+
+        nf = ndf
+        for n in range(1, n_layers):
+            nf_prev = nf
+            nf = min(nf * 2, 512)
+            sequence += [[
+                nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw),
+                norm_layer(nf), nn.LeakyReLU(0.2, True)
+            ]]
+
+        nf_prev = nf
+        nf = min(nf * 2, 512)
+        sequence += [[
+            nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
+            norm_layer(nf),
+            nn.LeakyReLU(0.2, True)
+        ]]
+
+        if use_sigmoid:
+            sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw), nn.Sigmoid()]]
+        else:
+            sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
+
+        if getIntermFeat:
+            for n in range(len(sequence)):
+                setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
+        else:
+            sequence_stream = []
+            for n in range(len(sequence)):
+                sequence_stream += sequence[n]
+            self.model = nn.Sequential(*sequence_stream)
+
+    def forward(self, input):
+        if self.getIntermFeat:
+            res = [input]
+            for n in range(self.n_layers+2):
+                model = getattr(self, 'model'+str(n))
+                res.append(model(res[-1]))
+            return res[1:]
+        else:
+            return self.model(input)        
+
+from torchvision import models
+class Vgg19(torch.nn.Module):
+    def __init__(self, requires_grad=False):
+        super(Vgg19, self).__init__()
+        vgg_pretrained_features = models.vgg19(pretrained=True).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        for x in range(2):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(2, 7):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(7, 12):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(12, 21):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(21, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h_relu1 = self.slice1(X)
+        h_relu2 = self.slice2(h_relu1)        
+        h_relu3 = self.slice3(h_relu2)        
+        h_relu4 = self.slice4(h_relu3)        
+        h_relu5 = self.slice5(h_relu4)                
+        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
+        return out
diff --git a/mlops/mlflow/registry/Swimswap/models/pix2pixHD_model.py b/mlops/mlflow/registry/Swimswap/models/pix2pixHD_model.py
new file mode 100644
index 0000000..fafdec0
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/models/pix2pixHD_model.py
@@ -0,0 +1,304 @@
+import numpy as np
+import torch
+import os
+from torch.autograd import Variable
+from util.image_pool import ImagePool
+from .base_model import BaseModel
+from . import networks
+
+class Pix2PixHDModel(BaseModel):
+    def name(self):
+        return 'Pix2PixHDModel'
+    
+    def init_loss_filter(self, use_gan_feat_loss, use_vgg_loss):
+        flags = (True, use_gan_feat_loss, use_vgg_loss, True, True)
+        def loss_filter(g_gan, g_gan_feat, g_vgg, d_real, d_fake):
+            return [l for (l,f) in zip((g_gan,g_gan_feat,g_vgg,d_real,d_fake),flags) if f]
+        return loss_filter
+    
+    def initialize(self, opt):
+        BaseModel.initialize(self, opt)
+        if opt.resize_or_crop != 'none' or not opt.isTrain: # when training at full res this causes OOM
+            torch.backends.cudnn.benchmark = True
+        self.isTrain = opt.isTrain
+        self.use_features = opt.instance_feat or opt.label_feat
+        self.gen_features = self.use_features and not self.opt.load_features
+        input_nc = opt.label_nc if opt.label_nc != 0 else opt.input_nc
+
+        ##### define networks        
+        # Generator network
+        netG_input_nc = input_nc        
+        if not opt.no_instance:
+            netG_input_nc += 1
+        if self.use_features:
+            netG_input_nc += opt.feat_num                  
+        self.netG = networks.define_G(netG_input_nc, opt.output_nc, opt.ngf, opt.netG, 
+                                      opt.n_downsample_global, opt.n_blocks_global, opt.n_local_enhancers, 
+                                      opt.n_blocks_local, opt.norm, gpu_ids=self.gpu_ids)        
+
+        # Discriminator network
+        if self.isTrain:
+            use_sigmoid = opt.no_lsgan
+            netD_input_nc = input_nc + opt.output_nc
+            if not opt.no_instance:
+                netD_input_nc += 1
+            self.netD = networks.define_D(netD_input_nc, opt.ndf, opt.n_layers_D, opt.norm, use_sigmoid, 
+                                          opt.num_D, not opt.no_ganFeat_loss, gpu_ids=self.gpu_ids)
+
+        ### Encoder network
+        if self.gen_features:          
+            self.netE = networks.define_G(opt.output_nc, opt.feat_num, opt.nef, 'encoder', 
+                                          opt.n_downsample_E, norm=opt.norm, gpu_ids=self.gpu_ids)  
+        if self.opt.verbose:
+                print('---------- Networks initialized -------------')
+
+        # load networks
+        if not self.isTrain or opt.continue_train or opt.load_pretrain:
+            pretrained_path = '' if not self.isTrain else opt.load_pretrain
+            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)            
+            if self.isTrain:
+                self.load_network(self.netD, 'D', opt.which_epoch, pretrained_path)  
+            if self.gen_features:
+                self.load_network(self.netE, 'E', opt.which_epoch, pretrained_path)              
+
+        # set loss functions and optimizers
+        if self.isTrain:
+            if opt.pool_size > 0 and (len(self.gpu_ids)) > 1:
+                raise NotImplementedError("Fake Pool Not Implemented for MultiGPU")
+            self.fake_pool = ImagePool(opt.pool_size)
+            self.old_lr = opt.lr
+
+            # define loss functions
+            self.loss_filter = self.init_loss_filter(not opt.no_ganFeat_loss, not opt.no_vgg_loss)
+            
+            self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor)   
+            self.criterionFeat = torch.nn.L1Loss()
+            if not opt.no_vgg_loss:             
+                self.criterionVGG = networks.VGGLoss(self.gpu_ids)
+                
+        
+            # Names so we can breakout loss
+            self.loss_names = self.loss_filter('G_GAN','G_GAN_Feat','G_VGG','D_real', 'D_fake')
+
+            # initialize optimizers
+            # optimizer G
+            if opt.niter_fix_global > 0:                
+                import sys
+                if sys.version_info >= (3,0):
+                    finetune_list = set()
+                else:
+                    from sets import Set
+                    finetune_list = Set()
+
+                params_dict = dict(self.netG.named_parameters())
+                params = []
+                for key, value in params_dict.items():       
+                    if key.startswith('model' + str(opt.n_local_enhancers)):                    
+                        params += [value]
+                        finetune_list.add(key.split('.')[0])  
+                print('------------- Only training the local enhancer network (for %d epochs) ------------' % opt.niter_fix_global)
+                print('The layers that are finetuned are ', sorted(finetune_list))                         
+            else:
+                params = list(self.netG.parameters())
+            if self.gen_features:              
+                params += list(self.netE.parameters())         
+            self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))                            
+
+            # optimizer D                        
+            params = list(self.netD.parameters())    
+            self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+
+    def encode_input(self, label_map, inst_map=None, real_image=None, feat_map=None, infer=False):             
+        if self.opt.label_nc == 0:
+            input_label = label_map.data.cuda()
+        else:
+            # create one-hot vector for label map 
+            size = label_map.size()
+            oneHot_size = (size[0], self.opt.label_nc, size[2], size[3])
+            input_label = torch.cuda.FloatTensor(torch.Size(oneHot_size)).zero_()
+            input_label = input_label.scatter_(1, label_map.data.long().cuda(), 1.0)
+            if self.opt.data_type == 16:
+                input_label = input_label.half()
+
+        # get edges from instance map
+        if not self.opt.no_instance:
+            inst_map = inst_map.data.cuda()
+            edge_map = self.get_edges(inst_map)
+            input_label = torch.cat((input_label, edge_map), dim=1)         
+        input_label = Variable(input_label, volatile=infer)
+
+        # real images for training
+        if real_image is not None:
+            real_image = Variable(real_image.data.cuda())
+
+        # instance map for feature encoding
+        if self.use_features:
+            # get precomputed feature maps
+            if self.opt.load_features:
+                feat_map = Variable(feat_map.data.cuda())
+            if self.opt.label_feat:
+                inst_map = label_map.cuda()
+
+        return input_label, inst_map, real_image, feat_map
+
+    def discriminate(self, input_label, test_image, use_pool=False):
+        input_concat = torch.cat((input_label, test_image.detach()), dim=1)
+        if use_pool:            
+            fake_query = self.fake_pool.query(input_concat)
+            return self.netD.forward(fake_query)
+        else:
+            return self.netD.forward(input_concat)
+
+    def forward(self, label, inst, image, feat, infer=False):
+        # Encode Inputs
+        input_label, inst_map, real_image, feat_map = self.encode_input(label, inst, image, feat)  
+
+        # Fake Generation
+        if self.use_features:
+            if not self.opt.load_features:
+                feat_map = self.netE.forward(real_image, inst_map)                     
+            input_concat = torch.cat((input_label, feat_map), dim=1)                        
+        else:
+            input_concat = input_label
+        fake_image = self.netG.forward(input_concat)
+
+        # Fake Detection and Loss
+        pred_fake_pool = self.discriminate(input_label, fake_image, use_pool=True)
+        loss_D_fake = self.criterionGAN(pred_fake_pool, False)        
+
+        # Real Detection and Loss        
+        pred_real = self.discriminate(input_label, real_image)
+        loss_D_real = self.criterionGAN(pred_real, True)
+
+        # GAN loss (Fake Passability Loss)        
+        pred_fake = self.netD.forward(torch.cat((input_label, fake_image), dim=1))        
+        loss_G_GAN = self.criterionGAN(pred_fake, True)               
+        
+        # GAN feature matching loss
+        loss_G_GAN_Feat = 0
+        if not self.opt.no_ganFeat_loss:
+            feat_weights = 4.0 / (self.opt.n_layers_D + 1)
+            D_weights = 1.0 / self.opt.num_D
+            for i in range(self.opt.num_D):
+                for j in range(len(pred_fake[i])-1):
+                    loss_G_GAN_Feat += D_weights * feat_weights * \
+                        self.criterionFeat(pred_fake[i][j], pred_real[i][j].detach()) * self.opt.lambda_feat
+                   
+        # VGG feature matching loss
+        loss_G_VGG = 0
+        if not self.opt.no_vgg_loss:
+            loss_G_VGG = self.criterionVGG(fake_image, real_image) * self.opt.lambda_feat
+        
+        # Only return the fake_B image if necessary to save BW
+        return [ self.loss_filter( loss_G_GAN, loss_G_GAN_Feat, loss_G_VGG, loss_D_real, loss_D_fake ), None if not infer else fake_image ]
+
+    def inference(self, label, inst, image=None):
+        # Encode Inputs        
+        image = Variable(image) if image is not None else None
+        input_label, inst_map, real_image, _ = self.encode_input(Variable(label), Variable(inst), image, infer=True)
+
+        # Fake Generation
+        if self.use_features:
+            if self.opt.use_encoded_image:
+                # encode the real image to get feature map
+                feat_map = self.netE.forward(real_image, inst_map)
+            else:
+                # sample clusters from precomputed features             
+                feat_map = self.sample_features(inst_map)
+            input_concat = torch.cat((input_label, feat_map), dim=1)                        
+        else:
+            input_concat = input_label        
+           
+        if torch.__version__.startswith('0.4'):
+            with torch.no_grad():
+                fake_image = self.netG.forward(input_concat)
+        else:
+            fake_image = self.netG.forward(input_concat)
+        return fake_image
+
+    def sample_features(self, inst): 
+        # read precomputed feature clusters 
+        cluster_path = os.path.join(self.opt.checkpoints_dir, self.opt.name, self.opt.cluster_path)        
+        features_clustered = np.load(cluster_path, encoding='latin1').item()
+
+        # randomly sample from the feature clusters
+        inst_np = inst.cpu().numpy().astype(int)                                      
+        feat_map = self.Tensor(inst.size()[0], self.opt.feat_num, inst.size()[2], inst.size()[3])
+        for i in np.unique(inst_np):    
+            label = i if i < 1000 else i//1000
+            if label in features_clustered:
+                feat = features_clustered[label]
+                cluster_idx = np.random.randint(0, feat.shape[0]) 
+                                            
+                idx = (inst == int(i)).nonzero()
+                for k in range(self.opt.feat_num):                                    
+                    feat_map[idx[:,0], idx[:,1] + k, idx[:,2], idx[:,3]] = feat[cluster_idx, k]
+        if self.opt.data_type==16:
+            feat_map = feat_map.half()
+        return feat_map
+
+    def encode_features(self, image, inst):
+        image = Variable(image.cuda(), volatile=True)
+        feat_num = self.opt.feat_num
+        h, w = inst.size()[2], inst.size()[3]
+        block_num = 32
+        feat_map = self.netE.forward(image, inst.cuda())
+        inst_np = inst.cpu().numpy().astype(int)
+        feature = {}
+        for i in range(self.opt.label_nc):
+            feature[i] = np.zeros((0, feat_num+1))
+        for i in np.unique(inst_np):
+            label = i if i < 1000 else i//1000
+            idx = (inst == int(i)).nonzero()
+            num = idx.size()[0]
+            idx = idx[num//2,:]
+            val = np.zeros((1, feat_num+1))                        
+            for k in range(feat_num):
+                val[0, k] = feat_map[idx[0], idx[1] + k, idx[2], idx[3]].data[0]            
+            val[0, feat_num] = float(num) / (h * w // block_num)
+            feature[label] = np.append(feature[label], val, axis=0)
+        return feature
+
+    def get_edges(self, t):
+        edge = torch.cuda.ByteTensor(t.size()).zero_()
+        edge[:,:,:,1:] = edge[:,:,:,1:] | (t[:,:,:,1:] != t[:,:,:,:-1])
+        edge[:,:,:,:-1] = edge[:,:,:,:-1] | (t[:,:,:,1:] != t[:,:,:,:-1])
+        edge[:,:,1:,:] = edge[:,:,1:,:] | (t[:,:,1:,:] != t[:,:,:-1,:])
+        edge[:,:,:-1,:] = edge[:,:,:-1,:] | (t[:,:,1:,:] != t[:,:,:-1,:])
+        if self.opt.data_type==16:
+            return edge.half()
+        else:
+            return edge.float()
+
+    def save(self, which_epoch):
+        self.save_network(self.netG, 'G', which_epoch, self.gpu_ids)
+        self.save_network(self.netD, 'D', which_epoch, self.gpu_ids)
+        if self.gen_features:
+            self.save_network(self.netE, 'E', which_epoch, self.gpu_ids)
+
+    def update_fixed_params(self):
+        # after fixing the global generator for a number of iterations, also start finetuning it
+        params = list(self.netG.parameters())
+        if self.gen_features:
+            params += list(self.netE.parameters())           
+        self.optimizer_G = torch.optim.Adam(params, lr=self.opt.lr, betas=(self.opt.beta1, 0.999))
+        if self.opt.verbose:
+            print('------------ Now also finetuning global generator -----------')
+
+    def update_learning_rate(self):
+        lrd = self.opt.lr / self.opt.niter_decay
+        lr = self.old_lr - lrd        
+        for param_group in self.optimizer_D.param_groups:
+            param_group['lr'] = lr
+        for param_group in self.optimizer_G.param_groups:
+            param_group['lr'] = lr
+        if self.opt.verbose:
+            print('update learning rate: %f -> %f' % (self.old_lr, lr))
+        self.old_lr = lr
+
+class InferenceModel(Pix2PixHDModel):
+    def forward(self, inp):
+        label, inst = inp
+        return self.inference(label, inst)
+
+        
diff --git a/mlops/mlflow/registry/Swimswap/models/projected_model.py b/mlops/mlflow/registry/Swimswap/models/projected_model.py
new file mode 100644
index 0000000..15d70f9
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/models/projected_model.py
@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: fs_model_fix_idnorm_donggp_saveoptim copy.py
+# Created Date: Wednesday January 12th 2022
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Saturday, 13th May 2023 9:56:35 am
+# Modified By: Chen Xuanhong
+# Copyright (c) 2022 Shanghai Jiao Tong University
+#############################################################
+
+
+import torch
+import torch.nn as nn
+
+from .base_model import BaseModel
+from .fs_networks_fix import Generator_Adain_Upsample
+
+from pg_modules.projected_discriminator import ProjectedDiscriminator
+
+def compute_grad2(d_out, x_in):
+    batch_size = x_in.size(0)
+    grad_dout = torch.autograd.grad(
+        outputs=d_out.sum(), inputs=x_in,
+        create_graph=True, retain_graph=True, only_inputs=True
+    )[0]
+    grad_dout2 = grad_dout.pow(2)
+    assert(grad_dout2.size() == x_in.size())
+    reg = grad_dout2.view(batch_size, -1).sum(1)
+    return reg
+
+class fsModel(BaseModel):
+    def name(self):
+        return 'fsModel'
+
+    def initialize(self, opt):
+        BaseModel.initialize(self, opt)
+        # if opt.resize_or_crop != 'none' or not opt.isTrain:  # when training at full res this causes OOM
+        self.isTrain = opt.isTrain
+
+        # Generator network
+        self.netG = Generator_Adain_Upsample(input_nc=3, output_nc=3, latent_size=512, n_blocks=9, deep=opt.Gdeep)
+        self.netG.cuda()
+
+        # Id network
+        netArc_checkpoint = opt.Arc_path
+        netArc_checkpoint = torch.load(netArc_checkpoint, map_location=torch.device("cpu"))
+        self.netArc = netArc_checkpoint
+        self.netArc = self.netArc.cuda()
+        self.netArc.eval()
+        self.netArc.requires_grad_(False)
+        if not self.isTrain:
+            pretrained_path =  opt.checkpoints_dir
+            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)
+            return
+        self.netD = ProjectedDiscriminator(diffaug=False, interp224=False, **{})
+        # self.netD.feature_network.requires_grad_(False)
+        self.netD.cuda()
+
+
+        if self.isTrain:
+            # define loss functions
+            self.criterionFeat  = nn.L1Loss()
+            self.criterionRec   = nn.L1Loss()
+
+
+           # initialize optimizers
+
+            # optimizer G
+            params = list(self.netG.parameters())
+            self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.99),eps=1e-8)
+
+            # optimizer D
+            params = list(self.netD.parameters())
+            self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.99),eps=1e-8)
+
+        # load networks
+        if opt.continue_train:
+            pretrained_path = '' if not self.isTrain else opt.load_pretrain
+            # print (pretrained_path)
+            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)
+            self.load_network(self.netD, 'D', opt.which_epoch, pretrained_path)
+            self.load_optim(self.optimizer_G, 'G', opt.which_epoch, pretrained_path)
+            self.load_optim(self.optimizer_D, 'D', opt.which_epoch, pretrained_path)
+        torch.cuda.empty_cache()
+
+    def cosin_metric(self, x1, x2):
+        #return np.dot(x1, x2) / (np.linalg.norm(x1) * np.linalg.norm(x2))
+        return torch.sum(x1 * x2, dim=1) / (torch.norm(x1, dim=1) * torch.norm(x2, dim=1))
+
+
+
+    def save(self, which_epoch):
+        self.save_network(self.netG, 'G', which_epoch)
+        self.save_network(self.netD, 'D', which_epoch)
+        self.save_optim(self.optimizer_G, 'G', which_epoch)
+        self.save_optim(self.optimizer_D, 'D', which_epoch)
+        '''if self.gen_features:
+            self.save_network(self.netE, 'E', which_epoch, self.gpu_ids)'''
+
+    def update_fixed_params(self):
+        # after fixing the global generator for a number of iterations, also start finetuning it
+        params = list(self.netG.parameters())
+        if self.gen_features:
+            params += list(self.netE.parameters())
+        self.optimizer_G = torch.optim.Adam(params, lr=self.opt.lr, betas=(self.opt.beta1, 0.999))
+        if self.opt.verbose:
+            print('------------ Now also finetuning global generator -----------')
+
+    def update_learning_rate(self):
+        lrd = self.opt.lr / self.opt.niter_decay
+        lr = self.old_lr - lrd
+        for param_group in self.optimizer_D.param_groups:
+            param_group['lr'] = lr
+        for param_group in self.optimizer_G.param_groups:
+            param_group['lr'] = lr
+        if self.opt.verbose:
+            print('update learning rate: %f -> %f' % (self.old_lr, lr))
+        self.old_lr = lr
+
+
diff --git a/mlops/mlflow/registry/Swimswap/models/projectionhead.py b/mlops/mlflow/registry/Swimswap/models/projectionhead.py
new file mode 100644
index 0000000..a0d18f1
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/models/projectionhead.py
@@ -0,0 +1,14 @@
+import torch.nn as nn
+
+class ProjectionHead(nn.Module):
+    def __init__(self, proj_dim=256):
+        super(ProjectionHead, self).__init__()
+
+        self.proj = nn.Sequential(
+            nn.Linear(proj_dim, proj_dim), 
+            nn.ReLU(),
+            nn.Linear(proj_dim, proj_dim), 
+        )
+
+    def forward(self, x):
+        return self.proj(x)
\ No newline at end of file
diff --git a/mlops/mlflow/registry/Swimswap/models/ui_model.py b/mlops/mlflow/registry/Swimswap/models/ui_model.py
new file mode 100644
index 0000000..c5b3433
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/models/ui_model.py
@@ -0,0 +1,347 @@
+import torch
+from torch.autograd import Variable
+from collections import OrderedDict
+import numpy as np
+import os
+from PIL import Image
+import util.util as util
+from .base_model import BaseModel
+from . import networks
+
+class UIModel(BaseModel):
+    def name(self):
+        return 'UIModel'
+
+    def initialize(self, opt):
+        assert(not opt.isTrain)
+        BaseModel.initialize(self, opt)
+        self.use_features = opt.instance_feat or opt.label_feat
+
+        netG_input_nc = opt.label_nc
+        if not opt.no_instance:
+            netG_input_nc += 1            
+        if self.use_features:   
+            netG_input_nc += opt.feat_num           
+
+        self.netG = networks.define_G(netG_input_nc, opt.output_nc, opt.ngf, opt.netG, 
+                                      opt.n_downsample_global, opt.n_blocks_global, opt.n_local_enhancers, 
+                                      opt.n_blocks_local, opt.norm, gpu_ids=self.gpu_ids)            
+        self.load_network(self.netG, 'G', opt.which_epoch)
+
+        print('---------- Networks initialized -------------')
+
+    def toTensor(self, img, normalize=False):
+        tensor = torch.from_numpy(np.array(img, np.int32, copy=False))
+        tensor = tensor.view(1, img.size[1], img.size[0], len(img.mode))    
+        tensor = tensor.transpose(1, 2).transpose(1, 3).contiguous()
+        if normalize:
+            return (tensor.float()/255.0 - 0.5) / 0.5        
+        return tensor.float()
+
+    def load_image(self, label_path, inst_path, feat_path):
+        opt = self.opt
+        # read label map
+        label_img = Image.open(label_path)    
+        if label_path.find('face') != -1:
+            label_img = label_img.convert('L')
+        ow, oh = label_img.size    
+        w = opt.loadSize
+        h = int(w * oh / ow)    
+        label_img = label_img.resize((w, h), Image.NEAREST)
+        label_map = self.toTensor(label_img)           
+        
+        # onehot vector input for label map
+        self.label_map = label_map.cuda()
+        oneHot_size = (1, opt.label_nc, h, w)
+        input_label = self.Tensor(torch.Size(oneHot_size)).zero_()
+        self.input_label = input_label.scatter_(1, label_map.long().cuda(), 1.0)
+
+        # read instance map
+        if not opt.no_instance:
+            inst_img = Image.open(inst_path)        
+            inst_img = inst_img.resize((w, h), Image.NEAREST)            
+            self.inst_map = self.toTensor(inst_img).cuda()
+            self.edge_map = self.get_edges(self.inst_map)          
+            self.net_input = Variable(torch.cat((self.input_label, self.edge_map), dim=1), volatile=True)
+        else:
+            self.net_input = Variable(self.input_label, volatile=True)  
+        
+        self.features_clustered = np.load(feat_path).item()
+        self.object_map = self.inst_map if opt.instance_feat else self.label_map 
+                       
+        object_np = self.object_map.cpu().numpy().astype(int) 
+        self.feat_map = self.Tensor(1, opt.feat_num, h, w).zero_()                 
+        self.cluster_indices = np.zeros(self.opt.label_nc, np.uint8)
+        for i in np.unique(object_np):    
+            label = i if i < 1000 else i//1000
+            if label in self.features_clustered:
+                feat = self.features_clustered[label]
+                np.random.seed(i+1)
+                cluster_idx = np.random.randint(0, feat.shape[0])
+                self.cluster_indices[label] = cluster_idx
+                idx = (self.object_map == i).nonzero()                    
+                self.set_features(idx, feat, cluster_idx)
+
+        self.net_input_original = self.net_input.clone()        
+        self.label_map_original = self.label_map.clone()
+        self.feat_map_original = self.feat_map.clone()
+        if not opt.no_instance:
+            self.inst_map_original = self.inst_map.clone()        
+
+    def reset(self):
+        self.net_input = self.net_input_prev = self.net_input_original.clone()        
+        self.label_map = self.label_map_prev = self.label_map_original.clone()
+        self.feat_map = self.feat_map_prev = self.feat_map_original.clone()
+        if not self.opt.no_instance:
+            self.inst_map = self.inst_map_prev = self.inst_map_original.clone()
+        self.object_map = self.inst_map if self.opt.instance_feat else self.label_map 
+
+    def undo(self):        
+        self.net_input = self.net_input_prev
+        self.label_map = self.label_map_prev
+        self.feat_map = self.feat_map_prev
+        if not self.opt.no_instance:
+            self.inst_map = self.inst_map_prev
+        self.object_map = self.inst_map if self.opt.instance_feat else self.label_map 
+            
+    # get boundary map from instance map
+    def get_edges(self, t):
+        edge = torch.cuda.ByteTensor(t.size()).zero_()
+        edge[:,:,:,1:] = edge[:,:,:,1:] | (t[:,:,:,1:] != t[:,:,:,:-1])
+        edge[:,:,:,:-1] = edge[:,:,:,:-1] | (t[:,:,:,1:] != t[:,:,:,:-1])
+        edge[:,:,1:,:] = edge[:,:,1:,:] | (t[:,:,1:,:] != t[:,:,:-1,:])
+        edge[:,:,:-1,:] = edge[:,:,:-1,:] | (t[:,:,1:,:] != t[:,:,:-1,:])
+        return edge.float()
+
+    # change the label at the source position to the label at the target position
+    def change_labels(self, click_src, click_tgt): 
+        y_src, x_src = click_src[0], click_src[1]
+        y_tgt, x_tgt = click_tgt[0], click_tgt[1]
+        label_src = int(self.label_map[0, 0, y_src, x_src])
+        inst_src = self.inst_map[0, 0, y_src, x_src]
+        label_tgt = int(self.label_map[0, 0, y_tgt, x_tgt])
+        inst_tgt = self.inst_map[0, 0, y_tgt, x_tgt]
+
+        idx_src = (self.inst_map == inst_src).nonzero()         
+        # need to change 3 things: label map, instance map, and feature map
+        if idx_src.shape:
+            # backup current maps
+            self.backup_current_state() 
+
+            # change both the label map and the network input
+            self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
+            self.net_input[idx_src[:,0], idx_src[:,1] + label_src, idx_src[:,2], idx_src[:,3]] = 0
+            self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1                                    
+            
+            # update the instance map (and the network input)
+            if inst_tgt > 1000:
+                # if different instances have different ids, give the new object a new id
+                tgt_indices = (self.inst_map > label_tgt * 1000) & (self.inst_map < (label_tgt+1) * 1000)
+                inst_tgt = self.inst_map[tgt_indices].max() + 1
+            self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = inst_tgt
+            self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
+
+            # also copy the source features to the target position      
+            idx_tgt = (self.inst_map == inst_tgt).nonzero()    
+            if idx_tgt.shape:
+                self.copy_features(idx_src, idx_tgt[0,:])
+
+        self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
+
+    # add strokes of target label in the image
+    def add_strokes(self, click_src, label_tgt, bw, save):
+        # get the region of the new strokes (bw is the brush width)        
+        size = self.net_input.size()
+        h, w = size[2], size[3]
+        idx_src = torch.LongTensor(bw**2, 4).fill_(0)
+        for i in range(bw):
+            idx_src[i*bw:(i+1)*bw, 2] = min(h-1, max(0, click_src[0]-bw//2 + i))
+            for j in range(bw):
+                idx_src[i*bw+j, 3] = min(w-1, max(0, click_src[1]-bw//2 + j))
+        idx_src = idx_src.cuda()
+        
+        # again, need to update 3 things
+        if idx_src.shape:
+            # backup current maps
+            if save:
+                self.backup_current_state()
+
+            # update the label map (and the network input) in the stroke region            
+            self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
+            for k in range(self.opt.label_nc):
+                self.net_input[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = 0
+            self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1                 
+
+            # update the instance map (and the network input)
+            self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
+            self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
+            
+            # also update the features if available
+            if self.opt.instance_feat:                                            
+                feat = self.features_clustered[label_tgt]
+                #np.random.seed(label_tgt+1)   
+                #cluster_idx = np.random.randint(0, feat.shape[0])
+                cluster_idx = self.cluster_indices[label_tgt]
+                self.set_features(idx_src, feat, cluster_idx)                                                  
+        
+        self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
+
+    # add an object to the clicked position with selected style
+    def add_objects(self, click_src, label_tgt, mask, style_id=0):
+        y, x = click_src[0], click_src[1]
+        mask = np.transpose(mask, (2, 0, 1))[np.newaxis,...]        
+        idx_src = torch.from_numpy(mask).cuda().nonzero()        
+        idx_src[:,2] += y
+        idx_src[:,3] += x
+
+        # backup current maps
+        self.backup_current_state()
+
+        # update label map
+        self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt        
+        for k in range(self.opt.label_nc):
+            self.net_input[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = 0
+        self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1            
+
+        # update instance map
+        self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
+        self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
+                
+        # update feature map
+        self.set_features(idx_src, self.feat, style_id)                
+        
+        self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
+
+    def single_forward(self, net_input, feat_map):
+        net_input = torch.cat((net_input, feat_map), dim=1)
+        fake_image = self.netG.forward(net_input)
+
+        if fake_image.size()[0] == 1:
+            return fake_image.data[0]        
+        return fake_image.data
+
+
+    # generate all outputs for different styles
+    def style_forward(self, click_pt, style_id=-1):           
+        if click_pt is None:            
+            self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
+            self.crop = None
+            self.mask = None        
+        else:                       
+            instToChange = int(self.object_map[0, 0, click_pt[0], click_pt[1]])
+            self.instToChange = instToChange
+            label = instToChange if instToChange < 1000 else instToChange//1000        
+            self.feat = self.features_clustered[label]
+            self.fake_image = []
+            self.mask = self.object_map == instToChange
+            idx = self.mask.nonzero()
+            self.get_crop_region(idx)            
+            if idx.size():                
+                if style_id == -1:
+                    (min_y, min_x, max_y, max_x) = self.crop
+                    ### original
+                    for cluster_idx in range(self.opt.multiple_output):
+                        self.set_features(idx, self.feat, cluster_idx)
+                        fake_image = self.single_forward(self.net_input, self.feat_map)
+                        fake_image = util.tensor2im(fake_image[:,min_y:max_y,min_x:max_x])
+                        self.fake_image.append(fake_image)    
+                    """### To speed up previewing different style results, either crop or downsample the label maps
+                    if instToChange > 1000:
+                        (min_y, min_x, max_y, max_x) = self.crop                                                
+                        ### crop                                                
+                        _, _, h, w = self.net_input.size()
+                        offset = 512
+                        y_start, x_start = max(0, min_y-offset), max(0, min_x-offset)
+                        y_end, x_end = min(h, (max_y + offset)), min(w, (max_x + offset))
+                        y_region = slice(y_start, y_start+(y_end-y_start)//16*16)
+                        x_region = slice(x_start, x_start+(x_end-x_start)//16*16)
+                        net_input = self.net_input[:,:,y_region,x_region]                    
+                        for cluster_idx in range(self.opt.multiple_output):  
+                            self.set_features(idx, self.feat, cluster_idx)
+                            fake_image = self.single_forward(net_input, self.feat_map[:,:,y_region,x_region])                            
+                            fake_image = util.tensor2im(fake_image[:,min_y-y_start:max_y-y_start,min_x-x_start:max_x-x_start])
+                            self.fake_image.append(fake_image)
+                    else:
+                        ### downsample
+                        (min_y, min_x, max_y, max_x) = [crop//2 for crop in self.crop]                    
+                        net_input = self.net_input[:,:,::2,::2]                    
+                        size = net_input.size()
+                        net_input_batch = net_input.expand(self.opt.multiple_output, size[1], size[2], size[3])             
+                        for cluster_idx in range(self.opt.multiple_output):  
+                            self.set_features(idx, self.feat, cluster_idx)
+                            feat_map = self.feat_map[:,:,::2,::2]
+                            if cluster_idx == 0:
+                                feat_map_batch = feat_map
+                            else:
+                                feat_map_batch = torch.cat((feat_map_batch, feat_map), dim=0)
+                        fake_image_batch = self.single_forward(net_input_batch, feat_map_batch)
+                        for i in range(self.opt.multiple_output):
+                            self.fake_image.append(util.tensor2im(fake_image_batch[i,:,min_y:max_y,min_x:max_x]))"""
+                                        
+                else:
+                    self.set_features(idx, self.feat, style_id)
+                    self.cluster_indices[label] = style_id
+                    self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))        
+
+    def backup_current_state(self):
+        self.net_input_prev = self.net_input.clone()
+        self.label_map_prev = self.label_map.clone() 
+        self.inst_map_prev = self.inst_map.clone() 
+        self.feat_map_prev = self.feat_map.clone() 
+
+    # crop the ROI and get the mask of the object
+    def get_crop_region(self, idx):
+        size = self.net_input.size()
+        h, w = size[2], size[3]
+        min_y, min_x = idx[:,2].min(), idx[:,3].min()
+        max_y, max_x = idx[:,2].max(), idx[:,3].max()             
+        crop_min = 128
+        if max_y - min_y < crop_min:
+            min_y = max(0, (max_y + min_y) // 2 - crop_min // 2)
+            max_y = min(h-1, min_y + crop_min)
+        if max_x - min_x < crop_min:
+            min_x = max(0, (max_x + min_x) // 2 - crop_min // 2)
+            max_x = min(w-1, min_x + crop_min)
+        self.crop = (min_y, min_x, max_y, max_x)           
+        self.mask = self.mask[:,:, min_y:max_y, min_x:max_x]
+
+    # update the feature map once a new object is added or the label is changed
+    def update_features(self, cluster_idx, mask=None, click_pt=None):        
+        self.feat_map_prev = self.feat_map.clone()
+        # adding a new object
+        if mask is not None:
+            y, x = click_pt[0], click_pt[1]
+            mask = np.transpose(mask, (2,0,1))[np.newaxis,...]        
+            idx = torch.from_numpy(mask).cuda().nonzero()        
+            idx[:,2] += y
+            idx[:,3] += x    
+        # changing the label of an existing object 
+        else:            
+            idx = (self.object_map == self.instToChange).nonzero()              
+
+        # update feature map
+        self.set_features(idx, self.feat, cluster_idx)        
+
+    # set the class features to the target feature
+    def set_features(self, idx, feat, cluster_idx):        
+        for k in range(self.opt.feat_num):
+            self.feat_map[idx[:,0], idx[:,1] + k, idx[:,2], idx[:,3]] = feat[cluster_idx, k] 
+
+    # copy the features at the target position to the source position
+    def copy_features(self, idx_src, idx_tgt):        
+        for k in range(self.opt.feat_num):
+            val = self.feat_map[idx_tgt[0], idx_tgt[1] + k, idx_tgt[2], idx_tgt[3]]
+            self.feat_map[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = val 
+
+    def get_current_visuals(self, getLabel=False):                              
+        mask = self.mask     
+        if self.mask is not None:
+            mask = np.transpose(self.mask[0].cpu().float().numpy(), (1,2,0)).astype(np.uint8)        
+
+        dict_list = [('fake_image', self.fake_image), ('mask', mask)]
+
+        if getLabel: # only output label map if needed to save bandwidth
+            label = util.tensor2label(self.net_input.data[0], self.opt.label_nc)                    
+            dict_list += [('label', label)]
+
+        return OrderedDict(dict_list)
\ No newline at end of file
diff --git a/mlops/mlflow/registry/Swimswap/options/base_options.py b/mlops/mlflow/registry/Swimswap/options/base_options.py
new file mode 100644
index 0000000..f5e5e8c
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/options/base_options.py
@@ -0,0 +1,104 @@
+import argparse
+import os
+from util import util
+import torch
+
+class BaseOptions():
+    def __init__(self):
+        self.parser = argparse.ArgumentParser()
+        self.initialized = False
+
+    def initialize(self):
+        # experiment specifics
+        self.parser.add_argument('--name', type=str, default='people', help='name of the experiment. It decides where to store samples and models')
+        self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
+        self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
+        # self.parser.add_argument('--model', type=str, default='pix2pixHD', help='which model to use')
+        self.parser.add_argument('--norm', type=str, default='batch', help='instance normalization or batch normalization')
+        self.parser.add_argument('--use_dropout', action='store_true', help='use dropout for the generator')
+        self.parser.add_argument('--data_type', default=32, type=int, choices=[8, 16, 32], help="Supported data type i.e. 8, 16, 32 bit")
+        self.parser.add_argument('--verbose', action='store_true', default=False, help='toggles verbose')
+        self.parser.add_argument('--fp16', action='store_true', default=False, help='train with AMP')
+        self.parser.add_argument('--local_rank', type=int, default=0, help='local rank for distributed training')
+        self.parser.add_argument('--isTrain', type=bool, default=True, help='local rank for distributed training')
+
+        # input/output sizes       
+        self.parser.add_argument('--batchSize', type=int, default=8, help='input batch size')
+        self.parser.add_argument('--loadSize', type=int, default=1024, help='scale images to this size')
+        self.parser.add_argument('--fineSize', type=int, default=512, help='then crop to this size')
+        self.parser.add_argument('--label_nc', type=int, default=0, help='# of input label channels')
+        self.parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels')
+        self.parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')
+
+        # for setting inputs
+        self.parser.add_argument('--dataroot', type=str, default='./SimSwap/datasets/cityscapes/') 
+        self.parser.add_argument('--resize_or_crop', type=str, default='scale_width', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop]')
+        self.parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')        
+        self.parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data argumentation') 
+        self.parser.add_argument('--nThreads', default=2, type=int, help='# threads for loading data')                
+        self.parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
+
+        # for displays
+        self.parser.add_argument('--display_winsize', type=int, default=512,  help='display window size')
+        self.parser.add_argument('--tf_log', action='store_true', help='if specified, use tensorboard logging. Requires tensorflow installed')
+
+        # for generator
+        self.parser.add_argument('--netG', type=str, default='global', help='selects model to use for netG')
+        self.parser.add_argument('--latent_size', type=int, default=512, help='latent size of Adain layer')
+        self.parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
+        self.parser.add_argument('--n_downsample_global', type=int, default=3, help='number of downsampling layers in netG')
+        self.parser.add_argument('--n_blocks_global', type=int, default=6, help='number of residual blocks in the global generator network')
+        self.parser.add_argument('--n_blocks_local', type=int, default=3, help='number of residual blocks in the local enhancer network')
+        self.parser.add_argument('--n_local_enhancers', type=int, default=1, help='number of local enhancers to use')        
+        self.parser.add_argument('--niter_fix_global', type=int, default=0, help='number of epochs that we only train the outmost local enhancer')        
+
+        # for instance-wise features
+        self.parser.add_argument('--no_instance', action='store_true', help='if specified, do *not* add instance map as input')        
+        self.parser.add_argument('--instance_feat', action='store_true', help='if specified, add encoded instance features as input')
+        self.parser.add_argument('--label_feat', action='store_true', help='if specified, add encoded label features as input')        
+        self.parser.add_argument('--feat_num', type=int, default=3, help='vector length for encoded features')        
+        self.parser.add_argument('--load_features', action='store_true', help='if specified, load precomputed feature maps')
+        self.parser.add_argument('--n_downsample_E', type=int, default=4, help='# of downsampling layers in encoder') 
+        self.parser.add_argument('--nef', type=int, default=16, help='# of encoder filters in the first conv layer')        
+        self.parser.add_argument('--n_clusters', type=int, default=10, help='number of clusters for features')
+        self.parser.add_argument('--image_size', type=int, default=224, help='number of clusters for features')
+        self.parser.add_argument('--norm_G', type=str, default='spectralspadesyncbatch3x3', help='instance normalization or batch normalization')
+        self.parser.add_argument('--semantic_nc', type=int, default=3, help='number of clusters for features')
+        self.initialized = True
+
+    def parse(self, save=True):
+        if not self.initialized:
+            self.initialize()
+        self.opt = self.parser.parse_args()
+        self.opt.isTrain = self.isTrain   # train or test
+
+        str_ids = self.opt.gpu_ids.split(',')
+        self.opt.gpu_ids = []
+        for str_id in str_ids:
+            id = int(str_id)
+            if id >= 0:
+                self.opt.gpu_ids.append(id)
+        
+        # set gpu ids
+        if len(self.opt.gpu_ids) > 0:
+            torch.cuda.set_device(self.opt.gpu_ids[0])
+
+        args = vars(self.opt)
+
+        print('------------ Options -------------')
+        for k, v in sorted(args.items()):
+            print('%s: %s' % (str(k), str(v)))
+        print('-------------- End ----------------')
+
+        # save to the disk
+        if self.opt.isTrain:
+            expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name)
+            util.mkdirs(expr_dir)
+            if save and not self.opt.continue_train:
+                file_name = os.path.join(expr_dir, 'opt.txt')
+                with open(file_name, 'wt') as opt_file:
+                    opt_file.write('------------ Options -------------\n')
+                    for k, v in sorted(args.items()):
+                        opt_file.write('%s: %s\n' % (str(k), str(v)))
+                    opt_file.write('-------------- End ----------------\n')
+        return self.opt
diff --git a/mlops/mlflow/registry/Swimswap/options/test_options.py b/mlops/mlflow/registry/Swimswap/options/test_options.py
new file mode 100644
index 0000000..e46e435
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/options/test_options.py
@@ -0,0 +1,38 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-23 17:08:08
+Description: 
+'''
+from .base_options import BaseOptions
+
+class TestOptions(BaseOptions):
+    def initialize(self):
+        BaseOptions.initialize(self)
+        self.parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.')
+        self.parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.')
+        self.parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
+        self.parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
+        self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
+        self.parser.add_argument('--how_many', type=int, default=50, help='how many test images to run')       
+        self.parser.add_argument('--cluster_path', type=str, default='features_clustered_010.npy', help='the path for clustered results of encoded features')
+        self.parser.add_argument('--use_encoded_image', action='store_true', help='if specified, encode the real image to get the feature map')
+        self.parser.add_argument("--export_onnx", type=str, help="export ONNX model to a given file")
+        self.parser.add_argument("--engine", type=str, help="run serialized TRT engine")
+        self.parser.add_argument("--onnx", type=str, help="run ONNX model via TRT")        
+        self.parser.add_argument("--Arc_path", type=str, default='SimSwap/arcface_model/arcface_checkpoint.tar', help="run ONNX model via TRT")
+        self.parser.add_argument("--pic_a_path", type=str, default='G:/swap_data/ID/elon-musk-hero-image.jpeg', help="Person who provides identity information")
+        self.parser.add_argument("--pic_b_path", type=str, default='./demo_file/multi_people.jpg', help="Person who provides information other than their identity")
+        self.parser.add_argument("--pic_specific_path", type=str, default='./crop_224/zrf.jpg', help="The specific person to be swapped")
+        self.parser.add_argument("--multisepcific_dir", type=str, default='./demo_file/multispecific', help="Dir for multi specific")
+        self.parser.add_argument("--video_path", type=str, default='G:/swap_data/video/HSB_Demo_Trim.mp4', help="path for the video to swap")
+        self.parser.add_argument("--temp_path", type=str, default='./temp_results', help="path to save temporarily images")
+        self.parser.add_argument("--output_path", type=str, default='./output/', help="results path")
+        self.parser.add_argument('--id_thres', type=float, default=0.03, help='how many test images to run')
+        self.parser.add_argument('--no_simswaplogo', action='store_true', help='Remove the watermark')
+        self.parser.add_argument('--use_mask', action='store_true', help='Use mask for better result')
+        self.parser.add_argument('--crop_size', type=int, default=224, help='Crop of size of input image')
+        
+        self.isTrain = False
\ No newline at end of file
diff --git a/mlops/mlflow/registry/Swimswap/parsing_model/model.py b/mlops/mlflow/registry/Swimswap/parsing_model/model.py
new file mode 100644
index 0000000..d434836
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/parsing_model/model.py
@@ -0,0 +1,283 @@
+#!/usr/bin/python
+# -*- encoding: utf-8 -*-
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+
+from parsing_model.resnet import Resnet18
+# from modules.bn import InPlaceABNSync as BatchNorm2d
+
+
+class ConvBNReLU(nn.Module):
+    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
+        super(ConvBNReLU, self).__init__()
+        self.conv = nn.Conv2d(in_chan,
+                out_chan,
+                kernel_size = ks,
+                stride = stride,
+                padding = padding,
+                bias = False)
+        self.bn = nn.BatchNorm2d(out_chan)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = F.relu(self.bn(x))
+        return x
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+class BiSeNetOutput(nn.Module):
+    def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
+        super(BiSeNetOutput, self).__init__()
+        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
+        self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.conv_out(x)
+        return x
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class AttentionRefinementModule(nn.Module):
+    def __init__(self, in_chan, out_chan, *args, **kwargs):
+        super(AttentionRefinementModule, self).__init__()
+        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
+        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
+        self.bn_atten = nn.BatchNorm2d(out_chan)
+        self.sigmoid_atten = nn.Sigmoid()
+        self.init_weight()
+
+    def forward(self, x):
+        feat = self.conv(x)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv_atten(atten)
+        atten = self.bn_atten(atten)
+        atten = self.sigmoid_atten(atten)
+        out = torch.mul(feat, atten)
+        return out
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+
+class ContextPath(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super(ContextPath, self).__init__()
+        self.resnet = Resnet18()
+        self.arm16 = AttentionRefinementModule(256, 128)
+        self.arm32 = AttentionRefinementModule(512, 128)
+        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
+
+        self.init_weight()
+
+    def forward(self, x):
+        H0, W0 = x.size()[2:]
+        feat8, feat16, feat32 = self.resnet(x)
+        H8, W8 = feat8.size()[2:]
+        H16, W16 = feat16.size()[2:]
+        H32, W32 = feat32.size()[2:]
+
+        avg = F.avg_pool2d(feat32, feat32.size()[2:])
+        avg = self.conv_avg(avg)
+        avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
+
+        feat32_arm = self.arm32(feat32)
+        feat32_sum = feat32_arm + avg_up
+        feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
+        feat32_up = self.conv_head32(feat32_up)
+
+        feat16_arm = self.arm16(feat16)
+        feat16_sum = feat16_arm + feat32_up
+        feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
+        feat16_up = self.conv_head16(feat16_up)
+
+        return feat8, feat16_up, feat32_up  # x8, x8, x16
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+### This is not used, since I replace this with the resnet feature with the same size
+class SpatialPath(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super(SpatialPath, self).__init__()
+        self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
+        self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
+        self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
+        self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
+        self.init_weight()
+
+    def forward(self, x):
+        feat = self.conv1(x)
+        feat = self.conv2(feat)
+        feat = self.conv3(feat)
+        feat = self.conv_out(feat)
+        return feat
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class FeatureFusionModule(nn.Module):
+    def __init__(self, in_chan, out_chan, *args, **kwargs):
+        super(FeatureFusionModule, self).__init__()
+        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
+        self.conv1 = nn.Conv2d(out_chan,
+                out_chan//4,
+                kernel_size = 1,
+                stride = 1,
+                padding = 0,
+                bias = False)
+        self.conv2 = nn.Conv2d(out_chan//4,
+                out_chan,
+                kernel_size = 1,
+                stride = 1,
+                padding = 0,
+                bias = False)
+        self.relu = nn.ReLU(inplace=True)
+        self.sigmoid = nn.Sigmoid()
+        self.init_weight()
+
+    def forward(self, fsp, fcp):
+        fcat = torch.cat([fsp, fcp], dim=1)
+        feat = self.convblk(fcat)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv1(atten)
+        atten = self.relu(atten)
+        atten = self.conv2(atten)
+        atten = self.sigmoid(atten)
+        feat_atten = torch.mul(feat, atten)
+        feat_out = feat_atten + feat
+        return feat_out
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class BiSeNet(nn.Module):
+    def __init__(self, n_classes, *args, **kwargs):
+        super(BiSeNet, self).__init__()
+        self.cp = ContextPath()
+        ## here self.sp is deleted
+        self.ffm = FeatureFusionModule(256, 256)
+        self.conv_out = BiSeNetOutput(256, 256, n_classes)
+        self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
+        self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
+        self.init_weight()
+
+    def forward(self, x):
+        H, W = x.size()[2:]
+        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # here return res3b1 feature
+        feat_sp = feat_res8  # use res3b1 feature to replace spatial path feature
+        feat_fuse = self.ffm(feat_sp, feat_cp8)
+
+        feat_out = self.conv_out(feat_fuse)
+        feat_out16 = self.conv_out16(feat_cp8)
+        feat_out32 = self.conv_out32(feat_cp16)
+
+        feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
+        feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
+        feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
+        return feat_out, feat_out16, feat_out32
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
+        for name, child in self.named_children():
+            child_wd_params, child_nowd_params = child.get_params()
+            if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
+                lr_mul_wd_params += child_wd_params
+                lr_mul_nowd_params += child_nowd_params
+            else:
+                wd_params += child_wd_params
+                nowd_params += child_nowd_params
+        return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
+
+
+if __name__ == "__main__":
+    net = BiSeNet(19)
+    net.cuda()
+    net.eval()
+    in_ten = torch.randn(16, 3, 640, 480).cuda()
+    out, out16, out32 = net(in_ten)
+    print(out.shape)
+
+    net.get_params()
diff --git a/mlops/mlflow/registry/Swimswap/parsing_model/resnet.py b/mlops/mlflow/registry/Swimswap/parsing_model/resnet.py
new file mode 100644
index 0000000..aa2bf95
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/parsing_model/resnet.py
@@ -0,0 +1,109 @@
+#!/usr/bin/python
+# -*- encoding: utf-8 -*-
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.model_zoo as modelzoo
+
+# from modules.bn import InPlaceABNSync as BatchNorm2d
+
+resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    def __init__(self, in_chan, out_chan, stride=1):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(in_chan, out_chan, stride)
+        self.bn1 = nn.BatchNorm2d(out_chan)
+        self.conv2 = conv3x3(out_chan, out_chan)
+        self.bn2 = nn.BatchNorm2d(out_chan)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = None
+        if in_chan != out_chan or stride != 1:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(in_chan, out_chan,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(out_chan),
+                )
+
+    def forward(self, x):
+        residual = self.conv1(x)
+        residual = F.relu(self.bn1(residual))
+        residual = self.conv2(residual)
+        residual = self.bn2(residual)
+
+        shortcut = x
+        if self.downsample is not None:
+            shortcut = self.downsample(x)
+
+        out = shortcut + residual
+        out = self.relu(out)
+        return out
+
+
+def create_layer_basic(in_chan, out_chan, bnum, stride=1):
+    layers = [BasicBlock(in_chan, out_chan, stride=stride)]
+    for i in range(bnum-1):
+        layers.append(BasicBlock(out_chan, out_chan, stride=1))
+    return nn.Sequential(*layers)
+
+
+class Resnet18(nn.Module):
+    def __init__(self):
+        super(Resnet18, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
+        self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
+        self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
+        self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = F.relu(self.bn1(x))
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        feat8 = self.layer2(x) # 1/8
+        feat16 = self.layer3(feat8) # 1/16
+        feat32 = self.layer4(feat16) # 1/32
+        return feat8, feat16, feat32
+
+    def init_weight(self):
+        state_dict = modelzoo.load_url(resnet18_url)
+        self_state_dict = self.state_dict()
+        for k, v in state_dict.items():
+            if 'fc' in k: continue
+            self_state_dict.update({k: v})
+        self.load_state_dict(self_state_dict)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module,  nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+if __name__ == "__main__":
+    net = Resnet18()
+    x = torch.randn(16, 3, 224, 224)
+    out = net(x)
+    print(out[0].size())
+    print(out[1].size())
+    print(out[2].size())
+    net.get_params()
diff --git a/mlops/mlflow/registry/Swimswap/pg_modules/blocks.py b/mlops/mlflow/registry/Swimswap/pg_modules/blocks.py
new file mode 100644
index 0000000..78bd113
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/pg_modules/blocks.py
@@ -0,0 +1,325 @@
+import functools
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.utils import spectral_norm
+
+
+### single layers
+
+
+def conv2d(*args, **kwargs):
+    return spectral_norm(nn.Conv2d(*args, **kwargs))
+
+
+def convTranspose2d(*args, **kwargs):
+    return spectral_norm(nn.ConvTranspose2d(*args, **kwargs))
+
+
+def embedding(*args, **kwargs):
+    return spectral_norm(nn.Embedding(*args, **kwargs))
+
+
+def linear(*args, **kwargs):
+    return spectral_norm(nn.Linear(*args, **kwargs))
+
+
+def NormLayer(c, mode='batch'):
+    if mode == 'group':
+        return nn.GroupNorm(c//2, c)
+    elif mode == 'batch':
+        return nn.BatchNorm2d(c)
+
+
+### Activations
+
+
+class GLU(nn.Module):
+    def forward(self, x):
+        nc = x.size(1)
+        assert nc % 2 == 0, 'channels dont divide 2!'
+        nc = int(nc/2)
+        return x[:, :nc] * torch.sigmoid(x[:, nc:])
+
+
+class Swish(nn.Module):
+    def forward(self, feat):
+        return feat * torch.sigmoid(feat)
+
+
+### Upblocks
+
+
+class InitLayer(nn.Module):
+    def __init__(self, nz, channel, sz=4):
+        super().__init__()
+
+        self.init = nn.Sequential(
+            convTranspose2d(nz, channel*2, sz, 1, 0, bias=False),
+            NormLayer(channel*2),
+            GLU(),
+        )
+
+    def forward(self, noise):
+        noise = noise.view(noise.shape[0], -1, 1, 1)
+        return self.init(noise)
+
+
+def UpBlockSmall(in_planes, out_planes):
+    block = nn.Sequential(
+        nn.Upsample(scale_factor=2, mode='nearest'),
+        conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False),
+        NormLayer(out_planes*2), GLU())
+    return block
+
+
+class UpBlockSmallCond(nn.Module):
+    def __init__(self, in_planes, out_planes, z_dim):
+        super().__init__()
+        self.in_planes = in_planes
+        self.out_planes = out_planes
+        self.up = nn.Upsample(scale_factor=2, mode='nearest')
+        self.conv = conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False)
+
+        which_bn = functools.partial(CCBN, which_linear=linear, input_size=z_dim)
+        self.bn = which_bn(2*out_planes)
+        self.act = GLU()
+
+    def forward(self, x, c):
+        x = self.up(x)
+        x = self.conv(x)
+        x = self.bn(x, c)
+        x = self.act(x)
+        return x
+
+
+def UpBlockBig(in_planes, out_planes):
+    block = nn.Sequential(
+        nn.Upsample(scale_factor=2, mode='nearest'),
+        conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False),
+        NoiseInjection(),
+        NormLayer(out_planes*2), GLU(),
+        conv2d(out_planes, out_planes*2, 3, 1, 1, bias=False),
+        NoiseInjection(),
+        NormLayer(out_planes*2), GLU()
+        )
+    return block
+
+
+class UpBlockBigCond(nn.Module):
+    def __init__(self, in_planes, out_planes, z_dim):
+        super().__init__()
+        self.in_planes = in_planes
+        self.out_planes = out_planes
+        self.up = nn.Upsample(scale_factor=2, mode='nearest')
+        self.conv1 = conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False)
+        self.conv2 = conv2d(out_planes, out_planes*2, 3, 1, 1, bias=False)
+
+        which_bn = functools.partial(CCBN, which_linear=linear, input_size=z_dim)
+        self.bn1 = which_bn(2*out_planes)
+        self.bn2 = which_bn(2*out_planes)
+        self.act = GLU()
+        self.noise = NoiseInjection()
+
+    def forward(self, x, c):
+        # block 1
+        x = self.up(x)
+        x = self.conv1(x)
+        x = self.noise(x)
+        x = self.bn1(x, c)
+        x = self.act(x)
+
+        # block 2
+        x = self.conv2(x)
+        x = self.noise(x)
+        x = self.bn2(x, c)
+        x = self.act(x)
+
+        return x
+
+
+class SEBlock(nn.Module):
+    def __init__(self, ch_in, ch_out):
+        super().__init__()
+        self.main = nn.Sequential(
+            nn.AdaptiveAvgPool2d(4),
+            conv2d(ch_in, ch_out, 4, 1, 0, bias=False),
+            Swish(),
+            conv2d(ch_out, ch_out, 1, 1, 0, bias=False),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, feat_small, feat_big):
+        return feat_big * self.main(feat_small)
+
+
+### Downblocks
+
+
+class SeparableConv2d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, bias=False):
+        super(SeparableConv2d, self).__init__()
+        self.depthwise = conv2d(in_channels, in_channels, kernel_size=kernel_size,
+            groups=in_channels, bias=bias, padding=1)
+        self.pointwise = conv2d(in_channels, out_channels,
+            kernel_size=1, bias=bias)
+
+    def forward(self, x):
+        out = self.depthwise(x)
+        out = self.pointwise(out)
+        return out
+
+
+class DownBlock(nn.Module):
+    def __init__(self, in_planes, out_planes, separable=False):
+        super().__init__()
+        if not separable:
+            self.main = nn.Sequential(
+                conv2d(in_planes, out_planes, 4, 2, 1),
+                NormLayer(out_planes),
+                nn.LeakyReLU(0.2, inplace=True),
+            )
+        else:
+            self.main = nn.Sequential(
+                SeparableConv2d(in_planes, out_planes, 3),
+                NormLayer(out_planes),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.AvgPool2d(2, 2),
+            )
+
+    def forward(self, feat):
+        return self.main(feat)
+
+
+class DownBlockPatch(nn.Module):
+    def __init__(self, in_planes, out_planes, separable=False):
+        super().__init__()
+        self.main = nn.Sequential(
+            DownBlock(in_planes, out_planes, separable),
+            conv2d(out_planes, out_planes, 1, 1, 0, bias=False),
+            NormLayer(out_planes),
+            nn.LeakyReLU(0.2, inplace=True),
+        )
+
+    def forward(self, feat):
+        return self.main(feat)
+
+
+### CSM
+
+
+class ResidualConvUnit(nn.Module):
+    def __init__(self, cin, activation, bn):
+        super().__init__()
+        self.conv = nn.Conv2d(cin, cin, kernel_size=3, stride=1, padding=1, bias=True)
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        return self.skip_add.add(self.conv(x), x)
+
+
+class FeatureFusionBlock(nn.Module):
+    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True, lowest=False):
+        super().__init__()
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.expand = expand
+        out_features = features
+        if self.expand==True:
+            out_features = features//2
+
+        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, *xs):
+        output = xs[0]
+
+        if len(xs) == 2:
+            output = self.skip_add.add(output, xs[1])
+
+        output = nn.functional.interpolate(
+            output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
+        )
+
+        output = self.out_conv(output)
+
+        return output
+
+
+### Misc
+
+
+class NoiseInjection(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.weight = nn.Parameter(torch.zeros(1), requires_grad=True)
+
+    def forward(self, feat, noise=None):
+        if noise is None:
+            batch, _, height, width = feat.shape
+            noise = torch.randn(batch, 1, height, width).to(feat.device)
+
+        return feat + self.weight * noise
+
+
+class CCBN(nn.Module):
+    ''' conditional batchnorm '''
+    def __init__(self, output_size, input_size, which_linear, eps=1e-5, momentum=0.1):
+        super().__init__()
+        self.output_size, self.input_size = output_size, input_size
+
+        # Prepare gain and bias layers
+        self.gain = which_linear(input_size, output_size)
+        self.bias = which_linear(input_size, output_size)
+
+        # epsilon to avoid dividing by 0
+        self.eps = eps
+        # Momentum
+        self.momentum = momentum
+
+        self.register_buffer('stored_mean', torch.zeros(output_size))
+        self.register_buffer('stored_var', torch.ones(output_size))
+
+    def forward(self, x, y):
+        # Calculate class-conditional gains and biases
+        gain = (1 + self.gain(y)).view(y.size(0), -1, 1, 1)
+        bias = self.bias(y).view(y.size(0), -1, 1, 1)
+        out = F.batch_norm(x, self.stored_mean, self.stored_var, None, None,
+                           self.training, 0.1, self.eps)
+        return out * gain + bias
+
+
+class Interpolate(nn.Module):
+    """Interpolation module."""
+
+    def __init__(self, size, mode='bilinear', align_corners=False):
+        """Init.
+        Args:
+            scale_factor (float): scaling
+            mode (str): interpolation mode
+        """
+        super(Interpolate, self).__init__()
+
+        self.interp = nn.functional.interpolate
+        self.size = size
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        """Forward pass.
+        Args:
+            x (tensor): input
+        Returns:
+            tensor: interpolated data
+        """
+
+        x = self.interp(
+            x,
+            size=self.size,
+            mode=self.mode,
+            align_corners=self.align_corners,
+        )
+
+        return x
diff --git a/mlops/mlflow/registry/Swimswap/pg_modules/diffaug.py b/mlops/mlflow/registry/Swimswap/pg_modules/diffaug.py
new file mode 100644
index 0000000..54020be
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/pg_modules/diffaug.py
@@ -0,0 +1,76 @@
+# Differentiable Augmentation for Data-Efficient GAN Training
+# Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han
+# https://arxiv.org/pdf/2006.10738
+
+import torch
+import torch.nn.functional as F
+
+
+def DiffAugment(x, policy='', channels_first=True):
+    if policy:
+        if not channels_first:
+            x = x.permute(0, 3, 1, 2)
+        for p in policy.split(','):
+            for f in AUGMENT_FNS[p]:
+                x = f(x)
+        if not channels_first:
+            x = x.permute(0, 2, 3, 1)
+        x = x.contiguous()
+    return x
+
+
+def rand_brightness(x):
+    x = x + (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) - 0.5)
+    return x
+
+
+def rand_saturation(x):
+    x_mean = x.mean(dim=1, keepdim=True)
+    x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) * 2) + x_mean
+    return x
+
+
+def rand_contrast(x):
+    x_mean = x.mean(dim=[1, 2, 3], keepdim=True)
+    x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) + 0.5) + x_mean
+    return x
+
+
+def rand_translation(x, ratio=0.125):
+    shift_x, shift_y = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
+    translation_x = torch.randint(-shift_x, shift_x + 1, size=[x.size(0), 1, 1], device=x.device)
+    translation_y = torch.randint(-shift_y, shift_y + 1, size=[x.size(0), 1, 1], device=x.device)
+    grid_batch, grid_x, grid_y = torch.meshgrid(
+        torch.arange(x.size(0), dtype=torch.long, device=x.device),
+        torch.arange(x.size(2), dtype=torch.long, device=x.device),
+        torch.arange(x.size(3), dtype=torch.long, device=x.device),
+    )
+    grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1)
+    grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1)
+    x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0])
+    x = x_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x, grid_y].permute(0, 3, 1, 2)
+    return x
+
+
+def rand_cutout(x, ratio=0.2):
+    cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
+    offset_x = torch.randint(0, x.size(2) + (1 - cutout_size[0] % 2), size=[x.size(0), 1, 1], device=x.device)
+    offset_y = torch.randint(0, x.size(3) + (1 - cutout_size[1] % 2), size=[x.size(0), 1, 1], device=x.device)
+    grid_batch, grid_x, grid_y = torch.meshgrid(
+        torch.arange(x.size(0), dtype=torch.long, device=x.device),
+        torch.arange(cutout_size[0], dtype=torch.long, device=x.device),
+        torch.arange(cutout_size[1], dtype=torch.long, device=x.device),
+    )
+    grid_x = torch.clamp(grid_x + offset_x - cutout_size[0] // 2, min=0, max=x.size(2) - 1)
+    grid_y = torch.clamp(grid_y + offset_y - cutout_size[1] // 2, min=0, max=x.size(3) - 1)
+    mask = torch.ones(x.size(0), x.size(2), x.size(3), dtype=x.dtype, device=x.device)
+    mask[grid_batch, grid_x, grid_y] = 0
+    x = x * mask.unsqueeze(1)
+    return x
+
+
+AUGMENT_FNS = {
+    'color': [rand_brightness, rand_saturation, rand_contrast],
+    'translation': [rand_translation],
+    'cutout': [rand_cutout],
+}
diff --git a/mlops/mlflow/registry/Swimswap/pg_modules/projected_discriminator.py b/mlops/mlflow/registry/Swimswap/pg_modules/projected_discriminator.py
new file mode 100644
index 0000000..d0c879f
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/pg_modules/projected_discriminator.py
@@ -0,0 +1,191 @@
+from functools import partial
+import numpy as np
+import torch
+import torch.nn as nn
+
+from pg_modules.blocks import DownBlock, DownBlockPatch, conv2d
+from pg_modules.projector import F_RandomProj
+from pg_modules.diffaug import DiffAugment
+
+
+class SingleDisc(nn.Module):
+    def __init__(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False):
+        super().__init__()
+        channel_dict = {4: 512, 8: 512, 16: 256, 32: 128, 64: 64, 128: 64,
+                        256: 32, 512: 16, 1024: 8}
+
+        # interpolate for start sz that are not powers of two
+        if start_sz not in channel_dict.keys():
+            sizes = np.array(list(channel_dict.keys()))
+            start_sz = sizes[np.argmin(abs(sizes - start_sz))]
+        self.start_sz = start_sz
+
+        # if given ndf, allocate all layers with the same ndf
+        if ndf is None:
+            nfc = channel_dict
+        else:
+            nfc = {k: ndf for k, v in channel_dict.items()}
+
+        # for feature map discriminators with nfc not in channel_dict
+        # this is the case for the pretrained backbone (midas.pretrained)
+        if nc is not None and head is None:
+            nfc[start_sz] = nc
+
+        layers = []
+
+        # Head if the initial input is the full modality
+        if head:
+            layers += [conv2d(nc, nfc[256], 3, 1, 1, bias=False),
+                       nn.LeakyReLU(0.2, inplace=True)]
+
+        # Down Blocks
+        DB = partial(DownBlockPatch, separable=separable) if patch else partial(DownBlock, separable=separable)
+        while start_sz > end_sz:
+            layers.append(DB(nfc[start_sz],  nfc[start_sz//2]))
+            start_sz = start_sz // 2
+
+        layers.append(conv2d(nfc[end_sz], 1, 4, 1, 0, bias=False))
+        self.main = nn.Sequential(*layers)
+
+    def forward(self, x, c):
+        return self.main(x)
+
+
+class SingleDiscCond(nn.Module):
+    def __init__(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False, c_dim=1000, cmap_dim=64, embedding_dim=128):
+        super().__init__()
+        self.cmap_dim = cmap_dim
+
+        # midas channels
+        channel_dict = {4: 512, 8: 512, 16: 256, 32: 128, 64: 64, 128: 64,
+                        256: 32, 512: 16, 1024: 8}
+
+        # interpolate for start sz that are not powers of two
+        if start_sz not in channel_dict.keys():
+            sizes = np.array(list(channel_dict.keys()))
+            start_sz = sizes[np.argmin(abs(sizes - start_sz))]
+        self.start_sz = start_sz
+
+        # if given ndf, allocate all layers with the same ndf
+        if ndf is None:
+            nfc = channel_dict
+        else:
+            nfc = {k: ndf for k, v in channel_dict.items()}
+
+        # for feature map discriminators with nfc not in channel_dict
+        # this is the case for the pretrained backbone (midas.pretrained)
+        if nc is not None and head is None:
+            nfc[start_sz] = nc
+
+        layers = []
+
+        # Head if the initial input is the full modality
+        if head:
+            layers += [conv2d(nc, nfc[256], 3, 1, 1, bias=False),
+                       nn.LeakyReLU(0.2, inplace=True)]
+
+        # Down Blocks
+        DB = partial(DownBlockPatch, separable=separable) if patch else partial(DownBlock, separable=separable)
+        while start_sz > end_sz:
+            layers.append(DB(nfc[start_sz],  nfc[start_sz//2]))
+            start_sz = start_sz // 2
+        self.main = nn.Sequential(*layers)
+
+        # additions for conditioning on class information
+        self.cls = conv2d(nfc[end_sz], self.cmap_dim, 4, 1, 0, bias=False)
+        self.embed = nn.Embedding(num_embeddings=c_dim, embedding_dim=embedding_dim)
+        self.embed_proj = nn.Sequential(
+            nn.Linear(self.embed.embedding_dim, self.cmap_dim),
+            nn.LeakyReLU(0.2, inplace=True),
+        )
+
+    def forward(self, x, c):
+        h = self.main(x)
+        out = self.cls(h)
+
+        # conditioning via projection
+        cmap = self.embed_proj(self.embed(c.argmax(1))).unsqueeze(-1).unsqueeze(-1)
+        out = (out * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
+
+        return out
+
+
+class MultiScaleD(nn.Module):
+    def __init__(
+        self,
+        channels,
+        resolutions,
+        num_discs=4,
+        proj_type=2,  # 0 = no projection, 1 = cross channel mixing, 2 = cross scale mixing
+        cond=0,
+        separable=False,
+        patch=False,
+        **kwargs,
+    ):
+        super().__init__()
+
+        assert num_discs in [1, 2, 3, 4]
+
+        # the first disc is on the lowest level of the backbone
+        self.disc_in_channels = channels[:num_discs]
+        self.disc_in_res = resolutions[:num_discs]
+        Disc = SingleDiscCond if cond else SingleDisc
+
+        mini_discs = []
+        for i, (cin, res) in enumerate(zip(self.disc_in_channels, self.disc_in_res)):
+            start_sz = res if not patch else 16
+            mini_discs += [str(i), Disc(nc=cin, start_sz=start_sz, end_sz=8, separable=separable, patch=patch)],
+        self.mini_discs = nn.ModuleDict(mini_discs)
+
+    def forward(self, features, c):
+        all_logits = []
+        for k, disc in self.mini_discs.items():
+            res = disc(features[k], c).view(features[k].size(0), -1)
+            all_logits.append(res)
+
+        all_logits = torch.cat(all_logits, dim=1)
+        return all_logits
+
+
+class ProjectedDiscriminator(torch.nn.Module):
+    def __init__(
+        self,
+        diffaug=True,
+        interp224=True,
+        backbone_kwargs={},
+        **kwargs
+    ):
+        super().__init__()
+        self.diffaug = diffaug
+        self.interp224 = interp224
+        self.feature_network = F_RandomProj(**backbone_kwargs)
+        self.discriminator = MultiScaleD(
+            channels=self.feature_network.CHANNELS,
+            resolutions=self.feature_network.RESOLUTIONS,
+            **backbone_kwargs,
+        )
+
+    def train(self, mode=True):
+        self.feature_network = self.feature_network.train(False)
+        self.discriminator = self.discriminator.train(mode)
+        return self
+
+    def eval(self):
+        return self.train(False)
+    
+    def get_feature(self, x):
+        features = self.feature_network(x, get_features=True)
+        return features
+
+    def forward(self, x, c):
+        # if self.diffaug:
+        #     x = DiffAugment(x, policy='color,translation,cutout')
+
+        # if self.interp224:
+        #     x = F.interpolate(x, 224, mode='bilinear', align_corners=False)
+
+        features,backbone_features = self.feature_network(x)
+        logits = self.discriminator(features, c)
+
+        return logits,backbone_features
+
diff --git a/mlops/mlflow/registry/Swimswap/pg_modules/projector.py b/mlops/mlflow/registry/Swimswap/pg_modules/projector.py
new file mode 100644
index 0000000..610a482
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/pg_modules/projector.py
@@ -0,0 +1,158 @@
+import torch
+import torch.nn as nn
+import timm
+from pg_modules.blocks import FeatureFusionBlock
+
+
+def _make_scratch_ccm(scratch, in_channels, cout, expand=False):
+    # shapes
+    out_channels = [cout, cout*2, cout*4, cout*8] if expand else [cout]*4
+
+    scratch.layer0_ccm = nn.Conv2d(in_channels[0], out_channels[0], kernel_size=1, stride=1, padding=0, bias=True)
+    scratch.layer1_ccm = nn.Conv2d(in_channels[1], out_channels[1], kernel_size=1, stride=1, padding=0, bias=True)
+    scratch.layer2_ccm = nn.Conv2d(in_channels[2], out_channels[2], kernel_size=1, stride=1, padding=0, bias=True)
+    scratch.layer3_ccm = nn.Conv2d(in_channels[3], out_channels[3], kernel_size=1, stride=1, padding=0, bias=True)
+
+    scratch.CHANNELS = out_channels
+
+    return scratch
+
+
+def _make_scratch_csm(scratch, in_channels, cout, expand):
+    scratch.layer3_csm = FeatureFusionBlock(in_channels[3], nn.ReLU(False), expand=expand, lowest=True)
+    scratch.layer2_csm = FeatureFusionBlock(in_channels[2], nn.ReLU(False), expand=expand)
+    scratch.layer1_csm = FeatureFusionBlock(in_channels[1], nn.ReLU(False), expand=expand)
+    scratch.layer0_csm = FeatureFusionBlock(in_channels[0], nn.ReLU(False))
+
+    # last refinenet does not expand to save channels in higher dimensions
+    scratch.CHANNELS = [cout, cout, cout*2, cout*4] if expand else [cout]*4
+
+    return scratch
+
+
+def _make_efficientnet(model):
+    pretrained = nn.Module()
+    pretrained.layer0 = nn.Sequential(model.conv_stem, model.bn1, model.act1, *model.blocks[0:2])
+    pretrained.layer1 = nn.Sequential(*model.blocks[2:3])
+    pretrained.layer2 = nn.Sequential(*model.blocks[3:5])
+    pretrained.layer3 = nn.Sequential(*model.blocks[5:9])
+    return pretrained
+
+
+def calc_channels(pretrained, inp_res=224):
+    channels = []
+    tmp = torch.zeros(1, 3, inp_res, inp_res)
+
+    # forward pass
+    tmp = pretrained.layer0(tmp)
+    channels.append(tmp.shape[1])
+    tmp = pretrained.layer1(tmp)
+    channels.append(tmp.shape[1])
+    tmp = pretrained.layer2(tmp)
+    channels.append(tmp.shape[1])
+    tmp = pretrained.layer3(tmp)
+    channels.append(tmp.shape[1])
+
+    return channels
+
+
+def _make_projector(im_res, cout, proj_type, expand=False):
+    assert proj_type in [0, 1, 2], "Invalid projection type"
+
+    ### Build pretrained feature network
+    model = timm.create_model('tf_efficientnet_lite0', pretrained=True)
+    pretrained = _make_efficientnet(model)
+
+    # determine resolution of feature maps, this is later used to calculate the number
+    # of down blocks in the discriminators. Interestingly, the best results are achieved
+    # by fixing this to 256, ie., we use the same number of down blocks per discriminator
+    # independent of the dataset resolution
+    im_res = 256
+    pretrained.RESOLUTIONS = [im_res//4, im_res//8, im_res//16, im_res//32]
+    pretrained.CHANNELS = calc_channels(pretrained)
+
+    if proj_type == 0: return pretrained, None
+
+    ### Build CCM
+    scratch = nn.Module()
+    scratch = _make_scratch_ccm(scratch, in_channels=pretrained.CHANNELS, cout=cout, expand=expand)
+    pretrained.CHANNELS = scratch.CHANNELS
+
+    if proj_type == 1: return pretrained, scratch
+
+    ### build CSM
+    scratch = _make_scratch_csm(scratch, in_channels=scratch.CHANNELS, cout=cout, expand=expand)
+
+    # CSM upsamples x2 so the feature map resolution doubles
+    pretrained.RESOLUTIONS = [res*2 for res in pretrained.RESOLUTIONS]
+    pretrained.CHANNELS = scratch.CHANNELS
+
+    return pretrained, scratch
+
+
+class F_RandomProj(nn.Module):
+    def __init__(
+        self,
+        im_res=256,
+        cout=64,
+        expand=True,
+        proj_type=2,  # 0 = no projection, 1 = cross channel mixing, 2 = cross scale mixing
+        **kwargs,
+    ):
+        super().__init__()
+        self.proj_type = proj_type
+        self.cout = cout
+        self.expand = expand
+
+        # build pretrained feature network and random decoder (scratch)
+        self.pretrained, self.scratch = _make_projector(im_res=im_res, cout=self.cout, proj_type=self.proj_type, expand=self.expand)
+        self.CHANNELS = self.pretrained.CHANNELS
+        self.RESOLUTIONS = self.pretrained.RESOLUTIONS
+
+    def forward(self, x, get_features=False):
+        # predict feature maps
+        out0 = self.pretrained.layer0(x)
+        out1 = self.pretrained.layer1(out0)
+        out2 = self.pretrained.layer2(out1)
+        out3 = self.pretrained.layer3(out2)
+
+        # start enumerating at the lowest layer (this is where we put the first discriminator)
+        backbone_features = {
+            '0': out0,
+            '1': out1,
+            '2': out2,
+            '3': out3,
+        }
+        if get_features:
+            return backbone_features
+
+        if self.proj_type == 0: return backbone_features
+
+        out0_channel_mixed = self.scratch.layer0_ccm(backbone_features['0'])
+        out1_channel_mixed = self.scratch.layer1_ccm(backbone_features['1'])
+        out2_channel_mixed = self.scratch.layer2_ccm(backbone_features['2'])
+        out3_channel_mixed = self.scratch.layer3_ccm(backbone_features['3'])
+
+        out = {
+            '0': out0_channel_mixed,
+            '1': out1_channel_mixed,
+            '2': out2_channel_mixed,
+            '3': out3_channel_mixed,
+        }
+
+        if self.proj_type == 1: return out
+
+        # from bottom to top
+        out3_scale_mixed = self.scratch.layer3_csm(out3_channel_mixed)
+        out2_scale_mixed = self.scratch.layer2_csm(out3_scale_mixed, out2_channel_mixed)
+        out1_scale_mixed = self.scratch.layer1_csm(out2_scale_mixed, out1_channel_mixed)
+        out0_scale_mixed = self.scratch.layer0_csm(out1_scale_mixed, out0_channel_mixed)
+
+        out = {
+            '0': out0_scale_mixed,
+            '1': out1_scale_mixed,
+            '2': out2_scale_mixed,
+            '3': out3_scale_mixed,
+        }
+
+        return out, backbone_features
diff --git a/mlops/mlflow/registry/Swimswap/util/add_watermark.py b/mlops/mlflow/registry/Swimswap/util/add_watermark.py
new file mode 100644
index 0000000..a4c5d3d
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/util/add_watermark.py
@@ -0,0 +1,118 @@
+
+import cv2
+import numpy as np
+from PIL import Image
+import math
+import numpy as np
+# import torch
+# from torchvision import transforms
+
+def rotate_image(image, angle, center = None, scale = 1.0):
+    (h, w) = image.shape[:2]
+
+    if center is None:
+        center = (w / 2, h / 2)
+
+    # Perform the rotation
+    M = cv2.getRotationMatrix2D(center, angle, scale)
+    rotated = cv2.warpAffine(image, M, (w, h))
+
+    return rotated
+
+class watermark_image:
+    def __init__(self, logo_path, size=0.3, oritation="DR", margin=(5,20,20,20), angle=15, rgb_weight=(0,1,1.5), input_frame_shape=None) -> None:
+
+        logo_image = cv2.imread(logo_path, cv2.IMREAD_UNCHANGED)
+        h,w,c = logo_image.shape
+        if angle%360 != 0:
+            new_h = w*math.sin(angle/180*math.pi) + h*math.cos(angle/180*math.pi)
+            pad_h = int((new_h-h)//2)
+            
+            padding = np.zeros((pad_h, w, c), dtype=np.uint8)
+            logo_image = cv2.vconcat([logo_image, padding])
+            logo_image = cv2.vconcat([padding, logo_image])
+        
+            logo_image = rotate_image(logo_image, angle)
+        print(logo_image.shape)
+        self.logo_image = logo_image
+        
+        if self.logo_image.shape[2] < 4:
+            print("No alpha channel found!")
+            self.logo_image  = self.__addAlpha__(self.logo_image) #add alpha channel
+        self.size       = size
+        self.oritation  = oritation
+        self.margin     = margin
+        self.ori_shape  = self.logo_image.shape
+        self.resized    = False
+        self.rgb_weight = rgb_weight
+
+        self.logo_image[:, :, 2] = self.logo_image[:, :, 2]*self.rgb_weight[0]
+        self.logo_image[:, :, 1] = self.logo_image[:, :, 1]*self.rgb_weight[1]
+        self.logo_image[:, :, 0] = self.logo_image[:, :, 0]*self.rgb_weight[2]
+
+        if input_frame_shape is not None:
+
+            logo_w = input_frame_shape[1] * self.size
+            ratio  = logo_w / self.ori_shape[1]
+            logo_h = int(ratio * self.ori_shape[0])
+            logo_w = int(logo_w)
+
+            size   = (logo_w, logo_h)
+            self.logo_image = cv2.resize(self.logo_image, size, interpolation = cv2.INTER_CUBIC)
+            self.resized    = True
+            if oritation == "UL":
+                self.coor_h = self.margin[1]
+                self.coor_w = self.margin[0]
+            elif oritation == "UR":
+                self.coor_h = self.margin[1]
+                self.coor_w = input_frame_shape[1] - (logo_w + self.margin[2])
+            elif oritation == "DL":
+                self.coor_h = input_frame_shape[0] - (logo_h + self.margin[1])
+                self.coor_w = self.margin[0]
+            else:
+                self.coor_h = input_frame_shape[0] - (logo_h + self.margin[3])
+                self.coor_w = input_frame_shape[1] - (logo_w + self.margin[2])
+            self.logo_w = logo_w
+            self.logo_h = logo_h
+            self.mask = self.logo_image[:,:,3]
+            self.mask = cv2.bitwise_not(self.mask//255)
+            
+    def apply_frames(self, frame):
+
+        if not self.resized:
+            shape = frame.shape
+            logo_w = shape[1] * self.size
+            ratio  = logo_w / self.ori_shape[1]
+            logo_h = int(ratio * self.ori_shape[0])
+            logo_w = int(logo_w)
+
+            size   = (logo_w, logo_h)
+            self.logo_image = cv2.resize(self.logo_image, size, interpolation = cv2.INTER_CUBIC)
+            self.resized    = True
+            if self.oritation == "UL":
+                self.coor_h = self.margin[1]
+                self.coor_w = self.margin[0]
+            elif self.oritation == "UR":
+                self.coor_h = self.margin[1]
+                self.coor_w = shape[1] - (logo_w + self.margin[2])
+            elif self.oritation == "DL":
+                self.coor_h = shape[0] - (logo_h + self.margin[1])
+                self.coor_w = self.margin[0]
+            else:
+                self.coor_h = shape[0] - (logo_h + self.margin[3])
+                self.coor_w = shape[1] - (logo_w + self.margin[2])
+            self.logo_w = logo_w
+            self.logo_h = logo_h
+            self.mask = self.logo_image[:,:,3]
+            self.mask = cv2.bitwise_not(self.mask//255)
+            
+        original_frame = frame[self.coor_h:(self.coor_h+self.logo_h), self.coor_w:(self.coor_w+self.logo_w),:]
+        blending_logo   = cv2.add(self.logo_image[:,:,0:3],original_frame,mask = self.mask)
+        frame[self.coor_h:(self.coor_h+self.logo_h), self.coor_w:(self.coor_w+self.logo_w),:] = blending_logo 
+        return frame
+        
+    def __addAlpha__(self, image):
+        shape = image.shape
+        alpha_channel = np.ones((shape[0],shape[1],1),np.uint8)*255
+        return np.concatenate((image,alpha_channel),2)
+
diff --git a/mlops/mlflow/registry/Swimswap/util/gifswap.py b/mlops/mlflow/registry/Swimswap/util/gifswap.py
new file mode 100644
index 0000000..e0eaca9
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/util/gifswap.py
@@ -0,0 +1,129 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 19:19:52
+Description: 
+'''
+import os 
+import cv2
+import glob
+import torch
+import shutil
+import numpy as np
+from tqdm import tqdm
+from util.reverse2original import reverse2wholeimage
+import moviepy.editor as mp
+from moviepy.editor import AudioFileClip, VideoFileClip 
+from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
+import  time
+from util.add_watermark import watermark_image
+from util.norm import SpecificNorm
+from parsing_model.model import BiSeNet
+from array2gif import write_gif
+
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+def gif_swap(video_path, id_vetor, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo=False, use_mask=False):
+    video_forcheck = VideoFileClip(video_path)
+    if video_forcheck.audio is None:
+        no_audio = True
+    else:
+        no_audio = False
+
+    del video_forcheck
+
+    if not no_audio:
+        video_audio_clip = AudioFileClip(video_path)
+
+    video = cv2.VideoCapture(video_path)
+    logoclass = watermark_image('./SimSwap/simswaplogo/simswaplogo.png')
+    ret = True
+    frame_index = 0
+
+    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+
+    # video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    fps = video.get(cv2.CAP_PROP_FPS)
+    if  os.path.exists(temp_results_dir):
+            shutil.rmtree(temp_results_dir)
+
+    spNorm =SpecificNorm()
+    if use_mask:
+        n_classes = 19
+        net = BiSeNet(n_classes=n_classes)
+        net.cuda()
+        save_pth = os.path.join('./SimSwap/parsing_model/checkpoint', '79999_iter.pth')
+        net.load_state_dict(torch.load(save_pth))
+        net.eval()
+    else:
+        net =None
+
+    frames = []
+    # while ret:
+    for frame_index in tqdm(range(frame_count)): 
+        ret, frame = video.read()
+        if  ret:
+            detect_results = detect_model.get(frame,crop_size)
+
+            if detect_results is not None:
+                # print(frame_index)
+                # if not os.path.exists(temp_results_dir):
+                #         os.mkdir(temp_results_dir)
+                frame_align_crop_list = detect_results[0]
+                frame_mat_list = detect_results[1]
+                swap_result_list = []
+                frame_align_crop_tenor_list = []
+                for frame_align_crop in frame_align_crop_list:
+
+                    # BGR TO RGB
+                    # frame_align_crop_RGB = frame_align_crop[...,::-1]
+
+                    frame_align_crop_tenor = _totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+                    swap_result = swap_model(None, frame_align_crop_tenor, id_vetor, None, True)[0]
+                    # cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame) # 원본 프레임 img 저장
+                    swap_result_list.append(swap_result)
+                    frame_align_crop_tenor_list.append(frame_align_crop_tenor)
+
+
+                img = reverse2wholeimage(frame_align_crop_tenor_list,swap_result_list, frame_mat_list, crop_size, frame, logoclass,\
+                        os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), no_simswaplogo, pasring_model=net, use_mask=use_mask, norm=spNorm)
+                frames.append(img.tobytes())
+
+            else:
+                if not os.path.exists(temp_results_dir):
+                    os.mkdir(temp_results_dir)
+                frame = frame.astype(np.uint8)
+                # if not no_simswaplogo:
+                    # frame = logoclass.apply_frames(frame)
+                cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+        else:
+            break
+
+    video.release()
+
+    # image_filename_list = []
+    # path = os.path.join(temp_results_dir, '*.jpg')
+    # image_filenames = sorted(glob.glob(path))
+
+    # clips = ImageSequenceClip(image_filenames, fps = fps)
+
+    # if not no_audio:
+    #     clips = clips.set_audio(video_audio_clip)
+
+    # # if save_path.split('.')[-1] == 'gif':
+    # clips.write_gif(save_path)
+
+    # write_gif(frames, save_path, fps=fps)
+    return frames, fps
+    
+
diff --git a/mlops/mlflow/registry/Swimswap/util/html.py b/mlops/mlflow/registry/Swimswap/util/html.py
new file mode 100644
index 0000000..71c48ad
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/util/html.py
@@ -0,0 +1,63 @@
+import dominate
+from dominate.tags import *
+import os
+
+
+class HTML:
+    def __init__(self, web_dir, title, refresh=0):
+        self.title = title
+        self.web_dir = web_dir
+        self.img_dir = os.path.join(self.web_dir, 'images')
+        if not os.path.exists(self.web_dir):
+            os.makedirs(self.web_dir)
+        if not os.path.exists(self.img_dir):
+            os.makedirs(self.img_dir)
+
+        self.doc = dominate.document(title=title)
+        if refresh > 0:
+            with self.doc.head:
+                meta(http_equiv="refresh", content=str(refresh))
+
+    def get_image_dir(self):
+        return self.img_dir
+
+    def add_header(self, str):
+        with self.doc:
+            h3(str)
+
+    def add_table(self, border=1):
+        self.t = table(border=border, style="table-layout: fixed;")
+        self.doc.add(self.t)
+
+    def add_images(self, ims, txts, links, width=512):
+        self.add_table()
+        with self.t:
+            with tr():
+                for im, txt, link in zip(ims, txts, links):
+                    with td(style="word-wrap: break-word;", halign="center", valign="top"):
+                        with p():
+                            with a(href=os.path.join('images', link)):
+                                img(style="width:%dpx" % (width), src=os.path.join('images', im))
+                            br()
+                            p(txt)
+
+    def save(self):
+        html_file = '%s/index.html' % self.web_dir
+        f = open(html_file, 'wt')
+        f.write(self.doc.render())
+        f.close()
+
+
+if __name__ == '__main__':
+    html = HTML('web/', 'test_html')
+    html.add_header('hello world')
+
+    ims = []
+    txts = []
+    links = []
+    for n in range(4):
+        ims.append('image_%d.jpg' % n)
+        txts.append('text_%d' % n)
+        links.append('image_%d.jpg' % n)
+    html.add_images(ims, txts, links)
+    html.save()
diff --git a/mlops/mlflow/registry/Swimswap/util/image_pool.py b/mlops/mlflow/registry/Swimswap/util/image_pool.py
new file mode 100644
index 0000000..63e1877
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/util/image_pool.py
@@ -0,0 +1,31 @@
+import random
+import torch
+from torch.autograd import Variable
+class ImagePool():
+    def __init__(self, pool_size):
+        self.pool_size = pool_size
+        if self.pool_size > 0:
+            self.num_imgs = 0
+            self.images = []
+
+    def query(self, images):
+        if self.pool_size == 0:
+            return images
+        return_images = []
+        for image in images.data:
+            image = torch.unsqueeze(image, 0)
+            if self.num_imgs < self.pool_size:
+                self.num_imgs = self.num_imgs + 1
+                self.images.append(image)
+                return_images.append(image)
+            else:
+                p = random.uniform(0, 1)
+                if p > 0.5:
+                    random_id = random.randint(0, self.pool_size-1)
+                    tmp = self.images[random_id].clone()
+                    self.images[random_id] = image
+                    return_images.append(tmp)
+                else:
+                    return_images.append(image)
+        return_images = Variable(torch.cat(return_images, 0))
+        return return_images
diff --git a/mlops/mlflow/registry/Swimswap/util/json_config.py b/mlops/mlflow/registry/Swimswap/util/json_config.py
new file mode 100644
index 0000000..c68fbff
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/util/json_config.py
@@ -0,0 +1,15 @@
+import  json
+
+
+def readConfig(path):
+    with open(path,'r') as cf:
+        nodelocaltionstr = cf.read()
+        nodelocaltioninf = json.loads(nodelocaltionstr)
+        if isinstance(nodelocaltioninf,str):
+            nodelocaltioninf = json.loads(nodelocaltioninf)
+    return nodelocaltioninf
+
+def writeConfig(path, info):
+    with open(path, 'w') as cf:
+        configjson  = json.dumps(info, indent=4)
+        cf.writelines(configjson)
\ No newline at end of file
diff --git a/mlops/mlflow/registry/Swimswap/util/logo_class.py b/mlops/mlflow/registry/Swimswap/util/logo_class.py
new file mode 100644
index 0000000..044dce3
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/util/logo_class.py
@@ -0,0 +1,44 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: logo_class.py
+# Created Date: Tuesday June 29th 2021
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Monday, 11th October 2021 12:39:55 am
+# Modified By: Chen Xuanhong
+# Copyright (c) 2021 Shanghai Jiao Tong University
+#############################################################
+
+class logo_class:
+    
+    @staticmethod
+    def print_group_logo():
+        logo_str = """
+
+███╗   ██╗██████╗ ███████╗██╗ ██████╗     ███████╗     ██╗████████╗██╗   ██╗
+████╗  ██║██╔══██╗██╔════╝██║██╔════╝     ██╔════╝     ██║╚══██╔══╝██║   ██║
+██╔██╗ ██║██████╔╝███████╗██║██║  ███╗    ███████╗     ██║   ██║   ██║   ██║
+██║╚██╗██║██╔══██╗╚════██║██║██║   ██║    ╚════██║██   ██║   ██║   ██║   ██║
+██║ ╚████║██║  ██║███████║██║╚██████╔╝    ███████║╚█████╔╝   ██║   ╚██████╔╝
+╚═╝  ╚═══╝╚═╝  ╚═╝╚══════╝╚═╝ ╚═════╝     ╚══════╝ ╚════╝    ╚═╝    ╚═════╝ 
+Neural Rendering Special Interesting Group of SJTU
+                                                                            
+        """
+        print(logo_str)
+
+    @staticmethod
+    def print_start_training():
+        logo_str = """
+   _____  __                __     ______              _         _              
+  / ___/ / /_ ____ _ _____ / /_   /_  __/_____ ____ _ (_)____   (_)____   ____ _
+  \__ \ / __// __ `// ___// __/    / /  / ___// __ `// // __ \ / // __ \ / __ `/
+ ___/ // /_ / /_/ // /   / /_     / /  / /   / /_/ // // / / // // / / // /_/ / 
+/____/ \__/ \__,_//_/    \__/    /_/  /_/    \__,_//_//_/ /_//_//_/ /_/ \__, /  
+                                                                       /____/   
+        """
+        print(logo_str)
+
+if __name__=="__main__":
+    # logo_class.print_group_logo()
+    logo_class.print_start_training()
\ No newline at end of file
diff --git a/mlops/mlflow/registry/Swimswap/util/norm.py b/mlops/mlflow/registry/Swimswap/util/norm.py
new file mode 100644
index 0000000..981adcf
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/util/norm.py
@@ -0,0 +1,25 @@
+import torch.nn as nn
+import numpy as np
+import torch
+class SpecificNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(SpecificNorm, self).__init__()
+        self.mean = np.array([0.485, 0.456, 0.406])
+        self.mean = torch.from_numpy(self.mean).float().cuda()
+        self.mean = self.mean.view([1, 3, 1, 1])
+
+        self.std = np.array([0.229, 0.224, 0.225])
+        self.std = torch.from_numpy(self.std).float().cuda()
+        self.std = self.std.view([1, 3, 1, 1])
+
+    def forward(self, x):
+        mean = self.mean.expand([1, 3, x.shape[2], x.shape[3]])
+        std = self.std.expand([1, 3, x.shape[2], x.shape[3]])
+
+        x = (x - mean) / std
+
+        return x
\ No newline at end of file
diff --git a/mlops/mlflow/registry/Swimswap/util/plot.py b/mlops/mlflow/registry/Swimswap/util/plot.py
new file mode 100644
index 0000000..0da1c75
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/util/plot.py
@@ -0,0 +1,37 @@
+import numpy as np
+import math
+import PIL
+
+def postprocess(x):
+    """[0,1] to uint8."""
+    
+    x = np.clip(255 * x, 0, 255)
+    x = np.cast[np.uint8](x)
+    return x
+
+def tile(X, rows, cols):
+    """Tile images for display."""
+    tiling = np.zeros((rows * X.shape[1], cols * X.shape[2], X.shape[3]), dtype = X.dtype)
+    for i in range(rows):
+        for j in range(cols):
+            idx = i * cols + j
+            if idx < X.shape[0]:
+                img = X[idx,...]
+                tiling[
+                        i*X.shape[1]:(i+1)*X.shape[1],
+                        j*X.shape[2]:(j+1)*X.shape[2],
+                        :] = img
+    return tiling
+
+
+def plot_batch(X, out_path):
+    """Save batch of images tiled."""
+    n_channels = X.shape[3]
+    if n_channels > 3:
+        X = X[:,:,:,np.random.choice(n_channels, size = 3)]
+    X = postprocess(X)
+    rc = math.sqrt(X.shape[0])
+    rows = cols = math.ceil(rc)
+    canvas = tile(X, rows, cols)
+    canvas = np.squeeze(canvas)
+    PIL.Image.fromarray(canvas).save(out_path)
\ No newline at end of file
diff --git a/mlops/mlflow/registry/Swimswap/util/reverse2original.py b/mlops/mlflow/registry/Swimswap/util/reverse2original.py
new file mode 100644
index 0000000..81305b8
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/util/reverse2original.py
@@ -0,0 +1,176 @@
+import cv2
+import numpy as np
+# import  time
+import torch
+from torch.nn import functional as F
+import torch.nn as nn
+
+
+def encode_segmentation_rgb(segmentation, no_neck=True):
+    parse = segmentation
+
+    face_part_ids = [1, 2, 3, 4, 5, 6, 10, 12, 13] if no_neck else [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14]
+    mouth_id = 11
+    # hair_id = 17
+    face_map = np.zeros([parse.shape[0], parse.shape[1]])
+    mouth_map = np.zeros([parse.shape[0], parse.shape[1]])
+    # hair_map = np.zeros([parse.shape[0], parse.shape[1]])
+
+    for valid_id in face_part_ids:
+        valid_index = np.where(parse==valid_id)
+        face_map[valid_index] = 255
+    valid_index = np.where(parse==mouth_id)
+    mouth_map[valid_index] = 255
+    # valid_index = np.where(parse==hair_id)
+    # hair_map[valid_index] = 255
+    #return np.stack([face_map, mouth_map,hair_map], axis=2)
+    return np.stack([face_map, mouth_map], axis=2)
+
+
+class SoftErosion(nn.Module):
+    def __init__(self, kernel_size=15, threshold=0.6, iterations=1):
+        super(SoftErosion, self).__init__()
+        r = kernel_size // 2
+        self.padding = r
+        self.iterations = iterations
+        self.threshold = threshold
+
+        # Create kernel
+        y_indices, x_indices = torch.meshgrid(torch.arange(0., kernel_size), torch.arange(0., kernel_size))
+        dist = torch.sqrt((x_indices - r) ** 2 + (y_indices - r) ** 2)
+        kernel = dist.max() - dist
+        kernel /= kernel.sum()
+        kernel = kernel.view(1, 1, *kernel.shape)
+        self.register_buffer('weight', kernel)
+
+    def forward(self, x):
+        x = x.float()
+        for i in range(self.iterations - 1):
+            x = torch.min(x, F.conv2d(x, weight=self.weight, groups=x.shape[1], padding=self.padding))
+        x = F.conv2d(x, weight=self.weight, groups=x.shape[1], padding=self.padding)
+
+        mask = x >= self.threshold
+        x[mask] = 1.0
+        x[~mask] /= x[~mask].max()
+
+        return x, mask
+
+
+def postprocess(swapped_face, target, target_mask,smooth_mask):
+    # target_mask = cv2.resize(target_mask, (self.size,  self.size))
+
+    mask_tensor = torch.from_numpy(target_mask.copy().transpose((2, 0, 1))).float().mul_(1/255.0).cuda()
+    face_mask_tensor = mask_tensor[0] + mask_tensor[1]
+    
+    soft_face_mask_tensor, _ = smooth_mask(face_mask_tensor.unsqueeze_(0).unsqueeze_(0))
+    soft_face_mask_tensor.squeeze_()
+
+    soft_face_mask = soft_face_mask_tensor.cpu().numpy()
+    soft_face_mask = soft_face_mask[:, :, np.newaxis]
+
+    result =  swapped_face * soft_face_mask + target * (1 - soft_face_mask)
+    result = result[:,:,::-1]# .astype(np.uint8)
+    return result
+
+def reverse2wholeimage(b_align_crop_tenor_list, swaped_imgs, mats, crop_size, oriimg, logoclass, save_path='', \
+                    no_simswaplogo=False, pasring_model=None, norm=None, use_mask=False):
+
+    target_image_list = []
+    img_mask_list = []
+    if use_mask:
+        smooth_mask = SoftErosion(kernel_size=17, threshold=0.9, iterations=7).cuda()
+    else:
+        pass
+
+    # print(len(swaped_imgs))
+    # print(mats)
+    # print(len(b_align_crop_tenor_list))
+    for swaped_img, mat ,source_img in zip(swaped_imgs, mats, b_align_crop_tenor_list):
+        swaped_img = swaped_img.cpu().detach().numpy().transpose((1, 2, 0))
+        img_white = np.full((crop_size,crop_size), 255, dtype=float)
+
+        # inverse the Affine transformation matrix
+        mat_rev = np.zeros([2,3])
+        div1 = mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0]
+        mat_rev[0][0] = mat[1][1]/div1
+        mat_rev[0][1] = -mat[0][1]/div1
+        mat_rev[0][2] = -(mat[0][2]*mat[1][1]-mat[0][1]*mat[1][2])/div1
+        div2 = mat[0][1]*mat[1][0]-mat[0][0]*mat[1][1]
+        mat_rev[1][0] = mat[1][0]/div2
+        mat_rev[1][1] = -mat[0][0]/div2
+        mat_rev[1][2] = -(mat[0][2]*mat[1][0]-mat[0][0]*mat[1][2])/div2
+
+        orisize = (oriimg.shape[1], oriimg.shape[0])
+        if use_mask:
+            source_img_norm = norm(source_img)
+            source_img_512  = F.interpolate(source_img_norm,size=(512,512))
+            out = pasring_model(source_img_512)[0]
+            parsing = out.squeeze(0).detach().cpu().numpy().argmax(0)
+            vis_parsing_anno = parsing.copy().astype(np.uint8)
+            tgt_mask = encode_segmentation_rgb(vis_parsing_anno)
+            if tgt_mask.sum() >= 5000:
+                # face_mask_tensor = tgt_mask[...,0] + tgt_mask[...,1]
+                target_mask = cv2.resize(tgt_mask, (crop_size,  crop_size))
+                # print(source_img)
+                target_image_parsing = postprocess(swaped_img, source_img[0].cpu().detach().numpy().transpose((1, 2, 0)), target_mask,smooth_mask)
+                
+
+                target_image = cv2.warpAffine(target_image_parsing, mat_rev, orisize)
+                # target_image_parsing = cv2.warpAffine(swaped_img, mat_rev, orisize)
+            else:
+                target_image = cv2.warpAffine(swaped_img, mat_rev, orisize)[..., ::-1]
+        else:
+            target_image = cv2.warpAffine(swaped_img, mat_rev, orisize)
+        # source_image   = cv2.warpAffine(source_img, mat_rev, orisize)
+
+        img_white = cv2.warpAffine(img_white, mat_rev, orisize)
+
+
+        img_white[img_white>20] =255
+
+        img_mask = img_white
+
+        # if use_mask:
+        #     kernel = np.ones((40,40),np.uint8)
+        #     img_mask = cv2.erode(img_mask,kernel,iterations = 1)
+        # else:
+        kernel = np.ones((40,40),np.uint8)
+        img_mask = cv2.erode(img_mask,kernel,iterations = 1)
+        kernel_size = (20, 20)
+        blur_size = tuple(2*i+1 for i in kernel_size)
+        img_mask = cv2.GaussianBlur(img_mask, blur_size, 0)
+
+        # kernel = np.ones((10,10),np.uint8)
+        # img_mask = cv2.erode(img_mask,kernel,iterations = 1)
+
+
+
+        img_mask /= 255
+
+        img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1])
+
+        # pasing mask
+
+        # target_image_parsing = postprocess(target_image, source_image, tgt_mask)
+
+        if use_mask:
+            target_image = np.array(target_image, dtype=np.float) * 255
+        else:
+            target_image = np.array(target_image, dtype=np.float)[..., ::-1] * 255
+
+
+        img_mask_list.append(img_mask)
+        target_image_list.append(target_image)
+        
+
+    # target_image /= 255
+    # target_image = 0
+    img = np.array(oriimg, dtype=np.float)
+    for img_mask, target_image in zip(img_mask_list, target_image_list):
+        img = img_mask * target_image + (1-img_mask) * img
+        
+    final_img = img.astype(np.uint8)
+    # if not no_simswaplogo:
+    #     final_img = logoclass.apply_frames(final_img)
+    # cv2.imwrite(save_path, final_img) # swap img 저장
+    return final_img
diff --git a/mlops/mlflow/registry/Swimswap/util/save_heatmap.py b/mlops/mlflow/registry/Swimswap/util/save_heatmap.py
new file mode 100644
index 0000000..71ce4c9
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/util/save_heatmap.py
@@ -0,0 +1,57 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: save_heatmap.py
+# Created Date: Friday January 15th 2021
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Wednesday, 19th January 2022 1:22:47 am
+# Modified By: Chen Xuanhong
+# Copyright (c) 2021 Shanghai Jiao Tong University
+#############################################################
+
+import os
+import shutil
+import seaborn as sns
+import matplotlib.pyplot as plt
+import cv2
+import numpy as np
+
+def SaveHeatmap(heatmaps, path, row=-1, dpi=72):
+    """
+    The input tensor must be B X 1 X H X W
+    """
+    batch_size = heatmaps.shape[0]
+    temp_path  = ".temp/"
+    if not os.path.exists(temp_path):
+        os.makedirs(temp_path)
+    final_img = None
+    if row < 1:
+        col = batch_size
+        row = 1
+    else:
+        col = batch_size // row
+        if row * col <batch_size:
+            col +=1
+    
+    row_i = 0
+    col_i = 0
+    
+    for i in range(batch_size):
+        img_path = os.path.join(temp_path,'temp_batch_{}.png'.format(i))
+        sns.heatmap(heatmaps[i,0,:,:],vmin=0,vmax=heatmaps[i,0,:,:].max(),cbar=False)
+        plt.savefig(img_path, dpi=dpi, bbox_inches = 'tight', pad_inches = 0)
+        img = cv2.imread(img_path)
+        if i == 0:
+            H,W,C = img.shape
+            final_img = np.zeros((H*row,W*col,C))
+        final_img[H*row_i:H*(row_i+1),W*col_i:W*(col_i+1),:] = img
+        col_i += 1
+        if col_i >= col:
+            col_i = 0
+            row_i += 1
+    cv2.imwrite(path,final_img)
+
+if __name__ == "__main__":
+    random_map = np.random.randn(16,1,10,10)
+    SaveHeatmap(random_map,"./wocao.png",1)
diff --git a/mlops/mlflow/registry/Swimswap/util/util.py b/mlops/mlflow/registry/Swimswap/util/util.py
new file mode 100644
index 0000000..f4f79ec
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/util/util.py
@@ -0,0 +1,100 @@
+from __future__ import print_function
+import torch
+import numpy as np
+from PIL import Image
+import numpy as np
+import os
+
+# Converts a Tensor into a Numpy array
+# |imtype|: the desired type of the converted numpy array
+def tensor2im(image_tensor, imtype=np.uint8, normalize=True):
+    if isinstance(image_tensor, list):
+        image_numpy = []
+        for i in range(len(image_tensor)):
+            image_numpy.append(tensor2im(image_tensor[i], imtype, normalize))
+        return image_numpy
+    image_numpy = image_tensor.cpu().float().numpy()
+    if normalize:
+        image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
+    else:
+        image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0      
+    image_numpy = np.clip(image_numpy, 0, 255)
+    if image_numpy.shape[2] == 1 or image_numpy.shape[2] > 3:        
+        image_numpy = image_numpy[:,:,0]
+    return image_numpy.astype(imtype)
+
+# Converts a one-hot tensor into a colorful label map
+def tensor2label(label_tensor, n_label, imtype=np.uint8):
+    if n_label == 0:
+        return tensor2im(label_tensor, imtype)
+    label_tensor = label_tensor.cpu().float()    
+    if label_tensor.size()[0] > 1:
+        label_tensor = label_tensor.max(0, keepdim=True)[1]
+    label_tensor = Colorize(n_label)(label_tensor)
+    label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0))
+    return label_numpy.astype(imtype)
+
+def save_image(image_numpy, image_path):
+    image_pil = Image.fromarray(image_numpy)
+    image_pil.save(image_path)
+
+def mkdirs(paths):
+    if isinstance(paths, list) and not isinstance(paths, str):
+        for path in paths:
+            mkdir(path)
+    else:
+        mkdir(paths)
+
+def mkdir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+###############################################################################
+# Code from
+# https://github.com/ycszen/pytorch-seg/blob/master/transform.py
+# Modified so it complies with the Citscape label map colors
+###############################################################################
+def uint82bin(n, count=8):
+    """returns the binary of integer n, count refers to amount of bits"""
+    return ''.join([str((n >> y) & 1) for y in range(count-1, -1, -1)])
+
+def labelcolormap(N):
+    if N == 35: # cityscape
+        cmap = np.array([(  0,  0,  0), (  0,  0,  0), (  0,  0,  0), (  0,  0,  0), (  0,  0,  0), (111, 74,  0), ( 81,  0, 81),
+                     (128, 64,128), (244, 35,232), (250,170,160), (230,150,140), ( 70, 70, 70), (102,102,156), (190,153,153),
+                     (180,165,180), (150,100,100), (150,120, 90), (153,153,153), (153,153,153), (250,170, 30), (220,220,  0),
+                     (107,142, 35), (152,251,152), ( 70,130,180), (220, 20, 60), (255,  0,  0), (  0,  0,142), (  0,  0, 70),
+                     (  0, 60,100), (  0,  0, 90), (  0,  0,110), (  0, 80,100), (  0,  0,230), (119, 11, 32), (  0,  0,142)], 
+                     dtype=np.uint8)
+    else:
+        cmap = np.zeros((N, 3), dtype=np.uint8)
+        for i in range(N):
+            r, g, b = 0, 0, 0
+            id = i
+            for j in range(7):
+                str_id = uint82bin(id)
+                r = r ^ (np.uint8(str_id[-1]) << (7-j))
+                g = g ^ (np.uint8(str_id[-2]) << (7-j))
+                b = b ^ (np.uint8(str_id[-3]) << (7-j))
+                id = id >> 3
+            cmap[i, 0] = r
+            cmap[i, 1] = g
+            cmap[i, 2] = b
+    return cmap
+
+class Colorize(object):
+    def __init__(self, n=35):
+        self.cmap = labelcolormap(n)
+        self.cmap = torch.from_numpy(self.cmap[:n])
+
+    def __call__(self, gray_image):
+        size = gray_image.size()
+        color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0)
+
+        for label in range(0, len(self.cmap)):
+            mask = (label == gray_image[0]).cpu()
+            color_image[0][mask] = self.cmap[label][0]
+            color_image[1][mask] = self.cmap[label][1]
+            color_image[2][mask] = self.cmap[label][2]
+
+        return color_image
diff --git a/mlops/mlflow/registry/Swimswap/util/videoswap.py b/mlops/mlflow/registry/Swimswap/util/videoswap.py
new file mode 100644
index 0000000..99294d5
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/util/videoswap.py
@@ -0,0 +1,121 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 19:19:52
+Description: 
+'''
+import os 
+import cv2
+import glob
+import torch
+import shutil
+import numpy as np
+from tqdm import tqdm
+from util.reverse2original import reverse2wholeimage
+import moviepy.editor as mp
+from moviepy.editor import AudioFileClip, VideoFileClip 
+from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
+import  time
+from util.add_watermark import watermark_image
+from util.norm import SpecificNorm
+from parsing_model.model import BiSeNet
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+def video_swap(video_path, id_vetor, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False,use_mask =False):
+    video_forcheck = VideoFileClip(video_path)
+    if video_forcheck.audio is None:
+        no_audio = True
+    else:
+        no_audio = False
+
+    del video_forcheck
+
+    if not no_audio:
+        video_audio_clip = AudioFileClip(video_path)
+
+    video = cv2.VideoCapture(video_path)
+    logoclass = watermark_image('./simswaplogo/simswaplogo.png')
+    ret = True
+    frame_index = 0
+
+    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+
+    # video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    fps = video.get(cv2.CAP_PROP_FPS)
+    if  os.path.exists(temp_results_dir):
+            shutil.rmtree(temp_results_dir)
+
+    spNorm =SpecificNorm()
+    if use_mask:
+        n_classes = 19
+        net = BiSeNet(n_classes=n_classes)
+        net.cuda()
+        save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
+        net.load_state_dict(torch.load(save_pth))
+        net.eval()
+    else:
+        net =None
+
+    # while ret:
+    for frame_index in tqdm(range(frame_count)): 
+        ret, frame = video.read()
+        if  ret:
+            detect_results = detect_model.get(frame,crop_size)
+
+            if detect_results is not None:
+                # print(frame_index)
+                if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+                frame_align_crop_list = detect_results[0]
+                frame_mat_list = detect_results[1]
+                swap_result_list = []
+                frame_align_crop_tenor_list = []
+                for frame_align_crop in frame_align_crop_list:
+
+                    # BGR TO RGB
+                    # frame_align_crop_RGB = frame_align_crop[...,::-1]
+
+                    frame_align_crop_tenor = _totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+                    swap_result = swap_model(None, frame_align_crop_tenor, id_vetor, None, True)[0]
+                    cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+                    swap_result_list.append(swap_result)
+                    frame_align_crop_tenor_list.append(frame_align_crop_tenor)
+
+                    
+
+                reverse2wholeimage(frame_align_crop_tenor_list,swap_result_list, frame_mat_list, crop_size, frame, logoclass,\
+                    os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,use_mask=use_mask, norm = spNorm)
+
+            else:
+                if not os.path.exists(temp_results_dir):
+                    os.mkdir(temp_results_dir)
+                frame = frame.astype(np.uint8)
+                if not no_simswaplogo:
+                    frame = logoclass.apply_frames(frame)
+                cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+        else:
+            break
+
+    video.release()
+
+    # image_filename_list = []
+    path = os.path.join(temp_results_dir,'*.jpg')
+    image_filenames = sorted(glob.glob(path))
+
+    clips = ImageSequenceClip(image_filenames,fps = fps)
+
+    if not no_audio:
+        clips = clips.set_audio(video_audio_clip)
+
+
+    clips.write_videofile(save_path,audio_codec='aac')
diff --git a/mlops/mlflow/registry/Swimswap/util/videoswap_multispecific.py b/mlops/mlflow/registry/Swimswap/util/videoswap_multispecific.py
new file mode 100644
index 0000000..40e9488
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/util/videoswap_multispecific.py
@@ -0,0 +1,146 @@
+import os 
+import cv2
+import glob
+import torch
+import shutil
+import numpy as np
+from tqdm import tqdm
+from util.reverse2original import reverse2wholeimage
+import moviepy.editor as mp
+from moviepy.editor import AudioFileClip, VideoFileClip 
+from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
+import  time
+from util.add_watermark import watermark_image
+from util.norm import SpecificNorm
+import torch.nn.functional as F
+from parsing_model.model import BiSeNet
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+def video_swap(video_path, target_id_norm_list,source_specific_id_nonorm_list,id_thres, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False,use_mask =False):
+    video_forcheck = VideoFileClip(video_path)
+    if video_forcheck.audio is None:
+        no_audio = True
+    else:
+        no_audio = False
+
+    del video_forcheck
+
+    if not no_audio:
+        video_audio_clip = AudioFileClip(video_path)
+
+    video = cv2.VideoCapture(video_path)
+    logoclass = watermark_image('./simswaplogo/simswaplogo.png')
+    ret = True
+    frame_index = 0
+
+    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+
+    # video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    fps = video.get(cv2.CAP_PROP_FPS)
+    if  os.path.exists(temp_results_dir):
+            shutil.rmtree(temp_results_dir)
+
+    spNorm =SpecificNorm()
+    mse = torch.nn.MSELoss().cuda()
+
+    if use_mask:
+        n_classes = 19
+        net = BiSeNet(n_classes=n_classes)
+        net.cuda()
+        save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
+        net.load_state_dict(torch.load(save_pth))
+        net.eval()
+    else:
+        net =None
+
+    # while ret:
+    for frame_index in tqdm(range(frame_count)): 
+        ret, frame = video.read()
+        if  ret:
+            detect_results = detect_model.get(frame,crop_size)
+
+            if detect_results is not None:
+                # print(frame_index)
+                if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+                frame_align_crop_list = detect_results[0]
+                frame_mat_list = detect_results[1]
+
+                id_compare_values = [] 
+                frame_align_crop_tenor_list = []
+                for frame_align_crop in frame_align_crop_list:
+
+                    # BGR TO RGB
+                    # frame_align_crop_RGB = frame_align_crop[...,::-1]
+
+                    frame_align_crop_tenor = _totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+                    frame_align_crop_tenor_arcnorm = spNorm(frame_align_crop_tenor)
+                    frame_align_crop_tenor_arcnorm_downsample = F.interpolate(frame_align_crop_tenor_arcnorm, size=(112,112))
+                    frame_align_crop_crop_id_nonorm = swap_model.netArc(frame_align_crop_tenor_arcnorm_downsample)
+                    id_compare_values.append([])
+                    for source_specific_id_nonorm_tmp in source_specific_id_nonorm_list:
+                        id_compare_values[-1].append(mse(frame_align_crop_crop_id_nonorm,source_specific_id_nonorm_tmp).detach().cpu().numpy())
+                    frame_align_crop_tenor_list.append(frame_align_crop_tenor)
+
+                id_compare_values_array = np.array(id_compare_values).transpose(1,0)
+                min_indexs = np.argmin(id_compare_values_array,axis=0)
+                min_value = np.min(id_compare_values_array,axis=0)
+
+                swap_result_list = [] 
+                swap_result_matrix_list = []
+                swap_result_ori_pic_list = []
+                for tmp_index, min_index in enumerate(min_indexs):
+                    if min_value[tmp_index] < id_thres:
+                        swap_result = swap_model(None, frame_align_crop_tenor_list[tmp_index], target_id_norm_list[min_index], None, True)[0]
+                        swap_result_list.append(swap_result)
+                        swap_result_matrix_list.append(frame_mat_list[tmp_index])
+                        swap_result_ori_pic_list.append(frame_align_crop_tenor_list[tmp_index])
+                    else:
+                        pass
+
+
+
+                if len(swap_result_list) !=0:
+                    
+                    reverse2wholeimage(swap_result_ori_pic_list,swap_result_list, swap_result_matrix_list, crop_size, frame, logoclass,\
+                        os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,use_mask=use_mask, norm = spNorm)
+                else:
+                    if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+                    frame = frame.astype(np.uint8)
+                    if not no_simswaplogo:
+                        frame = logoclass.apply_frames(frame)
+                    cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+
+            else:
+                if not os.path.exists(temp_results_dir):
+                    os.mkdir(temp_results_dir)
+                frame = frame.astype(np.uint8)
+                if not no_simswaplogo:
+                    frame = logoclass.apply_frames(frame)
+                cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+        else:
+            break
+
+    video.release()
+
+    # image_filename_list = []
+    path = os.path.join(temp_results_dir,'*.jpg')
+    image_filenames = sorted(glob.glob(path))
+
+    clips = ImageSequenceClip(image_filenames,fps = fps)
+
+    if not no_audio:
+        clips = clips.set_audio(video_audio_clip)
+
+
+    clips.write_videofile(save_path,audio_codec='aac')
+
diff --git a/mlops/mlflow/registry/Swimswap/util/videoswap_specific.py b/mlops/mlflow/registry/Swimswap/util/videoswap_specific.py
new file mode 100644
index 0000000..8177efb
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/util/videoswap_specific.py
@@ -0,0 +1,130 @@
+import os 
+import cv2
+import glob
+import torch
+import shutil
+import numpy as np
+from tqdm import tqdm
+from util.reverse2original import reverse2wholeimage
+import moviepy.editor as mp
+from moviepy.editor import AudioFileClip, VideoFileClip 
+from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
+import  time
+from util.add_watermark import watermark_image
+from util.norm import SpecificNorm
+import torch.nn.functional as F
+from parsing_model.model import BiSeNet
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+def video_swap(video_path, id_vetor,specific_person_id_nonorm,id_thres, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False,use_mask =False):
+    video_forcheck = VideoFileClip(video_path)
+    if video_forcheck.audio is None:
+        no_audio = True
+    else:
+        no_audio = False
+
+    del video_forcheck
+
+    if not no_audio:
+        video_audio_clip = AudioFileClip(video_path)
+
+    video = cv2.VideoCapture(video_path)
+    logoclass = watermark_image('./simswaplogo/simswaplogo.png')
+    ret = True
+    frame_index = 0
+
+    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+
+    # video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    fps = video.get(cv2.CAP_PROP_FPS)
+    if  os.path.exists(temp_results_dir):
+            shutil.rmtree(temp_results_dir)
+
+    spNorm =SpecificNorm()
+    mse = torch.nn.MSELoss().cuda()
+
+    if use_mask:
+        n_classes = 19
+        net = BiSeNet(n_classes=n_classes)
+        net.cuda()
+        save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
+        net.load_state_dict(torch.load(save_pth))
+        net.eval()
+    else:
+        net =None
+
+    # while ret:
+    for frame_index in tqdm(range(frame_count)): 
+        ret, frame = video.read()
+        if  ret:
+            detect_results = detect_model.get(frame,crop_size)
+
+            if detect_results is not None:
+                # print(frame_index)
+                if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+                frame_align_crop_list = detect_results[0]
+                frame_mat_list = detect_results[1]
+
+                id_compare_values = [] 
+                frame_align_crop_tenor_list = []
+                for frame_align_crop in frame_align_crop_list:
+
+                    # BGR TO RGB
+                    # frame_align_crop_RGB = frame_align_crop[...,::-1]
+
+                    frame_align_crop_tenor = _totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+                    frame_align_crop_tenor_arcnorm = spNorm(frame_align_crop_tenor)
+                    frame_align_crop_tenor_arcnorm_downsample = F.interpolate(frame_align_crop_tenor_arcnorm, size=(112,112))
+                    frame_align_crop_crop_id_nonorm = swap_model.netArc(frame_align_crop_tenor_arcnorm_downsample)
+
+                    id_compare_values.append(mse(frame_align_crop_crop_id_nonorm,specific_person_id_nonorm).detach().cpu().numpy())
+                    frame_align_crop_tenor_list.append(frame_align_crop_tenor)
+                id_compare_values_array = np.array(id_compare_values)
+                min_index = np.argmin(id_compare_values_array)
+                min_value = id_compare_values_array[min_index]
+                if min_value < id_thres:
+                    swap_result = swap_model(None, frame_align_crop_tenor_list[min_index], id_vetor, None, True)[0]
+                
+                    reverse2wholeimage([frame_align_crop_tenor_list[min_index]], [swap_result], [frame_mat_list[min_index]], crop_size, frame, logoclass,\
+                        os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,use_mask= use_mask, norm = spNorm)
+                else:
+                    if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+                    frame = frame.astype(np.uint8)
+                    if not no_simswaplogo:
+                        frame = logoclass.apply_frames(frame)
+                    cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+
+            else:
+                if not os.path.exists(temp_results_dir):
+                    os.mkdir(temp_results_dir)
+                frame = frame.astype(np.uint8)
+                if not no_simswaplogo:
+                    frame = logoclass.apply_frames(frame)
+                cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+        else:
+            break
+
+    video.release()
+
+    # image_filename_list = []
+    path = os.path.join(temp_results_dir,'*.jpg')
+    image_filenames = sorted(glob.glob(path))
+
+    clips = ImageSequenceClip(image_filenames,fps = fps)
+
+    if not no_audio:
+        clips = clips.set_audio(video_audio_clip)
+
+
+    clips.write_videofile(save_path,audio_codec='aac')
+
diff --git a/mlops/mlflow/registry/Swimswap/util/visualizer.py b/mlops/mlflow/registry/Swimswap/util/visualizer.py
new file mode 100644
index 0000000..584ac45
--- /dev/null
+++ b/mlops/mlflow/registry/Swimswap/util/visualizer.py
@@ -0,0 +1,131 @@
+import numpy as np
+import os
+import ntpath
+import time
+from . import util
+from . import html
+import scipy.misc
+try:
+    from StringIO import StringIO  # Python 2.7
+except ImportError:
+    from io import BytesIO         # Python 3.x
+
+class Visualizer():
+    def __init__(self, opt):
+        # self.opt = opt
+        self.tf_log = opt.tf_log
+        self.use_html = opt.isTrain and not opt.no_html
+        self.win_size = opt.display_winsize
+        self.name = opt.name
+        if self.tf_log:
+            import tensorflow as tf
+            self.tf = tf
+            self.log_dir = os.path.join(opt.checkpoints_dir, opt.name, 'logs')
+            self.writer = tf.summary.FileWriter(self.log_dir)
+
+        if self.use_html:
+            self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
+            self.img_dir = os.path.join(self.web_dir, 'images')
+            print('create web directory %s...' % self.web_dir)
+            util.mkdirs([self.web_dir, self.img_dir])
+        self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
+        with open(self.log_name, "a") as log_file:
+            now = time.strftime("%c")
+            log_file.write('================ Training Loss (%s) ================\n' % now)
+
+    # |visuals|: dictionary of images to display or save
+    def display_current_results(self, visuals, epoch, step):
+        if self.tf_log: # show images in tensorboard output
+            img_summaries = []
+            for label, image_numpy in visuals.items():
+                # Write the image to a string
+                try:
+                    s = StringIO()
+                except:
+                    s = BytesIO()
+                scipy.misc.toimage(image_numpy).save(s, format="jpeg")
+                # Create an Image object
+                img_sum = self.tf.Summary.Image(encoded_image_string=s.getvalue(), height=image_numpy.shape[0], width=image_numpy.shape[1])
+                # Create a Summary value
+                img_summaries.append(self.tf.Summary.Value(tag=label, image=img_sum))
+
+            # Create and write Summary
+            summary = self.tf.Summary(value=img_summaries)
+            self.writer.add_summary(summary, step)
+
+        if self.use_html: # save images to a html file
+            for label, image_numpy in visuals.items():
+                if isinstance(image_numpy, list):
+                    for i in range(len(image_numpy)):
+                        img_path = os.path.join(self.img_dir, 'epoch%.3d_%s_%d.jpg' % (epoch, label, i))
+                        util.save_image(image_numpy[i], img_path)
+                else:
+                    img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.jpg' % (epoch, label))
+                    util.save_image(image_numpy, img_path)
+
+            # update website
+            webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=30)
+            for n in range(epoch, 0, -1):
+                webpage.add_header('epoch [%d]' % n)
+                ims = []
+                txts = []
+                links = []
+
+                for label, image_numpy in visuals.items():
+                    if isinstance(image_numpy, list):
+                        for i in range(len(image_numpy)):
+                            img_path = 'epoch%.3d_%s_%d.jpg' % (n, label, i)
+                            ims.append(img_path)
+                            txts.append(label+str(i))
+                            links.append(img_path)
+                    else:
+                        img_path = 'epoch%.3d_%s.jpg' % (n, label)
+                        ims.append(img_path)
+                        txts.append(label)
+                        links.append(img_path)
+                if len(ims) < 10:
+                    webpage.add_images(ims, txts, links, width=self.win_size)
+                else:
+                    num = int(round(len(ims)/2.0))
+                    webpage.add_images(ims[:num], txts[:num], links[:num], width=self.win_size)
+                    webpage.add_images(ims[num:], txts[num:], links[num:], width=self.win_size)
+            webpage.save()
+
+    # errors: dictionary of error labels and values
+    def plot_current_errors(self, errors, step):
+        if self.tf_log:
+            for tag, value in errors.items():
+                summary = self.tf.Summary(value=[self.tf.Summary.Value(tag=tag, simple_value=value)])
+                self.writer.add_summary(summary, step)
+
+    # errors: same format as |errors| of plotCurrentErrors
+    def print_current_errors(self, epoch, i, errors, t):
+        message = '(epoch: %d, iters: %d, time: %.3f) ' % (epoch, i, t)
+        for k, v in errors.items():
+            if v != 0:
+                message += '%s: %.3f ' % (k, v)
+
+        print(message)
+        with open(self.log_name, "a") as log_file:
+            log_file.write('%s\n' % message)
+
+    # save image to the disk
+    def save_images(self, webpage, visuals, image_path):
+        image_dir = webpage.get_image_dir()
+        short_path = ntpath.basename(image_path[0])
+        name = os.path.splitext(short_path)[0]
+
+        webpage.add_header(name)
+        ims = []
+        txts = []
+        links = []
+
+        for label, image_numpy in visuals.items():
+            image_name = '%s_%s.jpg' % (name, label)
+            save_path = os.path.join(image_dir, image_name)
+            util.save_image(image_numpy, save_path)
+
+            ims.append(image_name)
+            txts.append(label)
+            links.append(image_name)
+        webpage.add_images(ims, txts, links, width=self.win_size)
diff --git a/mlops/mlflow/train/sf2f/GETTING_STARTED.md b/mlops/mlflow/train/sf2f/GETTING_STARTED.md
new file mode 100644
index 0000000..1241f5a
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/GETTING_STARTED.md
@@ -0,0 +1,120 @@
+## Environment Setup
+Create python env:
+```
+conda create -n sf2f python=3.6
+conda activate sf2f
+```
+Our repo is developed with python 3.6, CUDA 10.1, and PyTorch 1.7.1. We suggest you to install the PyTorch version which is suitable for your machine, according to [PyTorch Official Website](https://pytorch.org/).
+```
+# This is the installation command for our environment
+conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch
+
+```
+
+Next, install other python dependencies:
+```
+pip install -r requirements.txt
+```
+
+## Prepare Dataset
+
+**Face Data.** Please download the high-quality face data from [HQ-VoxCeleb](https://github.com/BAI-Yeqi/HQ-VoxCeleb).
+
+**Speech Data.** Please download speech data from VoxCeleb1 and VoxCeleb2 on their [Offical Website](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/).
+
+The speech data need to be converted to mel-spectrograms via STFT with 30ms frame duration, please process the data using our script:
+```
+python ./scripts/convert_wav_to_mel.py --n_jobs 5
+```
+`convert_wav_to_mel.py` used parallel processes to speed up the mel extraction. Feel free to adjust `n_jobs` to adapt to the configuration of your machine. 
+
+**Train-val Split.** To create train-validation split:
+```
+python ./scripts/create_split_json.py
+```
+
+**Dataset Structure.** Refer to the following picture for ```data/``` folder structure.
+<img src="./images/data_VoxCeleb.png" align="center" width="60%">
+
+
+## Download VGGFace2 Resnet Checkpoint
+We developed VGGFace Score (VFS) with pretrained ResNet model on VGGFace2 Dataset (https://github.com/cydonia999/VGGFace2-pytorch).
+
+To make sure VGGFace Score can be evaluated, download this https://drive.google.com/file/d/1A94PAAnwk6L7hXdBXLFosB_s0SzEhAFU/view to ```./scripts/weights/resnet50_ft_weights.pkl```.
+
+
+
+## Launch Training
+
+To train SF2F 1st-stage, where only encoder & decoder is learned:
+```
+python train.py \
+  --path_opt options/vox/sf2f/sf2f_1st_stage.yaml \
+  --batch_size 256 \
+  --visualize_every 10 \
+  --epochs 12000 \
+  --eval_epochs 15
+```
+
+To train SF2F 2nd-stage, where fuser is learned while encoder & decoder are frozen:
+```
+python train.py \
+  --path_opt options/vox/sf2f/sf2f_fuser.yaml \
+  --batch_size 256 \
+  --visualize_every 10 \
+  --epochs 50 \
+  --eval_epochs 1 \
+  --eval_mode_after 100 \
+  --train_fuser_only True \
+  --pretrained_path \
+    output/{sf2f_1st_stage_experiment_pretrained_model}.pt \
+  --learning_rate 1e-4
+```
+
+To train baseline voice2face:
+```
+python train.py \
+  --path_opt options/vox/baseline/v2f.yaml \
+  --batch_size 256 \
+  --visualize_every 10 \
+  --epochs 12000 \
+  --eval_epochs 15
+```
+
+## Visualize Training Process
+
+The training process is visualized with [tensorboard](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/04-utils/tensorboard).
+
+Start the **tensorboard** server:
+```
+tensorboard --logdir='./output' --port=8097
+```
+(Optional) If working on a remote server, mapping the remote tensorboard server to local:
+```
+ssh -N -L localhost:8000:localhost:8097 user@your_remote_server
+```
+Visualize the training process by opening `localhost:8097` (from local)  `localhost:8000` (from remote)
+
+
+## Test
+To evaluate 1st-stage SF2F and baseline models:
+```
+python test.py \
+  --path_opt xxxx.yaml \
+  --batch_size 1 \
+  --checkpoint_start_from output/xxxx/best_with_model.pt \
+  --recall_method cos_sim
+```
+
+To evaluate SF2F with fuser:
+```
+python test.py \
+  --path_opt options/vox/sf2f/sf2f_fuser.yaml \
+  --batch_size 1 \
+  --checkpoint_start_from \
+    output/{sf2f_fuser_experiment_name} \
+  --recall_method cos_sim \
+  --face_gen_mode naive \
+  --train_fuser_only True \
+  --checkpoint L1 cos R10 epoch_2 epoch_4 epoch_6 epoch_8 epoch_10 epoch_12
+```
diff --git a/mlops/mlflow/train/sf2f/datasets/__init__.py b/mlops/mlflow/train/sf2f/datasets/__init__.py
new file mode 100644
index 0000000..1b99502
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/datasets/__init__.py
@@ -0,0 +1,10 @@
+#!/usr/bin/python
+#
+from .utils import imagenet_preprocess, imagenet_deprocess, \
+    imagenet_deprocess_batch, fast_imagenet_deprocess_batch, \
+        fast_mel_deprocess_batch, set_mel_transform, deprocess_and_save, \
+            window_segment
+#from .coco import CocoSceneGraphDataset as coco
+#from .vg import VgSceneGraphDataset as visual_genome
+from .vox_dataset import VoxDataset
+from .build_dataset import build_dataset, build_loaders
diff --git a/mlops/mlflow/train/sf2f/datasets/build_dataset.py b/mlops/mlflow/train/sf2f/datasets/build_dataset.py
new file mode 100644
index 0000000..6bc91c6
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/datasets/build_dataset.py
@@ -0,0 +1,64 @@
+import json
+import os
+import os.path as osp
+import numpy as np
+from datasets import VoxDataset
+from torch.utils.data import DataLoader
+
+
+VOX_DIR = os.path.join('./data', 'VoxCeleb')
+
+
+def build_vox_dsets(data_opts, batch_size, image_size):
+    dset_kwargs = {
+        'data_dir': osp.join(data_opts['root_dir']),
+        'image_size': image_size,
+        'face_type': data_opts.get('face_type', 'masked'),
+        'image_normalize_method': \
+            data_opts.get('image_normalize_method', 'imagenet'),
+        'mel_normalize_method': \
+            data_opts.get('mel_normalize_method', 'vox_mel'),
+        'split_set': 'train',
+        'split_json': \
+            data_opts.get('split_json', os.path.join(VOX_DIR, 'split.json'))
+    }
+    train_dset = VoxDataset(**dset_kwargs)
+    iter_per_epoch = len(train_dset) // batch_size
+    print('There are %d iterations per epoch' % iter_per_epoch)
+
+    # make some modification to dset_kwargs so that it can be used to create val_dset
+    # dset_kwargs['max_samples'] = data_opts["num_val_samples"]
+    # val_dset = visual_genome(**dset_kwargs)
+    dset_kwargs['split_set'] = 'val'
+    val_dset = VoxDataset(**dset_kwargs)
+
+    dset_kwargs['split_set'] = 'test'
+    test_dset = VoxDataset(**dset_kwargs)
+
+    return train_dset, val_dset, test_dset
+
+
+def build_dataset(opts):
+    if opts["dataset"] == "vox":
+        return build_vox_dsets(opts["data_opts"], opts["batch_size"],
+                              opts["image_size"])
+    else:
+        raise ValueError("Unrecognized dataset: {}".format(opts["dataset"]))
+
+
+def build_loaders(opts):
+    train_dset, val_dset, test_dset = build_dataset(opts)
+
+    loader_kwargs = {
+        'batch_size': opts["batch_size"],
+        'num_workers': opts["workers"],
+        'shuffle': True,
+        "drop_last": True,
+        'collate_fn': train_dset.collate_fn,
+    }
+    train_loader = DataLoader(train_dset, **loader_kwargs)
+    loader_kwargs['shuffle'] = False
+    loader_kwargs['drop_last'] = False
+    val_loader = DataLoader(val_dset, **loader_kwargs)
+    test_loader = DataLoader(test_dset, **loader_kwargs)
+    return train_loader, val_loader, test_loader
diff --git a/mlops/mlflow/train/sf2f/datasets/utils.py b/mlops/mlflow/train/sf2f/datasets/utils.py
new file mode 100644
index 0000000..c2bb4e4
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/datasets/utils.py
@@ -0,0 +1,158 @@
+import PIL
+import torch
+import torchvision.transforms as T
+import numpy as np
+
+# Imagenet macros
+IMAGENET_MEAN = [0.485, 0.456, 0.406]
+IMAGENET_STD = [0.229, 0.224, 0.225]
+
+INV_IMAGENET_MEAN = [-m for m in IMAGENET_MEAN]
+INV_IMAGENET_STD = [1.0 / s for s in IMAGENET_STD]
+
+STANDARD_MEAN = [0.5, 0.5, 0.5]
+STANDARD_STD = [0.5, 0.5, 0.5]
+
+INV_STANDARD_MEAN = [-m for m in STANDARD_MEAN]
+INV_STANDARD_STD = [1.0 / s for s in STANDARD_STD]
+
+VOX_MEL_MEAN = [10.9915,]
+VOX_MEL_STD = [3.1661,]
+
+def imagenet_preprocess(normalize_method='imagenet'):
+    if normalize_method == 'imagenet':
+        return T.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)
+    elif normalize_method == 'standard':
+        return T.Normalize(mean=STANDARD_MEAN, std=STANDARD_STD)
+    elif normalize_method == 'vox_mel':
+        return T.Normalize(mean=VOX_MEL_MEAN, std=VOX_MEL_STD)
+
+
+def rescale(x):
+    lo, hi = x.min(), x.max()
+    return x.sub(lo).div(hi - lo + 1e-5)
+
+
+def imagenet_deprocess(rescale_image=True, normalize_method='imagenet'):
+    if normalize_method == 'imagenet':
+        transforms = [
+            T.Normalize(mean=[0, 0, 0], std=INV_IMAGENET_STD),
+            T.Normalize(mean=INV_IMAGENET_MEAN, std=[1.0, 1.0, 1.0]),
+        ]
+    elif normalize_method == 'standard':
+        transforms = [
+            T.Normalize(mean=[0, 0, 0], std=INV_STANDARD_STD),
+            T.Normalize(mean=INV_STANDARD_MEAN, std=[1.0, 1.0, 1.0]),
+        ]
+    if rescale_image:
+        transforms.append(rescale)
+    return T.Compose(transforms)
+
+
+def fast_imagenet_deprocess_batch(imgs, normalize_method='imagenet'):
+    '''
+    ImageNet deprocess batch's non-loop and backpropable implementation
+    '''
+    if normalize_method == 'imagenet':
+        mean = IMAGENET_MEAN
+        std = IMAGENET_STD
+    elif normalize_method == 'standard':
+        mean = STANDARD_MEAN
+        std = STANDARD_STD
+    mean = torch.tensor(mean).type(imgs.type())
+    std = torch.tensor(std).type(imgs.type())
+    # Initialize broadcasting Dims
+    mean = mean.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+    std = std.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+
+    img_de = imgs * std + mean
+    img_de = img_de.mul(255).clamp(0, 255)
+
+    return img_de
+
+def set_mel_transform(mel_normalize_method=None):
+    mel_transform = [T.ToTensor(), ]
+    print('Called mel_normalize_method:',
+        mel_normalize_method)
+    if mel_normalize_method is not None:
+        mel_transform.append(imagenet_preprocess(
+            normalize_method=mel_normalize_method))
+    mel_transform.append(torch.squeeze)
+    mel_transform = T.Compose(mel_transform)
+    return mel_transform
+
+def fast_mel_deprocess_batch(log_mels, normalize_method='vox_mel'):
+    '''
+    Mel Spectrogram deprocess batch's non-loop and backpropable implementation
+    '''
+    if normalize_method == 'vox_mel':
+        mean = VOX_MEL_MEAN[0]
+        std = VOX_MEL_STD[0]
+    mean = torch.tensor(mean).type(log_mels.type())
+    std = torch.tensor(std).type(log_mels.type())
+    log_mels_de = log_mels * std + mean
+
+    return log_mels_de
+
+
+def window_segment(log_mel, window_length, stride_length):
+    """
+    Sliding window segmentation on a log_mel tensor
+    """
+    mel_length = log_mel.shape[1]
+    # Calulate the number of windows that can be generated
+    num_window = 1 + (mel_length - window_length) // stride_length
+    # Sliding Window
+    segments = []
+    for i in range(0, num_window):
+        start_time = i * stride_length
+        segment = log_mel[:, start_time:start_time + window_length]
+        segments.append(segment)
+    segments = torch.stack(segments)
+    return segments
+
+
+def imagenet_deprocess_batch(imgs, rescale=False, normalize_method='imagenet'):
+    """
+    Input:
+    - imgs: FloatTensor of shape (N, C, H, W) giving preprocessed images
+
+    Output:
+    - imgs_de: ByteTensor of shape (N, C, H, W) giving deprocessed images
+      in the range [0, 255]
+    """
+    if isinstance(imgs, torch.autograd.Variable):
+        imgs = imgs.data
+    imgs = imgs.cpu().clone()
+    deprocess_fn = imagenet_deprocess(
+        rescale_image=rescale, normalize_method=normalize_method)
+    imgs_de = []
+    for i in range(imgs.size(0)):
+        img_de = deprocess_fn(imgs[i])[None]
+        img_de = img_de.mul(255).clamp(0, 255).byte()
+        imgs_de.append(img_de)
+    imgs_de = torch.cat(imgs_de, dim=0)
+    return imgs_de
+
+
+def deprocess_and_save(image, normalize_method, save_path):
+    '''
+    Deprocess a image Tensor generated by VoxDataset, and save its image as a
+    jpg
+    '''
+    deprocess_fn = imagenet_deprocess(normalize_method=normalize_method)
+    image = deprocess_fn(image)
+    #print('deprocessed image shape:', image.shape)
+    img = np.array(image)
+    img = np.transpose(img, (1, 2, 0))
+    img = (img * 255).astype(np.uint8)
+    #print(img.shape)
+    #print(np.max(img), np.min(img))
+    img = PIL.Image.fromarray(img)
+    img.save(save_path)
+
+
+def unpack_var(v):
+    if isinstance(v, torch.autograd.Variable):
+        return v.data
+    return v
diff --git a/mlops/mlflow/train/sf2f/datasets/vox_dataset.py b/mlops/mlflow/train/sf2f/datasets/vox_dataset.py
new file mode 100644
index 0000000..d3dbc0f
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/datasets/vox_dataset.py
@@ -0,0 +1,465 @@
+'''
+VoxCeleb DataSet
+'''
+
+
+import json, os
+import numpy as np
+import random
+#import pandas as pd
+#import h5py
+import pickle
+import PIL
+from PIL import Image
+import torch
+from torch.utils.data import Dataset, DataLoader
+from torch.utils.data.dataloader import default_collate
+import torchvision.transforms as T
+#from skimage.transform import resize as imresize
+try:
+    from .utils import imagenet_preprocess, imagenet_deprocess_batch, \
+        fast_imagenet_deprocess_batch, fast_mel_deprocess_batch
+except:
+    from utils import imagenet_preprocess, imagenet_deprocess_batch, \
+        fast_imagenet_deprocess_batch, fast_mel_deprocess_batch
+
+
+VOX_DIR = os.path.join('./data', 'VoxCeleb')
+
+class VoxDataset(Dataset):
+    def __init__(self,
+                 data_dir,
+                 image_size=(64, 64),
+                 face_type='masked',
+                 image_normalize_method='imagenet',
+                 mel_normalize_method='vox_mel',
+                 nframe_range=(100, 150),
+                 split_set='train',
+                 split_json=os.path.join(VOX_DIR, 'split.json'),
+                 return_mel_segments=False,
+                 mel_seg_window_stride=(125, 125),
+                 image_left_right_avg=False,
+                 image_random_hflip=False):
+        '''
+        A PyTorch Dataset for loading VoxCeleb 1 & 2 human speech
+        (as mel spectrograms) and face (as image)
+
+        Inputs:
+        - data: Path to a directory where vox1 & vox2 data are held
+        - face_type: 'masked' or 'origin'
+        - image_size: Shape (h, w) to output the image
+        - image_normalize_method: Method to normalize the image, 'imagenet' or
+            'standard'
+        - return_mel_segments: Return several segments of mel spectrogram
+        - mel_seg_window_stride: Tuple (int, int), defines the window size and
+            stride size when segmenting the mel spectrogram with sliding window
+        - image_left_right_avg: flip the image, average the original and flipped
+            image
+        - image_random_hflip: Add random horizontal image flip to training set
+
+        '''
+        self.data_dir = data_dir
+        self.image_size = image_size
+        self.face_type = face_type
+        self.face_dir = self.face_type + '_faces'
+        self.image_normalize_method = image_normalize_method
+        self.mel_normalize_method = mel_normalize_method
+        self.nframe_range = nframe_range
+        self.split_set = split_set
+        self.split_json = split_json
+        self.return_mel_segments = return_mel_segments
+        self.mel_seg_window_stride = mel_seg_window_stride
+        self.shuffle_mel_segments = True
+        # This attribute is added to make the return segment mode to start from
+        # a random time
+        # Thus improve the randomness in fuser data
+        self.mel_segments_rand_start = False
+        self.image_left_right_avg = image_left_right_avg
+        self.image_random_hflip = image_random_hflip
+
+        self.load_split_dict()
+        self.list_available_names()
+        self.set_image_transform()
+        self.set_mel_transform()
+
+    def __len__(self):
+        return len(self.available_names)
+
+    def set_length(self, length):
+        self.available_names = self.available_names[0:length]
+
+    def __getitem__(self, index):
+        '''
+        Given an index, randomly return a face and a mel_spectrogram of this guy
+        '''
+        sub_dataset, name = self.available_names[index]
+        # Face Image
+        image_dir = os.path.join(
+            self.data_dir, sub_dataset, self.face_dir, name)
+        image_jpg = random.choice(os.listdir(image_dir))
+        image_path = os.path.join(image_dir, image_jpg)
+
+        with open(image_path, 'rb') as f:
+            with PIL.Image.open(f) as image:
+                WW, HH = image.size
+                #print("PIL Image:", np.array(image))
+                image = image.convert('RGB')
+                if self.image_left_right_avg:
+                    arr = (np.array(image) / 2.0 + \
+                        np.array(T.functional.hflip(image)) / 2.0).astype(
+                            np.uint8)
+                    image = PIL.Image.fromarray(arr, mode="RGB")
+                image = self.image_transform(image)
+
+        # Mel Spectrogram
+        mel_gram_dir = os.path.join(
+            self.data_dir, sub_dataset, 'mel_spectrograms', name)
+        mel_gram_pickle = random.choice(os.listdir(mel_gram_dir))
+        mel_gram_path = os.path.join(mel_gram_dir, mel_gram_pickle)
+        if not self.return_mel_segments:
+            # Return single segment
+            log_mel = self.load_mel_gram(mel_gram_path)
+            log_mel = self.mel_transform(log_mel)
+        else:
+            log_mel = self.get_all_mel_segments_of_id(
+                index, shuffle=self.shuffle_mel_segments)
+
+        human_id = torch.tensor(index)
+
+        return image, log_mel, human_id
+
+    def get_all_faces_of_id(self, index):
+        '''
+        Given a id, return all the faces of him as a batch tensor, with shape
+        (N, C, H, W)
+        '''
+        sub_dataset, name = self.available_names[index]
+        faces = []
+        # Face Image
+        image_dir = os.path.join(
+            self.data_dir, sub_dataset, self.face_dir, name)
+        for image_jpg in os.listdir(image_dir):
+            image_path = os.path.join(image_dir, image_jpg)
+            with open(image_path, 'rb') as f:
+                with PIL.Image.open(f) as image:
+                    WW, HH = image.size
+                    #print("PIL Image:", np.array(image))
+                    image = self.image_transform(image.convert('RGB'))
+                    faces.append(image)
+        faces = torch.stack(faces)
+
+        return faces
+
+    def get_all_mel_segments_of_id(self,
+                                   index,
+                                   shuffle=False):
+        '''
+        Given a id, return all the speech segments of him as a batch tensor,
+        with shape (N, C, L)
+        '''
+        sub_dataset, name = self.available_names[index]
+        window_length, stride_length = self.mel_seg_window_stride
+        segments = []
+        # Mel Spectrogram
+        mel_gram_dir = os.path.join(
+            self.data_dir, sub_dataset, 'mel_spectrograms', name)
+        mel_gram_list = os.listdir(mel_gram_dir)
+        if shuffle:
+            random.shuffle(mel_gram_list)
+        else:
+            mel_gram_list.sort()
+        seg_count = 0
+        for mel_gram_pickle in mel_gram_list:
+            mel_gram_path = os.path.join(mel_gram_dir, mel_gram_pickle)
+            log_mel = self.load_mel_gram(mel_gram_path)
+            log_mel = self.mel_transform(log_mel)
+            mel_length = log_mel.shape[1]
+            if self.mel_segments_rand_start:
+
+                start = np.random.randint(mel_length - window_length) if mel_length > window_length else 0
+
+                log_mel = log_mel[:, start:]
+                mel_length = log_mel.shape[1]
+            # Calulate the number of windows that can be generated
+            num_window = 1 + (mel_length - window_length) // stride_length
+            # Sliding Window
+            for i in range(0, num_window):
+                start_time = i * stride_length
+                segment = log_mel[:, start_time:start_time + window_length]
+                segments.append(segment)
+                seg_count = seg_count + 1
+                if seg_count == 20: # 20
+                    segments = torch.stack(segments)
+                    return segments
+        segments = torch.stack(segments)
+        return segments
+
+    def set_image_transform(self):
+        print('Dataloader: called set_image_size', self.image_size)
+        image_transform = [T.Resize(self.image_size), T.ToTensor()]
+        if self.image_random_hflip and self.split_set == 'train':
+            image_transform = [T.RandomHorizontalFlip(p=0.5),] + \
+                image_transform
+        if self.image_normalize_method is not None:
+            print('Dataloader: called image_normalize_method',
+                self.image_normalize_method)
+            image_transform.append(imagenet_preprocess(
+                normalize_method=self.image_normalize_method))
+        self.image_transform = T.Compose(image_transform)
+
+    def set_mel_transform(self):
+        mel_transform = [T.ToTensor(), ]
+        print('Dataloader: called mel_normalize_method',
+            self.mel_normalize_method)
+        if self.mel_normalize_method is not None:
+            mel_transform.append(imagenet_preprocess(
+                normalize_method=self.mel_normalize_method))
+        mel_transform.append(torch.squeeze)
+        self.mel_transform = T.Compose(mel_transform)
+
+    def load_split_dict(self):
+        '''
+        Load the train, val, test set information from split.json
+        '''
+        with open(self.split_json) as json_file:
+            self.split_dict = json.load(json_file)
+
+    def list_available_names(self):
+        '''
+        Find the intersection of speech and face data
+        '''
+        self.available_names = []
+        # List VoxCeleb1 data:
+        for sub_dataset in (['vox1']): #, 'vox2'
+            mel_gram_available = os.listdir(
+                os.path.join(self.data_dir, sub_dataset, 'mel_spectrograms'))
+            face_available = os.listdir(
+                os.path.join(self.data_dir, sub_dataset, self.face_dir))
+            available = \
+                set(mel_gram_available).intersection(face_available)
+            for name in available:
+                if name in self.split_dict[sub_dataset][self.split_set]:
+                    self.available_names.append((sub_dataset, name))
+
+        self.available_names.sort()
+
+    def load_mel_gram(self, mel_pickle):
+        '''
+        Load a speech's mel spectrogram from pickle file.
+
+        Format of the pickled data:
+            LogMel_Features
+            spkid
+            clipid
+            wavid
+
+        Inputs:
+        - mel_pickle: Path to the mel spectrogram to be loaded.
+        '''
+        # open a file, where you stored the pickled data
+        file = open(mel_pickle, 'rb')
+        # dump information to that file
+        data = pickle.load(file)
+        # close the file
+        file.close()
+        log_mel = data['LogMel_Features']
+        #log_mel = np.transpose(log_mel, axes=None)
+
+        return log_mel
+
+    def crop_or_pad(self, log_mel, out_frame):
+        '''
+        Log_mel padding/cropping function to cooperate with collate_fn
+        '''
+        freq, cur_frame = log_mel.shape
+        if cur_frame >= out_frame:
+            # Just crop
+            start = np.random.randint(0, cur_frame-out_frame+1)
+            log_mel = log_mel[..., start:start+out_frame]
+        else:
+            # Padding
+            zero_padding = np.zeros((freq, out_frame-cur_frame))
+            zero_padding = self.mel_transform(zero_padding)
+            if len(zero_padding.shape) == 1:
+                zero_padding = zero_padding.view([-1, 1])
+            log_mel = torch.cat([log_mel, zero_padding], -1)
+
+        return log_mel
+
+    def collate_fn(self, batch):
+        min_nframe, max_nframe = self.nframe_range
+        assert min_nframe <= max_nframe
+        np.random.seed()
+        num_frame = np.random.randint(min_nframe, max_nframe+1)
+        #start = np.random.randint(0, max_nframe-num_frame+1)
+        #batch = [(item[0], item[1][..., start:start+num_frame], item[2])
+        #         for item in batch]
+
+        batch = [(item[0],
+                  self.crop_or_pad(item[1], num_frame),
+                  item[2]) for item in batch]
+        return default_collate(batch)
+
+    def count_faces(self):
+        '''
+        Count the number of faces in the dataset
+        '''
+        total_count = 0
+        for index in range(len(self.available_names)):
+            sub_dataset, name = self.available_names[index]
+            # Face Image
+            image_dir = os.path.join(
+                self.data_dir, sub_dataset, self.face_dir, name)
+            cur_count = len(os.listdir(image_dir))
+            total_count = total_count + cur_count
+        print('Number of faces in current dataset: {}'.format(total_count))
+        return total_count
+
+    def count_speech(self):
+        '''
+        Given a id, return all the speech segments of him as a batch tensor,
+        with shape (N, C, L)
+        '''
+        total_count = 0
+        for index in range(len(self.available_names)):
+            sub_dataset, name = self.available_names[index]
+            window_length, stride_length = self.mel_seg_window_stride
+            # Mel Spectrogram
+            mel_gram_dir = os.path.join(
+                self.data_dir, sub_dataset, 'mel_spectrograms', name)
+            mel_gram_list = os.listdir(mel_gram_dir)
+            cur_count = len(mel_gram_list)
+            total_count = total_count + cur_count
+        print('Number of speech in current dataset: {}'.format(total_count))
+        return total_count
+
+
+if __name__ == '__main__':
+    '''
+    from utils import imagenet_deprocess, deprocess_and_save
+
+    # Config
+    image_size = (256, 256)
+    #image_normalize_method = 'imagenet'
+    image_normalize_method = 'standard'
+    mel_normalize_method = 'vox_mel'
+    test_case_dir = os.path.join('./data', 'test_cases')
+    os.makedirs(test_case_dir, exist_ok=True)
+
+    # Dataset
+    vox_dataset = VoxDataset(
+        data_dir=VOX_DIR,
+        image_size=image_size,
+        image_normalize_method=image_normalize_method,
+        mel_normalize_method=mel_normalize_method)
+    np_mel = vox_dataset.load_mel_gram(mel_pickle= \
+        './data/VoxCeleb/vox1/mel_spectrograms/A.J._Buckley' + \
+            '/id10001_J9lHsKG98U8_00026.pickle')
+    print('np_mel.shape:', np_mel.shape)
+
+    print('dataset length:', len(vox_dataset))
+
+    # Try out 1 case
+    # image, log_mel, mel_len = vox_dataset[220]
+    image, log_mel, human_id = vox_dataset[220]
+    print('image shape:', image.shape)
+    print('log_mel shape:', log_mel.shape)
+    print('human_id:', human_id)
+    # print(mel_len)
+    deprocess_and_save(image, image_normalize_method,
+        os.path.join(test_case_dir, 'vox_dataset_image_test_1.jpg'))
+
+    log_mel = np.array(log_mel)
+    print(log_mel)
+    print(np.max(log_mel), np.min(log_mel))
+
+    # Unit test after train test split update:
+    print('### Testing splitted dataset ###')
+    print('Test identities:', vox_dataset.split_dict['vox1']['test'])
+    '''
+
+    for split_set in ['train', 'val', 'test']:
+        vox_dataset = VoxDataset(
+            data_dir=VOX_DIR,
+            image_size=(64, 64),
+            nframe_range=(300, 600),
+            face_type='masked',
+            image_normalize_method='imagenet',
+            mel_normalize_method='vox_mel',
+            split_set=split_set,
+            split_json=os.path.join(VOX_DIR, 'split.json'))
+        print('Length of {} set: {}'.format(split_set, len(vox_dataset)))
+        vox_dataset.count_faces()
+        vox_dataset.count_speech()
+
+    # Collate Function dataloader test
+    loader_kwargs = {
+        'batch_size': 16,
+        'num_workers': 8,
+        'shuffle': False,
+        "drop_last": True,
+        'collate_fn': vox_dataset.collate_fn,
+    }
+    val_loader = DataLoader(vox_dataset, **loader_kwargs)
+    for iter, batch in enumerate(val_loader):
+        images, log_mels, human_ids = batch
+        print('log_mels.shape:', log_mels.shape)
+        if iter > 10000:
+            break
+
+    for e in range(1000):
+        for iter, batch in enumerate(val_loader):
+            images, log_mels, human_ids = batch
+            print('log_mels.shape:', log_mels.shape)
+            if iter > 10000:
+                break
+
+    '''
+
+    #### Test utils imagenet_deprocess_batch and fast_imagenet_deprocess_batch
+    imgs_de = imagenet_deprocess_batch(
+        images,
+        rescale=False,
+        normalize_method='imagenet')
+    imgs_de_fast = fast_imagenet_deprocess_batch(
+        images,
+        normalize_method='imagenet')
+
+    print('imgs_de:', imgs_de[0])
+    print('imgs_de_fast:', imgs_de_fast[0])
+
+    #### Test utils fast_mel_deprocess_batch
+    log_mels_de = fast_mel_deprocess_batch(
+        log_mels,
+        normalize_method='vox_mel')
+
+    print('log_mels_de:', log_mels_de[0])
+
+    # Test DataLoader Deepcopy
+    from copy import deepcopy
+    val_loader_copy = deepcopy(val_loader)
+    val_loader_copy.dataset.set_length(100)
+    print('length of copied val dataset: {}'.format(
+        len(val_loader_copy.dataset)))
+    print('length of val dataset: {}'.format(
+        len(val_loader.dataset)))
+
+    # Test get all faces
+    faces = val_loader.dataset.get_all_faces_of_id(3)
+    print('get_all_faces_of_id:', faces.shape)
+
+    # Test get all mel segments
+    segments = val_loader.dataset.get_all_mel_segments_of_id(3)
+    print('get_all_mel_segments_of_id:', segments.shape)
+
+    # Test return many segments mode
+    val_loader.collate_fn = default_collate
+    val_loader.dataset.return_mel_segments = True
+    val_loader.dataset.mel_seg_window_stride = (125, 60)
+    for iter, batch in enumerate(val_loader):
+        images, log_mels, human_ids = batch
+        print('log_mels.shape (many segments mode):', log_mels.shape)
+        if iter > 10000:
+            break
+
+    '''
diff --git a/mlops/mlflow/train/sf2f/infer.py b/mlops/mlflow/train/sf2f/infer.py
new file mode 100644
index 0000000..a795e8e
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/infer.py
@@ -0,0 +1,121 @@
+'''
+author: Bai Yeqi
+email: yeqi.bai@yitu-inc.com
+'''
+'''
+Reload the generator checkpoint, inference with provided audio, in form of .wav
+    or .m4a
+
+We infer with different policies:
+    1. Generate the face conditioned on each segment
+    2. Generate the face conditioned on the whole speech
+    3. Generate the face with the fuser
+'''
+
+
+import os
+import shutil
+import glob
+import pyprind
+import glog as log
+import torch
+import numpy as np
+from imageio import imwrite
+
+import models
+from utils.wav2mel import wav_to_mel
+from datasets import imagenet_deprocess_batch, set_mel_transform, \
+    deprocess_and_save, window_segment
+from options.opts import args, options
+
+
+torch.backends.cudnn.benchmark = True
+
+
+def main():
+    global args, options
+    print(args)
+    device = torch.device('cuda')
+    float_dtype = torch.cuda.FloatTensor
+    long_dtype = torch.cuda.LongTensor
+
+    image_normalize_method = options["data"]["data_opts"].get(
+        'image_normalize_method', 'imagenet')
+    print('image_normalize_method:', image_normalize_method)
+    mel_normalize_method = options["data"]["data_opts"].get(
+        'mel_normalize_method', 'vox_mel')
+    mel_transform = set_mel_transform(mel_normalize_method)
+
+    # Model
+    log.info("Building Generative Model...")
+    print(options["generator"])
+    model, _ = models.build_model(
+            options["generator"],
+            image_size=options["data"]["image_size"],
+            checkpoint_start_from=args.checkpoint_start_from)
+    model.cuda().eval()
+
+    exp_dir = os.path.dirname(args.checkpoint_start_from)
+    result_dir = os.path.join(exp_dir, 'infer_result')
+    try:
+        shutil.copytree(args.input_wav_dir, result_dir)
+    except:
+        shutil.rmtree(result_dir)
+        shutil.copytree(args.input_wav_dir, result_dir)
+    voice_path = os.path.join(result_dir, '*.wav')
+    voice_list = glob.glob(voice_path)
+    for filename in voice_list:
+        result_sub_dir = filename.replace('.wav', '')
+        os.makedirs(result_sub_dir, exist_ok=True)
+        # Load mel_spectrogram
+        log_mel = wav_to_mel(filename)
+        log_mel = mel_transform(log_mel).type(float_dtype)
+        #print(log_mel)
+        log_mel_segs = window_segment(
+            log_mel, window_length=125, stride_length=63)
+        log_mel = log_mel.unsqueeze(0)
+
+        # image generated by directly feed in mel gram
+        with torch.no_grad():
+            img_d_fed, others = model(log_mel)
+        if isinstance(img_d_fed, tuple):
+            img_d_fed = img_d_fed[-1]
+        img_d_fed = img_d_fed.cpu().detach()
+        img_d_fed = imagenet_deprocess_batch(
+            img_d_fed, normalize_method=image_normalize_method)
+
+        for j in range(img_d_fed.shape[0]):
+            img_np = img_d_fed[j].numpy().transpose(1, 2, 0)
+            img_path = os.path.join(result_sub_dir, 'd_fed_%d.png' % j)
+            imwrite(img_path, img_np)
+
+        # image generated by each segment
+        with torch.no_grad():
+            imgs_from_segs, others = model(log_mel_segs)
+        if isinstance(imgs_from_segs, tuple):
+            imgs_from_segs = imgs_from_segs[-1]
+        imgs_from_segs = imgs_from_segs.cpu().detach()
+        imgs_from_segs = imagenet_deprocess_batch(
+            imgs_from_segs, normalize_method=image_normalize_method)
+        for j in range(imgs_from_segs.shape[0]):
+            img_np = imgs_from_segs[j].numpy().transpose(1, 2, 0)
+            img_path = os.path.join(result_sub_dir, 'seg_%d.png' % j)
+            imwrite(img_path, img_np)
+
+        # image generated by fuser
+        if args.fuser_infer:
+            with torch.no_grad():
+                imgs_fused, others = model(log_mel_segs.unsqueeze(0))
+            if isinstance(imgs_fused, tuple):
+                imgs_fused = imgs_fused[-1]
+            imgs_fused = imgs_fused.cpu().detach()
+            imgs_fused = imagenet_deprocess_batch(
+                imgs_fused, normalize_method=image_normalize_method)
+            for j in range(imgs_fused.shape[0]):
+                img_np = imgs_fused[j].numpy().transpose(1, 2, 0)
+                img_path = os.path.join(result_sub_dir, 'fused_%d.png' % j)
+                imwrite(img_path, img_np)
+        print(f"Done. file name : {filename}")
+
+if __name__ == '__main__':
+    main()
diff --git a/mlops/mlflow/train/sf2f/inference_fuser.py b/mlops/mlflow/train/sf2f/inference_fuser.py
new file mode 100644
index 0000000..aa7fa89
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/inference_fuser.py
@@ -0,0 +1,92 @@
+'''
+author: Bai Yeqi
+email: yeqi.bai@yitu-inc.com
+'''
+'''
+Reload the generator checkpoint, inference with provided audio, in form of .wav
+    or .m4a
+
+We infer with different policies:
+    1. Generate the face conditioned on each segment
+    2. Generate the face conditioned on the whole speech
+    3. Generate the face with the fuser
+'''
+
+
+import os
+import shutil
+import glob
+import pyprind
+import glog as log
+import torch
+import numpy as np
+from imageio import imwrite
+
+import models
+from utils.wav2mel import wav_to_mel
+from datasets import imagenet_deprocess_batch, set_mel_transform, \
+    deprocess_and_save, window_segment
+from options.opts import args, options
+
+
+torch.backends.cudnn.benchmark = True
+
+
+def main():
+    global args, options
+    print(args)
+    device = torch.device('cuda')
+    float_dtype = torch.cuda.FloatTensor
+    long_dtype = torch.cuda.LongTensor
+
+    image_normalize_method = options["data"]["data_opts"].get(
+        'image_normalize_method', 'imagenet')
+    print('image_normalize_method:', image_normalize_method)
+    mel_normalize_method = options["data"]["data_opts"].get(
+        'mel_normalize_method', 'vox_mel')
+    mel_transform = set_mel_transform(mel_normalize_method)
+
+    # Model
+    log.info("Building Generative Model...")
+    print(options["generator"])
+    model, _ = models.build_model(
+            options["generator"],
+            image_size=options["data"]["image_size"],
+            checkpoint_start_from=args.checkpoint_start_from)
+    model.cuda().eval()
+    
+    # voice_path = os.path.join(args.input_wav_dir, '*.wav')
+    # voice_list = glob.glob(voice_path)
+    # filename = voice_list[0]
+    filename = os.path.join(args.input_wav_file)
+    assert os.path.exists(filename), "File not found: {}".format(filename)
+
+    # for filename in voice_list:
+    # result_sub_dir = filename.replace('.wav', '')
+    # os.makedirs(result_sub_dir, exist_ok=True) # 결과 폴더 생성
+    # Load mel_spectrogram
+    log_mel = wav_to_mel(filename)
+    log_mel = mel_transform(log_mel).type(float_dtype)
+    #print(log_mel)
+    log_mel_segs = window_segment(
+        log_mel, window_length=125, stride_length=63)
+    log_mel = log_mel.unsqueeze(0)
+
+    # image generated by fuser
+    # if args.fuser_infer:
+    with torch.no_grad():
+        imgs_fused, others = model(log_mel_segs.unsqueeze(0))
+    if isinstance(imgs_fused, tuple):
+        imgs_fused = imgs_fused[-1]
+    imgs_fused = imgs_fused.cpu().detach()
+    imgs_fused = imagenet_deprocess_batch(
+        imgs_fused, normalize_method=image_normalize_method)
+    for j in range(imgs_fused.shape[0]):
+        img_np = imgs_fused[j].numpy().transpose(1, 2, 0) # 64x64x3
+        # img_path = os.path.join(result_sub_dir, 'fused_%d.png' % j) # 이미지 저장할 파일
+        # imwrite(img_path, img_np) # 이미지 저장
+
+    return img_np.tobytes()
+
+if __name__ == '__main__':
+    main()
diff --git a/mlops/mlflow/train/sf2f/model_registry.py b/mlops/mlflow/train/sf2f/model_registry.py
new file mode 100644
index 0000000..6e40efb
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/model_registry.py
@@ -0,0 +1,65 @@
+from options.opts import  options
+import models
+import torch
+import os, glob
+import mlflow
+from utils.wav2mel import wav_to_mel
+from datasets import imagenet_deprocess_batch, set_mel_transform, \
+    deprocess_and_save, window_segment
+    
+os.environ["MLFLOW_S3_ENDPOINT_URL"] = ""
+os.environ["MLFLOW_TRACKING_URI"] = ""
+os.environ["AWS_ACCESS_KEY_ID"] = ""
+os.environ["AWS_SECRET_ACCESS_KEY"] = ""
+mlflow.set_experiment("new-exp")
+model, _ = models.build_model(
+            options["generator"],
+            image_size=[128,128],
+            checkpoint_start_from="/home/hojun/Documents/project/boostcamp/final_project/mlops/temp/voice2face-mlops/best_IS_with_model.pt")
+model.cuda().eval()
+    
+voice_path = os.path.join("/home/hojun/Documents/project/boostcamp/final_project/mlops/mlflow/train/mlflow/sf2f/data/", '*.wav')
+voice_list = glob.glob(voice_path)
+filename = voice_list[0]
+print(filename)
+
+
+
+#데이터 전처리
+mel_transform = set_mel_transform("vox_mel")
+image_normalize_method = 'imagenet'
+log_mel = wav_to_mel(filename)
+float_dtype = torch.cuda.FloatTensor
+log_mel = mel_transform(log_mel).type(float_dtype)
+
+log_mel_segs = window_segment(
+log_mel, window_length=125, stride_length=63)
+log_mel = log_mel.unsqueeze(0)
+
+with torch.no_grad():
+    imgs_fused, others = model(log_mel_segs.unsqueeze(0))
+    print("dksldklwkdlw : ",type(imgs_fused))
+if isinstance(imgs_fused, tuple):
+    imgs_fused = imgs_fused[-1]
+imgs_fused = imgs_fused.cpu().detach()
+imgs_fused = imagenet_deprocess_batch(
+    imgs_fused, normalize_method=image_normalize_method)
+for j in range(imgs_fused.shape[0]):
+    img_np = imgs_fused[j].numpy().transpose(1, 2, 0) # 64x64x3
+# img_np = torch.from_numpy(img_np)
+# log_mel_segs = log_mel_segs.unsqueeze(0).cpu().numpy()
+print(type(img_np))
+print(type(log_mel_segs))
+signature = mlflow.models.signature.infer_signature(model_input=log_mel_segs.unsqueeze(0).cpu().numpy(), model_output=img_np)
+input_sample = log_mel_segs.unsqueeze(0).cpu().numpy()
+
+
+
+with mlflow.start_run():
+    mlflow.pytorch.log_model(
+        pytorch_model=model,
+        artifact_path = "sf2f_pytorch",
+        signature= signature,
+        input_example = input_sample,
+        # pip_requirements = "rec.txt"
+    )
\ No newline at end of file
diff --git a/mlops/mlflow/train/sf2f/models/__init__.py b/mlops/mlflow/train/sf2f/models/__init__.py
new file mode 100644
index 0000000..694bee5
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/models/__init__.py
@@ -0,0 +1,19 @@
+from .model_setup import *
+from .encoder_decoder import EncoderDecoder
+
+# Lower level encoder decoders
+from .voice_encoders import V2F1DCNN
+from .face_decoders import V2FDecoder
+
+# Progressive GAN
+# from .pgan import PGan_Net
+# from .pgan_dis import PGAN_Discriminator
+
+# Discriminator
+from .discriminators import PatchDiscriminator
+from .discriminators import AcDiscriminator
+from .discriminators import AcCropDiscriminator
+from .discriminators import CondPatchDiscriminator
+
+# Facenet for Perceptual Loss and Evaluation
+from .inception_resnet_v1 import InceptionResnetV1, fixed_image_standardization
diff --git a/mlops/mlflow/train/sf2f/models/attention.py b/mlops/mlflow/train/sf2f/models/attention.py
new file mode 100644
index 0000000..e74b2af
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/models/attention.py
@@ -0,0 +1,223 @@
+'''
+Attenion implementation
+Adapted from torchnlp:
+    https://github.com/PetrochukM/PyTorch-NLP/tree/master/torchnlp/nn
+'''
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from math import sqrt
+try:
+    from .layers import get_activation, get_normalization_2d
+except:
+    from layers import get_activation, get_normalization_2d
+
+
+class AttnBlock(nn.Module):
+    def __init__(self,
+                 attn_dim_in,
+                 attn_dim_out,
+                 attn_num_query=16,
+                 attn_type='general',
+                 normalization='none'):
+        super(AttnBlock, self).__init__()
+        self.attn_layer = AttnLayer(
+            attn_dim_in,
+            attn_dim_out,
+            attn_num_query,
+            attn_type,
+            normalization)
+
+    def forward(self, cat_tensor, context):
+        N, C, H, W = cat_tensor.shape
+        attn_map = self.attn_layer(context.transpose(1, 2))
+        #print('attn_map 1:', attn_map.shape)
+        attn_map = F.interpolate(attn_map, (H, W))
+        #print('attn_map 2:', attn_map.shape)
+        map_out = torch.cat([cat_tensor, attn_map], 1)
+
+        return map_out
+
+    def return_attn_weights(self):
+        return self.attn_layer.attn_weights
+
+
+class AttnLayer(nn.Module):
+    def __init__(self,
+                 dim_in,
+                 dim_out,
+                 num_query=16,
+                 attention_type='general',
+                 normalization='none'):
+        super(AttnLayer, self).__init__()
+        self.num_query = num_query
+        self.query = nn.Parameter(
+            torch.randn((1, num_query, dim_in),
+            requires_grad=True))
+        self.attention = Attention(
+            dimensions=dim_in,
+            dim_out=dim_out,
+            attention_type=attention_type)
+        self.norm_layer = get_normalization_2d(
+            dim_out, normalization)
+        # Cache Attenion Weights for visualization
+        self.attn_weights = None
+
+    def forward(self, context):
+        N, L, D_in = context.shape
+        # Repeat query layer over batch dim
+        query = self.query.repeat(N, 1, 1)
+        # **output** (:class:`torch.FloatTensor`
+        # [batch size, output length, dim_out]
+        attn_out, attn_weights = self.attention(query, context)
+        self.attn_weights = attn_weights
+        N, Q_L, D_out = attn_out.shape
+        # [batch size, dim_out, output length]
+        attn_out = torch.transpose(attn_out, 1, 2)
+        #  [batch size, dimensions, H, W]
+        H = W = int(sqrt(self.num_query))
+        attn_map = attn_out.view(
+            N, D_out, H, W)
+        if self.norm_layer is not None:
+            attn_map = self.norm_layer(attn_map)
+
+        return attn_map
+
+
+class Attention(nn.Module):
+    """ Applies attention mechanism on the `context` using the `query`.
+
+    **Thank you** to IBM for their initial implementation of :class:`Attention`. Here is
+    their `License
+    <https://github.com/IBM/pytorch-seq2seq/blob/master/LICENSE>`__.
+
+    Args:
+        dimensions (int): Dimensionality of the query and context.
+        attention_type (str, optional): How to compute the attention score:
+
+            * dot: :math:`score(H_j,q) = H_j^T q`
+            * general: :math:`score(H_j, q) = H_j^T W_a q`
+
+    Example:
+
+         >>> attention = Attention(256)
+         >>> query = torch.randn(5, 1, 256)
+         >>> context = torch.randn(5, 5, 256)
+         >>> output, weights = attention(query, context)
+         >>> output.size()
+         torch.Size([5, 1, 256])
+         >>> weights.size()
+         torch.Size([5, 1, 5])
+    """
+
+    def __init__(self,
+                 dimensions,
+                 dim_out,
+                 attention_type='general',
+                 linear_out_layer=True,
+                 ignore_tanh=False):
+        super(Attention, self).__init__()
+
+        if attention_type not in ['dot', 'general']:
+            raise ValueError('Invalid attention type selected.')
+
+        # Attention Setup
+        self.attention_type = attention_type
+        if self.attention_type == 'general':
+            self.linear_in = nn.Linear(dimensions, dimensions, bias=False)
+        self.softmax = nn.Softmax(dim=-1)
+
+        # Output Setup
+        self.linear_out_layer = linear_out_layer
+        if self.linear_out_layer:
+            self.dim_out = dim_out
+            self.linear_out = nn.Linear(dimensions * 2, dim_out, bias=False)
+
+        self.ignore_tanh = ignore_tanh
+        if not ignore_tanh:
+            self.tanh = nn.Tanh()
+
+    def forward(self, query, context):
+        """
+        Args:
+            query (:class:`torch.FloatTensor` [batch size, output length,
+                dimensions]): Sequence of queries to query the context.
+            context (:class:`torch.FloatTensor` [batch size, query length,
+                dimensions]): Data overwhich to apply the attention mechanism.
+
+        Returns:
+            :class:`tuple` with `output` and `weights`:
+            * **output** (:class:`torch.LongTensor` [batch size, output length,
+                dimensions]):
+                    Tensor containing the attended features.
+            * **weights** (:class:`torch.FloatTensor` [batch size,
+                output length, query length]):
+                    Tensor containing attention weights.
+        """
+        batch_size, output_len, dimensions = query.size()
+        query_len = context.size(1)
+
+        if self.attention_type == "general":
+            query = query.reshape(batch_size * output_len, dimensions)
+            query = self.linear_in(query)
+            query = query.reshape(batch_size, output_len, dimensions)
+
+        # TODO: Include mask on PADDING_INDEX?
+
+        # (batch_size, output_len, dimensions) * \
+        # (batch_size, query_len, dimensions) ->
+        # (batch_size, output_len, query_len)
+        # batch matrix-matrix product
+        attention_scores = torch.bmm(
+            query, context.transpose(1, 2).contiguous())
+
+        # Compute weights across every context sequence
+        attention_scores = attention_scores.view(
+            batch_size * output_len, query_len)
+        attention_weights = self.softmax(attention_scores)
+        attention_weights = attention_weights.view(
+            batch_size, output_len, query_len)
+
+        # (batch_size, output_len, query_len) * \
+        # (batch_size, query_len, dimensions) ->
+        # (batch_size, output_len, dimensions)
+        mix = torch.bmm(attention_weights, context)
+
+        if self.linear_out_layer:
+            # Concatenate the attention output with the context and process the
+            # concatenated feature with a fc output layer
+            # concat -> (batch_size * output_len, 2*dimensions)
+            combined = torch.cat((mix, query), dim=2)
+            combined = combined.view(batch_size * output_len, 2 * dimensions)
+            # Apply linear_out on every 2nd dimension of concat
+            # output -> (batch_size, output_len, dimensions)
+            output = self.linear_out(combined).view(
+                batch_size, output_len, self.dim_out)
+        else:
+            # Directly output the attention output
+            output = mix
+
+        if not self.ignore_tanh:
+            output = self.tanh(output)
+
+        return output, attention_weights
+
+
+if __name__ == '__main__':
+    attention = Attention(256, 100, linear_out_layer=False)
+    query = torch.randn(5, 1, 256)
+    context = torch.randn(5, 5, 256)
+    cat_tensor = torch.randn(5, 170, 128, 128)
+    output, weights = attention(query, context)
+    print('output.shape:', output.shape)
+    '''
+    attn_layer = AttnLayer(256, 110, 16)
+    attn_map = attn_layer(context)
+    print('attn_map.shape:', attn_map.shape)
+    for param in attn_layer.parameters():
+        print(type(param.data), param.size())
+
+    attn_block = AttnBlock(256, 110, 16, normalization='batch')
+    cat_map = attn_block(cat_tensor, context.transpose(1, 2))
+    print('cat_map.shape:', cat_map.shape)
+    '''
diff --git a/mlops/mlflow/train/sf2f/models/crn.py b/mlops/mlflow/train/sf2f/models/crn.py
new file mode 100644
index 0000000..33cd9a3
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/models/crn.py
@@ -0,0 +1,152 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .layers import get_normalization_2d
+from .layers import get_activation
+
+
+"""
+Cascaded refinement network architecture, as described in:
+Qifeng Chen and Vladlen Koltun,
+"Photographic Image Synthesis with Cascaded Refinement Networks",
+ICCV 2017
+"""
+
+
+class RefinementModule(nn.Module):
+    def __init__(self, layout_dim, input_dim, output_dim,
+                 normalization='instance', activation='leakyrelu'):
+        super(RefinementModule, self).__init__()
+
+        layers = []
+        layers.append(nn.Conv2d(layout_dim + input_dim, output_dim,
+                                kernel_size=3, padding=1))
+        layers.append(get_normalization_2d(output_dim, normalization))
+        layers.append(get_activation(activation))
+        layers.append(nn.Conv2d(output_dim, output_dim,
+                                kernel_size=3, padding=1))
+        layers.append(get_normalization_2d(output_dim, normalization))
+        layers.append(get_activation(activation))
+        layers = [layer for layer in layers if layer is not None]
+        for layer in layers:
+            if isinstance(layer, nn.Conv2d):
+                nn.init.kaiming_normal_(layer.weight)
+        self.net = nn.Sequential(*layers)
+
+    def forward(self, layout, feats):
+        _, _, HH, WW = layout.size()
+        _, _, H, W = feats.size()
+        assert HH >= H
+        if HH > H:
+            factor = round(HH // H)
+            assert HH % factor == 0
+            assert WW % factor == 0 and WW // factor == W
+            layout = F.avg_pool2d(layout, kernel_size=factor, stride=factor)
+        net_input = torch.cat([layout, feats], dim=1)
+        out = self.net(net_input)
+        return out
+
+
+class RefinementNetwork(nn.Module):
+    def __init__(self, dims, normalization='instance',
+                 activation='leakyrelu',
+                 use_tanh=False,
+                 use_deconv=False,
+                 multi_resolution=False,):
+        super(RefinementNetwork, self).__init__()
+        layout_dim = dims[0]
+        self.refinement_modules = nn.ModuleList()
+        self.upsample_modules = nn.ModuleList()
+        self.multi_resolution = multi_resolution
+        for i in range(1, len(dims)):
+            input_dim = 1 if i == 1 else (dims[i] if use_deconv else dims[i-1])
+            output_dim = dims[i]
+            mod = RefinementModule(layout_dim, input_dim, output_dim,
+                                   normalization=normalization, activation=activation)
+            self.refinement_modules.append(mod)
+            if use_deconv:
+                if i == 1:
+                    mod = nn.Sequential()
+                else:
+                    mod = nn.Sequential(
+                        nn.ConvTranspose2d(dims[i-1], dims[i],
+                            kernel_size=2, stride=2,),
+                        get_normalization_2d(dims[i], normalization),
+                        get_activation(activation)
+                    )
+                self.upsample_modules.append(mod)
+        output_conv_layers = None
+        if not multi_resolution:
+            output_conv_layers = [
+                nn.Conv2d(dims[-1], dims[-1], kernel_size=3, padding=1),
+                get_activation(activation),
+                nn.Conv2d(dims[-1], 3, kernel_size=1, padding=0),
+            ]
+            if use_tanh:
+                ## added according to Pix2Pix-HD
+                output_conv_layers.append(nn.Tanh(),)
+            nn.init.kaiming_normal_(output_conv_layers[0].weight)
+            nn.init.kaiming_normal_(output_conv_layers[2].weight)
+        else:
+            output_conv_layers_low = [
+                nn.Conv2d(dims[-2], dims[-2], kernel_size=3, padding=1),
+                get_activation(activation),
+                nn.Conv2d(dims[-2], 3, kernel_size=1, padding=0),
+            ]
+            output_conv_layers_high = [
+                nn.Conv2d(dims[-1], dims[-1], kernel_size=3, padding=1),
+                get_activation(activation),
+                nn.Conv2d(dims[-1], 3, kernel_size=1, padding=0),
+            ]
+            if use_tanh:
+                ## added according to Pix2Pix-HD
+                output_conv_layers_low.append(nn.Tanh(),)
+                output_conv_layers_high.append(nn.Tanh(),)
+
+        if output_conv_layers:
+            self.output_conv = nn.Sequential(*output_conv_layers)
+        else:
+            assert output_conv_layers_low
+            assert output_conv_layers_high
+            self.output_conv_low = nn.Sequential(*output_conv_layers_low)
+            self.output_conv_high = nn.Sequential(*output_conv_layers_high)
+
+    def forward(self, layout):
+        """
+        Output will have same size as layout
+        """
+        # H, W = self.output_size
+        N, _, H, W = layout.size()
+        self.layout = layout
+
+        # Figure out size of input
+        input_H, input_W = H, W
+        for _ in range(len(self.refinement_modules)):
+            input_H //= 2
+            input_W //= 2
+
+        assert input_H != 0
+        assert input_W != 0
+
+        feats = torch.zeros(N, 1, input_H, input_W).to(layout)
+        if not self.multi_resolution:
+            for i, mod in enumerate(self.refinement_modules):
+                if i == 0 or len(self.upsample_modules) == 0:
+                    feats = F.interpolate(feats, scale_factor=2, mode='nearest')
+                else:
+                    feats = self.upsample_modules[i](feats)
+                feats = mod(layout, feats)
+            out = self.output_conv(feats)
+            return (out, feats)
+        elif self.multi_resolution:
+            for i, mod in enumerate(self.refinement_modules):
+                if i == 0 or len(self.upsample_modules) == 0:
+                    feats = F.interpolate(feats, scale_factor=2, mode='nearest')
+                else:
+                    feats = self.upsample_modules[i](feats)
+                feats = mod(layout, feats)
+                if i == len(self.refinement_modules) - 2:
+                    out_low = self.output_conv_low(feats)
+            out_high = self.output_conv_high(feats)
+            return (out_high, out_low)
diff --git a/mlops/mlflow/train/sf2f/models/discriminators.py b/mlops/mlflow/train/sf2f/models/discriminators.py
new file mode 100644
index 0000000..f0f276d
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/models/discriminators.py
@@ -0,0 +1,110 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from utils.bilinear import crop_bbox_batch, uncrop_bbox
+from utils.box_utils import box_union, box_in_region
+from .layers import GlobalAvgPool, Flatten, get_activation, build_cnn
+import models
+
+
+class PatchDiscriminator(nn.Module):
+    def __init__(self, arch, normalization='batch', activation='leakyrelu-0.2',
+                 padding='same', pooling='avg', input_size=(128, 128),
+                 layout_dim=0):
+        super(PatchDiscriminator, self).__init__()
+        input_dim = 3 + layout_dim
+        arch = 'I%d,%s' % (input_dim, arch)
+        cnn_kwargs = {
+            'arch': arch,
+            'normalization': normalization,
+            'activation': activation,
+            'pooling': pooling,
+            'padding': padding,
+        }
+        self.cnn, output_dim = build_cnn(**cnn_kwargs)
+        self.classifier = nn.Conv2d(output_dim, 1, kernel_size=1, stride=1)
+
+    def forward(self, x):
+        return self.classifier(self.cnn(x))
+
+
+class AcDiscriminator(nn.Module):
+    def __init__(self, arch, normalization='none', activation='relu',
+                 padding='same', pooling='avg', num_id=None):
+        super(AcDiscriminator, self).__init__()
+        # self.vocab = vocab
+
+        cnn_kwargs = {
+            'arch': arch,
+            'normalization': normalization,
+            'activation': activation,
+            'pooling': pooling,
+            'padding': padding,
+        }
+        cnn, D = build_cnn(**cnn_kwargs)
+        self.cnn = nn.Sequential(cnn, GlobalAvgPool(), nn.Linear(D, 1024))
+        self.num_id = num_id
+
+        self.real_classifier = nn.Linear(1024, 1)
+        self.id_classifier = nn.Linear(1024, num_id)
+
+    def forward(self, x, y):
+        if x.dim() == 3:
+            x = x[:, None]
+
+        vecs = self.cnn(x)
+        real_scores = self.real_classifier(vecs)
+        id_scores = self.id_classifier(vecs)
+        ac_loss = F.cross_entropy(id_scores, y)
+        return real_scores, ac_loss
+
+
+class AcCropDiscriminator(nn.Module):
+    def __init__(self, arch, normalization='none', activation='relu',
+                 object_size=64, padding='same', pooling='avg'):
+        super(AcCropDiscriminator, self).__init__()
+        self.discriminator = AcDiscriminator(arch, normalization,
+                                             activation, padding, pooling)
+        self.object_size = object_size
+
+    def forward(self, imgs, objs, boxes, obj_to_img,
+                object_crops=None, **kwargs):
+        if object_crops is None:
+            object_crops = crop_bbox_batch(imgs, boxes, obj_to_img, self.object_size)
+        real_scores, ac_loss = self.discriminator(object_crops, objs)
+        return real_scores, ac_loss, object_crops
+
+
+# Conditional Discriminator
+class CondPatchDiscriminator(nn.Module):
+    def __init__(self, arch, normalization='batch', activation='leakyrelu-0.2',
+                 padding='same', pooling='avg', input_size=(128, 128),
+                 cond_dim=0):
+        super(CondPatchDiscriminator, self).__init__()
+        input_dim = 3
+        arch = 'I%d,%s' % (input_dim, arch)
+        cnn_kwargs = {
+            'arch': arch,
+            'normalization': normalization,
+            'activation': activation,
+            'pooling': pooling,
+            'padding': padding,
+        }
+        self.cnn, output_dim = build_cnn(**cnn_kwargs)
+        self.joint_conv = nn.Sequential(
+            nn.Conv2d(output_dim+cond_dim, output_dim, \
+                kernel_size=3, stride=1, padding=1, bias=False),
+            nn.BatchNorm2d(output_dim),
+            nn.LeakyReLU(0.2, inplace=True)
+        )
+        self.classifier = nn.Conv2d(output_dim, 1, kernel_size=1, stride=1)
+
+    def forward(self, x, cond_vecs):
+        if len(cond_vecs) == 2:
+            cond_vecs = cond_vecs.view(-1, cond_vecs.size(1), 1, 1)
+        x = self.cnn(x)
+        cond_vecs = cond_vecs.expand(-1, cond_vecs.size(1), x.size(2), x.size(3))
+        x = torch.cat([x, cond_vecs], dim=1)
+        x = self.joint_conv(x)
+        return self.classifier(x)
diff --git a/mlops/mlflow/train/sf2f/models/encoder_decoder.py b/mlops/mlflow/train/sf2f/models/encoder_decoder.py
new file mode 100644
index 0000000..d564d86
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/models/encoder_decoder.py
@@ -0,0 +1,87 @@
+'''
+Flexible Encoder-Decoder Framework
+'''
+
+
+import torch
+import torch.nn as nn
+import functools
+from torch.autograd import Variable
+import numpy as np
+
+try:
+    from .model_collection import model_collection
+except:
+    from model_collection import model_collection
+#from .voice_encoders import V2F1DCNN
+#from .face_decoders import V2FDecoder
+
+
+class EncoderDecoder(nn.Module):
+    def __init__(self,
+                 encoder_arch,
+                 encoder_kwargs,
+                 decoder_arch,
+                 decoder_kwargs,
+                 image_size):
+        super(EncoderDecoder, self).__init__()
+        # Initialize Encoder Decoder
+        self.encoder = getattr(model_collection, encoder_arch)(**encoder_kwargs)
+        self.decoder = getattr(model_collection, decoder_arch)(**decoder_kwargs)
+        #self.model = nn.Sequential(
+        #    self.encoder,
+        #    self.decoder
+        #)
+
+    def forward(self, x, average_voice_embedding=False):
+        '''
+        average_encoder_embedding: an option for evaluation, not needed for
+        training
+        '''
+        others = {}
+        if self.encoder.return_seq:
+            emb, seq_emb = self.encoder(x)
+            imgs_pred = self.decoder(emb, seq_emb)
+        else:
+            emb = self.encoder(x)
+            if average_voice_embedding:
+                emb = torch.mean(emb, 0, keepdim=True)
+            imgs_pred = self.decoder(emb)
+        others['cond'] = emb
+        return imgs_pred, others
+
+    def train_fuser_only(self):
+        print('Training Attention Fuser Only')
+        for name, param in self.named_parameters():
+            if 'attn_fuser' in name:
+                param.requires_grad = True
+            else:
+                param.requires_grad = False
+
+
+if __name__ == '__main__':
+    # Demo Input
+    log_mels = torch.ones((16, 40, 127))
+
+    # Test V2F 1D CNN
+    v2f_id_cnn_kwargs = {
+        'input_channel': 40,
+        'channels': [256, 384, 576, 864],
+        'output_channel': 64,
+        }
+    # Test V2F 1D CNN
+    v2f_decoder_kwargs = {
+        'input_channel': 64,
+        'channels': [1024, 512, 256, 128, 64],
+        'output_channel': 3,
+        }
+
+    baseline_v2f = EncoderDecoder(
+        encoder_arch='V2F1DCNN',
+        encoder_kwargs=v2f_id_cnn_kwargs,
+        decoder_arch='V2FDecoder',
+        decoder_kwargs=v2f_decoder_kwargs
+    )
+
+    print(baseline_v2f)
+    print('baseline_v2f Output shape:', baseline_v2f(log_mels).shape)
diff --git a/mlops/mlflow/train/sf2f/models/face_decoders.py b/mlops/mlflow/train/sf2f/models/face_decoders.py
new file mode 100644
index 0000000..a05a8b7
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/models/face_decoders.py
@@ -0,0 +1,583 @@
+'''
+Implementations of different Face Decoders
+'''
+
+
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+import torch.nn.functional as F
+import functools
+import numpy as np
+from collections import OrderedDict
+try:
+    from .attention import AttnBlock
+except:
+    from attention import AttnBlock
+try:
+    from .layers import get_activation, get_normalization_2d
+except:
+    from layers import get_activation, get_normalization_2d
+try:
+    from .networks import upBlock, GLU
+except:
+    from networks import upBlock, GLU
+
+
+class DeConv2DBLK(nn.Module):
+    def __init__(self,
+                 input_channel,
+                 output_channel,
+                 kernel_size,
+                 stride,
+                 padding,
+                 activation='relu',
+                 normalization='none'):
+        super(DeConv2DBLK, self).__init__()
+        self.layers = []
+        self.layers.append(
+            nn.ConvTranspose2d(
+                input_channel,
+                output_channel,
+                kernel_size,
+                stride,
+                padding,
+                bias=True))
+        self.layers.append(get_activation(activation))
+        self.layers.append(
+            get_normalization_2d(
+                output_channel,
+                normalization))
+        self.layers = [layer for layer in self.layers if \
+            layer is not None]
+        self.model = nn.Sequential(*self.layers)
+
+    def forward(self, x):
+        x = self.model(x)
+        return x
+
+
+class ExtraFeatureBlock(nn.Module):
+    def __init__(self,
+                 dim_in,
+                 dim_hidden,
+                 normalization='none'):
+        super(ExtraFeatureBlock, self).__init__()
+        self.projection = nn.Linear(dim_in, dim_hidden, bias=True)
+        self.norm_layer = get_normalization_2d(
+            dim_hidden // 16, normalization)
+
+    def forward(self, embedding, cat_tensor):
+        '''
+        Transform the embedding, then concat it to the hidden tensor map
+        '''
+        # Projected Embedding
+        #print('embedding:', embedding.shape)
+        N, D_in, _, _ = embedding.shape
+        proj_emb = self.projection(embedding.view(N, -1))
+        N, D_hid = proj_emb.shape
+        feat_map = proj_emb.view(N, -1, 4, 4)
+        if self.norm_layer is not None:
+            feat_map = self.norm_layer(feat_map)
+        N, C, H, W = cat_tensor.shape
+        feat_map = F.interpolate(feat_map, (H, W))
+        map_out = torch.cat([cat_tensor, feat_map], 1)
+
+        return map_out
+
+
+class V2FDecoder(nn.Module):
+    def __init__(self,
+                 input_channel,
+                 channels,
+                 output_channel,
+                 normalization='none'):
+        super(V2FDecoder, self).__init__()
+        output_size = 2 ** (len(channels) + 1)
+        print('V2FDecoder: According to your channels list, ' + \
+            'the output will be generated with shape ({}, {})'.format(
+                output_size, output_size))
+        #torch.nn.ConvTranspose2d(
+        #in_channels, out_channels, kernel_size, stride=1, padding=0,
+        #output_padding=0, groups=1, bias=True,
+        #dilation=1, padding_mode='zeros')
+        self.layers = []
+        # First Layer
+        layer_id = 0
+        self.layers.append(
+            DeConv2DBLK(input_channel,
+                        channels[0],
+                        kernel_size=4,
+                        stride=1,
+                        padding=0,
+                        activation='relu',
+                        normalization=normalization))
+        # Hidden Layers
+        for layer_id in range(1, len(channels)):
+            self.layers.append(
+                DeConv2DBLK(channels[layer_id - 1],
+                            channels[layer_id],
+                            kernel_size=4,
+                            stride=2,
+                            padding=1,
+                            activation='relu',
+                            normalization=normalization))
+        # Output Layer
+        layer_id = layer_id + 1
+        self.layers.append(
+            DeConv2DBLK(channels[layer_id - 1],
+                        output_channel,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        activation='none',
+                        normalization='none'))
+        self.model = nn.Sequential(*self.layers)
+
+    def forward(self, x):
+        x = self.model(x)
+        return x
+
+
+class MRDecoder(nn.Module):
+    def __init__(self,
+                 input_channel,
+                 base_block_channels,
+                 mid_block_channel=32,
+                 high_block_channel=None,
+                 output_channel=3,
+                 normalization='none',
+                 use_up_block=False,
+                 extra_feature_map=False):
+        super(MRDecoder, self).__init__()
+        output_sizes = []
+        output_sizes.append(
+            2 ** (len(base_block_channels) + 1))
+        output_sizes.append(
+            2 ** (len(base_block_channels) + 2))
+        if high_block_channel is not None:
+            self.build_high_img = True
+            output_sizes.append(
+                2 ** (len(base_block_channels) + 3))
+        else:
+            self.build_high_img = False
+        print('MRDecoder: According to your channels list:')
+        for i, output_size in enumerate(output_sizes):
+            print('\tOutput {} will be generated with shape ({}, {})'.format(
+                i, output_size, output_size))
+
+        self.input_channel = input_channel
+        self.base_block_channels = base_block_channels
+        self.mid_block_channel = mid_block_channel
+        self.high_block_channel = high_block_channel
+        self.output_channel = output_channel
+        self.normalization = normalization
+        self.use_up_block = use_up_block
+        self.extra_feature_map = extra_feature_map
+
+        # Output Layer
+        self.build_base_block()
+        self.build_low_block()
+        self.build_mid_block()
+        if self.build_high_img:
+            self.build_high_block()
+
+    def build_base_block(self):
+        #torch.nn.ConvTranspose2d(
+        #in_channels, out_channels, kernel_size, stride=1, padding=0,
+        #output_padding=0, groups=1, bias=True,
+        #dilation=1, padding_mode='zeros')
+        self.base_block_layers = []
+        # First Layer
+        layer_id = 0
+        self.base_block_layers.append(
+            DeConv2DBLK(self.input_channel,
+                        self.base_block_channels[0],
+                        kernel_size=4,
+                        stride=1,
+                        padding=0,
+                        activation='relu',
+                        normalization=self.normalization))
+        # Hidden Layers
+        for layer_id in range(1, len(self.base_block_channels)):
+            self.base_block_layers.append(
+                DeConv2DBLK(self.base_block_channels[layer_id - 1],
+                            self.base_block_channels[layer_id],
+                            kernel_size=4,
+                            stride=2,
+                            padding=1,
+                            activation='relu',
+                            normalization=self.normalization))
+        self.base_block = nn.Sequential(*self.base_block_layers)
+
+    def build_low_block(self):
+        self.low_output_layer = \
+            DeConv2DBLK(self.base_block_channels[-1],
+                        self.output_channel,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        activation='none',
+                        normalization='none')
+
+    def build_mid_block(self):
+        extra_channel = 0
+        if self.extra_feature_map:
+            # Mid-resolution ExtraFeatureBlock
+            self.mid_ef_block = ExtraFeatureBlock(
+                dim_in=self.input_channel,
+                dim_hidden=self.input_channel,
+                normalization=self.normalization)
+            extra_channel = self.input_channel // 16
+        if self.use_up_block:
+            self.mid_hidden_layer = \
+                upBlock(self.base_block_channels[-1] + extra_channel,
+                        self.mid_block_channel)
+        else:
+            self.mid_hidden_layer = \
+                DeConv2DBLK(self.base_block_channels[-1] + extra_channel,
+                            self.mid_block_channel,
+                            kernel_size=4,
+                            stride=2,
+                            padding=1,
+                            activation='relu',
+                            normalization=self.normalization)
+        self.mid_output_layer = \
+            DeConv2DBLK(self.mid_block_channel,
+                        self.output_channel,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        activation='none',
+                        normalization='none')
+
+    def build_high_block(self):
+        extra_channel = 0
+        if self.extra_feature_map:
+            # High-resolution ExtraFeatureBlock
+            self.high_ef_block = ExtraFeatureBlock(
+                dim_in=self.input_channel,
+                dim_hidden=self.input_channel,
+                normalization=self.normalization)
+            extra_channel = self.input_channel // 16
+        if self.use_up_block:
+            self.high_hidden_layer = \
+                upBlock(self.mid_block_channel + extra_channel,
+                        self.high_block_channel)
+        else:
+            self.high_hidden_layer = \
+                DeConv2DBLK(self.mid_block_channel + extra_channel,
+                            self.high_block_channel,
+                            kernel_size=4,
+                            stride=2,
+                            padding=1,
+                            activation='relu',
+                            normalization=self.normalization)
+        self.high_output_layer = \
+            DeConv2DBLK(self.high_block_channel,
+                        self.output_channel,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        activation='none',
+                        normalization='none')
+
+    def forward(self, x):
+        low_code = self.base_block(x)
+        low_imgs = self.low_output_layer(low_code)
+        #print('low_code:', low_code.shape)
+        if self.extra_feature_map:
+            low_code = self.mid_ef_block(x, low_code)
+            #print('low_code:', low_code.shape)
+        mid_code = self.mid_hidden_layer(low_code)
+        mid_imgs = self.mid_output_layer(mid_code)
+        if self.build_high_img:
+            #print('mid_code:', mid_code.shape)
+            if self.extra_feature_map:
+                mid_code = self.high_ef_block(x, mid_code)
+                #print('mid_code:', mid_code.shape)
+            high_code = self.high_hidden_layer(mid_code)
+            high_imgs = self.high_output_layer(high_code)
+            return (low_imgs, mid_imgs, high_imgs)
+        else:
+            return (low_imgs, mid_imgs)
+
+
+class AttnV2FDecoder(nn.Module):
+    def __init__(self,
+                 input_channel,
+                 cnn_channels,
+                 attn_channels,
+                 output_channel,
+                 seq_in_dim,
+                 normalization='none'):
+        super(AttnV2FDecoder, self).__init__()
+        output_size = 2 ** (len(cnn_channels) + 1)
+        print('V2FDecoder: According to your channels list, ' + \
+            'the output will be generated with shape ({}, {})'.format(
+                output_size, output_size))
+        #torch.nn.ConvTranspose2d(
+        #in_channels, out_channels, kernel_size, stride=1, padding=0,
+        #output_padding=0, groups=1, bias=True,
+        #dilation=1, padding_mode='zeros')
+        self.layers = []
+        self.layer_names = []
+        # First Layer
+        layer_id = 0
+        self.layers.append(
+            DeConv2DBLK(input_channel,
+                        cnn_channels[0],
+                        kernel_size=4,
+                        stride=1,
+                        padding=0,
+                        activation='relu',
+                        normalization=normalization))
+        self.layer_names.append('DeConv2DBLK{}'.format(layer_id))
+        # Hidden Layers
+        for layer_id in range(1, len(cnn_channels)):
+            self.layers.append(
+                DeConv2DBLK(
+                    cnn_channels[layer_id - 1]+attn_channels[layer_id - 1],
+                    cnn_channels[layer_id],
+                    kernel_size=4,
+                    stride=2,
+                    padding=1,
+                    activation='relu',
+                    normalization=normalization))
+            self.layer_names.append('DeConv2DBLK{}'.format(layer_id))
+            attn_channel = attn_channels[layer_id]
+            if attn_channel > 0:
+                self.layers.append(
+                    AttnBlock(attn_dim_in=seq_in_dim,
+                              attn_dim_out=attn_channel,
+                              attn_num_query=16,
+                              normalization=normalization))
+                self.layer_names.append('AttnBlock{}'.format(layer_id))
+        # Output Layer
+        layer_id = layer_id + 1
+        self.layers.append(
+            DeConv2DBLK(cnn_channels[layer_id - 1]+attn_channels[layer_id - 1],
+                        output_channel,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        activation='none',
+                        normalization='none'))
+        self.layer_names.append('DeConv2DBLK{}'.format(layer_id))
+
+        for layer_id, layer in enumerate(self.layers):
+            self.add_module(self.layer_names[layer_id], layer)
+
+    def forward(self, x, seq_feat):
+        for layer, name in zip(self.layers, self.layer_names):
+            if 'AttnBlock' in name:
+                x = layer(x, seq_feat)
+            else:
+                x = layer(x)
+            #print(x.shape)
+        return x
+
+    def return_attn_weights(self):
+        for layer, name in zip(self.layers, self.layer_names):
+            if 'AttnBlock' in name:
+                return layer.return_attn_weights()
+
+
+class FaceGanDecoder(nn.Module):
+    def __init__(self,
+                 image_size=(64, 64),
+                 normalization='batch',
+                 activation='leakyrelu-0.2',
+                 mlp_normalization='none',
+                 noise_dim=128,
+                 **kwargs):
+
+        super(FaceGanDecoder, self).__init__()
+        if len(kwargs) > 0:
+            print('WARNING: Model got unexpected kwargs ', kwargs)
+
+        self.dim = noise_dim
+        self.image_size = image_size
+
+        self.fc = nn.Sequential(
+            nn.Linear(self.dim, self.dim * 4 * 4 * 2, bias=False),
+            nn.BatchNorm1d(self.dim * 4 * 4 * 2),
+            GLU())
+
+        up_num, up_layers = [], []
+        num_in, num_out = 1, 1
+        up_num = range(1, int(np.log2(image_size[0]))-1)
+
+        for i, num in enumerate(up_num):
+            if i == 0:
+                num_in = num
+            num_out = 2**num
+            up_layers.append(upBlock(self.dim//num_in, self.dim//num_out))
+            num_in = num_out
+
+        self.up_layers = nn.Sequential(*up_layers)
+
+        gnet_layers = [
+            nn.Conv2d(self.dim//num_out, self.dim//num_out, kernel_size=3, padding=1),
+            get_activation('leakyrelu'),
+            nn.Conv2d(self.dim//num_out, 3, kernel_size=1, padding=0),
+        ]
+        self.gnet = nn.Sequential(*gnet_layers)
+
+    def forward(self, x):
+        """
+        Required Inputs:
+        - placeholder: This is a placeholder.
+
+        Optional Inputs:
+        - placeholder: This is a placeholder.
+        """
+
+        vecs = self.fc(x.view(-1, 512))
+        vecs = vecs.view(x.size()[0], -1, 4, 4)
+
+        img_code = self.up_layers(vecs)
+        img_pred = self.gnet(img_code)
+
+        return img_pred
+
+
+class FaceGanDecoder_v2(nn.Module):
+    def __init__(self,
+                 image_size=(64, 64),
+                 normalization='batch',
+                 activation='leakyrelu-0.2',
+                 mlp_normalization='none',
+                 noise_dim=128,
+                 **kwargs):
+
+        super(FaceGanDecoder_v2, self).__init__()
+        if len(kwargs) > 0:
+            print('WARNING: Model got unexpected kwargs ', kwargs)
+
+        self.dim = noise_dim
+        self.image_size = image_size
+
+        self.fc = nn.Sequential(
+            nn.Linear(self.dim, self.dim * 4 * 4 * 2, bias=False),
+            nn.BatchNorm1d(self.dim * 4 * 4 * 2),
+            GLU())
+
+        up_num, up_layers = [], []
+        num_in, num_out = 1, 1
+        up_num = range(1, int(np.log2(image_size[0]))-1)
+
+        for i, num in enumerate(up_num):
+            if i == 0:
+                num_in = num
+            num_out = 2**num
+            up_layers.append(upBlock(self.dim//num_in, self.dim//num_out))
+            num_in = num_out
+
+        self.up_low_layers = nn.Sequential(*up_layers)
+        self.up_mid_layer = upBlock(self.dim//num_out, self.dim//num_out//2)
+
+        gnet_layers = [
+            nn.Conv2d(self.dim//num_out, self.dim//num_out, kernel_size=3, padding=1),
+            get_activation('leakyrelu'),
+            nn.Conv2d(self.dim//num_out, 3, kernel_size=1, padding=0),
+        ]
+        self.gnet_low = nn.Sequential(*gnet_layers)
+
+        gnet_layers = [
+            nn.Conv2d(self.dim//num_out//2, self.dim//num_out//2, kernel_size=3, padding=1),
+            get_activation('leakyrelu'),
+            nn.Conv2d(self.dim//num_out//2, 3, kernel_size=1, padding=0),
+        ]
+        self.gnet_mid = nn.Sequential(*gnet_layers)
+
+    def forward(self, x):
+        """
+        Required Inputs:
+        - placeholder: This is a placeholder.
+
+        Optional Inputs:
+        - placeholder: This is a placeholder.
+        """
+
+        vecs = self.fc(x.view(-1, 512))
+        vecs = vecs.view(x.size()[0], -1, 4, 4)
+
+        img_code_low = self.up_low_layers(vecs)
+        img_pred_low = self.gnet_low(img_code_low)
+
+        img_code_mid = self.up_mid_layer(img_code_low)
+        img_pred_mid = self.gnet_mid(img_code_mid)
+
+        return (img_pred_low, img_pred_mid)
+
+
+if __name__ == '__main__':
+    # Demo Input
+    voice_embeddings = torch.ones((16, 512, 1, 1))
+    seq_voice_embeddings = torch.ones((16, 512, 270))
+
+    # Test V2F 1D CNN
+    mr_decoder_kwargs = {
+        'input_channel': 512,
+        'base_block_channels': [1024, 512, 256, 128, 64],
+        'mid_block_channel': 32,
+        'high_block_channel': 32,
+        'output_channel': 3,
+        'normalization': 'batch',
+        'use_up_block': True,
+        'extra_feature_map': True,
+        }
+    mr_decoder = MRDecoder(**mr_decoder_kwargs)
+    print(mr_decoder)
+    for i, imgs in enumerate(mr_decoder(voice_embeddings)):
+        print('MRDecoder Output {} shape:'.format(i), imgs.shape)
+
+    # Test V2F Decoder
+    v2f_decoder_kwargs = {
+        'input_channel': 512,
+        'channels': [1024, 512, 256, 128, 64],
+        'output_channel': 3,
+        'normalization': 'batch',
+        }
+    v2f_decoder = V2FDecoder(**v2f_decoder_kwargs)
+    print(v2f_decoder)
+    print('V2FDecoder Output shape:', v2f_decoder(voice_embeddings).shape)
+    print('V2F Sequential:')
+    print(v2f_decoder.model[5])
+    for name, param in v2f_decoder.model[5].named_parameters():
+        print(name)
+    #for module in v2f_decoder.model:
+    #    print(module)
+
+    v2f_decoder_kwargs = {
+        'input_channel': 512,
+        'cnn_channels': [1024, 512, 256, 128, 64],
+        'attn_channels': [0, 0, 128, 0, 32],
+        'output_channel': 3,
+        'seq_in_dim': 512,
+        'normalization': 'none',
+        }
+    attn_v2f_decoder = AttnV2FDecoder(**v2f_decoder_kwargs)
+    print(attn_v2f_decoder)
+    print('AttnV2FDecoder Output shape:', \
+        attn_v2f_decoder(voice_embeddings, seq_voice_embeddings).shape)
+    print('Return Attention Weights:',
+          attn_v2f_decoder.return_attn_weights().shape,
+          attn_v2f_decoder.return_attn_weights()[0][0][0:10])
+
+    dimLatentVector = 512
+    depthScale0 = 512
+
+    pgandecoder = PganDecoder(dimLatent=dimLatentVector,
+                          depthScale0=depthScale0,
+                          initBiasToZero=True,
+                          leakyReluLeak=0.2,
+                          normalization=True,
+                          generationActivation=None,
+                          dimOutput=3,
+                          equalizedlR=True,
+                          depthOtherScales=[512,512,512,256])
+
+    img = pgandecoder(voice_embeddings)
+    
diff --git a/mlops/mlflow/train/sf2f/models/fusers.py b/mlops/mlflow/train/sf2f/models/fusers.py
new file mode 100644
index 0000000..ecc8043
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/models/fusers.py
@@ -0,0 +1,136 @@
+'''
+Implementations of different Fusers
+'''
+
+
+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+import numpy as np
+import math
+
+try:
+    from attention import Attention
+except:
+    from .attention import Attention
+
+
+class AttentionFuserV1(nn.Module):
+    def __init__(self,
+                 dimensions,
+                 dim_out,
+                 attention_type='general',
+                 linear_out_layer=True,
+                 ignore_tanh=False):
+        '''
+        1 Layer Attnetion Fuser
+        '''
+        nn.Module.__init__(self)
+        self.attention = Attention(
+            dimensions,
+            dim_out,
+            attention_type=attention_type,
+            linear_out_layer=linear_out_layer,
+            ignore_tanh=ignore_tanh)
+
+    def forward(self, embeddings):
+        '''
+        seq_in: tensor with shape [N, D, L]
+        '''
+        # Embeddings: [B, N, C]
+        embeddings, attention_weights = \
+            self.attention(embeddings, embeddings)
+        # [B, C, N]
+        embeddings = embeddings.transpose(1, 2)
+        #print('Transposed Shape:', embeddings.shape)
+        embeddings = F.avg_pool1d(
+            embeddings, embeddings.size()[2], stride=1)
+        embeddings = embeddings.view(embeddings.size()[0], -1, 1, 1)
+        #print('Attention Fuser Output Shape:', embeddings.shape)
+        return embeddings
+
+
+class AttentionFuserV2(nn.Module):
+    def __init__(self,
+                 dimensions,
+                 attention_type='general'):
+        '''
+        2-Layer Attention
+        '''
+        nn.Module.__init__(self)
+        self.attention_1 = Attention(
+            dimensions,
+            dimensions,
+            attention_type=attention_type,
+            linear_out_layer=True,
+            ignore_tanh=False)
+        self.attention_2 = Attention(
+            dimensions,
+            dimensions,
+            attention_type=attention_type,
+            linear_out_layer=True,
+            ignore_tanh=True)
+
+    def forward(self, embeddings):
+        '''
+        seq_in: tensor with shape [N, D, L]
+        '''
+        # Embeddings: [B, N, C]
+        embeddings, attention_weights = \
+            self.attention_1(embeddings, embeddings)
+        embeddings, attention_weights = \
+            self.attention_2(embeddings, embeddings)
+        # [B, C, N]
+        embeddings = embeddings.transpose(1, 2)
+        #print('Transposed Shape:', embeddings.shape)
+        embeddings = F.avg_pool1d(
+            embeddings, embeddings.size()[2], stride=1)
+        embeddings = embeddings.view(embeddings.size()[0], -1, 1, 1)
+        #print('Attention Fuser Output Shape:', embeddings.shape)
+        return embeddings
+
+
+class AttentionFuserV3(nn.Module):
+    def __init__(self,
+                 dimensions,
+                 attention_type='general'):
+        '''
+        2-Layer Attention with Residual Connection
+        '''
+        nn.Module.__init__(self)
+        self.attention_1 = Attention(
+            dimensions,
+            dimensions,
+            attention_type=attention_type,
+            linear_out_layer=True,
+            ignore_tanh=False)
+        self.attention_2 = Attention(
+            dimensions*2,
+            dimensions,
+            attention_type=attention_type,
+            linear_out_layer=True,
+            ignore_tanh=True)
+
+    def forward(self, input):
+        '''
+        seq_in: tensor with shape [N, D, L]
+        '''
+        # Embeddings: [B, N, C]
+        hidden, attention_weights = \
+            self.attention_1(input, input)
+        hidden = F.normalize(hidden, dim=2)
+        # Residual Connection
+        combined = torch.cat((hidden, input), dim=2)
+        # 2nd Attention
+        embeddings, attention_weights = \
+            self.attention_2(combined, combined)
+        # [B, C, N]
+        embeddings = embeddings.transpose(1, 2)
+        #print('Transposed Shape:', embeddings.shape)
+        embeddings = F.avg_pool1d(
+            embeddings, embeddings.size()[2], stride=1)
+        embeddings = embeddings.view(embeddings.size()[0], -1, 1, 1)
+        #print('Attention Fuser Output Shape:', embeddings.shape)
+        return embeddings
diff --git a/mlops/mlflow/train/sf2f/models/inception_resnet_v1.py b/mlops/mlflow/train/sf2f/models/inception_resnet_v1.py
new file mode 100644
index 0000000..f95a1f8
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/models/inception_resnet_v1.py
@@ -0,0 +1,439 @@
+'''
+Reference: https://github.com/timesler/facenet-pytorch/ \
+    blob/master/models/inception_resnet_v1.py
+
+Both pretrained models were trained on 160x160 px images,
+so will perform best if applied to images resized to this shape.
+For best results, images should also be cropped to the face using
+MTCNN (see below).
+'''
+
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+import requests
+from requests.adapters import HTTPAdapter
+import os
+
+
+class BasicConv2d(nn.Module):
+
+    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
+        super().__init__()
+        self.conv = nn.Conv2d(
+            in_planes, out_planes,
+            kernel_size=kernel_size, stride=stride,
+            padding=padding, bias=False
+        ) # verify bias false
+        self.bn = nn.BatchNorm2d(
+            out_planes,
+            eps=0.001, # value found in tensorflow
+            momentum=0.1, # default pytorch value
+            affine=True
+        )
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Block35(nn.Module):
+
+    def __init__(self, scale=1.0):
+        super().__init__()
+
+        self.scale = scale
+
+        self.branch0 = BasicConv2d(256, 32, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(256, 32, kernel_size=1, stride=1),
+            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(256, 32, kernel_size=1, stride=1),
+            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.conv2d = nn.Conv2d(96, 256, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        out = self.relu(out)
+        return out
+
+
+class Block17(nn.Module):
+
+    def __init__(self, scale=1.0):
+        super().__init__()
+
+        self.scale = scale
+
+        self.branch0 = BasicConv2d(896, 128, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(896, 128, kernel_size=1, stride=1),
+            BasicConv2d(128, 128, kernel_size=(1,7), stride=1, padding=(0,3)),
+            BasicConv2d(128, 128, kernel_size=(7,1), stride=1, padding=(3,0))
+        )
+
+        self.conv2d = nn.Conv2d(256, 896, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        out = self.relu(out)
+        return out
+
+
+class Block8(nn.Module):
+
+    def __init__(self, scale=1.0, noReLU=False):
+        super().__init__()
+
+        self.scale = scale
+        self.noReLU = noReLU
+
+        self.branch0 = BasicConv2d(1792, 192, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1792, 192, kernel_size=1, stride=1),
+            BasicConv2d(192, 192, kernel_size=(1,3), stride=1, padding=(0,1)),
+            BasicConv2d(192, 192, kernel_size=(3,1), stride=1, padding=(1,0))
+        )
+
+        self.conv2d = nn.Conv2d(384, 1792, kernel_size=1, stride=1)
+        if not self.noReLU:
+            self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        if not self.noReLU:
+            out = self.relu(out)
+        return out
+
+
+class Mixed_6a(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+        self.branch0 = BasicConv2d(256, 384, kernel_size=3, stride=2)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(256, 192, kernel_size=1, stride=1),
+            BasicConv2d(192, 192, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(192, 256, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        return out
+
+
+class Mixed_7a(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+        self.branch0 = nn.Sequential(
+            BasicConv2d(896, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 384, kernel_size=3, stride=2)
+        )
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(896, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 256, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(896, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(256, 256, kernel_size=3, stride=2)
+        )
+
+        self.branch3 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class InceptionResnetV1(nn.Module):
+    """Inception Resnet V1 model with optional loading of pretrained weights.
+
+    Model parameters can be loaded based on pretraining on the VGGFace2 or CASIA-Webface
+    datasets. Pretrained state_dicts are automatically downloaded on model instantiation if
+    requested and cached in the torch cache. Subsequent instantiations use the cache rather than
+    redownloading.
+
+    Keyword Arguments:
+        pretrained {str} -- Optional pretraining dataset. Either 'vggface2' or 'casia-webface'.
+            (default: {None})
+        classify {bool} -- Whether the model should output classification probabilities or feature
+            embeddings. (default: {False})
+        num_classes {int} -- Number of output classes. If 'pretrained' is set and num_classes not
+            equal to that used for the pretrained model, the final linear layer will be randomly
+            initialized. (default: {None})
+        dropout_prob {float} -- Dropout probability. (default: {0.6})
+    """
+    def __init__(self,
+                 pretrained=None,
+                 classify=False,
+                 num_classes=None,
+                 dropout_prob=0.6,
+                 device=None,
+                 auto_input_resize=True,
+                 return_pooling=False):
+        super().__init__()
+
+        # Set simple attributes
+        self.pretrained = pretrained
+        self.classify = classify
+        self.num_classes = num_classes
+        self.auto_input_resize = auto_input_resize
+
+        if pretrained == 'vggface2':
+            tmp_classes = 8631
+        elif pretrained == 'casia-webface':
+            tmp_classes = 10575
+        elif pretrained is None and self.num_classes is None:
+            raise Exception('At least one of "pretrained" or "num_classes" must be specified')
+        else:
+            tmp_classes = self.num_classes
+
+        if self.auto_input_resize:
+            self.input_resize = nn.UpsamplingBilinear2d(size=(140, 140))
+        self.return_pooling = return_pooling
+        # Define layers
+        self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
+        self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
+        self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.maxpool_3a = nn.MaxPool2d(3, stride=2)
+        self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
+        self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
+        self.conv2d_4b = BasicConv2d(192, 256, kernel_size=3, stride=2)
+        self.repeat_1 = nn.Sequential(
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+        )
+        self.mixed_6a = Mixed_6a()
+        self.repeat_2 = nn.Sequential(
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+        )
+        self.mixed_7a = Mixed_7a()
+        self.repeat_3 = nn.Sequential(
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+        )
+        self.block8 = Block8(noReLU=True)
+        self.avgpool_1a = nn.AdaptiveAvgPool2d(1)
+        self.dropout = nn.Dropout(dropout_prob)
+        self.last_linear = nn.Linear(1792, 512, bias=False)
+        self.last_bn = nn.BatchNorm1d(512, eps=0.001, momentum=0.1, affine=True)
+        self.logits = nn.Linear(512, tmp_classes)
+
+        if pretrained is not None:
+            load_weights(self, pretrained)
+
+        if self.num_classes is not None:
+            self.logits = nn.Linear(512, self.num_classes)
+
+        self.device = torch.device('cpu')
+        if device is not None:
+            self.device = device
+            self.to(device)
+
+    def forward(self, x):
+        """
+        Calculate embeddings or probabilities given a batch of input image
+        tensors.
+
+        Arguments:
+            x {torch.tensor} -- Batch of image tensors representing faces.
+
+        Returns:
+            torch.tensor -- Batch of embeddings or softmax probabilities.
+        """
+        if self.auto_input_resize:
+            x = self.input_resize(x)
+        x = self.conv2d_1a(x)
+        x = self.conv2d_2a(x)
+        x = self.conv2d_2b(x)
+        x = self.maxpool_3a(x)
+        x = self.conv2d_3b(x)
+        x = self.conv2d_4a(x)
+        x = self.conv2d_4b(x)
+        x = self.repeat_1(x)
+        x = self.mixed_6a(x)
+        x = self.repeat_2(x)
+        x = self.mixed_7a(x)
+        x = self.repeat_3(x)
+        x = self.block8(x)
+        x = self.avgpool_1a(x)
+        if self.return_pooling:
+            pooled = x.view(x.shape[0], -1)
+            pooled = F.normalize(pooled, p=2, dim=1)
+            return pooled
+        x = self.dropout(x)
+        x = self.last_linear(x.view(x.shape[0], -1))
+        x = self.last_bn(x)
+        x = F.normalize(x, p=2, dim=1)
+        if self.classify:
+            x = self.logits(x)
+        return x
+
+    def get_hidden_states(self, x):
+        """
+        Calculate embeddings or probabilities given a batch of input image
+        tensors.
+
+        Arguments:
+            x {torch.tensor} -- Batch of image tensors representing faces.
+
+        Returns:
+            torch.tensor -- Batch of embeddings or softmax probabilities.
+        """
+        if self.auto_input_resize:
+            x = self.input_resize(x)
+        conv2d_1a = self.conv2d_1a(x)
+        conv2d_2a = self.conv2d_2a(conv2d_1a)
+        conv2d_2b = self.conv2d_2b(conv2d_2a)
+        maxpool_3a = self.maxpool_3a(conv2d_2b)
+        conv2d_3b = self.conv2d_3b(maxpool_3a)
+        conv2d_4a = self.conv2d_4a(conv2d_3b)
+        conv2d_4b = self.conv2d_4b(conv2d_4a)
+        repeat_1 = self.repeat_1(conv2d_4b)
+        mixed_6a = self.mixed_6a(repeat_1)
+        repeat_2 = self.repeat_2(mixed_6a)
+        mixed_7a = self.mixed_7a(repeat_2)
+        repeat_3 = self.repeat_3(mixed_7a)
+        block8 = self.block8(repeat_3)
+        avgpool_1a = self.avgpool_1a(block8)
+        dropout = self.dropout(avgpool_1a)
+        last_linear = self.last_linear(dropout.view(x.shape[0], -1))
+        last_bn = self.last_bn(last_linear)
+        x = F.normalize(last_bn, p=2, dim=1)
+        if self.classify:
+            x = self.logits(x)
+        out = [repeat_1, repeat_2, repeat_3, x]
+        return out
+
+
+def load_weights(mdl, name):
+    """Download pretrained state_dict and load into model.
+
+    Arguments:
+        mdl {torch.nn.Module} -- Pytorch model.
+        name {str} -- Name of dataset that was used to generate pretrained state_dict.
+
+    Raises:
+        ValueError: If 'pretrained' not equal to 'vggface2' or 'casia-webface'.
+    """
+    if name == 'vggface2':
+        features_path = \
+            'https://drive.google.com/uc?export=download&' + \
+                'id=1cWLH_hPns8kSfMz9kKl9PsG5aNV2VSMn'
+        logits_path = \
+            'https://drive.google.com/uc?export=download' + \
+                '&id=1mAie3nzZeno9UIzFXvmVZrDG3kwML46X'
+    elif name == 'casia-webface':
+        features_path = 'https://drive.google.com/uc?export=download&id=1LSHHee_IQj5W3vjBcRyVaALv4py1XaGy'
+        logits_path = 'https://drive.google.com/uc?export=download&id=1QrhPgn1bGlDxAil2uc07ctunCQoDnCzT'
+    else:
+        raise ValueError('Pretrained models only exist for "vggface2" and "casia-webface"')
+
+    model_dir = os.path.join(get_torch_home(), 'checkpoints')
+    os.makedirs(model_dir, exist_ok=True)
+
+    state_dict = {}
+    for i, path in enumerate([features_path, logits_path]):
+        cached_file = os.path.join(
+            model_dir, '{}_{}.pt'.format(name, path[-10:]))
+        #print('Cashed File Location:', cached_file)
+        if not os.path.exists(cached_file):
+            print('Downloading parameters ({}/2)'.format(i+1))
+            s = requests.Session()
+            s.mount('https://', HTTPAdapter(max_retries=10))
+            r = s.get(path, allow_redirects=True)
+            with open(cached_file, 'wb') as f:
+                f.write(r.content)
+        state_dict.update(torch.load(cached_file))
+
+    mdl.load_state_dict(state_dict)
+
+
+def get_torch_home():
+    torch_home = os.path.expanduser(
+        os.getenv(
+            'TORCH_HOME',
+            os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch')
+        )
+    )
+    return torch_home
+
+
+def fixed_image_standardization(image_tensor):
+    '''
+    This is the default preprocess function for this pretrained model
+    '''
+    processed_tensor = (image_tensor - 127.5) / 128.0
+    return processed_tensor
+
+
+if __name__ == '__main__':
+    model = InceptionResnetV1(pretrained='vggface2').eval()
+    print(model)
+    demo_imgs = torch.zeros(16, 3, 64, 64)
+    print('Output size 1:', model(demo_imgs).shape, model(demo_imgs)[0])
+
+    model_2 = InceptionResnetV1(
+        pretrained='vggface2',
+        auto_input_resize=False).eval()
+    demo_imgs = torch.zeros(16, 3, 140, 140)
+    print('Output size 2:', model(demo_imgs).shape, model_2(demo_imgs)[0])
diff --git a/mlops/mlflow/train/sf2f/models/layers.py b/mlops/mlflow/train/sf2f/models/layers.py
new file mode 100644
index 0000000..001e3eb
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/models/layers.py
@@ -0,0 +1,257 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+## We use the batchnorm in pytorch 0.3.x to avoid unexpected errors
+## Otherwise, some in-place modification will bring some errors
+# from models.utils.batchnorm import BatchNorm1d, BatchNorm2d
+
+
+
+def get_normalization_2d(channels, normalization):
+    if normalization == 'instance':
+        return nn.InstanceNorm2d(channels)
+    elif normalization == 'batch':
+        return nn.BatchNorm2d(channels)
+    elif normalization == 'none':
+        return None
+    else:
+        raise ValueError('Unrecognized normalization type "%s"' %
+                         normalization)
+
+
+def get_activation(name):
+    kwargs = {}
+    if name.lower().startswith('leakyrelu'):
+        if '-' in name:
+            slope = float(name.split('-')[1])
+            kwargs = {'negative_slope': slope}
+            name = 'leakyrelu'
+    activations = {
+        'relu': nn.ReLU,
+        'leakyrelu': nn.LeakyReLU,
+        'none': None,
+    }
+    if name.lower() not in activations:
+        raise ValueError('Invalid activation "%s"' % name)
+    if activations[name.lower()] is not None:
+        return activations[name.lower()](**kwargs)
+
+
+def weights_init(m):
+    raise NotImplementedError
+    classname = m.__class__.__name__
+    if classname.find('Conv') != -1:
+        m.weight.data.normal_(0.0, 0.02)
+        m.bias.data.fill_(0)
+    elif classname.find('BatchNorm2d') != -1:
+        m.weight.data.normal_(1.0, 0.02)
+        m.bias.data.fill_(0)
+    elif classname.find('BatchNorm1d') != -1:
+        m.weight.data.normal_(1.0, 0.02)
+        m.bias.data.fill_(0)
+    elif classname.find('Linear') != -1:
+        m.weight.data.normal_(0.0, 0.02)
+        m.bias.data.fill_(0)
+
+
+class Flatten(nn.Module):
+    def forward(self, x):
+        return x.view(x.size(0), -1)
+
+    def __repr__(self):
+        return 'Flatten()'
+
+
+class Unflatten(nn.Module):
+    def __init__(self, size):
+        super(Unflatten, self).__init__()
+        self.size = size
+
+    def forward(self, x):
+        return x.view(*self.size)
+
+    def __repr__(self):
+        size_str = ', '.join('%d' % d for d in self.size)
+        return 'Unflatten(%s)' % size_str
+
+
+class GlobalAvgPool(nn.Module):
+    def __init__(self, pooling="avg"):
+        super(GlobalAvgPool, self).__init__()
+        self.pooling = pooling
+    def forward(self, x):
+        N, C = x.size(0), x.size(1)
+        if self.pooling == "avg":
+            return x.view(N, C, -1).mean(dim=2)
+        elif self.pooling == "max":
+            return x.view(N, C, -1).max(dim=2)[0]
+        else:
+            assert False, "Unrecognized pooling: {}".format(self.pooling)
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, channels, normalization='batch', activation='relu',
+                 padding='same', kernel_size=3, init='default'):
+        super(ResidualBlock, self).__init__()
+        K = kernel_size
+        P = _get_padding(K, "same")
+        C = channels
+        self.padding = P
+        layers = [
+            get_normalization_2d(C, normalization),
+            get_activation(activation),
+            nn.Conv2d(C, C, kernel_size=K, padding=P),
+            get_normalization_2d(C, normalization),
+            get_activation(activation),
+            nn.Conv2d(C, C, kernel_size=K, padding=P),
+        ]
+        layers = [layer for layer in layers if layer is not None]
+        # for layer in layers:
+        #     _init_conv(layer, method=init)
+        self.net = nn.Sequential(*layers)
+
+    def forward(self, x):
+        P = self.padding
+        shortcut = x
+        y = self.net(x)
+        return shortcut + self.net(x)
+
+
+def _get_padding(K, mode):
+    """ Helper method to compute padding size """
+    if mode == 'valid':
+        return 0
+    elif mode == 'same':
+        assert K % 2 == 1, 'Invalid kernel size %d for "same" padding' % K
+        return (K - 1) // 2
+
+
+def build_cnn(arch, normalization='batch', activation='relu', padding='same',
+              pooling='max', init='default', non_linear_activated=False):
+    """
+    Build a CNN from an architecture string, which is a list of layer
+    specification strings. The overall architecture can be given as a list or as
+    a comma-separated string.
+
+    All convolutions *except for the first* are preceeded by normalization and
+    nonlinearity.
+
+    non_linear_activated: whether to add [non-linear activation / normalization]
+        Default is [False] (do not add these layers)
+
+    All other layers support the following:
+    - IX: Indicates that the number of input channels to the network is X.
+          Can only be used at the first layer; if not present then we assume
+          3 input channels.
+    - CK-X: KxK convolution with X output channels
+    - CK-X-S: KxK convolution with X output channels and stride S
+    - R: Residual block keeping the same number of channels
+    - UX: Nearest-neighbor upsampling with factor X
+    - PX: Spatial pooling with factor X
+    - FC-X-Y: Flatten followed by fully-connected layer
+
+    Returns a tuple of:
+    - cnn: An nn.Sequential
+    - channels: Number of output channels
+    """
+    if isinstance(arch, str):
+        arch = arch.split(',')
+    cur_C = 3
+    if len(arch) > 0 and arch[0][0] == 'I':
+        cur_C = int(arch[0][1:])
+        arch = arch[1:]
+
+    first_conv = True
+    flat = False
+    layers = []
+    for i, s in enumerate(arch):
+        if s[0] == 'C':
+            if not first_conv:
+                layers.append(get_normalization_2d(cur_C, normalization))
+                layers.append(get_activation(activation))
+            first_conv = False
+            vals = [int(i) for i in s[1:].split('-')]
+            P = None
+            if len(vals) == 2:
+                K, next_C = vals
+                stride = 1
+            elif len(vals) == 3:
+                K, next_C, stride = vals
+            elif len(vals) == 4:
+                # Add padding
+                K, next_C, stride, P = vals
+            # K, next_C = (int(i) for i in s[1:].split('-'))
+            if P is None:
+                P = _get_padding(K, padding)
+            conv = nn.Conv2d(cur_C, next_C, kernel_size=K,
+                             padding=P, stride=stride)
+            layers.append(conv)
+            # _init_conv(layers[-1], init)
+            cur_C = next_C
+        elif s[0] == 'R':
+            norm = 'none' if first_conv else normalization
+            res = ResidualBlock(cur_C, normalization=norm, activation=activation,
+                                padding=padding, init=init)
+            layers.append(res)
+            first_conv = False
+        elif s[0] == 'U':
+            factor = int(s[1:])
+            layers.append(nn.Upsample(scale_factor=factor, mode='nearest'))
+        elif s[0] == 'P':
+            factor = int(s[1:])
+            if pooling == 'max':
+                pool = nn.MaxPool2d(kernel_size=factor, stride=factor)
+            elif pooling == 'avg':
+                pool = nn.AvgPool2d(kernel_size=factor, stride=factor)
+            layers.append(pool)
+        elif s[:2] == 'FC':
+            _, Din, Dout = s.split('-')
+            Din, Dout = int(Din), int(Dout)
+            if not flat:
+                layers.append(Flatten())
+            flat = True
+            layers.append(nn.Linear(Din, Dout))
+            if i + 1 < len(arch):
+                layers.append(get_activation(activation))
+            cur_C = Dout
+        else:
+            raise ValueError('Invalid layer "%s"' % s)
+    if non_linear_activated:
+        layers.append(get_normalization_2d(cur_C, normalization))
+        layers.append(get_activation(activation))
+    layers = [layer for layer in layers if layer is not None]
+    #for layer in layers:
+    #    print(layer)
+
+    model = nn.Sequential(*layers)
+    return model, cur_C
+
+
+def build_mlp(dim_list, activation='relu', batch_norm='none',
+              dropout=0, final_nonlinearity=True,
+              start_nonlinearity=False,):
+    layers = []
+    for i in range(len(dim_list) - 1):
+        dim_in, dim_out = dim_list[i], dim_list[i + 1]
+        if start_nonlinearity and i == 0:
+            if batch_norm == 'batch':
+                layers.append(nn.BatchNorm1d(dim_in))
+            # elif batch_norm == 'instance':
+            #     layers.append(InstanceNorm1d(dim_in))
+            if activation == 'relu':
+                layers.append(nn.ReLU())
+            elif activation == 'leakyrelu':
+                layers.append(nn.LeakyReLU())
+        layers.append(nn.Linear(dim_in, dim_out))
+        final_layer = (i == len(dim_list) - 2)
+        if not final_layer or final_nonlinearity:
+            if batch_norm == 'batch':
+                layers.append(nn.BatchNorm1d(dim_out))
+            if activation == 'relu':
+                layers.append(nn.ReLU())
+            elif activation == 'leakyrelu':
+                layers.append(nn.LeakyReLU())
+        if dropout > 0:
+            layers.append(nn.Dropout(p=dropout))
+    model = nn.Sequential(*layers)
+    return model
diff --git a/mlops/mlflow/train/sf2f/models/model_collection.py b/mlops/mlflow/train/sf2f/models/model_collection.py
new file mode 100644
index 0000000..bf3e4ba
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/models/model_collection.py
@@ -0,0 +1,38 @@
+'''
+A collection of all the models, for the convenience of internal calling
+'''
+
+
+# Lower level encoder decoders
+try:
+    from .voice_encoders import V2F1DCNN, TransEncoder
+except:
+    from voice_encoders import V2F1DCNN, TransEncoder
+
+try:
+    from .face_decoders import V2FDecoder, AttnV2FDecoder, MRDecoder, \
+        FaceGanDecoder, FaceGanDecoder_v2
+except:
+    from face_decoders import V2FDecoder, AttnV2FDecoder, MRDecoder, \
+        FaceGanDecoder, FaceGanDecoder_v2
+
+try:
+    from .attention import Attention
+except:
+    from .attention import Attention
+
+
+class ModelCollection():
+    def __init__(self):
+        self.V2F1DCNN = V2F1DCNN
+        self.V2FDecoder = V2FDecoder
+        self.AttnV2FDecoder = AttnV2FDecoder
+        self.TransEncoder = TransEncoder
+        self.MRDecoder = MRDecoder
+        self.FaceGanDecoder = FaceGanDecoder
+        self.FaceGanDecoder_v2 = FaceGanDecoder_v2
+        # self.PganDecoder = PganDecoder
+        self.Attention = Attention
+
+
+model_collection = ModelCollection()
diff --git a/mlops/mlflow/train/sf2f/models/model_setup.py b/mlops/mlflow/train/sf2f/models/model_setup.py
new file mode 100644
index 0000000..94ef21a
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/models/model_setup.py
@@ -0,0 +1,99 @@
+import torch
+import models
+from copy import deepcopy
+from utils import update_values, load_model_state
+import glog as log
+
+def build_model(opts, image_size, checkpoint_start_from=None):
+    if checkpoint_start_from is not None:
+        log.info("Load checkpoint as initialization: {}".format(
+            checkpoint_start_from))
+        checkpoint = torch.load(checkpoint_start_from)
+        # kwarg aka keyword arguments
+        #kwargs = checkpoint['model_kwargs']
+        kwargs = deepcopy(opts["options"])
+        kwargs["image_size"] = image_size
+        model = getattr(models, opts["arch"])(**kwargs)
+        raw_state_dict = checkpoint['model_state']
+        state_dict = {}
+        for k, v in raw_state_dict.items():
+            if k.startswith('module.'):
+                k = k[7:]
+            state_dict[k] = v
+        #print("state_dict: ", state_dict.keys())
+        model = load_model_state(model, state_dict, strict=False)
+    else:
+        kwargs = deepcopy(opts["options"])
+        kwargs["image_size"] = image_size
+        model = getattr(models, opts["arch"])(**kwargs)
+    return model, kwargs
+
+def build_ac_discriminator(opts):
+    d_kwargs = deepcopy(opts["generic"])
+    discriminator = []
+    if "identity" in opts.keys():
+        d_kwargs = update_values(opts["identity"], d_kwargs)
+        dis = models.AcDiscriminator(**d_kwargs)
+        discriminator.append(dis)
+    if "identity_low" in opts.keys():
+        d_kwargs = update_values(opts["identity_low"], d_kwargs)
+        dis_low = models.AcDiscriminator(**d_kwargs)
+        discriminator.append(dis_low)
+    if "identity_mid" in opts.keys():
+        d_kwargs = update_values(opts["identity_mid"], d_kwargs)
+        dis_mid = models.AcDiscriminator(**d_kwargs)
+        discriminator.append(dis_mid)
+    if "identity_high" in opts.keys():
+        d_kwargs = update_values(opts["identity_high"], d_kwargs)
+        dis_high = models.AcDiscriminator(**d_kwargs)
+        discriminator.append(dis_high)
+    return discriminator, d_kwargs
+
+def build_img_discriminator(opts):
+    discriminator = []
+    if opts.get("pgan_dis") is not None:
+        d_kwargs = deepcopy(opts["pgan_dis"])
+        pgan_dis = models.PGAN_Discriminator(**d_kwargs)
+        discriminator.append(pgan_dis)
+    else:
+        d_kwargs = deepcopy(opts["generic"])
+        if "image" in opts.keys():
+            d_kwargs = update_values(opts["image"], d_kwargs)
+            dis = models.PatchDiscriminator(**d_kwargs)
+            discriminator.append(dis)
+        else:
+            if "image_low" in opts.keys():
+                d_kwargs = update_values(opts["image_low"], d_kwargs)
+                discriminator_low = models.PatchDiscriminator(**d_kwargs)
+                discriminator.append(discriminator_low)
+            if "image_mid" in opts.keys():
+                d_kwargs = update_values(opts["image_mid"], d_kwargs)
+                discriminator_mid = models.PatchDiscriminator(**d_kwargs)
+                discriminator.append(discriminator_mid)
+            if "image_high" in opts.keys():
+                d_kwargs = update_values(opts["image_high"], d_kwargs)
+                discriminator_high = models.PatchDiscriminator(**d_kwargs)
+                discriminator.append(discriminator_high)
+    return discriminator, d_kwargs
+
+def build_cond_discriminator(opts):
+    discriminator = []
+    d_kwargs = deepcopy(opts["generic"])
+    if "condition" in opts.keys():
+        d_kwargs = update_values(opts["condition"], d_kwargs)
+        dis = models.CondPatchDiscriminator(**d_kwargs)
+        discriminator.append(dis)
+    else:
+        if "cond_low" in opts.keys():
+            d_kwargs = update_values(opts["cond_low"], d_kwargs)
+            discriminator_low = models.CondPatchDiscriminator(**d_kwargs)
+            discriminator.append(discriminator_low)
+        if "cond_mid" in opts.keys():
+            d_kwargs = update_values(opts["cond_mid"], d_kwargs)
+            discriminator_mid = models.CondPatchDiscriminator(**d_kwargs)
+            discriminator.append(discriminator_mid)
+        if "cond_high" in opts.keys():
+            d_kwargs = update_values(opts["cond_high"], d_kwargs)
+            discriminator_high = models.CondPatchDiscriminator(**d_kwargs)
+            discriminator.append(discriminator_high)
+    return discriminator, d_kwargs
diff --git a/mlops/mlflow/train/sf2f/models/networks.py b/mlops/mlflow/train/sf2f/models/networks.py
new file mode 100644
index 0000000..28f3333
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/models/networks.py
@@ -0,0 +1,203 @@
+import torch
+import torch.nn as nn
+import functools
+from torch.autograd import Variable
+import numpy as np
+import torch.nn.functional as F
+import math
+from numpy import prod
+
+
+
+class VGGLoss(nn.Module):
+    def __init__(self):
+        super(VGGLoss, self).__init__()
+        self.vgg = Vgg19().cuda()
+        self.criterion = nn.L1Loss()
+        self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]
+
+    def forward(self, x, y):
+        x_vgg = self.vgg(x)
+        # we do not store gradients for y
+        with torch.no_grad():
+            y_vgg = self.vgg(y)
+        loss = 0
+        for i in range(len(x_vgg)):
+            loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i])
+        return loss
+
+from torchvision import models
+class Vgg19(torch.nn.Module):
+    def __init__(self, requires_grad=False, ignore_last=False):
+        super(Vgg19, self).__init__()
+        vgg_pretrained_features = models.vgg19(pretrained=True).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        if ignore_last:
+            self.slice5 = None
+        else:
+            self.slice5 = torch.nn.Sequential()
+        for x in range(2):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(2, 7):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(7, 12):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(12, 21):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        if self.slice5 is not None:
+            for x in range(21, 30):
+                self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h_relu1 = self.slice1(X)
+        h_relu2 = self.slice2(h_relu1)
+        h_relu3 = self.slice3(h_relu2)
+        h_relu4 = self.slice4(h_relu3)
+        if self.slice5 is None or h_relu4.size(3) < 3:
+            return [h_relu1, h_relu2, h_relu3, h_relu4,]
+        h_relu5 = self.slice5(h_relu4)
+        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
+        return out
+
+
+class GLU(nn.Module):
+    def __init__(self):
+        super(GLU, self).__init__()
+
+    def forward(self, x):
+        nc = x.size(1)
+        assert nc % 2 == 0, 'channels dont divide 2!'
+        nc = int(nc/2)
+        return x[:, :nc] * F.sigmoid(x[:, nc:])
+
+
+def upBlock(in_planes, out_planes):
+    block = nn.Sequential(
+        nn.Upsample(scale_factor=2, mode='nearest'),
+        nn.Conv2d(in_planes, out_planes * 2, kernel_size=3,
+           stride=1, padding=1, bias=False),
+        nn.BatchNorm2d(out_planes * 2),
+        GLU())
+    return block
+
+# The defination of Progressive GAN
+
+class NormalizationLayer(nn.Module):
+
+    def __init__(self):
+        super(NormalizationLayer, self).__init__()
+
+    def forward(self, x, epsilon=1e-8):
+        return x * (((x**2).mean(dim=1, keepdim=True) + epsilon).rsqrt())
+
+
+def Upscale2d(x, factor=2):
+    assert isinstance(factor, int) and factor >= 1
+    if factor == 1:
+        return x
+    s = x.size()
+    x = x.view(-1, s[1], s[2], 1, s[3], 1)
+    x = x.expand(-1, s[1], s[2], factor, s[3], factor)
+    x = x.contiguous().view(-1, s[1], s[2] * factor, s[3] * factor)
+    return x
+
+
+def getLayerNormalizationFactor(x):
+    r"""
+    Get He's constant for the given layer
+    https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
+    """
+    size = x.weight.size()
+    fan_in = prod(size[1:])
+
+    return math.sqrt(2.0 / fan_in)
+
+
+class ConstrainedLayer(nn.Module):
+    r"""
+    A handy refactor that allows the user to:
+    - initialize one layer's bias to zero
+    - apply He's initialization at runtime
+    """
+
+    def __init__(self,
+                 module,
+                 equalized=True,
+                 lrMul=1.0,
+                 initBiasToZero=True):
+        r"""
+        equalized (bool): if true, the layer's weight should evolve within
+                         the range (-1, 1)
+        initBiasToZero (bool): if true, bias will be initialized to zero
+        """
+
+        super(ConstrainedLayer, self).__init__()
+
+        self.module = module
+        self.equalized = equalized
+
+        if initBiasToZero:
+            self.module.bias.data.fill_(0)
+        if self.equalized:
+            self.module.weight.data.normal_(0, 1)
+            self.module.weight.data /= lrMul
+            self.weight = getLayerNormalizationFactor(self.module) * lrMul
+
+    def forward(self, x):
+
+        x = self.module(x)
+        if self.equalized:
+            x *= self.weight
+        return x
+
+
+class EqualizedConv2d(ConstrainedLayer):
+
+    def __init__(self,
+                 nChannelsPrevious,
+                 nChannels,
+                 kernelSize,
+                 padding=0,
+                 bias=True,
+                 **kwargs):
+        r"""
+        A nn.Conv2d module with specific constraints
+        Args:
+            nChannelsPrevious (int): number of channels in the previous layer
+            nChannels (int): number of channels of the current layer
+            kernelSize (int): size of the convolutional kernel
+            padding (int): convolution's padding
+            bias (bool): with bias ?
+        """
+
+        ConstrainedLayer.__init__(self,
+                                  nn.Conv2d(nChannelsPrevious, nChannels,
+                                            kernelSize, padding=padding,
+                                            bias=bias),
+                                  **kwargs)
+
+
+class EqualizedLinear(ConstrainedLayer):
+
+    def __init__(self,
+                 nChannelsPrevious,
+                 nChannels,
+                 bias=True,
+                 **kwargs):
+        r"""
+        A nn.Linear module with specific constraints
+        Args:
+            nChannelsPrevious (int): number of channels in the previous layer
+            nChannels (int): number of channels of the current layer
+            bias (bool): with bias ?
+        """
+
+        ConstrainedLayer.__init__(self,
+                                  nn.Linear(nChannelsPrevious, nChannels,
+                                  bias=bias), **kwargs)
diff --git a/mlops/mlflow/train/sf2f/models/perceptual.py b/mlops/mlflow/train/sf2f/models/perceptual.py
new file mode 100644
index 0000000..c22015f
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/models/perceptual.py
@@ -0,0 +1,111 @@
+import torch
+import torch.nn as nn
+import functools
+from torch.autograd import Variable
+import numpy as np
+from torchvision import models
+try:
+    from .inception_resnet_v1 import InceptionResnetV1
+except:
+    from inception_resnet_v1 import InceptionResnetV1
+
+
+class FaceNetLoss(nn.Module):
+    def __init__(self, cos_loss_weight=0.0):
+        super(FaceNetLoss, self).__init__()
+        self.facenet = InceptionResnetV1(pretrained='vggface2').eval().cuda()
+        self.criterion = nn.L1Loss()
+        self.weights = [1.0/16, 1.0/8, 1.0/4, 1.0]
+        self.cos_loss_weight = cos_loss_weight
+        if self.cos_loss_weight > 0.0:
+            self.cos_emb_loss = torch.nn.CosineEmbeddingLoss()
+            self.cos_target = torch.ones((1,), dtype=torch.int8).cuda()
+
+    def forward(self, x, y):
+        x_facenet = self.facenet.get_hidden_states(x)
+        # we do not store gradients for y
+        with torch.no_grad():
+            y_facenet = self.facenet.get_hidden_states(y)
+        loss = 0
+        for i in range(len(x_facenet)):
+            #print(x_facenet[i].shape)
+            #print(y_facenet[i].shape)
+            loss += self.weights[i] * \
+                self.criterion(x_facenet[i], y_facenet[i])
+        if self.cos_loss_weight > 0.0:
+            loss += self.cos_loss_weight * \
+                self.cos_emb_loss(
+                    x_facenet[-1],
+                    y_facenet[-1],
+                    self.cos_target)
+        return loss
+
+
+class VGGLoss(nn.Module):
+    def __init__(self):
+        super(VGGLoss, self).__init__()
+        self.vgg = Vgg19().cuda()
+        self.criterion = nn.L1Loss()
+        self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]
+
+    def forward(self, x, y):
+        x_vgg = self.vgg(x)
+        # we do not store gradients for y
+        with torch.no_grad():
+            y_vgg = self.vgg(y)
+        loss = 0
+        for i in range(len(x_vgg)):
+            loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i])
+        return loss
+
+
+class Vgg19(torch.nn.Module):
+    def __init__(self, requires_grad=False, ignore_last=False):
+        super(Vgg19, self).__init__()
+        vgg_pretrained_features = models.vgg19(pretrained=True).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        if ignore_last:
+            self.slice5 = None
+        else:
+            self.slice5 = torch.nn.Sequential()
+        for x in range(2):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(2, 7):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(7, 12):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(12, 21):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        if self.slice5 is not None:
+            for x in range(21, 30):
+                self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h_relu1 = self.slice1(X)
+        h_relu2 = self.slice2(h_relu1)
+        h_relu3 = self.slice3(h_relu2)
+        h_relu4 = self.slice4(h_relu3)
+        if self.slice5 is None or h_relu4.size(3) < 3:
+            return [h_relu1, h_relu2, h_relu3, h_relu4,]
+        h_relu5 = self.slice5(h_relu4)
+        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
+        return out
+
+
+if __name__ == '__main__':
+    demo_imgs = torch.ones((4, 3, 224, 224))
+    facenet = InceptionResnetV1(pretrained='vggface2').eval()
+    facenet_out = facenet.get_hidden_states(demo_imgs)
+    for i, feature in enumerate(facenet_out):
+        print('FaceNet Feature Shape {}:'.format(i), feature.shape)
+
+    vgg_19 = Vgg19()
+    vgg_out = vgg_19(demo_imgs)
+    for i, feature in enumerate(vgg_out):
+        print('VGG Feature Shape {}:'.format(i), feature.shape)
diff --git a/mlops/mlflow/train/sf2f/models/voice_encoders.py b/mlops/mlflow/train/sf2f/models/voice_encoders.py
new file mode 100644
index 0000000..f26acfb
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/models/voice_encoders.py
@@ -0,0 +1,474 @@
+'''
+Implementations of different Voice Encoders
+'''
+
+
+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+import numpy as np
+import math
+
+try:
+    from fusers import AttentionFuserV1, AttentionFuserV2, AttentionFuserV3
+except:
+    from .fusers import AttentionFuserV1, AttentionFuserV2, AttentionFuserV3
+
+
+class EncoderModelCollection():
+    def __init__(self):
+        self.AttentionFuserV1 = AttentionFuserV1
+        self.AttentionFuserV2 = AttentionFuserV2
+        self.AttentionFuserV3 = AttentionFuserV3
+
+encoder_model_collection = EncoderModelCollection()
+
+
+class TransposeLayer(nn.Module):
+    def __init__(self, dim_1, dim_2):
+        '''
+        Transpose Layer
+        '''
+        nn.Module.__init__(self)
+        self.dim_1 = dim_1
+        self.dim_2 = dim_2
+
+    def forward(self, x):
+        '''
+        seq_in: tensor with shape [N, D, L]
+        '''
+        x = x.transpose(self.dim_1, self.dim_2)
+        return x
+
+
+class CNN1DBlock(nn.Module):
+    def __init__(self,
+                 channel_in,
+                 channel_out,
+                 kernel=3,
+                 stride=2,
+                 padding=1):
+        '''
+        1D CNN block
+        '''
+        nn.Module.__init__(self)
+        self.layers = []
+        self.layers.append(
+            nn.Conv1d(channel_in,
+                      channel_out,
+                      kernel,
+                      stride,
+                      padding,
+                      bias=False))
+        self.layers.append(
+            nn.BatchNorm1d(channel_out, affine=True))
+        self.layers.append(
+            nn.ReLU(inplace=True))
+        self.model = nn.Sequential(*self.layers)
+
+    def forward(self, seq_in):
+        '''
+        seq_in: tensor with shape [N, D, L]
+        '''
+        seq_out = self.model(seq_in)
+        return seq_out
+
+
+class Inception1DBlock(nn.Module):
+    def __init__(self,
+                 channel_in,
+                 channel_k2,
+                 channel_k3,
+                 channel_k5,
+                 channel_k7):
+        '''
+        Basic building block of 1D Inception Encoder
+        '''
+        nn.Module.__init__(self)
+        self.conv_k2 = None
+        self.conv_k3 = None
+        self.conv_k5 = None
+
+        if channel_k2 > 0:
+            self.conv_k2 = CNN1DBlock(
+                channel_in,
+                channel_k2,
+                2, 2, 1)
+        if channel_k3 > 0:
+            self.conv_k3 = CNN1DBlock(
+                channel_in,
+                channel_k3,
+                3, 2, 1)
+        if channel_k5 > 0:
+            self.conv_k5 = CNN1DBlock(
+                channel_in,
+                channel_k3,
+                5, 2, 2)
+        if channel_k7 > 0:
+            self.conv_k7 = CNN1DBlock(
+                channel_in,
+                channel_k3,
+                7, 2, 3)
+
+    def forward(self, input):
+        output = []
+        if self.conv_k2 is not None:
+            c2_out = self.conv_k2(input)
+            output.append(c2_out)
+        if self.conv_k3 is not None:
+            c3_out = self.conv_k3(input)
+            output.append(c3_out)
+        if self.conv_k5 is not None:
+            c5_out = self.conv_k5(input)
+            output.append(c5_out)
+        if self.conv_k7 is not None:
+            c7_out = self.conv_k7(input)
+            output.append(c7_out)
+        #print(c2_out.shape, c3_out.shape, c5_out.shape, c7_out.shape)
+        if output[0].shape[-1] > output[1].shape[-1]:
+            output[0] = output[0][:, :, 0:-1]
+        output = torch.cat(output, 1)
+        #print(output.shape)
+        return output
+
+
+class PositionalEncoding(nn.Module):
+
+    def __init__(self, d_model, dropout=0.1, max_len=10000):
+        super(PositionalEncoding, self).__init__()
+        self.dropout = nn.Dropout(p=dropout)
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * \
+            (-math.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0).transpose(0, 1)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        x = x + self.pe[:x.size(0), :]
+        return self.dropout(x)
+
+
+class PosEmbLayer(nn.Module):
+    def __init__(self, max_length=2000, pos_embedding_dim=5):
+        '''
+        Postional embedding block
+        '''
+        nn.Module.__init__(self)
+
+        self.max_length = max_length
+        self.pos_embedding_dim = pos_embedding_dim
+        # Position Embedding
+        # Each position index in range (0, 2 * max_length - 1) have its own embedding
+        self.pos_embedding = nn.Embedding(
+            max_length, pos_embedding_dim, padding_idx=0)
+
+    def forward(self, seq_in):
+        '''
+        seq_in: tensor with shape [N, L, D]
+        '''
+        N, L, D = seq_in.shape
+        # (L, )
+        pos_idx = torch.tensor(np.arange(0, L))
+        # (1, L)
+        pos_idx = pos_idx.unsqueeze(0)
+        # (N, L)
+        pos_idx = pos_idx.repeat(N, 1).cuda()
+        # (N, L, D_pos)
+        pos_emb = self.pos_embedding(pos_idx).type(seq_in.type())
+        #pos_emb = torch.transpose(pos_emb, 1, 2)
+        seq_out = torch.cat([seq_in, pos_emb], 2)
+        # (N, L, D + D_pos)
+        return seq_out
+
+
+class TransEncoder(nn.Module):
+    def __init__(self,
+                 input_channel=40,
+                 cnn_channels=[512, ],
+                 transformer_dim=512,
+                 transformer_depth=3,
+                 return_seq=True,
+                 sin_pos_encoding=False,
+                 pos_embedding_dim=8,
+                 add_noise=False,
+                 normalize_embedding=False):
+        super(TransEncoder, self).__init__()
+        self.layers = []
+
+        if pos_embedding_dim > 0:
+            # Postional Embedding Layer
+            # (N, L, D)
+            self.layers.append(TransposeLayer(1, 2))
+            # (N, D, L)
+            self.layers.append(
+                PosEmbLayer(2000, pos_embedding_dim))
+            # (N, L, D)
+            self.layers.append(TransposeLayer(1, 2))
+
+        # CNN Layer for dimension conversion
+        self.layers.append(
+            CNN1DBlock(input_channel+pos_embedding_dim, cnn_channels[0]))
+        for cnn_layer_id in range(1, len(cnn_channels)):
+            self.layers.append(CNN1DBlock(
+                cnn_channels[cnn_layer_id - 1],
+                cnn_channels[cnn_layer_id]))
+
+        self.layers.append(TransposeLayer(1, 2))
+        # Sin Positional Encoding
+        if sin_pos_encoding:
+            self.layers.append(PositionalEncoding(transformer_dim))
+
+        # Transformer
+        trans_layer = nn.TransformerEncoderLayer(
+            transformer_dim,
+            8,
+            transformer_dim)
+        transformer_encoder = nn.TransformerEncoder(
+            trans_layer, transformer_depth)
+        self.layers.append(transformer_encoder)
+        self.layers.append(TransposeLayer(1, 2))
+
+        self.model = nn.Sequential(*self.layers)
+        self.add_noise = add_noise
+        self.normalize_embedding = normalize_embedding
+        self.return_seq = return_seq
+
+    def forward(self, seq_in):
+        seq_out = self.model(seq_in)
+        #for layer in self.layers:
+        #    print(layer)
+        #    x = layer(x)
+        #    print(x.shape)
+        embeddings = F.avg_pool1d(seq_out, seq_out.size()[2], stride=1)
+        embeddings = embeddings.view(embeddings.size()[0], -1, 1, 1)
+
+        if self.normalize_embedding:
+            embeddings = F.normalize(embeddings)
+        if self.add_noise:
+            noise = 0.05 * torch.randn(
+                embeddings.shape[0], embeddings.shape[1], 1, 1)
+            noise = noise.type(embeddings.type())
+            embeddings = embeddings + noise
+            if self.normalize_embedding:
+                embeddings = F.normalize(embeddings)
+
+        if self.return_seq:
+            return embeddings, seq_out
+        else:
+            return embeddings
+
+
+class V2F1DCNN(nn.Module):
+    def __init__(self,
+                 input_channel,
+                 channels,
+                 output_channel,
+                 add_noise=False,
+                 normalize_embedding=False,
+                 return_seq=False,
+                 inception_mode=False,
+                 segments_fusion=False,
+                 normalize_fusion=False,
+                 fuser_arch='Attention',
+                 fuser_kwargs=None):
+        super(V2F1DCNN, self).__init__()
+        if inception_mode:
+            self.model = nn.Sequential(
+                Inception1DBlock(
+                    channel_in=input_channel,
+                    channel_k2=channels[0]//4,
+                    channel_k3=channels[0]//4,
+                    channel_k5=channels[0]//4,
+                    channel_k7=channels[0]//4),
+                Inception1DBlock(
+                    channel_in=channels[0],
+                    channel_k2=channels[1]//4,
+                    channel_k3=channels[1]//4,
+                    channel_k5=channels[1]//4,
+                    channel_k7=channels[1]//4),
+                Inception1DBlock(
+                    channel_in=channels[1],
+                    channel_k2=channels[2]//4,
+                    channel_k3=channels[2]//4,
+                    channel_k5=channels[2]//4,
+                    channel_k7=channels[2]//4),
+                Inception1DBlock(
+                    channel_in=channels[2],
+                    channel_k2=channels[3]//4,
+                    channel_k3=channels[3]//4,
+                    channel_k5=channels[3]//4,
+                    channel_k7=channels[3]//4),
+                nn.Conv1d(channels[3], output_channel, 3, 2, 1, bias=True),
+            )
+        else:
+            self.model = nn.Sequential(
+                nn.Conv1d(input_channel, channels[0], 3, 2, 1, bias=False),
+                nn.BatchNorm1d(channels[0], affine=True),
+                nn.ReLU(inplace=True),
+                nn.Conv1d(channels[0], channels[1], 3, 2, 1, bias=False),
+                nn.BatchNorm1d(channels[1], affine=True),
+                nn.ReLU(inplace=True),
+                nn.Conv1d(channels[1], channels[2], 3, 2, 1, bias=False),
+                nn.BatchNorm1d(channels[2], affine=True),
+                nn.ReLU(inplace=True),
+                nn.Conv1d(channels[2], channels[3], 3, 2, 1, bias=False),
+                nn.BatchNorm1d(channels[3], affine=True),
+                nn.ReLU(inplace=True),
+                nn.Conv1d(channels[3], output_channel, 3, 2, 1, bias=True),
+            )
+        self.add_noise = add_noise
+        self.normalize_embedding = normalize_embedding
+        self.return_seq = return_seq
+        self.output_channel = output_channel
+
+        self.segments_fusion = segments_fusion
+        self.normalize_fusion = normalize_fusion
+        if segments_fusion:
+            #self.attn_fuser = Attention(
+            #    output_channel,
+            #    output_channel,
+            #    ignore_tanh=True)
+            self.attn_fuser = \
+                getattr(encoder_model_collection, fuser_arch)(**fuser_kwargs)
+
+    def forward(self, x):
+        # In case more than one mel segment per person is passed
+        if len(x.shape) == 4:
+            fusion_mode = True
+            B, N, C, L = x.shape
+            #print('Fusion Mode On! Input Shape:', x.shape)
+            x = x.view(B*N, C, L)
+        else:
+            fusion_mode = False
+            B, C, L = x.shape
+        x = self.model(x)
+        embeddings = F.avg_pool1d(x, x.size()[2], stride=1)
+        embeddings = embeddings.view(embeddings.size()[0], -1, 1, 1)
+
+        if self.normalize_embedding:
+            embeddings = F.normalize(embeddings)
+        if self.add_noise:
+            noise = 0.05 * torch.randn(x.shape[0], x.shape[1], 1, 1)
+            noise = noise.type(embeddings.type())
+            embeddings = embeddings + noise
+            if self.normalize_embedding:
+                embeddings = F.normalize(embeddings)
+
+        # Restore Tensor shape
+        if fusion_mode:
+            #print(embeddings.shape)
+            C_emb = embeddings.shape[1]
+            embeddings = embeddings.view(B, N, C_emb)
+            # Attention fusion
+            embeddings = self.attn_fuser(embeddings)
+            if self.normalize_fusion:
+                embeddings = F.normalize(embeddings)
+
+        if self.return_seq:
+            return embeddings, x
+        else:
+            return embeddings
+
+    def print_param(self):
+        print('All parameters:')
+        for name, param in self.named_parameters():
+            print(name)
+
+    def print_trainable_param(model):
+        print('Trainable Parameters:')
+        for name, param in model.named_parameters():
+            if param.requires_grad:
+                print(name)
+
+    def train_fuser_only(self):
+        print('Training Attention Fuser Only')
+        for name, param in self.named_parameters():
+            if 'attn_fuser' in name:
+                param.requires_grad = True
+            else:
+                param.requires_grad = False
+
+    def init_attn_fusion(self):
+        self.attn_fuser = Attention(
+            self.output_channel,
+            self.output_channel,
+            ignore_tanh=True)
+
+if __name__ == '__main__':
+    import time
+    # Demo Input
+    log_mels = torch.ones((16, 40, 151))
+    pos_log_mel = torch.ones((16, 48, 150)).transpose(1, 2)
+
+    # Test Inception1DBlock
+    incept_block = Inception1DBlock(
+        channel_in=40,
+        channel_k2=64,
+        channel_k3=64,
+        channel_k5=64,
+        channel_k7=64)
+    incept_block(log_mels)
+
+    # Test V2F 1D CNN
+    log_mel_segs = torch.ones((16, 20, 40, 150))
+    v2f_id_cnn_kwargs = {
+        'input_channel': 40,
+        'channels': [256, 384, 576, 864],
+        'output_channel': 64,
+        'segments_fusion': True,
+        'inception_mode': True,
+        }
+    v2f_id_cnn_fuse = V2F1DCNN(**v2f_id_cnn_kwargs)
+    print(v2f_id_cnn_fuse)
+    print('V2F1DCNN Output shape:', v2f_id_cnn_fuse(log_mel_segs).shape)
+    v2f_id_cnn_fuse.print_param()
+    v2f_id_cnn_fuse.train_fuser_only()
+    v2f_id_cnn_fuse.print_trainable_param()
+
+    # test a simple transformer layer
+    trans_layer = nn.TransformerEncoderLayer(48, 8,
+        dim_feedforward=48, dropout=0.1, activation='relu')
+    print(trans_layer(pos_log_mel).shape)
+
+
+    # Transformer
+    trans_kwargs = {
+        'input_channel': 40,
+        'cnn_channels': [512, 512],
+        'transformer_dim': 512,
+        'transformer_depth': 2,
+        'return_seq': True,
+        'pos_embedding_dim': 0, #8
+        'sin_pos_encoding': True, #False
+    }
+    trans_encoder = TransEncoder(**trans_kwargs).cuda()
+    print(trans_encoder)
+    emb, seq_emb = trans_encoder(log_mels.cuda())
+    print('TransEncoder Output shape:', emb.shape, seq_emb.shape)
+
+    # Test V2F 1D CNN
+    v2f_id_cnn_kwargs = {
+        'input_channel': 40,
+        'channels': [256, 384, 576, 864],
+        'output_channel': 64,
+        }
+    v2f_id_cnn = V2F1DCNN(**v2f_id_cnn_kwargs)
+    print(v2f_id_cnn)
+    print('V2F1DCNN Output shape:', v2f_id_cnn(log_mels).shape)
+
+    # Test V2F 1D CNN Sequential Return
+    v2f_id_cnn_kwargs = {
+        'input_channel': 40,
+        'channels': [256, 384, 576, 864],
+        'output_channel': 64,
+        'return_seq': True,
+        }
+    v2f_id_cnn = V2F1DCNN(**v2f_id_cnn_kwargs)
+    print(v2f_id_cnn)
+    emb, seq_emb = v2f_id_cnn(log_mels)
+    print('V2F1DCNN Output shape:', emb.shape, seq_emb.shape)
diff --git a/mlops/mlflow/train/sf2f/options/__init__.py b/mlops/mlflow/train/sf2f/options/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mlops/mlflow/train/sf2f/options/data_opts/vox.yaml b/mlops/mlflow/train/sf2f/options/data_opts/vox.yaml
new file mode 100644
index 0000000..d0d3207
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/options/data_opts/vox.yaml
@@ -0,0 +1,7 @@
+root_dir: /home/data/VoxCeleb
+face_type: masked
+image_normalize_method: imagenet #standard
+mel_normalize_method: vox_mel
+nframe_range: (100, 150)
+split_set: train
+split_json: /home/data/VoxCeleb/split.json
diff --git a/mlops/mlflow/train/sf2f/options/opts.py b/mlops/mlflow/train/sf2f/options/opts.py
new file mode 100644
index 0000000..988372f
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/options/opts.py
@@ -0,0 +1,92 @@
+import os
+import os.path as osp
+import argparse
+import yaml
+from utils import update_values
+from utils import int_tuple, float_tuple, str_tuple, bool_flag
+
+parser = argparse.ArgumentParser()
+
+# Optimization hyperparameters
+parser.add_argument('--batch_size', default=256, type=int)
+parser.add_argument('--epochs', default=200, type=int)
+parser.add_argument('--learning_rate', default=5e-4, type=float)
+# by default, it is disabled
+parser.add_argument('--decay_lr_epochs', default=10000, type=float)
+parser.add_argument('--beta1', type=float, default=0.9,
+                    help='momentum term of adam')
+parser.add_argument('--eval_epochs', default=1, type=int)
+parser.add_argument('--eval_mode_after', default=10000, type=int)
+parser.add_argument('--disable_l1_loss_after', default=10000000, type=int)
+parser.add_argument('--path_opts', type=str,
+                    default='options/vox/sf2f/sf2f_1st_stage.yaml', help="Options.")
+
+parser.add_argument('--workers', default=12, type=int)
+
+# Output options
+parser.add_argument('--print_every', default=100, type=int)
+parser.add_argument('--visualize_every', type=int,
+                    default=1, help="visualize to visdom.")
+parser.add_argument('--timing', action="store_true")
+parser.add_argument('--output_dir', type=str)
+parser.add_argument('--log_suffix', type=str)
+parser.add_argument('--checkpoint_name', default='checkpoint')
+parser.add_argument('--checkpoint_start_from', default=None)
+parser.add_argument('--checkpoint', nargs='+')
+parser.add_argument('--resume', type=str)
+parser.add_argument('--evaluate', action='store_true',
+    help="Set to evaluate the model.")
+parser.add_argument('--evaluate_train', action='store_true',
+    help="Set to evaluate the training set.")
+
+# For inference in run_model.py
+parser.add_argument('--output_demo_dir', default='output/results')
+parser.add_argument('--samples_path', default='samples')
+
+# For evaluation
+parser.add_argument('--recall_method', default='cos_sim')
+parser.add_argument('--facenet_return_pooling', default=False)
+# parser.add_argument('--face_gen_mode', default='average_facenet_embedding')
+parser.add_argument('--face_gen_mode', nargs='+')
+
+# For test.py
+parser.add_argument('--get_faces_from_different_segments', default=False)
+
+# Attention Fuser Mode
+parser.add_argument('--train_fuser_only', default=False)
+# Train the fuser and decoder
+parser.add_argument('--train_fuser_decoder', default=False)
+parser.add_argument('--pretrained_path', default=None)
+parser.add_argument('--freeze_discriminators', default=False)
+
+# For Inference
+parser.add_argument('--input_wav_dir', type=str, default='data/example_audio')
+parser.add_argument('--fuser_infer', default=False)
+parser.add_argument('--seed', type=int, default=311)
+args = parser.parse_args()
+
+options = {
+    "data": {
+        "batch_size": args.batch_size,
+        "workers": args.workers,
+        "data_opts": {},
+    },
+    "optim": {
+        "lr": args.learning_rate,
+        "epochs": args.epochs,
+        "eval_epochs": args.eval_epochs,
+    },
+    "logs": {
+        "output_dir": args.output_dir,
+    },
+}
+
+with open(args.path_opts, "r") as f:
+    options_yaml = yaml.full_load(f)
+with open(options_yaml["data"]["data_opts_path"], "r") as f:
+    data_opts = yaml.full_load(f)
+    options_yaml["data"]["data_opts"] = data_opts
+
+options = update_values(options, options_yaml)
+if args.log_suffix:
+    options["logs"]["name"] += "-" + args.log_suffix
diff --git a/mlops/mlflow/train/sf2f/options/vox/baseline/v2f.yaml b/mlops/mlflow/train/sf2f/options/vox/baseline/v2f.yaml
new file mode 100644
index 0000000..cd406bf
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/options/vox/baseline/v2f.yaml
@@ -0,0 +1,50 @@
+# The implementation of NeurIPS 19 V2F baseline model
+# Log-related settings
+logs:
+  name: v2f
+  output_dir: output/
+# data-related settings
+data:
+  dataset: vox
+  data_opts_path: options/data_opts/vox.yaml
+  image_size: [64, 64]
+# model related settings
+generator:
+  arch: EncoderDecoder
+  options:
+    encoder_arch: V2F1DCNN
+    encoder_kwargs:
+      input_channel: 40
+      channels: [256, 384, 576, 864]
+      output_channel: 512
+      add_noise: True
+      normalize_embedding: True
+    decoder_arch: V2FDecoder
+    decoder_kwargs:
+      input_channel: 512
+      channels: [1024, 512, 256, 128, 64]
+      output_channel: 3
+discriminator:
+  generic:
+    normalization: none
+    # padding mode is set to unused because we are specifying padding value
+    padding: unused
+    activation: leakyrelu-0.2
+  # image discrimintor is implemented in AC Discriminator
+  identity:
+    # 'C{kernel_size}-{channel_out}-{stride}-{padding}'
+    arch: 'C1-32-1-0,C4-64-2-1,C4-128-2-1,C4-256-2-1,C4-512-1-0'
+    num_id: 0 # will be updated in train.py
+optim:
+  # Discriminator Loss Weights
+  d_loss_weight: 1.0
+  d_img_weight: -1
+  ac_loss_weight: 1.0
+  # Generator Loss Weights
+  gan_loss_type: 'gan'
+  l1_pixel_loss_weight: 100.0
+  # Perceptual Loss
+eval:
+  facenet:
+    deprocess_and_preprocess: True
+    crop_faces: True
diff --git a/mlops/mlflow/train/sf2f/options/vox/sf2f/sf2f_1st_stage.yaml b/mlops/mlflow/train/sf2f/options/vox/sf2f/sf2f_1st_stage.yaml
new file mode 100644
index 0000000..c627b1d
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/options/vox/sf2f/sf2f_1st_stage.yaml
@@ -0,0 +1,52 @@
+# Optimal final version of encoder-decoder and Loss Function
+# Encoder: Inceptional 1D CNN
+# Decoder: Upsampling + CNN
+logs:
+  name: sf2f_1st_stage
+  output_dir: output/
+# data-related settings
+data:
+  dataset: vox
+  data_opts_path: options/data_opts/vox.yaml
+  image_size: [64, 64]
+# model related settings
+generator:
+  arch: EncoderDecoder
+  options:
+    encoder_arch: V2F1DCNN
+    encoder_kwargs:
+      input_channel: 40
+      channels: [256, 384, 576, 864]
+      output_channel: 512
+      normalize_embedding: True
+      inception_mode: True
+    decoder_arch: FaceGanDecoder
+    decoder_kwargs:
+      noise_dim: 512
+      mlp_normalization: none
+      normalization: batch
+      activation: leakyrelu-0.1
+discriminator:
+  generic:
+    normalization: batch
+    padding: valid
+    activation: leakyrelu-0.1
+  image:
+    arch: 'C4-64-2,C4-128-2,C4-256-2'
+  identity:
+    arch: 'C4-64-2,C4-128-2,C4-256-2'
+    num_id: 0 # will be updated in train.py
+optim:
+  # Discriminator Loss Weights
+  d_loss_weight: 1.0
+  d_img_weight: 1.0 #0.5
+  ac_loss_weight: 0.05
+  # Generator Loss Weights
+  gan_loss_type: 'gan'
+  l1_pixel_loss_weight: 10.0
+  # Perceptual Loss
+  perceptual_loss_weight: 100.0
+eval:
+  facenet:
+    deprocess_and_preprocess: True
+    crop_faces: True
diff --git a/mlops/mlflow/train/sf2f/options/vox/sf2f/sf2f_fuser.yaml b/mlops/mlflow/train/sf2f/options/vox/sf2f/sf2f_fuser.yaml
new file mode 100644
index 0000000..c354661
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/options/vox/sf2f/sf2f_fuser.yaml
@@ -0,0 +1,62 @@
+# Optimal final version of encoder-decoder and Loss Function
+# Encoder: Inceptional 1D CNN
+# Decoder: Upsampling + CNN
+# Fuser: Vanilla 1-layer attention 'general'
+logs:
+  name: sf2f_fuser
+  output_dir: sf2f/output/
+# data-related settings
+data:
+  dataset: vox
+  data_opts_path: /home/hojun/Documents/project/boostcamp/final_project/mlops/mlflow/train/mlflow/sf2f/options/data_opts/vox.yaml
+  image_size: [64, 64]
+# model related settings
+generator:
+  arch: EncoderDecoder
+  options:
+    encoder_arch: V2F1DCNN
+    encoder_kwargs:
+      input_channel: 40
+      channels: [256, 384, 576, 864]
+      output_channel: 512
+      normalize_embedding: True
+      inception_mode: True
+      segments_fusion: True
+      normalize_fusion: True
+      # Initialize Attention Fuser as a submodule of encoder
+      fuser_arch: AttentionFuserV1
+      fuser_kwargs:
+        dimensions: 512
+        dim_out: 512
+        attention_type: general
+        ignore_tanh: True
+    decoder_arch: FaceGanDecoder
+    decoder_kwargs:
+      noise_dim: 512
+      mlp_normalization: none
+      normalization: batch
+      activation: leakyrelu-0.1
+discriminator:
+  generic:
+    normalization: batch
+    padding: valid
+    activation: leakyrelu-0.1
+  image:
+    arch: 'C4-64-2,C4-128-2,C4-256-2'
+  identity:
+    arch: 'C4-64-2,C4-128-2,C4-256-2'
+    num_id: 0 # will be updated in train.py
+optim:
+  # Discriminator Loss Weights
+  d_loss_weight: 1.0
+  d_img_weight: 1.0 #0.5
+  ac_loss_weight: 0.05
+  # Generator Loss Weights
+  gan_loss_type: 'gan'
+  l1_pixel_loss_weight: 10.0
+  # Perceptual Loss
+  perceptual_loss_weight: 100.0
+eval:
+  facenet:
+    deprocess_and_preprocess: True
+    crop_faces: True
diff --git a/mlops/mlflow/train/sf2f/options/vox/sf2f/sf2f_mid_1st_stage.yaml b/mlops/mlflow/train/sf2f/options/vox/sf2f/sf2f_mid_1st_stage.yaml
new file mode 100644
index 0000000..6f0e407
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/options/vox/sf2f/sf2f_mid_1st_stage.yaml
@@ -0,0 +1,58 @@
+# Optimal final version of encoder-decoder and Loss Function
+# Encoder: Inceptional 1D CNN
+# Decoder: Upsampling + CNN
+# Mid resolution: with multi-reso training
+logs:
+  name: sf2f_mid_1st_stage
+  output_dir: output/
+# data-related settings
+data:
+  dataset: vox
+  data_opts_path: sf2f/options/data_opts/vox.yaml
+  image_size: [128, 128]
+# model related settings
+generator:
+  arch: EncoderDecoder
+  options:
+    encoder_arch: V2F1DCNN
+    encoder_kwargs:
+      input_channel: 40
+      channels: [256, 384, 576, 864]
+      output_channel: 512
+      normalize_embedding: True
+      inception_mode: True
+    decoder_arch: FaceGanDecoder_v2
+    decoder_kwargs:
+      noise_dim: 512
+      mlp_normalization: none
+      normalization: batch
+      activation: leakyrelu-0.1
+discriminator:
+  generic:
+    normalization: batch
+    padding: valid
+    activation: leakyrelu-0.1
+  image_low:
+    arch: 'C4-64-2,C4-128-2,C4-256-2'
+  image_mid:
+    arch: 'C4-64-2,C4-128-2,C4-256-2,C4-512-2'
+  identity_low:
+    arch: 'C4-64-2,C4-128-2,C4-256-2'
+    num_id: 0 # will be updated in train.py
+  identity_mid:
+    arch: 'C4-64-2,C4-128-2,C4-256-2,C4-512-2'
+    num_id: 0 # will be updated in train.py
+optim:
+  # Discriminator Loss Weights
+  d_loss_weight: 1.0
+  d_img_weight: 1.0 #0.5
+  ac_loss_weight: 0.05
+  # Generator Loss Weights
+  gan_loss_type: 'gan'
+  l1_pixel_loss_weight: 100.0
+  # Perceptual Loss
+  perceptual_loss_weight: 100.0
+eval:
+  facenet:
+    deprocess_and_preprocess: True
+    crop_faces: True
diff --git a/mlops/mlflow/train/sf2f/options/vox/sf2f/sf2f_mid_fuser.yaml b/mlops/mlflow/train/sf2f/options/vox/sf2f/sf2f_mid_fuser.yaml
new file mode 100644
index 0000000..ed021dc
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/options/vox/sf2f/sf2f_mid_fuser.yaml
@@ -0,0 +1,67 @@
+# Optimal final version of encoder-decoder and Loss Function
+# Encoder: Inceptional 1D CNN
+# Decoder: Upsampling + CNN
+# Mid resolution: with multi-reso training
+logs:
+  name: sf2f_mid_fuser
+  output_dir: output/
+# data-related settings
+data:
+  dataset: vox
+  data_opts_path: sf2f/options/data_opts/vox.yaml
+  image_size: [128, 128]
+# model related settings
+generator:
+  arch: EncoderDecoder
+  options:
+    encoder_arch: V2F1DCNN
+    encoder_kwargs:
+      input_channel: 40
+      channels: [256, 384, 576, 864]
+      output_channel: 512
+      normalize_embedding: True
+      inception_mode: True
+      segments_fusion: True
+      normalize_fusion: True
+      # Initialize Attention Fuser as a submodule of encoder
+      fuser_arch: AttentionFuserV1
+      fuser_kwargs:
+        dimensions: 512
+        dim_out: 512
+        attention_type: general
+        ignore_tanh: True
+    decoder_arch: FaceGanDecoder_v2
+    decoder_kwargs:
+      noise_dim: 512
+      mlp_normalization: none
+      normalization: batch
+      activation: leakyrelu-0.1
+discriminator:
+  generic:
+    normalization: batch
+    padding: valid
+    activation: leakyrelu-0.1
+  image_low:
+    arch: 'C4-64-2,C4-128-2,C4-256-2'
+  image_mid:
+    arch: 'C4-64-2,C4-128-2,C4-256-2,C4-512-2'
+  identity_low:
+    arch: 'C4-64-2,C4-128-2,C4-256-2'
+    num_id: 0 # will be updated in train.py
+  identity_mid:
+    arch: 'C4-64-2,C4-128-2,C4-256-2,C4-512-2'
+    num_id: 0 # will be updated in train.py
+optim:
+  # Discriminator Loss Weights
+  d_loss_weight: 1.0
+  d_img_weight: 1.0 #0.5
+  ac_loss_weight: 0.05
+  # Generator Loss Weights
+  gan_loss_type: 'gan'
+  l1_pixel_loss_weight: 100.0
+  # Perceptual Loss
+  perceptual_loss_weight: 100.0
+eval:
+  facenet:
+    deprocess_and_preprocess: True
+    crop_faces: True
diff --git a/mlops/mlflow/train/sf2f/scripts/DScore/__init__.py b/mlops/mlflow/train/sf2f/scripts/DScore/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mlops/mlflow/train/sf2f/scripts/DScore/models/__init__.py b/mlops/mlflow/train/sf2f/scripts/DScore/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mlops/mlflow/train/sf2f/scripts/DScore/models/base_model.py b/mlops/mlflow/train/sf2f/scripts/DScore/models/base_model.py
new file mode 100644
index 0000000..36f4d83
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/DScore/models/base_model.py
@@ -0,0 +1,60 @@
+import os
+import torch
+import scripts.DScore.util.util as util
+from torch.autograd import Variable
+from pdb import set_trace as st
+from IPython import embed
+
+class BaseModel():
+    def __init__(self):
+        pass;
+
+    def name(self):
+        return 'BaseModel'
+
+    def initialize(self, use_gpu=True):
+        self.use_gpu = use_gpu
+        self.Tensor = torch.cuda.FloatTensor if self.use_gpu else torch.Tensor
+        # self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)
+
+    def forward(self):
+        pass
+
+    def get_image_paths(self):
+        pass
+
+    def optimize_parameters(self):
+        pass
+
+    def get_current_visuals(self):
+        return self.input
+
+    def get_current_errors(self):
+        return {}
+
+    def save(self, label):
+        pass
+
+    # helper saving function that can be used by subclasses
+    def save_network(self, network, path, network_label, epoch_label):
+        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
+        save_path = os.path.join(path, save_filename)
+        torch.save(network.state_dict(), save_path)
+
+    # helper loading function that can be used by subclasses
+    def load_network(self, network, network_label, epoch_label):
+        # embed()
+        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
+        save_path = os.path.join(self.save_dir, save_filename)
+        print('Loading network from %s'%save_path)
+        network.load_state_dict(torch.load(save_path))
+
+    def update_learning_rate():
+        pass
+
+    def get_image_paths(self):
+        return self.image_paths
+
+    def save_done(self, flag=False):
+        np.save(os.path.join(self.save_dir, 'done_flag'),flag)
+        np.savetxt(os.path.join(self.save_dir, 'done_flag'),[flag,],fmt='%i')
diff --git a/mlops/mlflow/train/sf2f/scripts/DScore/models/dist_model.py b/mlops/mlflow/train/sf2f/scripts/DScore/models/dist_model.py
new file mode 100644
index 0000000..d11eee1
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/DScore/models/dist_model.py
@@ -0,0 +1,326 @@
+from __future__ import absolute_import
+
+import sys
+sys.path.append('..')
+sys.path.append('.')
+import numpy as np
+import torch
+from torch import nn
+import os
+from collections import OrderedDict
+from torch.autograd import Variable
+import itertools
+from .base_model import BaseModel
+from scipy.ndimage import zoom
+import fractions
+import functools
+import skimage.transform
+from IPython import embed
+
+from . import networks_basic as networks
+# from PerceptualSimilarity.util import util
+from scripts.DScore.util import util
+
+class DistModel(BaseModel):
+    def name(self):
+        return self.model_name
+
+    def initialize(self, model='net-lin', net='alex', pnet_rand=False, pnet_tune=False, model_path=None, colorspace='Lab', use_gpu=True, printNet=False, spatial=False, spatial_shape=None, spatial_order=1, spatial_factor=None, is_train=False, lr=.0001, beta1=0.5, version='0.1'):
+        '''
+        INPUTS
+            model - ['net-lin'] for linearly calibrated network
+                    ['net'] for off-the-shelf network
+                    ['L2'] for L2 distance in Lab colorspace
+                    ['SSIM'] for ssim in RGB colorspace
+            net - ['squeeze','alex','vgg']
+            model_path - if None, will look in weights/[NET_NAME].pth
+            colorspace - ['Lab','RGB'] colorspace to use for L2 and SSIM
+            use_gpu - bool - whether or not to use a GPU
+            printNet - bool - whether or not to print network architecture out
+            spatial - bool - whether to output an array containing varying distances across spatial dimensions
+            spatial_shape - if given, output spatial shape. if None then spatial shape is determined automatically via spatial_factor (see below).
+            spatial_factor - if given, specifies upsampling factor relative to the largest spatial extent of a convolutional layer. if None then resized to size of input images.
+            spatial_order - spline order of filter for upsampling in spatial mode, by default 1 (bilinear).
+            is_train - bool - [True] for training mode
+            lr - float - initial learning rate
+            beta1 - float - initial momentum term for adam
+            version - 0.1 for latest, 0.0 was original
+        '''
+        BaseModel.initialize(self, use_gpu=use_gpu)
+
+        self.model = model
+        self.net = net
+        self.use_gpu = use_gpu
+        self.is_train = is_train
+        self.spatial = spatial
+        self.spatial_shape = spatial_shape
+        self.spatial_order = spatial_order
+        self.spatial_factor = spatial_factor
+
+        self.model_name = '%s [%s]'%(model,net)
+        if(self.model == 'net-lin'): # pretrained net + linear layer
+            self.net = networks.PNetLin(use_gpu=use_gpu,pnet_rand=pnet_rand, pnet_tune=pnet_tune, pnet_type=net,use_dropout=True,spatial=spatial,version=version)
+            kw = {}
+            if not use_gpu:
+                kw['map_location'] = 'cpu'
+            if(model_path is None):
+                import inspect
+                # model_path = './PerceptualSimilarity/weights/v%s/%s.pth'%(version,net)
+                model_path = os.path.abspath(os.path.join(inspect.getfile(self.initialize), '..', '..', 'weights/v%s/%s.pth'%(version,net)))
+
+            if(not is_train):
+                # print('Loading model from: %s'%model_path)
+                self.net.load_state_dict(torch.load(model_path, **kw))
+
+        elif(self.model=='net'): # pretrained network
+            assert not self.spatial, 'spatial argument not supported yet for uncalibrated networks'
+            self.net = networks.PNet(use_gpu=use_gpu,pnet_type=net)
+            self.is_fake_net = True
+        elif(self.model in ['L2','l2']):
+            self.net = networks.L2(use_gpu=use_gpu,colorspace=colorspace) # not really a network, only for testing
+            self.model_name = 'L2'
+        elif(self.model in ['DSSIM','dssim','SSIM','ssim']):
+            self.net = networks.DSSIM(use_gpu=use_gpu,colorspace=colorspace)
+            self.model_name = 'SSIM'
+        else:
+            raise ValueError("Model [%s] not recognized." % self.model)
+
+        self.parameters = list(self.net.parameters())
+
+        if self.is_train: # training mode
+            # extra network on top to go from distances (d0,d1) => predicted human judgment (h*)
+            self.rankLoss = networks.BCERankingLoss(use_gpu=use_gpu)
+            self.parameters+=self.rankLoss.parameters
+            self.lr = lr
+            self.old_lr = lr
+            self.optimizer_net = torch.optim.Adam(self.parameters, lr=lr, betas=(beta1, 0.999))
+        else: # test mode
+            self.net.eval()
+
+        if(printNet):
+            print('---------- Networks initialized -------------')
+            networks.print_network(self.net)
+            print('-----------------------------------------------')
+
+    def forward_pair(self,in1,in2,retPerLayer=False):
+        if(retPerLayer):
+            return self.net.forward(in1,in2, retPerLayer=True)
+        else:
+            return self.net.forward(in1,in2)
+
+    def forward(self, in0, in1, retNumpy=True):
+        ''' Function computes the distance between image patches in0 and in1
+        INPUTS
+            in0, in1 - torch.Tensor object of shape Nx3xXxY - image patch scaled to [-1,1]
+            retNumpy - [False] to return as torch.Tensor, [True] to return as numpy array
+        OUTPUT
+            computed distances between in0 and in1
+        '''
+
+        self.input_ref = in0
+        self.input_p0 = in1
+
+        if(self.use_gpu):
+            self.input_ref = self.input_ref.cuda()
+            self.input_p0 = self.input_p0.cuda()
+
+        self.var_ref = Variable(self.input_ref,requires_grad=True)
+        self.var_p0 = Variable(self.input_p0,requires_grad=True)
+
+        self.d0 = self.forward_pair(self.var_ref, self.var_p0)
+        self.loss_total = self.d0
+
+        def convert_output(d0):
+            if(retNumpy):
+                ans = d0.cpu().data.numpy()
+                if not self.spatial:
+                    ans = ans.flatten()
+                else:
+                    assert(ans.shape[0] == 1 and len(ans.shape) == 4)
+                    return ans[0,...].transpose([1, 2, 0])                  # Reshape to usual numpy image format: (height, width, channels)
+                return ans
+            else:
+                return d0
+
+        if self.spatial:
+            L = [convert_output(x) for x in self.d0]
+            spatial_shape = self.spatial_shape
+            if spatial_shape is None:
+                if(self.spatial_factor is None):
+                    spatial_shape = (in0.size()[2],in0.size()[3])
+                else:
+                    spatial_shape = (max([x.shape[0] for x in L])*self.spatial_factor, max([x.shape[1] for x in L])*self.spatial_factor)
+
+            L = [skimage.transform.resize(x, spatial_shape, order=self.spatial_order, mode='edge') for x in L]
+
+            L = np.mean(np.concatenate(L, 2) * len(L), 2)
+            return L
+        else:
+            return convert_output(self.d0)
+
+    # ***** TRAINING FUNCTIONS *****
+    def optimize_parameters(self):
+        self.forward_train()
+        self.optimizer_net.zero_grad()
+        self.backward_train()
+        self.optimizer_net.step()
+        self.clamp_weights()
+
+    def clamp_weights(self):
+        for module in self.net.modules():
+            if(hasattr(module, 'weight') and module.kernel_size==(1,1)):
+                module.weight.data = torch.clamp(module.weight.data,min=0)
+
+    def set_input(self, data):
+        self.input_ref = data['ref']
+        self.input_p0 = data['p0']
+        self.input_p1 = data['p1']
+        self.input_judge = data['judge']
+
+        if(self.use_gpu):
+            self.input_ref = self.input_ref.cuda()
+            self.input_p0 = self.input_p0.cuda()
+            self.input_p1 = self.input_p1.cuda()
+            self.input_judge = self.input_judge.cuda()
+
+        self.var_ref = Variable(self.input_ref,requires_grad=True)
+        self.var_p0 = Variable(self.input_p0,requires_grad=True)
+        self.var_p1 = Variable(self.input_p1,requires_grad=True)
+
+    def forward_train(self): # run forward pass
+        self.d0 = self.forward_pair(self.var_ref, self.var_p0)
+        self.d1 = self.forward_pair(self.var_ref, self.var_p1)
+        self.acc_r = self.compute_accuracy(self.d0,self.d1,self.input_judge)
+
+        # var_judge
+        self.var_judge = Variable(1.*self.input_judge).view(self.d0.size())
+
+        self.loss_total = self.rankLoss.forward(self.d0, self.d1, self.var_judge*2.-1.)
+        return self.loss_total
+
+    def backward_train(self):
+        torch.mean(self.loss_total).backward()
+
+    def compute_accuracy(self,d0,d1,judge):
+        ''' d0, d1 are Variables, judge is a Tensor '''
+        d1_lt_d0 = (d1<d0).cpu().data.numpy().flatten()
+        judge_per = judge.cpu().numpy().flatten()
+        return d1_lt_d0*judge_per + (1-d1_lt_d0)*(1-judge_per)
+
+    def get_current_errors(self):
+        retDict = OrderedDict([('loss_total', self.loss_total.data.cpu().numpy()),
+                            ('acc_r', self.acc_r)])
+
+        for key in retDict.keys():
+            retDict[key] = np.mean(retDict[key])
+
+        return retDict
+
+    def get_current_visuals(self):
+        zoom_factor = 256/self.var_ref.data.size()[2]
+
+        ref_img = util.tensor2im(self.var_ref.data)
+        p0_img = util.tensor2im(self.var_p0.data)
+        p1_img = util.tensor2im(self.var_p1.data)
+
+        ref_img_vis = zoom(ref_img,[zoom_factor, zoom_factor, 1],order=0)
+        p0_img_vis = zoom(p0_img,[zoom_factor, zoom_factor, 1],order=0)
+        p1_img_vis = zoom(p1_img,[zoom_factor, zoom_factor, 1],order=0)
+
+        return OrderedDict([('ref', ref_img_vis),
+                            ('p0', p0_img_vis),
+                            ('p1', p1_img_vis)])
+
+    def save(self, path, label):
+        self.save_network(self.net, path, '', label)
+        self.save_network(self.rankLoss.net, path, 'rank', label)
+
+    def update_learning_rate(self,nepoch_decay):
+        lrd = self.lr / nepoch_decay
+        lr = self.old_lr - lrd
+
+        for param_group in self.optimizer_net.param_groups:
+            param_group['lr'] = lr
+
+        print('update lr [%s] decay: %f -> %f' % (type,self.old_lr, lr))
+        self.old_lr = lr
+
+
+
+def score_2afc_dataset(data_loader,func):
+    ''' Function computes Two Alternative Forced Choice (2AFC) score using
+        distance function 'func' in dataset 'data_loader'
+    INPUTS
+        data_loader - CustomDatasetDataLoader object - contains a TwoAFCDataset inside
+        func - callable distance function - calling d=func(in0,in1) should take 2
+            pytorch tensors with shape Nx3xXxY, and return numpy array of length N
+    OUTPUTS
+        [0] - 2AFC score in [0,1], fraction of time func agrees with human evaluators
+        [1] - dictionary with following elements
+            d0s,d1s - N arrays containing distances between reference patch to perturbed patches
+            gts - N array in [0,1], preferred patch selected by human evaluators
+                (closer to "0" for left patch p0, "1" for right patch p1,
+                "0.6" means 60pct people preferred right patch, 40pct preferred left)
+            scores - N array in [0,1], corresponding to what percentage function agreed with humans
+    CONSTS
+        N - number of test triplets in data_loader
+    '''
+
+    d0s = []
+    d1s = []
+    gts = []
+
+    # bar = pb.ProgressBar(max_value=data_loader.load_data().__len__())
+    for (i,data) in enumerate(data_loader.load_data()):
+        d0s+=func(data['ref'],data['p0']).tolist()
+        d1s+=func(data['ref'],data['p1']).tolist()
+        gts+=data['judge'].cpu().numpy().flatten().tolist()
+        # bar.update(i)
+
+    d0s = np.array(d0s)
+    d1s = np.array(d1s)
+    gts = np.array(gts)
+    scores = (d0s<d1s)*(1.-gts) + (d1s<d0s)*gts + (d1s==d0s)*.5
+
+    return(np.mean(scores), dict(d0s=d0s,d1s=d1s,gts=gts,scores=scores))
+
+def score_jnd_dataset(data_loader,func):
+    ''' Function computes JND score using distance function 'func' in dataset 'data_loader'
+    INPUTS
+        data_loader - CustomDatasetDataLoader object - contains a JNDDataset inside
+        func - callable distance function - calling d=func(in0,in1) should take 2
+            pytorch tensors with shape Nx3xXxY, and return numpy array of length N
+    OUTPUTS
+        [0] - JND score in [0,1], mAP score (area under precision-recall curve)
+        [1] - dictionary with following elements
+            ds - N array containing distances between two patches shown to human evaluator
+            sames - N array containing fraction of people who thought the two patches were identical
+    CONSTS
+        N - number of test triplets in data_loader
+    '''
+
+    ds = []
+    gts = []
+
+    # bar = pb.ProgressBar(max_value=data_loader.load_data().__len__())
+    for (i,data) in enumerate(data_loader.load_data()):
+        ds+=func(data['p0'],data['p1']).tolist()
+        gts+=data['same'].cpu().numpy().flatten().tolist()
+        # bar.update(i)
+
+    sames = np.array(gts)
+    ds = np.array(ds)
+
+    sorted_inds = np.argsort(ds)
+    ds_sorted = ds[sorted_inds]
+    sames_sorted = sames[sorted_inds]
+
+    TPs = np.cumsum(sames_sorted)
+    FPs = np.cumsum(1-sames_sorted)
+    FNs = np.sum(sames_sorted)-TPs
+
+    precs = TPs/(TPs+FPs)
+    recs = TPs/(TPs+FNs)
+    score = util.voc_ap(recs,precs)
+
+    return(score, dict(ds=ds,sames=sames))
diff --git a/mlops/mlflow/train/sf2f/scripts/DScore/models/models.py b/mlops/mlflow/train/sf2f/scripts/DScore/models/models.py
new file mode 100644
index 0000000..97cad61
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/DScore/models/models.py
@@ -0,0 +1,11 @@
+from __future__ import absolute_import
+
+def create_model(opt):
+    model = None
+    print(opt.model)
+    from .siam_model import *
+    model = DistModel()
+    model.initialize(opt, opt.batchSize, )
+    print("model [%s] was created" % (model.name()))
+    return model
+
diff --git a/mlops/mlflow/train/sf2f/scripts/DScore/models/networks_basic.py b/mlops/mlflow/train/sf2f/scripts/DScore/models/networks_basic.py
new file mode 100644
index 0000000..783ebf6
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/DScore/models/networks_basic.py
@@ -0,0 +1,268 @@
+from __future__ import absolute_import
+
+import sys
+sys.path.append('..')
+sys.path.append('.')
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+import numpy as np
+from pdb import set_trace as st
+from skimage import color
+from IPython import embed
+from . import pretrained_networks as pn
+
+# from PerceptualSimilarity.util import util
+from scripts.DScore.util import util
+
+# Off-the-shelf deep network
+class PNet(nn.Module):
+    '''Pre-trained network with all channels equally weighted by default'''
+    def __init__(self, pnet_type='vgg', pnet_rand=False, use_gpu=True):
+        super(PNet, self).__init__()
+
+        self.use_gpu = use_gpu
+
+        self.pnet_type = pnet_type
+        self.pnet_rand = pnet_rand
+
+        self.shift = torch.autograd.Variable(torch.Tensor([-.030, -.088, -.188]).view(1,3,1,1))
+        self.scale = torch.autograd.Variable(torch.Tensor([.458, .448, .450]).view(1,3,1,1))
+
+        if(self.pnet_type in ['vgg','vgg16']):
+            self.net = pn.vgg16(pretrained=not self.pnet_rand,requires_grad=False)
+        elif(self.pnet_type=='alex'):
+            self.net = pn.alexnet(pretrained=not self.pnet_rand,requires_grad=False)
+        elif(self.pnet_type[:-2]=='resnet'):
+            self.net = pn.resnet(pretrained=not self.pnet_rand,requires_grad=False, num=int(self.pnet_type[-2:]))
+        elif(self.pnet_type=='squeeze'):
+            self.net = pn.squeezenet(pretrained=not self.pnet_rand,requires_grad=False)
+
+        self.L = self.net.N_slices
+
+        if(use_gpu):
+            self.net.cuda()
+            self.shift = self.shift.cuda()
+            self.scale = self.scale.cuda()
+
+    def forward(self, in0, in1, retPerLayer=False):
+        in0_sc = (in0 - self.shift.expand_as(in0))/self.scale.expand_as(in0)
+        in1_sc = (in1 - self.shift.expand_as(in0))/self.scale.expand_as(in0)
+
+        outs0 = self.net.forward(in0_sc)
+        outs1 = self.net.forward(in1_sc)
+
+        if(retPerLayer):
+            all_scores = []
+        for (kk,out0) in enumerate(outs0):
+            cur_score = (1.-util.cos_sim(outs0[kk],outs1[kk]))
+            if(kk==0):
+                val = 1.*cur_score
+            else:
+                # val = val + self.lambda_feat_layers[kk]*cur_score
+                val = val + cur_score
+            if(retPerLayer):
+                all_scores+=[cur_score]
+
+        if(retPerLayer):
+            return (val, all_scores)
+        else:
+            return val
+
+# Learned perceptual metric
+class PNetLin(nn.Module):
+    def __init__(self, pnet_type='vgg', pnet_rand=False, pnet_tune=False, use_dropout=True, use_gpu=True, spatial=False, version='0.1'):
+        super(PNetLin, self).__init__()
+
+        self.use_gpu = use_gpu
+        self.pnet_type = pnet_type
+        self.pnet_tune = pnet_tune
+        self.pnet_rand = pnet_rand
+        self.spatial = spatial
+        self.version = version
+
+        if(self.pnet_type in ['vgg','vgg16']):
+            net_type = pn.vgg16
+            self.chns = [64,128,256,512,512]
+        elif(self.pnet_type=='alex'):
+            net_type = pn.alexnet
+            self.chns = [64,192,384,256,256]
+        elif(self.pnet_type=='squeeze'):
+            net_type = pn.squeezenet
+            self.chns = [64,128,256,384,384,512,512]
+
+        if(self.pnet_tune):
+            self.net = net_type(pretrained=not self.pnet_rand,requires_grad=True)
+        else:
+            self.net = [net_type(pretrained=not self.pnet_rand,requires_grad=False),]
+
+        self.lin0 = NetLinLayer(self.chns[0],use_dropout=use_dropout)
+        self.lin1 = NetLinLayer(self.chns[1],use_dropout=use_dropout)
+        self.lin2 = NetLinLayer(self.chns[2],use_dropout=use_dropout)
+        self.lin3 = NetLinLayer(self.chns[3],use_dropout=use_dropout)
+        self.lin4 = NetLinLayer(self.chns[4],use_dropout=use_dropout)
+        self.lins = [self.lin0,self.lin1,self.lin2,self.lin3,self.lin4]
+        if(self.pnet_type=='squeeze'): # 7 layers for squeezenet
+            self.lin5 = NetLinLayer(self.chns[5],use_dropout=use_dropout)
+            self.lin6 = NetLinLayer(self.chns[6],use_dropout=use_dropout)
+            self.lins+=[self.lin5,self.lin6]
+
+        self.shift = torch.autograd.Variable(torch.Tensor([-.030, -.088, -.188]).view(1,3,1,1))
+        self.scale = torch.autograd.Variable(torch.Tensor([.458, .448, .450]).view(1,3,1,1))
+
+        if(use_gpu):
+            if(self.pnet_tune):
+                self.net.cuda()
+            else:
+                self.net[0].cuda()
+            self.shift = self.shift.cuda()
+            self.scale = self.scale.cuda()
+            self.lin0.cuda()
+            self.lin1.cuda()
+            self.lin2.cuda()
+            self.lin3.cuda()
+            self.lin4.cuda()
+            if(self.pnet_type=='squeeze'):
+                self.lin5.cuda()
+                self.lin6.cuda()
+
+    def forward(self, in0, in1):
+        in0_sc = (in0 - self.shift.expand_as(in0))/self.scale.expand_as(in0)
+        in1_sc = (in1 - self.shift.expand_as(in0))/self.scale.expand_as(in0)
+
+        if(self.version=='0.0'):
+            # v0.0 - original release had a bug, where input was not scaled
+            in0_input = in0
+            in1_input = in1
+        else:
+            # v0.1
+            in0_input = in0_sc
+            in1_input = in1_sc
+
+        if(self.pnet_tune):
+            outs0 = self.net.forward(in0_input)
+            outs1 = self.net.forward(in1_input)
+        else:
+            outs0 = self.net[0].forward(in0_input)
+            outs1 = self.net[0].forward(in1_input)
+
+        feats0 = {}
+        feats1 = {}
+        diffs = [0]*len(outs0)
+
+        for (kk,out0) in enumerate(outs0):
+            feats0[kk] = util.normalize_tensor(outs0[kk])
+            feats1[kk] = util.normalize_tensor(outs1[kk])
+            diffs[kk] = (feats0[kk]-feats1[kk])**2
+
+        if self.spatial:
+            lin_models = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
+            if(self.pnet_type=='squeeze'):
+                lin_models.extend([self.lin5, self.lin6])
+            res = [lin_models[kk].model(diffs[kk]) for kk in range(len(diffs))]
+            return res
+
+        val = torch.mean(torch.mean(self.lin0.model(diffs[0]),dim=3),dim=2)
+        val = val + torch.mean(torch.mean(self.lin1.model(diffs[1]),dim=3),dim=2)
+        val = val + torch.mean(torch.mean(self.lin2.model(diffs[2]),dim=3),dim=2)
+        val = val + torch.mean(torch.mean(self.lin3.model(diffs[3]),dim=3),dim=2)
+        val = val + torch.mean(torch.mean(self.lin4.model(diffs[4]),dim=3),dim=2)
+        if(self.pnet_type=='squeeze'):
+            val = val + torch.mean(torch.mean(self.lin5.model(diffs[5]),dim=3),dim=2)
+            val = val + torch.mean(torch.mean(self.lin6.model(diffs[6]),dim=3),dim=2)
+
+        val = val.view(val.size()[0],val.size()[1],1,1)
+
+        return val
+
+class Dist2LogitLayer(nn.Module):
+    ''' takes 2 distances, puts through fc layers, spits out value between [0,1] (if use_sigmoid is True) '''
+    def __init__(self, chn_mid=32,use_sigmoid=True):
+        super(Dist2LogitLayer, self).__init__()
+        layers = [nn.Conv2d(5, chn_mid, 1, stride=1, padding=0, bias=True),]
+        layers += [nn.LeakyReLU(0.2,True),]
+        layers += [nn.Conv2d(chn_mid, chn_mid, 1, stride=1, padding=0, bias=True),]
+        layers += [nn.LeakyReLU(0.2,True),]
+        layers += [nn.Conv2d(chn_mid, 1, 1, stride=1, padding=0, bias=True),]
+        if(use_sigmoid):
+            layers += [nn.Sigmoid(),]
+        self.model = nn.Sequential(*layers)
+
+    def forward(self,d0,d1,eps=0.1):
+        return self.model.forward(torch.cat((d0,d1,d0-d1,d0/(d1+eps),d1/(d0+eps)),dim=1))
+
+class BCERankingLoss(nn.Module):
+    def __init__(self, use_gpu=True, chn_mid=32):
+        super(BCERankingLoss, self).__init__()
+        self.use_gpu = use_gpu
+        self.net = Dist2LogitLayer(chn_mid=chn_mid)
+        self.parameters = list(self.net.parameters())
+        self.loss = torch.nn.BCELoss()
+        self.model = nn.Sequential(*[self.net])
+
+        if(self.use_gpu):
+            self.net.cuda()
+
+    def forward(self, d0, d1, judge):
+        per = (judge+1.)/2.
+        if(self.use_gpu):
+            per = per.cuda()
+        self.logit = self.net.forward(d0,d1)
+        return self.loss(self.logit, per)
+
+class NetLinLayer(nn.Module):
+    ''' A single linear layer which does a 1x1 conv '''
+    def __init__(self, chn_in, chn_out=1, use_dropout=False):
+        super(NetLinLayer, self).__init__()
+
+        layers = [nn.Dropout(),] if(use_dropout) else []
+        layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),]
+        self.model = nn.Sequential(*layers)
+
+
+# L2, DSSIM metrics
+class FakeNet(nn.Module):
+    def __init__(self, use_gpu=True, colorspace='Lab'):
+        super(FakeNet, self).__init__()
+        self.use_gpu = use_gpu
+        self.colorspace=colorspace
+
+class L2(FakeNet):
+
+    def forward(self, in0, in1):
+        assert(in0.size()[0]==1) # currently only supports batchSize 1
+
+        if(self.colorspace=='RGB'):
+            (N,C,X,Y) = in0.size()
+            value = torch.mean(torch.mean(torch.mean((in0-in1)**2,dim=1).view(N,1,X,Y),dim=2).view(N,1,1,Y),dim=3).view(N)
+            return value
+        elif(self.colorspace=='Lab'):
+            value = util.l2(util.tensor2np(util.tensor2tensorlab(in0.data,to_norm=False)),
+                util.tensor2np(util.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float')
+            ret_var = Variable( torch.Tensor((value,) ) )
+            if(self.use_gpu):
+                ret_var = ret_var.cuda()
+            return ret_var
+
+class DSSIM(FakeNet):
+
+    def forward(self, in0, in1):
+        assert(in0.size()[0]==1) # currently only supports batchSize 1
+
+        if(self.colorspace=='RGB'):
+            value = util.dssim(1.*util.tensor2im(in0.data), 1.*util.tensor2im(in1.data), range=255.).astype('float')
+        elif(self.colorspace=='Lab'):
+            value = util.dssim(util.tensor2np(util.tensor2tensorlab(in0.data,to_norm=False)),
+                util.tensor2np(util.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float')
+        ret_var = Variable( torch.Tensor((value,) ) )
+        if(self.use_gpu):
+            ret_var = ret_var.cuda()
+        return ret_var
+
+def print_network(net):
+    num_params = 0
+    for param in net.parameters():
+        num_params += param.numel()
+    print('Network',net)
+    print('Total number of parameters: %d' % num_params)
diff --git a/mlops/mlflow/train/sf2f/scripts/DScore/models/pretrained_networks.py b/mlops/mlflow/train/sf2f/scripts/DScore/models/pretrained_networks.py
new file mode 100644
index 0000000..f5c70dc
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/DScore/models/pretrained_networks.py
@@ -0,0 +1,181 @@
+from collections import namedtuple
+import torch
+from torchvision import models
+from IPython import embed
+
+class squeezenet(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True):
+        super(squeezenet, self).__init__()
+        pretrained_features = models.squeezenet1_1(pretrained=pretrained).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        self.slice6 = torch.nn.Sequential()
+        self.slice7 = torch.nn.Sequential()
+        self.N_slices = 7
+        for x in range(2):
+            self.slice1.add_module(str(x), pretrained_features[x])
+        for x in range(2,5):
+            self.slice2.add_module(str(x), pretrained_features[x])
+        for x in range(5, 8):
+            self.slice3.add_module(str(x), pretrained_features[x])
+        for x in range(8, 10):
+            self.slice4.add_module(str(x), pretrained_features[x])
+        for x in range(10, 11):
+            self.slice5.add_module(str(x), pretrained_features[x])
+        for x in range(11, 12):
+            self.slice6.add_module(str(x), pretrained_features[x])
+        for x in range(12, 13):
+            self.slice7.add_module(str(x), pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h = self.slice1(X)
+        h_relu1 = h
+        h = self.slice2(h)
+        h_relu2 = h
+        h = self.slice3(h)
+        h_relu3 = h
+        h = self.slice4(h)
+        h_relu4 = h
+        h = self.slice5(h)
+        h_relu5 = h
+        h = self.slice6(h)
+        h_relu6 = h
+        h = self.slice7(h)
+        h_relu7 = h
+        vgg_outputs = namedtuple("SqueezeOutputs", ['relu1','relu2','relu3','relu4','relu5','relu6','relu7'])
+        out = vgg_outputs(h_relu1,h_relu2,h_relu3,h_relu4,h_relu5,h_relu6,h_relu7)
+
+        return out
+
+
+class alexnet(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True):
+        super(alexnet, self).__init__()
+        alexnet_pretrained_features = models.alexnet(pretrained=pretrained).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        self.N_slices = 5
+        for x in range(2):
+            self.slice1.add_module(str(x), alexnet_pretrained_features[x])
+        for x in range(2, 5):
+            self.slice2.add_module(str(x), alexnet_pretrained_features[x])
+        for x in range(5, 8):
+            self.slice3.add_module(str(x), alexnet_pretrained_features[x])
+        for x in range(8, 10):
+            self.slice4.add_module(str(x), alexnet_pretrained_features[x])
+        for x in range(10, 12):
+            self.slice5.add_module(str(x), alexnet_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h = self.slice1(X)
+        h_relu1 = h
+        h = self.slice2(h)
+        h_relu2 = h
+        h = self.slice3(h)
+        h_relu3 = h
+        h = self.slice4(h)
+        h_relu4 = h
+        h = self.slice5(h)
+        h_relu5 = h
+        alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5'])
+        out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)
+
+        return out
+
+class vgg16(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True):
+        super(vgg16, self).__init__()
+        vgg_pretrained_features = models.vgg16(pretrained=pretrained).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        self.N_slices = 5
+        for x in range(4):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(4, 9):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(9, 16):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(16, 23):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(23, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h = self.slice1(X)
+        h_relu1_2 = h
+        h = self.slice2(h)
+        h_relu2_2 = h
+        h = self.slice3(h)
+        h_relu3_3 = h
+        h = self.slice4(h)
+        h_relu4_3 = h
+        h = self.slice5(h)
+        h_relu5_3 = h
+        vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
+        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
+
+        return out
+
+
+
+class resnet(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True, num=18):
+        super(resnet, self).__init__()
+        if(num==18):
+            self.net = models.resnet18(pretrained=pretrained)
+        elif(num==34):
+            self.net = models.resnet34(pretrained=pretrained)
+        elif(num==50):
+            self.net = models.resnet50(pretrained=pretrained)
+        elif(num==101):
+            self.net = models.resnet101(pretrained=pretrained)
+        elif(num==152):
+            self.net = models.resnet152(pretrained=pretrained)
+        self.N_slices = 5
+
+        self.conv1 = self.net.conv1
+        self.bn1 = self.net.bn1
+        self.relu = self.net.relu
+        self.maxpool = self.net.maxpool
+        self.layer1 = self.net.layer1
+        self.layer2 = self.net.layer2
+        self.layer3 = self.net.layer3
+        self.layer4 = self.net.layer4
+
+    def forward(self, X):
+        h = self.conv1(X)
+        h = self.bn1(h)
+        h = self.relu(h)
+        h_relu1 = h
+        h = self.maxpool(h)
+        h = self.layer1(h)
+        h_conv2 = h
+        h = self.layer2(h)
+        h_conv3 = h
+        h = self.layer3(h)
+        h_conv4 = h
+        h = self.layer4(h)
+        h_conv5 = h
+
+        outputs = namedtuple("Outputs", ['relu1','conv2','conv3','conv4','conv5'])
+        out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5)
+
+        return out
diff --git a/mlops/mlflow/train/sf2f/scripts/DScore/util/__init__.py b/mlops/mlflow/train/sf2f/scripts/DScore/util/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mlops/mlflow/train/sf2f/scripts/DScore/util/html.py b/mlops/mlflow/train/sf2f/scripts/DScore/util/html.py
new file mode 100644
index 0000000..3a113e2
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/DScore/util/html.py
@@ -0,0 +1,66 @@
+import dominate
+from dominate.tags import *
+import os
+
+
+class HTML:
+    def __init__(self, web_dir, title, image_subdir='', reflesh=0):
+        self.title = title
+        self.web_dir = web_dir
+        # self.img_dir = os.path.join(self.web_dir, )
+        self.img_subdir = image_subdir
+        self.img_dir = os.path.join(self.web_dir, image_subdir)
+        if not os.path.exists(self.web_dir):
+            os.makedirs(self.web_dir)
+        if not os.path.exists(self.img_dir):
+            os.makedirs(self.img_dir)
+        # print(self.img_dir)
+
+        self.doc = dominate.document(title=title)
+        if reflesh > 0:
+            with self.doc.head:
+                meta(http_equiv="reflesh", content=str(reflesh))
+
+    def get_image_dir(self):
+        return self.img_dir
+
+    def add_header(self, str):
+        with self.doc:
+            h3(str)
+
+    def add_table(self, border=1):
+        self.t = table(border=border, style="table-layout: fixed;")
+        self.doc.add(self.t)
+
+    def add_images(self, ims, txts, links, width=400):
+        self.add_table()
+        with self.t:
+            with tr():
+                for im, txt, link in zip(ims, txts, links):
+                    with td(style="word-wrap: break-word;", halign="center", valign="top"):
+                        with p():
+                            with a(href=os.path.join(link)):
+                                img(style="width:%dpx" % width, src=os.path.join(im))
+                            br()
+                            p(txt)
+
+    def save(self,file='index'):
+        html_file = '%s/%s.html' % (self.web_dir,file)
+        f = open(html_file, 'wt')
+        f.write(self.doc.render())
+        f.close()
+
+
+if __name__ == '__main__':
+    html = HTML('web/', 'test_html')
+    html.add_header('hello world')
+
+    ims = []
+    txts = []
+    links = []
+    for n in range(4):
+        ims.append('image_%d.png' % n)
+        txts.append('text_%d' % n)
+        links.append('image_%d.png' % n)
+    html.add_images(ims, txts, links)
+    html.save()
diff --git a/mlops/mlflow/train/sf2f/scripts/DScore/util/util.py b/mlops/mlflow/train/sf2f/scripts/DScore/util/util.py
new file mode 100644
index 0000000..1a84327
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/DScore/util/util.py
@@ -0,0 +1,452 @@
+from __future__ import print_function
+
+import numpy as np
+from PIL import Image
+import inspect
+import re
+import numpy as np
+import os
+import collections
+import matplotlib.pyplot as plt
+from scipy.ndimage.interpolation import zoom
+from skimage.measure import compare_ssim
+import torch
+from IPython import embed
+# import cv2
+from datetime import datetime
+
+def datetime_str():
+    now = datetime.now()
+    return '%04d-%02d-%02d-%02d-%02d-%02d'%(now.year,now.month,now.day,now.hour,now.minute,now.second)
+
+def read_text_file(in_path):
+    fid = open(in_path,'r')
+
+    vals = []
+    cur_line = fid.readline()
+    while(cur_line!=''):
+        vals.append(float(cur_line))
+        cur_line = fid.readline()
+
+    fid.close()
+    return np.array(vals)
+
+def bootstrap(in_vec,num_samples=100,bootfunc=np.mean):
+    from astropy import stats
+    return stats.bootstrap(np.array(in_vec),bootnum=num_samples,bootfunc=bootfunc)
+
+def rand_flip(input1,input2):
+    if(np.random.binomial(1,.5)==1):
+        return (input1,input2)
+    else:
+        return (input2,input1)
+
+def l2(p0, p1, range=255.):
+    return .5*np.mean((p0 / range - p1 / range)**2)
+
+def psnr(p0, p1, peak=255.):
+    return 10*np.log10(peak**2/np.mean((1.*p0-1.*p1)**2))
+
+def dssim(p0, p1, range=255.):
+    # embed()
+    return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2.
+
+def rgb2lab(in_img,mean_cent=False):
+    from skimage import color
+    img_lab = color.rgb2lab(in_img)
+    if(mean_cent):
+        img_lab[:,:,0] = img_lab[:,:,0]-50
+    return img_lab
+
+def normalize_blob(in_feat,eps=1e-10):
+    norm_factor = np.sqrt(np.sum(in_feat**2,axis=1,keepdims=True))
+    return in_feat/(norm_factor+eps)
+
+def cos_sim_blob(in0,in1):
+    in0_norm = normalize_blob(in0)
+    in1_norm = normalize_blob(in1)
+    (N,C,X,Y) = in0_norm.shape
+
+    return np.mean(np.mean(np.sum(in0_norm*in1_norm,axis=1),axis=1),axis=1)
+
+def normalize_tensor(in_feat,eps=1e-10):
+    # norm_factor = torch.sqrt(torch.sum(in_feat**2,dim=1)).view(in_feat.size()[0],1,in_feat.size()[2],in_feat.size()[3]).repeat(1,in_feat.size()[1],1,1)
+    norm_factor = torch.sqrt(torch.sum(in_feat**2,dim=1)).view(in_feat.size()[0],1,in_feat.size()[2],in_feat.size()[3])
+    return in_feat/(norm_factor.expand_as(in_feat)+eps)
+
+def cos_sim(in0,in1):
+    in0_norm = normalize_tensor(in0)
+    in1_norm = normalize_tensor(in1)
+    N = in0.size()[0]
+    X = in0.size()[2]
+    Y = in0.size()[3]
+
+    return torch.mean(torch.mean(torch.sum(in0_norm*in1_norm,dim=1).view(N,1,X,Y),dim=2).view(N,1,1,Y),dim=3).view(N)
+
+# Converts a Tensor into a Numpy array
+# |imtype|: the desired type of the conve
+
+def tensor2np(tensor_obj):
+    # change dimension of a tensor object into a numpy array
+    return tensor_obj[0].cpu().float().numpy().transpose((1,2,0))
+
+def np2tensor(np_obj):
+     # change dimenion of np array into tensor array
+    return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
+
+def tensor2tensorlab(image_tensor,to_norm=True,mc_only=False):
+    # image tensor to lab tensor
+    from skimage import color
+
+    img = tensor2im(image_tensor)
+    # print('img_rgb',img.flatten())
+    img_lab = color.rgb2lab(img)
+    # print('img_lab',img_lab.flatten())
+    if(mc_only):
+        img_lab[:,:,0] = img_lab[:,:,0]-50
+    if(to_norm and not mc_only):
+        img_lab[:,:,0] = img_lab[:,:,0]-50
+        img_lab = img_lab/100.
+
+    return np2tensor(img_lab)
+
+def tensorlab2tensor(lab_tensor,return_inbnd=False):
+    from skimage import color
+    import warnings
+    warnings.filterwarnings("ignore")
+
+    lab = tensor2np(lab_tensor)*100.
+    lab[:,:,0] = lab[:,:,0]+50
+    # print('lab',lab)
+
+    rgb_back = 255.*np.clip(color.lab2rgb(lab.astype('float')),0,1)
+    # print('rgb',rgb_back)
+    if(return_inbnd):
+        # convert back to lab, see if we match
+        lab_back = color.rgb2lab(rgb_back.astype('uint8'))
+        # print('lab_back',lab_back)
+        # print('lab==lab_back',np.isclose(lab_back,lab,atol=1.))
+        # print('lab-lab_back',np.abs(lab-lab_back))
+        mask = 1.*np.isclose(lab_back,lab,atol=2.)
+        mask = np2tensor(np.prod(mask,axis=2)[:,:,np.newaxis])
+        return (im2tensor(rgb_back),mask)
+    else:
+        return im2tensor(rgb_back)
+
+def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
+# def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.):
+    image_numpy = image_tensor[0].cpu().float().numpy()
+    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
+    return image_numpy.astype(imtype)
+
+def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.):
+# def im2tensor(image, imtype=np.uint8, cent=1., factor=1.):
+    return torch.Tensor((image / factor - cent)
+                        [:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
+
+def tensor2vec(vector_tensor):
+    return vector_tensor.data.cpu().numpy()[:, :, 0, 0]
+
+def diagnose_network(net, name='network'):
+    mean = 0.0
+    count = 0
+    for param in net.parameters():
+        if param.grad is not None:
+            mean += torch.mean(torch.abs(param.grad.data))
+            count += 1
+    if count > 0:
+        mean = mean / count
+    print(name)
+    print(mean)
+
+def grab_patch(img_in, P, yy, xx):
+    return img_in[yy:yy+P,xx:xx+P,:]
+
+# def load_image(path):
+#     if(path[-3:] == 'dng'):
+#         import rawpy
+#         with rawpy.imread(path) as raw:
+#             img = raw.postprocess()
+#         # img = plt.imread(path)
+#     elif(path[-3:]=='bmp' or path[-3:]=='jpg' or path[-3:]=='png'):
+#         import cv2
+#         return cv2.imread(path)[:,:,::-1]
+#     else:
+#         img = (255*plt.imread(path)[:,:,:3]).astype('uint8')
+#
+#     return img
+
+
+def resize_image(img, max_size=256):
+    [Y, X] = img.shape[:2]
+
+    # resize
+    max_dim = max([Y, X])
+    zoom_factor = 1. * max_size / max_dim
+    img = zoom(img, [zoom_factor, zoom_factor, 1])
+
+    return img
+
+def resize_image_zoom(img, zoom_factor=1., order=3):
+    if(zoom_factor==1):
+        return img
+    else:
+        return zoom(img, [zoom_factor, zoom_factor, 1], order=order)
+
+def save_image(image_numpy, image_path, ):
+    image_pil = Image.fromarray(image_numpy)
+    image_pil.save(image_path)
+
+
+def prep_display_image(img, dtype='uint8'):
+    if(dtype == 'uint8'):
+        return np.clip(img, 0, 255).astype('uint8')
+    else:
+        return np.clip(img, 0, 1.)
+
+
+def info(object, spacing=10, collapse=1):
+    """Print methods and doc strings.
+    Takes module, class, list, dictionary, or string."""
+    methodList = [
+        e for e in dir(object) if isinstance(
+            getattr(
+                object,
+                e),
+            collections.Callable)]
+    processFunc = collapse and (lambda s: " ".join(s.split())) or (lambda s: s)
+    print("\n".join(["%s %s" %
+                     (method.ljust(spacing),
+                      processFunc(str(getattr(object, method).__doc__)))
+                     for method in methodList]))
+
+
+def varname(p):
+    for line in inspect.getframeinfo(inspect.currentframe().f_back)[3]:
+        m = re.search(r'\bvarname\s*\(\s*([A-Za-z_][A-Za-z0-9_]*)\s*\)', line)
+        if m:
+            return m.group(1)
+
+
+def print_numpy(x, val=True, shp=False):
+    x = x.astype(np.float64)
+    if shp:
+        print('shape,', x.shape)
+    if val:
+        x = x.flatten()
+        print(
+            'mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' %
+            (np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))
+
+
+def mkdirs(paths):
+    if isinstance(paths, list) and not isinstance(paths, str):
+        for path in paths:
+            mkdir(path)
+    else:
+        mkdir(paths)
+
+
+def mkdir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+
+def rgb2lab(input):
+    from skimage import color
+    return color.rgb2lab(input / 255.)
+
+
+def montage(
+    imgs,
+    PAD=5,
+    RATIO=16 / 9.,
+    EXTRA_PAD=(
+        False,
+        False),
+        MM=-1,
+        NN=-1,
+        primeDir=0,
+        verbose=False,
+        returnGridPos=False,
+        backClr=np.array(
+            (0,
+             0,
+             0))):
+    # INPUTS
+    #   imgs        YxXxMxN or YxXxN
+    #   PAD         scalar              number of pixels in between
+    #   RATIO       scalar              target ratio of cols/rows
+    #   MM          scalar              # rows, if specified, overrides RATIO
+    #   NN          scalar              # columns, if specified, overrides RATIO
+    #   primeDir    scalar              0 for top-to-bottom, 1 for left-to-right
+    # OUTPUTS
+    #   mont_imgs   MM*Y x NN*X x M     big image with everything montaged
+    # def montage(imgs, PAD=5, RATIO=16/9., MM=-1, NN=-1, primeDir=0,
+    # verbose=False, forceFloat=False):
+    if(imgs.ndim == 3):
+        toExp = True
+        imgs = imgs[:, :, np.newaxis, :]
+    else:
+        toExp = False
+
+    Y = imgs.shape[0]
+    X = imgs.shape[1]
+    M = imgs.shape[2]
+    N = imgs.shape[3]
+
+    PADS = np.array((PAD))
+    if(PADS.flatten().size == 1):
+        PADY = PADS
+        PADX = PADS
+    else:
+        PADY = PADS[0]
+        PADX = PADS[1]
+
+    if(MM == -1 and NN == -1):
+        NN = np.ceil(np.sqrt(1.0 * N * RATIO))
+        MM = np.ceil(1.0 * N / NN)
+        NN = np.ceil(1.0 * N / MM)
+    elif(MM == -1):
+        MM = np.ceil(1.0 * N / NN)
+    elif(NN == -1):
+        NN = np.ceil(1.0 * N / MM)
+
+    if(primeDir == 0):  # write top-to-bottom
+        [grid_mm, grid_nn] = np.meshgrid(
+            np.arange(MM, dtype='uint'), np.arange(NN, dtype='uint'))
+    elif(primeDir == 1):  # write left-to-right
+        [grid_nn, grid_mm] = np.meshgrid(
+            np.arange(NN, dtype='uint'), np.arange(MM, dtype='uint'))
+
+    grid_mm = np.uint(grid_mm.flatten()[0:N])
+    grid_nn = np.uint(grid_nn.flatten()[0:N])
+
+    EXTRA_PADY = EXTRA_PAD[0] * PADY
+    EXTRA_PADX = EXTRA_PAD[0] * PADX
+
+    # mont_imgs = np.zeros(((Y+PAD)*MM-PAD, (X+PAD)*NN-PAD, M), dtype=use_dtype)
+    mont_imgs = np.zeros(
+        (np.uint(
+            (Y + PADY) * MM - PADY + EXTRA_PADY),
+            np.uint(
+            (X + PADX) * NN - PADX + EXTRA_PADX),
+            M),
+        dtype=imgs.dtype)
+    mont_imgs = mont_imgs + \
+        backClr.flatten()[np.newaxis, np.newaxis, :].astype(mont_imgs.dtype)
+
+    for ii in np.random.permutation(N):
+        # print imgs[:,:,:,ii].shape
+        # mont_imgs[grid_mm[ii]*(Y+PAD):(grid_mm[ii]*(Y+PAD)+Y), grid_nn[ii]*(X+PAD):(grid_nn[ii]*(X+PAD)+X),:]
+        mont_imgs[np.uint(grid_mm[ii] *
+                          (Y +
+                           PADY)):np.uint((grid_mm[ii] *
+                                           (Y +
+                                            PADY) +
+                                           Y)), np.uint(grid_nn[ii] *
+                                                        (X +
+                                                         PADX)):np.uint((grid_nn[ii] *
+                                                                         (X +
+                                                                          PADX) +
+                                                                         X)), :] = imgs[:, :, :, ii]
+
+    if(M == 1):
+        imgs = imgs.reshape(imgs.shape[0], imgs.shape[1], imgs.shape[3])
+
+    if(toExp):
+        mont_imgs = mont_imgs[:, :, 0]
+
+    if(returnGridPos):
+        # return (mont_imgs,np.concatenate((grid_mm[:,:,np.newaxis]*(Y+PAD),
+        # grid_nn[:,:,np.newaxis]*(X+PAD)),axis=2))
+        return (mont_imgs, np.concatenate(
+            (grid_mm[:, np.newaxis] * (Y + PADY), grid_nn[:, np.newaxis] * (X + PADX)), axis=1))
+        # return (mont_imgs, (grid_mm,grid_nn))
+    else:
+        return mont_imgs
+
+class zeroClipper(object):
+    def __init__(self, frequency=1):
+        self.frequency = frequency
+
+    def __call__(self, module):
+        embed()
+        if hasattr(module, 'weight'):
+            # module.weight.data = torch.max(module.weight.data, 0)
+            module.weight.data = torch.max(module.weight.data, 0) + 100
+
+def flatten_nested_list(nested_list):
+    # only works for list of list
+    accum = []
+    for sublist in nested_list:
+        for item in sublist:
+            accum.append(item)
+    return accum
+
+def read_file(in_path,list_lines=False):
+    agg_str = ''
+    f = open(in_path,'r')
+    cur_line = f.readline()
+    while(cur_line!=''):
+        agg_str+=cur_line
+        cur_line = f.readline()
+    f.close()
+    if(list_lines==False):
+        return agg_str.replace('\n','')
+    else:
+        line_list = agg_str.split('\n')
+        ret_list = []
+        for item in line_list:
+            if(item!=''):
+                ret_list.append(item)
+        return ret_list
+
+def read_csv_file_as_text(in_path):
+    agg_str = []
+    f = open(in_path,'r')
+    cur_line = f.readline()
+    while(cur_line!=''):
+        agg_str.append(cur_line)
+        cur_line = f.readline()
+    f.close()
+    return agg_str
+
+def random_swap(obj0,obj1):
+    if(np.random.rand() < .5):
+        return (obj0,obj1,0)
+    else:
+        return (obj1,obj0,1)
+
+def voc_ap(rec, prec, use_07_metric=False):
+    """ ap = voc_ap(rec, prec, [use_07_metric])
+    Compute VOC AP given precision and recall.
+    If use_07_metric is true, uses the
+    VOC 07 11 point method (default:False).
+    """
+    if use_07_metric:
+        # 11 point metric
+        ap = 0.
+        for t in np.arange(0., 1.1, 0.1):
+            if np.sum(rec >= t) == 0:
+                p = 0
+            else:
+                p = np.max(prec[rec >= t])
+            ap = ap + p / 11.
+    else:
+        # correct AP calculation
+        # first append sentinel values at the end
+        mrec = np.concatenate(([0.], rec, [1.]))
+        mpre = np.concatenate(([0.], prec, [0.]))
+
+        # compute the precision envelope
+        for i in range(mpre.size - 1, 0, -1):
+            mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
+
+        # to calculate area under PR curve, look for points
+        # where X axis (recall) changes value
+        i = np.where(mrec[1:] != mrec[:-1])[0]
+
+        # and sum (\Delta recall) * prec
+        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
+    return ap
diff --git a/mlops/mlflow/train/sf2f/scripts/DScore/util/visualizer.py b/mlops/mlflow/train/sf2f/scripts/DScore/util/visualizer.py
new file mode 100644
index 0000000..3609705
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/DScore/util/visualizer.py
@@ -0,0 +1,216 @@
+import numpy as np
+import os
+import time
+from . import util
+from . import html
+# from pdb import set_trace as st
+import matplotlib.pyplot as plt
+import math
+# from IPython import embed
+
+def zoom_to_res(img,res=256,order=0,axis=0):
+    # img   3xXxX
+    from scipy.ndimage import zoom
+    zoom_factor = res/img.shape[1]
+    if(axis==0):
+        return zoom(img,[1,zoom_factor,zoom_factor],order=order)
+    elif(axis==2):
+        return zoom(img,[zoom_factor,zoom_factor,1],order=order)
+
+class Visualizer():
+    def __init__(self, opt):
+        # self.opt = opt
+        self.display_id = opt.display_id
+        # self.use_html = opt.is_train and not opt.no_html
+        self.win_size = opt.display_winsize
+        self.name = opt.name
+        self.display_cnt = 0 # display_current_results counter
+        self.display_cnt_high = 0
+        self.use_html = opt.use_html
+
+        if self.display_id > 0:
+            import visdom
+            self.vis = visdom.Visdom(port = opt.display_port)
+
+        self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
+        util.mkdirs([self.web_dir,])
+        if self.use_html:
+            self.img_dir = os.path.join(self.web_dir, 'images')
+            print('create web directory %s...' % self.web_dir)
+            util.mkdirs([self.img_dir,])
+
+    # |visuals|: dictionary of images to display or save
+    def display_current_results(self, visuals, epoch, nrows=None, res=256):
+        if self.display_id > 0: # show images in the browser
+            title = self.name
+            if(nrows is None):
+                nrows = int(math.ceil(len(visuals.items()) / 2.0))
+            images = []
+            idx = 0
+            for label, image_numpy in visuals.items():
+                title += " | " if idx % nrows == 0 else ", "
+                title += label
+                img = image_numpy.transpose([2, 0, 1])
+                img = zoom_to_res(img,res=res,order=0)
+                images.append(img)
+                idx += 1
+            if len(visuals.items()) % 2 != 0:
+                white_image = np.ones_like(image_numpy.transpose([2, 0, 1]))*255
+                white_image = zoom_to_res(white_image,res=res,order=0)
+                images.append(white_image)
+            self.vis.images(images, nrow=nrows, win=self.display_id + 1,
+                            opts=dict(title=title))
+
+        if self.use_html: # save images to a html file
+            for label, image_numpy in visuals.items():
+                img_path = os.path.join(self.img_dir, 'epoch%.3d_cnt%.6d_%s.png' % (epoch, self.display_cnt, label))
+                util.save_image(zoom_to_res(image_numpy, res=res, axis=2), img_path)
+
+            self.display_cnt += 1
+            self.display_cnt_high = np.maximum(self.display_cnt_high, self.display_cnt)
+
+            # update website
+            webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, reflesh=1)
+            for n in range(epoch, 0, -1):
+                webpage.add_header('epoch [%d]' % n)
+                if(n==epoch):
+                    high = self.display_cnt
+                else:
+                    high = self.display_cnt_high
+                for c in range(high-1,-1,-1):
+                    ims = []
+                    txts = []
+                    links = []
+
+                    for label, image_numpy in visuals.items():
+                        img_path = 'epoch%.3d_cnt%.6d_%s.png' % (n, c, label)
+                        ims.append(os.path.join('images',img_path))
+                        txts.append(label)
+                        links.append(os.path.join('images',img_path))
+                    webpage.add_images(ims, txts, links, width=self.win_size)
+            webpage.save()
+
+    # save errors into a directory
+    def plot_current_errors_save(self, epoch, counter_ratio, opt, errors,keys='+ALL',name='loss', to_plot=False):
+        if not hasattr(self, 'plot_data'):
+            self.plot_data = {'X':[],'Y':[], 'legend':list(errors.keys())}
+        self.plot_data['X'].append(epoch + counter_ratio)
+        self.plot_data['Y'].append([errors[k] for k in self.plot_data['legend']])
+
+        # embed()
+        if(keys=='+ALL'):
+            plot_keys = self.plot_data['legend']
+        else:
+            plot_keys = keys
+
+        if(to_plot):
+            (f,ax) = plt.subplots(1,1)
+        for (k,kname) in enumerate(plot_keys):
+            kk = np.where(np.array(self.plot_data['legend'])==kname)[0][0]
+            x = self.plot_data['X']
+            y = np.array(self.plot_data['Y'])[:,kk]
+            if(to_plot):
+                ax.plot(x, y, 'o-', label=kname)
+            np.save(os.path.join(self.web_dir,'%s_x')%kname,x)
+            np.save(os.path.join(self.web_dir,'%s_y')%kname,y)
+
+        if(to_plot):
+            plt.legend(loc=0,fontsize='small')
+            plt.xlabel('epoch')
+            plt.ylabel('Value')
+            f.savefig(os.path.join(self.web_dir,'%s.png'%name))
+            f.clf()
+            plt.close()
+
+    # errors: dictionary of error labels and values
+    def plot_current_errors(self, epoch, counter_ratio, opt, errors):
+        if not hasattr(self, 'plot_data'):
+            self.plot_data = {'X':[],'Y':[], 'legend':list(errors.keys())}
+        self.plot_data['X'].append(epoch + counter_ratio)
+        self.plot_data['Y'].append([errors[k] for k in self.plot_data['legend']])
+        self.vis.line(
+            X=np.stack([np.array(self.plot_data['X'])]*len(self.plot_data['legend']),1),
+            Y=np.array(self.plot_data['Y']),
+            opts={
+                'title': self.name + ' loss over time',
+                'legend': self.plot_data['legend'],
+                'xlabel': 'epoch',
+                'ylabel': 'loss'},
+            win=self.display_id)
+
+    # errors: same format as |errors| of plotCurrentErrors
+    def print_current_errors(self, epoch, i, errors, t, t2=-1, t2o=-1, fid=None):
+        message = '(ep: %d, it: %d, t: %.3f[s], ept: %.2f/%.2f[h]) ' % (epoch, i, t, t2o, t2)
+        message += (', ').join(['%s: %.3f' % (k, v) for k, v in errors.items()])
+
+        print(message)
+        if(fid is not None):
+            fid.write('%s\n'%message)
+
+
+    # save image to the disk
+    def save_images_simple(self, webpage, images, names, in_txts, prefix='', res=256):
+        image_dir = webpage.get_image_dir()
+        ims = []
+        txts = []
+        links = []
+
+        for name, image_numpy, txt in zip(names, images, in_txts):
+            image_name = '%s_%s.png' % (prefix, name)
+            save_path = os.path.join(image_dir, image_name)
+            if(res is not None):
+                util.save_image(zoom_to_res(image_numpy,res=res,axis=2), save_path)
+            else:
+                util.save_image(image_numpy, save_path)
+
+            ims.append(os.path.join(webpage.img_subdir,image_name))
+            # txts.append(name)
+            txts.append(txt)
+            links.append(os.path.join(webpage.img_subdir,image_name))
+        # embed()
+        webpage.add_images(ims, txts, links, width=self.win_size)
+
+    # save image to the disk
+    def save_images(self, webpage, images, names, image_path, title=''):
+        image_dir = webpage.get_image_dir()
+        # short_path = ntpath.basename(image_path)
+        # name = os.path.splitext(short_path)[0]
+        # name = short_path
+        # webpage.add_header('%s, %s' % (name, title))
+        ims = []
+        txts = []
+        links = []
+
+        for label, image_numpy in zip(names, images):
+            image_name = '%s.jpg' % (label,)
+            save_path = os.path.join(image_dir, image_name)
+            util.save_image(image_numpy, save_path)
+
+            ims.append(image_name)
+            txts.append(label)
+            links.append(image_name)
+        webpage.add_images(ims, txts, links, width=self.win_size)
+
+    # save image to the disk
+    # def save_images(self, webpage, visuals, image_path, short=False):
+    #     image_dir = webpage.get_image_dir()
+    #     if short:
+    #         short_path = ntpath.basename(image_path)
+    #         name = os.path.splitext(short_path)[0]
+    #     else:
+    #         name = image_path
+
+    #     webpage.add_header(name)
+    #     ims = []
+    #     txts = []
+    #     links = []
+
+    #     for label, image_numpy in visuals.items():
+    #         image_name = '%s_%s.png' % (name, label)
+    #         save_path = os.path.join(image_dir, image_name)
+    #         util.save_image(image_numpy, save_path)
+
+    #         ims.append(image_name)
+    #         txts.append(label)
+    #         links.append(image_name)
+    #     webpage.add_images(ims, txts, links, width=self.win_size)
diff --git a/mlops/mlflow/train/sf2f/scripts/build_demo_set.py b/mlops/mlflow/train/sf2f/scripts/build_demo_set.py
new file mode 100644
index 0000000..127a76e
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/build_demo_set.py
@@ -0,0 +1,85 @@
+'''
+Build a demo inference dataset
+'''
+
+
+import os
+import shutil
+import json
+import pandas as pd
+from PIL import Image
+from pydub import AudioSegment
+
+
+DATA_DIR = './data'
+vox2_face_dir = os.path.join(DATA_DIR, 'VoxCeleb', 'vox2', 'masked_faces')
+vox2_wav_dir = os.path.join(DATA_DIR, 'VoxCeleb', 'raw_wav', 'vox2')
+vox2_meta_csv = os.path.join(DATA_DIR, 'VoxCeleb', 'vox2', 'full_vox2_meta.csv')
+vox_split_json = os.path.join(DATA_DIR, 'VoxCeleb', 'split.json')
+demo_dir = os.path.join(DATA_DIR, 'VoxCeleb', 'demo_data')
+
+def main():
+    try:
+        shutil.rmtree(demo_dir)
+    except:
+        pass
+    os.makedirs(demo_dir, exist_ok=True)
+    #print(os.listdir(vox2_face_dir))
+    #print(os.listdir(vox2_wav_dir))
+    with open(vox_split_json) as json_file:
+        split_dict = json.load(json_file)
+    # A list of names in the vox2 test set (in out splition)
+    test_list = split_dict['vox2']['test']
+    # meta_csv
+    meta_df = pd.read_csv(vox2_meta_csv)
+    print(meta_df.head(5))
+    name2id = {}
+    name2set = {}
+    for i in range(len(meta_df)):
+        name = meta_df['Name'][i]
+        id = meta_df['VoxCeleb2ID'][i]
+        split_set = meta_df['Set'][i]
+        name2id[name] = id
+        name2set[name] = split_set
+    #print(name2id)
+    available_names = set(test_list).intersection(
+        set(os.listdir(vox2_face_dir)))
+    available_names = list(available_names)
+    for name in available_names[:50]:
+        id = name2id[name]
+        split_set = name2set[name]
+        face_dir = os.path.join(vox2_face_dir, name)
+        #print(os.listdir(face_dir))
+        wav_dir = os.path.join(vox2_wav_dir, split_set, id)
+        # Avoid PermissionError
+        try:
+            #print(os.listdir(wav_dir))
+            wav_sub_dir_list = os.listdir(wav_dir)
+        except PermissionError:
+            continue
+        save_dir = os.path.join(demo_dir, name)
+        os.makedirs(save_dir, exist_ok=True)
+        for i, jpg in enumerate(os.listdir(face_dir)):
+            face_jpg = os.path.join(face_dir, jpg)
+            img = Image.open(face_jpg)
+            for reso, w in [('low', 64), ('mid', 128), ('high', 256)]:
+                save_path = os.path.join(save_dir, '{}_{}.jpg'.format(i, reso))
+                cur_img = img.resize((w, w))
+                cur_img.save(save_path)
+        # select 2 wavs
+        for j, wav_sub_dir in enumerate(wav_sub_dir_list[:2]):
+            #print(wav_sub_dir)
+            wav_sub_dir = os.path.join(wav_dir, wav_sub_dir)
+            wav_path = os.path.join(wav_sub_dir, os.listdir(wav_sub_dir)[0])
+            wav_save_dir = os.path.join(save_dir, 'audio_{}'.format(j))
+            os.makedirs(wav_save_dir, exist_ok=True)
+            try:
+                track = AudioSegment.from_file(wav_path, 'm4a')
+                wav_save_path = os.path.join(wav_save_dir, '{}.wav'.format(j))
+                file_handle = track.export(wav_save_path, format='wav')
+            except:
+                print("ERROR CONVERTING " + str(wav_path))
+        print(name)
+
+if __name__ == '__main__':
+    main()
diff --git a/mlops/mlflow/train/sf2f/scripts/compute_diversity_score.py b/mlops/mlflow/train/sf2f/scripts/compute_diversity_score.py
new file mode 100644
index 0000000..52e338c
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/compute_diversity_score.py
@@ -0,0 +1,28 @@
+from scripts.DScore.models import dist_model as dm
+from scripts.DScore.util import util
+import numpy as np
+from pyprind import prog_bar
+
+def compute_diversity_score(images_A, images_B, use_gpu):
+    assert(type(images_A) == list)
+    assert(type(images_B) == list)
+    assert(len(images_A) == len(images_B))
+
+    img_pair_num = len(images_A)
+
+    ## Initializing the model
+    model = dm.DistModel()
+    model.initialize(model='net-lin', net='alex', use_gpu=use_gpu)
+
+    distance = []
+    for i in prog_bar(range(img_pair_num), title="Calculating Diversity Scores", width=50):
+        img_A = util.im2tensor(images_A[i])
+        img_B = util.im2tensor(images_B[i])
+        dist = model.forward(img_A, img_B)
+        distance.append(dist)
+
+    return np.mean(distance), np.std(distance)
+
+
+if __name__ == '__main__':
+    print("please Call from other file.")
diff --git a/mlops/mlflow/train/sf2f/scripts/compute_fid_score.py b/mlops/mlflow/train/sf2f/scripts/compute_fid_score.py
new file mode 100644
index 0000000..d7a7212
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/compute_fid_score.py
@@ -0,0 +1,230 @@
+# Code derived from https://github.com/openai/improved-gan/tree/master/inception_score
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os.path
+import sys
+import tarfile
+
+import numpy as np
+from six.moves import urllib
+import tensorflow as tf
+import glob
+import scipy.misc
+from scipy import linalg
+import math
+import sys
+from pyprind import prog_bar
+
+tf.compat.v1.disable_v2_behavior()
+
+
+try:
+    MODEL_DIR = '/mnt/.tensorflow/imagenet'#'/mnt/imagenet'
+    os.listdir(MODEL_DIR)
+except:
+    MODEL_DIR = './.cache/.tensorflow/imagenet'
+DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
+softmax = None
+last_layer = None
+
+config = tf.compat.v1.ConfigProto()
+config.gpu_options.per_process_gpu_memory_fraction = 0.3
+
+
+def inception_forward(images, layer):
+    assert (type(images[0]) == np.ndarray)
+    assert (len(images[0].shape) == 3)
+    assert (np.max(images[0]) > 10)
+    assert (np.min(images[0]) >= 0.0)
+    # bs = 100
+    bs = 1
+    # images = images.transpose(0, 2, 3, 1)
+    #with tf.Session(config=config) as sess:
+    preds = []
+    n_batches = int(math.ceil(float(len(images)) / float(bs)))
+    # for i in range(n_batches):
+    for i in prog_bar(range(n_batches), title="Calculating FID Scores", width=50):
+        # sys.stdout.write(".")
+        # sys.stdout.flush()
+        inps = images[(i * bs):min((i + 1) * bs, len(images))]
+        for inp in inps:
+            pred = fw_sess.run(layer, {'ExpandDims:0': [inp]})
+            preds.append(pred)
+    preds = np.array(preds)
+        # preds = np.concatenate(preds, 0)
+    return preds
+
+
+def get_mean_and_cov(images):
+    before_preds = inception_forward(images, last_layer)
+    m = np.mean(before_preds, 0)
+    cov = np.cov(before_preds, rowvar=False)
+    return m, cov
+
+
+def get_fid(images, ref_stats=None, images_ref=None, splits=10):
+    before_preds = inception_forward(images, last_layer)
+    if ref_stats is None:
+        if images_ref is None:
+            raise ValueError('images_ref should be provided if ref_stats is None')
+        m_ref, cov_ref = get_mean_and_cov(images_ref)
+    fids = []
+    for i in range(splits):
+        part = before_preds[(i * before_preds.shape[0] // splits):((i + 1) * before_preds.shape[0] // splits), :]
+        m_gen = np.mean(part, 0)
+        cov_gen = np.cov(part, rowvar=False)
+        fid = np.sum((m_ref - m_gen) ** 2) + np.trace(
+            cov_ref + cov_gen - 2 * scipy.linalg.sqrtm(np.dot(cov_ref, cov_gen)))
+        fids.append(fid)
+    return np.mean(fids), np.std(fids)
+
+
+# Call this function with list of images. Each of elements should be a
+# numpy array with values ranging from 0 to 255.
+def get_inception_score(images, splits=10):
+    preds = inception_forward(images, softmax)
+    scores = []
+    for i in range(splits):
+        part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :]
+        kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
+        kl = np.mean(np.sum(kl, 1))
+        scores.append(np.exp(kl))
+    return np.mean(scores), np.std(scores)
+
+
+def get_inception_pred(images):
+    preds = inception_forward(images, softmax)
+    return preds
+
+
+def get_fid_pred(images):
+    preds = inception_forward(images, last_layer)
+    return preds
+
+def close_sess():
+    fw_sess.close()
+
+
+# This function is called automatically.
+def _init_inception():
+    global softmax
+    global last_layer
+    if not os.path.exists(MODEL_DIR):
+        os.makedirs(MODEL_DIR)
+    filename = DATA_URL.split('/')[-1]
+    filepath = os.path.join(MODEL_DIR, filename)
+    if not os.path.exists(filepath):
+        def _progress(count, block_size, total_size):
+            sys.stdout.write('\r>> Downloading %s %.1f%%' % (
+                filename, float(count * block_size) / float(total_size) * 100.0))
+            sys.stdout.flush()
+
+        filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
+        print()
+        statinfo = os.stat(filepath)
+        print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
+    tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR)
+    with tf.io.gfile.GFile(os.path.join(
+            MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:
+        graph_def = tf.compat.v1.GraphDef()
+        graph_def.ParseFromString(f.read())
+        _ = tf.import_graph_def(graph_def, name='')
+    # Works with an arbitrary minibatch size.
+    with tf.compat.v1.Session(config=config) as sess:
+        pool3 = sess.graph.get_tensor_by_name('pool_3:0')
+        ops = pool3.graph.get_operations()
+        for op_idx, op in enumerate(ops):
+            for o in op.outputs:
+                shape = o.get_shape()
+                shape = [s.value for s in shape]
+                new_shape = []
+                for j, s in enumerate(shape):
+                    if s == 1 and j == 0:
+                        new_shape.append(None)
+                    else:
+                        new_shape.append(s)
+                # o.set_shape(tf.TensorShape(new_shape))
+                o.set_shape = tf.TensorShape(new_shape)
+        w = sess.graph.get_operation_by_name("softmax/logits/MatMul").inputs[1]
+        last_layer = tf.squeeze(pool3)
+        logits = tf.matmul(tf.expand_dims(last_layer, 0), w)
+        softmax = tf.nn.softmax(logits[0])
+
+    global fw_sess
+    fw_sess = tf.compat.v1.Session(config=config)
+
+
+if softmax is None:
+    _init_inception()
+
+
+
+def calculate_activation_statistics(act):
+    """Calculation of the statistics used by the FID.
+    Params:
+    -- act      : Numpy array of dimension (n_images, dim (e.g. 2048)).
+    Returns:
+    -- mu    : The mean over samples of the activations of the pool_3 layer of
+               the inception model.
+    -- sigma : The covariance matrix of the activations of the pool_3 layer of
+               the inception model.
+    """
+    mu = np.mean(act, axis=0)
+    sigma = np.cov(act, rowvar=False)
+    return mu, sigma
+
+
+def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
+    """Numpy implementation of the Frechet Distance.
+    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
+    and X_2 ~ N(mu_2, C_2) is
+            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
+    Stable version by Dougal J. Sutherland.
+    Params:
+    -- mu1   : Numpy array containing the activations of a layer of the
+               inception net (like returned by the function 'get_predictions')
+               for generated samples.
+    -- mu2   : The sample mean over activations, precalculated on an
+               representive data set.
+    -- sigma1: The covariance matrix over activations for generated samples.
+    -- sigma2: The covariance matrix over activations, precalculated on an
+               representive data set.
+    Returns:
+    --   : The Frechet Distance.
+    """
+
+    mu1 = np.atleast_1d(mu1)
+    mu2 = np.atleast_1d(mu2)
+
+    sigma1 = np.atleast_2d(sigma1)
+    sigma2 = np.atleast_2d(sigma2)
+
+    assert mu1.shape == mu2.shape, \
+        'Training and test mean vectors have different lengths'
+    assert sigma1.shape == sigma2.shape, \
+        'Training and test covariances have different dimensions'
+
+    diff = mu1 - mu2
+
+    # Product might be almost singular
+    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+    if not np.isfinite(covmean).all():
+        msg = ('fid calculation produces singular product; '
+               'adding %s to diagonal of cov estimates') % eps
+        print(msg)
+        offset = np.eye(sigma1.shape[0]) * eps
+        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+    # Numerical error might give slight imaginary component
+    if np.iscomplexobj(covmean):
+        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+            m = np.max(np.abs(covmean.imag))
+            raise ValueError('Imaginary component {}'.format(m))
+        covmean = covmean.real
+
+    tr_covmean = np.trace(covmean)
+
+    return (diff.dot(diff) + np.trace(sigma1) +
+            np.trace(sigma2) - 2 * tr_covmean)
diff --git a/mlops/mlflow/train/sf2f/scripts/compute_inception_score.py b/mlops/mlflow/train/sf2f/scripts/compute_inception_score.py
new file mode 100644
index 0000000..85992e2
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/compute_inception_score.py
@@ -0,0 +1,229 @@
+# Code adapted from
+# https://github.com/openai/improved-gan/blob/master/inception_score/model.py
+# which was in turn derived from
+# tensorflow/tensorflow/models/image/imagenet/classify_image.py
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import os
+import sys
+import tarfile
+import h5py
+
+import numpy as np
+from six.moves import urllib
+import tensorflow as tf
+import glob
+# from scipy.misc import imread, imresize
+from cv2 import resize as imresize
+from imageio import imread
+
+from imageio import imwrite
+import math
+from pyprind import prog_bar
+
+tf.compat.v1.disable_v2_behavior()
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--input_npy_file', default=None)
+parser.add_argument('--input_image_dir', default=None)
+parser.add_argument('--input_image_dir_list', default=None)
+parser.add_argument('--input_image_superdir', default=None)
+parser.add_argument('--image_size', default=None, type=int)
+
+# Most papers use 50k samples and 10 splits but I don't have that much
+# data so I'll use 3 splits for everything
+parser.add_argument('--num_splits', default=3, type=int)
+parser.add_argument('--tensor_layout', default='NHWC', choices=['NHWC', 'NCHW'])
+
+IMAGE_EXTS = ['.png', '.jpg', '.jpeg']
+
+def main(args):
+    got_npy_file = args.input_npy_file is not None
+    got_image_dir = args.input_image_dir is not None
+    got_image_dir_list = args.input_image_dir_list is not None
+    got_image_superdir = args.input_image_superdir is not None
+    inputs = [got_npy_file, got_image_dir, got_image_dir_list, got_image_superdir]
+    if sum(inputs) != 1:
+        # raise ValueError('Must give exactly one input type')
+        images = read_images()
+        mean, std = get_inception_score(images)
+        print('Inception mean: ', mean)
+        print('Inception std: ', std)
+
+
+    if args.input_npy_file is not None:
+        images = np.load(args.input_npy_file)
+        images = np.split(image, images.shape[0], axis=0)
+        images = [img[0] for img in images]
+        mean, std = get_inception_score(args, images)
+        print('Inception mean: ', mean)
+        print('Inception std: ', std)
+    elif args.input_image_dir is not None:
+        images = load_images(args, args.input_image_dir)
+        import pdb; pdb.set_trace()
+        print(type(images))
+        import pdb; pdb.set_trace()
+        exit()
+        mean, std = get_inception_score(args, images)
+        print('Inception mean: ', mean)
+        print('Inception std: ', std)
+    elif got_image_dir_list:
+        with open(args.input_image_dir_list, 'r') as f:
+            dir_list = [line.strip() for line in f]
+        for image_dir in dir_list:
+            images = load_images(args, image_dir)
+            mean, std = get_inception_score(args, images)
+            print('Inception mean: ', mean)
+            print('Inception std: ', std)
+            print()
+    elif got_image_superdir:
+        for fn in sorted(os.listdir(args.input_image_superdir)):
+            print(fn)
+            if not fn.startswith('result'): continue
+            image_dir = os.path.join(args.input_image_superdir, fn, 'images')
+            if not os.path.isdir(image_dir): continue
+            images = load_images(args, image_dir)
+            mean, std = get_inception_score(args, images)
+            print('Inception mean: ', mean)
+            print('Inception std: ', std)
+            print()
+
+def load_images(args, image_dir):
+    print('Loading images from ', image_dir)
+    images = []
+    args.tensor_layout = 'NHWC'
+    for fn in os.listdir(image_dir):
+        ext = os.path.splitext(fn)[1].lower()
+        if ext not in IMAGE_EXTS:
+            continue
+        img_path = os.path.join(image_dir, fn)
+        img = imread(img_path)
+        if args.image_size is not None:
+            img = imresize(img, (args.image_size, args.image_size))
+        images.append(img)
+    print('Found %d images' % len(images))
+    return images
+
+def read_images():
+    img_dir = './data/vg/images/'
+    vg_dataset = []
+    print("Begin to load test...")
+    test_file = "./data/visual_genome/val.h5"
+    test_set = h5py.File(test_file, 'r')
+    test_set_name = np.array(test_set['image_paths'])
+    for iter, img_name in enumerate(test_set_name):
+        img_path = os.path.join(img_dir, img_name)
+        img = imread(img_path)
+        if len(img.shape) != 3:
+            continue
+        vg_dataset.append(img)
+    print('Found %d images' % len(vg_dataset))
+    return vg_dataset
+try:
+    MODEL_DIR = '/mnt/.tensorflow/imagenet'#'/mnt/imagenet'
+    os.listdir(MODEL_DIR)
+except:
+    MODEL_DIR = './.cache/.tensorflow/imagenet'
+DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
+softmax = None
+
+# Call this function with list of images. Each of elements should be a
+# numpy array with values ranging from 0 to 255.
+def get_inception_score(images):
+    splits = 3
+    layout = 'NHWC'
+
+    assert(type(images) == list)
+    assert(type(images[0]) == np.ndarray)
+    assert(len(images[0].shape) == 3)
+    # assert(np.max(images[0]) > 10)
+    # assert(np.min(images[0]) >= 0.0)
+    inps = []
+    for img in images:
+        img = img.astype(np.float32)
+        inps.append(np.expand_dims(img, 0))
+    bs = 1
+    # bs = 100
+    with tf.compat.v1.Session() as sess:
+        preds = []
+        n_batches = int(math.ceil(float(len(inps)) / float(bs)))
+        n_preds = 0
+        for i in prog_bar(range(n_batches), title="Calculating Inception Scores", width=50):
+            # import pdb; pdb.set_trace()
+            inp = inps[(i * bs):min((i + 1) * bs, len(inps))]
+
+            inp = np.concatenate(inp, 0)
+            if layout == 'NCHW':
+                inp = inp.transpose(0, 2, 3, 1)
+            # print(inp.shape)
+            pred = sess.run(softmax, {'ExpandDims:0': inp})
+            preds.append(pred)
+            n_preds += pred.shape[0]
+            # print('Ran %d / %d images' % (n_preds, len(images)))
+            # import pdb; pdb.set_trace()
+        preds = np.concatenate(preds, 0)
+        scores = []
+        for i in range(splits):
+            part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :]
+            kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
+            kl = np.mean(np.sum(kl, 1))
+            scores.append(np.exp(kl))
+        return np.mean(scores), np.std(scores)
+
+# This function is called automatically.
+def _init_inception():
+    global softmax
+    if not os.path.exists(MODEL_DIR):
+        os.makedirs(MODEL_DIR)
+    filename = DATA_URL.split('/')[-1]
+    filepath = os.path.join(MODEL_DIR, filename)
+    if not os.path.exists(filepath):
+        def _progress(count, block_size, total_size):
+            sys.stdout.write('\r>> Downloading %s %.1f%%' % (
+                filename, float(count * block_size) / float(total_size) * 100.0))
+            sys.stdout.flush()
+        filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
+        print()
+        statinfo = os.stat(filepath)
+        print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
+    tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR)
+    with tf.io.gfile.GFile(os.path.join(
+        MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:
+        #
+        graph_def = tf.compat.v1.GraphDef()
+        graph_def.ParseFromString(f.read())
+        _ = tf.import_graph_def(graph_def, name='')
+    # Works with an arbitrary minibatch size.
+    with tf.compat.v1.Session() as sess:
+        pool3 = sess.graph.get_tensor_by_name('pool_3:0')
+        ops = pool3.graph.get_operations()
+        for op_idx, op in enumerate(ops):
+            for o in op.outputs:
+                shape = o.get_shape()
+                shape = [s.value for s in shape]
+                new_shape = []
+                for j, s in enumerate(shape):
+                    if s == 1 and j == 0:
+                        new_shape.append(None)
+                    else:
+                        new_shape.append(s)
+                # o._shape = tf.TensorShape(new_shape)
+                o.set_shape = tf.TensorShape(new_shape)
+        w = sess.graph.get_operation_by_name("softmax/logits/MatMul").inputs[1]
+        pool3 = tf.squeeze(pool3)
+        pool3 = tf.reshape(pool3, (1, 2048))
+        # logits = tf.matmul(tf.squeeze(pool3), w)
+        logits = tf.matmul(pool3, w)
+        softmax = tf.nn.softmax(logits)
+
+if softmax is None:
+    _init_inception()
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    main(args)
diff --git a/mlops/mlflow/train/sf2f/scripts/compute_mel_mean_var.py b/mlops/mlflow/train/sf2f/scripts/compute_mel_mean_var.py
new file mode 100644
index 0000000..d730e17
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/compute_mel_mean_var.py
@@ -0,0 +1,55 @@
+# -*- coding: utf-8 -*-
+'''
+Compute the mean and variance of VoxCeleb dataset's mel spectrograms
+'''
+
+
+import os
+import numpy as np
+import sys
+import torch
+from torch.utils.data import DataLoader
+sys.path.append('./')
+print(sys.path)
+from datasets.vox_dataset import VoxDataset
+
+
+def mean():
+    epoch_num = 100
+    vox_dataset = VoxDataset(
+        data_dir='./data/VoxCeleb',
+        image_size=(64, 64),
+        image_normalize_method=None,
+        mel_normalize_method=None)
+    loader_kwargs = {
+        'batch_size': 128,
+        'num_workers': 8,
+        'shuffle': False,
+        "drop_last": True,
+    }
+    print(vox_dataset)
+    vox_loader = DataLoader(vox_dataset, **loader_kwargs)
+
+    total_mean = torch.tensor(0.0)
+    total_std = torch.tensor(0.0)
+    min_length = 10000000
+    for epoch in range(epoch_num):
+        for iter, batch in enumerate(vox_loader):
+            #images, log_mels, mel_length = batch
+            images, log_mels = batch
+            total_mean = total_mean + torch.mean(log_mels)
+            total_std = total_std + torch.std(log_mels)
+            #cur_min_length = torch.min(mel_length)
+            #if cur_min_length < min_length:
+            #    min_length = cur_min_length
+        print(epoch)
+
+    avg_mean = (total_mean / epoch_num) / (iter + 1)
+    avg_std = (total_std / epoch_num) / (iter + 1)
+
+    print('Dataset Mel Spectrogram Mean:', avg_mean)
+    print('Dataset Mel Spectrogram Standard Deviation:', avg_std)
+    print('Dataset minimum mel spectrogram length:', min_length)
+
+if __name__ == '__main__':
+    mean()
diff --git a/mlops/mlflow/train/sf2f/scripts/compute_vggface_score.py b/mlops/mlflow/train/sf2f/scripts/compute_vggface_score.py
new file mode 100644
index 0000000..8868c2b
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/compute_vggface_score.py
@@ -0,0 +1,291 @@
+import argparse
+import os
+import sys
+import torch
+import torch.nn as nn
+import math
+import pickle
+import numpy as np
+import torch.nn.functional as F
+import requests
+from requests.adapters import HTTPAdapter
+
+
+__all__ = ['ResNet', 'resnet50']
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, num_classes=1000, include_top=True):
+        self.inplanes = 64
+        super(ResNet, self).__init__()
+        self.include_top = include_top
+
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0, ceil_mode=True)
+
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AvgPool2d(7, stride=1)
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+
+        if not self.include_top:
+            return x
+
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+
+    def resnet50(**kwargs):
+        """Constructs a ResNet-50 model.
+        """
+        model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
+        return model
+
+
+def load_state_dict(model, fname):
+    """
+    Set parameters converted from Caffe models authors of VGGFace2 provide.
+    See https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/.
+    Arguments:
+        model: model
+        fname: file name of parameters converted from a Caffe model, assuming the file format is Pickle.
+    """
+    with open(fname, 'rb') as f:
+        weights = pickle.load(f, encoding='latin1')
+
+    own_state = model.state_dict()
+    for name, param in weights.items():
+        if name in own_state:
+            try:
+                own_state[name].copy_(torch.from_numpy(param))
+            except Exception:
+                raise RuntimeError('While copying the parameter named {}, whose dimensions in the model are {} and whose '\
+                                   'dimensions in the checkpoint are {}.'.format(name, own_state[name].size(), param.size()))
+        else:
+            raise KeyError('unexpected key "{}" in state_dict'.format(name))
+
+
+def transform(img):
+    img = img[:, :, ::-1]  # RGB -> BGR
+    img = img.astype(np.float32)
+    img -= self.mean_bgr
+    img = img.transpose(2, 0, 1)  # C x H x W
+    img = torch.from_numpy(img).float()
+    return img
+
+def download_weight_for_small_file(weight_file):
+    '''
+    Not working for large files
+    '''
+    weight_url = 'https://drive.google.com/uc?export=download' + \
+        '&id=1A94PAAnwk6L7hXdBXLFosB_s0SzEhAFU'
+    if not os.path.exists(weight_file):
+        print('Downloading VGGFace resnet50_ft_weight.pkl (1/1)')
+        s = requests.Session()
+        s.mount('https://', HTTPAdapter(max_retries=10))
+        r = s.get(weight_url, allow_redirects=True)
+        with open(weight_file, 'wb') as f:
+            f.write(r.content)
+
+def download_weight(weight_file):
+    '''
+    Reference:
+        https://medium.com/@acpanjan/
+            download-google-drive-files-using-wget-3c2c025a8b99
+    '''
+    if not os.path.exists(weight_file):
+        cmd = "bash scripts/download_vggface_weights.sh"
+        print(cmd)
+        os.system(cmd)
+
+def get_vggface_score(imgs):
+    N_IDENTITY = 8631
+    # include_top = True if args.cmd != 'extract' else False
+    include_top = True
+    weight_file = 'scripts/weights/resnet50_ft_weight.pkl'
+    # mean_bgr = np.array([91.4953, 103.8827, 131.0912])  # from resnet50_ft.prototxt
+    model = ResNet.resnet50(num_classes=N_IDENTITY, include_top=include_top)
+    download_weight(weight_file)
+    load_state_dict(model, weight_file)
+    model = model.cuda()
+    model.eval()
+    mean_rgb = np.array([131.0912, 103.8827, 91.4953])
+    # mean_rgb = torch.Tensor([131.0912, 103.8827, 91.4953]).cuda()
+    preds = []
+    for img in imgs:
+        # img = img.permute(1, 2, 0) - mean_rgb
+        img = img - mean_rgb
+        img = torch.Tensor(img).cuda().permute(2, 0, 1)
+        img = nn.UpsamplingBilinear2d(size=(224, 224))(img.view(1, 3, img.size(1), img.size(2)))
+        pred = model(img)
+        pred = F.softmax(pred, dim=1)
+        preds.append(pred.data.cpu().numpy())
+    preds = np.concatenate(preds, 0)
+    # preds = model(imgs)
+
+    np.random.shuffle(preds)
+    splits = 5
+    scores = []
+    for i in range(splits):
+        part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :]
+        kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
+        kl = np.mean(np.sum(kl, 1))
+        scores.append(np.exp(kl))
+
+    return np.mean(scores), np.std(scores)
+
+
+def get_vggface_act(imgs):
+    N_IDENTITY = 8631
+    # include_top = True if args.cmd != 'extract' else False
+    include_top = True
+    weight_file = 'scripts/weights/resnet50_ft_weight.pkl'
+    # mean_bgr = np.array([91.4953, 103.8827, 131.0912])  # from resnet50_ft.prototxt
+    model = ResNet.resnet50(num_classes=N_IDENTITY, include_top=include_top)
+    load_state_dict(model, weight_file)
+    model = model.cuda()
+    model.eval()
+    mean_rgb = np.array([131.0912, 103.8827, 91.4953])
+    # mean_rgb = torch.Tensor([131.0912, 103.8827, 91.4953]).cuda()
+    preds = []
+    for img in imgs:
+        # img = img.permute(1, 2, 0) - mean_rgb
+        img = img - mean_rgb
+        img = torch.Tensor(img).cuda().permute(2, 0, 1)
+        img = nn.UpsamplingBilinear2d(size=(224, 224))(img.view(1, 3, img.size(1), img.size(2)))
+        pred = model(img)
+        pred = F.softmax(pred, dim=1)
+        preds.append(pred.data.cpu().numpy())
+    preds = np.concatenate(preds, 0)
+    return preds
+
+
+def main():
+    N_IDENTITY = 8631
+    # include_top = True if args.cmd != 'extract' else False
+    include_top = False
+    weight_file = 'scripts/weights/resnet50_ft_weights.pkl'
+    model = ResNet.resnet50(num_classes=N_IDENTITY, include_top=include_top)
+    load_state_dict(model, weight_file)
+    model = model.cuda()
+
+if __name__ == '__main__':
+    main()
diff --git a/mlops/mlflow/train/sf2f/scripts/convert_wav_to_mel.py b/mlops/mlflow/train/sf2f/scripts/convert_wav_to_mel.py
new file mode 100644
index 0000000..ff334fa
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/convert_wav_to_mel.py
@@ -0,0 +1,216 @@
+'''
+Convert raw_wavs in VoxCeleb dataSet to mel_gram
+
+    1. Build two id2name mapping for vox1 & vox2
+    2. Create a folder for each identity with his/her name
+    3. for each raw wav file, convert it to mel_gram,
+        save it under the identity folder
+'''
+
+
+import warnings
+warnings.filterwarnings("ignore")
+import argparse
+import os
+import shutil
+import gc
+import time
+import pickle
+import numpy as np
+import pandas as pd
+import multiprocessing as mp
+from tensorflow.io import gfile
+import sys
+sys.path.append('./')
+from utils.wav2mel import wav_to_mel
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from utils.filter_pickle import filtering_pickle
+
+
+VOX_DIR = os.path.join('/home/data/VoxCeleb')
+vox1_raw = os.path.join(VOX_DIR, 'raw_wav', 'vox1')
+# vox2_raw = os.path.join(VOX_DIR, 'raw_wav', 'vox2')
+vox1_meta_csv = os.path.join(VOX_DIR, 'vox1', 'vox1_meta.csv')
+# vox2_meta_csv = os.path.join(VOX_DIR, 'vox2', 'full_vox2_meta.csv')
+
+parser = argparse.ArgumentParser()
+
+# arguments:
+#parser.add_argument('--output_folder',
+#    default=os.path.join(PROC_DIR, 'VoxCeleb1', 'faces'),
+#    help='folder that contains the outputs')
+
+args = parser.parse_args()
+
+class WavConvertor:
+    def __init__(self):
+        self.load_metadata()
+        self.get_id2name()
+        self.get_wav_dirs()
+        self.create_output_dirs()
+
+    def load_metadata(self):
+        self.vox1_df = pd.read_csv(vox1_meta_csv, sep='\t')
+        # self.vox2_df = pd.read_csv(vox2_meta_csv, sep=',')
+        #print(self.vox1_df.head())
+        #print(self.vox1_df.columns)
+        #print(self.vox2_df.head())
+        #print(self.vox2_df.columns)
+
+    def get_id2name(self):
+        self.vox1_id2name = dict(
+            zip(self.vox1_df['VoxCeleb1 ID'], self.vox1_df['VGGFace1 ID']))
+        # self.vox2_id2name = dict(
+        #     zip(self.vox2_df['VoxCeleb2ID'], self.vox2_df['Name']))
+        #print((self.vox2_id2name))
+
+    def convert_identity(self, wav_dir, mel_home_dir, dataset):
+        '''
+        Create a mel_gram folder for the given identity
+
+        Arguments:
+            1. wav_dir (str): path to the identity's raw_wav folder
+            2. mel_home_dir (str): path to the identity's raw_wav folder
+            3. dataset (str): 'vox1' or 'vox2'
+        '''
+        spkid = wav_dir.split('/')[-1]
+        # In case the input path ends with '/'
+        if spkid == '':
+            spkid = wav_dir.split('/')[-2]
+        if dataset == 'vox1':
+            name = self.vox1_id2name[spkid]
+        # elif dataset == 'vox2':
+        #     name = self.vox2_id2name[spkid]
+        else:
+            raise ValueError("Invalid dataset argument")
+        #print(name)
+
+        # Create mel_gram directory of the speaker
+        mel_dir = os.path.join(mel_home_dir, name)
+        gfile.mkdir(mel_dir)
+
+        clipids = os.listdir(wav_dir)
+        for clipid in clipids:
+            clip_dir = os.path.join(wav_dir, clipid)
+            wav_files = os.listdir(clip_dir)
+            for wav_file in wav_files:
+                # Read and process the wav
+                wav_path = os.path.join(clip_dir, wav_file)
+                try:
+                    wavid = wav_file.replace('.wav', '').replace('.m4a', '')
+                    pickle_name = "{}_{}_{}.pickle".format(
+                        spkid, clipid, wavid)
+                    pickle_path = os.path.join(
+                        mel_dir, pickle_name)
+                    if os.path.exists(pickle_path):
+                        # Skip if exists
+                        continue
+                    # Vox1 use .wav format, Vox2 use .m4a format
+                    log_mel = wav_to_mel(wav_path)
+                    pickle_dict = {
+                        'LogMel_Features': log_mel,
+                        'spkid': spkid,
+                        'clipid': clipid,
+                        'wavid': wavid
+                    }
+                    pickle.dump(
+                        pickle_dict,
+                        open(pickle_path, "wb")
+                    )
+                except IndexError:
+                    pass
+        gc.collect()
+
+    def get_wav_dirs(self):
+        '''
+        Generate a list containing paths to the wav_dir of all the speakers
+        '''
+        # The original VoxCeleb dataset comes with test set and dev set
+        vox1_dev = os.path.join(vox1_raw, 'dev')
+        vox1_test = os.path.join(vox1_raw, 'test')
+        # vox2_dev = os.path.join(vox2_raw, 'dev')
+        # vox2_test = os.path.join(vox2_raw, 'test')
+        # Get Vox1 wav_dir paths
+        vox1_wav_dirs = []
+        for wav_dir in os.listdir(vox1_dev):
+            vox1_wav_dirs.append(os.path.join(vox1_dev, wav_dir))
+        for wav_dir in os.listdir(vox1_test):
+            vox1_wav_dirs.append(os.path.join(vox1_test, wav_dir))
+        # Get Vox2 wav_dir paths
+        # vox2_wav_dirs = []
+        # for wav_dir in os.listdir(vox2_dev):
+        #     vox2_wav_dirs.append(os.path.join(vox2_dev, wav_dir))
+        # for wav_dir in os.listdir(vox2_test):
+        #     vox2_wav_dirs.append(os.path.join(vox2_test, wav_dir))
+        self.vox1_wav_dirs = vox1_wav_dirs
+        # self.vox2_wav_dirs = vox2_wav_dirs
+        return 0
+
+    def create_output_dirs(self):
+        self.vox1_mel = os.path.join(VOX_DIR, 'vox1', 'mel_spectrograms')
+        # self.vox2_mel = os.path.join(VOX_DIR, 'vox2', 'mel_spectrograms')
+        if not gfile.exists(self.vox1_mel):
+            gfile.mkdir(self.vox1_mel)
+        # gfile.mkdir(self.vox2_mel)
+
+    def _worker(self, job_id, infos):
+            for i, info in enumerate(infos):
+                self.convert_identity(info[0], info[1], info[2])
+                print('job #{} prcess {} {} done'.format(job_id, i, info[0]))
+                shutil.rmtree(info[0]) # remove complete file
+                
+    def convert_wav_to_mel(self, n_jobs=1):
+
+        infos = []
+        for wav_dir in self.vox1_wav_dirs:
+            infos.append((wav_dir, self.vox1_mel, 'vox1'))
+        print(len(infos))
+        # l1 = len(infos)
+        # for wav_dir in self.vox2_wav_dirs:
+        #     infos.append((wav_dir, self.vox2_mel, 'vox2'))
+        # print(len(infos) - l1)
+        
+        n_wav_dirs = len(infos)
+        n_jobs = n_jobs if n_jobs <= n_wav_dirs else n_wav_dirs
+        n_wav_dirs_per_job = n_wav_dirs // n_jobs
+        process_index = []
+        for ii in range(n_jobs):
+            process_index.append([ii*n_wav_dirs_per_job, (ii+1)*n_wav_dirs_per_job])
+        if n_jobs * n_wav_dirs_per_job != n_wav_dirs:
+            process_index[-1][-1] = n_wav_dirs
+        
+        futures = set()
+        with ProcessPoolExecutor() as executor:
+            for job_id in range(n_jobs):
+                # future = executor.submit(_worker, process_index[job_id][0], process_index[job_id][1])
+                future = executor.submit(self._worker, job_id, infos[process_index[job_id][0]:process_index[job_id][1]])
+                futures.add(future)
+                print('submit job {}, {}-{}'.format(job_id, process_index[job_id][0], process_index[job_id][1]))
+            for future in as_completed(futures):
+                pass
+        
+        print("Done.")
+
+
+
+def main():
+    
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--n_jobs', '-n_jobs', type=int, default=5)
+    args = parser.parse_args()
+    
+    wav_convertor = WavConvertor()
+    wav_convertor.convert_wav_to_mel(args.n_jobs)
+    #gfile.mkdir('./data/test')
+    #wav_convertor.convert_identity(
+    #    'data/VoxCeleb/raw_wav/vox1/dev/id10001/',
+    #    './data/test', 'vox1')
+
+    dir_path = './sf2f/data/VoxCeleb/vox1/mel_spectrograms' # wav_convertor.vox1_mel
+    filtering_pickle(dir_path)
+
+
+if __name__ == '__main__':
+    print(os.listdir(VOX_DIR))
+    main()
diff --git a/mlops/mlflow/train/sf2f/scripts/create_split_json.py b/mlops/mlflow/train/sf2f/scripts/create_split_json.py
new file mode 100644
index 0000000..3bb5b8f
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/create_split_json.py
@@ -0,0 +1,88 @@
+'''
+This script generates a train/test/val split json for VoxCeleb dataset
+
+vox1_meta.csv could be download from VoxCeleb official website:
+    https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/vox1_meta.csv
+'''
+
+
+import os
+import json
+import pandas as pd
+
+VOX_DIR = os.path.join('/home/data/VoxCeleb')
+vox1_meta_csv = os.path.join(VOX_DIR, 'vox1', 'vox1_meta.csv')
+# vox2_meta_csv = os.path.join(VOX_DIR, 'vox2', 'full_vox2_meta.csv')
+# output:
+split_json = os.path.join(VOX_DIR, 'split.json')
+
+
+def main():
+    vox1_df = pd.read_csv(vox1_meta_csv, sep='\t')
+    # vox2_df = pd.read_csv(vox2_meta_csv, sep=',')
+
+    print(vox1_df.head())
+    # print(vox2_df.head())
+
+    split_dict = {
+        'vox1': {'train':[], 'val':[], 'test':[]},
+        # 'vox2': {'train':[], 'val':[], 'test':[]}
+        }
+
+    for i in range(len(vox1_df)):
+        name = vox1_df['VGGFace1 ID'].iloc[i]
+        if i % 10 == 8:
+            split_dict['vox1']['test'].append(name)
+        elif i % 10 == 9:
+            split_dict['vox1']['val'].append(name)
+        else:
+            split_dict['vox1']['train'].append(name)
+
+    # for i in range(len(vox2_df)):
+    #     name = vox2_df['Name'].iloc[i]
+    #     if i % 10 == 8:
+    #         split_dict['vox2']['test'].append(name)
+    #     elif i % 10 == 9:
+    #         split_dict['vox2']['val'].append(name)
+    #     else:
+    #         split_dict['vox2']['train'].append(name)
+
+    '''
+    for i in range(len(vox1_df)):
+        name = vox1_df['VGGFace1 ID'].iloc[i]
+        set = vox1_df['Set'].iloc[i]
+        if set == 'test':
+            split_dict['vox1']['test'].append(name)
+        if set == 'dev':
+            if i % 10 == 9:
+                split_dict['vox1']['val'].append(name)
+            else:
+                split_dict['vox1']['train'].append(name)
+
+    for i in range(len(vox2_df)):
+        name = vox2_df['Name'].iloc[i]
+        set = vox2_df['Set'].iloc[i]
+        if set == 'test':
+            split_dict['vox2']['test'].append(name)
+        if set == 'dev':
+            if i % 10 == 9:
+                split_dict['vox2']['val'].append(name)
+            else:
+                split_dict['vox2']['train'].append(name)
+    '''
+
+    print(len(split_dict['vox1']['train']),
+          len(split_dict['vox1']['val']),
+          len(split_dict['vox1']['test']))
+    # print(len(split_dict['vox2']['train']),
+    #       len(split_dict['vox2']['val']),
+    #       len(split_dict['vox2']['test']))
+
+    with open(split_json, 'w') as outfile:
+        json.dump(split_dict, outfile)
+
+    return 0
+
+
+if __name__ == '__main__':
+    main()
diff --git a/mlops/mlflow/train/sf2f/scripts/download_vggface_weights.sh b/mlops/mlflow/train/sf2f/scripts/download_vggface_weights.sh
new file mode 100644
index 0000000..2d59ada
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/download_vggface_weights.sh
@@ -0,0 +1,4 @@
+mkdir './scripts/weights/'
+wget --load-cookies /tmp/cookies.txt \
+    "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1A94PAAnwk6L7hXdBXLFosB_s0SzEhAFU' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1A94PAAnwk6L7hXdBXLFosB_s0SzEhAFU" \
+        -O 'scripts/weights/resnet50_ft_weight.pkl' && rm -rf /tmp/cookies.txt
\ No newline at end of file
diff --git a/mlops/mlflow/train/sf2f/scripts/install_requirements.py b/mlops/mlflow/train/sf2f/scripts/install_requirements.py
new file mode 100644
index 0000000..b531402
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/install_requirements.py
@@ -0,0 +1,50 @@
+import os
+from multiprocessing import Process
+
+
+def pip_install(package):
+    cmd = 'pip install {}'.format(package)
+    print(cmd)
+    try:
+        os.system(cmd)
+        return 0
+    except:
+        print('###########################')
+        print('Exception')
+        print('###########################')
+        pip_install(package)
+
+
+def txt2list(txt_path):
+    '''
+    Read a txt file and return a list
+    Arguments:
+        txt_path (str): path to save the text file
+    '''
+    output = []
+    f = open(txt_path, 'r')
+    for line in f.readlines():
+        output.append(line.replace('\n', ''))
+    f.close()
+    print('{} loaded.'.format(txt_path))
+    return output
+
+
+def install_once():
+    packages = txt2list('./requirements.txt')
+    procs = []
+
+    # instantiating process with arguments
+    for package in packages:
+        proc = Process(target=pip_install, args=(package,))
+        procs.append(proc)
+        proc.start()
+
+    # complete the processes
+    for proc in procs:
+        proc.join()
+
+
+if __name__ == "__main__":  # confirms that the code is under main function
+    for i in range(100):
+        install_once()
diff --git a/mlops/mlflow/train/sf2f/scripts/print_args.py b/mlops/mlflow/train/sf2f/scripts/print_args.py
new file mode 100644
index 0000000..5ccaa26
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/print_args.py
@@ -0,0 +1,22 @@
+"""
+Tiny utility to print the command-line args used for a checkpoint
+"""
+
+
+import argparse
+import torch
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('checkpoint')
+
+
+def main(args):
+    checkpoint = torch.load(args.checkpoint, map_location='cpu')
+    for k, v in checkpoint['args'].items():
+        print(k, v)
+
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    main(args)
diff --git a/mlops/mlflow/train/sf2f/scripts/sample_mel_spectrograms.py b/mlops/mlflow/train/sf2f/scripts/sample_mel_spectrograms.py
new file mode 100644
index 0000000..deb63a8
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/sample_mel_spectrograms.py
@@ -0,0 +1,52 @@
+'''
+This file samples several mel spectrograms from the VoxCeleb Dataset, and
+    visualize them as png images
+'''
+
+
+import os
+import PIL
+
+import sys
+sys.path.append('./')
+#print(sys.path)
+from datasets import VoxDataset, fast_mel_deprocess_batch
+from utils.visualization.plot import plot_mel_spectrogram #get_np_plot,
+from tensorflow.io.gfile import mkdir
+
+
+VOX_DIR = os.path.join('./data', 'VoxCeleb')
+output_dir = os.path.join('./output', 'sampled_mel_spectrograms')
+mkdir(output_dir)
+
+
+def main():
+    vox_dataset = VoxDataset(
+        data_dir=VOX_DIR,
+        image_size=(64, 64),
+        nframe_range=(300, 600),
+        face_type='masked',
+        image_normalize_method='imagenet',
+        mel_normalize_method='vox_mel',
+        mel_seg_window_stride=(125, 63),
+        split_set='test',
+        split_json=os.path.join(VOX_DIR, 'split.json'))
+
+    log_mels = vox_dataset.get_all_mel_segments_of_id(5)
+
+    log_mels_de = fast_mel_deprocess_batch(log_mels, 'vox_mel')
+
+    for i in range(len(log_mels_de)):
+        log_mel = log_mels_de[i].cpu().detach().numpy()
+        buf = plot_mel_spectrogram(
+            log_mel,
+            colorbar=False,
+            label=False,
+            coordinate=False,
+            remove_boarder=True)
+        log_mel_img = PIL.Image.open(buf)
+        log_mel_img.save(os.path.join(output_dir, '{}.png'.format(i)))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/mlops/mlflow/train/sf2f/scripts/strip_checkpoint.py b/mlops/mlflow/train/sf2f/scripts/strip_checkpoint.py
new file mode 100644
index 0000000..f91831b
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/strip_checkpoint.py
@@ -0,0 +1,53 @@
+"""
+Checkpoints saved by train.py contain not only model parameters but also
+optimizer states, losses, a history of generated images, and other statistics.
+This information is very useful for development and debugging models, but makes
+the saved checkpoints very large. This utility script strips away all extra
+information from saved checkpoints, keeping only the saved models.
+"""
+
+
+import argparse
+import os
+import torch
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--input_checkpoint', default=None)
+parser.add_argument('--output_checkpoint', default=None)
+parser.add_argument('--input_dir', default=None)
+parser.add_argument('--output_dir', default=None)
+parser.add_argument('--keep_discriminators', type=int, default=1)
+
+
+def main(args):
+    if args.input_checkpoint is not None:
+        handle_checkpoint(args, args.input_checkpoint, args.output_checkpoint)
+    if args.input_dir is not None:
+        handle_dir(args, args.input_dir, args.output_dir)
+
+
+def handle_dir(args, input_dir, output_dir):
+    for fn in os.listdir(input_dir):
+        if not fn.endswith('.pt'):
+            continue
+        input_path = os.path.join(input_dir, fn)
+        output_path = os.path.join(output_dir, fn)
+        handle_checkpoint(args, input_path, output_path)
+
+
+def handle_checkpoint(args, input_path, output_path):
+    input_checkpoint = torch.load(input_path)
+    keep = ['args', 'model_state', 'model_kwargs']
+    if args.keep_discriminators == 1:
+        keep += ['d_img_state', 'd_img_kwargs', 'd_obj_state', 'd_obj_kwargs']
+    output_checkpoint = {}
+    for k, v in input_checkpoint.items():
+        if k in keep:
+            output_checkpoint[k] = v
+    torch.save(output_checkpoint, output_path)
+
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    main(args)
diff --git a/mlops/mlflow/train/sf2f/scripts/strip_old_args.py b/mlops/mlflow/train/sf2f/scripts/strip_old_args.py
new file mode 100644
index 0000000..578cece
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/strip_old_args.py
@@ -0,0 +1,55 @@
+"""
+This utility script removes deprecated kwargs in checkpoints.
+"""
+
+
+import argparse
+import os
+import torch
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--input_checkpoint', default=None)
+parser.add_argument('--input_dir', default=None)
+
+
+DEPRECATED_KWARGS = {
+    'model_kwargs': [
+        'vec_noise_dim', 'gconv_mode', 'box_anchor', 'decouple_obj_predictions',
+    ],
+}
+
+
+def main(args):
+    got_checkpoint = (args.input_checkpoint is not None)
+    got_dir = (args.input_dir is not None)
+    assert got_checkpoint != got_dir, "Must give exactly one of checkpoint or dir"
+    if got_checkpoint:
+        handle_checkpoint(args.input_checkpoint)
+    elif got_dir:
+        handle_dir(args.input_dir)
+
+
+def handle_dir(dir_path):
+    for fn in os.listdir(dir_path):
+        if not fn.endswith('.pt'):
+            continue
+        checkpoint_path = os.path.join(dir_path, fn)
+        handle_checkpoint(checkpoint_path)
+
+
+def handle_checkpoint(checkpoint_path):
+    print('Stripping old args from checkpoint "%s"' % checkpoint_path)
+    checkpoint = torch.load(checkpoint_path)
+    for group, deprecated in DEPRECATED_KWARGS.items():
+        assert group in checkpoint
+        for k in deprecated:
+            if k in checkpoint[group]:
+                print('Removing key "%s" from "%s"' % (k, group))
+                del checkpoint[group][k]
+    torch.save(checkpoint, checkpoint_path)
+
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    main(args)
diff --git a/mlops/mlflow/train/sf2f/scripts/watch_data.py b/mlops/mlflow/train/sf2f/scripts/watch_data.py
new file mode 100644
index 0000000..8cd319c
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/scripts/watch_data.py
@@ -0,0 +1,111 @@
+import os
+import numpy as np
+import random
+import pickle
+import json
+import PIL
+from PIL import Image
+
+DIR = './data/VoxCeleb/'
+
+
+def main():
+    dir_1 = os.path.join(DIR, 'vox1/masked_faces')
+    names = os.listdir(dir_1)
+    print("Length of names: ", len(names))  # 1187
+    img_num_1 = 0
+    all_W1, all_H1 = 0, 0
+    num1_50, num1_100, num1_200, num1_300, num1_400, num1_600 = 0, 0, 0, 0, 0, 0
+    for i, name in enumerate(names):
+        path = os.path.join(dir_1, name)
+        images = os.listdir(path)
+        for j, image in enumerate(images):
+            image_path = os.path.join(path, image)
+            with open(image_path, 'rb') as f:
+                with PIL.Image.open(f) as image:
+                    WW, HH = image.size
+                    img_num_1 += 1
+                    all_W1 += WW
+                    all_H1 += HH
+                    # print("PIL Image:", np.array(image))
+                    if (WW >= 50 and WW < 100) or (HH >= 50 and HH < 100):
+                        num1_50 += 1
+                    elif (WW >= 100 and WW < 200) or (HH >= 100 and HH < 200):
+                        num1_100 += 1
+                    elif (WW >= 200 and WW < 300) or (HH >= 200 and HH < 300):
+                        num1_200 += 1
+                    elif (WW >= 300 and WW < 400) or (HH >= 300 and HH < 400):
+                        num1_300 += 1
+                    elif (WW >= 400 and WW < 600) or (HH >= 400 and HH < 600):
+                        num1_400 += 1
+                    elif (WW >= 600) or (HH >= 600):
+                        num1_600 += 1
+
+    print("======== Vox1 =========")
+    print("Number of vox1: ", img_num_1)
+    print("Average W: ", all_W1/img_num_1)
+    print("Average H: ", all_H1/img_num_1)
+    print("50~100: ", num1_50)
+    print("100~200: ", num1_100)
+    print("200~300: ", num1_200)
+    print("300~400: ", num1_300)
+    print("400~600: ", num1_400)
+    print("600+: ", num1_600)
+    print("\n")
+
+    dir_2 = os.path.join(DIR, 'vox2/masked_faces')
+    names = os.listdir(dir_2)
+    print("Length of names: ", len(names))  # 2850
+    img_num_2 = 0
+    all_W2, all_H2 = 0, 0
+    num2_50, num2_100, num2_200, num2_300, num2_400, num2_600 = 0, 0, 0, 0, 0, 0
+    for i, name in enumerate(names):
+        path = os.path.join(dir_2, name)
+        images = os.listdir(path)
+        for j, image in enumerate(images):
+            image_path = os.path.join(path, image)
+            with open(image_path, 'rb') as f:
+                with PIL.Image.open(f) as image:
+                    WW, HH = image.size
+                    img_num_2 += 1
+                    all_W2 += WW
+                    all_H2 += HH
+                    # print("PIL Image:", np.array(image))
+                    if (WW >= 50 and WW < 100) or (HH >= 50 and HH < 100):
+                        num2_50 += 1
+                    elif (WW >= 100 and WW < 200) or (HH >= 100 and HH < 200):
+                        num2_100 += 1
+                    elif (WW >= 200 and WW < 300) or (HH >= 200 and HH < 300):
+                        num2_200 += 1
+                    elif (WW >= 300 and WW < 400) or (HH >= 300 and HH < 400):
+                        num2_300 += 1
+                    elif (WW >= 400 and WW < 600) or (HH >= 400 and HH < 600):
+                        num2_400 += 1
+                    elif (WW >= 600) or (HH >= 600):
+                        num2_600 += 1
+
+    print("======== Vox2 =========")
+    print("Number of vox2: ", img_num_2)
+    print("Average W: ", all_W2/img_num_2)
+    print("Average H: ", all_H2/img_num_2)
+    print("50~100: ", num2_50)
+    print("100~200: ", num2_100)
+    print("200~300: ", num2_200)
+    print("300~400: ", num2_300)
+    print("400~600: ", num2_400)
+    print("600+: ", num2_600)
+    print("\n")
+
+    print("======== ALL DATASET ========")
+    print("Number of images: ", img_num_1 + img_num_2)
+    print("Average W: ", (all_W1 + all_W2) / (img_num_1 + img_num_2))
+    print("Average H: ", (all_H1 + all_H2) / (img_num_1 + img_num_2))
+    print("50~100: ", num1_50 + num2_50)
+    print("100~200: ", num1_100 + num2_100)
+    print("200~300: ", num1_200 + num2_200)
+    print("300~400: ", num1_300 + num2_300)
+    print("400~600: ", num1_400 + num2_400)
+    print("600+: ", num1_600 + num2_600)
+
+if __name__ == '__main__':
+    main()
diff --git a/mlops/mlflow/train/sf2f/train.py b/mlops/mlflow/train/sf2f/train.py
new file mode 100644
index 0000000..ab8304e
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/train.py
@@ -0,0 +1,854 @@
+import functools
+import os
+import json
+import math
+from collections import defaultdict
+import random
+import time
+import pyprind
+import glog as log
+from shutil import copyfile
+
+import numpy as np
+import torch
+import torch.optim as optim
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data.dataloader import default_collate
+import mlflow
+
+from datasets import imagenet_deprocess_batch
+import datasets
+import models
+import models.perceptual
+from utils.losses import get_gan_losses
+from utils import timeit, LossManager
+from options.opts import args, options
+from utils.logger import Logger
+from utils import tensor2im
+from utils.utils import load_my_state_dict
+# losseds need to be modified
+from utils.training_utils import add_loss, check_model, calculate_model_losses
+from utils.training_utils import visualize_sample
+from utils.evaluate import evaluate
+from utils.evaluate_fid import evaluate_fid
+from utils.s2f_evaluator import S2fEvaluator
+torch.backends.cudnn.benchmark = True
+
+
+def main():
+    global args, options
+    print(args)
+    print(options['data'])
+    # Set random seed, deterministic
+    torch.cuda.manual_seed(args.seed)
+    torch.manual_seed(args.seed)
+    np.random.seed(args.seed)
+    random.seed(args.seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    #visualize_attn=options['eval'].get('visualize_attn', False)
+    #print('########### visualize_attn: {} ###########'.format(visualize_attn))
+    float_dtype = torch.cuda.FloatTensor
+    long_dtype = torch.cuda.LongTensor
+    log.info("Building loader...")
+    train_loader, val_loader, test_loader = \
+        datasets.build_loaders(options["data"])
+    # Fuser Logic
+    if args.train_fuser_only:
+        train_loader.collate_fn = default_collate
+        train_loader.dataset.return_mel_segments = True
+        train_loader.dataset.mel_segments_rand_start = True
+        val_loader.collate_fn = default_collate
+        val_loader.dataset.return_mel_segments = True
+        s2f_face_gen_mode = 'naive'
+    else:
+        s2f_face_gen_mode = 'average_facenet_embedding'
+    # End of fuser logic
+    #s2f_val_evaluator = S2fEvaluator(val_loader, options)
+    s2f_val_evaluator = S2fEvaluator(
+        val_loader,
+        options,
+        extraction_size=100, #[100,200,300],
+        hq_emb_dict=True,
+        face_gen_mode=s2f_face_gen_mode)
+
+    # [:len(checkpoint[term_name]['id_classifier.bias'])] class 맞추기
+    # if restore_path is not None and os.path.isfile(restore_path):
+    train_loader.dataset.available_names = train_loader.dataset.available_names[:729]
+   
+    # Get total number of people in training set
+    num_train_id = len(train_loader.dataset)
+    # Initialize softmax neuron value
+    for ac_net in ['identity', 'identity_low', 'identity_mid', 'identity_high']:
+        if options['discriminator'].get(ac_net) is not None:
+            options['discriminator'][ac_net]['num_id'] = num_train_id
+
+    log.info("Building Generative Model...")
+    model, model_kwargs = models.build_model(
+        options["generator"],
+        image_size=options["data"]["image_size"],
+        checkpoint_start_from=args.checkpoint_start_from)
+    model.type(float_dtype)
+    # Fuser logic
+    if args.train_fuser_only:
+        # Hardcode to freeze batchnorm layers
+        model.eval()
+        if args.train_fuser_decoder:
+            model.encoder.train_fuser_only()
+        else:
+            model.train_fuser_only()
+    # End of fuser logic
+    print(model)
+
+    optimizer = torch.optim.Adam(
+            filter(lambda x: x.requires_grad, model.parameters()),
+            lr=args.learning_rate,
+            betas=(args.beta1, 0.999),)
+
+    if (options["optim"]["d_loss_weight"] < 0 or \
+        options["optim"]["d_img_weight"] < 0):
+        # Ignore image discriminator
+        img_discriminator = None
+        d_img_kwargs = {}
+        log.info("Ignoring Image Discriminator.")
+    else:
+        img_discriminator, d_img_kwargs = models.build_img_discriminator(
+            options["discriminator"])
+        log.info("Done Building Image Discriminator.")
+
+    if (options["optim"]["d_loss_weight"] < 0 or \
+        options["optim"]["ac_loss_weight"] < 0):
+        # Ignore AC discriminator
+        ac_discriminator = None
+        ac_img_kwargs = {}
+        log.info("Ignoring Auxilary Classifier Discriminator.")
+    else:
+        ac_discriminator, ac_img_kwargs = models.build_ac_discriminator(
+            options["discriminator"])
+        #print('AC Discriminator:', ac_discriminator)
+        log.info("Done Building Auxilary Classifier Discriminator.")
+
+    if (options["optim"]["d_loss_weight"] < 0 or \
+        options["optim"].get("cond_loss_weight", -1) < 0):
+        # Ignore Conditional discriminator
+        cond_discriminator = None
+        cond_d_kwargs = {}
+        log.info("Ignoring Conditional Discriminator.")
+    else:
+        cond_discriminator, cond_d_kwargs = models.build_cond_discriminator(
+            options["discriminator"])
+        #print('Conditional Discriminator:', cond_discriminator)
+        log.info("Done Building Conditional Discriminator.")
+
+    perceptual_module = None
+    if options["optim"].get("perceptual_loss_weight", -1) > 0:
+        ploss_name = options.get("perceptual", {}).get("arch", "FaceNetLoss")
+        ploss_cos_weight = options["optim"].get("cos_percept_loss_weight", -1)
+        perceptual_module = getattr(
+            models.perceptual,
+            ploss_name)(cos_loss_weight=ploss_cos_weight)
+        log.info("Done Building Perceptual {} Module.".format(ploss_name))
+        if ploss_cos_weight > 0:
+            log.info("Perceptual Cos Loss Weight: {}".format(ploss_cos_weight))
+    else:
+        log.info("Ignoring Perceptual Module.")
+
+    gan_g_loss, gan_d_loss = get_gan_losses(options["optim"]["gan_loss_type"])
+
+    optimizer_d_img = []
+    if img_discriminator is not None:
+        for i in range(len(img_discriminator)):
+            img_discriminator[i].type(float_dtype)
+            img_discriminator[i].train()
+            print(img_discriminator[i])
+            optimizer_d_img.append(torch.optim.Adam(
+                    filter(lambda x: x.requires_grad,
+                           img_discriminator[i].parameters()),
+                    lr=args.learning_rate,
+                    betas=(args.beta1, 0.999),))
+
+    optimizer_d_ac = []
+    if ac_discriminator is not None:
+        for i in range(len(ac_discriminator)):
+            ac_discriminator[i].type(float_dtype)
+            ac_discriminator[i].train()
+            print(ac_discriminator[i])
+            optimizer_d_ac.append(torch.optim.Adam(
+                    filter(lambda x: x.requires_grad,
+                           ac_discriminator[i].parameters()),
+                    lr=args.learning_rate,
+                    betas=(args.beta1, 0.999),))
+
+    optimizer_cond_d = []
+    if cond_discriminator is not None:
+        for i in range(len(cond_discriminator)):
+            cond_discriminator[i].type(float_dtype)
+            cond_discriminator[i].train()
+            print(cond_discriminator[i])
+            optimizer_cond_d.append(torch.optim.Adam(
+                    filter(lambda x: x.requires_grad,
+                           cond_discriminator[i].parameters()),
+                    lr=args.learning_rate,
+                    betas=(args.beta1, 0.999),))
+
+    restore_path = None
+    if args.resume is not None:
+        restore_path = '%s_with_model.pt' % args.checkpoint_name
+        restore_path = os.path.join(
+            options["logs"]["output_dir"], args.resume, restore_path)
+
+    if restore_path is not None and os.path.isfile(restore_path):
+        log.info('Restoring from checkpoint: {}'.format(restore_path))
+        checkpoint = torch.load(restore_path)
+        model.load_state_dict(checkpoint['model_state'])
+        optimizer.load_state_dict(checkpoint['optim_state'])
+
+        if img_discriminator is not None:
+            for i in range(len(img_discriminator)):
+                term_name = 'd_img_state_%d' % i
+                img_discriminator[i].load_state_dict(checkpoint[term_name])
+                term_name = 'd_img_optim_state_%d' % i
+                optimizer_d_img[i].load_state_dict(checkpoint[term_name])
+
+        if ac_discriminator is not None:
+            for i in range(len(ac_discriminator)):
+                term_name = 'd_ac_state_%d' % i
+                ac_discriminator[i].load_state_dict(checkpoint[term_name])
+                term_name = 'd_ac_optim_state_%d' % i
+                optimizer_d_ac[i].load_state_dict(checkpoint[term_name])
+
+        if cond_discriminator is not None:
+            for i in range(len(cond_discriminator)):
+                term_name = 'd_img_state_%d' % i
+                cond_discriminator[i].load_state_dict(checkpoint[term_name])
+                term_name = 'd_img_optim_state_%d' % i
+                optimizer_cond_d[i].load_state_dict(checkpoint[term_name])
+
+        t = checkpoint['counters']['t'] + 1
+        if 0 <= args.eval_mode_after <= t:
+            model.eval()
+        else:
+            model.train()
+        # Reset epoch here later
+        start_epoch = checkpoint['counters']['epoch'] + 1
+        log_path = os.path.join(options["logs"]["output_dir"], args.resume,)
+        lr = checkpoint.get('learning_rate', args.learning_rate)
+        best_inception = checkpoint["counters"].get("best_inception", (0., 0.))
+        best_vfs = checkpoint["counters"].get("best_vfs", (0., 0.))
+        best_recall_1 = checkpoint["counters"].get("best_recall_1", 0.)
+        best_recall_5 = checkpoint["counters"].get("best_recall_5", 0.)
+        best_recall_10 = checkpoint["counters"].get("best_recall_10", 0.)
+        best_cos = checkpoint["counters"].get("best_cos", 0.)
+        best_L1 = checkpoint["counters"].get("best_L1", 100000.0)
+        options = checkpoint.get("options", options)
+    else:
+        t, start_epoch, best_inception, best_vfs = 0, 0, (0., 0.), (0., 0.)
+        best_recall_1, best_recall_5, best_recall_10 = 0.0, 0.0, 0.0
+        best_cos, best_L1 = 0.0, 100000.0
+        lr = args.learning_rate
+        checkpoint = {
+            'args': args.__dict__,
+            'options': options,
+            'model_kwargs': model_kwargs,
+            'd_img_kwargs': d_img_kwargs,
+            'train_losses': defaultdict(list),
+            'checkpoint_ts': [],
+            'train_batch_data': [],
+            'train_samples': [],
+            'train_iou': [],
+            'train_inception': [],
+            'lr': [],
+            'val_batch_data': [],
+            'val_samples': [],
+            'val_losses': defaultdict(list),
+            'val_iou': [],
+            'val_inception': [],
+            'norm_d': [],
+            'norm_g': [],
+            'counters': {
+                't': None,
+                'epoch': None,
+                'best_inception': None,
+                'best_vfs': None,
+                'best_recall_1': None,
+                'best_recall_5': None,
+                'best_recall_10': None,
+                'best_cos': None,
+                'best_L1': None,
+            },
+            'model_state': None,
+            'model_best_state': None,
+            'optim_state': None,
+            'd_img_state': None,
+            'd_img_best_state': None,
+            'd_img_optim_state': None,
+            'd_ac_state': None,
+            'd_ac_optim_state': None,
+        }
+
+        log_path = os.path.join(
+            options["logs"]["output_dir"],
+            options["logs"]["name"] + "-" + time.strftime("%Y%m%d-%H%M%S")
+        )
+
+    ### Fuser Logic
+    if args.pretrained_path is not None and \
+        os.path.isfile(args.pretrained_path):
+        # Load
+        log.info('Loading Pretrained Model: {}'.format(args.pretrained_path))
+        pre_checkpoint = torch.load(args.pretrained_path)
+        #model.load_state_dict(pre_checkpoint['model_state'])
+        load_my_state_dict(model, pre_checkpoint['model_state'])
+        #optimizer.load_state_dict(pre_checkpoint['optim_state'])
+
+        if img_discriminator is not None:
+            for i in range(len(img_discriminator)):
+                term_name = 'd_img_state_%d' % i
+                img_discriminator[i].load_state_dict(pre_checkpoint[term_name])
+                #term_name = 'd_img_optim_state_%d' % i
+                #optimizer_d_img[i].load_state_dict(pre_checkpoint[term_name])
+
+        if ac_discriminator is not None:
+            for i in range(len(ac_discriminator)):
+                term_name = 'd_ac_state_%d' % i
+                ac_discriminator[i].load_state_dict(pre_checkpoint[term_name])
+                #term_name = 'd_ac_optim_state_%d' % i
+                #optimizer_d_ac[i].load_state_dict(pre_checkpoint[term_name])
+    ### End of fuser logic
+    logger = Logger(log_path)
+    log.info("Logging to: {}".format(log_path))
+    # save the current config yaml
+    copyfile(args.path_opts,
+             os.path.join(log_path, options["logs"]["name"] + '.yaml'))
+
+    model = nn.DataParallel(model.cuda())
+    if ac_discriminator is not None:
+        for i in range(len(ac_discriminator)):
+            ac_discriminator[i] = nn.DataParallel(ac_discriminator[i].cuda())
+    if img_discriminator is not None:
+        for i in range(len(img_discriminator)):
+            img_discriminator[i] = nn.DataParallel(img_discriminator[i].cuda())
+    if cond_discriminator is not None:
+        for i in range(len(cond_discriminator)):
+            cond_discriminator[i] = nn.DataParallel(cond_discriminator[i].cuda())
+    perceptual_module = nn.DataParallel(perceptual_module.cuda()) if \
+        perceptual_module else None
+
+    if args.evaluate:
+        assert args.resume is not None
+        if args.evaluate_train:
+            log.info("Evaluting the training set.")
+            train_mean, train_std, train_vfs_mean, train_vfs_std = \
+                evaluate(model, train_loader, options)
+            log.info("Inception score: {} ({})".format(train_mean, train_std))
+            log.info("VggFace score: {} ({})".format(
+                train_vfs_mean, train_vfs_std))
+        log.info("Evaluting the validation set.")
+        val_mean, val_std, vfs_mean, vfs_std = evaluate(
+            model, val_loader, options)
+        log.info("Inception score: {} ({})".format(val_mean, val_std))
+        log.info("VggFace score: {} ({})".format(vfs_mean, vfs_std))
+        fid_score = evaluate_fid(model, val_loader, options)
+        log.info("FID score: {}".format(fid_score))
+        return 0
+
+
+    got_best_IS = True
+    got_best_VFS = True
+    got_best_R1 = True
+    got_best_R5 = True
+    got_best_R10 = True
+    got_best_cos = True
+    got_best_L1 = True
+    others = None
+    for epoch in range(start_epoch, args.epochs):
+        if epoch >= args.eval_mode_after and model.training:
+            log.info('[Epoch {}/{}] switching to eval mode'.format(
+                epoch, args.epochs))
+            model.eval()
+            if epoch == args.eval_mode_after:
+                optimizer = optim.Adam(
+                        filter(lambda x: x.requires_grad, model.parameters()),
+                        lr=lr,
+                        betas=(args.beta1, 0.999),)
+        if epoch >= args.disable_l1_loss_after and \
+            options["optim"]["l1_pixel_loss_weight"] > 1e-10:
+            #
+            log.info('[Epoch {}/{}] Disable L1 Loss'.format(epoch, args.epochs))
+            options["optim"]["l1_pixel_loss_weight"] = 0
+        start_time = time.time()
+        for iter, batch in enumerate(pyprind.prog_bar(
+            train_loader,
+            title="[Epoch {}/{}]".format(epoch, args.epochs),
+            width=50)):
+            # An iteration
+            if args.timing:
+                print("Loading Time: {} ms".format(
+                    (time.time() - start_time) * 1000))
+            t += 1
+            ######### unpack the data #########
+            imgs, log_mels, human_ids = batch
+            imgs = imgs.cuda()
+            log_mels = log_mels.type(float_dtype)
+            human_ids = human_ids.type(long_dtype)
+            ###################################
+            with timeit('forward', args.timing):
+                #model_out = model(imgs)
+                model_out = model(log_mels)
+                """
+                imgs_pred: generated images
+                others: placeholder for other output
+                """
+                imgs_pred, others = model_out
+
+            if t % args.visualize_every == 0:
+                #print('Performing Visualization...')
+                training_status = model.training
+                model.eval()
+                samples = visualize_sample(
+                    model,
+                    imgs,
+                    log_mels,
+                    options["data"]["data_opts"]["image_normalize_method"],
+                    visualize_attn=options['eval'].get('visualize_attn', False))
+                model.train(mode=training_status)
+                logger.image_summary(samples, t, tag="vis")
+
+            with timeit('G_loss', args.timing):
+                #model_boxes = None
+                # Skip the pixel loss if not using GT boxes
+                skip_pixel_loss = False
+
+                # calculate L1 loss between imgs and imgs_self
+                total_loss, losses = calculate_model_losses(
+                    options["optim"], skip_pixel_loss, imgs, imgs_pred,)
+
+                if img_discriminator is not None:
+                    for i in range(len(img_discriminator)):
+                        # TODO: Here we need to choose the two methods:
+                        # 1. determine whether imgs_pred is tuple or not
+                        # 2. change the return (imgs_pred) of model to be tuple
+                        if isinstance(imgs_pred, tuple):
+                            scores_fake = img_discriminator[i](imgs_pred[i])
+                        else:
+                            scores_fake = img_discriminator[i](imgs_pred)
+                        weight = options["optim"]["d_loss_weight"] * \
+                            options["optim"]["d_img_weight"]
+                        loss_name = 'g_gan_img_loss_%d' % i
+                        total_loss = add_loss(total_loss,
+                            gan_g_loss(scores_fake), losses, loss_name, weight)
+
+                if ac_discriminator is not None:
+                    for i in range(len(ac_discriminator)):
+                        if isinstance(imgs_pred, tuple):
+                            scores_fake, ac_loss = ac_discriminator[i](
+                                imgs_pred[i], human_ids)
+                        else:
+                            scores_fake, ac_loss = ac_discriminator[i](
+                                imgs_pred, human_ids)
+                        weight = options["optim"]["d_loss_weight"] * \
+                            options["optim"]["ac_loss_weight"]
+                        loss_name = 'ac_loss_%d' % i
+                        total_loss = add_loss(total_loss,
+                                              ac_loss.mean(),
+                                              losses,
+                                              loss_name,
+                                              weight)
+                        loss_name = 'g_gan_ac_loss_%d' % i
+                        total_loss = add_loss(total_loss,
+                                              gan_g_loss(scores_fake),
+                                              losses,
+                                              loss_name,
+                                              weight)
+
+                if cond_discriminator is not None:
+                    for i in range(len(cond_discriminator)):
+                        # TODO: check whether condition is in others or not ?
+                        cond_vecs = others['cond']
+                        if isinstance(imgs_pred, tuple):
+                            scores_fake = cond_discriminator[i](
+                                imgs_pred[i], cond_vecs)
+                        else:
+                            scores_fake = cond_discriminator[i](
+                                imgs_pred, cond_vecs)
+                        weight = options["optim"]["d_loss_weight"] * \
+                            options["optim"]["cond_loss_weight"]
+                        loss_name = 'g_cond_loss_%d' % i
+                        total_loss = add_loss(total_loss,
+                            gan_g_loss(scores_fake), losses, loss_name, weight)
+
+                if options["optim"].get("perceptual_loss_weight", -1) > 0:
+                    if isinstance(imgs_pred, tuple):
+                        # Multi-Resolution Pixel Loss
+                        for i, img_pred in enumerate(imgs_pred):
+                            loss_name = 'img_perceptual_loss_%d' % i
+                            img = imgs
+                            while img.size()[2] != img_pred.size()[2]:
+                                img = F.interpolate(
+                                    img, scale_factor=0.5, mode='nearest')
+                            if s2f_val_evaluator.do_deprocess_and_preprocess:
+                                img_pred_in = \
+                                    s2f_val_evaluator.deprocess_and_preprocess(
+                                        img_pred)
+                                img_in = \
+                                    s2f_val_evaluator.deprocess_and_preprocess(
+                                        img)
+                            if s2f_val_evaluator.crop_faces:
+                                img_pred_in = \
+                                    s2f_val_evaluator.crop_vgg_box(img_pred_in)
+                                img_in = s2f_val_evaluator.crop_vgg_box(img_in)
+                            perceptual_loss = perceptual_module(
+                                img_pred_in, img_in)
+                            perceptual_loss = perceptual_loss.mean()
+                            weight = options["optim"]["perceptual_loss_weight"]
+                            total_loss = add_loss(total_loss, perceptual_loss,
+                                                  losses, loss_name,
+                                                  weight)
+                    else:
+                        if s2f_val_evaluator.do_deprocess_and_preprocess:
+                            imgs_pred_in = \
+                                s2f_val_evaluator.deprocess_and_preprocess(
+                                    imgs_pred)
+                            imgs_in = \
+                                s2f_val_evaluator.deprocess_and_preprocess(imgs)
+                        if s2f_val_evaluator.crop_faces:
+                            imgs_pred_in = \
+                                s2f_val_evaluator.crop_vgg_box(imgs_pred_in)
+                            imgs_in = s2f_val_evaluator.crop_vgg_box(imgs_in)
+                        perceptual_loss = perceptual_module(
+                            imgs_pred_in, imgs_in)
+                        perceptual_loss = perceptual_loss.mean()
+                        weight = options["optim"]["perceptual_loss_weight"]
+                        total_loss = add_loss(total_loss, perceptual_loss,
+                                              losses, "img_perceptual_loss",
+                                              weight)
+
+            losses['total_loss'] = total_loss.item()
+            if not math.isfinite(losses['total_loss']):
+                log.warn('WARNING: Got loss = NaN, not backpropping')
+                continue
+
+            optimizer.zero_grad()
+            with timeit('backward', args.timing):
+                total_loss.backward()
+            optimizer.step()
+
+            total_loss_d = None
+            ac_loss_real = None
+            ac_loss_fake = None
+            d_losses = {}
+
+            with timeit('D_loss', args.timing):
+                if img_discriminator is not None:
+                    d_img_losses = LossManager()
+                    for i in range(len(img_discriminator)):
+                        if isinstance(imgs_pred, tuple):
+                            imgs_fake = imgs_pred[i].detach()
+                        else:
+                            imgs_fake = imgs_pred.detach()
+                        imgs_real = imgs.detach()
+                        while imgs_real.size()[2] != imgs_fake.size()[2]:
+                            imgs_real = F.interpolate(
+                                imgs_real, scale_factor=0.5, mode='nearest')
+
+                        scores_fake = img_discriminator[i](imgs_fake)
+                        scores_real = img_discriminator[i](imgs_real)
+
+                        d_img_gan_loss = gan_d_loss(scores_real, scores_fake)
+                        d_img_losses.add_loss(
+                            d_img_gan_loss, 'd_img_gan_loss_%d' % i)
+
+                    for i in range(len(img_discriminator)):
+                        optimizer_d_img[i].zero_grad()
+                    d_img_losses.total_loss.backward()
+                    for i in range(len(img_discriminator)):
+                        optimizer_d_img[i].step()
+
+                if ac_discriminator is not None:
+                    d_ac_losses = LossManager()
+                    for i in range(len(ac_discriminator)):
+                        if isinstance(imgs_pred, tuple):
+                            imgs_fake = imgs_pred[i].detach()
+                        else:
+                            imgs_fake = imgs_pred.detach()
+
+                        imgs_real = imgs.detach()
+                        while imgs_real.size()[2] != imgs_fake.size()[2]:
+                            imgs_real = F.interpolate(
+                                imgs_real, scale_factor=0.5, mode='nearest')
+
+                        scores_real, ac_loss_real= ac_discriminator[i](
+                            imgs_real, human_ids)
+                        scores_fake, ac_loss_fake = ac_discriminator[i](
+                            imgs_fake, human_ids)
+
+                        d_ac_gan_loss = gan_d_loss(scores_real, scores_fake)
+                        d_ac_losses.add_loss(
+                            d_ac_gan_loss, 'd_ac_gan_loss_%d' % i)
+                        d_ac_losses.add_loss(
+                            ac_loss_real.mean(), 'd_ac_loss_real_%d' % i)
+
+                    for i in range(len(ac_discriminator)):
+                        optimizer_d_ac[i].zero_grad()
+                    d_ac_losses.total_loss.backward()
+                    for i in range(len(ac_discriminator)):
+                        optimizer_d_ac[i].step()
+
+                if cond_discriminator is not None:
+                    cond_d_losses = LossManager()
+                    for i in range(len(cond_discriminator)):
+                        if isinstance(imgs_pred, tuple):
+                            imgs_fake = imgs_pred[i].detach()
+                        else:
+                            imgs_fake = imgs_pred.detach()
+                        imgs_real = imgs.detach()
+                        cond_vecs = others['cond'].detach()
+                        while imgs_real.size()[2] != imgs_fake.size()[2]:
+                            imgs_real = F.interpolate(
+                                imgs_real, scale_factor=0.5, mode='nearest')
+
+                        scores_fake = cond_discriminator[i](
+                            imgs_fake, cond_vecs)
+                        scores_real = cond_discriminator[i](
+                            imgs_real, cond_vecs)
+
+                        cond_d_gan_loss = gan_d_loss(scores_real, scores_fake)
+                        cond_d_losses.add_loss(
+                            cond_d_gan_loss, 'cond_d_gan_loss_%d' % i)
+
+                    for i in range(len(cond_discriminator)):
+                        optimizer_cond_d[i].zero_grad()
+                    cond_d_losses.total_loss.backward()
+                    for i in range(len(cond_discriminator)):
+                        optimizer_cond_d[i].step()
+
+            # Logging generative model losses
+            for name, val in losses.items():
+                logger.scalar_summary("loss/{}".format(name), val, t)
+            if img_discriminator is not None:
+                for name, val in d_img_losses.items():
+                    logger.scalar_summary("d_loss/{}".format(name), val, t)
+            if ac_discriminator is not None:
+                for name, val in d_ac_losses.items():
+                    logger.scalar_summary("d_loss/{}".format(name), val, t)
+            if cond_discriminator is not None:
+                for name, val in cond_d_losses.items():
+                    logger.scalar_summary("d_loss/{}".format(name), val, t)
+            start_time = time.time()
+
+        if epoch % args.eval_epochs == 0:
+            log.info('[Epoch {}/{}] checking on val'.format(
+                epoch, args.epochs)
+            )
+            val_results = check_model(
+                args, options, epoch, val_loader, model)
+            val_losses, val_samples, val_inception, val_vfs = val_results
+            # call evaluate_s2f_metrics() here
+            val_facenet_L2_dist, val_facenet_L1_dist, val_facenet_cos_sim, \
+                val_recall_tuple, val_ih_sim  = \
+                    s2f_val_evaluator.get_metrics(
+                        model, recall_method='cos_sim', get_ih_sim=True)
+            val_recall_at_1, val_recall_at_2, val_recall_at_5, \
+                val_recall_at_10, val_recall_at_20, \
+                    val_recall_at_50 = val_recall_tuple
+            # Update the best of metrics
+            if val_inception[0] > best_inception[0]:
+                got_best_IS = True
+                best_inception = val_inception
+            if val_vfs[0] > best_vfs[0]:
+                got_best_VFS = True
+                best_vfs = val_vfs
+            if val_recall_at_1 > best_recall_1:
+                got_best_R1 = True
+                best_recall_1 = val_recall_at_1
+            if val_recall_at_5 > best_recall_5:
+                got_best_R5 = True
+                best_recall_5 = val_recall_at_5
+            if val_recall_at_10 > best_recall_10:
+                got_best_R10 = True
+                best_recall_10 = val_recall_at_10
+            if val_facenet_cos_sim > best_cos:
+                got_best_cos = True
+                best_cos = val_facenet_cos_sim
+            if val_facenet_L1_dist < best_L1:
+                got_best_L1 = True
+                best_L1 = val_facenet_L1_dist
+            checkpoint['counters']['best_inception'] = best_inception
+            checkpoint['counters']['best_vfs'] = best_vfs
+            checkpoint['val_samples'].append(val_samples)
+            # checkpoint['val_batch_data'].append(val_batch_data)
+            for k, v in val_losses.items():
+                checkpoint['val_losses'][k].append(v)
+                logger.scalar_summary("ckpt/val_{}".format(k), v, epoch)
+            logger.scalar_summary("ckpt/val_inception", val_inception[0], epoch)
+            logger.scalar_summary("ckpt/val_facenet_L2_dist",
+                val_facenet_L2_dist, epoch)
+            logger.scalar_summary("ckpt/val_facenet_L1_dist",
+                val_facenet_L1_dist, epoch)
+            logger.scalar_summary("ckpt/val_facenet_cos_sim",
+                val_facenet_cos_sim, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_1",
+                val_recall_at_1, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_2",
+                val_recall_at_2, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_5",
+                val_recall_at_5, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_10",
+                val_recall_at_10, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_20",
+                val_recall_at_20, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_50",
+                val_recall_at_50, epoch)
+            logger.scalar_summary("ckpt/val_ih_sim",
+                val_ih_sim, epoch)
+            logger.scalar_summary("ckpt/val_vfs",
+                val_vfs[0], epoch)
+            # Add speech2face metrics here..
+            #
+            logger.image_summary(val_samples, epoch, tag="ckpt_val")
+            # log.info('[Epoch {}/{}] val iou: {}'.format(
+            # epoch, args.epochs, val_avg_iou))
+            log.info('[Epoch {}/{}] val inception score: {} ({})'.format(
+                    epoch, args.epochs, val_inception[0], val_inception[1]))
+            log.info('[Epoch {}/{}] best inception scores: {} ({})'.format(
+                    epoch, args.epochs, best_inception[0], best_inception[1]))
+            log.info('[Epoch {}/{}] val vfs scores: {} ({})'.format(
+                    epoch, args.epochs, val_vfs[0], val_vfs[1]))
+            log.info('[Epoch {}/{}] best vfs scores: {} ({})'.format(
+                    epoch, args.epochs, best_vfs[0], best_vfs[1]))
+            log.info('[Epoch {}/{}] val recall at 5: {}, '.format(
+                     epoch, args.epochs, val_recall_at_5) + \
+                        'best recall at 5: {}'.format(best_recall_5))
+            log.info('[Epoch {}/{}] val recall at 10: {}, '.format(
+                     epoch, args.epochs, val_recall_at_10) + \
+                        'best recall at 10: {}'.format(best_recall_10))
+            log.info('[Epoch {}/{}] val cosine similarity: {}, '.format(
+                     epoch, args.epochs, val_facenet_cos_sim) + \
+                        'best cosine similarity: {}'.format(best_cos))
+            log.info('[Epoch {}/{}] val L1 distance: {}, '.format(
+                     epoch, args.epochs, val_facenet_L1_dist) + \
+                            'best L1 distance: {}'.format(best_L1))
+
+            checkpoint['model_state'] = model.module.state_dict()
+
+            if img_discriminator is not None:
+                for i in range(len(img_discriminator)):
+                    term_name = 'd_img_state_%d' % i
+                    checkpoint[term_name] = \
+                        img_discriminator[i].module.state_dict()
+                    term_name = 'd_img_optim_state_%d' % i
+                    checkpoint[term_name] = \
+                        optimizer_d_img[i].state_dict()
+
+            if ac_discriminator is not None:
+                for i in range(len(ac_discriminator)):
+                    term_name = 'd_ac_state_%d' % i
+                    checkpoint[term_name] = \
+                        ac_discriminator[i].module.state_dict()
+                    term_name = 'd_ac_optim_state_%d' % i
+                    checkpoint[term_name] = \
+                        optimizer_d_ac[i].state_dict()
+
+            if cond_discriminator is not None:
+                for i in range(len(cond_discriminator)):
+                    term_name = 'cond_d_state_%d' % i
+                    checkpoint[term_name] = \
+                        cond_discriminator[i].module.state_dict()
+                    term_name = 'cond_d_optim_state_%d' % i
+                    checkpoint[term_name] = \
+                        optimizer_cond_d[i].state_dict()
+
+            checkpoint['optim_state'] = optimizer.state_dict()
+            checkpoint['counters']['epoch'] = epoch
+            checkpoint['counters']['t'] = t
+            checkpoint['counters']['best_inception'] = best_inception
+            checkpoint['counters']['best_vfs'] = best_vfs
+            checkpoint['counters']['best_recall_1'] = best_recall_1
+            checkpoint['counters']['best_recall_5'] = best_recall_5
+            checkpoint['counters']['best_recall_10'] = best_recall_10
+            checkpoint['counters']['best_cos'] = best_cos
+            checkpoint['counters']['best_L1'] = best_L1
+            checkpoint['lr'] = lr
+            checkpoint_path = os.path.join(
+                log_path,
+                '%s_with_model.pt' % args.checkpoint_name)
+            log.info('[Epoch {}/{}] Saving checkpoint: {}'.format(
+                epoch, args.epochs, checkpoint_path))
+            torch.save(checkpoint, checkpoint_path)
+            if got_best_IS:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_IS_with_model.pt'))
+                got_best_IS = False
+            if got_best_VFS:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_VFS_with_model.pt'))
+                got_best_VFS = False
+            if got_best_R1:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_R1_with_model.pt'))
+                got_best_R1 = False
+            if got_best_R5:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_R5_with_model.pt'))
+                got_best_R5 = False
+            if got_best_R10:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_R10_with_model.pt'))
+                got_best_R10 = False
+            if got_best_L1:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_L1_with_model.pt'))
+                got_best_L1 = False
+            if got_best_cos:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_cos_with_model.pt'))
+                got_best_cos = False
+
+        if epoch > 0 and epoch % 1000 == 0:
+            print('Saving checkpoint for Epoch {}.'.format(epoch))
+            copyfile(
+                checkpoint_path,
+                os.path.join(log_path, 'epoch_{}_model.pt'.format(epoch)))
+        # Fuser Logic
+        elif args.train_fuser_only and epoch > 0 and epoch % 1 == 0:
+            print('Saving checkpoint for Epoch {}.'.format(epoch))
+            copyfile(
+                checkpoint_path,
+                os.path.join(log_path, 'epoch_{}_model.pt'.format(epoch)))
+        # End of fuser logic
+
+        if epoch >= args.decay_lr_epochs:
+            lr_end = args.learning_rate * 1e-3
+            decay_frac = (epoch - args.decay_lr_epochs + 1) / \
+                (args.epochs - args.decay_lr_epochs + 1e-5)
+            lr = args.learning_rate - decay_frac * (args.learning_rate - lr_end)
+            for param_group in optimizer.param_groups:
+                param_group["lr"] = lr
+            if img_discriminator is not None:
+                for i in range(len(optimizer_d_img)):
+                    for param_group in optimizer_d_img[i].param_groups:
+                        param_group["lr"] = lr
+                # for param_group in optimizer_d_img.param_groups:
+                #     param_group["lr"] = lr
+            log.info('[Epoch {}/{}] learning rate: {}'.format(
+                epoch+1, args.epochs, lr))
+
+        logger.scalar_summary("ckpt/learning_rate", lr, epoch)
+
+    # Evaluating after the whole training process.
+    log.info("Evaluting the validation set.")
+    is_mean, is_std, vfs_mean, vfs_std = evaluate(model, val_loader, options)
+    log.info("Inception score: {} ({})".format(is_mean, is_std))
+    log.info("VggFace score: {} ({})".format(vfs_mean, vfs_std))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/mlops/mlflow/train/sf2f/train_registry.py b/mlops/mlflow/train/sf2f/train_registry.py
new file mode 100644
index 0000000..cd86b03
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/train_registry.py
@@ -0,0 +1,885 @@
+import functools
+import os
+import json
+import math
+from collections import defaultdict
+import random
+import time
+import pyprind
+import glog as log
+from shutil import copyfile
+
+import numpy as np
+import torch
+import torch.optim as optim
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data.dataloader import default_collate
+import mlflow, wandb
+
+from datasets import imagenet_deprocess_batch
+from datasets import window_segment
+import datasets
+import models
+import models.perceptual
+from utils.losses import get_gan_losses
+from utils import timeit, LossManager
+from options.opts import args, options
+from utils.logger import Logger
+from utils import tensor2im
+from utils.utils import load_my_state_dict
+# losseds need to be modified
+from utils.training_utils import add_loss, check_model, calculate_model_losses
+from utils.training_utils import visualize_sample
+from utils.evaluate import evaluate
+from utils.evaluate_fid import evaluate_fid
+from utils.s2f_evaluator import S2fEvaluator
+from utils.mlflow_wandb import mlflow_wandb_logging
+
+
+torch.backends.cudnn.benchmark = True
+
+
+def main():
+    global args, options
+    print(args)
+    print(options['data'])
+    # Set random seed, deterministic
+    torch.cuda.manual_seed(args.seed)
+    torch.manual_seed(args.seed)
+    np.random.seed(args.seed)
+    random.seed(args.seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    #visualize_attn=options['eval'].get('visualize_attn', False)
+    #print('########### visualize_attn: {} ###########'.format(visualize_attn))
+    float_dtype = torch.cuda.FloatTensor
+    long_dtype = torch.cuda.LongTensor
+    log.info("Building loader...")
+    train_loader, val_loader, test_loader = \
+        datasets.build_loaders(options["data"])
+    # Fuser Logic
+    if args.train_fuser_only:
+        train_loader.collate_fn = default_collate
+        train_loader.dataset.return_mel_segments = True
+        train_loader.dataset.mel_segments_rand_start = True
+        val_loader.collate_fn = default_collate
+        val_loader.dataset.return_mel_segments = True
+        s2f_face_gen_mode = 'naive'
+    else:
+        s2f_face_gen_mode = 'average_facenet_embedding'
+    # End of fuser logic
+    #s2f_val_evaluator = S2fEvaluator(val_loader, options)
+    s2f_val_evaluator = S2fEvaluator(
+        val_loader,
+        options,
+        extraction_size=100, #[100,200,300],
+        hq_emb_dict=True,
+        face_gen_mode=s2f_face_gen_mode)
+
+    # [:len(checkpoint[term_name]['id_classifier.bias'])] class 맞추기
+    # if restore_path is not None and os.path.isfile(restore_path):
+    train_loader.dataset.available_names = train_loader.dataset.available_names[:729]
+   
+    # Get total number of people in training set
+    num_train_id = len(train_loader.dataset)
+    # Initialize softmax neuron value
+    for ac_net in ['identity', 'identity_low', 'identity_mid', 'identity_high']:
+        if options['discriminator'].get(ac_net) is not None:
+            options['discriminator'][ac_net]['num_id'] = num_train_id
+
+    log.info("Building Generative Model...")
+    model, model_kwargs = models.build_model(
+        options["generator"],
+        image_size=options["data"]["image_size"],
+        checkpoint_start_from=args.checkpoint_start_from)
+    model.type(float_dtype)
+    # Fuser logic
+    if args.train_fuser_only:
+        # Hardcode to freeze batchnorm layers
+        model.eval()
+        if args.train_fuser_decoder:
+            model.encoder.train_fuser_only()
+        else:
+            model.train_fuser_only()
+    # End of fuser logic
+    print(model)
+
+    optimizer = torch.optim.Adam(
+            filter(lambda x: x.requires_grad, model.parameters()),
+            lr=args.learning_rate,
+            betas=(args.beta1, 0.999),)
+
+    if (options["optim"]["d_loss_weight"] < 0 or \
+        options["optim"]["d_img_weight"] < 0):
+        # Ignore image discriminator
+        img_discriminator = None
+        d_img_kwargs = {}
+        log.info("Ignoring Image Discriminator.")
+    else:
+        img_discriminator, d_img_kwargs = models.build_img_discriminator(
+            options["discriminator"])
+        log.info("Done Building Image Discriminator.")
+
+    if (options["optim"]["d_loss_weight"] < 0 or \
+        options["optim"]["ac_loss_weight"] < 0):
+        # Ignore AC discriminator
+        ac_discriminator = None
+        ac_img_kwargs = {}
+        log.info("Ignoring Auxilary Classifier Discriminator.")
+    else:
+        ac_discriminator, ac_img_kwargs = models.build_ac_discriminator(
+            options["discriminator"])
+        #print('AC Discriminator:', ac_discriminator)
+        log.info("Done Building Auxilary Classifier Discriminator.")
+
+    if (options["optim"]["d_loss_weight"] < 0 or \
+        options["optim"].get("cond_loss_weight", -1) < 0):
+        # Ignore Conditional discriminator
+        cond_discriminator = None
+        cond_d_kwargs = {}
+        log.info("Ignoring Conditional Discriminator.")
+    else:
+        cond_discriminator, cond_d_kwargs = models.build_cond_discriminator(
+            options["discriminator"])
+        #print('Conditional Discriminator:', cond_discriminator)
+        log.info("Done Building Conditional Discriminator.")
+
+    perceptual_module = None
+    if options["optim"].get("perceptual_loss_weight", -1) > 0:
+        ploss_name = options.get("perceptual", {}).get("arch", "FaceNetLoss")
+        ploss_cos_weight = options["optim"].get("cos_percept_loss_weight", -1)
+        perceptual_module = getattr(
+            models.perceptual,
+            ploss_name)(cos_loss_weight=ploss_cos_weight)
+        log.info("Done Building Perceptual {} Module.".format(ploss_name))
+        if ploss_cos_weight > 0:
+            log.info("Perceptual Cos Loss Weight: {}".format(ploss_cos_weight))
+    else:
+        log.info("Ignoring Perceptual Module.")
+
+    gan_g_loss, gan_d_loss = get_gan_losses(options["optim"]["gan_loss_type"])
+
+    optimizer_d_img = []
+    if img_discriminator is not None:
+        for i in range(len(img_discriminator)):
+            img_discriminator[i].type(float_dtype)
+            img_discriminator[i].train()
+            print(img_discriminator[i])
+            optimizer_d_img.append(torch.optim.Adam(
+                    filter(lambda x: x.requires_grad,
+                           img_discriminator[i].parameters()),
+                    lr=args.learning_rate,
+                    betas=(args.beta1, 0.999),))
+
+    optimizer_d_ac = []
+    if ac_discriminator is not None:
+        for i in range(len(ac_discriminator)):
+            ac_discriminator[i].type(float_dtype)
+            ac_discriminator[i].train()
+            print(ac_discriminator[i])
+            optimizer_d_ac.append(torch.optim.Adam(
+                    filter(lambda x: x.requires_grad,
+                           ac_discriminator[i].parameters()),
+                    lr=args.learning_rate,
+                    betas=(args.beta1, 0.999),))
+
+    optimizer_cond_d = []
+    if cond_discriminator is not None:
+        for i in range(len(cond_discriminator)):
+            cond_discriminator[i].type(float_dtype)
+            cond_discriminator[i].train()
+            print(cond_discriminator[i])
+            optimizer_cond_d.append(torch.optim.Adam(
+                    filter(lambda x: x.requires_grad,
+                           cond_discriminator[i].parameters()),
+                    lr=args.learning_rate,
+                    betas=(args.beta1, 0.999),))
+
+    restore_path = None
+    if args.resume is not None:
+        restore_path = '%s_with_model.pt' % args.checkpoint_name
+        restore_path = os.path.join(
+            options["logs"]["output_dir"], args.resume, restore_path)
+
+    if restore_path is not None and os.path.isfile(restore_path):
+        log.info('Restoring from checkpoint: {}'.format(restore_path))
+        checkpoint = torch.load(restore_path)
+        model.load_state_dict(checkpoint['model_state'])
+        optimizer.load_state_dict(checkpoint['optim_state'])
+
+        if img_discriminator is not None:
+            for i in range(len(img_discriminator)):
+                term_name = 'd_img_state_%d' % i
+                img_discriminator[i].load_state_dict(checkpoint[term_name])
+                term_name = 'd_img_optim_state_%d' % i
+                optimizer_d_img[i].load_state_dict(checkpoint[term_name])
+
+        if ac_discriminator is not None:
+            for i in range(len(ac_discriminator)):
+                term_name = 'd_ac_state_%d' % i
+                ac_discriminator[i].load_state_dict(checkpoint[term_name])
+                term_name = 'd_ac_optim_state_%d' % i
+                optimizer_d_ac[i].load_state_dict(checkpoint[term_name])
+
+        if cond_discriminator is not None:
+            for i in range(len(cond_discriminator)):
+                term_name = 'd_img_state_%d' % i
+                cond_discriminator[i].load_state_dict(checkpoint[term_name])
+                term_name = 'd_img_optim_state_%d' % i
+                optimizer_cond_d[i].load_state_dict(checkpoint[term_name])
+
+        t = checkpoint['counters']['t'] + 1
+        if 0 <= args.eval_mode_after <= t:
+            model.eval()
+        else:
+            model.train()
+        # Reset epoch here later
+        start_epoch = checkpoint['counters']['epoch'] + 1
+        log_path = os.path.join(options["logs"]["output_dir"], args.resume,)
+        lr = checkpoint.get('learning_rate', args.learning_rate)
+        best_inception = checkpoint["counters"].get("best_inception", (0., 0.))
+        best_vfs = checkpoint["counters"].get("best_vfs", (0., 0.))
+        best_recall_1 = checkpoint["counters"].get("best_recall_1", 0.)
+        best_recall_5 = checkpoint["counters"].get("best_recall_5", 0.)
+        best_recall_10 = checkpoint["counters"].get("best_recall_10", 0.)
+        best_cos = checkpoint["counters"].get("best_cos", 0.)
+        best_L1 = checkpoint["counters"].get("best_L1", 100000.0)
+        options = checkpoint.get("options", options)
+    else:
+        t, start_epoch, best_inception, best_vfs = 0, 0, (0., 0.), (0., 0.)
+        best_recall_1, best_recall_5, best_recall_10 = 0.0, 0.0, 0.0
+        best_cos, best_L1 = 0.0, 100000.0
+        lr = args.learning_rate
+        checkpoint = {
+            'args': args.__dict__,
+            'options': options,
+            'model_kwargs': model_kwargs,
+            'd_img_kwargs': d_img_kwargs,
+            'train_losses': defaultdict(list),
+            'checkpoint_ts': [],
+            'train_batch_data': [],
+            'train_samples': [],
+            'train_iou': [],
+            'train_inception': [],
+            'lr': [],
+            'val_batch_data': [],
+            'val_samples': [],
+            'val_losses': defaultdict(list),
+            'val_iou': [],
+            'val_inception': [],
+            'norm_d': [],
+            'norm_g': [],
+            'counters': {
+                't': None,
+                'epoch': None,
+                'best_inception': None,
+                'best_vfs': None,
+                'best_recall_1': None,
+                'best_recall_5': None,
+                'best_recall_10': None,
+                'best_cos': None,
+                'best_L1': None,
+            },
+            'model_state': None,
+            'model_best_state': None,
+            'optim_state': None,
+            'd_img_state': None,
+            'd_img_best_state': None,
+            'd_img_optim_state': None,
+            'd_ac_state': None,
+            'd_ac_optim_state': None,
+        }
+
+        log_path = os.path.join(
+            options["logs"]["output_dir"],
+            options["logs"]["name"] + "-" + time.strftime("%Y%m%d-%H%M%S")
+        )
+
+    ### Fuser Logic
+    if args.pretrained_path is not None and \
+        os.path.isfile(args.pretrained_path):
+        # Load
+        log.info('Loading Pretrained Model: {}'.format(args.pretrained_path))
+        pre_checkpoint = torch.load(args.pretrained_path)
+        #model.load_state_dict(pre_checkpoint['model_state'])
+        load_my_state_dict(model, pre_checkpoint['model_state'])
+        #optimizer.load_state_dict(pre_checkpoint['optim_state'])
+
+        if img_discriminator is not None:
+            for i in range(len(img_discriminator)):
+                term_name = 'd_img_state_%d' % i
+                img_discriminator[i].load_state_dict(pre_checkpoint[term_name])
+                #term_name = 'd_img_optim_state_%d' % i
+                #optimizer_d_img[i].load_state_dict(pre_checkpoint[term_name])
+
+        if ac_discriminator is not None:
+            for i in range(len(ac_discriminator)):
+                term_name = 'd_ac_state_%d' % i
+                ac_discriminator[i].load_state_dict(pre_checkpoint[term_name])
+                #term_name = 'd_ac_optim_state_%d' % i
+                #optimizer_d_ac[i].load_state_dict(pre_checkpoint[term_name])
+    ### End of fuser logic
+    logger = Logger(log_path)
+    log.info("Logging to: {}".format(log_path))
+    # save the current config yaml
+    copyfile(args.path_opts,
+             os.path.join(log_path, options["logs"]["name"] + '.yaml'))
+
+    model = nn.DataParallel(model.cuda())
+    if ac_discriminator is not None:
+        for i in range(len(ac_discriminator)):
+            ac_discriminator[i] = nn.DataParallel(ac_discriminator[i].cuda())
+    if img_discriminator is not None:
+        for i in range(len(img_discriminator)):
+            img_discriminator[i] = nn.DataParallel(img_discriminator[i].cuda())
+    if cond_discriminator is not None:
+        for i in range(len(cond_discriminator)):
+            cond_discriminator[i] = nn.DataParallel(cond_discriminator[i].cuda())
+    perceptual_module = nn.DataParallel(perceptual_module.cuda()) if \
+        perceptual_module else None
+
+    if args.evaluate:
+        assert args.resume is not None
+        if args.evaluate_train:
+            log.info("Evaluting the training set.")
+            train_mean, train_std, train_vfs_mean, train_vfs_std = \
+                evaluate(model, train_loader, options)
+            log.info("Inception score: {} ({})".format(train_mean, train_std))
+            log.info("VggFace score: {} ({})".format(
+                train_vfs_mean, train_vfs_std))
+        log.info("Evaluting the validation set.")
+        val_mean, val_std, vfs_mean, vfs_std = evaluate(
+            model, val_loader, options)
+        log.info("Inception score: {} ({})".format(val_mean, val_std))
+        log.info("VggFace score: {} ({})".format(vfs_mean, vfs_std))
+        fid_score = evaluate_fid(model, val_loader, options)
+        log.info("FID score: {}".format(fid_score))
+        return 0
+
+
+    got_best_IS = True
+    got_best_VFS = True
+    got_best_R1 = True
+    got_best_R5 = True
+    got_best_R10 = True
+    got_best_cos = True
+    got_best_L1 = True
+    others = None
+    for epoch in range(start_epoch, args.epochs):
+        if epoch >= args.eval_mode_after and model.training:
+            log.info('[Epoch {}/{}] switching to eval mode'.format(
+                epoch, args.epochs))
+            model.eval()
+            if epoch == args.eval_mode_after:
+                optimizer = optim.Adam(
+                        filter(lambda x: x.requires_grad, model.parameters()),
+                        lr=lr,
+                        betas=(args.beta1, 0.999),)
+        if epoch >= args.disable_l1_loss_after and \
+            options["optim"]["l1_pixel_loss_weight"] > 1e-10:
+            #
+            log.info('[Epoch {}/{}] Disable L1 Loss'.format(epoch, args.epochs))
+            options["optim"]["l1_pixel_loss_weight"] = 0
+        start_time = time.time()
+        for iter, batch in enumerate(pyprind.prog_bar(
+            train_loader,
+            title="[Epoch {}/{}]".format(epoch, args.epochs),
+            width=50)):
+            # An iteration
+            if args.timing:
+                print("Loading Time: {} ms".format(
+                    (time.time() - start_time) * 1000))
+            t += 1
+            ######### unpack the data #########
+            imgs, log_mels, human_ids = batch
+            imgs = imgs.cuda()
+            log_mels = log_mels.type(float_dtype)
+            human_ids = human_ids.type(long_dtype)
+            ###################################
+            # log_mel_segs = window_segment(log_mels[0], window_length=125, stride_length=63)
+            ###################################
+            with timeit('forward', args.timing):
+                #model_out = model(imgs)
+                model_out = model(log_mels)
+                """
+                imgs_pred: generated images
+                others: placeholder for other output
+                """
+                imgs_pred, others = model_out
+
+            if t % args.visualize_every == 0:
+                #print('Performing Visualization...')
+                training_status = model.training
+                model.eval()
+                samples = visualize_sample(
+                    model,
+                    imgs,
+                    log_mels,
+                    options["data"]["data_opts"]["image_normalize_method"],
+                    visualize_attn=options['eval'].get('visualize_attn', False))
+                model.train(mode=training_status)
+                logger.image_summary(samples, t, tag="vis")
+
+            with timeit('G_loss', args.timing):
+                #model_boxes = None
+                # Skip the pixel loss if not using GT boxes
+                skip_pixel_loss = False
+
+                # calculate L1 loss between imgs and imgs_self
+                total_loss, losses = calculate_model_losses(
+                    options["optim"], skip_pixel_loss, imgs, imgs_pred,)
+
+                if img_discriminator is not None:
+                    for i in range(len(img_discriminator)):
+                        # TODO: Here we need to choose the two methods:
+                        # 1. determine whether imgs_pred is tuple or not
+                        # 2. change the return (imgs_pred) of model to be tuple
+                        if isinstance(imgs_pred, tuple):
+                            scores_fake = img_discriminator[i](imgs_pred[i])
+                        else:
+                            scores_fake = img_discriminator[i](imgs_pred)
+                        weight = options["optim"]["d_loss_weight"] * \
+                            options["optim"]["d_img_weight"]
+                        loss_name = 'g_gan_img_loss_%d' % i
+                        total_loss = add_loss(total_loss,
+                            gan_g_loss(scores_fake), losses, loss_name, weight)
+
+                if ac_discriminator is not None:
+                    for i in range(len(ac_discriminator)):
+                        if isinstance(imgs_pred, tuple):
+                            scores_fake, ac_loss = ac_discriminator[i](
+                                imgs_pred[i], human_ids)
+                        else:
+                            scores_fake, ac_loss = ac_discriminator[i](
+                                imgs_pred, human_ids)
+                        weight = options["optim"]["d_loss_weight"] * \
+                            options["optim"]["ac_loss_weight"]
+                        loss_name = 'ac_loss_%d' % i
+                        total_loss = add_loss(total_loss,
+                                              ac_loss.mean(),
+                                              losses,
+                                              loss_name,
+                                              weight)
+                        loss_name = 'g_gan_ac_loss_%d' % i
+                        total_loss = add_loss(total_loss,
+                                              gan_g_loss(scores_fake),
+                                              losses,
+                                              loss_name,
+                                              weight)
+
+                if cond_discriminator is not None:
+                    for i in range(len(cond_discriminator)):
+                        # TODO: check whether condition is in others or not ?
+                        cond_vecs = others['cond']
+                        if isinstance(imgs_pred, tuple):
+                            scores_fake = cond_discriminator[i](
+                                imgs_pred[i], cond_vecs)
+                        else:
+                            scores_fake = cond_discriminator[i](
+                                imgs_pred, cond_vecs)
+                        weight = options["optim"]["d_loss_weight"] * \
+                            options["optim"]["cond_loss_weight"]
+                        loss_name = 'g_cond_loss_%d' % i
+                        total_loss = add_loss(total_loss,
+                            gan_g_loss(scores_fake), losses, loss_name, weight)
+
+                if options["optim"].get("perceptual_loss_weight", -1) > 0:
+                    if isinstance(imgs_pred, tuple):
+                        # Multi-Resolution Pixel Loss
+                        for i, img_pred in enumerate(imgs_pred):
+                            loss_name = 'img_perceptual_loss_%d' % i
+                            img = imgs
+                            while img.size()[2] != img_pred.size()[2]:
+                                img = F.interpolate(
+                                    img, scale_factor=0.5, mode='nearest')
+                            if s2f_val_evaluator.do_deprocess_and_preprocess:
+                                img_pred_in = \
+                                    s2f_val_evaluator.deprocess_and_preprocess(
+                                        img_pred)
+                                img_in = \
+                                    s2f_val_evaluator.deprocess_and_preprocess(
+                                        img)
+                            if s2f_val_evaluator.crop_faces:
+                                img_pred_in = \
+                                    s2f_val_evaluator.crop_vgg_box(img_pred_in)
+                                img_in = s2f_val_evaluator.crop_vgg_box(img_in)
+                            perceptual_loss = perceptual_module(
+                                img_pred_in, img_in)
+                            perceptual_loss = perceptual_loss.mean()
+                            weight = options["optim"]["perceptual_loss_weight"]
+                            total_loss = add_loss(total_loss, perceptual_loss,
+                                                  losses, loss_name,
+                                                  weight)
+                    else:
+                        if s2f_val_evaluator.do_deprocess_and_preprocess:
+                            imgs_pred_in = \
+                                s2f_val_evaluator.deprocess_and_preprocess(
+                                    imgs_pred)
+                            imgs_in = \
+                                s2f_val_evaluator.deprocess_and_preprocess(imgs)
+                        if s2f_val_evaluator.crop_faces:
+                            imgs_pred_in = \
+                                s2f_val_evaluator.crop_vgg_box(imgs_pred_in)
+                            imgs_in = s2f_val_evaluator.crop_vgg_box(imgs_in)
+                        perceptual_loss = perceptual_module(
+                            imgs_pred_in, imgs_in)
+                        perceptual_loss = perceptual_loss.mean()
+                        weight = options["optim"]["perceptual_loss_weight"]
+                        total_loss = add_loss(total_loss, perceptual_loss,
+                                              losses, "img_perceptual_loss",
+                                              weight)
+
+            losses['total_loss'] = total_loss.item()
+            if not math.isfinite(losses['total_loss']):
+                log.warn('WARNING: Got loss = NaN, not backpropping')
+                continue
+
+            optimizer.zero_grad()
+            with timeit('backward', args.timing):
+                total_loss.backward()
+            optimizer.step()
+
+            total_loss_d = None
+            ac_loss_real = None
+            ac_loss_fake = None
+            d_losses = {}
+
+            with timeit('D_loss', args.timing):
+                if img_discriminator is not None:
+                    d_img_losses = LossManager()
+                    for i in range(len(img_discriminator)):
+                        if isinstance(imgs_pred, tuple):
+                            imgs_fake = imgs_pred[i].detach()
+                        else:
+                            imgs_fake = imgs_pred.detach()
+                        imgs_real = imgs.detach()
+                        while imgs_real.size()[2] != imgs_fake.size()[2]:
+                            imgs_real = F.interpolate(
+                                imgs_real, scale_factor=0.5, mode='nearest')
+
+                        scores_fake = img_discriminator[i](imgs_fake)
+                        scores_real = img_discriminator[i](imgs_real)
+
+                        d_img_gan_loss = gan_d_loss(scores_real, scores_fake)
+                        d_img_losses.add_loss(
+                            d_img_gan_loss, 'd_img_gan_loss_%d' % i)
+
+                    for i in range(len(img_discriminator)):
+                        optimizer_d_img[i].zero_grad()
+                    d_img_losses.total_loss.backward()
+                    for i in range(len(img_discriminator)):
+                        optimizer_d_img[i].step()
+
+                if ac_discriminator is not None:
+                    d_ac_losses = LossManager()
+                    for i in range(len(ac_discriminator)):
+                        if isinstance(imgs_pred, tuple):
+                            imgs_fake = imgs_pred[i].detach()
+                        else:
+                            imgs_fake = imgs_pred.detach()
+
+                        imgs_real = imgs.detach()
+                        while imgs_real.size()[2] != imgs_fake.size()[2]:
+                            imgs_real = F.interpolate(
+                                imgs_real, scale_factor=0.5, mode='nearest')
+
+                        scores_real, ac_loss_real= ac_discriminator[i](
+                            imgs_real, human_ids)
+                        scores_fake, ac_loss_fake = ac_discriminator[i](
+                            imgs_fake, human_ids)
+
+                        d_ac_gan_loss = gan_d_loss(scores_real, scores_fake)
+                        d_ac_losses.add_loss(
+                            d_ac_gan_loss, 'd_ac_gan_loss_%d' % i)
+                        d_ac_losses.add_loss(
+                            ac_loss_real.mean(), 'd_ac_loss_real_%d' % i)
+
+                    for i in range(len(ac_discriminator)):
+                        optimizer_d_ac[i].zero_grad()
+                    d_ac_losses.total_loss.backward()
+                    for i in range(len(ac_discriminator)):
+                        optimizer_d_ac[i].step()
+
+                if cond_discriminator is not None:
+                    cond_d_losses = LossManager()
+                    for i in range(len(cond_discriminator)):
+                        if isinstance(imgs_pred, tuple):
+                            imgs_fake = imgs_pred[i].detach()
+                        else:
+                            imgs_fake = imgs_pred.detach()
+                        imgs_real = imgs.detach()
+                        cond_vecs = others['cond'].detach()
+                        while imgs_real.size()[2] != imgs_fake.size()[2]:
+                            imgs_real = F.interpolate(
+                                imgs_real, scale_factor=0.5, mode='nearest')
+
+                        scores_fake = cond_discriminator[i](
+                            imgs_fake, cond_vecs)
+                        scores_real = cond_discriminator[i](
+                            imgs_real, cond_vecs)
+
+                        cond_d_gan_loss = gan_d_loss(scores_real, scores_fake)
+                        cond_d_losses.add_loss(
+                            cond_d_gan_loss, 'cond_d_gan_loss_%d' % i)
+
+                    for i in range(len(cond_discriminator)):
+                        optimizer_cond_d[i].zero_grad()
+                    cond_d_losses.total_loss.backward()
+                    for i in range(len(cond_discriminator)):
+                        optimizer_cond_d[i].step()
+
+            # Logging generative model losses
+            for name, val in losses.items():
+                logger.scalar_summary("loss/{}".format(name), val, t)
+            if img_discriminator is not None:
+                for name, val in d_img_losses.items():
+                    logger.scalar_summary("d_loss/{}".format(name), val, t)
+            if ac_discriminator is not None:
+                for name, val in d_ac_losses.items():
+                    logger.scalar_summary("d_loss/{}".format(name), val, t)
+            if cond_discriminator is not None:
+                for name, val in cond_d_losses.items():
+                    logger.scalar_summary("d_loss/{}".format(name), val, t)
+            start_time = time.time()
+
+        if epoch % args.eval_epochs == 0:
+            log.info('[Epoch {}/{}] checking on val'.format(
+                epoch, args.epochs)
+            )
+            val_results = check_model(
+                args, options, epoch, val_loader, model)
+            val_losses, val_samples, val_inception, val_vfs = val_results
+            # call evaluate_s2f_metrics() here
+            val_facenet_L2_dist, val_facenet_L1_dist, val_facenet_cos_sim, \
+                val_recall_tuple, val_ih_sim  = \
+                    s2f_val_evaluator.get_metrics(
+                        model, recall_method='cos_sim', get_ih_sim=True)
+            val_recall_at_1, val_recall_at_2, val_recall_at_5, \
+                val_recall_at_10, val_recall_at_20, \
+                    val_recall_at_50 = val_recall_tuple
+            if log_mels.dim() == 2:
+                log_mel_segs = window_segment(log_mels, window_length=62, stride_length=31)
+            elif log_mels.dim() == 3:
+                log_mel_segs = window_segment(log_mels[0], window_length=62, stride_length=31)
+            
+            signature = mlflow.models.signature.infer_signature(model_input=log_mel_segs.unsqueeze(0).cpu().numpy(), model_output=model_out[0].detach().cpu().numpy())
+            
+            log_dict = {"val_losses":val_losses['L1_pixel_loss'], "val_inception":val_inception[0], "val_vfs":val_vfs[0], "val_facenet_L2_dist":val_facenet_L2_dist, "val_facenet_L1_dist":val_facenet_L1_dist, "val_facenet_cos_sim":val_facenet_cos_sim, "val_ih_sim":val_ih_sim, "val_recall_at_1":val_recall_at_1, "val_recall_at_2":val_recall_at_2, "val_recall_at_5":val_recall_at_5, "val_recall_at_10":val_recall_at_10, "val_recall_at_20":val_recall_at_20, "val_recall_at_50":val_recall_at_50}
+            mlflow_wandb_logging(log_dict,model,signature,log_mel_segs.unsqueeze(0).cpu().numpy(),val_samples['samples'])
+            # Update the best of metrics
+            if val_inception[0] > best_inception[0]:
+                got_best_IS = True
+                best_inception = val_inception
+            if val_vfs[0] > best_vfs[0]:
+                got_best_VFS = True
+                best_vfs = val_vfs
+            if val_recall_at_1 > best_recall_1:
+                got_best_R1 = True
+                best_recall_1 = val_recall_at_1
+            if val_recall_at_5 > best_recall_5:
+                got_best_R5 = True
+                best_recall_5 = val_recall_at_5
+            if val_recall_at_10 > best_recall_10:
+                got_best_R10 = True
+                best_recall_10 = val_recall_at_10
+            if val_facenet_cos_sim > best_cos:
+                got_best_cos = True
+                best_cos = val_facenet_cos_sim
+            if val_facenet_L1_dist < best_L1:
+                got_best_L1 = True
+                best_L1 = val_facenet_L1_dist
+            
+            checkpoint['counters']['best_inception'] = best_inception
+            checkpoint['counters']['best_vfs'] = best_vfs
+            checkpoint['val_samples'].append(val_samples)
+            # checkpoint['val_batch_data'].append(val_batch_data)
+            for k, v in val_losses.items():
+                checkpoint['val_losses'][k].append(v)
+                logger.scalar_summary("ckpt/val_{}".format(k), v, epoch)
+            logger.scalar_summary("ckpt/val_inception", val_inception[0], epoch)
+            logger.scalar_summary("ckpt/val_facenet_L2_dist",
+                val_facenet_L2_dist, epoch)
+            logger.scalar_summary("ckpt/val_facenet_L1_dist",
+                val_facenet_L1_dist, epoch)
+            logger.scalar_summary("ckpt/val_facenet_cos_sim",
+                val_facenet_cos_sim, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_1",
+                val_recall_at_1, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_2",
+                val_recall_at_2, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_5",
+                val_recall_at_5, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_10",
+                val_recall_at_10, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_20",
+                val_recall_at_20, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_50",
+                val_recall_at_50, epoch)
+            logger.scalar_summary("ckpt/val_ih_sim",
+                val_ih_sim, epoch)
+            logger.scalar_summary("ckpt/val_vfs",
+                val_vfs[0], epoch)
+            # Add speech2face metrics here..
+            #
+            logger.image_summary(val_samples, epoch, tag="ckpt_val")
+            # log.info('[Epoch {}/{}] val iou: {}'.format(
+            # epoch, args.epochs, val_avg_iou))
+            log.info('[Epoch {}/{}] val inception score: {} ({})'.format(
+                    epoch, args.epochs, val_inception[0], val_inception[1]))
+            log.info('[Epoch {}/{}] best inception scores: {} ({})'.format(
+                    epoch, args.epochs, best_inception[0], best_inception[1]))
+            log.info('[Epoch {}/{}] val vfs scores: {} ({})'.format(
+                    epoch, args.epochs, val_vfs[0], val_vfs[1]))
+            log.info('[Epoch {}/{}] best vfs scores: {} ({})'.format(
+                    epoch, args.epochs, best_vfs[0], best_vfs[1]))
+            log.info('[Epoch {}/{}] val recall at 5: {}, '.format(
+                     epoch, args.epochs, val_recall_at_5) + \
+                        'best recall at 5: {}'.format(best_recall_5))
+            log.info('[Epoch {}/{}] val recall at 10: {}, '.format(
+                     epoch, args.epochs, val_recall_at_10) + \
+                        'best recall at 10: {}'.format(best_recall_10))
+            log.info('[Epoch {}/{}] val cosine similarity: {}, '.format(
+                     epoch, args.epochs, val_facenet_cos_sim) + \
+                        'best cosine similarity: {}'.format(best_cos))
+            log.info('[Epoch {}/{}] val L1 distance: {}, '.format(
+                     epoch, args.epochs, val_facenet_L1_dist) + \
+                            'best L1 distance: {}'.format(best_L1))
+
+            checkpoint['model_state'] = model.module.state_dict()
+
+            if img_discriminator is not None:
+                for i in range(len(img_discriminator)):
+                    term_name = 'd_img_state_%d' % i
+                    checkpoint[term_name] = \
+                        img_discriminator[i].module.state_dict()
+                    term_name = 'd_img_optim_state_%d' % i
+                    checkpoint[term_name] = \
+                        optimizer_d_img[i].state_dict()
+
+            if ac_discriminator is not None:
+                for i in range(len(ac_discriminator)):
+                    term_name = 'd_ac_state_%d' % i
+                    checkpoint[term_name] = \
+                        ac_discriminator[i].module.state_dict()
+                    term_name = 'd_ac_optim_state_%d' % i
+                    checkpoint[term_name] = \
+                        optimizer_d_ac[i].state_dict()
+
+            if cond_discriminator is not None:
+                for i in range(len(cond_discriminator)):
+                    term_name = 'cond_d_state_%d' % i
+                    checkpoint[term_name] = \
+                        cond_discriminator[i].module.state_dict()
+                    term_name = 'cond_d_optim_state_%d' % i
+                    checkpoint[term_name] = \
+                        optimizer_cond_d[i].state_dict()
+
+            checkpoint['optim_state'] = optimizer.state_dict()
+            checkpoint['counters']['epoch'] = epoch
+            checkpoint['counters']['t'] = t
+            checkpoint['counters']['best_inception'] = best_inception
+            checkpoint['counters']['best_vfs'] = best_vfs
+            checkpoint['counters']['best_recall_1'] = best_recall_1
+            checkpoint['counters']['best_recall_5'] = best_recall_5
+            checkpoint['counters']['best_recall_10'] = best_recall_10
+            checkpoint['counters']['best_cos'] = best_cos
+            checkpoint['counters']['best_L1'] = best_L1
+            checkpoint['lr'] = lr
+            checkpoint_path = os.path.join(
+                log_path,
+                '%s_with_model.pt' % args.checkpoint_name)
+            log.info('[Epoch {}/{}] Saving checkpoint: {}'.format(
+                epoch, args.epochs, checkpoint_path))
+            torch.save(checkpoint, checkpoint_path)
+            if got_best_IS:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_IS_with_model.pt'))
+                got_best_IS = False
+            if got_best_VFS:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_VFS_with_model.pt'))
+                got_best_VFS = False
+            if got_best_R1:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_R1_with_model.pt'))
+                got_best_R1 = False
+            if got_best_R5:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_R5_with_model.pt'))
+                got_best_R5 = False
+            if got_best_R10:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_R10_with_model.pt'))
+                got_best_R10 = False
+            if got_best_L1:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_L1_with_model.pt'))
+                got_best_L1 = False
+            if got_best_cos:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_cos_with_model.pt'))
+                got_best_cos = False
+
+        if epoch > 0 and epoch % 1000 == 0:
+            print('Saving checkpoint for Epoch {}.'.format(epoch))
+            copyfile(
+                checkpoint_path,
+                os.path.join(log_path, 'epoch_{}_model.pt'.format(epoch)))
+        # Fuser Logic
+        elif args.train_fuser_only and epoch > 0 and epoch % 1 == 0:
+            print('Saving checkpoint for Epoch {}.'.format(epoch))
+            copyfile(
+                checkpoint_path,
+                os.path.join(log_path, 'epoch_{}_model.pt'.format(epoch)))
+        # End of fuser logic
+
+        if epoch >= args.decay_lr_epochs:
+            lr_end = args.learning_rate * 1e-3
+            decay_frac = (epoch - args.decay_lr_epochs + 1) / \
+                (args.epochs - args.decay_lr_epochs + 1e-5)
+            lr = args.learning_rate - decay_frac * (args.learning_rate - lr_end)
+            for param_group in optimizer.param_groups:
+                param_group["lr"] = lr
+            if img_discriminator is not None:
+                for i in range(len(optimizer_d_img)):
+                    for param_group in optimizer_d_img[i].param_groups:
+                        param_group["lr"] = lr
+                # for param_group in optimizer_d_img.param_groups:
+                #     param_group["lr"] = lr
+            log.info('[Epoch {}/{}] learning rate: {}'.format(
+                epoch+1, args.epochs, lr))
+
+        logger.scalar_summary("ckpt/learning_rate", lr, epoch)
+
+    # Evaluating after the whole training process.
+    log.info("Evaluting the validation set.")
+    is_mean, is_std, vfs_mean, vfs_std = evaluate(model, val_loader, options)
+    log.info("Inception score: {} ({})".format(is_mean, is_std))
+    log.info("VggFace score: {} ({})".format(vfs_mean, vfs_std))
+
+
+if __name__ == '__main__':
+    os.environ["MLFLOW_S3_ENDPOINT_URL"] = ""
+    os.environ["MLFLOW_TRACKING_URI"] = ""
+    os.environ["AWS_ACCESS_KEY_ID"] = ""
+    os.environ["AWS_SECRET_ACCESS_KEY"] = ""
+    mlflow.start_run()
+    mlflow.set_experiment("sf2f_voxceleb")
+    wandb.init(
+            project="Voice2Face",
+            config={
+                "architecture": "sf2f",
+                "dataset": "Voxceleb",
+                "notes": "test",
+            },
+            name="sf2f_voxceleb_1",
+    )
+    main()
diff --git a/mlops/mlflow/train/sf2f/utils/__init__.py b/mlops/mlflow/train/sf2f/utils/__init__.py
new file mode 100644
index 0000000..55e5f84
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/__init__.py
@@ -0,0 +1 @@
+from .common import *
diff --git a/mlops/mlflow/train/sf2f/utils/bilinear.py b/mlops/mlflow/train/sf2f/utils/bilinear.py
new file mode 100644
index 0000000..7a12100
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/bilinear.py
@@ -0,0 +1,296 @@
+import numpy as np
+import torch
+import torch.nn.functional as F
+from .common import timeit
+
+
+"""
+Functions for performing differentiable bilinear cropping of images, for use in
+the object discriminator
+"""
+
+def crop_bbox_batch(feats, bbox, bbox_to_feats, HH, WW=None, backend='cudnn'):
+    """
+    Inputs:
+    - feats: FloatTensor of shape (N, C, H, W)
+    - bbox: FloatTensor of shape (B, 4) giving bounding box coordinates
+    - bbox_to_feats: LongTensor of shape (B,) mapping boxes to feature maps;
+      each element is in the range [0, N) and bbox_to_feats[b] = i means that
+      bbox[b] will be cropped from feats[i].
+    - HH, WW: Size of the output crops
+
+    Returns:
+    - crops: FloatTensor of shape (B, C, HH, WW) where crops[i] uses bbox[i] to
+      crop from feats[bbox_to_feats[i]].
+    """
+    if backend == 'cudnn':
+        return crop_bbox_batch_cudnn(feats, bbox, bbox_to_feats, HH, WW)
+    N, C, H, W = feats.size()
+    B = bbox.size(0)
+    if WW is None:
+        WW = HH
+    dtype, device = feats.dtype, feats.device
+    crops = torch.zeros(B, C, HH, WW, dtype=dtype, device=device)
+    for i in range(N):
+        idx = (bbox_to_feats == i).nonzero()
+        if idx.dim() == 0:
+            continue
+        idx = idx.view(-1)
+        n = idx.size(0)
+        cur_feats = feats[i].view(1, C, H, W).expand(n, C, H, W).contiguous()
+        cur_bbox = bbox[idx]
+        cur_crops = crop_bbox(cur_feats, cur_bbox, HH, WW)
+        crops[idx] = cur_crops
+    return crops
+
+
+def _invperm(p):
+    N = p.size(0)
+    eye = torch.arange(0, N).type_as(p)
+    pp = (eye[:, None] == p).nonzero()[:, 1]
+    return pp
+
+
+def crop_bbox_batch_cudnn(feats, bbox, bbox_to_feats, HH, WW=None):
+    N, C, H, W = feats.size()
+    B = bbox.size(0)
+    if WW is None:
+        WW = HH
+    dtype = feats.type()
+
+    feats_flat, bbox_flat, all_idx = [], [], []
+    for i in range(N):
+        idx = (bbox_to_feats == i).nonzero()
+        if idx.dim() == 0:
+            continue
+        idx = idx.view(-1)
+        n = idx.size(0)
+        cur_feats = feats[i].view(1, C, H, W).expand(n, C, H, W).contiguous()
+        cur_bbox = bbox[idx]
+
+        feats_flat.append(cur_feats)
+        bbox_flat.append(cur_bbox)
+        all_idx.append(idx)
+
+    feats_flat = torch.cat(feats_flat, dim=0)
+    bbox_flat = torch.cat(bbox_flat, dim=0)
+    crops = crop_bbox(feats_flat, bbox_flat, HH, WW, backend='cudnn')
+
+    # If the crops were sequential (all_idx is identity permutation) then we can
+    # simply return them; otherwise we need to permute crops by the inverse
+    # permutation from all_idx.
+    all_idx = torch.cat(all_idx, dim=0)
+    eye = torch.arange(0, B).type_as(all_idx)
+    if (all_idx == eye).all():
+        return crops
+    return crops[_invperm(all_idx)]
+
+
+def crop_bbox(feats, bbox, HH, WW=None, backend='cudnn'):
+    """
+    Take differentiable crops of feats specified by bbox.
+
+    Inputs:
+    - feats: Tensor of shape (N, C, H, W)
+    - bbox: Bounding box coordinates of shape (N, 4) in the format
+      [x0, y0, x1, y1] in the [0, 1] coordinate space.
+    - HH, WW: Size of the output crops.
+
+    Returns:
+    - crops: Tensor of shape (N, C, HH, WW) where crops[i] is the portion of
+      feats[i] specified by bbox[i], reshaped to (HH, WW) using bilinear sampling.
+    """
+    N = feats.size(0)
+    assert bbox.size(0) == N
+    assert bbox.size(1) == 4
+    if WW is None:
+        WW = HH
+    if backend == 'cudnn':
+        # Change box from [0, 1] to [-1, 1] coordinate system
+        bbox = 2 * bbox - 1
+    x0, y0 = bbox[:, 0], bbox[:, 1]
+    x1, y1 = bbox[:, 2], bbox[:, 3]
+    X = tensor_linspace(x0, x1, steps=WW).view(N, 1, WW).expand(N, HH, WW)
+    Y = tensor_linspace(y0, y1, steps=HH).view(N, HH, 1).expand(N, HH, WW)
+    if backend == 'jj':
+        return bilinear_sample(feats, X, Y)
+    elif backend == 'cudnn':
+        grid = torch.stack([X, Y], dim=3)
+        return F.grid_sample(feats, grid)
+
+
+def uncrop_bbox(feats, bbox, H, W=None, fill_value=0):
+    """
+    Inverse operation to crop_bbox; construct output images where the feature maps
+    from feats have been reshaped and placed into the positions specified by bbox.
+
+    Inputs:
+    - feats: Tensor of shape (N, C, HH, WW)
+    - bbox: Bounding box coordinates of shape (N, 4) in the format
+      [x0, y0, x1, y1] in the [0, 1] coordinate space.
+    - H, W: Size of output.
+    - fill_value: Portions of the output image that are outside the bounding box
+      will be filled with this value.
+
+    Returns:
+    - out: Tensor of shape (N, C, H, W) where the portion of out[i] given by
+      bbox[i] contains feats[i], reshaped using bilinear sampling.
+    """
+    N, C = feats.size(0), feats.size(1)
+    assert bbox.size(0) == N
+    assert bbox.size(1) == 4
+    if W is None:
+        W = H
+
+    x0, y0 = bbox[:, 0], bbox[:, 1]
+    x1, y1 = bbox[:, 2], bbox[:, 3]
+    ww = x1 - x0
+    hh = y1 - y0
+
+    x0 = x0.contiguous().view(N, 1).expand(N, H)
+    x1 = x1.contiguous().view(N, 1).expand(N, H)
+    ww = ww.view(N, 1).expand(N, H)
+
+    y0 = y0.contiguous().view(N, 1).expand(N, W)
+    y1 = y1.contiguous().view(N, 1).expand(N, W)
+    hh = hh.view(N, 1).expand(N, W)
+
+    X = torch.linspace(0, 1, steps=W).view(1, W).expand(N, W).to(feats)
+    Y = torch.linspace(0, 1, steps=H).view(1, H).expand(N, H).to(feats)
+
+    X = (X - x0) / ww
+    Y = (Y - y0) / hh
+
+    # For ByteTensors, (x + y).clamp(max=1) gives logical_or
+    X_out_mask = ((X < 0) + (X > 1)).view(N, 1, W).expand(N, H, W)
+    Y_out_mask = ((Y < 0) + (Y > 1)).view(N, H, 1).expand(N, H, W)
+    out_mask = (X_out_mask + Y_out_mask).clamp(max=1)
+    out_mask = out_mask.view(N, 1, H, W).expand(N, C, H, W)
+
+    X = X.view(N, 1, W).expand(N, H, W)
+    Y = Y.view(N, H, 1).expand(N, H, W)
+
+    grid = torch.stack([X, Y], dim=3)
+    out = F.grid_sample(feats, grid)
+    out[out_mask] = fill_value
+    return out
+
+
+def bilinear_sample(feats, X, Y):
+    """
+    Perform bilinear sampling on the features in feats using the sampling grid
+    given by X and Y.
+
+    Inputs:
+    - feats: Tensor holding input feature map, of shape (N, C, H, W)
+    - X, Y: Tensors holding x and y coordinates of the sampling
+      grids; both have shape shape (N, HH, WW) and have elements in the range [0, 1].
+    Returns:
+    - out: Tensor of shape (B, C, HH, WW) where out[i] is computed
+      by sampling from feats[idx[i]] using the sampling grid (X[i], Y[i]).
+    """
+    N, C, H, W = feats.size()
+    assert X.size() == Y.size()
+    assert X.size(0) == N
+    _, HH, WW = X.size()
+
+    X = X.mul(W)
+    Y = Y.mul(H)
+
+    # Get the x and y coordinates for the four samples
+    x0 = X.floor().clamp(min=0, max=W-1)
+    x1 = (x0 + 1).clamp(min=0, max=W-1)
+    y0 = Y.floor().clamp(min=0, max=H-1)
+    y1 = (y0 + 1).clamp(min=0, max=H-1)
+
+    # In numpy we could do something like feats[i, :, y0, x0] to pull out
+    # the elements of feats at coordinates y0 and x0, but PyTorch doesn't
+    # yet support this style of indexing. Instead we have to use the gather
+    # method, which only allows us to index along one dimension at a time;
+    # therefore we will collapse the features (BB, C, H, W) into (BB, C, H * W)
+    # and index along the last dimension. Below we generate linear indices into
+    # the collapsed last dimension for each of the four combinations we need.
+    y0x0_idx = (W * y0 + x0).view(N, 1, HH * WW).expand(N, C, HH * WW)
+    y1x0_idx = (W * y1 + x0).view(N, 1, HH * WW).expand(N, C, HH * WW)
+    y0x1_idx = (W * y0 + x1).view(N, 1, HH * WW).expand(N, C, HH * WW)
+    y1x1_idx = (W * y1 + x1).view(N, 1, HH * WW).expand(N, C, HH * WW)
+
+    # Actually use gather to pull out the values from feats corresponding
+    # to our four samples, then reshape them to (BB, C, HH, WW)
+    feats_flat = feats.view(N, C, H * W)
+    v1 = feats_flat.gather(2, y0x0_idx.long()).view(N, C, HH, WW)
+    v2 = feats_flat.gather(2, y1x0_idx.long()).view(N, C, HH, WW)
+    v3 = feats_flat.gather(2, y0x1_idx.long()).view(N, C, HH, WW)
+    v4 = feats_flat.gather(2, y1x1_idx.long()).view(N, C, HH, WW)
+
+    # Compute the weights for the four samples
+    w1 = ((x1 - X) * (y1 - Y)).view(N, 1, HH, WW).expand(N, C, HH, WW)
+    w2 = ((x1 - X) * (Y - y0)).view(N, 1, HH, WW).expand(N, C, HH, WW)
+    w3 = ((X - x0) * (y1 - Y)).view(N, 1, HH, WW).expand(N, C, HH, WW)
+    w4 = ((X - x0) * (Y - y0)).view(N, 1, HH, WW).expand(N, C, HH, WW)
+
+    # Multiply the samples by the weights to give our interpolated results.
+    out = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4
+    return out
+
+
+def tensor_linspace(start, end, steps=10):
+    """
+    Vectorized version of torch.linspace.
+
+    Inputs:
+    - start: Tensor of any shape
+    - end: Tensor of the same shape as start
+    - steps: Integer
+
+    Returns:
+    - out: Tensor of shape start.size() + (steps,), such that
+      out.select(-1, 0) == start, out.select(-1, -1) == end,
+      and the other elements of out linearly interpolate between
+      start and end.
+    """
+    assert start.size() == end.size()
+    view_size = start.size() + (1,)
+    w_size = (1,) * start.dim() + (steps,)
+    out_size = start.size() + (steps,)
+
+    start_w = torch.linspace(1, 0, steps=steps).to(start)
+    start_w = start_w.view(w_size).expand(out_size)
+    end_w = torch.linspace(0, 1, steps=steps).to(start)
+    end_w = end_w.view(w_size).expand(out_size)
+
+    start = start.contiguous().view(view_size).expand(out_size)
+    end = end.contiguous().view(view_size).expand(out_size)
+
+    out = start_w * start + end_w * end
+    return out
+
+
+if __name__ == '__main__':
+    import numpy as np
+    # from scipy.misc import imread, imsave, imresize
+    from cv2 import imresize
+    from imageio import imread
+    from imageio import imwrite as imsave
+
+    cat = imresize(imread('cat.jpg'), (256, 256))
+    dog = imresize(imread('dog.jpg'), (256, 256))
+    feats = torch.stack([
+        torch.from_numpy(cat.transpose(2, 0, 1).astype(np.float32)),
+        torch.from_numpy(dog.transpose(2, 0, 1).astype(np.float32))],
+        dim=0)
+
+    boxes = torch.FloatTensor([
+        [0, 0, 1, 1],
+        [0.25, 0.25, 0.75, 0.75],
+        [0, 0, 0.5, 0.5],
+    ])
+
+    box_to_feats = torch.LongTensor([1, 0, 1]).cuda()
+
+    feats, boxes = feats.cuda(), boxes.cuda()
+    crops = crop_bbox_batch_cudnn(feats, boxes, box_to_feats, 128)
+    for i in range(crops.size(0)):
+        crop_np = crops.data[i].cpu().numpy().transpose(
+            1, 2, 0).astype(np.uint8)
+        imsave('out%d.png' % i, crop_np)
diff --git a/mlops/mlflow/train/sf2f/utils/box_utils.py b/mlops/mlflow/train/sf2f/utils/box_utils.py
new file mode 100644
index 0000000..898e543
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/box_utils.py
@@ -0,0 +1,133 @@
+import torch
+import numpy as np
+"""
+Utilities for dealing with bounding boxes
+"""
+
+
+def box_union(box1, box2):
+    assert isinstance(box1, torch.Tensor)
+    return torch.cat(
+            [torch.min(
+                torch.stack([box1[:, :2], box2[:, :2],], dim=0),
+                dim=0,
+                keepdim=False,
+            )[0],
+            torch.max(
+                torch.stack([box1[:, 2:], box2[:, 2:],], dim=0),
+                dim=0,
+                keepdim=False,
+            )[0],]
+        , dim=1)
+
+def box_in_region(boxes, regions):
+    region_w = regions[:, 2] - regions[:, 0]
+    region_h = regions[:, 3] - regions[:, 1]
+    assert (region_w > 0).all() and (region_h > 0).all()
+    result = torch.zeros_like(regions)
+    result[:, 0::2] = (boxes[:, 0::2] - regions[:, 0::2]) / region_w
+    result[:, 1::2] = (boxes[:, 1::2] - regions[:, 1::2]) / region_h
+    return result
+
+
+def apply_box_transform(anchors, transforms):
+    """
+    Apply box transforms to a set of anchor boxes.
+
+    Inputs:
+    - anchors: Anchor boxes of shape (N, 4), where each anchor is specified
+      in the form [xc, yc, w, h]
+    - transforms: Box transforms of shape (N, 4) where each transform is
+      specified as [tx, ty, tw, th]
+
+    Returns:
+    - boxes: Transformed boxes of shape (N, 4) where each box is in the
+      format [xc, yc, w, h]
+    """
+    # Unpack anchors
+    xa, ya = anchors[:, 0], anchors[:, 1]
+    wa, ha = anchors[:, 2], anchors[:, 3]
+
+    # Unpack transforms
+    tx, ty = transforms[:, 0], transforms[:, 1]
+    tw, th = transforms[:, 2], transforms[:, 3]
+
+    x = xa + tx * wa
+    y = ya + ty * ha
+    w = wa * tw.exp()
+    h = ha * th.exp()
+
+    boxes = torch.stack([x, y, w, h], dim=1)
+    return boxes
+
+
+def invert_box_transform(anchors, boxes):
+    """
+    Compute the box transform that, when applied to anchors, would give boxes.
+
+    Inputs:
+    - anchors: Box anchors of shape (N, 4) in the format [xc, yc, w, h]
+    - boxes: Target boxes of shape (N, 4) in the format [xc, yc, w, h]
+
+    Returns:
+    - transforms: Box transforms of shape (N, 4) in the format [tx, ty, tw, th]
+    """
+    # Unpack anchors
+    xa, ya = anchors[:, 0], anchors[:, 1]
+    wa, ha = anchors[:, 2], anchors[:, 3]
+
+    # Unpack boxes
+    x, y = boxes[:, 0], boxes[:, 1]
+    w, h = boxes[:, 2], boxes[:, 3]
+
+    tx = (x - xa) / wa
+    ty = (y - ya) / ha
+    tw = w.log() - wa.log()
+    th = h.log() - ha.log()
+
+    transforms = torch.stack([tx, ty, tw, th], dim=1)
+    return transforms
+
+
+def centers_to_extents(boxes):
+    """
+    Convert boxes from [xc, yc, w, h] format to [x0, y0, x1, y1] format
+
+    Input:
+    - boxes: Input boxes of shape (N, 4) in [xc, yc, w, h] format
+
+    Returns:
+    - boxes: Output boxes of shape (N, 4) in [x0, y0, x1, y1] format
+    """
+    xc, yc = boxes[:, 0], boxes[:, 1]
+    w, h = boxes[:, 2], boxes[:, 3]
+
+    x0 = xc - w / 2
+    x1 = x0 + w
+    y0 = yc - h / 2
+    y1 = y0 + h
+
+    boxes_out = torch.stack([x0, y0, x1, y1], dim=1)
+    return boxes_out
+
+
+def extents_to_centers(boxes):
+    """
+    Convert boxes from [x0, y0, x1, y1] format to [xc, yc, w, h] format
+
+    Input:
+    - boxes: Input boxes of shape (N, 4) in [x0, y0, x1, y1] format
+
+    Returns:
+    - boxes: Output boxes of shape (N, 4) in [xc, yc, w, h] format
+    """
+    x0, y0 = boxes[:, 0], boxes[:, 1]
+    x1, y1 = boxes[:, 2], boxes[:, 3]
+
+    xc = 0.5 * (x0 + x1)
+    yc = 0.5 * (y0 + y1)
+    w = x1 - x0
+    h = y1 - y0
+
+    boxes_out = torch.stack([xc, yc, w, h], dim=1)
+    return boxes_out
diff --git a/mlops/mlflow/train/sf2f/utils/common.py b/mlops/mlflow/train/sf2f/utils/common.py
new file mode 100644
index 0000000..2248793
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/common.py
@@ -0,0 +1,192 @@
+import os
+import time
+import inspect
+import subprocess
+from contextlib import contextmanager
+
+import torch
+from PIL import Image
+import numpy as np
+from collections import OrderedDict
+
+def int_tuple(s):
+    return tuple(int(i) for i in s.split(','))
+
+
+def float_tuple(s):
+    return tuple(float(i) for i in s.split(','))
+
+
+def str_tuple(s):
+    return tuple(s.split(','))
+
+
+def set_trainable(model, requires_grad):
+    set_trainable_param(model.parameters(), requires_grad)
+
+def set_trainable_param(parameters, requires_grad):
+    for param in parameters:
+        param.requires_grad = requires_grad
+
+
+def update_values(dict_from, dict_to):
+    for key, value in dict_from.items():
+        if isinstance(value, dict) and key in dict_to.keys():
+            update_values(dict_from[key], dict_to[key])
+        elif value is not None or key not in dict_to.keys():
+            dict_to[key] = dict_from[key]
+    return dict_to
+
+
+def params_count(model):
+    count = 0
+    for p in model.parameters():
+        c = 1
+        for i in range(p.dim()):
+            c *= p.size(i)
+        count += c
+    return count
+
+
+def bool_flag(s):
+    if s == '1':
+        return True
+    elif s == '0':
+        return False
+    msg = 'Invalid value "%s" for bool flag (should be 0 or 1)'
+    raise ValueError(msg % s)
+
+
+def lineno():
+    return inspect.currentframe().f_back.f_lineno
+
+
+def get_gpu_memory():
+    torch.cuda.synchronize()
+    opts = [
+        'nvidia-smi', '-q', '--gpu=' + str(0), '|', 'grep', '"Used GPU Memory"'
+    ]
+    cmd = str.join(' ', opts)
+    ps = subprocess.Popen(
+        cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+    output = ps.communicate()[0].decode('utf-8')
+    output = output.split("\n")[1].split(":")
+    consumed_mem = int(output[1].strip().split(" ")[0])
+    return consumed_mem
+
+# Converts a Tensor into an image array (numpy)
+# |imtype|: the desired type of the converted numpy array
+
+
+def tensor2im(input_image, imtype=np.uint8):
+    if not isinstance(input_image, torch.Tensor):
+        return input_image
+    image_numpy = input_image.numpy()
+    if image_numpy.ndim == 3:
+        if image_numpy.shape[0] == 1:
+            image_numpy = np.tile(image_numpy, (3, 1, 1))
+        elif image_numpy.shape[0] == 3:
+            image_numpy = np.transpose(image_numpy, (1, 2, 0))
+    elif image_numpy.ndim == 4:
+        if image_numpy.shape[1] == 1:
+            image_numpy = np.tile(image_numpy, (1, 3, 1, 1))
+        elif image_numpy.shape[1] == 3:
+            image_numpy = np.transpose(image_numpy, (0, 2, 3, 1))
+    else:
+        raise ValueError("Only support images or batches")
+    return image_numpy.astype(imtype)
+
+
+def save_image(image_numpy, image_path):
+    image_pil = Image.fromarray(image_numpy)
+    image_pil.save(image_path)
+
+
+def mkdirs(paths):
+    if isinstance(paths, list) and not isinstance(paths, str):
+        for path in paths:
+            mkdir(path)
+    else:
+        mkdir(paths)
+
+
+def mkdir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+
+@contextmanager
+def timeit(msg, should_time=True):
+    if should_time:
+        torch.cuda.synchronize()
+        t0 = time.time()
+    yield
+    if should_time:
+        torch.cuda.synchronize()
+        t1 = time.time()
+        duration = (t1 - t0) * 1000.0
+        print('%s: %.2f ms' % (msg, duration))
+
+
+class LossManager(object):
+    def __init__(self):
+        self.total_loss = None
+        self.all_losses = {}
+
+    def add_loss(self, loss, name, weight=1.0):
+        cur_loss = loss * weight
+        if self.total_loss is not None:
+            self.total_loss += cur_loss
+        else:
+            self.total_loss = cur_loss
+
+        self.all_losses[name] = cur_loss.data.cpu().item()
+
+    def items(self):
+        return self.all_losses.items()
+
+
+def load_model_state(model, state_dict, strict=True):
+    # sometimes if the state_dict is saved from DataParallel model, the keys
+    # has a prefix "module.", so we remove them before Loading
+    new_state_dict = OrderedDict()
+    if not isinstance(model, torch.nn.DataParallel):
+        for k, v in state_dict.items():
+            k = k[7:] if k.startswith("module") else k
+            new_state_dict[k] = v
+    else:
+        new_state_dict = state_dict
+    if strict:
+        # requires the model's state_dict to be exactly same as the new_state_dict
+        model.load_state_dict(new_state_dict)
+    else:
+        # only load parameters that are same size and type, and print warnings
+        # for parameters that don't match
+        own_state = model.state_dict()
+        for name, param in new_state_dict.items():
+            if name in own_state:
+                if isinstance(param, torch.nn.Parameter):
+                    # backwards compatibility for serialized parameters
+                    param = param.data
+                if own_state[name].size() == param.size():
+                    own_state[name].copy_(param)
+                else:
+                    print(
+                        "Warning: While copying the parameter named {}, "
+                        "whose dimensions in the model are {} and "
+                        "whose dimensions in the checkpoint are {}.".format(
+                            name, own_state[name].size(), param.size()
+                        )
+                    )
+            else:
+                print(
+                    "Warning: Parameter named {} is not used "
+                    "by this model.".format(name)
+                )
+        for name, _ in own_state.items():
+            if name not in new_state_dict:
+                print(
+                    "Warning: Parameter named {} in the model "
+                    "is not initialized.".format(name)
+                )
+    return model
diff --git a/mlops/mlflow/train/sf2f/utils/evaluate.py b/mlops/mlflow/train/sf2f/utils/evaluate.py
new file mode 100644
index 0000000..81f27cc
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/evaluate.py
@@ -0,0 +1,67 @@
+import pyprind
+import numpy as np
+import torch
+import torch.nn.functional as F
+import glog as log
+
+from datasets import imagenet_deprocess_batch
+from scripts.compute_inception_score import get_inception_score
+from scripts.compute_vggface_score import get_vggface_score
+
+
+VGG_BOX = [0.235, 0.195, 0.765, 0.915]
+def crop_vgg_box(imgs):
+    # with correct cropping & correct processing
+    left, top, right, bottom = VGG_BOX
+    # = [0.235015, 0.19505739, 0.76817876, 0.9154963]
+    N, C, H, W = imgs.shape
+    left = int(left * W)
+    right = int(right * W)
+    top = int(top * H)
+    bottom = int(bottom * H)
+    imgs = imgs[:, :, top:bottom+1, left:right+1]
+    return imgs
+
+def evaluate(model, data_loader, options):
+    '''
+    Evaluate the current model...
+    '''
+    normalize_method = options["data"]["data_opts"].get(
+        'normalize_method', 'imagenet')
+    model.eval()
+    log.info("Evaluating with Inception Scores.")
+    images = []
+    imgs_gt = []
+    for iter, batch in enumerate(pyprind.prog_bar(data_loader,
+                                  title="[Generating Images]",
+                                  width=50)):
+        ######### unpack the data #########
+        imgs, log_mels, human_ids = batch
+        imgs = imgs.cuda()
+        imgs_gt.append(imgs[0])
+        ###################################
+        with torch.no_grad():
+            float_dtype = torch.cuda.FloatTensor
+            log_mels = log_mels.type(float_dtype)
+            model_out = model(log_mels)
+            """
+            imgs_pred: generated images
+            others: placeholder for other output
+            """
+        imgs_pred, others = model_out
+        img = imagenet_deprocess_batch(
+            imgs_pred, normalize_method=normalize_method)
+        img = crop_vgg_box(img)
+        for i in range(img.shape[0]):
+            img_np = img[i].numpy().transpose(1, 2, 0)
+            images.append(img_np)
+    log.info("Computing inception scores...")
+    is_mean, is_std = get_inception_score(images)
+    log.info("Computing VF scores...")
+    vfs_mean, vfs_std = get_vggface_score(images)
+
+    return is_mean, is_std, vfs_mean, vfs_std
+
+
+if __name__ == '__main__':
+    evaluate()
diff --git a/mlops/mlflow/train/sf2f/utils/evaluate_fid.py b/mlops/mlflow/train/sf2f/utils/evaluate_fid.py
new file mode 100644
index 0000000..90da53d
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/evaluate_fid.py
@@ -0,0 +1,82 @@
+import pyprind
+import numpy as np
+import torch
+import torch.nn.functional as F
+import glog as log
+
+from datasets import imagenet_deprocess_batch
+from scripts.compute_fid_score import calculate_activation_statistics, calculate_frechet_distance
+import scripts.compute_fid_score as inception_score
+from scripts.compute_vggface_score import get_vggface_act
+
+VGG_BOX = [0.235, 0.195, 0.765, 0.915]
+def crop_vgg_box(imgs):
+    # with correct cropping & correct processing
+    left, top, right, bottom = VGG_BOX
+    # = [0.235015, 0.19505739, 0.76817876, 0.9154963]
+    N, C, H, W = imgs.shape
+    left = int(left * W)
+    right = int(right * W)
+    top = int(top * H)
+    bottom = int(bottom * H)
+    imgs = imgs[:, :, top:bottom+1, left:right+1]
+    return imgs
+
+def evaluate_fid(model, data_loader, options):
+    '''
+    Evaluate the current model...
+    '''
+    normalize_method = options["data"]["data_opts"].get(
+        'normalize_method', 'imagenet')
+    model.eval()
+    log.info("Evaluating with Inception Scores.")
+    images = []
+    imgs_gt = []
+    for iter, batch in enumerate(pyprind.prog_bar(data_loader,
+                                  title="[Generating Images]",
+                                  width=50)):
+        ######### unpack the data #########
+        imgs, log_mels, human_ids = batch
+        imgs = imgs.cuda()
+        ###################################
+        with torch.no_grad():
+            float_dtype = torch.cuda.FloatTensor
+            log_mels = log_mels.type(float_dtype)
+            model_out = model(log_mels)
+            """
+            imgs_pred: generated images
+            others: placeholder for other output
+            """
+        imgs_pred, others = model_out
+        img = imagenet_deprocess_batch(
+            imgs_pred, normalize_method=normalize_method)
+        img = crop_vgg_box(img)
+        for i in range(img.shape[0]):
+            img_np = img[i].numpy().transpose(1, 2, 0)
+            images.append(img_np)
+
+        img = imagenet_deprocess_batch(
+            imgs, normalize_method=normalize_method)
+        img = crop_vgg_box(img)
+        for i in range(img.shape[0]):
+            img_np = img[i].numpy().transpose(1, 2, 0)
+            imgs_gt.append(img_np)
+
+    log.info("Computing FID scores...")
+    acts_set = inception_score.get_fid_pred(imgs_gt)
+    fake_acts_set = inception_score.get_fid_pred(images)
+    real_mu, real_sigma = calculate_activation_statistics(acts_set)
+    fake_mu, fake_sigma = calculate_activation_statistics(fake_acts_set)
+    fid_score = calculate_frechet_distance(real_mu, real_sigma, fake_mu, fake_sigma)
+
+    acts_set = get_vggface_act(imgs_gt)
+    fake_acts_set = get_vggface_act(images)
+    real_mu, real_sigma = calculate_activation_statistics(acts_set)
+    fake_mu, fake_sigma = calculate_activation_statistics(fake_acts_set)
+    fvfd_score = calculate_frechet_distance(real_mu, real_sigma, fake_mu, fake_sigma)
+
+    return fid_score
+
+
+if __name__ == '__main__':
+    evaluate()
diff --git a/mlops/mlflow/train/sf2f/utils/filter_pickle.py b/mlops/mlflow/train/sf2f/utils/filter_pickle.py
new file mode 100644
index 0000000..ccf5646
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/filter_pickle.py
@@ -0,0 +1,35 @@
+import pickle
+import os
+
+
+def check_pickle(mel_pickle):
+    """
+    Load a mel pickle file and remove the file if it is empty
+
+    Args:
+        mel_pickle: str, path to the mel pickle file
+    Returns:
+        None
+    """
+    file = open(mel_pickle, 'rb')
+    try:
+        data = pickle.load(file)
+    except:
+        print('Error loading:', mel_pickle)
+        os.remove(mel_pickle)
+        
+    file.close()
+
+def filtering_pickle(dir_path):
+    for dir in os.listdir(dir_path):
+        data_path = os.path.join(dir_path, dir)
+        for id in os.listdir(data_path):
+            data = os.path.join(data_path, id)
+            check_pickle(data)
+    print("Filtering Done.")
+
+
+# if __name__=="__main__":
+#     dir_path = './data/VoxCeleb/vox1/mel_spectrograms' # wav_convertor.vox1_mel
+#     filtering_pickle(dir_path)
+
diff --git a/mlops/mlflow/train/sf2f/utils/logger.py b/mlops/mlflow/train/sf2f/utils/logger.py
new file mode 100644
index 0000000..c21f383
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/logger.py
@@ -0,0 +1,93 @@
+# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
+import tensorflow as tf
+import numpy as np
+# import scipy.misc
+from PIL.Image import fromarray
+
+try:
+    from StringIO import StringIO  # Python 2.7
+except ImportError:
+    from io import BytesIO         # Python 3.x
+    
+tf.compat.v1.disable_v2_behavior()
+
+
+class Logger(object):
+
+    def __init__(self, log_dir):
+        """Create a summary writer logging to log_dir."""
+        self.writer = tf.compat.v1.summary.FileWriter(log_dir)
+        # self.writer = tf.compat.v1.summary.create_file_writer(log_dir)
+
+    def scalar_summary(self, tag, value, step):
+        """Log a scalar variable."""
+        summary = tf.compat.v1.Summary(
+            value=[tf.compat.v1.Summary.Value(tag=tag, simple_value=value)])
+        self.writer.add_summary(summary, step)
+
+    def image_summary(self, images, step, tag="vis"):
+        """Log a list of images."""
+
+        img_summaries = []
+        if isinstance(images, list):
+            for i, img in enumerate(images):
+                _tag = '%s/%d' % (tag, i)
+                # Create a Summary value
+                img_summaries.append(self.add_image(img, _tag))
+        elif isinstance(images, dict):
+            for key, img in images.items():
+                _tag = '%s/%s' % (tag, key)
+                # Create a Summary value
+                img_summaries.append(self.add_image(img, _tag))
+        else:
+            raise ValueError("Either a list or a dict is supported.")
+
+        # Create and write Summary
+        summary = tf.compat.v1.Summary(value=img_summaries)
+        self.writer.add_summary(summary, step)
+
+    def histo_summary(self, tag, values, step, bins=1000):
+        """Log a histogram of the tensor of values."""
+
+        # Create a histogram using numpy
+        counts, bin_edges = np.histogram(values, bins=bins)
+
+        # Fill the fields of the histogram proto
+        hist = tf.HistogramProto()
+        hist.min = float(np.min(values))
+        hist.max = float(np.max(values))
+        hist.num = int(np.prod(values.shape))
+        hist.sum = float(np.sum(values))
+        hist.sum_squares = float(np.sum(values**2))
+
+        # Drop the start of the first bin
+        bin_edges = bin_edges[1:]
+
+        # Add bin edges and counts
+        for edge in bin_edges:
+            hist.bucket_limit.append(edge)
+        for c in counts:
+            hist.bucket.append(c)
+
+        # Create and write Summary
+        summary = tf.compat.v1.Summary(
+            value=[tf.compat.v1.Summary.Value(tag=tag, histo=hist)])
+        self.writer.add_summary(summary, step)
+        self.writer.flush()
+
+    def add_image(self, img, tag):
+        # Write the image to a string
+        try:
+            s = StringIO()
+        except:
+            s = BytesIO()
+        # scipy.misc.toimage(img).save(s, format="png")
+        fromarray(img).save(s, format="png")
+
+
+        # Create an Image object
+        img_sum = tf.compat.v1.Summary.Image(encoded_image_string=s.getvalue(),
+                                   height=img.shape[0],
+                                   width=img.shape[1])
+
+        return tf.compat.v1.Summary.Value(tag=tag, image=img_sum)
diff --git a/mlops/mlflow/train/sf2f/utils/losses.py b/mlops/mlflow/train/sf2f/utils/losses.py
new file mode 100644
index 0000000..55fcb70
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/losses.py
@@ -0,0 +1,144 @@
+import torch
+import torch.nn.functional as F
+
+
+def get_gan_losses(gan_type):
+    """
+    Returns the generator and discriminator loss for a particular GAN type.
+
+    The returned functions have the following API:
+    loss_g = g_loss(scores_fake)
+    loss_d = d_loss(scores_real, scores_fake)
+    """
+    if gan_type == 'gan':
+        return gan_g_loss, gan_d_loss
+    elif gan_type == 'wgan':
+        return wgan_g_loss, wgan_d_loss
+    elif gan_type == 'lsgan':
+        return lsgan_g_loss, lsgan_d_loss
+    else:
+        raise ValueError('Unrecognized GAN type "%s"' % gan_type)
+
+
+def bce_loss(input, target):
+    """
+    Numerically stable version of the binary cross-entropy loss function.
+
+    As per https://github.com/pytorch/pytorch/issues/751
+    See the TensorFlow docs for a derivation of this formula:
+    https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits
+
+    Inputs:
+    - input: PyTorch Tensor of shape (N, ) giving scores.
+    - target: PyTorch Tensor of shape (N,) containing 0 and 1 giving targets.
+
+    Returns:
+    - A PyTorch Tensor containing the mean BCE loss over the minibatch of
+      input data.
+    """
+    neg_abs = -input.abs()
+    loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
+    return loss.mean()
+
+
+def _make_targets(x, y):
+    """
+    Inputs:
+    - x: PyTorch Tensor
+    - y: Python scalar
+
+    Outputs:
+    - out: PyTorch Variable with same shape and dtype as x, but filled with y
+    """
+    return torch.full_like(x, y)
+
+
+def gan_g_loss(scores_fake):
+    """
+    Input:
+    - scores_fake: Tensor of shape (N,) containing scores for fake samples
+
+    Output:
+    - loss: Variable of shape (,) giving GAN generator loss
+    """
+    if scores_fake.dim() > 1:
+        scores_fake = scores_fake.view(-1)
+    y_fake = _make_targets(scores_fake, 1)
+    return bce_loss(scores_fake, y_fake)
+
+
+def gan_d_loss(scores_real, scores_fake):
+    """
+    Input:
+    - scores_real: Tensor of shape (N,) giving scores for real samples
+    - scores_fake: Tensor of shape (N,) giving scores for fake samples
+
+    Output:
+    - loss: Tensor of shape (,) giving GAN discriminator loss
+    """
+    assert scores_real.size() == scores_fake.size()
+    if scores_real.dim() > 1:
+        scores_real = scores_real.view(-1)
+        scores_fake = scores_fake.view(-1)
+    y_real = _make_targets(scores_real, 1)
+    y_fake = _make_targets(scores_fake, 0)
+    loss_real = bce_loss(scores_real, y_real)
+    loss_fake = bce_loss(scores_fake, y_fake)
+    return loss_real + loss_fake
+
+
+def wgan_g_loss(scores_fake):
+    """
+    Input:
+    - scores_fake: Tensor of shape (N,) containing scores for fake samples
+
+    Output:
+    - loss: Tensor of shape (,) giving WGAN generator loss
+    """
+    return -scores_fake.mean()
+
+
+def wgan_d_loss(scores_real, scores_fake):
+    """
+    Input:
+    - scores_real: Tensor of shape (N,) giving scores for real samples
+    - scores_fake: Tensor of shape (N,) giving scores for fake samples
+
+    Output:
+    - loss: Tensor of shape (,) giving WGAN discriminator loss
+    """
+    return scores_fake.mean() - scores_real.mean()
+
+
+def lsgan_g_loss(scores_fake):
+    if scores_fake.dim() > 1:
+        scores_fake = scores_fake.view(-1)
+    y_fake = _make_targets(scores_fake, 1)
+    return F.mse_loss(scores_fake.sigmoid(), y_fake)
+
+
+def lsgan_d_loss(scores_real, scores_fake):
+    assert scores_real.size() == scores_fake.size()
+    if scores_real.dim() > 1:
+        scores_real = scores_real.view(-1)
+        scores_fake = scores_fake.view(-1)
+    y_real = _make_targets(scores_real, 1)
+    y_fake = _make_targets(scores_fake, 0)
+    loss_real = F.mse_loss(scores_real.sigmoid(), y_real)
+    loss_fake = F.mse_loss(scores_fake.sigmoid(), y_fake)
+    return loss_real + loss_fake
+
+
+def gradient_penalty(x_real, x_fake, f, gamma=1.0):
+    N = x_real.size(0)
+    device, dtype = x_real.device, x_real.dtype
+    eps = torch.randn(N, 1, 1, 1, device=device, dtype=dtype)
+    x_hat = eps * x_real + (1 - eps) * x_fake
+    x_hat_score = f(x_hat)
+    if x_hat_score.dim() > 1:
+        x_hat_score = x_hat_score.view(x_hat_score.size(0), -1).mean(dim=1)
+    x_hat_score = x_hat_score.sum()
+    grad_x_hat, = torch.autograd.grad(x_hat_score, x_hat, create_graph=True)
+    grad_x_hat_norm = grad_x_hat.contiguous().view(N, -1).norm(p=2, dim=1)
+    gp_loss = (grad_x_hat_norm - gamma).pow(2).div(gamma * gamma).mean()
+    return gp_loss
diff --git a/mlops/mlflow/train/sf2f/utils/metrics.py b/mlops/mlflow/train/sf2f/utils/metrics.py
new file mode 100644
index 0000000..f337aaf
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/metrics.py
@@ -0,0 +1,50 @@
+import torch
+
+def accuracy(output, target, topk=(1,)):
+    """Computes the accuracy over the k top predictions for the specified values of k"""
+    with torch.no_grad():
+        maxk = max(topk)
+        batch_size = target.size(0)
+
+        _, pred = output.topk(maxk, 1, True, True)
+        pred = pred.t()
+        correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+        res = []
+        for k in topk:
+            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+            res.append(correct_k.mul_(100.0 / batch_size).item())
+        return res
+
+
+def intersection(bbox_pred, bbox_gt):
+    max_xy = torch.min(bbox_pred[:, 2:], bbox_gt[:, 2:])
+    min_xy = torch.max(bbox_pred[:, :2], bbox_gt[:, :2])
+    inter = torch.clamp((max_xy - min_xy), min=0)
+    return inter[:, 0] * inter[:, 1]
+
+
+def jaccard(bbox_pred, bbox_gt):
+    inter = intersection(bbox_pred, bbox_gt)
+    area_pred = (bbox_pred[:, 2] - bbox_pred[:, 0]) * (bbox_pred[:, 3] -
+                                                       bbox_pred[:, 1])
+    area_gt = (bbox_gt[:, 2] - bbox_gt[:, 0]) * (bbox_gt[:, 3] -
+                                                 bbox_gt[:, 1])
+    union = area_pred + area_gt - inter
+    iou = torch.div(inter, union)
+    return torch.sum(iou)
+
+
+def get_total_norm(parameters, norm_type=2):
+    if norm_type == float('inf'):
+        total_norm = max(p.grad.data.abs().max() for p in parameters)
+    else:
+        total_norm = 0
+        for p in parameters:
+            try:
+                param_norm = p.grad.data.norm(norm_type)
+                total_norm += param_norm ** norm_type
+                total_norm = total_norm ** (1. / norm_type)
+            except:
+                continue
+    return total_norm
diff --git a/mlops/mlflow/train/sf2f/utils/mlflow_wandb.py b/mlops/mlflow/train/sf2f/utils/mlflow_wandb.py
new file mode 100644
index 0000000..5ce4f04
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/mlflow_wandb.py
@@ -0,0 +1,15 @@
+import mlflow
+import wandb
+
+def mlflow_wandb_logging(log_dict,model,signature,input_sample,sample_image):
+    #mlflow Log metric & model
+    mlflow.log_metrics(log_dict)
+    mlflow.pytorch.log_model(
+        pytorch_model=model,
+        artifact_path = "sf2f_pytorch",
+        signature= signature,
+        input_example = input_sample,
+        # pip_requirements = "rec.txt"
+    )
+    log_dict['sample'] = [wandb.Image(sample_image, caption=f"Sample")]
+    wandb.log(log_dict)
diff --git a/mlops/mlflow/train/sf2f/utils/pgan_utils.py b/mlops/mlflow/train/sf2f/utils/pgan_utils.py
new file mode 100644
index 0000000..efb4e52
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/pgan_utils.py
@@ -0,0 +1,45 @@
+import os
+import time
+import json
+import math
+import torch
+
+
+def num_flat_features(x):
+    size = x.size()[1:]  # all dimensions except the batch dimension
+    num_features = 1
+    for s in size:
+        num_features *= s
+    return num_features
+
+
+def miniBatchStdDev(x, subGroupSize=4):
+    r"""
+    Add a minibatch standard deviation channel to the current layer.
+    In other words:
+        1) Compute the standard deviation of the feature map over the minibatch
+        2) Get the mean, over all pixels and all channels of thsi ValueError
+        3) expand the layer and cocatenate it with the input
+    Args:
+        - x (tensor): previous layer
+        - subGroupSize (int): size of the mini-batches on which the standard deviation
+        should be computed
+    """
+    size = x.size()
+    subGroupSize = min(size[0], subGroupSize)
+    if size[0] % subGroupSize != 0:
+        subGroupSize = size[0]
+    G = int(size[0] / subGroupSize)
+    if subGroupSize > 1:
+        y = x.view(-1, subGroupSize, size[1], size[2], size[3])
+        y = torch.var(y, 1)
+        y = torch.sqrt(y + 1e-8)
+        y = y.view(G, -1)
+        y = torch.mean(y, 1).view(G, 1)
+        y = y.expand(G, size[2]*size[3]).view((G, 1, 1, size[2], size[3]))
+        y = y.expand(G, subGroupSize, -1, -1, -1)
+        y = y.contiguous().view((-1, 1, size[2], size[3]))
+    else:
+        y = torch.zeros(x.size(0), 1, x.size(2), x.size(3), device=x.device)
+
+    return torch.cat([x, y], dim=1)
diff --git a/mlops/mlflow/train/sf2f/utils/s2f_evaluator.py b/mlops/mlflow/train/sf2f/utils/s2f_evaluator.py
new file mode 100644
index 0000000..9341cc0
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/s2f_evaluator.py
@@ -0,0 +1,449 @@
+'''
+This file contains the utils for evaluation, and is used to cooperate with \
+    training_utils.py and test.py
+'''
+
+
+import os
+import json
+import math
+from collections import defaultdict
+import time
+from copy import deepcopy
+
+import numpy as np
+import torch
+import torch.optim as optim
+import torch.nn as nn
+import torch.nn.functional as F
+from scipy.stats import entropy
+from torchvision.models import inception_v3
+from pyprind import prog_bar
+from tensorflow.io import gfile
+from imageio import imwrite
+
+from datasets import fast_imagenet_deprocess_batch, imagenet_deprocess_batch
+import datasets
+import models
+from models import InceptionResnetV1, fixed_image_standardization
+
+
+# left, top, right, bottom
+VGG_BOX = [0.235, 0.195, 0.765, 0.915]
+
+
+class S2fEvaluator:
+    def __init__(self,
+                 loader,
+                 options,
+                 nframe_range=None,
+                 extraction_size=100,
+                 hq_emb_dict=True,
+                 face_gen_mode='naive',
+                 facenet_return_pooling=False):
+        '''
+        Inputs:
+            loader
+            options
+        '''
+        self.loader = deepcopy(loader)
+        # This makes sure same group of meg_spectrograms is used for fuser
+        self.loader.dataset.shuffle_mel_segments = False
+        if nframe_range is not None:
+            self.loader.dataset.nframe_range = nframe_range
+        self.facenet = InceptionResnetV1(
+            pretrained='vggface2',
+            auto_input_resize=True,
+            return_pooling=facenet_return_pooling).cuda().eval()
+        self.float_dtype = torch.cuda.FloatTensor
+        self.long_dtype = torch.cuda.LongTensor
+        self.options = options
+        self.image_normalize_method= \
+                self.options["data"]["data_opts"]["image_normalize_method"]
+        self.do_deprocess_and_preprocess = \
+            self.options["eval"]["facenet"]["deprocess_and_preprocess"]
+        # crop faces according to VGG average bounding box
+        self.crop_faces = \
+            self.options["eval"]["facenet"]["crop_faces"]
+        self.extraction_size = extraction_size
+        self.hq_emb_dict = hq_emb_dict
+        self.face_gen_mode = face_gen_mode
+
+        self.get_dataset_embeddings()
+
+        # Implement Evaluation Metrics
+        self.L2_dist = \
+            nn.PairwiseDistance(p=2.0, eps=1e-06, keepdim=False)
+        self.L1_dist = \
+            nn.PairwiseDistance(p=1.0, eps=1e-06, keepdim=False)
+        self.cos_sim = nn.CosineSimilarity(dim=-1, eps=1e-08)
+
+    def deprocess_and_preprocess(self, imgs):
+        '''
+        For a batch of real / generated image, perform 'imagenet' or 'standard'
+        deprocess, and FaceNet Preprocess
+        '''
+        #print('Begin:', imgs[0])
+        imgs = fast_imagenet_deprocess_batch(
+            imgs,
+            normalize_method=self.image_normalize_method)
+        #print('Our distribution:', imgs[0])
+        imgs = fixed_image_standardization(imgs)
+        #print('fixed_image_standardization:', imgs[0])
+        return imgs
+
+    def crop_vgg_box(self, imgs):
+        # with correct cropping & correct processing
+        left, top, right, bottom = VGG_BOX
+        # = [0.235015, 0.19505739, 0.76817876, 0.9154963]
+        N, C, H, W = imgs.shape
+        left = int(left * W)
+        right = int(right * W)
+        top = int(top * H)
+        bottom = int(bottom * H)
+        imgs = imgs[:, :, top:bottom+1, left:right+1]
+        return imgs
+
+    def get_dataset_embeddings(self):
+        with torch.no_grad():
+            # To avoid influence to the running_mean/var of BatchNorm Layers
+            embedding_batches = []
+            if self.hq_emb_dict:
+                for i in prog_bar(
+                    range(len(self.loader.dataset)),
+                    title="[S2fEvaluator: " + \
+                        "Preparing FaceNet Embedding Dictionary]",
+                    width=50):
+                    # Loop logic
+                    ######### unpack the data #########
+                    imgs = self.loader.dataset.get_all_faces_of_id(i)
+                    imgs = imgs.cuda()
+                    ###################################
+                    if self.do_deprocess_and_preprocess:
+                        imgs = self.deprocess_and_preprocess(imgs)
+                    if self.crop_faces:
+                        imgs = self.crop_vgg_box(imgs)
+                    embeddings = self.facenet(imgs)
+                    embeddings = torch.mean(embeddings, 0, keepdim=True)
+                    embedding_batches.append(embeddings)
+            else:
+                for batch in prog_bar(
+                    self.loader,
+                    title="[S2fEvaluator: " + \
+                        "Preparing FaceNet Embedding Dictionary]",
+                    width=50):
+                    # Loop logic
+                    ######### unpack the data #########
+                    imgs, log_mels, human_ids = batch
+                    imgs = imgs.cuda()
+                    ###################################
+                    if self.do_deprocess_and_preprocess:
+                        imgs = self.deprocess_and_preprocess(imgs)
+                    if self.crop_faces:
+                        imgs = self.crop_vgg_box(imgs)
+                    embeddings = self.facenet(imgs)
+                    embedding_batches.append(embeddings)
+            self.dataset_embedding = torch.cat(embedding_batches, 0)
+        print("S2fEvaluator: dataset_embedding shape:",
+              self.dataset_embedding.shape)
+
+    def get_pred_img_embeddings(self, model):
+        '''
+        This function focus on evaluating the speech-to-face specific metrics,
+        Including:
+            1. Deep feature (FaceNet Feature) Similarity
+            2. Extraction Recall@K
+            3. Landmark Correlation
+        '''
+        training_status = model.training
+        model.eval()
+        pred_img_embedding_batches = []
+        with torch.no_grad():
+            # To avoid influence to the running_mean/var of BatchNorm Layers
+            if self.face_gen_mode == 'naive':
+                for batch in prog_bar(
+                    self.loader,
+                    title="[S2fEvaluator: " + \
+                        "Getting FaceNet Embedding for Predicted Images]",
+                    width=50):
+                    # Loop logic
+                    ######### unpack the data #########
+                    imgs, log_mels, human_ids = batch
+                    imgs = imgs.cuda()
+                    log_mels = log_mels.type(self.float_dtype)
+                    human_ids = human_ids.type(self.long_dtype)
+                    ###################################
+                    # Run the model as it has been run during training
+                    model_out = model(log_mels)
+                    imgs_pred, others = model_out
+                    # Multi-Reso Case
+                    if isinstance(imgs_pred, tuple):
+                        imgs_pred = imgs_pred[-1]
+                    if self.do_deprocess_and_preprocess:
+                        imgs_pred = self.deprocess_and_preprocess(imgs_pred)
+                    if self.crop_faces:
+                        imgs_pred = self.crop_vgg_box(imgs_pred)
+                    pred_img_embeddings = self.facenet(imgs_pred)
+                    pred_img_embedding_batches.append(pred_img_embeddings)
+            elif self.face_gen_mode == 'average_facenet_embedding':
+                for i in prog_bar(
+                    range(len(self.loader.dataset)),
+                    title="[S2fEvaluator: " + \
+                        "Getting FaceNet Embedding for Predicted Images, " + \
+                            "with average Facenet embedding policy]",
+                    width=50):
+                    # Loop logic
+                    ######### unpack the data #########
+                    log_mels = self.loader.dataset.get_all_mel_segments_of_id(i)
+                    log_mels = log_mels.type(self.float_dtype)
+                    ###################################
+                    # Run the model as it has been run during training
+                    model_out = model(log_mels)
+                    imgs_pred, others = model_out
+                    if isinstance(imgs_pred, tuple):
+                        imgs_pred = imgs_pred[-1]
+                    if self.do_deprocess_and_preprocess:
+                        imgs_pred = self.deprocess_and_preprocess(imgs_pred)
+                    if self.crop_faces:
+                        imgs_pred = self.crop_vgg_box(imgs_pred)
+                    pred_img_embeddings = self.facenet(imgs_pred)
+                    pred_img_embeddings = torch.mean(
+                        pred_img_embeddings, 0, keepdim=True)
+                    pred_img_embedding_batches.append(pred_img_embeddings)
+            elif self.face_gen_mode == 'average_voice_embedding':
+                for i in prog_bar(
+                    range(len(self.loader.dataset)),
+                    title="[S2fEvaluator: " + \
+                        "Getting FaceNet Embedding for Predicted Images, " + \
+                            "with average voice embedding policy]",
+                    width=50):
+                    # Loop logic
+                    ######### unpack the data #########
+                    log_mels = self.loader.dataset.get_all_mel_segments_of_id(i)
+                    log_mels = log_mels.type(self.float_dtype)
+                    ###################################
+                    # Run the model as it has been run during training
+                    model_out = model(log_mels, average_voice_embedding=True)
+                    imgs_pred, others = model_out
+                    if isinstance(imgs_pred, tuple):
+                        imgs_pred = imgs_pred[-1]
+                    if self.do_deprocess_and_preprocess:
+                        imgs_pred = self.deprocess_and_preprocess(imgs_pred)
+                    if self.crop_faces:
+                        imgs_pred = self.crop_vgg_box(imgs_pred)
+                    pred_img_embeddings = self.facenet(imgs_pred)
+                    #pred_img_embeddings = torch.mean(
+                    #    pred_img_embeddings, 0, keepdim=True)
+                    pred_img_embedding_batches.append(pred_img_embeddings)
+            pred_img_embedding = torch.cat(pred_img_embedding_batches, 0)
+        #print("S2fEvaluator: pred_img_embedding shape:",
+        #      pred_img_embedding.shape)
+        model.train(mode=training_status)
+
+        return pred_img_embedding
+
+    def L1_query(self, x, y):
+        '''
+        Given x, extract top K similar features from y, based on L1 distance
+        x in shape (N_x, D)
+        y in shape (N_y, D)
+        '''
+        # (N_x, D) --> (N_x, 1, D)
+        x = x.unsqueeze(1)
+        # Initialize: (N_x, )
+        x_ids = torch.tensor(np.arange(x.shape[0])).cpu()
+        # (N_y, D) --> (1, N_y, D)
+        y = y.unsqueeze(0)
+        # Output: (N_x, N_y, D)
+        L1_table = torch.abs(x - y)
+        # (N_x, N_y, D) --> (N_x, N_y)
+        L1_table = torch.mean(L1_table, dim=-1)
+        L1_table = torch.neg(L1_table)
+        # Top K: (N_x, K)
+        top_1_vals, top_1_indices = torch.topk(L1_table, 1, dim=-1)
+        top_5_vals, top_5_indices = torch.topk(L1_table, 5, dim=-1)
+        top_10_vals, top_10_indices = torch.topk(L1_table, 10, dim=-1)
+        top_50_vals, top_50_indices = torch.topk(L1_table, 50, dim=-1)
+
+        recall_at_1 = self.in_top_k(top_1_indices.cpu(), x_ids)
+        recall_at_5 = self.in_top_k(top_5_indices.cpu(), x_ids)
+        recall_at_10 = self.in_top_k(top_10_indices.cpu(), x_ids)
+        recall_at_50 = self.in_top_k(top_50_indices.cpu(), x_ids)
+
+        return recall_at_1, recall_at_5, recall_at_10, recall_at_50
+
+    def cos_query(self, x, y):
+        '''
+        Given x, extract top K similar features from y, based on L1 distance
+        x in shape (N_x, D)
+        y in shape (N_y, D)
+        '''
+        # (N_x, D) --> (N_x, 1, D)
+        x = x.unsqueeze(1)
+        # Initialize: (N_x, )
+        x_ids = torch.tensor(np.arange(x.shape[0])).cpu()
+        # (N_y, D) --> (1, N_y, D)
+        y = y.unsqueeze(0)
+        # Output: (N_x, N_y)
+        cos_table = self.cos_sim(x, y)
+
+        # Top K: (N_x, K)
+        top_1_vals, top_1_indices = torch.topk(cos_table, 1, dim=-1)
+        top_2_vals, top_2_indices = torch.topk(cos_table, 2, dim=-1)
+        top_5_vals, top_5_indices = torch.topk(cos_table, 5, dim=-1)
+        top_10_vals, top_10_indices = torch.topk(cos_table, 10, dim=-1)
+        top_20_vals, top_20_indices = torch.topk(cos_table, 20, dim=-1)
+        top_50_vals, top_50_indices = torch.topk(cos_table, 50, dim=-1)
+
+        recall_at_1 = self.in_top_k(top_1_indices.cpu(), x_ids)
+        recall_at_2 = self.in_top_k(top_2_indices.cpu(), x_ids)
+        recall_at_5 = self.in_top_k(top_5_indices.cpu(), x_ids)
+        recall_at_10 = self.in_top_k(top_10_indices.cpu(), x_ids)
+        recall_at_20 = self.in_top_k(top_20_indices.cpu(), x_ids)
+        recall_at_50 = self.in_top_k(top_50_indices.cpu(), x_ids)
+
+        recall_tuple = (recall_at_1, recall_at_2, recall_at_5, recall_at_10, \
+            recall_at_20, recall_at_50)
+
+        return recall_tuple
+
+    def cal_ih_sim(self, x, y):
+        '''
+        For an existing face embedding distribution, calculate the inter-human
+        similarity
+
+        Arguments:
+            x in shape (N_x, D)
+            x in shape (N_y, D)
+        '''
+        # (N_x, D) --> (N_x, 1, D)
+        y = y.unsqueeze(0)
+        x = x.unsqueeze(1)
+        # Output: (N_x, N_y)
+        cos_table = self.cos_sim(x, y)
+        cos_table = cos_table.detach().cpu().numpy()
+        ih_sum = 0.0
+        for i in range(cos_table.shape[0]):
+            for j in range(cos_table.shape[1]):
+                if i != j:
+                    ih_sum = ih_sum + cos_table[i, j]
+        ih_sim = ih_sum / float(cos_table.shape[0] * (cos_table.shape[1] - 1))
+        return ih_sim
+
+    def in_top_k(self, top_k_indices, gt_labels):
+        results = []
+        for i, top_k_id in enumerate(top_k_indices):
+            gt_label = gt_labels[i]
+            #print(gt_label, top_k_id)
+            if gt_label in top_k_id:
+                results.append(1.0)
+            else:
+                results.append(0.0)
+            #print('results:', results)
+        return np.mean(results)
+
+    def get_metrics(self, model, recall_method='cos_sim', get_ih_sim=False):
+        '''
+        Arguments:
+            recall_method: the similarity metric to use for retrival
+            get_ih_sim: get the inter-human similarity
+        '''
+        pred_img_embedding = self.get_pred_img_embeddings(model)
+        L2_dist = self.L2_dist(self.dataset_embedding, pred_img_embedding)
+        L2_dist = torch.mean(L2_dist).item()
+        L1_dist = self.L1_dist(self.dataset_embedding, pred_img_embedding)
+        L1_dist = torch.mean(L1_dist).item()
+        cos_sim = self.cos_sim(self.dataset_embedding, pred_img_embedding)
+        cos_sim = torch.mean(cos_sim).item()
+
+        # We might use only 100 id for extraction
+        if self.extraction_size is None:
+            pred_emb_to_use = pred_img_embedding
+            data_emb_to_use = self.dataset_embedding
+        elif isinstance(self.extraction_size, list):
+            pred_emb_to_use = [pred_img_embedding[0:self.extraction_size[0]], \
+                pred_img_embedding[self.extraction_size[0]:self.extraction_size[1]], \
+                pred_img_embedding[self.extraction_size[1]:self.extraction_size[2]]]
+            data_emb_to_use = [self.dataset_embedding[0:self.extraction_size[0]], \
+                self.dataset_embedding[self.extraction_size[0]:self.extraction_size[1]], \
+                self.dataset_embedding[self.extraction_size[1]:self.extraction_size[2]]]
+        else:
+            pred_emb_to_use = pred_img_embedding[0:self.extraction_size]
+            data_emb_to_use = self.dataset_embedding[0:self.extraction_size]
+
+        if recall_method == 'L1':
+            if isinstance(pred_emb_to_use, list):
+                recall_temp = []
+                for i, pred_emb in enumerate(pred_emb_to_use):
+                    recall_temp.append(self.L1_query(pred_emb, data_emb_to_use[i]))
+                recall_tuple = tuple(np.mean(np.array(recall_temp), axis=0))
+            else:
+                recall_tuple = self.L1_query(pred_emb_to_use, data_emb_to_use)
+        elif recall_method == 'cos_sim':
+            if isinstance(pred_emb_to_use, list):
+                recall_temp = []
+                for i, pred_emb in enumerate(pred_emb_to_use):
+                    recall_temp.append(self.cos_query(pred_emb, data_emb_to_use[i]))
+                recall_tuple = tuple(np.mean(np.array(recall_temp), axis=0))
+            else:
+                recall_tuple = self.cos_query(pred_emb_to_use, data_emb_to_use)
+
+        if get_ih_sim:
+            ih_sim = self.cal_ih_sim(pred_img_embedding, self.dataset_embedding)
+            return L2_dist, L1_dist, cos_sim, recall_tuple, ih_sim
+        else:
+            return L2_dist, L1_dist, cos_sim, recall_tuple
+
+    def get_faces_from_different_segments(self, model, output_dir):
+        #gfile.MkDir(output_dir)
+        os.makedirs(output_dir, exist_ok=True)
+        temp_loader = deepcopy(self.loader)
+        for i in prog_bar(
+            range(len(self.loader.dataset)),
+            title="[S2fEvaluator: " + \
+                "Generating Faces from Different Speech Segments]",
+            width=50):
+            ######### unpack the data #########
+            imgs = temp_loader.dataset.get_all_faces_of_id(i)
+            log_mels = temp_loader.dataset.get_all_mel_segments_of_id(i)
+            imgs = imgs.cuda()
+            log_mels = log_mels.type(self.float_dtype) #cuda()
+            #human_ids = human_ids.type(long_dtype)
+            ###################################
+            with torch.no_grad():
+                model_out = model(log_mels)
+            imgs_pred, _ = model_out
+            if isinstance(imgs_pred, tuple):
+                imgs_pred = imgs_pred[-1]
+            imgs = imagenet_deprocess_batch(
+                imgs, normalize_method=self.image_normalize_method)
+            imgs_pred = imagenet_deprocess_batch(
+                imgs_pred, normalize_method=self.image_normalize_method)
+            #print(imgs.shape, imgs_pred.shape)
+            identity_dir = os.path.join(output_dir, str(i))
+            gfile.MkDir(identity_dir)
+
+            for j in range(imgs.shape[0]):
+                img_np = imgs[j].numpy().transpose(1, 2, 0)
+                img_path = os.path.join(identity_dir, 'origin_%d.png' % j)
+                imwrite(img_path, img_np)
+
+            for k in range(imgs_pred.shape[0]):
+                img_np = imgs_pred[k].numpy().transpose(1, 2, 0)
+                img_path = os.path.join(identity_dir, 'pred_%d.png' % k)
+                imwrite(img_path, img_np)
+
+    def L2_distances(self, x, y=None):
+        '''
+        Input: x is a Nxd matrix
+               y is an optional Mxd matirx
+        Output: dist is a NxM matrix where dist[i,j] is the square norm between x[i,:] and y[j,:]
+                if y is not given then use 'y=x'.
+        i.e. dist[i,j] = ||x[i,:]-y[j,:]||^2
+        '''
+        if y is not None:
+             differences = x.unsqueeze(1) - y.unsqueeze(0)
+        else:
+            differences = x.unsqueeze(1) - x.unsqueeze(0)
+        distances = torch.sum(differences * differences, -1)
+        return distances
diff --git a/mlops/mlflow/train/sf2f/utils/training_utils.py b/mlops/mlflow/train/sf2f/utils/training_utils.py
new file mode 100644
index 0000000..b917b7f
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/training_utils.py
@@ -0,0 +1,250 @@
+import os
+import json
+import math
+from collections import defaultdict
+import time
+
+import numpy as np
+import torch
+import torch.optim as optim
+import torch.nn as nn
+import torch.nn.functional as F
+from scipy.stats import entropy
+from torchvision.models import inception_v3
+from pyprind import prog_bar
+
+from datasets import imagenet_deprocess_batch, fast_mel_deprocess_batch
+import datasets
+import models
+from utils.metrics import jaccard
+from utils import tensor2im
+from utils.visualization.plot import get_np_plot, plot_mel_spectrogram, \
+    plot_attention
+
+from scripts.compute_vggface_score import get_vggface_score
+
+
+def add_loss_with_tensor(total_loss,
+                         curr_loss,
+                         loss_dict,
+                         loss_name,
+                         weight=1):
+    curr_loss = curr_loss * weight
+    loss_dict[loss_name] = curr_loss
+    if total_loss is not None:
+        total_loss += curr_loss
+    else:
+        total_loss = curr_loss
+    return total_loss
+
+def add_loss(total_loss, curr_loss, loss_dict, loss_name, weight=1):
+    curr_loss = curr_loss * weight
+    loss_dict[loss_name] = curr_loss.item()
+    if total_loss is not None:
+        total_loss += curr_loss
+    else:
+        total_loss = curr_loss
+    return total_loss
+
+
+def visualize_sample(model,
+                     imgs,
+                     log_mels,
+                     image_normalize_method,
+                     mel_normalize_method='vox_mel',
+                     visualize_attn=False):
+    '''
+    Prepare data for tensorboard
+    '''
+    samples = []
+    # add the ground-truth images
+    samples.append(imgs[:1])
+
+    with torch.no_grad():
+        model_out = model(log_mels)
+        imgs_pred, others = model_out
+        # add the reconstructed images
+        if isinstance(imgs_pred, tuple):
+            if len(imgs_pred) >= 2:
+                low_imgs_pred = imgs_pred[0]
+                # Rescale
+                while low_imgs_pred.size()[2] != imgs_pred[-1].size()[2]:
+                    low_imgs_pred = F.interpolate(
+                        low_imgs_pred, scale_factor=2, mode='nearest')
+                samples.append(low_imgs_pred[:1])
+            if len(imgs_pred) == 3:
+                mid_imgs_pred = imgs_pred[1]
+                while mid_imgs_pred.size()[2] != imgs_pred[-1].size()[2]:
+                    mid_imgs_pred = F.interpolate(
+                        mid_imgs_pred, scale_factor=2, mode='nearest')
+                samples.append(mid_imgs_pred[:1])
+            imgs_pred = imgs_pred[-1]
+        samples.append(imgs_pred[:1])
+
+        log_mels_de = fast_mel_deprocess_batch(log_mels, mel_normalize_method)
+        log_mel = log_mels_de[0].cpu().detach().numpy()
+
+        if visualize_attn:
+            attn_weights = model.module.decoder.return_attn_weights()
+            attn_weight = attn_weights[0].cpu().detach().numpy()
+
+    samples = torch.cat(samples, dim=3)
+    samples = {
+            "samples": tensor2im(
+                imagenet_deprocess_batch(
+                    samples,
+                    rescale=False,
+                    normalize_method=image_normalize_method
+                ).squeeze(0)
+            ),
+            #"mel_spectrogram": get_np_plot(
+            #    plot_mel_spectrogram(
+            #        log_mel)
+            #)
+    }
+    if visualize_attn:
+        samples['attention_weights'] = get_np_plot(
+            plot_attention(attn_weight))
+
+    # Draw Scene Graphs
+    #sg_array = draw_scene_graph(objs[obj_to_img == 0],
+    #                 triples[triple_to_img == 0],
+    #                 vocab=vocab)
+    #samples["scene_graph"] = sg_array
+
+    return samples
+
+
+def check_model(args,
+                options,
+                t,
+                loader,
+                model):
+    training_status = model.training
+    model.eval()
+    float_dtype = torch.cuda.FloatTensor
+    long_dtype = torch.cuda.LongTensor
+    num_samples = 0
+    all_losses = defaultdict(list)
+    inception_module = nn.DataParallel(
+        inception_v3(
+            pretrained=True,
+            transform_input=False).cuda())
+    inception_module.eval()
+    preds = []
+    images = []
+    with torch.no_grad():
+        # To avoid influence to the running_mean/var of BatchNorm Layers
+        for batch in prog_bar(
+            loader,
+            title="[Validating Inception Score and Pixel Loss]",
+            width=50):
+            # Loop logic
+            ######### unpack the data #########
+            imgs, log_mels, human_ids = batch
+            imgs = imgs.cuda()
+            log_mels = log_mels.type(float_dtype)
+            human_ids = human_ids.type(long_dtype)
+            ###################################
+            # Run the model as it has been run during training
+            model_out = model(log_mels)
+            imgs_pred, others = model_out
+
+            skip_pixel_loss = False
+            total_loss, losses = calculate_model_losses(
+                options["optim"],
+                skip_pixel_loss,
+                imgs,
+                imgs_pred,
+                get_item=True)
+
+            if isinstance(imgs_pred, tuple):
+                imgs_pred = imgs_pred[-1]
+            image = imagenet_deprocess_batch(
+                imgs_pred,
+                normalize_method=options["data"]["data_opts"]["image_normalize_method"])
+            for i in range(image.shape[0]):
+                img_np = image[i].numpy().transpose(1, 2, 0)
+                images.append(img_np)
+
+            # check inception scores
+            x = F.interpolate(imgs_pred, (299, 299), mode="bilinear")
+            x = inception_module(x)
+            preds.append(F.softmax(x).cpu().numpy())
+
+            for loss_name, loss_val in losses.items():
+                all_losses[loss_name].append(loss_val)
+            num_samples += imgs.size(0)
+
+        samples = visualize_sample(
+            model,
+            imgs,
+            log_mels,
+            options["data"]["data_opts"]["image_normalize_method"],
+            visualize_attn=options['eval'].get('visualize_attn', False))
+        mean_losses = {k: np.mean(v) for k, v in all_losses.items()}
+
+        # calculate the inception scores
+        splits = 5
+        preds = np.concatenate(preds, axis=0)
+        # Now compute the mean kl-div
+        split_scores = []
+        N = preds.shape[0]
+        for k in range(splits):
+            part = preds[k * (N // splits): (k+1) * (N // splits), :]
+            py = np.mean(part, axis=0)
+            scores = []
+            for i in range(part.shape[0]):
+                pyx = part[i, :]
+                scores.append(entropy(pyx, py))
+            split_scores.append(np.exp(np.mean(scores)))
+        inception_score = (np.mean(split_scores), np.std(split_scores))
+
+    vf_score = get_vggface_score(images)
+
+    out = [mean_losses, samples, inception_score, vf_score]
+    model.train(mode=training_status)
+    return tuple(out)
+
+
+def calculate_model_losses(opts,
+                           skip_pixel_loss,
+                           imgs,
+                           imgs_pred,
+                           get_item=False):
+    if get_item:
+        add_loss_fn = add_loss
+    else:
+        add_loss_fn = add_loss_with_tensor
+    total_loss = torch.zeros(1).to(imgs)
+    losses = {}
+
+    l1_pixel_weight = opts["l1_pixel_loss_weight"]
+    if skip_pixel_loss:
+        l1_pixel_weight = 0
+
+    if isinstance(imgs_pred, tuple):
+        # Multi-Resolution Pixel Loss
+        for i, img_pred in enumerate(imgs_pred):
+            loss_name = 'L1_pixel_loss_%d' % i
+            img = imgs
+            while img.size()[2] != img_pred.size()[2]:
+                img = F.interpolate(img, scale_factor=0.5, mode='nearest')
+            l1_pixel_loss = F.l1_loss(img_pred, img)
+            total_loss = add_loss_fn(
+                total_loss,
+                l1_pixel_loss,
+                losses,
+                loss_name,
+                l1_pixel_weight)
+    else:
+        # Single Resolution
+        l1_pixel_loss = F.l1_loss(imgs_pred, imgs)
+        total_loss = add_loss_fn(
+            total_loss,
+            l1_pixel_loss,
+            losses,
+            'L1_pixel_loss',
+            l1_pixel_weight)
+
+    return total_loss, losses
diff --git a/mlops/mlflow/train/sf2f/utils/utils.py b/mlops/mlflow/train/sf2f/utils/utils.py
new file mode 100644
index 0000000..0157a82
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/utils.py
@@ -0,0 +1,89 @@
+import time
+import inspect
+import subprocess
+from contextlib import contextmanager
+
+import torch
+from torch import nn
+
+def int_tuple(s):
+    return tuple(int(i) for i in s.split(','))
+
+
+def float_tuple(s):
+    return tuple(float(i) for i in s.split(','))
+
+
+def str_tuple(s):
+    return tuple(s.split(','))
+
+
+def bool_flag(s):
+    if s == '1':
+        return True
+    elif s == '0':
+        return False
+    msg = 'Invalid value "%s" for bool flag (should be 0 or 1)'
+    raise ValueError(msg % s)
+
+
+def lineno():
+    return inspect.currentframe().f_back.f_lineno
+
+
+def get_gpu_memory():
+    torch.cuda.synchronize()
+    opts = [
+        'nvidia-smi', '-q', '--gpu=' + str(0), '|', 'grep', '"Used GPU Memory"'
+    ]
+    cmd = str.join(' ', opts)
+    ps = subprocess.Popen(
+        cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+    output = ps.communicate()[0].decode('utf-8')
+    output = output.split("\n")[1].split(":")
+    consumed_mem = int(output[1].strip().split(" ")[0])
+    return consumed_mem
+
+
+@contextmanager
+def timeit(msg, should_time=True):
+    if should_time:
+        torch.cuda.synchronize()
+        t0 = time.time()
+    yield
+    if should_time:
+        torch.cuda.synchronize()
+        t1 = time.time()
+        duration = (t1 - t0) * 1000.0
+        print('%s: %.2f ms' % (msg, duration))
+
+
+class LossManager(object):
+    def __init__(self):
+        self.total_loss = None
+        self.all_losses = {}
+
+    def add_loss(self, loss, name, weight=1.0):
+        cur_loss = loss * weight
+        if self.total_loss is not None:
+            self.total_loss += cur_loss
+        else:
+            self.total_loss = cur_loss
+
+        self.all_losses[name] = cur_loss.data.cpu().item()
+
+    def items(self):
+        return self.all_losses.items()
+
+
+def load_my_state_dict(model, state_dict):
+    print('Loading my state_dict')
+    own_state = model.state_dict()
+    for name, param in state_dict.items():
+        if name not in own_state:
+            continue
+        if isinstance(param, nn.Parameter):
+            # backwards compatibility for serialized parameters
+            param = param.data
+        own_state[name].copy_(param)
+        print('{} Loaded!'.format(name))
diff --git a/mlops/mlflow/train/sf2f/utils/vad_ex.py b/mlops/mlflow/train/sf2f/utils/vad_ex.py
new file mode 100644
index 0000000..f8b489e
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/vad_ex.py
@@ -0,0 +1,174 @@
+import collections
+import contextlib
+import sys
+import wave
+from pydub import AudioSegment
+import webrtcvad
+
+# Modified https://github.com/wiseman/py-webrtcvad/blob/master/example.py
+
+def read_wave(path):
+    """Reads a .wav file.
+    Takes the path, and returns (PCM audio data, sample rate).
+    """
+    with contextlib.closing(wave.open(path, 'rb')) as wf:
+        num_channels = wf.getnchannels()
+        #assert num_channels == 1
+        sample_width = wf.getsampwidth()
+        assert sample_width == 2
+        sample_rate = wf.getframerate()
+        #assert sample_rate in (8000, 16000, 32000)
+        pcm_data = wf.readframes(wf.getnframes())
+        return pcm_data, sample_rate
+
+def read_libri(path):
+    mf = AudioSegment.from_file(path, "wav")
+    sample_rate = mf.frame_rate
+    pcm_data = mf.raw_data
+    return pcm_data, sample_rate
+
+
+def read_m4a(path):
+    mf = AudioSegment.from_file(path, "m4a")
+    sample_rate = mf.frame_rate
+    pcm_data = mf.raw_data
+    return pcm_data, sample_rate
+
+
+def write_wave(path, audio, sample_rate):
+    """Writes a .wav file.
+    Takes path, PCM audio data, and sample rate.
+    """
+    with contextlib.closing(wave.open(path, 'wb')) as wf:
+        wf.setnchannels(1)
+        wf.setsampwidth(2)
+        wf.setframerate(sample_rate)
+        wf.writeframes(audio)
+
+
+class Frame(object):
+    """Represents a "frame" of audio data."""
+    def __init__(self, bytes, timestamp, duration):
+        self.bytes = bytes
+        self.timestamp = timestamp
+        self.duration = duration
+
+
+def frame_generator(frame_duration_ms, audio, sample_rate):
+    """Generates audio frames from PCM audio data.
+    Takes the desired frame duration in milliseconds, the PCM data, and
+    the sample rate.
+    Yields Frames of the requested duration.
+    """
+    n = int(sample_rate * (frame_duration_ms / 1000.0) * 2)
+    offset = 0
+    timestamp = 0.0
+    duration = (float(n) / sample_rate) / 2.0
+    while offset + n < len(audio):
+        yield Frame(audio[offset:offset + n], timestamp, duration)
+        timestamp += duration
+        offset += n
+
+
+def vad_collector(sample_rate, frame_duration_ms,
+                  padding_duration_ms, vad, frames):
+    """Filters out non-voiced audio frames.
+    Given a webrtcvad.Vad and a source of audio frames, yields only
+    the voiced audio.
+    Uses a padded, sliding window algorithm over the audio frames.
+    When more than 90% of the frames in the window are voiced (as
+    reported by the VAD), the collector triggers and begins yielding
+    audio frames. Then the collector waits until 90% of the frames in
+    the window are unvoiced to detrigger.
+    The window is padded at the front and back to provide a small
+    amount of silence or the beginnings/endings of speech around the
+    voiced frames.
+    Arguments:
+    sample_rate - The audio sample rate, in Hz.
+    frame_duration_ms - The frame duration in milliseconds.
+    padding_duration_ms - The amount to pad the window, in milliseconds.
+    vad - An instance of webrtcvad.Vad.
+    frames - a source of audio frames (sequence or generator).
+    Returns: A generator that yields PCM audio data.
+    """
+    num_padding_frames = int(padding_duration_ms / frame_duration_ms)
+    # We use a deque for our sliding window/ring buffer.
+    ring_buffer = collections.deque(maxlen=num_padding_frames)
+    # We have two states: TRIGGERED and NOTTRIGGERED. We start in the
+    # NOTTRIGGERED state.
+    triggered = False
+
+    voiced_frames = []
+    for frame in frames:
+        is_speech = vad.is_speech(frame.bytes, sample_rate)
+
+        #sys.stdout.write('1' if is_speech else '0')
+        if not triggered:
+            ring_buffer.append((frame, is_speech))
+            num_voiced = len([f for f, speech in ring_buffer if speech])
+            # If we're NOTTRIGGERED and more than 90% of the frames in
+            # the ring buffer are voiced frames, then enter the
+            # TRIGGERED state.
+            if num_voiced > 0.9 * ring_buffer.maxlen:
+                triggered = True
+                #sys.stdout.write('+(%s)' % (ring_buffer[0][0].timestamp,))
+                # We want to yield all the audio we see from now until
+                # we are NOTTRIGGERED, but we have to start with the
+                # audio that's already in the ring buffer.
+                for f, s in ring_buffer:
+                    voiced_frames.append(f)
+                ring_buffer.clear()
+        else:
+            # We're in the TRIGGERED state, so collect the audio data
+            # and add it to the ring buffer.
+            voiced_frames.append(frame)
+            ring_buffer.append((frame, is_speech))
+            num_unvoiced = len([f for f, speech in ring_buffer if not speech])
+            # If more than 90% of the frames in the ring buffer are
+            # unvoiced, then enter NOTTRIGGERED and yield whatever
+            # audio we've collected.
+            if num_unvoiced > 0.9 * ring_buffer.maxlen:
+                #sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
+                triggered = False
+                yield b''.join([f.bytes for f in voiced_frames])
+                ring_buffer.clear()
+                voiced_frames = []
+    #if triggered:
+    #    sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
+    #sys.stdout.write('\n')
+    # If we have any leftover voiced audio when we run out of input,
+    # yield it.
+    if voiced_frames:
+        yield b''.join([f.bytes for f in voiced_frames])
+
+
+def main(args):
+    if len(args) != 2:
+        sys.stderr.write(
+            'Usage: example.py <aggressiveness> <path to wav file>\n')
+        sys.exit(1)
+    audio, sample_rate = read_wave(args[1])
+    #audio, sample_rate = read_m4a(args[1])
+    #audio, sample_rate = read_libri(args[1])
+    vad = webrtcvad.Vad(int(args[0]))
+    frames = frame_generator(30, audio, sample_rate)
+    frames = list(frames)
+    segments = vad_collector(sample_rate, 30, 300, vad, frames)
+    total_wav = b""
+    for i, segment in enumerate(segments):
+    	total_wav += segment
+
+
+    path = 'test.wav'
+    write_wave(path, total_wav, sample_rate)
+
+
+if __name__ == '__main__':
+    #main(sys.argv[1:])
+    import os
+    WAV_DIR = os.path.join('./data', 'VoxCeleb', 'raw_wav')
+    wav_path = 'vox1/test/id10270/zjwijMp0Qyw/00001.wav'
+    wav_path = os.path.join(WAV_DIR, wav_path)
+
+    audio, sample_rate = read_wave(wav_path)
+    print(audio, sample_rate)
diff --git a/mlops/mlflow/train/sf2f/utils/visualization/__init__.py b/mlops/mlflow/train/sf2f/utils/visualization/__init__.py
new file mode 100644
index 0000000..2b54423
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/visualization/__init__.py
@@ -0,0 +1,2 @@
+from .vis import *
+# from .visualizer import Visualizer
diff --git a/mlops/mlflow/train/sf2f/utils/visualization/html.py b/mlops/mlflow/train/sf2f/utils/visualization/html.py
new file mode 100644
index 0000000..1e71687
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/visualization/html.py
@@ -0,0 +1,65 @@
+import dominate
+from dominate.tags import *
+import os
+
+
+class HTML:
+    def __init__(self, web_dir, title, reflesh=0):
+        self.title = title
+        self.web_dir = web_dir
+        self.img_dir = os.path.join(self.web_dir, 'images')
+        if not os.path.exists(self.web_dir):
+            os.makedirs(self.web_dir)
+        if not os.path.exists(self.img_dir):
+            os.makedirs(self.img_dir)
+        # print(self.img_dir)
+
+        self.doc = dominate.document(title=title)
+        if reflesh > 0:
+            with self.doc.head:
+                meta(http_equiv="reflesh", content=str(reflesh))
+
+    def get_image_dir(self):
+        return self.img_dir
+
+    def add_header(self, str):
+        with self.doc:
+            h3(str)
+
+    def add_table(self, border=1):
+        self.t = table(border=border, style="table-layout: fixed;")
+        self.doc.add(self.t)
+
+    def add_images(self, ims, txts, links, width=400):
+        self.add_table()
+        with self.t:
+            with tr():
+                for im, txt, link in zip(ims, txts, links):
+                    with td(style="word-wrap: break-word;", halign="center", valign="top"):
+                        with p():
+                            with a(href=os.path.join('images', link)):
+                                img(style="width:%dpx" %
+                                    width, src=os.path.join('images', im))
+                            br()
+                            p(txt)
+
+    def save(self):
+        html_file = '%s/index.html' % self.web_dir
+        f = open(html_file, 'wt')
+        f.write(self.doc.render())
+        f.close()
+
+
+if __name__ == '__main__':
+    html = HTML('web/', 'test_html')
+    html.add_header('hello world')
+
+    ims = []
+    txts = []
+    links = []
+    for n in range(4):
+        ims.append('image_%d.png' % n)
+        txts.append('text_%d' % n)
+        links.append('image_%d.png' % n)
+    html.add_images(ims, txts, links)
+    html.save()
diff --git a/mlops/mlflow/train/sf2f/utils/visualization/plot.py b/mlops/mlflow/train/sf2f/utils/visualization/plot.py
new file mode 100644
index 0000000..7f24e9d
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/visualization/plot.py
@@ -0,0 +1,199 @@
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import io
+import tensorflow as tf
+import PIL
+import numpy as np
+
+
+def plot_attention(attn_mat, path=None, info=None):
+    num_query_head, seq_len = attn_mat.shape
+    fig, ax = plt.subplots()
+    fig.set_size_inches(10, 10)
+
+    querys = ['query {}'.format(i) for i in range(num_query_head)]
+    steps = ['steps {}'.format(j) for j in range(seq_len)]
+
+    im, cbar = heatmap(attn_mat, querys, steps, ax=ax,
+                       cmap="YlGn", cbarlabel="Attention Weight")
+    annotate_heatmap(im)
+
+    fig.tight_layout()
+    if path is not None:
+        plt.savefig(path, format='png')
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    plt.clf()
+    plt.cla()
+    plt.close(fig)
+    return buf
+
+
+def plot_mel_spectrogram(log_mels,
+                         path=None,
+                         info=None,
+                         colorbar=True,
+                         label=True,
+                         coordinate=True,
+                         remove_boarder=False):
+    fig, ax = plt.subplots()
+    if not coordinate:
+        #ax.format_coord = lambda x, y: ''
+        plt.axis('off')
+    fig.set_size_inches(5, 3)
+    im = ax.imshow(
+        log_mels,
+        aspect='auto',
+        origin='lower',
+        interpolation='none')
+    im.set_cmap('viridis')
+    if colorbar:
+        fig.colorbar(im, ax=ax)
+    xlabel = 'Time'
+    if info is not None:
+        xlabel += '\n\n' + info
+    if label:
+        plt.xlabel(xlabel)
+        plt.ylabel('Frequency')
+    plt.tight_layout()
+    if path is not None:
+        plt.savefig(path, format='png')
+    buf = io.BytesIO()
+    if not remove_boarder:
+        plt.savefig(buf, format='png')
+    else:
+        plt.savefig(buf, format='png', bbox_inches='tight')
+    buf.seek(0)
+    plt.clf()
+    plt.cla()
+    plt.close(fig)
+    return buf
+
+
+def get_np_plot(plot_buf):
+    image = PIL.Image.open(plot_buf)
+    WW, HH = image.size
+
+    return np.array(image)
+
+
+def heatmap(data, row_labels, col_labels, ax=None,
+            cbar_kw={}, cbarlabel="", **kwargs):
+    """
+    Create a heatmap from a numpy array and two lists of labels.
+
+    Parameters
+    ----------
+    data
+        A 2D numpy array of shape (N, M).
+    row_labels
+        A list or array of length N with the labels for the rows.
+    col_labels
+        A list or array of length M with the labels for the columns.
+    ax
+        A `matplotlib.axes.Axes` instance to which the heatmap is plotted.  If
+        not provided, use current axes or create a new one.  Optional.
+    cbar_kw
+        A dictionary with arguments to `matplotlib.Figure.colorbar`.  Optional.
+    cbarlabel
+        The label for the colorbar.  Optional.
+    **kwargs
+        All other arguments are forwarded to `imshow`.
+    """
+
+    if not ax:
+        ax = plt.gca()
+
+    # Plot the heatmap
+    im = ax.imshow(data, **kwargs)
+
+    # Create colorbar
+    cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
+    cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom")
+
+    # We want to show all ticks...
+    ax.set_xticks(np.arange(data.shape[1]))
+    ax.set_yticks(np.arange(data.shape[0]))
+    # ... and label them with the respective list entries.
+    ax.set_xticklabels(col_labels)
+    ax.set_yticklabels(row_labels)
+
+    # Let the horizontal axes labeling appear on top.
+    ax.tick_params(top=True, bottom=False,
+                   labeltop=True, labelbottom=False)
+
+    # Rotate the tick labels and set their alignment.
+    plt.setp(ax.get_xticklabels(), rotation=-30, ha="right",
+             rotation_mode="anchor")
+
+    # Turn spines off and create white grid.
+    for edge, spine in ax.spines.items():
+        spine.set_visible(False)
+
+    ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)
+    ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)
+    ax.grid(which="minor", color="w", linestyle='-', linewidth=3)
+    ax.tick_params(which="minor", bottom=False, left=False)
+
+    return im, cbar
+
+
+def annotate_heatmap(im, data=None, valfmt="{x:.2f}",
+                     textcolors=["black", "white"],
+                     threshold=None, **textkw):
+    """
+    A function to annotate a heatmap.
+
+    Parameters
+    ----------
+    im
+        The AxesImage to be labeled.
+    data
+        Data used to annotate.  If None, the image's data is used.  Optional.
+    valfmt
+        The format of the annotations inside the heatmap.  This should either
+        use the string format method, e.g. "$ {x:.2f}", or be a
+        `matplotlib.ticker.Formatter`.  Optional.
+    textcolors
+        A list or array of two color specifications.  The first is used for
+        values below a threshold, the second for those above.  Optional.
+    threshold
+        Value in data units according to which the colors from textcolors are
+        applied.  If None (the default) uses the middle of the colormap as
+        separation.  Optional.
+    **kwargs
+        All other arguments are forwarded to each call to `text` used to create
+        the text labels.
+    """
+
+    if not isinstance(data, (list, np.ndarray)):
+        data = im.get_array()
+
+    # Normalize the threshold to the images color range.
+    if threshold is not None:
+        threshold = im.norm(threshold)
+    else:
+        threshold = im.norm(data.max())/2.
+
+    # Set default alignment to center, but allow it to be
+    # overwritten by textkw.
+    kw = dict(horizontalalignment="center",
+              verticalalignment="center")
+    kw.update(textkw)
+
+    # Get the formatter in case a string is supplied
+    if isinstance(valfmt, str):
+        valfmt = matplotlib.ticker.StrMethodFormatter(valfmt)
+
+    # Loop over the data and create a `Text` for each "pixel".
+    # Change the text's color depending on the data.
+    texts = []
+    for i in range(data.shape[0]):
+        for j in range(data.shape[1]):
+            kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)])
+            text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)
+            texts.append(text)
+
+    return texts
diff --git a/mlops/mlflow/train/sf2f/utils/visualization/tsne.py b/mlops/mlflow/train/sf2f/utils/visualization/tsne.py
new file mode 100644
index 0000000..62f8791
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/visualization/tsne.py
@@ -0,0 +1,40 @@
+'''
+Visualize embeddings via TSNE
+'''
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from sklearn.manifold import TSNE
+
+
+def visualize_distribution(
+        embeddings,
+        num_class,
+        sample_per_class,
+        class_names=None):
+    '''
+    Inputs:
+        embeddings: numpy array with shape
+            [num_class * sample_per_class, emb_dim]
+    '''
+    emb_2d = TSNE(n_components=2).fit_transform(embeddings)
+    emb_2d = emb_2d.reshape([num_class, sample_per_class, 2])
+    fig, axes = plt.subplots()
+    fig.set_size_inches(18.5, 10.5)
+    if class_names is None:
+        class_names = [' ' for i in range(num_class)]
+    color_list = ['b', 'g', 'r', 'c', 'm', 'y', 'k', \
+        'orange', 'purple', 'pink']
+    for i, class_name in enumerate(class_names):
+        x = emb_2d[i, :, 0]
+        y = emb_2d[i, :, 1]
+        axes.scatter(x=x, y=y, c=color_list[i], label=class_name)
+
+    axes.set_xlabel("Component 1")
+    axes.set_ylabel("Component 2")
+    axes.legend(loc='upper left')
+    axes.set_xlim(-300, 300)
+    axes.set_ylim(-300, 300)
+    plt.show()
diff --git a/mlops/mlflow/train/sf2f/utils/visualization/vis.py b/mlops/mlflow/train/sf2f/utils/visualization/vis.py
new file mode 100644
index 0000000..de412bd
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/visualization/vis.py
@@ -0,0 +1,219 @@
+import tempfile
+import os
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Rectangle
+from imageio import imread
+import time
+
+
+"""
+Utilities for making visualizations.
+"""
+
+
+def draw_layout(vocab, objs, boxes, masks=None, size=256,
+                show_boxes=False, bgcolor=(0, 0, 0)):
+    if bgcolor == 'white':
+        bgcolor = (255, 255, 255)
+
+    cmap = plt.get_cmap('rainbow')
+    colors = cmap(np.linspace(0, 1, len(objs)))
+
+    with torch.no_grad():
+        objs = objs.cpu().clone()
+        boxes = boxes.cpu().clone()
+        boxes *= size
+
+        if masks is not None:
+            masks = masks.cpu().clone()
+
+        bgcolor = np.asarray(bgcolor)
+        bg = np.ones((size, size, 1)) * bgcolor
+        plt.imshow(bg.astype(np.uint8))
+
+        plt.gca().set_xlim(0, size)
+        plt.gca().set_ylim(size, 0)
+        plt.gca().set_aspect(1.0, adjustable='box')
+
+        for i, obj in enumerate(objs):
+            name = vocab['object_idx_to_name'][obj]
+            if name == '__image__':
+                continue
+            box = boxes[i]
+
+            if masks is None:
+                continue
+            mask = masks[i].numpy()
+            mask /= mask.max()
+
+            r, g, b, a = colors[i]
+            colored_mask = mask[:, :, None] * np.asarray(colors[i])
+
+            x0, y0, x1, y1 = box
+            plt.imshow(colored_mask, extent=(x0, x1, y1, y0),
+                       interpolation='bicubic', alpha=1.0)
+
+        if show_boxes:
+            for i, obj in enumerate(objs):
+                name = vocab['object_idx_to_name'][obj]
+                if name == '__image__':
+                    continue
+                box = boxes[i]
+
+                draw_box(box, colors[i], name)
+
+
+def draw_box(box, color, text=None):
+    """
+    Draw a bounding box using pyplot, optionally with a text box label.
+
+    Inputs:
+    - box: Tensor or list with 4 elements: [x0, y0, x1, y1] in [0, W] x [0, H]
+           coordinate system.
+    - color: pyplot color to use for the box.
+    - text: (Optional) String; if provided then draw a label for this box.
+    """
+    TEXT_BOX_HEIGHT = 10
+    if torch.is_tensor(box) and box.dim() == 2:
+        box = box.view(-1)
+        assert box.size(0) == 4
+    x0, y0, x1, y1 = box
+    assert y1 > y0, box
+    assert x1 > x0, box
+    w, h = x1 - x0, y1 - y0
+    rect = Rectangle((x0, y0), w, h, fc='none', lw=2, ec=color)
+    plt.gca().add_patch(rect)
+    if text is not None:
+        text_rect = Rectangle(
+            (x0, y0), w, TEXT_BOX_HEIGHT, fc=color, alpha=0.5)
+        plt.gca().add_patch(text_rect)
+        tx = 0.5 * (x0 + x1)
+        ty = y0 + TEXT_BOX_HEIGHT / 2.0
+        plt.text(tx, ty, text, va='center', ha='center')
+
+
+def draw_scene_graph(objs, triples, vocab=None, **kwargs):
+    """
+    Use GraphViz to draw a scene graph. If vocab is not passed then we assume
+    that objs and triples are python lists containing strings for object and
+    relationship names.
+
+    Using this requires that GraphViz is installed. On Ubuntu 16.04 this is easy:
+    sudo apt-get install graphviz
+    """
+    output_filename = kwargs.pop('output_filename',
+                                 'graph-{}.png'.format(time.strftime("%Y%m%d-%H%M%S")))
+    orientation = kwargs.pop('orientation', 'V')
+    edge_width = kwargs.pop('edge_width', 6)
+    arrow_size = kwargs.pop('arrow_size', 1.5)
+    binary_edge_weight = kwargs.pop('binary_edge_weight', 1.2)
+    ignore_dummies = kwargs.pop('ignore_dummies', True)
+
+    if orientation not in ['V', 'H']:
+        raise ValueError('Invalid orientation "%s"' % orientation)
+    rankdir = {'H': 'LR', 'V': 'TD'}[orientation]
+
+    if vocab is not None:
+        # Decode object and relationship names
+        assert torch.is_tensor(objs)
+        assert torch.is_tensor(triples)
+        objs_list, triples_list = [], []
+        for i in range(objs.size(0)):
+            objs_list.append(vocab['object_idx_to_name'][objs[i].item()])
+        for i in range(triples.size(0)):
+            s = triples[i, 0].item()
+            p = vocab['pred_idx_to_name'][triples[i, 1].item()]
+            # p = triples[i, 1].item()
+            o = triples[i, 2].item()
+            triples_list.append([s, p, o])
+        objs, triples = objs_list, triples_list
+
+    # General setup, and style for object nodes
+    lines = [
+        'digraph{',
+        'graph [size="5,3",ratio="compress",dpi="300",bgcolor="transparent"]',
+        'rankdir=%s' % rankdir,
+        'nodesep="0.5"',
+        'ranksep="0.5"',
+        'node [shape="box",style="rounded,filled",fontsize="48",color="none"]',
+        'node [fillcolor="lightpink1"]',
+    ]
+    # Output nodes for objects
+    for i, obj in enumerate(objs):
+        if ignore_dummies and obj == '__image__':
+            continue
+        lines.append('%d [label="%s"]' % (i, obj))
+
+    # Output relationships
+    next_node_id = len(objs)
+    lines.append('node [fillcolor="lightblue1"]')
+    for s, p, o in triples:
+        if ignore_dummies and p == '__in_image__':
+            continue
+        lines += [
+            '%d [label="%s"]' % (next_node_id, p),
+            '%d->%d [penwidth=%f,arrowsize=%f,weight=%f]' % (
+                s, next_node_id, edge_width, arrow_size, binary_edge_weight),
+            '%d->%d [penwidth=%f,arrowsize=%f,weight=%f]' % (
+                next_node_id, o, edge_width, arrow_size, binary_edge_weight)
+        ]
+        next_node_id += 1
+    lines.append('}')
+
+    # Now it gets slightly hacky. Write the graphviz spec to a temporary
+    # text file
+    ff, dot_filename = tempfile.mkstemp()
+    with open(dot_filename, 'w') as f:
+        for line in lines:
+            f.write('%s\n' % line)
+    os.close(ff)
+
+    # Shell out to invoke graphviz; this will save the resulting image to disk,
+    # so we read it, delete it, then return it.
+    output_format = os.path.splitext(output_filename)[1][1:]
+    os.system('dot -T%s %s > %s' %
+              (output_format, dot_filename, output_filename))
+    os.remove(dot_filename)
+    try:
+        img = imread(output_filename)
+        os.remove(output_filename)
+    except:
+        print("####################################")
+        print("Fail to load image: {}".format(output_filename))
+        print("####################################")
+        img = np.zeros((400, 400, 3))
+
+    return np.array(img)
+
+
+if __name__ == '__main__':
+    o_idx_to_name = ['cat', 'dog', 'hat', 'skateboard']
+    p_idx_to_name = ['riding', 'wearing', 'on', 'next to', 'above']
+    o_name_to_idx = {s: i for i, s in enumerate(o_idx_to_name)}
+    p_name_to_idx = {s: i for i, s in enumerate(p_idx_to_name)}
+    vocab = {
+        'object_idx_to_name': o_idx_to_name,
+        'object_name_to_idx': o_name_to_idx,
+        'pred_idx_to_name': p_idx_to_name,
+        'pred_name_to_idx': p_name_to_idx,
+    }
+
+    objs = [
+        'cat',
+        'cat',
+        'skateboard',
+        'hat',
+    ]
+    objs = torch.LongTensor([o_name_to_idx[o] for o in objs])
+    triples = [
+        [0, 'next to', 1],
+        [0, 'riding', 2],
+        [1, 'wearing', 3],
+        [3, 'above', 2],
+    ]
+    triples = [[s, p_name_to_idx[p], o] for s, p, o in triples]
+    triples = torch.LongTensor(triples)
+
+    draw_scene_graph(objs, triples, vocab, orientation='V')
diff --git a/mlops/mlflow/train/sf2f/utils/wav2mel.py b/mlops/mlflow/train/sf2f/utils/wav2mel.py
new file mode 100644
index 0000000..35a822d
--- /dev/null
+++ b/mlops/mlflow/train/sf2f/utils/wav2mel.py
@@ -0,0 +1,104 @@
+'''
+Convert *.wav or *.m4a into mel spectrogram\
+
+for unit test, run:
+    python utils/wav2mel.py
+'''
+
+
+import random
+import numpy as np
+import argparse
+import time
+from _thread import start_new_thread
+import queue
+from python_speech_features import logfbank
+import webrtcvad
+try:
+    import vad_ex
+except:
+    from utils import vad_ex
+
+
+def vad_process(path):
+    # VAD Process
+    if path.endswith('.wav'):
+        audio, sample_rate = vad_ex.read_wave(path)
+    elif path.endswith('.m4a'):
+        audio, sample_rate = vad_ex.read_m4a(path)
+    else:
+        raise TypeError('Unsupported file type: {}'.format(path.split('.')[-1]))
+
+    vad = webrtcvad.Vad(1)
+    frames = vad_ex.frame_generator(30, audio, sample_rate)
+    frames = list(frames)
+    segments = vad_ex.vad_collector(sample_rate, 30, 300, vad, frames)
+    total_wav = b""
+    for i, segment in enumerate(segments):
+        total_wav += segment
+    # Without writing, unpack total_wav into numpy [N,1] array
+    # 16bit PCM 기준 dtype=np.int16
+    wav_arr = np.frombuffer(total_wav, dtype=np.int16)
+    #print("read audio data from byte string. np array of shape:" + \
+    #    str(wav_arr.shape))
+    return wav_arr, sample_rate
+
+
+def wav_to_mel(path, nfilt=40):
+    '''
+    Output shape: (nfilt, length)
+    '''
+    wav_arr, sample_rate = vad_process(path)
+    #print("sample_rate:", sample_rate)
+    logmel_feats = logfbank(
+        wav_arr,
+        samplerate=sample_rate,
+        nfilt=nfilt)
+    #print("created logmel feats from audio data. np array of shape:" \
+    #    + str(logmel_feats.shape))
+    return np.transpose(logmel_feats)
+
+
+if __name__ == '__main__':
+    import os
+    VOX_DIR = os.path.join('./data', 'VoxCeleb')
+    WAV_DIR = os.path.join('./data', 'VoxCeleb', 'raw_wav')
+    #wav_path = 'vox1/test/id10270/zjwijMp0Qyw/00001.wav'
+    wav_path = 'vox1/dev/id10001/J9lHsKG98U8/00026.wav'
+    wav_path = os.path.join(WAV_DIR, wav_path)
+    # m4a_path = 'vox2/test/id04253/dfbCPe2xOPA/00257.m4a'
+    # m4a_path = os.path.join(WAV_DIR, m4a_path)
+
+    #wav_arr, sample_rate = vad_process(wav_path)
+    #print(wav_arr, sample_rate)
+    #wav_arr, sample_rate = vad_process(m4a_path)
+    #print(wav_arr, sample_rate)
+
+    print(wav_to_mel(wav_path).shape)
+    # print(wav_to_mel(m4a_path).shape)
+
+    # Compare with preprocessed pickles
+    import sys
+    sys.path.append('./')
+    from datasets.vox_dataset import VoxDataset
+    # Config
+    image_size = (256, 256)
+    #image_normalize_method = 'imagenet'
+    image_normalize_method = 'standard'
+    mel_normalize_method = 'vox_mel'
+    test_case_dir = os.path.join('./data', 'test_cases')
+    os.makedirs(test_case_dir, exist_ok=True)
+
+    # Dataset
+    vox_dataset = VoxDataset(
+        data_dir=VOX_DIR,
+        image_size=image_size,
+        image_normalize_method=image_normalize_method,
+        mel_normalize_method=mel_normalize_method)
+    np_mel = vox_dataset.load_mel_gram(mel_pickle= \
+        './data/VoxCeleb/vox1/mel_spectrograms/A.J._Buckley' + \
+            '/id10001_J9lHsKG98U8_00026.pickle')
+    print('Load from pickle:', np_mel.shape)
+    print('Calculated by program:', wav_to_mel(wav_path).shape)
+
+    print(np_mel - wav_to_mel(wav_path))
diff --git a/mlops/serving/SwimSwap/Dockerfile.swimswap b/mlops/serving/SwimSwap/Dockerfile.swimswap
new file mode 100644
index 0000000..73d85e1
--- /dev/null
+++ b/mlops/serving/SwimSwap/Dockerfile.swimswap
@@ -0,0 +1,37 @@
+FROM pytorch/pytorch:2.2.1-cuda11.8-cudnn8-runtime
+
+ENV base_path /workspace
+COPY . ${base_path}
+
+RUN mkdir -p ${base_path}/insightface_func/models \
+${base_path}/parsing_model/checkpoint \
+${base_path}/arcface_model
+
+RUN apt update -y
+RUN apt install -y libgl1-mesa-glx \
+libglib2.0-0 \
+unzip
+
+ADD https://storage.makezenerator.com:9000/voice2face-public/model_resource/swimswap/79999_iter.pth ${base_path}/parsing_model/checkpoint
+
+ADD https://storage.makezenerator.com:9000/voice2face-public/model_resource/swimswap/antelope.zip ${base_path}/insightface_func/models
+
+RUN unzip ${base_path}/insightface_func/models/antelope.zip -d ${base_path}/insightface_func/models/
+
+
+RUN pip install imageio \
+imageio-ffmpeg \
+insightface==0.2.1 \
+onnxruntime \
+onnxruntime-gpu \
+mlflow \
+minio \
+boto3 \
+flask \
+flask_cors \
+gunicorn \
+python-dotenv
+
+
+# ENTRYPOINT gunicorn -w 1 -b 0.0.0.0:3001 app:app --reload --timeout 90
+# CMD [ "python", "inference.py" ]
\ No newline at end of file
diff --git a/mlops/serving/SwimSwap/app.py b/mlops/serving/SwimSwap/app.py
new file mode 100644
index 0000000..7c33eae
--- /dev/null
+++ b/mlops/serving/SwimSwap/app.py
@@ -0,0 +1,40 @@
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from inference import face_synthesis_gif
+from config import MINIO_ENDPOINT
+
+app = Flask(__name__)
+# app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024 # 용량제한
+app.config.update(DEBUG=True)
+
+CORS(app, resources={r'*': {'origins': '*'}}, supports_credentials=True)
+
+@app.route('/imagetovideo', methods=['POST'])
+def inference():
+    make_json = request.get_json()
+    request_id = make_json['request_id']
+    result_id = make_json['result_id']
+    voice_image_url = make_json['voice_image_url']
+    gender = make_json['gender']
+    age = make_json['age']
+    gif_dict = {"woman" : 'hj', "man" : 'tae'}
+    
+    try: 
+        video_url = f"https://{MINIO_ENDPOINT}/voice2face-public/site/result/{gif_dict[gender]}_24fps_square.mp4"
+        
+        result, voice_video_url = face_synthesis_gif(voice_image_url, video_url, int(request_id), int(result_id))
+        
+        if result == 400:
+            raise Exception(voice_video_url)
+
+        return jsonify({'status_code' : 200,
+                'voice_image_url' : voice_image_url,
+                'voice_video_url' : voice_video_url})
+
+    except Exception as ex:
+        print(ex)
+        return jsonify({"status_code" : 400, "error": str(ex)}) #false->400
+
+if __name__ == "__main__":
+    app.run(port=3001, debug=True)
+
diff --git a/mlops/serving/SwimSwap/config.py b/mlops/serving/SwimSwap/config.py
new file mode 100644
index 0000000..1385184
--- /dev/null
+++ b/mlops/serving/SwimSwap/config.py
@@ -0,0 +1,11 @@
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+MLFLOW_S3_ENDPOINT_URL=os.getenv('MLFLOW_S3_ENDPOINT_URL')
+MLFLOW_TRACKING_URI=os.getenv('MLFLOW_TRACKING_URI')
+AWS_ACCESS_KEY=os.getenv('AWS_ACCESS_KEY')
+AWS_SECRET_ACCESS_KEY=os.getenv('AWS_SECRET_ACCESS_KEY')
+MINIO_BUCKET=os.getenv('MINIO_BUCKET')
+MINIO_ENDPOINT=os.getenv('MINIO_ENDPOINT')
diff --git a/mlops/serving/SwimSwap/data/hj_24fps_square.mp4 b/mlops/serving/SwimSwap/data/hj_24fps_square.mp4
new file mode 100644
index 0000000..8c433d1
Binary files /dev/null and b/mlops/serving/SwimSwap/data/hj_24fps_square.mp4 differ
diff --git a/mlops/serving/SwimSwap/data/realface.jpg b/mlops/serving/SwimSwap/data/realface.jpg
new file mode 100644
index 0000000..61590bd
Binary files /dev/null and b/mlops/serving/SwimSwap/data/realface.jpg differ
diff --git a/mlops/serving/SwimSwap/data/tae_24fps_square.mp4 b/mlops/serving/SwimSwap/data/tae_24fps_square.mp4
new file mode 100644
index 0000000..06e42ed
Binary files /dev/null and b/mlops/serving/SwimSwap/data/tae_24fps_square.mp4 differ
diff --git a/mlops/serving/SwimSwap/inference.py b/mlops/serving/SwimSwap/inference.py
new file mode 100644
index 0000000..9573a02
--- /dev/null
+++ b/mlops/serving/SwimSwap/inference.py
@@ -0,0 +1,98 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 19:00:38
+Description: 
+'''
+
+import cv2
+import torch
+import fractions
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+from torchvision import transforms
+from insightface_func.face_detect_crop_single import Face_detect_crop
+from util.mp4swap import mp4_swap
+from util.upload_minio import upload_object
+import os
+import mlflow
+from minio import Minio
+import tempfile
+
+from config import MLFLOW_S3_ENDPOINT_URL, MLFLOW_TRACKING_URI, AWS_ACCESS_KEY, AWS_SECRET_ACCESS_KEY, MINIO_BUCKET, MINIO_ENDPOINT
+
+torch.cuda.empty_cache()
+
+os.environ["MLFLOW_S3_ENDPOINT_URL"] = MLFLOW_S3_ENDPOINT_URL
+os.environ["MLFLOW_TRACKING_URI"] = MLFLOW_TRACKING_URI
+os.environ["AWS_ACCESS_KEY_ID"] = AWS_ACCESS_KEY
+os.environ["AWS_SECRET_ACCESS_KEY"] = AWS_SECRET_ACCESS_KEY
+os.environ["MINIO_BUCKET"] = MINIO_BUCKET
+os.environ["MINIO_ENDPOINT"] = MINIO_ENDPOINT
+
+print(MINIO_ENDPOINT, AWS_ACCESS_KEY, AWS_SECRET_ACCESS_KEY,MINIO_BUCKET)
+client = Minio(MINIO_ENDPOINT, AWS_ACCESS_KEY, AWS_SECRET_ACCESS_KEY, secure=True)
+
+model = mlflow.pytorch.load_model("runs:/c7430d77a51a454cad5f315f38af9104/swimswap_pytorch").eval()
+
+def face_synthesis_gif(face_image_url,base_video_url,request_id,result_id):
+    def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
+
+
+    transformer_Arcface = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    crop_size = 224
+    try:
+        with tempfile.NamedTemporaryFile(delete=False) as tmpfile:
+            object_path = "/".join(face_image_url.split("/")[4:])
+            VOICE_MINIO_BUCKET = face_image_url.split("/")[3]
+            
+            print(VOICE_MINIO_BUCKET)
+            client.fget_object(VOICE_MINIO_BUCKET, object_path, tmpfile.name)
+            save_url = f"web_artifact/output/{request_id}_{result_id}_video.mp4"
+            
+            app = Face_detect_crop(name='antelope', root='./insightface_func/models')
+            app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640),mode="{}")
+            with torch.no_grad():
+                pic_a = tmpfile.name
+                # img_a = Image.open(pic_a).convert('RGB')
+                img_a_whole = cv2.imread(pic_a)
+
+        try:
+            img_a_align_crop, _ = app.get(img = img_a_whole,crop_size=crop_size)
+        except TypeError:
+            return 400, "low quality image"
+            
+        img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) 
+        img_a = transformer_Arcface(img_a_align_crop_pil)
+        img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
+        # convert numpy to tensor
+        img_id = img_id.cuda()
+
+
+        #create latent id
+        img_id_downsample = F.interpolate(img_id, size=(112,112))
+        latend_id = model.netArc(img_id_downsample)
+        latend_id = F.normalize(latend_id, p=2, dim=1)
+        content_type = 'video/mp4'
+        os.makedirs(os.path.dirname(save_url),exist_ok=True)
+        make_flag = mp4_swap(base_video_url, latend_id, model, app, save_url,\
+                        no_simswaplogo=True, use_mask=True, crop_size=crop_size)
+        if make_flag:
+            with open(save_url, 'rb') as file_data:
+                file_stat = os.stat(save_url)
+                upload_object(client, save_url, file_data,file_stat.st_size,VOICE_MINIO_BUCKET,content_type)
+                os.remove(save_url)
+        else:
+            return 400, "make_flag error"
+        return 200, f"https://{MINIO_ENDPOINT}/{MINIO_BUCKET}/{save_url}"
+    except Exception as ex:
+        return 400, str(ex)
+
+
+
diff --git a/mlops/serving/SwimSwap/insightface_func/__init__.py b/mlops/serving/SwimSwap/insightface_func/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mlops/serving/SwimSwap/insightface_func/face_detect_crop_multi.py b/mlops/serving/SwimSwap/insightface_func/face_detect_crop_multi.py
new file mode 100644
index 0000000..d75c36b
--- /dev/null
+++ b/mlops/serving/SwimSwap/insightface_func/face_detect_crop_multi.py
@@ -0,0 +1,100 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 16:45:41
+Description: 
+'''
+from __future__ import division
+import collections
+import numpy as np
+import glob
+import os
+import os.path as osp
+import cv2
+from insightface.model_zoo import model_zoo
+from insightface_func.utils import face_align_ffhqandnewarc as face_align
+
+__all__ = ['Face_detect_crop', 'Face']
+
+Face = collections.namedtuple('Face', [
+    'bbox', 'kps', 'det_score', 'embedding', 'gender', 'age',
+    'embedding_norm', 'normed_embedding',
+    'landmark'
+])
+
+Face.__new__.__defaults__ = (None, ) * len(Face._fields)
+
+
+class Face_detect_crop:
+    def __init__(self, name, root='~/.insightface_func/models'):
+        self.models = {}
+        root = os.path.expanduser(root)
+        onnx_files = glob.glob(osp.join(root, name, '*.onnx'))
+        onnx_files = sorted(onnx_files)
+        for onnx_file in onnx_files:
+            if onnx_file.find('_selfgen_')>0:
+                #print('ignore:', onnx_file)
+                continue
+            model = model_zoo.get_model(onnx_file)
+            if model.taskname not in self.models:
+                print('find model:', onnx_file, model.taskname)
+                self.models[model.taskname] = model
+            else:
+                print('duplicated model task type, ignore:', onnx_file, model.taskname)
+                del model
+        assert 'detection' in self.models
+        self.det_model = self.models['detection']
+
+
+    def prepare(self, ctx_id, det_thresh=0.5, det_size=(640, 640), mode ='None'):
+        self.det_thresh = det_thresh
+        self.mode = mode
+        assert det_size is not None
+        print('set det-size:', det_size)
+        self.det_size = det_size
+        for taskname, model in self.models.items():
+            if taskname=='detection':
+                model.prepare(ctx_id, input_size=det_size)
+            else:
+                model.prepare(ctx_id)
+
+    def get(self, img, crop_size, max_num=0):
+        bboxes, kpss = self.det_model.detect(img,
+                                             threshold=self.det_thresh,
+                                             max_num=max_num,
+                                             metric='default')
+        if bboxes.shape[0] == 0:
+            return None
+        ret = []
+        # for i in range(bboxes.shape[0]):
+        #     bbox = bboxes[i, 0:4]
+        #     det_score = bboxes[i, 4]
+        #     kps = None
+        #     if kpss is not None:
+        #         kps = kpss[i]
+        #     M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') 
+        #     align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+        align_img_list = []
+        M_list = []
+        for i in range(bboxes.shape[0]):
+            kps = None
+            if kpss is not None:
+                kps = kpss[i]
+            M, _ = face_align.estimate_norm(kps, crop_size, mode = self.mode) 
+            align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+            align_img_list.append(align_img)
+            M_list.append(M)
+
+        # det_score = bboxes[..., 4]
+
+        # best_index = np.argmax(det_score)
+
+        # kps = None
+        # if kpss is not None:
+        #     kps = kpss[best_index]
+        # M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') 
+        # align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+        
+        return align_img_list, M_list
diff --git a/mlops/serving/SwimSwap/insightface_func/face_detect_crop_single.py b/mlops/serving/SwimSwap/insightface_func/face_detect_crop_single.py
new file mode 100644
index 0000000..f3f648f
--- /dev/null
+++ b/mlops/serving/SwimSwap/insightface_func/face_detect_crop_single.py
@@ -0,0 +1,97 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 16:46:04
+Description: 
+'''
+from __future__ import division
+import collections
+import numpy as np
+import glob
+import os
+import os.path as osp
+import cv2
+from insightface.model_zoo import model_zoo
+from insightface_func.utils import face_align_ffhqandnewarc as face_align
+
+__all__ = ['Face_detect_crop', 'Face']
+
+Face = collections.namedtuple('Face', [
+    'bbox', 'kps', 'det_score', 'embedding', 'gender', 'age',
+    'embedding_norm', 'normed_embedding',
+    'landmark'
+])
+
+Face.__new__.__defaults__ = (None, ) * len(Face._fields)
+
+
+class Face_detect_crop:
+    def __init__(self, name, root='~/.insightface_func/models'):
+        self.models = {}
+        root = os.path.expanduser(root)
+        onnx_files = glob.glob(osp.join(root, name, '*.onnx'))
+        onnx_files = sorted(onnx_files)
+        for onnx_file in onnx_files:
+            if onnx_file.find('_selfgen_')>0:
+                #print('ignore:', onnx_file)
+                continue
+            model = model_zoo.get_model(onnx_file)
+            if model.taskname not in self.models:
+                print('find model:', onnx_file, model.taskname)
+                self.models[model.taskname] = model
+            else:
+                print('duplicated model task type, ignore:', onnx_file, model.taskname)
+                del model
+        assert 'detection' in self.models
+        self.det_model = self.models['detection']
+
+
+    def prepare(self, ctx_id, det_thresh=0.5, det_size=(640, 640), mode ='None'):
+        self.det_thresh = det_thresh
+        self.mode = mode
+        assert det_size is not None
+        print('set det-size:', det_size)
+        self.det_size = det_size
+        for taskname, model in self.models.items():
+            if taskname=='detection':
+                model.prepare(ctx_id, input_size=det_size)
+            else:
+                model.prepare(ctx_id)
+
+    def get(self, img, crop_size, max_num=0):
+        bboxes, kpss = self.det_model.detect(img,
+                                             threshold=self.det_thresh,
+                                             max_num=max_num,
+                                             metric='default')
+        if bboxes.shape[0] == 0:
+            return None
+        # ret = []
+        # for i in range(bboxes.shape[0]):
+        #     bbox = bboxes[i, 0:4]
+        #     det_score = bboxes[i, 4]
+        #     kps = None
+        #     if kpss is not None:
+        #         kps = kpss[i]
+        #     M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') 
+        #     align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+        # for i in range(bboxes.shape[0]):
+        #     kps = None
+        #     if kpss is not None:
+        #         kps = kpss[i]
+        #     M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') 
+        #     align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+
+        det_score = bboxes[..., 4]
+
+        # select the face with the hightest detection score
+        best_index = np.argmax(det_score)
+
+        kps = None
+        if kpss is not None:
+            kps = kpss[best_index]
+        M, _ = face_align.estimate_norm(kps, crop_size, mode = self.mode) 
+        align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+        
+        return [align_img], [M]
diff --git a/mlops/serving/SwimSwap/insightface_func/utils/face_align_ffhqandnewarc.py b/mlops/serving/SwimSwap/insightface_func/utils/face_align_ffhqandnewarc.py
new file mode 100644
index 0000000..2f5aeb7
--- /dev/null
+++ b/mlops/serving/SwimSwap/insightface_func/utils/face_align_ffhqandnewarc.py
@@ -0,0 +1,159 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-15 19:42:42
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-15 20:01:47
+Description: 
+'''
+
+import cv2
+import numpy as np
+from skimage import transform as trans
+
+src1 = np.array([[51.642, 50.115], [57.617, 49.990], [35.740, 69.007],
+                 [51.157, 89.050], [57.025, 89.702]],
+                dtype=np.float32)
+#<--left
+src2 = np.array([[45.031, 50.118], [65.568, 50.872], [39.677, 68.111],
+                 [45.177, 86.190], [64.246, 86.758]],
+                dtype=np.float32)
+
+#---frontal
+src3 = np.array([[39.730, 51.138], [72.270, 51.138], [56.000, 68.493],
+                 [42.463, 87.010], [69.537, 87.010]],
+                dtype=np.float32)
+
+#-->right
+src4 = np.array([[46.845, 50.872], [67.382, 50.118], [72.737, 68.111],
+                 [48.167, 86.758], [67.236, 86.190]],
+                dtype=np.float32)
+
+#-->right profile
+src5 = np.array([[54.796, 49.990], [60.771, 50.115], [76.673, 69.007],
+                 [55.388, 89.702], [61.257, 89.050]],
+                dtype=np.float32)
+
+src = np.array([src1, src2, src3, src4, src5])
+src_map = src
+
+ffhq_src = np.array([[192.98138, 239.94708], [318.90277, 240.1936], [256.63416, 314.01935],
+                                           [201.26117, 371.41043], [313.08905, 371.15118]])
+ffhq_src = np.expand_dims(ffhq_src, axis=0)
+
+# arcface_src = np.array(
+#     [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366],
+#      [41.5493, 92.3655], [70.7299, 92.2041]],
+#     dtype=np.float32)
+
+# arcface_src = np.expand_dims(arcface_src, axis=0)
+
+# In[66]:
+
+
+# lmk is prediction; src is template
+def estimate_norm(lmk, image_size=112, mode='ffhq'):
+    assert lmk.shape == (5, 2)
+    tform = trans.SimilarityTransform()
+    lmk_tran = np.insert(lmk, 2, values=np.ones(5), axis=1)
+    min_M = []
+    min_index = []
+    min_error = float('inf')
+    if mode == 'ffhq':
+        # assert image_size == 112
+        src = ffhq_src * image_size / 512
+    else:
+        src = src_map * image_size / 112
+    for i in np.arange(src.shape[0]):
+        tform.estimate(lmk, src[i])
+        M = tform.params[0:2, :]
+        results = np.dot(M, lmk_tran.T)
+        results = results.T
+        error = np.sum(np.sqrt(np.sum((results - src[i])**2, axis=1)))
+        #         print(error)
+        if error < min_error:
+            min_error = error
+            min_M = M
+            min_index = i
+    return min_M, min_index
+
+
+def norm_crop(img, landmark, image_size=112, mode='ffhq'):
+    if mode == 'Both':
+        M_None, _ = estimate_norm(landmark, image_size, mode = 'newarc')
+        M_ffhq, _ = estimate_norm(landmark, image_size, mode='ffhq')
+        warped_None = cv2.warpAffine(img, M_None, (image_size, image_size), borderValue=0.0)
+        warped_ffhq = cv2.warpAffine(img, M_ffhq, (image_size, image_size), borderValue=0.0)
+        return warped_ffhq, warped_None
+    else:
+        M, pose_index = estimate_norm(landmark, image_size, mode)
+        warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
+        return warped
+
+def square_crop(im, S):
+    if im.shape[0] > im.shape[1]:
+        height = S
+        width = int(float(im.shape[1]) / im.shape[0] * S)
+        scale = float(S) / im.shape[0]
+    else:
+        width = S
+        height = int(float(im.shape[0]) / im.shape[1] * S)
+        scale = float(S) / im.shape[1]
+    resized_im = cv2.resize(im, (width, height))
+    det_im = np.zeros((S, S, 3), dtype=np.uint8)
+    det_im[:resized_im.shape[0], :resized_im.shape[1], :] = resized_im
+    return det_im, scale
+
+
+def transform(data, center, output_size, scale, rotation):
+    scale_ratio = scale
+    rot = float(rotation) * np.pi / 180.0
+    #translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio)
+    t1 = trans.SimilarityTransform(scale=scale_ratio)
+    cx = center[0] * scale_ratio
+    cy = center[1] * scale_ratio
+    t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy))
+    t3 = trans.SimilarityTransform(rotation=rot)
+    t4 = trans.SimilarityTransform(translation=(output_size / 2,
+                                                output_size / 2))
+    t = t1 + t2 + t3 + t4
+    M = t.params[0:2]
+    cropped = cv2.warpAffine(data,
+                             M, (output_size, output_size),
+                             borderValue=0.0)
+    return cropped, M
+
+
+def trans_points2d(pts, M):
+    new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
+    for i in range(pts.shape[0]):
+        pt = pts[i]
+        new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
+        new_pt = np.dot(M, new_pt)
+        #print('new_pt', new_pt.shape, new_pt)
+        new_pts[i] = new_pt[0:2]
+
+    return new_pts
+
+
+def trans_points3d(pts, M):
+    scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1])
+    #print(scale)
+    new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
+    for i in range(pts.shape[0]):
+        pt = pts[i]
+        new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
+        new_pt = np.dot(M, new_pt)
+        #print('new_pt', new_pt.shape, new_pt)
+        new_pts[i][0:2] = new_pt[0:2]
+        new_pts[i][2] = pts[i][2] * scale
+
+    return new_pts
+
+
+def trans_points(pts, M):
+    if pts.shape[1] == 2:
+        return trans_points2d(pts, M)
+    else:
+        return trans_points3d(pts, M)
+
diff --git a/mlops/serving/SwimSwap/models/__init__.py b/mlops/serving/SwimSwap/models/__init__.py
new file mode 100644
index 0000000..289de91
--- /dev/null
+++ b/mlops/serving/SwimSwap/models/__init__.py
@@ -0,0 +1,4 @@
+from .arcface_models import ArcMarginModel
+from .arcface_models import ResNet
+from .arcface_models import IRBlock
+from .arcface_models import SEBlock
\ No newline at end of file
diff --git a/mlops/serving/SwimSwap/models/arcface_models.py b/mlops/serving/SwimSwap/models/arcface_models.py
new file mode 100644
index 0000000..39a6ac5
--- /dev/null
+++ b/mlops/serving/SwimSwap/models/arcface_models.py
@@ -0,0 +1,163 @@
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn import Parameter
+from .config import device, num_classes
+
+
+
+class SEBlock(nn.Module):
+    def __init__(self, channel, reduction=16):
+        super(SEBlock, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction),
+            nn.PReLU(),
+            nn.Linear(channel // reduction, channel),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1, 1)
+        return x * y
+
+
+class IRBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
+        super(IRBlock, self).__init__()
+        self.bn0 = nn.BatchNorm2d(inplanes)
+        self.conv1 = conv3x3(inplanes, inplanes)
+        self.bn1 = nn.BatchNorm2d(inplanes)
+        self.prelu = nn.PReLU()
+        self.conv2 = conv3x3(inplanes, planes, stride)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+        self.use_se = use_se
+        if self.use_se:
+            self.se = SEBlock(planes)
+
+    def forward(self, x):
+        residual = x
+        out = self.bn0(x)
+        out = self.conv1(out)
+        out = self.bn1(out)
+        out = self.prelu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        if self.use_se:
+            out = self.se(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.prelu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, use_se=True):
+        self.inplanes = 64
+        self.use_se = use_se
+        super(ResNet, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.prelu = nn.PReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.bn2 = nn.BatchNorm2d(512)
+        self.dropout = nn.Dropout()
+        self.fc = nn.Linear(512 * 7 * 7, 512)
+        self.bn3 = nn.BatchNorm1d(512)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.xavier_normal_(m.weight)
+            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.xavier_normal_(m.weight)
+                nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
+        self.inplanes = planes
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, use_se=self.use_se))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.prelu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.bn2(x)
+        x = self.dropout(x)
+        # feature = x
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        x = self.bn3(x)
+
+        return x
+
+
+class ArcMarginModel(nn.Module):
+    def __init__(self, args):
+        super(ArcMarginModel, self).__init__()
+
+        self.weight = Parameter(torch.FloatTensor(num_classes, args.emb_size))
+        nn.init.xavier_uniform_(self.weight)
+
+        self.easy_margin = args.easy_margin
+        self.m = args.margin_m
+        self.s = args.margin_s
+
+        self.cos_m = math.cos(self.m)
+        self.sin_m = math.sin(self.m)
+        self.th = math.cos(math.pi - self.m)
+        self.mm = math.sin(math.pi - self.m) * self.m
+
+    def forward(self, input, label):
+        x = F.normalize(input)
+        W = F.normalize(self.weight)
+        cosine = F.linear(x, W)
+        sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
+        phi = cosine * self.cos_m - sine * self.sin_m  # cos(theta + m)
+        if self.easy_margin:
+            phi = torch.where(cosine > 0, phi, cosine)
+        else:
+            phi = torch.where(cosine > self.th, phi, cosine - self.mm)
+        one_hot = torch.zeros(cosine.size(), device=device)
+        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
+        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
+        output *= self.s
+        return output
\ No newline at end of file
diff --git a/mlops/serving/SwimSwap/models/base_model.py b/mlops/serving/SwimSwap/models/base_model.py
new file mode 100644
index 0000000..0a6474a
--- /dev/null
+++ b/mlops/serving/SwimSwap/models/base_model.py
@@ -0,0 +1,138 @@
+import os
+import torch
+import sys
+
+class BaseModel(torch.nn.Module):
+    def name(self):
+        return 'BaseModel'
+
+    def initialize(self, opt):
+        self.opt = opt
+        self.gpu_ids = opt.gpu_ids
+        self.isTrain = opt.isTrain
+        self.Tensor = torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor
+        self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)
+
+    def set_input(self, input):
+        self.input = input
+
+    def forward(self):
+        pass
+
+    # used in test time, no backprop
+    def test(self):
+        pass
+
+    def get_image_paths(self):
+        pass
+
+    def optimize_parameters(self):
+        pass
+
+    def get_current_visuals(self):
+        return self.input
+
+    def get_current_errors(self):
+        return {}
+
+    def save(self, label):
+        pass
+    
+    # helper saving function that can be used by subclasses
+    def save_network(self, network, network_label, epoch_label, gpu_ids=None):
+        save_filename = '{}_net_{}.pth'.format(epoch_label, network_label)
+        save_path = os.path.join(self.save_dir, save_filename)
+        torch.save(network.cpu().state_dict(), save_path)
+        if torch.cuda.is_available():
+            network.cuda()
+
+    def save_optim(self, network, network_label, epoch_label, gpu_ids=None):
+        save_filename = '{}_optim_{}.pth'.format(epoch_label, network_label)
+        save_path = os.path.join(self.save_dir, save_filename)
+        torch.save(network.state_dict(), save_path)
+
+
+    # helper loading function that can be used by subclasses
+    def load_network(self, network, network_label, epoch_label, save_dir=''):        
+        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
+        if not save_dir:
+            save_dir = self.save_dir
+        save_path = os.path.join(save_dir, save_filename)        
+        if not os.path.isfile(save_path):
+            print('%s not exists yet!' % save_path)
+            if network_label == 'G':
+                raise('Generator must exist!')
+        else:
+            #network.load_state_dict(torch.load(save_path))
+            try:
+                network.load_state_dict(torch.load(save_path))
+            except:   
+                pretrained_dict = torch.load(save_path)                
+                model_dict = network.state_dict()
+                try:
+                    pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}                    
+                    network.load_state_dict(pretrained_dict)
+                    if self.opt.verbose:
+                        print('Pretrained network %s has excessive layers; Only loading layers that are used' % network_label)
+                except:
+                    print('Pretrained network %s has fewer layers; The following are not initialized:' % network_label)
+                    for k, v in pretrained_dict.items():                      
+                        if v.size() == model_dict[k].size():
+                            model_dict[k] = v
+
+                    if sys.version_info >= (3,0):
+                        not_initialized = set()
+                    else:
+                        from sets import Set
+                        not_initialized = Set()                    
+
+                    for k, v in model_dict.items():
+                        if k not in pretrained_dict or v.size() != pretrained_dict[k].size():
+                            not_initialized.add(k.split('.')[0])
+                    
+                    print(sorted(not_initialized))
+                    network.load_state_dict(model_dict)
+
+    # helper loading function that can be used by subclasses
+    def load_optim(self, network, network_label, epoch_label, save_dir=''):        
+        save_filename = '%s_optim_%s.pth' % (epoch_label, network_label)
+        if not save_dir:
+            save_dir = self.save_dir
+        save_path = os.path.join(save_dir, save_filename)        
+        if not os.path.isfile(save_path):
+            print('%s not exists yet!' % save_path)
+            if network_label == 'G':
+                raise('Generator must exist!')
+        else:
+            #network.load_state_dict(torch.load(save_path))
+            try:
+                network.load_state_dict(torch.load(save_path, map_location=torch.device("cpu")))
+            except:   
+                pretrained_dict = torch.load(save_path, map_location=torch.device("cpu"))                
+                model_dict = network.state_dict()
+                try:
+                    pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}                    
+                    network.load_state_dict(pretrained_dict)
+                    if self.opt.verbose:
+                        print('Pretrained network %s has excessive layers; Only loading layers that are used' % network_label)
+                except:
+                    print('Pretrained network %s has fewer layers; The following are not initialized:' % network_label)
+                    for k, v in pretrained_dict.items():                      
+                        if v.size() == model_dict[k].size():
+                            model_dict[k] = v
+
+                    if sys.version_info >= (3,0):
+                        not_initialized = set()
+                    else:
+                        from sets import Set
+                        not_initialized = Set()                    
+
+                    for k, v in model_dict.items():
+                        if k not in pretrained_dict or v.size() != pretrained_dict[k].size():
+                            not_initialized.add(k.split('.')[0])
+                    
+                    print(sorted(not_initialized))
+                    network.load_state_dict(model_dict)                  
+
+    def update_learning_rate():
+        pass
diff --git a/mlops/serving/SwimSwap/models/config.py b/mlops/serving/SwimSwap/models/config.py
new file mode 100644
index 0000000..eb83edb
--- /dev/null
+++ b/mlops/serving/SwimSwap/models/config.py
@@ -0,0 +1,28 @@
+import os
+
+import torch
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # sets device for model and PyTorch tensors
+
+# Model parameters
+image_w = 112
+image_h = 112
+channel = 3
+emb_size = 512
+
+# Training parameters
+num_workers = 1  # for data-loading; right now, only 1 works with h5py
+grad_clip = 5.  # clip gradients at an absolute value of
+print_freq = 100  # print training/validation stats  every __ batches
+checkpoint = None  # path to checkpoint, None if none
+
+# Data parameters
+num_classes = 93431
+num_samples = 5179510
+DATA_DIR = 'data'
+# faces_ms1m_folder = 'data/faces_ms1m_112x112'
+faces_ms1m_folder = 'data/ms1m-retinaface-t1'
+path_imgidx = os.path.join(faces_ms1m_folder, 'train.idx')
+path_imgrec = os.path.join(faces_ms1m_folder, 'train.rec')
+IMG_DIR = 'data/images'
+pickle_file = 'data/faces_ms1m_112x112.pickle'
diff --git a/mlops/serving/SwimSwap/models/fs_model.py b/mlops/serving/SwimSwap/models/fs_model.py
new file mode 100644
index 0000000..95ac877
--- /dev/null
+++ b/mlops/serving/SwimSwap/models/fs_model.py
@@ -0,0 +1,242 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import os
+from torch.autograd import Variable
+from .base_model import BaseModel
+from . import networks
+
+class SpecificNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(SpecificNorm, self).__init__()
+        self.mean = np.array([0.485, 0.456, 0.406])
+        self.mean = torch.from_numpy(self.mean).float().cuda()
+        self.mean = self.mean.view([1, 3, 1, 1])
+
+        self.std = np.array([0.229, 0.224, 0.225])
+        self.std = torch.from_numpy(self.std).float().cuda()
+        self.std = self.std.view([1, 3, 1, 1])
+
+    def forward(self, x):
+        mean = self.mean.expand([1, 3, x.shape[2], x.shape[3]])
+        std = self.std.expand([1, 3, x.shape[2], x.shape[3]])
+
+        x = (x - mean) / std
+
+        return x
+
+class fsModel(BaseModel):
+    def name(self):
+        return 'fsModel'
+
+    def init_loss_filter(self, use_gan_feat_loss, use_vgg_loss):
+        flags = (True, use_gan_feat_loss, use_vgg_loss, True, True, True, True, True)
+
+        def loss_filter(g_gan, g_gan_feat, g_vgg, g_id, g_rec, g_mask, d_real, d_fake):
+            return [l for (l, f) in zip((g_gan, g_gan_feat, g_vgg, g_id, g_rec, g_mask, d_real, d_fake), flags) if f]
+
+        return loss_filter
+
+    def initialize(self, opt):
+        BaseModel.initialize(self, opt)
+        if opt.resize_or_crop != 'none' or not opt.isTrain:  # when training at full res this causes OOM
+            torch.backends.cudnn.benchmark = True
+        self.isTrain = opt.isTrain
+
+        device = torch.device("cuda:0")
+
+        if opt.crop_size == 224:
+            from .fs_networks import Generator_Adain_Upsample, Discriminator
+        elif opt.crop_size == 512:
+            from .fs_networks_512 import Generator_Adain_Upsample, Discriminator
+
+        # Generator network
+        self.netG = Generator_Adain_Upsample(input_nc=3, output_nc=3, latent_size=512, n_blocks=9, deep=False)
+        self.netG.to(device)
+
+        # Id network
+        netArc_checkpoint = opt.Arc_path
+        netArc_checkpoint = torch.load(netArc_checkpoint, map_location=torch.device("cpu"))
+        self.netArc = netArc_checkpoint
+        self.netArc = self.netArc.to(device)
+        self.netArc.eval()
+
+        if not self.isTrain:
+            pretrained_path = '' if not self.isTrain else opt.load_pretrain
+            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)
+            return
+
+        # Discriminator network
+        if opt.gan_mode == 'original':
+            use_sigmoid = True
+        else:
+            use_sigmoid = False
+        self.netD1 = Discriminator(input_nc=3, use_sigmoid=use_sigmoid)
+        self.netD2 = Discriminator(input_nc=3, use_sigmoid=use_sigmoid)
+        self.netD1.to(device)
+        self.netD2.to(device)
+
+        #
+        self.spNorm =SpecificNorm()
+        self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
+
+        # load networks
+        if opt.continue_train or opt.load_pretrain:
+            pretrained_path = '' if not self.isTrain else opt.load_pretrain
+            # print (pretrained_path)
+            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)
+            self.load_network(self.netD1, 'D1', opt.which_epoch, pretrained_path)
+            self.load_network(self.netD2, 'D2', opt.which_epoch, pretrained_path)
+
+
+
+        if self.isTrain:
+            # define loss functions
+            self.loss_filter = self.init_loss_filter(not opt.no_ganFeat_loss, not opt.no_vgg_loss)
+
+            self.criterionGAN = networks.GANLoss(opt.gan_mode, tensor=self.Tensor, opt=self.opt)
+            self.criterionFeat = nn.L1Loss()
+            self.criterionRec = nn.L1Loss()
+
+            # Names so we can breakout loss
+            self.loss_names = self.loss_filter('G_GAN', 'G_GAN_Feat', 'G_VGG', 'G_ID', 'G_Rec', 'D_GP',
+                                               'D_real', 'D_fake')
+
+           # initialize optimizers
+
+            # optimizer G
+            params = list(self.netG.parameters())
+            self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+
+            # optimizer D
+            params = list(self.netD1.parameters()) + list(self.netD2.parameters())
+            self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+
+    def _gradinet_penalty_D(self, netD, img_att, img_fake):
+        # interpolate sample
+        bs = img_fake.shape[0]
+        alpha = torch.rand(bs, 1, 1, 1).expand_as(img_fake).cuda()
+        interpolated = Variable(alpha * img_att + (1 - alpha) * img_fake, requires_grad=True)
+        pred_interpolated = netD.forward(interpolated)
+        pred_interpolated = pred_interpolated[-1]
+
+        # compute gradients
+        grad = torch.autograd.grad(outputs=pred_interpolated,
+                                   inputs=interpolated,
+                                   grad_outputs=torch.ones(pred_interpolated.size()).cuda(),
+                                   retain_graph=True,
+                                   create_graph=True,
+                                   only_inputs=True)[0]
+
+        # penalize gradients
+        grad = grad.view(grad.size(0), -1)
+        grad_l2norm = torch.sqrt(torch.sum(grad ** 2, dim=1))
+        loss_d_gp = torch.mean((grad_l2norm - 1) ** 2)
+
+        return loss_d_gp
+
+    def cosin_metric(self, x1, x2):
+        #return np.dot(x1, x2) / (np.linalg.norm(x1) * np.linalg.norm(x2))
+        return torch.sum(x1 * x2, dim=1) / (torch.norm(x1, dim=1) * torch.norm(x2, dim=1))
+
+    def forward(self, img_id, img_att, latent_id, latent_att, for_G=False):
+        loss_D_fake, loss_D_real, loss_D_GP = 0, 0, 0
+        loss_G_GAN, loss_G_GAN_Feat, loss_G_VGG, loss_G_ID, loss_G_Rec = 0,0,0,0,0
+
+        img_fake = self.netG.forward(img_att, latent_id)
+        if not self.isTrain:
+            return img_fake
+        img_fake_downsample = self.downsample(img_fake)
+        img_att_downsample = self.downsample(img_att)
+
+
+
+        # D_Fake
+        fea1_fake = self.netD1.forward(img_fake.detach())
+        fea2_fake = self.netD2.forward(img_fake_downsample.detach())
+        pred_fake = [fea1_fake, fea2_fake]
+        loss_D_fake = self.criterionGAN(pred_fake, False, for_discriminator=True)
+
+
+        # D_Feal
+        fea1_real = self.netD1.forward(img_att)
+        fea2_real = self.netD2.forward(img_att_downsample)
+        pred_real = [fea1_real, fea2_real]
+        fea_real = [fea1_real, fea2_real]
+        loss_D_real = self.criterionGAN(pred_real, True, for_discriminator=True)
+        #print('=====================D_Real========================')
+
+        # D_GP
+
+        loss_D_GP = 0
+
+        # G_GAN
+        fea1_fake = self.netD1.forward(img_fake)
+        fea2_fake = self.netD2.forward(img_fake_downsample)
+        #pred_fake = [fea1_fake[-1], fea2_fake[-1]]
+        pred_fake = [fea1_fake, fea2_fake]
+        fea_fake = [fea1_fake, fea2_fake]
+        loss_G_GAN = self.criterionGAN(pred_fake, True, for_discriminator=False)
+
+        # GAN feature matching loss
+        n_layers_D = 4
+        num_D = 2
+        if not self.opt.no_ganFeat_loss:
+            feat_weights = 4.0 / (n_layers_D + 1)
+            D_weights = 1.0 / num_D
+            for i in range(num_D):
+                for j in range(0, len(fea_fake[i]) - 1):
+                    loss_G_GAN_Feat += D_weights * feat_weights * \
+                                       self.criterionFeat(fea_fake[i][j],
+                                                          fea_real[i][j].detach()) * self.opt.lambda_feat
+
+
+        #G_ID
+        img_fake_down = F.interpolate(img_fake, size=(112,112))
+        img_fake_down = self.spNorm(img_fake_down)
+        latent_fake = self.netArc(img_fake_down)
+        loss_G_ID = (1 - self.cosin_metric(latent_fake, latent_id))
+        #print('=====================G_ID========================')
+        #print(loss_G_ID)
+
+        #G_Rec
+        loss_G_Rec = self.criterionRec(img_fake, img_att) * self.opt.lambda_rec
+
+        # Only return the fake_B image if necessary to save BW
+        return [self.loss_filter(loss_G_GAN, loss_G_GAN_Feat, loss_G_VGG, loss_G_ID, loss_G_Rec, loss_D_GP, loss_D_real, loss_D_fake),
+                img_fake]
+
+
+    def save(self, which_epoch):
+        self.save_network(self.netG, 'G', which_epoch, self.gpu_ids)
+        self.save_network(self.netD1, 'D1', which_epoch, self.gpu_ids)
+        self.save_network(self.netD2, 'D2', which_epoch, self.gpu_ids)
+        '''if self.gen_features:
+            self.save_network(self.netE, 'E', which_epoch, self.gpu_ids)'''
+
+    def update_fixed_params(self):
+        # after fixing the global generator for a number of iterations, also start finetuning it
+        params = list(self.netG.parameters())
+        if self.gen_features:
+            params += list(self.netE.parameters())
+        self.optimizer_G = torch.optim.Adam(params, lr=self.opt.lr, betas=(self.opt.beta1, 0.999))
+        if self.opt.verbose:
+            print('------------ Now also finetuning global generator -----------')
+
+    def update_learning_rate(self):
+        lrd = self.opt.lr / self.opt.niter_decay
+        lr = self.old_lr - lrd
+        for param_group in self.optimizer_D.param_groups:
+            param_group['lr'] = lr
+        for param_group in self.optimizer_G.param_groups:
+            param_group['lr'] = lr
+        if self.opt.verbose:
+            print('update learning rate: %f -> %f' % (self.old_lr, lr))
+        self.old_lr = lr
+
+
diff --git a/mlops/serving/SwimSwap/models/fs_networks.py b/mlops/serving/SwimSwap/models/fs_networks.py
new file mode 100644
index 0000000..227f6fa
--- /dev/null
+++ b/mlops/serving/SwimSwap/models/fs_networks.py
@@ -0,0 +1,215 @@
+"""
+Copyright (C) 2019 NVIDIA Corporation.  All rights reserved.
+Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+"""
+
+import torch
+import torch.nn as nn
+
+
+class InstanceNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(InstanceNorm, self).__init__()
+        self.epsilon = epsilon
+
+    def forward(self, x):
+        x   = x - torch.mean(x, (2, 3), True)
+        tmp = torch.mul(x, x) # or x ** 2
+        tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
+        return x * tmp
+
+class ApplyStyle(nn.Module):
+    """
+        @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
+    """
+    def __init__(self, latent_size, channels):
+        super(ApplyStyle, self).__init__()
+        self.linear = nn.Linear(latent_size, channels * 2)
+
+    def forward(self, x, latent):
+        style = self.linear(latent)  # style => [batch_size, n_channels*2]
+        shape = [-1, 2, x.size(1), 1, 1]
+        style = style.view(shape)    # [batch_size, 2, n_channels, ...]
+        #x = x * (style[:, 0] + 1.) + style[:, 1]
+        x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1
+        return x
+
+class ResnetBlock_Adain(nn.Module):
+    def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
+        super(ResnetBlock_Adain, self).__init__()
+
+        p = 0
+        conv1 = []
+        if padding_type == 'reflect':
+            conv1 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv1 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()]
+        self.conv1 = nn.Sequential(*conv1)
+        self.style1 = ApplyStyle(latent_size, dim)
+        self.act1 = activation
+
+        p = 0
+        conv2 = []
+        if padding_type == 'reflect':
+            conv2 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv2 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
+        self.conv2 = nn.Sequential(*conv2)
+        self.style2 = ApplyStyle(latent_size, dim)
+
+
+    def forward(self, x, dlatents_in_slice):
+        y = self.conv1(x)
+        y = self.style1(y, dlatents_in_slice)
+        y = self.act1(y)
+        y = self.conv2(y)
+        y = self.style2(y, dlatents_in_slice)
+        out = x + y
+        return out
+
+
+
+class Generator_Adain_Upsample(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, n_blocks=6, deep=False,
+                 norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_Upsample, self).__init__()
+        activation = nn.ReLU(True)
+        self.deep = deep
+
+        self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 64, kernel_size=7, padding=0),
+                                         norm_layer(64), activation)
+        ### downsample
+        self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(128), activation)
+        self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(256), activation)
+        self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(512), activation)
+        if self.deep:
+            self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
+                                       norm_layer(512), activation)
+
+        ### resnet blocks
+        BN = []
+        for i in range(n_blocks):
+            BN += [
+                ResnetBlock_Adain(512, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        if self.deep:
+            self.up4 = nn.Sequential(
+                nn.Upsample(scale_factor=2, mode='bilinear'),
+                nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
+                nn.BatchNorm2d(512), activation
+            )
+        self.up3 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(256), activation
+        )
+        self.up2 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(128), activation
+        )
+        self.up1 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(64), activation
+        )
+        self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, kernel_size=7, padding=0),
+                                        nn.Tanh())
+
+    def forward(self, input, dlatents):
+        x = input  # 3*224*224
+
+        skip1 = self.first_layer(x)
+        skip2 = self.down1(skip1)
+        skip3 = self.down2(skip2)
+        if self.deep:
+            skip4 = self.down3(skip3)
+            x = self.down4(skip4)
+        else:
+            x = self.down3(skip3)
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+
+        if self.deep:
+            x = self.up4(x)
+        x = self.up3(x)
+        x = self.up2(x)
+        x = self.up1(x)
+        x = self.last_layer(x)
+        x = (x + 1) / 2
+
+        return x
+
+class Discriminator(nn.Module):
+    def __init__(self, input_nc, norm_layer=nn.BatchNorm2d, use_sigmoid=False):
+        super(Discriminator, self).__init__()
+
+        kw = 4
+        padw = 1
+        self.down1 = nn.Sequential(
+            nn.Conv2d(input_nc, 64, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)
+        )
+        self.down2 = nn.Sequential(
+            nn.Conv2d(64, 128, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(128), nn.LeakyReLU(0.2, True)
+        )
+        self.down3 = nn.Sequential(
+            nn.Conv2d(128, 256, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(256), nn.LeakyReLU(0.2, True)
+        )
+        self.down4 = nn.Sequential(
+            nn.Conv2d(256, 512, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(512), nn.LeakyReLU(0.2, True)
+        )
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(512, 512, kernel_size=kw, stride=1, padding=padw),
+            norm_layer(512),
+            nn.LeakyReLU(0.2, True)
+        )
+
+        if use_sigmoid:
+            self.conv2 = nn.Sequential(
+                nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw), nn.Sigmoid()
+            )
+        else:
+            self.conv2 = nn.Sequential(
+                nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw)
+            )
+
+    def forward(self, input):
+        out = []
+        x = self.down1(input)
+        out.append(x)
+        x = self.down2(x)
+        out.append(x)
+        x = self.down3(x)
+        out.append(x)
+        x = self.down4(x)
+        out.append(x)
+        x = self.conv1(x)
+        out.append(x)
+        x = self.conv2(x)
+        out.append(x)
+        
+        return out
\ No newline at end of file
diff --git a/mlops/serving/SwimSwap/models/fs_networks_512.py b/mlops/serving/SwimSwap/models/fs_networks_512.py
new file mode 100644
index 0000000..1b9f9c8
--- /dev/null
+++ b/mlops/serving/SwimSwap/models/fs_networks_512.py
@@ -0,0 +1,232 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 16:55:48
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 16:58:06
+Description: 
+'''
+"""
+Copyright (C) 2019 NVIDIA Corporation.  All rights reserved.
+Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+"""
+
+import torch
+import torch.nn as nn
+
+
+class InstanceNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(InstanceNorm, self).__init__()
+        self.epsilon = epsilon
+
+    def forward(self, x):
+        x   = x - torch.mean(x, (2, 3), True)
+        tmp = torch.mul(x, x) # or x ** 2
+        tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
+        return x * tmp
+
+class ApplyStyle(nn.Module):
+    """
+        @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
+    """
+    def __init__(self, latent_size, channels):
+        super(ApplyStyle, self).__init__()
+        self.linear = nn.Linear(latent_size, channels * 2)
+
+    def forward(self, x, latent):
+        style = self.linear(latent)  # style => [batch_size, n_channels*2]
+        shape = [-1, 2, x.size(1), 1, 1]
+        style = style.view(shape)    # [batch_size, 2, n_channels, ...]
+        #x = x * (style[:, 0] + 1.) + style[:, 1]
+        x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1
+        return x
+
+class ResnetBlock_Adain(nn.Module):
+    def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
+        super(ResnetBlock_Adain, self).__init__()
+
+        p = 0
+        conv1 = []
+        if padding_type == 'reflect':
+            conv1 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv1 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()]
+        self.conv1 = nn.Sequential(*conv1)
+        self.style1 = ApplyStyle(latent_size, dim)
+        self.act1 = activation
+
+        p = 0
+        conv2 = []
+        if padding_type == 'reflect':
+            conv2 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv2 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
+        self.conv2 = nn.Sequential(*conv2)
+        self.style2 = ApplyStyle(latent_size, dim)
+
+
+    def forward(self, x, dlatents_in_slice):
+        y = self.conv1(x)
+        y = self.style1(y, dlatents_in_slice)
+        y = self.act1(y)
+        y = self.conv2(y)
+        y = self.style2(y, dlatents_in_slice)
+        out = x + y
+        return out
+
+
+
+class Generator_Adain_Upsample(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, n_blocks=6, deep=False,
+                 norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_Upsample, self).__init__()
+        activation = nn.ReLU(True)
+        self.deep = deep
+
+        self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 32, kernel_size=7, padding=0),
+                                         norm_layer(32), activation)
+        ### downsample
+        self.down0 = nn.Sequential(nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(64), activation)
+        self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(128), activation)
+        self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(256), activation)
+        self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(512), activation)
+        if self.deep:
+            self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
+                                       norm_layer(512), activation)
+
+        ### resnet blocks
+        BN = []
+        for i in range(n_blocks):
+            BN += [
+                ResnetBlock_Adain(512, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        if self.deep:
+            self.up4 = nn.Sequential(
+                nn.Upsample(scale_factor=2, mode='bilinear'),
+                nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
+                nn.BatchNorm2d(512), activation
+            )
+        self.up3 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(256), activation
+        )
+        self.up2 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(128), activation
+        )
+        self.up1 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(64), activation
+        )
+        self.up0 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(32), activation
+        )
+        self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(32, output_nc, kernel_size=7, padding=0),
+                                        nn.Tanh())
+
+    def forward(self, input, dlatents):
+        x = input  # 3*224*224
+
+        skip0 = self.first_layer(x)
+        skip1 = self.down0(skip0)
+        skip2 = self.down1(skip1)
+        skip3 = self.down2(skip2)
+        if self.deep:
+            skip4 = self.down3(skip3)
+            x = self.down4(skip4)
+        else:
+            x = self.down3(skip3)
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+
+        if self.deep:
+            x = self.up4(x)
+        x = self.up3(x)
+        x = self.up2(x)
+        x = self.up1(x)
+        x = self.up0(x)
+        x = self.last_layer(x)
+        x = (x + 1) / 2
+
+        return x
+
+class Discriminator(nn.Module):
+    def __init__(self, input_nc, norm_layer=nn.BatchNorm2d, use_sigmoid=False):
+        super(Discriminator, self).__init__()
+
+        kw = 4
+        padw = 1
+        self.down1 = nn.Sequential(
+            nn.Conv2d(input_nc, 64, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)
+        )
+        self.down2 = nn.Sequential(
+            nn.Conv2d(64, 128, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(128), nn.LeakyReLU(0.2, True)
+        )
+        self.down3 = nn.Sequential(
+            nn.Conv2d(128, 256, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(256), nn.LeakyReLU(0.2, True)
+        )
+        self.down4 = nn.Sequential(
+            nn.Conv2d(256, 512, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(512), nn.LeakyReLU(0.2, True)
+        )
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(512, 512, kernel_size=kw, stride=1, padding=padw),
+            norm_layer(512),
+            nn.LeakyReLU(0.2, True)
+        )
+
+        if use_sigmoid:
+            self.conv2 = nn.Sequential(
+                nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw), nn.Sigmoid()
+            )
+        else:
+            self.conv2 = nn.Sequential(
+                nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw)
+            )
+
+    def forward(self, input):
+        out = []
+        x = self.down1(input)
+        out.append(x)
+        x = self.down2(x)
+        out.append(x)
+        x = self.down3(x)
+        out.append(x)
+        x = self.down4(x)
+        out.append(x)
+        x = self.conv1(x)
+        out.append(x)
+        x = self.conv2(x)
+        out.append(x)
+        
+        return out
diff --git a/mlops/serving/SwimSwap/models/fs_networks_fix.py b/mlops/serving/SwimSwap/models/fs_networks_fix.py
new file mode 100644
index 0000000..c7b0525
--- /dev/null
+++ b/mlops/serving/SwimSwap/models/fs_networks_fix.py
@@ -0,0 +1,172 @@
+"""
+Copyright (C) 2019 NVIDIA Corporation.  All rights reserved.
+Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+"""
+
+import torch
+import torch.nn as nn
+
+
+class InstanceNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(InstanceNorm, self).__init__()
+        self.epsilon = epsilon
+
+    def forward(self, x):
+        x   = x - torch.mean(x, (2, 3), True)
+        tmp = torch.mul(x, x) # or x ** 2
+        tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
+        return x * tmp
+
+class ApplyStyle(nn.Module):
+    """
+        @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
+    """
+    def __init__(self, latent_size, channels):
+        super(ApplyStyle, self).__init__()
+        self.linear = nn.Linear(latent_size, channels * 2)
+
+    def forward(self, x, latent):
+        style = self.linear(latent)  # style => [batch_size, n_channels*2]
+        shape = [-1, 2, x.size(1), 1, 1]
+        style = style.view(shape)    # [batch_size, 2, n_channels, ...]
+        #x = x * (style[:, 0] + 1.) + style[:, 1]
+        x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1
+        return x
+
+class ResnetBlock_Adain(nn.Module):
+    def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
+        super(ResnetBlock_Adain, self).__init__()
+
+        p = 0
+        conv1 = []
+        if padding_type == 'reflect':
+            conv1 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv1 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()]
+        self.conv1 = nn.Sequential(*conv1)
+        self.style1 = ApplyStyle(latent_size, dim)
+        self.act1 = activation
+
+        p = 0
+        conv2 = []
+        if padding_type == 'reflect':
+            conv2 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv2 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
+        self.conv2 = nn.Sequential(*conv2)
+        self.style2 = ApplyStyle(latent_size, dim)
+
+
+    def forward(self, x, dlatents_in_slice):
+        y = self.conv1(x)
+        y = self.style1(y, dlatents_in_slice)
+        y = self.act1(y)
+        y = self.conv2(y)
+        y = self.style2(y, dlatents_in_slice)
+        out = x + y
+        return out
+
+
+
+class Generator_Adain_Upsample(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, n_blocks=6, deep=False,
+                 norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_Upsample, self).__init__()
+
+        activation = nn.ReLU(True)
+        
+        self.deep = deep
+        
+        self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 64, kernel_size=7, padding=0),
+                                         norm_layer(64), activation)
+        ### downsample
+        self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(128), activation)
+        self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(256), activation)
+        self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(512), activation)
+                                   
+        if self.deep:
+            self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
+                                       norm_layer(512), activation)
+
+        ### resnet blocks
+        BN = []
+        for i in range(n_blocks):
+            BN += [
+                ResnetBlock_Adain(512, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        if self.deep:
+            self.up4 = nn.Sequential(
+                nn.Upsample(scale_factor=2, mode='bilinear',align_corners=False),
+                nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
+                nn.BatchNorm2d(512), activation
+            )
+        self.up3 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear',align_corners=False),
+            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(256), activation
+        )
+        self.up2 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear',align_corners=False),
+            nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(128), activation
+        )
+        self.up1 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear',align_corners=False),
+            nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(64), activation
+        )
+        self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, kernel_size=7, padding=0))
+
+    def forward(self, input, dlatents):
+        x = input  # 3*224*224
+
+        skip1 = self.first_layer(x)
+        skip2 = self.down1(skip1)
+        skip3 = self.down2(skip2)
+        if self.deep:
+            skip4 = self.down3(skip3)
+            x = self.down4(skip4)
+        else:
+            x = self.down3(skip3)
+        bot = []
+        bot.append(x)
+        features = []
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+            bot.append(x)
+
+        if self.deep:
+            x = self.up4(x)
+            features.append(x)
+        x = self.up3(x)
+        features.append(x)
+        x = self.up2(x)
+        features.append(x)
+        x = self.up1(x)
+        features.append(x)
+        x = self.last_layer(x)
+        # x = (x + 1) / 2
+
+        # return x, bot, features, dlatents
+        return x
\ No newline at end of file
diff --git a/mlops/serving/SwimSwap/models/models.py b/mlops/serving/SwimSwap/models/models.py
new file mode 100644
index 0000000..bf9e84e
--- /dev/null
+++ b/mlops/serving/SwimSwap/models/models.py
@@ -0,0 +1,177 @@
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn import Parameter
+from .config import device, num_classes
+
+
+def create_model(opt):
+    #from .pix2pixHD_model import Pix2PixHDModel, InferenceModel
+    from .fs_model import fsModel
+    model = fsModel()
+
+    model.initialize(opt)
+    if opt.verbose:
+        print("model [%s] was created" % (model.name()))
+
+    if opt.isTrain and len(opt.gpu_ids) and not opt.fp16:
+        model = torch.nn.DataParallel(model, device_ids=opt.gpu_ids)
+
+    return model
+
+
+
+class SEBlock(nn.Module):
+    def __init__(self, channel, reduction=16):
+        super(SEBlock, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction),
+            nn.PReLU(),
+            nn.Linear(channel // reduction, channel),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1, 1)
+        return x * y
+
+
+class IRBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
+        super(IRBlock, self).__init__()
+        self.bn0 = nn.BatchNorm2d(inplanes)
+        self.conv1 = conv3x3(inplanes, inplanes)
+        self.bn1 = nn.BatchNorm2d(inplanes)
+        self.prelu = nn.PReLU()
+        self.conv2 = conv3x3(inplanes, planes, stride)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+        self.use_se = use_se
+        if self.use_se:
+            self.se = SEBlock(planes)
+
+    def forward(self, x):
+        residual = x
+        out = self.bn0(x)
+        out = self.conv1(out)
+        out = self.bn1(out)
+        out = self.prelu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        if self.use_se:
+            out = self.se(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.prelu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, use_se=True):
+        self.inplanes = 64
+        self.use_se = use_se
+        super(ResNet, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.prelu = nn.PReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.bn2 = nn.BatchNorm2d(512)
+        self.dropout = nn.Dropout()
+        self.fc = nn.Linear(512 * 7 * 7, 512)
+        self.bn3 = nn.BatchNorm1d(512)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.xavier_normal_(m.weight)
+            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.xavier_normal_(m.weight)
+                nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
+        self.inplanes = planes
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, use_se=self.use_se))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.prelu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.bn2(x)
+        x = self.dropout(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        x = self.bn3(x)
+
+        return x
+
+
+class ArcMarginModel(nn.Module):
+    def __init__(self, args):
+        super(ArcMarginModel, self).__init__()
+
+        self.weight = Parameter(torch.FloatTensor(num_classes, args.emb_size))
+        nn.init.xavier_uniform_(self.weight)
+
+        self.easy_margin = args.easy_margin
+        self.m = args.margin_m
+        self.s = args.margin_s
+
+        self.cos_m = math.cos(self.m)
+        self.sin_m = math.sin(self.m)
+        self.th = math.cos(math.pi - self.m)
+        self.mm = math.sin(math.pi - self.m) * self.m
+
+    def forward(self, input, label):
+        x = F.normalize(input)
+        W = F.normalize(self.weight)
+        cosine = F.linear(x, W)
+        sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
+        phi = cosine * self.cos_m - sine * self.sin_m  # cos(theta + m)
+        if self.easy_margin:
+            phi = torch.where(cosine > 0, phi, cosine)
+        else:
+            phi = torch.where(cosine > self.th, phi, cosine - self.mm)
+        one_hot = torch.zeros(cosine.size(), device=device)
+        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
+        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
+        output *= self.s
+        return output
diff --git a/mlops/serving/SwimSwap/models/networks.py b/mlops/serving/SwimSwap/models/networks.py
new file mode 100644
index 0000000..3c7eb4d
--- /dev/null
+++ b/mlops/serving/SwimSwap/models/networks.py
@@ -0,0 +1,845 @@
+import torch
+import torch.nn as nn
+import functools
+from torch.autograd import Variable
+import numpy as np
+from torchvision import transforms
+import torch.nn.functional as F
+
+###############################################################################
+# Functions
+###############################################################################
+def weights_init(m):
+    classname = m.__class__.__name__
+    if classname.find('Conv') != -1:
+        m.weight.data.normal_(0.0, 0.02)
+    elif classname.find('BatchNorm2d') != -1:
+        m.weight.data.normal_(1.0, 0.02)
+        m.bias.data.fill_(0)
+
+def get_norm_layer(norm_type='instance'):
+    if norm_type == 'batch':
+        norm_layer = functools.partial(nn.BatchNorm2d, affine=True)
+    elif norm_type == 'instance':
+        norm_layer = functools.partial(nn.InstanceNorm2d, affine=False)
+    else:
+        raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
+    return norm_layer
+
+def define_G(input_nc, output_nc, ngf, netG, n_downsample_global=3, n_blocks_global=9, n_local_enhancers=1, 
+             n_blocks_local=3, norm='instance', gpu_ids=[]):    
+    norm_layer = get_norm_layer(norm_type=norm)     
+    if netG == 'global':    
+        netG = GlobalGenerator(input_nc, output_nc, ngf, n_downsample_global, n_blocks_global, norm_layer)       
+    elif netG == 'local':        
+        netG = LocalEnhancer(input_nc, output_nc, ngf, n_downsample_global, n_blocks_global, 
+                                  n_local_enhancers, n_blocks_local, norm_layer)
+    elif netG == 'encoder':
+        netG = Encoder(input_nc, output_nc, ngf, n_downsample_global, norm_layer)
+    else:
+        raise('generator not implemented!')
+    print(netG)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())   
+        netG.cuda(gpu_ids[0])
+    netG.apply(weights_init)
+    return netG
+
+def define_G_Adain(input_nc, output_nc, latent_size, ngf, netG, n_downsample_global=2, n_blocks_global=4, norm='instance', gpu_ids=[]):
+    norm_layer = get_norm_layer(norm_type=norm)
+    netG = Generator_Adain(input_nc, output_nc, latent_size, ngf, n_downsample_global, n_blocks_global, norm_layer)
+    print(netG)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        netG.cuda(gpu_ids[0])
+    netG.apply(weights_init)
+    return netG
+
+def define_G_Adain_Mask(input_nc, output_nc, latent_size, ngf, netG, n_downsample_global=2, n_blocks_global=4, norm='instance', gpu_ids=[]):
+    norm_layer = get_norm_layer(norm_type=norm)
+    netG = Generator_Adain_Mask(input_nc, output_nc, latent_size, ngf, n_downsample_global, n_blocks_global, norm_layer)
+    print(netG)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        netG.cuda(gpu_ids[0])
+    netG.apply(weights_init)
+    return netG
+
+def define_G_Adain_Upsample(input_nc, output_nc, latent_size, ngf, netG, n_downsample_global=2, n_blocks_global=4, norm='instance', gpu_ids=[]):
+    norm_layer = get_norm_layer(norm_type=norm)
+    netG = Generator_Adain_Upsample(input_nc, output_nc, latent_size, ngf, n_downsample_global, n_blocks_global, norm_layer)
+    print(netG)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        netG.cuda(gpu_ids[0])
+    netG.apply(weights_init)
+    return netG
+
+def define_G_Adain_2(input_nc, output_nc, latent_size, ngf, netG, n_downsample_global=2, n_blocks_global=4, norm='instance', gpu_ids=[]):
+    norm_layer = get_norm_layer(norm_type=norm)
+    netG = Generator_Adain_2(input_nc, output_nc, latent_size, ngf, n_downsample_global, n_blocks_global, norm_layer)
+    print(netG)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        netG.cuda(gpu_ids[0])
+    netG.apply(weights_init)
+    return netG
+
+def define_D(input_nc, ndf, n_layers_D, norm='instance', use_sigmoid=False, num_D=1, getIntermFeat=False, gpu_ids=[]):        
+    norm_layer = get_norm_layer(norm_type=norm)   
+    netD = MultiscaleDiscriminator(input_nc, ndf, n_layers_D, norm_layer, use_sigmoid, num_D, getIntermFeat)   
+    print(netD)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        netD.cuda(gpu_ids[0])
+    netD.apply(weights_init)
+    return netD
+
+def print_network(net):
+    if isinstance(net, list):
+        net = net[0]
+    num_params = 0
+    for param in net.parameters():
+        num_params += param.numel()
+    print(net)
+    print('Total number of parameters: %d' % num_params)
+
+##############################################################################
+# Losses
+##############################################################################
+class GANLoss(nn.Module):
+    def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0,
+                 tensor=torch.FloatTensor, opt=None):
+        super(GANLoss, self).__init__()
+        self.real_label = target_real_label
+        self.fake_label = target_fake_label
+        self.real_label_tensor = None
+        self.fake_label_tensor = None
+        self.zero_tensor = None
+        self.Tensor = tensor
+        self.gan_mode = gan_mode
+        self.opt = opt
+        if gan_mode == 'ls':
+            pass
+        elif gan_mode == 'original':
+            pass
+        elif gan_mode == 'w':
+            pass
+        elif gan_mode == 'hinge':
+            pass
+        else:
+            raise ValueError('Unexpected gan_mode {}'.format(gan_mode))
+
+    def get_target_tensor(self, input, target_is_real):
+        if target_is_real:
+            if self.real_label_tensor is None:
+                self.real_label_tensor = self.Tensor(1).fill_(self.real_label)
+                self.real_label_tensor.requires_grad_(False)
+            return self.real_label_tensor.expand_as(input)
+        else:
+            if self.fake_label_tensor is None:
+                self.fake_label_tensor = self.Tensor(1).fill_(self.fake_label)
+                self.fake_label_tensor.requires_grad_(False)
+            return self.fake_label_tensor.expand_as(input)
+
+    def get_zero_tensor(self, input):
+        if self.zero_tensor is None:
+            self.zero_tensor = self.Tensor(1).fill_(0)
+            self.zero_tensor.requires_grad_(False)
+        return self.zero_tensor.expand_as(input)
+
+    def loss(self, input, target_is_real, for_discriminator=True):
+        if self.gan_mode == 'original':  # cross entropy loss
+            target_tensor = self.get_target_tensor(input, target_is_real)
+            loss = F.binary_cross_entropy_with_logits(input, target_tensor)
+            return loss
+        elif self.gan_mode == 'ls':
+            target_tensor = self.get_target_tensor(input, target_is_real)
+            return F.mse_loss(input, target_tensor)
+        elif self.gan_mode == 'hinge':
+            if for_discriminator:
+                if target_is_real:
+                    minval = torch.min(input - 1, self.get_zero_tensor(input))
+                    loss = -torch.mean(minval)
+                else:
+                    minval = torch.min(-input - 1, self.get_zero_tensor(input))
+                    loss = -torch.mean(minval)
+            else:
+                assert target_is_real, "The generator's hinge loss must be aiming for real"
+                loss = -torch.mean(input)
+            return loss
+        else:
+            # wgan
+            if target_is_real:
+                return -input.mean()
+            else:
+                return input.mean()
+
+    def __call__(self, input, target_is_real, for_discriminator=True):
+        # computing loss is a bit complicated because |input| may not be
+        # a tensor, but list of tensors in case of multiscale discriminator
+        if isinstance(input, list):
+            loss = 0
+            for pred_i in input:
+                if isinstance(pred_i, list):
+                    pred_i = pred_i[-1]
+                loss_tensor = self.loss(pred_i, target_is_real, for_discriminator)
+                bs = 1 if len(loss_tensor.size()) == 0 else loss_tensor.size(0)
+                new_loss = torch.mean(loss_tensor.view(bs, -1), dim=1)
+                loss += new_loss
+            return loss / len(input)
+        else:
+            return self.loss(input, target_is_real, for_discriminator)
+
+class VGGLoss(nn.Module):
+    def __init__(self, gpu_ids):
+        super(VGGLoss, self).__init__()        
+        self.vgg = Vgg19().cuda()
+        self.criterion = nn.L1Loss()
+        self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]        
+
+    def forward(self, x, y):              
+        x_vgg, y_vgg = self.vgg(x), self.vgg(y)
+        loss = 0
+        for i in range(len(x_vgg)):
+            loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())        
+        return loss
+
+##############################################################################
+# Generator
+##############################################################################
+class LocalEnhancer(nn.Module):
+    def __init__(self, input_nc, output_nc, ngf=32, n_downsample_global=3, n_blocks_global=9, 
+                 n_local_enhancers=1, n_blocks_local=3, norm_layer=nn.BatchNorm2d, padding_type='reflect'):        
+        super(LocalEnhancer, self).__init__()
+        self.n_local_enhancers = n_local_enhancers
+        
+        ###### global generator model #####           
+        ngf_global = ngf * (2**n_local_enhancers)
+        model_global = GlobalGenerator(input_nc, output_nc, ngf_global, n_downsample_global, n_blocks_global, norm_layer).model        
+        model_global = [model_global[i] for i in range(len(model_global)-3)] # get rid of final convolution layers        
+        self.model = nn.Sequential(*model_global)                
+
+        ###### local enhancer layers #####
+        for n in range(1, n_local_enhancers+1):
+            ### downsample            
+            ngf_global = ngf * (2**(n_local_enhancers-n))
+            model_downsample = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf_global, kernel_size=7, padding=0), 
+                                norm_layer(ngf_global), nn.ReLU(True),
+                                nn.Conv2d(ngf_global, ngf_global * 2, kernel_size=3, stride=2, padding=1), 
+                                norm_layer(ngf_global * 2), nn.ReLU(True)]
+            ### residual blocks
+            model_upsample = []
+            for i in range(n_blocks_local):
+                model_upsample += [ResnetBlock(ngf_global * 2, padding_type=padding_type, norm_layer=norm_layer)]
+
+            ### upsample
+            model_upsample += [nn.ConvTranspose2d(ngf_global * 2, ngf_global, kernel_size=3, stride=2, padding=1, output_padding=1), 
+                               norm_layer(ngf_global), nn.ReLU(True)]      
+
+            ### final convolution
+            if n == n_local_enhancers:                
+                model_upsample += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]                       
+            
+            setattr(self, 'model'+str(n)+'_1', nn.Sequential(*model_downsample))
+            setattr(self, 'model'+str(n)+'_2', nn.Sequential(*model_upsample))                  
+        
+        self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
+
+    def forward(self, input): 
+        ### create input pyramid
+        input_downsampled = [input]
+        for i in range(self.n_local_enhancers):
+            input_downsampled.append(self.downsample(input_downsampled[-1]))
+
+        ### output at coarest level
+        output_prev = self.model(input_downsampled[-1])        
+        ### build up one layer at a time
+        for n_local_enhancers in range(1, self.n_local_enhancers+1):
+            model_downsample = getattr(self, 'model'+str(n_local_enhancers)+'_1')
+            model_upsample = getattr(self, 'model'+str(n_local_enhancers)+'_2')            
+            input_i = input_downsampled[self.n_local_enhancers-n_local_enhancers]            
+            output_prev = model_upsample(model_downsample(input_i) + output_prev)
+        return output_prev
+
+class GlobalGenerator(nn.Module):
+    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, 
+                 padding_type='reflect'):
+        assert(n_blocks >= 0)
+        super(GlobalGenerator, self).__init__()        
+        activation = nn.ReLU(True)        
+
+        model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2**i
+            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), activation]
+
+        ### resnet blocks
+        mult = 2**n_downsampling
+        for i in range(n_blocks):
+            model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer)]
+        
+        ### upsample         
+        for i in range(n_downsampling):
+            mult = 2**(n_downsampling - i)
+            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, output_padding=1),
+                       norm_layer(int(ngf * mult / 2)), activation]
+        model += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]        
+        self.model = nn.Sequential(*model)
+            
+    def forward(self, input):
+        return self.model(input)             
+        
+# Define a resnet block
+class ResnetBlock(nn.Module):
+    def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False):
+        super(ResnetBlock, self).__init__()
+        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout)
+
+    def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout):
+        conv_block = []
+        p = 0
+        if padding_type == 'reflect':
+            conv_block += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv_block += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+
+        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p),
+                       norm_layer(dim),
+                       activation]
+        if use_dropout:
+            conv_block += [nn.Dropout(0.5)]
+
+        p = 0
+        if padding_type == 'reflect':
+            conv_block += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv_block += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p),
+                       norm_layer(dim)]
+
+        return nn.Sequential(*conv_block)
+
+    def forward(self, x):
+        out = x + self.conv_block(x)
+        return out
+
+class InstanceNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(InstanceNorm, self).__init__()
+        self.epsilon = epsilon
+
+    def forward(self, x):
+        x   = x - torch.mean(x, (2, 3), True)
+        tmp = torch.mul(x, x) # or x ** 2
+        tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
+        return x * tmp
+
+class SpecificNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(SpecificNorm, self).__init__()
+        self.mean = np.array([0.485, 0.456, 0.406])
+        self.mean = torch.from_numpy(self.mean).float().cuda()
+        self.mean = self.mean.view([1, 3, 1, 1])
+
+        self.std = np.array([0.229, 0.224, 0.225])
+        self.std = torch.from_numpy(self.std).float().cuda()
+        self.std = self.std.view([1, 3, 1, 1])
+
+    def forward(self, x):
+        mean = self.mean.expand([1, 3, x.shape[2], x.shape[3]])
+        std = self.std.expand([1, 3, x.shape[2], x.shape[3]])
+
+        x = (x - mean) / std
+
+        return x
+
+class ApplyStyle(nn.Module):
+    """
+        @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
+    """
+    def __init__(self, latent_size, channels):
+        super(ApplyStyle, self).__init__()
+        self.linear = nn.Linear(latent_size, channels * 2)
+
+    def forward(self, x, latent):
+        style = self.linear(latent)  # style => [batch_size, n_channels*2]
+        shape = [-1, 2, x.size(1), 1, 1]
+        style = style.view(shape)    # [batch_size, 2, n_channels, ...]
+        x = x * (style[:, 0] + 1.) + style[:, 1]
+        return x
+
+class ResnetBlock_Adain(nn.Module):
+    def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
+        super(ResnetBlock_Adain, self).__init__()
+
+        p = 0
+        conv1 = []
+        if padding_type == 'reflect':
+            conv1 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv1 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()]
+        self.conv1 = nn.Sequential(*conv1)
+        self.style1 = ApplyStyle(latent_size, dim)
+        self.act1 = activation
+
+        p = 0
+        conv2 = []
+        if padding_type == 'reflect':
+            conv2 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv2 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
+        self.conv2 = nn.Sequential(*conv2)
+        self.style2 = ApplyStyle(latent_size, dim)
+
+
+    def forward(self, x, dlatents_in_slice):
+        y = self.conv1(x)
+        y = self.style1(y, dlatents_in_slice)
+        y = self.act1(y)
+        y = self.conv2(y)
+        y = self.style2(y, dlatents_in_slice)
+        out = x + y
+        return out
+
+class UpBlock_Adain(nn.Module):
+    def __init__(self, dim_in, dim_out, latent_size, padding_type, activation=nn.ReLU(True)):
+        super(UpBlock_Adain, self).__init__()
+
+        p = 0
+        conv1 = [nn.Upsample(scale_factor=2, mode='bilinear')]
+        if padding_type == 'reflect':
+            conv1 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv1 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv1 += [nn.Conv2d(dim_in, dim_out, kernel_size=3, padding = p), InstanceNorm()]
+        self.conv1 = nn.Sequential(*conv1)
+        self.style1 = ApplyStyle(latent_size, dim_out)
+        self.act1 = activation
+
+
+    def forward(self, x, dlatents_in_slice):
+        y = self.conv1(x)
+        y = self.style1(y, dlatents_in_slice)
+        y = self.act1(y)
+        return y
+
+class Encoder(nn.Module):
+    def __init__(self, input_nc, output_nc, ngf=32, n_downsampling=4, norm_layer=nn.BatchNorm2d):
+        super(Encoder, self).__init__()        
+        self.output_nc = output_nc        
+
+        model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), 
+                 norm_layer(ngf), nn.ReLU(True)]             
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2**i
+            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), nn.ReLU(True)]
+
+        ### upsample         
+        for i in range(n_downsampling):
+            mult = 2**(n_downsampling - i)
+            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, output_padding=1),
+                       norm_layer(int(ngf * mult / 2)), nn.ReLU(True)]        
+
+        model += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]
+        self.model = nn.Sequential(*model) 
+
+    def forward(self, input, inst):
+        outputs = self.model(input)
+
+        # instance-wise average pooling
+        outputs_mean = outputs.clone()
+        inst_list = np.unique(inst.cpu().numpy().astype(int))        
+        for i in inst_list:
+            for b in range(input.size()[0]):
+                indices = (inst[b:b+1] == int(i)).nonzero() # n x 4            
+                for j in range(self.output_nc):
+                    output_ins = outputs[indices[:,0] + b, indices[:,1] + j, indices[:,2], indices[:,3]]                    
+                    mean_feat = torch.mean(output_ins).expand_as(output_ins)                                        
+                    outputs_mean[indices[:,0] + b, indices[:,1] + j, indices[:,2], indices[:,3]] = mean_feat                       
+        return outputs_mean
+
+
+class Generator_Adain(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, ngf=64, n_downsampling=2, n_blocks=4, norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain, self).__init__()
+        activation = nn.ReLU(True)
+
+        Enc = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2 ** i
+            Enc += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), activation]
+        self.Encoder = nn.Sequential(*Enc)
+
+        ### resnet blocks
+        BN = []
+        mult = 2 ** n_downsampling
+        for i in range(n_blocks):
+            BN += [ResnetBlock_Adain(ngf*mult, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+        '''self.ResBlockAdain1 = ResnetBlock_Adain(ngf * mult, latent_size=latent_size, padding_type=padding_type,
+                                                activation=activation)
+        self.ResBlockAdain2 = ResnetBlock_Adain(ngf * mult, latent_size=latent_size, padding_type=padding_type,
+                                                activation=activation)
+        self.ResBlockAdain3 = ResnetBlock_Adain(ngf * mult, latent_size=latent_size, padding_type=padding_type,
+                                                activation=activation)
+        self.ResBlockAdain4 = ResnetBlock_Adain(ngf * mult, latent_size=latent_size, padding_type=padding_type,
+                                                activation=activation)'''
+
+        ### upsample
+        Dec = []
+        for i in range(n_downsampling):
+            mult = 2 ** (n_downsampling - i)
+            Dec += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1,
+                                         output_padding=1),
+                      norm_layer(int(ngf * mult / 2)), activation]
+        Dec += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]
+
+        self.Decoder = nn.Sequential(*Dec)
+        #self.model = nn.Sequential(*model)
+        self.spNorm = SpecificNorm()
+
+    def forward(self, input, dlatents):
+        x = input
+        x = self.Encoder(x)
+
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+        '''x = self.ResBlockAdain1(x, dlatents)
+        x = self.ResBlockAdain2(x, dlatents)
+        x = self.ResBlockAdain3(x, dlatents)
+        x = self.ResBlockAdain4(x, dlatents)'''
+
+        x = self.Decoder(x)
+
+        x = (x + 1) / 2
+        x = self.spNorm(x)
+
+        return x
+
+class Generator_Adain_Mask(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, ngf=64, n_downsampling=2, n_blocks=4, norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_Mask, self).__init__()
+        activation = nn.ReLU(True)
+
+        Enc = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2 ** i
+            Enc += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), activation]
+        self.Encoder = nn.Sequential(*Enc)
+
+        ### resnet blocks
+        BN = []
+        mult = 2 ** n_downsampling
+        for i in range(n_blocks):
+            BN += [ResnetBlock_Adain(ngf*mult, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        ### upsample
+        Dec = []
+        for i in range(n_downsampling):
+            mult = 2 ** (n_downsampling - i)
+            Dec += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1,
+                                         output_padding=1),
+                      norm_layer(int(ngf * mult / 2)), activation]
+        Fake_out = [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]
+        Mast_out = [nn.ReflectionPad2d(3), nn.Conv2d(ngf, 1, kernel_size=7, padding=0), nn.Sigmoid()]
+
+        self.Decoder = nn.Sequential(*Dec)
+        #self.model = nn.Sequential(*model)
+        self.spNorm = SpecificNorm()
+        self.Fake_out = nn.Sequential(*Fake_out)
+        self.Mask_out = nn.Sequential(*Mast_out)
+
+    def forward(self, input, dlatents):
+        x = input
+        x = self.Encoder(x)
+
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+
+        x = self.Decoder(x)
+
+        fake_out = self.Fake_out(x)
+        mask_out = self.Mask_out(x)
+
+        fake_out = (fake_out + 1) / 2
+        fake_out = self.spNorm(fake_out)
+
+        generated = fake_out * mask_out + input * (1-mask_out)
+
+        return generated, mask_out
+
+class Generator_Adain_Upsample(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, ngf=64, n_downsampling=2, n_blocks=4, norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_Upsample, self).__init__()
+        activation = nn.ReLU(True)
+
+        Enc = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2 ** i
+            Enc += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), activation]
+        self.Encoder = nn.Sequential(*Enc)
+
+        ### resnet blocks
+        BN = []
+        mult = 2 ** n_downsampling
+        for i in range(n_blocks):
+            BN += [ResnetBlock_Adain(ngf*mult, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        ### upsample
+        Dec = []
+        for i in range(n_downsampling):
+            mult = 2 ** (n_downsampling - i)
+            '''Dec += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1,
+                                         output_padding=1),
+                      norm_layer(int(ngf * mult / 2)), activation]'''
+            Dec += [nn.Upsample(scale_factor=2, mode='bilinear'),
+                    nn.Conv2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=1, padding=1),
+                    norm_layer(int(ngf * mult / 2)), activation]
+        Dec += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]
+
+        self.Decoder = nn.Sequential(*Dec)
+        self.spNorm = SpecificNorm()
+
+    def forward(self, input, dlatents):
+        x = input
+        x = self.Encoder(x)
+
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+
+        x = self.Decoder(x)
+
+        x = (x + 1) / 2
+        x = self.spNorm(x)
+
+        return x
+
+class Generator_Adain_2(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, ngf=64, n_downsampling=2, n_blocks=4, norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_2, self).__init__()
+        activation = nn.ReLU(True)
+
+        Enc = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2 ** i
+            Enc += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), activation]
+        self.Encoder = nn.Sequential(*Enc)
+
+        ### resnet blocks
+        BN = []
+        mult = 2 ** n_downsampling
+        for i in range(n_blocks):
+            BN += [ResnetBlock_Adain(ngf*mult, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        ### upsample
+        Dec = []
+        for i in range(n_downsampling):
+            mult = 2 ** (n_downsampling - i)
+            Dec += [UpBlock_Adain(dim_in=ngf * mult, dim_out=int(ngf * mult / 2), latent_size=latent_size, padding_type=padding_type)]
+        layer_out = [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]
+
+        self.Decoder = nn.Sequential(*Dec)
+        #self.model = nn.Sequential(*model)
+        self.spNorm = SpecificNorm()
+        self.layer_out = nn.Sequential(*layer_out)
+
+    def forward(self, input, dlatents):
+        x = input
+        x = self.Encoder(x)
+
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+
+        for i in range(len(self.Decoder)):
+            x = self.Decoder[i](x, dlatents)
+
+        x = self.layer_out(x)
+
+        x = (x + 1) / 2
+        x = self.spNorm(x)
+
+        return x
+
+class MultiscaleDiscriminator(nn.Module):
+    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, 
+                 use_sigmoid=False, num_D=3, getIntermFeat=False):
+        super(MultiscaleDiscriminator, self).__init__()
+        self.num_D = num_D
+        self.n_layers = n_layers
+        self.getIntermFeat = getIntermFeat
+     
+        for i in range(num_D):
+            netD = NLayerDiscriminator(input_nc, ndf, n_layers, norm_layer, use_sigmoid, getIntermFeat)
+            if getIntermFeat:                                
+                for j in range(n_layers+2):
+                    setattr(self, 'scale'+str(i)+'_layer'+str(j), getattr(netD, 'model'+str(j)))                                   
+            else:
+                setattr(self, 'layer'+str(i), netD.model)
+
+        self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
+
+    def singleD_forward(self, model, input):
+        if self.getIntermFeat:
+            result = [input]
+            for i in range(len(model)):
+                result.append(model[i](result[-1]))
+            return result[1:]
+        else:
+            return [model(input)]
+
+    def forward(self, input):        
+        num_D = self.num_D
+        result = []
+        input_downsampled = input
+        for i in range(num_D):
+            if self.getIntermFeat:
+                model = [getattr(self, 'scale'+str(num_D-1-i)+'_layer'+str(j)) for j in range(self.n_layers+2)]
+            else:
+                model = getattr(self, 'layer'+str(num_D-1-i))
+            result.append(self.singleD_forward(model, input_downsampled))
+            if i != (num_D-1):
+                input_downsampled = self.downsample(input_downsampled)
+        return result
+        
+# Defines the PatchGAN discriminator with the specified arguments.
+class NLayerDiscriminator(nn.Module):
+    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, getIntermFeat=False):
+        super(NLayerDiscriminator, self).__init__()
+        self.getIntermFeat = getIntermFeat
+        self.n_layers = n_layers
+
+        kw = 4
+        padw = 1
+        sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]]
+
+        nf = ndf
+        for n in range(1, n_layers):
+            nf_prev = nf
+            nf = min(nf * 2, 512)
+            sequence += [[
+                nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw),
+                norm_layer(nf), nn.LeakyReLU(0.2, True)
+            ]]
+
+        nf_prev = nf
+        nf = min(nf * 2, 512)
+        sequence += [[
+            nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
+            norm_layer(nf),
+            nn.LeakyReLU(0.2, True)
+        ]]
+
+        if use_sigmoid:
+            sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw), nn.Sigmoid()]]
+        else:
+            sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
+
+        if getIntermFeat:
+            for n in range(len(sequence)):
+                setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
+        else:
+            sequence_stream = []
+            for n in range(len(sequence)):
+                sequence_stream += sequence[n]
+            self.model = nn.Sequential(*sequence_stream)
+
+    def forward(self, input):
+        if self.getIntermFeat:
+            res = [input]
+            for n in range(self.n_layers+2):
+                model = getattr(self, 'model'+str(n))
+                res.append(model(res[-1]))
+            return res[1:]
+        else:
+            return self.model(input)        
+
+from torchvision import models
+class Vgg19(torch.nn.Module):
+    def __init__(self, requires_grad=False):
+        super(Vgg19, self).__init__()
+        vgg_pretrained_features = models.vgg19(pretrained=True).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        for x in range(2):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(2, 7):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(7, 12):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(12, 21):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(21, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h_relu1 = self.slice1(X)
+        h_relu2 = self.slice2(h_relu1)        
+        h_relu3 = self.slice3(h_relu2)        
+        h_relu4 = self.slice4(h_relu3)        
+        h_relu5 = self.slice5(h_relu4)                
+        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
+        return out
diff --git a/mlops/serving/SwimSwap/models/pix2pixHD_model.py b/mlops/serving/SwimSwap/models/pix2pixHD_model.py
new file mode 100644
index 0000000..fafdec0
--- /dev/null
+++ b/mlops/serving/SwimSwap/models/pix2pixHD_model.py
@@ -0,0 +1,304 @@
+import numpy as np
+import torch
+import os
+from torch.autograd import Variable
+from util.image_pool import ImagePool
+from .base_model import BaseModel
+from . import networks
+
+class Pix2PixHDModel(BaseModel):
+    def name(self):
+        return 'Pix2PixHDModel'
+    
+    def init_loss_filter(self, use_gan_feat_loss, use_vgg_loss):
+        flags = (True, use_gan_feat_loss, use_vgg_loss, True, True)
+        def loss_filter(g_gan, g_gan_feat, g_vgg, d_real, d_fake):
+            return [l for (l,f) in zip((g_gan,g_gan_feat,g_vgg,d_real,d_fake),flags) if f]
+        return loss_filter
+    
+    def initialize(self, opt):
+        BaseModel.initialize(self, opt)
+        if opt.resize_or_crop != 'none' or not opt.isTrain: # when training at full res this causes OOM
+            torch.backends.cudnn.benchmark = True
+        self.isTrain = opt.isTrain
+        self.use_features = opt.instance_feat or opt.label_feat
+        self.gen_features = self.use_features and not self.opt.load_features
+        input_nc = opt.label_nc if opt.label_nc != 0 else opt.input_nc
+
+        ##### define networks        
+        # Generator network
+        netG_input_nc = input_nc        
+        if not opt.no_instance:
+            netG_input_nc += 1
+        if self.use_features:
+            netG_input_nc += opt.feat_num                  
+        self.netG = networks.define_G(netG_input_nc, opt.output_nc, opt.ngf, opt.netG, 
+                                      opt.n_downsample_global, opt.n_blocks_global, opt.n_local_enhancers, 
+                                      opt.n_blocks_local, opt.norm, gpu_ids=self.gpu_ids)        
+
+        # Discriminator network
+        if self.isTrain:
+            use_sigmoid = opt.no_lsgan
+            netD_input_nc = input_nc + opt.output_nc
+            if not opt.no_instance:
+                netD_input_nc += 1
+            self.netD = networks.define_D(netD_input_nc, opt.ndf, opt.n_layers_D, opt.norm, use_sigmoid, 
+                                          opt.num_D, not opt.no_ganFeat_loss, gpu_ids=self.gpu_ids)
+
+        ### Encoder network
+        if self.gen_features:          
+            self.netE = networks.define_G(opt.output_nc, opt.feat_num, opt.nef, 'encoder', 
+                                          opt.n_downsample_E, norm=opt.norm, gpu_ids=self.gpu_ids)  
+        if self.opt.verbose:
+                print('---------- Networks initialized -------------')
+
+        # load networks
+        if not self.isTrain or opt.continue_train or opt.load_pretrain:
+            pretrained_path = '' if not self.isTrain else opt.load_pretrain
+            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)            
+            if self.isTrain:
+                self.load_network(self.netD, 'D', opt.which_epoch, pretrained_path)  
+            if self.gen_features:
+                self.load_network(self.netE, 'E', opt.which_epoch, pretrained_path)              
+
+        # set loss functions and optimizers
+        if self.isTrain:
+            if opt.pool_size > 0 and (len(self.gpu_ids)) > 1:
+                raise NotImplementedError("Fake Pool Not Implemented for MultiGPU")
+            self.fake_pool = ImagePool(opt.pool_size)
+            self.old_lr = opt.lr
+
+            # define loss functions
+            self.loss_filter = self.init_loss_filter(not opt.no_ganFeat_loss, not opt.no_vgg_loss)
+            
+            self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor)   
+            self.criterionFeat = torch.nn.L1Loss()
+            if not opt.no_vgg_loss:             
+                self.criterionVGG = networks.VGGLoss(self.gpu_ids)
+                
+        
+            # Names so we can breakout loss
+            self.loss_names = self.loss_filter('G_GAN','G_GAN_Feat','G_VGG','D_real', 'D_fake')
+
+            # initialize optimizers
+            # optimizer G
+            if opt.niter_fix_global > 0:                
+                import sys
+                if sys.version_info >= (3,0):
+                    finetune_list = set()
+                else:
+                    from sets import Set
+                    finetune_list = Set()
+
+                params_dict = dict(self.netG.named_parameters())
+                params = []
+                for key, value in params_dict.items():       
+                    if key.startswith('model' + str(opt.n_local_enhancers)):                    
+                        params += [value]
+                        finetune_list.add(key.split('.')[0])  
+                print('------------- Only training the local enhancer network (for %d epochs) ------------' % opt.niter_fix_global)
+                print('The layers that are finetuned are ', sorted(finetune_list))                         
+            else:
+                params = list(self.netG.parameters())
+            if self.gen_features:              
+                params += list(self.netE.parameters())         
+            self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))                            
+
+            # optimizer D                        
+            params = list(self.netD.parameters())    
+            self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+
+    def encode_input(self, label_map, inst_map=None, real_image=None, feat_map=None, infer=False):             
+        if self.opt.label_nc == 0:
+            input_label = label_map.data.cuda()
+        else:
+            # create one-hot vector for label map 
+            size = label_map.size()
+            oneHot_size = (size[0], self.opt.label_nc, size[2], size[3])
+            input_label = torch.cuda.FloatTensor(torch.Size(oneHot_size)).zero_()
+            input_label = input_label.scatter_(1, label_map.data.long().cuda(), 1.0)
+            if self.opt.data_type == 16:
+                input_label = input_label.half()
+
+        # get edges from instance map
+        if not self.opt.no_instance:
+            inst_map = inst_map.data.cuda()
+            edge_map = self.get_edges(inst_map)
+            input_label = torch.cat((input_label, edge_map), dim=1)         
+        input_label = Variable(input_label, volatile=infer)
+
+        # real images for training
+        if real_image is not None:
+            real_image = Variable(real_image.data.cuda())
+
+        # instance map for feature encoding
+        if self.use_features:
+            # get precomputed feature maps
+            if self.opt.load_features:
+                feat_map = Variable(feat_map.data.cuda())
+            if self.opt.label_feat:
+                inst_map = label_map.cuda()
+
+        return input_label, inst_map, real_image, feat_map
+
+    def discriminate(self, input_label, test_image, use_pool=False):
+        input_concat = torch.cat((input_label, test_image.detach()), dim=1)
+        if use_pool:            
+            fake_query = self.fake_pool.query(input_concat)
+            return self.netD.forward(fake_query)
+        else:
+            return self.netD.forward(input_concat)
+
+    def forward(self, label, inst, image, feat, infer=False):
+        # Encode Inputs
+        input_label, inst_map, real_image, feat_map = self.encode_input(label, inst, image, feat)  
+
+        # Fake Generation
+        if self.use_features:
+            if not self.opt.load_features:
+                feat_map = self.netE.forward(real_image, inst_map)                     
+            input_concat = torch.cat((input_label, feat_map), dim=1)                        
+        else:
+            input_concat = input_label
+        fake_image = self.netG.forward(input_concat)
+
+        # Fake Detection and Loss
+        pred_fake_pool = self.discriminate(input_label, fake_image, use_pool=True)
+        loss_D_fake = self.criterionGAN(pred_fake_pool, False)        
+
+        # Real Detection and Loss        
+        pred_real = self.discriminate(input_label, real_image)
+        loss_D_real = self.criterionGAN(pred_real, True)
+
+        # GAN loss (Fake Passability Loss)        
+        pred_fake = self.netD.forward(torch.cat((input_label, fake_image), dim=1))        
+        loss_G_GAN = self.criterionGAN(pred_fake, True)               
+        
+        # GAN feature matching loss
+        loss_G_GAN_Feat = 0
+        if not self.opt.no_ganFeat_loss:
+            feat_weights = 4.0 / (self.opt.n_layers_D + 1)
+            D_weights = 1.0 / self.opt.num_D
+            for i in range(self.opt.num_D):
+                for j in range(len(pred_fake[i])-1):
+                    loss_G_GAN_Feat += D_weights * feat_weights * \
+                        self.criterionFeat(pred_fake[i][j], pred_real[i][j].detach()) * self.opt.lambda_feat
+                   
+        # VGG feature matching loss
+        loss_G_VGG = 0
+        if not self.opt.no_vgg_loss:
+            loss_G_VGG = self.criterionVGG(fake_image, real_image) * self.opt.lambda_feat
+        
+        # Only return the fake_B image if necessary to save BW
+        return [ self.loss_filter( loss_G_GAN, loss_G_GAN_Feat, loss_G_VGG, loss_D_real, loss_D_fake ), None if not infer else fake_image ]
+
+    def inference(self, label, inst, image=None):
+        # Encode Inputs        
+        image = Variable(image) if image is not None else None
+        input_label, inst_map, real_image, _ = self.encode_input(Variable(label), Variable(inst), image, infer=True)
+
+        # Fake Generation
+        if self.use_features:
+            if self.opt.use_encoded_image:
+                # encode the real image to get feature map
+                feat_map = self.netE.forward(real_image, inst_map)
+            else:
+                # sample clusters from precomputed features             
+                feat_map = self.sample_features(inst_map)
+            input_concat = torch.cat((input_label, feat_map), dim=1)                        
+        else:
+            input_concat = input_label        
+           
+        if torch.__version__.startswith('0.4'):
+            with torch.no_grad():
+                fake_image = self.netG.forward(input_concat)
+        else:
+            fake_image = self.netG.forward(input_concat)
+        return fake_image
+
+    def sample_features(self, inst): 
+        # read precomputed feature clusters 
+        cluster_path = os.path.join(self.opt.checkpoints_dir, self.opt.name, self.opt.cluster_path)        
+        features_clustered = np.load(cluster_path, encoding='latin1').item()
+
+        # randomly sample from the feature clusters
+        inst_np = inst.cpu().numpy().astype(int)                                      
+        feat_map = self.Tensor(inst.size()[0], self.opt.feat_num, inst.size()[2], inst.size()[3])
+        for i in np.unique(inst_np):    
+            label = i if i < 1000 else i//1000
+            if label in features_clustered:
+                feat = features_clustered[label]
+                cluster_idx = np.random.randint(0, feat.shape[0]) 
+                                            
+                idx = (inst == int(i)).nonzero()
+                for k in range(self.opt.feat_num):                                    
+                    feat_map[idx[:,0], idx[:,1] + k, idx[:,2], idx[:,3]] = feat[cluster_idx, k]
+        if self.opt.data_type==16:
+            feat_map = feat_map.half()
+        return feat_map
+
+    def encode_features(self, image, inst):
+        image = Variable(image.cuda(), volatile=True)
+        feat_num = self.opt.feat_num
+        h, w = inst.size()[2], inst.size()[3]
+        block_num = 32
+        feat_map = self.netE.forward(image, inst.cuda())
+        inst_np = inst.cpu().numpy().astype(int)
+        feature = {}
+        for i in range(self.opt.label_nc):
+            feature[i] = np.zeros((0, feat_num+1))
+        for i in np.unique(inst_np):
+            label = i if i < 1000 else i//1000
+            idx = (inst == int(i)).nonzero()
+            num = idx.size()[0]
+            idx = idx[num//2,:]
+            val = np.zeros((1, feat_num+1))                        
+            for k in range(feat_num):
+                val[0, k] = feat_map[idx[0], idx[1] + k, idx[2], idx[3]].data[0]            
+            val[0, feat_num] = float(num) / (h * w // block_num)
+            feature[label] = np.append(feature[label], val, axis=0)
+        return feature
+
+    def get_edges(self, t):
+        edge = torch.cuda.ByteTensor(t.size()).zero_()
+        edge[:,:,:,1:] = edge[:,:,:,1:] | (t[:,:,:,1:] != t[:,:,:,:-1])
+        edge[:,:,:,:-1] = edge[:,:,:,:-1] | (t[:,:,:,1:] != t[:,:,:,:-1])
+        edge[:,:,1:,:] = edge[:,:,1:,:] | (t[:,:,1:,:] != t[:,:,:-1,:])
+        edge[:,:,:-1,:] = edge[:,:,:-1,:] | (t[:,:,1:,:] != t[:,:,:-1,:])
+        if self.opt.data_type==16:
+            return edge.half()
+        else:
+            return edge.float()
+
+    def save(self, which_epoch):
+        self.save_network(self.netG, 'G', which_epoch, self.gpu_ids)
+        self.save_network(self.netD, 'D', which_epoch, self.gpu_ids)
+        if self.gen_features:
+            self.save_network(self.netE, 'E', which_epoch, self.gpu_ids)
+
+    def update_fixed_params(self):
+        # after fixing the global generator for a number of iterations, also start finetuning it
+        params = list(self.netG.parameters())
+        if self.gen_features:
+            params += list(self.netE.parameters())           
+        self.optimizer_G = torch.optim.Adam(params, lr=self.opt.lr, betas=(self.opt.beta1, 0.999))
+        if self.opt.verbose:
+            print('------------ Now also finetuning global generator -----------')
+
+    def update_learning_rate(self):
+        lrd = self.opt.lr / self.opt.niter_decay
+        lr = self.old_lr - lrd        
+        for param_group in self.optimizer_D.param_groups:
+            param_group['lr'] = lr
+        for param_group in self.optimizer_G.param_groups:
+            param_group['lr'] = lr
+        if self.opt.verbose:
+            print('update learning rate: %f -> %f' % (self.old_lr, lr))
+        self.old_lr = lr
+
+class InferenceModel(Pix2PixHDModel):
+    def forward(self, inp):
+        label, inst = inp
+        return self.inference(label, inst)
+
+        
diff --git a/mlops/serving/SwimSwap/models/projected_model.py b/mlops/serving/SwimSwap/models/projected_model.py
new file mode 100644
index 0000000..15d70f9
--- /dev/null
+++ b/mlops/serving/SwimSwap/models/projected_model.py
@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: fs_model_fix_idnorm_donggp_saveoptim copy.py
+# Created Date: Wednesday January 12th 2022
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Saturday, 13th May 2023 9:56:35 am
+# Modified By: Chen Xuanhong
+# Copyright (c) 2022 Shanghai Jiao Tong University
+#############################################################
+
+
+import torch
+import torch.nn as nn
+
+from .base_model import BaseModel
+from .fs_networks_fix import Generator_Adain_Upsample
+
+from pg_modules.projected_discriminator import ProjectedDiscriminator
+
+def compute_grad2(d_out, x_in):
+    batch_size = x_in.size(0)
+    grad_dout = torch.autograd.grad(
+        outputs=d_out.sum(), inputs=x_in,
+        create_graph=True, retain_graph=True, only_inputs=True
+    )[0]
+    grad_dout2 = grad_dout.pow(2)
+    assert(grad_dout2.size() == x_in.size())
+    reg = grad_dout2.view(batch_size, -1).sum(1)
+    return reg
+
+class fsModel(BaseModel):
+    def name(self):
+        return 'fsModel'
+
+    def initialize(self, opt):
+        BaseModel.initialize(self, opt)
+        # if opt.resize_or_crop != 'none' or not opt.isTrain:  # when training at full res this causes OOM
+        self.isTrain = opt.isTrain
+
+        # Generator network
+        self.netG = Generator_Adain_Upsample(input_nc=3, output_nc=3, latent_size=512, n_blocks=9, deep=opt.Gdeep)
+        self.netG.cuda()
+
+        # Id network
+        netArc_checkpoint = opt.Arc_path
+        netArc_checkpoint = torch.load(netArc_checkpoint, map_location=torch.device("cpu"))
+        self.netArc = netArc_checkpoint
+        self.netArc = self.netArc.cuda()
+        self.netArc.eval()
+        self.netArc.requires_grad_(False)
+        if not self.isTrain:
+            pretrained_path =  opt.checkpoints_dir
+            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)
+            return
+        self.netD = ProjectedDiscriminator(diffaug=False, interp224=False, **{})
+        # self.netD.feature_network.requires_grad_(False)
+        self.netD.cuda()
+
+
+        if self.isTrain:
+            # define loss functions
+            self.criterionFeat  = nn.L1Loss()
+            self.criterionRec   = nn.L1Loss()
+
+
+           # initialize optimizers
+
+            # optimizer G
+            params = list(self.netG.parameters())
+            self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.99),eps=1e-8)
+
+            # optimizer D
+            params = list(self.netD.parameters())
+            self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.99),eps=1e-8)
+
+        # load networks
+        if opt.continue_train:
+            pretrained_path = '' if not self.isTrain else opt.load_pretrain
+            # print (pretrained_path)
+            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)
+            self.load_network(self.netD, 'D', opt.which_epoch, pretrained_path)
+            self.load_optim(self.optimizer_G, 'G', opt.which_epoch, pretrained_path)
+            self.load_optim(self.optimizer_D, 'D', opt.which_epoch, pretrained_path)
+        torch.cuda.empty_cache()
+
+    def cosin_metric(self, x1, x2):
+        #return np.dot(x1, x2) / (np.linalg.norm(x1) * np.linalg.norm(x2))
+        return torch.sum(x1 * x2, dim=1) / (torch.norm(x1, dim=1) * torch.norm(x2, dim=1))
+
+
+
+    def save(self, which_epoch):
+        self.save_network(self.netG, 'G', which_epoch)
+        self.save_network(self.netD, 'D', which_epoch)
+        self.save_optim(self.optimizer_G, 'G', which_epoch)
+        self.save_optim(self.optimizer_D, 'D', which_epoch)
+        '''if self.gen_features:
+            self.save_network(self.netE, 'E', which_epoch, self.gpu_ids)'''
+
+    def update_fixed_params(self):
+        # after fixing the global generator for a number of iterations, also start finetuning it
+        params = list(self.netG.parameters())
+        if self.gen_features:
+            params += list(self.netE.parameters())
+        self.optimizer_G = torch.optim.Adam(params, lr=self.opt.lr, betas=(self.opt.beta1, 0.999))
+        if self.opt.verbose:
+            print('------------ Now also finetuning global generator -----------')
+
+    def update_learning_rate(self):
+        lrd = self.opt.lr / self.opt.niter_decay
+        lr = self.old_lr - lrd
+        for param_group in self.optimizer_D.param_groups:
+            param_group['lr'] = lr
+        for param_group in self.optimizer_G.param_groups:
+            param_group['lr'] = lr
+        if self.opt.verbose:
+            print('update learning rate: %f -> %f' % (self.old_lr, lr))
+        self.old_lr = lr
+
+
diff --git a/mlops/serving/SwimSwap/models/projectionhead.py b/mlops/serving/SwimSwap/models/projectionhead.py
new file mode 100644
index 0000000..a0d18f1
--- /dev/null
+++ b/mlops/serving/SwimSwap/models/projectionhead.py
@@ -0,0 +1,14 @@
+import torch.nn as nn
+
+class ProjectionHead(nn.Module):
+    def __init__(self, proj_dim=256):
+        super(ProjectionHead, self).__init__()
+
+        self.proj = nn.Sequential(
+            nn.Linear(proj_dim, proj_dim), 
+            nn.ReLU(),
+            nn.Linear(proj_dim, proj_dim), 
+        )
+
+    def forward(self, x):
+        return self.proj(x)
\ No newline at end of file
diff --git a/mlops/serving/SwimSwap/models/ui_model.py b/mlops/serving/SwimSwap/models/ui_model.py
new file mode 100644
index 0000000..c5b3433
--- /dev/null
+++ b/mlops/serving/SwimSwap/models/ui_model.py
@@ -0,0 +1,347 @@
+import torch
+from torch.autograd import Variable
+from collections import OrderedDict
+import numpy as np
+import os
+from PIL import Image
+import util.util as util
+from .base_model import BaseModel
+from . import networks
+
+class UIModel(BaseModel):
+    def name(self):
+        return 'UIModel'
+
+    def initialize(self, opt):
+        assert(not opt.isTrain)
+        BaseModel.initialize(self, opt)
+        self.use_features = opt.instance_feat or opt.label_feat
+
+        netG_input_nc = opt.label_nc
+        if not opt.no_instance:
+            netG_input_nc += 1            
+        if self.use_features:   
+            netG_input_nc += opt.feat_num           
+
+        self.netG = networks.define_G(netG_input_nc, opt.output_nc, opt.ngf, opt.netG, 
+                                      opt.n_downsample_global, opt.n_blocks_global, opt.n_local_enhancers, 
+                                      opt.n_blocks_local, opt.norm, gpu_ids=self.gpu_ids)            
+        self.load_network(self.netG, 'G', opt.which_epoch)
+
+        print('---------- Networks initialized -------------')
+
+    def toTensor(self, img, normalize=False):
+        tensor = torch.from_numpy(np.array(img, np.int32, copy=False))
+        tensor = tensor.view(1, img.size[1], img.size[0], len(img.mode))    
+        tensor = tensor.transpose(1, 2).transpose(1, 3).contiguous()
+        if normalize:
+            return (tensor.float()/255.0 - 0.5) / 0.5        
+        return tensor.float()
+
+    def load_image(self, label_path, inst_path, feat_path):
+        opt = self.opt
+        # read label map
+        label_img = Image.open(label_path)    
+        if label_path.find('face') != -1:
+            label_img = label_img.convert('L')
+        ow, oh = label_img.size    
+        w = opt.loadSize
+        h = int(w * oh / ow)    
+        label_img = label_img.resize((w, h), Image.NEAREST)
+        label_map = self.toTensor(label_img)           
+        
+        # onehot vector input for label map
+        self.label_map = label_map.cuda()
+        oneHot_size = (1, opt.label_nc, h, w)
+        input_label = self.Tensor(torch.Size(oneHot_size)).zero_()
+        self.input_label = input_label.scatter_(1, label_map.long().cuda(), 1.0)
+
+        # read instance map
+        if not opt.no_instance:
+            inst_img = Image.open(inst_path)        
+            inst_img = inst_img.resize((w, h), Image.NEAREST)            
+            self.inst_map = self.toTensor(inst_img).cuda()
+            self.edge_map = self.get_edges(self.inst_map)          
+            self.net_input = Variable(torch.cat((self.input_label, self.edge_map), dim=1), volatile=True)
+        else:
+            self.net_input = Variable(self.input_label, volatile=True)  
+        
+        self.features_clustered = np.load(feat_path).item()
+        self.object_map = self.inst_map if opt.instance_feat else self.label_map 
+                       
+        object_np = self.object_map.cpu().numpy().astype(int) 
+        self.feat_map = self.Tensor(1, opt.feat_num, h, w).zero_()                 
+        self.cluster_indices = np.zeros(self.opt.label_nc, np.uint8)
+        for i in np.unique(object_np):    
+            label = i if i < 1000 else i//1000
+            if label in self.features_clustered:
+                feat = self.features_clustered[label]
+                np.random.seed(i+1)
+                cluster_idx = np.random.randint(0, feat.shape[0])
+                self.cluster_indices[label] = cluster_idx
+                idx = (self.object_map == i).nonzero()                    
+                self.set_features(idx, feat, cluster_idx)
+
+        self.net_input_original = self.net_input.clone()        
+        self.label_map_original = self.label_map.clone()
+        self.feat_map_original = self.feat_map.clone()
+        if not opt.no_instance:
+            self.inst_map_original = self.inst_map.clone()        
+
+    def reset(self):
+        self.net_input = self.net_input_prev = self.net_input_original.clone()        
+        self.label_map = self.label_map_prev = self.label_map_original.clone()
+        self.feat_map = self.feat_map_prev = self.feat_map_original.clone()
+        if not self.opt.no_instance:
+            self.inst_map = self.inst_map_prev = self.inst_map_original.clone()
+        self.object_map = self.inst_map if self.opt.instance_feat else self.label_map 
+
+    def undo(self):        
+        self.net_input = self.net_input_prev
+        self.label_map = self.label_map_prev
+        self.feat_map = self.feat_map_prev
+        if not self.opt.no_instance:
+            self.inst_map = self.inst_map_prev
+        self.object_map = self.inst_map if self.opt.instance_feat else self.label_map 
+            
+    # get boundary map from instance map
+    def get_edges(self, t):
+        edge = torch.cuda.ByteTensor(t.size()).zero_()
+        edge[:,:,:,1:] = edge[:,:,:,1:] | (t[:,:,:,1:] != t[:,:,:,:-1])
+        edge[:,:,:,:-1] = edge[:,:,:,:-1] | (t[:,:,:,1:] != t[:,:,:,:-1])
+        edge[:,:,1:,:] = edge[:,:,1:,:] | (t[:,:,1:,:] != t[:,:,:-1,:])
+        edge[:,:,:-1,:] = edge[:,:,:-1,:] | (t[:,:,1:,:] != t[:,:,:-1,:])
+        return edge.float()
+
+    # change the label at the source position to the label at the target position
+    def change_labels(self, click_src, click_tgt): 
+        y_src, x_src = click_src[0], click_src[1]
+        y_tgt, x_tgt = click_tgt[0], click_tgt[1]
+        label_src = int(self.label_map[0, 0, y_src, x_src])
+        inst_src = self.inst_map[0, 0, y_src, x_src]
+        label_tgt = int(self.label_map[0, 0, y_tgt, x_tgt])
+        inst_tgt = self.inst_map[0, 0, y_tgt, x_tgt]
+
+        idx_src = (self.inst_map == inst_src).nonzero()         
+        # need to change 3 things: label map, instance map, and feature map
+        if idx_src.shape:
+            # backup current maps
+            self.backup_current_state() 
+
+            # change both the label map and the network input
+            self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
+            self.net_input[idx_src[:,0], idx_src[:,1] + label_src, idx_src[:,2], idx_src[:,3]] = 0
+            self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1                                    
+            
+            # update the instance map (and the network input)
+            if inst_tgt > 1000:
+                # if different instances have different ids, give the new object a new id
+                tgt_indices = (self.inst_map > label_tgt * 1000) & (self.inst_map < (label_tgt+1) * 1000)
+                inst_tgt = self.inst_map[tgt_indices].max() + 1
+            self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = inst_tgt
+            self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
+
+            # also copy the source features to the target position      
+            idx_tgt = (self.inst_map == inst_tgt).nonzero()    
+            if idx_tgt.shape:
+                self.copy_features(idx_src, idx_tgt[0,:])
+
+        self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
+
+    # add strokes of target label in the image
+    def add_strokes(self, click_src, label_tgt, bw, save):
+        # get the region of the new strokes (bw is the brush width)        
+        size = self.net_input.size()
+        h, w = size[2], size[3]
+        idx_src = torch.LongTensor(bw**2, 4).fill_(0)
+        for i in range(bw):
+            idx_src[i*bw:(i+1)*bw, 2] = min(h-1, max(0, click_src[0]-bw//2 + i))
+            for j in range(bw):
+                idx_src[i*bw+j, 3] = min(w-1, max(0, click_src[1]-bw//2 + j))
+        idx_src = idx_src.cuda()
+        
+        # again, need to update 3 things
+        if idx_src.shape:
+            # backup current maps
+            if save:
+                self.backup_current_state()
+
+            # update the label map (and the network input) in the stroke region            
+            self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
+            for k in range(self.opt.label_nc):
+                self.net_input[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = 0
+            self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1                 
+
+            # update the instance map (and the network input)
+            self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
+            self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
+            
+            # also update the features if available
+            if self.opt.instance_feat:                                            
+                feat = self.features_clustered[label_tgt]
+                #np.random.seed(label_tgt+1)   
+                #cluster_idx = np.random.randint(0, feat.shape[0])
+                cluster_idx = self.cluster_indices[label_tgt]
+                self.set_features(idx_src, feat, cluster_idx)                                                  
+        
+        self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
+
+    # add an object to the clicked position with selected style
+    def add_objects(self, click_src, label_tgt, mask, style_id=0):
+        y, x = click_src[0], click_src[1]
+        mask = np.transpose(mask, (2, 0, 1))[np.newaxis,...]        
+        idx_src = torch.from_numpy(mask).cuda().nonzero()        
+        idx_src[:,2] += y
+        idx_src[:,3] += x
+
+        # backup current maps
+        self.backup_current_state()
+
+        # update label map
+        self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt        
+        for k in range(self.opt.label_nc):
+            self.net_input[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = 0
+        self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1            
+
+        # update instance map
+        self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
+        self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
+                
+        # update feature map
+        self.set_features(idx_src, self.feat, style_id)                
+        
+        self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
+
+    def single_forward(self, net_input, feat_map):
+        net_input = torch.cat((net_input, feat_map), dim=1)
+        fake_image = self.netG.forward(net_input)
+
+        if fake_image.size()[0] == 1:
+            return fake_image.data[0]        
+        return fake_image.data
+
+
+    # generate all outputs for different styles
+    def style_forward(self, click_pt, style_id=-1):           
+        if click_pt is None:            
+            self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
+            self.crop = None
+            self.mask = None        
+        else:                       
+            instToChange = int(self.object_map[0, 0, click_pt[0], click_pt[1]])
+            self.instToChange = instToChange
+            label = instToChange if instToChange < 1000 else instToChange//1000        
+            self.feat = self.features_clustered[label]
+            self.fake_image = []
+            self.mask = self.object_map == instToChange
+            idx = self.mask.nonzero()
+            self.get_crop_region(idx)            
+            if idx.size():                
+                if style_id == -1:
+                    (min_y, min_x, max_y, max_x) = self.crop
+                    ### original
+                    for cluster_idx in range(self.opt.multiple_output):
+                        self.set_features(idx, self.feat, cluster_idx)
+                        fake_image = self.single_forward(self.net_input, self.feat_map)
+                        fake_image = util.tensor2im(fake_image[:,min_y:max_y,min_x:max_x])
+                        self.fake_image.append(fake_image)    
+                    """### To speed up previewing different style results, either crop or downsample the label maps
+                    if instToChange > 1000:
+                        (min_y, min_x, max_y, max_x) = self.crop                                                
+                        ### crop                                                
+                        _, _, h, w = self.net_input.size()
+                        offset = 512
+                        y_start, x_start = max(0, min_y-offset), max(0, min_x-offset)
+                        y_end, x_end = min(h, (max_y + offset)), min(w, (max_x + offset))
+                        y_region = slice(y_start, y_start+(y_end-y_start)//16*16)
+                        x_region = slice(x_start, x_start+(x_end-x_start)//16*16)
+                        net_input = self.net_input[:,:,y_region,x_region]                    
+                        for cluster_idx in range(self.opt.multiple_output):  
+                            self.set_features(idx, self.feat, cluster_idx)
+                            fake_image = self.single_forward(net_input, self.feat_map[:,:,y_region,x_region])                            
+                            fake_image = util.tensor2im(fake_image[:,min_y-y_start:max_y-y_start,min_x-x_start:max_x-x_start])
+                            self.fake_image.append(fake_image)
+                    else:
+                        ### downsample
+                        (min_y, min_x, max_y, max_x) = [crop//2 for crop in self.crop]                    
+                        net_input = self.net_input[:,:,::2,::2]                    
+                        size = net_input.size()
+                        net_input_batch = net_input.expand(self.opt.multiple_output, size[1], size[2], size[3])             
+                        for cluster_idx in range(self.opt.multiple_output):  
+                            self.set_features(idx, self.feat, cluster_idx)
+                            feat_map = self.feat_map[:,:,::2,::2]
+                            if cluster_idx == 0:
+                                feat_map_batch = feat_map
+                            else:
+                                feat_map_batch = torch.cat((feat_map_batch, feat_map), dim=0)
+                        fake_image_batch = self.single_forward(net_input_batch, feat_map_batch)
+                        for i in range(self.opt.multiple_output):
+                            self.fake_image.append(util.tensor2im(fake_image_batch[i,:,min_y:max_y,min_x:max_x]))"""
+                                        
+                else:
+                    self.set_features(idx, self.feat, style_id)
+                    self.cluster_indices[label] = style_id
+                    self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))        
+
+    def backup_current_state(self):
+        self.net_input_prev = self.net_input.clone()
+        self.label_map_prev = self.label_map.clone() 
+        self.inst_map_prev = self.inst_map.clone() 
+        self.feat_map_prev = self.feat_map.clone() 
+
+    # crop the ROI and get the mask of the object
+    def get_crop_region(self, idx):
+        size = self.net_input.size()
+        h, w = size[2], size[3]
+        min_y, min_x = idx[:,2].min(), idx[:,3].min()
+        max_y, max_x = idx[:,2].max(), idx[:,3].max()             
+        crop_min = 128
+        if max_y - min_y < crop_min:
+            min_y = max(0, (max_y + min_y) // 2 - crop_min // 2)
+            max_y = min(h-1, min_y + crop_min)
+        if max_x - min_x < crop_min:
+            min_x = max(0, (max_x + min_x) // 2 - crop_min // 2)
+            max_x = min(w-1, min_x + crop_min)
+        self.crop = (min_y, min_x, max_y, max_x)           
+        self.mask = self.mask[:,:, min_y:max_y, min_x:max_x]
+
+    # update the feature map once a new object is added or the label is changed
+    def update_features(self, cluster_idx, mask=None, click_pt=None):        
+        self.feat_map_prev = self.feat_map.clone()
+        # adding a new object
+        if mask is not None:
+            y, x = click_pt[0], click_pt[1]
+            mask = np.transpose(mask, (2,0,1))[np.newaxis,...]        
+            idx = torch.from_numpy(mask).cuda().nonzero()        
+            idx[:,2] += y
+            idx[:,3] += x    
+        # changing the label of an existing object 
+        else:            
+            idx = (self.object_map == self.instToChange).nonzero()              
+
+        # update feature map
+        self.set_features(idx, self.feat, cluster_idx)        
+
+    # set the class features to the target feature
+    def set_features(self, idx, feat, cluster_idx):        
+        for k in range(self.opt.feat_num):
+            self.feat_map[idx[:,0], idx[:,1] + k, idx[:,2], idx[:,3]] = feat[cluster_idx, k] 
+
+    # copy the features at the target position to the source position
+    def copy_features(self, idx_src, idx_tgt):        
+        for k in range(self.opt.feat_num):
+            val = self.feat_map[idx_tgt[0], idx_tgt[1] + k, idx_tgt[2], idx_tgt[3]]
+            self.feat_map[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = val 
+
+    def get_current_visuals(self, getLabel=False):                              
+        mask = self.mask     
+        if self.mask is not None:
+            mask = np.transpose(self.mask[0].cpu().float().numpy(), (1,2,0)).astype(np.uint8)        
+
+        dict_list = [('fake_image', self.fake_image), ('mask', mask)]
+
+        if getLabel: # only output label map if needed to save bandwidth
+            label = util.tensor2label(self.net_input.data[0], self.opt.label_nc)                    
+            dict_list += [('label', label)]
+
+        return OrderedDict(dict_list)
\ No newline at end of file
diff --git a/mlops/serving/SwimSwap/options/base_options.py b/mlops/serving/SwimSwap/options/base_options.py
new file mode 100644
index 0000000..f5e5e8c
--- /dev/null
+++ b/mlops/serving/SwimSwap/options/base_options.py
@@ -0,0 +1,104 @@
+import argparse
+import os
+from util import util
+import torch
+
+class BaseOptions():
+    def __init__(self):
+        self.parser = argparse.ArgumentParser()
+        self.initialized = False
+
+    def initialize(self):
+        # experiment specifics
+        self.parser.add_argument('--name', type=str, default='people', help='name of the experiment. It decides where to store samples and models')
+        self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
+        self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
+        # self.parser.add_argument('--model', type=str, default='pix2pixHD', help='which model to use')
+        self.parser.add_argument('--norm', type=str, default='batch', help='instance normalization or batch normalization')
+        self.parser.add_argument('--use_dropout', action='store_true', help='use dropout for the generator')
+        self.parser.add_argument('--data_type', default=32, type=int, choices=[8, 16, 32], help="Supported data type i.e. 8, 16, 32 bit")
+        self.parser.add_argument('--verbose', action='store_true', default=False, help='toggles verbose')
+        self.parser.add_argument('--fp16', action='store_true', default=False, help='train with AMP')
+        self.parser.add_argument('--local_rank', type=int, default=0, help='local rank for distributed training')
+        self.parser.add_argument('--isTrain', type=bool, default=True, help='local rank for distributed training')
+
+        # input/output sizes       
+        self.parser.add_argument('--batchSize', type=int, default=8, help='input batch size')
+        self.parser.add_argument('--loadSize', type=int, default=1024, help='scale images to this size')
+        self.parser.add_argument('--fineSize', type=int, default=512, help='then crop to this size')
+        self.parser.add_argument('--label_nc', type=int, default=0, help='# of input label channels')
+        self.parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels')
+        self.parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')
+
+        # for setting inputs
+        self.parser.add_argument('--dataroot', type=str, default='./SimSwap/datasets/cityscapes/') 
+        self.parser.add_argument('--resize_or_crop', type=str, default='scale_width', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop]')
+        self.parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')        
+        self.parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data argumentation') 
+        self.parser.add_argument('--nThreads', default=2, type=int, help='# threads for loading data')                
+        self.parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
+
+        # for displays
+        self.parser.add_argument('--display_winsize', type=int, default=512,  help='display window size')
+        self.parser.add_argument('--tf_log', action='store_true', help='if specified, use tensorboard logging. Requires tensorflow installed')
+
+        # for generator
+        self.parser.add_argument('--netG', type=str, default='global', help='selects model to use for netG')
+        self.parser.add_argument('--latent_size', type=int, default=512, help='latent size of Adain layer')
+        self.parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
+        self.parser.add_argument('--n_downsample_global', type=int, default=3, help='number of downsampling layers in netG')
+        self.parser.add_argument('--n_blocks_global', type=int, default=6, help='number of residual blocks in the global generator network')
+        self.parser.add_argument('--n_blocks_local', type=int, default=3, help='number of residual blocks in the local enhancer network')
+        self.parser.add_argument('--n_local_enhancers', type=int, default=1, help='number of local enhancers to use')        
+        self.parser.add_argument('--niter_fix_global', type=int, default=0, help='number of epochs that we only train the outmost local enhancer')        
+
+        # for instance-wise features
+        self.parser.add_argument('--no_instance', action='store_true', help='if specified, do *not* add instance map as input')        
+        self.parser.add_argument('--instance_feat', action='store_true', help='if specified, add encoded instance features as input')
+        self.parser.add_argument('--label_feat', action='store_true', help='if specified, add encoded label features as input')        
+        self.parser.add_argument('--feat_num', type=int, default=3, help='vector length for encoded features')        
+        self.parser.add_argument('--load_features', action='store_true', help='if specified, load precomputed feature maps')
+        self.parser.add_argument('--n_downsample_E', type=int, default=4, help='# of downsampling layers in encoder') 
+        self.parser.add_argument('--nef', type=int, default=16, help='# of encoder filters in the first conv layer')        
+        self.parser.add_argument('--n_clusters', type=int, default=10, help='number of clusters for features')
+        self.parser.add_argument('--image_size', type=int, default=224, help='number of clusters for features')
+        self.parser.add_argument('--norm_G', type=str, default='spectralspadesyncbatch3x3', help='instance normalization or batch normalization')
+        self.parser.add_argument('--semantic_nc', type=int, default=3, help='number of clusters for features')
+        self.initialized = True
+
+    def parse(self, save=True):
+        if not self.initialized:
+            self.initialize()
+        self.opt = self.parser.parse_args()
+        self.opt.isTrain = self.isTrain   # train or test
+
+        str_ids = self.opt.gpu_ids.split(',')
+        self.opt.gpu_ids = []
+        for str_id in str_ids:
+            id = int(str_id)
+            if id >= 0:
+                self.opt.gpu_ids.append(id)
+        
+        # set gpu ids
+        if len(self.opt.gpu_ids) > 0:
+            torch.cuda.set_device(self.opt.gpu_ids[0])
+
+        args = vars(self.opt)
+
+        print('------------ Options -------------')
+        for k, v in sorted(args.items()):
+            print('%s: %s' % (str(k), str(v)))
+        print('-------------- End ----------------')
+
+        # save to the disk
+        if self.opt.isTrain:
+            expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name)
+            util.mkdirs(expr_dir)
+            if save and not self.opt.continue_train:
+                file_name = os.path.join(expr_dir, 'opt.txt')
+                with open(file_name, 'wt') as opt_file:
+                    opt_file.write('------------ Options -------------\n')
+                    for k, v in sorted(args.items()):
+                        opt_file.write('%s: %s\n' % (str(k), str(v)))
+                    opt_file.write('-------------- End ----------------\n')
+        return self.opt
diff --git a/mlops/serving/SwimSwap/options/test_options.py b/mlops/serving/SwimSwap/options/test_options.py
new file mode 100644
index 0000000..e46e435
--- /dev/null
+++ b/mlops/serving/SwimSwap/options/test_options.py
@@ -0,0 +1,38 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-23 17:08:08
+Description: 
+'''
+from .base_options import BaseOptions
+
+class TestOptions(BaseOptions):
+    def initialize(self):
+        BaseOptions.initialize(self)
+        self.parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.')
+        self.parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.')
+        self.parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
+        self.parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
+        self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
+        self.parser.add_argument('--how_many', type=int, default=50, help='how many test images to run')       
+        self.parser.add_argument('--cluster_path', type=str, default='features_clustered_010.npy', help='the path for clustered results of encoded features')
+        self.parser.add_argument('--use_encoded_image', action='store_true', help='if specified, encode the real image to get the feature map')
+        self.parser.add_argument("--export_onnx", type=str, help="export ONNX model to a given file")
+        self.parser.add_argument("--engine", type=str, help="run serialized TRT engine")
+        self.parser.add_argument("--onnx", type=str, help="run ONNX model via TRT")        
+        self.parser.add_argument("--Arc_path", type=str, default='SimSwap/arcface_model/arcface_checkpoint.tar', help="run ONNX model via TRT")
+        self.parser.add_argument("--pic_a_path", type=str, default='G:/swap_data/ID/elon-musk-hero-image.jpeg', help="Person who provides identity information")
+        self.parser.add_argument("--pic_b_path", type=str, default='./demo_file/multi_people.jpg', help="Person who provides information other than their identity")
+        self.parser.add_argument("--pic_specific_path", type=str, default='./crop_224/zrf.jpg', help="The specific person to be swapped")
+        self.parser.add_argument("--multisepcific_dir", type=str, default='./demo_file/multispecific', help="Dir for multi specific")
+        self.parser.add_argument("--video_path", type=str, default='G:/swap_data/video/HSB_Demo_Trim.mp4', help="path for the video to swap")
+        self.parser.add_argument("--temp_path", type=str, default='./temp_results', help="path to save temporarily images")
+        self.parser.add_argument("--output_path", type=str, default='./output/', help="results path")
+        self.parser.add_argument('--id_thres', type=float, default=0.03, help='how many test images to run')
+        self.parser.add_argument('--no_simswaplogo', action='store_true', help='Remove the watermark')
+        self.parser.add_argument('--use_mask', action='store_true', help='Use mask for better result')
+        self.parser.add_argument('--crop_size', type=int, default=224, help='Crop of size of input image')
+        
+        self.isTrain = False
\ No newline at end of file
diff --git a/mlops/serving/SwimSwap/parsing_model/model.py b/mlops/serving/SwimSwap/parsing_model/model.py
new file mode 100644
index 0000000..d434836
--- /dev/null
+++ b/mlops/serving/SwimSwap/parsing_model/model.py
@@ -0,0 +1,283 @@
+#!/usr/bin/python
+# -*- encoding: utf-8 -*-
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+
+from parsing_model.resnet import Resnet18
+# from modules.bn import InPlaceABNSync as BatchNorm2d
+
+
+class ConvBNReLU(nn.Module):
+    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
+        super(ConvBNReLU, self).__init__()
+        self.conv = nn.Conv2d(in_chan,
+                out_chan,
+                kernel_size = ks,
+                stride = stride,
+                padding = padding,
+                bias = False)
+        self.bn = nn.BatchNorm2d(out_chan)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = F.relu(self.bn(x))
+        return x
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+class BiSeNetOutput(nn.Module):
+    def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
+        super(BiSeNetOutput, self).__init__()
+        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
+        self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.conv_out(x)
+        return x
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class AttentionRefinementModule(nn.Module):
+    def __init__(self, in_chan, out_chan, *args, **kwargs):
+        super(AttentionRefinementModule, self).__init__()
+        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
+        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
+        self.bn_atten = nn.BatchNorm2d(out_chan)
+        self.sigmoid_atten = nn.Sigmoid()
+        self.init_weight()
+
+    def forward(self, x):
+        feat = self.conv(x)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv_atten(atten)
+        atten = self.bn_atten(atten)
+        atten = self.sigmoid_atten(atten)
+        out = torch.mul(feat, atten)
+        return out
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+
+class ContextPath(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super(ContextPath, self).__init__()
+        self.resnet = Resnet18()
+        self.arm16 = AttentionRefinementModule(256, 128)
+        self.arm32 = AttentionRefinementModule(512, 128)
+        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
+
+        self.init_weight()
+
+    def forward(self, x):
+        H0, W0 = x.size()[2:]
+        feat8, feat16, feat32 = self.resnet(x)
+        H8, W8 = feat8.size()[2:]
+        H16, W16 = feat16.size()[2:]
+        H32, W32 = feat32.size()[2:]
+
+        avg = F.avg_pool2d(feat32, feat32.size()[2:])
+        avg = self.conv_avg(avg)
+        avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
+
+        feat32_arm = self.arm32(feat32)
+        feat32_sum = feat32_arm + avg_up
+        feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
+        feat32_up = self.conv_head32(feat32_up)
+
+        feat16_arm = self.arm16(feat16)
+        feat16_sum = feat16_arm + feat32_up
+        feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
+        feat16_up = self.conv_head16(feat16_up)
+
+        return feat8, feat16_up, feat32_up  # x8, x8, x16
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+### This is not used, since I replace this with the resnet feature with the same size
+class SpatialPath(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super(SpatialPath, self).__init__()
+        self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
+        self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
+        self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
+        self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
+        self.init_weight()
+
+    def forward(self, x):
+        feat = self.conv1(x)
+        feat = self.conv2(feat)
+        feat = self.conv3(feat)
+        feat = self.conv_out(feat)
+        return feat
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class FeatureFusionModule(nn.Module):
+    def __init__(self, in_chan, out_chan, *args, **kwargs):
+        super(FeatureFusionModule, self).__init__()
+        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
+        self.conv1 = nn.Conv2d(out_chan,
+                out_chan//4,
+                kernel_size = 1,
+                stride = 1,
+                padding = 0,
+                bias = False)
+        self.conv2 = nn.Conv2d(out_chan//4,
+                out_chan,
+                kernel_size = 1,
+                stride = 1,
+                padding = 0,
+                bias = False)
+        self.relu = nn.ReLU(inplace=True)
+        self.sigmoid = nn.Sigmoid()
+        self.init_weight()
+
+    def forward(self, fsp, fcp):
+        fcat = torch.cat([fsp, fcp], dim=1)
+        feat = self.convblk(fcat)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv1(atten)
+        atten = self.relu(atten)
+        atten = self.conv2(atten)
+        atten = self.sigmoid(atten)
+        feat_atten = torch.mul(feat, atten)
+        feat_out = feat_atten + feat
+        return feat_out
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class BiSeNet(nn.Module):
+    def __init__(self, n_classes, *args, **kwargs):
+        super(BiSeNet, self).__init__()
+        self.cp = ContextPath()
+        ## here self.sp is deleted
+        self.ffm = FeatureFusionModule(256, 256)
+        self.conv_out = BiSeNetOutput(256, 256, n_classes)
+        self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
+        self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
+        self.init_weight()
+
+    def forward(self, x):
+        H, W = x.size()[2:]
+        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # here return res3b1 feature
+        feat_sp = feat_res8  # use res3b1 feature to replace spatial path feature
+        feat_fuse = self.ffm(feat_sp, feat_cp8)
+
+        feat_out = self.conv_out(feat_fuse)
+        feat_out16 = self.conv_out16(feat_cp8)
+        feat_out32 = self.conv_out32(feat_cp16)
+
+        feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
+        feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
+        feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
+        return feat_out, feat_out16, feat_out32
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
+        for name, child in self.named_children():
+            child_wd_params, child_nowd_params = child.get_params()
+            if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
+                lr_mul_wd_params += child_wd_params
+                lr_mul_nowd_params += child_nowd_params
+            else:
+                wd_params += child_wd_params
+                nowd_params += child_nowd_params
+        return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
+
+
+if __name__ == "__main__":
+    net = BiSeNet(19)
+    net.cuda()
+    net.eval()
+    in_ten = torch.randn(16, 3, 640, 480).cuda()
+    out, out16, out32 = net(in_ten)
+    print(out.shape)
+
+    net.get_params()
diff --git a/mlops/serving/SwimSwap/parsing_model/resnet.py b/mlops/serving/SwimSwap/parsing_model/resnet.py
new file mode 100644
index 0000000..aa2bf95
--- /dev/null
+++ b/mlops/serving/SwimSwap/parsing_model/resnet.py
@@ -0,0 +1,109 @@
+#!/usr/bin/python
+# -*- encoding: utf-8 -*-
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.model_zoo as modelzoo
+
+# from modules.bn import InPlaceABNSync as BatchNorm2d
+
+resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    def __init__(self, in_chan, out_chan, stride=1):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(in_chan, out_chan, stride)
+        self.bn1 = nn.BatchNorm2d(out_chan)
+        self.conv2 = conv3x3(out_chan, out_chan)
+        self.bn2 = nn.BatchNorm2d(out_chan)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = None
+        if in_chan != out_chan or stride != 1:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(in_chan, out_chan,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(out_chan),
+                )
+
+    def forward(self, x):
+        residual = self.conv1(x)
+        residual = F.relu(self.bn1(residual))
+        residual = self.conv2(residual)
+        residual = self.bn2(residual)
+
+        shortcut = x
+        if self.downsample is not None:
+            shortcut = self.downsample(x)
+
+        out = shortcut + residual
+        out = self.relu(out)
+        return out
+
+
+def create_layer_basic(in_chan, out_chan, bnum, stride=1):
+    layers = [BasicBlock(in_chan, out_chan, stride=stride)]
+    for i in range(bnum-1):
+        layers.append(BasicBlock(out_chan, out_chan, stride=1))
+    return nn.Sequential(*layers)
+
+
+class Resnet18(nn.Module):
+    def __init__(self):
+        super(Resnet18, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
+        self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
+        self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
+        self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = F.relu(self.bn1(x))
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        feat8 = self.layer2(x) # 1/8
+        feat16 = self.layer3(feat8) # 1/16
+        feat32 = self.layer4(feat16) # 1/32
+        return feat8, feat16, feat32
+
+    def init_weight(self):
+        state_dict = modelzoo.load_url(resnet18_url)
+        self_state_dict = self.state_dict()
+        for k, v in state_dict.items():
+            if 'fc' in k: continue
+            self_state_dict.update({k: v})
+        self.load_state_dict(self_state_dict)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module,  nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+if __name__ == "__main__":
+    net = Resnet18()
+    x = torch.randn(16, 3, 224, 224)
+    out = net(x)
+    print(out[0].size())
+    print(out[1].size())
+    print(out[2].size())
+    net.get_params()
diff --git a/mlops/serving/SwimSwap/pg_modules/blocks.py b/mlops/serving/SwimSwap/pg_modules/blocks.py
new file mode 100644
index 0000000..78bd113
--- /dev/null
+++ b/mlops/serving/SwimSwap/pg_modules/blocks.py
@@ -0,0 +1,325 @@
+import functools
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.utils import spectral_norm
+
+
+### single layers
+
+
+def conv2d(*args, **kwargs):
+    return spectral_norm(nn.Conv2d(*args, **kwargs))
+
+
+def convTranspose2d(*args, **kwargs):
+    return spectral_norm(nn.ConvTranspose2d(*args, **kwargs))
+
+
+def embedding(*args, **kwargs):
+    return spectral_norm(nn.Embedding(*args, **kwargs))
+
+
+def linear(*args, **kwargs):
+    return spectral_norm(nn.Linear(*args, **kwargs))
+
+
+def NormLayer(c, mode='batch'):
+    if mode == 'group':
+        return nn.GroupNorm(c//2, c)
+    elif mode == 'batch':
+        return nn.BatchNorm2d(c)
+
+
+### Activations
+
+
+class GLU(nn.Module):
+    def forward(self, x):
+        nc = x.size(1)
+        assert nc % 2 == 0, 'channels dont divide 2!'
+        nc = int(nc/2)
+        return x[:, :nc] * torch.sigmoid(x[:, nc:])
+
+
+class Swish(nn.Module):
+    def forward(self, feat):
+        return feat * torch.sigmoid(feat)
+
+
+### Upblocks
+
+
+class InitLayer(nn.Module):
+    def __init__(self, nz, channel, sz=4):
+        super().__init__()
+
+        self.init = nn.Sequential(
+            convTranspose2d(nz, channel*2, sz, 1, 0, bias=False),
+            NormLayer(channel*2),
+            GLU(),
+        )
+
+    def forward(self, noise):
+        noise = noise.view(noise.shape[0], -1, 1, 1)
+        return self.init(noise)
+
+
+def UpBlockSmall(in_planes, out_planes):
+    block = nn.Sequential(
+        nn.Upsample(scale_factor=2, mode='nearest'),
+        conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False),
+        NormLayer(out_planes*2), GLU())
+    return block
+
+
+class UpBlockSmallCond(nn.Module):
+    def __init__(self, in_planes, out_planes, z_dim):
+        super().__init__()
+        self.in_planes = in_planes
+        self.out_planes = out_planes
+        self.up = nn.Upsample(scale_factor=2, mode='nearest')
+        self.conv = conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False)
+
+        which_bn = functools.partial(CCBN, which_linear=linear, input_size=z_dim)
+        self.bn = which_bn(2*out_planes)
+        self.act = GLU()
+
+    def forward(self, x, c):
+        x = self.up(x)
+        x = self.conv(x)
+        x = self.bn(x, c)
+        x = self.act(x)
+        return x
+
+
+def UpBlockBig(in_planes, out_planes):
+    block = nn.Sequential(
+        nn.Upsample(scale_factor=2, mode='nearest'),
+        conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False),
+        NoiseInjection(),
+        NormLayer(out_planes*2), GLU(),
+        conv2d(out_planes, out_planes*2, 3, 1, 1, bias=False),
+        NoiseInjection(),
+        NormLayer(out_planes*2), GLU()
+        )
+    return block
+
+
+class UpBlockBigCond(nn.Module):
+    def __init__(self, in_planes, out_planes, z_dim):
+        super().__init__()
+        self.in_planes = in_planes
+        self.out_planes = out_planes
+        self.up = nn.Upsample(scale_factor=2, mode='nearest')
+        self.conv1 = conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False)
+        self.conv2 = conv2d(out_planes, out_planes*2, 3, 1, 1, bias=False)
+
+        which_bn = functools.partial(CCBN, which_linear=linear, input_size=z_dim)
+        self.bn1 = which_bn(2*out_planes)
+        self.bn2 = which_bn(2*out_planes)
+        self.act = GLU()
+        self.noise = NoiseInjection()
+
+    def forward(self, x, c):
+        # block 1
+        x = self.up(x)
+        x = self.conv1(x)
+        x = self.noise(x)
+        x = self.bn1(x, c)
+        x = self.act(x)
+
+        # block 2
+        x = self.conv2(x)
+        x = self.noise(x)
+        x = self.bn2(x, c)
+        x = self.act(x)
+
+        return x
+
+
+class SEBlock(nn.Module):
+    def __init__(self, ch_in, ch_out):
+        super().__init__()
+        self.main = nn.Sequential(
+            nn.AdaptiveAvgPool2d(4),
+            conv2d(ch_in, ch_out, 4, 1, 0, bias=False),
+            Swish(),
+            conv2d(ch_out, ch_out, 1, 1, 0, bias=False),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, feat_small, feat_big):
+        return feat_big * self.main(feat_small)
+
+
+### Downblocks
+
+
+class SeparableConv2d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, bias=False):
+        super(SeparableConv2d, self).__init__()
+        self.depthwise = conv2d(in_channels, in_channels, kernel_size=kernel_size,
+            groups=in_channels, bias=bias, padding=1)
+        self.pointwise = conv2d(in_channels, out_channels,
+            kernel_size=1, bias=bias)
+
+    def forward(self, x):
+        out = self.depthwise(x)
+        out = self.pointwise(out)
+        return out
+
+
+class DownBlock(nn.Module):
+    def __init__(self, in_planes, out_planes, separable=False):
+        super().__init__()
+        if not separable:
+            self.main = nn.Sequential(
+                conv2d(in_planes, out_planes, 4, 2, 1),
+                NormLayer(out_planes),
+                nn.LeakyReLU(0.2, inplace=True),
+            )
+        else:
+            self.main = nn.Sequential(
+                SeparableConv2d(in_planes, out_planes, 3),
+                NormLayer(out_planes),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.AvgPool2d(2, 2),
+            )
+
+    def forward(self, feat):
+        return self.main(feat)
+
+
+class DownBlockPatch(nn.Module):
+    def __init__(self, in_planes, out_planes, separable=False):
+        super().__init__()
+        self.main = nn.Sequential(
+            DownBlock(in_planes, out_planes, separable),
+            conv2d(out_planes, out_planes, 1, 1, 0, bias=False),
+            NormLayer(out_planes),
+            nn.LeakyReLU(0.2, inplace=True),
+        )
+
+    def forward(self, feat):
+        return self.main(feat)
+
+
+### CSM
+
+
+class ResidualConvUnit(nn.Module):
+    def __init__(self, cin, activation, bn):
+        super().__init__()
+        self.conv = nn.Conv2d(cin, cin, kernel_size=3, stride=1, padding=1, bias=True)
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        return self.skip_add.add(self.conv(x), x)
+
+
+class FeatureFusionBlock(nn.Module):
+    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True, lowest=False):
+        super().__init__()
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.expand = expand
+        out_features = features
+        if self.expand==True:
+            out_features = features//2
+
+        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, *xs):
+        output = xs[0]
+
+        if len(xs) == 2:
+            output = self.skip_add.add(output, xs[1])
+
+        output = nn.functional.interpolate(
+            output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
+        )
+
+        output = self.out_conv(output)
+
+        return output
+
+
+### Misc
+
+
+class NoiseInjection(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.weight = nn.Parameter(torch.zeros(1), requires_grad=True)
+
+    def forward(self, feat, noise=None):
+        if noise is None:
+            batch, _, height, width = feat.shape
+            noise = torch.randn(batch, 1, height, width).to(feat.device)
+
+        return feat + self.weight * noise
+
+
+class CCBN(nn.Module):
+    ''' conditional batchnorm '''
+    def __init__(self, output_size, input_size, which_linear, eps=1e-5, momentum=0.1):
+        super().__init__()
+        self.output_size, self.input_size = output_size, input_size
+
+        # Prepare gain and bias layers
+        self.gain = which_linear(input_size, output_size)
+        self.bias = which_linear(input_size, output_size)
+
+        # epsilon to avoid dividing by 0
+        self.eps = eps
+        # Momentum
+        self.momentum = momentum
+
+        self.register_buffer('stored_mean', torch.zeros(output_size))
+        self.register_buffer('stored_var', torch.ones(output_size))
+
+    def forward(self, x, y):
+        # Calculate class-conditional gains and biases
+        gain = (1 + self.gain(y)).view(y.size(0), -1, 1, 1)
+        bias = self.bias(y).view(y.size(0), -1, 1, 1)
+        out = F.batch_norm(x, self.stored_mean, self.stored_var, None, None,
+                           self.training, 0.1, self.eps)
+        return out * gain + bias
+
+
+class Interpolate(nn.Module):
+    """Interpolation module."""
+
+    def __init__(self, size, mode='bilinear', align_corners=False):
+        """Init.
+        Args:
+            scale_factor (float): scaling
+            mode (str): interpolation mode
+        """
+        super(Interpolate, self).__init__()
+
+        self.interp = nn.functional.interpolate
+        self.size = size
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        """Forward pass.
+        Args:
+            x (tensor): input
+        Returns:
+            tensor: interpolated data
+        """
+
+        x = self.interp(
+            x,
+            size=self.size,
+            mode=self.mode,
+            align_corners=self.align_corners,
+        )
+
+        return x
diff --git a/mlops/serving/SwimSwap/pg_modules/diffaug.py b/mlops/serving/SwimSwap/pg_modules/diffaug.py
new file mode 100644
index 0000000..54020be
--- /dev/null
+++ b/mlops/serving/SwimSwap/pg_modules/diffaug.py
@@ -0,0 +1,76 @@
+# Differentiable Augmentation for Data-Efficient GAN Training
+# Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han
+# https://arxiv.org/pdf/2006.10738
+
+import torch
+import torch.nn.functional as F
+
+
+def DiffAugment(x, policy='', channels_first=True):
+    if policy:
+        if not channels_first:
+            x = x.permute(0, 3, 1, 2)
+        for p in policy.split(','):
+            for f in AUGMENT_FNS[p]:
+                x = f(x)
+        if not channels_first:
+            x = x.permute(0, 2, 3, 1)
+        x = x.contiguous()
+    return x
+
+
+def rand_brightness(x):
+    x = x + (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) - 0.5)
+    return x
+
+
+def rand_saturation(x):
+    x_mean = x.mean(dim=1, keepdim=True)
+    x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) * 2) + x_mean
+    return x
+
+
+def rand_contrast(x):
+    x_mean = x.mean(dim=[1, 2, 3], keepdim=True)
+    x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) + 0.5) + x_mean
+    return x
+
+
+def rand_translation(x, ratio=0.125):
+    shift_x, shift_y = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
+    translation_x = torch.randint(-shift_x, shift_x + 1, size=[x.size(0), 1, 1], device=x.device)
+    translation_y = torch.randint(-shift_y, shift_y + 1, size=[x.size(0), 1, 1], device=x.device)
+    grid_batch, grid_x, grid_y = torch.meshgrid(
+        torch.arange(x.size(0), dtype=torch.long, device=x.device),
+        torch.arange(x.size(2), dtype=torch.long, device=x.device),
+        torch.arange(x.size(3), dtype=torch.long, device=x.device),
+    )
+    grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1)
+    grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1)
+    x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0])
+    x = x_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x, grid_y].permute(0, 3, 1, 2)
+    return x
+
+
+def rand_cutout(x, ratio=0.2):
+    cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
+    offset_x = torch.randint(0, x.size(2) + (1 - cutout_size[0] % 2), size=[x.size(0), 1, 1], device=x.device)
+    offset_y = torch.randint(0, x.size(3) + (1 - cutout_size[1] % 2), size=[x.size(0), 1, 1], device=x.device)
+    grid_batch, grid_x, grid_y = torch.meshgrid(
+        torch.arange(x.size(0), dtype=torch.long, device=x.device),
+        torch.arange(cutout_size[0], dtype=torch.long, device=x.device),
+        torch.arange(cutout_size[1], dtype=torch.long, device=x.device),
+    )
+    grid_x = torch.clamp(grid_x + offset_x - cutout_size[0] // 2, min=0, max=x.size(2) - 1)
+    grid_y = torch.clamp(grid_y + offset_y - cutout_size[1] // 2, min=0, max=x.size(3) - 1)
+    mask = torch.ones(x.size(0), x.size(2), x.size(3), dtype=x.dtype, device=x.device)
+    mask[grid_batch, grid_x, grid_y] = 0
+    x = x * mask.unsqueeze(1)
+    return x
+
+
+AUGMENT_FNS = {
+    'color': [rand_brightness, rand_saturation, rand_contrast],
+    'translation': [rand_translation],
+    'cutout': [rand_cutout],
+}
diff --git a/mlops/serving/SwimSwap/pg_modules/projected_discriminator.py b/mlops/serving/SwimSwap/pg_modules/projected_discriminator.py
new file mode 100644
index 0000000..d0c879f
--- /dev/null
+++ b/mlops/serving/SwimSwap/pg_modules/projected_discriminator.py
@@ -0,0 +1,191 @@
+from functools import partial
+import numpy as np
+import torch
+import torch.nn as nn
+
+from pg_modules.blocks import DownBlock, DownBlockPatch, conv2d
+from pg_modules.projector import F_RandomProj
+from pg_modules.diffaug import DiffAugment
+
+
+class SingleDisc(nn.Module):
+    def __init__(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False):
+        super().__init__()
+        channel_dict = {4: 512, 8: 512, 16: 256, 32: 128, 64: 64, 128: 64,
+                        256: 32, 512: 16, 1024: 8}
+
+        # interpolate for start sz that are not powers of two
+        if start_sz not in channel_dict.keys():
+            sizes = np.array(list(channel_dict.keys()))
+            start_sz = sizes[np.argmin(abs(sizes - start_sz))]
+        self.start_sz = start_sz
+
+        # if given ndf, allocate all layers with the same ndf
+        if ndf is None:
+            nfc = channel_dict
+        else:
+            nfc = {k: ndf for k, v in channel_dict.items()}
+
+        # for feature map discriminators with nfc not in channel_dict
+        # this is the case for the pretrained backbone (midas.pretrained)
+        if nc is not None and head is None:
+            nfc[start_sz] = nc
+
+        layers = []
+
+        # Head if the initial input is the full modality
+        if head:
+            layers += [conv2d(nc, nfc[256], 3, 1, 1, bias=False),
+                       nn.LeakyReLU(0.2, inplace=True)]
+
+        # Down Blocks
+        DB = partial(DownBlockPatch, separable=separable) if patch else partial(DownBlock, separable=separable)
+        while start_sz > end_sz:
+            layers.append(DB(nfc[start_sz],  nfc[start_sz//2]))
+            start_sz = start_sz // 2
+
+        layers.append(conv2d(nfc[end_sz], 1, 4, 1, 0, bias=False))
+        self.main = nn.Sequential(*layers)
+
+    def forward(self, x, c):
+        return self.main(x)
+
+
+class SingleDiscCond(nn.Module):
+    def __init__(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False, c_dim=1000, cmap_dim=64, embedding_dim=128):
+        super().__init__()
+        self.cmap_dim = cmap_dim
+
+        # midas channels
+        channel_dict = {4: 512, 8: 512, 16: 256, 32: 128, 64: 64, 128: 64,
+                        256: 32, 512: 16, 1024: 8}
+
+        # interpolate for start sz that are not powers of two
+        if start_sz not in channel_dict.keys():
+            sizes = np.array(list(channel_dict.keys()))
+            start_sz = sizes[np.argmin(abs(sizes - start_sz))]
+        self.start_sz = start_sz
+
+        # if given ndf, allocate all layers with the same ndf
+        if ndf is None:
+            nfc = channel_dict
+        else:
+            nfc = {k: ndf for k, v in channel_dict.items()}
+
+        # for feature map discriminators with nfc not in channel_dict
+        # this is the case for the pretrained backbone (midas.pretrained)
+        if nc is not None and head is None:
+            nfc[start_sz] = nc
+
+        layers = []
+
+        # Head if the initial input is the full modality
+        if head:
+            layers += [conv2d(nc, nfc[256], 3, 1, 1, bias=False),
+                       nn.LeakyReLU(0.2, inplace=True)]
+
+        # Down Blocks
+        DB = partial(DownBlockPatch, separable=separable) if patch else partial(DownBlock, separable=separable)
+        while start_sz > end_sz:
+            layers.append(DB(nfc[start_sz],  nfc[start_sz//2]))
+            start_sz = start_sz // 2
+        self.main = nn.Sequential(*layers)
+
+        # additions for conditioning on class information
+        self.cls = conv2d(nfc[end_sz], self.cmap_dim, 4, 1, 0, bias=False)
+        self.embed = nn.Embedding(num_embeddings=c_dim, embedding_dim=embedding_dim)
+        self.embed_proj = nn.Sequential(
+            nn.Linear(self.embed.embedding_dim, self.cmap_dim),
+            nn.LeakyReLU(0.2, inplace=True),
+        )
+
+    def forward(self, x, c):
+        h = self.main(x)
+        out = self.cls(h)
+
+        # conditioning via projection
+        cmap = self.embed_proj(self.embed(c.argmax(1))).unsqueeze(-1).unsqueeze(-1)
+        out = (out * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
+
+        return out
+
+
+class MultiScaleD(nn.Module):
+    def __init__(
+        self,
+        channels,
+        resolutions,
+        num_discs=4,
+        proj_type=2,  # 0 = no projection, 1 = cross channel mixing, 2 = cross scale mixing
+        cond=0,
+        separable=False,
+        patch=False,
+        **kwargs,
+    ):
+        super().__init__()
+
+        assert num_discs in [1, 2, 3, 4]
+
+        # the first disc is on the lowest level of the backbone
+        self.disc_in_channels = channels[:num_discs]
+        self.disc_in_res = resolutions[:num_discs]
+        Disc = SingleDiscCond if cond else SingleDisc
+
+        mini_discs = []
+        for i, (cin, res) in enumerate(zip(self.disc_in_channels, self.disc_in_res)):
+            start_sz = res if not patch else 16
+            mini_discs += [str(i), Disc(nc=cin, start_sz=start_sz, end_sz=8, separable=separable, patch=patch)],
+        self.mini_discs = nn.ModuleDict(mini_discs)
+
+    def forward(self, features, c):
+        all_logits = []
+        for k, disc in self.mini_discs.items():
+            res = disc(features[k], c).view(features[k].size(0), -1)
+            all_logits.append(res)
+
+        all_logits = torch.cat(all_logits, dim=1)
+        return all_logits
+
+
+class ProjectedDiscriminator(torch.nn.Module):
+    def __init__(
+        self,
+        diffaug=True,
+        interp224=True,
+        backbone_kwargs={},
+        **kwargs
+    ):
+        super().__init__()
+        self.diffaug = diffaug
+        self.interp224 = interp224
+        self.feature_network = F_RandomProj(**backbone_kwargs)
+        self.discriminator = MultiScaleD(
+            channels=self.feature_network.CHANNELS,
+            resolutions=self.feature_network.RESOLUTIONS,
+            **backbone_kwargs,
+        )
+
+    def train(self, mode=True):
+        self.feature_network = self.feature_network.train(False)
+        self.discriminator = self.discriminator.train(mode)
+        return self
+
+    def eval(self):
+        return self.train(False)
+    
+    def get_feature(self, x):
+        features = self.feature_network(x, get_features=True)
+        return features
+
+    def forward(self, x, c):
+        # if self.diffaug:
+        #     x = DiffAugment(x, policy='color,translation,cutout')
+
+        # if self.interp224:
+        #     x = F.interpolate(x, 224, mode='bilinear', align_corners=False)
+
+        features,backbone_features = self.feature_network(x)
+        logits = self.discriminator(features, c)
+
+        return logits,backbone_features
+
diff --git a/mlops/serving/SwimSwap/pg_modules/projector.py b/mlops/serving/SwimSwap/pg_modules/projector.py
new file mode 100644
index 0000000..610a482
--- /dev/null
+++ b/mlops/serving/SwimSwap/pg_modules/projector.py
@@ -0,0 +1,158 @@
+import torch
+import torch.nn as nn
+import timm
+from pg_modules.blocks import FeatureFusionBlock
+
+
+def _make_scratch_ccm(scratch, in_channels, cout, expand=False):
+    # shapes
+    out_channels = [cout, cout*2, cout*4, cout*8] if expand else [cout]*4
+
+    scratch.layer0_ccm = nn.Conv2d(in_channels[0], out_channels[0], kernel_size=1, stride=1, padding=0, bias=True)
+    scratch.layer1_ccm = nn.Conv2d(in_channels[1], out_channels[1], kernel_size=1, stride=1, padding=0, bias=True)
+    scratch.layer2_ccm = nn.Conv2d(in_channels[2], out_channels[2], kernel_size=1, stride=1, padding=0, bias=True)
+    scratch.layer3_ccm = nn.Conv2d(in_channels[3], out_channels[3], kernel_size=1, stride=1, padding=0, bias=True)
+
+    scratch.CHANNELS = out_channels
+
+    return scratch
+
+
+def _make_scratch_csm(scratch, in_channels, cout, expand):
+    scratch.layer3_csm = FeatureFusionBlock(in_channels[3], nn.ReLU(False), expand=expand, lowest=True)
+    scratch.layer2_csm = FeatureFusionBlock(in_channels[2], nn.ReLU(False), expand=expand)
+    scratch.layer1_csm = FeatureFusionBlock(in_channels[1], nn.ReLU(False), expand=expand)
+    scratch.layer0_csm = FeatureFusionBlock(in_channels[0], nn.ReLU(False))
+
+    # last refinenet does not expand to save channels in higher dimensions
+    scratch.CHANNELS = [cout, cout, cout*2, cout*4] if expand else [cout]*4
+
+    return scratch
+
+
+def _make_efficientnet(model):
+    pretrained = nn.Module()
+    pretrained.layer0 = nn.Sequential(model.conv_stem, model.bn1, model.act1, *model.blocks[0:2])
+    pretrained.layer1 = nn.Sequential(*model.blocks[2:3])
+    pretrained.layer2 = nn.Sequential(*model.blocks[3:5])
+    pretrained.layer3 = nn.Sequential(*model.blocks[5:9])
+    return pretrained
+
+
+def calc_channels(pretrained, inp_res=224):
+    channels = []
+    tmp = torch.zeros(1, 3, inp_res, inp_res)
+
+    # forward pass
+    tmp = pretrained.layer0(tmp)
+    channels.append(tmp.shape[1])
+    tmp = pretrained.layer1(tmp)
+    channels.append(tmp.shape[1])
+    tmp = pretrained.layer2(tmp)
+    channels.append(tmp.shape[1])
+    tmp = pretrained.layer3(tmp)
+    channels.append(tmp.shape[1])
+
+    return channels
+
+
+def _make_projector(im_res, cout, proj_type, expand=False):
+    assert proj_type in [0, 1, 2], "Invalid projection type"
+
+    ### Build pretrained feature network
+    model = timm.create_model('tf_efficientnet_lite0', pretrained=True)
+    pretrained = _make_efficientnet(model)
+
+    # determine resolution of feature maps, this is later used to calculate the number
+    # of down blocks in the discriminators. Interestingly, the best results are achieved
+    # by fixing this to 256, ie., we use the same number of down blocks per discriminator
+    # independent of the dataset resolution
+    im_res = 256
+    pretrained.RESOLUTIONS = [im_res//4, im_res//8, im_res//16, im_res//32]
+    pretrained.CHANNELS = calc_channels(pretrained)
+
+    if proj_type == 0: return pretrained, None
+
+    ### Build CCM
+    scratch = nn.Module()
+    scratch = _make_scratch_ccm(scratch, in_channels=pretrained.CHANNELS, cout=cout, expand=expand)
+    pretrained.CHANNELS = scratch.CHANNELS
+
+    if proj_type == 1: return pretrained, scratch
+
+    ### build CSM
+    scratch = _make_scratch_csm(scratch, in_channels=scratch.CHANNELS, cout=cout, expand=expand)
+
+    # CSM upsamples x2 so the feature map resolution doubles
+    pretrained.RESOLUTIONS = [res*2 for res in pretrained.RESOLUTIONS]
+    pretrained.CHANNELS = scratch.CHANNELS
+
+    return pretrained, scratch
+
+
+class F_RandomProj(nn.Module):
+    def __init__(
+        self,
+        im_res=256,
+        cout=64,
+        expand=True,
+        proj_type=2,  # 0 = no projection, 1 = cross channel mixing, 2 = cross scale mixing
+        **kwargs,
+    ):
+        super().__init__()
+        self.proj_type = proj_type
+        self.cout = cout
+        self.expand = expand
+
+        # build pretrained feature network and random decoder (scratch)
+        self.pretrained, self.scratch = _make_projector(im_res=im_res, cout=self.cout, proj_type=self.proj_type, expand=self.expand)
+        self.CHANNELS = self.pretrained.CHANNELS
+        self.RESOLUTIONS = self.pretrained.RESOLUTIONS
+
+    def forward(self, x, get_features=False):
+        # predict feature maps
+        out0 = self.pretrained.layer0(x)
+        out1 = self.pretrained.layer1(out0)
+        out2 = self.pretrained.layer2(out1)
+        out3 = self.pretrained.layer3(out2)
+
+        # start enumerating at the lowest layer (this is where we put the first discriminator)
+        backbone_features = {
+            '0': out0,
+            '1': out1,
+            '2': out2,
+            '3': out3,
+        }
+        if get_features:
+            return backbone_features
+
+        if self.proj_type == 0: return backbone_features
+
+        out0_channel_mixed = self.scratch.layer0_ccm(backbone_features['0'])
+        out1_channel_mixed = self.scratch.layer1_ccm(backbone_features['1'])
+        out2_channel_mixed = self.scratch.layer2_ccm(backbone_features['2'])
+        out3_channel_mixed = self.scratch.layer3_ccm(backbone_features['3'])
+
+        out = {
+            '0': out0_channel_mixed,
+            '1': out1_channel_mixed,
+            '2': out2_channel_mixed,
+            '3': out3_channel_mixed,
+        }
+
+        if self.proj_type == 1: return out
+
+        # from bottom to top
+        out3_scale_mixed = self.scratch.layer3_csm(out3_channel_mixed)
+        out2_scale_mixed = self.scratch.layer2_csm(out3_scale_mixed, out2_channel_mixed)
+        out1_scale_mixed = self.scratch.layer1_csm(out2_scale_mixed, out1_channel_mixed)
+        out0_scale_mixed = self.scratch.layer0_csm(out1_scale_mixed, out0_channel_mixed)
+
+        out = {
+            '0': out0_scale_mixed,
+            '1': out1_scale_mixed,
+            '2': out2_scale_mixed,
+            '3': out3_scale_mixed,
+        }
+
+        return out, backbone_features
diff --git a/mlops/serving/SwimSwap/requirement.txt b/mlops/serving/SwimSwap/requirement.txt
new file mode 100644
index 0000000..737caba
--- /dev/null
+++ b/mlops/serving/SwimSwap/requirement.txt
@@ -0,0 +1,18 @@
+imageio
+insightface==0.2.1
+onnxruntime
+moviepy
+onnxruntime-gpu
+
+pip install imageio \
+insightface==0.2.1 \
+onnxruntime \
+moviepy \
+onnxruntime-gpu \
+mlflow \
+minio \
+boto3
+
+# apt update -y
+# apt install -y libgl1-mesa-glx \
+libglib2.0-0
diff --git a/mlops/serving/SwimSwap/util/add_watermark.py b/mlops/serving/SwimSwap/util/add_watermark.py
new file mode 100644
index 0000000..a4c5d3d
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/add_watermark.py
@@ -0,0 +1,118 @@
+
+import cv2
+import numpy as np
+from PIL import Image
+import math
+import numpy as np
+# import torch
+# from torchvision import transforms
+
+def rotate_image(image, angle, center = None, scale = 1.0):
+    (h, w) = image.shape[:2]
+
+    if center is None:
+        center = (w / 2, h / 2)
+
+    # Perform the rotation
+    M = cv2.getRotationMatrix2D(center, angle, scale)
+    rotated = cv2.warpAffine(image, M, (w, h))
+
+    return rotated
+
+class watermark_image:
+    def __init__(self, logo_path, size=0.3, oritation="DR", margin=(5,20,20,20), angle=15, rgb_weight=(0,1,1.5), input_frame_shape=None) -> None:
+
+        logo_image = cv2.imread(logo_path, cv2.IMREAD_UNCHANGED)
+        h,w,c = logo_image.shape
+        if angle%360 != 0:
+            new_h = w*math.sin(angle/180*math.pi) + h*math.cos(angle/180*math.pi)
+            pad_h = int((new_h-h)//2)
+            
+            padding = np.zeros((pad_h, w, c), dtype=np.uint8)
+            logo_image = cv2.vconcat([logo_image, padding])
+            logo_image = cv2.vconcat([padding, logo_image])
+        
+            logo_image = rotate_image(logo_image, angle)
+        print(logo_image.shape)
+        self.logo_image = logo_image
+        
+        if self.logo_image.shape[2] < 4:
+            print("No alpha channel found!")
+            self.logo_image  = self.__addAlpha__(self.logo_image) #add alpha channel
+        self.size       = size
+        self.oritation  = oritation
+        self.margin     = margin
+        self.ori_shape  = self.logo_image.shape
+        self.resized    = False
+        self.rgb_weight = rgb_weight
+
+        self.logo_image[:, :, 2] = self.logo_image[:, :, 2]*self.rgb_weight[0]
+        self.logo_image[:, :, 1] = self.logo_image[:, :, 1]*self.rgb_weight[1]
+        self.logo_image[:, :, 0] = self.logo_image[:, :, 0]*self.rgb_weight[2]
+
+        if input_frame_shape is not None:
+
+            logo_w = input_frame_shape[1] * self.size
+            ratio  = logo_w / self.ori_shape[1]
+            logo_h = int(ratio * self.ori_shape[0])
+            logo_w = int(logo_w)
+
+            size   = (logo_w, logo_h)
+            self.logo_image = cv2.resize(self.logo_image, size, interpolation = cv2.INTER_CUBIC)
+            self.resized    = True
+            if oritation == "UL":
+                self.coor_h = self.margin[1]
+                self.coor_w = self.margin[0]
+            elif oritation == "UR":
+                self.coor_h = self.margin[1]
+                self.coor_w = input_frame_shape[1] - (logo_w + self.margin[2])
+            elif oritation == "DL":
+                self.coor_h = input_frame_shape[0] - (logo_h + self.margin[1])
+                self.coor_w = self.margin[0]
+            else:
+                self.coor_h = input_frame_shape[0] - (logo_h + self.margin[3])
+                self.coor_w = input_frame_shape[1] - (logo_w + self.margin[2])
+            self.logo_w = logo_w
+            self.logo_h = logo_h
+            self.mask = self.logo_image[:,:,3]
+            self.mask = cv2.bitwise_not(self.mask//255)
+            
+    def apply_frames(self, frame):
+
+        if not self.resized:
+            shape = frame.shape
+            logo_w = shape[1] * self.size
+            ratio  = logo_w / self.ori_shape[1]
+            logo_h = int(ratio * self.ori_shape[0])
+            logo_w = int(logo_w)
+
+            size   = (logo_w, logo_h)
+            self.logo_image = cv2.resize(self.logo_image, size, interpolation = cv2.INTER_CUBIC)
+            self.resized    = True
+            if self.oritation == "UL":
+                self.coor_h = self.margin[1]
+                self.coor_w = self.margin[0]
+            elif self.oritation == "UR":
+                self.coor_h = self.margin[1]
+                self.coor_w = shape[1] - (logo_w + self.margin[2])
+            elif self.oritation == "DL":
+                self.coor_h = shape[0] - (logo_h + self.margin[1])
+                self.coor_w = self.margin[0]
+            else:
+                self.coor_h = shape[0] - (logo_h + self.margin[3])
+                self.coor_w = shape[1] - (logo_w + self.margin[2])
+            self.logo_w = logo_w
+            self.logo_h = logo_h
+            self.mask = self.logo_image[:,:,3]
+            self.mask = cv2.bitwise_not(self.mask//255)
+            
+        original_frame = frame[self.coor_h:(self.coor_h+self.logo_h), self.coor_w:(self.coor_w+self.logo_w),:]
+        blending_logo   = cv2.add(self.logo_image[:,:,0:3],original_frame,mask = self.mask)
+        frame[self.coor_h:(self.coor_h+self.logo_h), self.coor_w:(self.coor_w+self.logo_w),:] = blending_logo 
+        return frame
+        
+    def __addAlpha__(self, image):
+        shape = image.shape
+        alpha_channel = np.ones((shape[0],shape[1],1),np.uint8)*255
+        return np.concatenate((image,alpha_channel),2)
+
diff --git a/mlops/serving/SwimSwap/util/gifswap.py b/mlops/serving/SwimSwap/util/gifswap.py
new file mode 100644
index 0000000..a852769
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/gifswap.py
@@ -0,0 +1,129 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 19:19:52
+Description: 
+'''
+import os 
+import cv2
+import glob
+import torch
+import shutil
+import numpy as np
+from tqdm import tqdm
+from util.reverse2original import reverse2wholeimage
+import moviepy.editor as mp
+from moviepy.editor import AudioFileClip, VideoFileClip 
+from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
+import  time
+from util.add_watermark import watermark_image
+from util.norm import SpecificNorm
+from parsing_model.model import BiSeNet
+from array2gif import write_gif
+import imageio.v3 as iio
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+def gif_swap(video_path, id_vetor, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo=False, use_mask=False):
+    video_forcheck = VideoFileClip(video_path)
+    if video_forcheck.audio is None:
+        no_audio = True
+    else:
+        no_audio = False
+
+    del video_forcheck
+
+    if not no_audio:
+        video_audio_clip = AudioFileClip(video_path)
+
+    video = cv2.VideoCapture(video_path)
+    logoclass = watermark_image('/home/hojun/Documents/project/boostcamp/final_project/mlops/pipeline/serving/sf2f/realface.jpg')
+    ret = True
+    frame_index = 0
+
+    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+
+    # video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    fps = video.get(cv2.CAP_PROP_FPS)
+    if  os.path.exists(temp_results_dir):
+            shutil.rmtree(temp_results_dir)
+
+    spNorm =SpecificNorm()
+    if use_mask:
+        n_classes = 19
+        net = BiSeNet(n_classes=n_classes)
+        net.cuda()
+        save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
+        net.load_state_dict(torch.load(save_pth))
+        net.eval()
+    else:
+        net =None
+
+    frames = []
+    # while ret:
+    for frame_index in tqdm(range(frame_count)): 
+        ret, frame = video.read()
+        if  ret:
+            detect_results = detect_model.get(frame,crop_size)
+
+            if detect_results is not None:
+                # print(frame_index)
+                # if not os.path.exists(temp_results_dir):
+                #         os.mkdir(temp_results_dir)
+                frame_align_crop_list = detect_results[0]
+                frame_mat_list = detect_results[1]
+                swap_result_list = []
+                frame_align_crop_tenor_list = []
+                for frame_align_crop in frame_align_crop_list:
+
+                    # BGR TO RGB
+                    # frame_align_crop_RGB = frame_align_crop[...,::-1]
+
+                    frame_align_crop_tenor = _totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+                    swap_result = swap_model(None, frame_align_crop_tenor, id_vetor, None, True)[0]
+                    # cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame) # 원본 프레임 img 저장
+                    swap_result_list.append(swap_result)
+                    frame_align_crop_tenor_list.append(frame_align_crop_tenor)
+
+
+                img = reverse2wholeimage(frame_align_crop_tenor_list,swap_result_list, frame_mat_list, crop_size, frame, logoclass,\
+                        os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), no_simswaplogo, pasring_model=net, use_mask=use_mask, norm=spNorm)
+                
+                frame = frame.astype(np.uint8)
+                if not os.path.exists(temp_results_dir):
+                    os.mkdir(temp_results_dir)
+                cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), img)
+                # frames.append(img.tobytes())
+                frames.append(img)
+
+            else:
+                if not os.path.exists(temp_results_dir):
+                    os.mkdir(temp_results_dir)
+                frame = frame.astype(np.uint8)
+                # if not no_simswaplogo:
+                    # frame = logoclass.apply_frames(frame)
+                cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+                print(1)
+        else:
+            break
+    
+    print(type(frames[0]))
+    print(np.unique(frames[0]))
+    # for idx in range(len(frames)):
+    #     # if np.where(frames[idx]>255,True,False):
+    #         print(np.where(frames[idx]>255))
+    iio.imwrite(save_path,
+                    frames)
+    # write_gif(frames, save_path, fps=fps)
+    return frames, fps
+    
+
diff --git a/mlops/serving/SwimSwap/util/html.py b/mlops/serving/SwimSwap/util/html.py
new file mode 100644
index 0000000..71c48ad
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/html.py
@@ -0,0 +1,63 @@
+import dominate
+from dominate.tags import *
+import os
+
+
+class HTML:
+    def __init__(self, web_dir, title, refresh=0):
+        self.title = title
+        self.web_dir = web_dir
+        self.img_dir = os.path.join(self.web_dir, 'images')
+        if not os.path.exists(self.web_dir):
+            os.makedirs(self.web_dir)
+        if not os.path.exists(self.img_dir):
+            os.makedirs(self.img_dir)
+
+        self.doc = dominate.document(title=title)
+        if refresh > 0:
+            with self.doc.head:
+                meta(http_equiv="refresh", content=str(refresh))
+
+    def get_image_dir(self):
+        return self.img_dir
+
+    def add_header(self, str):
+        with self.doc:
+            h3(str)
+
+    def add_table(self, border=1):
+        self.t = table(border=border, style="table-layout: fixed;")
+        self.doc.add(self.t)
+
+    def add_images(self, ims, txts, links, width=512):
+        self.add_table()
+        with self.t:
+            with tr():
+                for im, txt, link in zip(ims, txts, links):
+                    with td(style="word-wrap: break-word;", halign="center", valign="top"):
+                        with p():
+                            with a(href=os.path.join('images', link)):
+                                img(style="width:%dpx" % (width), src=os.path.join('images', im))
+                            br()
+                            p(txt)
+
+    def save(self):
+        html_file = '%s/index.html' % self.web_dir
+        f = open(html_file, 'wt')
+        f.write(self.doc.render())
+        f.close()
+
+
+if __name__ == '__main__':
+    html = HTML('web/', 'test_html')
+    html.add_header('hello world')
+
+    ims = []
+    txts = []
+    links = []
+    for n in range(4):
+        ims.append('image_%d.jpg' % n)
+        txts.append('text_%d' % n)
+        links.append('image_%d.jpg' % n)
+    html.add_images(ims, txts, links)
+    html.save()
diff --git a/mlops/serving/SwimSwap/util/image_pool.py b/mlops/serving/SwimSwap/util/image_pool.py
new file mode 100644
index 0000000..63e1877
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/image_pool.py
@@ -0,0 +1,31 @@
+import random
+import torch
+from torch.autograd import Variable
+class ImagePool():
+    def __init__(self, pool_size):
+        self.pool_size = pool_size
+        if self.pool_size > 0:
+            self.num_imgs = 0
+            self.images = []
+
+    def query(self, images):
+        if self.pool_size == 0:
+            return images
+        return_images = []
+        for image in images.data:
+            image = torch.unsqueeze(image, 0)
+            if self.num_imgs < self.pool_size:
+                self.num_imgs = self.num_imgs + 1
+                self.images.append(image)
+                return_images.append(image)
+            else:
+                p = random.uniform(0, 1)
+                if p > 0.5:
+                    random_id = random.randint(0, self.pool_size-1)
+                    tmp = self.images[random_id].clone()
+                    self.images[random_id] = image
+                    return_images.append(tmp)
+                else:
+                    return_images.append(image)
+        return_images = Variable(torch.cat(return_images, 0))
+        return return_images
diff --git a/mlops/serving/SwimSwap/util/json_config.py b/mlops/serving/SwimSwap/util/json_config.py
new file mode 100644
index 0000000..c68fbff
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/json_config.py
@@ -0,0 +1,15 @@
+import  json
+
+
+def readConfig(path):
+    with open(path,'r') as cf:
+        nodelocaltionstr = cf.read()
+        nodelocaltioninf = json.loads(nodelocaltionstr)
+        if isinstance(nodelocaltioninf,str):
+            nodelocaltioninf = json.loads(nodelocaltioninf)
+    return nodelocaltioninf
+
+def writeConfig(path, info):
+    with open(path, 'w') as cf:
+        configjson  = json.dumps(info, indent=4)
+        cf.writelines(configjson)
\ No newline at end of file
diff --git a/mlops/serving/SwimSwap/util/logo_class.py b/mlops/serving/SwimSwap/util/logo_class.py
new file mode 100644
index 0000000..044dce3
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/logo_class.py
@@ -0,0 +1,44 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: logo_class.py
+# Created Date: Tuesday June 29th 2021
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Monday, 11th October 2021 12:39:55 am
+# Modified By: Chen Xuanhong
+# Copyright (c) 2021 Shanghai Jiao Tong University
+#############################################################
+
+class logo_class:
+    
+    @staticmethod
+    def print_group_logo():
+        logo_str = """
+
+███╗   ██╗██████╗ ███████╗██╗ ██████╗     ███████╗     ██╗████████╗██╗   ██╗
+████╗  ██║██╔══██╗██╔════╝██║██╔════╝     ██╔════╝     ██║╚══██╔══╝██║   ██║
+██╔██╗ ██║██████╔╝███████╗██║██║  ███╗    ███████╗     ██║   ██║   ██║   ██║
+██║╚██╗██║██╔══██╗╚════██║██║██║   ██║    ╚════██║██   ██║   ██║   ██║   ██║
+██║ ╚████║██║  ██║███████║██║╚██████╔╝    ███████║╚█████╔╝   ██║   ╚██████╔╝
+╚═╝  ╚═══╝╚═╝  ╚═╝╚══════╝╚═╝ ╚═════╝     ╚══════╝ ╚════╝    ╚═╝    ╚═════╝ 
+Neural Rendering Special Interesting Group of SJTU
+                                                                            
+        """
+        print(logo_str)
+
+    @staticmethod
+    def print_start_training():
+        logo_str = """
+   _____  __                __     ______              _         _              
+  / ___/ / /_ ____ _ _____ / /_   /_  __/_____ ____ _ (_)____   (_)____   ____ _
+  \__ \ / __// __ `// ___// __/    / /  / ___// __ `// // __ \ / // __ \ / __ `/
+ ___/ // /_ / /_/ // /   / /_     / /  / /   / /_/ // // / / // // / / // /_/ / 
+/____/ \__/ \__,_//_/    \__/    /_/  /_/    \__,_//_//_/ /_//_//_/ /_/ \__, /  
+                                                                       /____/   
+        """
+        print(logo_str)
+
+if __name__=="__main__":
+    # logo_class.print_group_logo()
+    logo_class.print_start_training()
\ No newline at end of file
diff --git a/mlops/serving/SwimSwap/util/mp4swap.py b/mlops/serving/SwimSwap/util/mp4swap.py
new file mode 100644
index 0000000..c6950fe
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/mp4swap.py
@@ -0,0 +1,96 @@
+import cv2
+import os
+import torch
+import numpy as np
+from tqdm import tqdm
+from util.reverse2original import reverse2wholeimage
+from util.norm import SpecificNorm
+from parsing_model.model import BiSeNet
+import tempfile, requests
+import imageio
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+def mp4_swap(video_path, id_vetor, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo=False, use_mask=False):
+
+        r = requests.get(video_path)
+        with tempfile.NamedTemporaryFile(delete=False) as tmpfile:
+            tmpfile.write(r.content)
+            tmp_video_path = tmpfile.name
+        
+        video = cv2.VideoCapture(tmp_video_path)
+
+        logoclass = None
+        ret = True
+        frame_index = 0
+
+        frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+
+        video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+
+        video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+        
+        fps = video.get(cv2.CAP_PROP_FPS)
+        
+        spNorm =SpecificNorm()
+        if use_mask:
+            n_classes = 19
+            net = BiSeNet(n_classes=n_classes)
+            net.cuda()
+            save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
+            net.load_state_dict(torch.load(save_pth))
+            net.eval()
+        else:
+            net =None
+
+        frames = []
+        # while ret:
+        for frame_index in tqdm(range(frame_count)): 
+            ret, frame = video.read()
+            if  ret:
+                detect_results = detect_model.get(frame,crop_size)
+
+                if detect_results is not None:
+                    frame_align_crop_list = detect_results[0]
+                    frame_mat_list = detect_results[1]
+                    swap_result_list = []
+                    frame_align_crop_tenor_list = []
+                    for frame_align_crop in frame_align_crop_list:
+                        frame_align_crop_tenor = _totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+                        swap_result = swap_model(None, frame_align_crop_tenor, id_vetor, None, True)[0]
+                        swap_result_list.append(swap_result)
+                        frame_align_crop_tenor_list.append(frame_align_crop_tenor)
+
+
+                    img = reverse2wholeimage(frame_align_crop_tenor_list,swap_result_list, frame_mat_list, crop_size, frame, logoclass,\
+                            os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), no_simswaplogo, pasring_model=net, use_mask=use_mask, norm=spNorm)
+                    
+                    frame = frame.astype(np.uint8)
+                    if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+           
+                    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+                    frames.append(img)
+
+                else:
+                    if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+                    frame = frame.astype(np.uint8)
+                    frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
+                    frames.append(frame)
+            else:
+                break
+        
+        
+        with imageio.get_writer(save_path, fps=fps) as video:
+            for image in frames:
+                video.append_data(image)
+        return True
+    # except:
+    #     return False
+
diff --git a/mlops/serving/SwimSwap/util/norm.py b/mlops/serving/SwimSwap/util/norm.py
new file mode 100644
index 0000000..981adcf
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/norm.py
@@ -0,0 +1,25 @@
+import torch.nn as nn
+import numpy as np
+import torch
+class SpecificNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(SpecificNorm, self).__init__()
+        self.mean = np.array([0.485, 0.456, 0.406])
+        self.mean = torch.from_numpy(self.mean).float().cuda()
+        self.mean = self.mean.view([1, 3, 1, 1])
+
+        self.std = np.array([0.229, 0.224, 0.225])
+        self.std = torch.from_numpy(self.std).float().cuda()
+        self.std = self.std.view([1, 3, 1, 1])
+
+    def forward(self, x):
+        mean = self.mean.expand([1, 3, x.shape[2], x.shape[3]])
+        std = self.std.expand([1, 3, x.shape[2], x.shape[3]])
+
+        x = (x - mean) / std
+
+        return x
\ No newline at end of file
diff --git a/mlops/serving/SwimSwap/util/plot.py b/mlops/serving/SwimSwap/util/plot.py
new file mode 100644
index 0000000..0da1c75
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/plot.py
@@ -0,0 +1,37 @@
+import numpy as np
+import math
+import PIL
+
+def postprocess(x):
+    """[0,1] to uint8."""
+    
+    x = np.clip(255 * x, 0, 255)
+    x = np.cast[np.uint8](x)
+    return x
+
+def tile(X, rows, cols):
+    """Tile images for display."""
+    tiling = np.zeros((rows * X.shape[1], cols * X.shape[2], X.shape[3]), dtype = X.dtype)
+    for i in range(rows):
+        for j in range(cols):
+            idx = i * cols + j
+            if idx < X.shape[0]:
+                img = X[idx,...]
+                tiling[
+                        i*X.shape[1]:(i+1)*X.shape[1],
+                        j*X.shape[2]:(j+1)*X.shape[2],
+                        :] = img
+    return tiling
+
+
+def plot_batch(X, out_path):
+    """Save batch of images tiled."""
+    n_channels = X.shape[3]
+    if n_channels > 3:
+        X = X[:,:,:,np.random.choice(n_channels, size = 3)]
+    X = postprocess(X)
+    rc = math.sqrt(X.shape[0])
+    rows = cols = math.ceil(rc)
+    canvas = tile(X, rows, cols)
+    canvas = np.squeeze(canvas)
+    PIL.Image.fromarray(canvas).save(out_path)
\ No newline at end of file
diff --git a/mlops/serving/SwimSwap/util/reverse2original.py b/mlops/serving/SwimSwap/util/reverse2original.py
new file mode 100644
index 0000000..171021b
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/reverse2original.py
@@ -0,0 +1,176 @@
+import cv2
+import numpy as np
+# import  time
+import torch
+from torch.nn import functional as F
+import torch.nn as nn
+
+
+def encode_segmentation_rgb(segmentation, no_neck=True):
+    parse = segmentation
+
+    face_part_ids = [1, 2, 3, 4, 5, 6, 10, 12, 13] if no_neck else [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14]
+    mouth_id = 11
+    # hair_id = 17
+    face_map = np.zeros([parse.shape[0], parse.shape[1]])
+    mouth_map = np.zeros([parse.shape[0], parse.shape[1]])
+    # hair_map = np.zeros([parse.shape[0], parse.shape[1]])
+
+    for valid_id in face_part_ids:
+        valid_index = np.where(parse==valid_id)
+        face_map[valid_index] = 255
+    valid_index = np.where(parse==mouth_id)
+    mouth_map[valid_index] = 255
+    # valid_index = np.where(parse==hair_id)
+    # hair_map[valid_index] = 255
+    #return np.stack([face_map, mouth_map,hair_map], axis=2)
+    return np.stack([face_map, mouth_map], axis=2)
+
+
+class SoftErosion(nn.Module):
+    def __init__(self, kernel_size=15, threshold=0.6, iterations=1):
+        super(SoftErosion, self).__init__()
+        r = kernel_size // 2
+        self.padding = r
+        self.iterations = iterations
+        self.threshold = threshold
+
+        # Create kernel
+        y_indices, x_indices = torch.meshgrid(torch.arange(0., kernel_size), torch.arange(0., kernel_size))
+        dist = torch.sqrt((x_indices - r) ** 2 + (y_indices - r) ** 2)
+        kernel = dist.max() - dist
+        kernel /= kernel.sum()
+        kernel = kernel.view(1, 1, *kernel.shape)
+        self.register_buffer('weight', kernel)
+
+    def forward(self, x):
+        x = x.float()
+        for i in range(self.iterations - 1):
+            x = torch.min(x, F.conv2d(x, weight=self.weight, groups=x.shape[1], padding=self.padding))
+        x = F.conv2d(x, weight=self.weight, groups=x.shape[1], padding=self.padding)
+
+        mask = x >= self.threshold
+        x[mask] = 1.0
+        x[~mask] /= x[~mask].max()
+
+        return x, mask
+
+
+def postprocess(swapped_face, target, target_mask,smooth_mask):
+    # target_mask = cv2.resize(target_mask, (self.size,  self.size))
+
+    mask_tensor = torch.from_numpy(target_mask.copy().transpose((2, 0, 1))).float().mul_(1/255.0).cuda()
+    face_mask_tensor = mask_tensor[0] + mask_tensor[1]
+    
+    soft_face_mask_tensor, _ = smooth_mask(face_mask_tensor.unsqueeze_(0).unsqueeze_(0))
+    soft_face_mask_tensor.squeeze_()
+
+    soft_face_mask = soft_face_mask_tensor.cpu().numpy()
+    soft_face_mask = soft_face_mask[:, :, np.newaxis]
+
+    result =  swapped_face * soft_face_mask + target * (1 - soft_face_mask)
+    result = result[:,:,::-1]# .astype(np.uint8)
+    return result
+
+def reverse2wholeimage(b_align_crop_tenor_list, swaped_imgs, mats, crop_size, oriimg, logoclass, save_path='', \
+                    no_simswaplogo=False, pasring_model=None, norm=None, use_mask=False):
+
+    target_image_list = []
+    img_mask_list = []
+    if use_mask:
+        smooth_mask = SoftErosion(kernel_size=17, threshold=0.9, iterations=7).cuda()
+    else:
+        pass
+
+    # print(len(swaped_imgs))
+    # print(mats)
+    # print(len(b_align_crop_tenor_list))
+    for swaped_img, mat ,source_img in zip(swaped_imgs, mats, b_align_crop_tenor_list):
+        swaped_img = swaped_img.cpu().detach().numpy().transpose((1, 2, 0))
+        img_white = np.full((crop_size,crop_size), 255, dtype=float)
+
+        # inverse the Affine transformation matrix
+        mat_rev = np.zeros([2,3])
+        div1 = mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0]
+        mat_rev[0][0] = mat[1][1]/div1
+        mat_rev[0][1] = -mat[0][1]/div1
+        mat_rev[0][2] = -(mat[0][2]*mat[1][1]-mat[0][1]*mat[1][2])/div1
+        div2 = mat[0][1]*mat[1][0]-mat[0][0]*mat[1][1]
+        mat_rev[1][0] = mat[1][0]/div2
+        mat_rev[1][1] = -mat[0][0]/div2
+        mat_rev[1][2] = -(mat[0][2]*mat[1][0]-mat[0][0]*mat[1][2])/div2
+
+        orisize = (oriimg.shape[1], oriimg.shape[0])
+        if use_mask:
+            source_img_norm = norm(source_img)
+            source_img_512  = F.interpolate(source_img_norm,size=(512,512))
+            out = pasring_model(source_img_512)[0]
+            parsing = out.squeeze(0).detach().cpu().numpy().argmax(0)
+            vis_parsing_anno = parsing.copy().astype(np.uint8)
+            tgt_mask = encode_segmentation_rgb(vis_parsing_anno)
+            if tgt_mask.sum() >= 5000:
+                # face_mask_tensor = tgt_mask[...,0] + tgt_mask[...,1]
+                target_mask = cv2.resize(tgt_mask, (crop_size,  crop_size))
+                # print(source_img)
+                target_image_parsing = postprocess(swaped_img, source_img[0].cpu().detach().numpy().transpose((1, 2, 0)), target_mask,smooth_mask)
+                
+
+                target_image = cv2.warpAffine(target_image_parsing, mat_rev, orisize)
+                # target_image_parsing = cv2.warpAffine(swaped_img, mat_rev, orisize)
+            else:
+                target_image = cv2.warpAffine(swaped_img, mat_rev, orisize)[..., ::-1]
+        else:
+            target_image = cv2.warpAffine(swaped_img, mat_rev, orisize)
+        # source_image   = cv2.warpAffine(source_img, mat_rev, orisize)
+
+        img_white = cv2.warpAffine(img_white, mat_rev, orisize)
+
+
+        img_white[img_white>20] =255
+
+        img_mask = img_white
+
+        # if use_mask:
+        #     kernel = np.ones((40,40),np.uint8)
+        #     img_mask = cv2.erode(img_mask,kernel,iterations = 1)
+        # else:
+        kernel = np.ones((40,40),np.uint8)
+        img_mask = cv2.erode(img_mask,kernel,iterations = 1)
+        kernel_size = (20, 20)
+        blur_size = tuple(2*i+1 for i in kernel_size)
+        img_mask = cv2.GaussianBlur(img_mask, blur_size, 0)
+
+        # kernel = np.ones((10,10),np.uint8)
+        # img_mask = cv2.erode(img_mask,kernel,iterations = 1)
+
+
+
+        img_mask /= 255
+
+        img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1])
+
+        # pasing mask
+
+        # target_image_parsing = postprocess(target_image, source_image, tgt_mask)
+
+        if use_mask:
+            target_image = np.array(target_image, dtype=np.float64) * 255
+        else:
+            target_image = np.array(target_image, dtype=np.float64)[..., ::-1] * 255
+
+
+        img_mask_list.append(img_mask)
+        target_image_list.append(target_image)
+        
+
+    # target_image /= 255
+    # target_image = 0
+    img = np.array(oriimg, dtype=np.float64)
+    for img_mask, target_image in zip(img_mask_list, target_image_list):
+        img = img_mask * target_image + (1-img_mask) * img
+        
+    final_img = img.astype(np.uint8)
+    # if not no_simswaplogo:
+    #     final_img = logoclass.apply_frames(final_img)
+    # cv2.imwrite(save_path, final_img) # swap img 저장
+    return final_img
diff --git a/mlops/serving/SwimSwap/util/save_heatmap.py b/mlops/serving/SwimSwap/util/save_heatmap.py
new file mode 100644
index 0000000..71ce4c9
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/save_heatmap.py
@@ -0,0 +1,57 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: save_heatmap.py
+# Created Date: Friday January 15th 2021
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Wednesday, 19th January 2022 1:22:47 am
+# Modified By: Chen Xuanhong
+# Copyright (c) 2021 Shanghai Jiao Tong University
+#############################################################
+
+import os
+import shutil
+import seaborn as sns
+import matplotlib.pyplot as plt
+import cv2
+import numpy as np
+
+def SaveHeatmap(heatmaps, path, row=-1, dpi=72):
+    """
+    The input tensor must be B X 1 X H X W
+    """
+    batch_size = heatmaps.shape[0]
+    temp_path  = ".temp/"
+    if not os.path.exists(temp_path):
+        os.makedirs(temp_path)
+    final_img = None
+    if row < 1:
+        col = batch_size
+        row = 1
+    else:
+        col = batch_size // row
+        if row * col <batch_size:
+            col +=1
+    
+    row_i = 0
+    col_i = 0
+    
+    for i in range(batch_size):
+        img_path = os.path.join(temp_path,'temp_batch_{}.png'.format(i))
+        sns.heatmap(heatmaps[i,0,:,:],vmin=0,vmax=heatmaps[i,0,:,:].max(),cbar=False)
+        plt.savefig(img_path, dpi=dpi, bbox_inches = 'tight', pad_inches = 0)
+        img = cv2.imread(img_path)
+        if i == 0:
+            H,W,C = img.shape
+            final_img = np.zeros((H*row,W*col,C))
+        final_img[H*row_i:H*(row_i+1),W*col_i:W*(col_i+1),:] = img
+        col_i += 1
+        if col_i >= col:
+            col_i = 0
+            row_i += 1
+    cv2.imwrite(path,final_img)
+
+if __name__ == "__main__":
+    random_map = np.random.randn(16,1,10,10)
+    SaveHeatmap(random_map,"./wocao.png",1)
diff --git a/mlops/serving/SwimSwap/util/upload_minio.py b/mlops/serving/SwimSwap/util/upload_minio.py
new file mode 100644
index 0000000..f1ed6c4
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/upload_minio.py
@@ -0,0 +1,22 @@
+import os
+from minio import Minio
+
+ALLOWED_EXTENSIONS = {"txt", "pdf", "png", "jpg", "jpeg", "gif"}
+ACCESS_KEY = os.environ.get("AWS_ACCESS_KEY_ID")
+SECRET_KEY = os.environ.get("AWS_SECRET_ACCESS_KEY")
+BUCKET_NAME = os.environ.get("MINIO_BUCKET")
+MINIO_API_HOST = os.environ.get("MINIO_ENDPOINT")
+
+def upload_object(client, filename, data, length, BUCKET_NAME,content_type=None):
+
+    # Make bucket if not exist.
+    found = client.bucket_exists(BUCKET_NAME)
+    if not found:
+        client.make_bucket(BUCKET_NAME)
+    else:
+        print(f"Bucket {BUCKET_NAME} already exists")
+    if content_type is not None:
+        client.put_object(BUCKET_NAME, filename, data, length,content_type=content_type)
+    else:
+        client.put_object(BUCKET_NAME, filename, data, length)
+    print(f"{filename} is successfully uploaded to bucket {BUCKET_NAME}.")
\ No newline at end of file
diff --git a/mlops/serving/SwimSwap/util/util.py b/mlops/serving/SwimSwap/util/util.py
new file mode 100644
index 0000000..f4f79ec
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/util.py
@@ -0,0 +1,100 @@
+from __future__ import print_function
+import torch
+import numpy as np
+from PIL import Image
+import numpy as np
+import os
+
+# Converts a Tensor into a Numpy array
+# |imtype|: the desired type of the converted numpy array
+def tensor2im(image_tensor, imtype=np.uint8, normalize=True):
+    if isinstance(image_tensor, list):
+        image_numpy = []
+        for i in range(len(image_tensor)):
+            image_numpy.append(tensor2im(image_tensor[i], imtype, normalize))
+        return image_numpy
+    image_numpy = image_tensor.cpu().float().numpy()
+    if normalize:
+        image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
+    else:
+        image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0      
+    image_numpy = np.clip(image_numpy, 0, 255)
+    if image_numpy.shape[2] == 1 or image_numpy.shape[2] > 3:        
+        image_numpy = image_numpy[:,:,0]
+    return image_numpy.astype(imtype)
+
+# Converts a one-hot tensor into a colorful label map
+def tensor2label(label_tensor, n_label, imtype=np.uint8):
+    if n_label == 0:
+        return tensor2im(label_tensor, imtype)
+    label_tensor = label_tensor.cpu().float()    
+    if label_tensor.size()[0] > 1:
+        label_tensor = label_tensor.max(0, keepdim=True)[1]
+    label_tensor = Colorize(n_label)(label_tensor)
+    label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0))
+    return label_numpy.astype(imtype)
+
+def save_image(image_numpy, image_path):
+    image_pil = Image.fromarray(image_numpy)
+    image_pil.save(image_path)
+
+def mkdirs(paths):
+    if isinstance(paths, list) and not isinstance(paths, str):
+        for path in paths:
+            mkdir(path)
+    else:
+        mkdir(paths)
+
+def mkdir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+###############################################################################
+# Code from
+# https://github.com/ycszen/pytorch-seg/blob/master/transform.py
+# Modified so it complies with the Citscape label map colors
+###############################################################################
+def uint82bin(n, count=8):
+    """returns the binary of integer n, count refers to amount of bits"""
+    return ''.join([str((n >> y) & 1) for y in range(count-1, -1, -1)])
+
+def labelcolormap(N):
+    if N == 35: # cityscape
+        cmap = np.array([(  0,  0,  0), (  0,  0,  0), (  0,  0,  0), (  0,  0,  0), (  0,  0,  0), (111, 74,  0), ( 81,  0, 81),
+                     (128, 64,128), (244, 35,232), (250,170,160), (230,150,140), ( 70, 70, 70), (102,102,156), (190,153,153),
+                     (180,165,180), (150,100,100), (150,120, 90), (153,153,153), (153,153,153), (250,170, 30), (220,220,  0),
+                     (107,142, 35), (152,251,152), ( 70,130,180), (220, 20, 60), (255,  0,  0), (  0,  0,142), (  0,  0, 70),
+                     (  0, 60,100), (  0,  0, 90), (  0,  0,110), (  0, 80,100), (  0,  0,230), (119, 11, 32), (  0,  0,142)], 
+                     dtype=np.uint8)
+    else:
+        cmap = np.zeros((N, 3), dtype=np.uint8)
+        for i in range(N):
+            r, g, b = 0, 0, 0
+            id = i
+            for j in range(7):
+                str_id = uint82bin(id)
+                r = r ^ (np.uint8(str_id[-1]) << (7-j))
+                g = g ^ (np.uint8(str_id[-2]) << (7-j))
+                b = b ^ (np.uint8(str_id[-3]) << (7-j))
+                id = id >> 3
+            cmap[i, 0] = r
+            cmap[i, 1] = g
+            cmap[i, 2] = b
+    return cmap
+
+class Colorize(object):
+    def __init__(self, n=35):
+        self.cmap = labelcolormap(n)
+        self.cmap = torch.from_numpy(self.cmap[:n])
+
+    def __call__(self, gray_image):
+        size = gray_image.size()
+        color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0)
+
+        for label in range(0, len(self.cmap)):
+            mask = (label == gray_image[0]).cpu()
+            color_image[0][mask] = self.cmap[label][0]
+            color_image[1][mask] = self.cmap[label][1]
+            color_image[2][mask] = self.cmap[label][2]
+
+        return color_image
diff --git a/mlops/serving/SwimSwap/util/videoswap.py b/mlops/serving/SwimSwap/util/videoswap.py
new file mode 100644
index 0000000..99294d5
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/videoswap.py
@@ -0,0 +1,121 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 19:19:52
+Description: 
+'''
+import os 
+import cv2
+import glob
+import torch
+import shutil
+import numpy as np
+from tqdm import tqdm
+from util.reverse2original import reverse2wholeimage
+import moviepy.editor as mp
+from moviepy.editor import AudioFileClip, VideoFileClip 
+from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
+import  time
+from util.add_watermark import watermark_image
+from util.norm import SpecificNorm
+from parsing_model.model import BiSeNet
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+def video_swap(video_path, id_vetor, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False,use_mask =False):
+    video_forcheck = VideoFileClip(video_path)
+    if video_forcheck.audio is None:
+        no_audio = True
+    else:
+        no_audio = False
+
+    del video_forcheck
+
+    if not no_audio:
+        video_audio_clip = AudioFileClip(video_path)
+
+    video = cv2.VideoCapture(video_path)
+    logoclass = watermark_image('./simswaplogo/simswaplogo.png')
+    ret = True
+    frame_index = 0
+
+    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+
+    # video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    fps = video.get(cv2.CAP_PROP_FPS)
+    if  os.path.exists(temp_results_dir):
+            shutil.rmtree(temp_results_dir)
+
+    spNorm =SpecificNorm()
+    if use_mask:
+        n_classes = 19
+        net = BiSeNet(n_classes=n_classes)
+        net.cuda()
+        save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
+        net.load_state_dict(torch.load(save_pth))
+        net.eval()
+    else:
+        net =None
+
+    # while ret:
+    for frame_index in tqdm(range(frame_count)): 
+        ret, frame = video.read()
+        if  ret:
+            detect_results = detect_model.get(frame,crop_size)
+
+            if detect_results is not None:
+                # print(frame_index)
+                if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+                frame_align_crop_list = detect_results[0]
+                frame_mat_list = detect_results[1]
+                swap_result_list = []
+                frame_align_crop_tenor_list = []
+                for frame_align_crop in frame_align_crop_list:
+
+                    # BGR TO RGB
+                    # frame_align_crop_RGB = frame_align_crop[...,::-1]
+
+                    frame_align_crop_tenor = _totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+                    swap_result = swap_model(None, frame_align_crop_tenor, id_vetor, None, True)[0]
+                    cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+                    swap_result_list.append(swap_result)
+                    frame_align_crop_tenor_list.append(frame_align_crop_tenor)
+
+                    
+
+                reverse2wholeimage(frame_align_crop_tenor_list,swap_result_list, frame_mat_list, crop_size, frame, logoclass,\
+                    os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,use_mask=use_mask, norm = spNorm)
+
+            else:
+                if not os.path.exists(temp_results_dir):
+                    os.mkdir(temp_results_dir)
+                frame = frame.astype(np.uint8)
+                if not no_simswaplogo:
+                    frame = logoclass.apply_frames(frame)
+                cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+        else:
+            break
+
+    video.release()
+
+    # image_filename_list = []
+    path = os.path.join(temp_results_dir,'*.jpg')
+    image_filenames = sorted(glob.glob(path))
+
+    clips = ImageSequenceClip(image_filenames,fps = fps)
+
+    if not no_audio:
+        clips = clips.set_audio(video_audio_clip)
+
+
+    clips.write_videofile(save_path,audio_codec='aac')
diff --git a/mlops/serving/SwimSwap/util/videoswap_multispecific.py b/mlops/serving/SwimSwap/util/videoswap_multispecific.py
new file mode 100644
index 0000000..40e9488
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/videoswap_multispecific.py
@@ -0,0 +1,146 @@
+import os 
+import cv2
+import glob
+import torch
+import shutil
+import numpy as np
+from tqdm import tqdm
+from util.reverse2original import reverse2wholeimage
+import moviepy.editor as mp
+from moviepy.editor import AudioFileClip, VideoFileClip 
+from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
+import  time
+from util.add_watermark import watermark_image
+from util.norm import SpecificNorm
+import torch.nn.functional as F
+from parsing_model.model import BiSeNet
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+def video_swap(video_path, target_id_norm_list,source_specific_id_nonorm_list,id_thres, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False,use_mask =False):
+    video_forcheck = VideoFileClip(video_path)
+    if video_forcheck.audio is None:
+        no_audio = True
+    else:
+        no_audio = False
+
+    del video_forcheck
+
+    if not no_audio:
+        video_audio_clip = AudioFileClip(video_path)
+
+    video = cv2.VideoCapture(video_path)
+    logoclass = watermark_image('./simswaplogo/simswaplogo.png')
+    ret = True
+    frame_index = 0
+
+    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+
+    # video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    fps = video.get(cv2.CAP_PROP_FPS)
+    if  os.path.exists(temp_results_dir):
+            shutil.rmtree(temp_results_dir)
+
+    spNorm =SpecificNorm()
+    mse = torch.nn.MSELoss().cuda()
+
+    if use_mask:
+        n_classes = 19
+        net = BiSeNet(n_classes=n_classes)
+        net.cuda()
+        save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
+        net.load_state_dict(torch.load(save_pth))
+        net.eval()
+    else:
+        net =None
+
+    # while ret:
+    for frame_index in tqdm(range(frame_count)): 
+        ret, frame = video.read()
+        if  ret:
+            detect_results = detect_model.get(frame,crop_size)
+
+            if detect_results is not None:
+                # print(frame_index)
+                if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+                frame_align_crop_list = detect_results[0]
+                frame_mat_list = detect_results[1]
+
+                id_compare_values = [] 
+                frame_align_crop_tenor_list = []
+                for frame_align_crop in frame_align_crop_list:
+
+                    # BGR TO RGB
+                    # frame_align_crop_RGB = frame_align_crop[...,::-1]
+
+                    frame_align_crop_tenor = _totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+                    frame_align_crop_tenor_arcnorm = spNorm(frame_align_crop_tenor)
+                    frame_align_crop_tenor_arcnorm_downsample = F.interpolate(frame_align_crop_tenor_arcnorm, size=(112,112))
+                    frame_align_crop_crop_id_nonorm = swap_model.netArc(frame_align_crop_tenor_arcnorm_downsample)
+                    id_compare_values.append([])
+                    for source_specific_id_nonorm_tmp in source_specific_id_nonorm_list:
+                        id_compare_values[-1].append(mse(frame_align_crop_crop_id_nonorm,source_specific_id_nonorm_tmp).detach().cpu().numpy())
+                    frame_align_crop_tenor_list.append(frame_align_crop_tenor)
+
+                id_compare_values_array = np.array(id_compare_values).transpose(1,0)
+                min_indexs = np.argmin(id_compare_values_array,axis=0)
+                min_value = np.min(id_compare_values_array,axis=0)
+
+                swap_result_list = [] 
+                swap_result_matrix_list = []
+                swap_result_ori_pic_list = []
+                for tmp_index, min_index in enumerate(min_indexs):
+                    if min_value[tmp_index] < id_thres:
+                        swap_result = swap_model(None, frame_align_crop_tenor_list[tmp_index], target_id_norm_list[min_index], None, True)[0]
+                        swap_result_list.append(swap_result)
+                        swap_result_matrix_list.append(frame_mat_list[tmp_index])
+                        swap_result_ori_pic_list.append(frame_align_crop_tenor_list[tmp_index])
+                    else:
+                        pass
+
+
+
+                if len(swap_result_list) !=0:
+                    
+                    reverse2wholeimage(swap_result_ori_pic_list,swap_result_list, swap_result_matrix_list, crop_size, frame, logoclass,\
+                        os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,use_mask=use_mask, norm = spNorm)
+                else:
+                    if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+                    frame = frame.astype(np.uint8)
+                    if not no_simswaplogo:
+                        frame = logoclass.apply_frames(frame)
+                    cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+
+            else:
+                if not os.path.exists(temp_results_dir):
+                    os.mkdir(temp_results_dir)
+                frame = frame.astype(np.uint8)
+                if not no_simswaplogo:
+                    frame = logoclass.apply_frames(frame)
+                cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+        else:
+            break
+
+    video.release()
+
+    # image_filename_list = []
+    path = os.path.join(temp_results_dir,'*.jpg')
+    image_filenames = sorted(glob.glob(path))
+
+    clips = ImageSequenceClip(image_filenames,fps = fps)
+
+    if not no_audio:
+        clips = clips.set_audio(video_audio_clip)
+
+
+    clips.write_videofile(save_path,audio_codec='aac')
+
diff --git a/mlops/serving/SwimSwap/util/videoswap_specific.py b/mlops/serving/SwimSwap/util/videoswap_specific.py
new file mode 100644
index 0000000..8177efb
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/videoswap_specific.py
@@ -0,0 +1,130 @@
+import os 
+import cv2
+import glob
+import torch
+import shutil
+import numpy as np
+from tqdm import tqdm
+from util.reverse2original import reverse2wholeimage
+import moviepy.editor as mp
+from moviepy.editor import AudioFileClip, VideoFileClip 
+from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
+import  time
+from util.add_watermark import watermark_image
+from util.norm import SpecificNorm
+import torch.nn.functional as F
+from parsing_model.model import BiSeNet
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+def video_swap(video_path, id_vetor,specific_person_id_nonorm,id_thres, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False,use_mask =False):
+    video_forcheck = VideoFileClip(video_path)
+    if video_forcheck.audio is None:
+        no_audio = True
+    else:
+        no_audio = False
+
+    del video_forcheck
+
+    if not no_audio:
+        video_audio_clip = AudioFileClip(video_path)
+
+    video = cv2.VideoCapture(video_path)
+    logoclass = watermark_image('./simswaplogo/simswaplogo.png')
+    ret = True
+    frame_index = 0
+
+    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+
+    # video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    fps = video.get(cv2.CAP_PROP_FPS)
+    if  os.path.exists(temp_results_dir):
+            shutil.rmtree(temp_results_dir)
+
+    spNorm =SpecificNorm()
+    mse = torch.nn.MSELoss().cuda()
+
+    if use_mask:
+        n_classes = 19
+        net = BiSeNet(n_classes=n_classes)
+        net.cuda()
+        save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
+        net.load_state_dict(torch.load(save_pth))
+        net.eval()
+    else:
+        net =None
+
+    # while ret:
+    for frame_index in tqdm(range(frame_count)): 
+        ret, frame = video.read()
+        if  ret:
+            detect_results = detect_model.get(frame,crop_size)
+
+            if detect_results is not None:
+                # print(frame_index)
+                if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+                frame_align_crop_list = detect_results[0]
+                frame_mat_list = detect_results[1]
+
+                id_compare_values = [] 
+                frame_align_crop_tenor_list = []
+                for frame_align_crop in frame_align_crop_list:
+
+                    # BGR TO RGB
+                    # frame_align_crop_RGB = frame_align_crop[...,::-1]
+
+                    frame_align_crop_tenor = _totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+                    frame_align_crop_tenor_arcnorm = spNorm(frame_align_crop_tenor)
+                    frame_align_crop_tenor_arcnorm_downsample = F.interpolate(frame_align_crop_tenor_arcnorm, size=(112,112))
+                    frame_align_crop_crop_id_nonorm = swap_model.netArc(frame_align_crop_tenor_arcnorm_downsample)
+
+                    id_compare_values.append(mse(frame_align_crop_crop_id_nonorm,specific_person_id_nonorm).detach().cpu().numpy())
+                    frame_align_crop_tenor_list.append(frame_align_crop_tenor)
+                id_compare_values_array = np.array(id_compare_values)
+                min_index = np.argmin(id_compare_values_array)
+                min_value = id_compare_values_array[min_index]
+                if min_value < id_thres:
+                    swap_result = swap_model(None, frame_align_crop_tenor_list[min_index], id_vetor, None, True)[0]
+                
+                    reverse2wholeimage([frame_align_crop_tenor_list[min_index]], [swap_result], [frame_mat_list[min_index]], crop_size, frame, logoclass,\
+                        os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,use_mask= use_mask, norm = spNorm)
+                else:
+                    if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+                    frame = frame.astype(np.uint8)
+                    if not no_simswaplogo:
+                        frame = logoclass.apply_frames(frame)
+                    cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+
+            else:
+                if not os.path.exists(temp_results_dir):
+                    os.mkdir(temp_results_dir)
+                frame = frame.astype(np.uint8)
+                if not no_simswaplogo:
+                    frame = logoclass.apply_frames(frame)
+                cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+        else:
+            break
+
+    video.release()
+
+    # image_filename_list = []
+    path = os.path.join(temp_results_dir,'*.jpg')
+    image_filenames = sorted(glob.glob(path))
+
+    clips = ImageSequenceClip(image_filenames,fps = fps)
+
+    if not no_audio:
+        clips = clips.set_audio(video_audio_clip)
+
+
+    clips.write_videofile(save_path,audio_codec='aac')
+
diff --git a/mlops/serving/SwimSwap/util/visualizer.py b/mlops/serving/SwimSwap/util/visualizer.py
new file mode 100644
index 0000000..584ac45
--- /dev/null
+++ b/mlops/serving/SwimSwap/util/visualizer.py
@@ -0,0 +1,131 @@
+import numpy as np
+import os
+import ntpath
+import time
+from . import util
+from . import html
+import scipy.misc
+try:
+    from StringIO import StringIO  # Python 2.7
+except ImportError:
+    from io import BytesIO         # Python 3.x
+
+class Visualizer():
+    def __init__(self, opt):
+        # self.opt = opt
+        self.tf_log = opt.tf_log
+        self.use_html = opt.isTrain and not opt.no_html
+        self.win_size = opt.display_winsize
+        self.name = opt.name
+        if self.tf_log:
+            import tensorflow as tf
+            self.tf = tf
+            self.log_dir = os.path.join(opt.checkpoints_dir, opt.name, 'logs')
+            self.writer = tf.summary.FileWriter(self.log_dir)
+
+        if self.use_html:
+            self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
+            self.img_dir = os.path.join(self.web_dir, 'images')
+            print('create web directory %s...' % self.web_dir)
+            util.mkdirs([self.web_dir, self.img_dir])
+        self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
+        with open(self.log_name, "a") as log_file:
+            now = time.strftime("%c")
+            log_file.write('================ Training Loss (%s) ================\n' % now)
+
+    # |visuals|: dictionary of images to display or save
+    def display_current_results(self, visuals, epoch, step):
+        if self.tf_log: # show images in tensorboard output
+            img_summaries = []
+            for label, image_numpy in visuals.items():
+                # Write the image to a string
+                try:
+                    s = StringIO()
+                except:
+                    s = BytesIO()
+                scipy.misc.toimage(image_numpy).save(s, format="jpeg")
+                # Create an Image object
+                img_sum = self.tf.Summary.Image(encoded_image_string=s.getvalue(), height=image_numpy.shape[0], width=image_numpy.shape[1])
+                # Create a Summary value
+                img_summaries.append(self.tf.Summary.Value(tag=label, image=img_sum))
+
+            # Create and write Summary
+            summary = self.tf.Summary(value=img_summaries)
+            self.writer.add_summary(summary, step)
+
+        if self.use_html: # save images to a html file
+            for label, image_numpy in visuals.items():
+                if isinstance(image_numpy, list):
+                    for i in range(len(image_numpy)):
+                        img_path = os.path.join(self.img_dir, 'epoch%.3d_%s_%d.jpg' % (epoch, label, i))
+                        util.save_image(image_numpy[i], img_path)
+                else:
+                    img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.jpg' % (epoch, label))
+                    util.save_image(image_numpy, img_path)
+
+            # update website
+            webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=30)
+            for n in range(epoch, 0, -1):
+                webpage.add_header('epoch [%d]' % n)
+                ims = []
+                txts = []
+                links = []
+
+                for label, image_numpy in visuals.items():
+                    if isinstance(image_numpy, list):
+                        for i in range(len(image_numpy)):
+                            img_path = 'epoch%.3d_%s_%d.jpg' % (n, label, i)
+                            ims.append(img_path)
+                            txts.append(label+str(i))
+                            links.append(img_path)
+                    else:
+                        img_path = 'epoch%.3d_%s.jpg' % (n, label)
+                        ims.append(img_path)
+                        txts.append(label)
+                        links.append(img_path)
+                if len(ims) < 10:
+                    webpage.add_images(ims, txts, links, width=self.win_size)
+                else:
+                    num = int(round(len(ims)/2.0))
+                    webpage.add_images(ims[:num], txts[:num], links[:num], width=self.win_size)
+                    webpage.add_images(ims[num:], txts[num:], links[num:], width=self.win_size)
+            webpage.save()
+
+    # errors: dictionary of error labels and values
+    def plot_current_errors(self, errors, step):
+        if self.tf_log:
+            for tag, value in errors.items():
+                summary = self.tf.Summary(value=[self.tf.Summary.Value(tag=tag, simple_value=value)])
+                self.writer.add_summary(summary, step)
+
+    # errors: same format as |errors| of plotCurrentErrors
+    def print_current_errors(self, epoch, i, errors, t):
+        message = '(epoch: %d, iters: %d, time: %.3f) ' % (epoch, i, t)
+        for k, v in errors.items():
+            if v != 0:
+                message += '%s: %.3f ' % (k, v)
+
+        print(message)
+        with open(self.log_name, "a") as log_file:
+            log_file.write('%s\n' % message)
+
+    # save image to the disk
+    def save_images(self, webpage, visuals, image_path):
+        image_dir = webpage.get_image_dir()
+        short_path = ntpath.basename(image_path[0])
+        name = os.path.splitext(short_path)[0]
+
+        webpage.add_header(name)
+        ims = []
+        txts = []
+        links = []
+
+        for label, image_numpy in visuals.items():
+            image_name = '%s_%s.jpg' % (name, label)
+            save_path = os.path.join(image_dir, image_name)
+            util.save_image(image_numpy, save_path)
+
+            ims.append(image_name)
+            txts.append(label)
+            links.append(image_name)
+        webpage.add_images(ims, txts, links, width=self.win_size)
diff --git a/mlops/serving/docker-compose.yaml b/mlops/serving/docker-compose.yaml
new file mode 100644
index 0000000..4c88dae
--- /dev/null
+++ b/mlops/serving/docker-compose.yaml
@@ -0,0 +1,46 @@
+version: "3"
+
+services:
+  sf2f:
+    build:
+      context: ./sf2f/
+      dockerfile: Dockerfile.sf2f
+    container_name: sf2f-server
+    # ports:
+    #   - 3002:3002
+    env_file:
+      .env.serving
+    command: gunicorn -w 4 -b 0.0.0.0:3002 app:app --reload --timeout 2000
+    deploy:
+      resources:
+        reservations:
+          devices:
+          - driver: nvidia
+            count: 1
+            capabilities: [gpu]
+    # networks:
+    #   - inference
+    network_mode: "host"
+  swimswap:
+    build:
+      context: ./SwimSwap/
+      dockerfile: Dockerfile.swimswap
+    container_name: swimswap-server
+    # ports:
+    #   - 3001:3001
+    env_file:
+      .env.serving
+    command: gunicorn -w 4 -b 0.0.0.0:3001 app:app --timeout 2700
+    deploy:
+      resources:
+        reservations:
+          devices:
+          - driver: nvidia
+            count: 1
+            capabilities: [gpu]
+    network_mode: "host"
+#     networks:
+#       - inference
+# networks:
+#   inference:
+#     driver: host
\ No newline at end of file
diff --git a/mlops/serving/requirements.txt b/mlops/serving/requirements.txt
new file mode 100644
index 0000000..7b49dcd
--- /dev/null
+++ b/mlops/serving/requirements.txt
@@ -0,0 +1,15 @@
+flask
+flask_cors
+glog
+python_speech_features 
+webrtcvad 
+pydub 
+imageio 
+insightface==0.2.1 
+onnxruntime 
+moviepy 
+onnxruntime-gpu 
+mlflow 
+minio 
+boto3
+gunicorn
\ No newline at end of file
diff --git a/mlops/serving/sf2f/Dockerfile.sf2f b/mlops/serving/sf2f/Dockerfile.sf2f
new file mode 100644
index 0000000..cd8b00c
--- /dev/null
+++ b/mlops/serving/sf2f/Dockerfile.sf2f
@@ -0,0 +1,23 @@
+FROM pytorch/pytorch:2.2.1-cuda11.8-cudnn8-runtime
+
+ENV base_path /workspace/
+COPY . ${base_path}
+
+RUN apt update -y
+RUN apt install -y gcc
+
+RUN pip install glog \
+python_speech_features \
+webrtcvad \
+pydub \
+mlflow \
+minio \
+boto3 \
+flask \
+flask_cors \
+gunicorn \
+python-dotenv
+
+EXPOSE 3002
+
+# CMD [ "python", "inference.py" ]
\ No newline at end of file
diff --git a/mlops/serving/sf2f/app.py b/mlops/serving/sf2f/app.py
new file mode 100644
index 0000000..fda2aa2
--- /dev/null
+++ b/mlops/serving/sf2f/app.py
@@ -0,0 +1,57 @@
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from inference import generate_voice_to_face
+import json
+import requests
+
+app = Flask(__name__)
+app.config.update(DEBUG=True)
+CORS(app, resources={r'*': {'origins': '*'}}, supports_credentials=True)
+
+@app.route('/makevideo', methods=['POST'])
+def inference():
+    make_json = request.get_json()
+    print(make_json)
+    
+    request_id = make_json['request_id']
+    result_id = make_json['result_id']
+    age = make_json['age']
+    gender = make_json['gender']
+    voice_url = make_json['voice_url']
+
+    try:
+        if not all([request_id, result_id, voice_url, gender, age]):
+            raise Exception('Missing required parameters')
+        
+        status_code, message = generate_voice_to_face(voice_url, request_id, result_id)
+        if status_code == 400:
+            raise Exception(message)
+
+        voice_image_url = message
+        video_make_json = json.dumps({
+            "request_id" : request_id,
+            "result_id" : result_id,
+            "voice_image_url" : message,
+            "age":age,
+            "gender":gender
+        })
+        headers = {"user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.89 Safari/537.36",
+                   "Content-Type":"application/json"}
+        imagetovideo_response = requests.post("http://0.0.0.0:3001/imagetovideo", data=video_make_json, headers=headers)
+        response_data = imagetovideo_response.json()
+
+        if response_data.get("status_code") == 400:
+            raise Exception(response_data.get('error'))
+        
+        return jsonify({'status_code' : 200,
+                'voice_image_url' : voice_image_url,
+                'voice_video_url' : response_data.get("voice_video_url")})
+    except Exception as ex:
+        print(ex)
+        if voice_image_url != None:
+            return jsonify({"status_code" : 404, "voice_image_url": voice_image_url, "error": str(ex)}) #false->400
+        else:
+            return jsonify({"status_code" : 400, "error": str(ex)}) #false->400
+
+if __name__ == "__main__":
+    app.run(port=3002, debug=True)
\ No newline at end of file
diff --git a/mlops/serving/sf2f/config.py b/mlops/serving/sf2f/config.py
new file mode 100644
index 0000000..1385184
--- /dev/null
+++ b/mlops/serving/sf2f/config.py
@@ -0,0 +1,11 @@
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+MLFLOW_S3_ENDPOINT_URL=os.getenv('MLFLOW_S3_ENDPOINT_URL')
+MLFLOW_TRACKING_URI=os.getenv('MLFLOW_TRACKING_URI')
+AWS_ACCESS_KEY=os.getenv('AWS_ACCESS_KEY')
+AWS_SECRET_ACCESS_KEY=os.getenv('AWS_SECRET_ACCESS_KEY')
+MINIO_BUCKET=os.getenv('MINIO_BUCKET')
+MINIO_ENDPOINT=os.getenv('MINIO_ENDPOINT')
diff --git a/mlops/serving/sf2f/datasets/__init__.py b/mlops/serving/sf2f/datasets/__init__.py
new file mode 100644
index 0000000..1b99502
--- /dev/null
+++ b/mlops/serving/sf2f/datasets/__init__.py
@@ -0,0 +1,10 @@
+#!/usr/bin/python
+#
+from .utils import imagenet_preprocess, imagenet_deprocess, \
+    imagenet_deprocess_batch, fast_imagenet_deprocess_batch, \
+        fast_mel_deprocess_batch, set_mel_transform, deprocess_and_save, \
+            window_segment
+#from .coco import CocoSceneGraphDataset as coco
+#from .vg import VgSceneGraphDataset as visual_genome
+from .vox_dataset import VoxDataset
+from .build_dataset import build_dataset, build_loaders
diff --git a/mlops/serving/sf2f/datasets/build_dataset.py b/mlops/serving/sf2f/datasets/build_dataset.py
new file mode 100644
index 0000000..6bc91c6
--- /dev/null
+++ b/mlops/serving/sf2f/datasets/build_dataset.py
@@ -0,0 +1,64 @@
+import json
+import os
+import os.path as osp
+import numpy as np
+from datasets import VoxDataset
+from torch.utils.data import DataLoader
+
+
+VOX_DIR = os.path.join('./data', 'VoxCeleb')
+
+
+def build_vox_dsets(data_opts, batch_size, image_size):
+    dset_kwargs = {
+        'data_dir': osp.join(data_opts['root_dir']),
+        'image_size': image_size,
+        'face_type': data_opts.get('face_type', 'masked'),
+        'image_normalize_method': \
+            data_opts.get('image_normalize_method', 'imagenet'),
+        'mel_normalize_method': \
+            data_opts.get('mel_normalize_method', 'vox_mel'),
+        'split_set': 'train',
+        'split_json': \
+            data_opts.get('split_json', os.path.join(VOX_DIR, 'split.json'))
+    }
+    train_dset = VoxDataset(**dset_kwargs)
+    iter_per_epoch = len(train_dset) // batch_size
+    print('There are %d iterations per epoch' % iter_per_epoch)
+
+    # make some modification to dset_kwargs so that it can be used to create val_dset
+    # dset_kwargs['max_samples'] = data_opts["num_val_samples"]
+    # val_dset = visual_genome(**dset_kwargs)
+    dset_kwargs['split_set'] = 'val'
+    val_dset = VoxDataset(**dset_kwargs)
+
+    dset_kwargs['split_set'] = 'test'
+    test_dset = VoxDataset(**dset_kwargs)
+
+    return train_dset, val_dset, test_dset
+
+
+def build_dataset(opts):
+    if opts["dataset"] == "vox":
+        return build_vox_dsets(opts["data_opts"], opts["batch_size"],
+                              opts["image_size"])
+    else:
+        raise ValueError("Unrecognized dataset: {}".format(opts["dataset"]))
+
+
+def build_loaders(opts):
+    train_dset, val_dset, test_dset = build_dataset(opts)
+
+    loader_kwargs = {
+        'batch_size': opts["batch_size"],
+        'num_workers': opts["workers"],
+        'shuffle': True,
+        "drop_last": True,
+        'collate_fn': train_dset.collate_fn,
+    }
+    train_loader = DataLoader(train_dset, **loader_kwargs)
+    loader_kwargs['shuffle'] = False
+    loader_kwargs['drop_last'] = False
+    val_loader = DataLoader(val_dset, **loader_kwargs)
+    test_loader = DataLoader(test_dset, **loader_kwargs)
+    return train_loader, val_loader, test_loader
diff --git a/mlops/serving/sf2f/datasets/utils.py b/mlops/serving/sf2f/datasets/utils.py
new file mode 100644
index 0000000..c2bb4e4
--- /dev/null
+++ b/mlops/serving/sf2f/datasets/utils.py
@@ -0,0 +1,158 @@
+import PIL
+import torch
+import torchvision.transforms as T
+import numpy as np
+
+# Imagenet macros
+IMAGENET_MEAN = [0.485, 0.456, 0.406]
+IMAGENET_STD = [0.229, 0.224, 0.225]
+
+INV_IMAGENET_MEAN = [-m for m in IMAGENET_MEAN]
+INV_IMAGENET_STD = [1.0 / s for s in IMAGENET_STD]
+
+STANDARD_MEAN = [0.5, 0.5, 0.5]
+STANDARD_STD = [0.5, 0.5, 0.5]
+
+INV_STANDARD_MEAN = [-m for m in STANDARD_MEAN]
+INV_STANDARD_STD = [1.0 / s for s in STANDARD_STD]
+
+VOX_MEL_MEAN = [10.9915,]
+VOX_MEL_STD = [3.1661,]
+
+def imagenet_preprocess(normalize_method='imagenet'):
+    if normalize_method == 'imagenet':
+        return T.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)
+    elif normalize_method == 'standard':
+        return T.Normalize(mean=STANDARD_MEAN, std=STANDARD_STD)
+    elif normalize_method == 'vox_mel':
+        return T.Normalize(mean=VOX_MEL_MEAN, std=VOX_MEL_STD)
+
+
+def rescale(x):
+    lo, hi = x.min(), x.max()
+    return x.sub(lo).div(hi - lo + 1e-5)
+
+
+def imagenet_deprocess(rescale_image=True, normalize_method='imagenet'):
+    if normalize_method == 'imagenet':
+        transforms = [
+            T.Normalize(mean=[0, 0, 0], std=INV_IMAGENET_STD),
+            T.Normalize(mean=INV_IMAGENET_MEAN, std=[1.0, 1.0, 1.0]),
+        ]
+    elif normalize_method == 'standard':
+        transforms = [
+            T.Normalize(mean=[0, 0, 0], std=INV_STANDARD_STD),
+            T.Normalize(mean=INV_STANDARD_MEAN, std=[1.0, 1.0, 1.0]),
+        ]
+    if rescale_image:
+        transforms.append(rescale)
+    return T.Compose(transforms)
+
+
+def fast_imagenet_deprocess_batch(imgs, normalize_method='imagenet'):
+    '''
+    ImageNet deprocess batch's non-loop and backpropable implementation
+    '''
+    if normalize_method == 'imagenet':
+        mean = IMAGENET_MEAN
+        std = IMAGENET_STD
+    elif normalize_method == 'standard':
+        mean = STANDARD_MEAN
+        std = STANDARD_STD
+    mean = torch.tensor(mean).type(imgs.type())
+    std = torch.tensor(std).type(imgs.type())
+    # Initialize broadcasting Dims
+    mean = mean.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+    std = std.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+
+    img_de = imgs * std + mean
+    img_de = img_de.mul(255).clamp(0, 255)
+
+    return img_de
+
+def set_mel_transform(mel_normalize_method=None):
+    mel_transform = [T.ToTensor(), ]
+    print('Called mel_normalize_method:',
+        mel_normalize_method)
+    if mel_normalize_method is not None:
+        mel_transform.append(imagenet_preprocess(
+            normalize_method=mel_normalize_method))
+    mel_transform.append(torch.squeeze)
+    mel_transform = T.Compose(mel_transform)
+    return mel_transform
+
+def fast_mel_deprocess_batch(log_mels, normalize_method='vox_mel'):
+    '''
+    Mel Spectrogram deprocess batch's non-loop and backpropable implementation
+    '''
+    if normalize_method == 'vox_mel':
+        mean = VOX_MEL_MEAN[0]
+        std = VOX_MEL_STD[0]
+    mean = torch.tensor(mean).type(log_mels.type())
+    std = torch.tensor(std).type(log_mels.type())
+    log_mels_de = log_mels * std + mean
+
+    return log_mels_de
+
+
+def window_segment(log_mel, window_length, stride_length):
+    """
+    Sliding window segmentation on a log_mel tensor
+    """
+    mel_length = log_mel.shape[1]
+    # Calulate the number of windows that can be generated
+    num_window = 1 + (mel_length - window_length) // stride_length
+    # Sliding Window
+    segments = []
+    for i in range(0, num_window):
+        start_time = i * stride_length
+        segment = log_mel[:, start_time:start_time + window_length]
+        segments.append(segment)
+    segments = torch.stack(segments)
+    return segments
+
+
+def imagenet_deprocess_batch(imgs, rescale=False, normalize_method='imagenet'):
+    """
+    Input:
+    - imgs: FloatTensor of shape (N, C, H, W) giving preprocessed images
+
+    Output:
+    - imgs_de: ByteTensor of shape (N, C, H, W) giving deprocessed images
+      in the range [0, 255]
+    """
+    if isinstance(imgs, torch.autograd.Variable):
+        imgs = imgs.data
+    imgs = imgs.cpu().clone()
+    deprocess_fn = imagenet_deprocess(
+        rescale_image=rescale, normalize_method=normalize_method)
+    imgs_de = []
+    for i in range(imgs.size(0)):
+        img_de = deprocess_fn(imgs[i])[None]
+        img_de = img_de.mul(255).clamp(0, 255).byte()
+        imgs_de.append(img_de)
+    imgs_de = torch.cat(imgs_de, dim=0)
+    return imgs_de
+
+
+def deprocess_and_save(image, normalize_method, save_path):
+    '''
+    Deprocess a image Tensor generated by VoxDataset, and save its image as a
+    jpg
+    '''
+    deprocess_fn = imagenet_deprocess(normalize_method=normalize_method)
+    image = deprocess_fn(image)
+    #print('deprocessed image shape:', image.shape)
+    img = np.array(image)
+    img = np.transpose(img, (1, 2, 0))
+    img = (img * 255).astype(np.uint8)
+    #print(img.shape)
+    #print(np.max(img), np.min(img))
+    img = PIL.Image.fromarray(img)
+    img.save(save_path)
+
+
+def unpack_var(v):
+    if isinstance(v, torch.autograd.Variable):
+        return v.data
+    return v
diff --git a/mlops/serving/sf2f/datasets/vox_dataset.py b/mlops/serving/sf2f/datasets/vox_dataset.py
new file mode 100644
index 0000000..d3dbc0f
--- /dev/null
+++ b/mlops/serving/sf2f/datasets/vox_dataset.py
@@ -0,0 +1,465 @@
+'''
+VoxCeleb DataSet
+'''
+
+
+import json, os
+import numpy as np
+import random
+#import pandas as pd
+#import h5py
+import pickle
+import PIL
+from PIL import Image
+import torch
+from torch.utils.data import Dataset, DataLoader
+from torch.utils.data.dataloader import default_collate
+import torchvision.transforms as T
+#from skimage.transform import resize as imresize
+try:
+    from .utils import imagenet_preprocess, imagenet_deprocess_batch, \
+        fast_imagenet_deprocess_batch, fast_mel_deprocess_batch
+except:
+    from utils import imagenet_preprocess, imagenet_deprocess_batch, \
+        fast_imagenet_deprocess_batch, fast_mel_deprocess_batch
+
+
+VOX_DIR = os.path.join('./data', 'VoxCeleb')
+
+class VoxDataset(Dataset):
+    def __init__(self,
+                 data_dir,
+                 image_size=(64, 64),
+                 face_type='masked',
+                 image_normalize_method='imagenet',
+                 mel_normalize_method='vox_mel',
+                 nframe_range=(100, 150),
+                 split_set='train',
+                 split_json=os.path.join(VOX_DIR, 'split.json'),
+                 return_mel_segments=False,
+                 mel_seg_window_stride=(125, 125),
+                 image_left_right_avg=False,
+                 image_random_hflip=False):
+        '''
+        A PyTorch Dataset for loading VoxCeleb 1 & 2 human speech
+        (as mel spectrograms) and face (as image)
+
+        Inputs:
+        - data: Path to a directory where vox1 & vox2 data are held
+        - face_type: 'masked' or 'origin'
+        - image_size: Shape (h, w) to output the image
+        - image_normalize_method: Method to normalize the image, 'imagenet' or
+            'standard'
+        - return_mel_segments: Return several segments of mel spectrogram
+        - mel_seg_window_stride: Tuple (int, int), defines the window size and
+            stride size when segmenting the mel spectrogram with sliding window
+        - image_left_right_avg: flip the image, average the original and flipped
+            image
+        - image_random_hflip: Add random horizontal image flip to training set
+
+        '''
+        self.data_dir = data_dir
+        self.image_size = image_size
+        self.face_type = face_type
+        self.face_dir = self.face_type + '_faces'
+        self.image_normalize_method = image_normalize_method
+        self.mel_normalize_method = mel_normalize_method
+        self.nframe_range = nframe_range
+        self.split_set = split_set
+        self.split_json = split_json
+        self.return_mel_segments = return_mel_segments
+        self.mel_seg_window_stride = mel_seg_window_stride
+        self.shuffle_mel_segments = True
+        # This attribute is added to make the return segment mode to start from
+        # a random time
+        # Thus improve the randomness in fuser data
+        self.mel_segments_rand_start = False
+        self.image_left_right_avg = image_left_right_avg
+        self.image_random_hflip = image_random_hflip
+
+        self.load_split_dict()
+        self.list_available_names()
+        self.set_image_transform()
+        self.set_mel_transform()
+
+    def __len__(self):
+        return len(self.available_names)
+
+    def set_length(self, length):
+        self.available_names = self.available_names[0:length]
+
+    def __getitem__(self, index):
+        '''
+        Given an index, randomly return a face and a mel_spectrogram of this guy
+        '''
+        sub_dataset, name = self.available_names[index]
+        # Face Image
+        image_dir = os.path.join(
+            self.data_dir, sub_dataset, self.face_dir, name)
+        image_jpg = random.choice(os.listdir(image_dir))
+        image_path = os.path.join(image_dir, image_jpg)
+
+        with open(image_path, 'rb') as f:
+            with PIL.Image.open(f) as image:
+                WW, HH = image.size
+                #print("PIL Image:", np.array(image))
+                image = image.convert('RGB')
+                if self.image_left_right_avg:
+                    arr = (np.array(image) / 2.0 + \
+                        np.array(T.functional.hflip(image)) / 2.0).astype(
+                            np.uint8)
+                    image = PIL.Image.fromarray(arr, mode="RGB")
+                image = self.image_transform(image)
+
+        # Mel Spectrogram
+        mel_gram_dir = os.path.join(
+            self.data_dir, sub_dataset, 'mel_spectrograms', name)
+        mel_gram_pickle = random.choice(os.listdir(mel_gram_dir))
+        mel_gram_path = os.path.join(mel_gram_dir, mel_gram_pickle)
+        if not self.return_mel_segments:
+            # Return single segment
+            log_mel = self.load_mel_gram(mel_gram_path)
+            log_mel = self.mel_transform(log_mel)
+        else:
+            log_mel = self.get_all_mel_segments_of_id(
+                index, shuffle=self.shuffle_mel_segments)
+
+        human_id = torch.tensor(index)
+
+        return image, log_mel, human_id
+
+    def get_all_faces_of_id(self, index):
+        '''
+        Given a id, return all the faces of him as a batch tensor, with shape
+        (N, C, H, W)
+        '''
+        sub_dataset, name = self.available_names[index]
+        faces = []
+        # Face Image
+        image_dir = os.path.join(
+            self.data_dir, sub_dataset, self.face_dir, name)
+        for image_jpg in os.listdir(image_dir):
+            image_path = os.path.join(image_dir, image_jpg)
+            with open(image_path, 'rb') as f:
+                with PIL.Image.open(f) as image:
+                    WW, HH = image.size
+                    #print("PIL Image:", np.array(image))
+                    image = self.image_transform(image.convert('RGB'))
+                    faces.append(image)
+        faces = torch.stack(faces)
+
+        return faces
+
+    def get_all_mel_segments_of_id(self,
+                                   index,
+                                   shuffle=False):
+        '''
+        Given a id, return all the speech segments of him as a batch tensor,
+        with shape (N, C, L)
+        '''
+        sub_dataset, name = self.available_names[index]
+        window_length, stride_length = self.mel_seg_window_stride
+        segments = []
+        # Mel Spectrogram
+        mel_gram_dir = os.path.join(
+            self.data_dir, sub_dataset, 'mel_spectrograms', name)
+        mel_gram_list = os.listdir(mel_gram_dir)
+        if shuffle:
+            random.shuffle(mel_gram_list)
+        else:
+            mel_gram_list.sort()
+        seg_count = 0
+        for mel_gram_pickle in mel_gram_list:
+            mel_gram_path = os.path.join(mel_gram_dir, mel_gram_pickle)
+            log_mel = self.load_mel_gram(mel_gram_path)
+            log_mel = self.mel_transform(log_mel)
+            mel_length = log_mel.shape[1]
+            if self.mel_segments_rand_start:
+
+                start = np.random.randint(mel_length - window_length) if mel_length > window_length else 0
+
+                log_mel = log_mel[:, start:]
+                mel_length = log_mel.shape[1]
+            # Calulate the number of windows that can be generated
+            num_window = 1 + (mel_length - window_length) // stride_length
+            # Sliding Window
+            for i in range(0, num_window):
+                start_time = i * stride_length
+                segment = log_mel[:, start_time:start_time + window_length]
+                segments.append(segment)
+                seg_count = seg_count + 1
+                if seg_count == 20: # 20
+                    segments = torch.stack(segments)
+                    return segments
+        segments = torch.stack(segments)
+        return segments
+
+    def set_image_transform(self):
+        print('Dataloader: called set_image_size', self.image_size)
+        image_transform = [T.Resize(self.image_size), T.ToTensor()]
+        if self.image_random_hflip and self.split_set == 'train':
+            image_transform = [T.RandomHorizontalFlip(p=0.5),] + \
+                image_transform
+        if self.image_normalize_method is not None:
+            print('Dataloader: called image_normalize_method',
+                self.image_normalize_method)
+            image_transform.append(imagenet_preprocess(
+                normalize_method=self.image_normalize_method))
+        self.image_transform = T.Compose(image_transform)
+
+    def set_mel_transform(self):
+        mel_transform = [T.ToTensor(), ]
+        print('Dataloader: called mel_normalize_method',
+            self.mel_normalize_method)
+        if self.mel_normalize_method is not None:
+            mel_transform.append(imagenet_preprocess(
+                normalize_method=self.mel_normalize_method))
+        mel_transform.append(torch.squeeze)
+        self.mel_transform = T.Compose(mel_transform)
+
+    def load_split_dict(self):
+        '''
+        Load the train, val, test set information from split.json
+        '''
+        with open(self.split_json) as json_file:
+            self.split_dict = json.load(json_file)
+
+    def list_available_names(self):
+        '''
+        Find the intersection of speech and face data
+        '''
+        self.available_names = []
+        # List VoxCeleb1 data:
+        for sub_dataset in (['vox1']): #, 'vox2'
+            mel_gram_available = os.listdir(
+                os.path.join(self.data_dir, sub_dataset, 'mel_spectrograms'))
+            face_available = os.listdir(
+                os.path.join(self.data_dir, sub_dataset, self.face_dir))
+            available = \
+                set(mel_gram_available).intersection(face_available)
+            for name in available:
+                if name in self.split_dict[sub_dataset][self.split_set]:
+                    self.available_names.append((sub_dataset, name))
+
+        self.available_names.sort()
+
+    def load_mel_gram(self, mel_pickle):
+        '''
+        Load a speech's mel spectrogram from pickle file.
+
+        Format of the pickled data:
+            LogMel_Features
+            spkid
+            clipid
+            wavid
+
+        Inputs:
+        - mel_pickle: Path to the mel spectrogram to be loaded.
+        '''
+        # open a file, where you stored the pickled data
+        file = open(mel_pickle, 'rb')
+        # dump information to that file
+        data = pickle.load(file)
+        # close the file
+        file.close()
+        log_mel = data['LogMel_Features']
+        #log_mel = np.transpose(log_mel, axes=None)
+
+        return log_mel
+
+    def crop_or_pad(self, log_mel, out_frame):
+        '''
+        Log_mel padding/cropping function to cooperate with collate_fn
+        '''
+        freq, cur_frame = log_mel.shape
+        if cur_frame >= out_frame:
+            # Just crop
+            start = np.random.randint(0, cur_frame-out_frame+1)
+            log_mel = log_mel[..., start:start+out_frame]
+        else:
+            # Padding
+            zero_padding = np.zeros((freq, out_frame-cur_frame))
+            zero_padding = self.mel_transform(zero_padding)
+            if len(zero_padding.shape) == 1:
+                zero_padding = zero_padding.view([-1, 1])
+            log_mel = torch.cat([log_mel, zero_padding], -1)
+
+        return log_mel
+
+    def collate_fn(self, batch):
+        min_nframe, max_nframe = self.nframe_range
+        assert min_nframe <= max_nframe
+        np.random.seed()
+        num_frame = np.random.randint(min_nframe, max_nframe+1)
+        #start = np.random.randint(0, max_nframe-num_frame+1)
+        #batch = [(item[0], item[1][..., start:start+num_frame], item[2])
+        #         for item in batch]
+
+        batch = [(item[0],
+                  self.crop_or_pad(item[1], num_frame),
+                  item[2]) for item in batch]
+        return default_collate(batch)
+
+    def count_faces(self):
+        '''
+        Count the number of faces in the dataset
+        '''
+        total_count = 0
+        for index in range(len(self.available_names)):
+            sub_dataset, name = self.available_names[index]
+            # Face Image
+            image_dir = os.path.join(
+                self.data_dir, sub_dataset, self.face_dir, name)
+            cur_count = len(os.listdir(image_dir))
+            total_count = total_count + cur_count
+        print('Number of faces in current dataset: {}'.format(total_count))
+        return total_count
+
+    def count_speech(self):
+        '''
+        Given a id, return all the speech segments of him as a batch tensor,
+        with shape (N, C, L)
+        '''
+        total_count = 0
+        for index in range(len(self.available_names)):
+            sub_dataset, name = self.available_names[index]
+            window_length, stride_length = self.mel_seg_window_stride
+            # Mel Spectrogram
+            mel_gram_dir = os.path.join(
+                self.data_dir, sub_dataset, 'mel_spectrograms', name)
+            mel_gram_list = os.listdir(mel_gram_dir)
+            cur_count = len(mel_gram_list)
+            total_count = total_count + cur_count
+        print('Number of speech in current dataset: {}'.format(total_count))
+        return total_count
+
+
+if __name__ == '__main__':
+    '''
+    from utils import imagenet_deprocess, deprocess_and_save
+
+    # Config
+    image_size = (256, 256)
+    #image_normalize_method = 'imagenet'
+    image_normalize_method = 'standard'
+    mel_normalize_method = 'vox_mel'
+    test_case_dir = os.path.join('./data', 'test_cases')
+    os.makedirs(test_case_dir, exist_ok=True)
+
+    # Dataset
+    vox_dataset = VoxDataset(
+        data_dir=VOX_DIR,
+        image_size=image_size,
+        image_normalize_method=image_normalize_method,
+        mel_normalize_method=mel_normalize_method)
+    np_mel = vox_dataset.load_mel_gram(mel_pickle= \
+        './data/VoxCeleb/vox1/mel_spectrograms/A.J._Buckley' + \
+            '/id10001_J9lHsKG98U8_00026.pickle')
+    print('np_mel.shape:', np_mel.shape)
+
+    print('dataset length:', len(vox_dataset))
+
+    # Try out 1 case
+    # image, log_mel, mel_len = vox_dataset[220]
+    image, log_mel, human_id = vox_dataset[220]
+    print('image shape:', image.shape)
+    print('log_mel shape:', log_mel.shape)
+    print('human_id:', human_id)
+    # print(mel_len)
+    deprocess_and_save(image, image_normalize_method,
+        os.path.join(test_case_dir, 'vox_dataset_image_test_1.jpg'))
+
+    log_mel = np.array(log_mel)
+    print(log_mel)
+    print(np.max(log_mel), np.min(log_mel))
+
+    # Unit test after train test split update:
+    print('### Testing splitted dataset ###')
+    print('Test identities:', vox_dataset.split_dict['vox1']['test'])
+    '''
+
+    for split_set in ['train', 'val', 'test']:
+        vox_dataset = VoxDataset(
+            data_dir=VOX_DIR,
+            image_size=(64, 64),
+            nframe_range=(300, 600),
+            face_type='masked',
+            image_normalize_method='imagenet',
+            mel_normalize_method='vox_mel',
+            split_set=split_set,
+            split_json=os.path.join(VOX_DIR, 'split.json'))
+        print('Length of {} set: {}'.format(split_set, len(vox_dataset)))
+        vox_dataset.count_faces()
+        vox_dataset.count_speech()
+
+    # Collate Function dataloader test
+    loader_kwargs = {
+        'batch_size': 16,
+        'num_workers': 8,
+        'shuffle': False,
+        "drop_last": True,
+        'collate_fn': vox_dataset.collate_fn,
+    }
+    val_loader = DataLoader(vox_dataset, **loader_kwargs)
+    for iter, batch in enumerate(val_loader):
+        images, log_mels, human_ids = batch
+        print('log_mels.shape:', log_mels.shape)
+        if iter > 10000:
+            break
+
+    for e in range(1000):
+        for iter, batch in enumerate(val_loader):
+            images, log_mels, human_ids = batch
+            print('log_mels.shape:', log_mels.shape)
+            if iter > 10000:
+                break
+
+    '''
+
+    #### Test utils imagenet_deprocess_batch and fast_imagenet_deprocess_batch
+    imgs_de = imagenet_deprocess_batch(
+        images,
+        rescale=False,
+        normalize_method='imagenet')
+    imgs_de_fast = fast_imagenet_deprocess_batch(
+        images,
+        normalize_method='imagenet')
+
+    print('imgs_de:', imgs_de[0])
+    print('imgs_de_fast:', imgs_de_fast[0])
+
+    #### Test utils fast_mel_deprocess_batch
+    log_mels_de = fast_mel_deprocess_batch(
+        log_mels,
+        normalize_method='vox_mel')
+
+    print('log_mels_de:', log_mels_de[0])
+
+    # Test DataLoader Deepcopy
+    from copy import deepcopy
+    val_loader_copy = deepcopy(val_loader)
+    val_loader_copy.dataset.set_length(100)
+    print('length of copied val dataset: {}'.format(
+        len(val_loader_copy.dataset)))
+    print('length of val dataset: {}'.format(
+        len(val_loader.dataset)))
+
+    # Test get all faces
+    faces = val_loader.dataset.get_all_faces_of_id(3)
+    print('get_all_faces_of_id:', faces.shape)
+
+    # Test get all mel segments
+    segments = val_loader.dataset.get_all_mel_segments_of_id(3)
+    print('get_all_mel_segments_of_id:', segments.shape)
+
+    # Test return many segments mode
+    val_loader.collate_fn = default_collate
+    val_loader.dataset.return_mel_segments = True
+    val_loader.dataset.mel_seg_window_stride = (125, 60)
+    for iter, batch in enumerate(val_loader):
+        images, log_mels, human_ids = batch
+        print('log_mels.shape (many segments mode):', log_mels.shape)
+        if iter > 10000:
+            break
+
+    '''
diff --git a/mlops/serving/sf2f/inference.py b/mlops/serving/sf2f/inference.py
new file mode 100644
index 0000000..fd6ef3f
--- /dev/null
+++ b/mlops/serving/sf2f/inference.py
@@ -0,0 +1,92 @@
+import models
+import torch
+import os, glob
+import mlflow
+from utils.wav2mel import wav_to_mel
+from utils.upload_minio import upload_object
+from datasets import imagenet_deprocess_batch, set_mel_transform, \
+    deprocess_and_save, window_segment
+import mlflow
+from PIL import Image
+import io
+from minio import Minio
+from minio.error import S3Error
+from flask import jsonify
+from config import MLFLOW_S3_ENDPOINT_URL, MLFLOW_TRACKING_URI, AWS_ACCESS_KEY, AWS_SECRET_ACCESS_KEY, MINIO_BUCKET, MINIO_ENDPOINT
+import subprocess
+import tempfile
+
+torch.cuda.empty_cache()
+model_url = "runs:/54d4991723104ba9b048df217bd32ce6/sf2f_pytorch"
+
+#docker compose에서 지정해줘야함 Fastapi 
+os.environ["MLFLOW_S3_ENDPOINT_URL"] = MLFLOW_S3_ENDPOINT_URL
+os.environ["MLFLOW_TRACKING_URI"] = MLFLOW_TRACKING_URI
+os.environ["AWS_ACCESS_KEY_ID"] = AWS_ACCESS_KEY
+os.environ["AWS_SECRET_ACCESS_KEY"] = AWS_SECRET_ACCESS_KEY
+os.environ["MINIO_BUCKET"] = MINIO_BUCKET
+os.environ["MINIO_ENDPOINT"] = MINIO_ENDPOINT
+
+print(MINIO_ENDPOINT, AWS_ACCESS_KEY, AWS_SECRET_ACCESS_KEY,MINIO_BUCKET)
+client = Minio(MINIO_ENDPOINT, AWS_ACCESS_KEY, AWS_SECRET_ACCESS_KEY, secure=True)
+
+model = mlflow.pytorch.load_model(model_url).cuda().eval()
+
+def load_voice_to_face(model_url):
+    global model
+    model =  mlflow.pytorch.load_model(model_url).cuda().eval()
+    
+def generate_voice_to_face(voice_url,request_id,result_id):
+    #file 정보를 받아오는 
+    temp_folder_path = "./temp/"
+    os.makedirs(temp_folder_path,exist_ok=True)
+    save_path = os.path.join(temp_folder_path, os.path.basename(voice_url))
+    object_path = "/".join(voice_url.split("/")[4:])
+    GET_BUCKET_NAME = voice_url.split("/")[3]
+    
+    file_path = f"web_artifact/output/{request_id}_{result_id}_image.png"
+    save_url = f"https://{MINIO_ENDPOINT}/{MINIO_BUCKET}/{file_path}"
+    try:
+        with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+            with tempfile.NamedTemporaryFile(delete=False) as save_temp_file:
+                save_temp_path = save_temp_file.name
+            # MinIO 객체를 임시 파일로 다운로드
+                client.fget_object(GET_BUCKET_NAME, object_path, temp_file.name)
+                # wav 파일로 변환 => ffmpeg 명령어 실행 
+                subprocess.run(['ffmpeg', '-i', temp_file.name,'-acodec', 'pcm_s16le', '-ar', '48000', '-ac', '1', save_path])
+                print(save_path)
+                mel_transform = set_mel_transform("vox_mel")
+                image_normalize_method = 'imagenet'
+                log_mel = wav_to_mel(save_path)
+        log_mel = mel_transform(log_mel).type(torch.cuda.FloatTensor)
+
+        log_mel_segs = window_segment(log_mel, window_length=125, stride_length=63)
+        log_mel = log_mel.unsqueeze(0)
+
+        with torch.no_grad():
+            imgs_fused, others = model(log_mel_segs.unsqueeze(0))
+        if isinstance(imgs_fused, tuple):
+            imgs_fused = imgs_fused[-1]
+        imgs_fused = imgs_fused.cpu().detach()
+        imgs_fused = imagenet_deprocess_batch(imgs_fused, normalize_method=image_normalize_method)
+        for j in range(imgs_fused.shape[0]):
+            img_np = imgs_fused[j].numpy().transpose(1, 2, 0) # 64x64x3
+
+        pil_image = Image.fromarray(img_np)
+        a = Image.open("ss_korean.png")
+        # Save the image to an in-memory file
+        in_mem_file = io.BytesIO()
+        pil_image.save(in_mem_file, format="PNG")
+        in_mem_file.seek(0)
+        img_byte_arr = in_mem_file.getvalue()
+        
+        upload_object(client, file_path, in_mem_file, len(img_byte_arr), MINIO_BUCKET)
+        os.remove(save_path)
+        print(save_url)
+        return 200, save_url
+    except Exception as ex:
+        print(ex)
+        os.remove(save_path)
+        return 400, str(ex)
+# generate_voice_to_face("/home/hojun/Documents/project/boostcamp/final_project/mlops/pipeline/serving/sf2f/녹음_남자목소리_여잘노래.wav")
+# generate_voice_to_face("http://223.130.133.236:9000/voice2face-public/mzRequest/00107_00109_voice_2024-03-22.wav",0,0)
\ No newline at end of file
diff --git a/mlops/serving/sf2f/models/__init__.py b/mlops/serving/sf2f/models/__init__.py
new file mode 100644
index 0000000..694bee5
--- /dev/null
+++ b/mlops/serving/sf2f/models/__init__.py
@@ -0,0 +1,19 @@
+from .model_setup import *
+from .encoder_decoder import EncoderDecoder
+
+# Lower level encoder decoders
+from .voice_encoders import V2F1DCNN
+from .face_decoders import V2FDecoder
+
+# Progressive GAN
+# from .pgan import PGan_Net
+# from .pgan_dis import PGAN_Discriminator
+
+# Discriminator
+from .discriminators import PatchDiscriminator
+from .discriminators import AcDiscriminator
+from .discriminators import AcCropDiscriminator
+from .discriminators import CondPatchDiscriminator
+
+# Facenet for Perceptual Loss and Evaluation
+from .inception_resnet_v1 import InceptionResnetV1, fixed_image_standardization
diff --git a/mlops/serving/sf2f/models/attention.py b/mlops/serving/sf2f/models/attention.py
new file mode 100644
index 0000000..e74b2af
--- /dev/null
+++ b/mlops/serving/sf2f/models/attention.py
@@ -0,0 +1,223 @@
+'''
+Attenion implementation
+Adapted from torchnlp:
+    https://github.com/PetrochukM/PyTorch-NLP/tree/master/torchnlp/nn
+'''
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from math import sqrt
+try:
+    from .layers import get_activation, get_normalization_2d
+except:
+    from layers import get_activation, get_normalization_2d
+
+
+class AttnBlock(nn.Module):
+    def __init__(self,
+                 attn_dim_in,
+                 attn_dim_out,
+                 attn_num_query=16,
+                 attn_type='general',
+                 normalization='none'):
+        super(AttnBlock, self).__init__()
+        self.attn_layer = AttnLayer(
+            attn_dim_in,
+            attn_dim_out,
+            attn_num_query,
+            attn_type,
+            normalization)
+
+    def forward(self, cat_tensor, context):
+        N, C, H, W = cat_tensor.shape
+        attn_map = self.attn_layer(context.transpose(1, 2))
+        #print('attn_map 1:', attn_map.shape)
+        attn_map = F.interpolate(attn_map, (H, W))
+        #print('attn_map 2:', attn_map.shape)
+        map_out = torch.cat([cat_tensor, attn_map], 1)
+
+        return map_out
+
+    def return_attn_weights(self):
+        return self.attn_layer.attn_weights
+
+
+class AttnLayer(nn.Module):
+    def __init__(self,
+                 dim_in,
+                 dim_out,
+                 num_query=16,
+                 attention_type='general',
+                 normalization='none'):
+        super(AttnLayer, self).__init__()
+        self.num_query = num_query
+        self.query = nn.Parameter(
+            torch.randn((1, num_query, dim_in),
+            requires_grad=True))
+        self.attention = Attention(
+            dimensions=dim_in,
+            dim_out=dim_out,
+            attention_type=attention_type)
+        self.norm_layer = get_normalization_2d(
+            dim_out, normalization)
+        # Cache Attenion Weights for visualization
+        self.attn_weights = None
+
+    def forward(self, context):
+        N, L, D_in = context.shape
+        # Repeat query layer over batch dim
+        query = self.query.repeat(N, 1, 1)
+        # **output** (:class:`torch.FloatTensor`
+        # [batch size, output length, dim_out]
+        attn_out, attn_weights = self.attention(query, context)
+        self.attn_weights = attn_weights
+        N, Q_L, D_out = attn_out.shape
+        # [batch size, dim_out, output length]
+        attn_out = torch.transpose(attn_out, 1, 2)
+        #  [batch size, dimensions, H, W]
+        H = W = int(sqrt(self.num_query))
+        attn_map = attn_out.view(
+            N, D_out, H, W)
+        if self.norm_layer is not None:
+            attn_map = self.norm_layer(attn_map)
+
+        return attn_map
+
+
+class Attention(nn.Module):
+    """ Applies attention mechanism on the `context` using the `query`.
+
+    **Thank you** to IBM for their initial implementation of :class:`Attention`. Here is
+    their `License
+    <https://github.com/IBM/pytorch-seq2seq/blob/master/LICENSE>`__.
+
+    Args:
+        dimensions (int): Dimensionality of the query and context.
+        attention_type (str, optional): How to compute the attention score:
+
+            * dot: :math:`score(H_j,q) = H_j^T q`
+            * general: :math:`score(H_j, q) = H_j^T W_a q`
+
+    Example:
+
+         >>> attention = Attention(256)
+         >>> query = torch.randn(5, 1, 256)
+         >>> context = torch.randn(5, 5, 256)
+         >>> output, weights = attention(query, context)
+         >>> output.size()
+         torch.Size([5, 1, 256])
+         >>> weights.size()
+         torch.Size([5, 1, 5])
+    """
+
+    def __init__(self,
+                 dimensions,
+                 dim_out,
+                 attention_type='general',
+                 linear_out_layer=True,
+                 ignore_tanh=False):
+        super(Attention, self).__init__()
+
+        if attention_type not in ['dot', 'general']:
+            raise ValueError('Invalid attention type selected.')
+
+        # Attention Setup
+        self.attention_type = attention_type
+        if self.attention_type == 'general':
+            self.linear_in = nn.Linear(dimensions, dimensions, bias=False)
+        self.softmax = nn.Softmax(dim=-1)
+
+        # Output Setup
+        self.linear_out_layer = linear_out_layer
+        if self.linear_out_layer:
+            self.dim_out = dim_out
+            self.linear_out = nn.Linear(dimensions * 2, dim_out, bias=False)
+
+        self.ignore_tanh = ignore_tanh
+        if not ignore_tanh:
+            self.tanh = nn.Tanh()
+
+    def forward(self, query, context):
+        """
+        Args:
+            query (:class:`torch.FloatTensor` [batch size, output length,
+                dimensions]): Sequence of queries to query the context.
+            context (:class:`torch.FloatTensor` [batch size, query length,
+                dimensions]): Data overwhich to apply the attention mechanism.
+
+        Returns:
+            :class:`tuple` with `output` and `weights`:
+            * **output** (:class:`torch.LongTensor` [batch size, output length,
+                dimensions]):
+                    Tensor containing the attended features.
+            * **weights** (:class:`torch.FloatTensor` [batch size,
+                output length, query length]):
+                    Tensor containing attention weights.
+        """
+        batch_size, output_len, dimensions = query.size()
+        query_len = context.size(1)
+
+        if self.attention_type == "general":
+            query = query.reshape(batch_size * output_len, dimensions)
+            query = self.linear_in(query)
+            query = query.reshape(batch_size, output_len, dimensions)
+
+        # TODO: Include mask on PADDING_INDEX?
+
+        # (batch_size, output_len, dimensions) * \
+        # (batch_size, query_len, dimensions) ->
+        # (batch_size, output_len, query_len)
+        # batch matrix-matrix product
+        attention_scores = torch.bmm(
+            query, context.transpose(1, 2).contiguous())
+
+        # Compute weights across every context sequence
+        attention_scores = attention_scores.view(
+            batch_size * output_len, query_len)
+        attention_weights = self.softmax(attention_scores)
+        attention_weights = attention_weights.view(
+            batch_size, output_len, query_len)
+
+        # (batch_size, output_len, query_len) * \
+        # (batch_size, query_len, dimensions) ->
+        # (batch_size, output_len, dimensions)
+        mix = torch.bmm(attention_weights, context)
+
+        if self.linear_out_layer:
+            # Concatenate the attention output with the context and process the
+            # concatenated feature with a fc output layer
+            # concat -> (batch_size * output_len, 2*dimensions)
+            combined = torch.cat((mix, query), dim=2)
+            combined = combined.view(batch_size * output_len, 2 * dimensions)
+            # Apply linear_out on every 2nd dimension of concat
+            # output -> (batch_size, output_len, dimensions)
+            output = self.linear_out(combined).view(
+                batch_size, output_len, self.dim_out)
+        else:
+            # Directly output the attention output
+            output = mix
+
+        if not self.ignore_tanh:
+            output = self.tanh(output)
+
+        return output, attention_weights
+
+
+if __name__ == '__main__':
+    attention = Attention(256, 100, linear_out_layer=False)
+    query = torch.randn(5, 1, 256)
+    context = torch.randn(5, 5, 256)
+    cat_tensor = torch.randn(5, 170, 128, 128)
+    output, weights = attention(query, context)
+    print('output.shape:', output.shape)
+    '''
+    attn_layer = AttnLayer(256, 110, 16)
+    attn_map = attn_layer(context)
+    print('attn_map.shape:', attn_map.shape)
+    for param in attn_layer.parameters():
+        print(type(param.data), param.size())
+
+    attn_block = AttnBlock(256, 110, 16, normalization='batch')
+    cat_map = attn_block(cat_tensor, context.transpose(1, 2))
+    print('cat_map.shape:', cat_map.shape)
+    '''
diff --git a/mlops/serving/sf2f/models/crn.py b/mlops/serving/sf2f/models/crn.py
new file mode 100644
index 0000000..33cd9a3
--- /dev/null
+++ b/mlops/serving/sf2f/models/crn.py
@@ -0,0 +1,152 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .layers import get_normalization_2d
+from .layers import get_activation
+
+
+"""
+Cascaded refinement network architecture, as described in:
+Qifeng Chen and Vladlen Koltun,
+"Photographic Image Synthesis with Cascaded Refinement Networks",
+ICCV 2017
+"""
+
+
+class RefinementModule(nn.Module):
+    def __init__(self, layout_dim, input_dim, output_dim,
+                 normalization='instance', activation='leakyrelu'):
+        super(RefinementModule, self).__init__()
+
+        layers = []
+        layers.append(nn.Conv2d(layout_dim + input_dim, output_dim,
+                                kernel_size=3, padding=1))
+        layers.append(get_normalization_2d(output_dim, normalization))
+        layers.append(get_activation(activation))
+        layers.append(nn.Conv2d(output_dim, output_dim,
+                                kernel_size=3, padding=1))
+        layers.append(get_normalization_2d(output_dim, normalization))
+        layers.append(get_activation(activation))
+        layers = [layer for layer in layers if layer is not None]
+        for layer in layers:
+            if isinstance(layer, nn.Conv2d):
+                nn.init.kaiming_normal_(layer.weight)
+        self.net = nn.Sequential(*layers)
+
+    def forward(self, layout, feats):
+        _, _, HH, WW = layout.size()
+        _, _, H, W = feats.size()
+        assert HH >= H
+        if HH > H:
+            factor = round(HH // H)
+            assert HH % factor == 0
+            assert WW % factor == 0 and WW // factor == W
+            layout = F.avg_pool2d(layout, kernel_size=factor, stride=factor)
+        net_input = torch.cat([layout, feats], dim=1)
+        out = self.net(net_input)
+        return out
+
+
+class RefinementNetwork(nn.Module):
+    def __init__(self, dims, normalization='instance',
+                 activation='leakyrelu',
+                 use_tanh=False,
+                 use_deconv=False,
+                 multi_resolution=False,):
+        super(RefinementNetwork, self).__init__()
+        layout_dim = dims[0]
+        self.refinement_modules = nn.ModuleList()
+        self.upsample_modules = nn.ModuleList()
+        self.multi_resolution = multi_resolution
+        for i in range(1, len(dims)):
+            input_dim = 1 if i == 1 else (dims[i] if use_deconv else dims[i-1])
+            output_dim = dims[i]
+            mod = RefinementModule(layout_dim, input_dim, output_dim,
+                                   normalization=normalization, activation=activation)
+            self.refinement_modules.append(mod)
+            if use_deconv:
+                if i == 1:
+                    mod = nn.Sequential()
+                else:
+                    mod = nn.Sequential(
+                        nn.ConvTranspose2d(dims[i-1], dims[i],
+                            kernel_size=2, stride=2,),
+                        get_normalization_2d(dims[i], normalization),
+                        get_activation(activation)
+                    )
+                self.upsample_modules.append(mod)
+        output_conv_layers = None
+        if not multi_resolution:
+            output_conv_layers = [
+                nn.Conv2d(dims[-1], dims[-1], kernel_size=3, padding=1),
+                get_activation(activation),
+                nn.Conv2d(dims[-1], 3, kernel_size=1, padding=0),
+            ]
+            if use_tanh:
+                ## added according to Pix2Pix-HD
+                output_conv_layers.append(nn.Tanh(),)
+            nn.init.kaiming_normal_(output_conv_layers[0].weight)
+            nn.init.kaiming_normal_(output_conv_layers[2].weight)
+        else:
+            output_conv_layers_low = [
+                nn.Conv2d(dims[-2], dims[-2], kernel_size=3, padding=1),
+                get_activation(activation),
+                nn.Conv2d(dims[-2], 3, kernel_size=1, padding=0),
+            ]
+            output_conv_layers_high = [
+                nn.Conv2d(dims[-1], dims[-1], kernel_size=3, padding=1),
+                get_activation(activation),
+                nn.Conv2d(dims[-1], 3, kernel_size=1, padding=0),
+            ]
+            if use_tanh:
+                ## added according to Pix2Pix-HD
+                output_conv_layers_low.append(nn.Tanh(),)
+                output_conv_layers_high.append(nn.Tanh(),)
+
+        if output_conv_layers:
+            self.output_conv = nn.Sequential(*output_conv_layers)
+        else:
+            assert output_conv_layers_low
+            assert output_conv_layers_high
+            self.output_conv_low = nn.Sequential(*output_conv_layers_low)
+            self.output_conv_high = nn.Sequential(*output_conv_layers_high)
+
+    def forward(self, layout):
+        """
+        Output will have same size as layout
+        """
+        # H, W = self.output_size
+        N, _, H, W = layout.size()
+        self.layout = layout
+
+        # Figure out size of input
+        input_H, input_W = H, W
+        for _ in range(len(self.refinement_modules)):
+            input_H //= 2
+            input_W //= 2
+
+        assert input_H != 0
+        assert input_W != 0
+
+        feats = torch.zeros(N, 1, input_H, input_W).to(layout)
+        if not self.multi_resolution:
+            for i, mod in enumerate(self.refinement_modules):
+                if i == 0 or len(self.upsample_modules) == 0:
+                    feats = F.interpolate(feats, scale_factor=2, mode='nearest')
+                else:
+                    feats = self.upsample_modules[i](feats)
+                feats = mod(layout, feats)
+            out = self.output_conv(feats)
+            return (out, feats)
+        elif self.multi_resolution:
+            for i, mod in enumerate(self.refinement_modules):
+                if i == 0 or len(self.upsample_modules) == 0:
+                    feats = F.interpolate(feats, scale_factor=2, mode='nearest')
+                else:
+                    feats = self.upsample_modules[i](feats)
+                feats = mod(layout, feats)
+                if i == len(self.refinement_modules) - 2:
+                    out_low = self.output_conv_low(feats)
+            out_high = self.output_conv_high(feats)
+            return (out_high, out_low)
diff --git a/mlops/serving/sf2f/models/discriminators.py b/mlops/serving/sf2f/models/discriminators.py
new file mode 100644
index 0000000..f0f276d
--- /dev/null
+++ b/mlops/serving/sf2f/models/discriminators.py
@@ -0,0 +1,110 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from utils.bilinear import crop_bbox_batch, uncrop_bbox
+from utils.box_utils import box_union, box_in_region
+from .layers import GlobalAvgPool, Flatten, get_activation, build_cnn
+import models
+
+
+class PatchDiscriminator(nn.Module):
+    def __init__(self, arch, normalization='batch', activation='leakyrelu-0.2',
+                 padding='same', pooling='avg', input_size=(128, 128),
+                 layout_dim=0):
+        super(PatchDiscriminator, self).__init__()
+        input_dim = 3 + layout_dim
+        arch = 'I%d,%s' % (input_dim, arch)
+        cnn_kwargs = {
+            'arch': arch,
+            'normalization': normalization,
+            'activation': activation,
+            'pooling': pooling,
+            'padding': padding,
+        }
+        self.cnn, output_dim = build_cnn(**cnn_kwargs)
+        self.classifier = nn.Conv2d(output_dim, 1, kernel_size=1, stride=1)
+
+    def forward(self, x):
+        return self.classifier(self.cnn(x))
+
+
+class AcDiscriminator(nn.Module):
+    def __init__(self, arch, normalization='none', activation='relu',
+                 padding='same', pooling='avg', num_id=None):
+        super(AcDiscriminator, self).__init__()
+        # self.vocab = vocab
+
+        cnn_kwargs = {
+            'arch': arch,
+            'normalization': normalization,
+            'activation': activation,
+            'pooling': pooling,
+            'padding': padding,
+        }
+        cnn, D = build_cnn(**cnn_kwargs)
+        self.cnn = nn.Sequential(cnn, GlobalAvgPool(), nn.Linear(D, 1024))
+        self.num_id = num_id
+
+        self.real_classifier = nn.Linear(1024, 1)
+        self.id_classifier = nn.Linear(1024, num_id)
+
+    def forward(self, x, y):
+        if x.dim() == 3:
+            x = x[:, None]
+
+        vecs = self.cnn(x)
+        real_scores = self.real_classifier(vecs)
+        id_scores = self.id_classifier(vecs)
+        ac_loss = F.cross_entropy(id_scores, y)
+        return real_scores, ac_loss
+
+
+class AcCropDiscriminator(nn.Module):
+    def __init__(self, arch, normalization='none', activation='relu',
+                 object_size=64, padding='same', pooling='avg'):
+        super(AcCropDiscriminator, self).__init__()
+        self.discriminator = AcDiscriminator(arch, normalization,
+                                             activation, padding, pooling)
+        self.object_size = object_size
+
+    def forward(self, imgs, objs, boxes, obj_to_img,
+                object_crops=None, **kwargs):
+        if object_crops is None:
+            object_crops = crop_bbox_batch(imgs, boxes, obj_to_img, self.object_size)
+        real_scores, ac_loss = self.discriminator(object_crops, objs)
+        return real_scores, ac_loss, object_crops
+
+
+# Conditional Discriminator
+class CondPatchDiscriminator(nn.Module):
+    def __init__(self, arch, normalization='batch', activation='leakyrelu-0.2',
+                 padding='same', pooling='avg', input_size=(128, 128),
+                 cond_dim=0):
+        super(CondPatchDiscriminator, self).__init__()
+        input_dim = 3
+        arch = 'I%d,%s' % (input_dim, arch)
+        cnn_kwargs = {
+            'arch': arch,
+            'normalization': normalization,
+            'activation': activation,
+            'pooling': pooling,
+            'padding': padding,
+        }
+        self.cnn, output_dim = build_cnn(**cnn_kwargs)
+        self.joint_conv = nn.Sequential(
+            nn.Conv2d(output_dim+cond_dim, output_dim, \
+                kernel_size=3, stride=1, padding=1, bias=False),
+            nn.BatchNorm2d(output_dim),
+            nn.LeakyReLU(0.2, inplace=True)
+        )
+        self.classifier = nn.Conv2d(output_dim, 1, kernel_size=1, stride=1)
+
+    def forward(self, x, cond_vecs):
+        if len(cond_vecs) == 2:
+            cond_vecs = cond_vecs.view(-1, cond_vecs.size(1), 1, 1)
+        x = self.cnn(x)
+        cond_vecs = cond_vecs.expand(-1, cond_vecs.size(1), x.size(2), x.size(3))
+        x = torch.cat([x, cond_vecs], dim=1)
+        x = self.joint_conv(x)
+        return self.classifier(x)
diff --git a/mlops/serving/sf2f/models/encoder_decoder.py b/mlops/serving/sf2f/models/encoder_decoder.py
new file mode 100644
index 0000000..d564d86
--- /dev/null
+++ b/mlops/serving/sf2f/models/encoder_decoder.py
@@ -0,0 +1,87 @@
+'''
+Flexible Encoder-Decoder Framework
+'''
+
+
+import torch
+import torch.nn as nn
+import functools
+from torch.autograd import Variable
+import numpy as np
+
+try:
+    from .model_collection import model_collection
+except:
+    from model_collection import model_collection
+#from .voice_encoders import V2F1DCNN
+#from .face_decoders import V2FDecoder
+
+
+class EncoderDecoder(nn.Module):
+    def __init__(self,
+                 encoder_arch,
+                 encoder_kwargs,
+                 decoder_arch,
+                 decoder_kwargs,
+                 image_size):
+        super(EncoderDecoder, self).__init__()
+        # Initialize Encoder Decoder
+        self.encoder = getattr(model_collection, encoder_arch)(**encoder_kwargs)
+        self.decoder = getattr(model_collection, decoder_arch)(**decoder_kwargs)
+        #self.model = nn.Sequential(
+        #    self.encoder,
+        #    self.decoder
+        #)
+
+    def forward(self, x, average_voice_embedding=False):
+        '''
+        average_encoder_embedding: an option for evaluation, not needed for
+        training
+        '''
+        others = {}
+        if self.encoder.return_seq:
+            emb, seq_emb = self.encoder(x)
+            imgs_pred = self.decoder(emb, seq_emb)
+        else:
+            emb = self.encoder(x)
+            if average_voice_embedding:
+                emb = torch.mean(emb, 0, keepdim=True)
+            imgs_pred = self.decoder(emb)
+        others['cond'] = emb
+        return imgs_pred, others
+
+    def train_fuser_only(self):
+        print('Training Attention Fuser Only')
+        for name, param in self.named_parameters():
+            if 'attn_fuser' in name:
+                param.requires_grad = True
+            else:
+                param.requires_grad = False
+
+
+if __name__ == '__main__':
+    # Demo Input
+    log_mels = torch.ones((16, 40, 127))
+
+    # Test V2F 1D CNN
+    v2f_id_cnn_kwargs = {
+        'input_channel': 40,
+        'channels': [256, 384, 576, 864],
+        'output_channel': 64,
+        }
+    # Test V2F 1D CNN
+    v2f_decoder_kwargs = {
+        'input_channel': 64,
+        'channels': [1024, 512, 256, 128, 64],
+        'output_channel': 3,
+        }
+
+    baseline_v2f = EncoderDecoder(
+        encoder_arch='V2F1DCNN',
+        encoder_kwargs=v2f_id_cnn_kwargs,
+        decoder_arch='V2FDecoder',
+        decoder_kwargs=v2f_decoder_kwargs
+    )
+
+    print(baseline_v2f)
+    print('baseline_v2f Output shape:', baseline_v2f(log_mels).shape)
diff --git a/mlops/serving/sf2f/models/face_decoders.py b/mlops/serving/sf2f/models/face_decoders.py
new file mode 100644
index 0000000..a05a8b7
--- /dev/null
+++ b/mlops/serving/sf2f/models/face_decoders.py
@@ -0,0 +1,583 @@
+'''
+Implementations of different Face Decoders
+'''
+
+
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+import torch.nn.functional as F
+import functools
+import numpy as np
+from collections import OrderedDict
+try:
+    from .attention import AttnBlock
+except:
+    from attention import AttnBlock
+try:
+    from .layers import get_activation, get_normalization_2d
+except:
+    from layers import get_activation, get_normalization_2d
+try:
+    from .networks import upBlock, GLU
+except:
+    from networks import upBlock, GLU
+
+
+class DeConv2DBLK(nn.Module):
+    def __init__(self,
+                 input_channel,
+                 output_channel,
+                 kernel_size,
+                 stride,
+                 padding,
+                 activation='relu',
+                 normalization='none'):
+        super(DeConv2DBLK, self).__init__()
+        self.layers = []
+        self.layers.append(
+            nn.ConvTranspose2d(
+                input_channel,
+                output_channel,
+                kernel_size,
+                stride,
+                padding,
+                bias=True))
+        self.layers.append(get_activation(activation))
+        self.layers.append(
+            get_normalization_2d(
+                output_channel,
+                normalization))
+        self.layers = [layer for layer in self.layers if \
+            layer is not None]
+        self.model = nn.Sequential(*self.layers)
+
+    def forward(self, x):
+        x = self.model(x)
+        return x
+
+
+class ExtraFeatureBlock(nn.Module):
+    def __init__(self,
+                 dim_in,
+                 dim_hidden,
+                 normalization='none'):
+        super(ExtraFeatureBlock, self).__init__()
+        self.projection = nn.Linear(dim_in, dim_hidden, bias=True)
+        self.norm_layer = get_normalization_2d(
+            dim_hidden // 16, normalization)
+
+    def forward(self, embedding, cat_tensor):
+        '''
+        Transform the embedding, then concat it to the hidden tensor map
+        '''
+        # Projected Embedding
+        #print('embedding:', embedding.shape)
+        N, D_in, _, _ = embedding.shape
+        proj_emb = self.projection(embedding.view(N, -1))
+        N, D_hid = proj_emb.shape
+        feat_map = proj_emb.view(N, -1, 4, 4)
+        if self.norm_layer is not None:
+            feat_map = self.norm_layer(feat_map)
+        N, C, H, W = cat_tensor.shape
+        feat_map = F.interpolate(feat_map, (H, W))
+        map_out = torch.cat([cat_tensor, feat_map], 1)
+
+        return map_out
+
+
+class V2FDecoder(nn.Module):
+    def __init__(self,
+                 input_channel,
+                 channels,
+                 output_channel,
+                 normalization='none'):
+        super(V2FDecoder, self).__init__()
+        output_size = 2 ** (len(channels) + 1)
+        print('V2FDecoder: According to your channels list, ' + \
+            'the output will be generated with shape ({}, {})'.format(
+                output_size, output_size))
+        #torch.nn.ConvTranspose2d(
+        #in_channels, out_channels, kernel_size, stride=1, padding=0,
+        #output_padding=0, groups=1, bias=True,
+        #dilation=1, padding_mode='zeros')
+        self.layers = []
+        # First Layer
+        layer_id = 0
+        self.layers.append(
+            DeConv2DBLK(input_channel,
+                        channels[0],
+                        kernel_size=4,
+                        stride=1,
+                        padding=0,
+                        activation='relu',
+                        normalization=normalization))
+        # Hidden Layers
+        for layer_id in range(1, len(channels)):
+            self.layers.append(
+                DeConv2DBLK(channels[layer_id - 1],
+                            channels[layer_id],
+                            kernel_size=4,
+                            stride=2,
+                            padding=1,
+                            activation='relu',
+                            normalization=normalization))
+        # Output Layer
+        layer_id = layer_id + 1
+        self.layers.append(
+            DeConv2DBLK(channels[layer_id - 1],
+                        output_channel,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        activation='none',
+                        normalization='none'))
+        self.model = nn.Sequential(*self.layers)
+
+    def forward(self, x):
+        x = self.model(x)
+        return x
+
+
+class MRDecoder(nn.Module):
+    def __init__(self,
+                 input_channel,
+                 base_block_channels,
+                 mid_block_channel=32,
+                 high_block_channel=None,
+                 output_channel=3,
+                 normalization='none',
+                 use_up_block=False,
+                 extra_feature_map=False):
+        super(MRDecoder, self).__init__()
+        output_sizes = []
+        output_sizes.append(
+            2 ** (len(base_block_channels) + 1))
+        output_sizes.append(
+            2 ** (len(base_block_channels) + 2))
+        if high_block_channel is not None:
+            self.build_high_img = True
+            output_sizes.append(
+                2 ** (len(base_block_channels) + 3))
+        else:
+            self.build_high_img = False
+        print('MRDecoder: According to your channels list:')
+        for i, output_size in enumerate(output_sizes):
+            print('\tOutput {} will be generated with shape ({}, {})'.format(
+                i, output_size, output_size))
+
+        self.input_channel = input_channel
+        self.base_block_channels = base_block_channels
+        self.mid_block_channel = mid_block_channel
+        self.high_block_channel = high_block_channel
+        self.output_channel = output_channel
+        self.normalization = normalization
+        self.use_up_block = use_up_block
+        self.extra_feature_map = extra_feature_map
+
+        # Output Layer
+        self.build_base_block()
+        self.build_low_block()
+        self.build_mid_block()
+        if self.build_high_img:
+            self.build_high_block()
+
+    def build_base_block(self):
+        #torch.nn.ConvTranspose2d(
+        #in_channels, out_channels, kernel_size, stride=1, padding=0,
+        #output_padding=0, groups=1, bias=True,
+        #dilation=1, padding_mode='zeros')
+        self.base_block_layers = []
+        # First Layer
+        layer_id = 0
+        self.base_block_layers.append(
+            DeConv2DBLK(self.input_channel,
+                        self.base_block_channels[0],
+                        kernel_size=4,
+                        stride=1,
+                        padding=0,
+                        activation='relu',
+                        normalization=self.normalization))
+        # Hidden Layers
+        for layer_id in range(1, len(self.base_block_channels)):
+            self.base_block_layers.append(
+                DeConv2DBLK(self.base_block_channels[layer_id - 1],
+                            self.base_block_channels[layer_id],
+                            kernel_size=4,
+                            stride=2,
+                            padding=1,
+                            activation='relu',
+                            normalization=self.normalization))
+        self.base_block = nn.Sequential(*self.base_block_layers)
+
+    def build_low_block(self):
+        self.low_output_layer = \
+            DeConv2DBLK(self.base_block_channels[-1],
+                        self.output_channel,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        activation='none',
+                        normalization='none')
+
+    def build_mid_block(self):
+        extra_channel = 0
+        if self.extra_feature_map:
+            # Mid-resolution ExtraFeatureBlock
+            self.mid_ef_block = ExtraFeatureBlock(
+                dim_in=self.input_channel,
+                dim_hidden=self.input_channel,
+                normalization=self.normalization)
+            extra_channel = self.input_channel // 16
+        if self.use_up_block:
+            self.mid_hidden_layer = \
+                upBlock(self.base_block_channels[-1] + extra_channel,
+                        self.mid_block_channel)
+        else:
+            self.mid_hidden_layer = \
+                DeConv2DBLK(self.base_block_channels[-1] + extra_channel,
+                            self.mid_block_channel,
+                            kernel_size=4,
+                            stride=2,
+                            padding=1,
+                            activation='relu',
+                            normalization=self.normalization)
+        self.mid_output_layer = \
+            DeConv2DBLK(self.mid_block_channel,
+                        self.output_channel,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        activation='none',
+                        normalization='none')
+
+    def build_high_block(self):
+        extra_channel = 0
+        if self.extra_feature_map:
+            # High-resolution ExtraFeatureBlock
+            self.high_ef_block = ExtraFeatureBlock(
+                dim_in=self.input_channel,
+                dim_hidden=self.input_channel,
+                normalization=self.normalization)
+            extra_channel = self.input_channel // 16
+        if self.use_up_block:
+            self.high_hidden_layer = \
+                upBlock(self.mid_block_channel + extra_channel,
+                        self.high_block_channel)
+        else:
+            self.high_hidden_layer = \
+                DeConv2DBLK(self.mid_block_channel + extra_channel,
+                            self.high_block_channel,
+                            kernel_size=4,
+                            stride=2,
+                            padding=1,
+                            activation='relu',
+                            normalization=self.normalization)
+        self.high_output_layer = \
+            DeConv2DBLK(self.high_block_channel,
+                        self.output_channel,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        activation='none',
+                        normalization='none')
+
+    def forward(self, x):
+        low_code = self.base_block(x)
+        low_imgs = self.low_output_layer(low_code)
+        #print('low_code:', low_code.shape)
+        if self.extra_feature_map:
+            low_code = self.mid_ef_block(x, low_code)
+            #print('low_code:', low_code.shape)
+        mid_code = self.mid_hidden_layer(low_code)
+        mid_imgs = self.mid_output_layer(mid_code)
+        if self.build_high_img:
+            #print('mid_code:', mid_code.shape)
+            if self.extra_feature_map:
+                mid_code = self.high_ef_block(x, mid_code)
+                #print('mid_code:', mid_code.shape)
+            high_code = self.high_hidden_layer(mid_code)
+            high_imgs = self.high_output_layer(high_code)
+            return (low_imgs, mid_imgs, high_imgs)
+        else:
+            return (low_imgs, mid_imgs)
+
+
+class AttnV2FDecoder(nn.Module):
+    def __init__(self,
+                 input_channel,
+                 cnn_channels,
+                 attn_channels,
+                 output_channel,
+                 seq_in_dim,
+                 normalization='none'):
+        super(AttnV2FDecoder, self).__init__()
+        output_size = 2 ** (len(cnn_channels) + 1)
+        print('V2FDecoder: According to your channels list, ' + \
+            'the output will be generated with shape ({}, {})'.format(
+                output_size, output_size))
+        #torch.nn.ConvTranspose2d(
+        #in_channels, out_channels, kernel_size, stride=1, padding=0,
+        #output_padding=0, groups=1, bias=True,
+        #dilation=1, padding_mode='zeros')
+        self.layers = []
+        self.layer_names = []
+        # First Layer
+        layer_id = 0
+        self.layers.append(
+            DeConv2DBLK(input_channel,
+                        cnn_channels[0],
+                        kernel_size=4,
+                        stride=1,
+                        padding=0,
+                        activation='relu',
+                        normalization=normalization))
+        self.layer_names.append('DeConv2DBLK{}'.format(layer_id))
+        # Hidden Layers
+        for layer_id in range(1, len(cnn_channels)):
+            self.layers.append(
+                DeConv2DBLK(
+                    cnn_channels[layer_id - 1]+attn_channels[layer_id - 1],
+                    cnn_channels[layer_id],
+                    kernel_size=4,
+                    stride=2,
+                    padding=1,
+                    activation='relu',
+                    normalization=normalization))
+            self.layer_names.append('DeConv2DBLK{}'.format(layer_id))
+            attn_channel = attn_channels[layer_id]
+            if attn_channel > 0:
+                self.layers.append(
+                    AttnBlock(attn_dim_in=seq_in_dim,
+                              attn_dim_out=attn_channel,
+                              attn_num_query=16,
+                              normalization=normalization))
+                self.layer_names.append('AttnBlock{}'.format(layer_id))
+        # Output Layer
+        layer_id = layer_id + 1
+        self.layers.append(
+            DeConv2DBLK(cnn_channels[layer_id - 1]+attn_channels[layer_id - 1],
+                        output_channel,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        activation='none',
+                        normalization='none'))
+        self.layer_names.append('DeConv2DBLK{}'.format(layer_id))
+
+        for layer_id, layer in enumerate(self.layers):
+            self.add_module(self.layer_names[layer_id], layer)
+
+    def forward(self, x, seq_feat):
+        for layer, name in zip(self.layers, self.layer_names):
+            if 'AttnBlock' in name:
+                x = layer(x, seq_feat)
+            else:
+                x = layer(x)
+            #print(x.shape)
+        return x
+
+    def return_attn_weights(self):
+        for layer, name in zip(self.layers, self.layer_names):
+            if 'AttnBlock' in name:
+                return layer.return_attn_weights()
+
+
+class FaceGanDecoder(nn.Module):
+    def __init__(self,
+                 image_size=(64, 64),
+                 normalization='batch',
+                 activation='leakyrelu-0.2',
+                 mlp_normalization='none',
+                 noise_dim=128,
+                 **kwargs):
+
+        super(FaceGanDecoder, self).__init__()
+        if len(kwargs) > 0:
+            print('WARNING: Model got unexpected kwargs ', kwargs)
+
+        self.dim = noise_dim
+        self.image_size = image_size
+
+        self.fc = nn.Sequential(
+            nn.Linear(self.dim, self.dim * 4 * 4 * 2, bias=False),
+            nn.BatchNorm1d(self.dim * 4 * 4 * 2),
+            GLU())
+
+        up_num, up_layers = [], []
+        num_in, num_out = 1, 1
+        up_num = range(1, int(np.log2(image_size[0]))-1)
+
+        for i, num in enumerate(up_num):
+            if i == 0:
+                num_in = num
+            num_out = 2**num
+            up_layers.append(upBlock(self.dim//num_in, self.dim//num_out))
+            num_in = num_out
+
+        self.up_layers = nn.Sequential(*up_layers)
+
+        gnet_layers = [
+            nn.Conv2d(self.dim//num_out, self.dim//num_out, kernel_size=3, padding=1),
+            get_activation('leakyrelu'),
+            nn.Conv2d(self.dim//num_out, 3, kernel_size=1, padding=0),
+        ]
+        self.gnet = nn.Sequential(*gnet_layers)
+
+    def forward(self, x):
+        """
+        Required Inputs:
+        - placeholder: This is a placeholder.
+
+        Optional Inputs:
+        - placeholder: This is a placeholder.
+        """
+
+        vecs = self.fc(x.view(-1, 512))
+        vecs = vecs.view(x.size()[0], -1, 4, 4)
+
+        img_code = self.up_layers(vecs)
+        img_pred = self.gnet(img_code)
+
+        return img_pred
+
+
+class FaceGanDecoder_v2(nn.Module):
+    def __init__(self,
+                 image_size=(64, 64),
+                 normalization='batch',
+                 activation='leakyrelu-0.2',
+                 mlp_normalization='none',
+                 noise_dim=128,
+                 **kwargs):
+
+        super(FaceGanDecoder_v2, self).__init__()
+        if len(kwargs) > 0:
+            print('WARNING: Model got unexpected kwargs ', kwargs)
+
+        self.dim = noise_dim
+        self.image_size = image_size
+
+        self.fc = nn.Sequential(
+            nn.Linear(self.dim, self.dim * 4 * 4 * 2, bias=False),
+            nn.BatchNorm1d(self.dim * 4 * 4 * 2),
+            GLU())
+
+        up_num, up_layers = [], []
+        num_in, num_out = 1, 1
+        up_num = range(1, int(np.log2(image_size[0]))-1)
+
+        for i, num in enumerate(up_num):
+            if i == 0:
+                num_in = num
+            num_out = 2**num
+            up_layers.append(upBlock(self.dim//num_in, self.dim//num_out))
+            num_in = num_out
+
+        self.up_low_layers = nn.Sequential(*up_layers)
+        self.up_mid_layer = upBlock(self.dim//num_out, self.dim//num_out//2)
+
+        gnet_layers = [
+            nn.Conv2d(self.dim//num_out, self.dim//num_out, kernel_size=3, padding=1),
+            get_activation('leakyrelu'),
+            nn.Conv2d(self.dim//num_out, 3, kernel_size=1, padding=0),
+        ]
+        self.gnet_low = nn.Sequential(*gnet_layers)
+
+        gnet_layers = [
+            nn.Conv2d(self.dim//num_out//2, self.dim//num_out//2, kernel_size=3, padding=1),
+            get_activation('leakyrelu'),
+            nn.Conv2d(self.dim//num_out//2, 3, kernel_size=1, padding=0),
+        ]
+        self.gnet_mid = nn.Sequential(*gnet_layers)
+
+    def forward(self, x):
+        """
+        Required Inputs:
+        - placeholder: This is a placeholder.
+
+        Optional Inputs:
+        - placeholder: This is a placeholder.
+        """
+
+        vecs = self.fc(x.view(-1, 512))
+        vecs = vecs.view(x.size()[0], -1, 4, 4)
+
+        img_code_low = self.up_low_layers(vecs)
+        img_pred_low = self.gnet_low(img_code_low)
+
+        img_code_mid = self.up_mid_layer(img_code_low)
+        img_pred_mid = self.gnet_mid(img_code_mid)
+
+        return (img_pred_low, img_pred_mid)
+
+
+if __name__ == '__main__':
+    # Demo Input
+    voice_embeddings = torch.ones((16, 512, 1, 1))
+    seq_voice_embeddings = torch.ones((16, 512, 270))
+
+    # Test V2F 1D CNN
+    mr_decoder_kwargs = {
+        'input_channel': 512,
+        'base_block_channels': [1024, 512, 256, 128, 64],
+        'mid_block_channel': 32,
+        'high_block_channel': 32,
+        'output_channel': 3,
+        'normalization': 'batch',
+        'use_up_block': True,
+        'extra_feature_map': True,
+        }
+    mr_decoder = MRDecoder(**mr_decoder_kwargs)
+    print(mr_decoder)
+    for i, imgs in enumerate(mr_decoder(voice_embeddings)):
+        print('MRDecoder Output {} shape:'.format(i), imgs.shape)
+
+    # Test V2F Decoder
+    v2f_decoder_kwargs = {
+        'input_channel': 512,
+        'channels': [1024, 512, 256, 128, 64],
+        'output_channel': 3,
+        'normalization': 'batch',
+        }
+    v2f_decoder = V2FDecoder(**v2f_decoder_kwargs)
+    print(v2f_decoder)
+    print('V2FDecoder Output shape:', v2f_decoder(voice_embeddings).shape)
+    print('V2F Sequential:')
+    print(v2f_decoder.model[5])
+    for name, param in v2f_decoder.model[5].named_parameters():
+        print(name)
+    #for module in v2f_decoder.model:
+    #    print(module)
+
+    v2f_decoder_kwargs = {
+        'input_channel': 512,
+        'cnn_channels': [1024, 512, 256, 128, 64],
+        'attn_channels': [0, 0, 128, 0, 32],
+        'output_channel': 3,
+        'seq_in_dim': 512,
+        'normalization': 'none',
+        }
+    attn_v2f_decoder = AttnV2FDecoder(**v2f_decoder_kwargs)
+    print(attn_v2f_decoder)
+    print('AttnV2FDecoder Output shape:', \
+        attn_v2f_decoder(voice_embeddings, seq_voice_embeddings).shape)
+    print('Return Attention Weights:',
+          attn_v2f_decoder.return_attn_weights().shape,
+          attn_v2f_decoder.return_attn_weights()[0][0][0:10])
+
+    dimLatentVector = 512
+    depthScale0 = 512
+
+    pgandecoder = PganDecoder(dimLatent=dimLatentVector,
+                          depthScale0=depthScale0,
+                          initBiasToZero=True,
+                          leakyReluLeak=0.2,
+                          normalization=True,
+                          generationActivation=None,
+                          dimOutput=3,
+                          equalizedlR=True,
+                          depthOtherScales=[512,512,512,256])
+
+    img = pgandecoder(voice_embeddings)
+    
diff --git a/mlops/serving/sf2f/models/fusers.py b/mlops/serving/sf2f/models/fusers.py
new file mode 100644
index 0000000..ecc8043
--- /dev/null
+++ b/mlops/serving/sf2f/models/fusers.py
@@ -0,0 +1,136 @@
+'''
+Implementations of different Fusers
+'''
+
+
+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+import numpy as np
+import math
+
+try:
+    from attention import Attention
+except:
+    from .attention import Attention
+
+
+class AttentionFuserV1(nn.Module):
+    def __init__(self,
+                 dimensions,
+                 dim_out,
+                 attention_type='general',
+                 linear_out_layer=True,
+                 ignore_tanh=False):
+        '''
+        1 Layer Attnetion Fuser
+        '''
+        nn.Module.__init__(self)
+        self.attention = Attention(
+            dimensions,
+            dim_out,
+            attention_type=attention_type,
+            linear_out_layer=linear_out_layer,
+            ignore_tanh=ignore_tanh)
+
+    def forward(self, embeddings):
+        '''
+        seq_in: tensor with shape [N, D, L]
+        '''
+        # Embeddings: [B, N, C]
+        embeddings, attention_weights = \
+            self.attention(embeddings, embeddings)
+        # [B, C, N]
+        embeddings = embeddings.transpose(1, 2)
+        #print('Transposed Shape:', embeddings.shape)
+        embeddings = F.avg_pool1d(
+            embeddings, embeddings.size()[2], stride=1)
+        embeddings = embeddings.view(embeddings.size()[0], -1, 1, 1)
+        #print('Attention Fuser Output Shape:', embeddings.shape)
+        return embeddings
+
+
+class AttentionFuserV2(nn.Module):
+    def __init__(self,
+                 dimensions,
+                 attention_type='general'):
+        '''
+        2-Layer Attention
+        '''
+        nn.Module.__init__(self)
+        self.attention_1 = Attention(
+            dimensions,
+            dimensions,
+            attention_type=attention_type,
+            linear_out_layer=True,
+            ignore_tanh=False)
+        self.attention_2 = Attention(
+            dimensions,
+            dimensions,
+            attention_type=attention_type,
+            linear_out_layer=True,
+            ignore_tanh=True)
+
+    def forward(self, embeddings):
+        '''
+        seq_in: tensor with shape [N, D, L]
+        '''
+        # Embeddings: [B, N, C]
+        embeddings, attention_weights = \
+            self.attention_1(embeddings, embeddings)
+        embeddings, attention_weights = \
+            self.attention_2(embeddings, embeddings)
+        # [B, C, N]
+        embeddings = embeddings.transpose(1, 2)
+        #print('Transposed Shape:', embeddings.shape)
+        embeddings = F.avg_pool1d(
+            embeddings, embeddings.size()[2], stride=1)
+        embeddings = embeddings.view(embeddings.size()[0], -1, 1, 1)
+        #print('Attention Fuser Output Shape:', embeddings.shape)
+        return embeddings
+
+
+class AttentionFuserV3(nn.Module):
+    def __init__(self,
+                 dimensions,
+                 attention_type='general'):
+        '''
+        2-Layer Attention with Residual Connection
+        '''
+        nn.Module.__init__(self)
+        self.attention_1 = Attention(
+            dimensions,
+            dimensions,
+            attention_type=attention_type,
+            linear_out_layer=True,
+            ignore_tanh=False)
+        self.attention_2 = Attention(
+            dimensions*2,
+            dimensions,
+            attention_type=attention_type,
+            linear_out_layer=True,
+            ignore_tanh=True)
+
+    def forward(self, input):
+        '''
+        seq_in: tensor with shape [N, D, L]
+        '''
+        # Embeddings: [B, N, C]
+        hidden, attention_weights = \
+            self.attention_1(input, input)
+        hidden = F.normalize(hidden, dim=2)
+        # Residual Connection
+        combined = torch.cat((hidden, input), dim=2)
+        # 2nd Attention
+        embeddings, attention_weights = \
+            self.attention_2(combined, combined)
+        # [B, C, N]
+        embeddings = embeddings.transpose(1, 2)
+        #print('Transposed Shape:', embeddings.shape)
+        embeddings = F.avg_pool1d(
+            embeddings, embeddings.size()[2], stride=1)
+        embeddings = embeddings.view(embeddings.size()[0], -1, 1, 1)
+        #print('Attention Fuser Output Shape:', embeddings.shape)
+        return embeddings
diff --git a/mlops/serving/sf2f/models/inception_resnet_v1.py b/mlops/serving/sf2f/models/inception_resnet_v1.py
new file mode 100644
index 0000000..f95a1f8
--- /dev/null
+++ b/mlops/serving/sf2f/models/inception_resnet_v1.py
@@ -0,0 +1,439 @@
+'''
+Reference: https://github.com/timesler/facenet-pytorch/ \
+    blob/master/models/inception_resnet_v1.py
+
+Both pretrained models were trained on 160x160 px images,
+so will perform best if applied to images resized to this shape.
+For best results, images should also be cropped to the face using
+MTCNN (see below).
+'''
+
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+import requests
+from requests.adapters import HTTPAdapter
+import os
+
+
+class BasicConv2d(nn.Module):
+
+    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
+        super().__init__()
+        self.conv = nn.Conv2d(
+            in_planes, out_planes,
+            kernel_size=kernel_size, stride=stride,
+            padding=padding, bias=False
+        ) # verify bias false
+        self.bn = nn.BatchNorm2d(
+            out_planes,
+            eps=0.001, # value found in tensorflow
+            momentum=0.1, # default pytorch value
+            affine=True
+        )
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Block35(nn.Module):
+
+    def __init__(self, scale=1.0):
+        super().__init__()
+
+        self.scale = scale
+
+        self.branch0 = BasicConv2d(256, 32, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(256, 32, kernel_size=1, stride=1),
+            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(256, 32, kernel_size=1, stride=1),
+            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.conv2d = nn.Conv2d(96, 256, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        out = self.relu(out)
+        return out
+
+
+class Block17(nn.Module):
+
+    def __init__(self, scale=1.0):
+        super().__init__()
+
+        self.scale = scale
+
+        self.branch0 = BasicConv2d(896, 128, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(896, 128, kernel_size=1, stride=1),
+            BasicConv2d(128, 128, kernel_size=(1,7), stride=1, padding=(0,3)),
+            BasicConv2d(128, 128, kernel_size=(7,1), stride=1, padding=(3,0))
+        )
+
+        self.conv2d = nn.Conv2d(256, 896, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        out = self.relu(out)
+        return out
+
+
+class Block8(nn.Module):
+
+    def __init__(self, scale=1.0, noReLU=False):
+        super().__init__()
+
+        self.scale = scale
+        self.noReLU = noReLU
+
+        self.branch0 = BasicConv2d(1792, 192, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1792, 192, kernel_size=1, stride=1),
+            BasicConv2d(192, 192, kernel_size=(1,3), stride=1, padding=(0,1)),
+            BasicConv2d(192, 192, kernel_size=(3,1), stride=1, padding=(1,0))
+        )
+
+        self.conv2d = nn.Conv2d(384, 1792, kernel_size=1, stride=1)
+        if not self.noReLU:
+            self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        if not self.noReLU:
+            out = self.relu(out)
+        return out
+
+
+class Mixed_6a(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+        self.branch0 = BasicConv2d(256, 384, kernel_size=3, stride=2)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(256, 192, kernel_size=1, stride=1),
+            BasicConv2d(192, 192, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(192, 256, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        return out
+
+
+class Mixed_7a(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+        self.branch0 = nn.Sequential(
+            BasicConv2d(896, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 384, kernel_size=3, stride=2)
+        )
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(896, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 256, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(896, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(256, 256, kernel_size=3, stride=2)
+        )
+
+        self.branch3 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class InceptionResnetV1(nn.Module):
+    """Inception Resnet V1 model with optional loading of pretrained weights.
+
+    Model parameters can be loaded based on pretraining on the VGGFace2 or CASIA-Webface
+    datasets. Pretrained state_dicts are automatically downloaded on model instantiation if
+    requested and cached in the torch cache. Subsequent instantiations use the cache rather than
+    redownloading.
+
+    Keyword Arguments:
+        pretrained {str} -- Optional pretraining dataset. Either 'vggface2' or 'casia-webface'.
+            (default: {None})
+        classify {bool} -- Whether the model should output classification probabilities or feature
+            embeddings. (default: {False})
+        num_classes {int} -- Number of output classes. If 'pretrained' is set and num_classes not
+            equal to that used for the pretrained model, the final linear layer will be randomly
+            initialized. (default: {None})
+        dropout_prob {float} -- Dropout probability. (default: {0.6})
+    """
+    def __init__(self,
+                 pretrained=None,
+                 classify=False,
+                 num_classes=None,
+                 dropout_prob=0.6,
+                 device=None,
+                 auto_input_resize=True,
+                 return_pooling=False):
+        super().__init__()
+
+        # Set simple attributes
+        self.pretrained = pretrained
+        self.classify = classify
+        self.num_classes = num_classes
+        self.auto_input_resize = auto_input_resize
+
+        if pretrained == 'vggface2':
+            tmp_classes = 8631
+        elif pretrained == 'casia-webface':
+            tmp_classes = 10575
+        elif pretrained is None and self.num_classes is None:
+            raise Exception('At least one of "pretrained" or "num_classes" must be specified')
+        else:
+            tmp_classes = self.num_classes
+
+        if self.auto_input_resize:
+            self.input_resize = nn.UpsamplingBilinear2d(size=(140, 140))
+        self.return_pooling = return_pooling
+        # Define layers
+        self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
+        self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
+        self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.maxpool_3a = nn.MaxPool2d(3, stride=2)
+        self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
+        self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
+        self.conv2d_4b = BasicConv2d(192, 256, kernel_size=3, stride=2)
+        self.repeat_1 = nn.Sequential(
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+        )
+        self.mixed_6a = Mixed_6a()
+        self.repeat_2 = nn.Sequential(
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+        )
+        self.mixed_7a = Mixed_7a()
+        self.repeat_3 = nn.Sequential(
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+        )
+        self.block8 = Block8(noReLU=True)
+        self.avgpool_1a = nn.AdaptiveAvgPool2d(1)
+        self.dropout = nn.Dropout(dropout_prob)
+        self.last_linear = nn.Linear(1792, 512, bias=False)
+        self.last_bn = nn.BatchNorm1d(512, eps=0.001, momentum=0.1, affine=True)
+        self.logits = nn.Linear(512, tmp_classes)
+
+        if pretrained is not None:
+            load_weights(self, pretrained)
+
+        if self.num_classes is not None:
+            self.logits = nn.Linear(512, self.num_classes)
+
+        self.device = torch.device('cpu')
+        if device is not None:
+            self.device = device
+            self.to(device)
+
+    def forward(self, x):
+        """
+        Calculate embeddings or probabilities given a batch of input image
+        tensors.
+
+        Arguments:
+            x {torch.tensor} -- Batch of image tensors representing faces.
+
+        Returns:
+            torch.tensor -- Batch of embeddings or softmax probabilities.
+        """
+        if self.auto_input_resize:
+            x = self.input_resize(x)
+        x = self.conv2d_1a(x)
+        x = self.conv2d_2a(x)
+        x = self.conv2d_2b(x)
+        x = self.maxpool_3a(x)
+        x = self.conv2d_3b(x)
+        x = self.conv2d_4a(x)
+        x = self.conv2d_4b(x)
+        x = self.repeat_1(x)
+        x = self.mixed_6a(x)
+        x = self.repeat_2(x)
+        x = self.mixed_7a(x)
+        x = self.repeat_3(x)
+        x = self.block8(x)
+        x = self.avgpool_1a(x)
+        if self.return_pooling:
+            pooled = x.view(x.shape[0], -1)
+            pooled = F.normalize(pooled, p=2, dim=1)
+            return pooled
+        x = self.dropout(x)
+        x = self.last_linear(x.view(x.shape[0], -1))
+        x = self.last_bn(x)
+        x = F.normalize(x, p=2, dim=1)
+        if self.classify:
+            x = self.logits(x)
+        return x
+
+    def get_hidden_states(self, x):
+        """
+        Calculate embeddings or probabilities given a batch of input image
+        tensors.
+
+        Arguments:
+            x {torch.tensor} -- Batch of image tensors representing faces.
+
+        Returns:
+            torch.tensor -- Batch of embeddings or softmax probabilities.
+        """
+        if self.auto_input_resize:
+            x = self.input_resize(x)
+        conv2d_1a = self.conv2d_1a(x)
+        conv2d_2a = self.conv2d_2a(conv2d_1a)
+        conv2d_2b = self.conv2d_2b(conv2d_2a)
+        maxpool_3a = self.maxpool_3a(conv2d_2b)
+        conv2d_3b = self.conv2d_3b(maxpool_3a)
+        conv2d_4a = self.conv2d_4a(conv2d_3b)
+        conv2d_4b = self.conv2d_4b(conv2d_4a)
+        repeat_1 = self.repeat_1(conv2d_4b)
+        mixed_6a = self.mixed_6a(repeat_1)
+        repeat_2 = self.repeat_2(mixed_6a)
+        mixed_7a = self.mixed_7a(repeat_2)
+        repeat_3 = self.repeat_3(mixed_7a)
+        block8 = self.block8(repeat_3)
+        avgpool_1a = self.avgpool_1a(block8)
+        dropout = self.dropout(avgpool_1a)
+        last_linear = self.last_linear(dropout.view(x.shape[0], -1))
+        last_bn = self.last_bn(last_linear)
+        x = F.normalize(last_bn, p=2, dim=1)
+        if self.classify:
+            x = self.logits(x)
+        out = [repeat_1, repeat_2, repeat_3, x]
+        return out
+
+
+def load_weights(mdl, name):
+    """Download pretrained state_dict and load into model.
+
+    Arguments:
+        mdl {torch.nn.Module} -- Pytorch model.
+        name {str} -- Name of dataset that was used to generate pretrained state_dict.
+
+    Raises:
+        ValueError: If 'pretrained' not equal to 'vggface2' or 'casia-webface'.
+    """
+    if name == 'vggface2':
+        features_path = \
+            'https://drive.google.com/uc?export=download&' + \
+                'id=1cWLH_hPns8kSfMz9kKl9PsG5aNV2VSMn'
+        logits_path = \
+            'https://drive.google.com/uc?export=download' + \
+                '&id=1mAie3nzZeno9UIzFXvmVZrDG3kwML46X'
+    elif name == 'casia-webface':
+        features_path = 'https://drive.google.com/uc?export=download&id=1LSHHee_IQj5W3vjBcRyVaALv4py1XaGy'
+        logits_path = 'https://drive.google.com/uc?export=download&id=1QrhPgn1bGlDxAil2uc07ctunCQoDnCzT'
+    else:
+        raise ValueError('Pretrained models only exist for "vggface2" and "casia-webface"')
+
+    model_dir = os.path.join(get_torch_home(), 'checkpoints')
+    os.makedirs(model_dir, exist_ok=True)
+
+    state_dict = {}
+    for i, path in enumerate([features_path, logits_path]):
+        cached_file = os.path.join(
+            model_dir, '{}_{}.pt'.format(name, path[-10:]))
+        #print('Cashed File Location:', cached_file)
+        if not os.path.exists(cached_file):
+            print('Downloading parameters ({}/2)'.format(i+1))
+            s = requests.Session()
+            s.mount('https://', HTTPAdapter(max_retries=10))
+            r = s.get(path, allow_redirects=True)
+            with open(cached_file, 'wb') as f:
+                f.write(r.content)
+        state_dict.update(torch.load(cached_file))
+
+    mdl.load_state_dict(state_dict)
+
+
+def get_torch_home():
+    torch_home = os.path.expanduser(
+        os.getenv(
+            'TORCH_HOME',
+            os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch')
+        )
+    )
+    return torch_home
+
+
+def fixed_image_standardization(image_tensor):
+    '''
+    This is the default preprocess function for this pretrained model
+    '''
+    processed_tensor = (image_tensor - 127.5) / 128.0
+    return processed_tensor
+
+
+if __name__ == '__main__':
+    model = InceptionResnetV1(pretrained='vggface2').eval()
+    print(model)
+    demo_imgs = torch.zeros(16, 3, 64, 64)
+    print('Output size 1:', model(demo_imgs).shape, model(demo_imgs)[0])
+
+    model_2 = InceptionResnetV1(
+        pretrained='vggface2',
+        auto_input_resize=False).eval()
+    demo_imgs = torch.zeros(16, 3, 140, 140)
+    print('Output size 2:', model(demo_imgs).shape, model_2(demo_imgs)[0])
diff --git a/mlops/serving/sf2f/models/layers.py b/mlops/serving/sf2f/models/layers.py
new file mode 100644
index 0000000..001e3eb
--- /dev/null
+++ b/mlops/serving/sf2f/models/layers.py
@@ -0,0 +1,257 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+## We use the batchnorm in pytorch 0.3.x to avoid unexpected errors
+## Otherwise, some in-place modification will bring some errors
+# from models.utils.batchnorm import BatchNorm1d, BatchNorm2d
+
+
+
+def get_normalization_2d(channels, normalization):
+    if normalization == 'instance':
+        return nn.InstanceNorm2d(channels)
+    elif normalization == 'batch':
+        return nn.BatchNorm2d(channels)
+    elif normalization == 'none':
+        return None
+    else:
+        raise ValueError('Unrecognized normalization type "%s"' %
+                         normalization)
+
+
+def get_activation(name):
+    kwargs = {}
+    if name.lower().startswith('leakyrelu'):
+        if '-' in name:
+            slope = float(name.split('-')[1])
+            kwargs = {'negative_slope': slope}
+            name = 'leakyrelu'
+    activations = {
+        'relu': nn.ReLU,
+        'leakyrelu': nn.LeakyReLU,
+        'none': None,
+    }
+    if name.lower() not in activations:
+        raise ValueError('Invalid activation "%s"' % name)
+    if activations[name.lower()] is not None:
+        return activations[name.lower()](**kwargs)
+
+
+def weights_init(m):
+    raise NotImplementedError
+    classname = m.__class__.__name__
+    if classname.find('Conv') != -1:
+        m.weight.data.normal_(0.0, 0.02)
+        m.bias.data.fill_(0)
+    elif classname.find('BatchNorm2d') != -1:
+        m.weight.data.normal_(1.0, 0.02)
+        m.bias.data.fill_(0)
+    elif classname.find('BatchNorm1d') != -1:
+        m.weight.data.normal_(1.0, 0.02)
+        m.bias.data.fill_(0)
+    elif classname.find('Linear') != -1:
+        m.weight.data.normal_(0.0, 0.02)
+        m.bias.data.fill_(0)
+
+
+class Flatten(nn.Module):
+    def forward(self, x):
+        return x.view(x.size(0), -1)
+
+    def __repr__(self):
+        return 'Flatten()'
+
+
+class Unflatten(nn.Module):
+    def __init__(self, size):
+        super(Unflatten, self).__init__()
+        self.size = size
+
+    def forward(self, x):
+        return x.view(*self.size)
+
+    def __repr__(self):
+        size_str = ', '.join('%d' % d for d in self.size)
+        return 'Unflatten(%s)' % size_str
+
+
+class GlobalAvgPool(nn.Module):
+    def __init__(self, pooling="avg"):
+        super(GlobalAvgPool, self).__init__()
+        self.pooling = pooling
+    def forward(self, x):
+        N, C = x.size(0), x.size(1)
+        if self.pooling == "avg":
+            return x.view(N, C, -1).mean(dim=2)
+        elif self.pooling == "max":
+            return x.view(N, C, -1).max(dim=2)[0]
+        else:
+            assert False, "Unrecognized pooling: {}".format(self.pooling)
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, channels, normalization='batch', activation='relu',
+                 padding='same', kernel_size=3, init='default'):
+        super(ResidualBlock, self).__init__()
+        K = kernel_size
+        P = _get_padding(K, "same")
+        C = channels
+        self.padding = P
+        layers = [
+            get_normalization_2d(C, normalization),
+            get_activation(activation),
+            nn.Conv2d(C, C, kernel_size=K, padding=P),
+            get_normalization_2d(C, normalization),
+            get_activation(activation),
+            nn.Conv2d(C, C, kernel_size=K, padding=P),
+        ]
+        layers = [layer for layer in layers if layer is not None]
+        # for layer in layers:
+        #     _init_conv(layer, method=init)
+        self.net = nn.Sequential(*layers)
+
+    def forward(self, x):
+        P = self.padding
+        shortcut = x
+        y = self.net(x)
+        return shortcut + self.net(x)
+
+
+def _get_padding(K, mode):
+    """ Helper method to compute padding size """
+    if mode == 'valid':
+        return 0
+    elif mode == 'same':
+        assert K % 2 == 1, 'Invalid kernel size %d for "same" padding' % K
+        return (K - 1) // 2
+
+
+def build_cnn(arch, normalization='batch', activation='relu', padding='same',
+              pooling='max', init='default', non_linear_activated=False):
+    """
+    Build a CNN from an architecture string, which is a list of layer
+    specification strings. The overall architecture can be given as a list or as
+    a comma-separated string.
+
+    All convolutions *except for the first* are preceeded by normalization and
+    nonlinearity.
+
+    non_linear_activated: whether to add [non-linear activation / normalization]
+        Default is [False] (do not add these layers)
+
+    All other layers support the following:
+    - IX: Indicates that the number of input channels to the network is X.
+          Can only be used at the first layer; if not present then we assume
+          3 input channels.
+    - CK-X: KxK convolution with X output channels
+    - CK-X-S: KxK convolution with X output channels and stride S
+    - R: Residual block keeping the same number of channels
+    - UX: Nearest-neighbor upsampling with factor X
+    - PX: Spatial pooling with factor X
+    - FC-X-Y: Flatten followed by fully-connected layer
+
+    Returns a tuple of:
+    - cnn: An nn.Sequential
+    - channels: Number of output channels
+    """
+    if isinstance(arch, str):
+        arch = arch.split(',')
+    cur_C = 3
+    if len(arch) > 0 and arch[0][0] == 'I':
+        cur_C = int(arch[0][1:])
+        arch = arch[1:]
+
+    first_conv = True
+    flat = False
+    layers = []
+    for i, s in enumerate(arch):
+        if s[0] == 'C':
+            if not first_conv:
+                layers.append(get_normalization_2d(cur_C, normalization))
+                layers.append(get_activation(activation))
+            first_conv = False
+            vals = [int(i) for i in s[1:].split('-')]
+            P = None
+            if len(vals) == 2:
+                K, next_C = vals
+                stride = 1
+            elif len(vals) == 3:
+                K, next_C, stride = vals
+            elif len(vals) == 4:
+                # Add padding
+                K, next_C, stride, P = vals
+            # K, next_C = (int(i) for i in s[1:].split('-'))
+            if P is None:
+                P = _get_padding(K, padding)
+            conv = nn.Conv2d(cur_C, next_C, kernel_size=K,
+                             padding=P, stride=stride)
+            layers.append(conv)
+            # _init_conv(layers[-1], init)
+            cur_C = next_C
+        elif s[0] == 'R':
+            norm = 'none' if first_conv else normalization
+            res = ResidualBlock(cur_C, normalization=norm, activation=activation,
+                                padding=padding, init=init)
+            layers.append(res)
+            first_conv = False
+        elif s[0] == 'U':
+            factor = int(s[1:])
+            layers.append(nn.Upsample(scale_factor=factor, mode='nearest'))
+        elif s[0] == 'P':
+            factor = int(s[1:])
+            if pooling == 'max':
+                pool = nn.MaxPool2d(kernel_size=factor, stride=factor)
+            elif pooling == 'avg':
+                pool = nn.AvgPool2d(kernel_size=factor, stride=factor)
+            layers.append(pool)
+        elif s[:2] == 'FC':
+            _, Din, Dout = s.split('-')
+            Din, Dout = int(Din), int(Dout)
+            if not flat:
+                layers.append(Flatten())
+            flat = True
+            layers.append(nn.Linear(Din, Dout))
+            if i + 1 < len(arch):
+                layers.append(get_activation(activation))
+            cur_C = Dout
+        else:
+            raise ValueError('Invalid layer "%s"' % s)
+    if non_linear_activated:
+        layers.append(get_normalization_2d(cur_C, normalization))
+        layers.append(get_activation(activation))
+    layers = [layer for layer in layers if layer is not None]
+    #for layer in layers:
+    #    print(layer)
+
+    model = nn.Sequential(*layers)
+    return model, cur_C
+
+
+def build_mlp(dim_list, activation='relu', batch_norm='none',
+              dropout=0, final_nonlinearity=True,
+              start_nonlinearity=False,):
+    layers = []
+    for i in range(len(dim_list) - 1):
+        dim_in, dim_out = dim_list[i], dim_list[i + 1]
+        if start_nonlinearity and i == 0:
+            if batch_norm == 'batch':
+                layers.append(nn.BatchNorm1d(dim_in))
+            # elif batch_norm == 'instance':
+            #     layers.append(InstanceNorm1d(dim_in))
+            if activation == 'relu':
+                layers.append(nn.ReLU())
+            elif activation == 'leakyrelu':
+                layers.append(nn.LeakyReLU())
+        layers.append(nn.Linear(dim_in, dim_out))
+        final_layer = (i == len(dim_list) - 2)
+        if not final_layer or final_nonlinearity:
+            if batch_norm == 'batch':
+                layers.append(nn.BatchNorm1d(dim_out))
+            if activation == 'relu':
+                layers.append(nn.ReLU())
+            elif activation == 'leakyrelu':
+                layers.append(nn.LeakyReLU())
+        if dropout > 0:
+            layers.append(nn.Dropout(p=dropout))
+    model = nn.Sequential(*layers)
+    return model
diff --git a/mlops/serving/sf2f/models/model_collection.py b/mlops/serving/sf2f/models/model_collection.py
new file mode 100644
index 0000000..bf3e4ba
--- /dev/null
+++ b/mlops/serving/sf2f/models/model_collection.py
@@ -0,0 +1,38 @@
+'''
+A collection of all the models, for the convenience of internal calling
+'''
+
+
+# Lower level encoder decoders
+try:
+    from .voice_encoders import V2F1DCNN, TransEncoder
+except:
+    from voice_encoders import V2F1DCNN, TransEncoder
+
+try:
+    from .face_decoders import V2FDecoder, AttnV2FDecoder, MRDecoder, \
+        FaceGanDecoder, FaceGanDecoder_v2
+except:
+    from face_decoders import V2FDecoder, AttnV2FDecoder, MRDecoder, \
+        FaceGanDecoder, FaceGanDecoder_v2
+
+try:
+    from .attention import Attention
+except:
+    from .attention import Attention
+
+
+class ModelCollection():
+    def __init__(self):
+        self.V2F1DCNN = V2F1DCNN
+        self.V2FDecoder = V2FDecoder
+        self.AttnV2FDecoder = AttnV2FDecoder
+        self.TransEncoder = TransEncoder
+        self.MRDecoder = MRDecoder
+        self.FaceGanDecoder = FaceGanDecoder
+        self.FaceGanDecoder_v2 = FaceGanDecoder_v2
+        # self.PganDecoder = PganDecoder
+        self.Attention = Attention
+
+
+model_collection = ModelCollection()
diff --git a/mlops/serving/sf2f/models/model_setup.py b/mlops/serving/sf2f/models/model_setup.py
new file mode 100644
index 0000000..94ef21a
--- /dev/null
+++ b/mlops/serving/sf2f/models/model_setup.py
@@ -0,0 +1,99 @@
+import torch
+import models
+from copy import deepcopy
+from utils import update_values, load_model_state
+import glog as log
+
+def build_model(opts, image_size, checkpoint_start_from=None):
+    if checkpoint_start_from is not None:
+        log.info("Load checkpoint as initialization: {}".format(
+            checkpoint_start_from))
+        checkpoint = torch.load(checkpoint_start_from)
+        # kwarg aka keyword arguments
+        #kwargs = checkpoint['model_kwargs']
+        kwargs = deepcopy(opts["options"])
+        kwargs["image_size"] = image_size
+        model = getattr(models, opts["arch"])(**kwargs)
+        raw_state_dict = checkpoint['model_state']
+        state_dict = {}
+        for k, v in raw_state_dict.items():
+            if k.startswith('module.'):
+                k = k[7:]
+            state_dict[k] = v
+        #print("state_dict: ", state_dict.keys())
+        model = load_model_state(model, state_dict, strict=False)
+    else:
+        kwargs = deepcopy(opts["options"])
+        kwargs["image_size"] = image_size
+        model = getattr(models, opts["arch"])(**kwargs)
+    return model, kwargs
+
+def build_ac_discriminator(opts):
+    d_kwargs = deepcopy(opts["generic"])
+    discriminator = []
+    if "identity" in opts.keys():
+        d_kwargs = update_values(opts["identity"], d_kwargs)
+        dis = models.AcDiscriminator(**d_kwargs)
+        discriminator.append(dis)
+    if "identity_low" in opts.keys():
+        d_kwargs = update_values(opts["identity_low"], d_kwargs)
+        dis_low = models.AcDiscriminator(**d_kwargs)
+        discriminator.append(dis_low)
+    if "identity_mid" in opts.keys():
+        d_kwargs = update_values(opts["identity_mid"], d_kwargs)
+        dis_mid = models.AcDiscriminator(**d_kwargs)
+        discriminator.append(dis_mid)
+    if "identity_high" in opts.keys():
+        d_kwargs = update_values(opts["identity_high"], d_kwargs)
+        dis_high = models.AcDiscriminator(**d_kwargs)
+        discriminator.append(dis_high)
+    return discriminator, d_kwargs
+
+def build_img_discriminator(opts):
+    discriminator = []
+    if opts.get("pgan_dis") is not None:
+        d_kwargs = deepcopy(opts["pgan_dis"])
+        pgan_dis = models.PGAN_Discriminator(**d_kwargs)
+        discriminator.append(pgan_dis)
+    else:
+        d_kwargs = deepcopy(opts["generic"])
+        if "image" in opts.keys():
+            d_kwargs = update_values(opts["image"], d_kwargs)
+            dis = models.PatchDiscriminator(**d_kwargs)
+            discriminator.append(dis)
+        else:
+            if "image_low" in opts.keys():
+                d_kwargs = update_values(opts["image_low"], d_kwargs)
+                discriminator_low = models.PatchDiscriminator(**d_kwargs)
+                discriminator.append(discriminator_low)
+            if "image_mid" in opts.keys():
+                d_kwargs = update_values(opts["image_mid"], d_kwargs)
+                discriminator_mid = models.PatchDiscriminator(**d_kwargs)
+                discriminator.append(discriminator_mid)
+            if "image_high" in opts.keys():
+                d_kwargs = update_values(opts["image_high"], d_kwargs)
+                discriminator_high = models.PatchDiscriminator(**d_kwargs)
+                discriminator.append(discriminator_high)
+    return discriminator, d_kwargs
+
+def build_cond_discriminator(opts):
+    discriminator = []
+    d_kwargs = deepcopy(opts["generic"])
+    if "condition" in opts.keys():
+        d_kwargs = update_values(opts["condition"], d_kwargs)
+        dis = models.CondPatchDiscriminator(**d_kwargs)
+        discriminator.append(dis)
+    else:
+        if "cond_low" in opts.keys():
+            d_kwargs = update_values(opts["cond_low"], d_kwargs)
+            discriminator_low = models.CondPatchDiscriminator(**d_kwargs)
+            discriminator.append(discriminator_low)
+        if "cond_mid" in opts.keys():
+            d_kwargs = update_values(opts["cond_mid"], d_kwargs)
+            discriminator_mid = models.CondPatchDiscriminator(**d_kwargs)
+            discriminator.append(discriminator_mid)
+        if "cond_high" in opts.keys():
+            d_kwargs = update_values(opts["cond_high"], d_kwargs)
+            discriminator_high = models.CondPatchDiscriminator(**d_kwargs)
+            discriminator.append(discriminator_high)
+    return discriminator, d_kwargs
diff --git a/mlops/serving/sf2f/models/networks.py b/mlops/serving/sf2f/models/networks.py
new file mode 100644
index 0000000..28f3333
--- /dev/null
+++ b/mlops/serving/sf2f/models/networks.py
@@ -0,0 +1,203 @@
+import torch
+import torch.nn as nn
+import functools
+from torch.autograd import Variable
+import numpy as np
+import torch.nn.functional as F
+import math
+from numpy import prod
+
+
+
+class VGGLoss(nn.Module):
+    def __init__(self):
+        super(VGGLoss, self).__init__()
+        self.vgg = Vgg19().cuda()
+        self.criterion = nn.L1Loss()
+        self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]
+
+    def forward(self, x, y):
+        x_vgg = self.vgg(x)
+        # we do not store gradients for y
+        with torch.no_grad():
+            y_vgg = self.vgg(y)
+        loss = 0
+        for i in range(len(x_vgg)):
+            loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i])
+        return loss
+
+from torchvision import models
+class Vgg19(torch.nn.Module):
+    def __init__(self, requires_grad=False, ignore_last=False):
+        super(Vgg19, self).__init__()
+        vgg_pretrained_features = models.vgg19(pretrained=True).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        if ignore_last:
+            self.slice5 = None
+        else:
+            self.slice5 = torch.nn.Sequential()
+        for x in range(2):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(2, 7):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(7, 12):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(12, 21):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        if self.slice5 is not None:
+            for x in range(21, 30):
+                self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h_relu1 = self.slice1(X)
+        h_relu2 = self.slice2(h_relu1)
+        h_relu3 = self.slice3(h_relu2)
+        h_relu4 = self.slice4(h_relu3)
+        if self.slice5 is None or h_relu4.size(3) < 3:
+            return [h_relu1, h_relu2, h_relu3, h_relu4,]
+        h_relu5 = self.slice5(h_relu4)
+        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
+        return out
+
+
+class GLU(nn.Module):
+    def __init__(self):
+        super(GLU, self).__init__()
+
+    def forward(self, x):
+        nc = x.size(1)
+        assert nc % 2 == 0, 'channels dont divide 2!'
+        nc = int(nc/2)
+        return x[:, :nc] * F.sigmoid(x[:, nc:])
+
+
+def upBlock(in_planes, out_planes):
+    block = nn.Sequential(
+        nn.Upsample(scale_factor=2, mode='nearest'),
+        nn.Conv2d(in_planes, out_planes * 2, kernel_size=3,
+           stride=1, padding=1, bias=False),
+        nn.BatchNorm2d(out_planes * 2),
+        GLU())
+    return block
+
+# The defination of Progressive GAN
+
+class NormalizationLayer(nn.Module):
+
+    def __init__(self):
+        super(NormalizationLayer, self).__init__()
+
+    def forward(self, x, epsilon=1e-8):
+        return x * (((x**2).mean(dim=1, keepdim=True) + epsilon).rsqrt())
+
+
+def Upscale2d(x, factor=2):
+    assert isinstance(factor, int) and factor >= 1
+    if factor == 1:
+        return x
+    s = x.size()
+    x = x.view(-1, s[1], s[2], 1, s[3], 1)
+    x = x.expand(-1, s[1], s[2], factor, s[3], factor)
+    x = x.contiguous().view(-1, s[1], s[2] * factor, s[3] * factor)
+    return x
+
+
+def getLayerNormalizationFactor(x):
+    r"""
+    Get He's constant for the given layer
+    https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
+    """
+    size = x.weight.size()
+    fan_in = prod(size[1:])
+
+    return math.sqrt(2.0 / fan_in)
+
+
+class ConstrainedLayer(nn.Module):
+    r"""
+    A handy refactor that allows the user to:
+    - initialize one layer's bias to zero
+    - apply He's initialization at runtime
+    """
+
+    def __init__(self,
+                 module,
+                 equalized=True,
+                 lrMul=1.0,
+                 initBiasToZero=True):
+        r"""
+        equalized (bool): if true, the layer's weight should evolve within
+                         the range (-1, 1)
+        initBiasToZero (bool): if true, bias will be initialized to zero
+        """
+
+        super(ConstrainedLayer, self).__init__()
+
+        self.module = module
+        self.equalized = equalized
+
+        if initBiasToZero:
+            self.module.bias.data.fill_(0)
+        if self.equalized:
+            self.module.weight.data.normal_(0, 1)
+            self.module.weight.data /= lrMul
+            self.weight = getLayerNormalizationFactor(self.module) * lrMul
+
+    def forward(self, x):
+
+        x = self.module(x)
+        if self.equalized:
+            x *= self.weight
+        return x
+
+
+class EqualizedConv2d(ConstrainedLayer):
+
+    def __init__(self,
+                 nChannelsPrevious,
+                 nChannels,
+                 kernelSize,
+                 padding=0,
+                 bias=True,
+                 **kwargs):
+        r"""
+        A nn.Conv2d module with specific constraints
+        Args:
+            nChannelsPrevious (int): number of channels in the previous layer
+            nChannels (int): number of channels of the current layer
+            kernelSize (int): size of the convolutional kernel
+            padding (int): convolution's padding
+            bias (bool): with bias ?
+        """
+
+        ConstrainedLayer.__init__(self,
+                                  nn.Conv2d(nChannelsPrevious, nChannels,
+                                            kernelSize, padding=padding,
+                                            bias=bias),
+                                  **kwargs)
+
+
+class EqualizedLinear(ConstrainedLayer):
+
+    def __init__(self,
+                 nChannelsPrevious,
+                 nChannels,
+                 bias=True,
+                 **kwargs):
+        r"""
+        A nn.Linear module with specific constraints
+        Args:
+            nChannelsPrevious (int): number of channels in the previous layer
+            nChannels (int): number of channels of the current layer
+            bias (bool): with bias ?
+        """
+
+        ConstrainedLayer.__init__(self,
+                                  nn.Linear(nChannelsPrevious, nChannels,
+                                  bias=bias), **kwargs)
diff --git a/mlops/serving/sf2f/models/perceptual.py b/mlops/serving/sf2f/models/perceptual.py
new file mode 100644
index 0000000..c22015f
--- /dev/null
+++ b/mlops/serving/sf2f/models/perceptual.py
@@ -0,0 +1,111 @@
+import torch
+import torch.nn as nn
+import functools
+from torch.autograd import Variable
+import numpy as np
+from torchvision import models
+try:
+    from .inception_resnet_v1 import InceptionResnetV1
+except:
+    from inception_resnet_v1 import InceptionResnetV1
+
+
+class FaceNetLoss(nn.Module):
+    def __init__(self, cos_loss_weight=0.0):
+        super(FaceNetLoss, self).__init__()
+        self.facenet = InceptionResnetV1(pretrained='vggface2').eval().cuda()
+        self.criterion = nn.L1Loss()
+        self.weights = [1.0/16, 1.0/8, 1.0/4, 1.0]
+        self.cos_loss_weight = cos_loss_weight
+        if self.cos_loss_weight > 0.0:
+            self.cos_emb_loss = torch.nn.CosineEmbeddingLoss()
+            self.cos_target = torch.ones((1,), dtype=torch.int8).cuda()
+
+    def forward(self, x, y):
+        x_facenet = self.facenet.get_hidden_states(x)
+        # we do not store gradients for y
+        with torch.no_grad():
+            y_facenet = self.facenet.get_hidden_states(y)
+        loss = 0
+        for i in range(len(x_facenet)):
+            #print(x_facenet[i].shape)
+            #print(y_facenet[i].shape)
+            loss += self.weights[i] * \
+                self.criterion(x_facenet[i], y_facenet[i])
+        if self.cos_loss_weight > 0.0:
+            loss += self.cos_loss_weight * \
+                self.cos_emb_loss(
+                    x_facenet[-1],
+                    y_facenet[-1],
+                    self.cos_target)
+        return loss
+
+
+class VGGLoss(nn.Module):
+    def __init__(self):
+        super(VGGLoss, self).__init__()
+        self.vgg = Vgg19().cuda()
+        self.criterion = nn.L1Loss()
+        self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]
+
+    def forward(self, x, y):
+        x_vgg = self.vgg(x)
+        # we do not store gradients for y
+        with torch.no_grad():
+            y_vgg = self.vgg(y)
+        loss = 0
+        for i in range(len(x_vgg)):
+            loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i])
+        return loss
+
+
+class Vgg19(torch.nn.Module):
+    def __init__(self, requires_grad=False, ignore_last=False):
+        super(Vgg19, self).__init__()
+        vgg_pretrained_features = models.vgg19(pretrained=True).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        if ignore_last:
+            self.slice5 = None
+        else:
+            self.slice5 = torch.nn.Sequential()
+        for x in range(2):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(2, 7):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(7, 12):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(12, 21):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        if self.slice5 is not None:
+            for x in range(21, 30):
+                self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h_relu1 = self.slice1(X)
+        h_relu2 = self.slice2(h_relu1)
+        h_relu3 = self.slice3(h_relu2)
+        h_relu4 = self.slice4(h_relu3)
+        if self.slice5 is None or h_relu4.size(3) < 3:
+            return [h_relu1, h_relu2, h_relu3, h_relu4,]
+        h_relu5 = self.slice5(h_relu4)
+        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
+        return out
+
+
+if __name__ == '__main__':
+    demo_imgs = torch.ones((4, 3, 224, 224))
+    facenet = InceptionResnetV1(pretrained='vggface2').eval()
+    facenet_out = facenet.get_hidden_states(demo_imgs)
+    for i, feature in enumerate(facenet_out):
+        print('FaceNet Feature Shape {}:'.format(i), feature.shape)
+
+    vgg_19 = Vgg19()
+    vgg_out = vgg_19(demo_imgs)
+    for i, feature in enumerate(vgg_out):
+        print('VGG Feature Shape {}:'.format(i), feature.shape)
diff --git a/mlops/serving/sf2f/models/voice_encoders.py b/mlops/serving/sf2f/models/voice_encoders.py
new file mode 100644
index 0000000..f26acfb
--- /dev/null
+++ b/mlops/serving/sf2f/models/voice_encoders.py
@@ -0,0 +1,474 @@
+'''
+Implementations of different Voice Encoders
+'''
+
+
+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+import numpy as np
+import math
+
+try:
+    from fusers import AttentionFuserV1, AttentionFuserV2, AttentionFuserV3
+except:
+    from .fusers import AttentionFuserV1, AttentionFuserV2, AttentionFuserV3
+
+
+class EncoderModelCollection():
+    def __init__(self):
+        self.AttentionFuserV1 = AttentionFuserV1
+        self.AttentionFuserV2 = AttentionFuserV2
+        self.AttentionFuserV3 = AttentionFuserV3
+
+encoder_model_collection = EncoderModelCollection()
+
+
+class TransposeLayer(nn.Module):
+    def __init__(self, dim_1, dim_2):
+        '''
+        Transpose Layer
+        '''
+        nn.Module.__init__(self)
+        self.dim_1 = dim_1
+        self.dim_2 = dim_2
+
+    def forward(self, x):
+        '''
+        seq_in: tensor with shape [N, D, L]
+        '''
+        x = x.transpose(self.dim_1, self.dim_2)
+        return x
+
+
+class CNN1DBlock(nn.Module):
+    def __init__(self,
+                 channel_in,
+                 channel_out,
+                 kernel=3,
+                 stride=2,
+                 padding=1):
+        '''
+        1D CNN block
+        '''
+        nn.Module.__init__(self)
+        self.layers = []
+        self.layers.append(
+            nn.Conv1d(channel_in,
+                      channel_out,
+                      kernel,
+                      stride,
+                      padding,
+                      bias=False))
+        self.layers.append(
+            nn.BatchNorm1d(channel_out, affine=True))
+        self.layers.append(
+            nn.ReLU(inplace=True))
+        self.model = nn.Sequential(*self.layers)
+
+    def forward(self, seq_in):
+        '''
+        seq_in: tensor with shape [N, D, L]
+        '''
+        seq_out = self.model(seq_in)
+        return seq_out
+
+
+class Inception1DBlock(nn.Module):
+    def __init__(self,
+                 channel_in,
+                 channel_k2,
+                 channel_k3,
+                 channel_k5,
+                 channel_k7):
+        '''
+        Basic building block of 1D Inception Encoder
+        '''
+        nn.Module.__init__(self)
+        self.conv_k2 = None
+        self.conv_k3 = None
+        self.conv_k5 = None
+
+        if channel_k2 > 0:
+            self.conv_k2 = CNN1DBlock(
+                channel_in,
+                channel_k2,
+                2, 2, 1)
+        if channel_k3 > 0:
+            self.conv_k3 = CNN1DBlock(
+                channel_in,
+                channel_k3,
+                3, 2, 1)
+        if channel_k5 > 0:
+            self.conv_k5 = CNN1DBlock(
+                channel_in,
+                channel_k3,
+                5, 2, 2)
+        if channel_k7 > 0:
+            self.conv_k7 = CNN1DBlock(
+                channel_in,
+                channel_k3,
+                7, 2, 3)
+
+    def forward(self, input):
+        output = []
+        if self.conv_k2 is not None:
+            c2_out = self.conv_k2(input)
+            output.append(c2_out)
+        if self.conv_k3 is not None:
+            c3_out = self.conv_k3(input)
+            output.append(c3_out)
+        if self.conv_k5 is not None:
+            c5_out = self.conv_k5(input)
+            output.append(c5_out)
+        if self.conv_k7 is not None:
+            c7_out = self.conv_k7(input)
+            output.append(c7_out)
+        #print(c2_out.shape, c3_out.shape, c5_out.shape, c7_out.shape)
+        if output[0].shape[-1] > output[1].shape[-1]:
+            output[0] = output[0][:, :, 0:-1]
+        output = torch.cat(output, 1)
+        #print(output.shape)
+        return output
+
+
+class PositionalEncoding(nn.Module):
+
+    def __init__(self, d_model, dropout=0.1, max_len=10000):
+        super(PositionalEncoding, self).__init__()
+        self.dropout = nn.Dropout(p=dropout)
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * \
+            (-math.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0).transpose(0, 1)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        x = x + self.pe[:x.size(0), :]
+        return self.dropout(x)
+
+
+class PosEmbLayer(nn.Module):
+    def __init__(self, max_length=2000, pos_embedding_dim=5):
+        '''
+        Postional embedding block
+        '''
+        nn.Module.__init__(self)
+
+        self.max_length = max_length
+        self.pos_embedding_dim = pos_embedding_dim
+        # Position Embedding
+        # Each position index in range (0, 2 * max_length - 1) have its own embedding
+        self.pos_embedding = nn.Embedding(
+            max_length, pos_embedding_dim, padding_idx=0)
+
+    def forward(self, seq_in):
+        '''
+        seq_in: tensor with shape [N, L, D]
+        '''
+        N, L, D = seq_in.shape
+        # (L, )
+        pos_idx = torch.tensor(np.arange(0, L))
+        # (1, L)
+        pos_idx = pos_idx.unsqueeze(0)
+        # (N, L)
+        pos_idx = pos_idx.repeat(N, 1).cuda()
+        # (N, L, D_pos)
+        pos_emb = self.pos_embedding(pos_idx).type(seq_in.type())
+        #pos_emb = torch.transpose(pos_emb, 1, 2)
+        seq_out = torch.cat([seq_in, pos_emb], 2)
+        # (N, L, D + D_pos)
+        return seq_out
+
+
+class TransEncoder(nn.Module):
+    def __init__(self,
+                 input_channel=40,
+                 cnn_channels=[512, ],
+                 transformer_dim=512,
+                 transformer_depth=3,
+                 return_seq=True,
+                 sin_pos_encoding=False,
+                 pos_embedding_dim=8,
+                 add_noise=False,
+                 normalize_embedding=False):
+        super(TransEncoder, self).__init__()
+        self.layers = []
+
+        if pos_embedding_dim > 0:
+            # Postional Embedding Layer
+            # (N, L, D)
+            self.layers.append(TransposeLayer(1, 2))
+            # (N, D, L)
+            self.layers.append(
+                PosEmbLayer(2000, pos_embedding_dim))
+            # (N, L, D)
+            self.layers.append(TransposeLayer(1, 2))
+
+        # CNN Layer for dimension conversion
+        self.layers.append(
+            CNN1DBlock(input_channel+pos_embedding_dim, cnn_channels[0]))
+        for cnn_layer_id in range(1, len(cnn_channels)):
+            self.layers.append(CNN1DBlock(
+                cnn_channels[cnn_layer_id - 1],
+                cnn_channels[cnn_layer_id]))
+
+        self.layers.append(TransposeLayer(1, 2))
+        # Sin Positional Encoding
+        if sin_pos_encoding:
+            self.layers.append(PositionalEncoding(transformer_dim))
+
+        # Transformer
+        trans_layer = nn.TransformerEncoderLayer(
+            transformer_dim,
+            8,
+            transformer_dim)
+        transformer_encoder = nn.TransformerEncoder(
+            trans_layer, transformer_depth)
+        self.layers.append(transformer_encoder)
+        self.layers.append(TransposeLayer(1, 2))
+
+        self.model = nn.Sequential(*self.layers)
+        self.add_noise = add_noise
+        self.normalize_embedding = normalize_embedding
+        self.return_seq = return_seq
+
+    def forward(self, seq_in):
+        seq_out = self.model(seq_in)
+        #for layer in self.layers:
+        #    print(layer)
+        #    x = layer(x)
+        #    print(x.shape)
+        embeddings = F.avg_pool1d(seq_out, seq_out.size()[2], stride=1)
+        embeddings = embeddings.view(embeddings.size()[0], -1, 1, 1)
+
+        if self.normalize_embedding:
+            embeddings = F.normalize(embeddings)
+        if self.add_noise:
+            noise = 0.05 * torch.randn(
+                embeddings.shape[0], embeddings.shape[1], 1, 1)
+            noise = noise.type(embeddings.type())
+            embeddings = embeddings + noise
+            if self.normalize_embedding:
+                embeddings = F.normalize(embeddings)
+
+        if self.return_seq:
+            return embeddings, seq_out
+        else:
+            return embeddings
+
+
+class V2F1DCNN(nn.Module):
+    def __init__(self,
+                 input_channel,
+                 channels,
+                 output_channel,
+                 add_noise=False,
+                 normalize_embedding=False,
+                 return_seq=False,
+                 inception_mode=False,
+                 segments_fusion=False,
+                 normalize_fusion=False,
+                 fuser_arch='Attention',
+                 fuser_kwargs=None):
+        super(V2F1DCNN, self).__init__()
+        if inception_mode:
+            self.model = nn.Sequential(
+                Inception1DBlock(
+                    channel_in=input_channel,
+                    channel_k2=channels[0]//4,
+                    channel_k3=channels[0]//4,
+                    channel_k5=channels[0]//4,
+                    channel_k7=channels[0]//4),
+                Inception1DBlock(
+                    channel_in=channels[0],
+                    channel_k2=channels[1]//4,
+                    channel_k3=channels[1]//4,
+                    channel_k5=channels[1]//4,
+                    channel_k7=channels[1]//4),
+                Inception1DBlock(
+                    channel_in=channels[1],
+                    channel_k2=channels[2]//4,
+                    channel_k3=channels[2]//4,
+                    channel_k5=channels[2]//4,
+                    channel_k7=channels[2]//4),
+                Inception1DBlock(
+                    channel_in=channels[2],
+                    channel_k2=channels[3]//4,
+                    channel_k3=channels[3]//4,
+                    channel_k5=channels[3]//4,
+                    channel_k7=channels[3]//4),
+                nn.Conv1d(channels[3], output_channel, 3, 2, 1, bias=True),
+            )
+        else:
+            self.model = nn.Sequential(
+                nn.Conv1d(input_channel, channels[0], 3, 2, 1, bias=False),
+                nn.BatchNorm1d(channels[0], affine=True),
+                nn.ReLU(inplace=True),
+                nn.Conv1d(channels[0], channels[1], 3, 2, 1, bias=False),
+                nn.BatchNorm1d(channels[1], affine=True),
+                nn.ReLU(inplace=True),
+                nn.Conv1d(channels[1], channels[2], 3, 2, 1, bias=False),
+                nn.BatchNorm1d(channels[2], affine=True),
+                nn.ReLU(inplace=True),
+                nn.Conv1d(channels[2], channels[3], 3, 2, 1, bias=False),
+                nn.BatchNorm1d(channels[3], affine=True),
+                nn.ReLU(inplace=True),
+                nn.Conv1d(channels[3], output_channel, 3, 2, 1, bias=True),
+            )
+        self.add_noise = add_noise
+        self.normalize_embedding = normalize_embedding
+        self.return_seq = return_seq
+        self.output_channel = output_channel
+
+        self.segments_fusion = segments_fusion
+        self.normalize_fusion = normalize_fusion
+        if segments_fusion:
+            #self.attn_fuser = Attention(
+            #    output_channel,
+            #    output_channel,
+            #    ignore_tanh=True)
+            self.attn_fuser = \
+                getattr(encoder_model_collection, fuser_arch)(**fuser_kwargs)
+
+    def forward(self, x):
+        # In case more than one mel segment per person is passed
+        if len(x.shape) == 4:
+            fusion_mode = True
+            B, N, C, L = x.shape
+            #print('Fusion Mode On! Input Shape:', x.shape)
+            x = x.view(B*N, C, L)
+        else:
+            fusion_mode = False
+            B, C, L = x.shape
+        x = self.model(x)
+        embeddings = F.avg_pool1d(x, x.size()[2], stride=1)
+        embeddings = embeddings.view(embeddings.size()[0], -1, 1, 1)
+
+        if self.normalize_embedding:
+            embeddings = F.normalize(embeddings)
+        if self.add_noise:
+            noise = 0.05 * torch.randn(x.shape[0], x.shape[1], 1, 1)
+            noise = noise.type(embeddings.type())
+            embeddings = embeddings + noise
+            if self.normalize_embedding:
+                embeddings = F.normalize(embeddings)
+
+        # Restore Tensor shape
+        if fusion_mode:
+            #print(embeddings.shape)
+            C_emb = embeddings.shape[1]
+            embeddings = embeddings.view(B, N, C_emb)
+            # Attention fusion
+            embeddings = self.attn_fuser(embeddings)
+            if self.normalize_fusion:
+                embeddings = F.normalize(embeddings)
+
+        if self.return_seq:
+            return embeddings, x
+        else:
+            return embeddings
+
+    def print_param(self):
+        print('All parameters:')
+        for name, param in self.named_parameters():
+            print(name)
+
+    def print_trainable_param(model):
+        print('Trainable Parameters:')
+        for name, param in model.named_parameters():
+            if param.requires_grad:
+                print(name)
+
+    def train_fuser_only(self):
+        print('Training Attention Fuser Only')
+        for name, param in self.named_parameters():
+            if 'attn_fuser' in name:
+                param.requires_grad = True
+            else:
+                param.requires_grad = False
+
+    def init_attn_fusion(self):
+        self.attn_fuser = Attention(
+            self.output_channel,
+            self.output_channel,
+            ignore_tanh=True)
+
+if __name__ == '__main__':
+    import time
+    # Demo Input
+    log_mels = torch.ones((16, 40, 151))
+    pos_log_mel = torch.ones((16, 48, 150)).transpose(1, 2)
+
+    # Test Inception1DBlock
+    incept_block = Inception1DBlock(
+        channel_in=40,
+        channel_k2=64,
+        channel_k3=64,
+        channel_k5=64,
+        channel_k7=64)
+    incept_block(log_mels)
+
+    # Test V2F 1D CNN
+    log_mel_segs = torch.ones((16, 20, 40, 150))
+    v2f_id_cnn_kwargs = {
+        'input_channel': 40,
+        'channels': [256, 384, 576, 864],
+        'output_channel': 64,
+        'segments_fusion': True,
+        'inception_mode': True,
+        }
+    v2f_id_cnn_fuse = V2F1DCNN(**v2f_id_cnn_kwargs)
+    print(v2f_id_cnn_fuse)
+    print('V2F1DCNN Output shape:', v2f_id_cnn_fuse(log_mel_segs).shape)
+    v2f_id_cnn_fuse.print_param()
+    v2f_id_cnn_fuse.train_fuser_only()
+    v2f_id_cnn_fuse.print_trainable_param()
+
+    # test a simple transformer layer
+    trans_layer = nn.TransformerEncoderLayer(48, 8,
+        dim_feedforward=48, dropout=0.1, activation='relu')
+    print(trans_layer(pos_log_mel).shape)
+
+
+    # Transformer
+    trans_kwargs = {
+        'input_channel': 40,
+        'cnn_channels': [512, 512],
+        'transformer_dim': 512,
+        'transformer_depth': 2,
+        'return_seq': True,
+        'pos_embedding_dim': 0, #8
+        'sin_pos_encoding': True, #False
+    }
+    trans_encoder = TransEncoder(**trans_kwargs).cuda()
+    print(trans_encoder)
+    emb, seq_emb = trans_encoder(log_mels.cuda())
+    print('TransEncoder Output shape:', emb.shape, seq_emb.shape)
+
+    # Test V2F 1D CNN
+    v2f_id_cnn_kwargs = {
+        'input_channel': 40,
+        'channels': [256, 384, 576, 864],
+        'output_channel': 64,
+        }
+    v2f_id_cnn = V2F1DCNN(**v2f_id_cnn_kwargs)
+    print(v2f_id_cnn)
+    print('V2F1DCNN Output shape:', v2f_id_cnn(log_mels).shape)
+
+    # Test V2F 1D CNN Sequential Return
+    v2f_id_cnn_kwargs = {
+        'input_channel': 40,
+        'channels': [256, 384, 576, 864],
+        'output_channel': 64,
+        'return_seq': True,
+        }
+    v2f_id_cnn = V2F1DCNN(**v2f_id_cnn_kwargs)
+    print(v2f_id_cnn)
+    emb, seq_emb = v2f_id_cnn(log_mels)
+    print('V2F1DCNN Output shape:', emb.shape, seq_emb.shape)
diff --git a/mlops/serving/sf2f/requirement.txt b/mlops/serving/sf2f/requirement.txt
new file mode 100644
index 0000000..bebdee4
--- /dev/null
+++ b/mlops/serving/sf2f/requirement.txt
@@ -0,0 +1,10 @@
+glog
+python_speech_features
+webrtcvad
+pydub
+mlflow
+boto3
+
+##apt
+#apt update
+#apt install gcc
\ No newline at end of file
diff --git a/mlops/serving/sf2f/utils/__init__.py b/mlops/serving/sf2f/utils/__init__.py
new file mode 100644
index 0000000..55e5f84
--- /dev/null
+++ b/mlops/serving/sf2f/utils/__init__.py
@@ -0,0 +1 @@
+from .common import *
diff --git a/mlops/serving/sf2f/utils/bilinear.py b/mlops/serving/sf2f/utils/bilinear.py
new file mode 100644
index 0000000..7a12100
--- /dev/null
+++ b/mlops/serving/sf2f/utils/bilinear.py
@@ -0,0 +1,296 @@
+import numpy as np
+import torch
+import torch.nn.functional as F
+from .common import timeit
+
+
+"""
+Functions for performing differentiable bilinear cropping of images, for use in
+the object discriminator
+"""
+
+def crop_bbox_batch(feats, bbox, bbox_to_feats, HH, WW=None, backend='cudnn'):
+    """
+    Inputs:
+    - feats: FloatTensor of shape (N, C, H, W)
+    - bbox: FloatTensor of shape (B, 4) giving bounding box coordinates
+    - bbox_to_feats: LongTensor of shape (B,) mapping boxes to feature maps;
+      each element is in the range [0, N) and bbox_to_feats[b] = i means that
+      bbox[b] will be cropped from feats[i].
+    - HH, WW: Size of the output crops
+
+    Returns:
+    - crops: FloatTensor of shape (B, C, HH, WW) where crops[i] uses bbox[i] to
+      crop from feats[bbox_to_feats[i]].
+    """
+    if backend == 'cudnn':
+        return crop_bbox_batch_cudnn(feats, bbox, bbox_to_feats, HH, WW)
+    N, C, H, W = feats.size()
+    B = bbox.size(0)
+    if WW is None:
+        WW = HH
+    dtype, device = feats.dtype, feats.device
+    crops = torch.zeros(B, C, HH, WW, dtype=dtype, device=device)
+    for i in range(N):
+        idx = (bbox_to_feats == i).nonzero()
+        if idx.dim() == 0:
+            continue
+        idx = idx.view(-1)
+        n = idx.size(0)
+        cur_feats = feats[i].view(1, C, H, W).expand(n, C, H, W).contiguous()
+        cur_bbox = bbox[idx]
+        cur_crops = crop_bbox(cur_feats, cur_bbox, HH, WW)
+        crops[idx] = cur_crops
+    return crops
+
+
+def _invperm(p):
+    N = p.size(0)
+    eye = torch.arange(0, N).type_as(p)
+    pp = (eye[:, None] == p).nonzero()[:, 1]
+    return pp
+
+
+def crop_bbox_batch_cudnn(feats, bbox, bbox_to_feats, HH, WW=None):
+    N, C, H, W = feats.size()
+    B = bbox.size(0)
+    if WW is None:
+        WW = HH
+    dtype = feats.type()
+
+    feats_flat, bbox_flat, all_idx = [], [], []
+    for i in range(N):
+        idx = (bbox_to_feats == i).nonzero()
+        if idx.dim() == 0:
+            continue
+        idx = idx.view(-1)
+        n = idx.size(0)
+        cur_feats = feats[i].view(1, C, H, W).expand(n, C, H, W).contiguous()
+        cur_bbox = bbox[idx]
+
+        feats_flat.append(cur_feats)
+        bbox_flat.append(cur_bbox)
+        all_idx.append(idx)
+
+    feats_flat = torch.cat(feats_flat, dim=0)
+    bbox_flat = torch.cat(bbox_flat, dim=0)
+    crops = crop_bbox(feats_flat, bbox_flat, HH, WW, backend='cudnn')
+
+    # If the crops were sequential (all_idx is identity permutation) then we can
+    # simply return them; otherwise we need to permute crops by the inverse
+    # permutation from all_idx.
+    all_idx = torch.cat(all_idx, dim=0)
+    eye = torch.arange(0, B).type_as(all_idx)
+    if (all_idx == eye).all():
+        return crops
+    return crops[_invperm(all_idx)]
+
+
+def crop_bbox(feats, bbox, HH, WW=None, backend='cudnn'):
+    """
+    Take differentiable crops of feats specified by bbox.
+
+    Inputs:
+    - feats: Tensor of shape (N, C, H, W)
+    - bbox: Bounding box coordinates of shape (N, 4) in the format
+      [x0, y0, x1, y1] in the [0, 1] coordinate space.
+    - HH, WW: Size of the output crops.
+
+    Returns:
+    - crops: Tensor of shape (N, C, HH, WW) where crops[i] is the portion of
+      feats[i] specified by bbox[i], reshaped to (HH, WW) using bilinear sampling.
+    """
+    N = feats.size(0)
+    assert bbox.size(0) == N
+    assert bbox.size(1) == 4
+    if WW is None:
+        WW = HH
+    if backend == 'cudnn':
+        # Change box from [0, 1] to [-1, 1] coordinate system
+        bbox = 2 * bbox - 1
+    x0, y0 = bbox[:, 0], bbox[:, 1]
+    x1, y1 = bbox[:, 2], bbox[:, 3]
+    X = tensor_linspace(x0, x1, steps=WW).view(N, 1, WW).expand(N, HH, WW)
+    Y = tensor_linspace(y0, y1, steps=HH).view(N, HH, 1).expand(N, HH, WW)
+    if backend == 'jj':
+        return bilinear_sample(feats, X, Y)
+    elif backend == 'cudnn':
+        grid = torch.stack([X, Y], dim=3)
+        return F.grid_sample(feats, grid)
+
+
+def uncrop_bbox(feats, bbox, H, W=None, fill_value=0):
+    """
+    Inverse operation to crop_bbox; construct output images where the feature maps
+    from feats have been reshaped and placed into the positions specified by bbox.
+
+    Inputs:
+    - feats: Tensor of shape (N, C, HH, WW)
+    - bbox: Bounding box coordinates of shape (N, 4) in the format
+      [x0, y0, x1, y1] in the [0, 1] coordinate space.
+    - H, W: Size of output.
+    - fill_value: Portions of the output image that are outside the bounding box
+      will be filled with this value.
+
+    Returns:
+    - out: Tensor of shape (N, C, H, W) where the portion of out[i] given by
+      bbox[i] contains feats[i], reshaped using bilinear sampling.
+    """
+    N, C = feats.size(0), feats.size(1)
+    assert bbox.size(0) == N
+    assert bbox.size(1) == 4
+    if W is None:
+        W = H
+
+    x0, y0 = bbox[:, 0], bbox[:, 1]
+    x1, y1 = bbox[:, 2], bbox[:, 3]
+    ww = x1 - x0
+    hh = y1 - y0
+
+    x0 = x0.contiguous().view(N, 1).expand(N, H)
+    x1 = x1.contiguous().view(N, 1).expand(N, H)
+    ww = ww.view(N, 1).expand(N, H)
+
+    y0 = y0.contiguous().view(N, 1).expand(N, W)
+    y1 = y1.contiguous().view(N, 1).expand(N, W)
+    hh = hh.view(N, 1).expand(N, W)
+
+    X = torch.linspace(0, 1, steps=W).view(1, W).expand(N, W).to(feats)
+    Y = torch.linspace(0, 1, steps=H).view(1, H).expand(N, H).to(feats)
+
+    X = (X - x0) / ww
+    Y = (Y - y0) / hh
+
+    # For ByteTensors, (x + y).clamp(max=1) gives logical_or
+    X_out_mask = ((X < 0) + (X > 1)).view(N, 1, W).expand(N, H, W)
+    Y_out_mask = ((Y < 0) + (Y > 1)).view(N, H, 1).expand(N, H, W)
+    out_mask = (X_out_mask + Y_out_mask).clamp(max=1)
+    out_mask = out_mask.view(N, 1, H, W).expand(N, C, H, W)
+
+    X = X.view(N, 1, W).expand(N, H, W)
+    Y = Y.view(N, H, 1).expand(N, H, W)
+
+    grid = torch.stack([X, Y], dim=3)
+    out = F.grid_sample(feats, grid)
+    out[out_mask] = fill_value
+    return out
+
+
+def bilinear_sample(feats, X, Y):
+    """
+    Perform bilinear sampling on the features in feats using the sampling grid
+    given by X and Y.
+
+    Inputs:
+    - feats: Tensor holding input feature map, of shape (N, C, H, W)
+    - X, Y: Tensors holding x and y coordinates of the sampling
+      grids; both have shape shape (N, HH, WW) and have elements in the range [0, 1].
+    Returns:
+    - out: Tensor of shape (B, C, HH, WW) where out[i] is computed
+      by sampling from feats[idx[i]] using the sampling grid (X[i], Y[i]).
+    """
+    N, C, H, W = feats.size()
+    assert X.size() == Y.size()
+    assert X.size(0) == N
+    _, HH, WW = X.size()
+
+    X = X.mul(W)
+    Y = Y.mul(H)
+
+    # Get the x and y coordinates for the four samples
+    x0 = X.floor().clamp(min=0, max=W-1)
+    x1 = (x0 + 1).clamp(min=0, max=W-1)
+    y0 = Y.floor().clamp(min=0, max=H-1)
+    y1 = (y0 + 1).clamp(min=0, max=H-1)
+
+    # In numpy we could do something like feats[i, :, y0, x0] to pull out
+    # the elements of feats at coordinates y0 and x0, but PyTorch doesn't
+    # yet support this style of indexing. Instead we have to use the gather
+    # method, which only allows us to index along one dimension at a time;
+    # therefore we will collapse the features (BB, C, H, W) into (BB, C, H * W)
+    # and index along the last dimension. Below we generate linear indices into
+    # the collapsed last dimension for each of the four combinations we need.
+    y0x0_idx = (W * y0 + x0).view(N, 1, HH * WW).expand(N, C, HH * WW)
+    y1x0_idx = (W * y1 + x0).view(N, 1, HH * WW).expand(N, C, HH * WW)
+    y0x1_idx = (W * y0 + x1).view(N, 1, HH * WW).expand(N, C, HH * WW)
+    y1x1_idx = (W * y1 + x1).view(N, 1, HH * WW).expand(N, C, HH * WW)
+
+    # Actually use gather to pull out the values from feats corresponding
+    # to our four samples, then reshape them to (BB, C, HH, WW)
+    feats_flat = feats.view(N, C, H * W)
+    v1 = feats_flat.gather(2, y0x0_idx.long()).view(N, C, HH, WW)
+    v2 = feats_flat.gather(2, y1x0_idx.long()).view(N, C, HH, WW)
+    v3 = feats_flat.gather(2, y0x1_idx.long()).view(N, C, HH, WW)
+    v4 = feats_flat.gather(2, y1x1_idx.long()).view(N, C, HH, WW)
+
+    # Compute the weights for the four samples
+    w1 = ((x1 - X) * (y1 - Y)).view(N, 1, HH, WW).expand(N, C, HH, WW)
+    w2 = ((x1 - X) * (Y - y0)).view(N, 1, HH, WW).expand(N, C, HH, WW)
+    w3 = ((X - x0) * (y1 - Y)).view(N, 1, HH, WW).expand(N, C, HH, WW)
+    w4 = ((X - x0) * (Y - y0)).view(N, 1, HH, WW).expand(N, C, HH, WW)
+
+    # Multiply the samples by the weights to give our interpolated results.
+    out = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4
+    return out
+
+
+def tensor_linspace(start, end, steps=10):
+    """
+    Vectorized version of torch.linspace.
+
+    Inputs:
+    - start: Tensor of any shape
+    - end: Tensor of the same shape as start
+    - steps: Integer
+
+    Returns:
+    - out: Tensor of shape start.size() + (steps,), such that
+      out.select(-1, 0) == start, out.select(-1, -1) == end,
+      and the other elements of out linearly interpolate between
+      start and end.
+    """
+    assert start.size() == end.size()
+    view_size = start.size() + (1,)
+    w_size = (1,) * start.dim() + (steps,)
+    out_size = start.size() + (steps,)
+
+    start_w = torch.linspace(1, 0, steps=steps).to(start)
+    start_w = start_w.view(w_size).expand(out_size)
+    end_w = torch.linspace(0, 1, steps=steps).to(start)
+    end_w = end_w.view(w_size).expand(out_size)
+
+    start = start.contiguous().view(view_size).expand(out_size)
+    end = end.contiguous().view(view_size).expand(out_size)
+
+    out = start_w * start + end_w * end
+    return out
+
+
+if __name__ == '__main__':
+    import numpy as np
+    # from scipy.misc import imread, imsave, imresize
+    from cv2 import imresize
+    from imageio import imread
+    from imageio import imwrite as imsave
+
+    cat = imresize(imread('cat.jpg'), (256, 256))
+    dog = imresize(imread('dog.jpg'), (256, 256))
+    feats = torch.stack([
+        torch.from_numpy(cat.transpose(2, 0, 1).astype(np.float32)),
+        torch.from_numpy(dog.transpose(2, 0, 1).astype(np.float32))],
+        dim=0)
+
+    boxes = torch.FloatTensor([
+        [0, 0, 1, 1],
+        [0.25, 0.25, 0.75, 0.75],
+        [0, 0, 0.5, 0.5],
+    ])
+
+    box_to_feats = torch.LongTensor([1, 0, 1]).cuda()
+
+    feats, boxes = feats.cuda(), boxes.cuda()
+    crops = crop_bbox_batch_cudnn(feats, boxes, box_to_feats, 128)
+    for i in range(crops.size(0)):
+        crop_np = crops.data[i].cpu().numpy().transpose(
+            1, 2, 0).astype(np.uint8)
+        imsave('out%d.png' % i, crop_np)
diff --git a/mlops/serving/sf2f/utils/box_utils.py b/mlops/serving/sf2f/utils/box_utils.py
new file mode 100644
index 0000000..898e543
--- /dev/null
+++ b/mlops/serving/sf2f/utils/box_utils.py
@@ -0,0 +1,133 @@
+import torch
+import numpy as np
+"""
+Utilities for dealing with bounding boxes
+"""
+
+
+def box_union(box1, box2):
+    assert isinstance(box1, torch.Tensor)
+    return torch.cat(
+            [torch.min(
+                torch.stack([box1[:, :2], box2[:, :2],], dim=0),
+                dim=0,
+                keepdim=False,
+            )[0],
+            torch.max(
+                torch.stack([box1[:, 2:], box2[:, 2:],], dim=0),
+                dim=0,
+                keepdim=False,
+            )[0],]
+        , dim=1)
+
+def box_in_region(boxes, regions):
+    region_w = regions[:, 2] - regions[:, 0]
+    region_h = regions[:, 3] - regions[:, 1]
+    assert (region_w > 0).all() and (region_h > 0).all()
+    result = torch.zeros_like(regions)
+    result[:, 0::2] = (boxes[:, 0::2] - regions[:, 0::2]) / region_w
+    result[:, 1::2] = (boxes[:, 1::2] - regions[:, 1::2]) / region_h
+    return result
+
+
+def apply_box_transform(anchors, transforms):
+    """
+    Apply box transforms to a set of anchor boxes.
+
+    Inputs:
+    - anchors: Anchor boxes of shape (N, 4), where each anchor is specified
+      in the form [xc, yc, w, h]
+    - transforms: Box transforms of shape (N, 4) where each transform is
+      specified as [tx, ty, tw, th]
+
+    Returns:
+    - boxes: Transformed boxes of shape (N, 4) where each box is in the
+      format [xc, yc, w, h]
+    """
+    # Unpack anchors
+    xa, ya = anchors[:, 0], anchors[:, 1]
+    wa, ha = anchors[:, 2], anchors[:, 3]
+
+    # Unpack transforms
+    tx, ty = transforms[:, 0], transforms[:, 1]
+    tw, th = transforms[:, 2], transforms[:, 3]
+
+    x = xa + tx * wa
+    y = ya + ty * ha
+    w = wa * tw.exp()
+    h = ha * th.exp()
+
+    boxes = torch.stack([x, y, w, h], dim=1)
+    return boxes
+
+
+def invert_box_transform(anchors, boxes):
+    """
+    Compute the box transform that, when applied to anchors, would give boxes.
+
+    Inputs:
+    - anchors: Box anchors of shape (N, 4) in the format [xc, yc, w, h]
+    - boxes: Target boxes of shape (N, 4) in the format [xc, yc, w, h]
+
+    Returns:
+    - transforms: Box transforms of shape (N, 4) in the format [tx, ty, tw, th]
+    """
+    # Unpack anchors
+    xa, ya = anchors[:, 0], anchors[:, 1]
+    wa, ha = anchors[:, 2], anchors[:, 3]
+
+    # Unpack boxes
+    x, y = boxes[:, 0], boxes[:, 1]
+    w, h = boxes[:, 2], boxes[:, 3]
+
+    tx = (x - xa) / wa
+    ty = (y - ya) / ha
+    tw = w.log() - wa.log()
+    th = h.log() - ha.log()
+
+    transforms = torch.stack([tx, ty, tw, th], dim=1)
+    return transforms
+
+
+def centers_to_extents(boxes):
+    """
+    Convert boxes from [xc, yc, w, h] format to [x0, y0, x1, y1] format
+
+    Input:
+    - boxes: Input boxes of shape (N, 4) in [xc, yc, w, h] format
+
+    Returns:
+    - boxes: Output boxes of shape (N, 4) in [x0, y0, x1, y1] format
+    """
+    xc, yc = boxes[:, 0], boxes[:, 1]
+    w, h = boxes[:, 2], boxes[:, 3]
+
+    x0 = xc - w / 2
+    x1 = x0 + w
+    y0 = yc - h / 2
+    y1 = y0 + h
+
+    boxes_out = torch.stack([x0, y0, x1, y1], dim=1)
+    return boxes_out
+
+
+def extents_to_centers(boxes):
+    """
+    Convert boxes from [x0, y0, x1, y1] format to [xc, yc, w, h] format
+
+    Input:
+    - boxes: Input boxes of shape (N, 4) in [x0, y0, x1, y1] format
+
+    Returns:
+    - boxes: Output boxes of shape (N, 4) in [xc, yc, w, h] format
+    """
+    x0, y0 = boxes[:, 0], boxes[:, 1]
+    x1, y1 = boxes[:, 2], boxes[:, 3]
+
+    xc = 0.5 * (x0 + x1)
+    yc = 0.5 * (y0 + y1)
+    w = x1 - x0
+    h = y1 - y0
+
+    boxes_out = torch.stack([xc, yc, w, h], dim=1)
+    return boxes_out
diff --git a/mlops/serving/sf2f/utils/common.py b/mlops/serving/sf2f/utils/common.py
new file mode 100644
index 0000000..2248793
--- /dev/null
+++ b/mlops/serving/sf2f/utils/common.py
@@ -0,0 +1,192 @@
+import os
+import time
+import inspect
+import subprocess
+from contextlib import contextmanager
+
+import torch
+from PIL import Image
+import numpy as np
+from collections import OrderedDict
+
+def int_tuple(s):
+    return tuple(int(i) for i in s.split(','))
+
+
+def float_tuple(s):
+    return tuple(float(i) for i in s.split(','))
+
+
+def str_tuple(s):
+    return tuple(s.split(','))
+
+
+def set_trainable(model, requires_grad):
+    set_trainable_param(model.parameters(), requires_grad)
+
+def set_trainable_param(parameters, requires_grad):
+    for param in parameters:
+        param.requires_grad = requires_grad
+
+
+def update_values(dict_from, dict_to):
+    for key, value in dict_from.items():
+        if isinstance(value, dict) and key in dict_to.keys():
+            update_values(dict_from[key], dict_to[key])
+        elif value is not None or key not in dict_to.keys():
+            dict_to[key] = dict_from[key]
+    return dict_to
+
+
+def params_count(model):
+    count = 0
+    for p in model.parameters():
+        c = 1
+        for i in range(p.dim()):
+            c *= p.size(i)
+        count += c
+    return count
+
+
+def bool_flag(s):
+    if s == '1':
+        return True
+    elif s == '0':
+        return False
+    msg = 'Invalid value "%s" for bool flag (should be 0 or 1)'
+    raise ValueError(msg % s)
+
+
+def lineno():
+    return inspect.currentframe().f_back.f_lineno
+
+
+def get_gpu_memory():
+    torch.cuda.synchronize()
+    opts = [
+        'nvidia-smi', '-q', '--gpu=' + str(0), '|', 'grep', '"Used GPU Memory"'
+    ]
+    cmd = str.join(' ', opts)
+    ps = subprocess.Popen(
+        cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+    output = ps.communicate()[0].decode('utf-8')
+    output = output.split("\n")[1].split(":")
+    consumed_mem = int(output[1].strip().split(" ")[0])
+    return consumed_mem
+
+# Converts a Tensor into an image array (numpy)
+# |imtype|: the desired type of the converted numpy array
+
+
+def tensor2im(input_image, imtype=np.uint8):
+    if not isinstance(input_image, torch.Tensor):
+        return input_image
+    image_numpy = input_image.numpy()
+    if image_numpy.ndim == 3:
+        if image_numpy.shape[0] == 1:
+            image_numpy = np.tile(image_numpy, (3, 1, 1))
+        elif image_numpy.shape[0] == 3:
+            image_numpy = np.transpose(image_numpy, (1, 2, 0))
+    elif image_numpy.ndim == 4:
+        if image_numpy.shape[1] == 1:
+            image_numpy = np.tile(image_numpy, (1, 3, 1, 1))
+        elif image_numpy.shape[1] == 3:
+            image_numpy = np.transpose(image_numpy, (0, 2, 3, 1))
+    else:
+        raise ValueError("Only support images or batches")
+    return image_numpy.astype(imtype)
+
+
+def save_image(image_numpy, image_path):
+    image_pil = Image.fromarray(image_numpy)
+    image_pil.save(image_path)
+
+
+def mkdirs(paths):
+    if isinstance(paths, list) and not isinstance(paths, str):
+        for path in paths:
+            mkdir(path)
+    else:
+        mkdir(paths)
+
+
+def mkdir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+
+@contextmanager
+def timeit(msg, should_time=True):
+    if should_time:
+        torch.cuda.synchronize()
+        t0 = time.time()
+    yield
+    if should_time:
+        torch.cuda.synchronize()
+        t1 = time.time()
+        duration = (t1 - t0) * 1000.0
+        print('%s: %.2f ms' % (msg, duration))
+
+
+class LossManager(object):
+    def __init__(self):
+        self.total_loss = None
+        self.all_losses = {}
+
+    def add_loss(self, loss, name, weight=1.0):
+        cur_loss = loss * weight
+        if self.total_loss is not None:
+            self.total_loss += cur_loss
+        else:
+            self.total_loss = cur_loss
+
+        self.all_losses[name] = cur_loss.data.cpu().item()
+
+    def items(self):
+        return self.all_losses.items()
+
+
+def load_model_state(model, state_dict, strict=True):
+    # sometimes if the state_dict is saved from DataParallel model, the keys
+    # has a prefix "module.", so we remove them before Loading
+    new_state_dict = OrderedDict()
+    if not isinstance(model, torch.nn.DataParallel):
+        for k, v in state_dict.items():
+            k = k[7:] if k.startswith("module") else k
+            new_state_dict[k] = v
+    else:
+        new_state_dict = state_dict
+    if strict:
+        # requires the model's state_dict to be exactly same as the new_state_dict
+        model.load_state_dict(new_state_dict)
+    else:
+        # only load parameters that are same size and type, and print warnings
+        # for parameters that don't match
+        own_state = model.state_dict()
+        for name, param in new_state_dict.items():
+            if name in own_state:
+                if isinstance(param, torch.nn.Parameter):
+                    # backwards compatibility for serialized parameters
+                    param = param.data
+                if own_state[name].size() == param.size():
+                    own_state[name].copy_(param)
+                else:
+                    print(
+                        "Warning: While copying the parameter named {}, "
+                        "whose dimensions in the model are {} and "
+                        "whose dimensions in the checkpoint are {}.".format(
+                            name, own_state[name].size(), param.size()
+                        )
+                    )
+            else:
+                print(
+                    "Warning: Parameter named {} is not used "
+                    "by this model.".format(name)
+                )
+        for name, _ in own_state.items():
+            if name not in new_state_dict:
+                print(
+                    "Warning: Parameter named {} in the model "
+                    "is not initialized.".format(name)
+                )
+    return model
diff --git a/mlops/serving/sf2f/utils/evaluate.py b/mlops/serving/sf2f/utils/evaluate.py
new file mode 100644
index 0000000..81f27cc
--- /dev/null
+++ b/mlops/serving/sf2f/utils/evaluate.py
@@ -0,0 +1,67 @@
+import pyprind
+import numpy as np
+import torch
+import torch.nn.functional as F
+import glog as log
+
+from datasets import imagenet_deprocess_batch
+from scripts.compute_inception_score import get_inception_score
+from scripts.compute_vggface_score import get_vggface_score
+
+
+VGG_BOX = [0.235, 0.195, 0.765, 0.915]
+def crop_vgg_box(imgs):
+    # with correct cropping & correct processing
+    left, top, right, bottom = VGG_BOX
+    # = [0.235015, 0.19505739, 0.76817876, 0.9154963]
+    N, C, H, W = imgs.shape
+    left = int(left * W)
+    right = int(right * W)
+    top = int(top * H)
+    bottom = int(bottom * H)
+    imgs = imgs[:, :, top:bottom+1, left:right+1]
+    return imgs
+
+def evaluate(model, data_loader, options):
+    '''
+    Evaluate the current model...
+    '''
+    normalize_method = options["data"]["data_opts"].get(
+        'normalize_method', 'imagenet')
+    model.eval()
+    log.info("Evaluating with Inception Scores.")
+    images = []
+    imgs_gt = []
+    for iter, batch in enumerate(pyprind.prog_bar(data_loader,
+                                  title="[Generating Images]",
+                                  width=50)):
+        ######### unpack the data #########
+        imgs, log_mels, human_ids = batch
+        imgs = imgs.cuda()
+        imgs_gt.append(imgs[0])
+        ###################################
+        with torch.no_grad():
+            float_dtype = torch.cuda.FloatTensor
+            log_mels = log_mels.type(float_dtype)
+            model_out = model(log_mels)
+            """
+            imgs_pred: generated images
+            others: placeholder for other output
+            """
+        imgs_pred, others = model_out
+        img = imagenet_deprocess_batch(
+            imgs_pred, normalize_method=normalize_method)
+        img = crop_vgg_box(img)
+        for i in range(img.shape[0]):
+            img_np = img[i].numpy().transpose(1, 2, 0)
+            images.append(img_np)
+    log.info("Computing inception scores...")
+    is_mean, is_std = get_inception_score(images)
+    log.info("Computing VF scores...")
+    vfs_mean, vfs_std = get_vggface_score(images)
+
+    return is_mean, is_std, vfs_mean, vfs_std
+
+
+if __name__ == '__main__':
+    evaluate()
diff --git a/mlops/serving/sf2f/utils/evaluate_fid.py b/mlops/serving/sf2f/utils/evaluate_fid.py
new file mode 100644
index 0000000..90da53d
--- /dev/null
+++ b/mlops/serving/sf2f/utils/evaluate_fid.py
@@ -0,0 +1,82 @@
+import pyprind
+import numpy as np
+import torch
+import torch.nn.functional as F
+import glog as log
+
+from datasets import imagenet_deprocess_batch
+from scripts.compute_fid_score import calculate_activation_statistics, calculate_frechet_distance
+import scripts.compute_fid_score as inception_score
+from scripts.compute_vggface_score import get_vggface_act
+
+VGG_BOX = [0.235, 0.195, 0.765, 0.915]
+def crop_vgg_box(imgs):
+    # with correct cropping & correct processing
+    left, top, right, bottom = VGG_BOX
+    # = [0.235015, 0.19505739, 0.76817876, 0.9154963]
+    N, C, H, W = imgs.shape
+    left = int(left * W)
+    right = int(right * W)
+    top = int(top * H)
+    bottom = int(bottom * H)
+    imgs = imgs[:, :, top:bottom+1, left:right+1]
+    return imgs
+
+def evaluate_fid(model, data_loader, options):
+    '''
+    Evaluate the current model...
+    '''
+    normalize_method = options["data"]["data_opts"].get(
+        'normalize_method', 'imagenet')
+    model.eval()
+    log.info("Evaluating with Inception Scores.")
+    images = []
+    imgs_gt = []
+    for iter, batch in enumerate(pyprind.prog_bar(data_loader,
+                                  title="[Generating Images]",
+                                  width=50)):
+        ######### unpack the data #########
+        imgs, log_mels, human_ids = batch
+        imgs = imgs.cuda()
+        ###################################
+        with torch.no_grad():
+            float_dtype = torch.cuda.FloatTensor
+            log_mels = log_mels.type(float_dtype)
+            model_out = model(log_mels)
+            """
+            imgs_pred: generated images
+            others: placeholder for other output
+            """
+        imgs_pred, others = model_out
+        img = imagenet_deprocess_batch(
+            imgs_pred, normalize_method=normalize_method)
+        img = crop_vgg_box(img)
+        for i in range(img.shape[0]):
+            img_np = img[i].numpy().transpose(1, 2, 0)
+            images.append(img_np)
+
+        img = imagenet_deprocess_batch(
+            imgs, normalize_method=normalize_method)
+        img = crop_vgg_box(img)
+        for i in range(img.shape[0]):
+            img_np = img[i].numpy().transpose(1, 2, 0)
+            imgs_gt.append(img_np)
+
+    log.info("Computing FID scores...")
+    acts_set = inception_score.get_fid_pred(imgs_gt)
+    fake_acts_set = inception_score.get_fid_pred(images)
+    real_mu, real_sigma = calculate_activation_statistics(acts_set)
+    fake_mu, fake_sigma = calculate_activation_statistics(fake_acts_set)
+    fid_score = calculate_frechet_distance(real_mu, real_sigma, fake_mu, fake_sigma)
+
+    acts_set = get_vggface_act(imgs_gt)
+    fake_acts_set = get_vggface_act(images)
+    real_mu, real_sigma = calculate_activation_statistics(acts_set)
+    fake_mu, fake_sigma = calculate_activation_statistics(fake_acts_set)
+    fvfd_score = calculate_frechet_distance(real_mu, real_sigma, fake_mu, fake_sigma)
+
+    return fid_score
+
+
+if __name__ == '__main__':
+    evaluate()
diff --git a/mlops/serving/sf2f/utils/filter_pickle.py b/mlops/serving/sf2f/utils/filter_pickle.py
new file mode 100644
index 0000000..ccf5646
--- /dev/null
+++ b/mlops/serving/sf2f/utils/filter_pickle.py
@@ -0,0 +1,35 @@
+import pickle
+import os
+
+
+def check_pickle(mel_pickle):
+    """
+    Load a mel pickle file and remove the file if it is empty
+
+    Args:
+        mel_pickle: str, path to the mel pickle file
+    Returns:
+        None
+    """
+    file = open(mel_pickle, 'rb')
+    try:
+        data = pickle.load(file)
+    except:
+        print('Error loading:', mel_pickle)
+        os.remove(mel_pickle)
+        
+    file.close()
+
+def filtering_pickle(dir_path):
+    for dir in os.listdir(dir_path):
+        data_path = os.path.join(dir_path, dir)
+        for id in os.listdir(data_path):
+            data = os.path.join(data_path, id)
+            check_pickle(data)
+    print("Filtering Done.")
+
+
+# if __name__=="__main__":
+#     dir_path = './data/VoxCeleb/vox1/mel_spectrograms' # wav_convertor.vox1_mel
+#     filtering_pickle(dir_path)
+
diff --git a/mlops/serving/sf2f/utils/logger.py b/mlops/serving/sf2f/utils/logger.py
new file mode 100644
index 0000000..c21f383
--- /dev/null
+++ b/mlops/serving/sf2f/utils/logger.py
@@ -0,0 +1,93 @@
+# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
+import tensorflow as tf
+import numpy as np
+# import scipy.misc
+from PIL.Image import fromarray
+
+try:
+    from StringIO import StringIO  # Python 2.7
+except ImportError:
+    from io import BytesIO         # Python 3.x
+    
+tf.compat.v1.disable_v2_behavior()
+
+
+class Logger(object):
+
+    def __init__(self, log_dir):
+        """Create a summary writer logging to log_dir."""
+        self.writer = tf.compat.v1.summary.FileWriter(log_dir)
+        # self.writer = tf.compat.v1.summary.create_file_writer(log_dir)
+
+    def scalar_summary(self, tag, value, step):
+        """Log a scalar variable."""
+        summary = tf.compat.v1.Summary(
+            value=[tf.compat.v1.Summary.Value(tag=tag, simple_value=value)])
+        self.writer.add_summary(summary, step)
+
+    def image_summary(self, images, step, tag="vis"):
+        """Log a list of images."""
+
+        img_summaries = []
+        if isinstance(images, list):
+            for i, img in enumerate(images):
+                _tag = '%s/%d' % (tag, i)
+                # Create a Summary value
+                img_summaries.append(self.add_image(img, _tag))
+        elif isinstance(images, dict):
+            for key, img in images.items():
+                _tag = '%s/%s' % (tag, key)
+                # Create a Summary value
+                img_summaries.append(self.add_image(img, _tag))
+        else:
+            raise ValueError("Either a list or a dict is supported.")
+
+        # Create and write Summary
+        summary = tf.compat.v1.Summary(value=img_summaries)
+        self.writer.add_summary(summary, step)
+
+    def histo_summary(self, tag, values, step, bins=1000):
+        """Log a histogram of the tensor of values."""
+
+        # Create a histogram using numpy
+        counts, bin_edges = np.histogram(values, bins=bins)
+
+        # Fill the fields of the histogram proto
+        hist = tf.HistogramProto()
+        hist.min = float(np.min(values))
+        hist.max = float(np.max(values))
+        hist.num = int(np.prod(values.shape))
+        hist.sum = float(np.sum(values))
+        hist.sum_squares = float(np.sum(values**2))
+
+        # Drop the start of the first bin
+        bin_edges = bin_edges[1:]
+
+        # Add bin edges and counts
+        for edge in bin_edges:
+            hist.bucket_limit.append(edge)
+        for c in counts:
+            hist.bucket.append(c)
+
+        # Create and write Summary
+        summary = tf.compat.v1.Summary(
+            value=[tf.compat.v1.Summary.Value(tag=tag, histo=hist)])
+        self.writer.add_summary(summary, step)
+        self.writer.flush()
+
+    def add_image(self, img, tag):
+        # Write the image to a string
+        try:
+            s = StringIO()
+        except:
+            s = BytesIO()
+        # scipy.misc.toimage(img).save(s, format="png")
+        fromarray(img).save(s, format="png")
+
+
+        # Create an Image object
+        img_sum = tf.compat.v1.Summary.Image(encoded_image_string=s.getvalue(),
+                                   height=img.shape[0],
+                                   width=img.shape[1])
+
+        return tf.compat.v1.Summary.Value(tag=tag, image=img_sum)
diff --git a/mlops/serving/sf2f/utils/losses.py b/mlops/serving/sf2f/utils/losses.py
new file mode 100644
index 0000000..55fcb70
--- /dev/null
+++ b/mlops/serving/sf2f/utils/losses.py
@@ -0,0 +1,144 @@
+import torch
+import torch.nn.functional as F
+
+
+def get_gan_losses(gan_type):
+    """
+    Returns the generator and discriminator loss for a particular GAN type.
+
+    The returned functions have the following API:
+    loss_g = g_loss(scores_fake)
+    loss_d = d_loss(scores_real, scores_fake)
+    """
+    if gan_type == 'gan':
+        return gan_g_loss, gan_d_loss
+    elif gan_type == 'wgan':
+        return wgan_g_loss, wgan_d_loss
+    elif gan_type == 'lsgan':
+        return lsgan_g_loss, lsgan_d_loss
+    else:
+        raise ValueError('Unrecognized GAN type "%s"' % gan_type)
+
+
+def bce_loss(input, target):
+    """
+    Numerically stable version of the binary cross-entropy loss function.
+
+    As per https://github.com/pytorch/pytorch/issues/751
+    See the TensorFlow docs for a derivation of this formula:
+    https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits
+
+    Inputs:
+    - input: PyTorch Tensor of shape (N, ) giving scores.
+    - target: PyTorch Tensor of shape (N,) containing 0 and 1 giving targets.
+
+    Returns:
+    - A PyTorch Tensor containing the mean BCE loss over the minibatch of
+      input data.
+    """
+    neg_abs = -input.abs()
+    loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
+    return loss.mean()
+
+
+def _make_targets(x, y):
+    """
+    Inputs:
+    - x: PyTorch Tensor
+    - y: Python scalar
+
+    Outputs:
+    - out: PyTorch Variable with same shape and dtype as x, but filled with y
+    """
+    return torch.full_like(x, y)
+
+
+def gan_g_loss(scores_fake):
+    """
+    Input:
+    - scores_fake: Tensor of shape (N,) containing scores for fake samples
+
+    Output:
+    - loss: Variable of shape (,) giving GAN generator loss
+    """
+    if scores_fake.dim() > 1:
+        scores_fake = scores_fake.view(-1)
+    y_fake = _make_targets(scores_fake, 1)
+    return bce_loss(scores_fake, y_fake)
+
+
+def gan_d_loss(scores_real, scores_fake):
+    """
+    Input:
+    - scores_real: Tensor of shape (N,) giving scores for real samples
+    - scores_fake: Tensor of shape (N,) giving scores for fake samples
+
+    Output:
+    - loss: Tensor of shape (,) giving GAN discriminator loss
+    """
+    assert scores_real.size() == scores_fake.size()
+    if scores_real.dim() > 1:
+        scores_real = scores_real.view(-1)
+        scores_fake = scores_fake.view(-1)
+    y_real = _make_targets(scores_real, 1)
+    y_fake = _make_targets(scores_fake, 0)
+    loss_real = bce_loss(scores_real, y_real)
+    loss_fake = bce_loss(scores_fake, y_fake)
+    return loss_real + loss_fake
+
+
+def wgan_g_loss(scores_fake):
+    """
+    Input:
+    - scores_fake: Tensor of shape (N,) containing scores for fake samples
+
+    Output:
+    - loss: Tensor of shape (,) giving WGAN generator loss
+    """
+    return -scores_fake.mean()
+
+
+def wgan_d_loss(scores_real, scores_fake):
+    """
+    Input:
+    - scores_real: Tensor of shape (N,) giving scores for real samples
+    - scores_fake: Tensor of shape (N,) giving scores for fake samples
+
+    Output:
+    - loss: Tensor of shape (,) giving WGAN discriminator loss
+    """
+    return scores_fake.mean() - scores_real.mean()
+
+
+def lsgan_g_loss(scores_fake):
+    if scores_fake.dim() > 1:
+        scores_fake = scores_fake.view(-1)
+    y_fake = _make_targets(scores_fake, 1)
+    return F.mse_loss(scores_fake.sigmoid(), y_fake)
+
+
+def lsgan_d_loss(scores_real, scores_fake):
+    assert scores_real.size() == scores_fake.size()
+    if scores_real.dim() > 1:
+        scores_real = scores_real.view(-1)
+        scores_fake = scores_fake.view(-1)
+    y_real = _make_targets(scores_real, 1)
+    y_fake = _make_targets(scores_fake, 0)
+    loss_real = F.mse_loss(scores_real.sigmoid(), y_real)
+    loss_fake = F.mse_loss(scores_fake.sigmoid(), y_fake)
+    return loss_real + loss_fake
+
+
+def gradient_penalty(x_real, x_fake, f, gamma=1.0):
+    N = x_real.size(0)
+    device, dtype = x_real.device, x_real.dtype
+    eps = torch.randn(N, 1, 1, 1, device=device, dtype=dtype)
+    x_hat = eps * x_real + (1 - eps) * x_fake
+    x_hat_score = f(x_hat)
+    if x_hat_score.dim() > 1:
+        x_hat_score = x_hat_score.view(x_hat_score.size(0), -1).mean(dim=1)
+    x_hat_score = x_hat_score.sum()
+    grad_x_hat, = torch.autograd.grad(x_hat_score, x_hat, create_graph=True)
+    grad_x_hat_norm = grad_x_hat.contiguous().view(N, -1).norm(p=2, dim=1)
+    gp_loss = (grad_x_hat_norm - gamma).pow(2).div(gamma * gamma).mean()
+    return gp_loss
diff --git a/mlops/serving/sf2f/utils/metrics.py b/mlops/serving/sf2f/utils/metrics.py
new file mode 100644
index 0000000..f337aaf
--- /dev/null
+++ b/mlops/serving/sf2f/utils/metrics.py
@@ -0,0 +1,50 @@
+import torch
+
+def accuracy(output, target, topk=(1,)):
+    """Computes the accuracy over the k top predictions for the specified values of k"""
+    with torch.no_grad():
+        maxk = max(topk)
+        batch_size = target.size(0)
+
+        _, pred = output.topk(maxk, 1, True, True)
+        pred = pred.t()
+        correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+        res = []
+        for k in topk:
+            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+            res.append(correct_k.mul_(100.0 / batch_size).item())
+        return res
+
+
+def intersection(bbox_pred, bbox_gt):
+    max_xy = torch.min(bbox_pred[:, 2:], bbox_gt[:, 2:])
+    min_xy = torch.max(bbox_pred[:, :2], bbox_gt[:, :2])
+    inter = torch.clamp((max_xy - min_xy), min=0)
+    return inter[:, 0] * inter[:, 1]
+
+
+def jaccard(bbox_pred, bbox_gt):
+    inter = intersection(bbox_pred, bbox_gt)
+    area_pred = (bbox_pred[:, 2] - bbox_pred[:, 0]) * (bbox_pred[:, 3] -
+                                                       bbox_pred[:, 1])
+    area_gt = (bbox_gt[:, 2] - bbox_gt[:, 0]) * (bbox_gt[:, 3] -
+                                                 bbox_gt[:, 1])
+    union = area_pred + area_gt - inter
+    iou = torch.div(inter, union)
+    return torch.sum(iou)
+
+
+def get_total_norm(parameters, norm_type=2):
+    if norm_type == float('inf'):
+        total_norm = max(p.grad.data.abs().max() for p in parameters)
+    else:
+        total_norm = 0
+        for p in parameters:
+            try:
+                param_norm = p.grad.data.norm(norm_type)
+                total_norm += param_norm ** norm_type
+                total_norm = total_norm ** (1. / norm_type)
+            except:
+                continue
+    return total_norm
diff --git a/mlops/serving/sf2f/utils/mlflow_wandb.py b/mlops/serving/sf2f/utils/mlflow_wandb.py
new file mode 100644
index 0000000..5ce4f04
--- /dev/null
+++ b/mlops/serving/sf2f/utils/mlflow_wandb.py
@@ -0,0 +1,15 @@
+import mlflow
+import wandb
+
+def mlflow_wandb_logging(log_dict,model,signature,input_sample,sample_image):
+    #mlflow Log metric & model
+    mlflow.log_metrics(log_dict)
+    mlflow.pytorch.log_model(
+        pytorch_model=model,
+        artifact_path = "sf2f_pytorch",
+        signature= signature,
+        input_example = input_sample,
+        # pip_requirements = "rec.txt"
+    )
+    log_dict['sample'] = [wandb.Image(sample_image, caption=f"Sample")]
+    wandb.log(log_dict)
diff --git a/mlops/serving/sf2f/utils/pgan_utils.py b/mlops/serving/sf2f/utils/pgan_utils.py
new file mode 100644
index 0000000..efb4e52
--- /dev/null
+++ b/mlops/serving/sf2f/utils/pgan_utils.py
@@ -0,0 +1,45 @@
+import os
+import time
+import json
+import math
+import torch
+
+
+def num_flat_features(x):
+    size = x.size()[1:]  # all dimensions except the batch dimension
+    num_features = 1
+    for s in size:
+        num_features *= s
+    return num_features
+
+
+def miniBatchStdDev(x, subGroupSize=4):
+    r"""
+    Add a minibatch standard deviation channel to the current layer.
+    In other words:
+        1) Compute the standard deviation of the feature map over the minibatch
+        2) Get the mean, over all pixels and all channels of thsi ValueError
+        3) expand the layer and cocatenate it with the input
+    Args:
+        - x (tensor): previous layer
+        - subGroupSize (int): size of the mini-batches on which the standard deviation
+        should be computed
+    """
+    size = x.size()
+    subGroupSize = min(size[0], subGroupSize)
+    if size[0] % subGroupSize != 0:
+        subGroupSize = size[0]
+    G = int(size[0] / subGroupSize)
+    if subGroupSize > 1:
+        y = x.view(-1, subGroupSize, size[1], size[2], size[3])
+        y = torch.var(y, 1)
+        y = torch.sqrt(y + 1e-8)
+        y = y.view(G, -1)
+        y = torch.mean(y, 1).view(G, 1)
+        y = y.expand(G, size[2]*size[3]).view((G, 1, 1, size[2], size[3]))
+        y = y.expand(G, subGroupSize, -1, -1, -1)
+        y = y.contiguous().view((-1, 1, size[2], size[3]))
+    else:
+        y = torch.zeros(x.size(0), 1, x.size(2), x.size(3), device=x.device)
+
+    return torch.cat([x, y], dim=1)
diff --git a/mlops/serving/sf2f/utils/s2f_evaluator.py b/mlops/serving/sf2f/utils/s2f_evaluator.py
new file mode 100644
index 0000000..9341cc0
--- /dev/null
+++ b/mlops/serving/sf2f/utils/s2f_evaluator.py
@@ -0,0 +1,449 @@
+'''
+This file contains the utils for evaluation, and is used to cooperate with \
+    training_utils.py and test.py
+'''
+
+
+import os
+import json
+import math
+from collections import defaultdict
+import time
+from copy import deepcopy
+
+import numpy as np
+import torch
+import torch.optim as optim
+import torch.nn as nn
+import torch.nn.functional as F
+from scipy.stats import entropy
+from torchvision.models import inception_v3
+from pyprind import prog_bar
+from tensorflow.io import gfile
+from imageio import imwrite
+
+from datasets import fast_imagenet_deprocess_batch, imagenet_deprocess_batch
+import datasets
+import models
+from models import InceptionResnetV1, fixed_image_standardization
+
+
+# left, top, right, bottom
+VGG_BOX = [0.235, 0.195, 0.765, 0.915]
+
+
+class S2fEvaluator:
+    def __init__(self,
+                 loader,
+                 options,
+                 nframe_range=None,
+                 extraction_size=100,
+                 hq_emb_dict=True,
+                 face_gen_mode='naive',
+                 facenet_return_pooling=False):
+        '''
+        Inputs:
+            loader
+            options
+        '''
+        self.loader = deepcopy(loader)
+        # This makes sure same group of meg_spectrograms is used for fuser
+        self.loader.dataset.shuffle_mel_segments = False
+        if nframe_range is not None:
+            self.loader.dataset.nframe_range = nframe_range
+        self.facenet = InceptionResnetV1(
+            pretrained='vggface2',
+            auto_input_resize=True,
+            return_pooling=facenet_return_pooling).cuda().eval()
+        self.float_dtype = torch.cuda.FloatTensor
+        self.long_dtype = torch.cuda.LongTensor
+        self.options = options
+        self.image_normalize_method= \
+                self.options["data"]["data_opts"]["image_normalize_method"]
+        self.do_deprocess_and_preprocess = \
+            self.options["eval"]["facenet"]["deprocess_and_preprocess"]
+        # crop faces according to VGG average bounding box
+        self.crop_faces = \
+            self.options["eval"]["facenet"]["crop_faces"]
+        self.extraction_size = extraction_size
+        self.hq_emb_dict = hq_emb_dict
+        self.face_gen_mode = face_gen_mode
+
+        self.get_dataset_embeddings()
+
+        # Implement Evaluation Metrics
+        self.L2_dist = \
+            nn.PairwiseDistance(p=2.0, eps=1e-06, keepdim=False)
+        self.L1_dist = \
+            nn.PairwiseDistance(p=1.0, eps=1e-06, keepdim=False)
+        self.cos_sim = nn.CosineSimilarity(dim=-1, eps=1e-08)
+
+    def deprocess_and_preprocess(self, imgs):
+        '''
+        For a batch of real / generated image, perform 'imagenet' or 'standard'
+        deprocess, and FaceNet Preprocess
+        '''
+        #print('Begin:', imgs[0])
+        imgs = fast_imagenet_deprocess_batch(
+            imgs,
+            normalize_method=self.image_normalize_method)
+        #print('Our distribution:', imgs[0])
+        imgs = fixed_image_standardization(imgs)
+        #print('fixed_image_standardization:', imgs[0])
+        return imgs
+
+    def crop_vgg_box(self, imgs):
+        # with correct cropping & correct processing
+        left, top, right, bottom = VGG_BOX
+        # = [0.235015, 0.19505739, 0.76817876, 0.9154963]
+        N, C, H, W = imgs.shape
+        left = int(left * W)
+        right = int(right * W)
+        top = int(top * H)
+        bottom = int(bottom * H)
+        imgs = imgs[:, :, top:bottom+1, left:right+1]
+        return imgs
+
+    def get_dataset_embeddings(self):
+        with torch.no_grad():
+            # To avoid influence to the running_mean/var of BatchNorm Layers
+            embedding_batches = []
+            if self.hq_emb_dict:
+                for i in prog_bar(
+                    range(len(self.loader.dataset)),
+                    title="[S2fEvaluator: " + \
+                        "Preparing FaceNet Embedding Dictionary]",
+                    width=50):
+                    # Loop logic
+                    ######### unpack the data #########
+                    imgs = self.loader.dataset.get_all_faces_of_id(i)
+                    imgs = imgs.cuda()
+                    ###################################
+                    if self.do_deprocess_and_preprocess:
+                        imgs = self.deprocess_and_preprocess(imgs)
+                    if self.crop_faces:
+                        imgs = self.crop_vgg_box(imgs)
+                    embeddings = self.facenet(imgs)
+                    embeddings = torch.mean(embeddings, 0, keepdim=True)
+                    embedding_batches.append(embeddings)
+            else:
+                for batch in prog_bar(
+                    self.loader,
+                    title="[S2fEvaluator: " + \
+                        "Preparing FaceNet Embedding Dictionary]",
+                    width=50):
+                    # Loop logic
+                    ######### unpack the data #########
+                    imgs, log_mels, human_ids = batch
+                    imgs = imgs.cuda()
+                    ###################################
+                    if self.do_deprocess_and_preprocess:
+                        imgs = self.deprocess_and_preprocess(imgs)
+                    if self.crop_faces:
+                        imgs = self.crop_vgg_box(imgs)
+                    embeddings = self.facenet(imgs)
+                    embedding_batches.append(embeddings)
+            self.dataset_embedding = torch.cat(embedding_batches, 0)
+        print("S2fEvaluator: dataset_embedding shape:",
+              self.dataset_embedding.shape)
+
+    def get_pred_img_embeddings(self, model):
+        '''
+        This function focus on evaluating the speech-to-face specific metrics,
+        Including:
+            1. Deep feature (FaceNet Feature) Similarity
+            2. Extraction Recall@K
+            3. Landmark Correlation
+        '''
+        training_status = model.training
+        model.eval()
+        pred_img_embedding_batches = []
+        with torch.no_grad():
+            # To avoid influence to the running_mean/var of BatchNorm Layers
+            if self.face_gen_mode == 'naive':
+                for batch in prog_bar(
+                    self.loader,
+                    title="[S2fEvaluator: " + \
+                        "Getting FaceNet Embedding for Predicted Images]",
+                    width=50):
+                    # Loop logic
+                    ######### unpack the data #########
+                    imgs, log_mels, human_ids = batch
+                    imgs = imgs.cuda()
+                    log_mels = log_mels.type(self.float_dtype)
+                    human_ids = human_ids.type(self.long_dtype)
+                    ###################################
+                    # Run the model as it has been run during training
+                    model_out = model(log_mels)
+                    imgs_pred, others = model_out
+                    # Multi-Reso Case
+                    if isinstance(imgs_pred, tuple):
+                        imgs_pred = imgs_pred[-1]
+                    if self.do_deprocess_and_preprocess:
+                        imgs_pred = self.deprocess_and_preprocess(imgs_pred)
+                    if self.crop_faces:
+                        imgs_pred = self.crop_vgg_box(imgs_pred)
+                    pred_img_embeddings = self.facenet(imgs_pred)
+                    pred_img_embedding_batches.append(pred_img_embeddings)
+            elif self.face_gen_mode == 'average_facenet_embedding':
+                for i in prog_bar(
+                    range(len(self.loader.dataset)),
+                    title="[S2fEvaluator: " + \
+                        "Getting FaceNet Embedding for Predicted Images, " + \
+                            "with average Facenet embedding policy]",
+                    width=50):
+                    # Loop logic
+                    ######### unpack the data #########
+                    log_mels = self.loader.dataset.get_all_mel_segments_of_id(i)
+                    log_mels = log_mels.type(self.float_dtype)
+                    ###################################
+                    # Run the model as it has been run during training
+                    model_out = model(log_mels)
+                    imgs_pred, others = model_out
+                    if isinstance(imgs_pred, tuple):
+                        imgs_pred = imgs_pred[-1]
+                    if self.do_deprocess_and_preprocess:
+                        imgs_pred = self.deprocess_and_preprocess(imgs_pred)
+                    if self.crop_faces:
+                        imgs_pred = self.crop_vgg_box(imgs_pred)
+                    pred_img_embeddings = self.facenet(imgs_pred)
+                    pred_img_embeddings = torch.mean(
+                        pred_img_embeddings, 0, keepdim=True)
+                    pred_img_embedding_batches.append(pred_img_embeddings)
+            elif self.face_gen_mode == 'average_voice_embedding':
+                for i in prog_bar(
+                    range(len(self.loader.dataset)),
+                    title="[S2fEvaluator: " + \
+                        "Getting FaceNet Embedding for Predicted Images, " + \
+                            "with average voice embedding policy]",
+                    width=50):
+                    # Loop logic
+                    ######### unpack the data #########
+                    log_mels = self.loader.dataset.get_all_mel_segments_of_id(i)
+                    log_mels = log_mels.type(self.float_dtype)
+                    ###################################
+                    # Run the model as it has been run during training
+                    model_out = model(log_mels, average_voice_embedding=True)
+                    imgs_pred, others = model_out
+                    if isinstance(imgs_pred, tuple):
+                        imgs_pred = imgs_pred[-1]
+                    if self.do_deprocess_and_preprocess:
+                        imgs_pred = self.deprocess_and_preprocess(imgs_pred)
+                    if self.crop_faces:
+                        imgs_pred = self.crop_vgg_box(imgs_pred)
+                    pred_img_embeddings = self.facenet(imgs_pred)
+                    #pred_img_embeddings = torch.mean(
+                    #    pred_img_embeddings, 0, keepdim=True)
+                    pred_img_embedding_batches.append(pred_img_embeddings)
+            pred_img_embedding = torch.cat(pred_img_embedding_batches, 0)
+        #print("S2fEvaluator: pred_img_embedding shape:",
+        #      pred_img_embedding.shape)
+        model.train(mode=training_status)
+
+        return pred_img_embedding
+
+    def L1_query(self, x, y):
+        '''
+        Given x, extract top K similar features from y, based on L1 distance
+        x in shape (N_x, D)
+        y in shape (N_y, D)
+        '''
+        # (N_x, D) --> (N_x, 1, D)
+        x = x.unsqueeze(1)
+        # Initialize: (N_x, )
+        x_ids = torch.tensor(np.arange(x.shape[0])).cpu()
+        # (N_y, D) --> (1, N_y, D)
+        y = y.unsqueeze(0)
+        # Output: (N_x, N_y, D)
+        L1_table = torch.abs(x - y)
+        # (N_x, N_y, D) --> (N_x, N_y)
+        L1_table = torch.mean(L1_table, dim=-1)
+        L1_table = torch.neg(L1_table)
+        # Top K: (N_x, K)
+        top_1_vals, top_1_indices = torch.topk(L1_table, 1, dim=-1)
+        top_5_vals, top_5_indices = torch.topk(L1_table, 5, dim=-1)
+        top_10_vals, top_10_indices = torch.topk(L1_table, 10, dim=-1)
+        top_50_vals, top_50_indices = torch.topk(L1_table, 50, dim=-1)
+
+        recall_at_1 = self.in_top_k(top_1_indices.cpu(), x_ids)
+        recall_at_5 = self.in_top_k(top_5_indices.cpu(), x_ids)
+        recall_at_10 = self.in_top_k(top_10_indices.cpu(), x_ids)
+        recall_at_50 = self.in_top_k(top_50_indices.cpu(), x_ids)
+
+        return recall_at_1, recall_at_5, recall_at_10, recall_at_50
+
+    def cos_query(self, x, y):
+        '''
+        Given x, extract top K similar features from y, based on L1 distance
+        x in shape (N_x, D)
+        y in shape (N_y, D)
+        '''
+        # (N_x, D) --> (N_x, 1, D)
+        x = x.unsqueeze(1)
+        # Initialize: (N_x, )
+        x_ids = torch.tensor(np.arange(x.shape[0])).cpu()
+        # (N_y, D) --> (1, N_y, D)
+        y = y.unsqueeze(0)
+        # Output: (N_x, N_y)
+        cos_table = self.cos_sim(x, y)
+
+        # Top K: (N_x, K)
+        top_1_vals, top_1_indices = torch.topk(cos_table, 1, dim=-1)
+        top_2_vals, top_2_indices = torch.topk(cos_table, 2, dim=-1)
+        top_5_vals, top_5_indices = torch.topk(cos_table, 5, dim=-1)
+        top_10_vals, top_10_indices = torch.topk(cos_table, 10, dim=-1)
+        top_20_vals, top_20_indices = torch.topk(cos_table, 20, dim=-1)
+        top_50_vals, top_50_indices = torch.topk(cos_table, 50, dim=-1)
+
+        recall_at_1 = self.in_top_k(top_1_indices.cpu(), x_ids)
+        recall_at_2 = self.in_top_k(top_2_indices.cpu(), x_ids)
+        recall_at_5 = self.in_top_k(top_5_indices.cpu(), x_ids)
+        recall_at_10 = self.in_top_k(top_10_indices.cpu(), x_ids)
+        recall_at_20 = self.in_top_k(top_20_indices.cpu(), x_ids)
+        recall_at_50 = self.in_top_k(top_50_indices.cpu(), x_ids)
+
+        recall_tuple = (recall_at_1, recall_at_2, recall_at_5, recall_at_10, \
+            recall_at_20, recall_at_50)
+
+        return recall_tuple
+
+    def cal_ih_sim(self, x, y):
+        '''
+        For an existing face embedding distribution, calculate the inter-human
+        similarity
+
+        Arguments:
+            x in shape (N_x, D)
+            x in shape (N_y, D)
+        '''
+        # (N_x, D) --> (N_x, 1, D)
+        y = y.unsqueeze(0)
+        x = x.unsqueeze(1)
+        # Output: (N_x, N_y)
+        cos_table = self.cos_sim(x, y)
+        cos_table = cos_table.detach().cpu().numpy()
+        ih_sum = 0.0
+        for i in range(cos_table.shape[0]):
+            for j in range(cos_table.shape[1]):
+                if i != j:
+                    ih_sum = ih_sum + cos_table[i, j]
+        ih_sim = ih_sum / float(cos_table.shape[0] * (cos_table.shape[1] - 1))
+        return ih_sim
+
+    def in_top_k(self, top_k_indices, gt_labels):
+        results = []
+        for i, top_k_id in enumerate(top_k_indices):
+            gt_label = gt_labels[i]
+            #print(gt_label, top_k_id)
+            if gt_label in top_k_id:
+                results.append(1.0)
+            else:
+                results.append(0.0)
+            #print('results:', results)
+        return np.mean(results)
+
+    def get_metrics(self, model, recall_method='cos_sim', get_ih_sim=False):
+        '''
+        Arguments:
+            recall_method: the similarity metric to use for retrival
+            get_ih_sim: get the inter-human similarity
+        '''
+        pred_img_embedding = self.get_pred_img_embeddings(model)
+        L2_dist = self.L2_dist(self.dataset_embedding, pred_img_embedding)
+        L2_dist = torch.mean(L2_dist).item()
+        L1_dist = self.L1_dist(self.dataset_embedding, pred_img_embedding)
+        L1_dist = torch.mean(L1_dist).item()
+        cos_sim = self.cos_sim(self.dataset_embedding, pred_img_embedding)
+        cos_sim = torch.mean(cos_sim).item()
+
+        # We might use only 100 id for extraction
+        if self.extraction_size is None:
+            pred_emb_to_use = pred_img_embedding
+            data_emb_to_use = self.dataset_embedding
+        elif isinstance(self.extraction_size, list):
+            pred_emb_to_use = [pred_img_embedding[0:self.extraction_size[0]], \
+                pred_img_embedding[self.extraction_size[0]:self.extraction_size[1]], \
+                pred_img_embedding[self.extraction_size[1]:self.extraction_size[2]]]
+            data_emb_to_use = [self.dataset_embedding[0:self.extraction_size[0]], \
+                self.dataset_embedding[self.extraction_size[0]:self.extraction_size[1]], \
+                self.dataset_embedding[self.extraction_size[1]:self.extraction_size[2]]]
+        else:
+            pred_emb_to_use = pred_img_embedding[0:self.extraction_size]
+            data_emb_to_use = self.dataset_embedding[0:self.extraction_size]
+
+        if recall_method == 'L1':
+            if isinstance(pred_emb_to_use, list):
+                recall_temp = []
+                for i, pred_emb in enumerate(pred_emb_to_use):
+                    recall_temp.append(self.L1_query(pred_emb, data_emb_to_use[i]))
+                recall_tuple = tuple(np.mean(np.array(recall_temp), axis=0))
+            else:
+                recall_tuple = self.L1_query(pred_emb_to_use, data_emb_to_use)
+        elif recall_method == 'cos_sim':
+            if isinstance(pred_emb_to_use, list):
+                recall_temp = []
+                for i, pred_emb in enumerate(pred_emb_to_use):
+                    recall_temp.append(self.cos_query(pred_emb, data_emb_to_use[i]))
+                recall_tuple = tuple(np.mean(np.array(recall_temp), axis=0))
+            else:
+                recall_tuple = self.cos_query(pred_emb_to_use, data_emb_to_use)
+
+        if get_ih_sim:
+            ih_sim = self.cal_ih_sim(pred_img_embedding, self.dataset_embedding)
+            return L2_dist, L1_dist, cos_sim, recall_tuple, ih_sim
+        else:
+            return L2_dist, L1_dist, cos_sim, recall_tuple
+
+    def get_faces_from_different_segments(self, model, output_dir):
+        #gfile.MkDir(output_dir)
+        os.makedirs(output_dir, exist_ok=True)
+        temp_loader = deepcopy(self.loader)
+        for i in prog_bar(
+            range(len(self.loader.dataset)),
+            title="[S2fEvaluator: " + \
+                "Generating Faces from Different Speech Segments]",
+            width=50):
+            ######### unpack the data #########
+            imgs = temp_loader.dataset.get_all_faces_of_id(i)
+            log_mels = temp_loader.dataset.get_all_mel_segments_of_id(i)
+            imgs = imgs.cuda()
+            log_mels = log_mels.type(self.float_dtype) #cuda()
+            #human_ids = human_ids.type(long_dtype)
+            ###################################
+            with torch.no_grad():
+                model_out = model(log_mels)
+            imgs_pred, _ = model_out
+            if isinstance(imgs_pred, tuple):
+                imgs_pred = imgs_pred[-1]
+            imgs = imagenet_deprocess_batch(
+                imgs, normalize_method=self.image_normalize_method)
+            imgs_pred = imagenet_deprocess_batch(
+                imgs_pred, normalize_method=self.image_normalize_method)
+            #print(imgs.shape, imgs_pred.shape)
+            identity_dir = os.path.join(output_dir, str(i))
+            gfile.MkDir(identity_dir)
+
+            for j in range(imgs.shape[0]):
+                img_np = imgs[j].numpy().transpose(1, 2, 0)
+                img_path = os.path.join(identity_dir, 'origin_%d.png' % j)
+                imwrite(img_path, img_np)
+
+            for k in range(imgs_pred.shape[0]):
+                img_np = imgs_pred[k].numpy().transpose(1, 2, 0)
+                img_path = os.path.join(identity_dir, 'pred_%d.png' % k)
+                imwrite(img_path, img_np)
+
+    def L2_distances(self, x, y=None):
+        '''
+        Input: x is a Nxd matrix
+               y is an optional Mxd matirx
+        Output: dist is a NxM matrix where dist[i,j] is the square norm between x[i,:] and y[j,:]
+                if y is not given then use 'y=x'.
+        i.e. dist[i,j] = ||x[i,:]-y[j,:]||^2
+        '''
+        if y is not None:
+             differences = x.unsqueeze(1) - y.unsqueeze(0)
+        else:
+            differences = x.unsqueeze(1) - x.unsqueeze(0)
+        distances = torch.sum(differences * differences, -1)
+        return distances
diff --git a/mlops/serving/sf2f/utils/training_utils.py b/mlops/serving/sf2f/utils/training_utils.py
new file mode 100644
index 0000000..b917b7f
--- /dev/null
+++ b/mlops/serving/sf2f/utils/training_utils.py
@@ -0,0 +1,250 @@
+import os
+import json
+import math
+from collections import defaultdict
+import time
+
+import numpy as np
+import torch
+import torch.optim as optim
+import torch.nn as nn
+import torch.nn.functional as F
+from scipy.stats import entropy
+from torchvision.models import inception_v3
+from pyprind import prog_bar
+
+from datasets import imagenet_deprocess_batch, fast_mel_deprocess_batch
+import datasets
+import models
+from utils.metrics import jaccard
+from utils import tensor2im
+from utils.visualization.plot import get_np_plot, plot_mel_spectrogram, \
+    plot_attention
+
+from scripts.compute_vggface_score import get_vggface_score
+
+
+def add_loss_with_tensor(total_loss,
+                         curr_loss,
+                         loss_dict,
+                         loss_name,
+                         weight=1):
+    curr_loss = curr_loss * weight
+    loss_dict[loss_name] = curr_loss
+    if total_loss is not None:
+        total_loss += curr_loss
+    else:
+        total_loss = curr_loss
+    return total_loss
+
+def add_loss(total_loss, curr_loss, loss_dict, loss_name, weight=1):
+    curr_loss = curr_loss * weight
+    loss_dict[loss_name] = curr_loss.item()
+    if total_loss is not None:
+        total_loss += curr_loss
+    else:
+        total_loss = curr_loss
+    return total_loss
+
+
+def visualize_sample(model,
+                     imgs,
+                     log_mels,
+                     image_normalize_method,
+                     mel_normalize_method='vox_mel',
+                     visualize_attn=False):
+    '''
+    Prepare data for tensorboard
+    '''
+    samples = []
+    # add the ground-truth images
+    samples.append(imgs[:1])
+
+    with torch.no_grad():
+        model_out = model(log_mels)
+        imgs_pred, others = model_out
+        # add the reconstructed images
+        if isinstance(imgs_pred, tuple):
+            if len(imgs_pred) >= 2:
+                low_imgs_pred = imgs_pred[0]
+                # Rescale
+                while low_imgs_pred.size()[2] != imgs_pred[-1].size()[2]:
+                    low_imgs_pred = F.interpolate(
+                        low_imgs_pred, scale_factor=2, mode='nearest')
+                samples.append(low_imgs_pred[:1])
+            if len(imgs_pred) == 3:
+                mid_imgs_pred = imgs_pred[1]
+                while mid_imgs_pred.size()[2] != imgs_pred[-1].size()[2]:
+                    mid_imgs_pred = F.interpolate(
+                        mid_imgs_pred, scale_factor=2, mode='nearest')
+                samples.append(mid_imgs_pred[:1])
+            imgs_pred = imgs_pred[-1]
+        samples.append(imgs_pred[:1])
+
+        log_mels_de = fast_mel_deprocess_batch(log_mels, mel_normalize_method)
+        log_mel = log_mels_de[0].cpu().detach().numpy()
+
+        if visualize_attn:
+            attn_weights = model.module.decoder.return_attn_weights()
+            attn_weight = attn_weights[0].cpu().detach().numpy()
+
+    samples = torch.cat(samples, dim=3)
+    samples = {
+            "samples": tensor2im(
+                imagenet_deprocess_batch(
+                    samples,
+                    rescale=False,
+                    normalize_method=image_normalize_method
+                ).squeeze(0)
+            ),
+            #"mel_spectrogram": get_np_plot(
+            #    plot_mel_spectrogram(
+            #        log_mel)
+            #)
+    }
+    if visualize_attn:
+        samples['attention_weights'] = get_np_plot(
+            plot_attention(attn_weight))
+
+    # Draw Scene Graphs
+    #sg_array = draw_scene_graph(objs[obj_to_img == 0],
+    #                 triples[triple_to_img == 0],
+    #                 vocab=vocab)
+    #samples["scene_graph"] = sg_array
+
+    return samples
+
+
+def check_model(args,
+                options,
+                t,
+                loader,
+                model):
+    training_status = model.training
+    model.eval()
+    float_dtype = torch.cuda.FloatTensor
+    long_dtype = torch.cuda.LongTensor
+    num_samples = 0
+    all_losses = defaultdict(list)
+    inception_module = nn.DataParallel(
+        inception_v3(
+            pretrained=True,
+            transform_input=False).cuda())
+    inception_module.eval()
+    preds = []
+    images = []
+    with torch.no_grad():
+        # To avoid influence to the running_mean/var of BatchNorm Layers
+        for batch in prog_bar(
+            loader,
+            title="[Validating Inception Score and Pixel Loss]",
+            width=50):
+            # Loop logic
+            ######### unpack the data #########
+            imgs, log_mels, human_ids = batch
+            imgs = imgs.cuda()
+            log_mels = log_mels.type(float_dtype)
+            human_ids = human_ids.type(long_dtype)
+            ###################################
+            # Run the model as it has been run during training
+            model_out = model(log_mels)
+            imgs_pred, others = model_out
+
+            skip_pixel_loss = False
+            total_loss, losses = calculate_model_losses(
+                options["optim"],
+                skip_pixel_loss,
+                imgs,
+                imgs_pred,
+                get_item=True)
+
+            if isinstance(imgs_pred, tuple):
+                imgs_pred = imgs_pred[-1]
+            image = imagenet_deprocess_batch(
+                imgs_pred,
+                normalize_method=options["data"]["data_opts"]["image_normalize_method"])
+            for i in range(image.shape[0]):
+                img_np = image[i].numpy().transpose(1, 2, 0)
+                images.append(img_np)
+
+            # check inception scores
+            x = F.interpolate(imgs_pred, (299, 299), mode="bilinear")
+            x = inception_module(x)
+            preds.append(F.softmax(x).cpu().numpy())
+
+            for loss_name, loss_val in losses.items():
+                all_losses[loss_name].append(loss_val)
+            num_samples += imgs.size(0)
+
+        samples = visualize_sample(
+            model,
+            imgs,
+            log_mels,
+            options["data"]["data_opts"]["image_normalize_method"],
+            visualize_attn=options['eval'].get('visualize_attn', False))
+        mean_losses = {k: np.mean(v) for k, v in all_losses.items()}
+
+        # calculate the inception scores
+        splits = 5
+        preds = np.concatenate(preds, axis=0)
+        # Now compute the mean kl-div
+        split_scores = []
+        N = preds.shape[0]
+        for k in range(splits):
+            part = preds[k * (N // splits): (k+1) * (N // splits), :]
+            py = np.mean(part, axis=0)
+            scores = []
+            for i in range(part.shape[0]):
+                pyx = part[i, :]
+                scores.append(entropy(pyx, py))
+            split_scores.append(np.exp(np.mean(scores)))
+        inception_score = (np.mean(split_scores), np.std(split_scores))
+
+    vf_score = get_vggface_score(images)
+
+    out = [mean_losses, samples, inception_score, vf_score]
+    model.train(mode=training_status)
+    return tuple(out)
+
+
+def calculate_model_losses(opts,
+                           skip_pixel_loss,
+                           imgs,
+                           imgs_pred,
+                           get_item=False):
+    if get_item:
+        add_loss_fn = add_loss
+    else:
+        add_loss_fn = add_loss_with_tensor
+    total_loss = torch.zeros(1).to(imgs)
+    losses = {}
+
+    l1_pixel_weight = opts["l1_pixel_loss_weight"]
+    if skip_pixel_loss:
+        l1_pixel_weight = 0
+
+    if isinstance(imgs_pred, tuple):
+        # Multi-Resolution Pixel Loss
+        for i, img_pred in enumerate(imgs_pred):
+            loss_name = 'L1_pixel_loss_%d' % i
+            img = imgs
+            while img.size()[2] != img_pred.size()[2]:
+                img = F.interpolate(img, scale_factor=0.5, mode='nearest')
+            l1_pixel_loss = F.l1_loss(img_pred, img)
+            total_loss = add_loss_fn(
+                total_loss,
+                l1_pixel_loss,
+                losses,
+                loss_name,
+                l1_pixel_weight)
+    else:
+        # Single Resolution
+        l1_pixel_loss = F.l1_loss(imgs_pred, imgs)
+        total_loss = add_loss_fn(
+            total_loss,
+            l1_pixel_loss,
+            losses,
+            'L1_pixel_loss',
+            l1_pixel_weight)
+
+    return total_loss, losses
diff --git a/mlops/serving/sf2f/utils/upload_minio.py b/mlops/serving/sf2f/utils/upload_minio.py
new file mode 100644
index 0000000..cd4997e
--- /dev/null
+++ b/mlops/serving/sf2f/utils/upload_minio.py
@@ -0,0 +1,20 @@
+import os
+from minio import Minio
+
+ALLOWED_EXTENSIONS = {"txt", "pdf", "png", "jpg", "jpeg", "gif"}
+ACCESS_KEY = os.environ.get("AWS_ACCESS_KEY_ID")
+SECRET_KEY = os.environ.get("AWS_SECRET_ACCESS_KEY")
+BUCKET_NAME = os.environ.get("MINIO_BUCKET")
+MINIO_API_HOST = os.environ.get("MINIO_ENDPOINT")
+
+def upload_object(client, filename, data, length, BUCKET_NAME):
+
+    # Make bucket if not exist.
+    found = client.bucket_exists(BUCKET_NAME)
+    if not found:
+        client.make_bucket(BUCKET_NAME)
+    else:
+        print(f"Bucket {BUCKET_NAME} already exists")
+
+    client.put_object(BUCKET_NAME, filename, data, length)
+    print(f"{filename} is successfully uploaded to bucket {BUCKET_NAME}.")
\ No newline at end of file
diff --git a/mlops/serving/sf2f/utils/utils.py b/mlops/serving/sf2f/utils/utils.py
new file mode 100644
index 0000000..0157a82
--- /dev/null
+++ b/mlops/serving/sf2f/utils/utils.py
@@ -0,0 +1,89 @@
+import time
+import inspect
+import subprocess
+from contextlib import contextmanager
+
+import torch
+from torch import nn
+
+def int_tuple(s):
+    return tuple(int(i) for i in s.split(','))
+
+
+def float_tuple(s):
+    return tuple(float(i) for i in s.split(','))
+
+
+def str_tuple(s):
+    return tuple(s.split(','))
+
+
+def bool_flag(s):
+    if s == '1':
+        return True
+    elif s == '0':
+        return False
+    msg = 'Invalid value "%s" for bool flag (should be 0 or 1)'
+    raise ValueError(msg % s)
+
+
+def lineno():
+    return inspect.currentframe().f_back.f_lineno
+
+
+def get_gpu_memory():
+    torch.cuda.synchronize()
+    opts = [
+        'nvidia-smi', '-q', '--gpu=' + str(0), '|', 'grep', '"Used GPU Memory"'
+    ]
+    cmd = str.join(' ', opts)
+    ps = subprocess.Popen(
+        cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+    output = ps.communicate()[0].decode('utf-8')
+    output = output.split("\n")[1].split(":")
+    consumed_mem = int(output[1].strip().split(" ")[0])
+    return consumed_mem
+
+
+@contextmanager
+def timeit(msg, should_time=True):
+    if should_time:
+        torch.cuda.synchronize()
+        t0 = time.time()
+    yield
+    if should_time:
+        torch.cuda.synchronize()
+        t1 = time.time()
+        duration = (t1 - t0) * 1000.0
+        print('%s: %.2f ms' % (msg, duration))
+
+
+class LossManager(object):
+    def __init__(self):
+        self.total_loss = None
+        self.all_losses = {}
+
+    def add_loss(self, loss, name, weight=1.0):
+        cur_loss = loss * weight
+        if self.total_loss is not None:
+            self.total_loss += cur_loss
+        else:
+            self.total_loss = cur_loss
+
+        self.all_losses[name] = cur_loss.data.cpu().item()
+
+    def items(self):
+        return self.all_losses.items()
+
+
+def load_my_state_dict(model, state_dict):
+    print('Loading my state_dict')
+    own_state = model.state_dict()
+    for name, param in state_dict.items():
+        if name not in own_state:
+            continue
+        if isinstance(param, nn.Parameter):
+            # backwards compatibility for serialized parameters
+            param = param.data
+        own_state[name].copy_(param)
+        print('{} Loaded!'.format(name))
diff --git a/mlops/serving/sf2f/utils/vad_ex.py b/mlops/serving/sf2f/utils/vad_ex.py
new file mode 100644
index 0000000..4a9407f
--- /dev/null
+++ b/mlops/serving/sf2f/utils/vad_ex.py
@@ -0,0 +1,179 @@
+import collections
+import contextlib
+import sys
+import wave
+from pydub import AudioSegment
+import webrtcvad
+from io import BytesIO
+import requests
+import subprocess
+
+# Modified https://github.com/wiseman/py-webrtcvad/blob/master/example.py
+
+def read_wave(path):
+    """Reads a .wav file.
+    Takes the path, and returns (PCM audio data, sample rate).
+    """
+    with contextlib.closing(wave.open(path, 'rb')) as wf:
+        num_channels = wf.getnchannels()
+        #assert num_channels == 1
+        sample_width = wf.getsampwidth()
+        print(num_channels, sample_width)
+        #assert sample_width == 2
+        sample_rate = wf.getframerate()
+        #assert sample_rate in (8000, 16000, 32000)
+        pcm_data = wf.readframes(wf.getnframes())
+        return pcm_data, sample_rate
+
+
+def read_libri(path):
+    mf = AudioSegment.from_file(path, "wav")
+    sample_rate = mf.frame_rate
+    pcm_data = mf.raw_data
+    return pcm_data, sample_rate
+
+
+def read_m4a(path):
+    mf = AudioSegment.from_file(path, "m4a")
+    sample_rate = mf.frame_rate
+    pcm_data = mf.raw_data
+    return pcm_data, sample_rate
+
+
+def write_wave(path, audio, sample_rate):
+    """Writes a .wav file.
+    Takes path, PCM audio data, and sample rate.
+    """
+    with contextlib.closing(wave.open(path, 'wb')) as wf:
+        wf.setnchannels(1)
+        wf.setsampwidth(2)
+        wf.setframerate(sample_rate)
+        wf.writeframes(audio)
+
+
+class Frame(object):
+    """Represents a "frame" of audio data."""
+    def __init__(self, bytes, timestamp, duration):
+        self.bytes = bytes
+        self.timestamp = timestamp
+        self.duration = duration
+
+
+def frame_generator(frame_duration_ms, audio, sample_rate):
+    """Generates audio frames from PCM audio data.
+    Takes the desired frame duration in milliseconds, the PCM data, and
+    the sample rate.
+    Yields Frames of the requested duration.
+    """
+    n = int(sample_rate * (frame_duration_ms / 1000.0) * 2)
+    offset = 0
+    timestamp = 0.0
+    duration = (float(n) / sample_rate) / 2.0
+    while offset + n < len(audio):
+        yield Frame(audio[offset:offset + n], timestamp, duration)
+        timestamp += duration
+        offset += n
+
+
+def vad_collector(sample_rate, frame_duration_ms,
+                  padding_duration_ms, vad, frames):
+    """Filters out non-voiced audio frames.
+    Given a webrtcvad.Vad and a source of audio frames, yields only
+    the voiced audio.
+    Uses a padded, sliding window algorithm over the audio frames.
+    When more than 90% of the frames in the window are voiced (as
+    reported by the VAD), the collector triggers and begins yielding
+    audio frames. Then the collector waits until 90% of the frames in
+    the window are unvoiced to detrigger.
+    The window is padded at the front and back to provide a small
+    amount of silence or the beginnings/endings of speech around the
+    voiced frames.
+    Arguments:
+    sample_rate - The audio sample rate, in Hz.
+    frame_duration_ms - The frame duration in milliseconds.
+    padding_duration_ms - The amount to pad the window, in milliseconds.
+    vad - An instance of webrtcvad.Vad.
+    frames - a source of audio frames (sequence or generator).
+    Returns: A generator that yields PCM audio data.
+    """
+    num_padding_frames = int(padding_duration_ms / frame_duration_ms)
+    # We use a deque for our sliding window/ring buffer.
+    ring_buffer = collections.deque(maxlen=num_padding_frames)
+    # We have two states: TRIGGERED and NOTTRIGGERED. We start in the
+    # NOTTRIGGERED state.
+    triggered = False
+
+    voiced_frames = []
+    for frame in frames:
+        is_speech = vad.is_speech(frame.bytes, sample_rate)
+
+        #sys.stdout.write('1' if is_speech else '0')
+        if not triggered:
+            ring_buffer.append((frame, is_speech))
+            num_voiced = len([f for f, speech in ring_buffer if speech])
+            # If we're NOTTRIGGERED and more than 90% of the frames in
+            # the ring buffer are voiced frames, then enter the
+            # TRIGGERED state.
+            if num_voiced > 0.9 * ring_buffer.maxlen:
+                triggered = True
+                #sys.stdout.write('+(%s)' % (ring_buffer[0][0].timestamp,))
+                # We want to yield all the audio we see from now until
+                # we are NOTTRIGGERED, but we have to start with the
+                # audio that's already in the ring buffer.
+                for f, s in ring_buffer:
+                    voiced_frames.append(f)
+                ring_buffer.clear()
+        else:
+            # We're in the TRIGGERED state, so collect the audio data
+            # and add it to the ring buffer.
+            voiced_frames.append(frame)
+            ring_buffer.append((frame, is_speech))
+            num_unvoiced = len([f for f, speech in ring_buffer if not speech])
+            # If more than 90% of the frames in the ring buffer are
+            # unvoiced, then enter NOTTRIGGERED and yield whatever
+            # audio we've collected.
+            if num_unvoiced > 0.9 * ring_buffer.maxlen:
+                #sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
+                triggered = False
+                yield b''.join([f.bytes for f in voiced_frames])
+                ring_buffer.clear()
+                voiced_frames = []
+    #if triggered:
+    #    sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
+    #sys.stdout.write('\n')
+    # If we have any leftover voiced audio when we run out of input,
+    # yield it.
+    if voiced_frames:
+        yield b''.join([f.bytes for f in voiced_frames])
+
+
+def main(args):
+    if len(args) != 2:
+        sys.stderr.write(
+            'Usage: example.py <aggressiveness> <path to wav file>\n')
+        sys.exit(1)
+    audio, sample_rate = read_wave(args[1])
+    #audio, sample_rate = read_m4a(args[1])
+    #audio, sample_rate = read_libri(args[1])
+    vad = webrtcvad.Vad(int(args[0]))
+    frames = frame_generator(30, audio, sample_rate)
+    frames = list(frames)
+    segments = vad_collector(sample_rate, 30, 300, vad, frames)
+    total_wav = b""
+    for i, segment in enumerate(segments):
+    	total_wav += segment
+
+
+    path = 'test.wav'
+    write_wave(path, total_wav, sample_rate)
+
+
+if __name__ == '__main__':
+    #main(sys.argv[1:])
+    import os
+    WAV_DIR = os.path.join('./data', 'VoxCeleb', 'raw_wav')
+    wav_path = 'vox1/test/id10270/zjwijMp0Qyw/00001.wav'
+    wav_path = os.path.join(WAV_DIR, wav_path)
+
+    audio, sample_rate = read_wave(wav_path)
+    print(audio, sample_rate)
diff --git a/mlops/serving/sf2f/utils/visualization/__init__.py b/mlops/serving/sf2f/utils/visualization/__init__.py
new file mode 100644
index 0000000..2b54423
--- /dev/null
+++ b/mlops/serving/sf2f/utils/visualization/__init__.py
@@ -0,0 +1,2 @@
+from .vis import *
+# from .visualizer import Visualizer
diff --git a/mlops/serving/sf2f/utils/visualization/html.py b/mlops/serving/sf2f/utils/visualization/html.py
new file mode 100644
index 0000000..1e71687
--- /dev/null
+++ b/mlops/serving/sf2f/utils/visualization/html.py
@@ -0,0 +1,65 @@
+import dominate
+from dominate.tags import *
+import os
+
+
+class HTML:
+    def __init__(self, web_dir, title, reflesh=0):
+        self.title = title
+        self.web_dir = web_dir
+        self.img_dir = os.path.join(self.web_dir, 'images')
+        if not os.path.exists(self.web_dir):
+            os.makedirs(self.web_dir)
+        if not os.path.exists(self.img_dir):
+            os.makedirs(self.img_dir)
+        # print(self.img_dir)
+
+        self.doc = dominate.document(title=title)
+        if reflesh > 0:
+            with self.doc.head:
+                meta(http_equiv="reflesh", content=str(reflesh))
+
+    def get_image_dir(self):
+        return self.img_dir
+
+    def add_header(self, str):
+        with self.doc:
+            h3(str)
+
+    def add_table(self, border=1):
+        self.t = table(border=border, style="table-layout: fixed;")
+        self.doc.add(self.t)
+
+    def add_images(self, ims, txts, links, width=400):
+        self.add_table()
+        with self.t:
+            with tr():
+                for im, txt, link in zip(ims, txts, links):
+                    with td(style="word-wrap: break-word;", halign="center", valign="top"):
+                        with p():
+                            with a(href=os.path.join('images', link)):
+                                img(style="width:%dpx" %
+                                    width, src=os.path.join('images', im))
+                            br()
+                            p(txt)
+
+    def save(self):
+        html_file = '%s/index.html' % self.web_dir
+        f = open(html_file, 'wt')
+        f.write(self.doc.render())
+        f.close()
+
+
+if __name__ == '__main__':
+    html = HTML('web/', 'test_html')
+    html.add_header('hello world')
+
+    ims = []
+    txts = []
+    links = []
+    for n in range(4):
+        ims.append('image_%d.png' % n)
+        txts.append('text_%d' % n)
+        links.append('image_%d.png' % n)
+    html.add_images(ims, txts, links)
+    html.save()
diff --git a/mlops/serving/sf2f/utils/visualization/plot.py b/mlops/serving/sf2f/utils/visualization/plot.py
new file mode 100644
index 0000000..7f24e9d
--- /dev/null
+++ b/mlops/serving/sf2f/utils/visualization/plot.py
@@ -0,0 +1,199 @@
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import io
+import tensorflow as tf
+import PIL
+import numpy as np
+
+
+def plot_attention(attn_mat, path=None, info=None):
+    num_query_head, seq_len = attn_mat.shape
+    fig, ax = plt.subplots()
+    fig.set_size_inches(10, 10)
+
+    querys = ['query {}'.format(i) for i in range(num_query_head)]
+    steps = ['steps {}'.format(j) for j in range(seq_len)]
+
+    im, cbar = heatmap(attn_mat, querys, steps, ax=ax,
+                       cmap="YlGn", cbarlabel="Attention Weight")
+    annotate_heatmap(im)
+
+    fig.tight_layout()
+    if path is not None:
+        plt.savefig(path, format='png')
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    plt.clf()
+    plt.cla()
+    plt.close(fig)
+    return buf
+
+
+def plot_mel_spectrogram(log_mels,
+                         path=None,
+                         info=None,
+                         colorbar=True,
+                         label=True,
+                         coordinate=True,
+                         remove_boarder=False):
+    fig, ax = plt.subplots()
+    if not coordinate:
+        #ax.format_coord = lambda x, y: ''
+        plt.axis('off')
+    fig.set_size_inches(5, 3)
+    im = ax.imshow(
+        log_mels,
+        aspect='auto',
+        origin='lower',
+        interpolation='none')
+    im.set_cmap('viridis')
+    if colorbar:
+        fig.colorbar(im, ax=ax)
+    xlabel = 'Time'
+    if info is not None:
+        xlabel += '\n\n' + info
+    if label:
+        plt.xlabel(xlabel)
+        plt.ylabel('Frequency')
+    plt.tight_layout()
+    if path is not None:
+        plt.savefig(path, format='png')
+    buf = io.BytesIO()
+    if not remove_boarder:
+        plt.savefig(buf, format='png')
+    else:
+        plt.savefig(buf, format='png', bbox_inches='tight')
+    buf.seek(0)
+    plt.clf()
+    plt.cla()
+    plt.close(fig)
+    return buf
+
+
+def get_np_plot(plot_buf):
+    image = PIL.Image.open(plot_buf)
+    WW, HH = image.size
+
+    return np.array(image)
+
+
+def heatmap(data, row_labels, col_labels, ax=None,
+            cbar_kw={}, cbarlabel="", **kwargs):
+    """
+    Create a heatmap from a numpy array and two lists of labels.
+
+    Parameters
+    ----------
+    data
+        A 2D numpy array of shape (N, M).
+    row_labels
+        A list or array of length N with the labels for the rows.
+    col_labels
+        A list or array of length M with the labels for the columns.
+    ax
+        A `matplotlib.axes.Axes` instance to which the heatmap is plotted.  If
+        not provided, use current axes or create a new one.  Optional.
+    cbar_kw
+        A dictionary with arguments to `matplotlib.Figure.colorbar`.  Optional.
+    cbarlabel
+        The label for the colorbar.  Optional.
+    **kwargs
+        All other arguments are forwarded to `imshow`.
+    """
+
+    if not ax:
+        ax = plt.gca()
+
+    # Plot the heatmap
+    im = ax.imshow(data, **kwargs)
+
+    # Create colorbar
+    cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
+    cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom")
+
+    # We want to show all ticks...
+    ax.set_xticks(np.arange(data.shape[1]))
+    ax.set_yticks(np.arange(data.shape[0]))
+    # ... and label them with the respective list entries.
+    ax.set_xticklabels(col_labels)
+    ax.set_yticklabels(row_labels)
+
+    # Let the horizontal axes labeling appear on top.
+    ax.tick_params(top=True, bottom=False,
+                   labeltop=True, labelbottom=False)
+
+    # Rotate the tick labels and set their alignment.
+    plt.setp(ax.get_xticklabels(), rotation=-30, ha="right",
+             rotation_mode="anchor")
+
+    # Turn spines off and create white grid.
+    for edge, spine in ax.spines.items():
+        spine.set_visible(False)
+
+    ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)
+    ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)
+    ax.grid(which="minor", color="w", linestyle='-', linewidth=3)
+    ax.tick_params(which="minor", bottom=False, left=False)
+
+    return im, cbar
+
+
+def annotate_heatmap(im, data=None, valfmt="{x:.2f}",
+                     textcolors=["black", "white"],
+                     threshold=None, **textkw):
+    """
+    A function to annotate a heatmap.
+
+    Parameters
+    ----------
+    im
+        The AxesImage to be labeled.
+    data
+        Data used to annotate.  If None, the image's data is used.  Optional.
+    valfmt
+        The format of the annotations inside the heatmap.  This should either
+        use the string format method, e.g. "$ {x:.2f}", or be a
+        `matplotlib.ticker.Formatter`.  Optional.
+    textcolors
+        A list or array of two color specifications.  The first is used for
+        values below a threshold, the second for those above.  Optional.
+    threshold
+        Value in data units according to which the colors from textcolors are
+        applied.  If None (the default) uses the middle of the colormap as
+        separation.  Optional.
+    **kwargs
+        All other arguments are forwarded to each call to `text` used to create
+        the text labels.
+    """
+
+    if not isinstance(data, (list, np.ndarray)):
+        data = im.get_array()
+
+    # Normalize the threshold to the images color range.
+    if threshold is not None:
+        threshold = im.norm(threshold)
+    else:
+        threshold = im.norm(data.max())/2.
+
+    # Set default alignment to center, but allow it to be
+    # overwritten by textkw.
+    kw = dict(horizontalalignment="center",
+              verticalalignment="center")
+    kw.update(textkw)
+
+    # Get the formatter in case a string is supplied
+    if isinstance(valfmt, str):
+        valfmt = matplotlib.ticker.StrMethodFormatter(valfmt)
+
+    # Loop over the data and create a `Text` for each "pixel".
+    # Change the text's color depending on the data.
+    texts = []
+    for i in range(data.shape[0]):
+        for j in range(data.shape[1]):
+            kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)])
+            text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)
+            texts.append(text)
+
+    return texts
diff --git a/mlops/serving/sf2f/utils/visualization/tsne.py b/mlops/serving/sf2f/utils/visualization/tsne.py
new file mode 100644
index 0000000..62f8791
--- /dev/null
+++ b/mlops/serving/sf2f/utils/visualization/tsne.py
@@ -0,0 +1,40 @@
+'''
+Visualize embeddings via TSNE
+'''
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from sklearn.manifold import TSNE
+
+
+def visualize_distribution(
+        embeddings,
+        num_class,
+        sample_per_class,
+        class_names=None):
+    '''
+    Inputs:
+        embeddings: numpy array with shape
+            [num_class * sample_per_class, emb_dim]
+    '''
+    emb_2d = TSNE(n_components=2).fit_transform(embeddings)
+    emb_2d = emb_2d.reshape([num_class, sample_per_class, 2])
+    fig, axes = plt.subplots()
+    fig.set_size_inches(18.5, 10.5)
+    if class_names is None:
+        class_names = [' ' for i in range(num_class)]
+    color_list = ['b', 'g', 'r', 'c', 'm', 'y', 'k', \
+        'orange', 'purple', 'pink']
+    for i, class_name in enumerate(class_names):
+        x = emb_2d[i, :, 0]
+        y = emb_2d[i, :, 1]
+        axes.scatter(x=x, y=y, c=color_list[i], label=class_name)
+
+    axes.set_xlabel("Component 1")
+    axes.set_ylabel("Component 2")
+    axes.legend(loc='upper left')
+    axes.set_xlim(-300, 300)
+    axes.set_ylim(-300, 300)
+    plt.show()
diff --git a/mlops/serving/sf2f/utils/visualization/vis.py b/mlops/serving/sf2f/utils/visualization/vis.py
new file mode 100644
index 0000000..de412bd
--- /dev/null
+++ b/mlops/serving/sf2f/utils/visualization/vis.py
@@ -0,0 +1,219 @@
+import tempfile
+import os
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Rectangle
+from imageio import imread
+import time
+
+
+"""
+Utilities for making visualizations.
+"""
+
+
+def draw_layout(vocab, objs, boxes, masks=None, size=256,
+                show_boxes=False, bgcolor=(0, 0, 0)):
+    if bgcolor == 'white':
+        bgcolor = (255, 255, 255)
+
+    cmap = plt.get_cmap('rainbow')
+    colors = cmap(np.linspace(0, 1, len(objs)))
+
+    with torch.no_grad():
+        objs = objs.cpu().clone()
+        boxes = boxes.cpu().clone()
+        boxes *= size
+
+        if masks is not None:
+            masks = masks.cpu().clone()
+
+        bgcolor = np.asarray(bgcolor)
+        bg = np.ones((size, size, 1)) * bgcolor
+        plt.imshow(bg.astype(np.uint8))
+
+        plt.gca().set_xlim(0, size)
+        plt.gca().set_ylim(size, 0)
+        plt.gca().set_aspect(1.0, adjustable='box')
+
+        for i, obj in enumerate(objs):
+            name = vocab['object_idx_to_name'][obj]
+            if name == '__image__':
+                continue
+            box = boxes[i]
+
+            if masks is None:
+                continue
+            mask = masks[i].numpy()
+            mask /= mask.max()
+
+            r, g, b, a = colors[i]
+            colored_mask = mask[:, :, None] * np.asarray(colors[i])
+
+            x0, y0, x1, y1 = box
+            plt.imshow(colored_mask, extent=(x0, x1, y1, y0),
+                       interpolation='bicubic', alpha=1.0)
+
+        if show_boxes:
+            for i, obj in enumerate(objs):
+                name = vocab['object_idx_to_name'][obj]
+                if name == '__image__':
+                    continue
+                box = boxes[i]
+
+                draw_box(box, colors[i], name)
+
+
+def draw_box(box, color, text=None):
+    """
+    Draw a bounding box using pyplot, optionally with a text box label.
+
+    Inputs:
+    - box: Tensor or list with 4 elements: [x0, y0, x1, y1] in [0, W] x [0, H]
+           coordinate system.
+    - color: pyplot color to use for the box.
+    - text: (Optional) String; if provided then draw a label for this box.
+    """
+    TEXT_BOX_HEIGHT = 10
+    if torch.is_tensor(box) and box.dim() == 2:
+        box = box.view(-1)
+        assert box.size(0) == 4
+    x0, y0, x1, y1 = box
+    assert y1 > y0, box
+    assert x1 > x0, box
+    w, h = x1 - x0, y1 - y0
+    rect = Rectangle((x0, y0), w, h, fc='none', lw=2, ec=color)
+    plt.gca().add_patch(rect)
+    if text is not None:
+        text_rect = Rectangle(
+            (x0, y0), w, TEXT_BOX_HEIGHT, fc=color, alpha=0.5)
+        plt.gca().add_patch(text_rect)
+        tx = 0.5 * (x0 + x1)
+        ty = y0 + TEXT_BOX_HEIGHT / 2.0
+        plt.text(tx, ty, text, va='center', ha='center')
+
+
+def draw_scene_graph(objs, triples, vocab=None, **kwargs):
+    """
+    Use GraphViz to draw a scene graph. If vocab is not passed then we assume
+    that objs and triples are python lists containing strings for object and
+    relationship names.
+
+    Using this requires that GraphViz is installed. On Ubuntu 16.04 this is easy:
+    sudo apt-get install graphviz
+    """
+    output_filename = kwargs.pop('output_filename',
+                                 'graph-{}.png'.format(time.strftime("%Y%m%d-%H%M%S")))
+    orientation = kwargs.pop('orientation', 'V')
+    edge_width = kwargs.pop('edge_width', 6)
+    arrow_size = kwargs.pop('arrow_size', 1.5)
+    binary_edge_weight = kwargs.pop('binary_edge_weight', 1.2)
+    ignore_dummies = kwargs.pop('ignore_dummies', True)
+
+    if orientation not in ['V', 'H']:
+        raise ValueError('Invalid orientation "%s"' % orientation)
+    rankdir = {'H': 'LR', 'V': 'TD'}[orientation]
+
+    if vocab is not None:
+        # Decode object and relationship names
+        assert torch.is_tensor(objs)
+        assert torch.is_tensor(triples)
+        objs_list, triples_list = [], []
+        for i in range(objs.size(0)):
+            objs_list.append(vocab['object_idx_to_name'][objs[i].item()])
+        for i in range(triples.size(0)):
+            s = triples[i, 0].item()
+            p = vocab['pred_idx_to_name'][triples[i, 1].item()]
+            # p = triples[i, 1].item()
+            o = triples[i, 2].item()
+            triples_list.append([s, p, o])
+        objs, triples = objs_list, triples_list
+
+    # General setup, and style for object nodes
+    lines = [
+        'digraph{',
+        'graph [size="5,3",ratio="compress",dpi="300",bgcolor="transparent"]',
+        'rankdir=%s' % rankdir,
+        'nodesep="0.5"',
+        'ranksep="0.5"',
+        'node [shape="box",style="rounded,filled",fontsize="48",color="none"]',
+        'node [fillcolor="lightpink1"]',
+    ]
+    # Output nodes for objects
+    for i, obj in enumerate(objs):
+        if ignore_dummies and obj == '__image__':
+            continue
+        lines.append('%d [label="%s"]' % (i, obj))
+
+    # Output relationships
+    next_node_id = len(objs)
+    lines.append('node [fillcolor="lightblue1"]')
+    for s, p, o in triples:
+        if ignore_dummies and p == '__in_image__':
+            continue
+        lines += [
+            '%d [label="%s"]' % (next_node_id, p),
+            '%d->%d [penwidth=%f,arrowsize=%f,weight=%f]' % (
+                s, next_node_id, edge_width, arrow_size, binary_edge_weight),
+            '%d->%d [penwidth=%f,arrowsize=%f,weight=%f]' % (
+                next_node_id, o, edge_width, arrow_size, binary_edge_weight)
+        ]
+        next_node_id += 1
+    lines.append('}')
+
+    # Now it gets slightly hacky. Write the graphviz spec to a temporary
+    # text file
+    ff, dot_filename = tempfile.mkstemp()
+    with open(dot_filename, 'w') as f:
+        for line in lines:
+            f.write('%s\n' % line)
+    os.close(ff)
+
+    # Shell out to invoke graphviz; this will save the resulting image to disk,
+    # so we read it, delete it, then return it.
+    output_format = os.path.splitext(output_filename)[1][1:]
+    os.system('dot -T%s %s > %s' %
+              (output_format, dot_filename, output_filename))
+    os.remove(dot_filename)
+    try:
+        img = imread(output_filename)
+        os.remove(output_filename)
+    except:
+        print("####################################")
+        print("Fail to load image: {}".format(output_filename))
+        print("####################################")
+        img = np.zeros((400, 400, 3))
+
+    return np.array(img)
+
+
+if __name__ == '__main__':
+    o_idx_to_name = ['cat', 'dog', 'hat', 'skateboard']
+    p_idx_to_name = ['riding', 'wearing', 'on', 'next to', 'above']
+    o_name_to_idx = {s: i for i, s in enumerate(o_idx_to_name)}
+    p_name_to_idx = {s: i for i, s in enumerate(p_idx_to_name)}
+    vocab = {
+        'object_idx_to_name': o_idx_to_name,
+        'object_name_to_idx': o_name_to_idx,
+        'pred_idx_to_name': p_idx_to_name,
+        'pred_name_to_idx': p_name_to_idx,
+    }
+
+    objs = [
+        'cat',
+        'cat',
+        'skateboard',
+        'hat',
+    ]
+    objs = torch.LongTensor([o_name_to_idx[o] for o in objs])
+    triples = [
+        [0, 'next to', 1],
+        [0, 'riding', 2],
+        [1, 'wearing', 3],
+        [3, 'above', 2],
+    ]
+    triples = [[s, p_name_to_idx[p], o] for s, p, o in triples]
+    triples = torch.LongTensor(triples)
+
+    draw_scene_graph(objs, triples, vocab, orientation='V')
diff --git a/mlops/serving/sf2f/utils/wav2mel.py b/mlops/serving/sf2f/utils/wav2mel.py
new file mode 100644
index 0000000..35a822d
--- /dev/null
+++ b/mlops/serving/sf2f/utils/wav2mel.py
@@ -0,0 +1,104 @@
+'''
+Convert *.wav or *.m4a into mel spectrogram\
+
+for unit test, run:
+    python utils/wav2mel.py
+'''
+
+
+import random
+import numpy as np
+import argparse
+import time
+from _thread import start_new_thread
+import queue
+from python_speech_features import logfbank
+import webrtcvad
+try:
+    import vad_ex
+except:
+    from utils import vad_ex
+
+
+def vad_process(path):
+    # VAD Process
+    if path.endswith('.wav'):
+        audio, sample_rate = vad_ex.read_wave(path)
+    elif path.endswith('.m4a'):
+        audio, sample_rate = vad_ex.read_m4a(path)
+    else:
+        raise TypeError('Unsupported file type: {}'.format(path.split('.')[-1]))
+
+    vad = webrtcvad.Vad(1)
+    frames = vad_ex.frame_generator(30, audio, sample_rate)
+    frames = list(frames)
+    segments = vad_ex.vad_collector(sample_rate, 30, 300, vad, frames)
+    total_wav = b""
+    for i, segment in enumerate(segments):
+        total_wav += segment
+    # Without writing, unpack total_wav into numpy [N,1] array
+    # 16bit PCM 기준 dtype=np.int16
+    wav_arr = np.frombuffer(total_wav, dtype=np.int16)
+    #print("read audio data from byte string. np array of shape:" + \
+    #    str(wav_arr.shape))
+    return wav_arr, sample_rate
+
+
+def wav_to_mel(path, nfilt=40):
+    '''
+    Output shape: (nfilt, length)
+    '''
+    wav_arr, sample_rate = vad_process(path)
+    #print("sample_rate:", sample_rate)
+    logmel_feats = logfbank(
+        wav_arr,
+        samplerate=sample_rate,
+        nfilt=nfilt)
+    #print("created logmel feats from audio data. np array of shape:" \
+    #    + str(logmel_feats.shape))
+    return np.transpose(logmel_feats)
+
+
+if __name__ == '__main__':
+    import os
+    VOX_DIR = os.path.join('./data', 'VoxCeleb')
+    WAV_DIR = os.path.join('./data', 'VoxCeleb', 'raw_wav')
+    #wav_path = 'vox1/test/id10270/zjwijMp0Qyw/00001.wav'
+    wav_path = 'vox1/dev/id10001/J9lHsKG98U8/00026.wav'
+    wav_path = os.path.join(WAV_DIR, wav_path)
+    # m4a_path = 'vox2/test/id04253/dfbCPe2xOPA/00257.m4a'
+    # m4a_path = os.path.join(WAV_DIR, m4a_path)
+
+    #wav_arr, sample_rate = vad_process(wav_path)
+    #print(wav_arr, sample_rate)
+    #wav_arr, sample_rate = vad_process(m4a_path)
+    #print(wav_arr, sample_rate)
+
+    print(wav_to_mel(wav_path).shape)
+    # print(wav_to_mel(m4a_path).shape)
+
+    # Compare with preprocessed pickles
+    import sys
+    sys.path.append('./')
+    from datasets.vox_dataset import VoxDataset
+    # Config
+    image_size = (256, 256)
+    #image_normalize_method = 'imagenet'
+    image_normalize_method = 'standard'
+    mel_normalize_method = 'vox_mel'
+    test_case_dir = os.path.join('./data', 'test_cases')
+    os.makedirs(test_case_dir, exist_ok=True)
+
+    # Dataset
+    vox_dataset = VoxDataset(
+        data_dir=VOX_DIR,
+        image_size=image_size,
+        image_normalize_method=image_normalize_method,
+        mel_normalize_method=mel_normalize_method)
+    np_mel = vox_dataset.load_mel_gram(mel_pickle= \
+        './data/VoxCeleb/vox1/mel_spectrograms/A.J._Buckley' + \
+            '/id10001_J9lHsKG98U8_00026.pickle')
+    print('Load from pickle:', np_mel.shape)
+    print('Calculated by program:', wav_to_mel(wav_path).shape)
+
+    print(np_mel - wav_to_mel(wav_path))
diff --git a/modeling/.gitignore b/modeling/.gitignore
new file mode 100644
index 0000000..42e9eb0
--- /dev/null
+++ b/modeling/.gitignore
@@ -0,0 +1,186 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+data/
+*.pth
+*.npy
+*.ipynb
+*.pkl
+*.png
+*.jpg
+*.mp4
+*.zip
+*.onnx
+*.tar
+*.ipynb
+
+.vscode/
+output/
+wandb
+log
+results
+checkpoints
+nohup*
+SimSwap/arcface_model/
+SimSwap/checkpoints/
+SimSwap/parsing_model/checkpoint/
+
+secret.py
\ No newline at end of file
diff --git a/modeling/README.md b/modeling/README.md
new file mode 100644
index 0000000..f705793
--- /dev/null
+++ b/modeling/README.md
@@ -0,0 +1,170 @@
+# Voice2Face-Modeling
+<img src="https://img.shields.io/badge/PyTorch-EE4C2C?style=for-the-badge&logo=PyTorch&logoColor=white">  <img src="https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=Python&logoColor=white">  <img src="https://img.shields.io/badge/opencv-5C3EE8?style=for-the-badge&logo=opencv&logoColor=white">  <img src="https://img.shields.io/badge/github-181717?style=for-the-badge&logo=github&logoColor=white">  <img src="https://img.shields.io/badge/git-F05032?style=for-the-badge&logo=git&logoColor=white"> <img src="https://img.shields.io/badge/Docker-2496ED?style=for-the-badge&logo=Docker&logoColor=white">  <img src="https://img.shields.io/badge/NCP-03C75A?style=for-the-badge&logo=Naver&logoColor=white"> <img src="https://img.shields.io/badge/Linux-FCC624?style=for-the-badge&logo=Linux&logoColor=white">
+
+## Project Structure
+
+```  
+modeling
+┣ pytorch_template
+┃ ┣ config
+┃ ┣ models
+┃ ┣ modules
+┃ ┣ predict.py
+┃ ┗ train.py
+┣ sf2f
+┃ ┣ datasets
+┃ ┣ models
+┃ ┣ options
+┃ ┣ scripts
+┃ ┣ utils
+┃ ┣ infer.py
+┃ ┣ inference_fuser.py
+┃ ┣ test.py
+┃ ┗ train.py
+┣ SimSwap
+┃ ┣ inference_swap.py
+┃ ┗ test_*_swap*.py
+┣ wcgan-gp
+┃ ┣ dataset.py
+┃ ┣ inference_options.py
+┃ ┣ inference.py
+┃ ┣ model.py
+┃ ┣ train_options.py
+┃ ┣ train.py
+┃ ┗ utils.py
+┣ README.md
+┗ requirements.txt
+```
+
+## Usage
+
+#### pytorch_template
+
+ - `config`: 모델을 동작시킬 arguments를 모두 포함하는 config 파일들을 모아 놓은 폴더입니다.
+ - `models`: model을 정의하고 build하는 코드들을 저장하는 폴더입니다.
+ - `modules`: 모델을 학습시키는데 필요한 부가적인 파일들을 저장해 둔 폴더입니다. (dataloader, loss function, scaler, optimizer, metrics 등)
+ - `predict.py` : 학습한 모델을 통해 inference를 실행시키는 코드입니다.
+ - `train.py` : config 파일을 통해 build한 후 모델을 학습시키는 코드입니다.
+
+#### sf2f
+
+ - `datasets` : dataset을 build하는 코드들을 모아둔 폴더입니다.
+ - `models` : voice encoder, embedding fuser, decoder, discriminator 등의 모델 전반적인 구조를 선언하고 build하는 파일들을 모아둔 폴더입니다.
+ - `options` : 사용할 모델의 구조와 데이터 셋, 하이퍼파라미터 등의 arguments를 정리해 둔 config 파일들을 모아 놓은 폴더입니다.
+ - `scripts` : mel spectrogram 변환, fid score 계산, inception score 계산 등의 기능들을 모아둔 폴더입니다.
+ - `utils` : loss, evalutation, metrics, training utils 등의 모델 학습과 평가지표를 구성한 파일들을 모아둔 폴더입니다.
+ - `infer.py`: voice를 spectrogram으로 변환시켜 입력하였을 때 encoder_decoder 구조만으로 결과를 생성하도록 하는 코드입니다.
+ - `inference_fuser.py`: voice를 spectrogram으로 변환시켜 입력하였을 때 encoder_decoder 구조와 embedding fuser를 통해 향상된 결과를 생성하도록 하는 코드입니다.
+ - `test.py`: 모델의 성능을 평가하기 위해 새로운 데이터셋으로 test를 진행하는 코드입니다.
+ - `train.py`: 모델을 학습시키기 위해 사용되는 코드로, stage-1과 stage-2로 변경해서 사용할 수 있도록 구성되어 있습니다.
+
+#### SimSwap
+
+ - `inference_swap.py` : face image와 target video를 입력받고, frames 별로 얼굴을 합성한 후 최종 video의 frames과 fps를 출력하는 코드입니다.
+ - ` test_*_swap*.py ` : 다양한 얼굴 합성을 수행합니다. 이미지/영상의 얼굴 하나에만, 이미지/영상의 모든 얼굴, 이미지/영상의 특정 얼굴 등 파일에 따라 target을 바꿀 수 있습니다.
+
+#### wcgan-gp
+
+ - `dataset.py` : argument로 지정된 dataset을 불러오도록 선언해둔 코드입니다.
+ - `inference_options.py` : 학습된 모델에 inference를 위해 필요한 arguments를 parameter로 받는 코드입니다.
+ - `inference.py` : arguments를 통해 성별과 나이를 condition으로 주어 모델을 통해 64x64 face image를 생성하는 코드입니다.
+ - `model.py` : 모델을 (conditional GAN, Discriminator, convolutional block 등) 선언하여 build할 수 있도록 하는 코드입니다.
+ - `train_options.py` : 모델을 학습시킬 때 사용되는 arguments를 parameter로 받는 코드로, pre-train 혹은 finetuning을 진행할 수 있도록 제어할 수 있는 argument도 있습니다.
+ - `train.py` : arguments를 통해 모델을 선언하고 학습시키는 코드입니다.
+ - `utils.py` : Gradient Penalty Loss와 그 이외의 추가적인 optinal한 함수들을 모아둔 코드입니다.
+
+## Getting Started
+
+  
+### Setting up Vitual Enviornment
+
+  
+1. Initialize and update the server
+
+```
+
+su -
+
+source .bashrc
+
+```
+
+  
+
+2. Create and Activate a virtual environment in the project directory
+
+  
+
+```
+
+conda create -n env python=3.8
+
+conda activate env
+
+```
+
+  
+
+4. To deactivate and exit the virtual environment, simply run:
+
+  
+
+```
+
+deactivate
+
+```
+
+  
+
+### Install Requirements
+
+  
+
+To Install the necessary packages liksted in `requirements.txt`, run the following command while your virtual environment is activated:
+
+```
+
+pip install -r requirements.txt
+
+```
+### SF2F
+[Prepare](https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/blob/feat/modeling/modeling/sf2f/GETTING_STARTED.md#download-vggface2-resnet-checkpoint) \
+[Training](https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/blob/feat/modeling/modeling/sf2f/GETTING_STARTED.md#launch-training) \
+Inference for a service that generates faces from voices :
+```
+python sf2f/inference_swap.py --checkpoint_start_from CHEKPOINT_DIR/CHECKPOINT.pt --input_wav_file WAV_DIR/VOICE.wav
+```
+
+
+### WCGAN-GP
+Training script for pre-train and finetuning model that generates faces from condition(gender, age) :
+```
+# train shell
+python wcgan-gp/main.py --gpu_ids 0 --use_gpu True --mode {train/finetune/inference} --train_continue {on/off} --checkpoint_path {./checkpoint} --checkpoint_name {epoch1.pth} --checkpoint_fine_tune_name {fine_tune/epoch1.pth} --dataset_path /workspace/data --dataset_name celeba --dataset_fine_tune_name VoxCeleb --output_path ./results --num_epoch 100 --num_freq_save 5 --batch_size 128 --lr_G 2e-4 --lr_D 2e-4 --lr_policy {linear/step/plateau/cosine} --lr_G_weight_decay 1e-5 --lr_D_weight_decay 1e-5 --nch_ker 64 --fine_tune True --fine_tune_nume_epoch 100 --fine_tune_num_freq_save 5
+```
+
+Inference for a service that generates faces from condition(gender, age) :
+```
+# inference shell
+python wcgan/main.py --mode {inference} --fine_tune {True, HQ-VoxCeleb dataset에 fine tune 진행여부} --output_path {inference 결과 저장 위치} --checkpoint_name {경로/checkpoint를 불러올 주소와 이름} --input_gendere {m/man/male/f/female/woman} --input_age {0 - 100}
+```
+
+
+### SimSwap
+[Prepare](https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/blob/feat/modeling/modeling/SimSwap/docs/guidance/preparation.md) \
+[Training](https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/tree/feat/modeling/modeling/SimSwap#training) \
+[Inference for image or video face swapping](https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/blob/feat/modeling/modeling/SimSwap/docs/guidance/usage.md) \
+Inference for a service that swaps only one face within the video :
+```
+python SimSwap/inference_swap.py --Arc_path SimSwap/arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/hj.png --video_path ./demo_file/tae_24fps.mp4
+```
+
+## References
+- [Speech Fusion to Face](https://arxiv.org/pdf/2006.05888.pdf) 
+- [Conditional GAN](https://arxiv.org/pdf/1411.1784.pdf)
+- [Sim Swap](https://arxiv.org/pdf/2106.06340v1.pdf)
+- [Pytorch Template](https://github.com/victoresque/pytorch-template)
+
+## Links
+- [Origin github](https://github.com/Make-Zenerator/voice2face-modeling.git)
diff --git a/modeling/SimSwap/LICENSE b/modeling/SimSwap/LICENSE
new file mode 100644
index 0000000..f2915d9
--- /dev/null
+++ b/modeling/SimSwap/LICENSE
@@ -0,0 +1,399 @@
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+
+Creative Commons may be contacted at creativecommons.org.
diff --git a/modeling/SimSwap/README.md b/modeling/SimSwap/README.md
new file mode 100644
index 0000000..92a7f6a
--- /dev/null
+++ b/modeling/SimSwap/README.md
@@ -0,0 +1,229 @@
+# SimSwap: An Efficient Framework For High Fidelity Face Swapping
+## Proceedings of the 28th ACM International Conference on Multimedia
+**The official repository with Pytorch**
+
+**Our method can realize **arbitrary face swapping** on images and videos with **one single trained model**.**
+
+Training and test code are now available!
+[ <a href="https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/train.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/train.ipynb)
+
+We are working with our incoming paper SimSwap++, keeping expecting!
+
+The high resolution version of ***SimSwap-HQ*** is supported!
+
+[![simswaplogo](/docs/img/logo1.png)](https://github.com/neuralchen/SimSwap)
+
+Our paper can be downloaded from [[Arxiv]](https://arxiv.org/pdf/2106.06340v1.pdf) [[ACM DOI]](https://dl.acm.org/doi/10.1145/3394171.3413630)
+
+
+### This project also received support from [SocialBook](https://socialbook.io).
+<!-- [![logo](./simswaplogo/socialbook_logo.2020.357eed90add7705e54a8.svg)](https://socialbook.io) -->
+<img width=30% src="./simswaplogo/socialbook_logo.2020.357eed90add7705e54a8.svg"/>
+
+<!-- [[Google Drive]](https://drive.google.com/file/d/1fcfWOGt1mkBo7F0gXVKitf8GJMAXQxZD/view?usp=sharing) 
+[[Baidu Drive ]](https://pan.baidu.com/s/1-TKFuycRNUKut8hn4IimvA) Password: ```ummt``` -->
+
+## Attention
+***This project is for technical and academic use only. Please do not apply it to illegal and unethical scenarios.***
+
+***In the event of violation of the legal and ethical requirements of the user's country or region, this code repository is exempt from liability***
+
+***Please do not ignore the content at the end of this README!***
+
+If you find this project useful, please star it. It is the greatest appreciation of our work.
+
+## Top News <img width=8% src="./docs/img/new.gif"/>
+
+**`2023-09-26`**: We fixed bugs in colab!
+
+**`2023-04-25`**: We fixed the "AttributeError: 'SGD' object has no attribute 'defaults' now" bug. If you have already downloaded **arcface_checkpoint.tar**, please **download it again**. Also, you also need to update the scripts in ```./models/```.
+
+**`2022-04-21`**: For resource limited users, we provide the cropped VGGFace2-224 dataset [[Google Driver] VGGFace2-224 (10.8G)](https://drive.google.com/file/d/19pWvdEHS-CEG6tW3PdxdtZ5QEymVjImc/view?usp=sharing) [[Baidu Driver]](https://pan.baidu.com/s/1OiwLJHVBSYB4AY2vEcfN0A) [Password: lrod].
+
+**`2022-04-20`**: Training scripts are now available. We highly recommend that you guys train the simswap model with our released high quality dataset [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ).
+
+**`2021-11-24`**: We have trained a beta version of ***SimSwap-HQ*** on [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) and open sourced the checkpoint of this model (if you think the Simswap 512 is cool, please star our  [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) repo). Please don’t forget to go to [Preparation](./docs/guidance/preparation.md) and [Inference for image or video face swapping](./docs/guidance/usage.md) to check the latest set up.
+
+**`2021-11-23`**: The google drive link of [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) is released. 
+
+**`2021-11-17`**: We released a high resolution face dataset [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) and the method to generate this dataset. This dataset is for research purpose. 
+
+**`2021-08-30`**: Docker has been supported, please refer [here](https://replicate.ai/neuralchen/simswap-image) for details.
+
+**`2021-08-17`**: We have updated the [Preparation](./docs/guidance/preparation.md), The main change is that the gpu version of onnx is now installed by default, Now the time to process a video is greatly reduced.
+
+**`2021-07-19`**: ***Obvious border abruptness has been resolved***. We add the ability to using mask and upgrade the old algorithm for better visual effect, please go to [Inference for image or video face swapping](./docs/guidance/usage.md) for details. Please don’t forget to go to [Preparation](./docs/guidance/preparation.md) to check the latest set up. (Thanks for the help from [@woctezuma](https://github.com/woctezuma) and [@instant-high](https://github.com/instant-high))
+
+## The first open source high resolution dataset for face swapping!!!
+## High Resolution Dataset [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ)
+
+[![logo](./docs/img/vggface2_hq_compare.png)](https://github.com/NNNNAI/VGGFace2-HQ)
+
+
+
+
+## Dependencies
+- python3.6+
+- pytorch1.5+
+- torchvision
+- opencv
+- pillow
+- numpy
+- imageio
+- moviepy
+- insightface
+- ***timm==0.5.4***
+
+## Training
+
+[Preparation](./docs/guidance/preparation.md)
+
+The training script is slightly different from the original version, e.g., we replace the patch discriminator with the projected discriminator, which saves a lot of hardware overhead and achieves slightly better results.
+
+In order to ensure the normal training, the batch size must be greater than 1.
+
+Friendly reminder, due to the difference in training settings, the user-trained model will have subtle differences in visual effects from the pre-trained model we provide.
+
+- Train 224 models with VGGFace2 224*224 [[Google Driver] VGGFace2-224 (10.8G)](https://drive.google.com/file/d/19pWvdEHS-CEG6tW3PdxdtZ5QEymVjImc/view?usp=sharing) [[Baidu Driver] ](https://pan.baidu.com/s/1OiwLJHVBSYB4AY2vEcfN0A) [Password: lrod]
+
+For faster convergence and better results, a large batch size (more than 16) is recommended!
+
+***We recommend training more than 400K iterations (batch size is 16), 600K~800K will be better, more iterations will not be recommended.***
+
+
+```
+python train.py --name simswap224_test --batchSize 8  --gpu_ids 0 --dataset /path/to/VGGFace2HQ --Gdeep False
+```
+
+[Colab demo for training 224 model][ <a href="https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/train.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/train.ipynb)
+
+For faster convergence and better results, a large batch size (more than 16) is recommended!
+
+- Train 512 models with VGGFace2-HQ 512*512 [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ).
+```
+python train.py --name simswap512_test  --batchSize 16  --gpu_ids 0 --dataset /path/to/VGGFace2HQ --Gdeep True
+```
+
+
+
+## Inference with a pretrained SimSwap model
+[Preparation](./docs/guidance/preparation.md)
+
+[Inference for image or video face swapping](./docs/guidance/usage.md)
+
+[Colab demo](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/SimSwap%20colab.ipynb)
+
+<div style="background: yellow; width:140px; font-weight:bold;font-family: sans-serif;">Stronger feature</div>
+
+[Colab for switching specific faces in multi-face videos][ <a href="https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/MultiSpecific.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/MultiSpecific.ipynb)
+
+[Image face swapping demo & Docker image on Replicate](https://replicate.ai/neuralchen/simswap-image)
+
+
+
+## Video
+<img src="./docs/img/video.webp"/>
+<div>
+<img width=24% src="./docs/img/anni.webp"/>
+<img width=24% src="./docs/img/chenglong.webp"/>
+<img width=24% src="./docs/img/zhoujielun.webp"/>
+<img width=24% src="./docs/img/zhuyin.webp"/>
+</div>
+<div>
+<img width=49% src="./docs/img/mama_mask_short.webp"/>
+<img width=49% src="./docs/img/mama_mask_wuyifan_short.webp"/>
+</div>
+
+## Results
+![Results1](/docs/img/results1.PNG)
+
+![Results2](/docs/img/total.PNG)
+
+
+<!-- ![video2](/docs/img/anni.webp)
+![video3](/docs/img/chenglong.webp)
+![video4](/docs/img/zhoujielun.webp)
+![video5](/docs/img/zhuyin.webp) -->
+
+
+**High-quality videos can be found in the link below:**
+
+[[Mama(video) 1080p]](https://drive.google.com/file/d/1mnSlwzz7f4H2O7UwApAHo64mgK4xSNyK/view?usp=sharing)
+
+[[Google Drive link for video 1]](https://drive.google.com/file/d/1hdne7Gw39d34zt3w1NYV3Ln5cT8PfCNm/view?usp=sharing)
+
+[[Google Drive link for video 2]](https://drive.google.com/file/d/1bDEg_pVeFYLnf9QLSMuG8bsjbRPk0X5_/view?usp=sharing)
+
+[[Google Drive link for video 3]](https://drive.google.com/file/d/1oftHAnLmgFis4XURcHTccGSWbWSXYKK1/view?usp=sharing)
+
+[[Baidu Drive link for video]](https://pan.baidu.com/s/1WTS6jm2TY17bYJurw57LUg ) Password: ```b26n```
+
+[[Online Video]](https://www.bilibili.com/video/BV12v411p7j5/)
+
+## User case
+If you have some interesting results after using our project and are willing to share, you can contact us by email or share directly on the issue. Later, we may make a separate section to show these results, which should be cool.
+
+At the same time, if you have suggestions for our project, please feel free to ask questions in the issue, or contact us directly via email: [email1](mailto:chenxuanhongzju@outlook.com), [email2](mailto:nicklau26@foxmail.com), [email3](mailto:ziangliu824@gmail.com). (All three can be contacted, just choose any one)
+
+## License
+For academic and non-commercial use only.The whole project is under the CC-BY-NC 4.0 license. See [LICENSE](https://github.com/neuralchen/SimSwap/blob/main/LICENSE) for additional details.
+
+
+## To cite our paper
+```
+@inproceedings{DBLP:conf/mm/ChenCNG20,
+  author    = {Renwang Chen and
+               Xuanhong Chen and
+               Bingbing Ni and
+               Yanhao Ge},
+  title     = {SimSwap: An Efficient Framework For High Fidelity Face Swapping},
+  booktitle = {{MM} '20: The 28th {ACM} International Conference on Multimedia},
+  year      = {2020}
+}
+```
+
+## Related Projects
+
+**Please visit our another ACMMM2020 high-quality style transfer project**
+
+[![logo](./docs/img/logo.png)](https://github.com/neuralchen/ASMAGAN)
+
+[![title](/docs/img/title.png)](https://github.com/neuralchen/ASMAGAN)
+
+**Please visit our AAAI2021 sketch based rendering project**
+
+[![logo](./docs/img/girl2.gif)](https://github.com/TZYSJTU/Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale)
+[![title](/docs/img/girl2-RGB.png)](https://github.com/TZYSJTU/Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale)
+
+**Please visit our high resolution face dataset VGGFace2-HQ**
+
+[![logo](./docs/img/vggface2_hq_compare.png)](https://github.com/NNNNAI/VGGFace2-HQ)
+
+Learn about our other projects 
+
+[[VGGFace2-HQ]](https://github.com/NNNNAI/VGGFace2-HQ);
+
+[[RainNet]](https://neuralchen.github.io/RainNet);
+
+[[Sketch Generation]](https://github.com/TZYSJTU/Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale);
+
+[[CooGAN]](https://github.com/neuralchen/CooGAN);
+
+[[Knowledge Style Transfer]](https://github.com/AceSix/Knowledge_Transfer);
+
+[[SimSwap]](https://github.com/neuralchen/SimSwap);
+
+[[ASMA-GAN]](https://github.com/neuralchen/ASMAGAN);
+
+[[SNGAN-Projection-pytorch]](https://github.com/neuralchen/SNGAN_Projection)
+
+[[Pretrained_VGG19]](https://github.com/neuralchen/Pretrained_VGG19).
+
+## Acknowledgements
+
+<!--ts-->
+* [Deepfacelab](https://github.com/iperov/DeepFaceLab)
+* [Insightface](https://github.com/deepinsight/insightface)
+* [Face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch)
+* [BiSeNet](https://github.com/CoinCheung/BiSeNet)
+<!--te-->
diff --git a/modeling/SimSwap/cog.yaml b/modeling/SimSwap/cog.yaml
new file mode 100644
index 0000000..280c369
--- /dev/null
+++ b/modeling/SimSwap/cog.yaml
@@ -0,0 +1,20 @@
+build:
+  gpu: true
+  python_version: "3.8"
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+  python_packages:
+    - "imageio==2.9.0"
+    - "torch==1.8.0"
+    - "torchvision==0.9.0"
+    - "numpy==1.21.1"
+    - "insightface==0.2.1"
+    - "ipython==7.21.0"
+    - "Pillow==8.3.1"
+    - "opencv-python==4.5.3.56"
+    - "Fraction==1.5.1"
+    - "onnxruntime-gpu==1.8.1"
+    - "moviepy==1.0.3"
+
+predict: "predict.py:Predictor"
diff --git a/modeling/SimSwap/docs/css/bootstrap-theme.min.css b/modeling/SimSwap/docs/css/bootstrap-theme.min.css
new file mode 100644
index 0000000..5e39401
--- /dev/null
+++ b/modeling/SimSwap/docs/css/bootstrap-theme.min.css
@@ -0,0 +1,6 @@
+/*!
+ * Bootstrap v3.3.7 (http://getbootstrap.com)
+ * Copyright 2011-2016 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */.btn-danger,.btn-default,.btn-info,.btn-primary,.btn-success,.btn-warning{text-shadow:0 -1px 0 rgba(0,0,0,.2);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075)}.btn-danger.active,.btn-danger:active,.btn-default.active,.btn-default:active,.btn-info.active,.btn-info:active,.btn-primary.active,.btn-primary:active,.btn-success.active,.btn-success:active,.btn-warning.active,.btn-warning:active{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-danger.disabled,.btn-danger[disabled],.btn-default.disabled,.btn-default[disabled],.btn-info.disabled,.btn-info[disabled],.btn-primary.disabled,.btn-primary[disabled],.btn-success.disabled,.btn-success[disabled],.btn-warning.disabled,.btn-warning[disabled],fieldset[disabled] .btn-danger,fieldset[disabled] .btn-default,fieldset[disabled] .btn-info,fieldset[disabled] 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diff --git a/modeling/SimSwap/docs/css/bootstrap.min.css b/modeling/SimSwap/docs/css/bootstrap.min.css
new file mode 100644
index 0000000..ed3905e
--- /dev/null
+++ b/modeling/SimSwap/docs/css/bootstrap.min.css
@@ -0,0 +1,6 @@
+/*!
+ * Bootstrap v3.3.7 (http://getbootstrap.com)
+ * Copyright 2011-2016 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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+/*# sourceMappingURL=bootstrap.min.css.map */
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diff --git a/modeling/SimSwap/docs/css/ie10-viewport-bug-workaround.css b/modeling/SimSwap/docs/css/ie10-viewport-bug-workaround.css
new file mode 100644
index 0000000..4b9518e
--- /dev/null
+++ b/modeling/SimSwap/docs/css/ie10-viewport-bug-workaround.css
@@ -0,0 +1,13 @@
+/*!
+ * IE10 viewport hack for Surface/desktop Windows 8 bug
+ * Copyright 2014-2015 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+
+/*
+ * See the Getting Started docs for more information:
+ * http://getbootstrap.com/getting-started/#support-ie10-width
+ */
+@-ms-viewport     { width: device-width; }
+@-o-viewport      { width: device-width; }
+@viewport         { width: device-width; }
diff --git a/modeling/SimSwap/docs/css/jumbotron.css b/modeling/SimSwap/docs/css/jumbotron.css
new file mode 100644
index 0000000..179d408
--- /dev/null
+++ b/modeling/SimSwap/docs/css/jumbotron.css
@@ -0,0 +1,5 @@
+/* Move down content because we have a fixed navbar that is 50px tall */
+body {
+  padding-top: 50px;
+  padding-bottom: 20px;
+}
diff --git a/modeling/SimSwap/docs/css/page.css b/modeling/SimSwap/docs/css/page.css
new file mode 100644
index 0000000..614330a
--- /dev/null
+++ b/modeling/SimSwap/docs/css/page.css
@@ -0,0 +1,49 @@
+.which-image-container {
+    display: flex;
+    flex-wrap: wrap;
+    align-items: center;
+    flex-direction: column;
+    justify-content: space-between;
+    height: 100%;
+}
+
+.which-image-container :nth-child(2) {
+    display: flex;
+    flex-wrap: wrap;
+    align-items: center;
+    flex-direction: column;
+    justify-content: flex-end;
+}
+
+.which-image {
+    display: inline-block;
+    flex: 1;
+    flex-basis: 45%;
+    margin: 1% 1% 1% 1%;
+    width: 100%;
+    height: auto;
+
+}
+
+.which-image img {
+    float: right;
+    width: 100%;
+}
+
+.image-display2 img {
+    float: right;
+    width: 100%;
+}
+/* .image-display{
+    align-items: center;
+}
+.image-display2{
+    align-items: center;
+} */
+.select-show {
+    border-style: dashed;
+    border-width: 2px;
+    border-color: purple;
+
+    /* padding: 2px; */
+}
\ No newline at end of file
diff --git a/modeling/SimSwap/docs/favicon.ico b/modeling/SimSwap/docs/favicon.ico
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new file mode 100644
index 0000000..94fb549
--- /dev/null
+++ b/modeling/SimSwap/docs/fonts/glyphicons-halflings-regular.svg
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+<?xml version="1.0" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
+<svg xmlns="http://www.w3.org/2000/svg">
+<metadata></metadata>
+<defs>
+<font id="glyphicons_halflingsregular" horiz-adv-x="1200" >
+<font-face units-per-em="1200" ascent="960" descent="-240" />
+<missing-glyph horiz-adv-x="500" />
+<glyph horiz-adv-x="0" />
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+
+# Preparation
+
+### Installation
+**We highly recommand that you use Anaconda for Installation**
+```
+conda create -n simswap python=3.6
+conda activate simswap
+conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch
+(option): pip install --ignore-installed imageio
+pip install insightface==0.2.1 onnxruntime moviepy
+(option): pip install onnxruntime-gpu  (If you want to reduce the inference time)(It will be diffcult to install onnxruntime-gpu , the specify version of onnxruntime-gpu may depends on your machine and cuda version.)
+```
+- ***We have now updated the prepare document. The main change gpu version of onnx is supported now. If you have configured the environment before, now use pip install onnxruntime-gpu ,You can increase the computing speed.***
+- We use the face detection and alignment methods from **[insightface](https://github.com/deepinsight/insightface)** for image preprocessing. Please download the relative files and unzip them to ./insightface_func/models from [this link](https://onedrive.live.com/?authkey=%21ADJ0aAOSsc90neY&cid=4A83B6B633B029CC&id=4A83B6B633B029CC%215837&parId=4A83B6B633B029CC%215834&action=locate).
+- We use the face parsing from **[face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch)** for image postprocessing. Please download the relative file and place it in ./parsing_model/checkpoint from [this link](https://drive.google.com/file/d/154JgKpzCPW82qINcVieuPH3fZ2e0P812/view).
+- The pytorch and cuda versions above are most recommanded. They may vary.
+- Using insightface with different versions is not recommanded. Please use this specific version.
+- These settings are tested valid on both Windows and Ubuntu.
+
+### Pretrained model
+There are two archive files in the drive: **checkpoints.zip** and **arcface_checkpoint.tar**
+
+- **Copy the arcface_checkpoint.tar into ./arcface_model**
+- **Unzip checkpoints.zip, place it in the root dir ./**
+
+[[Google Drive]](https://drive.google.com/drive/folders/1jV6_0FIMPC53FZ2HzZNJZGMe55bbu17R?usp=sharing)
+[[Baidu Drive]](https://pan.baidu.com/s/1wFV11RVZMHqd-ky4YpLdcA) Password: ```jd2v```
+
+**Simswap 512 (optional)**
+
+The checkpoint of **Simswap 512 beta version** has been uploaded in [Github release](https://github.com/neuralchen/SimSwap/releases/download/512_beta/512.zip).If you want to experience Simswap 512, feel free to try.
+- **Unzip 512.zip, place it in the root dir ./checkpoints**.
+
+
+### Note
+We expect users to have GPU with at least 3G memory. For those who do not, we provide [[Colab Notebook implementation]](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/SimSwap%20colab.ipynb).
diff --git a/modeling/SimSwap/docs/guidance/usage.md b/modeling/SimSwap/docs/guidance/usage.md
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+<!--
+ * @FilePath: \SimSwap\docs\guidance\usage.md
+ * @Author: AceSix
+ * @Date: 2021-06-28 10:01:40
+ * @LastEditors: AceSix
+ * @LastEditTime: 2021-06-28 10:05:11
+ * Copyright (C) 2021 SJTU. All rights reserved.
+-->
+
+# Usage
+
+###### Before running, please make sure you have installed the environment and downloaded requested files according to the [preparation guidance](./preparation.md).
+###### The below example command lines are using mask by default.
+
+### Simple face swapping for already face-aligned images
+```
+python test_one_image.py --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path crop_224/6.jpg --pic_b_path crop_224/ds.jpg --output_path output/ 
+```
+
+### Face swapping for video
+
+- Swap only one face within the video(the one with highest confidence by face detection).
+```
+python test_video_swapsingle.py --crop_size 224 --use_mask --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_swapsingle.mp4 --temp_path ./temp_results 
+```
+- Swap all faces within the video.
+```
+python test_video_swapmulti.py --crop_size 224 --use_mask --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_swapmulti.mp4 --temp_path ./temp_results 
+```
+- Swap the ***specific*** face within the video.
+```
+python test_video_swapspecific.py --crop_size 224 --use_mask --pic_specific_path ./demo_file/specific1.png --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_specific.mp4 --temp_path ./temp_results 
+```
+When changing the specified face, you need to give a picture of the person whose face is to be changed. Then assign the picture path to the argument "***--pic_specific_path***". This picture should be a front face and show the entire head and neck, which can help accurately change the face (if you still don’t know how to choose the picture, you can refer to the specific*.png of [./demo_file/](https://github.com/neuralchen/SimSwap/tree/main/demo_file)). It would be better if this picture was taken from the video to be changed.
+
+- Swap ***multi specific*** face with **multi specific id** within the video.
+```
+python test_video_swap_multispecific.py --crop_size 224 --use_mask  --name people --Arc_path arcface_model/arcface_checkpoint.tar --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_multispecific.mp4 --temp_path ./temp_results --multisepcific_dir ./demo_file/multispecific 
+```
+The folder you assign to ***"--multisepcific_dir"*** should be looked like:
+```
+$Your folder name$
+
+├── DST_01.jpg(png)
+└── DST_02.jpg(png)
+└──...
+└── SRC_01.jpg(png)
+└── SRC_02.jpg(png)
+└──...
+```
+The result is that the face corresponding to SRC_01.jpg (png) in the video will be replaced by the face corresponding to DST_01.jpg (png). Then the character corresponding to SRC_02.jpg(png) will be replaced by the face of DST_02.jpg(png), and so on. Note that when using your own data and naming it, do not remove the **0** in SRC_(DST_)**0**1.jpg(png), etc.
+
+
+
+### Face swapping for Arbitrary images
+
+- Swap only one face within one image(the one with highest confidence by face detection). The result would be saved to ./output/result_whole_swapsingle.jpg
+```
+python test_wholeimage_swapsingle.py --crop_size 224 --use_mask  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/ 
+```
+- Swap all faces within one image. The result would be saved to ./output/result_whole_swapmulti.jpg
+```
+python test_wholeimage_swapmulti.py --crop_size 224 --use_mask  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/ 
+```
+- Swap **specific** face within one image. The result would be saved to ./output/result_whole_swapspecific.jpg
+```
+python test_wholeimage_swapspecific.py --crop_size 224 --use_mask  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/ --pic_specific_path ./demo_file/specific2.png 
+```
+- Swap **multi specific** face with **multi specific id** within one image. The result would be saved to ./output/result_whole_swap_multispecific.jpg
+```
+python test_wholeimage_swap_multispecific.py --crop_size 224 --use_mask  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/ --multisepcific_dir ./demo_file/multispecific 
+```
+### About using Simswap 512 (beta version)
+We trained a beta version of Simswap 512 on [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) and open sourced the model (if you think the Simswap 512 is cool, please star our  [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) repo).
+
+The usage of applying Simswap 512 is to modify the value of the argument: "***--crop_size***" to 512 , take the command line of "Swap **multi specific** face with **multi specific id** within one image." as an example, the following command line can get the result without watermark:
+```
+python test_wholeimage_swap_multispecific.py --crop_size 512 --use_mask  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/ --multisepcific_dir ./demo_file/multispecific 
+```
+The effect of Simswap 512 is shown below.
+<img src="../img/result_whole_swap_multispecific_512.jpg"/>
+
+### About watermark of simswap logo
+The above example command lines are to add the simswap logo as the watermark by default. After our discussion, we have added a hyper parameter to control whether to remove watermark.
+
+The usage of removing the watermark is to add an argument: "***--no_simswaplogo***" to the command line, take the command line of "Swap all faces within one image" as an example, the following command line can get the result without watermark:
+```
+python test_wholeimage_swapmulti.py --no_simswaplogo  --crop_size 224 --use_mask  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --pic_b_path ./demo_file/multi_people.jpg --output_path ./output/
+```
+### About using mask for better result
+We provide two methods to paste the face back to the original image after changing the face: Using mask or using bounding box. At present, the effect of using mask is the best. All the above code examples are using mask. If you want to use the bounding box, you only need to remove the --use_mask in the code example.
+Difference between using mask and not using mask can be found [here](https://imgsli.com/NjE3OTA).
+
+### Difference between single face swapping and all face swapping are shown below.
+<img src="../img/multi_face_comparison.png"/>
+
+
+
+
+### Parameters
+|  Parameters   | Function  |
+|  :----  | :----  |
+| --name  | The SimSwap training logs name |
+| --pic_a_path  | Path of image with the target face |
+| --pic_b_path  | Path of image with the source face to swap |
+| --pic_specific_path  | Path of image with the specific face to be swapped |
+|--multisepcific_dir  |Path of image folder for multi specific face swapping|
+| --video_path  | Path of video with the source face to swap |
+| --temp_path  | Path to store intermediate files  |
+| --output_path  | Path of directory to store the face swapping result  |
+| --no_simswaplogo  |The hyper parameter to control whether to remove watermark |
+| --use_mask  |The hyper parameter to control whether to use face parsing for the better visual effects(I recommend to use)|
+
+### Note
+We expect users to have GPU with at least 3G memory.the For those who do not, we will provide Colab Notebook implementation in the future.
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diff --git a/modeling/SimSwap/docs/index.html b/modeling/SimSwap/docs/index.html
new file mode 100644
index 0000000..2b48b1a
--- /dev/null
+++ b/modeling/SimSwap/docs/index.html
@@ -0,0 +1,345 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="utf-8">
+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <!-- The above 3 meta tags *must* come first in the head; any other head content must come *after* these tags -->
+    <meta name="description" content="SimSwap">
+    <meta name="author" content="">
+    <link rel="icon" href="./favicon.ico">
+
+    <title>SimSwap</title>
+
+    <!-- Bootstrap core CSS -->
+    <link href="./css/bootstrap.min.css" rel="stylesheet">
+
+    <!-- IE10 viewport hack for Surface/desktop Windows 8 bug -->
+    <link href="./css/ie10-viewport-bug-workaround.css" rel="stylesheet">
+
+    <!-- Custom styles for this template -->
+    <link href="./css/jumbotron.css" rel="stylesheet">
+    <link href="./css/page.css" rel="stylesheet">
+
+    <!-- Just for debugging purposes. Don't actually copy these 2 lines! -->
+    <!--[if lt IE 9]><script src="../../assets/js/ie8-responsive-file-warning.js"></script><![endif]-->
+    <script src="./js/ie-emulation-modes-warning.js"></script>
+    <script src="./js/which-image.js"></script>
+
+    <!-- HTML5 shim and Respond.js for IE8 support of HTML5 elements and media queries -->
+    <!--[if lt IE 9]>
+      <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+      <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+    <![endif]-->
+</head>
+
+<body>
+
+    <nav class="navbar navbar-inverse navbar-fixed-top">
+        <div class="container">
+            <div class="navbar-header">
+                <!-- <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false" aria-controls="navbar">
+            <span class="sr-only">Toggle navigation</span>
+            <span class="icon-bar"></span>
+            <span class="icon-bar"></span>
+            <span class="icon-bar"></span>
+          </button> -->
+                <a class="navbar-brand" href="#">SimSwap</a>
+            </div>
+            <!-- <div id="navbar" class="navbar-collapse collapse">
+          <form class="navbar-form navbar-right">
+            <div class="form-group">
+              <input type="text" placeholder="Email" class="form-control">
+            </div>
+            <div class="form-group">
+              <input type="password" placeholder="Password" class="form-control">
+            </div>
+            <button type="submit" class="btn btn-success">Sign in</button>
+          </form>
+        </div>/.navbar-collapse -->
+        </div>
+    </nav>
+
+    <!-- Main jumbotron for a primary marketing message or call to action -->
+    <div class="jumbotron jumbotron-fluid" style="background: url('./img/nrsig.png'); background-size: 33%;">
+        <div height=100% width=100% style="background: rgba(255,255,255, 1); background-size: 33%;">
+        </div>
+        <div class="container">
+            <div class="row">
+                <div class="col-md-6 col-md-offset-3 text-center">
+                    <img src="./img/logo2.png" alt="SimSwap" class="img-rounded" style="width: 100%;">
+                </div>
+            </div>
+
+            <div class="row text-center">
+                <div class="col-md-* text-center" style="color:white;">
+                    <h2>SimSwap: An Efficient Framework For High Fidelity Face Swapping</h2>
+                </div>
+            </div>
+
+            <div class="row">
+                <div class="col-md-* text-center" style="color:white;">
+                    <h4>Renwang Chen<sup>*</sup>, Xuanhong Chen<sup>*</sup>, Bingbing Ni, Yanhao Ge</h4>
+                </div>
+            </div>
+            <div class="row">
+                <div class="col-md-* text-center" style="color:white;">
+                    <h4>Shanghai Jiao Tong University, Tencent, China</h4>
+                </div>
+            </div>
+            <div class="row">
+                <div class="col-md-2 col-md-offset-5 text-center" style="color:white;">
+                    <h5><sup>*</sup>Equal contribution.</h5>
+                </div>
+            </div>
+            <div class="row">
+                <h5 class="page-header"></h5>
+            </div>
+            <div class="row">
+                <div class="col-md-6 col-md-offset-3 text-center">
+                    <div class="row">
+                        <div class="col-md-4 col-sm-4 text-center">
+                            <p>
+                                <a class="btn btn-primary btn-lg" href="https://github.com/neuralchen/SimSwap" role="button" target="_blank"> <span class="glyphicon glyphicon-star" aria-hidden="true"></span> Code &raquo;</a>
+                            </p>
+                        </div>
+                        <div class="col-md-4 col-sm-4 text-center">
+                            <p><a class="btn btn-primary btn-lg" href="#downloads" role="button"><span class="glyphicon glyphicon-cloud-download" aria-hidden="true"></span> Download &raquo;</a></p>
+                        </div>
+                        <div class="col-md-4 col-sm-4 text-center">
+                            <p><a class="btn btn-primary btn-lg" href="https://arxiv.org/abs/2106.06340" role="button" target="_blank"><span class="glyphicon glyphicon-save-file" aria-hidden="true"></span> Paper &raquo;</a></p>
+                        </div>
+                    </div>
+                </div>
+            </div>
+
+        </div>
+    </div>
+
+    <div class="container">
+        <!-- Example row of columns -->
+        <div class="row">
+            <div class="col-md-*">
+                <h2>Abstract</h2>
+            </div>
+        </div>
+        <div class="row">
+            <div class="col-md-* text-justify" style="line-height:150%; font-size: large;">
+                <p>
+                    We propose an efficient framework, called <strong><i>Simple Swap (SimSwap)</i></strong>, aiming for generalized and high fidelity face swapping. In contrast to previous approaches that either lack the ability to generalize to arbitrary
+                    identity or fail to preserve attributes like facial expression and gaze direction, our framework is capable of transferring the identity of an arbitrary source face into an arbitrary target face while preserving the attributes of the
+                    target face. We overcome the above defects in the following two ways. First, we present the <i>ID Injection Module (IIM)</i> which transfers the identity information of the source face into the target face at feature level. By using
+                    this module, we extend the architecture of an identityspecific face swapping algorithm to a framework for arbitrary face swapping. Second, we propose the <i>Weak Feature Matching Loss</i> which efficiently helps our framework to preserve
+                    the facial attributes in an implicit way. Extensive experiments on wild faces demonstrate that our <i>SimSwap</i> is able to achieve competitive identity performance while preserving attributes better than previous state-of-the-art
+                    methods. The code is already available on github: https://github.com/neuralchen/SimSwap.
+                </p>
+            </div>
+        </div>
+        <div class="row">
+            <h5 class="page-header"></h5>
+        </div>
+
+        <div class="row">
+            <div class="col-md-*">
+                <h2>SimSwap Demo</h2>
+            </div>
+        </div>
+        <div class="row">
+            <div class="col-md-*">
+                <h3>Single Face Video Swap</h3>
+            </div>
+        </div>
+        <div class="row select-show">
+            <div class="col-md-12">
+                <div class="col-md-3 which-image-container">
+                    <div class="which-image" id=anni>
+                        <img src="./img/id/anni.jpg" onclick="select_source(0)" />
+                    </div>
+                    <div class="which-image" id=chenglong>
+                        <img src="./img/id/zhoujielun.jpg" onclick="select_source(1)" />
+                    </div>
+                </div>
+                <div class="col-md-3 which-image-container">
+                    <div class="which-image" id=zhoujielun>
+                        <img src="./img/id/chenglong.jpg" onclick="select_source(2)" />
+                    </div>
+                    <div class="which-image" id=zhuyin>
+                        <img src="./img/id/zhuyin.jpg" onclick="select_source(3)" />
+                    </div>
+                </div>
+                <div class="col-md-6 image-display" height=100%>
+                    <img id=jiroujinlun src="./img/anni.webp" />
+                </div>
+            </div>
+        </div>
+        <div class="row select-show" style="margin-top: 10px;">
+            <div class="col-md-12">
+                <div class="col-md-3 which-image-container">
+                    <div class="which-image" id=Iron_man>
+                        <img src="./img/id/Iron_man.jpg" onclick="select_source2(0)" />
+                    </div>
+                </div>
+                <div class="col-md-3 which-image-container">
+                    <div class="which-image" id=wuyifan>
+                        <img src="./img/id/wuyifan.png" onclick="select_source2(1)" />
+                    </div>
+                </div>
+                <div class="col-md-6 image-display2">
+                    <img id=mama src="./img/mama_mask_short.webp" />
+                </div>
+            </div>
+        </div>
+        <div class="row">
+            <div class="col-md-*">
+                <h3>Multiple Faces Video Swap</h2>
+            </div>
+        </div>
+        <div class="row">
+            [TODO]
+        </div>
+
+        <div class="row">
+            <div class="col-md-*">
+                <h3>An overview of model capacity</h2>
+            </div>
+        </div>
+        <div class="row">
+            <div class="col-md-12 col-sm-12">
+                <img src="./img/results1.PNG" style="width: 100%;" />
+            </div>
+
+        </div>
+        <div class="row">
+            <div class="col-md-12 col-sm-12">
+                <img src="./img/total.PNG" style="width: 100%;" />
+            </div>
+        </div>
+
+        <div class="row">
+            <h5 class="page-header"></h5>
+        </div>
+        <div class="row">
+            <div class="col-md-*">
+                <h2>Downloads</h2>
+            </div>
+        </div>
+        <div id="downloads" class="row">
+            <div class="col-md-6 col-sm-6 text-center">
+                <p>
+                    <a class="btn btn-success btn-lg" href="https://github.com/neuralchen/SimSwap" role="button" target="_blank"> <span class="glyphicon glyphicon-hdd" aria-hidden="true"></span> Donwnload via Google Drive &raquo;</a>
+                </p>
+            </div>
+            <div class="col-md-6 col-sm-6 text-center">
+                <p><a class="btn btn-success btn-lg" href="#downloads" role="button" target="_blank"><span class="glyphicon glyphicon-cloud-download" aria-hidden="true"></span> Download via Baidu Drive &raquo;</a></p>
+                <span class="label label-info">password: xxxx</span>
+            </div>
+        </div>
+
+        <div class="row">
+            <h5 class="page-header"></h5>
+        </div>
+        <div class="row">
+            <div class="col-md-*">
+                <h2>Citation</h2>
+            </div>
+        </div>
+        <div class="row">
+            <div class="col-md-*">
+                <div class="well">
+                    <!-- @misc{chen2020rainnet,<br/> &ensp;&ensp;title={RainNet: A Large-Scale Dataset for Spatial Precipitation Downscaling},<br/> &ensp;&ensp;author={Xuanhong Chen and Kairui Feng and Naiyuan Liu and Yifan Lu and Zhengyan Tong and Bingbing
+                    Ni and Ziang Liu and Ning Lin},<br/> &ensp;&ensp;year={2020},
+                    <br/> &ensp;&ensp;eprint={2012.09700},
+                    <br/> &ensp;&ensp;archivePrefix={arXiv},
+                    <br/> &ensp;&ensp;primaryClass={cs.CV}
+                    <br/> }  -->
+                    @article{Chen_2020,
+                    <br/> &ensp;&ensp;title={SimSwap},
+                    <br/> &ensp;&ensp;ISBN={9781450379885},
+                    <br/> &ensp;&ensp;url={http://dx.doi.org/10.1145/3394171.3413630},
+                    <br/> &ensp;&ensp;DOI={10.1145/3394171.3413630},
+                    <br/> &ensp;&ensp;journal={Proceedings of the 28th ACM International Conference on Multimedia},
+                    <br/> &ensp;&ensp;publisher={ACM},
+                    <br/> &ensp;&ensp;author={Chen, Renwang and Chen, Xuanhong and Ni, Bingbing and Ge, Yanhao},
+                    <br/> &ensp;&ensp;year={2020},
+                    <br/> &ensp;&ensp;month={Oct}
+                    <br/> }
+                </div>
+            </div>
+        </div>
+
+        <div class="row">
+            <h5 class="page-header"></h5>
+        </div>
+        <div class="row">
+            <div class="col-md-*">
+                <h2>Contact</h2>
+            </div>
+        </div>
+        <div class="row">
+            <div class="col-md-*">
+                <p>Please concat Renwang Chen <span class="label label-primary">applebananac@sjtu.edu.cn</span> and Xuanhong Chen <span class="label label-primary">xuanhongchenzju@outlook.com</span> for questions about the paper. </p>
+            </div>
+        </div>
+        <!-- <div class="row">
+        <h5 class="page-header"></h5>
+      </div>
+      <div class="row">
+        <div class="col-md-*">
+          <h3>High Resolution Precipitation Maps</h2>
+        </div>
+      </div>
+      <div class="row">
+        <div class="col-md-6 col-sm-6">
+          <img src="./img/HRGT_201109091_201109123.gif" style="width: 100%;"/>
+        </div>
+        <div class="col-md-6 col-sm-6">
+          <img src="./img/HRGT_201108607_201108655.gif" style="width: 100%;"/>
+        </div>
+      </div>
+
+      <div class="row">
+        <div class="col-md-*">
+          <h3>Low Resolution Precipitation Maps</h2>
+        </div>
+      </div>
+      <div class="row">
+        <div class="col-md-6 col-sm-6">
+          <img src="./img/LRGT_201109091_201109123.gif" style="width: 100%;"/>
+        </div>
+        <div class="col-md-6 col-sm-6">
+          <img src="./img/LRGT_201108607_201108655.gif" style="width: 100%;"/>
+        </div>
+      </div> -->
+
+        <hr>
+
+        <footer>
+            <p><a>Accessibility</a></p>
+        </footer>
+    </div>
+    <!-- /container -->
+
+
+    <!-- Bootstrap core JavaScript
+    ================================================== -->
+    <!-- Placed at the end of the document so the pages load faster -->
+    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.12.4/jquery.min.js"></script>
+    <script>
+        window.jQuery || document.write('<script src="./js/vendor/jquery.min.js"><\/script>')
+    </script>
+    <script src="./js/bootstrap.min.js"></script>
+    <!-- IE10 viewport hack for Surface/desktop Windows 8 bug -->
+    <script src="./js/ie10-viewport-bug-workaround.js"></script>
+    <script>
+        document.getElementById('Iron_man').style.borderWidth = '5px';
+        document.getElementById('Iron_man').style.borderColor = 'red';
+        document.getElementById('Iron_man').style.borderStyle = 'outset';
+
+        document.getElementById('anni').style.borderWidth = '5px';
+        document.getElementById('anni').style.borderColor = 'red';
+        document.getElementById('anni').style.borderStyle = 'outset';
+    </script>
+</body>
+
+</html>
\ No newline at end of file
diff --git a/modeling/SimSwap/docs/js/bootstrap.min.js b/modeling/SimSwap/docs/js/bootstrap.min.js
new file mode 100644
index 0000000..9bcd2fc
--- /dev/null
+++ b/modeling/SimSwap/docs/js/bootstrap.min.js
@@ -0,0 +1,7 @@
+/*!
+ * Bootstrap v3.3.7 (http://getbootstrap.com)
+ * Copyright 2011-2016 Twitter, Inc.
+ * Licensed under the MIT license
+ */
+if("undefined"==typeof jQuery)throw new Error("Bootstrap's JavaScript requires jQuery");+function(a){"use strict";var b=a.fn.jquery.split(" ")[0].split(".");if(b[0]<2&&b[1]<9||1==b[0]&&9==b[1]&&b[2]<1||b[0]>3)throw new Error("Bootstrap's JavaScript requires jQuery version 1.9.1 or higher, but lower than version 4")}(jQuery),+function(a){"use strict";function b(){var a=document.createElement("bootstrap"),b={WebkitTransition:"webkitTransitionEnd",MozTransition:"transitionend",OTransition:"oTransitionEnd otransitionend",transition:"transitionend"};for(var c in b)if(void 0!==a.style[c])return{end:b[c]};return!1}a.fn.emulateTransitionEnd=function(b){var c=!1,d=this;a(this).one("bsTransitionEnd",function(){c=!0});var e=function(){c||a(d).trigger(a.support.transition.end)};return 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+this.activeTarget=b,this.clear();var c=this.selector+'[data-target="'+b+'"],'+this.selector+'[href="'+b+'"]',d=a(c).parents("li").addClass("active");d.parent(".dropdown-menu").length&&(d=d.closest("li.dropdown").addClass("active")),d.trigger("activate.bs.scrollspy")},b.prototype.clear=function(){a(this.selector).parentsUntil(this.options.target,".active").removeClass("active")};var d=a.fn.scrollspy;a.fn.scrollspy=c,a.fn.scrollspy.Constructor=b,a.fn.scrollspy.noConflict=function(){return a.fn.scrollspy=d,this},a(window).on("load.bs.scrollspy.data-api",function(){a('[data-spy="scroll"]').each(function(){var b=a(this);c.call(b,b.data())})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tab");e||d.data("bs.tab",e=new c(this)),"string"==typeof b&&e[b]()})}var c=function(b){this.element=a(b)};c.VERSION="3.3.7",c.TRANSITION_DURATION=150,c.prototype.show=function(){var b=this.element,c=b.closest("ul:not(.dropdown-menu)"),d=b.data("target");if(d||(d=b.attr("href"),d=d&&d.replace(/.*(?=#[^\s]*$)/,"")),!b.parent("li").hasClass("active")){var e=c.find(".active:last a"),f=a.Event("hide.bs.tab",{relatedTarget:b[0]}),g=a.Event("show.bs.tab",{relatedTarget:e[0]});if(e.trigger(f),b.trigger(g),!g.isDefaultPrevented()&&!f.isDefaultPrevented()){var h=a(d);this.activate(b.closest("li"),c),this.activate(h,h.parent(),function(){e.trigger({type:"hidden.bs.tab",relatedTarget:b[0]}),b.trigger({type:"shown.bs.tab",relatedTarget:e[0]})})}}},c.prototype.activate=function(b,d,e){function f(){g.removeClass("active").find("> .dropdown-menu > .active").removeClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!1),b.addClass("active").find('[data-toggle="tab"]').attr("aria-expanded",!0),h?(b[0].offsetWidth,b.addClass("in")):b.removeClass("fade"),b.parent(".dropdown-menu").length&&b.closest("li.dropdown").addClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!0),e&&e()}var g=d.find("> .active"),h=e&&a.support.transition&&(g.length&&g.hasClass("fade")||!!d.find("> .fade").length);g.length&&h?g.one("bsTransitionEnd",f).emulateTransitionEnd(c.TRANSITION_DURATION):f(),g.removeClass("in")};var d=a.fn.tab;a.fn.tab=b,a.fn.tab.Constructor=c,a.fn.tab.noConflict=function(){return a.fn.tab=d,this};var e=function(c){c.preventDefault(),b.call(a(this),"show")};a(document).on("click.bs.tab.data-api",'[data-toggle="tab"]',e).on("click.bs.tab.data-api",'[data-toggle="pill"]',e)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.affix"),f="object"==typeof b&&b;e||d.data("bs.affix",e=new c(this,f)),"string"==typeof b&&e[b]()})}var c=function(b,d){this.options=a.extend({},c.DEFAULTS,d),this.$target=a(this.options.target).on("scroll.bs.affix.data-api",a.proxy(this.checkPosition,this)).on("click.bs.affix.data-api",a.proxy(this.checkPositionWithEventLoop,this)),this.$element=a(b),this.affixed=null,this.unpin=null,this.pinnedOffset=null,this.checkPosition()};c.VERSION="3.3.7",c.RESET="affix affix-top affix-bottom",c.DEFAULTS={offset:0,target:window},c.prototype.getState=function(a,b,c,d){var e=this.$target.scrollTop(),f=this.$element.offset(),g=this.$target.height();if(null!=c&&"top"==this.affixed)return e<c&&"top";if("bottom"==this.affixed)return null!=c?!(e+this.unpin<=f.top)&&"bottom":!(e+g<=a-d)&&"bottom";var h=null==this.affixed,i=h?e:f.top,j=h?g:b;return null!=c&&e<=c?"top":null!=d&&i+j>=a-d&&"bottom"},c.prototype.getPinnedOffset=function(){if(this.pinnedOffset)return this.pinnedOffset;this.$element.removeClass(c.RESET).addClass("affix");var a=this.$target.scrollTop(),b=this.$element.offset();return this.pinnedOffset=b.top-a},c.prototype.checkPositionWithEventLoop=function(){setTimeout(a.proxy(this.checkPosition,this),1)},c.prototype.checkPosition=function(){if(this.$element.is(":visible")){var b=this.$element.height(),d=this.options.offset,e=d.top,f=d.bottom,g=Math.max(a(document).height(),a(document.body).height());"object"!=typeof d&&(f=e=d),"function"==typeof e&&(e=d.top(this.$element)),"function"==typeof f&&(f=d.bottom(this.$element));var h=this.getState(g,b,e,f);if(this.affixed!=h){null!=this.unpin&&this.$element.css("top","");var i="affix"+(h?"-"+h:""),j=a.Event(i+".bs.affix");if(this.$element.trigger(j),j.isDefaultPrevented())return;this.affixed=h,this.unpin="bottom"==h?this.getPinnedOffset():null,this.$element.removeClass(c.RESET).addClass(i).trigger(i.replace("affix","affixed")+".bs.affix")}"bottom"==h&&this.$element.offset({top:g-b-f})}};var d=a.fn.affix;a.fn.affix=b,a.fn.affix.Constructor=c,a.fn.affix.noConflict=function(){return a.fn.affix=d,this},a(window).on("load",function(){a('[data-spy="affix"]').each(function(){var c=a(this),d=c.data();d.offset=d.offset||{},null!=d.offsetBottom&&(d.offset.bottom=d.offsetBottom),null!=d.offsetTop&&(d.offset.top=d.offsetTop),b.call(c,d)})})}(jQuery);
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diff --git a/modeling/SimSwap/docs/js/ie-emulation-modes-warning.js b/modeling/SimSwap/docs/js/ie-emulation-modes-warning.js
new file mode 100644
index 0000000..3f97ba5
--- /dev/null
+++ b/modeling/SimSwap/docs/js/ie-emulation-modes-warning.js
@@ -0,0 +1,51 @@
+// NOTICE!! DO NOT USE ANY OF THIS JAVASCRIPT
+// IT'S JUST JUNK FOR OUR DOCS!
+// ++++++++++++++++++++++++++++++++++++++++++
+/*!
+ * Copyright 2014-2015 Twitter, Inc.
+ *
+ * Licensed under the Creative Commons Attribution 3.0 Unported License. For
+ * details, see https://creativecommons.org/licenses/by/3.0/.
+ */
+// Intended to prevent false-positive bug reports about Bootstrap not working properly in old versions of IE due to folks testing using IE's unreliable emulation modes.
+(function () {
+  'use strict';
+
+  function emulatedIEMajorVersion() {
+    var groups = /MSIE ([0-9.]+)/.exec(window.navigator.userAgent)
+    if (groups === null) {
+      return null
+    }
+    var ieVersionNum = parseInt(groups[1], 10)
+    var ieMajorVersion = Math.floor(ieVersionNum)
+    return ieMajorVersion
+  }
+
+  function actualNonEmulatedIEMajorVersion() {
+    // Detects the actual version of IE in use, even if it's in an older-IE emulation mode.
+    // IE JavaScript conditional compilation docs: https://msdn.microsoft.com/library/121hztk3%28v=vs.94%29.aspx
+    // @cc_on docs: https://msdn.microsoft.com/library/8ka90k2e%28v=vs.94%29.aspx
+    var jscriptVersion = new Function('/*@cc_on return @_jscript_version; @*/')() // jshint ignore:line
+    if (jscriptVersion === undefined) {
+      return 11 // IE11+ not in emulation mode
+    }
+    if (jscriptVersion < 9) {
+      return 8 // IE8 (or lower; haven't tested on IE<8)
+    }
+    return jscriptVersion // IE9 or IE10 in any mode, or IE11 in non-IE11 mode
+  }
+
+  var ua = window.navigator.userAgent
+  if (ua.indexOf('Opera') > -1 || ua.indexOf('Presto') > -1) {
+    return // Opera, which might pretend to be IE
+  }
+  var emulated = emulatedIEMajorVersion()
+  if (emulated === null) {
+    return // Not IE
+  }
+  var nonEmulated = actualNonEmulatedIEMajorVersion()
+
+  if (emulated !== nonEmulated) {
+    window.alert('WARNING: You appear to be using IE' + nonEmulated + ' in IE' + emulated + ' emulation mode.\nIE emulation modes can behave significantly differently from ACTUAL older versions of IE.\nPLEASE DON\'T FILE BOOTSTRAP BUGS based on testing in IE emulation modes!')
+  }
+})();
diff --git a/modeling/SimSwap/docs/js/ie10-viewport-bug-workaround.js b/modeling/SimSwap/docs/js/ie10-viewport-bug-workaround.js
new file mode 100644
index 0000000..479a6eb
--- /dev/null
+++ b/modeling/SimSwap/docs/js/ie10-viewport-bug-workaround.js
@@ -0,0 +1,23 @@
+/*!
+ * IE10 viewport hack for Surface/desktop Windows 8 bug
+ * Copyright 2014-2015 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+
+// See the Getting Started docs for more information:
+// http://getbootstrap.com/getting-started/#support-ie10-width
+
+(function () {
+  'use strict';
+
+  if (navigator.userAgent.match(/IEMobile\/10\.0/)) {
+    var msViewportStyle = document.createElement('style')
+    msViewportStyle.appendChild(
+      document.createTextNode(
+        '@-ms-viewport{width:auto!important}'
+      )
+    )
+    document.querySelector('head').appendChild(msViewportStyle)
+  }
+
+})();
diff --git a/modeling/SimSwap/docs/js/vendor/jquery.min.js b/modeling/SimSwap/docs/js/vendor/jquery.min.js
new file mode 100644
index 0000000..e836475
--- /dev/null
+++ b/modeling/SimSwap/docs/js/vendor/jquery.min.js
@@ -0,0 +1,5 @@
+/*! jQuery v1.12.4 | (c) jQuery Foundation | jquery.org/license */
+!function(a,b){"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?window:this,function(a,b){var c=[],d=a.document,e=c.slice,f=c.concat,g=c.push,h=c.indexOf,i={},j=i.toString,k=i.hasOwnProperty,l={},m="1.12.4",n=function(a,b){return new n.fn.init(a,b)},o=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,p=/^-ms-/,q=/-([\da-z])/gi,r=function(a,b){return b.toUpperCase()};n.fn=n.prototype={jquery:m,constructor:n,selector:"",length:0,toArray:function(){return e.call(this)},get:function(a){return null!=a?0>a?this[a+this.length]:this[a]:e.call(this)},pushStack:function(a){var b=n.merge(this.constructor(),a);return b.prevObject=this,b.context=this.context,b},each:function(a){return n.each(this,a)},map:function(a){return this.pushStack(n.map(this,function(b,c){return a.call(b,c,b)}))},slice:function(){return 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+padding:"inner"+a,content:b,"":"outer"+a},function(c,d){n.fn[d]=function(d,e){var f=arguments.length&&(c||"boolean"!=typeof d),g=c||(d===!0||e===!0?"margin":"border");return Y(this,function(b,c,d){var e;return n.isWindow(b)?b.document.documentElement["client"+a]:9===b.nodeType?(e=b.documentElement,Math.max(b.body["scroll"+a],e["scroll"+a],b.body["offset"+a],e["offset"+a],e["client"+a])):void 0===d?n.css(b,c,g):n.style(b,c,d,g)},b,f?d:void 0,f,null)}})}),n.fn.extend({bind:function(a,b,c){return this.on(a,null,b,c)},unbind:function(a,b){return this.off(a,null,b)},delegate:function(a,b,c,d){return this.on(b,a,c,d)},undelegate:function(a,b,c){return 1===arguments.length?this.off(a,"**"):this.off(b,a||"**",c)}}),n.fn.size=function(){return this.length},n.fn.andSelf=n.fn.addBack,"function"==typeof define&&define.amd&&define("jquery",[],function(){return n});var nc=a.jQuery,oc=a.$;return n.noConflict=function(b){return a.$===n&&(a.$=oc),b&&a.jQuery===n&&(a.jQuery=nc),n},b||(a.jQuery=a.$=n),n});
diff --git a/modeling/SimSwap/docs/js/which-image.js b/modeling/SimSwap/docs/js/which-image.js
new file mode 100644
index 0000000..867bf20
--- /dev/null
+++ b/modeling/SimSwap/docs/js/which-image.js
@@ -0,0 +1,50 @@
+/*
+ * @FilePath: \SimSwap\docs\js\which-image.js
+ * @Author: Ziang Liu
+ * @Date: 2021-07-03 16:34:56
+ * @LastEditors: AceSix
+ * @LastEditTime: 2021-07-20 00:46:27
+ * Copyright (C) 2021 SJTU. All rights reserved.
+ */
+
+
+
+function select_source(number) {
+    var items = ['anni', 'chenglong', 'zhoujielun', 'zhuyin'];
+    var item_id = items[number];
+
+    for (i = 0; i < 4; i++) {
+        if (number == i) {
+            document.getElementById(items[i]).style.borderWidth = '5px';
+            document.getElementById(items[i]).style.borderColor = 'red';
+            document.getElementById(items[i]).style.borderStyle = 'outset';
+        } else {
+            document.getElementById(items[i]).style.border = 'none';
+        }
+    }
+    document.getElementById('jiroujinlun').src = './img/' + item_id + '.webp';
+
+}
+
+function select_source2(number) {
+    var items = ['Iron_man', 'wuyifan'];
+    var item_id = items[number];
+
+    for (i = 0; i < 2; i++) {
+        if (number == i) {
+            document.getElementById(items[i]).style.borderWidth = '5px';
+            document.getElementById(items[i]).style.borderColor = 'red';
+            document.getElementById(items[i]).style.borderStyle = 'outset';
+        } else {
+            document.getElementById(items[i]).style.border = 'none';
+        }
+    }
+    if (item_id=='Iron_man'){
+        document.getElementById('mama').src = './img/mama_mask_short.webp';
+    }
+    else{
+        document.getElementById('mama').src = './img/mama_mask_wuyifan_short.webp';
+
+    }
+    
+}
\ No newline at end of file
diff --git a/modeling/SimSwap/download-weights.sh b/modeling/SimSwap/download-weights.sh
new file mode 100644
index 0000000..54e6ea5
--- /dev/null
+++ b/modeling/SimSwap/download-weights.sh
@@ -0,0 +1,9 @@
+#!/bin/sh
+wget -P ./arcface_model https://github.com/neuralchen/SimSwap/releases/download/1.0/arcface_checkpoint.tar
+wget https://github.com/neuralchen/SimSwap/releases/download/1.0/checkpoints.zip
+unzip ./checkpoints.zip  -d ./checkpoints
+rm checkpoints.zip
+wget --no-check-certificate "https://sh23tw.dm.files.1drv.com/y4mmGiIkNVigkSwOKDcV3nwMJulRGhbtHdkheehR5TArc52UjudUYNXAEvKCii2O5LAmzGCGK6IfleocxuDeoKxDZkNzDRSt4ZUlEt8GlSOpCXAFEkBwaZimtWGDRbpIGpb_pz9Nq5jATBQpezBS6G_UtspWTkgrXHHxhviV2nWy8APPx134zOZrUIbkSF6xnsqzs3uZ_SEX_m9Rey0ykpx9w" -O antelope.zip
+mkdir -p insightface_func/models
+unzip ./antelope.zip -d ./insightface_func/models/
+rm antelope.zip
diff --git a/modeling/SimSwap/inference_swap.py b/modeling/SimSwap/inference_swap.py
new file mode 100644
index 0000000..0e1424b
--- /dev/null
+++ b/modeling/SimSwap/inference_swap.py
@@ -0,0 +1,92 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 19:00:38
+Description: 
+'''
+
+import cv2
+import torch
+import fractions
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+from torchvision import transforms
+from models.models import create_model
+from options.test_options import TestOptions
+from insightface_func.face_detect_crop_single import Face_detect_crop
+from util.gifswap import gif_swap
+import os
+
+def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
+
+transformer = transforms.Compose([
+        transforms.ToTensor(),
+        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+transformer_Arcface = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+# detransformer = transforms.Compose([
+#         transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
+#         transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
+#     ])
+
+def main():
+    opt = TestOptions().parse()
+
+    start_epoch, epoch_iter = 1, 0
+    crop_size = opt.crop_size
+
+    torch.nn.Module.dump_patches = True
+    if crop_size == 512:
+        opt.which_epoch = 550000
+        opt.name = '512'
+        mode = 'ffhq'
+    else:
+        mode = 'None'
+    model = create_model(opt)
+    model.eval()
+
+
+    app = Face_detect_crop(name='antelope', root='./SimSwap/insightface_func/models')
+    app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640),mode=mode)
+    with torch.no_grad():
+        pic_a = opt.pic_a_path
+        # img_a = Image.open(pic_a).convert('RGB')
+        img_a_whole = cv2.imread(pic_a)
+        img_a_align_crop, _ = app.get(img_a_whole,crop_size)
+        img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) 
+        img_a = transformer_Arcface(img_a_align_crop_pil)
+        img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
+
+        # pic_b = opt.pic_b_path
+        # img_b_whole = cv2.imread(pic_b)
+        # img_b_align_crop, b_mat = app.get(img_b_whole,crop_size)
+        # img_b_align_crop_pil = Image.fromarray(cv2.cvtColor(img_b_align_crop,cv2.COLOR_BGR2RGB)) 
+        # img_b = transformer(img_b_align_crop_pil)
+        # img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2])
+
+        # convert numpy to tensor
+        img_id = img_id.cuda()
+        # img_att = img_att.cuda()
+
+        #create latent id
+        img_id_downsample = F.interpolate(img_id, size=(112,112))
+        latend_id = model.netArc(img_id_downsample)
+        latend_id = F.normalize(latend_id, p=2, dim=1)
+
+        frames, fps = gif_swap(opt.video_path, latend_id, model, app, opt.output_path, temp_results_dir=opt.temp_path,\
+                        no_simswaplogo=opt.no_simswaplogo, use_mask=True, crop_size=crop_size)
+        
+    return frames, fps
+
+
+if __name__ == '__main__':
+    main()
+
diff --git a/modeling/SimSwap/insightface_func/__init__.py b/modeling/SimSwap/insightface_func/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/modeling/SimSwap/insightface_func/face_detect_crop_multi.py b/modeling/SimSwap/insightface_func/face_detect_crop_multi.py
new file mode 100644
index 0000000..d75c36b
--- /dev/null
+++ b/modeling/SimSwap/insightface_func/face_detect_crop_multi.py
@@ -0,0 +1,100 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 16:45:41
+Description: 
+'''
+from __future__ import division
+import collections
+import numpy as np
+import glob
+import os
+import os.path as osp
+import cv2
+from insightface.model_zoo import model_zoo
+from insightface_func.utils import face_align_ffhqandnewarc as face_align
+
+__all__ = ['Face_detect_crop', 'Face']
+
+Face = collections.namedtuple('Face', [
+    'bbox', 'kps', 'det_score', 'embedding', 'gender', 'age',
+    'embedding_norm', 'normed_embedding',
+    'landmark'
+])
+
+Face.__new__.__defaults__ = (None, ) * len(Face._fields)
+
+
+class Face_detect_crop:
+    def __init__(self, name, root='~/.insightface_func/models'):
+        self.models = {}
+        root = os.path.expanduser(root)
+        onnx_files = glob.glob(osp.join(root, name, '*.onnx'))
+        onnx_files = sorted(onnx_files)
+        for onnx_file in onnx_files:
+            if onnx_file.find('_selfgen_')>0:
+                #print('ignore:', onnx_file)
+                continue
+            model = model_zoo.get_model(onnx_file)
+            if model.taskname not in self.models:
+                print('find model:', onnx_file, model.taskname)
+                self.models[model.taskname] = model
+            else:
+                print('duplicated model task type, ignore:', onnx_file, model.taskname)
+                del model
+        assert 'detection' in self.models
+        self.det_model = self.models['detection']
+
+
+    def prepare(self, ctx_id, det_thresh=0.5, det_size=(640, 640), mode ='None'):
+        self.det_thresh = det_thresh
+        self.mode = mode
+        assert det_size is not None
+        print('set det-size:', det_size)
+        self.det_size = det_size
+        for taskname, model in self.models.items():
+            if taskname=='detection':
+                model.prepare(ctx_id, input_size=det_size)
+            else:
+                model.prepare(ctx_id)
+
+    def get(self, img, crop_size, max_num=0):
+        bboxes, kpss = self.det_model.detect(img,
+                                             threshold=self.det_thresh,
+                                             max_num=max_num,
+                                             metric='default')
+        if bboxes.shape[0] == 0:
+            return None
+        ret = []
+        # for i in range(bboxes.shape[0]):
+        #     bbox = bboxes[i, 0:4]
+        #     det_score = bboxes[i, 4]
+        #     kps = None
+        #     if kpss is not None:
+        #         kps = kpss[i]
+        #     M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') 
+        #     align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+        align_img_list = []
+        M_list = []
+        for i in range(bboxes.shape[0]):
+            kps = None
+            if kpss is not None:
+                kps = kpss[i]
+            M, _ = face_align.estimate_norm(kps, crop_size, mode = self.mode) 
+            align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+            align_img_list.append(align_img)
+            M_list.append(M)
+
+        # det_score = bboxes[..., 4]
+
+        # best_index = np.argmax(det_score)
+
+        # kps = None
+        # if kpss is not None:
+        #     kps = kpss[best_index]
+        # M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') 
+        # align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+        
+        return align_img_list, M_list
diff --git a/modeling/SimSwap/insightface_func/face_detect_crop_single.py b/modeling/SimSwap/insightface_func/face_detect_crop_single.py
new file mode 100644
index 0000000..f3f648f
--- /dev/null
+++ b/modeling/SimSwap/insightface_func/face_detect_crop_single.py
@@ -0,0 +1,97 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 16:46:04
+Description: 
+'''
+from __future__ import division
+import collections
+import numpy as np
+import glob
+import os
+import os.path as osp
+import cv2
+from insightface.model_zoo import model_zoo
+from insightface_func.utils import face_align_ffhqandnewarc as face_align
+
+__all__ = ['Face_detect_crop', 'Face']
+
+Face = collections.namedtuple('Face', [
+    'bbox', 'kps', 'det_score', 'embedding', 'gender', 'age',
+    'embedding_norm', 'normed_embedding',
+    'landmark'
+])
+
+Face.__new__.__defaults__ = (None, ) * len(Face._fields)
+
+
+class Face_detect_crop:
+    def __init__(self, name, root='~/.insightface_func/models'):
+        self.models = {}
+        root = os.path.expanduser(root)
+        onnx_files = glob.glob(osp.join(root, name, '*.onnx'))
+        onnx_files = sorted(onnx_files)
+        for onnx_file in onnx_files:
+            if onnx_file.find('_selfgen_')>0:
+                #print('ignore:', onnx_file)
+                continue
+            model = model_zoo.get_model(onnx_file)
+            if model.taskname not in self.models:
+                print('find model:', onnx_file, model.taskname)
+                self.models[model.taskname] = model
+            else:
+                print('duplicated model task type, ignore:', onnx_file, model.taskname)
+                del model
+        assert 'detection' in self.models
+        self.det_model = self.models['detection']
+
+
+    def prepare(self, ctx_id, det_thresh=0.5, det_size=(640, 640), mode ='None'):
+        self.det_thresh = det_thresh
+        self.mode = mode
+        assert det_size is not None
+        print('set det-size:', det_size)
+        self.det_size = det_size
+        for taskname, model in self.models.items():
+            if taskname=='detection':
+                model.prepare(ctx_id, input_size=det_size)
+            else:
+                model.prepare(ctx_id)
+
+    def get(self, img, crop_size, max_num=0):
+        bboxes, kpss = self.det_model.detect(img,
+                                             threshold=self.det_thresh,
+                                             max_num=max_num,
+                                             metric='default')
+        if bboxes.shape[0] == 0:
+            return None
+        # ret = []
+        # for i in range(bboxes.shape[0]):
+        #     bbox = bboxes[i, 0:4]
+        #     det_score = bboxes[i, 4]
+        #     kps = None
+        #     if kpss is not None:
+        #         kps = kpss[i]
+        #     M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') 
+        #     align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+        # for i in range(bboxes.shape[0]):
+        #     kps = None
+        #     if kpss is not None:
+        #         kps = kpss[i]
+        #     M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') 
+        #     align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+
+        det_score = bboxes[..., 4]
+
+        # select the face with the hightest detection score
+        best_index = np.argmax(det_score)
+
+        kps = None
+        if kpss is not None:
+            kps = kpss[best_index]
+        M, _ = face_align.estimate_norm(kps, crop_size, mode = self.mode) 
+        align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0)
+        
+        return [align_img], [M]
diff --git a/modeling/SimSwap/insightface_func/utils/face_align_ffhqandnewarc.py b/modeling/SimSwap/insightface_func/utils/face_align_ffhqandnewarc.py
new file mode 100644
index 0000000..2f5aeb7
--- /dev/null
+++ b/modeling/SimSwap/insightface_func/utils/face_align_ffhqandnewarc.py
@@ -0,0 +1,159 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-15 19:42:42
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-15 20:01:47
+Description: 
+'''
+
+import cv2
+import numpy as np
+from skimage import transform as trans
+
+src1 = np.array([[51.642, 50.115], [57.617, 49.990], [35.740, 69.007],
+                 [51.157, 89.050], [57.025, 89.702]],
+                dtype=np.float32)
+#<--left
+src2 = np.array([[45.031, 50.118], [65.568, 50.872], [39.677, 68.111],
+                 [45.177, 86.190], [64.246, 86.758]],
+                dtype=np.float32)
+
+#---frontal
+src3 = np.array([[39.730, 51.138], [72.270, 51.138], [56.000, 68.493],
+                 [42.463, 87.010], [69.537, 87.010]],
+                dtype=np.float32)
+
+#-->right
+src4 = np.array([[46.845, 50.872], [67.382, 50.118], [72.737, 68.111],
+                 [48.167, 86.758], [67.236, 86.190]],
+                dtype=np.float32)
+
+#-->right profile
+src5 = np.array([[54.796, 49.990], [60.771, 50.115], [76.673, 69.007],
+                 [55.388, 89.702], [61.257, 89.050]],
+                dtype=np.float32)
+
+src = np.array([src1, src2, src3, src4, src5])
+src_map = src
+
+ffhq_src = np.array([[192.98138, 239.94708], [318.90277, 240.1936], [256.63416, 314.01935],
+                                           [201.26117, 371.41043], [313.08905, 371.15118]])
+ffhq_src = np.expand_dims(ffhq_src, axis=0)
+
+# arcface_src = np.array(
+#     [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366],
+#      [41.5493, 92.3655], [70.7299, 92.2041]],
+#     dtype=np.float32)
+
+# arcface_src = np.expand_dims(arcface_src, axis=0)
+
+# In[66]:
+
+
+# lmk is prediction; src is template
+def estimate_norm(lmk, image_size=112, mode='ffhq'):
+    assert lmk.shape == (5, 2)
+    tform = trans.SimilarityTransform()
+    lmk_tran = np.insert(lmk, 2, values=np.ones(5), axis=1)
+    min_M = []
+    min_index = []
+    min_error = float('inf')
+    if mode == 'ffhq':
+        # assert image_size == 112
+        src = ffhq_src * image_size / 512
+    else:
+        src = src_map * image_size / 112
+    for i in np.arange(src.shape[0]):
+        tform.estimate(lmk, src[i])
+        M = tform.params[0:2, :]
+        results = np.dot(M, lmk_tran.T)
+        results = results.T
+        error = np.sum(np.sqrt(np.sum((results - src[i])**2, axis=1)))
+        #         print(error)
+        if error < min_error:
+            min_error = error
+            min_M = M
+            min_index = i
+    return min_M, min_index
+
+
+def norm_crop(img, landmark, image_size=112, mode='ffhq'):
+    if mode == 'Both':
+        M_None, _ = estimate_norm(landmark, image_size, mode = 'newarc')
+        M_ffhq, _ = estimate_norm(landmark, image_size, mode='ffhq')
+        warped_None = cv2.warpAffine(img, M_None, (image_size, image_size), borderValue=0.0)
+        warped_ffhq = cv2.warpAffine(img, M_ffhq, (image_size, image_size), borderValue=0.0)
+        return warped_ffhq, warped_None
+    else:
+        M, pose_index = estimate_norm(landmark, image_size, mode)
+        warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
+        return warped
+
+def square_crop(im, S):
+    if im.shape[0] > im.shape[1]:
+        height = S
+        width = int(float(im.shape[1]) / im.shape[0] * S)
+        scale = float(S) / im.shape[0]
+    else:
+        width = S
+        height = int(float(im.shape[0]) / im.shape[1] * S)
+        scale = float(S) / im.shape[1]
+    resized_im = cv2.resize(im, (width, height))
+    det_im = np.zeros((S, S, 3), dtype=np.uint8)
+    det_im[:resized_im.shape[0], :resized_im.shape[1], :] = resized_im
+    return det_im, scale
+
+
+def transform(data, center, output_size, scale, rotation):
+    scale_ratio = scale
+    rot = float(rotation) * np.pi / 180.0
+    #translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio)
+    t1 = trans.SimilarityTransform(scale=scale_ratio)
+    cx = center[0] * scale_ratio
+    cy = center[1] * scale_ratio
+    t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy))
+    t3 = trans.SimilarityTransform(rotation=rot)
+    t4 = trans.SimilarityTransform(translation=(output_size / 2,
+                                                output_size / 2))
+    t = t1 + t2 + t3 + t4
+    M = t.params[0:2]
+    cropped = cv2.warpAffine(data,
+                             M, (output_size, output_size),
+                             borderValue=0.0)
+    return cropped, M
+
+
+def trans_points2d(pts, M):
+    new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
+    for i in range(pts.shape[0]):
+        pt = pts[i]
+        new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
+        new_pt = np.dot(M, new_pt)
+        #print('new_pt', new_pt.shape, new_pt)
+        new_pts[i] = new_pt[0:2]
+
+    return new_pts
+
+
+def trans_points3d(pts, M):
+    scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1])
+    #print(scale)
+    new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
+    for i in range(pts.shape[0]):
+        pt = pts[i]
+        new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
+        new_pt = np.dot(M, new_pt)
+        #print('new_pt', new_pt.shape, new_pt)
+        new_pts[i][0:2] = new_pt[0:2]
+        new_pts[i][2] = pts[i][2] * scale
+
+    return new_pts
+
+
+def trans_points(pts, M):
+    if pts.shape[1] == 2:
+        return trans_points2d(pts, M)
+    else:
+        return trans_points3d(pts, M)
+
diff --git a/modeling/SimSwap/models/__init__.py b/modeling/SimSwap/models/__init__.py
new file mode 100644
index 0000000..289de91
--- /dev/null
+++ b/modeling/SimSwap/models/__init__.py
@@ -0,0 +1,4 @@
+from .arcface_models import ArcMarginModel
+from .arcface_models import ResNet
+from .arcface_models import IRBlock
+from .arcface_models import SEBlock
\ No newline at end of file
diff --git a/modeling/SimSwap/models/arcface_models.py b/modeling/SimSwap/models/arcface_models.py
new file mode 100644
index 0000000..39a6ac5
--- /dev/null
+++ b/modeling/SimSwap/models/arcface_models.py
@@ -0,0 +1,163 @@
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn import Parameter
+from .config import device, num_classes
+
+
+
+class SEBlock(nn.Module):
+    def __init__(self, channel, reduction=16):
+        super(SEBlock, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction),
+            nn.PReLU(),
+            nn.Linear(channel // reduction, channel),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1, 1)
+        return x * y
+
+
+class IRBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
+        super(IRBlock, self).__init__()
+        self.bn0 = nn.BatchNorm2d(inplanes)
+        self.conv1 = conv3x3(inplanes, inplanes)
+        self.bn1 = nn.BatchNorm2d(inplanes)
+        self.prelu = nn.PReLU()
+        self.conv2 = conv3x3(inplanes, planes, stride)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+        self.use_se = use_se
+        if self.use_se:
+            self.se = SEBlock(planes)
+
+    def forward(self, x):
+        residual = x
+        out = self.bn0(x)
+        out = self.conv1(out)
+        out = self.bn1(out)
+        out = self.prelu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        if self.use_se:
+            out = self.se(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.prelu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, use_se=True):
+        self.inplanes = 64
+        self.use_se = use_se
+        super(ResNet, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.prelu = nn.PReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.bn2 = nn.BatchNorm2d(512)
+        self.dropout = nn.Dropout()
+        self.fc = nn.Linear(512 * 7 * 7, 512)
+        self.bn3 = nn.BatchNorm1d(512)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.xavier_normal_(m.weight)
+            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.xavier_normal_(m.weight)
+                nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
+        self.inplanes = planes
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, use_se=self.use_se))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.prelu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.bn2(x)
+        x = self.dropout(x)
+        # feature = x
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        x = self.bn3(x)
+
+        return x
+
+
+class ArcMarginModel(nn.Module):
+    def __init__(self, args):
+        super(ArcMarginModel, self).__init__()
+
+        self.weight = Parameter(torch.FloatTensor(num_classes, args.emb_size))
+        nn.init.xavier_uniform_(self.weight)
+
+        self.easy_margin = args.easy_margin
+        self.m = args.margin_m
+        self.s = args.margin_s
+
+        self.cos_m = math.cos(self.m)
+        self.sin_m = math.sin(self.m)
+        self.th = math.cos(math.pi - self.m)
+        self.mm = math.sin(math.pi - self.m) * self.m
+
+    def forward(self, input, label):
+        x = F.normalize(input)
+        W = F.normalize(self.weight)
+        cosine = F.linear(x, W)
+        sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
+        phi = cosine * self.cos_m - sine * self.sin_m  # cos(theta + m)
+        if self.easy_margin:
+            phi = torch.where(cosine > 0, phi, cosine)
+        else:
+            phi = torch.where(cosine > self.th, phi, cosine - self.mm)
+        one_hot = torch.zeros(cosine.size(), device=device)
+        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
+        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
+        output *= self.s
+        return output
\ No newline at end of file
diff --git a/modeling/SimSwap/models/base_model.py b/modeling/SimSwap/models/base_model.py
new file mode 100644
index 0000000..0a6474a
--- /dev/null
+++ b/modeling/SimSwap/models/base_model.py
@@ -0,0 +1,138 @@
+import os
+import torch
+import sys
+
+class BaseModel(torch.nn.Module):
+    def name(self):
+        return 'BaseModel'
+
+    def initialize(self, opt):
+        self.opt = opt
+        self.gpu_ids = opt.gpu_ids
+        self.isTrain = opt.isTrain
+        self.Tensor = torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor
+        self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)
+
+    def set_input(self, input):
+        self.input = input
+
+    def forward(self):
+        pass
+
+    # used in test time, no backprop
+    def test(self):
+        pass
+
+    def get_image_paths(self):
+        pass
+
+    def optimize_parameters(self):
+        pass
+
+    def get_current_visuals(self):
+        return self.input
+
+    def get_current_errors(self):
+        return {}
+
+    def save(self, label):
+        pass
+    
+    # helper saving function that can be used by subclasses
+    def save_network(self, network, network_label, epoch_label, gpu_ids=None):
+        save_filename = '{}_net_{}.pth'.format(epoch_label, network_label)
+        save_path = os.path.join(self.save_dir, save_filename)
+        torch.save(network.cpu().state_dict(), save_path)
+        if torch.cuda.is_available():
+            network.cuda()
+
+    def save_optim(self, network, network_label, epoch_label, gpu_ids=None):
+        save_filename = '{}_optim_{}.pth'.format(epoch_label, network_label)
+        save_path = os.path.join(self.save_dir, save_filename)
+        torch.save(network.state_dict(), save_path)
+
+
+    # helper loading function that can be used by subclasses
+    def load_network(self, network, network_label, epoch_label, save_dir=''):        
+        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
+        if not save_dir:
+            save_dir = self.save_dir
+        save_path = os.path.join(save_dir, save_filename)        
+        if not os.path.isfile(save_path):
+            print('%s not exists yet!' % save_path)
+            if network_label == 'G':
+                raise('Generator must exist!')
+        else:
+            #network.load_state_dict(torch.load(save_path))
+            try:
+                network.load_state_dict(torch.load(save_path))
+            except:   
+                pretrained_dict = torch.load(save_path)                
+                model_dict = network.state_dict()
+                try:
+                    pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}                    
+                    network.load_state_dict(pretrained_dict)
+                    if self.opt.verbose:
+                        print('Pretrained network %s has excessive layers; Only loading layers that are used' % network_label)
+                except:
+                    print('Pretrained network %s has fewer layers; The following are not initialized:' % network_label)
+                    for k, v in pretrained_dict.items():                      
+                        if v.size() == model_dict[k].size():
+                            model_dict[k] = v
+
+                    if sys.version_info >= (3,0):
+                        not_initialized = set()
+                    else:
+                        from sets import Set
+                        not_initialized = Set()                    
+
+                    for k, v in model_dict.items():
+                        if k not in pretrained_dict or v.size() != pretrained_dict[k].size():
+                            not_initialized.add(k.split('.')[0])
+                    
+                    print(sorted(not_initialized))
+                    network.load_state_dict(model_dict)
+
+    # helper loading function that can be used by subclasses
+    def load_optim(self, network, network_label, epoch_label, save_dir=''):        
+        save_filename = '%s_optim_%s.pth' % (epoch_label, network_label)
+        if not save_dir:
+            save_dir = self.save_dir
+        save_path = os.path.join(save_dir, save_filename)        
+        if not os.path.isfile(save_path):
+            print('%s not exists yet!' % save_path)
+            if network_label == 'G':
+                raise('Generator must exist!')
+        else:
+            #network.load_state_dict(torch.load(save_path))
+            try:
+                network.load_state_dict(torch.load(save_path, map_location=torch.device("cpu")))
+            except:   
+                pretrained_dict = torch.load(save_path, map_location=torch.device("cpu"))                
+                model_dict = network.state_dict()
+                try:
+                    pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}                    
+                    network.load_state_dict(pretrained_dict)
+                    if self.opt.verbose:
+                        print('Pretrained network %s has excessive layers; Only loading layers that are used' % network_label)
+                except:
+                    print('Pretrained network %s has fewer layers; The following are not initialized:' % network_label)
+                    for k, v in pretrained_dict.items():                      
+                        if v.size() == model_dict[k].size():
+                            model_dict[k] = v
+
+                    if sys.version_info >= (3,0):
+                        not_initialized = set()
+                    else:
+                        from sets import Set
+                        not_initialized = Set()                    
+
+                    for k, v in model_dict.items():
+                        if k not in pretrained_dict or v.size() != pretrained_dict[k].size():
+                            not_initialized.add(k.split('.')[0])
+                    
+                    print(sorted(not_initialized))
+                    network.load_state_dict(model_dict)                  
+
+    def update_learning_rate():
+        pass
diff --git a/modeling/SimSwap/models/config.py b/modeling/SimSwap/models/config.py
new file mode 100644
index 0000000..eb83edb
--- /dev/null
+++ b/modeling/SimSwap/models/config.py
@@ -0,0 +1,28 @@
+import os
+
+import torch
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # sets device for model and PyTorch tensors
+
+# Model parameters
+image_w = 112
+image_h = 112
+channel = 3
+emb_size = 512
+
+# Training parameters
+num_workers = 1  # for data-loading; right now, only 1 works with h5py
+grad_clip = 5.  # clip gradients at an absolute value of
+print_freq = 100  # print training/validation stats  every __ batches
+checkpoint = None  # path to checkpoint, None if none
+
+# Data parameters
+num_classes = 93431
+num_samples = 5179510
+DATA_DIR = 'data'
+# faces_ms1m_folder = 'data/faces_ms1m_112x112'
+faces_ms1m_folder = 'data/ms1m-retinaface-t1'
+path_imgidx = os.path.join(faces_ms1m_folder, 'train.idx')
+path_imgrec = os.path.join(faces_ms1m_folder, 'train.rec')
+IMG_DIR = 'data/images'
+pickle_file = 'data/faces_ms1m_112x112.pickle'
diff --git a/modeling/SimSwap/models/fs_model.py b/modeling/SimSwap/models/fs_model.py
new file mode 100644
index 0000000..95ac877
--- /dev/null
+++ b/modeling/SimSwap/models/fs_model.py
@@ -0,0 +1,242 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import os
+from torch.autograd import Variable
+from .base_model import BaseModel
+from . import networks
+
+class SpecificNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(SpecificNorm, self).__init__()
+        self.mean = np.array([0.485, 0.456, 0.406])
+        self.mean = torch.from_numpy(self.mean).float().cuda()
+        self.mean = self.mean.view([1, 3, 1, 1])
+
+        self.std = np.array([0.229, 0.224, 0.225])
+        self.std = torch.from_numpy(self.std).float().cuda()
+        self.std = self.std.view([1, 3, 1, 1])
+
+    def forward(self, x):
+        mean = self.mean.expand([1, 3, x.shape[2], x.shape[3]])
+        std = self.std.expand([1, 3, x.shape[2], x.shape[3]])
+
+        x = (x - mean) / std
+
+        return x
+
+class fsModel(BaseModel):
+    def name(self):
+        return 'fsModel'
+
+    def init_loss_filter(self, use_gan_feat_loss, use_vgg_loss):
+        flags = (True, use_gan_feat_loss, use_vgg_loss, True, True, True, True, True)
+
+        def loss_filter(g_gan, g_gan_feat, g_vgg, g_id, g_rec, g_mask, d_real, d_fake):
+            return [l for (l, f) in zip((g_gan, g_gan_feat, g_vgg, g_id, g_rec, g_mask, d_real, d_fake), flags) if f]
+
+        return loss_filter
+
+    def initialize(self, opt):
+        BaseModel.initialize(self, opt)
+        if opt.resize_or_crop != 'none' or not opt.isTrain:  # when training at full res this causes OOM
+            torch.backends.cudnn.benchmark = True
+        self.isTrain = opt.isTrain
+
+        device = torch.device("cuda:0")
+
+        if opt.crop_size == 224:
+            from .fs_networks import Generator_Adain_Upsample, Discriminator
+        elif opt.crop_size == 512:
+            from .fs_networks_512 import Generator_Adain_Upsample, Discriminator
+
+        # Generator network
+        self.netG = Generator_Adain_Upsample(input_nc=3, output_nc=3, latent_size=512, n_blocks=9, deep=False)
+        self.netG.to(device)
+
+        # Id network
+        netArc_checkpoint = opt.Arc_path
+        netArc_checkpoint = torch.load(netArc_checkpoint, map_location=torch.device("cpu"))
+        self.netArc = netArc_checkpoint
+        self.netArc = self.netArc.to(device)
+        self.netArc.eval()
+
+        if not self.isTrain:
+            pretrained_path = '' if not self.isTrain else opt.load_pretrain
+            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)
+            return
+
+        # Discriminator network
+        if opt.gan_mode == 'original':
+            use_sigmoid = True
+        else:
+            use_sigmoid = False
+        self.netD1 = Discriminator(input_nc=3, use_sigmoid=use_sigmoid)
+        self.netD2 = Discriminator(input_nc=3, use_sigmoid=use_sigmoid)
+        self.netD1.to(device)
+        self.netD2.to(device)
+
+        #
+        self.spNorm =SpecificNorm()
+        self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
+
+        # load networks
+        if opt.continue_train or opt.load_pretrain:
+            pretrained_path = '' if not self.isTrain else opt.load_pretrain
+            # print (pretrained_path)
+            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)
+            self.load_network(self.netD1, 'D1', opt.which_epoch, pretrained_path)
+            self.load_network(self.netD2, 'D2', opt.which_epoch, pretrained_path)
+
+
+
+        if self.isTrain:
+            # define loss functions
+            self.loss_filter = self.init_loss_filter(not opt.no_ganFeat_loss, not opt.no_vgg_loss)
+
+            self.criterionGAN = networks.GANLoss(opt.gan_mode, tensor=self.Tensor, opt=self.opt)
+            self.criterionFeat = nn.L1Loss()
+            self.criterionRec = nn.L1Loss()
+
+            # Names so we can breakout loss
+            self.loss_names = self.loss_filter('G_GAN', 'G_GAN_Feat', 'G_VGG', 'G_ID', 'G_Rec', 'D_GP',
+                                               'D_real', 'D_fake')
+
+           # initialize optimizers
+
+            # optimizer G
+            params = list(self.netG.parameters())
+            self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+
+            # optimizer D
+            params = list(self.netD1.parameters()) + list(self.netD2.parameters())
+            self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+
+    def _gradinet_penalty_D(self, netD, img_att, img_fake):
+        # interpolate sample
+        bs = img_fake.shape[0]
+        alpha = torch.rand(bs, 1, 1, 1).expand_as(img_fake).cuda()
+        interpolated = Variable(alpha * img_att + (1 - alpha) * img_fake, requires_grad=True)
+        pred_interpolated = netD.forward(interpolated)
+        pred_interpolated = pred_interpolated[-1]
+
+        # compute gradients
+        grad = torch.autograd.grad(outputs=pred_interpolated,
+                                   inputs=interpolated,
+                                   grad_outputs=torch.ones(pred_interpolated.size()).cuda(),
+                                   retain_graph=True,
+                                   create_graph=True,
+                                   only_inputs=True)[0]
+
+        # penalize gradients
+        grad = grad.view(grad.size(0), -1)
+        grad_l2norm = torch.sqrt(torch.sum(grad ** 2, dim=1))
+        loss_d_gp = torch.mean((grad_l2norm - 1) ** 2)
+
+        return loss_d_gp
+
+    def cosin_metric(self, x1, x2):
+        #return np.dot(x1, x2) / (np.linalg.norm(x1) * np.linalg.norm(x2))
+        return torch.sum(x1 * x2, dim=1) / (torch.norm(x1, dim=1) * torch.norm(x2, dim=1))
+
+    def forward(self, img_id, img_att, latent_id, latent_att, for_G=False):
+        loss_D_fake, loss_D_real, loss_D_GP = 0, 0, 0
+        loss_G_GAN, loss_G_GAN_Feat, loss_G_VGG, loss_G_ID, loss_G_Rec = 0,0,0,0,0
+
+        img_fake = self.netG.forward(img_att, latent_id)
+        if not self.isTrain:
+            return img_fake
+        img_fake_downsample = self.downsample(img_fake)
+        img_att_downsample = self.downsample(img_att)
+
+
+
+        # D_Fake
+        fea1_fake = self.netD1.forward(img_fake.detach())
+        fea2_fake = self.netD2.forward(img_fake_downsample.detach())
+        pred_fake = [fea1_fake, fea2_fake]
+        loss_D_fake = self.criterionGAN(pred_fake, False, for_discriminator=True)
+
+
+        # D_Feal
+        fea1_real = self.netD1.forward(img_att)
+        fea2_real = self.netD2.forward(img_att_downsample)
+        pred_real = [fea1_real, fea2_real]
+        fea_real = [fea1_real, fea2_real]
+        loss_D_real = self.criterionGAN(pred_real, True, for_discriminator=True)
+        #print('=====================D_Real========================')
+
+        # D_GP
+
+        loss_D_GP = 0
+
+        # G_GAN
+        fea1_fake = self.netD1.forward(img_fake)
+        fea2_fake = self.netD2.forward(img_fake_downsample)
+        #pred_fake = [fea1_fake[-1], fea2_fake[-1]]
+        pred_fake = [fea1_fake, fea2_fake]
+        fea_fake = [fea1_fake, fea2_fake]
+        loss_G_GAN = self.criterionGAN(pred_fake, True, for_discriminator=False)
+
+        # GAN feature matching loss
+        n_layers_D = 4
+        num_D = 2
+        if not self.opt.no_ganFeat_loss:
+            feat_weights = 4.0 / (n_layers_D + 1)
+            D_weights = 1.0 / num_D
+            for i in range(num_D):
+                for j in range(0, len(fea_fake[i]) - 1):
+                    loss_G_GAN_Feat += D_weights * feat_weights * \
+                                       self.criterionFeat(fea_fake[i][j],
+                                                          fea_real[i][j].detach()) * self.opt.lambda_feat
+
+
+        #G_ID
+        img_fake_down = F.interpolate(img_fake, size=(112,112))
+        img_fake_down = self.spNorm(img_fake_down)
+        latent_fake = self.netArc(img_fake_down)
+        loss_G_ID = (1 - self.cosin_metric(latent_fake, latent_id))
+        #print('=====================G_ID========================')
+        #print(loss_G_ID)
+
+        #G_Rec
+        loss_G_Rec = self.criterionRec(img_fake, img_att) * self.opt.lambda_rec
+
+        # Only return the fake_B image if necessary to save BW
+        return [self.loss_filter(loss_G_GAN, loss_G_GAN_Feat, loss_G_VGG, loss_G_ID, loss_G_Rec, loss_D_GP, loss_D_real, loss_D_fake),
+                img_fake]
+
+
+    def save(self, which_epoch):
+        self.save_network(self.netG, 'G', which_epoch, self.gpu_ids)
+        self.save_network(self.netD1, 'D1', which_epoch, self.gpu_ids)
+        self.save_network(self.netD2, 'D2', which_epoch, self.gpu_ids)
+        '''if self.gen_features:
+            self.save_network(self.netE, 'E', which_epoch, self.gpu_ids)'''
+
+    def update_fixed_params(self):
+        # after fixing the global generator for a number of iterations, also start finetuning it
+        params = list(self.netG.parameters())
+        if self.gen_features:
+            params += list(self.netE.parameters())
+        self.optimizer_G = torch.optim.Adam(params, lr=self.opt.lr, betas=(self.opt.beta1, 0.999))
+        if self.opt.verbose:
+            print('------------ Now also finetuning global generator -----------')
+
+    def update_learning_rate(self):
+        lrd = self.opt.lr / self.opt.niter_decay
+        lr = self.old_lr - lrd
+        for param_group in self.optimizer_D.param_groups:
+            param_group['lr'] = lr
+        for param_group in self.optimizer_G.param_groups:
+            param_group['lr'] = lr
+        if self.opt.verbose:
+            print('update learning rate: %f -> %f' % (self.old_lr, lr))
+        self.old_lr = lr
+
+
diff --git a/modeling/SimSwap/models/fs_networks.py b/modeling/SimSwap/models/fs_networks.py
new file mode 100644
index 0000000..227f6fa
--- /dev/null
+++ b/modeling/SimSwap/models/fs_networks.py
@@ -0,0 +1,215 @@
+"""
+Copyright (C) 2019 NVIDIA Corporation.  All rights reserved.
+Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+"""
+
+import torch
+import torch.nn as nn
+
+
+class InstanceNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(InstanceNorm, self).__init__()
+        self.epsilon = epsilon
+
+    def forward(self, x):
+        x   = x - torch.mean(x, (2, 3), True)
+        tmp = torch.mul(x, x) # or x ** 2
+        tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
+        return x * tmp
+
+class ApplyStyle(nn.Module):
+    """
+        @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
+    """
+    def __init__(self, latent_size, channels):
+        super(ApplyStyle, self).__init__()
+        self.linear = nn.Linear(latent_size, channels * 2)
+
+    def forward(self, x, latent):
+        style = self.linear(latent)  # style => [batch_size, n_channels*2]
+        shape = [-1, 2, x.size(1), 1, 1]
+        style = style.view(shape)    # [batch_size, 2, n_channels, ...]
+        #x = x * (style[:, 0] + 1.) + style[:, 1]
+        x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1
+        return x
+
+class ResnetBlock_Adain(nn.Module):
+    def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
+        super(ResnetBlock_Adain, self).__init__()
+
+        p = 0
+        conv1 = []
+        if padding_type == 'reflect':
+            conv1 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv1 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()]
+        self.conv1 = nn.Sequential(*conv1)
+        self.style1 = ApplyStyle(latent_size, dim)
+        self.act1 = activation
+
+        p = 0
+        conv2 = []
+        if padding_type == 'reflect':
+            conv2 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv2 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
+        self.conv2 = nn.Sequential(*conv2)
+        self.style2 = ApplyStyle(latent_size, dim)
+
+
+    def forward(self, x, dlatents_in_slice):
+        y = self.conv1(x)
+        y = self.style1(y, dlatents_in_slice)
+        y = self.act1(y)
+        y = self.conv2(y)
+        y = self.style2(y, dlatents_in_slice)
+        out = x + y
+        return out
+
+
+
+class Generator_Adain_Upsample(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, n_blocks=6, deep=False,
+                 norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_Upsample, self).__init__()
+        activation = nn.ReLU(True)
+        self.deep = deep
+
+        self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 64, kernel_size=7, padding=0),
+                                         norm_layer(64), activation)
+        ### downsample
+        self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(128), activation)
+        self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(256), activation)
+        self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(512), activation)
+        if self.deep:
+            self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
+                                       norm_layer(512), activation)
+
+        ### resnet blocks
+        BN = []
+        for i in range(n_blocks):
+            BN += [
+                ResnetBlock_Adain(512, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        if self.deep:
+            self.up4 = nn.Sequential(
+                nn.Upsample(scale_factor=2, mode='bilinear'),
+                nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
+                nn.BatchNorm2d(512), activation
+            )
+        self.up3 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(256), activation
+        )
+        self.up2 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(128), activation
+        )
+        self.up1 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(64), activation
+        )
+        self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, kernel_size=7, padding=0),
+                                        nn.Tanh())
+
+    def forward(self, input, dlatents):
+        x = input  # 3*224*224
+
+        skip1 = self.first_layer(x)
+        skip2 = self.down1(skip1)
+        skip3 = self.down2(skip2)
+        if self.deep:
+            skip4 = self.down3(skip3)
+            x = self.down4(skip4)
+        else:
+            x = self.down3(skip3)
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+
+        if self.deep:
+            x = self.up4(x)
+        x = self.up3(x)
+        x = self.up2(x)
+        x = self.up1(x)
+        x = self.last_layer(x)
+        x = (x + 1) / 2
+
+        return x
+
+class Discriminator(nn.Module):
+    def __init__(self, input_nc, norm_layer=nn.BatchNorm2d, use_sigmoid=False):
+        super(Discriminator, self).__init__()
+
+        kw = 4
+        padw = 1
+        self.down1 = nn.Sequential(
+            nn.Conv2d(input_nc, 64, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)
+        )
+        self.down2 = nn.Sequential(
+            nn.Conv2d(64, 128, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(128), nn.LeakyReLU(0.2, True)
+        )
+        self.down3 = nn.Sequential(
+            nn.Conv2d(128, 256, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(256), nn.LeakyReLU(0.2, True)
+        )
+        self.down4 = nn.Sequential(
+            nn.Conv2d(256, 512, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(512), nn.LeakyReLU(0.2, True)
+        )
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(512, 512, kernel_size=kw, stride=1, padding=padw),
+            norm_layer(512),
+            nn.LeakyReLU(0.2, True)
+        )
+
+        if use_sigmoid:
+            self.conv2 = nn.Sequential(
+                nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw), nn.Sigmoid()
+            )
+        else:
+            self.conv2 = nn.Sequential(
+                nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw)
+            )
+
+    def forward(self, input):
+        out = []
+        x = self.down1(input)
+        out.append(x)
+        x = self.down2(x)
+        out.append(x)
+        x = self.down3(x)
+        out.append(x)
+        x = self.down4(x)
+        out.append(x)
+        x = self.conv1(x)
+        out.append(x)
+        x = self.conv2(x)
+        out.append(x)
+        
+        return out
\ No newline at end of file
diff --git a/modeling/SimSwap/models/fs_networks_512.py b/modeling/SimSwap/models/fs_networks_512.py
new file mode 100644
index 0000000..1b9f9c8
--- /dev/null
+++ b/modeling/SimSwap/models/fs_networks_512.py
@@ -0,0 +1,232 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 16:55:48
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 16:58:06
+Description: 
+'''
+"""
+Copyright (C) 2019 NVIDIA Corporation.  All rights reserved.
+Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+"""
+
+import torch
+import torch.nn as nn
+
+
+class InstanceNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(InstanceNorm, self).__init__()
+        self.epsilon = epsilon
+
+    def forward(self, x):
+        x   = x - torch.mean(x, (2, 3), True)
+        tmp = torch.mul(x, x) # or x ** 2
+        tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
+        return x * tmp
+
+class ApplyStyle(nn.Module):
+    """
+        @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
+    """
+    def __init__(self, latent_size, channels):
+        super(ApplyStyle, self).__init__()
+        self.linear = nn.Linear(latent_size, channels * 2)
+
+    def forward(self, x, latent):
+        style = self.linear(latent)  # style => [batch_size, n_channels*2]
+        shape = [-1, 2, x.size(1), 1, 1]
+        style = style.view(shape)    # [batch_size, 2, n_channels, ...]
+        #x = x * (style[:, 0] + 1.) + style[:, 1]
+        x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1
+        return x
+
+class ResnetBlock_Adain(nn.Module):
+    def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
+        super(ResnetBlock_Adain, self).__init__()
+
+        p = 0
+        conv1 = []
+        if padding_type == 'reflect':
+            conv1 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv1 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()]
+        self.conv1 = nn.Sequential(*conv1)
+        self.style1 = ApplyStyle(latent_size, dim)
+        self.act1 = activation
+
+        p = 0
+        conv2 = []
+        if padding_type == 'reflect':
+            conv2 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv2 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
+        self.conv2 = nn.Sequential(*conv2)
+        self.style2 = ApplyStyle(latent_size, dim)
+
+
+    def forward(self, x, dlatents_in_slice):
+        y = self.conv1(x)
+        y = self.style1(y, dlatents_in_slice)
+        y = self.act1(y)
+        y = self.conv2(y)
+        y = self.style2(y, dlatents_in_slice)
+        out = x + y
+        return out
+
+
+
+class Generator_Adain_Upsample(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, n_blocks=6, deep=False,
+                 norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_Upsample, self).__init__()
+        activation = nn.ReLU(True)
+        self.deep = deep
+
+        self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 32, kernel_size=7, padding=0),
+                                         norm_layer(32), activation)
+        ### downsample
+        self.down0 = nn.Sequential(nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(64), activation)
+        self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(128), activation)
+        self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(256), activation)
+        self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(512), activation)
+        if self.deep:
+            self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
+                                       norm_layer(512), activation)
+
+        ### resnet blocks
+        BN = []
+        for i in range(n_blocks):
+            BN += [
+                ResnetBlock_Adain(512, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        if self.deep:
+            self.up4 = nn.Sequential(
+                nn.Upsample(scale_factor=2, mode='bilinear'),
+                nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
+                nn.BatchNorm2d(512), activation
+            )
+        self.up3 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(256), activation
+        )
+        self.up2 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(128), activation
+        )
+        self.up1 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(64), activation
+        )
+        self.up0 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear'),
+            nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(32), activation
+        )
+        self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(32, output_nc, kernel_size=7, padding=0),
+                                        nn.Tanh())
+
+    def forward(self, input, dlatents):
+        x = input  # 3*224*224
+
+        skip0 = self.first_layer(x)
+        skip1 = self.down0(skip0)
+        skip2 = self.down1(skip1)
+        skip3 = self.down2(skip2)
+        if self.deep:
+            skip4 = self.down3(skip3)
+            x = self.down4(skip4)
+        else:
+            x = self.down3(skip3)
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+
+        if self.deep:
+            x = self.up4(x)
+        x = self.up3(x)
+        x = self.up2(x)
+        x = self.up1(x)
+        x = self.up0(x)
+        x = self.last_layer(x)
+        x = (x + 1) / 2
+
+        return x
+
+class Discriminator(nn.Module):
+    def __init__(self, input_nc, norm_layer=nn.BatchNorm2d, use_sigmoid=False):
+        super(Discriminator, self).__init__()
+
+        kw = 4
+        padw = 1
+        self.down1 = nn.Sequential(
+            nn.Conv2d(input_nc, 64, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)
+        )
+        self.down2 = nn.Sequential(
+            nn.Conv2d(64, 128, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(128), nn.LeakyReLU(0.2, True)
+        )
+        self.down3 = nn.Sequential(
+            nn.Conv2d(128, 256, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(256), nn.LeakyReLU(0.2, True)
+        )
+        self.down4 = nn.Sequential(
+            nn.Conv2d(256, 512, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(512), nn.LeakyReLU(0.2, True)
+        )
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(512, 512, kernel_size=kw, stride=1, padding=padw),
+            norm_layer(512),
+            nn.LeakyReLU(0.2, True)
+        )
+
+        if use_sigmoid:
+            self.conv2 = nn.Sequential(
+                nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw), nn.Sigmoid()
+            )
+        else:
+            self.conv2 = nn.Sequential(
+                nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw)
+            )
+
+    def forward(self, input):
+        out = []
+        x = self.down1(input)
+        out.append(x)
+        x = self.down2(x)
+        out.append(x)
+        x = self.down3(x)
+        out.append(x)
+        x = self.down4(x)
+        out.append(x)
+        x = self.conv1(x)
+        out.append(x)
+        x = self.conv2(x)
+        out.append(x)
+        
+        return out
diff --git a/modeling/SimSwap/models/fs_networks_fix.py b/modeling/SimSwap/models/fs_networks_fix.py
new file mode 100644
index 0000000..c7b0525
--- /dev/null
+++ b/modeling/SimSwap/models/fs_networks_fix.py
@@ -0,0 +1,172 @@
+"""
+Copyright (C) 2019 NVIDIA Corporation.  All rights reserved.
+Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+"""
+
+import torch
+import torch.nn as nn
+
+
+class InstanceNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(InstanceNorm, self).__init__()
+        self.epsilon = epsilon
+
+    def forward(self, x):
+        x   = x - torch.mean(x, (2, 3), True)
+        tmp = torch.mul(x, x) # or x ** 2
+        tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
+        return x * tmp
+
+class ApplyStyle(nn.Module):
+    """
+        @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
+    """
+    def __init__(self, latent_size, channels):
+        super(ApplyStyle, self).__init__()
+        self.linear = nn.Linear(latent_size, channels * 2)
+
+    def forward(self, x, latent):
+        style = self.linear(latent)  # style => [batch_size, n_channels*2]
+        shape = [-1, 2, x.size(1), 1, 1]
+        style = style.view(shape)    # [batch_size, 2, n_channels, ...]
+        #x = x * (style[:, 0] + 1.) + style[:, 1]
+        x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1
+        return x
+
+class ResnetBlock_Adain(nn.Module):
+    def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
+        super(ResnetBlock_Adain, self).__init__()
+
+        p = 0
+        conv1 = []
+        if padding_type == 'reflect':
+            conv1 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv1 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()]
+        self.conv1 = nn.Sequential(*conv1)
+        self.style1 = ApplyStyle(latent_size, dim)
+        self.act1 = activation
+
+        p = 0
+        conv2 = []
+        if padding_type == 'reflect':
+            conv2 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv2 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
+        self.conv2 = nn.Sequential(*conv2)
+        self.style2 = ApplyStyle(latent_size, dim)
+
+
+    def forward(self, x, dlatents_in_slice):
+        y = self.conv1(x)
+        y = self.style1(y, dlatents_in_slice)
+        y = self.act1(y)
+        y = self.conv2(y)
+        y = self.style2(y, dlatents_in_slice)
+        out = x + y
+        return out
+
+
+
+class Generator_Adain_Upsample(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, n_blocks=6, deep=False,
+                 norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_Upsample, self).__init__()
+
+        activation = nn.ReLU(True)
+        
+        self.deep = deep
+        
+        self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 64, kernel_size=7, padding=0),
+                                         norm_layer(64), activation)
+        ### downsample
+        self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(128), activation)
+        self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(256), activation)
+        self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
+                                   norm_layer(512), activation)
+                                   
+        if self.deep:
+            self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
+                                       norm_layer(512), activation)
+
+        ### resnet blocks
+        BN = []
+        for i in range(n_blocks):
+            BN += [
+                ResnetBlock_Adain(512, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        if self.deep:
+            self.up4 = nn.Sequential(
+                nn.Upsample(scale_factor=2, mode='bilinear',align_corners=False),
+                nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
+                nn.BatchNorm2d(512), activation
+            )
+        self.up3 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear',align_corners=False),
+            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(256), activation
+        )
+        self.up2 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear',align_corners=False),
+            nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(128), activation
+        )
+        self.up1 = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear',align_corners=False),
+            nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(64), activation
+        )
+        self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, kernel_size=7, padding=0))
+
+    def forward(self, input, dlatents):
+        x = input  # 3*224*224
+
+        skip1 = self.first_layer(x)
+        skip2 = self.down1(skip1)
+        skip3 = self.down2(skip2)
+        if self.deep:
+            skip4 = self.down3(skip3)
+            x = self.down4(skip4)
+        else:
+            x = self.down3(skip3)
+        bot = []
+        bot.append(x)
+        features = []
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+            bot.append(x)
+
+        if self.deep:
+            x = self.up4(x)
+            features.append(x)
+        x = self.up3(x)
+        features.append(x)
+        x = self.up2(x)
+        features.append(x)
+        x = self.up1(x)
+        features.append(x)
+        x = self.last_layer(x)
+        # x = (x + 1) / 2
+
+        # return x, bot, features, dlatents
+        return x
\ No newline at end of file
diff --git a/modeling/SimSwap/models/models.py b/modeling/SimSwap/models/models.py
new file mode 100644
index 0000000..bf9e84e
--- /dev/null
+++ b/modeling/SimSwap/models/models.py
@@ -0,0 +1,177 @@
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn import Parameter
+from .config import device, num_classes
+
+
+def create_model(opt):
+    #from .pix2pixHD_model import Pix2PixHDModel, InferenceModel
+    from .fs_model import fsModel
+    model = fsModel()
+
+    model.initialize(opt)
+    if opt.verbose:
+        print("model [%s] was created" % (model.name()))
+
+    if opt.isTrain and len(opt.gpu_ids) and not opt.fp16:
+        model = torch.nn.DataParallel(model, device_ids=opt.gpu_ids)
+
+    return model
+
+
+
+class SEBlock(nn.Module):
+    def __init__(self, channel, reduction=16):
+        super(SEBlock, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction),
+            nn.PReLU(),
+            nn.Linear(channel // reduction, channel),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1, 1)
+        return x * y
+
+
+class IRBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
+        super(IRBlock, self).__init__()
+        self.bn0 = nn.BatchNorm2d(inplanes)
+        self.conv1 = conv3x3(inplanes, inplanes)
+        self.bn1 = nn.BatchNorm2d(inplanes)
+        self.prelu = nn.PReLU()
+        self.conv2 = conv3x3(inplanes, planes, stride)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+        self.use_se = use_se
+        if self.use_se:
+            self.se = SEBlock(planes)
+
+    def forward(self, x):
+        residual = x
+        out = self.bn0(x)
+        out = self.conv1(out)
+        out = self.bn1(out)
+        out = self.prelu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        if self.use_se:
+            out = self.se(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.prelu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, use_se=True):
+        self.inplanes = 64
+        self.use_se = use_se
+        super(ResNet, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.prelu = nn.PReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.bn2 = nn.BatchNorm2d(512)
+        self.dropout = nn.Dropout()
+        self.fc = nn.Linear(512 * 7 * 7, 512)
+        self.bn3 = nn.BatchNorm1d(512)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.xavier_normal_(m.weight)
+            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.xavier_normal_(m.weight)
+                nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
+        self.inplanes = planes
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, use_se=self.use_se))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.prelu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.bn2(x)
+        x = self.dropout(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        x = self.bn3(x)
+
+        return x
+
+
+class ArcMarginModel(nn.Module):
+    def __init__(self, args):
+        super(ArcMarginModel, self).__init__()
+
+        self.weight = Parameter(torch.FloatTensor(num_classes, args.emb_size))
+        nn.init.xavier_uniform_(self.weight)
+
+        self.easy_margin = args.easy_margin
+        self.m = args.margin_m
+        self.s = args.margin_s
+
+        self.cos_m = math.cos(self.m)
+        self.sin_m = math.sin(self.m)
+        self.th = math.cos(math.pi - self.m)
+        self.mm = math.sin(math.pi - self.m) * self.m
+
+    def forward(self, input, label):
+        x = F.normalize(input)
+        W = F.normalize(self.weight)
+        cosine = F.linear(x, W)
+        sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
+        phi = cosine * self.cos_m - sine * self.sin_m  # cos(theta + m)
+        if self.easy_margin:
+            phi = torch.where(cosine > 0, phi, cosine)
+        else:
+            phi = torch.where(cosine > self.th, phi, cosine - self.mm)
+        one_hot = torch.zeros(cosine.size(), device=device)
+        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
+        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
+        output *= self.s
+        return output
diff --git a/modeling/SimSwap/models/networks.py b/modeling/SimSwap/models/networks.py
new file mode 100644
index 0000000..3c7eb4d
--- /dev/null
+++ b/modeling/SimSwap/models/networks.py
@@ -0,0 +1,845 @@
+import torch
+import torch.nn as nn
+import functools
+from torch.autograd import Variable
+import numpy as np
+from torchvision import transforms
+import torch.nn.functional as F
+
+###############################################################################
+# Functions
+###############################################################################
+def weights_init(m):
+    classname = m.__class__.__name__
+    if classname.find('Conv') != -1:
+        m.weight.data.normal_(0.0, 0.02)
+    elif classname.find('BatchNorm2d') != -1:
+        m.weight.data.normal_(1.0, 0.02)
+        m.bias.data.fill_(0)
+
+def get_norm_layer(norm_type='instance'):
+    if norm_type == 'batch':
+        norm_layer = functools.partial(nn.BatchNorm2d, affine=True)
+    elif norm_type == 'instance':
+        norm_layer = functools.partial(nn.InstanceNorm2d, affine=False)
+    else:
+        raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
+    return norm_layer
+
+def define_G(input_nc, output_nc, ngf, netG, n_downsample_global=3, n_blocks_global=9, n_local_enhancers=1, 
+             n_blocks_local=3, norm='instance', gpu_ids=[]):    
+    norm_layer = get_norm_layer(norm_type=norm)     
+    if netG == 'global':    
+        netG = GlobalGenerator(input_nc, output_nc, ngf, n_downsample_global, n_blocks_global, norm_layer)       
+    elif netG == 'local':        
+        netG = LocalEnhancer(input_nc, output_nc, ngf, n_downsample_global, n_blocks_global, 
+                                  n_local_enhancers, n_blocks_local, norm_layer)
+    elif netG == 'encoder':
+        netG = Encoder(input_nc, output_nc, ngf, n_downsample_global, norm_layer)
+    else:
+        raise('generator not implemented!')
+    print(netG)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())   
+        netG.cuda(gpu_ids[0])
+    netG.apply(weights_init)
+    return netG
+
+def define_G_Adain(input_nc, output_nc, latent_size, ngf, netG, n_downsample_global=2, n_blocks_global=4, norm='instance', gpu_ids=[]):
+    norm_layer = get_norm_layer(norm_type=norm)
+    netG = Generator_Adain(input_nc, output_nc, latent_size, ngf, n_downsample_global, n_blocks_global, norm_layer)
+    print(netG)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        netG.cuda(gpu_ids[0])
+    netG.apply(weights_init)
+    return netG
+
+def define_G_Adain_Mask(input_nc, output_nc, latent_size, ngf, netG, n_downsample_global=2, n_blocks_global=4, norm='instance', gpu_ids=[]):
+    norm_layer = get_norm_layer(norm_type=norm)
+    netG = Generator_Adain_Mask(input_nc, output_nc, latent_size, ngf, n_downsample_global, n_blocks_global, norm_layer)
+    print(netG)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        netG.cuda(gpu_ids[0])
+    netG.apply(weights_init)
+    return netG
+
+def define_G_Adain_Upsample(input_nc, output_nc, latent_size, ngf, netG, n_downsample_global=2, n_blocks_global=4, norm='instance', gpu_ids=[]):
+    norm_layer = get_norm_layer(norm_type=norm)
+    netG = Generator_Adain_Upsample(input_nc, output_nc, latent_size, ngf, n_downsample_global, n_blocks_global, norm_layer)
+    print(netG)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        netG.cuda(gpu_ids[0])
+    netG.apply(weights_init)
+    return netG
+
+def define_G_Adain_2(input_nc, output_nc, latent_size, ngf, netG, n_downsample_global=2, n_blocks_global=4, norm='instance', gpu_ids=[]):
+    norm_layer = get_norm_layer(norm_type=norm)
+    netG = Generator_Adain_2(input_nc, output_nc, latent_size, ngf, n_downsample_global, n_blocks_global, norm_layer)
+    print(netG)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        netG.cuda(gpu_ids[0])
+    netG.apply(weights_init)
+    return netG
+
+def define_D(input_nc, ndf, n_layers_D, norm='instance', use_sigmoid=False, num_D=1, getIntermFeat=False, gpu_ids=[]):        
+    norm_layer = get_norm_layer(norm_type=norm)   
+    netD = MultiscaleDiscriminator(input_nc, ndf, n_layers_D, norm_layer, use_sigmoid, num_D, getIntermFeat)   
+    print(netD)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        netD.cuda(gpu_ids[0])
+    netD.apply(weights_init)
+    return netD
+
+def print_network(net):
+    if isinstance(net, list):
+        net = net[0]
+    num_params = 0
+    for param in net.parameters():
+        num_params += param.numel()
+    print(net)
+    print('Total number of parameters: %d' % num_params)
+
+##############################################################################
+# Losses
+##############################################################################
+class GANLoss(nn.Module):
+    def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0,
+                 tensor=torch.FloatTensor, opt=None):
+        super(GANLoss, self).__init__()
+        self.real_label = target_real_label
+        self.fake_label = target_fake_label
+        self.real_label_tensor = None
+        self.fake_label_tensor = None
+        self.zero_tensor = None
+        self.Tensor = tensor
+        self.gan_mode = gan_mode
+        self.opt = opt
+        if gan_mode == 'ls':
+            pass
+        elif gan_mode == 'original':
+            pass
+        elif gan_mode == 'w':
+            pass
+        elif gan_mode == 'hinge':
+            pass
+        else:
+            raise ValueError('Unexpected gan_mode {}'.format(gan_mode))
+
+    def get_target_tensor(self, input, target_is_real):
+        if target_is_real:
+            if self.real_label_tensor is None:
+                self.real_label_tensor = self.Tensor(1).fill_(self.real_label)
+                self.real_label_tensor.requires_grad_(False)
+            return self.real_label_tensor.expand_as(input)
+        else:
+            if self.fake_label_tensor is None:
+                self.fake_label_tensor = self.Tensor(1).fill_(self.fake_label)
+                self.fake_label_tensor.requires_grad_(False)
+            return self.fake_label_tensor.expand_as(input)
+
+    def get_zero_tensor(self, input):
+        if self.zero_tensor is None:
+            self.zero_tensor = self.Tensor(1).fill_(0)
+            self.zero_tensor.requires_grad_(False)
+        return self.zero_tensor.expand_as(input)
+
+    def loss(self, input, target_is_real, for_discriminator=True):
+        if self.gan_mode == 'original':  # cross entropy loss
+            target_tensor = self.get_target_tensor(input, target_is_real)
+            loss = F.binary_cross_entropy_with_logits(input, target_tensor)
+            return loss
+        elif self.gan_mode == 'ls':
+            target_tensor = self.get_target_tensor(input, target_is_real)
+            return F.mse_loss(input, target_tensor)
+        elif self.gan_mode == 'hinge':
+            if for_discriminator:
+                if target_is_real:
+                    minval = torch.min(input - 1, self.get_zero_tensor(input))
+                    loss = -torch.mean(minval)
+                else:
+                    minval = torch.min(-input - 1, self.get_zero_tensor(input))
+                    loss = -torch.mean(minval)
+            else:
+                assert target_is_real, "The generator's hinge loss must be aiming for real"
+                loss = -torch.mean(input)
+            return loss
+        else:
+            # wgan
+            if target_is_real:
+                return -input.mean()
+            else:
+                return input.mean()
+
+    def __call__(self, input, target_is_real, for_discriminator=True):
+        # computing loss is a bit complicated because |input| may not be
+        # a tensor, but list of tensors in case of multiscale discriminator
+        if isinstance(input, list):
+            loss = 0
+            for pred_i in input:
+                if isinstance(pred_i, list):
+                    pred_i = pred_i[-1]
+                loss_tensor = self.loss(pred_i, target_is_real, for_discriminator)
+                bs = 1 if len(loss_tensor.size()) == 0 else loss_tensor.size(0)
+                new_loss = torch.mean(loss_tensor.view(bs, -1), dim=1)
+                loss += new_loss
+            return loss / len(input)
+        else:
+            return self.loss(input, target_is_real, for_discriminator)
+
+class VGGLoss(nn.Module):
+    def __init__(self, gpu_ids):
+        super(VGGLoss, self).__init__()        
+        self.vgg = Vgg19().cuda()
+        self.criterion = nn.L1Loss()
+        self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]        
+
+    def forward(self, x, y):              
+        x_vgg, y_vgg = self.vgg(x), self.vgg(y)
+        loss = 0
+        for i in range(len(x_vgg)):
+            loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())        
+        return loss
+
+##############################################################################
+# Generator
+##############################################################################
+class LocalEnhancer(nn.Module):
+    def __init__(self, input_nc, output_nc, ngf=32, n_downsample_global=3, n_blocks_global=9, 
+                 n_local_enhancers=1, n_blocks_local=3, norm_layer=nn.BatchNorm2d, padding_type='reflect'):        
+        super(LocalEnhancer, self).__init__()
+        self.n_local_enhancers = n_local_enhancers
+        
+        ###### global generator model #####           
+        ngf_global = ngf * (2**n_local_enhancers)
+        model_global = GlobalGenerator(input_nc, output_nc, ngf_global, n_downsample_global, n_blocks_global, norm_layer).model        
+        model_global = [model_global[i] for i in range(len(model_global)-3)] # get rid of final convolution layers        
+        self.model = nn.Sequential(*model_global)                
+
+        ###### local enhancer layers #####
+        for n in range(1, n_local_enhancers+1):
+            ### downsample            
+            ngf_global = ngf * (2**(n_local_enhancers-n))
+            model_downsample = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf_global, kernel_size=7, padding=0), 
+                                norm_layer(ngf_global), nn.ReLU(True),
+                                nn.Conv2d(ngf_global, ngf_global * 2, kernel_size=3, stride=2, padding=1), 
+                                norm_layer(ngf_global * 2), nn.ReLU(True)]
+            ### residual blocks
+            model_upsample = []
+            for i in range(n_blocks_local):
+                model_upsample += [ResnetBlock(ngf_global * 2, padding_type=padding_type, norm_layer=norm_layer)]
+
+            ### upsample
+            model_upsample += [nn.ConvTranspose2d(ngf_global * 2, ngf_global, kernel_size=3, stride=2, padding=1, output_padding=1), 
+                               norm_layer(ngf_global), nn.ReLU(True)]      
+
+            ### final convolution
+            if n == n_local_enhancers:                
+                model_upsample += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]                       
+            
+            setattr(self, 'model'+str(n)+'_1', nn.Sequential(*model_downsample))
+            setattr(self, 'model'+str(n)+'_2', nn.Sequential(*model_upsample))                  
+        
+        self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
+
+    def forward(self, input): 
+        ### create input pyramid
+        input_downsampled = [input]
+        for i in range(self.n_local_enhancers):
+            input_downsampled.append(self.downsample(input_downsampled[-1]))
+
+        ### output at coarest level
+        output_prev = self.model(input_downsampled[-1])        
+        ### build up one layer at a time
+        for n_local_enhancers in range(1, self.n_local_enhancers+1):
+            model_downsample = getattr(self, 'model'+str(n_local_enhancers)+'_1')
+            model_upsample = getattr(self, 'model'+str(n_local_enhancers)+'_2')            
+            input_i = input_downsampled[self.n_local_enhancers-n_local_enhancers]            
+            output_prev = model_upsample(model_downsample(input_i) + output_prev)
+        return output_prev
+
+class GlobalGenerator(nn.Module):
+    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, 
+                 padding_type='reflect'):
+        assert(n_blocks >= 0)
+        super(GlobalGenerator, self).__init__()        
+        activation = nn.ReLU(True)        
+
+        model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2**i
+            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), activation]
+
+        ### resnet blocks
+        mult = 2**n_downsampling
+        for i in range(n_blocks):
+            model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer)]
+        
+        ### upsample         
+        for i in range(n_downsampling):
+            mult = 2**(n_downsampling - i)
+            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, output_padding=1),
+                       norm_layer(int(ngf * mult / 2)), activation]
+        model += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]        
+        self.model = nn.Sequential(*model)
+            
+    def forward(self, input):
+        return self.model(input)             
+        
+# Define a resnet block
+class ResnetBlock(nn.Module):
+    def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False):
+        super(ResnetBlock, self).__init__()
+        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout)
+
+    def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout):
+        conv_block = []
+        p = 0
+        if padding_type == 'reflect':
+            conv_block += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv_block += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+
+        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p),
+                       norm_layer(dim),
+                       activation]
+        if use_dropout:
+            conv_block += [nn.Dropout(0.5)]
+
+        p = 0
+        if padding_type == 'reflect':
+            conv_block += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv_block += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p),
+                       norm_layer(dim)]
+
+        return nn.Sequential(*conv_block)
+
+    def forward(self, x):
+        out = x + self.conv_block(x)
+        return out
+
+class InstanceNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(InstanceNorm, self).__init__()
+        self.epsilon = epsilon
+
+    def forward(self, x):
+        x   = x - torch.mean(x, (2, 3), True)
+        tmp = torch.mul(x, x) # or x ** 2
+        tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
+        return x * tmp
+
+class SpecificNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(SpecificNorm, self).__init__()
+        self.mean = np.array([0.485, 0.456, 0.406])
+        self.mean = torch.from_numpy(self.mean).float().cuda()
+        self.mean = self.mean.view([1, 3, 1, 1])
+
+        self.std = np.array([0.229, 0.224, 0.225])
+        self.std = torch.from_numpy(self.std).float().cuda()
+        self.std = self.std.view([1, 3, 1, 1])
+
+    def forward(self, x):
+        mean = self.mean.expand([1, 3, x.shape[2], x.shape[3]])
+        std = self.std.expand([1, 3, x.shape[2], x.shape[3]])
+
+        x = (x - mean) / std
+
+        return x
+
+class ApplyStyle(nn.Module):
+    """
+        @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
+    """
+    def __init__(self, latent_size, channels):
+        super(ApplyStyle, self).__init__()
+        self.linear = nn.Linear(latent_size, channels * 2)
+
+    def forward(self, x, latent):
+        style = self.linear(latent)  # style => [batch_size, n_channels*2]
+        shape = [-1, 2, x.size(1), 1, 1]
+        style = style.view(shape)    # [batch_size, 2, n_channels, ...]
+        x = x * (style[:, 0] + 1.) + style[:, 1]
+        return x
+
+class ResnetBlock_Adain(nn.Module):
+    def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
+        super(ResnetBlock_Adain, self).__init__()
+
+        p = 0
+        conv1 = []
+        if padding_type == 'reflect':
+            conv1 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv1 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()]
+        self.conv1 = nn.Sequential(*conv1)
+        self.style1 = ApplyStyle(latent_size, dim)
+        self.act1 = activation
+
+        p = 0
+        conv2 = []
+        if padding_type == 'reflect':
+            conv2 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv2 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
+        self.conv2 = nn.Sequential(*conv2)
+        self.style2 = ApplyStyle(latent_size, dim)
+
+
+    def forward(self, x, dlatents_in_slice):
+        y = self.conv1(x)
+        y = self.style1(y, dlatents_in_slice)
+        y = self.act1(y)
+        y = self.conv2(y)
+        y = self.style2(y, dlatents_in_slice)
+        out = x + y
+        return out
+
+class UpBlock_Adain(nn.Module):
+    def __init__(self, dim_in, dim_out, latent_size, padding_type, activation=nn.ReLU(True)):
+        super(UpBlock_Adain, self).__init__()
+
+        p = 0
+        conv1 = [nn.Upsample(scale_factor=2, mode='bilinear')]
+        if padding_type == 'reflect':
+            conv1 += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv1 += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv1 += [nn.Conv2d(dim_in, dim_out, kernel_size=3, padding = p), InstanceNorm()]
+        self.conv1 = nn.Sequential(*conv1)
+        self.style1 = ApplyStyle(latent_size, dim_out)
+        self.act1 = activation
+
+
+    def forward(self, x, dlatents_in_slice):
+        y = self.conv1(x)
+        y = self.style1(y, dlatents_in_slice)
+        y = self.act1(y)
+        return y
+
+class Encoder(nn.Module):
+    def __init__(self, input_nc, output_nc, ngf=32, n_downsampling=4, norm_layer=nn.BatchNorm2d):
+        super(Encoder, self).__init__()        
+        self.output_nc = output_nc        
+
+        model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), 
+                 norm_layer(ngf), nn.ReLU(True)]             
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2**i
+            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), nn.ReLU(True)]
+
+        ### upsample         
+        for i in range(n_downsampling):
+            mult = 2**(n_downsampling - i)
+            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, output_padding=1),
+                       norm_layer(int(ngf * mult / 2)), nn.ReLU(True)]        
+
+        model += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]
+        self.model = nn.Sequential(*model) 
+
+    def forward(self, input, inst):
+        outputs = self.model(input)
+
+        # instance-wise average pooling
+        outputs_mean = outputs.clone()
+        inst_list = np.unique(inst.cpu().numpy().astype(int))        
+        for i in inst_list:
+            for b in range(input.size()[0]):
+                indices = (inst[b:b+1] == int(i)).nonzero() # n x 4            
+                for j in range(self.output_nc):
+                    output_ins = outputs[indices[:,0] + b, indices[:,1] + j, indices[:,2], indices[:,3]]                    
+                    mean_feat = torch.mean(output_ins).expand_as(output_ins)                                        
+                    outputs_mean[indices[:,0] + b, indices[:,1] + j, indices[:,2], indices[:,3]] = mean_feat                       
+        return outputs_mean
+
+
+class Generator_Adain(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, ngf=64, n_downsampling=2, n_blocks=4, norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain, self).__init__()
+        activation = nn.ReLU(True)
+
+        Enc = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2 ** i
+            Enc += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), activation]
+        self.Encoder = nn.Sequential(*Enc)
+
+        ### resnet blocks
+        BN = []
+        mult = 2 ** n_downsampling
+        for i in range(n_blocks):
+            BN += [ResnetBlock_Adain(ngf*mult, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+        '''self.ResBlockAdain1 = ResnetBlock_Adain(ngf * mult, latent_size=latent_size, padding_type=padding_type,
+                                                activation=activation)
+        self.ResBlockAdain2 = ResnetBlock_Adain(ngf * mult, latent_size=latent_size, padding_type=padding_type,
+                                                activation=activation)
+        self.ResBlockAdain3 = ResnetBlock_Adain(ngf * mult, latent_size=latent_size, padding_type=padding_type,
+                                                activation=activation)
+        self.ResBlockAdain4 = ResnetBlock_Adain(ngf * mult, latent_size=latent_size, padding_type=padding_type,
+                                                activation=activation)'''
+
+        ### upsample
+        Dec = []
+        for i in range(n_downsampling):
+            mult = 2 ** (n_downsampling - i)
+            Dec += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1,
+                                         output_padding=1),
+                      norm_layer(int(ngf * mult / 2)), activation]
+        Dec += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]
+
+        self.Decoder = nn.Sequential(*Dec)
+        #self.model = nn.Sequential(*model)
+        self.spNorm = SpecificNorm()
+
+    def forward(self, input, dlatents):
+        x = input
+        x = self.Encoder(x)
+
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+        '''x = self.ResBlockAdain1(x, dlatents)
+        x = self.ResBlockAdain2(x, dlatents)
+        x = self.ResBlockAdain3(x, dlatents)
+        x = self.ResBlockAdain4(x, dlatents)'''
+
+        x = self.Decoder(x)
+
+        x = (x + 1) / 2
+        x = self.spNorm(x)
+
+        return x
+
+class Generator_Adain_Mask(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, ngf=64, n_downsampling=2, n_blocks=4, norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_Mask, self).__init__()
+        activation = nn.ReLU(True)
+
+        Enc = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2 ** i
+            Enc += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), activation]
+        self.Encoder = nn.Sequential(*Enc)
+
+        ### resnet blocks
+        BN = []
+        mult = 2 ** n_downsampling
+        for i in range(n_blocks):
+            BN += [ResnetBlock_Adain(ngf*mult, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        ### upsample
+        Dec = []
+        for i in range(n_downsampling):
+            mult = 2 ** (n_downsampling - i)
+            Dec += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1,
+                                         output_padding=1),
+                      norm_layer(int(ngf * mult / 2)), activation]
+        Fake_out = [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]
+        Mast_out = [nn.ReflectionPad2d(3), nn.Conv2d(ngf, 1, kernel_size=7, padding=0), nn.Sigmoid()]
+
+        self.Decoder = nn.Sequential(*Dec)
+        #self.model = nn.Sequential(*model)
+        self.spNorm = SpecificNorm()
+        self.Fake_out = nn.Sequential(*Fake_out)
+        self.Mask_out = nn.Sequential(*Mast_out)
+
+    def forward(self, input, dlatents):
+        x = input
+        x = self.Encoder(x)
+
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+
+        x = self.Decoder(x)
+
+        fake_out = self.Fake_out(x)
+        mask_out = self.Mask_out(x)
+
+        fake_out = (fake_out + 1) / 2
+        fake_out = self.spNorm(fake_out)
+
+        generated = fake_out * mask_out + input * (1-mask_out)
+
+        return generated, mask_out
+
+class Generator_Adain_Upsample(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, ngf=64, n_downsampling=2, n_blocks=4, norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_Upsample, self).__init__()
+        activation = nn.ReLU(True)
+
+        Enc = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2 ** i
+            Enc += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), activation]
+        self.Encoder = nn.Sequential(*Enc)
+
+        ### resnet blocks
+        BN = []
+        mult = 2 ** n_downsampling
+        for i in range(n_blocks):
+            BN += [ResnetBlock_Adain(ngf*mult, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        ### upsample
+        Dec = []
+        for i in range(n_downsampling):
+            mult = 2 ** (n_downsampling - i)
+            '''Dec += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1,
+                                         output_padding=1),
+                      norm_layer(int(ngf * mult / 2)), activation]'''
+            Dec += [nn.Upsample(scale_factor=2, mode='bilinear'),
+                    nn.Conv2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=1, padding=1),
+                    norm_layer(int(ngf * mult / 2)), activation]
+        Dec += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]
+
+        self.Decoder = nn.Sequential(*Dec)
+        self.spNorm = SpecificNorm()
+
+    def forward(self, input, dlatents):
+        x = input
+        x = self.Encoder(x)
+
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+
+        x = self.Decoder(x)
+
+        x = (x + 1) / 2
+        x = self.spNorm(x)
+
+        return x
+
+class Generator_Adain_2(nn.Module):
+    def __init__(self, input_nc, output_nc, latent_size, ngf=64, n_downsampling=2, n_blocks=4, norm_layer=nn.BatchNorm2d,
+                 padding_type='reflect'):
+        assert (n_blocks >= 0)
+        super(Generator_Adain_2, self).__init__()
+        activation = nn.ReLU(True)
+
+        Enc = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2 ** i
+            Enc += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), activation]
+        self.Encoder = nn.Sequential(*Enc)
+
+        ### resnet blocks
+        BN = []
+        mult = 2 ** n_downsampling
+        for i in range(n_blocks):
+            BN += [ResnetBlock_Adain(ngf*mult, latent_size=latent_size, padding_type=padding_type, activation=activation)]
+        self.BottleNeck = nn.Sequential(*BN)
+
+        ### upsample
+        Dec = []
+        for i in range(n_downsampling):
+            mult = 2 ** (n_downsampling - i)
+            Dec += [UpBlock_Adain(dim_in=ngf * mult, dim_out=int(ngf * mult / 2), latent_size=latent_size, padding_type=padding_type)]
+        layer_out = [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]
+
+        self.Decoder = nn.Sequential(*Dec)
+        #self.model = nn.Sequential(*model)
+        self.spNorm = SpecificNorm()
+        self.layer_out = nn.Sequential(*layer_out)
+
+    def forward(self, input, dlatents):
+        x = input
+        x = self.Encoder(x)
+
+
+        for i in range(len(self.BottleNeck)):
+            x = self.BottleNeck[i](x, dlatents)
+
+        for i in range(len(self.Decoder)):
+            x = self.Decoder[i](x, dlatents)
+
+        x = self.layer_out(x)
+
+        x = (x + 1) / 2
+        x = self.spNorm(x)
+
+        return x
+
+class MultiscaleDiscriminator(nn.Module):
+    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, 
+                 use_sigmoid=False, num_D=3, getIntermFeat=False):
+        super(MultiscaleDiscriminator, self).__init__()
+        self.num_D = num_D
+        self.n_layers = n_layers
+        self.getIntermFeat = getIntermFeat
+     
+        for i in range(num_D):
+            netD = NLayerDiscriminator(input_nc, ndf, n_layers, norm_layer, use_sigmoid, getIntermFeat)
+            if getIntermFeat:                                
+                for j in range(n_layers+2):
+                    setattr(self, 'scale'+str(i)+'_layer'+str(j), getattr(netD, 'model'+str(j)))                                   
+            else:
+                setattr(self, 'layer'+str(i), netD.model)
+
+        self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
+
+    def singleD_forward(self, model, input):
+        if self.getIntermFeat:
+            result = [input]
+            for i in range(len(model)):
+                result.append(model[i](result[-1]))
+            return result[1:]
+        else:
+            return [model(input)]
+
+    def forward(self, input):        
+        num_D = self.num_D
+        result = []
+        input_downsampled = input
+        for i in range(num_D):
+            if self.getIntermFeat:
+                model = [getattr(self, 'scale'+str(num_D-1-i)+'_layer'+str(j)) for j in range(self.n_layers+2)]
+            else:
+                model = getattr(self, 'layer'+str(num_D-1-i))
+            result.append(self.singleD_forward(model, input_downsampled))
+            if i != (num_D-1):
+                input_downsampled = self.downsample(input_downsampled)
+        return result
+        
+# Defines the PatchGAN discriminator with the specified arguments.
+class NLayerDiscriminator(nn.Module):
+    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, getIntermFeat=False):
+        super(NLayerDiscriminator, self).__init__()
+        self.getIntermFeat = getIntermFeat
+        self.n_layers = n_layers
+
+        kw = 4
+        padw = 1
+        sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]]
+
+        nf = ndf
+        for n in range(1, n_layers):
+            nf_prev = nf
+            nf = min(nf * 2, 512)
+            sequence += [[
+                nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw),
+                norm_layer(nf), nn.LeakyReLU(0.2, True)
+            ]]
+
+        nf_prev = nf
+        nf = min(nf * 2, 512)
+        sequence += [[
+            nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
+            norm_layer(nf),
+            nn.LeakyReLU(0.2, True)
+        ]]
+
+        if use_sigmoid:
+            sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw), nn.Sigmoid()]]
+        else:
+            sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
+
+        if getIntermFeat:
+            for n in range(len(sequence)):
+                setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
+        else:
+            sequence_stream = []
+            for n in range(len(sequence)):
+                sequence_stream += sequence[n]
+            self.model = nn.Sequential(*sequence_stream)
+
+    def forward(self, input):
+        if self.getIntermFeat:
+            res = [input]
+            for n in range(self.n_layers+2):
+                model = getattr(self, 'model'+str(n))
+                res.append(model(res[-1]))
+            return res[1:]
+        else:
+            return self.model(input)        
+
+from torchvision import models
+class Vgg19(torch.nn.Module):
+    def __init__(self, requires_grad=False):
+        super(Vgg19, self).__init__()
+        vgg_pretrained_features = models.vgg19(pretrained=True).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        for x in range(2):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(2, 7):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(7, 12):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(12, 21):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(21, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h_relu1 = self.slice1(X)
+        h_relu2 = self.slice2(h_relu1)        
+        h_relu3 = self.slice3(h_relu2)        
+        h_relu4 = self.slice4(h_relu3)        
+        h_relu5 = self.slice5(h_relu4)                
+        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
+        return out
diff --git a/modeling/SimSwap/models/pix2pixHD_model.py b/modeling/SimSwap/models/pix2pixHD_model.py
new file mode 100644
index 0000000..fafdec0
--- /dev/null
+++ b/modeling/SimSwap/models/pix2pixHD_model.py
@@ -0,0 +1,304 @@
+import numpy as np
+import torch
+import os
+from torch.autograd import Variable
+from util.image_pool import ImagePool
+from .base_model import BaseModel
+from . import networks
+
+class Pix2PixHDModel(BaseModel):
+    def name(self):
+        return 'Pix2PixHDModel'
+    
+    def init_loss_filter(self, use_gan_feat_loss, use_vgg_loss):
+        flags = (True, use_gan_feat_loss, use_vgg_loss, True, True)
+        def loss_filter(g_gan, g_gan_feat, g_vgg, d_real, d_fake):
+            return [l for (l,f) in zip((g_gan,g_gan_feat,g_vgg,d_real,d_fake),flags) if f]
+        return loss_filter
+    
+    def initialize(self, opt):
+        BaseModel.initialize(self, opt)
+        if opt.resize_or_crop != 'none' or not opt.isTrain: # when training at full res this causes OOM
+            torch.backends.cudnn.benchmark = True
+        self.isTrain = opt.isTrain
+        self.use_features = opt.instance_feat or opt.label_feat
+        self.gen_features = self.use_features and not self.opt.load_features
+        input_nc = opt.label_nc if opt.label_nc != 0 else opt.input_nc
+
+        ##### define networks        
+        # Generator network
+        netG_input_nc = input_nc        
+        if not opt.no_instance:
+            netG_input_nc += 1
+        if self.use_features:
+            netG_input_nc += opt.feat_num                  
+        self.netG = networks.define_G(netG_input_nc, opt.output_nc, opt.ngf, opt.netG, 
+                                      opt.n_downsample_global, opt.n_blocks_global, opt.n_local_enhancers, 
+                                      opt.n_blocks_local, opt.norm, gpu_ids=self.gpu_ids)        
+
+        # Discriminator network
+        if self.isTrain:
+            use_sigmoid = opt.no_lsgan
+            netD_input_nc = input_nc + opt.output_nc
+            if not opt.no_instance:
+                netD_input_nc += 1
+            self.netD = networks.define_D(netD_input_nc, opt.ndf, opt.n_layers_D, opt.norm, use_sigmoid, 
+                                          opt.num_D, not opt.no_ganFeat_loss, gpu_ids=self.gpu_ids)
+
+        ### Encoder network
+        if self.gen_features:          
+            self.netE = networks.define_G(opt.output_nc, opt.feat_num, opt.nef, 'encoder', 
+                                          opt.n_downsample_E, norm=opt.norm, gpu_ids=self.gpu_ids)  
+        if self.opt.verbose:
+                print('---------- Networks initialized -------------')
+
+        # load networks
+        if not self.isTrain or opt.continue_train or opt.load_pretrain:
+            pretrained_path = '' if not self.isTrain else opt.load_pretrain
+            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)            
+            if self.isTrain:
+                self.load_network(self.netD, 'D', opt.which_epoch, pretrained_path)  
+            if self.gen_features:
+                self.load_network(self.netE, 'E', opt.which_epoch, pretrained_path)              
+
+        # set loss functions and optimizers
+        if self.isTrain:
+            if opt.pool_size > 0 and (len(self.gpu_ids)) > 1:
+                raise NotImplementedError("Fake Pool Not Implemented for MultiGPU")
+            self.fake_pool = ImagePool(opt.pool_size)
+            self.old_lr = opt.lr
+
+            # define loss functions
+            self.loss_filter = self.init_loss_filter(not opt.no_ganFeat_loss, not opt.no_vgg_loss)
+            
+            self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor)   
+            self.criterionFeat = torch.nn.L1Loss()
+            if not opt.no_vgg_loss:             
+                self.criterionVGG = networks.VGGLoss(self.gpu_ids)
+                
+        
+            # Names so we can breakout loss
+            self.loss_names = self.loss_filter('G_GAN','G_GAN_Feat','G_VGG','D_real', 'D_fake')
+
+            # initialize optimizers
+            # optimizer G
+            if opt.niter_fix_global > 0:                
+                import sys
+                if sys.version_info >= (3,0):
+                    finetune_list = set()
+                else:
+                    from sets import Set
+                    finetune_list = Set()
+
+                params_dict = dict(self.netG.named_parameters())
+                params = []
+                for key, value in params_dict.items():       
+                    if key.startswith('model' + str(opt.n_local_enhancers)):                    
+                        params += [value]
+                        finetune_list.add(key.split('.')[0])  
+                print('------------- Only training the local enhancer network (for %d epochs) ------------' % opt.niter_fix_global)
+                print('The layers that are finetuned are ', sorted(finetune_list))                         
+            else:
+                params = list(self.netG.parameters())
+            if self.gen_features:              
+                params += list(self.netE.parameters())         
+            self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))                            
+
+            # optimizer D                        
+            params = list(self.netD.parameters())    
+            self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+
+    def encode_input(self, label_map, inst_map=None, real_image=None, feat_map=None, infer=False):             
+        if self.opt.label_nc == 0:
+            input_label = label_map.data.cuda()
+        else:
+            # create one-hot vector for label map 
+            size = label_map.size()
+            oneHot_size = (size[0], self.opt.label_nc, size[2], size[3])
+            input_label = torch.cuda.FloatTensor(torch.Size(oneHot_size)).zero_()
+            input_label = input_label.scatter_(1, label_map.data.long().cuda(), 1.0)
+            if self.opt.data_type == 16:
+                input_label = input_label.half()
+
+        # get edges from instance map
+        if not self.opt.no_instance:
+            inst_map = inst_map.data.cuda()
+            edge_map = self.get_edges(inst_map)
+            input_label = torch.cat((input_label, edge_map), dim=1)         
+        input_label = Variable(input_label, volatile=infer)
+
+        # real images for training
+        if real_image is not None:
+            real_image = Variable(real_image.data.cuda())
+
+        # instance map for feature encoding
+        if self.use_features:
+            # get precomputed feature maps
+            if self.opt.load_features:
+                feat_map = Variable(feat_map.data.cuda())
+            if self.opt.label_feat:
+                inst_map = label_map.cuda()
+
+        return input_label, inst_map, real_image, feat_map
+
+    def discriminate(self, input_label, test_image, use_pool=False):
+        input_concat = torch.cat((input_label, test_image.detach()), dim=1)
+        if use_pool:            
+            fake_query = self.fake_pool.query(input_concat)
+            return self.netD.forward(fake_query)
+        else:
+            return self.netD.forward(input_concat)
+
+    def forward(self, label, inst, image, feat, infer=False):
+        # Encode Inputs
+        input_label, inst_map, real_image, feat_map = self.encode_input(label, inst, image, feat)  
+
+        # Fake Generation
+        if self.use_features:
+            if not self.opt.load_features:
+                feat_map = self.netE.forward(real_image, inst_map)                     
+            input_concat = torch.cat((input_label, feat_map), dim=1)                        
+        else:
+            input_concat = input_label
+        fake_image = self.netG.forward(input_concat)
+
+        # Fake Detection and Loss
+        pred_fake_pool = self.discriminate(input_label, fake_image, use_pool=True)
+        loss_D_fake = self.criterionGAN(pred_fake_pool, False)        
+
+        # Real Detection and Loss        
+        pred_real = self.discriminate(input_label, real_image)
+        loss_D_real = self.criterionGAN(pred_real, True)
+
+        # GAN loss (Fake Passability Loss)        
+        pred_fake = self.netD.forward(torch.cat((input_label, fake_image), dim=1))        
+        loss_G_GAN = self.criterionGAN(pred_fake, True)               
+        
+        # GAN feature matching loss
+        loss_G_GAN_Feat = 0
+        if not self.opt.no_ganFeat_loss:
+            feat_weights = 4.0 / (self.opt.n_layers_D + 1)
+            D_weights = 1.0 / self.opt.num_D
+            for i in range(self.opt.num_D):
+                for j in range(len(pred_fake[i])-1):
+                    loss_G_GAN_Feat += D_weights * feat_weights * \
+                        self.criterionFeat(pred_fake[i][j], pred_real[i][j].detach()) * self.opt.lambda_feat
+                   
+        # VGG feature matching loss
+        loss_G_VGG = 0
+        if not self.opt.no_vgg_loss:
+            loss_G_VGG = self.criterionVGG(fake_image, real_image) * self.opt.lambda_feat
+        
+        # Only return the fake_B image if necessary to save BW
+        return [ self.loss_filter( loss_G_GAN, loss_G_GAN_Feat, loss_G_VGG, loss_D_real, loss_D_fake ), None if not infer else fake_image ]
+
+    def inference(self, label, inst, image=None):
+        # Encode Inputs        
+        image = Variable(image) if image is not None else None
+        input_label, inst_map, real_image, _ = self.encode_input(Variable(label), Variable(inst), image, infer=True)
+
+        # Fake Generation
+        if self.use_features:
+            if self.opt.use_encoded_image:
+                # encode the real image to get feature map
+                feat_map = self.netE.forward(real_image, inst_map)
+            else:
+                # sample clusters from precomputed features             
+                feat_map = self.sample_features(inst_map)
+            input_concat = torch.cat((input_label, feat_map), dim=1)                        
+        else:
+            input_concat = input_label        
+           
+        if torch.__version__.startswith('0.4'):
+            with torch.no_grad():
+                fake_image = self.netG.forward(input_concat)
+        else:
+            fake_image = self.netG.forward(input_concat)
+        return fake_image
+
+    def sample_features(self, inst): 
+        # read precomputed feature clusters 
+        cluster_path = os.path.join(self.opt.checkpoints_dir, self.opt.name, self.opt.cluster_path)        
+        features_clustered = np.load(cluster_path, encoding='latin1').item()
+
+        # randomly sample from the feature clusters
+        inst_np = inst.cpu().numpy().astype(int)                                      
+        feat_map = self.Tensor(inst.size()[0], self.opt.feat_num, inst.size()[2], inst.size()[3])
+        for i in np.unique(inst_np):    
+            label = i if i < 1000 else i//1000
+            if label in features_clustered:
+                feat = features_clustered[label]
+                cluster_idx = np.random.randint(0, feat.shape[0]) 
+                                            
+                idx = (inst == int(i)).nonzero()
+                for k in range(self.opt.feat_num):                                    
+                    feat_map[idx[:,0], idx[:,1] + k, idx[:,2], idx[:,3]] = feat[cluster_idx, k]
+        if self.opt.data_type==16:
+            feat_map = feat_map.half()
+        return feat_map
+
+    def encode_features(self, image, inst):
+        image = Variable(image.cuda(), volatile=True)
+        feat_num = self.opt.feat_num
+        h, w = inst.size()[2], inst.size()[3]
+        block_num = 32
+        feat_map = self.netE.forward(image, inst.cuda())
+        inst_np = inst.cpu().numpy().astype(int)
+        feature = {}
+        for i in range(self.opt.label_nc):
+            feature[i] = np.zeros((0, feat_num+1))
+        for i in np.unique(inst_np):
+            label = i if i < 1000 else i//1000
+            idx = (inst == int(i)).nonzero()
+            num = idx.size()[0]
+            idx = idx[num//2,:]
+            val = np.zeros((1, feat_num+1))                        
+            for k in range(feat_num):
+                val[0, k] = feat_map[idx[0], idx[1] + k, idx[2], idx[3]].data[0]            
+            val[0, feat_num] = float(num) / (h * w // block_num)
+            feature[label] = np.append(feature[label], val, axis=0)
+        return feature
+
+    def get_edges(self, t):
+        edge = torch.cuda.ByteTensor(t.size()).zero_()
+        edge[:,:,:,1:] = edge[:,:,:,1:] | (t[:,:,:,1:] != t[:,:,:,:-1])
+        edge[:,:,:,:-1] = edge[:,:,:,:-1] | (t[:,:,:,1:] != t[:,:,:,:-1])
+        edge[:,:,1:,:] = edge[:,:,1:,:] | (t[:,:,1:,:] != t[:,:,:-1,:])
+        edge[:,:,:-1,:] = edge[:,:,:-1,:] | (t[:,:,1:,:] != t[:,:,:-1,:])
+        if self.opt.data_type==16:
+            return edge.half()
+        else:
+            return edge.float()
+
+    def save(self, which_epoch):
+        self.save_network(self.netG, 'G', which_epoch, self.gpu_ids)
+        self.save_network(self.netD, 'D', which_epoch, self.gpu_ids)
+        if self.gen_features:
+            self.save_network(self.netE, 'E', which_epoch, self.gpu_ids)
+
+    def update_fixed_params(self):
+        # after fixing the global generator for a number of iterations, also start finetuning it
+        params = list(self.netG.parameters())
+        if self.gen_features:
+            params += list(self.netE.parameters())           
+        self.optimizer_G = torch.optim.Adam(params, lr=self.opt.lr, betas=(self.opt.beta1, 0.999))
+        if self.opt.verbose:
+            print('------------ Now also finetuning global generator -----------')
+
+    def update_learning_rate(self):
+        lrd = self.opt.lr / self.opt.niter_decay
+        lr = self.old_lr - lrd        
+        for param_group in self.optimizer_D.param_groups:
+            param_group['lr'] = lr
+        for param_group in self.optimizer_G.param_groups:
+            param_group['lr'] = lr
+        if self.opt.verbose:
+            print('update learning rate: %f -> %f' % (self.old_lr, lr))
+        self.old_lr = lr
+
+class InferenceModel(Pix2PixHDModel):
+    def forward(self, inp):
+        label, inst = inp
+        return self.inference(label, inst)
+
+        
diff --git a/modeling/SimSwap/models/projected_model.py b/modeling/SimSwap/models/projected_model.py
new file mode 100644
index 0000000..15d70f9
--- /dev/null
+++ b/modeling/SimSwap/models/projected_model.py
@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: fs_model_fix_idnorm_donggp_saveoptim copy.py
+# Created Date: Wednesday January 12th 2022
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Saturday, 13th May 2023 9:56:35 am
+# Modified By: Chen Xuanhong
+# Copyright (c) 2022 Shanghai Jiao Tong University
+#############################################################
+
+
+import torch
+import torch.nn as nn
+
+from .base_model import BaseModel
+from .fs_networks_fix import Generator_Adain_Upsample
+
+from pg_modules.projected_discriminator import ProjectedDiscriminator
+
+def compute_grad2(d_out, x_in):
+    batch_size = x_in.size(0)
+    grad_dout = torch.autograd.grad(
+        outputs=d_out.sum(), inputs=x_in,
+        create_graph=True, retain_graph=True, only_inputs=True
+    )[0]
+    grad_dout2 = grad_dout.pow(2)
+    assert(grad_dout2.size() == x_in.size())
+    reg = grad_dout2.view(batch_size, -1).sum(1)
+    return reg
+
+class fsModel(BaseModel):
+    def name(self):
+        return 'fsModel'
+
+    def initialize(self, opt):
+        BaseModel.initialize(self, opt)
+        # if opt.resize_or_crop != 'none' or not opt.isTrain:  # when training at full res this causes OOM
+        self.isTrain = opt.isTrain
+
+        # Generator network
+        self.netG = Generator_Adain_Upsample(input_nc=3, output_nc=3, latent_size=512, n_blocks=9, deep=opt.Gdeep)
+        self.netG.cuda()
+
+        # Id network
+        netArc_checkpoint = opt.Arc_path
+        netArc_checkpoint = torch.load(netArc_checkpoint, map_location=torch.device("cpu"))
+        self.netArc = netArc_checkpoint
+        self.netArc = self.netArc.cuda()
+        self.netArc.eval()
+        self.netArc.requires_grad_(False)
+        if not self.isTrain:
+            pretrained_path =  opt.checkpoints_dir
+            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)
+            return
+        self.netD = ProjectedDiscriminator(diffaug=False, interp224=False, **{})
+        # self.netD.feature_network.requires_grad_(False)
+        self.netD.cuda()
+
+
+        if self.isTrain:
+            # define loss functions
+            self.criterionFeat  = nn.L1Loss()
+            self.criterionRec   = nn.L1Loss()
+
+
+           # initialize optimizers
+
+            # optimizer G
+            params = list(self.netG.parameters())
+            self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.99),eps=1e-8)
+
+            # optimizer D
+            params = list(self.netD.parameters())
+            self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.99),eps=1e-8)
+
+        # load networks
+        if opt.continue_train:
+            pretrained_path = '' if not self.isTrain else opt.load_pretrain
+            # print (pretrained_path)
+            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)
+            self.load_network(self.netD, 'D', opt.which_epoch, pretrained_path)
+            self.load_optim(self.optimizer_G, 'G', opt.which_epoch, pretrained_path)
+            self.load_optim(self.optimizer_D, 'D', opt.which_epoch, pretrained_path)
+        torch.cuda.empty_cache()
+
+    def cosin_metric(self, x1, x2):
+        #return np.dot(x1, x2) / (np.linalg.norm(x1) * np.linalg.norm(x2))
+        return torch.sum(x1 * x2, dim=1) / (torch.norm(x1, dim=1) * torch.norm(x2, dim=1))
+
+
+
+    def save(self, which_epoch):
+        self.save_network(self.netG, 'G', which_epoch)
+        self.save_network(self.netD, 'D', which_epoch)
+        self.save_optim(self.optimizer_G, 'G', which_epoch)
+        self.save_optim(self.optimizer_D, 'D', which_epoch)
+        '''if self.gen_features:
+            self.save_network(self.netE, 'E', which_epoch, self.gpu_ids)'''
+
+    def update_fixed_params(self):
+        # after fixing the global generator for a number of iterations, also start finetuning it
+        params = list(self.netG.parameters())
+        if self.gen_features:
+            params += list(self.netE.parameters())
+        self.optimizer_G = torch.optim.Adam(params, lr=self.opt.lr, betas=(self.opt.beta1, 0.999))
+        if self.opt.verbose:
+            print('------------ Now also finetuning global generator -----------')
+
+    def update_learning_rate(self):
+        lrd = self.opt.lr / self.opt.niter_decay
+        lr = self.old_lr - lrd
+        for param_group in self.optimizer_D.param_groups:
+            param_group['lr'] = lr
+        for param_group in self.optimizer_G.param_groups:
+            param_group['lr'] = lr
+        if self.opt.verbose:
+            print('update learning rate: %f -> %f' % (self.old_lr, lr))
+        self.old_lr = lr
+
+
diff --git a/modeling/SimSwap/models/projectionhead.py b/modeling/SimSwap/models/projectionhead.py
new file mode 100644
index 0000000..a0d18f1
--- /dev/null
+++ b/modeling/SimSwap/models/projectionhead.py
@@ -0,0 +1,14 @@
+import torch.nn as nn
+
+class ProjectionHead(nn.Module):
+    def __init__(self, proj_dim=256):
+        super(ProjectionHead, self).__init__()
+
+        self.proj = nn.Sequential(
+            nn.Linear(proj_dim, proj_dim), 
+            nn.ReLU(),
+            nn.Linear(proj_dim, proj_dim), 
+        )
+
+    def forward(self, x):
+        return self.proj(x)
\ No newline at end of file
diff --git a/modeling/SimSwap/models/ui_model.py b/modeling/SimSwap/models/ui_model.py
new file mode 100644
index 0000000..c5b3433
--- /dev/null
+++ b/modeling/SimSwap/models/ui_model.py
@@ -0,0 +1,347 @@
+import torch
+from torch.autograd import Variable
+from collections import OrderedDict
+import numpy as np
+import os
+from PIL import Image
+import util.util as util
+from .base_model import BaseModel
+from . import networks
+
+class UIModel(BaseModel):
+    def name(self):
+        return 'UIModel'
+
+    def initialize(self, opt):
+        assert(not opt.isTrain)
+        BaseModel.initialize(self, opt)
+        self.use_features = opt.instance_feat or opt.label_feat
+
+        netG_input_nc = opt.label_nc
+        if not opt.no_instance:
+            netG_input_nc += 1            
+        if self.use_features:   
+            netG_input_nc += opt.feat_num           
+
+        self.netG = networks.define_G(netG_input_nc, opt.output_nc, opt.ngf, opt.netG, 
+                                      opt.n_downsample_global, opt.n_blocks_global, opt.n_local_enhancers, 
+                                      opt.n_blocks_local, opt.norm, gpu_ids=self.gpu_ids)            
+        self.load_network(self.netG, 'G', opt.which_epoch)
+
+        print('---------- Networks initialized -------------')
+
+    def toTensor(self, img, normalize=False):
+        tensor = torch.from_numpy(np.array(img, np.int32, copy=False))
+        tensor = tensor.view(1, img.size[1], img.size[0], len(img.mode))    
+        tensor = tensor.transpose(1, 2).transpose(1, 3).contiguous()
+        if normalize:
+            return (tensor.float()/255.0 - 0.5) / 0.5        
+        return tensor.float()
+
+    def load_image(self, label_path, inst_path, feat_path):
+        opt = self.opt
+        # read label map
+        label_img = Image.open(label_path)    
+        if label_path.find('face') != -1:
+            label_img = label_img.convert('L')
+        ow, oh = label_img.size    
+        w = opt.loadSize
+        h = int(w * oh / ow)    
+        label_img = label_img.resize((w, h), Image.NEAREST)
+        label_map = self.toTensor(label_img)           
+        
+        # onehot vector input for label map
+        self.label_map = label_map.cuda()
+        oneHot_size = (1, opt.label_nc, h, w)
+        input_label = self.Tensor(torch.Size(oneHot_size)).zero_()
+        self.input_label = input_label.scatter_(1, label_map.long().cuda(), 1.0)
+
+        # read instance map
+        if not opt.no_instance:
+            inst_img = Image.open(inst_path)        
+            inst_img = inst_img.resize((w, h), Image.NEAREST)            
+            self.inst_map = self.toTensor(inst_img).cuda()
+            self.edge_map = self.get_edges(self.inst_map)          
+            self.net_input = Variable(torch.cat((self.input_label, self.edge_map), dim=1), volatile=True)
+        else:
+            self.net_input = Variable(self.input_label, volatile=True)  
+        
+        self.features_clustered = np.load(feat_path).item()
+        self.object_map = self.inst_map if opt.instance_feat else self.label_map 
+                       
+        object_np = self.object_map.cpu().numpy().astype(int) 
+        self.feat_map = self.Tensor(1, opt.feat_num, h, w).zero_()                 
+        self.cluster_indices = np.zeros(self.opt.label_nc, np.uint8)
+        for i in np.unique(object_np):    
+            label = i if i < 1000 else i//1000
+            if label in self.features_clustered:
+                feat = self.features_clustered[label]
+                np.random.seed(i+1)
+                cluster_idx = np.random.randint(0, feat.shape[0])
+                self.cluster_indices[label] = cluster_idx
+                idx = (self.object_map == i).nonzero()                    
+                self.set_features(idx, feat, cluster_idx)
+
+        self.net_input_original = self.net_input.clone()        
+        self.label_map_original = self.label_map.clone()
+        self.feat_map_original = self.feat_map.clone()
+        if not opt.no_instance:
+            self.inst_map_original = self.inst_map.clone()        
+
+    def reset(self):
+        self.net_input = self.net_input_prev = self.net_input_original.clone()        
+        self.label_map = self.label_map_prev = self.label_map_original.clone()
+        self.feat_map = self.feat_map_prev = self.feat_map_original.clone()
+        if not self.opt.no_instance:
+            self.inst_map = self.inst_map_prev = self.inst_map_original.clone()
+        self.object_map = self.inst_map if self.opt.instance_feat else self.label_map 
+
+    def undo(self):        
+        self.net_input = self.net_input_prev
+        self.label_map = self.label_map_prev
+        self.feat_map = self.feat_map_prev
+        if not self.opt.no_instance:
+            self.inst_map = self.inst_map_prev
+        self.object_map = self.inst_map if self.opt.instance_feat else self.label_map 
+            
+    # get boundary map from instance map
+    def get_edges(self, t):
+        edge = torch.cuda.ByteTensor(t.size()).zero_()
+        edge[:,:,:,1:] = edge[:,:,:,1:] | (t[:,:,:,1:] != t[:,:,:,:-1])
+        edge[:,:,:,:-1] = edge[:,:,:,:-1] | (t[:,:,:,1:] != t[:,:,:,:-1])
+        edge[:,:,1:,:] = edge[:,:,1:,:] | (t[:,:,1:,:] != t[:,:,:-1,:])
+        edge[:,:,:-1,:] = edge[:,:,:-1,:] | (t[:,:,1:,:] != t[:,:,:-1,:])
+        return edge.float()
+
+    # change the label at the source position to the label at the target position
+    def change_labels(self, click_src, click_tgt): 
+        y_src, x_src = click_src[0], click_src[1]
+        y_tgt, x_tgt = click_tgt[0], click_tgt[1]
+        label_src = int(self.label_map[0, 0, y_src, x_src])
+        inst_src = self.inst_map[0, 0, y_src, x_src]
+        label_tgt = int(self.label_map[0, 0, y_tgt, x_tgt])
+        inst_tgt = self.inst_map[0, 0, y_tgt, x_tgt]
+
+        idx_src = (self.inst_map == inst_src).nonzero()         
+        # need to change 3 things: label map, instance map, and feature map
+        if idx_src.shape:
+            # backup current maps
+            self.backup_current_state() 
+
+            # change both the label map and the network input
+            self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
+            self.net_input[idx_src[:,0], idx_src[:,1] + label_src, idx_src[:,2], idx_src[:,3]] = 0
+            self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1                                    
+            
+            # update the instance map (and the network input)
+            if inst_tgt > 1000:
+                # if different instances have different ids, give the new object a new id
+                tgt_indices = (self.inst_map > label_tgt * 1000) & (self.inst_map < (label_tgt+1) * 1000)
+                inst_tgt = self.inst_map[tgt_indices].max() + 1
+            self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = inst_tgt
+            self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
+
+            # also copy the source features to the target position      
+            idx_tgt = (self.inst_map == inst_tgt).nonzero()    
+            if idx_tgt.shape:
+                self.copy_features(idx_src, idx_tgt[0,:])
+
+        self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
+
+    # add strokes of target label in the image
+    def add_strokes(self, click_src, label_tgt, bw, save):
+        # get the region of the new strokes (bw is the brush width)        
+        size = self.net_input.size()
+        h, w = size[2], size[3]
+        idx_src = torch.LongTensor(bw**2, 4).fill_(0)
+        for i in range(bw):
+            idx_src[i*bw:(i+1)*bw, 2] = min(h-1, max(0, click_src[0]-bw//2 + i))
+            for j in range(bw):
+                idx_src[i*bw+j, 3] = min(w-1, max(0, click_src[1]-bw//2 + j))
+        idx_src = idx_src.cuda()
+        
+        # again, need to update 3 things
+        if idx_src.shape:
+            # backup current maps
+            if save:
+                self.backup_current_state()
+
+            # update the label map (and the network input) in the stroke region            
+            self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
+            for k in range(self.opt.label_nc):
+                self.net_input[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = 0
+            self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1                 
+
+            # update the instance map (and the network input)
+            self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
+            self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
+            
+            # also update the features if available
+            if self.opt.instance_feat:                                            
+                feat = self.features_clustered[label_tgt]
+                #np.random.seed(label_tgt+1)   
+                #cluster_idx = np.random.randint(0, feat.shape[0])
+                cluster_idx = self.cluster_indices[label_tgt]
+                self.set_features(idx_src, feat, cluster_idx)                                                  
+        
+        self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
+
+    # add an object to the clicked position with selected style
+    def add_objects(self, click_src, label_tgt, mask, style_id=0):
+        y, x = click_src[0], click_src[1]
+        mask = np.transpose(mask, (2, 0, 1))[np.newaxis,...]        
+        idx_src = torch.from_numpy(mask).cuda().nonzero()        
+        idx_src[:,2] += y
+        idx_src[:,3] += x
+
+        # backup current maps
+        self.backup_current_state()
+
+        # update label map
+        self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt        
+        for k in range(self.opt.label_nc):
+            self.net_input[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = 0
+        self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1            
+
+        # update instance map
+        self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
+        self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
+                
+        # update feature map
+        self.set_features(idx_src, self.feat, style_id)                
+        
+        self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
+
+    def single_forward(self, net_input, feat_map):
+        net_input = torch.cat((net_input, feat_map), dim=1)
+        fake_image = self.netG.forward(net_input)
+
+        if fake_image.size()[0] == 1:
+            return fake_image.data[0]        
+        return fake_image.data
+
+
+    # generate all outputs for different styles
+    def style_forward(self, click_pt, style_id=-1):           
+        if click_pt is None:            
+            self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
+            self.crop = None
+            self.mask = None        
+        else:                       
+            instToChange = int(self.object_map[0, 0, click_pt[0], click_pt[1]])
+            self.instToChange = instToChange
+            label = instToChange if instToChange < 1000 else instToChange//1000        
+            self.feat = self.features_clustered[label]
+            self.fake_image = []
+            self.mask = self.object_map == instToChange
+            idx = self.mask.nonzero()
+            self.get_crop_region(idx)            
+            if idx.size():                
+                if style_id == -1:
+                    (min_y, min_x, max_y, max_x) = self.crop
+                    ### original
+                    for cluster_idx in range(self.opt.multiple_output):
+                        self.set_features(idx, self.feat, cluster_idx)
+                        fake_image = self.single_forward(self.net_input, self.feat_map)
+                        fake_image = util.tensor2im(fake_image[:,min_y:max_y,min_x:max_x])
+                        self.fake_image.append(fake_image)    
+                    """### To speed up previewing different style results, either crop or downsample the label maps
+                    if instToChange > 1000:
+                        (min_y, min_x, max_y, max_x) = self.crop                                                
+                        ### crop                                                
+                        _, _, h, w = self.net_input.size()
+                        offset = 512
+                        y_start, x_start = max(0, min_y-offset), max(0, min_x-offset)
+                        y_end, x_end = min(h, (max_y + offset)), min(w, (max_x + offset))
+                        y_region = slice(y_start, y_start+(y_end-y_start)//16*16)
+                        x_region = slice(x_start, x_start+(x_end-x_start)//16*16)
+                        net_input = self.net_input[:,:,y_region,x_region]                    
+                        for cluster_idx in range(self.opt.multiple_output):  
+                            self.set_features(idx, self.feat, cluster_idx)
+                            fake_image = self.single_forward(net_input, self.feat_map[:,:,y_region,x_region])                            
+                            fake_image = util.tensor2im(fake_image[:,min_y-y_start:max_y-y_start,min_x-x_start:max_x-x_start])
+                            self.fake_image.append(fake_image)
+                    else:
+                        ### downsample
+                        (min_y, min_x, max_y, max_x) = [crop//2 for crop in self.crop]                    
+                        net_input = self.net_input[:,:,::2,::2]                    
+                        size = net_input.size()
+                        net_input_batch = net_input.expand(self.opt.multiple_output, size[1], size[2], size[3])             
+                        for cluster_idx in range(self.opt.multiple_output):  
+                            self.set_features(idx, self.feat, cluster_idx)
+                            feat_map = self.feat_map[:,:,::2,::2]
+                            if cluster_idx == 0:
+                                feat_map_batch = feat_map
+                            else:
+                                feat_map_batch = torch.cat((feat_map_batch, feat_map), dim=0)
+                        fake_image_batch = self.single_forward(net_input_batch, feat_map_batch)
+                        for i in range(self.opt.multiple_output):
+                            self.fake_image.append(util.tensor2im(fake_image_batch[i,:,min_y:max_y,min_x:max_x]))"""
+                                        
+                else:
+                    self.set_features(idx, self.feat, style_id)
+                    self.cluster_indices[label] = style_id
+                    self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))        
+
+    def backup_current_state(self):
+        self.net_input_prev = self.net_input.clone()
+        self.label_map_prev = self.label_map.clone() 
+        self.inst_map_prev = self.inst_map.clone() 
+        self.feat_map_prev = self.feat_map.clone() 
+
+    # crop the ROI and get the mask of the object
+    def get_crop_region(self, idx):
+        size = self.net_input.size()
+        h, w = size[2], size[3]
+        min_y, min_x = idx[:,2].min(), idx[:,3].min()
+        max_y, max_x = idx[:,2].max(), idx[:,3].max()             
+        crop_min = 128
+        if max_y - min_y < crop_min:
+            min_y = max(0, (max_y + min_y) // 2 - crop_min // 2)
+            max_y = min(h-1, min_y + crop_min)
+        if max_x - min_x < crop_min:
+            min_x = max(0, (max_x + min_x) // 2 - crop_min // 2)
+            max_x = min(w-1, min_x + crop_min)
+        self.crop = (min_y, min_x, max_y, max_x)           
+        self.mask = self.mask[:,:, min_y:max_y, min_x:max_x]
+
+    # update the feature map once a new object is added or the label is changed
+    def update_features(self, cluster_idx, mask=None, click_pt=None):        
+        self.feat_map_prev = self.feat_map.clone()
+        # adding a new object
+        if mask is not None:
+            y, x = click_pt[0], click_pt[1]
+            mask = np.transpose(mask, (2,0,1))[np.newaxis,...]        
+            idx = torch.from_numpy(mask).cuda().nonzero()        
+            idx[:,2] += y
+            idx[:,3] += x    
+        # changing the label of an existing object 
+        else:            
+            idx = (self.object_map == self.instToChange).nonzero()              
+
+        # update feature map
+        self.set_features(idx, self.feat, cluster_idx)        
+
+    # set the class features to the target feature
+    def set_features(self, idx, feat, cluster_idx):        
+        for k in range(self.opt.feat_num):
+            self.feat_map[idx[:,0], idx[:,1] + k, idx[:,2], idx[:,3]] = feat[cluster_idx, k] 
+
+    # copy the features at the target position to the source position
+    def copy_features(self, idx_src, idx_tgt):        
+        for k in range(self.opt.feat_num):
+            val = self.feat_map[idx_tgt[0], idx_tgt[1] + k, idx_tgt[2], idx_tgt[3]]
+            self.feat_map[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = val 
+
+    def get_current_visuals(self, getLabel=False):                              
+        mask = self.mask     
+        if self.mask is not None:
+            mask = np.transpose(self.mask[0].cpu().float().numpy(), (1,2,0)).astype(np.uint8)        
+
+        dict_list = [('fake_image', self.fake_image), ('mask', mask)]
+
+        if getLabel: # only output label map if needed to save bandwidth
+            label = util.tensor2label(self.net_input.data[0], self.opt.label_nc)                    
+            dict_list += [('label', label)]
+
+        return OrderedDict(dict_list)
\ No newline at end of file
diff --git a/modeling/SimSwap/options/base_options.py b/modeling/SimSwap/options/base_options.py
new file mode 100644
index 0000000..610bb22
--- /dev/null
+++ b/modeling/SimSwap/options/base_options.py
@@ -0,0 +1,104 @@
+import argparse
+import os
+from util import util
+import torch
+
+class BaseOptions():
+    def __init__(self):
+        self.parser = argparse.ArgumentParser()
+        self.initialized = False
+
+    def initialize(self):
+        # experiment specifics
+        self.parser.add_argument('--name', type=str, default='people', help='name of the experiment. It decides where to store samples and models')
+        self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
+        self.parser.add_argument('--checkpoints_dir', type=str, default='./SimSwap/checkpoints', help='models are saved here')
+        # self.parser.add_argument('--model', type=str, default='pix2pixHD', help='which model to use')
+        self.parser.add_argument('--norm', type=str, default='batch', help='instance normalization or batch normalization')
+        self.parser.add_argument('--use_dropout', action='store_true', help='use dropout for the generator')
+        self.parser.add_argument('--data_type', default=32, type=int, choices=[8, 16, 32], help="Supported data type i.e. 8, 16, 32 bit")
+        self.parser.add_argument('--verbose', action='store_true', default=False, help='toggles verbose')
+        self.parser.add_argument('--fp16', action='store_true', default=False, help='train with AMP')
+        self.parser.add_argument('--local_rank', type=int, default=0, help='local rank for distributed training')
+        self.parser.add_argument('--isTrain', type=bool, default=True, help='local rank for distributed training')
+
+        # input/output sizes       
+        self.parser.add_argument('--batchSize', type=int, default=8, help='input batch size')
+        self.parser.add_argument('--loadSize', type=int, default=1024, help='scale images to this size')
+        self.parser.add_argument('--fineSize', type=int, default=512, help='then crop to this size')
+        self.parser.add_argument('--label_nc', type=int, default=0, help='# of input label channels')
+        self.parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels')
+        self.parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')
+
+        # for setting inputs
+        self.parser.add_argument('--dataroot', type=str, default='./SimSwap/datasets/cityscapes/') 
+        self.parser.add_argument('--resize_or_crop', type=str, default='scale_width', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop]')
+        self.parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')        
+        self.parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data argumentation') 
+        self.parser.add_argument('--nThreads', default=2, type=int, help='# threads for loading data')                
+        self.parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
+
+        # for displays
+        self.parser.add_argument('--display_winsize', type=int, default=512,  help='display window size')
+        self.parser.add_argument('--tf_log', action='store_true', help='if specified, use tensorboard logging. Requires tensorflow installed')
+
+        # for generator
+        self.parser.add_argument('--netG', type=str, default='global', help='selects model to use for netG')
+        self.parser.add_argument('--latent_size', type=int, default=512, help='latent size of Adain layer')
+        self.parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
+        self.parser.add_argument('--n_downsample_global', type=int, default=3, help='number of downsampling layers in netG')
+        self.parser.add_argument('--n_blocks_global', type=int, default=6, help='number of residual blocks in the global generator network')
+        self.parser.add_argument('--n_blocks_local', type=int, default=3, help='number of residual blocks in the local enhancer network')
+        self.parser.add_argument('--n_local_enhancers', type=int, default=1, help='number of local enhancers to use')        
+        self.parser.add_argument('--niter_fix_global', type=int, default=0, help='number of epochs that we only train the outmost local enhancer')        
+
+        # for instance-wise features
+        self.parser.add_argument('--no_instance', action='store_true', help='if specified, do *not* add instance map as input')        
+        self.parser.add_argument('--instance_feat', action='store_true', help='if specified, add encoded instance features as input')
+        self.parser.add_argument('--label_feat', action='store_true', help='if specified, add encoded label features as input')        
+        self.parser.add_argument('--feat_num', type=int, default=3, help='vector length for encoded features')        
+        self.parser.add_argument('--load_features', action='store_true', help='if specified, load precomputed feature maps')
+        self.parser.add_argument('--n_downsample_E', type=int, default=4, help='# of downsampling layers in encoder') 
+        self.parser.add_argument('--nef', type=int, default=16, help='# of encoder filters in the first conv layer')        
+        self.parser.add_argument('--n_clusters', type=int, default=10, help='number of clusters for features')
+        self.parser.add_argument('--image_size', type=int, default=224, help='number of clusters for features')
+        self.parser.add_argument('--norm_G', type=str, default='spectralspadesyncbatch3x3', help='instance normalization or batch normalization')
+        self.parser.add_argument('--semantic_nc', type=int, default=3, help='number of clusters for features')
+        self.initialized = True
+
+    def parse(self, save=True):
+        if not self.initialized:
+            self.initialize()
+        self.opt = self.parser.parse_args()
+        self.opt.isTrain = self.isTrain   # train or test
+
+        str_ids = self.opt.gpu_ids.split(',')
+        self.opt.gpu_ids = []
+        for str_id in str_ids:
+            id = int(str_id)
+            if id >= 0:
+                self.opt.gpu_ids.append(id)
+        
+        # set gpu ids
+        if len(self.opt.gpu_ids) > 0:
+            torch.cuda.set_device(self.opt.gpu_ids[0])
+
+        args = vars(self.opt)
+
+        print('------------ Options -------------')
+        for k, v in sorted(args.items()):
+            print('%s: %s' % (str(k), str(v)))
+        print('-------------- End ----------------')
+
+        # save to the disk
+        if self.opt.isTrain:
+            expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name)
+            util.mkdirs(expr_dir)
+            if save and not self.opt.continue_train:
+                file_name = os.path.join(expr_dir, 'opt.txt')
+                with open(file_name, 'wt') as opt_file:
+                    opt_file.write('------------ Options -------------\n')
+                    for k, v in sorted(args.items()):
+                        opt_file.write('%s: %s\n' % (str(k), str(v)))
+                    opt_file.write('-------------- End ----------------\n')
+        return self.opt
diff --git a/modeling/SimSwap/options/test_options.py b/modeling/SimSwap/options/test_options.py
new file mode 100644
index 0000000..e46e435
--- /dev/null
+++ b/modeling/SimSwap/options/test_options.py
@@ -0,0 +1,38 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-23 17:08:08
+Description: 
+'''
+from .base_options import BaseOptions
+
+class TestOptions(BaseOptions):
+    def initialize(self):
+        BaseOptions.initialize(self)
+        self.parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.')
+        self.parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.')
+        self.parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
+        self.parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
+        self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
+        self.parser.add_argument('--how_many', type=int, default=50, help='how many test images to run')       
+        self.parser.add_argument('--cluster_path', type=str, default='features_clustered_010.npy', help='the path for clustered results of encoded features')
+        self.parser.add_argument('--use_encoded_image', action='store_true', help='if specified, encode the real image to get the feature map')
+        self.parser.add_argument("--export_onnx", type=str, help="export ONNX model to a given file")
+        self.parser.add_argument("--engine", type=str, help="run serialized TRT engine")
+        self.parser.add_argument("--onnx", type=str, help="run ONNX model via TRT")        
+        self.parser.add_argument("--Arc_path", type=str, default='SimSwap/arcface_model/arcface_checkpoint.tar', help="run ONNX model via TRT")
+        self.parser.add_argument("--pic_a_path", type=str, default='G:/swap_data/ID/elon-musk-hero-image.jpeg', help="Person who provides identity information")
+        self.parser.add_argument("--pic_b_path", type=str, default='./demo_file/multi_people.jpg', help="Person who provides information other than their identity")
+        self.parser.add_argument("--pic_specific_path", type=str, default='./crop_224/zrf.jpg', help="The specific person to be swapped")
+        self.parser.add_argument("--multisepcific_dir", type=str, default='./demo_file/multispecific', help="Dir for multi specific")
+        self.parser.add_argument("--video_path", type=str, default='G:/swap_data/video/HSB_Demo_Trim.mp4', help="path for the video to swap")
+        self.parser.add_argument("--temp_path", type=str, default='./temp_results', help="path to save temporarily images")
+        self.parser.add_argument("--output_path", type=str, default='./output/', help="results path")
+        self.parser.add_argument('--id_thres', type=float, default=0.03, help='how many test images to run')
+        self.parser.add_argument('--no_simswaplogo', action='store_true', help='Remove the watermark')
+        self.parser.add_argument('--use_mask', action='store_true', help='Use mask for better result')
+        self.parser.add_argument('--crop_size', type=int, default=224, help='Crop of size of input image')
+        
+        self.isTrain = False
\ No newline at end of file
diff --git a/modeling/SimSwap/options/train_options.py b/modeling/SimSwap/options/train_options.py
new file mode 100644
index 0000000..7a34357
--- /dev/null
+++ b/modeling/SimSwap/options/train_options.py
@@ -0,0 +1,39 @@
+from .base_options import BaseOptions
+
+class TrainOptions(BaseOptions):
+    def initialize(self):
+        BaseOptions.initialize(self)
+        # for displays
+        self.parser.add_argument('--display_freq', type=int, default=99, help='frequency of showing training results on screen')
+        self.parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console')
+        self.parser.add_argument('--save_latest_freq', type=int, default=10000, help='frequency of saving the latest results')
+        self.parser.add_argument('--save_epoch_freq', type=int, default=10000, help='frequency of saving checkpoints at the end of epochs')
+        self.parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/')
+        self.parser.add_argument('--debug', action='store_true', help='only do one epoch and displays at each iteration')
+
+        # for training
+        self.parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
+        self.parser.add_argument('--load_pretrain', type=str, default='', help='load the pretrained model from the specified location')
+        self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
+        self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
+        self.parser.add_argument('--niter', type=int, default=10000, help='# of iter at starting learning rate')
+        self.parser.add_argument('--niter_decay', type=int, default=10000, help='# of iter to linearly decay learning rate to zero')
+        self.parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam')
+        self.parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam')
+
+        # for discriminators        
+        self.parser.add_argument('--num_D', type=int, default=2, help='number of discriminators to use')
+        self.parser.add_argument('--n_layers_D', type=int, default=4, help='only used if which_model_netD==n_layers')
+        self.parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer')    
+        self.parser.add_argument('--lambda_feat', type=float, default=10.0, help='weight for feature matching loss')
+        self.parser.add_argument('--lambda_id', type=float, default=20.0, help='weight for id loss')
+        self.parser.add_argument('--lambda_rec', type=float, default=10.0, help='weight for reconstruction loss')
+        self.parser.add_argument('--lambda_GP', type=float, default=10.0, help='weight for gradient penalty loss')
+        self.parser.add_argument('--no_ganFeat_loss', action='store_true', help='if specified, do *not* use discriminator feature matching loss')
+        self.parser.add_argument('--no_vgg_loss', action='store_true', help='if specified, do *not* use VGG feature matching loss')        
+        self.parser.add_argument('--gan_mode', type=str, default='hinge', help='(ls|original|hinge)')
+        self.parser.add_argument('--pool_size', type=int, default=0, help='the size of image buffer that stores previously generated images')
+        self.parser.add_argument('--times_G', type=int, default=1,
+                                 help='time of training generator before traning discriminator')
+
+        self.isTrain = True
diff --git a/modeling/SimSwap/parsing_model/model.py b/modeling/SimSwap/parsing_model/model.py
new file mode 100644
index 0000000..d434836
--- /dev/null
+++ b/modeling/SimSwap/parsing_model/model.py
@@ -0,0 +1,283 @@
+#!/usr/bin/python
+# -*- encoding: utf-8 -*-
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+
+from parsing_model.resnet import Resnet18
+# from modules.bn import InPlaceABNSync as BatchNorm2d
+
+
+class ConvBNReLU(nn.Module):
+    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
+        super(ConvBNReLU, self).__init__()
+        self.conv = nn.Conv2d(in_chan,
+                out_chan,
+                kernel_size = ks,
+                stride = stride,
+                padding = padding,
+                bias = False)
+        self.bn = nn.BatchNorm2d(out_chan)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = F.relu(self.bn(x))
+        return x
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+class BiSeNetOutput(nn.Module):
+    def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
+        super(BiSeNetOutput, self).__init__()
+        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
+        self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.conv_out(x)
+        return x
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class AttentionRefinementModule(nn.Module):
+    def __init__(self, in_chan, out_chan, *args, **kwargs):
+        super(AttentionRefinementModule, self).__init__()
+        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
+        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
+        self.bn_atten = nn.BatchNorm2d(out_chan)
+        self.sigmoid_atten = nn.Sigmoid()
+        self.init_weight()
+
+    def forward(self, x):
+        feat = self.conv(x)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv_atten(atten)
+        atten = self.bn_atten(atten)
+        atten = self.sigmoid_atten(atten)
+        out = torch.mul(feat, atten)
+        return out
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+
+class ContextPath(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super(ContextPath, self).__init__()
+        self.resnet = Resnet18()
+        self.arm16 = AttentionRefinementModule(256, 128)
+        self.arm32 = AttentionRefinementModule(512, 128)
+        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
+
+        self.init_weight()
+
+    def forward(self, x):
+        H0, W0 = x.size()[2:]
+        feat8, feat16, feat32 = self.resnet(x)
+        H8, W8 = feat8.size()[2:]
+        H16, W16 = feat16.size()[2:]
+        H32, W32 = feat32.size()[2:]
+
+        avg = F.avg_pool2d(feat32, feat32.size()[2:])
+        avg = self.conv_avg(avg)
+        avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
+
+        feat32_arm = self.arm32(feat32)
+        feat32_sum = feat32_arm + avg_up
+        feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
+        feat32_up = self.conv_head32(feat32_up)
+
+        feat16_arm = self.arm16(feat16)
+        feat16_sum = feat16_arm + feat32_up
+        feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
+        feat16_up = self.conv_head16(feat16_up)
+
+        return feat8, feat16_up, feat32_up  # x8, x8, x16
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+### This is not used, since I replace this with the resnet feature with the same size
+class SpatialPath(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super(SpatialPath, self).__init__()
+        self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
+        self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
+        self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
+        self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
+        self.init_weight()
+
+    def forward(self, x):
+        feat = self.conv1(x)
+        feat = self.conv2(feat)
+        feat = self.conv3(feat)
+        feat = self.conv_out(feat)
+        return feat
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class FeatureFusionModule(nn.Module):
+    def __init__(self, in_chan, out_chan, *args, **kwargs):
+        super(FeatureFusionModule, self).__init__()
+        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
+        self.conv1 = nn.Conv2d(out_chan,
+                out_chan//4,
+                kernel_size = 1,
+                stride = 1,
+                padding = 0,
+                bias = False)
+        self.conv2 = nn.Conv2d(out_chan//4,
+                out_chan,
+                kernel_size = 1,
+                stride = 1,
+                padding = 0,
+                bias = False)
+        self.relu = nn.ReLU(inplace=True)
+        self.sigmoid = nn.Sigmoid()
+        self.init_weight()
+
+    def forward(self, fsp, fcp):
+        fcat = torch.cat([fsp, fcp], dim=1)
+        feat = self.convblk(fcat)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv1(atten)
+        atten = self.relu(atten)
+        atten = self.conv2(atten)
+        atten = self.sigmoid(atten)
+        feat_atten = torch.mul(feat, atten)
+        feat_out = feat_atten + feat
+        return feat_out
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class BiSeNet(nn.Module):
+    def __init__(self, n_classes, *args, **kwargs):
+        super(BiSeNet, self).__init__()
+        self.cp = ContextPath()
+        ## here self.sp is deleted
+        self.ffm = FeatureFusionModule(256, 256)
+        self.conv_out = BiSeNetOutput(256, 256, n_classes)
+        self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
+        self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
+        self.init_weight()
+
+    def forward(self, x):
+        H, W = x.size()[2:]
+        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # here return res3b1 feature
+        feat_sp = feat_res8  # use res3b1 feature to replace spatial path feature
+        feat_fuse = self.ffm(feat_sp, feat_cp8)
+
+        feat_out = self.conv_out(feat_fuse)
+        feat_out16 = self.conv_out16(feat_cp8)
+        feat_out32 = self.conv_out32(feat_cp16)
+
+        feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
+        feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
+        feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
+        return feat_out, feat_out16, feat_out32
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
+        for name, child in self.named_children():
+            child_wd_params, child_nowd_params = child.get_params()
+            if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
+                lr_mul_wd_params += child_wd_params
+                lr_mul_nowd_params += child_nowd_params
+            else:
+                wd_params += child_wd_params
+                nowd_params += child_nowd_params
+        return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
+
+
+if __name__ == "__main__":
+    net = BiSeNet(19)
+    net.cuda()
+    net.eval()
+    in_ten = torch.randn(16, 3, 640, 480).cuda()
+    out, out16, out32 = net(in_ten)
+    print(out.shape)
+
+    net.get_params()
diff --git a/modeling/SimSwap/parsing_model/resnet.py b/modeling/SimSwap/parsing_model/resnet.py
new file mode 100644
index 0000000..aa2bf95
--- /dev/null
+++ b/modeling/SimSwap/parsing_model/resnet.py
@@ -0,0 +1,109 @@
+#!/usr/bin/python
+# -*- encoding: utf-8 -*-
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.model_zoo as modelzoo
+
+# from modules.bn import InPlaceABNSync as BatchNorm2d
+
+resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    def __init__(self, in_chan, out_chan, stride=1):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(in_chan, out_chan, stride)
+        self.bn1 = nn.BatchNorm2d(out_chan)
+        self.conv2 = conv3x3(out_chan, out_chan)
+        self.bn2 = nn.BatchNorm2d(out_chan)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = None
+        if in_chan != out_chan or stride != 1:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(in_chan, out_chan,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(out_chan),
+                )
+
+    def forward(self, x):
+        residual = self.conv1(x)
+        residual = F.relu(self.bn1(residual))
+        residual = self.conv2(residual)
+        residual = self.bn2(residual)
+
+        shortcut = x
+        if self.downsample is not None:
+            shortcut = self.downsample(x)
+
+        out = shortcut + residual
+        out = self.relu(out)
+        return out
+
+
+def create_layer_basic(in_chan, out_chan, bnum, stride=1):
+    layers = [BasicBlock(in_chan, out_chan, stride=stride)]
+    for i in range(bnum-1):
+        layers.append(BasicBlock(out_chan, out_chan, stride=1))
+    return nn.Sequential(*layers)
+
+
+class Resnet18(nn.Module):
+    def __init__(self):
+        super(Resnet18, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
+        self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
+        self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
+        self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = F.relu(self.bn1(x))
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        feat8 = self.layer2(x) # 1/8
+        feat16 = self.layer3(feat8) # 1/16
+        feat32 = self.layer4(feat16) # 1/32
+        return feat8, feat16, feat32
+
+    def init_weight(self):
+        state_dict = modelzoo.load_url(resnet18_url)
+        self_state_dict = self.state_dict()
+        for k, v in state_dict.items():
+            if 'fc' in k: continue
+            self_state_dict.update({k: v})
+        self.load_state_dict(self_state_dict)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module,  nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+if __name__ == "__main__":
+    net = Resnet18()
+    x = torch.randn(16, 3, 224, 224)
+    out = net(x)
+    print(out[0].size())
+    print(out[1].size())
+    print(out[2].size())
+    net.get_params()
diff --git a/modeling/SimSwap/pg_modules/blocks.py b/modeling/SimSwap/pg_modules/blocks.py
new file mode 100644
index 0000000..78bd113
--- /dev/null
+++ b/modeling/SimSwap/pg_modules/blocks.py
@@ -0,0 +1,325 @@
+import functools
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.utils import spectral_norm
+
+
+### single layers
+
+
+def conv2d(*args, **kwargs):
+    return spectral_norm(nn.Conv2d(*args, **kwargs))
+
+
+def convTranspose2d(*args, **kwargs):
+    return spectral_norm(nn.ConvTranspose2d(*args, **kwargs))
+
+
+def embedding(*args, **kwargs):
+    return spectral_norm(nn.Embedding(*args, **kwargs))
+
+
+def linear(*args, **kwargs):
+    return spectral_norm(nn.Linear(*args, **kwargs))
+
+
+def NormLayer(c, mode='batch'):
+    if mode == 'group':
+        return nn.GroupNorm(c//2, c)
+    elif mode == 'batch':
+        return nn.BatchNorm2d(c)
+
+
+### Activations
+
+
+class GLU(nn.Module):
+    def forward(self, x):
+        nc = x.size(1)
+        assert nc % 2 == 0, 'channels dont divide 2!'
+        nc = int(nc/2)
+        return x[:, :nc] * torch.sigmoid(x[:, nc:])
+
+
+class Swish(nn.Module):
+    def forward(self, feat):
+        return feat * torch.sigmoid(feat)
+
+
+### Upblocks
+
+
+class InitLayer(nn.Module):
+    def __init__(self, nz, channel, sz=4):
+        super().__init__()
+
+        self.init = nn.Sequential(
+            convTranspose2d(nz, channel*2, sz, 1, 0, bias=False),
+            NormLayer(channel*2),
+            GLU(),
+        )
+
+    def forward(self, noise):
+        noise = noise.view(noise.shape[0], -1, 1, 1)
+        return self.init(noise)
+
+
+def UpBlockSmall(in_planes, out_planes):
+    block = nn.Sequential(
+        nn.Upsample(scale_factor=2, mode='nearest'),
+        conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False),
+        NormLayer(out_planes*2), GLU())
+    return block
+
+
+class UpBlockSmallCond(nn.Module):
+    def __init__(self, in_planes, out_planes, z_dim):
+        super().__init__()
+        self.in_planes = in_planes
+        self.out_planes = out_planes
+        self.up = nn.Upsample(scale_factor=2, mode='nearest')
+        self.conv = conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False)
+
+        which_bn = functools.partial(CCBN, which_linear=linear, input_size=z_dim)
+        self.bn = which_bn(2*out_planes)
+        self.act = GLU()
+
+    def forward(self, x, c):
+        x = self.up(x)
+        x = self.conv(x)
+        x = self.bn(x, c)
+        x = self.act(x)
+        return x
+
+
+def UpBlockBig(in_planes, out_planes):
+    block = nn.Sequential(
+        nn.Upsample(scale_factor=2, mode='nearest'),
+        conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False),
+        NoiseInjection(),
+        NormLayer(out_planes*2), GLU(),
+        conv2d(out_planes, out_planes*2, 3, 1, 1, bias=False),
+        NoiseInjection(),
+        NormLayer(out_planes*2), GLU()
+        )
+    return block
+
+
+class UpBlockBigCond(nn.Module):
+    def __init__(self, in_planes, out_planes, z_dim):
+        super().__init__()
+        self.in_planes = in_planes
+        self.out_planes = out_planes
+        self.up = nn.Upsample(scale_factor=2, mode='nearest')
+        self.conv1 = conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False)
+        self.conv2 = conv2d(out_planes, out_planes*2, 3, 1, 1, bias=False)
+
+        which_bn = functools.partial(CCBN, which_linear=linear, input_size=z_dim)
+        self.bn1 = which_bn(2*out_planes)
+        self.bn2 = which_bn(2*out_planes)
+        self.act = GLU()
+        self.noise = NoiseInjection()
+
+    def forward(self, x, c):
+        # block 1
+        x = self.up(x)
+        x = self.conv1(x)
+        x = self.noise(x)
+        x = self.bn1(x, c)
+        x = self.act(x)
+
+        # block 2
+        x = self.conv2(x)
+        x = self.noise(x)
+        x = self.bn2(x, c)
+        x = self.act(x)
+
+        return x
+
+
+class SEBlock(nn.Module):
+    def __init__(self, ch_in, ch_out):
+        super().__init__()
+        self.main = nn.Sequential(
+            nn.AdaptiveAvgPool2d(4),
+            conv2d(ch_in, ch_out, 4, 1, 0, bias=False),
+            Swish(),
+            conv2d(ch_out, ch_out, 1, 1, 0, bias=False),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, feat_small, feat_big):
+        return feat_big * self.main(feat_small)
+
+
+### Downblocks
+
+
+class SeparableConv2d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, bias=False):
+        super(SeparableConv2d, self).__init__()
+        self.depthwise = conv2d(in_channels, in_channels, kernel_size=kernel_size,
+            groups=in_channels, bias=bias, padding=1)
+        self.pointwise = conv2d(in_channels, out_channels,
+            kernel_size=1, bias=bias)
+
+    def forward(self, x):
+        out = self.depthwise(x)
+        out = self.pointwise(out)
+        return out
+
+
+class DownBlock(nn.Module):
+    def __init__(self, in_planes, out_planes, separable=False):
+        super().__init__()
+        if not separable:
+            self.main = nn.Sequential(
+                conv2d(in_planes, out_planes, 4, 2, 1),
+                NormLayer(out_planes),
+                nn.LeakyReLU(0.2, inplace=True),
+            )
+        else:
+            self.main = nn.Sequential(
+                SeparableConv2d(in_planes, out_planes, 3),
+                NormLayer(out_planes),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.AvgPool2d(2, 2),
+            )
+
+    def forward(self, feat):
+        return self.main(feat)
+
+
+class DownBlockPatch(nn.Module):
+    def __init__(self, in_planes, out_planes, separable=False):
+        super().__init__()
+        self.main = nn.Sequential(
+            DownBlock(in_planes, out_planes, separable),
+            conv2d(out_planes, out_planes, 1, 1, 0, bias=False),
+            NormLayer(out_planes),
+            nn.LeakyReLU(0.2, inplace=True),
+        )
+
+    def forward(self, feat):
+        return self.main(feat)
+
+
+### CSM
+
+
+class ResidualConvUnit(nn.Module):
+    def __init__(self, cin, activation, bn):
+        super().__init__()
+        self.conv = nn.Conv2d(cin, cin, kernel_size=3, stride=1, padding=1, bias=True)
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        return self.skip_add.add(self.conv(x), x)
+
+
+class FeatureFusionBlock(nn.Module):
+    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True, lowest=False):
+        super().__init__()
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.expand = expand
+        out_features = features
+        if self.expand==True:
+            out_features = features//2
+
+        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, *xs):
+        output = xs[0]
+
+        if len(xs) == 2:
+            output = self.skip_add.add(output, xs[1])
+
+        output = nn.functional.interpolate(
+            output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
+        )
+
+        output = self.out_conv(output)
+
+        return output
+
+
+### Misc
+
+
+class NoiseInjection(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.weight = nn.Parameter(torch.zeros(1), requires_grad=True)
+
+    def forward(self, feat, noise=None):
+        if noise is None:
+            batch, _, height, width = feat.shape
+            noise = torch.randn(batch, 1, height, width).to(feat.device)
+
+        return feat + self.weight * noise
+
+
+class CCBN(nn.Module):
+    ''' conditional batchnorm '''
+    def __init__(self, output_size, input_size, which_linear, eps=1e-5, momentum=0.1):
+        super().__init__()
+        self.output_size, self.input_size = output_size, input_size
+
+        # Prepare gain and bias layers
+        self.gain = which_linear(input_size, output_size)
+        self.bias = which_linear(input_size, output_size)
+
+        # epsilon to avoid dividing by 0
+        self.eps = eps
+        # Momentum
+        self.momentum = momentum
+
+        self.register_buffer('stored_mean', torch.zeros(output_size))
+        self.register_buffer('stored_var', torch.ones(output_size))
+
+    def forward(self, x, y):
+        # Calculate class-conditional gains and biases
+        gain = (1 + self.gain(y)).view(y.size(0), -1, 1, 1)
+        bias = self.bias(y).view(y.size(0), -1, 1, 1)
+        out = F.batch_norm(x, self.stored_mean, self.stored_var, None, None,
+                           self.training, 0.1, self.eps)
+        return out * gain + bias
+
+
+class Interpolate(nn.Module):
+    """Interpolation module."""
+
+    def __init__(self, size, mode='bilinear', align_corners=False):
+        """Init.
+        Args:
+            scale_factor (float): scaling
+            mode (str): interpolation mode
+        """
+        super(Interpolate, self).__init__()
+
+        self.interp = nn.functional.interpolate
+        self.size = size
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        """Forward pass.
+        Args:
+            x (tensor): input
+        Returns:
+            tensor: interpolated data
+        """
+
+        x = self.interp(
+            x,
+            size=self.size,
+            mode=self.mode,
+            align_corners=self.align_corners,
+        )
+
+        return x
diff --git a/modeling/SimSwap/pg_modules/diffaug.py b/modeling/SimSwap/pg_modules/diffaug.py
new file mode 100644
index 0000000..54020be
--- /dev/null
+++ b/modeling/SimSwap/pg_modules/diffaug.py
@@ -0,0 +1,76 @@
+# Differentiable Augmentation for Data-Efficient GAN Training
+# Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han
+# https://arxiv.org/pdf/2006.10738
+
+import torch
+import torch.nn.functional as F
+
+
+def DiffAugment(x, policy='', channels_first=True):
+    if policy:
+        if not channels_first:
+            x = x.permute(0, 3, 1, 2)
+        for p in policy.split(','):
+            for f in AUGMENT_FNS[p]:
+                x = f(x)
+        if not channels_first:
+            x = x.permute(0, 2, 3, 1)
+        x = x.contiguous()
+    return x
+
+
+def rand_brightness(x):
+    x = x + (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) - 0.5)
+    return x
+
+
+def rand_saturation(x):
+    x_mean = x.mean(dim=1, keepdim=True)
+    x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) * 2) + x_mean
+    return x
+
+
+def rand_contrast(x):
+    x_mean = x.mean(dim=[1, 2, 3], keepdim=True)
+    x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) + 0.5) + x_mean
+    return x
+
+
+def rand_translation(x, ratio=0.125):
+    shift_x, shift_y = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
+    translation_x = torch.randint(-shift_x, shift_x + 1, size=[x.size(0), 1, 1], device=x.device)
+    translation_y = torch.randint(-shift_y, shift_y + 1, size=[x.size(0), 1, 1], device=x.device)
+    grid_batch, grid_x, grid_y = torch.meshgrid(
+        torch.arange(x.size(0), dtype=torch.long, device=x.device),
+        torch.arange(x.size(2), dtype=torch.long, device=x.device),
+        torch.arange(x.size(3), dtype=torch.long, device=x.device),
+    )
+    grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1)
+    grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1)
+    x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0])
+    x = x_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x, grid_y].permute(0, 3, 1, 2)
+    return x
+
+
+def rand_cutout(x, ratio=0.2):
+    cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
+    offset_x = torch.randint(0, x.size(2) + (1 - cutout_size[0] % 2), size=[x.size(0), 1, 1], device=x.device)
+    offset_y = torch.randint(0, x.size(3) + (1 - cutout_size[1] % 2), size=[x.size(0), 1, 1], device=x.device)
+    grid_batch, grid_x, grid_y = torch.meshgrid(
+        torch.arange(x.size(0), dtype=torch.long, device=x.device),
+        torch.arange(cutout_size[0], dtype=torch.long, device=x.device),
+        torch.arange(cutout_size[1], dtype=torch.long, device=x.device),
+    )
+    grid_x = torch.clamp(grid_x + offset_x - cutout_size[0] // 2, min=0, max=x.size(2) - 1)
+    grid_y = torch.clamp(grid_y + offset_y - cutout_size[1] // 2, min=0, max=x.size(3) - 1)
+    mask = torch.ones(x.size(0), x.size(2), x.size(3), dtype=x.dtype, device=x.device)
+    mask[grid_batch, grid_x, grid_y] = 0
+    x = x * mask.unsqueeze(1)
+    return x
+
+
+AUGMENT_FNS = {
+    'color': [rand_brightness, rand_saturation, rand_contrast],
+    'translation': [rand_translation],
+    'cutout': [rand_cutout],
+}
diff --git a/modeling/SimSwap/pg_modules/projected_discriminator.py b/modeling/SimSwap/pg_modules/projected_discriminator.py
new file mode 100644
index 0000000..d0c879f
--- /dev/null
+++ b/modeling/SimSwap/pg_modules/projected_discriminator.py
@@ -0,0 +1,191 @@
+from functools import partial
+import numpy as np
+import torch
+import torch.nn as nn
+
+from pg_modules.blocks import DownBlock, DownBlockPatch, conv2d
+from pg_modules.projector import F_RandomProj
+from pg_modules.diffaug import DiffAugment
+
+
+class SingleDisc(nn.Module):
+    def __init__(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False):
+        super().__init__()
+        channel_dict = {4: 512, 8: 512, 16: 256, 32: 128, 64: 64, 128: 64,
+                        256: 32, 512: 16, 1024: 8}
+
+        # interpolate for start sz that are not powers of two
+        if start_sz not in channel_dict.keys():
+            sizes = np.array(list(channel_dict.keys()))
+            start_sz = sizes[np.argmin(abs(sizes - start_sz))]
+        self.start_sz = start_sz
+
+        # if given ndf, allocate all layers with the same ndf
+        if ndf is None:
+            nfc = channel_dict
+        else:
+            nfc = {k: ndf for k, v in channel_dict.items()}
+
+        # for feature map discriminators with nfc not in channel_dict
+        # this is the case for the pretrained backbone (midas.pretrained)
+        if nc is not None and head is None:
+            nfc[start_sz] = nc
+
+        layers = []
+
+        # Head if the initial input is the full modality
+        if head:
+            layers += [conv2d(nc, nfc[256], 3, 1, 1, bias=False),
+                       nn.LeakyReLU(0.2, inplace=True)]
+
+        # Down Blocks
+        DB = partial(DownBlockPatch, separable=separable) if patch else partial(DownBlock, separable=separable)
+        while start_sz > end_sz:
+            layers.append(DB(nfc[start_sz],  nfc[start_sz//2]))
+            start_sz = start_sz // 2
+
+        layers.append(conv2d(nfc[end_sz], 1, 4, 1, 0, bias=False))
+        self.main = nn.Sequential(*layers)
+
+    def forward(self, x, c):
+        return self.main(x)
+
+
+class SingleDiscCond(nn.Module):
+    def __init__(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False, c_dim=1000, cmap_dim=64, embedding_dim=128):
+        super().__init__()
+        self.cmap_dim = cmap_dim
+
+        # midas channels
+        channel_dict = {4: 512, 8: 512, 16: 256, 32: 128, 64: 64, 128: 64,
+                        256: 32, 512: 16, 1024: 8}
+
+        # interpolate for start sz that are not powers of two
+        if start_sz not in channel_dict.keys():
+            sizes = np.array(list(channel_dict.keys()))
+            start_sz = sizes[np.argmin(abs(sizes - start_sz))]
+        self.start_sz = start_sz
+
+        # if given ndf, allocate all layers with the same ndf
+        if ndf is None:
+            nfc = channel_dict
+        else:
+            nfc = {k: ndf for k, v in channel_dict.items()}
+
+        # for feature map discriminators with nfc not in channel_dict
+        # this is the case for the pretrained backbone (midas.pretrained)
+        if nc is not None and head is None:
+            nfc[start_sz] = nc
+
+        layers = []
+
+        # Head if the initial input is the full modality
+        if head:
+            layers += [conv2d(nc, nfc[256], 3, 1, 1, bias=False),
+                       nn.LeakyReLU(0.2, inplace=True)]
+
+        # Down Blocks
+        DB = partial(DownBlockPatch, separable=separable) if patch else partial(DownBlock, separable=separable)
+        while start_sz > end_sz:
+            layers.append(DB(nfc[start_sz],  nfc[start_sz//2]))
+            start_sz = start_sz // 2
+        self.main = nn.Sequential(*layers)
+
+        # additions for conditioning on class information
+        self.cls = conv2d(nfc[end_sz], self.cmap_dim, 4, 1, 0, bias=False)
+        self.embed = nn.Embedding(num_embeddings=c_dim, embedding_dim=embedding_dim)
+        self.embed_proj = nn.Sequential(
+            nn.Linear(self.embed.embedding_dim, self.cmap_dim),
+            nn.LeakyReLU(0.2, inplace=True),
+        )
+
+    def forward(self, x, c):
+        h = self.main(x)
+        out = self.cls(h)
+
+        # conditioning via projection
+        cmap = self.embed_proj(self.embed(c.argmax(1))).unsqueeze(-1).unsqueeze(-1)
+        out = (out * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
+
+        return out
+
+
+class MultiScaleD(nn.Module):
+    def __init__(
+        self,
+        channels,
+        resolutions,
+        num_discs=4,
+        proj_type=2,  # 0 = no projection, 1 = cross channel mixing, 2 = cross scale mixing
+        cond=0,
+        separable=False,
+        patch=False,
+        **kwargs,
+    ):
+        super().__init__()
+
+        assert num_discs in [1, 2, 3, 4]
+
+        # the first disc is on the lowest level of the backbone
+        self.disc_in_channels = channels[:num_discs]
+        self.disc_in_res = resolutions[:num_discs]
+        Disc = SingleDiscCond if cond else SingleDisc
+
+        mini_discs = []
+        for i, (cin, res) in enumerate(zip(self.disc_in_channels, self.disc_in_res)):
+            start_sz = res if not patch else 16
+            mini_discs += [str(i), Disc(nc=cin, start_sz=start_sz, end_sz=8, separable=separable, patch=patch)],
+        self.mini_discs = nn.ModuleDict(mini_discs)
+
+    def forward(self, features, c):
+        all_logits = []
+        for k, disc in self.mini_discs.items():
+            res = disc(features[k], c).view(features[k].size(0), -1)
+            all_logits.append(res)
+
+        all_logits = torch.cat(all_logits, dim=1)
+        return all_logits
+
+
+class ProjectedDiscriminator(torch.nn.Module):
+    def __init__(
+        self,
+        diffaug=True,
+        interp224=True,
+        backbone_kwargs={},
+        **kwargs
+    ):
+        super().__init__()
+        self.diffaug = diffaug
+        self.interp224 = interp224
+        self.feature_network = F_RandomProj(**backbone_kwargs)
+        self.discriminator = MultiScaleD(
+            channels=self.feature_network.CHANNELS,
+            resolutions=self.feature_network.RESOLUTIONS,
+            **backbone_kwargs,
+        )
+
+    def train(self, mode=True):
+        self.feature_network = self.feature_network.train(False)
+        self.discriminator = self.discriminator.train(mode)
+        return self
+
+    def eval(self):
+        return self.train(False)
+    
+    def get_feature(self, x):
+        features = self.feature_network(x, get_features=True)
+        return features
+
+    def forward(self, x, c):
+        # if self.diffaug:
+        #     x = DiffAugment(x, policy='color,translation,cutout')
+
+        # if self.interp224:
+        #     x = F.interpolate(x, 224, mode='bilinear', align_corners=False)
+
+        features,backbone_features = self.feature_network(x)
+        logits = self.discriminator(features, c)
+
+        return logits,backbone_features
+
diff --git a/modeling/SimSwap/pg_modules/projector.py b/modeling/SimSwap/pg_modules/projector.py
new file mode 100644
index 0000000..610a482
--- /dev/null
+++ b/modeling/SimSwap/pg_modules/projector.py
@@ -0,0 +1,158 @@
+import torch
+import torch.nn as nn
+import timm
+from pg_modules.blocks import FeatureFusionBlock
+
+
+def _make_scratch_ccm(scratch, in_channels, cout, expand=False):
+    # shapes
+    out_channels = [cout, cout*2, cout*4, cout*8] if expand else [cout]*4
+
+    scratch.layer0_ccm = nn.Conv2d(in_channels[0], out_channels[0], kernel_size=1, stride=1, padding=0, bias=True)
+    scratch.layer1_ccm = nn.Conv2d(in_channels[1], out_channels[1], kernel_size=1, stride=1, padding=0, bias=True)
+    scratch.layer2_ccm = nn.Conv2d(in_channels[2], out_channels[2], kernel_size=1, stride=1, padding=0, bias=True)
+    scratch.layer3_ccm = nn.Conv2d(in_channels[3], out_channels[3], kernel_size=1, stride=1, padding=0, bias=True)
+
+    scratch.CHANNELS = out_channels
+
+    return scratch
+
+
+def _make_scratch_csm(scratch, in_channels, cout, expand):
+    scratch.layer3_csm = FeatureFusionBlock(in_channels[3], nn.ReLU(False), expand=expand, lowest=True)
+    scratch.layer2_csm = FeatureFusionBlock(in_channels[2], nn.ReLU(False), expand=expand)
+    scratch.layer1_csm = FeatureFusionBlock(in_channels[1], nn.ReLU(False), expand=expand)
+    scratch.layer0_csm = FeatureFusionBlock(in_channels[0], nn.ReLU(False))
+
+    # last refinenet does not expand to save channels in higher dimensions
+    scratch.CHANNELS = [cout, cout, cout*2, cout*4] if expand else [cout]*4
+
+    return scratch
+
+
+def _make_efficientnet(model):
+    pretrained = nn.Module()
+    pretrained.layer0 = nn.Sequential(model.conv_stem, model.bn1, model.act1, *model.blocks[0:2])
+    pretrained.layer1 = nn.Sequential(*model.blocks[2:3])
+    pretrained.layer2 = nn.Sequential(*model.blocks[3:5])
+    pretrained.layer3 = nn.Sequential(*model.blocks[5:9])
+    return pretrained
+
+
+def calc_channels(pretrained, inp_res=224):
+    channels = []
+    tmp = torch.zeros(1, 3, inp_res, inp_res)
+
+    # forward pass
+    tmp = pretrained.layer0(tmp)
+    channels.append(tmp.shape[1])
+    tmp = pretrained.layer1(tmp)
+    channels.append(tmp.shape[1])
+    tmp = pretrained.layer2(tmp)
+    channels.append(tmp.shape[1])
+    tmp = pretrained.layer3(tmp)
+    channels.append(tmp.shape[1])
+
+    return channels
+
+
+def _make_projector(im_res, cout, proj_type, expand=False):
+    assert proj_type in [0, 1, 2], "Invalid projection type"
+
+    ### Build pretrained feature network
+    model = timm.create_model('tf_efficientnet_lite0', pretrained=True)
+    pretrained = _make_efficientnet(model)
+
+    # determine resolution of feature maps, this is later used to calculate the number
+    # of down blocks in the discriminators. Interestingly, the best results are achieved
+    # by fixing this to 256, ie., we use the same number of down blocks per discriminator
+    # independent of the dataset resolution
+    im_res = 256
+    pretrained.RESOLUTIONS = [im_res//4, im_res//8, im_res//16, im_res//32]
+    pretrained.CHANNELS = calc_channels(pretrained)
+
+    if proj_type == 0: return pretrained, None
+
+    ### Build CCM
+    scratch = nn.Module()
+    scratch = _make_scratch_ccm(scratch, in_channels=pretrained.CHANNELS, cout=cout, expand=expand)
+    pretrained.CHANNELS = scratch.CHANNELS
+
+    if proj_type == 1: return pretrained, scratch
+
+    ### build CSM
+    scratch = _make_scratch_csm(scratch, in_channels=scratch.CHANNELS, cout=cout, expand=expand)
+
+    # CSM upsamples x2 so the feature map resolution doubles
+    pretrained.RESOLUTIONS = [res*2 for res in pretrained.RESOLUTIONS]
+    pretrained.CHANNELS = scratch.CHANNELS
+
+    return pretrained, scratch
+
+
+class F_RandomProj(nn.Module):
+    def __init__(
+        self,
+        im_res=256,
+        cout=64,
+        expand=True,
+        proj_type=2,  # 0 = no projection, 1 = cross channel mixing, 2 = cross scale mixing
+        **kwargs,
+    ):
+        super().__init__()
+        self.proj_type = proj_type
+        self.cout = cout
+        self.expand = expand
+
+        # build pretrained feature network and random decoder (scratch)
+        self.pretrained, self.scratch = _make_projector(im_res=im_res, cout=self.cout, proj_type=self.proj_type, expand=self.expand)
+        self.CHANNELS = self.pretrained.CHANNELS
+        self.RESOLUTIONS = self.pretrained.RESOLUTIONS
+
+    def forward(self, x, get_features=False):
+        # predict feature maps
+        out0 = self.pretrained.layer0(x)
+        out1 = self.pretrained.layer1(out0)
+        out2 = self.pretrained.layer2(out1)
+        out3 = self.pretrained.layer3(out2)
+
+        # start enumerating at the lowest layer (this is where we put the first discriminator)
+        backbone_features = {
+            '0': out0,
+            '1': out1,
+            '2': out2,
+            '3': out3,
+        }
+        if get_features:
+            return backbone_features
+
+        if self.proj_type == 0: return backbone_features
+
+        out0_channel_mixed = self.scratch.layer0_ccm(backbone_features['0'])
+        out1_channel_mixed = self.scratch.layer1_ccm(backbone_features['1'])
+        out2_channel_mixed = self.scratch.layer2_ccm(backbone_features['2'])
+        out3_channel_mixed = self.scratch.layer3_ccm(backbone_features['3'])
+
+        out = {
+            '0': out0_channel_mixed,
+            '1': out1_channel_mixed,
+            '2': out2_channel_mixed,
+            '3': out3_channel_mixed,
+        }
+
+        if self.proj_type == 1: return out
+
+        # from bottom to top
+        out3_scale_mixed = self.scratch.layer3_csm(out3_channel_mixed)
+        out2_scale_mixed = self.scratch.layer2_csm(out3_scale_mixed, out2_channel_mixed)
+        out1_scale_mixed = self.scratch.layer1_csm(out2_scale_mixed, out1_channel_mixed)
+        out0_scale_mixed = self.scratch.layer0_csm(out1_scale_mixed, out0_channel_mixed)
+
+        out = {
+            '0': out0_scale_mixed,
+            '1': out1_scale_mixed,
+            '2': out2_scale_mixed,
+            '3': out3_scale_mixed,
+        }
+
+        return out, backbone_features
diff --git a/modeling/SimSwap/predict.py b/modeling/SimSwap/predict.py
new file mode 100644
index 0000000..3fe60e3
--- /dev/null
+++ b/modeling/SimSwap/predict.py
@@ -0,0 +1,101 @@
+import cog
+import tempfile
+from pathlib import Path
+import argparse
+import cv2
+import torch
+from PIL import Image
+import torch.nn.functional as F
+from torchvision import transforms
+from models.models import create_model
+from options.test_options import TestOptions
+from util.reverse2original import reverse2wholeimage
+from util.norm import SpecificNorm
+from test_wholeimage_swapmulti import _totensor
+from insightface_func.face_detect_crop_multi import Face_detect_crop as Face_detect_crop_multi
+from insightface_func.face_detect_crop_single import Face_detect_crop as Face_detect_crop_single
+
+
+class Predictor(cog.Predictor):
+    def setup(self):
+        self.transformer_Arcface = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+        ])
+
+    @cog.input("source", type=Path, help="source image")
+    @cog.input("target", type=Path, help="target image")
+    @cog.input("mode", type=str, options=['single', 'all'], default='all',
+               help="swap a single face (the one with highest confidence by face detection) or all faces in the target image")
+    def predict(self, source, target, mode='all'):
+
+        app = Face_detect_crop_multi(name='antelope', root='./insightface_func/models')
+
+        if mode == 'single':
+            app = Face_detect_crop_single(name='antelope', root='./insightface_func/models')
+
+        app.prepare(ctx_id=0, det_thresh=0.6, det_size=(640, 640))
+
+        options = TestOptions()
+        options.initialize()
+        opt = options.parser.parse_args(["--Arc_path", 'arcface_model/arcface_checkpoint.tar', "--pic_a_path", str(source),
+                                         "--pic_b_path", str(target), "--isTrain", False, "--no_simswaplogo"])
+
+        str_ids = opt.gpu_ids.split(',')
+        opt.gpu_ids = []
+        for str_id in str_ids:
+            id = int(str_id)
+            if id >= 0:
+                opt.gpu_ids.append(id)
+
+        # set gpu ids
+        if len(opt.gpu_ids) > 0:
+            torch.cuda.set_device(opt.gpu_ids[0])
+
+        torch.nn.Module.dump_patches = True
+        model = create_model(opt)
+        model.eval()
+
+        crop_size = opt.crop_size
+        spNorm = SpecificNorm()
+
+        with torch.no_grad():
+            pic_a = opt.pic_a_path
+            img_a_whole = cv2.imread(pic_a)
+            img_a_align_crop, _ = app.get(img_a_whole, crop_size)
+            img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0], cv2.COLOR_BGR2RGB))
+            img_a = self.transformer_Arcface(img_a_align_crop_pil)
+            img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
+
+            # convert numpy to tensor
+            img_id = img_id.cuda()
+
+            # create latent id
+            img_id_downsample = F.interpolate(img_id, size=(112,112))
+            latend_id = model.netArc(img_id_downsample)
+            latend_id = F.normalize(latend_id, p=2, dim=1)
+
+            ############## Forward Pass ######################
+
+            pic_b = opt.pic_b_path
+            img_b_whole = cv2.imread(pic_b)
+            img_b_align_crop_list, b_mat_list = app.get(img_b_whole, crop_size)
+
+            swap_result_list = []
+            b_align_crop_tenor_list = []
+
+            for b_align_crop in img_b_align_crop_list:
+                b_align_crop_tenor = _totensor(cv2.cvtColor(b_align_crop, cv2.COLOR_BGR2RGB))[None, ...].cuda()
+
+                swap_result = model(None, b_align_crop_tenor, latend_id, None, True)[0]
+                swap_result_list.append(swap_result)
+                b_align_crop_tenor_list.append(b_align_crop_tenor)
+
+            net = None
+
+            out_path = Path(tempfile.mkdtemp()) / "output.png"
+
+            reverse2wholeimage(b_align_crop_tenor_list, swap_result_list, b_mat_list, crop_size, img_b_whole, None,
+                               str(out_path), opt.no_simswaplogo,
+                               pasring_model=net, use_mask=opt.use_mask, norm=spNorm)
+            return out_path
diff --git a/modeling/SimSwap/simswaplogo/socialbook_logo.2020.357eed90add7705e54a8.svg b/modeling/SimSwap/simswaplogo/socialbook_logo.2020.357eed90add7705e54a8.svg
new file mode 100644
index 0000000..dd04f00
--- /dev/null
+++ b/modeling/SimSwap/simswaplogo/socialbook_logo.2020.357eed90add7705e54a8.svg
@@ -0,0 +1,64 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 24.1.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
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+<style type="text/css">
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diff --git a/modeling/SimSwap/test_one_image.py b/modeling/SimSwap/test_one_image.py
new file mode 100644
index 0000000..88e2f7f
--- /dev/null
+++ b/modeling/SimSwap/test_one_image.py
@@ -0,0 +1,86 @@
+
+import cv2
+import torch
+import fractions
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+from torchvision import transforms
+from models.models import create_model
+from options.test_options import TestOptions
+
+
+def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
+
+transformer = transforms.Compose([
+        transforms.ToTensor(),
+        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+transformer_Arcface = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+detransformer = transforms.Compose([
+        transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
+        transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
+    ])
+if __name__ == '__main__':
+    opt = TestOptions().parse()
+
+    start_epoch, epoch_iter = 1, 0
+
+    torch.nn.Module.dump_patches = True
+    model = create_model(opt)
+    model.eval()
+
+    with torch.no_grad():
+        
+        pic_a = opt.pic_a_path
+        img_a = Image.open(pic_a).convert('RGB')
+        img_a = transformer_Arcface(img_a)
+        img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
+
+        pic_b = opt.pic_b_path
+
+        img_b = Image.open(pic_b).convert('RGB')
+        img_b = transformer(img_b)
+        img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2])
+
+        # convert numpy to tensor
+        img_id = img_id.cuda()
+        img_att = img_att.cuda()
+
+        #create latent id
+        img_id_downsample = F.interpolate(img_id, size=(112,112))
+        latend_id = model.netArc(img_id_downsample)
+        latend_id = latend_id.detach().to('cpu')
+        latend_id = latend_id/np.linalg.norm(latend_id,axis=1,keepdims=True)
+        latend_id = latend_id.to('cuda')
+
+
+        ############## Forward Pass ######################
+        img_fake = model(img_id, img_att, latend_id, latend_id, True)
+
+
+        for i in range(img_id.shape[0]):
+            if i == 0:
+                row1 = img_id[i]
+                row2 = img_att[i]
+                row3 = img_fake[i]
+            else:
+                row1 = torch.cat([row1, img_id[i]], dim=2)
+                row2 = torch.cat([row2, img_att[i]], dim=2)
+                row3 = torch.cat([row3, img_fake[i]], dim=2)
+
+        #full = torch.cat([row1, row2, row3], dim=1).detach()
+        full = row3.detach()
+        full = full.permute(1, 2, 0)
+        output = full.to('cpu')
+        output = np.array(output)
+        output = output[..., ::-1]
+
+        output = output*255
+
+        cv2.imwrite(opt.output_path + 'result.jpg', output)
\ No newline at end of file
diff --git a/modeling/SimSwap/test_video_swap_multispecific.py b/modeling/SimSwap/test_video_swap_multispecific.py
new file mode 100644
index 0000000..fc9fd71
--- /dev/null
+++ b/modeling/SimSwap/test_video_swap_multispecific.py
@@ -0,0 +1,99 @@
+
+import cv2
+import torch
+import fractions
+from PIL import Image
+import torch.nn.functional as F
+from torchvision import transforms
+from models.models import create_model
+from options.test_options import TestOptions
+from insightface_func.face_detect_crop_multi import Face_detect_crop
+from util.videoswap_multispecific import video_swap
+import os
+import glob
+
+def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
+
+transformer = transforms.Compose([
+        transforms.ToTensor(),
+        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+transformer_Arcface = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+# detransformer = transforms.Compose([
+#         transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
+#         transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
+#     ])
+
+
+if __name__ == '__main__':
+    opt = TestOptions().parse()
+    pic_specific = opt.pic_specific_path
+    start_epoch, epoch_iter = 1, 0
+    crop_size = opt.crop_size
+
+    multisepcific_dir = opt.multisepcific_dir
+    torch.nn.Module.dump_patches = True
+    if crop_size == 512:
+        opt.which_epoch = 550000
+        opt.name = '512'
+        mode = 'ffhq'
+    else:
+        mode = 'None'
+    model = create_model(opt)
+    model.eval()
+
+
+    app = Face_detect_crop(name='antelope', root='./insightface_func/models')
+    app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640),mode=mode)
+
+    # The specific person to be swapped(source)
+
+    source_specific_id_nonorm_list = []
+    source_path = os.path.join(multisepcific_dir,'SRC_*')
+    source_specific_images_path = sorted(glob.glob(source_path))
+    with torch.no_grad():
+        for source_specific_image_path in source_specific_images_path:
+            specific_person_whole = cv2.imread(source_specific_image_path)
+            specific_person_align_crop, _ = app.get(specific_person_whole,crop_size)
+            specific_person_align_crop_pil = Image.fromarray(cv2.cvtColor(specific_person_align_crop[0],cv2.COLOR_BGR2RGB)) 
+            specific_person = transformer_Arcface(specific_person_align_crop_pil)
+            specific_person = specific_person.view(-1, specific_person.shape[0], specific_person.shape[1], specific_person.shape[2])
+            # convert numpy to tensor
+            specific_person = specific_person.cuda()
+            #create latent id
+            specific_person_downsample = F.interpolate(specific_person, size=(112,112))
+            specific_person_id_nonorm = model.netArc(specific_person_downsample)
+            source_specific_id_nonorm_list.append(specific_person_id_nonorm.clone())
+
+
+        # The person who provides id information (list)
+        target_id_norm_list = []
+        target_path = os.path.join(multisepcific_dir,'DST_*')
+        target_images_path = sorted(glob.glob(target_path))
+
+        for target_image_path in target_images_path:
+            img_a_whole = cv2.imread(target_image_path)
+            img_a_align_crop, _ = app.get(img_a_whole,crop_size)
+            img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) 
+            img_a = transformer_Arcface(img_a_align_crop_pil)
+            img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
+            # convert numpy to tensor
+            img_id = img_id.cuda()
+            #create latent id
+            img_id_downsample = F.interpolate(img_id, size=(112,112))
+            latend_id = model.netArc(img_id_downsample)
+            latend_id = F.normalize(latend_id, p=2, dim=1)
+            target_id_norm_list.append(latend_id.clone())
+
+        assert len(target_id_norm_list) == len(source_specific_id_nonorm_list), "The number of images in source and target directory must be same !!!"
+
+
+
+        video_swap(opt.video_path, target_id_norm_list,source_specific_id_nonorm_list, opt.id_thres, \
+            model, app, opt.output_path,temp_results_dir=opt.temp_path,no_simswaplogo=opt.no_simswaplogo,use_mask=opt.use_mask,crop_size=crop_size)
+
diff --git a/modeling/SimSwap/test_video_swapmulti.py b/modeling/SimSwap/test_video_swapmulti.py
new file mode 100644
index 0000000..37d7751
--- /dev/null
+++ b/modeling/SimSwap/test_video_swapmulti.py
@@ -0,0 +1,88 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 19:00:34
+Description: 
+'''
+
+import cv2
+import torch
+import fractions
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+from torchvision import transforms
+from models.models import create_model
+from options.test_options import TestOptions
+from insightface_func.face_detect_crop_multi import Face_detect_crop
+from util.videoswap import video_swap
+import os
+
+def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
+
+transformer = transforms.Compose([
+        transforms.ToTensor(),
+        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+transformer_Arcface = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+# detransformer = transforms.Compose([
+#         transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
+#         transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
+#     ])
+
+
+if __name__ == '__main__':
+    opt = TestOptions().parse()
+
+    start_epoch, epoch_iter = 1, 0
+    crop_size = opt.crop_size
+
+    torch.nn.Module.dump_patches = True
+
+    if crop_size == 512:
+        opt.which_epoch = 550000
+        opt.name = '512'
+        mode = 'ffhq'
+    else:
+        mode = 'None'
+    model = create_model(opt)
+    model.eval()
+
+    app = Face_detect_crop(name='antelope', root='./insightface_func/models')
+    app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640),mode = mode)
+
+    with torch.no_grad():
+        pic_a = opt.pic_a_path
+        # img_a = Image.open(pic_a).convert('RGB')
+        img_a_whole = cv2.imread(pic_a)
+        img_a_align_crop, _ = app.get(img_a_whole,crop_size)
+        img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) 
+        img_a = transformer_Arcface(img_a_align_crop_pil)
+        img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
+
+        # pic_b = opt.pic_b_path
+        # img_b_whole = cv2.imread(pic_b)
+        # img_b_align_crop, b_mat = app.get(img_b_whole,crop_size)
+        # img_b_align_crop_pil = Image.fromarray(cv2.cvtColor(img_b_align_crop,cv2.COLOR_BGR2RGB)) 
+        # img_b = transformer(img_b_align_crop_pil)
+        # img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2])
+
+        # convert numpy to tensor
+        img_id = img_id.cuda()
+        # img_att = img_att.cuda()
+
+        #create latent id
+        img_id_downsample = F.interpolate(img_id, size=(112,112))
+        latend_id = model.netArc(img_id_downsample)
+        latend_id = F.normalize(latend_id, p=2, dim=1)
+
+        video_swap(opt.video_path, latend_id, model, app, opt.output_path,temp_results_dir=opt.temp_path,\
+            no_simswaplogo=opt.no_simswaplogo,use_mask=opt.use_mask,crop_size=crop_size)
+
diff --git a/modeling/SimSwap/test_video_swapsingle.py b/modeling/SimSwap/test_video_swapsingle.py
new file mode 100644
index 0000000..23d26d3
--- /dev/null
+++ b/modeling/SimSwap/test_video_swapsingle.py
@@ -0,0 +1,86 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 19:00:38
+Description: 
+'''
+
+import cv2
+import torch
+import fractions
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+from torchvision import transforms
+from models.models import create_model
+from options.test_options import TestOptions
+from insightface_func.face_detect_crop_single import Face_detect_crop
+from util.videoswap import video_swap
+import os
+
+def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
+
+transformer = transforms.Compose([
+        transforms.ToTensor(),
+        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+transformer_Arcface = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+# detransformer = transforms.Compose([
+#         transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
+#         transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
+#     ])
+
+
+if __name__ == '__main__':
+    opt = TestOptions().parse()
+
+    start_epoch, epoch_iter = 1, 0
+    crop_size = opt.crop_size
+
+    torch.nn.Module.dump_patches = True
+    if crop_size == 512:
+        opt.which_epoch = 550000
+        opt.name = '512'
+        mode = 'ffhq'
+    else:
+        mode = 'None'
+    model = create_model(opt)
+    model.eval()
+
+
+    app = Face_detect_crop(name='antelope', root='./insightface_func/models')
+    app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640),mode=mode)
+    with torch.no_grad():
+        pic_a = opt.pic_a_path
+        # img_a = Image.open(pic_a).convert('RGB')
+        img_a_whole = cv2.imread(pic_a)
+        img_a_align_crop, _ = app.get(img_a_whole,crop_size)
+        img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) 
+        img_a = transformer_Arcface(img_a_align_crop_pil)
+        img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
+
+        # pic_b = opt.pic_b_path
+        # img_b_whole = cv2.imread(pic_b)
+        # img_b_align_crop, b_mat = app.get(img_b_whole,crop_size)
+        # img_b_align_crop_pil = Image.fromarray(cv2.cvtColor(img_b_align_crop,cv2.COLOR_BGR2RGB)) 
+        # img_b = transformer(img_b_align_crop_pil)
+        # img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2])
+
+        # convert numpy to tensor
+        img_id = img_id.cuda()
+        # img_att = img_att.cuda()
+
+        #create latent id
+        img_id_downsample = F.interpolate(img_id, size=(112,112))
+        latend_id = model.netArc(img_id_downsample)
+        latend_id = F.normalize(latend_id, p=2, dim=1)
+
+        video_swap(opt.video_path, latend_id, model, app, opt.output_path,temp_results_dir=opt.temp_path,\
+            no_simswaplogo=opt.no_simswaplogo,use_mask=opt.use_mask,crop_size=crop_size)
diff --git a/modeling/SimSwap/test_video_swapspecific.py b/modeling/SimSwap/test_video_swapspecific.py
new file mode 100644
index 0000000..3af643d
--- /dev/null
+++ b/modeling/SimSwap/test_video_swapspecific.py
@@ -0,0 +1,98 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 19:00:42
+Description: 
+'''
+
+import cv2
+import torch
+import fractions
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+from torchvision import transforms
+from models.models import create_model
+from options.test_options import TestOptions
+from insightface_func.face_detect_crop_multi import Face_detect_crop
+from util.videoswap_specific import video_swap
+import os
+
+def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
+
+transformer = transforms.Compose([
+        transforms.ToTensor(),
+        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+transformer_Arcface = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+# detransformer = transforms.Compose([
+#         transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
+#         transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
+#     ])
+
+
+if __name__ == '__main__':
+    opt = TestOptions().parse()
+    pic_specific = opt.pic_specific_path
+    start_epoch, epoch_iter = 1, 0
+    crop_size = opt.crop_size
+
+    torch.nn.Module.dump_patches = True
+    if crop_size == 512:
+        opt.which_epoch = 550000
+        opt.name = '512'
+        mode = 'ffhq'
+    else:
+        mode = 'None'
+    model = create_model(opt)
+    model.eval()
+
+
+    app = Face_detect_crop(name='antelope', root='./insightface_func/models')
+    app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640),mode=mode)
+    with torch.no_grad():
+        pic_a = opt.pic_a_path
+        # img_a = Image.open(pic_a).convert('RGB')
+        img_a_whole = cv2.imread(pic_a)
+        img_a_align_crop, _ = app.get(img_a_whole,crop_size)
+        img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) 
+        img_a = transformer_Arcface(img_a_align_crop_pil)
+        img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
+
+        # pic_b = opt.pic_b_path
+        # img_b_whole = cv2.imread(pic_b)
+        # img_b_align_crop, b_mat = app.get(img_b_whole,crop_size)
+        # img_b_align_crop_pil = Image.fromarray(cv2.cvtColor(img_b_align_crop,cv2.COLOR_BGR2RGB)) 
+        # img_b = transformer(img_b_align_crop_pil)
+        # img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2])
+
+        # convert numpy to tensor
+        img_id = img_id.cuda()
+        # img_att = img_att.cuda()
+
+        #create latent id
+        img_id_downsample = F.interpolate(img_id, size=(112,112))
+        latend_id = model.netArc(img_id_downsample)
+        latend_id = F.normalize(latend_id, p=2, dim=1)
+
+
+        # The specific person to be swapped
+        specific_person_whole = cv2.imread(pic_specific)
+        specific_person_align_crop, _ = app.get(specific_person_whole,crop_size)
+        specific_person_align_crop_pil = Image.fromarray(cv2.cvtColor(specific_person_align_crop[0],cv2.COLOR_BGR2RGB)) 
+        specific_person = transformer_Arcface(specific_person_align_crop_pil)
+        specific_person = specific_person.view(-1, specific_person.shape[0], specific_person.shape[1], specific_person.shape[2])
+        specific_person = specific_person.cuda()
+        specific_person_downsample = F.interpolate(specific_person, size=(112,112))
+        specific_person_id_nonorm = model.netArc(specific_person_downsample)
+
+        video_swap(opt.video_path, latend_id,specific_person_id_nonorm, opt.id_thres, \
+            model, app, opt.output_path,temp_results_dir=opt.temp_path,no_simswaplogo=opt.no_simswaplogo,use_mask=opt.use_mask,crop_size=crop_size)
+
diff --git a/modeling/SimSwap/test_wholeimage_swap_multispecific.py b/modeling/SimSwap/test_wholeimage_swap_multispecific.py
new file mode 100644
index 0000000..dca31af
--- /dev/null
+++ b/modeling/SimSwap/test_wholeimage_swap_multispecific.py
@@ -0,0 +1,176 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 19:19:22
+Description: 
+'''
+
+import cv2
+import torch
+import fractions
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+from torchvision import transforms
+from models.models import create_model
+from options.test_options import TestOptions
+from insightface_func.face_detect_crop_multi import Face_detect_crop
+from util.reverse2original import reverse2wholeimage
+import os
+from util.add_watermark import watermark_image
+import torch.nn as nn
+from util.norm import SpecificNorm
+import glob
+from parsing_model.model import BiSeNet
+
+def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
+
+transformer_Arcface = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+def _toarctensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+if __name__ == '__main__':
+    opt = TestOptions().parse()
+
+    start_epoch, epoch_iter = 1, 0
+    crop_size = opt.crop_size
+
+    multisepcific_dir = opt.multisepcific_dir
+
+    torch.nn.Module.dump_patches = True
+
+    if crop_size == 512:
+        opt.which_epoch = 550000
+        opt.name = '512'
+        mode = 'ffhq'
+    else:
+        mode = 'None'
+
+    logoclass = watermark_image('./simswaplogo/simswaplogo.png')
+    model = create_model(opt)
+    model.eval()
+    mse = torch.nn.MSELoss().cuda()
+
+    spNorm =SpecificNorm()
+
+
+    app = Face_detect_crop(name='antelope', root='./insightface_func/models')
+    app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640),mode = mode)
+
+    with torch.no_grad():
+        # The specific person to be swapped(source)
+
+        source_specific_id_nonorm_list = []
+        source_path = os.path.join(multisepcific_dir,'SRC_*')
+        source_specific_images_path = sorted(glob.glob(source_path))
+
+        for source_specific_image_path in source_specific_images_path:
+            specific_person_whole = cv2.imread(source_specific_image_path)
+            specific_person_align_crop, _ = app.get(specific_person_whole,crop_size)
+            specific_person_align_crop_pil = Image.fromarray(cv2.cvtColor(specific_person_align_crop[0],cv2.COLOR_BGR2RGB)) 
+            specific_person = transformer_Arcface(specific_person_align_crop_pil)
+            specific_person = specific_person.view(-1, specific_person.shape[0], specific_person.shape[1], specific_person.shape[2])
+            # convert numpy to tensor
+            specific_person = specific_person.cuda()
+            #create latent id
+            specific_person_downsample = F.interpolate(specific_person, size=(112,112))
+            specific_person_id_nonorm = model.netArc(specific_person_downsample)
+            source_specific_id_nonorm_list.append(specific_person_id_nonorm.clone())
+
+
+        # The person who provides id information (list)
+        target_id_norm_list = []
+        target_path = os.path.join(multisepcific_dir,'DST_*')
+        target_images_path = sorted(glob.glob(target_path))
+
+        for target_image_path in target_images_path:
+            img_a_whole = cv2.imread(target_image_path)
+            img_a_align_crop, _ = app.get(img_a_whole,crop_size)
+            img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) 
+            img_a = transformer_Arcface(img_a_align_crop_pil)
+            img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
+            # convert numpy to tensor
+            img_id = img_id.cuda()
+            #create latent id
+            img_id_downsample = F.interpolate(img_id, size=(112,112))
+            latend_id = model.netArc(img_id_downsample)
+            latend_id = F.normalize(latend_id, p=2, dim=1)
+            target_id_norm_list.append(latend_id.clone())
+
+        assert len(target_id_norm_list) == len(source_specific_id_nonorm_list), "The number of images in source and target directory must be same !!!"
+
+        ############## Forward Pass ######################
+
+        pic_b = opt.pic_b_path
+        img_b_whole = cv2.imread(pic_b)
+
+        img_b_align_crop_list, b_mat_list = app.get(img_b_whole,crop_size)
+        # detect_results = None
+        swap_result_list = []
+
+        id_compare_values = [] 
+        b_align_crop_tenor_list = []
+        for b_align_crop in img_b_align_crop_list:
+
+            b_align_crop_tenor = _totensor(cv2.cvtColor(b_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+            b_align_crop_tenor_arcnorm = spNorm(b_align_crop_tenor)
+            b_align_crop_tenor_arcnorm_downsample = F.interpolate(b_align_crop_tenor_arcnorm, size=(112,112))
+            b_align_crop_id_nonorm = model.netArc(b_align_crop_tenor_arcnorm_downsample)
+
+            id_compare_values.append([])
+            for source_specific_id_nonorm_tmp in source_specific_id_nonorm_list:
+                id_compare_values[-1].append(mse(b_align_crop_id_nonorm,source_specific_id_nonorm_tmp).detach().cpu().numpy())
+            b_align_crop_tenor_list.append(b_align_crop_tenor)
+
+        id_compare_values_array = np.array(id_compare_values).transpose(1,0)
+        min_indexs = np.argmin(id_compare_values_array,axis=0)
+        min_value = np.min(id_compare_values_array,axis=0)
+
+        swap_result_list = [] 
+        swap_result_matrix_list = []
+        swap_result_ori_pic_list = []
+
+        for tmp_index, min_index in enumerate(min_indexs):
+            if min_value[tmp_index] < opt.id_thres:
+                swap_result = model(None, b_align_crop_tenor_list[tmp_index], target_id_norm_list[min_index], None, True)[0]
+                swap_result_list.append(swap_result)
+                swap_result_matrix_list.append(b_mat_list[tmp_index])
+                swap_result_ori_pic_list.append(b_align_crop_tenor_list[tmp_index])
+            else:
+                pass
+
+        if len(swap_result_list) !=0:
+
+            if opt.use_mask:
+                n_classes = 19
+                net = BiSeNet(n_classes=n_classes)
+                net.cuda()
+                save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
+                net.load_state_dict(torch.load(save_pth))
+                net.eval()
+            else:
+                net =None
+        
+            reverse2wholeimage(swap_result_ori_pic_list, swap_result_list, swap_result_matrix_list, crop_size, img_b_whole, logoclass,\
+                os.path.join(opt.output_path, 'result_whole_swap_multispecific.jpg'), opt.no_simswaplogo,pasring_model =net,use_mask=opt.use_mask, norm = spNorm)
+
+            print(' ')
+
+            print('************ Done ! ************')
+        
+        else:
+            print('The people you specified are not found on the picture: {}'.format(pic_b))
diff --git a/modeling/SimSwap/test_wholeimage_swapmulti.py b/modeling/SimSwap/test_wholeimage_swapmulti.py
new file mode 100644
index 0000000..1938057
--- /dev/null
+++ b/modeling/SimSwap/test_wholeimage_swapmulti.py
@@ -0,0 +1,110 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 19:19:26
+Description: 
+'''
+
+import cv2
+import torch
+import fractions
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+from torchvision import transforms
+from models.models import create_model
+from options.test_options import TestOptions
+from insightface_func.face_detect_crop_multi import Face_detect_crop
+from util.reverse2original import reverse2wholeimage
+import os
+from util.add_watermark import watermark_image
+from util.norm import SpecificNorm
+from parsing_model.model import BiSeNet
+
+def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
+
+transformer_Arcface = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+if __name__ == '__main__':
+    opt = TestOptions().parse()
+
+    start_epoch, epoch_iter = 1, 0
+    crop_size = opt.crop_size
+
+    torch.nn.Module.dump_patches = True
+    if crop_size == 512:
+        opt.which_epoch = 550000
+        opt.name = '512'
+        mode = 'ffhq'
+    else:
+        mode = 'None'
+    logoclass = watermark_image('./simswaplogo/simswaplogo.png')
+    model = create_model(opt)
+    model.eval()
+    spNorm =SpecificNorm()
+
+    app = Face_detect_crop(name='antelope', root='./insightface_func/models')
+    app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640),mode=mode)
+
+    with torch.no_grad():
+        pic_a = opt.pic_a_path
+
+        img_a_whole = cv2.imread(pic_a)
+        img_a_align_crop, _ = app.get(img_a_whole,crop_size)
+        img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) 
+        img_a = transformer_Arcface(img_a_align_crop_pil)
+        img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
+
+        # convert numpy to tensor
+        img_id = img_id.cuda()
+
+        #create latent id
+        img_id_downsample = F.interpolate(img_id, size=(112,112))
+        latend_id = model.netArc(img_id_downsample)
+        latend_id = F.normalize(latend_id, p=2, dim=1)
+
+
+        ############## Forward Pass ######################
+
+        pic_b = opt.pic_b_path
+        img_b_whole = cv2.imread(pic_b)
+
+        img_b_align_crop_list, b_mat_list = app.get(img_b_whole,crop_size)
+        # detect_results = None
+        swap_result_list = []
+        b_align_crop_tenor_list = []
+
+        for b_align_crop in img_b_align_crop_list:
+
+            b_align_crop_tenor = _totensor(cv2.cvtColor(b_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+            swap_result = model(None, b_align_crop_tenor, latend_id, None, True)[0]
+            swap_result_list.append(swap_result)
+            b_align_crop_tenor_list.append(b_align_crop_tenor)
+
+
+        if opt.use_mask:
+            n_classes = 19
+            net = BiSeNet(n_classes=n_classes)
+            net.cuda()
+            save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
+            net.load_state_dict(torch.load(save_pth))
+            net.eval()
+        else:
+            net =None
+
+        reverse2wholeimage(b_align_crop_tenor_list,swap_result_list, b_mat_list, crop_size, img_b_whole, logoclass, \
+            os.path.join(opt.output_path, 'result_whole_swapmulti.jpg'),opt.no_simswaplogo,pasring_model =net,use_mask=opt.use_mask, norm = spNorm)
+        print(' ')
+
+        print('************ Done ! ************')
diff --git a/modeling/SimSwap/test_wholeimage_swapsingle.py b/modeling/SimSwap/test_wholeimage_swapsingle.py
new file mode 100644
index 0000000..e35d843
--- /dev/null
+++ b/modeling/SimSwap/test_wholeimage_swapsingle.py
@@ -0,0 +1,110 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 19:19:43
+Description: 
+'''
+
+import cv2
+import torch
+import fractions
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+from torchvision import transforms
+from models.models import create_model
+from options.test_options import TestOptions
+from insightface_func.face_detect_crop_single import Face_detect_crop
+from util.reverse2original import reverse2wholeimage
+import os
+from util.add_watermark import watermark_image
+from util.norm import SpecificNorm
+from parsing_model.model import BiSeNet
+
+def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
+
+transformer_Arcface = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+if __name__ == '__main__':
+    opt = TestOptions().parse()
+
+    start_epoch, epoch_iter = 1, 0
+    crop_size = opt.crop_size
+
+    torch.nn.Module.dump_patches = True
+    if crop_size == 512:
+        opt.which_epoch = 550000
+        opt.name = '512'
+        mode = 'ffhq'
+    else:
+        mode = 'None'
+    logoclass = watermark_image('./simswaplogo/simswaplogo.png')
+    model = create_model(opt)
+    model.eval()
+
+    spNorm =SpecificNorm()
+    app = Face_detect_crop(name='antelope', root='./insightface_func/models')
+    app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640),mode=mode)
+
+    with torch.no_grad():
+        pic_a = opt.pic_a_path
+
+        img_a_whole = cv2.imread(pic_a)
+        img_a_align_crop, _ = app.get(img_a_whole,crop_size)
+        img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) 
+        img_a = transformer_Arcface(img_a_align_crop_pil)
+        img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
+
+        # convert numpy to tensor
+        img_id = img_id.cuda()
+
+        #create latent id
+        img_id_downsample = F.interpolate(img_id, size=(112,112))
+        latend_id = model.netArc(img_id_downsample)
+        latend_id = F.normalize(latend_id, p=2, dim=1)
+
+
+        ############## Forward Pass ######################
+
+        pic_b = opt.pic_b_path
+        img_b_whole = cv2.imread(pic_b)
+
+        img_b_align_crop_list, b_mat_list = app.get(img_b_whole,crop_size)
+        # detect_results = None
+        swap_result_list = []
+
+        b_align_crop_tenor_list = []
+
+        for b_align_crop in img_b_align_crop_list:
+
+            b_align_crop_tenor = _totensor(cv2.cvtColor(b_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+            swap_result = model(None, b_align_crop_tenor, latend_id, None, True)[0]
+            swap_result_list.append(swap_result)
+            b_align_crop_tenor_list.append(b_align_crop_tenor)
+
+        if opt.use_mask:
+            n_classes = 19
+            net = BiSeNet(n_classes=n_classes)
+            net.cuda()
+            save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
+            net.load_state_dict(torch.load(save_pth))
+            net.eval()
+        else:
+            net =None
+
+        reverse2wholeimage(b_align_crop_tenor_list, swap_result_list, b_mat_list, crop_size, img_b_whole, logoclass, \
+            os.path.join(opt.output_path, 'result_whole_swapsingle.jpg'), opt.no_simswaplogo,pasring_model =net,use_mask=opt.use_mask, norm = spNorm)
+
+        print(' ')
+
+        print('************ Done ! ************')
diff --git a/modeling/SimSwap/test_wholeimage_swapspecific.py b/modeling/SimSwap/test_wholeimage_swapspecific.py
new file mode 100644
index 0000000..b9167f8
--- /dev/null
+++ b/modeling/SimSwap/test_wholeimage_swapspecific.py
@@ -0,0 +1,150 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 19:19:47
+Description: 
+'''
+
+import cv2
+import torch
+import fractions
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+from torchvision import transforms
+from models.models import create_model
+from options.test_options import TestOptions
+from insightface_func.face_detect_crop_multi import Face_detect_crop
+from util.reverse2original import reverse2wholeimage
+import os
+from util.add_watermark import watermark_image
+import torch.nn as nn
+from util.norm import SpecificNorm
+from parsing_model.model import BiSeNet
+
+def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
+
+transformer_Arcface = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+def _toarctensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+if __name__ == '__main__':
+    opt = TestOptions().parse()
+
+    start_epoch, epoch_iter = 1, 0
+    crop_size = opt.crop_size
+
+    torch.nn.Module.dump_patches = True
+    if crop_size == 512:
+        opt.which_epoch = 550000
+        opt.name = '512'
+        mode = 'ffhq'
+    else:
+        mode = 'None'
+    logoclass = watermark_image('./simswaplogo/simswaplogo.png')
+    model = create_model(opt)
+    model.eval()
+    mse = torch.nn.MSELoss().cuda()
+
+    spNorm =SpecificNorm()
+
+
+    app = Face_detect_crop(name='antelope', root='./insightface_func/models')
+    app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640),mode=mode)
+
+    pic_a = opt.pic_a_path
+    pic_specific = opt.pic_specific_path
+
+    # The person who provides id information 
+    img_a_whole = cv2.imread(pic_a)
+    img_a_align_crop, _ = app.get(img_a_whole,crop_size)
+    img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) 
+    img_a = transformer_Arcface(img_a_align_crop_pil)
+    img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
+
+    # convert numpy to tensor
+    img_id = img_id.cuda()
+
+    #create latent id
+    img_id_downsample = F.interpolate(img_id, size=(112,112))
+    latend_id = model.netArc(img_id_downsample)
+    latend_id = F.normalize(latend_id, p=2, dim=1)
+
+
+    # The specific person to be swapped
+    specific_person_whole = cv2.imread(pic_specific)
+    specific_person_align_crop, _ = app.get(specific_person_whole,crop_size)
+    specific_person_align_crop_pil = Image.fromarray(cv2.cvtColor(specific_person_align_crop[0],cv2.COLOR_BGR2RGB)) 
+    specific_person = transformer_Arcface(specific_person_align_crop_pil)
+    specific_person = specific_person.view(-1, specific_person.shape[0], specific_person.shape[1], specific_person.shape[2])
+
+    # convert numpy to tensor
+    specific_person = specific_person.cuda()
+
+    #create latent id
+    specific_person_downsample = F.interpolate(specific_person, size=(112,112))
+    specific_person_id_nonorm = model.netArc(specific_person_downsample)
+    # specific_person_id_norm = F.normalize(specific_person_id_nonorm, p=2, dim=1)
+
+    ############## Forward Pass ######################
+
+    pic_b = opt.pic_b_path
+    img_b_whole = cv2.imread(pic_b)
+
+    img_b_align_crop_list, b_mat_list = app.get(img_b_whole,crop_size)
+    # detect_results = None
+    swap_result_list = []
+
+    id_compare_values = [] 
+    b_align_crop_tenor_list = []
+    for b_align_crop in img_b_align_crop_list:
+
+        b_align_crop_tenor = _totensor(cv2.cvtColor(b_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+        b_align_crop_tenor_arcnorm = spNorm(b_align_crop_tenor)
+        b_align_crop_tenor_arcnorm_downsample = F.interpolate(b_align_crop_tenor_arcnorm, size=(112,112))
+        b_align_crop_id_nonorm = model.netArc(b_align_crop_tenor_arcnorm_downsample)
+
+        id_compare_values.append(mse(b_align_crop_id_nonorm,specific_person_id_nonorm).detach().cpu().numpy())
+        b_align_crop_tenor_list.append(b_align_crop_tenor)
+
+    id_compare_values_array = np.array(id_compare_values)
+    min_index = np.argmin(id_compare_values_array)
+    min_value = id_compare_values_array[min_index]
+
+    if opt.use_mask:
+        n_classes = 19
+        net = BiSeNet(n_classes=n_classes)
+        net.cuda()
+        save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
+        net.load_state_dict(torch.load(save_pth))
+        net.eval()
+    else:
+        net =None
+
+    if min_value < opt.id_thres:
+
+        swap_result = model(None, b_align_crop_tenor_list[min_index], latend_id, None, True)[0]
+
+        reverse2wholeimage([b_align_crop_tenor_list[min_index]], [swap_result], [b_mat_list[min_index]], crop_size, img_b_whole, logoclass, \
+            os.path.join(opt.output_path, 'result_whole_swapspecific.jpg'), opt.no_simswaplogo,pasring_model =net,use_mask=opt.use_mask, norm = spNorm)
+
+        print(' ')
+
+        print('************ Done ! ************')
+
+    else:
+        print('The person you specified is not found on the picture: {}'.format(pic_b))
diff --git a/modeling/SimSwap/train.py b/modeling/SimSwap/train.py
new file mode 100644
index 0000000..ebe5fde
--- /dev/null
+++ b/modeling/SimSwap/train.py
@@ -0,0 +1,290 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: train.py
+# Created Date: Monday December 27th 2021
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Friday, 22nd April 2022 10:49:26 am
+# Modified By: Chen Xuanhong
+# Copyright (c) 2021 Shanghai Jiao Tong University
+#############################################################
+
+import os
+import time
+import random
+import argparse
+import numpy as np
+
+import torch
+import torch.nn.functional as F
+from torch.backends import cudnn
+import torch.utils.tensorboard as tensorboard
+
+from util import util
+from util.plot import plot_batch
+
+from models.projected_model import fsModel
+from data.data_loader_Swapping import GetLoader
+
+def str2bool(v):
+    return v.lower() in ('true')
+
+class TrainOptions:
+    def __init__(self):
+        self.parser = argparse.ArgumentParser()
+        self.initialized = False
+        
+    def initialize(self):
+        self.parser.add_argument('--name', type=str, default='simswap', help='name of the experiment. It decides where to store samples and models')
+        self.parser.add_argument('--gpu_ids', default='0')
+        self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
+        self.parser.add_argument('--isTrain', type=str2bool, default='True')
+
+        # input/output sizes       
+        self.parser.add_argument('--batchSize', type=int, default=4, help='input batch size')       
+
+        # for displays
+        self.parser.add_argument('--use_tensorboard', type=str2bool, default='False')
+
+        # for training
+        self.parser.add_argument('--dataset', type=str, default="/path/to/VGGFace2", help='path to the face swapping dataset')
+        self.parser.add_argument('--continue_train', type=str2bool, default='False', help='continue training: load the latest model')
+        self.parser.add_argument('--load_pretrain', type=str, default='./checkpoints/simswap224_test', help='load the pretrained model from the specified location')
+        self.parser.add_argument('--which_epoch', type=str, default='10000', help='which epoch to load? set to latest to use latest cached model')
+        self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
+        self.parser.add_argument('--niter', type=int, default=10000, help='# of iter at starting learning rate')
+        self.parser.add_argument('--niter_decay', type=int, default=10000, help='# of iter to linearly decay learning rate to zero')
+        self.parser.add_argument('--beta1', type=float, default=0.0, help='momentum term of adam')
+        self.parser.add_argument('--lr', type=float, default=0.0004, help='initial learning rate for adam')
+        self.parser.add_argument('--Gdeep', type=str2bool, default='False')
+
+        # for discriminators         
+        self.parser.add_argument('--lambda_feat', type=float, default=10.0, help='weight for feature matching loss')
+        self.parser.add_argument('--lambda_id', type=float, default=30.0, help='weight for id loss')
+        self.parser.add_argument('--lambda_rec', type=float, default=10.0, help='weight for reconstruction loss') 
+
+        self.parser.add_argument("--Arc_path", type=str, default='arcface_model/arcface_checkpoint.tar', help="run ONNX model via TRT")
+        self.parser.add_argument("--total_step", type=int, default=1000000, help='total training step')
+        self.parser.add_argument("--log_frep", type=int, default=200, help='frequence for printing log information')
+        self.parser.add_argument("--sample_freq", type=int, default=1000, help='frequence for sampling')
+        self.parser.add_argument("--model_freq", type=int, default=10000, help='frequence for saving the model')
+
+        
+
+
+        self.isTrain = True
+        
+    def parse(self, save=True):
+        if not self.initialized:
+            self.initialize()
+        self.opt = self.parser.parse_args()
+        self.opt.isTrain = self.isTrain   # train or test
+
+        args = vars(self.opt)
+
+        print('------------ Options -------------')
+        for k, v in sorted(args.items()):
+            print('%s: %s' % (str(k), str(v)))
+        print('-------------- End ----------------')
+
+        # save to the disk
+        if self.opt.isTrain:
+            expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name)
+            util.mkdirs(expr_dir)
+            if save and not self.opt.continue_train:
+                file_name = os.path.join(expr_dir, 'opt.txt')
+                with open(file_name, 'wt') as opt_file:
+                    opt_file.write('------------ Options -------------\n')
+                    for k, v in sorted(args.items()):
+                        opt_file.write('%s: %s\n' % (str(k), str(v)))
+                    opt_file.write('-------------- End ----------------\n')
+        return self.opt
+
+
+if __name__ == '__main__':
+
+    opt         = TrainOptions().parse()
+    iter_path   = os.path.join(opt.checkpoints_dir, opt.name, 'iter.txt')
+
+    sample_path = os.path.join(opt.checkpoints_dir, opt.name, 'samples')
+
+    if not os.path.exists(sample_path):
+        os.makedirs(sample_path)
+    
+    log_path = os.path.join(opt.checkpoints_dir, opt.name, 'summary')
+
+    if not os.path.exists(log_path):
+        os.makedirs(log_path)
+
+    if opt.continue_train:
+        try:
+            start_epoch, epoch_iter = np.loadtxt(iter_path , delimiter=',', dtype=int)
+        except:
+            start_epoch, epoch_iter = 1, 0
+        print('Resuming from epoch %d at iteration %d' % (start_epoch, epoch_iter))        
+    else:    
+        start_epoch, epoch_iter = 1, 0
+
+    os.environ['CUDA_VISIBLE_DEVICES'] = str(opt.gpu_ids)
+    print("GPU used : ", str(opt.gpu_ids))
+
+    
+    cudnn.benchmark = True
+
+    
+
+    model = fsModel()
+
+    model.initialize(opt)
+
+    #####################################################
+    if opt.use_tensorboard:
+        tensorboard_writer  = tensorboard.SummaryWriter(log_path)
+        logger              = tensorboard_writer
+        
+    log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
+
+    with open(log_name, "a") as log_file:
+        now = time.strftime("%c")
+        log_file.write('================ Training Loss (%s) ================\n' % now)
+
+    optimizer_G, optimizer_D = model.optimizer_G, model.optimizer_D
+
+    loss_avg        = 0
+    refresh_count   = 0
+    imagenet_std    = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1)
+    imagenet_mean   = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1)
+
+    train_loader    = GetLoader(opt.dataset,opt.batchSize,8,1234)
+
+    randindex = [i for i in range(opt.batchSize)]
+    random.shuffle(randindex)
+
+    if not opt.continue_train:
+        start   = 0
+    else:
+        start   = int(opt.which_epoch)
+    total_step  = opt.total_step
+    import datetime
+    print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    
+    from util.logo_class import logo_class
+    logo_class.print_start_training()
+    model.netD.feature_network.requires_grad_(False)
+
+    # Training Cycle
+    for step in range(start, total_step):
+        model.netG.train()
+        for interval in range(2):
+            random.shuffle(randindex)
+            src_image1, src_image2  = train_loader.next()
+            
+            if step%2 == 0:
+                img_id = src_image2
+            else:
+                img_id = src_image2[randindex]
+
+            img_id_112      = F.interpolate(img_id,size=(112,112), mode='bicubic')
+            latent_id       = model.netArc(img_id_112)
+            latent_id       = F.normalize(latent_id, p=2, dim=1)
+            if interval:
+                
+                img_fake        = model.netG(src_image1, latent_id)
+                gen_logits,_    = model.netD(img_fake.detach(), None)
+                loss_Dgen       = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean()
+
+                real_logits,_   = model.netD(src_image2,None)
+                loss_Dreal      = (F.relu(torch.ones_like(real_logits) - real_logits)).mean()
+
+                loss_D          = loss_Dgen + loss_Dreal
+                optimizer_D.zero_grad()
+                loss_D.backward()
+                optimizer_D.step()
+            else:
+                
+                # model.netD.requires_grad_(True)
+                img_fake        = model.netG(src_image1, latent_id)
+                # G loss
+                gen_logits,feat = model.netD(img_fake, None)
+                
+                loss_Gmain      = (-gen_logits).mean()
+                img_fake_down   = F.interpolate(img_fake, size=(112,112), mode='bicubic')
+                latent_fake     = model.netArc(img_fake_down)
+                latent_fake     = F.normalize(latent_fake, p=2, dim=1)
+                loss_G_ID       = (1 - model.cosin_metric(latent_fake, latent_id)).mean()
+                real_feat       = model.netD.get_feature(src_image1)
+                feat_match_loss = model.criterionFeat(feat["3"],real_feat["3"]) 
+                loss_G          = loss_Gmain + loss_G_ID * opt.lambda_id + feat_match_loss * opt.lambda_feat
+                
+
+                if step%2 == 0:
+                    #G_Rec
+                    loss_G_Rec  = model.criterionRec(img_fake, src_image1) * opt.lambda_rec
+                    loss_G      += loss_G_Rec
+
+                optimizer_G.zero_grad()
+                loss_G.backward()
+                optimizer_G.step()
+                
+
+        ############## Display results and errors ##########
+        ### print out errors
+        # Print out log info
+        if (step + 1) % opt.log_frep == 0:
+            # errors = {k: v.data.item() if not isinstance(v, int) else v for k, v in loss_dict.items()}
+            errors = {
+                "G_Loss":loss_Gmain.item(),
+                "G_ID":loss_G_ID.item(),
+                "G_Rec":loss_G_Rec.item(),
+                "G_feat_match":feat_match_loss.item(),
+                "D_fake":loss_Dgen.item(),
+                "D_real":loss_Dreal.item(),
+                "D_loss":loss_D.item()
+            }
+            if opt.use_tensorboard:
+                for tag, value in errors.items():
+                    logger.add_scalar(tag, value, step)
+            message = '( step: %d, ) ' % (step)
+            for k, v in errors.items():
+                message += '%s: %.3f ' % (k, v)
+
+            print(message)
+            with open(log_name, "a") as log_file:
+                log_file.write('%s\n' % message)
+
+        ### display output images
+        if (step + 1) % opt.sample_freq == 0:
+            model.netG.eval()
+            with torch.no_grad():
+                imgs        = list()
+                zero_img    = (torch.zeros_like(src_image1[0,...]))
+                imgs.append(zero_img.cpu().numpy())
+                save_img    = ((src_image1.cpu())* imagenet_std + imagenet_mean).numpy()
+                for r in range(opt.batchSize):
+                    imgs.append(save_img[r,...])
+                arcface_112     = F.interpolate(src_image2,size=(112,112), mode='bicubic')
+                id_vector_src1  = model.netArc(arcface_112)
+                id_vector_src1  = F.normalize(id_vector_src1, p=2, dim=1)
+
+                for i in range(opt.batchSize):
+                    
+                    imgs.append(save_img[i,...])
+                    image_infer = src_image1[i, ...].repeat(opt.batchSize, 1, 1, 1)
+                    img_fake    = model.netG(image_infer, id_vector_src1).cpu()
+                    
+                    img_fake    = img_fake * imagenet_std
+                    img_fake    = img_fake + imagenet_mean
+                    img_fake    = img_fake.numpy()
+                    for j in range(opt.batchSize):
+                        imgs.append(img_fake[j,...])
+                print("Save test data")
+                imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1)
+                plot_batch(imgs, os.path.join(sample_path, 'step_'+str(step+1)+'.jpg'))
+
+        ### save latest model
+        if (step+1) % opt.model_freq==0:
+            print('saving the latest model (steps %d)' % (step+1))
+            model.save(step+1)            
+            np.savetxt(iter_path, (step+1, total_step), delimiter=',', fmt='%d')
+    wandb.finish()
\ No newline at end of file
diff --git a/modeling/SimSwap/util/add_watermark.py b/modeling/SimSwap/util/add_watermark.py
new file mode 100644
index 0000000..a4c5d3d
--- /dev/null
+++ b/modeling/SimSwap/util/add_watermark.py
@@ -0,0 +1,118 @@
+
+import cv2
+import numpy as np
+from PIL import Image
+import math
+import numpy as np
+# import torch
+# from torchvision import transforms
+
+def rotate_image(image, angle, center = None, scale = 1.0):
+    (h, w) = image.shape[:2]
+
+    if center is None:
+        center = (w / 2, h / 2)
+
+    # Perform the rotation
+    M = cv2.getRotationMatrix2D(center, angle, scale)
+    rotated = cv2.warpAffine(image, M, (w, h))
+
+    return rotated
+
+class watermark_image:
+    def __init__(self, logo_path, size=0.3, oritation="DR", margin=(5,20,20,20), angle=15, rgb_weight=(0,1,1.5), input_frame_shape=None) -> None:
+
+        logo_image = cv2.imread(logo_path, cv2.IMREAD_UNCHANGED)
+        h,w,c = logo_image.shape
+        if angle%360 != 0:
+            new_h = w*math.sin(angle/180*math.pi) + h*math.cos(angle/180*math.pi)
+            pad_h = int((new_h-h)//2)
+            
+            padding = np.zeros((pad_h, w, c), dtype=np.uint8)
+            logo_image = cv2.vconcat([logo_image, padding])
+            logo_image = cv2.vconcat([padding, logo_image])
+        
+            logo_image = rotate_image(logo_image, angle)
+        print(logo_image.shape)
+        self.logo_image = logo_image
+        
+        if self.logo_image.shape[2] < 4:
+            print("No alpha channel found!")
+            self.logo_image  = self.__addAlpha__(self.logo_image) #add alpha channel
+        self.size       = size
+        self.oritation  = oritation
+        self.margin     = margin
+        self.ori_shape  = self.logo_image.shape
+        self.resized    = False
+        self.rgb_weight = rgb_weight
+
+        self.logo_image[:, :, 2] = self.logo_image[:, :, 2]*self.rgb_weight[0]
+        self.logo_image[:, :, 1] = self.logo_image[:, :, 1]*self.rgb_weight[1]
+        self.logo_image[:, :, 0] = self.logo_image[:, :, 0]*self.rgb_weight[2]
+
+        if input_frame_shape is not None:
+
+            logo_w = input_frame_shape[1] * self.size
+            ratio  = logo_w / self.ori_shape[1]
+            logo_h = int(ratio * self.ori_shape[0])
+            logo_w = int(logo_w)
+
+            size   = (logo_w, logo_h)
+            self.logo_image = cv2.resize(self.logo_image, size, interpolation = cv2.INTER_CUBIC)
+            self.resized    = True
+            if oritation == "UL":
+                self.coor_h = self.margin[1]
+                self.coor_w = self.margin[0]
+            elif oritation == "UR":
+                self.coor_h = self.margin[1]
+                self.coor_w = input_frame_shape[1] - (logo_w + self.margin[2])
+            elif oritation == "DL":
+                self.coor_h = input_frame_shape[0] - (logo_h + self.margin[1])
+                self.coor_w = self.margin[0]
+            else:
+                self.coor_h = input_frame_shape[0] - (logo_h + self.margin[3])
+                self.coor_w = input_frame_shape[1] - (logo_w + self.margin[2])
+            self.logo_w = logo_w
+            self.logo_h = logo_h
+            self.mask = self.logo_image[:,:,3]
+            self.mask = cv2.bitwise_not(self.mask//255)
+            
+    def apply_frames(self, frame):
+
+        if not self.resized:
+            shape = frame.shape
+            logo_w = shape[1] * self.size
+            ratio  = logo_w / self.ori_shape[1]
+            logo_h = int(ratio * self.ori_shape[0])
+            logo_w = int(logo_w)
+
+            size   = (logo_w, logo_h)
+            self.logo_image = cv2.resize(self.logo_image, size, interpolation = cv2.INTER_CUBIC)
+            self.resized    = True
+            if self.oritation == "UL":
+                self.coor_h = self.margin[1]
+                self.coor_w = self.margin[0]
+            elif self.oritation == "UR":
+                self.coor_h = self.margin[1]
+                self.coor_w = shape[1] - (logo_w + self.margin[2])
+            elif self.oritation == "DL":
+                self.coor_h = shape[0] - (logo_h + self.margin[1])
+                self.coor_w = self.margin[0]
+            else:
+                self.coor_h = shape[0] - (logo_h + self.margin[3])
+                self.coor_w = shape[1] - (logo_w + self.margin[2])
+            self.logo_w = logo_w
+            self.logo_h = logo_h
+            self.mask = self.logo_image[:,:,3]
+            self.mask = cv2.bitwise_not(self.mask//255)
+            
+        original_frame = frame[self.coor_h:(self.coor_h+self.logo_h), self.coor_w:(self.coor_w+self.logo_w),:]
+        blending_logo   = cv2.add(self.logo_image[:,:,0:3],original_frame,mask = self.mask)
+        frame[self.coor_h:(self.coor_h+self.logo_h), self.coor_w:(self.coor_w+self.logo_w),:] = blending_logo 
+        return frame
+        
+    def __addAlpha__(self, image):
+        shape = image.shape
+        alpha_channel = np.ones((shape[0],shape[1],1),np.uint8)*255
+        return np.concatenate((image,alpha_channel),2)
+
diff --git a/modeling/SimSwap/util/gifswap.py b/modeling/SimSwap/util/gifswap.py
new file mode 100644
index 0000000..e0eaca9
--- /dev/null
+++ b/modeling/SimSwap/util/gifswap.py
@@ -0,0 +1,129 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 19:19:52
+Description: 
+'''
+import os 
+import cv2
+import glob
+import torch
+import shutil
+import numpy as np
+from tqdm import tqdm
+from util.reverse2original import reverse2wholeimage
+import moviepy.editor as mp
+from moviepy.editor import AudioFileClip, VideoFileClip 
+from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
+import  time
+from util.add_watermark import watermark_image
+from util.norm import SpecificNorm
+from parsing_model.model import BiSeNet
+from array2gif import write_gif
+
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+def gif_swap(video_path, id_vetor, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo=False, use_mask=False):
+    video_forcheck = VideoFileClip(video_path)
+    if video_forcheck.audio is None:
+        no_audio = True
+    else:
+        no_audio = False
+
+    del video_forcheck
+
+    if not no_audio:
+        video_audio_clip = AudioFileClip(video_path)
+
+    video = cv2.VideoCapture(video_path)
+    logoclass = watermark_image('./SimSwap/simswaplogo/simswaplogo.png')
+    ret = True
+    frame_index = 0
+
+    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+
+    # video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    fps = video.get(cv2.CAP_PROP_FPS)
+    if  os.path.exists(temp_results_dir):
+            shutil.rmtree(temp_results_dir)
+
+    spNorm =SpecificNorm()
+    if use_mask:
+        n_classes = 19
+        net = BiSeNet(n_classes=n_classes)
+        net.cuda()
+        save_pth = os.path.join('./SimSwap/parsing_model/checkpoint', '79999_iter.pth')
+        net.load_state_dict(torch.load(save_pth))
+        net.eval()
+    else:
+        net =None
+
+    frames = []
+    # while ret:
+    for frame_index in tqdm(range(frame_count)): 
+        ret, frame = video.read()
+        if  ret:
+            detect_results = detect_model.get(frame,crop_size)
+
+            if detect_results is not None:
+                # print(frame_index)
+                # if not os.path.exists(temp_results_dir):
+                #         os.mkdir(temp_results_dir)
+                frame_align_crop_list = detect_results[0]
+                frame_mat_list = detect_results[1]
+                swap_result_list = []
+                frame_align_crop_tenor_list = []
+                for frame_align_crop in frame_align_crop_list:
+
+                    # BGR TO RGB
+                    # frame_align_crop_RGB = frame_align_crop[...,::-1]
+
+                    frame_align_crop_tenor = _totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+                    swap_result = swap_model(None, frame_align_crop_tenor, id_vetor, None, True)[0]
+                    # cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame) # 원본 프레임 img 저장
+                    swap_result_list.append(swap_result)
+                    frame_align_crop_tenor_list.append(frame_align_crop_tenor)
+
+
+                img = reverse2wholeimage(frame_align_crop_tenor_list,swap_result_list, frame_mat_list, crop_size, frame, logoclass,\
+                        os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), no_simswaplogo, pasring_model=net, use_mask=use_mask, norm=spNorm)
+                frames.append(img.tobytes())
+
+            else:
+                if not os.path.exists(temp_results_dir):
+                    os.mkdir(temp_results_dir)
+                frame = frame.astype(np.uint8)
+                # if not no_simswaplogo:
+                    # frame = logoclass.apply_frames(frame)
+                cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+        else:
+            break
+
+    video.release()
+
+    # image_filename_list = []
+    # path = os.path.join(temp_results_dir, '*.jpg')
+    # image_filenames = sorted(glob.glob(path))
+
+    # clips = ImageSequenceClip(image_filenames, fps = fps)
+
+    # if not no_audio:
+    #     clips = clips.set_audio(video_audio_clip)
+
+    # # if save_path.split('.')[-1] == 'gif':
+    # clips.write_gif(save_path)
+
+    # write_gif(frames, save_path, fps=fps)
+    return frames, fps
+    
+
diff --git a/modeling/SimSwap/util/html.py b/modeling/SimSwap/util/html.py
new file mode 100644
index 0000000..71c48ad
--- /dev/null
+++ b/modeling/SimSwap/util/html.py
@@ -0,0 +1,63 @@
+import dominate
+from dominate.tags import *
+import os
+
+
+class HTML:
+    def __init__(self, web_dir, title, refresh=0):
+        self.title = title
+        self.web_dir = web_dir
+        self.img_dir = os.path.join(self.web_dir, 'images')
+        if not os.path.exists(self.web_dir):
+            os.makedirs(self.web_dir)
+        if not os.path.exists(self.img_dir):
+            os.makedirs(self.img_dir)
+
+        self.doc = dominate.document(title=title)
+        if refresh > 0:
+            with self.doc.head:
+                meta(http_equiv="refresh", content=str(refresh))
+
+    def get_image_dir(self):
+        return self.img_dir
+
+    def add_header(self, str):
+        with self.doc:
+            h3(str)
+
+    def add_table(self, border=1):
+        self.t = table(border=border, style="table-layout: fixed;")
+        self.doc.add(self.t)
+
+    def add_images(self, ims, txts, links, width=512):
+        self.add_table()
+        with self.t:
+            with tr():
+                for im, txt, link in zip(ims, txts, links):
+                    with td(style="word-wrap: break-word;", halign="center", valign="top"):
+                        with p():
+                            with a(href=os.path.join('images', link)):
+                                img(style="width:%dpx" % (width), src=os.path.join('images', im))
+                            br()
+                            p(txt)
+
+    def save(self):
+        html_file = '%s/index.html' % self.web_dir
+        f = open(html_file, 'wt')
+        f.write(self.doc.render())
+        f.close()
+
+
+if __name__ == '__main__':
+    html = HTML('web/', 'test_html')
+    html.add_header('hello world')
+
+    ims = []
+    txts = []
+    links = []
+    for n in range(4):
+        ims.append('image_%d.jpg' % n)
+        txts.append('text_%d' % n)
+        links.append('image_%d.jpg' % n)
+    html.add_images(ims, txts, links)
+    html.save()
diff --git a/modeling/SimSwap/util/image_pool.py b/modeling/SimSwap/util/image_pool.py
new file mode 100644
index 0000000..63e1877
--- /dev/null
+++ b/modeling/SimSwap/util/image_pool.py
@@ -0,0 +1,31 @@
+import random
+import torch
+from torch.autograd import Variable
+class ImagePool():
+    def __init__(self, pool_size):
+        self.pool_size = pool_size
+        if self.pool_size > 0:
+            self.num_imgs = 0
+            self.images = []
+
+    def query(self, images):
+        if self.pool_size == 0:
+            return images
+        return_images = []
+        for image in images.data:
+            image = torch.unsqueeze(image, 0)
+            if self.num_imgs < self.pool_size:
+                self.num_imgs = self.num_imgs + 1
+                self.images.append(image)
+                return_images.append(image)
+            else:
+                p = random.uniform(0, 1)
+                if p > 0.5:
+                    random_id = random.randint(0, self.pool_size-1)
+                    tmp = self.images[random_id].clone()
+                    self.images[random_id] = image
+                    return_images.append(tmp)
+                else:
+                    return_images.append(image)
+        return_images = Variable(torch.cat(return_images, 0))
+        return return_images
diff --git a/modeling/SimSwap/util/json_config.py b/modeling/SimSwap/util/json_config.py
new file mode 100644
index 0000000..c68fbff
--- /dev/null
+++ b/modeling/SimSwap/util/json_config.py
@@ -0,0 +1,15 @@
+import  json
+
+
+def readConfig(path):
+    with open(path,'r') as cf:
+        nodelocaltionstr = cf.read()
+        nodelocaltioninf = json.loads(nodelocaltionstr)
+        if isinstance(nodelocaltioninf,str):
+            nodelocaltioninf = json.loads(nodelocaltioninf)
+    return nodelocaltioninf
+
+def writeConfig(path, info):
+    with open(path, 'w') as cf:
+        configjson  = json.dumps(info, indent=4)
+        cf.writelines(configjson)
\ No newline at end of file
diff --git a/modeling/SimSwap/util/logo_class.py b/modeling/SimSwap/util/logo_class.py
new file mode 100644
index 0000000..044dce3
--- /dev/null
+++ b/modeling/SimSwap/util/logo_class.py
@@ -0,0 +1,44 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: logo_class.py
+# Created Date: Tuesday June 29th 2021
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Monday, 11th October 2021 12:39:55 am
+# Modified By: Chen Xuanhong
+# Copyright (c) 2021 Shanghai Jiao Tong University
+#############################################################
+
+class logo_class:
+    
+    @staticmethod
+    def print_group_logo():
+        logo_str = """
+
+███╗   ██╗██████╗ ███████╗██╗ ██████╗     ███████╗     ██╗████████╗██╗   ██╗
+████╗  ██║██╔══██╗██╔════╝██║██╔════╝     ██╔════╝     ██║╚══██╔══╝██║   ██║
+██╔██╗ ██║██████╔╝███████╗██║██║  ███╗    ███████╗     ██║   ██║   ██║   ██║
+██║╚██╗██║██╔══██╗╚════██║██║██║   ██║    ╚════██║██   ██║   ██║   ██║   ██║
+██║ ╚████║██║  ██║███████║██║╚██████╔╝    ███████║╚█████╔╝   ██║   ╚██████╔╝
+╚═╝  ╚═══╝╚═╝  ╚═╝╚══════╝╚═╝ ╚═════╝     ╚══════╝ ╚════╝    ╚═╝    ╚═════╝ 
+Neural Rendering Special Interesting Group of SJTU
+                                                                            
+        """
+        print(logo_str)
+
+    @staticmethod
+    def print_start_training():
+        logo_str = """
+   _____  __                __     ______              _         _              
+  / ___/ / /_ ____ _ _____ / /_   /_  __/_____ ____ _ (_)____   (_)____   ____ _
+  \__ \ / __// __ `// ___// __/    / /  / ___// __ `// // __ \ / // __ \ / __ `/
+ ___/ // /_ / /_/ // /   / /_     / /  / /   / /_/ // // / / // // / / // /_/ / 
+/____/ \__/ \__,_//_/    \__/    /_/  /_/    \__,_//_//_/ /_//_//_/ /_/ \__, /  
+                                                                       /____/   
+        """
+        print(logo_str)
+
+if __name__=="__main__":
+    # logo_class.print_group_logo()
+    logo_class.print_start_training()
\ No newline at end of file
diff --git a/modeling/SimSwap/util/norm.py b/modeling/SimSwap/util/norm.py
new file mode 100644
index 0000000..981adcf
--- /dev/null
+++ b/modeling/SimSwap/util/norm.py
@@ -0,0 +1,25 @@
+import torch.nn as nn
+import numpy as np
+import torch
+class SpecificNorm(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        """
+            @notice: avoid in-place ops.
+            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
+        """
+        super(SpecificNorm, self).__init__()
+        self.mean = np.array([0.485, 0.456, 0.406])
+        self.mean = torch.from_numpy(self.mean).float().cuda()
+        self.mean = self.mean.view([1, 3, 1, 1])
+
+        self.std = np.array([0.229, 0.224, 0.225])
+        self.std = torch.from_numpy(self.std).float().cuda()
+        self.std = self.std.view([1, 3, 1, 1])
+
+    def forward(self, x):
+        mean = self.mean.expand([1, 3, x.shape[2], x.shape[3]])
+        std = self.std.expand([1, 3, x.shape[2], x.shape[3]])
+
+        x = (x - mean) / std
+
+        return x
\ No newline at end of file
diff --git a/modeling/SimSwap/util/plot.py b/modeling/SimSwap/util/plot.py
new file mode 100644
index 0000000..0da1c75
--- /dev/null
+++ b/modeling/SimSwap/util/plot.py
@@ -0,0 +1,37 @@
+import numpy as np
+import math
+import PIL
+
+def postprocess(x):
+    """[0,1] to uint8."""
+    
+    x = np.clip(255 * x, 0, 255)
+    x = np.cast[np.uint8](x)
+    return x
+
+def tile(X, rows, cols):
+    """Tile images for display."""
+    tiling = np.zeros((rows * X.shape[1], cols * X.shape[2], X.shape[3]), dtype = X.dtype)
+    for i in range(rows):
+        for j in range(cols):
+            idx = i * cols + j
+            if idx < X.shape[0]:
+                img = X[idx,...]
+                tiling[
+                        i*X.shape[1]:(i+1)*X.shape[1],
+                        j*X.shape[2]:(j+1)*X.shape[2],
+                        :] = img
+    return tiling
+
+
+def plot_batch(X, out_path):
+    """Save batch of images tiled."""
+    n_channels = X.shape[3]
+    if n_channels > 3:
+        X = X[:,:,:,np.random.choice(n_channels, size = 3)]
+    X = postprocess(X)
+    rc = math.sqrt(X.shape[0])
+    rows = cols = math.ceil(rc)
+    canvas = tile(X, rows, cols)
+    canvas = np.squeeze(canvas)
+    PIL.Image.fromarray(canvas).save(out_path)
\ No newline at end of file
diff --git a/modeling/SimSwap/util/reverse2original.py b/modeling/SimSwap/util/reverse2original.py
new file mode 100644
index 0000000..81305b8
--- /dev/null
+++ b/modeling/SimSwap/util/reverse2original.py
@@ -0,0 +1,176 @@
+import cv2
+import numpy as np
+# import  time
+import torch
+from torch.nn import functional as F
+import torch.nn as nn
+
+
+def encode_segmentation_rgb(segmentation, no_neck=True):
+    parse = segmentation
+
+    face_part_ids = [1, 2, 3, 4, 5, 6, 10, 12, 13] if no_neck else [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14]
+    mouth_id = 11
+    # hair_id = 17
+    face_map = np.zeros([parse.shape[0], parse.shape[1]])
+    mouth_map = np.zeros([parse.shape[0], parse.shape[1]])
+    # hair_map = np.zeros([parse.shape[0], parse.shape[1]])
+
+    for valid_id in face_part_ids:
+        valid_index = np.where(parse==valid_id)
+        face_map[valid_index] = 255
+    valid_index = np.where(parse==mouth_id)
+    mouth_map[valid_index] = 255
+    # valid_index = np.where(parse==hair_id)
+    # hair_map[valid_index] = 255
+    #return np.stack([face_map, mouth_map,hair_map], axis=2)
+    return np.stack([face_map, mouth_map], axis=2)
+
+
+class SoftErosion(nn.Module):
+    def __init__(self, kernel_size=15, threshold=0.6, iterations=1):
+        super(SoftErosion, self).__init__()
+        r = kernel_size // 2
+        self.padding = r
+        self.iterations = iterations
+        self.threshold = threshold
+
+        # Create kernel
+        y_indices, x_indices = torch.meshgrid(torch.arange(0., kernel_size), torch.arange(0., kernel_size))
+        dist = torch.sqrt((x_indices - r) ** 2 + (y_indices - r) ** 2)
+        kernel = dist.max() - dist
+        kernel /= kernel.sum()
+        kernel = kernel.view(1, 1, *kernel.shape)
+        self.register_buffer('weight', kernel)
+
+    def forward(self, x):
+        x = x.float()
+        for i in range(self.iterations - 1):
+            x = torch.min(x, F.conv2d(x, weight=self.weight, groups=x.shape[1], padding=self.padding))
+        x = F.conv2d(x, weight=self.weight, groups=x.shape[1], padding=self.padding)
+
+        mask = x >= self.threshold
+        x[mask] = 1.0
+        x[~mask] /= x[~mask].max()
+
+        return x, mask
+
+
+def postprocess(swapped_face, target, target_mask,smooth_mask):
+    # target_mask = cv2.resize(target_mask, (self.size,  self.size))
+
+    mask_tensor = torch.from_numpy(target_mask.copy().transpose((2, 0, 1))).float().mul_(1/255.0).cuda()
+    face_mask_tensor = mask_tensor[0] + mask_tensor[1]
+    
+    soft_face_mask_tensor, _ = smooth_mask(face_mask_tensor.unsqueeze_(0).unsqueeze_(0))
+    soft_face_mask_tensor.squeeze_()
+
+    soft_face_mask = soft_face_mask_tensor.cpu().numpy()
+    soft_face_mask = soft_face_mask[:, :, np.newaxis]
+
+    result =  swapped_face * soft_face_mask + target * (1 - soft_face_mask)
+    result = result[:,:,::-1]# .astype(np.uint8)
+    return result
+
+def reverse2wholeimage(b_align_crop_tenor_list, swaped_imgs, mats, crop_size, oriimg, logoclass, save_path='', \
+                    no_simswaplogo=False, pasring_model=None, norm=None, use_mask=False):
+
+    target_image_list = []
+    img_mask_list = []
+    if use_mask:
+        smooth_mask = SoftErosion(kernel_size=17, threshold=0.9, iterations=7).cuda()
+    else:
+        pass
+
+    # print(len(swaped_imgs))
+    # print(mats)
+    # print(len(b_align_crop_tenor_list))
+    for swaped_img, mat ,source_img in zip(swaped_imgs, mats, b_align_crop_tenor_list):
+        swaped_img = swaped_img.cpu().detach().numpy().transpose((1, 2, 0))
+        img_white = np.full((crop_size,crop_size), 255, dtype=float)
+
+        # inverse the Affine transformation matrix
+        mat_rev = np.zeros([2,3])
+        div1 = mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0]
+        mat_rev[0][0] = mat[1][1]/div1
+        mat_rev[0][1] = -mat[0][1]/div1
+        mat_rev[0][2] = -(mat[0][2]*mat[1][1]-mat[0][1]*mat[1][2])/div1
+        div2 = mat[0][1]*mat[1][0]-mat[0][0]*mat[1][1]
+        mat_rev[1][0] = mat[1][0]/div2
+        mat_rev[1][1] = -mat[0][0]/div2
+        mat_rev[1][2] = -(mat[0][2]*mat[1][0]-mat[0][0]*mat[1][2])/div2
+
+        orisize = (oriimg.shape[1], oriimg.shape[0])
+        if use_mask:
+            source_img_norm = norm(source_img)
+            source_img_512  = F.interpolate(source_img_norm,size=(512,512))
+            out = pasring_model(source_img_512)[0]
+            parsing = out.squeeze(0).detach().cpu().numpy().argmax(0)
+            vis_parsing_anno = parsing.copy().astype(np.uint8)
+            tgt_mask = encode_segmentation_rgb(vis_parsing_anno)
+            if tgt_mask.sum() >= 5000:
+                # face_mask_tensor = tgt_mask[...,0] + tgt_mask[...,1]
+                target_mask = cv2.resize(tgt_mask, (crop_size,  crop_size))
+                # print(source_img)
+                target_image_parsing = postprocess(swaped_img, source_img[0].cpu().detach().numpy().transpose((1, 2, 0)), target_mask,smooth_mask)
+                
+
+                target_image = cv2.warpAffine(target_image_parsing, mat_rev, orisize)
+                # target_image_parsing = cv2.warpAffine(swaped_img, mat_rev, orisize)
+            else:
+                target_image = cv2.warpAffine(swaped_img, mat_rev, orisize)[..., ::-1]
+        else:
+            target_image = cv2.warpAffine(swaped_img, mat_rev, orisize)
+        # source_image   = cv2.warpAffine(source_img, mat_rev, orisize)
+
+        img_white = cv2.warpAffine(img_white, mat_rev, orisize)
+
+
+        img_white[img_white>20] =255
+
+        img_mask = img_white
+
+        # if use_mask:
+        #     kernel = np.ones((40,40),np.uint8)
+        #     img_mask = cv2.erode(img_mask,kernel,iterations = 1)
+        # else:
+        kernel = np.ones((40,40),np.uint8)
+        img_mask = cv2.erode(img_mask,kernel,iterations = 1)
+        kernel_size = (20, 20)
+        blur_size = tuple(2*i+1 for i in kernel_size)
+        img_mask = cv2.GaussianBlur(img_mask, blur_size, 0)
+
+        # kernel = np.ones((10,10),np.uint8)
+        # img_mask = cv2.erode(img_mask,kernel,iterations = 1)
+
+
+
+        img_mask /= 255
+
+        img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1])
+
+        # pasing mask
+
+        # target_image_parsing = postprocess(target_image, source_image, tgt_mask)
+
+        if use_mask:
+            target_image = np.array(target_image, dtype=np.float) * 255
+        else:
+            target_image = np.array(target_image, dtype=np.float)[..., ::-1] * 255
+
+
+        img_mask_list.append(img_mask)
+        target_image_list.append(target_image)
+        
+
+    # target_image /= 255
+    # target_image = 0
+    img = np.array(oriimg, dtype=np.float)
+    for img_mask, target_image in zip(img_mask_list, target_image_list):
+        img = img_mask * target_image + (1-img_mask) * img
+        
+    final_img = img.astype(np.uint8)
+    # if not no_simswaplogo:
+    #     final_img = logoclass.apply_frames(final_img)
+    # cv2.imwrite(save_path, final_img) # swap img 저장
+    return final_img
diff --git a/modeling/SimSwap/util/save_heatmap.py b/modeling/SimSwap/util/save_heatmap.py
new file mode 100644
index 0000000..71ce4c9
--- /dev/null
+++ b/modeling/SimSwap/util/save_heatmap.py
@@ -0,0 +1,57 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: save_heatmap.py
+# Created Date: Friday January 15th 2021
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Wednesday, 19th January 2022 1:22:47 am
+# Modified By: Chen Xuanhong
+# Copyright (c) 2021 Shanghai Jiao Tong University
+#############################################################
+
+import os
+import shutil
+import seaborn as sns
+import matplotlib.pyplot as plt
+import cv2
+import numpy as np
+
+def SaveHeatmap(heatmaps, path, row=-1, dpi=72):
+    """
+    The input tensor must be B X 1 X H X W
+    """
+    batch_size = heatmaps.shape[0]
+    temp_path  = ".temp/"
+    if not os.path.exists(temp_path):
+        os.makedirs(temp_path)
+    final_img = None
+    if row < 1:
+        col = batch_size
+        row = 1
+    else:
+        col = batch_size // row
+        if row * col <batch_size:
+            col +=1
+    
+    row_i = 0
+    col_i = 0
+    
+    for i in range(batch_size):
+        img_path = os.path.join(temp_path,'temp_batch_{}.png'.format(i))
+        sns.heatmap(heatmaps[i,0,:,:],vmin=0,vmax=heatmaps[i,0,:,:].max(),cbar=False)
+        plt.savefig(img_path, dpi=dpi, bbox_inches = 'tight', pad_inches = 0)
+        img = cv2.imread(img_path)
+        if i == 0:
+            H,W,C = img.shape
+            final_img = np.zeros((H*row,W*col,C))
+        final_img[H*row_i:H*(row_i+1),W*col_i:W*(col_i+1),:] = img
+        col_i += 1
+        if col_i >= col:
+            col_i = 0
+            row_i += 1
+    cv2.imwrite(path,final_img)
+
+if __name__ == "__main__":
+    random_map = np.random.randn(16,1,10,10)
+    SaveHeatmap(random_map,"./wocao.png",1)
diff --git a/modeling/SimSwap/util/util.py b/modeling/SimSwap/util/util.py
new file mode 100644
index 0000000..f4f79ec
--- /dev/null
+++ b/modeling/SimSwap/util/util.py
@@ -0,0 +1,100 @@
+from __future__ import print_function
+import torch
+import numpy as np
+from PIL import Image
+import numpy as np
+import os
+
+# Converts a Tensor into a Numpy array
+# |imtype|: the desired type of the converted numpy array
+def tensor2im(image_tensor, imtype=np.uint8, normalize=True):
+    if isinstance(image_tensor, list):
+        image_numpy = []
+        for i in range(len(image_tensor)):
+            image_numpy.append(tensor2im(image_tensor[i], imtype, normalize))
+        return image_numpy
+    image_numpy = image_tensor.cpu().float().numpy()
+    if normalize:
+        image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
+    else:
+        image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0      
+    image_numpy = np.clip(image_numpy, 0, 255)
+    if image_numpy.shape[2] == 1 or image_numpy.shape[2] > 3:        
+        image_numpy = image_numpy[:,:,0]
+    return image_numpy.astype(imtype)
+
+# Converts a one-hot tensor into a colorful label map
+def tensor2label(label_tensor, n_label, imtype=np.uint8):
+    if n_label == 0:
+        return tensor2im(label_tensor, imtype)
+    label_tensor = label_tensor.cpu().float()    
+    if label_tensor.size()[0] > 1:
+        label_tensor = label_tensor.max(0, keepdim=True)[1]
+    label_tensor = Colorize(n_label)(label_tensor)
+    label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0))
+    return label_numpy.astype(imtype)
+
+def save_image(image_numpy, image_path):
+    image_pil = Image.fromarray(image_numpy)
+    image_pil.save(image_path)
+
+def mkdirs(paths):
+    if isinstance(paths, list) and not isinstance(paths, str):
+        for path in paths:
+            mkdir(path)
+    else:
+        mkdir(paths)
+
+def mkdir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+###############################################################################
+# Code from
+# https://github.com/ycszen/pytorch-seg/blob/master/transform.py
+# Modified so it complies with the Citscape label map colors
+###############################################################################
+def uint82bin(n, count=8):
+    """returns the binary of integer n, count refers to amount of bits"""
+    return ''.join([str((n >> y) & 1) for y in range(count-1, -1, -1)])
+
+def labelcolormap(N):
+    if N == 35: # cityscape
+        cmap = np.array([(  0,  0,  0), (  0,  0,  0), (  0,  0,  0), (  0,  0,  0), (  0,  0,  0), (111, 74,  0), ( 81,  0, 81),
+                     (128, 64,128), (244, 35,232), (250,170,160), (230,150,140), ( 70, 70, 70), (102,102,156), (190,153,153),
+                     (180,165,180), (150,100,100), (150,120, 90), (153,153,153), (153,153,153), (250,170, 30), (220,220,  0),
+                     (107,142, 35), (152,251,152), ( 70,130,180), (220, 20, 60), (255,  0,  0), (  0,  0,142), (  0,  0, 70),
+                     (  0, 60,100), (  0,  0, 90), (  0,  0,110), (  0, 80,100), (  0,  0,230), (119, 11, 32), (  0,  0,142)], 
+                     dtype=np.uint8)
+    else:
+        cmap = np.zeros((N, 3), dtype=np.uint8)
+        for i in range(N):
+            r, g, b = 0, 0, 0
+            id = i
+            for j in range(7):
+                str_id = uint82bin(id)
+                r = r ^ (np.uint8(str_id[-1]) << (7-j))
+                g = g ^ (np.uint8(str_id[-2]) << (7-j))
+                b = b ^ (np.uint8(str_id[-3]) << (7-j))
+                id = id >> 3
+            cmap[i, 0] = r
+            cmap[i, 1] = g
+            cmap[i, 2] = b
+    return cmap
+
+class Colorize(object):
+    def __init__(self, n=35):
+        self.cmap = labelcolormap(n)
+        self.cmap = torch.from_numpy(self.cmap[:n])
+
+    def __call__(self, gray_image):
+        size = gray_image.size()
+        color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0)
+
+        for label in range(0, len(self.cmap)):
+            mask = (label == gray_image[0]).cpu()
+            color_image[0][mask] = self.cmap[label][0]
+            color_image[1][mask] = self.cmap[label][1]
+            color_image[2][mask] = self.cmap[label][2]
+
+        return color_image
diff --git a/modeling/SimSwap/util/videoswap.py b/modeling/SimSwap/util/videoswap.py
new file mode 100644
index 0000000..99294d5
--- /dev/null
+++ b/modeling/SimSwap/util/videoswap.py
@@ -0,0 +1,121 @@
+'''
+Author: Naiyuan liu
+Github: https://github.com/NNNNAI
+Date: 2021-11-23 17:03:58
+LastEditors: Naiyuan liu
+LastEditTime: 2021-11-24 19:19:52
+Description: 
+'''
+import os 
+import cv2
+import glob
+import torch
+import shutil
+import numpy as np
+from tqdm import tqdm
+from util.reverse2original import reverse2wholeimage
+import moviepy.editor as mp
+from moviepy.editor import AudioFileClip, VideoFileClip 
+from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
+import  time
+from util.add_watermark import watermark_image
+from util.norm import SpecificNorm
+from parsing_model.model import BiSeNet
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+def video_swap(video_path, id_vetor, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False,use_mask =False):
+    video_forcheck = VideoFileClip(video_path)
+    if video_forcheck.audio is None:
+        no_audio = True
+    else:
+        no_audio = False
+
+    del video_forcheck
+
+    if not no_audio:
+        video_audio_clip = AudioFileClip(video_path)
+
+    video = cv2.VideoCapture(video_path)
+    logoclass = watermark_image('./simswaplogo/simswaplogo.png')
+    ret = True
+    frame_index = 0
+
+    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+
+    # video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    fps = video.get(cv2.CAP_PROP_FPS)
+    if  os.path.exists(temp_results_dir):
+            shutil.rmtree(temp_results_dir)
+
+    spNorm =SpecificNorm()
+    if use_mask:
+        n_classes = 19
+        net = BiSeNet(n_classes=n_classes)
+        net.cuda()
+        save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
+        net.load_state_dict(torch.load(save_pth))
+        net.eval()
+    else:
+        net =None
+
+    # while ret:
+    for frame_index in tqdm(range(frame_count)): 
+        ret, frame = video.read()
+        if  ret:
+            detect_results = detect_model.get(frame,crop_size)
+
+            if detect_results is not None:
+                # print(frame_index)
+                if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+                frame_align_crop_list = detect_results[0]
+                frame_mat_list = detect_results[1]
+                swap_result_list = []
+                frame_align_crop_tenor_list = []
+                for frame_align_crop in frame_align_crop_list:
+
+                    # BGR TO RGB
+                    # frame_align_crop_RGB = frame_align_crop[...,::-1]
+
+                    frame_align_crop_tenor = _totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+                    swap_result = swap_model(None, frame_align_crop_tenor, id_vetor, None, True)[0]
+                    cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+                    swap_result_list.append(swap_result)
+                    frame_align_crop_tenor_list.append(frame_align_crop_tenor)
+
+                    
+
+                reverse2wholeimage(frame_align_crop_tenor_list,swap_result_list, frame_mat_list, crop_size, frame, logoclass,\
+                    os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,use_mask=use_mask, norm = spNorm)
+
+            else:
+                if not os.path.exists(temp_results_dir):
+                    os.mkdir(temp_results_dir)
+                frame = frame.astype(np.uint8)
+                if not no_simswaplogo:
+                    frame = logoclass.apply_frames(frame)
+                cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+        else:
+            break
+
+    video.release()
+
+    # image_filename_list = []
+    path = os.path.join(temp_results_dir,'*.jpg')
+    image_filenames = sorted(glob.glob(path))
+
+    clips = ImageSequenceClip(image_filenames,fps = fps)
+
+    if not no_audio:
+        clips = clips.set_audio(video_audio_clip)
+
+
+    clips.write_videofile(save_path,audio_codec='aac')
diff --git a/modeling/SimSwap/util/videoswap_multispecific.py b/modeling/SimSwap/util/videoswap_multispecific.py
new file mode 100644
index 0000000..40e9488
--- /dev/null
+++ b/modeling/SimSwap/util/videoswap_multispecific.py
@@ -0,0 +1,146 @@
+import os 
+import cv2
+import glob
+import torch
+import shutil
+import numpy as np
+from tqdm import tqdm
+from util.reverse2original import reverse2wholeimage
+import moviepy.editor as mp
+from moviepy.editor import AudioFileClip, VideoFileClip 
+from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
+import  time
+from util.add_watermark import watermark_image
+from util.norm import SpecificNorm
+import torch.nn.functional as F
+from parsing_model.model import BiSeNet
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+def video_swap(video_path, target_id_norm_list,source_specific_id_nonorm_list,id_thres, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False,use_mask =False):
+    video_forcheck = VideoFileClip(video_path)
+    if video_forcheck.audio is None:
+        no_audio = True
+    else:
+        no_audio = False
+
+    del video_forcheck
+
+    if not no_audio:
+        video_audio_clip = AudioFileClip(video_path)
+
+    video = cv2.VideoCapture(video_path)
+    logoclass = watermark_image('./simswaplogo/simswaplogo.png')
+    ret = True
+    frame_index = 0
+
+    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+
+    # video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    fps = video.get(cv2.CAP_PROP_FPS)
+    if  os.path.exists(temp_results_dir):
+            shutil.rmtree(temp_results_dir)
+
+    spNorm =SpecificNorm()
+    mse = torch.nn.MSELoss().cuda()
+
+    if use_mask:
+        n_classes = 19
+        net = BiSeNet(n_classes=n_classes)
+        net.cuda()
+        save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
+        net.load_state_dict(torch.load(save_pth))
+        net.eval()
+    else:
+        net =None
+
+    # while ret:
+    for frame_index in tqdm(range(frame_count)): 
+        ret, frame = video.read()
+        if  ret:
+            detect_results = detect_model.get(frame,crop_size)
+
+            if detect_results is not None:
+                # print(frame_index)
+                if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+                frame_align_crop_list = detect_results[0]
+                frame_mat_list = detect_results[1]
+
+                id_compare_values = [] 
+                frame_align_crop_tenor_list = []
+                for frame_align_crop in frame_align_crop_list:
+
+                    # BGR TO RGB
+                    # frame_align_crop_RGB = frame_align_crop[...,::-1]
+
+                    frame_align_crop_tenor = _totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+                    frame_align_crop_tenor_arcnorm = spNorm(frame_align_crop_tenor)
+                    frame_align_crop_tenor_arcnorm_downsample = F.interpolate(frame_align_crop_tenor_arcnorm, size=(112,112))
+                    frame_align_crop_crop_id_nonorm = swap_model.netArc(frame_align_crop_tenor_arcnorm_downsample)
+                    id_compare_values.append([])
+                    for source_specific_id_nonorm_tmp in source_specific_id_nonorm_list:
+                        id_compare_values[-1].append(mse(frame_align_crop_crop_id_nonorm,source_specific_id_nonorm_tmp).detach().cpu().numpy())
+                    frame_align_crop_tenor_list.append(frame_align_crop_tenor)
+
+                id_compare_values_array = np.array(id_compare_values).transpose(1,0)
+                min_indexs = np.argmin(id_compare_values_array,axis=0)
+                min_value = np.min(id_compare_values_array,axis=0)
+
+                swap_result_list = [] 
+                swap_result_matrix_list = []
+                swap_result_ori_pic_list = []
+                for tmp_index, min_index in enumerate(min_indexs):
+                    if min_value[tmp_index] < id_thres:
+                        swap_result = swap_model(None, frame_align_crop_tenor_list[tmp_index], target_id_norm_list[min_index], None, True)[0]
+                        swap_result_list.append(swap_result)
+                        swap_result_matrix_list.append(frame_mat_list[tmp_index])
+                        swap_result_ori_pic_list.append(frame_align_crop_tenor_list[tmp_index])
+                    else:
+                        pass
+
+
+
+                if len(swap_result_list) !=0:
+                    
+                    reverse2wholeimage(swap_result_ori_pic_list,swap_result_list, swap_result_matrix_list, crop_size, frame, logoclass,\
+                        os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,use_mask=use_mask, norm = spNorm)
+                else:
+                    if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+                    frame = frame.astype(np.uint8)
+                    if not no_simswaplogo:
+                        frame = logoclass.apply_frames(frame)
+                    cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+
+            else:
+                if not os.path.exists(temp_results_dir):
+                    os.mkdir(temp_results_dir)
+                frame = frame.astype(np.uint8)
+                if not no_simswaplogo:
+                    frame = logoclass.apply_frames(frame)
+                cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+        else:
+            break
+
+    video.release()
+
+    # image_filename_list = []
+    path = os.path.join(temp_results_dir,'*.jpg')
+    image_filenames = sorted(glob.glob(path))
+
+    clips = ImageSequenceClip(image_filenames,fps = fps)
+
+    if not no_audio:
+        clips = clips.set_audio(video_audio_clip)
+
+
+    clips.write_videofile(save_path,audio_codec='aac')
+
diff --git a/modeling/SimSwap/util/videoswap_specific.py b/modeling/SimSwap/util/videoswap_specific.py
new file mode 100644
index 0000000..8177efb
--- /dev/null
+++ b/modeling/SimSwap/util/videoswap_specific.py
@@ -0,0 +1,130 @@
+import os 
+import cv2
+import glob
+import torch
+import shutil
+import numpy as np
+from tqdm import tqdm
+from util.reverse2original import reverse2wholeimage
+import moviepy.editor as mp
+from moviepy.editor import AudioFileClip, VideoFileClip 
+from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
+import  time
+from util.add_watermark import watermark_image
+from util.norm import SpecificNorm
+import torch.nn.functional as F
+from parsing_model.model import BiSeNet
+
+def _totensor(array):
+    tensor = torch.from_numpy(array)
+    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
+    return img.float().div(255)
+
+def video_swap(video_path, id_vetor,specific_person_id_nonorm,id_thres, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False,use_mask =False):
+    video_forcheck = VideoFileClip(video_path)
+    if video_forcheck.audio is None:
+        no_audio = True
+    else:
+        no_audio = False
+
+    del video_forcheck
+
+    if not no_audio:
+        video_audio_clip = AudioFileClip(video_path)
+
+    video = cv2.VideoCapture(video_path)
+    logoclass = watermark_image('./simswaplogo/simswaplogo.png')
+    ret = True
+    frame_index = 0
+
+    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # video_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+
+    # video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    fps = video.get(cv2.CAP_PROP_FPS)
+    if  os.path.exists(temp_results_dir):
+            shutil.rmtree(temp_results_dir)
+
+    spNorm =SpecificNorm()
+    mse = torch.nn.MSELoss().cuda()
+
+    if use_mask:
+        n_classes = 19
+        net = BiSeNet(n_classes=n_classes)
+        net.cuda()
+        save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
+        net.load_state_dict(torch.load(save_pth))
+        net.eval()
+    else:
+        net =None
+
+    # while ret:
+    for frame_index in tqdm(range(frame_count)): 
+        ret, frame = video.read()
+        if  ret:
+            detect_results = detect_model.get(frame,crop_size)
+
+            if detect_results is not None:
+                # print(frame_index)
+                if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+                frame_align_crop_list = detect_results[0]
+                frame_mat_list = detect_results[1]
+
+                id_compare_values = [] 
+                frame_align_crop_tenor_list = []
+                for frame_align_crop in frame_align_crop_list:
+
+                    # BGR TO RGB
+                    # frame_align_crop_RGB = frame_align_crop[...,::-1]
+
+                    frame_align_crop_tenor = _totensor(cv2.cvtColor(frame_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
+
+                    frame_align_crop_tenor_arcnorm = spNorm(frame_align_crop_tenor)
+                    frame_align_crop_tenor_arcnorm_downsample = F.interpolate(frame_align_crop_tenor_arcnorm, size=(112,112))
+                    frame_align_crop_crop_id_nonorm = swap_model.netArc(frame_align_crop_tenor_arcnorm_downsample)
+
+                    id_compare_values.append(mse(frame_align_crop_crop_id_nonorm,specific_person_id_nonorm).detach().cpu().numpy())
+                    frame_align_crop_tenor_list.append(frame_align_crop_tenor)
+                id_compare_values_array = np.array(id_compare_values)
+                min_index = np.argmin(id_compare_values_array)
+                min_value = id_compare_values_array[min_index]
+                if min_value < id_thres:
+                    swap_result = swap_model(None, frame_align_crop_tenor_list[min_index], id_vetor, None, True)[0]
+                
+                    reverse2wholeimage([frame_align_crop_tenor_list[min_index]], [swap_result], [frame_mat_list[min_index]], crop_size, frame, logoclass,\
+                        os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,use_mask= use_mask, norm = spNorm)
+                else:
+                    if not os.path.exists(temp_results_dir):
+                        os.mkdir(temp_results_dir)
+                    frame = frame.astype(np.uint8)
+                    if not no_simswaplogo:
+                        frame = logoclass.apply_frames(frame)
+                    cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+
+            else:
+                if not os.path.exists(temp_results_dir):
+                    os.mkdir(temp_results_dir)
+                frame = frame.astype(np.uint8)
+                if not no_simswaplogo:
+                    frame = logoclass.apply_frames(frame)
+                cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
+        else:
+            break
+
+    video.release()
+
+    # image_filename_list = []
+    path = os.path.join(temp_results_dir,'*.jpg')
+    image_filenames = sorted(glob.glob(path))
+
+    clips = ImageSequenceClip(image_filenames,fps = fps)
+
+    if not no_audio:
+        clips = clips.set_audio(video_audio_clip)
+
+
+    clips.write_videofile(save_path,audio_codec='aac')
+
diff --git a/modeling/SimSwap/util/visualizer.py b/modeling/SimSwap/util/visualizer.py
new file mode 100644
index 0000000..584ac45
--- /dev/null
+++ b/modeling/SimSwap/util/visualizer.py
@@ -0,0 +1,131 @@
+import numpy as np
+import os
+import ntpath
+import time
+from . import util
+from . import html
+import scipy.misc
+try:
+    from StringIO import StringIO  # Python 2.7
+except ImportError:
+    from io import BytesIO         # Python 3.x
+
+class Visualizer():
+    def __init__(self, opt):
+        # self.opt = opt
+        self.tf_log = opt.tf_log
+        self.use_html = opt.isTrain and not opt.no_html
+        self.win_size = opt.display_winsize
+        self.name = opt.name
+        if self.tf_log:
+            import tensorflow as tf
+            self.tf = tf
+            self.log_dir = os.path.join(opt.checkpoints_dir, opt.name, 'logs')
+            self.writer = tf.summary.FileWriter(self.log_dir)
+
+        if self.use_html:
+            self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
+            self.img_dir = os.path.join(self.web_dir, 'images')
+            print('create web directory %s...' % self.web_dir)
+            util.mkdirs([self.web_dir, self.img_dir])
+        self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
+        with open(self.log_name, "a") as log_file:
+            now = time.strftime("%c")
+            log_file.write('================ Training Loss (%s) ================\n' % now)
+
+    # |visuals|: dictionary of images to display or save
+    def display_current_results(self, visuals, epoch, step):
+        if self.tf_log: # show images in tensorboard output
+            img_summaries = []
+            for label, image_numpy in visuals.items():
+                # Write the image to a string
+                try:
+                    s = StringIO()
+                except:
+                    s = BytesIO()
+                scipy.misc.toimage(image_numpy).save(s, format="jpeg")
+                # Create an Image object
+                img_sum = self.tf.Summary.Image(encoded_image_string=s.getvalue(), height=image_numpy.shape[0], width=image_numpy.shape[1])
+                # Create a Summary value
+                img_summaries.append(self.tf.Summary.Value(tag=label, image=img_sum))
+
+            # Create and write Summary
+            summary = self.tf.Summary(value=img_summaries)
+            self.writer.add_summary(summary, step)
+
+        if self.use_html: # save images to a html file
+            for label, image_numpy in visuals.items():
+                if isinstance(image_numpy, list):
+                    for i in range(len(image_numpy)):
+                        img_path = os.path.join(self.img_dir, 'epoch%.3d_%s_%d.jpg' % (epoch, label, i))
+                        util.save_image(image_numpy[i], img_path)
+                else:
+                    img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.jpg' % (epoch, label))
+                    util.save_image(image_numpy, img_path)
+
+            # update website
+            webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=30)
+            for n in range(epoch, 0, -1):
+                webpage.add_header('epoch [%d]' % n)
+                ims = []
+                txts = []
+                links = []
+
+                for label, image_numpy in visuals.items():
+                    if isinstance(image_numpy, list):
+                        for i in range(len(image_numpy)):
+                            img_path = 'epoch%.3d_%s_%d.jpg' % (n, label, i)
+                            ims.append(img_path)
+                            txts.append(label+str(i))
+                            links.append(img_path)
+                    else:
+                        img_path = 'epoch%.3d_%s.jpg' % (n, label)
+                        ims.append(img_path)
+                        txts.append(label)
+                        links.append(img_path)
+                if len(ims) < 10:
+                    webpage.add_images(ims, txts, links, width=self.win_size)
+                else:
+                    num = int(round(len(ims)/2.0))
+                    webpage.add_images(ims[:num], txts[:num], links[:num], width=self.win_size)
+                    webpage.add_images(ims[num:], txts[num:], links[num:], width=self.win_size)
+            webpage.save()
+
+    # errors: dictionary of error labels and values
+    def plot_current_errors(self, errors, step):
+        if self.tf_log:
+            for tag, value in errors.items():
+                summary = self.tf.Summary(value=[self.tf.Summary.Value(tag=tag, simple_value=value)])
+                self.writer.add_summary(summary, step)
+
+    # errors: same format as |errors| of plotCurrentErrors
+    def print_current_errors(self, epoch, i, errors, t):
+        message = '(epoch: %d, iters: %d, time: %.3f) ' % (epoch, i, t)
+        for k, v in errors.items():
+            if v != 0:
+                message += '%s: %.3f ' % (k, v)
+
+        print(message)
+        with open(self.log_name, "a") as log_file:
+            log_file.write('%s\n' % message)
+
+    # save image to the disk
+    def save_images(self, webpage, visuals, image_path):
+        image_dir = webpage.get_image_dir()
+        short_path = ntpath.basename(image_path[0])
+        name = os.path.splitext(short_path)[0]
+
+        webpage.add_header(name)
+        ims = []
+        txts = []
+        links = []
+
+        for label, image_numpy in visuals.items():
+            image_name = '%s_%s.jpg' % (name, label)
+            save_path = os.path.join(image_dir, image_name)
+            util.save_image(image_numpy, save_path)
+
+            ims.append(image_name)
+            txts.append(label)
+            links.append(image_name)
+        webpage.add_images(ims, txts, links, width=self.win_size)
diff --git a/modeling/pytorch_template/config/predict.yaml b/modeling/pytorch_template/config/predict.yaml
new file mode 100644
index 0000000..7ed071c
--- /dev/null
+++ b/modeling/pytorch_template/config/predict.yaml
@@ -0,0 +1,12 @@
+# System
+gpu_num: 0
+
+# Model
+train_serial: '20221025_145511' # ex) '20221012_174254'
+
+# Data
+batch_size: 16
+num_workers: 4
+
+# Logging
+verbose: false
diff --git a/modeling/pytorch_template/config/train.yaml b/modeling/pytorch_template/config/train.yaml
new file mode 100644
index 0000000..6656842
--- /dev/null
+++ b/modeling/pytorch_template/config/train.yaml
@@ -0,0 +1,66 @@
+# System
+gpu_num: 0
+seed: 42
+debug: False
+verbose: False
+
+# Wandb
+project_name : "asksalk"
+run_name : "askal"
+wandb_note : "saklas"
+
+mlflow_exp_name : "xp"
+# Train / val
+val_size: 0.2
+
+# Data Loader
+dataset_name : SegDataset
+input_width: 480
+input_height: 256
+scaler: normalize
+num_workers: 4
+shuffle: True
+drop_last: False
+
+# Model
+model:
+  model_name: Unet
+  args:
+    encoder_name: timm-efficientnet-b0 #timm-regnety_016
+    encoder_weights: noisy-student #imagenet
+    # depth: 5
+    classes: 4
+    activation: null
+
+# Trainer
+n_epochs: 100
+batch_size: 16
+loss: 
+  name: MeanCCELoss # MeanCCELoss
+  args:
+    weight: [1, 1, 1, 1]
+metrics: 
+  - miou
+  - iou1
+  - iou2
+  - iou3
+earlystopping_target: val_miou
+earlystopping_patience: 5
+optimizer:
+  name: AdamW
+  args:
+    lr: 5.0e-04
+    weight_decay: 5.0e-02
+scheduler:
+  name: CosineAnnealingLR
+  args:
+    T_max: 100
+    eta_min: 0
+
+# Logging
+plot:
+  - loss
+  - miou
+  - iou1
+  - iou2
+  - iou3
diff --git a/modeling/pytorch_template/models/utils.py b/modeling/pytorch_template/models/utils.py
new file mode 100644
index 0000000..356c6d7
--- /dev/null
+++ b/modeling/pytorch_template/models/utils.py
@@ -0,0 +1,32 @@
+import segmentation_models_pytorch as smp
+
+def get_model(model_str: str):
+    """모델 클래스 변수 설정
+    Args:
+        model_str (str): 모델 클래스명
+    Note:
+        model_str 이 모델 클래스명으로 정의돼야함
+        `model` 변수에 모델 클래스에 해당하는 인스턴스 할당
+    """
+
+
+    if model_str == 'Unet':
+        return smp.Unet
+    
+    elif model_str == 'FPN':
+        return smp.FPN
+
+    elif model_str == 'DeepLabV3Plus':
+        return smp.DeepLabV3Plus
+    
+    elif model_str == 'UnetPlusPlus':
+        return smp.UnetPlusPlus
+
+    elif model_str == 'PAN':
+        return smp.PAN
+
+    elif model_str == 'MAnet':
+        return smp.MAnet
+
+    elif model_str == 'PSPNet':
+        return smp.PSPNet
\ No newline at end of file
diff --git a/modeling/pytorch_template/modules/datasets.py b/modeling/pytorch_template/modules/datasets.py
new file mode 100644
index 0000000..76b70f6
--- /dev/null
+++ b/modeling/pytorch_template/modules/datasets.py
@@ -0,0 +1,63 @@
+"""Datasets
+"""
+
+from torch.utils.data import Dataset
+import numpy as np
+import cv2
+import os
+
+def get_dataset_function(loss_function_str: str):
+
+    if loss_function_str == 'SegDataset':
+        return SegDataset
+
+
+class SegDataset(Dataset):
+    """Dataset for image segmentation
+
+    Attributs:
+        x_dirs(list): 이미지 경로
+        y_dirs(list): 마스크 이미지 경로
+        input_size(list, tuple): 이미지 크기(width, height)
+        scaler(obj): 이미지 스케일러 함수
+        logger(obj): 로거 객체
+        verbose(bool): 세부 로깅 여부
+    """   
+    def __init__(self, paths, input_size, scaler, mode='train', logger=None, verbose=False):
+        
+        self.x_paths = paths
+        self.y_paths = list(map(lambda x : x.replace('x', 'y'),self.x_paths))
+        self.input_size = input_size
+        self.scaler = scaler
+        self.logger = logger
+        self.verbose = verbose
+        self.mode = mode
+
+
+    def __len__(self):
+        return len(self.x_paths)
+
+    def __getitem__(self, id_: int):
+        
+        filename = os.path.basename(self.x_paths[id_]) # Get filename for logging
+        x = cv2.imread(self.x_paths[id_], cv2.IMREAD_COLOR)
+        orig_size = x.shape
+
+        x = cv2.cvtColor(x, cv2.COLOR_BGR2RGB)
+        x = cv2.resize(x, self.input_size)
+        x = self.scaler(x)
+        x = np.transpose(x, (2, 0, 1))
+
+        if self.mode in ['train', 'valid']:
+            y = cv2.imread(self.y_paths[id_], cv2.IMREAD_GRAYSCALE)
+            y = cv2.resize(y, self.input_size, interpolation=cv2.INTER_NEAREST)
+
+            return x, y, filename
+
+        elif self.mode in ['test']:
+            return x, orig_size, filename
+
+        else:
+            assert False, f"Invalid mode : {self.mode}"
+
+
diff --git a/modeling/pytorch_template/modules/earlystoppers.py b/modeling/pytorch_template/modules/earlystoppers.py
new file mode 100644
index 0000000..a72f957
--- /dev/null
+++ b/modeling/pytorch_template/modules/earlystoppers.py
@@ -0,0 +1,50 @@
+"""Early stopping 클래스 정의
+"""
+import numpy as np
+import logging
+
+class EarlyStopper():
+    """Early stoppiing 여부 판단
+    
+    Attributes:
+        patience (int): target가 줄어들지 않아도 학습할 epoch 수
+        patience_counter (int): target 가 줄어들지 않을 때 마다 1씩 증가
+        best_target (float): 최소 target
+        stop (bool): True 일 때 학습 중단
+
+    """
+
+    def __init__(self, patience: int, mode:str='max', logger:logging.RootLogger=None)-> None:
+        """ 초기화
+        
+        Args:
+            patience (int): target가 줄어들지 않아도 학습할 epoch 수
+            mode (str): max tracks higher value, min tracks lower value
+
+        """
+        self.patience = patience
+        self.mode = mode
+        self.logger = logger
+
+        # Initiate
+        self.patience_counter = 0
+        self.stop = False
+        self.best_target = np.inf
+        self.logger.info(f"ES | Initiated, mode: {self.mode}, best score: {self.best_target}, patience: {self.patience}")
+        
+    def check_early_stopping(self, target: float)-> None:
+        target = -target if self.mode == 'max' else target
+
+        if target > self.best_target:
+            self.patience_counter += 1
+            self.logger.info(f"ES | {self.patience_counter}/{self.patience}, best:{abs(self.best_target)}, now:{abs(target)}")
+            
+            if self.patience_counter == self.patience:
+                self.logger.debug(f"ES | Stop {self.patience_counter}/{self.patience}")
+                self.stop = True 
+
+        elif target <= self.best_target:
+            self.patience_counter = 0
+            self.best_target = target
+            self.logger.info(f"ES | {self.patience_counter}/{self.patience}, best:{abs(self.best_target)}, now:{abs(target)}")
+            self.logger.info(f"ES | Set patience counter as {self.patience_counter}")
\ No newline at end of file
diff --git a/modeling/pytorch_template/modules/losses.py b/modeling/pytorch_template/modules/losses.py
new file mode 100644
index 0000000..ce2e902
--- /dev/null
+++ b/modeling/pytorch_template/modules/losses.py
@@ -0,0 +1,69 @@
+"""Losses
+    * https://github.com/JunMa11/SegLoss
+"""
+
+
+from torch.nn import functional as F
+import torch.nn as nn
+import torch
+
+def get_loss_function(loss_function_str: str):
+
+    if loss_function_str == 'MeanCCELoss':
+
+        return CCE
+
+    elif loss_function_str == 'GDLoss':
+
+        return GeneralizedDiceLoss
+
+class CCE(nn.Module):
+
+    def __init__(self, weight, **kwargs):
+        super(CCE, self).__init__()
+        
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.weight = torch.Tensor(weight).to(device)
+
+    def forward(self, inputs, targets):
+        
+        loss = F.cross_entropy(inputs, targets, reduction='none', weight=self.weight)
+        unique_values, unique_counts = torch.unique(targets, return_counts=True)
+        selected_weight = torch.index_select(input=self.weight, dim=0, index=unique_values)
+
+        numerator = loss.sum()                               # weighted losses
+        denominator = (unique_counts*selected_weight).sum()  # weigthed counts
+
+        loss = numerator/denominator
+
+        return loss
+
+
+class GeneralizedDiceLoss(nn.Module):
+    
+    def __init__(self, **kwargs):
+        super(GeneralizedDiceLoss, self).__init__()
+        self.scaler = nn.Softmax(dim=1)  # Softmax for loss
+
+    def forward(self, inputs, targets):
+
+        targets = targets.contiguous()
+        targets = torch.nn.functional.one_hot(targets.to(torch.int64), inputs.size()[1])  # B, H, W, C
+
+        inputs = inputs.contiguous()
+        inputs = self.scaler(inputs)
+        inputs = inputs.permute(0, 2, 3, 1)  # B, H, W, C
+
+        w = 1. / (torch.sum(targets, (0, 1, 2)) ** 2 + 1e-9)
+
+        numerator = targets * inputs
+        numerator = w * torch.sum(numerator, (0, 1, 2))
+        numerator = torch.sum(numerator)
+
+        denominator = targets + inputs
+        denominator = w * torch.sum(denominator, (0, 1, 2))
+        denominator = torch.sum(denominator)
+
+        dice = 2. * (numerator + 1e-9) / (denominator + 1e-9)
+
+        return 1. - dice
\ No newline at end of file
diff --git a/modeling/pytorch_template/modules/metrics.py b/modeling/pytorch_template/modules/metrics.py
new file mode 100644
index 0000000..79544d0
--- /dev/null
+++ b/modeling/pytorch_template/modules/metrics.py
@@ -0,0 +1,63 @@
+"""Metric 함수 정의
+"""
+
+import torch
+import numpy as np
+SMOOTH = 1e-6
+
+def get_metric_function(metric_function_str):
+    """
+    Add metrics, weights for weighted score
+    """
+
+    if metric_function_str == 'miou':
+        iou = Iou()
+        return iou.get_miou
+
+    elif metric_function_str == 'iou1':
+        iou =Iou(class_num=1)
+        return iou.get_iou 
+
+    elif metric_function_str == 'iou2':
+        iou =Iou(class_num=2)
+        return iou.get_iou
+
+    elif metric_function_str == 'iou3':
+        iou =Iou(class_num=3)
+        return iou.get_iou
+
+        
+class Iou:
+    
+    def __init__(self, class_num:int=0):
+        self.class_num = class_num
+        
+    def get_iou(self, outputs: torch.Tensor, labels: torch.Tensor):
+        mask_value = self.class_num
+
+        batch_size = outputs.size()[0]
+            
+        intersection = ((outputs.int() == mask_value) & (labels.int() == mask_value) & (outputs.int() == labels.int())).float()
+        intersection = intersection.view(batch_size, -1).sum(1)
+
+        union = ((outputs.int() == mask_value) | (labels.int() == mask_value)).float()
+        union = union.view(batch_size, -1).sum(1)
+
+        iou = (intersection + SMOOTH) / (union + SMOOTH)
+            
+        return iou.mean()
+
+    def get_miou(self, outputs: torch.Tensor, labels: torch.Tensor):
+        # Not exactly match the mIoU definition
+        batch_size = outputs.size()[0]
+    
+        intersection = ((outputs.int() > 0) & (labels.int() > 0) & (outputs.int() == labels.int())).float()
+        intersection = intersection.view(batch_size, -1).sum(1)
+    
+        union = ((outputs.int() > 0) | (labels.int() > 0)).float()
+        union = union.view(batch_size, -1).sum(1)
+    
+        iou = (intersection + SMOOTH) / (union + SMOOTH)
+    
+        return iou.mean()
+
diff --git a/modeling/pytorch_template/modules/optimizers.py b/modeling/pytorch_template/modules/optimizers.py
new file mode 100644
index 0000000..51f206a
--- /dev/null
+++ b/modeling/pytorch_template/modules/optimizers.py
@@ -0,0 +1,17 @@
+import torch.optim as optim
+
+def get_optimizer(optimizer_str: str)-> 'optimizer':
+
+    if optimizer_str == 'SGD':
+
+        optimizer = optim.SGD
+
+    elif optimizer_str == 'Adam':
+
+        optimizer = optim.Adam
+    
+    elif optimizer_str == 'AdamW':
+        
+        optimizer = optim.AdamW
+        
+    return optimizer
diff --git a/modeling/pytorch_template/modules/recorders.py b/modeling/pytorch_template/modules/recorders.py
new file mode 100644
index 0000000..3091787
--- /dev/null
+++ b/modeling/pytorch_template/modules/recorders.py
@@ -0,0 +1,126 @@
+"""PerformanceRecorder
+
+"""
+from matplotlib import pyplot as plt
+import pandas as pd
+import logging
+import torch
+import csv
+import os
+import wandb
+import mlflow
+
+
+class Recorder():
+
+    def __init__(self,
+                 record_dir: str,
+                 model: 'model',
+                 optimizer: 'optimizer',
+                 scheduler: 'scheduler',
+                 amp: 'amp'=None,
+                 logger: logging.RootLogger=None):
+        """Recorder 초기화
+            
+        Args:
+
+        Note:
+        """
+        
+        self.record_dir = record_dir
+        self.plot_dir = os.path.join(record_dir, 'plots')
+        self.record_filepath = os.path.join(self.record_dir, 'record.csv')
+        self.weight_path = os.path.join(record_dir, 'model.pt')
+
+        self.logger = logger
+        self.model = model
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+        self.amp = amp
+        os.makedirs(self.plot_dir, exist_ok=True)
+
+    def set_model(self, model: 'model'):
+        self.model = model
+
+    def set_logger(self, logger: logging.RootLogger):
+        self.logger = logger
+    
+    def wandb_mlflow_log(self, model, log_dict,sample_image=None):
+        log_dict['lr'] = log_dict['lr'][0]
+        log_dict = {k: int(v) for k, v in log_dict.items()}
+        mlflow.log_metrics(log_dict)
+        mlflow.pytorch.log_model(
+            pytorch_model=model,
+            artifact_path = "sf2f_pytorch",
+        )
+        if sample_image:
+            log_dict['sample'] = [wandb.Image(sample_image, caption=f"Sample")]
+        wandb.log(log_dict)
+
+
+    def add_row(self, row_dict: dict):
+        """Epoch 단위 성능 적재
+
+        Args:
+            row (list): 
+
+        """
+
+        fieldnames = list(row_dict.keys())
+
+        with open(self.record_filepath, newline='', mode='a') as f:
+            writer = csv.DictWriter(f, fieldnames=fieldnames)
+
+            if f.tell() == 0:
+                writer.writeheader()
+
+            writer.writerow(row_dict)
+        self.logger.debug(f"RECORDER | record saved: {self.record_filepath}")
+
+    def save_weight(self, epoch: int)-> None:
+        """Weight 저장
+            amp 추가
+        Args:
+            loss (float): validation loss
+            model (`model`): model
+        
+        """
+        check_point = {
+            'epoch': epoch + 1,
+            'model': self.model.state_dict(),
+            'optimizer': self.optimizer.state_dict(),
+            'scheduler': self.scheduler.state_dict() if self.scheduler else None,
+            'amp': self.amp.state_dict() if self.amp else None}
+
+        torch.save(check_point, self.weight_path)
+        self.logger.debug(f"RECORDER | epoch {epoch}, checkpoint saved: {self.weight_path}")
+
+    
+    def save_plot(self, plots: list):
+
+        record_df = pd.read_csv(self.record_filepath)
+        current_epoch = record_df['epoch_id'].max()
+        epoch_range = list(range(0, current_epoch+1))
+        color_list = ['red', 'blue']
+
+        for plot_name in plots:
+            columns = [f'train_{plot_name}', f'val_{plot_name}']
+
+            fig = plt.figure(figsize=(20, 8))
+            
+            for id_, column in enumerate(columns):
+                values = record_df[column].tolist()
+                plt.plot(epoch_range, values, marker='.', c=color_list[id_], label=column)
+             
+            plt.title(plot_name, fontsize=15)
+            plt.legend(loc='upper right')
+            plt.grid()
+            plt.xlabel('epoch')
+            plt.ylabel(plot_name)
+            plt.xticks(epoch_range, [str(i) for i in epoch_range])
+            plt.close(fig)
+            fig.savefig(os.path.join(self.plot_dir, plot_name +'.png'))
+
+            self.logger.debug(f"RECORDER | plot saved: {os.path.join(self.plot_dir, plot_name +'.png')}")
+
+
diff --git a/modeling/pytorch_template/modules/scalers.py b/modeling/pytorch_template/modules/scalers.py
new file mode 100644
index 0000000..561db77
--- /dev/null
+++ b/modeling/pytorch_template/modules/scalers.py
@@ -0,0 +1,33 @@
+"""Preprocessor
+
+"""
+
+import numpy as np
+import cv2
+
+def get_image_scaler(scaler_str: str):
+    
+    if scaler_str == 'normalize':
+        return normalize_image
+
+    elif scaler_str == 'normalize_histogram':
+        return normalize_histogram
+
+    else:
+        return None
+
+def normalize_image(image: np.ndarray, max_pixel_value:int = 255)->np.ndarray:
+    """Normalize image by pixel
+    """
+    normalized_image = image / max_pixel_value
+
+    return normalized_image
+
+
+def normalize_histogram(image: np.ndarray)-> np.ndarray:
+    """Normalize histogram
+    """
+    lab_image = cv2.cvtColor(image, cv2.COLOR_RGB2LAB)
+    lab_image[:, :, 0] = cv2.normalize(lab_image[:, :, 0], None, 0, 255 , cv2.NORM_MINMAX, dtype=cv2.CV_32F)
+    histogram_normalized_image = cv2.cvtColor(lab_image, cv2.COLOR_LAB2RGB)
+    return histogram_normalized_image
\ No newline at end of file
diff --git a/modeling/pytorch_template/modules/schedulers.py b/modeling/pytorch_template/modules/schedulers.py
new file mode 100644
index 0000000..40acf11
--- /dev/null
+++ b/modeling/pytorch_template/modules/schedulers.py
@@ -0,0 +1,15 @@
+from torch.optim import lr_scheduler
+
+def get_scheduler(scheduler_str)-> object:
+    
+    scheduler = None
+    
+    if scheduler_str == 'CosineAnnealingLR':
+
+        scheduler = lr_scheduler.CosineAnnealingLR
+
+    elif scheduler_str == 'CosineAnnealingWarmRestarts':
+
+        scheduler = lr_scheduler.CosineAnnealingWarmRestarts
+
+    return scheduler
\ No newline at end of file
diff --git a/modeling/pytorch_template/modules/trainer.py b/modeling/pytorch_template/modules/trainer.py
new file mode 100644
index 0000000..c1325ce
--- /dev/null
+++ b/modeling/pytorch_template/modules/trainer.py
@@ -0,0 +1,107 @@
+"""Trainer
+
+"""
+from tqdm import tqdm
+import torch
+
+class Trainer():
+    """Trainer
+    
+    Attribues:
+        model(object): 모델 객체
+        optimizer (object): optimizer 객체
+        scheduler (object): scheduler 객체
+        loss_func (object): loss 함수 객체
+        metric_funcs (dict): metric 함수 dict
+        device (str):  'cuda' | 'cpu'
+        logger (object): logger 객체
+        loss (float): loss
+        scores (dict): metric 별 score
+    """
+
+    def __init__(self,
+                model,
+                optimizer,
+                scheduler,
+                loss_func,
+                metric_funcs,
+                device,
+                logger=None):        
+        
+        self.model = model
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+        self.loss_func = loss_func
+        self.metric_funcs = metric_funcs
+        self.device = device
+        self.logger = logger
+
+        self.loss = 0
+        self.scores = {metric_name: 0 for metric_name, _ in self.metric_funcs.items()}
+
+    def train(self, dataloader, epoch_index=0):
+        
+        self.model.train()
+        for batch_id, (x, y, filename) in enumerate(tqdm(dataloader)):
+
+            # Load data to gpu
+            x = x.to(self.device, dtype=torch.float)
+            y = y.to(self.device, dtype=torch.long)
+            
+            # Inference
+            y_pred = self.model(x)
+            
+            # Loss
+            loss = self.loss_func(y_pred, y)
+
+            # Update
+            self.optimizer.zero_grad()
+            loss.backward()
+            self.optimizer.step()
+            
+            # Metric
+            for metric_name, metric_func in self.metric_funcs.items():
+                self.scores[metric_name] += metric_func(y_pred.argmax(1), y).item() / len(dataloader)
+            # History
+            self.loss += loss.item()
+            self.logger.debug(f"TRAINER | train epoch: {epoch_index}, batch: {batch_id}/{len(dataloader)-1}, loss: {loss.item()}")
+            self.logger.debug(f"TRAINER | {filename}")
+
+        self.scheduler.step()
+        self.loss = self.loss / len(dataloader)
+
+    def validate(self, dataloader, epoch_index=0):
+    
+        self.model.eval()
+
+        with torch.no_grad():
+
+            for batch_id, (x, y, filename) in enumerate(tqdm(dataloader)):
+
+                # Load data to gpu
+                x = x.to(self.device, dtype=torch.float)
+                y = y.to(self.device, dtype=torch.long)
+                
+                # Inference
+                y_pred = self.model(x)
+
+                # Loss
+                loss = self.loss_func(y_pred, y)
+
+                # Metric
+                for metric_name, metric_func in self.metric_funcs.items():
+                    self.scores[metric_name] += metric_func(y_pred.argmax(1), y).item() / len(dataloader)
+
+                # History
+                self.loss += loss.item()
+
+        self.loss = self.loss / len(dataloader)
+        self.logger.debug(f"TRAINER | val/test epoch: {epoch_index}, batch: {batch_id}/{len(dataloader)-1}, loss: {loss.item()}")
+        self.logger.debug(f"TRAINER | {filename}")
+        
+    def clear_history(self):
+
+        torch.cuda.empty_cache()
+        self.loss = 0
+        self.scores = {metric_name: 0 for metric_name, _ in self.metric_funcs.items()}
+        self.logger.debug(f"TRAINER | Clear history, loss: {self.loss}, score: {self.scores}")
\ No newline at end of file
diff --git a/modeling/pytorch_template/modules/utils.py b/modeling/pytorch_template/modules/utils.py
new file mode 100644
index 0000000..918d650
--- /dev/null
+++ b/modeling/pytorch_template/modules/utils.py
@@ -0,0 +1,86 @@
+"""공용 함수
+    * File IO
+    * Logger
+    * System
+"""
+import logging
+import pickle5 as pickle
+import json
+import yaml
+
+"""
+File IO
+"""
+def save_pickle(path, obj):
+    
+    with open(path, 'wb') as f:
+        
+        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
+
+
+def load_pickle(path):
+
+    with open(path, 'rb') as f:
+
+        return pickle.load(f)
+
+
+def save_json(path, obj, sort_keys=True)-> str:
+    
+    try:
+        
+        with open(path, 'w') as f:
+            
+            json.dump(obj, f, indent=4, sort_keys=sort_keys)
+        
+        msg = f"Json saved {path}"
+    
+    except Exception as e:
+        msg = f"Fail to save {e}"
+
+    return msg
+
+def load_json(path):
+
+	with open(path, 'r', encoding='utf-8') as f:
+
+		return json.load(f)
+
+
+def save_yaml(path, obj):
+	
+	with open(path, 'w') as f:
+
+		yaml.dump(obj, f, sort_keys=False)
+		
+
+def load_yaml(path):
+
+	with open(path, 'r') as f:
+		return yaml.load(f, Loader=yaml.FullLoader)
+
+"""
+Logger
+"""
+def get_logger(name: str, file_path: str, stream=False, level='info')-> logging.RootLogger:
+
+    level_map = {
+        'info': logging.INFO,
+        'debug': logging.DEBUG
+    }
+    
+    logger = logging.getLogger(name)
+    logger.setLevel(level_map[level])  # logging all levels
+    
+    formatter = logging.Formatter('%(asctime)s | %(name)s | %(levelname)s | %(message)s')
+    stream_handler = logging.StreamHandler()
+    file_handler = logging.FileHandler(file_path)
+
+    stream_handler.setFormatter(formatter)
+    file_handler.setFormatter(formatter)
+
+    if stream:
+        logger.addHandler(stream_handler)
+    logger.addHandler(file_handler)
+
+    return logger
\ No newline at end of file
diff --git a/modeling/pytorch_template/predict.py b/modeling/pytorch_template/predict.py
new file mode 100644
index 0000000..703312e
--- /dev/null
+++ b/modeling/pytorch_template/predict.py
@@ -0,0 +1,105 @@
+"""
+Predict
+"""
+from datetime import datetime
+from tqdm import tqdm
+import numpy as np
+import random, os, sys, torch, cv2, warnings
+from glob import glob
+from torch.utils.data import DataLoader
+
+prj_dir = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(prj_dir)
+
+from modules.utils import load_yaml, save_yaml, get_logger
+from modules.scalers import get_image_scaler
+from modules.datasets import get_dataset_function
+from models.utils import get_model
+warnings.filterwarnings('ignore')
+
+if __name__ == '__main__':
+
+    #! Load config
+    config = load_yaml(os.path.join(prj_dir, 'config', 'predict.yaml'))
+    train_config = load_yaml(os.path.join(prj_dir, 'results', 'train', config['train_serial'], 'train.yaml'))
+    
+    #! Set predict serial
+    pred_serial = config['train_serial'] + '_' + datetime.now().strftime("%Y%m%d_%H%M%S")
+
+    # Set random seed, deterministic
+    torch.cuda.manual_seed(train_config['seed'])
+    torch.manual_seed(train_config['seed'])
+    np.random.seed(train_config['seed'])
+    random.seed(train_config['seed'])
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+    # Set device(GPU/CPU)
+    os.environ['CUDA_VISIBLE_DEVICES'] = str(config['gpu_num'])
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    
+    # Create train result directory and set logger
+    pred_result_dir = os.path.join(prj_dir, 'results', 'pred', pred_serial)
+    pred_result_dir_mask = os.path.join(prj_dir, 'results', 'pred', pred_serial, 'mask')
+    os.makedirs(pred_result_dir, exist_ok=True)
+    os.makedirs(pred_result_dir_mask, exist_ok=True)
+
+    # Set logger
+    logging_level = 'debug' if config['verbose'] else 'info'
+    logger = get_logger(name='train',
+                        file_path=os.path.join(pred_result_dir, 'pred.log'),
+                        level=logging_level)
+
+    # Set data directory
+    test_dirs = os.path.join(prj_dir, 'data', 'test')
+    test_img_paths = glob(os.path.join(test_dirs, 'x', '*.png'))
+
+    #! Load data & create dataset for train 
+    test_dataset = get_dataset_function(train_config['dataset_name'])
+    test_dataset = test_dataset(paths=test_img_paths,
+                            input_size=[train_config['input_width'], train_config['input_height']],
+                            scaler=get_image_scaler(train_config['scaler']),
+                            mode='test',
+                            logger=logger)
+
+    # Create data loader
+    test_dataloader = DataLoader(dataset=test_dataset,
+                                batch_size=config['batch_size'],
+                                num_workers=config['num_workers'],
+                                shuffle=False,
+                                drop_last=False)
+    logger.info(f"Load test dataset: {len(test_dataset)}")
+
+    # Load architecture
+    model = get_model(model_str=train_config['model']['model_name'])
+    model = model(**config['model']['args']).to(device)
+    logger.info(f"Load model architecture: {train_config['model']['model_name']}")
+
+    #! Load weight
+    check_point_path = os.path.join(prj_dir, 'results', 'train', config['train_serial'], 'model.pt')
+    check_point = torch.load(check_point_path)
+    model.load_state_dict(check_point['model'])
+    logger.info(f"Load model weight, {check_point_path}")
+
+    # Save config
+    save_yaml(os.path.join(pred_result_dir, 'train_config.yml'), train_config)
+    save_yaml(os.path.join(pred_result_dir, 'predict_config.yml'), config)
+    
+    # Predict
+    logger.info(f"START PREDICTION")
+
+    model.eval()
+
+    with torch.no_grad():
+
+        for batch_id, (x, orig_size, filename) in enumerate(tqdm(test_dataloader)):
+            
+            x = x.to(device, dtype=torch.float)
+            y_pred = model(x)
+            y_pred_argmax = y_pred.argmax(1).cpu().numpy().astype(np.uint8)
+            orig_size = [(orig_size[0].tolist()[i], orig_size[1].tolist()[i]) for i in range(len(orig_size[0]))]
+            # Save predict result
+            for filename_, orig_size_, y_pred_ in zip(filename, orig_size, y_pred_argmax):
+                resized_img = cv2.resize(y_pred_, [orig_size_[1], orig_size_[0]], interpolation=cv2.INTER_NEAREST)
+                cv2.imwrite(os.path.join(pred_result_dir_mask, filename_), resized_img)
+    logger.info(f"END PREDICTION")
\ No newline at end of file
diff --git a/modeling/pytorch_template/train.py b/modeling/pytorch_template/train.py
new file mode 100644
index 0000000..0aed70b
--- /dev/null
+++ b/modeling/pytorch_template/train.py
@@ -0,0 +1,214 @@
+"""Train
+"""
+from datetime import datetime
+from time import time
+import numpy as np
+import shutil, random, os, sys, torch
+from glob import glob
+import mlflow, wandb
+from torch.utils.data import DataLoader
+from sklearn.model_selection import train_test_split
+from config import secret
+prj_dir = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(prj_dir)
+
+from modules.utils import load_yaml, get_logger
+from modules.metrics import get_metric_function
+from modules.earlystoppers import EarlyStopper
+from modules.losses import get_loss_function
+from modules.optimizers import get_optimizer
+from modules.schedulers import get_scheduler
+from modules.scalers import get_image_scaler
+from modules.datasets import get_dataset_function
+from modules.recorders import Recorder
+from modules.trainer import Trainer
+from models.utils import get_model
+
+if __name__ == '__main__':
+    # Load config
+    config_path = os.path.join(prj_dir, 'config', 'train.yaml')
+    config = load_yaml(config_path)
+    
+    # Set train serial: ex) 20211004
+    train_serial = datetime.now().strftime("%Y%m%d_%H%M%S")
+    train_serial = 'debug' if config['debug'] else train_serial
+    
+    ##mlflow set
+    os.environ["MLFLOW_S3_ENDPOINT_URL"] = secret.MLFLOW_S3_ENDPOINT_URL
+    os.environ["MLFLOW_TRACKING_URI"] = secret.MLFLOW_TRACKING_URI
+    os.environ["AWS_ACCESS_KEY_ID"] = secret.AWS_ACCESS_KEY_ID
+    os.environ["AWS_SECRET_ACCESS_KEY"] = secret.AWS_SECRET_ACCESS_KEY
+    
+    ## wandb
+    mlflow.start_run()
+    mlflow.set_experiment(config['mlflow_exp_name'])
+    wandb.init(
+            project=config['project_name'],
+            config={
+                "architecture": config['model']['model_name'],
+                "dataset": config['dataset_name'],
+                "notes": config['wandb_note'],
+            },
+            name=config['run_name'],
+    )
+
+    # Set random seed, deterministic
+    torch.cuda.manual_seed(config['seed'])
+    torch.manual_seed(config['seed'])
+    np.random.seed(config['seed'])
+    random.seed(config['seed'])
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+    # Set device(GPU/CPU)
+    os.environ['CUDA_VISIBLE_DEVICES'] = str(config['gpu_num'])
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    # Create train result directory and set logger
+    train_result_dir = os.path.join(prj_dir, 'results', 'train', train_serial)
+    os.makedirs(train_result_dir, exist_ok=True)
+
+    # Set logger
+    logging_level = 'debug' if config['verbose'] else 'info'
+    logger = get_logger(name='train',
+                        file_path=os.path.join(train_result_dir, 'train.log'),
+                        level=logging_level)
+
+
+    # Set data directory
+    train_dirs = os.path.join(prj_dir, 'data', 'train')
+
+    # Load data and create dataset for train 
+    # Load image scaler
+    train_img_paths = glob(os.path.join(train_dirs, 'x', '*.png'))
+    train_img_paths, val_img_paths = train_test_split(train_img_paths, test_size=config['val_size'], random_state=config['seed'], shuffle=True)
+    
+    train_dataset = get_dataset_function(config['dataset_name']) 
+    val_dataset = get_dataset_function(config['dataset_name'])
+    
+    train_dataset = train_dataset(paths=train_img_paths,
+                            input_size=[config['input_width'], config['input_height']],
+                            scaler=get_image_scaler(config['scaler']),
+                            logger=logger)
+    val_dataset = val_dataset(paths=val_img_paths,
+                            input_size=[config['input_width'], config['input_height']],
+                            scaler=get_image_scaler(config['scaler']),
+                            logger=logger)
+    # Create data loader
+    train_dataloader = DataLoader(dataset=train_dataset,
+                                batch_size=config['batch_size'],
+                                num_workers=config['num_workers'], 
+                                shuffle=config['shuffle'],
+                                drop_last=config['drop_last'])
+                                
+    val_dataloader = DataLoader(dataset=val_dataset,
+                                batch_size=config['batch_size'],
+                                num_workers=config['num_workers'], 
+                                shuffle=False,
+                                drop_last=config['drop_last'])
+
+    logger.info(f"Load dataset, train: {len(train_dataset)}, val: {len(val_dataset)}")
+    
+    # Load model
+    model = get_model(model_str=config['model']['model_name'])
+    model = model(**config['model']['args']).to(device)
+    logger.info(f"Load model architecture: {config['model']['model_name']}")
+
+    # Set optimizer
+    optimizer = get_optimizer(optimizer_str=config['optimizer']['name'])
+    optimizer = optimizer(model.parameters(), **config['optimizer']['args'])
+    
+    # Set Scheduler
+    scheduler = get_scheduler(scheduler_str=config['scheduler']['name'])
+    scheduler = scheduler(optimizer=optimizer, **config['scheduler']['args'])
+
+    # Set loss function
+    loss_func = get_loss_function(loss_function_str=config['loss']['name'])
+    loss_func = loss_func(**config['loss']['args'])
+
+    # Set metric
+    metric_funcs = {metric_name:get_metric_function(metric_name) for metric_name in config['metrics']}
+    logger.info(f"Load optimizer:{config['optimizer']['name']}, scheduler: {config['scheduler']['name']}, loss: {config['loss']['name']}, metric: {config['metrics']}")
+
+    # Set trainer
+    trainer = Trainer(model=model,
+                    optimizer=optimizer,
+                    scheduler=scheduler,
+                    loss_func=loss_func,
+                    metric_funcs=metric_funcs,
+                    device=device,
+                    logger=logger)
+    logger.info(f"Load trainer")
+
+    # Set early stopper
+    early_stopper = EarlyStopper(patience=config['earlystopping_patience'],
+                                logger=logger)
+    # Set recorder
+    recorder = Recorder(record_dir=train_result_dir,
+                        model=model,
+                        optimizer=optimizer,
+                        scheduler=scheduler,
+                        logger=logger)
+    logger.info("Load early stopper, recorder")
+
+    # Recorder - save train config
+    shutil.copy(config_path, os.path.join(recorder.record_dir, 'train.yaml'))
+
+    # Train
+    print("START TRAINING")
+    logger.info("START TRAINING")
+    for epoch_id in range(config['n_epochs']):
+        
+        # Initiate result row
+        row = dict()
+        row['epoch_id'] = epoch_id
+        row['train_serial'] = train_serial
+        row['lr'] = trainer.scheduler.get_last_lr()
+
+        # Train
+        print(f"Epoch {epoch_id}/{config['n_epochs']} Train..")
+        logger.info(f"Epoch {epoch_id}/{config['n_epochs']} Train..")
+        tic = time()
+        trainer.train(dataloader=train_dataloader, epoch_index=epoch_id)
+        toc = time()
+        # Write tarin result to result row
+        row['train_loss'] = trainer.loss  # Loss
+        for metric_name, metric_score in trainer.scores.items():
+            row[f'train_{metric_name}'] = metric_score
+
+        row['train_elapsed_time'] = round(toc-tic, 1)
+        # Clear
+        trainer.clear_history()
+
+        # Validation
+        print(f"Epoch {epoch_id}/{config['n_epochs']} Validation..")
+        logger.info(f"Epoch {epoch_id}/{config['n_epochs']} Validation..")
+        tic = time()
+        trainer.validate(dataloader=val_dataloader, epoch_index=epoch_id)
+        toc = time()
+        row['val_loss'] = trainer.loss
+        # row[f"val_{config['metric']}"] = trainer.score
+        for metric_name, metric_score in trainer.scores.items():
+            row[f'val_{metric_name}'] = metric_score
+        row['val_elapsed_time'] = round(toc-tic, 1)
+        trainer.clear_history()
+
+        # Performance record - row
+        recorder.add_row(row)
+        recorder.wandb_mlflow_log(model=model, log_dict=row, sample_image=None)
+        
+        # Performance record - plot
+        recorder.save_plot(config['plot'])
+
+        # Check early stopping
+        early_stopper.check_early_stopping(row[config['earlystopping_target']])
+        if early_stopper.patience_counter == 0:
+            recorder.save_weight(epoch=epoch_id)
+            
+        if early_stopper.stop:
+            print(f"Epoch {epoch_id}/{config['n_epochs']}, Stopped counter {early_stopper.patience_counter}/{config['earlystopping_patience']}")
+            logger.info(f"Epoch {epoch_id}/{config['n_epochs']}, Stopped counter {early_stopper.patience_counter}/{config['earlystopping_patience']}")
+            break
+
+    print("END TRAINING")
+    logger.info("END TRAINING")
diff --git a/modeling/sf2f/GETTING_STARTED.md b/modeling/sf2f/GETTING_STARTED.md
new file mode 100644
index 0000000..1241f5a
--- /dev/null
+++ b/modeling/sf2f/GETTING_STARTED.md
@@ -0,0 +1,120 @@
+## Environment Setup
+Create python env:
+```
+conda create -n sf2f python=3.6
+conda activate sf2f
+```
+Our repo is developed with python 3.6, CUDA 10.1, and PyTorch 1.7.1. We suggest you to install the PyTorch version which is suitable for your machine, according to [PyTorch Official Website](https://pytorch.org/).
+```
+# This is the installation command for our environment
+conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch
+
+```
+
+Next, install other python dependencies:
+```
+pip install -r requirements.txt
+```
+
+## Prepare Dataset
+
+**Face Data.** Please download the high-quality face data from [HQ-VoxCeleb](https://github.com/BAI-Yeqi/HQ-VoxCeleb).
+
+**Speech Data.** Please download speech data from VoxCeleb1 and VoxCeleb2 on their [Offical Website](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/).
+
+The speech data need to be converted to mel-spectrograms via STFT with 30ms frame duration, please process the data using our script:
+```
+python ./scripts/convert_wav_to_mel.py --n_jobs 5
+```
+`convert_wav_to_mel.py` used parallel processes to speed up the mel extraction. Feel free to adjust `n_jobs` to adapt to the configuration of your machine. 
+
+**Train-val Split.** To create train-validation split:
+```
+python ./scripts/create_split_json.py
+```
+
+**Dataset Structure.** Refer to the following picture for ```data/``` folder structure.
+<img src="./images/data_VoxCeleb.png" align="center" width="60%">
+
+
+## Download VGGFace2 Resnet Checkpoint
+We developed VGGFace Score (VFS) with pretrained ResNet model on VGGFace2 Dataset (https://github.com/cydonia999/VGGFace2-pytorch).
+
+To make sure VGGFace Score can be evaluated, download this https://drive.google.com/file/d/1A94PAAnwk6L7hXdBXLFosB_s0SzEhAFU/view to ```./scripts/weights/resnet50_ft_weights.pkl```.
+
+
+
+## Launch Training
+
+To train SF2F 1st-stage, where only encoder & decoder is learned:
+```
+python train.py \
+  --path_opt options/vox/sf2f/sf2f_1st_stage.yaml \
+  --batch_size 256 \
+  --visualize_every 10 \
+  --epochs 12000 \
+  --eval_epochs 15
+```
+
+To train SF2F 2nd-stage, where fuser is learned while encoder & decoder are frozen:
+```
+python train.py \
+  --path_opt options/vox/sf2f/sf2f_fuser.yaml \
+  --batch_size 256 \
+  --visualize_every 10 \
+  --epochs 50 \
+  --eval_epochs 1 \
+  --eval_mode_after 100 \
+  --train_fuser_only True \
+  --pretrained_path \
+    output/{sf2f_1st_stage_experiment_pretrained_model}.pt \
+  --learning_rate 1e-4
+```
+
+To train baseline voice2face:
+```
+python train.py \
+  --path_opt options/vox/baseline/v2f.yaml \
+  --batch_size 256 \
+  --visualize_every 10 \
+  --epochs 12000 \
+  --eval_epochs 15
+```
+
+## Visualize Training Process
+
+The training process is visualized with [tensorboard](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/04-utils/tensorboard).
+
+Start the **tensorboard** server:
+```
+tensorboard --logdir='./output' --port=8097
+```
+(Optional) If working on a remote server, mapping the remote tensorboard server to local:
+```
+ssh -N -L localhost:8000:localhost:8097 user@your_remote_server
+```
+Visualize the training process by opening `localhost:8097` (from local)  `localhost:8000` (from remote)
+
+
+## Test
+To evaluate 1st-stage SF2F and baseline models:
+```
+python test.py \
+  --path_opt xxxx.yaml \
+  --batch_size 1 \
+  --checkpoint_start_from output/xxxx/best_with_model.pt \
+  --recall_method cos_sim
+```
+
+To evaluate SF2F with fuser:
+```
+python test.py \
+  --path_opt options/vox/sf2f/sf2f_fuser.yaml \
+  --batch_size 1 \
+  --checkpoint_start_from \
+    output/{sf2f_fuser_experiment_name} \
+  --recall_method cos_sim \
+  --face_gen_mode naive \
+  --train_fuser_only True \
+  --checkpoint L1 cos R10 epoch_2 epoch_4 epoch_6 epoch_8 epoch_10 epoch_12
+```
diff --git a/modeling/sf2f/LICENSE b/modeling/sf2f/LICENSE
new file mode 100644
index 0000000..a5b2462
--- /dev/null
+++ b/modeling/sf2f/LICENSE
@@ -0,0 +1,201 @@
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+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
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+   Copyright 2022 Bai Yeqi
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diff --git a/modeling/sf2f/README.md b/modeling/sf2f/README.md
new file mode 100644
index 0000000..0fdc00c
--- /dev/null
+++ b/modeling/sf2f/README.md
@@ -0,0 +1,60 @@
+# ACMMM '22: Speech Fusion to Face: Bridging the Gap Between Human's Vocal Characteristics and Facial Imaging
+
+This is the officical PyTorch implementation for the paper *Speech Fusion to Face: Bridging the Gap Between Human's Vocal Characteristics and Facial Imaging*.
+
+The main paper of this work is published on ACMMM 2022 as a full paper [[acmdl](https://dl.acm.org/doi/10.1145/3503161.3547850)][[arxiv](https://arxiv.org/abs/2006.05888)]. Please refer to our [supplementary material](https://sf2f.github.io/) for more information about this paper.
+
+[![license](https://img.shields.io/github/license/BAI-Yeqi/SF2F_PyTorch.svg)](https://github.com/BAI-Yeqi/SF2F_PyTorch/blob/main/LICENSE)
+[![open issues](https://isitmaintained.com/badge/open/SF2F/SF2F_PyTorch.svg)](https://github.com/SF2F/SF2F_PyTorch/issues)
+[![docs](https://img.shields.io/badge/docs-latest-blue)](https://github.com/BAI-Yeqi/SF2F_PyTorch/blob/main/README.md)
+
+<img src="images/overview.png" align="center" width="50%">
+
+## Released Models
+
+We release low resolution and mid resolution versions of SF2F. Our baseline model, voice2face ([paper](https://proceedings.neurips.cc/paper/2019/file/eb9fc349601c69352c859c1faa287874-Paper.pdf)) which generates low resolution images by default, is also released. All released implementations are designed and experimented on HQ-VoxCeleb dataset.
+
+|    Model                                                    | Output Resolution | VGGFace Score    |
+|:-----------------------------------------------------------:|:-----------------:|:----------------:|
+| [voice2face](https://github.com/SF2F/SF2F_PyTorch/tree/main/options/vox/baseline/v2f.yaml)               | 64 $\times$ 64    | 15.47 $\pm$ 0.67 |
+| [SF2F (no fuser)](https://github.com/SF2F/SF2F_PyTorch/tree/main/options/vox/sf2f/sf2f_1st_stage.yaml)     | 64 $\times$ 64    | 18.59 $\pm$ 0.87 |
+| [SF2F](https://github.com/SF2F/SF2F_PyTorch/tree/main/options/vox/sf2f/sf2f_fuser.yaml)                    | 64 $\times$ 64    | 19.49 $\pm$ 0.59 |
+| [SF2F (no fuser)](https://github.com/SF2F/SF2F_PyTorch/tree/main/options/vox/sf2f/sf2f_mid_1st_stage.yaml) | 128 $\times$ 128  | 19.31 $\pm$ 0.65 |
+| [SF2F](https://github.com/SF2F/SF2F_PyTorch/tree/main/options/vox/sf2f/sf2f_mid_fuser.yaml)                | 128 $\times$ 128  | 20.10 $\pm$ 0.47 |
+
+Instructions on the training and testing of above models are introduced in [GETTING_STARTED](./GETTING_STARTED.md).
+
+## Implementation Details
+
+To provide the users of this repo a better understand of our implementation, we hereby introduces the implementation of key modules.
+
+**Voice Encoders.** The baseline voice encoder from voice2face is implemented as `V2F1DCNN` in `models/voice_encoders.py`. As mention in our main paper, we designed and implemented `Inception1DBlock` to improve the performance of voice encoder. When parameter `inception_mode` is set to `True`, `V2F1DCNN` is automatically built up with `Inception1DBlock`, which results in our proposed 1D-Inception based voice encoder. ([Jump to code](./models/voice_encoders.py#L266))
+
+**Face Decoders.** The baseline face decoder is implemented as `V2FDecoder` in `models/face_decoders.py`. Our enhanced face decoder is implemented as `FaceGanDecoder` in the same file. ([Jump to code](./models/face_decoders.py#L390))
+
+**Embedding Fuser.** Our proposed attention fuser is implemented as `AttentionFuserV1` in `models/fusers.py`. A graphical demonstration of embedding fuser is shown below. ([Jump to code](./models/fusers.py#L20))
+
+<img src="images/fuser_v2.png" align="center" width="50%">
+
+**Generative Models.** All generative models in this repo are implemented as `EncoderDecoder` in `models/encoder_decoder.py`. Encoder, decoder, and fusers will be initialized as attributes of `EncoderDecoder` class. ([Jump to code](./models/encoder_decoder.py#L20))
+
+**FaceNet Perceptual Loss.** FaceNet perceptual loss is implemented as `FaceNetLoss` in `models/perceptual.py`. ([Jump to code](./models/perceptual.py#L13))
+
+**VGGFace Score.** VGGFace Score is implemented in `scripts/compute_vggface_score.py`. ([Jump to code](./scripts/compute_vggface_score.py#L219))
+
+**Retrieval Metrics.** Retrieval metrics are implemented in `utils/s2f_evaluator.py`. ([Jump to code](./utils/s2f_evaluator.py#L35))
+
+## Getting Started
+To learn about environment setup, data preparation, launch of training, visualization, and evaluation, please refer to [GETTING_STARTED](./GETTING_STARTED.md).
+
+## Citation
+If you find this project useful in your research, please consider cite:
+```
+@inproceedings{bai2022speech,
+  title={Speech Fusion to Face: Bridging the Gap Between Human's Vocal Characteristics and Facial Imaging},
+  author={Bai, Yeqi and Ma, Tao and Wang, Lipo and Zhang, Zhenjie},
+  booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
+  pages={2042--2050},
+  year={2022}
+}
+```
diff --git a/modeling/sf2f/datasets/__init__.py b/modeling/sf2f/datasets/__init__.py
new file mode 100644
index 0000000..1b99502
--- /dev/null
+++ b/modeling/sf2f/datasets/__init__.py
@@ -0,0 +1,10 @@
+#!/usr/bin/python
+#
+from .utils import imagenet_preprocess, imagenet_deprocess, \
+    imagenet_deprocess_batch, fast_imagenet_deprocess_batch, \
+        fast_mel_deprocess_batch, set_mel_transform, deprocess_and_save, \
+            window_segment
+#from .coco import CocoSceneGraphDataset as coco
+#from .vg import VgSceneGraphDataset as visual_genome
+from .vox_dataset import VoxDataset
+from .build_dataset import build_dataset, build_loaders
diff --git a/modeling/sf2f/datasets/build_dataset.py b/modeling/sf2f/datasets/build_dataset.py
new file mode 100644
index 0000000..ecac9cb
--- /dev/null
+++ b/modeling/sf2f/datasets/build_dataset.py
@@ -0,0 +1,64 @@
+import json
+import os
+import os.path as osp
+import numpy as np
+from datasets import VoxDataset
+from torch.utils.data import DataLoader
+
+
+VOX_DIR = os.path.join('./data', 'VoxCeleb')
+
+
+def build_vox_dsets(data_opts, batch_size, image_size):
+    dset_kwargs = {
+        'data_dir': osp.join(data_opts['root_dir']),
+        'image_size': image_size,
+        'face_type': data_opts.get('face_type', 'masked'),
+        'image_normalize_method': \
+            data_opts.get('image_normalize_method', 'imagenet'),
+        'mel_normalize_method': \
+            data_opts.get('mel_normalize_method', 'vox_mel'),
+        'split_set': 'train',
+        'split_json': \
+            data_opts.get('split_json', os.path.join(VOX_DIR, 'split.json'))
+    }
+    train_dset = VoxDataset(**dset_kwargs)
+    iter_per_epoch = len(train_dset) // batch_size
+    print('There are %d iterations per epoch' % iter_per_epoch)
+
+    # make some modification to dset_kwargs so that it can be used to create val_dset
+    # dset_kwargs['max_samples'] = data_opts["num_val_samples"]
+    # val_dset = visual_genome(**dset_kwargs)
+    dset_kwargs['split_set'] = 'val'
+    val_dset = VoxDataset(**dset_kwargs)
+
+    dset_kwargs['split_set'] = 'test'
+    test_dset = VoxDataset(**dset_kwargs)
+
+    return train_dset, val_dset, test_dset
+
+
+def build_dataset(opts):
+    if opts["dataset"] == "vox":
+        return build_vox_dsets(opts["data_opts"], opts["batch_size"],
+                              opts["image_size"])
+    else:
+        raise ValueError("Unrecognized dataset: {}".format(opts["dataset"]))
+
+
+def build_loaders(opts):
+    train_dset, val_dset, test_dset = build_dataset(opts)
+
+    loader_kwargs = {
+        'batch_size': opts["batch_size"],
+        'num_workers': opts["workers"],
+        'shuffle': True,
+        "drop_last": True,
+        'collate_fn': train_dset.collate_fn,
+    }
+    train_loader = DataLoader(train_dset, **loader_kwargs)
+    loader_kwargs['shuffle'] = False
+    loader_kwargs['drop_last'] = False
+    val_loader = DataLoader(val_dset, **loader_kwargs)
+    test_loader = DataLoader(test_dset, **loader_kwargs)
+    return train_loader, val_loader, test_loader
diff --git a/modeling/sf2f/datasets/utils.py b/modeling/sf2f/datasets/utils.py
new file mode 100644
index 0000000..c2bb4e4
--- /dev/null
+++ b/modeling/sf2f/datasets/utils.py
@@ -0,0 +1,158 @@
+import PIL
+import torch
+import torchvision.transforms as T
+import numpy as np
+
+# Imagenet macros
+IMAGENET_MEAN = [0.485, 0.456, 0.406]
+IMAGENET_STD = [0.229, 0.224, 0.225]
+
+INV_IMAGENET_MEAN = [-m for m in IMAGENET_MEAN]
+INV_IMAGENET_STD = [1.0 / s for s in IMAGENET_STD]
+
+STANDARD_MEAN = [0.5, 0.5, 0.5]
+STANDARD_STD = [0.5, 0.5, 0.5]
+
+INV_STANDARD_MEAN = [-m for m in STANDARD_MEAN]
+INV_STANDARD_STD = [1.0 / s for s in STANDARD_STD]
+
+VOX_MEL_MEAN = [10.9915,]
+VOX_MEL_STD = [3.1661,]
+
+def imagenet_preprocess(normalize_method='imagenet'):
+    if normalize_method == 'imagenet':
+        return T.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)
+    elif normalize_method == 'standard':
+        return T.Normalize(mean=STANDARD_MEAN, std=STANDARD_STD)
+    elif normalize_method == 'vox_mel':
+        return T.Normalize(mean=VOX_MEL_MEAN, std=VOX_MEL_STD)
+
+
+def rescale(x):
+    lo, hi = x.min(), x.max()
+    return x.sub(lo).div(hi - lo + 1e-5)
+
+
+def imagenet_deprocess(rescale_image=True, normalize_method='imagenet'):
+    if normalize_method == 'imagenet':
+        transforms = [
+            T.Normalize(mean=[0, 0, 0], std=INV_IMAGENET_STD),
+            T.Normalize(mean=INV_IMAGENET_MEAN, std=[1.0, 1.0, 1.0]),
+        ]
+    elif normalize_method == 'standard':
+        transforms = [
+            T.Normalize(mean=[0, 0, 0], std=INV_STANDARD_STD),
+            T.Normalize(mean=INV_STANDARD_MEAN, std=[1.0, 1.0, 1.0]),
+        ]
+    if rescale_image:
+        transforms.append(rescale)
+    return T.Compose(transforms)
+
+
+def fast_imagenet_deprocess_batch(imgs, normalize_method='imagenet'):
+    '''
+    ImageNet deprocess batch's non-loop and backpropable implementation
+    '''
+    if normalize_method == 'imagenet':
+        mean = IMAGENET_MEAN
+        std = IMAGENET_STD
+    elif normalize_method == 'standard':
+        mean = STANDARD_MEAN
+        std = STANDARD_STD
+    mean = torch.tensor(mean).type(imgs.type())
+    std = torch.tensor(std).type(imgs.type())
+    # Initialize broadcasting Dims
+    mean = mean.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+    std = std.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+
+    img_de = imgs * std + mean
+    img_de = img_de.mul(255).clamp(0, 255)
+
+    return img_de
+
+def set_mel_transform(mel_normalize_method=None):
+    mel_transform = [T.ToTensor(), ]
+    print('Called mel_normalize_method:',
+        mel_normalize_method)
+    if mel_normalize_method is not None:
+        mel_transform.append(imagenet_preprocess(
+            normalize_method=mel_normalize_method))
+    mel_transform.append(torch.squeeze)
+    mel_transform = T.Compose(mel_transform)
+    return mel_transform
+
+def fast_mel_deprocess_batch(log_mels, normalize_method='vox_mel'):
+    '''
+    Mel Spectrogram deprocess batch's non-loop and backpropable implementation
+    '''
+    if normalize_method == 'vox_mel':
+        mean = VOX_MEL_MEAN[0]
+        std = VOX_MEL_STD[0]
+    mean = torch.tensor(mean).type(log_mels.type())
+    std = torch.tensor(std).type(log_mels.type())
+    log_mels_de = log_mels * std + mean
+
+    return log_mels_de
+
+
+def window_segment(log_mel, window_length, stride_length):
+    """
+    Sliding window segmentation on a log_mel tensor
+    """
+    mel_length = log_mel.shape[1]
+    # Calulate the number of windows that can be generated
+    num_window = 1 + (mel_length - window_length) // stride_length
+    # Sliding Window
+    segments = []
+    for i in range(0, num_window):
+        start_time = i * stride_length
+        segment = log_mel[:, start_time:start_time + window_length]
+        segments.append(segment)
+    segments = torch.stack(segments)
+    return segments
+
+
+def imagenet_deprocess_batch(imgs, rescale=False, normalize_method='imagenet'):
+    """
+    Input:
+    - imgs: FloatTensor of shape (N, C, H, W) giving preprocessed images
+
+    Output:
+    - imgs_de: ByteTensor of shape (N, C, H, W) giving deprocessed images
+      in the range [0, 255]
+    """
+    if isinstance(imgs, torch.autograd.Variable):
+        imgs = imgs.data
+    imgs = imgs.cpu().clone()
+    deprocess_fn = imagenet_deprocess(
+        rescale_image=rescale, normalize_method=normalize_method)
+    imgs_de = []
+    for i in range(imgs.size(0)):
+        img_de = deprocess_fn(imgs[i])[None]
+        img_de = img_de.mul(255).clamp(0, 255).byte()
+        imgs_de.append(img_de)
+    imgs_de = torch.cat(imgs_de, dim=0)
+    return imgs_de
+
+
+def deprocess_and_save(image, normalize_method, save_path):
+    '''
+    Deprocess a image Tensor generated by VoxDataset, and save its image as a
+    jpg
+    '''
+    deprocess_fn = imagenet_deprocess(normalize_method=normalize_method)
+    image = deprocess_fn(image)
+    #print('deprocessed image shape:', image.shape)
+    img = np.array(image)
+    img = np.transpose(img, (1, 2, 0))
+    img = (img * 255).astype(np.uint8)
+    #print(img.shape)
+    #print(np.max(img), np.min(img))
+    img = PIL.Image.fromarray(img)
+    img.save(save_path)
+
+
+def unpack_var(v):
+    if isinstance(v, torch.autograd.Variable):
+        return v.data
+    return v
diff --git a/modeling/sf2f/datasets/vox_dataset.py b/modeling/sf2f/datasets/vox_dataset.py
new file mode 100644
index 0000000..d3dbc0f
--- /dev/null
+++ b/modeling/sf2f/datasets/vox_dataset.py
@@ -0,0 +1,465 @@
+'''
+VoxCeleb DataSet
+'''
+
+
+import json, os
+import numpy as np
+import random
+#import pandas as pd
+#import h5py
+import pickle
+import PIL
+from PIL import Image
+import torch
+from torch.utils.data import Dataset, DataLoader
+from torch.utils.data.dataloader import default_collate
+import torchvision.transforms as T
+#from skimage.transform import resize as imresize
+try:
+    from .utils import imagenet_preprocess, imagenet_deprocess_batch, \
+        fast_imagenet_deprocess_batch, fast_mel_deprocess_batch
+except:
+    from utils import imagenet_preprocess, imagenet_deprocess_batch, \
+        fast_imagenet_deprocess_batch, fast_mel_deprocess_batch
+
+
+VOX_DIR = os.path.join('./data', 'VoxCeleb')
+
+class VoxDataset(Dataset):
+    def __init__(self,
+                 data_dir,
+                 image_size=(64, 64),
+                 face_type='masked',
+                 image_normalize_method='imagenet',
+                 mel_normalize_method='vox_mel',
+                 nframe_range=(100, 150),
+                 split_set='train',
+                 split_json=os.path.join(VOX_DIR, 'split.json'),
+                 return_mel_segments=False,
+                 mel_seg_window_stride=(125, 125),
+                 image_left_right_avg=False,
+                 image_random_hflip=False):
+        '''
+        A PyTorch Dataset for loading VoxCeleb 1 & 2 human speech
+        (as mel spectrograms) and face (as image)
+
+        Inputs:
+        - data: Path to a directory where vox1 & vox2 data are held
+        - face_type: 'masked' or 'origin'
+        - image_size: Shape (h, w) to output the image
+        - image_normalize_method: Method to normalize the image, 'imagenet' or
+            'standard'
+        - return_mel_segments: Return several segments of mel spectrogram
+        - mel_seg_window_stride: Tuple (int, int), defines the window size and
+            stride size when segmenting the mel spectrogram with sliding window
+        - image_left_right_avg: flip the image, average the original and flipped
+            image
+        - image_random_hflip: Add random horizontal image flip to training set
+
+        '''
+        self.data_dir = data_dir
+        self.image_size = image_size
+        self.face_type = face_type
+        self.face_dir = self.face_type + '_faces'
+        self.image_normalize_method = image_normalize_method
+        self.mel_normalize_method = mel_normalize_method
+        self.nframe_range = nframe_range
+        self.split_set = split_set
+        self.split_json = split_json
+        self.return_mel_segments = return_mel_segments
+        self.mel_seg_window_stride = mel_seg_window_stride
+        self.shuffle_mel_segments = True
+        # This attribute is added to make the return segment mode to start from
+        # a random time
+        # Thus improve the randomness in fuser data
+        self.mel_segments_rand_start = False
+        self.image_left_right_avg = image_left_right_avg
+        self.image_random_hflip = image_random_hflip
+
+        self.load_split_dict()
+        self.list_available_names()
+        self.set_image_transform()
+        self.set_mel_transform()
+
+    def __len__(self):
+        return len(self.available_names)
+
+    def set_length(self, length):
+        self.available_names = self.available_names[0:length]
+
+    def __getitem__(self, index):
+        '''
+        Given an index, randomly return a face and a mel_spectrogram of this guy
+        '''
+        sub_dataset, name = self.available_names[index]
+        # Face Image
+        image_dir = os.path.join(
+            self.data_dir, sub_dataset, self.face_dir, name)
+        image_jpg = random.choice(os.listdir(image_dir))
+        image_path = os.path.join(image_dir, image_jpg)
+
+        with open(image_path, 'rb') as f:
+            with PIL.Image.open(f) as image:
+                WW, HH = image.size
+                #print("PIL Image:", np.array(image))
+                image = image.convert('RGB')
+                if self.image_left_right_avg:
+                    arr = (np.array(image) / 2.0 + \
+                        np.array(T.functional.hflip(image)) / 2.0).astype(
+                            np.uint8)
+                    image = PIL.Image.fromarray(arr, mode="RGB")
+                image = self.image_transform(image)
+
+        # Mel Spectrogram
+        mel_gram_dir = os.path.join(
+            self.data_dir, sub_dataset, 'mel_spectrograms', name)
+        mel_gram_pickle = random.choice(os.listdir(mel_gram_dir))
+        mel_gram_path = os.path.join(mel_gram_dir, mel_gram_pickle)
+        if not self.return_mel_segments:
+            # Return single segment
+            log_mel = self.load_mel_gram(mel_gram_path)
+            log_mel = self.mel_transform(log_mel)
+        else:
+            log_mel = self.get_all_mel_segments_of_id(
+                index, shuffle=self.shuffle_mel_segments)
+
+        human_id = torch.tensor(index)
+
+        return image, log_mel, human_id
+
+    def get_all_faces_of_id(self, index):
+        '''
+        Given a id, return all the faces of him as a batch tensor, with shape
+        (N, C, H, W)
+        '''
+        sub_dataset, name = self.available_names[index]
+        faces = []
+        # Face Image
+        image_dir = os.path.join(
+            self.data_dir, sub_dataset, self.face_dir, name)
+        for image_jpg in os.listdir(image_dir):
+            image_path = os.path.join(image_dir, image_jpg)
+            with open(image_path, 'rb') as f:
+                with PIL.Image.open(f) as image:
+                    WW, HH = image.size
+                    #print("PIL Image:", np.array(image))
+                    image = self.image_transform(image.convert('RGB'))
+                    faces.append(image)
+        faces = torch.stack(faces)
+
+        return faces
+
+    def get_all_mel_segments_of_id(self,
+                                   index,
+                                   shuffle=False):
+        '''
+        Given a id, return all the speech segments of him as a batch tensor,
+        with shape (N, C, L)
+        '''
+        sub_dataset, name = self.available_names[index]
+        window_length, stride_length = self.mel_seg_window_stride
+        segments = []
+        # Mel Spectrogram
+        mel_gram_dir = os.path.join(
+            self.data_dir, sub_dataset, 'mel_spectrograms', name)
+        mel_gram_list = os.listdir(mel_gram_dir)
+        if shuffle:
+            random.shuffle(mel_gram_list)
+        else:
+            mel_gram_list.sort()
+        seg_count = 0
+        for mel_gram_pickle in mel_gram_list:
+            mel_gram_path = os.path.join(mel_gram_dir, mel_gram_pickle)
+            log_mel = self.load_mel_gram(mel_gram_path)
+            log_mel = self.mel_transform(log_mel)
+            mel_length = log_mel.shape[1]
+            if self.mel_segments_rand_start:
+
+                start = np.random.randint(mel_length - window_length) if mel_length > window_length else 0
+
+                log_mel = log_mel[:, start:]
+                mel_length = log_mel.shape[1]
+            # Calulate the number of windows that can be generated
+            num_window = 1 + (mel_length - window_length) // stride_length
+            # Sliding Window
+            for i in range(0, num_window):
+                start_time = i * stride_length
+                segment = log_mel[:, start_time:start_time + window_length]
+                segments.append(segment)
+                seg_count = seg_count + 1
+                if seg_count == 20: # 20
+                    segments = torch.stack(segments)
+                    return segments
+        segments = torch.stack(segments)
+        return segments
+
+    def set_image_transform(self):
+        print('Dataloader: called set_image_size', self.image_size)
+        image_transform = [T.Resize(self.image_size), T.ToTensor()]
+        if self.image_random_hflip and self.split_set == 'train':
+            image_transform = [T.RandomHorizontalFlip(p=0.5),] + \
+                image_transform
+        if self.image_normalize_method is not None:
+            print('Dataloader: called image_normalize_method',
+                self.image_normalize_method)
+            image_transform.append(imagenet_preprocess(
+                normalize_method=self.image_normalize_method))
+        self.image_transform = T.Compose(image_transform)
+
+    def set_mel_transform(self):
+        mel_transform = [T.ToTensor(), ]
+        print('Dataloader: called mel_normalize_method',
+            self.mel_normalize_method)
+        if self.mel_normalize_method is not None:
+            mel_transform.append(imagenet_preprocess(
+                normalize_method=self.mel_normalize_method))
+        mel_transform.append(torch.squeeze)
+        self.mel_transform = T.Compose(mel_transform)
+
+    def load_split_dict(self):
+        '''
+        Load the train, val, test set information from split.json
+        '''
+        with open(self.split_json) as json_file:
+            self.split_dict = json.load(json_file)
+
+    def list_available_names(self):
+        '''
+        Find the intersection of speech and face data
+        '''
+        self.available_names = []
+        # List VoxCeleb1 data:
+        for sub_dataset in (['vox1']): #, 'vox2'
+            mel_gram_available = os.listdir(
+                os.path.join(self.data_dir, sub_dataset, 'mel_spectrograms'))
+            face_available = os.listdir(
+                os.path.join(self.data_dir, sub_dataset, self.face_dir))
+            available = \
+                set(mel_gram_available).intersection(face_available)
+            for name in available:
+                if name in self.split_dict[sub_dataset][self.split_set]:
+                    self.available_names.append((sub_dataset, name))
+
+        self.available_names.sort()
+
+    def load_mel_gram(self, mel_pickle):
+        '''
+        Load a speech's mel spectrogram from pickle file.
+
+        Format of the pickled data:
+            LogMel_Features
+            spkid
+            clipid
+            wavid
+
+        Inputs:
+        - mel_pickle: Path to the mel spectrogram to be loaded.
+        '''
+        # open a file, where you stored the pickled data
+        file = open(mel_pickle, 'rb')
+        # dump information to that file
+        data = pickle.load(file)
+        # close the file
+        file.close()
+        log_mel = data['LogMel_Features']
+        #log_mel = np.transpose(log_mel, axes=None)
+
+        return log_mel
+
+    def crop_or_pad(self, log_mel, out_frame):
+        '''
+        Log_mel padding/cropping function to cooperate with collate_fn
+        '''
+        freq, cur_frame = log_mel.shape
+        if cur_frame >= out_frame:
+            # Just crop
+            start = np.random.randint(0, cur_frame-out_frame+1)
+            log_mel = log_mel[..., start:start+out_frame]
+        else:
+            # Padding
+            zero_padding = np.zeros((freq, out_frame-cur_frame))
+            zero_padding = self.mel_transform(zero_padding)
+            if len(zero_padding.shape) == 1:
+                zero_padding = zero_padding.view([-1, 1])
+            log_mel = torch.cat([log_mel, zero_padding], -1)
+
+        return log_mel
+
+    def collate_fn(self, batch):
+        min_nframe, max_nframe = self.nframe_range
+        assert min_nframe <= max_nframe
+        np.random.seed()
+        num_frame = np.random.randint(min_nframe, max_nframe+1)
+        #start = np.random.randint(0, max_nframe-num_frame+1)
+        #batch = [(item[0], item[1][..., start:start+num_frame], item[2])
+        #         for item in batch]
+
+        batch = [(item[0],
+                  self.crop_or_pad(item[1], num_frame),
+                  item[2]) for item in batch]
+        return default_collate(batch)
+
+    def count_faces(self):
+        '''
+        Count the number of faces in the dataset
+        '''
+        total_count = 0
+        for index in range(len(self.available_names)):
+            sub_dataset, name = self.available_names[index]
+            # Face Image
+            image_dir = os.path.join(
+                self.data_dir, sub_dataset, self.face_dir, name)
+            cur_count = len(os.listdir(image_dir))
+            total_count = total_count + cur_count
+        print('Number of faces in current dataset: {}'.format(total_count))
+        return total_count
+
+    def count_speech(self):
+        '''
+        Given a id, return all the speech segments of him as a batch tensor,
+        with shape (N, C, L)
+        '''
+        total_count = 0
+        for index in range(len(self.available_names)):
+            sub_dataset, name = self.available_names[index]
+            window_length, stride_length = self.mel_seg_window_stride
+            # Mel Spectrogram
+            mel_gram_dir = os.path.join(
+                self.data_dir, sub_dataset, 'mel_spectrograms', name)
+            mel_gram_list = os.listdir(mel_gram_dir)
+            cur_count = len(mel_gram_list)
+            total_count = total_count + cur_count
+        print('Number of speech in current dataset: {}'.format(total_count))
+        return total_count
+
+
+if __name__ == '__main__':
+    '''
+    from utils import imagenet_deprocess, deprocess_and_save
+
+    # Config
+    image_size = (256, 256)
+    #image_normalize_method = 'imagenet'
+    image_normalize_method = 'standard'
+    mel_normalize_method = 'vox_mel'
+    test_case_dir = os.path.join('./data', 'test_cases')
+    os.makedirs(test_case_dir, exist_ok=True)
+
+    # Dataset
+    vox_dataset = VoxDataset(
+        data_dir=VOX_DIR,
+        image_size=image_size,
+        image_normalize_method=image_normalize_method,
+        mel_normalize_method=mel_normalize_method)
+    np_mel = vox_dataset.load_mel_gram(mel_pickle= \
+        './data/VoxCeleb/vox1/mel_spectrograms/A.J._Buckley' + \
+            '/id10001_J9lHsKG98U8_00026.pickle')
+    print('np_mel.shape:', np_mel.shape)
+
+    print('dataset length:', len(vox_dataset))
+
+    # Try out 1 case
+    # image, log_mel, mel_len = vox_dataset[220]
+    image, log_mel, human_id = vox_dataset[220]
+    print('image shape:', image.shape)
+    print('log_mel shape:', log_mel.shape)
+    print('human_id:', human_id)
+    # print(mel_len)
+    deprocess_and_save(image, image_normalize_method,
+        os.path.join(test_case_dir, 'vox_dataset_image_test_1.jpg'))
+
+    log_mel = np.array(log_mel)
+    print(log_mel)
+    print(np.max(log_mel), np.min(log_mel))
+
+    # Unit test after train test split update:
+    print('### Testing splitted dataset ###')
+    print('Test identities:', vox_dataset.split_dict['vox1']['test'])
+    '''
+
+    for split_set in ['train', 'val', 'test']:
+        vox_dataset = VoxDataset(
+            data_dir=VOX_DIR,
+            image_size=(64, 64),
+            nframe_range=(300, 600),
+            face_type='masked',
+            image_normalize_method='imagenet',
+            mel_normalize_method='vox_mel',
+            split_set=split_set,
+            split_json=os.path.join(VOX_DIR, 'split.json'))
+        print('Length of {} set: {}'.format(split_set, len(vox_dataset)))
+        vox_dataset.count_faces()
+        vox_dataset.count_speech()
+
+    # Collate Function dataloader test
+    loader_kwargs = {
+        'batch_size': 16,
+        'num_workers': 8,
+        'shuffle': False,
+        "drop_last": True,
+        'collate_fn': vox_dataset.collate_fn,
+    }
+    val_loader = DataLoader(vox_dataset, **loader_kwargs)
+    for iter, batch in enumerate(val_loader):
+        images, log_mels, human_ids = batch
+        print('log_mels.shape:', log_mels.shape)
+        if iter > 10000:
+            break
+
+    for e in range(1000):
+        for iter, batch in enumerate(val_loader):
+            images, log_mels, human_ids = batch
+            print('log_mels.shape:', log_mels.shape)
+            if iter > 10000:
+                break
+
+    '''
+
+    #### Test utils imagenet_deprocess_batch and fast_imagenet_deprocess_batch
+    imgs_de = imagenet_deprocess_batch(
+        images,
+        rescale=False,
+        normalize_method='imagenet')
+    imgs_de_fast = fast_imagenet_deprocess_batch(
+        images,
+        normalize_method='imagenet')
+
+    print('imgs_de:', imgs_de[0])
+    print('imgs_de_fast:', imgs_de_fast[0])
+
+    #### Test utils fast_mel_deprocess_batch
+    log_mels_de = fast_mel_deprocess_batch(
+        log_mels,
+        normalize_method='vox_mel')
+
+    print('log_mels_de:', log_mels_de[0])
+
+    # Test DataLoader Deepcopy
+    from copy import deepcopy
+    val_loader_copy = deepcopy(val_loader)
+    val_loader_copy.dataset.set_length(100)
+    print('length of copied val dataset: {}'.format(
+        len(val_loader_copy.dataset)))
+    print('length of val dataset: {}'.format(
+        len(val_loader.dataset)))
+
+    # Test get all faces
+    faces = val_loader.dataset.get_all_faces_of_id(3)
+    print('get_all_faces_of_id:', faces.shape)
+
+    # Test get all mel segments
+    segments = val_loader.dataset.get_all_mel_segments_of_id(3)
+    print('get_all_mel_segments_of_id:', segments.shape)
+
+    # Test return many segments mode
+    val_loader.collate_fn = default_collate
+    val_loader.dataset.return_mel_segments = True
+    val_loader.dataset.mel_seg_window_stride = (125, 60)
+    for iter, batch in enumerate(val_loader):
+        images, log_mels, human_ids = batch
+        print('log_mels.shape (many segments mode):', log_mels.shape)
+        if iter > 10000:
+            break
+
+    '''
diff --git a/modeling/sf2f/infer.py b/modeling/sf2f/infer.py
new file mode 100644
index 0000000..a795e8e
--- /dev/null
+++ b/modeling/sf2f/infer.py
@@ -0,0 +1,121 @@
+'''
+author: Bai Yeqi
+email: yeqi.bai@yitu-inc.com
+'''
+'''
+Reload the generator checkpoint, inference with provided audio, in form of .wav
+    or .m4a
+
+We infer with different policies:
+    1. Generate the face conditioned on each segment
+    2. Generate the face conditioned on the whole speech
+    3. Generate the face with the fuser
+'''
+
+
+import os
+import shutil
+import glob
+import pyprind
+import glog as log
+import torch
+import numpy as np
+from imageio import imwrite
+
+import models
+from utils.wav2mel import wav_to_mel
+from datasets import imagenet_deprocess_batch, set_mel_transform, \
+    deprocess_and_save, window_segment
+from options.opts import args, options
+
+
+torch.backends.cudnn.benchmark = True
+
+
+def main():
+    global args, options
+    print(args)
+    device = torch.device('cuda')
+    float_dtype = torch.cuda.FloatTensor
+    long_dtype = torch.cuda.LongTensor
+
+    image_normalize_method = options["data"]["data_opts"].get(
+        'image_normalize_method', 'imagenet')
+    print('image_normalize_method:', image_normalize_method)
+    mel_normalize_method = options["data"]["data_opts"].get(
+        'mel_normalize_method', 'vox_mel')
+    mel_transform = set_mel_transform(mel_normalize_method)
+
+    # Model
+    log.info("Building Generative Model...")
+    print(options["generator"])
+    model, _ = models.build_model(
+            options["generator"],
+            image_size=options["data"]["image_size"],
+            checkpoint_start_from=args.checkpoint_start_from)
+    model.cuda().eval()
+
+    exp_dir = os.path.dirname(args.checkpoint_start_from)
+    result_dir = os.path.join(exp_dir, 'infer_result')
+    try:
+        shutil.copytree(args.input_wav_dir, result_dir)
+    except:
+        shutil.rmtree(result_dir)
+        shutil.copytree(args.input_wav_dir, result_dir)
+    voice_path = os.path.join(result_dir, '*.wav')
+    voice_list = glob.glob(voice_path)
+    for filename in voice_list:
+        result_sub_dir = filename.replace('.wav', '')
+        os.makedirs(result_sub_dir, exist_ok=True)
+        # Load mel_spectrogram
+        log_mel = wav_to_mel(filename)
+        log_mel = mel_transform(log_mel).type(float_dtype)
+        #print(log_mel)
+        log_mel_segs = window_segment(
+            log_mel, window_length=125, stride_length=63)
+        log_mel = log_mel.unsqueeze(0)
+
+        # image generated by directly feed in mel gram
+        with torch.no_grad():
+            img_d_fed, others = model(log_mel)
+        if isinstance(img_d_fed, tuple):
+            img_d_fed = img_d_fed[-1]
+        img_d_fed = img_d_fed.cpu().detach()
+        img_d_fed = imagenet_deprocess_batch(
+            img_d_fed, normalize_method=image_normalize_method)
+
+        for j in range(img_d_fed.shape[0]):
+            img_np = img_d_fed[j].numpy().transpose(1, 2, 0)
+            img_path = os.path.join(result_sub_dir, 'd_fed_%d.png' % j)
+            imwrite(img_path, img_np)
+
+        # image generated by each segment
+        with torch.no_grad():
+            imgs_from_segs, others = model(log_mel_segs)
+        if isinstance(imgs_from_segs, tuple):
+            imgs_from_segs = imgs_from_segs[-1]
+        imgs_from_segs = imgs_from_segs.cpu().detach()
+        imgs_from_segs = imagenet_deprocess_batch(
+            imgs_from_segs, normalize_method=image_normalize_method)
+        for j in range(imgs_from_segs.shape[0]):
+            img_np = imgs_from_segs[j].numpy().transpose(1, 2, 0)
+            img_path = os.path.join(result_sub_dir, 'seg_%d.png' % j)
+            imwrite(img_path, img_np)
+
+        # image generated by fuser
+        if args.fuser_infer:
+            with torch.no_grad():
+                imgs_fused, others = model(log_mel_segs.unsqueeze(0))
+            if isinstance(imgs_fused, tuple):
+                imgs_fused = imgs_fused[-1]
+            imgs_fused = imgs_fused.cpu().detach()
+            imgs_fused = imagenet_deprocess_batch(
+                imgs_fused, normalize_method=image_normalize_method)
+            for j in range(imgs_fused.shape[0]):
+                img_np = imgs_fused[j].numpy().transpose(1, 2, 0)
+                img_path = os.path.join(result_sub_dir, 'fused_%d.png' % j)
+                imwrite(img_path, img_np)
+        print(f"Done. file name : {filename}")
+
+if __name__ == '__main__':
+    main()
diff --git a/modeling/sf2f/inference_fuser.py b/modeling/sf2f/inference_fuser.py
new file mode 100644
index 0000000..aa7fa89
--- /dev/null
+++ b/modeling/sf2f/inference_fuser.py
@@ -0,0 +1,92 @@
+'''
+author: Bai Yeqi
+email: yeqi.bai@yitu-inc.com
+'''
+'''
+Reload the generator checkpoint, inference with provided audio, in form of .wav
+    or .m4a
+
+We infer with different policies:
+    1. Generate the face conditioned on each segment
+    2. Generate the face conditioned on the whole speech
+    3. Generate the face with the fuser
+'''
+
+
+import os
+import shutil
+import glob
+import pyprind
+import glog as log
+import torch
+import numpy as np
+from imageio import imwrite
+
+import models
+from utils.wav2mel import wav_to_mel
+from datasets import imagenet_deprocess_batch, set_mel_transform, \
+    deprocess_and_save, window_segment
+from options.opts import args, options
+
+
+torch.backends.cudnn.benchmark = True
+
+
+def main():
+    global args, options
+    print(args)
+    device = torch.device('cuda')
+    float_dtype = torch.cuda.FloatTensor
+    long_dtype = torch.cuda.LongTensor
+
+    image_normalize_method = options["data"]["data_opts"].get(
+        'image_normalize_method', 'imagenet')
+    print('image_normalize_method:', image_normalize_method)
+    mel_normalize_method = options["data"]["data_opts"].get(
+        'mel_normalize_method', 'vox_mel')
+    mel_transform = set_mel_transform(mel_normalize_method)
+
+    # Model
+    log.info("Building Generative Model...")
+    print(options["generator"])
+    model, _ = models.build_model(
+            options["generator"],
+            image_size=options["data"]["image_size"],
+            checkpoint_start_from=args.checkpoint_start_from)
+    model.cuda().eval()
+    
+    # voice_path = os.path.join(args.input_wav_dir, '*.wav')
+    # voice_list = glob.glob(voice_path)
+    # filename = voice_list[0]
+    filename = os.path.join(args.input_wav_file)
+    assert os.path.exists(filename), "File not found: {}".format(filename)
+
+    # for filename in voice_list:
+    # result_sub_dir = filename.replace('.wav', '')
+    # os.makedirs(result_sub_dir, exist_ok=True) # 결과 폴더 생성
+    # Load mel_spectrogram
+    log_mel = wav_to_mel(filename)
+    log_mel = mel_transform(log_mel).type(float_dtype)
+    #print(log_mel)
+    log_mel_segs = window_segment(
+        log_mel, window_length=125, stride_length=63)
+    log_mel = log_mel.unsqueeze(0)
+
+    # image generated by fuser
+    # if args.fuser_infer:
+    with torch.no_grad():
+        imgs_fused, others = model(log_mel_segs.unsqueeze(0))
+    if isinstance(imgs_fused, tuple):
+        imgs_fused = imgs_fused[-1]
+    imgs_fused = imgs_fused.cpu().detach()
+    imgs_fused = imagenet_deprocess_batch(
+        imgs_fused, normalize_method=image_normalize_method)
+    for j in range(imgs_fused.shape[0]):
+        img_np = imgs_fused[j].numpy().transpose(1, 2, 0) # 64x64x3
+        # img_path = os.path.join(result_sub_dir, 'fused_%d.png' % j) # 이미지 저장할 파일
+        # imwrite(img_path, img_np) # 이미지 저장
+
+    return img_np.tobytes()
+
+if __name__ == '__main__':
+    main()
diff --git a/modeling/sf2f/models/__init__.py b/modeling/sf2f/models/__init__.py
new file mode 100644
index 0000000..694bee5
--- /dev/null
+++ b/modeling/sf2f/models/__init__.py
@@ -0,0 +1,19 @@
+from .model_setup import *
+from .encoder_decoder import EncoderDecoder
+
+# Lower level encoder decoders
+from .voice_encoders import V2F1DCNN
+from .face_decoders import V2FDecoder
+
+# Progressive GAN
+# from .pgan import PGan_Net
+# from .pgan_dis import PGAN_Discriminator
+
+# Discriminator
+from .discriminators import PatchDiscriminator
+from .discriminators import AcDiscriminator
+from .discriminators import AcCropDiscriminator
+from .discriminators import CondPatchDiscriminator
+
+# Facenet for Perceptual Loss and Evaluation
+from .inception_resnet_v1 import InceptionResnetV1, fixed_image_standardization
diff --git a/modeling/sf2f/models/attention.py b/modeling/sf2f/models/attention.py
new file mode 100644
index 0000000..e74b2af
--- /dev/null
+++ b/modeling/sf2f/models/attention.py
@@ -0,0 +1,223 @@
+'''
+Attenion implementation
+Adapted from torchnlp:
+    https://github.com/PetrochukM/PyTorch-NLP/tree/master/torchnlp/nn
+'''
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from math import sqrt
+try:
+    from .layers import get_activation, get_normalization_2d
+except:
+    from layers import get_activation, get_normalization_2d
+
+
+class AttnBlock(nn.Module):
+    def __init__(self,
+                 attn_dim_in,
+                 attn_dim_out,
+                 attn_num_query=16,
+                 attn_type='general',
+                 normalization='none'):
+        super(AttnBlock, self).__init__()
+        self.attn_layer = AttnLayer(
+            attn_dim_in,
+            attn_dim_out,
+            attn_num_query,
+            attn_type,
+            normalization)
+
+    def forward(self, cat_tensor, context):
+        N, C, H, W = cat_tensor.shape
+        attn_map = self.attn_layer(context.transpose(1, 2))
+        #print('attn_map 1:', attn_map.shape)
+        attn_map = F.interpolate(attn_map, (H, W))
+        #print('attn_map 2:', attn_map.shape)
+        map_out = torch.cat([cat_tensor, attn_map], 1)
+
+        return map_out
+
+    def return_attn_weights(self):
+        return self.attn_layer.attn_weights
+
+
+class AttnLayer(nn.Module):
+    def __init__(self,
+                 dim_in,
+                 dim_out,
+                 num_query=16,
+                 attention_type='general',
+                 normalization='none'):
+        super(AttnLayer, self).__init__()
+        self.num_query = num_query
+        self.query = nn.Parameter(
+            torch.randn((1, num_query, dim_in),
+            requires_grad=True))
+        self.attention = Attention(
+            dimensions=dim_in,
+            dim_out=dim_out,
+            attention_type=attention_type)
+        self.norm_layer = get_normalization_2d(
+            dim_out, normalization)
+        # Cache Attenion Weights for visualization
+        self.attn_weights = None
+
+    def forward(self, context):
+        N, L, D_in = context.shape
+        # Repeat query layer over batch dim
+        query = self.query.repeat(N, 1, 1)
+        # **output** (:class:`torch.FloatTensor`
+        # [batch size, output length, dim_out]
+        attn_out, attn_weights = self.attention(query, context)
+        self.attn_weights = attn_weights
+        N, Q_L, D_out = attn_out.shape
+        # [batch size, dim_out, output length]
+        attn_out = torch.transpose(attn_out, 1, 2)
+        #  [batch size, dimensions, H, W]
+        H = W = int(sqrt(self.num_query))
+        attn_map = attn_out.view(
+            N, D_out, H, W)
+        if self.norm_layer is not None:
+            attn_map = self.norm_layer(attn_map)
+
+        return attn_map
+
+
+class Attention(nn.Module):
+    """ Applies attention mechanism on the `context` using the `query`.
+
+    **Thank you** to IBM for their initial implementation of :class:`Attention`. Here is
+    their `License
+    <https://github.com/IBM/pytorch-seq2seq/blob/master/LICENSE>`__.
+
+    Args:
+        dimensions (int): Dimensionality of the query and context.
+        attention_type (str, optional): How to compute the attention score:
+
+            * dot: :math:`score(H_j,q) = H_j^T q`
+            * general: :math:`score(H_j, q) = H_j^T W_a q`
+
+    Example:
+
+         >>> attention = Attention(256)
+         >>> query = torch.randn(5, 1, 256)
+         >>> context = torch.randn(5, 5, 256)
+         >>> output, weights = attention(query, context)
+         >>> output.size()
+         torch.Size([5, 1, 256])
+         >>> weights.size()
+         torch.Size([5, 1, 5])
+    """
+
+    def __init__(self,
+                 dimensions,
+                 dim_out,
+                 attention_type='general',
+                 linear_out_layer=True,
+                 ignore_tanh=False):
+        super(Attention, self).__init__()
+
+        if attention_type not in ['dot', 'general']:
+            raise ValueError('Invalid attention type selected.')
+
+        # Attention Setup
+        self.attention_type = attention_type
+        if self.attention_type == 'general':
+            self.linear_in = nn.Linear(dimensions, dimensions, bias=False)
+        self.softmax = nn.Softmax(dim=-1)
+
+        # Output Setup
+        self.linear_out_layer = linear_out_layer
+        if self.linear_out_layer:
+            self.dim_out = dim_out
+            self.linear_out = nn.Linear(dimensions * 2, dim_out, bias=False)
+
+        self.ignore_tanh = ignore_tanh
+        if not ignore_tanh:
+            self.tanh = nn.Tanh()
+
+    def forward(self, query, context):
+        """
+        Args:
+            query (:class:`torch.FloatTensor` [batch size, output length,
+                dimensions]): Sequence of queries to query the context.
+            context (:class:`torch.FloatTensor` [batch size, query length,
+                dimensions]): Data overwhich to apply the attention mechanism.
+
+        Returns:
+            :class:`tuple` with `output` and `weights`:
+            * **output** (:class:`torch.LongTensor` [batch size, output length,
+                dimensions]):
+                    Tensor containing the attended features.
+            * **weights** (:class:`torch.FloatTensor` [batch size,
+                output length, query length]):
+                    Tensor containing attention weights.
+        """
+        batch_size, output_len, dimensions = query.size()
+        query_len = context.size(1)
+
+        if self.attention_type == "general":
+            query = query.reshape(batch_size * output_len, dimensions)
+            query = self.linear_in(query)
+            query = query.reshape(batch_size, output_len, dimensions)
+
+        # TODO: Include mask on PADDING_INDEX?
+
+        # (batch_size, output_len, dimensions) * \
+        # (batch_size, query_len, dimensions) ->
+        # (batch_size, output_len, query_len)
+        # batch matrix-matrix product
+        attention_scores = torch.bmm(
+            query, context.transpose(1, 2).contiguous())
+
+        # Compute weights across every context sequence
+        attention_scores = attention_scores.view(
+            batch_size * output_len, query_len)
+        attention_weights = self.softmax(attention_scores)
+        attention_weights = attention_weights.view(
+            batch_size, output_len, query_len)
+
+        # (batch_size, output_len, query_len) * \
+        # (batch_size, query_len, dimensions) ->
+        # (batch_size, output_len, dimensions)
+        mix = torch.bmm(attention_weights, context)
+
+        if self.linear_out_layer:
+            # Concatenate the attention output with the context and process the
+            # concatenated feature with a fc output layer
+            # concat -> (batch_size * output_len, 2*dimensions)
+            combined = torch.cat((mix, query), dim=2)
+            combined = combined.view(batch_size * output_len, 2 * dimensions)
+            # Apply linear_out on every 2nd dimension of concat
+            # output -> (batch_size, output_len, dimensions)
+            output = self.linear_out(combined).view(
+                batch_size, output_len, self.dim_out)
+        else:
+            # Directly output the attention output
+            output = mix
+
+        if not self.ignore_tanh:
+            output = self.tanh(output)
+
+        return output, attention_weights
+
+
+if __name__ == '__main__':
+    attention = Attention(256, 100, linear_out_layer=False)
+    query = torch.randn(5, 1, 256)
+    context = torch.randn(5, 5, 256)
+    cat_tensor = torch.randn(5, 170, 128, 128)
+    output, weights = attention(query, context)
+    print('output.shape:', output.shape)
+    '''
+    attn_layer = AttnLayer(256, 110, 16)
+    attn_map = attn_layer(context)
+    print('attn_map.shape:', attn_map.shape)
+    for param in attn_layer.parameters():
+        print(type(param.data), param.size())
+
+    attn_block = AttnBlock(256, 110, 16, normalization='batch')
+    cat_map = attn_block(cat_tensor, context.transpose(1, 2))
+    print('cat_map.shape:', cat_map.shape)
+    '''
diff --git a/modeling/sf2f/models/crn.py b/modeling/sf2f/models/crn.py
new file mode 100644
index 0000000..33cd9a3
--- /dev/null
+++ b/modeling/sf2f/models/crn.py
@@ -0,0 +1,152 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .layers import get_normalization_2d
+from .layers import get_activation
+
+
+"""
+Cascaded refinement network architecture, as described in:
+Qifeng Chen and Vladlen Koltun,
+"Photographic Image Synthesis with Cascaded Refinement Networks",
+ICCV 2017
+"""
+
+
+class RefinementModule(nn.Module):
+    def __init__(self, layout_dim, input_dim, output_dim,
+                 normalization='instance', activation='leakyrelu'):
+        super(RefinementModule, self).__init__()
+
+        layers = []
+        layers.append(nn.Conv2d(layout_dim + input_dim, output_dim,
+                                kernel_size=3, padding=1))
+        layers.append(get_normalization_2d(output_dim, normalization))
+        layers.append(get_activation(activation))
+        layers.append(nn.Conv2d(output_dim, output_dim,
+                                kernel_size=3, padding=1))
+        layers.append(get_normalization_2d(output_dim, normalization))
+        layers.append(get_activation(activation))
+        layers = [layer for layer in layers if layer is not None]
+        for layer in layers:
+            if isinstance(layer, nn.Conv2d):
+                nn.init.kaiming_normal_(layer.weight)
+        self.net = nn.Sequential(*layers)
+
+    def forward(self, layout, feats):
+        _, _, HH, WW = layout.size()
+        _, _, H, W = feats.size()
+        assert HH >= H
+        if HH > H:
+            factor = round(HH // H)
+            assert HH % factor == 0
+            assert WW % factor == 0 and WW // factor == W
+            layout = F.avg_pool2d(layout, kernel_size=factor, stride=factor)
+        net_input = torch.cat([layout, feats], dim=1)
+        out = self.net(net_input)
+        return out
+
+
+class RefinementNetwork(nn.Module):
+    def __init__(self, dims, normalization='instance',
+                 activation='leakyrelu',
+                 use_tanh=False,
+                 use_deconv=False,
+                 multi_resolution=False,):
+        super(RefinementNetwork, self).__init__()
+        layout_dim = dims[0]
+        self.refinement_modules = nn.ModuleList()
+        self.upsample_modules = nn.ModuleList()
+        self.multi_resolution = multi_resolution
+        for i in range(1, len(dims)):
+            input_dim = 1 if i == 1 else (dims[i] if use_deconv else dims[i-1])
+            output_dim = dims[i]
+            mod = RefinementModule(layout_dim, input_dim, output_dim,
+                                   normalization=normalization, activation=activation)
+            self.refinement_modules.append(mod)
+            if use_deconv:
+                if i == 1:
+                    mod = nn.Sequential()
+                else:
+                    mod = nn.Sequential(
+                        nn.ConvTranspose2d(dims[i-1], dims[i],
+                            kernel_size=2, stride=2,),
+                        get_normalization_2d(dims[i], normalization),
+                        get_activation(activation)
+                    )
+                self.upsample_modules.append(mod)
+        output_conv_layers = None
+        if not multi_resolution:
+            output_conv_layers = [
+                nn.Conv2d(dims[-1], dims[-1], kernel_size=3, padding=1),
+                get_activation(activation),
+                nn.Conv2d(dims[-1], 3, kernel_size=1, padding=0),
+            ]
+            if use_tanh:
+                ## added according to Pix2Pix-HD
+                output_conv_layers.append(nn.Tanh(),)
+            nn.init.kaiming_normal_(output_conv_layers[0].weight)
+            nn.init.kaiming_normal_(output_conv_layers[2].weight)
+        else:
+            output_conv_layers_low = [
+                nn.Conv2d(dims[-2], dims[-2], kernel_size=3, padding=1),
+                get_activation(activation),
+                nn.Conv2d(dims[-2], 3, kernel_size=1, padding=0),
+            ]
+            output_conv_layers_high = [
+                nn.Conv2d(dims[-1], dims[-1], kernel_size=3, padding=1),
+                get_activation(activation),
+                nn.Conv2d(dims[-1], 3, kernel_size=1, padding=0),
+            ]
+            if use_tanh:
+                ## added according to Pix2Pix-HD
+                output_conv_layers_low.append(nn.Tanh(),)
+                output_conv_layers_high.append(nn.Tanh(),)
+
+        if output_conv_layers:
+            self.output_conv = nn.Sequential(*output_conv_layers)
+        else:
+            assert output_conv_layers_low
+            assert output_conv_layers_high
+            self.output_conv_low = nn.Sequential(*output_conv_layers_low)
+            self.output_conv_high = nn.Sequential(*output_conv_layers_high)
+
+    def forward(self, layout):
+        """
+        Output will have same size as layout
+        """
+        # H, W = self.output_size
+        N, _, H, W = layout.size()
+        self.layout = layout
+
+        # Figure out size of input
+        input_H, input_W = H, W
+        for _ in range(len(self.refinement_modules)):
+            input_H //= 2
+            input_W //= 2
+
+        assert input_H != 0
+        assert input_W != 0
+
+        feats = torch.zeros(N, 1, input_H, input_W).to(layout)
+        if not self.multi_resolution:
+            for i, mod in enumerate(self.refinement_modules):
+                if i == 0 or len(self.upsample_modules) == 0:
+                    feats = F.interpolate(feats, scale_factor=2, mode='nearest')
+                else:
+                    feats = self.upsample_modules[i](feats)
+                feats = mod(layout, feats)
+            out = self.output_conv(feats)
+            return (out, feats)
+        elif self.multi_resolution:
+            for i, mod in enumerate(self.refinement_modules):
+                if i == 0 or len(self.upsample_modules) == 0:
+                    feats = F.interpolate(feats, scale_factor=2, mode='nearest')
+                else:
+                    feats = self.upsample_modules[i](feats)
+                feats = mod(layout, feats)
+                if i == len(self.refinement_modules) - 2:
+                    out_low = self.output_conv_low(feats)
+            out_high = self.output_conv_high(feats)
+            return (out_high, out_low)
diff --git a/modeling/sf2f/models/discriminators.py b/modeling/sf2f/models/discriminators.py
new file mode 100644
index 0000000..f0f276d
--- /dev/null
+++ b/modeling/sf2f/models/discriminators.py
@@ -0,0 +1,110 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from utils.bilinear import crop_bbox_batch, uncrop_bbox
+from utils.box_utils import box_union, box_in_region
+from .layers import GlobalAvgPool, Flatten, get_activation, build_cnn
+import models
+
+
+class PatchDiscriminator(nn.Module):
+    def __init__(self, arch, normalization='batch', activation='leakyrelu-0.2',
+                 padding='same', pooling='avg', input_size=(128, 128),
+                 layout_dim=0):
+        super(PatchDiscriminator, self).__init__()
+        input_dim = 3 + layout_dim
+        arch = 'I%d,%s' % (input_dim, arch)
+        cnn_kwargs = {
+            'arch': arch,
+            'normalization': normalization,
+            'activation': activation,
+            'pooling': pooling,
+            'padding': padding,
+        }
+        self.cnn, output_dim = build_cnn(**cnn_kwargs)
+        self.classifier = nn.Conv2d(output_dim, 1, kernel_size=1, stride=1)
+
+    def forward(self, x):
+        return self.classifier(self.cnn(x))
+
+
+class AcDiscriminator(nn.Module):
+    def __init__(self, arch, normalization='none', activation='relu',
+                 padding='same', pooling='avg', num_id=None):
+        super(AcDiscriminator, self).__init__()
+        # self.vocab = vocab
+
+        cnn_kwargs = {
+            'arch': arch,
+            'normalization': normalization,
+            'activation': activation,
+            'pooling': pooling,
+            'padding': padding,
+        }
+        cnn, D = build_cnn(**cnn_kwargs)
+        self.cnn = nn.Sequential(cnn, GlobalAvgPool(), nn.Linear(D, 1024))
+        self.num_id = num_id
+
+        self.real_classifier = nn.Linear(1024, 1)
+        self.id_classifier = nn.Linear(1024, num_id)
+
+    def forward(self, x, y):
+        if x.dim() == 3:
+            x = x[:, None]
+
+        vecs = self.cnn(x)
+        real_scores = self.real_classifier(vecs)
+        id_scores = self.id_classifier(vecs)
+        ac_loss = F.cross_entropy(id_scores, y)
+        return real_scores, ac_loss
+
+
+class AcCropDiscriminator(nn.Module):
+    def __init__(self, arch, normalization='none', activation='relu',
+                 object_size=64, padding='same', pooling='avg'):
+        super(AcCropDiscriminator, self).__init__()
+        self.discriminator = AcDiscriminator(arch, normalization,
+                                             activation, padding, pooling)
+        self.object_size = object_size
+
+    def forward(self, imgs, objs, boxes, obj_to_img,
+                object_crops=None, **kwargs):
+        if object_crops is None:
+            object_crops = crop_bbox_batch(imgs, boxes, obj_to_img, self.object_size)
+        real_scores, ac_loss = self.discriminator(object_crops, objs)
+        return real_scores, ac_loss, object_crops
+
+
+# Conditional Discriminator
+class CondPatchDiscriminator(nn.Module):
+    def __init__(self, arch, normalization='batch', activation='leakyrelu-0.2',
+                 padding='same', pooling='avg', input_size=(128, 128),
+                 cond_dim=0):
+        super(CondPatchDiscriminator, self).__init__()
+        input_dim = 3
+        arch = 'I%d,%s' % (input_dim, arch)
+        cnn_kwargs = {
+            'arch': arch,
+            'normalization': normalization,
+            'activation': activation,
+            'pooling': pooling,
+            'padding': padding,
+        }
+        self.cnn, output_dim = build_cnn(**cnn_kwargs)
+        self.joint_conv = nn.Sequential(
+            nn.Conv2d(output_dim+cond_dim, output_dim, \
+                kernel_size=3, stride=1, padding=1, bias=False),
+            nn.BatchNorm2d(output_dim),
+            nn.LeakyReLU(0.2, inplace=True)
+        )
+        self.classifier = nn.Conv2d(output_dim, 1, kernel_size=1, stride=1)
+
+    def forward(self, x, cond_vecs):
+        if len(cond_vecs) == 2:
+            cond_vecs = cond_vecs.view(-1, cond_vecs.size(1), 1, 1)
+        x = self.cnn(x)
+        cond_vecs = cond_vecs.expand(-1, cond_vecs.size(1), x.size(2), x.size(3))
+        x = torch.cat([x, cond_vecs], dim=1)
+        x = self.joint_conv(x)
+        return self.classifier(x)
diff --git a/modeling/sf2f/models/encoder_decoder.py b/modeling/sf2f/models/encoder_decoder.py
new file mode 100644
index 0000000..d564d86
--- /dev/null
+++ b/modeling/sf2f/models/encoder_decoder.py
@@ -0,0 +1,87 @@
+'''
+Flexible Encoder-Decoder Framework
+'''
+
+
+import torch
+import torch.nn as nn
+import functools
+from torch.autograd import Variable
+import numpy as np
+
+try:
+    from .model_collection import model_collection
+except:
+    from model_collection import model_collection
+#from .voice_encoders import V2F1DCNN
+#from .face_decoders import V2FDecoder
+
+
+class EncoderDecoder(nn.Module):
+    def __init__(self,
+                 encoder_arch,
+                 encoder_kwargs,
+                 decoder_arch,
+                 decoder_kwargs,
+                 image_size):
+        super(EncoderDecoder, self).__init__()
+        # Initialize Encoder Decoder
+        self.encoder = getattr(model_collection, encoder_arch)(**encoder_kwargs)
+        self.decoder = getattr(model_collection, decoder_arch)(**decoder_kwargs)
+        #self.model = nn.Sequential(
+        #    self.encoder,
+        #    self.decoder
+        #)
+
+    def forward(self, x, average_voice_embedding=False):
+        '''
+        average_encoder_embedding: an option for evaluation, not needed for
+        training
+        '''
+        others = {}
+        if self.encoder.return_seq:
+            emb, seq_emb = self.encoder(x)
+            imgs_pred = self.decoder(emb, seq_emb)
+        else:
+            emb = self.encoder(x)
+            if average_voice_embedding:
+                emb = torch.mean(emb, 0, keepdim=True)
+            imgs_pred = self.decoder(emb)
+        others['cond'] = emb
+        return imgs_pred, others
+
+    def train_fuser_only(self):
+        print('Training Attention Fuser Only')
+        for name, param in self.named_parameters():
+            if 'attn_fuser' in name:
+                param.requires_grad = True
+            else:
+                param.requires_grad = False
+
+
+if __name__ == '__main__':
+    # Demo Input
+    log_mels = torch.ones((16, 40, 127))
+
+    # Test V2F 1D CNN
+    v2f_id_cnn_kwargs = {
+        'input_channel': 40,
+        'channels': [256, 384, 576, 864],
+        'output_channel': 64,
+        }
+    # Test V2F 1D CNN
+    v2f_decoder_kwargs = {
+        'input_channel': 64,
+        'channels': [1024, 512, 256, 128, 64],
+        'output_channel': 3,
+        }
+
+    baseline_v2f = EncoderDecoder(
+        encoder_arch='V2F1DCNN',
+        encoder_kwargs=v2f_id_cnn_kwargs,
+        decoder_arch='V2FDecoder',
+        decoder_kwargs=v2f_decoder_kwargs
+    )
+
+    print(baseline_v2f)
+    print('baseline_v2f Output shape:', baseline_v2f(log_mels).shape)
diff --git a/modeling/sf2f/models/face_decoders.py b/modeling/sf2f/models/face_decoders.py
new file mode 100644
index 0000000..a05a8b7
--- /dev/null
+++ b/modeling/sf2f/models/face_decoders.py
@@ -0,0 +1,583 @@
+'''
+Implementations of different Face Decoders
+'''
+
+
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+import torch.nn.functional as F
+import functools
+import numpy as np
+from collections import OrderedDict
+try:
+    from .attention import AttnBlock
+except:
+    from attention import AttnBlock
+try:
+    from .layers import get_activation, get_normalization_2d
+except:
+    from layers import get_activation, get_normalization_2d
+try:
+    from .networks import upBlock, GLU
+except:
+    from networks import upBlock, GLU
+
+
+class DeConv2DBLK(nn.Module):
+    def __init__(self,
+                 input_channel,
+                 output_channel,
+                 kernel_size,
+                 stride,
+                 padding,
+                 activation='relu',
+                 normalization='none'):
+        super(DeConv2DBLK, self).__init__()
+        self.layers = []
+        self.layers.append(
+            nn.ConvTranspose2d(
+                input_channel,
+                output_channel,
+                kernel_size,
+                stride,
+                padding,
+                bias=True))
+        self.layers.append(get_activation(activation))
+        self.layers.append(
+            get_normalization_2d(
+                output_channel,
+                normalization))
+        self.layers = [layer for layer in self.layers if \
+            layer is not None]
+        self.model = nn.Sequential(*self.layers)
+
+    def forward(self, x):
+        x = self.model(x)
+        return x
+
+
+class ExtraFeatureBlock(nn.Module):
+    def __init__(self,
+                 dim_in,
+                 dim_hidden,
+                 normalization='none'):
+        super(ExtraFeatureBlock, self).__init__()
+        self.projection = nn.Linear(dim_in, dim_hidden, bias=True)
+        self.norm_layer = get_normalization_2d(
+            dim_hidden // 16, normalization)
+
+    def forward(self, embedding, cat_tensor):
+        '''
+        Transform the embedding, then concat it to the hidden tensor map
+        '''
+        # Projected Embedding
+        #print('embedding:', embedding.shape)
+        N, D_in, _, _ = embedding.shape
+        proj_emb = self.projection(embedding.view(N, -1))
+        N, D_hid = proj_emb.shape
+        feat_map = proj_emb.view(N, -1, 4, 4)
+        if self.norm_layer is not None:
+            feat_map = self.norm_layer(feat_map)
+        N, C, H, W = cat_tensor.shape
+        feat_map = F.interpolate(feat_map, (H, W))
+        map_out = torch.cat([cat_tensor, feat_map], 1)
+
+        return map_out
+
+
+class V2FDecoder(nn.Module):
+    def __init__(self,
+                 input_channel,
+                 channels,
+                 output_channel,
+                 normalization='none'):
+        super(V2FDecoder, self).__init__()
+        output_size = 2 ** (len(channels) + 1)
+        print('V2FDecoder: According to your channels list, ' + \
+            'the output will be generated with shape ({}, {})'.format(
+                output_size, output_size))
+        #torch.nn.ConvTranspose2d(
+        #in_channels, out_channels, kernel_size, stride=1, padding=0,
+        #output_padding=0, groups=1, bias=True,
+        #dilation=1, padding_mode='zeros')
+        self.layers = []
+        # First Layer
+        layer_id = 0
+        self.layers.append(
+            DeConv2DBLK(input_channel,
+                        channels[0],
+                        kernel_size=4,
+                        stride=1,
+                        padding=0,
+                        activation='relu',
+                        normalization=normalization))
+        # Hidden Layers
+        for layer_id in range(1, len(channels)):
+            self.layers.append(
+                DeConv2DBLK(channels[layer_id - 1],
+                            channels[layer_id],
+                            kernel_size=4,
+                            stride=2,
+                            padding=1,
+                            activation='relu',
+                            normalization=normalization))
+        # Output Layer
+        layer_id = layer_id + 1
+        self.layers.append(
+            DeConv2DBLK(channels[layer_id - 1],
+                        output_channel,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        activation='none',
+                        normalization='none'))
+        self.model = nn.Sequential(*self.layers)
+
+    def forward(self, x):
+        x = self.model(x)
+        return x
+
+
+class MRDecoder(nn.Module):
+    def __init__(self,
+                 input_channel,
+                 base_block_channels,
+                 mid_block_channel=32,
+                 high_block_channel=None,
+                 output_channel=3,
+                 normalization='none',
+                 use_up_block=False,
+                 extra_feature_map=False):
+        super(MRDecoder, self).__init__()
+        output_sizes = []
+        output_sizes.append(
+            2 ** (len(base_block_channels) + 1))
+        output_sizes.append(
+            2 ** (len(base_block_channels) + 2))
+        if high_block_channel is not None:
+            self.build_high_img = True
+            output_sizes.append(
+                2 ** (len(base_block_channels) + 3))
+        else:
+            self.build_high_img = False
+        print('MRDecoder: According to your channels list:')
+        for i, output_size in enumerate(output_sizes):
+            print('\tOutput {} will be generated with shape ({}, {})'.format(
+                i, output_size, output_size))
+
+        self.input_channel = input_channel
+        self.base_block_channels = base_block_channels
+        self.mid_block_channel = mid_block_channel
+        self.high_block_channel = high_block_channel
+        self.output_channel = output_channel
+        self.normalization = normalization
+        self.use_up_block = use_up_block
+        self.extra_feature_map = extra_feature_map
+
+        # Output Layer
+        self.build_base_block()
+        self.build_low_block()
+        self.build_mid_block()
+        if self.build_high_img:
+            self.build_high_block()
+
+    def build_base_block(self):
+        #torch.nn.ConvTranspose2d(
+        #in_channels, out_channels, kernel_size, stride=1, padding=0,
+        #output_padding=0, groups=1, bias=True,
+        #dilation=1, padding_mode='zeros')
+        self.base_block_layers = []
+        # First Layer
+        layer_id = 0
+        self.base_block_layers.append(
+            DeConv2DBLK(self.input_channel,
+                        self.base_block_channels[0],
+                        kernel_size=4,
+                        stride=1,
+                        padding=0,
+                        activation='relu',
+                        normalization=self.normalization))
+        # Hidden Layers
+        for layer_id in range(1, len(self.base_block_channels)):
+            self.base_block_layers.append(
+                DeConv2DBLK(self.base_block_channels[layer_id - 1],
+                            self.base_block_channels[layer_id],
+                            kernel_size=4,
+                            stride=2,
+                            padding=1,
+                            activation='relu',
+                            normalization=self.normalization))
+        self.base_block = nn.Sequential(*self.base_block_layers)
+
+    def build_low_block(self):
+        self.low_output_layer = \
+            DeConv2DBLK(self.base_block_channels[-1],
+                        self.output_channel,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        activation='none',
+                        normalization='none')
+
+    def build_mid_block(self):
+        extra_channel = 0
+        if self.extra_feature_map:
+            # Mid-resolution ExtraFeatureBlock
+            self.mid_ef_block = ExtraFeatureBlock(
+                dim_in=self.input_channel,
+                dim_hidden=self.input_channel,
+                normalization=self.normalization)
+            extra_channel = self.input_channel // 16
+        if self.use_up_block:
+            self.mid_hidden_layer = \
+                upBlock(self.base_block_channels[-1] + extra_channel,
+                        self.mid_block_channel)
+        else:
+            self.mid_hidden_layer = \
+                DeConv2DBLK(self.base_block_channels[-1] + extra_channel,
+                            self.mid_block_channel,
+                            kernel_size=4,
+                            stride=2,
+                            padding=1,
+                            activation='relu',
+                            normalization=self.normalization)
+        self.mid_output_layer = \
+            DeConv2DBLK(self.mid_block_channel,
+                        self.output_channel,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        activation='none',
+                        normalization='none')
+
+    def build_high_block(self):
+        extra_channel = 0
+        if self.extra_feature_map:
+            # High-resolution ExtraFeatureBlock
+            self.high_ef_block = ExtraFeatureBlock(
+                dim_in=self.input_channel,
+                dim_hidden=self.input_channel,
+                normalization=self.normalization)
+            extra_channel = self.input_channel // 16
+        if self.use_up_block:
+            self.high_hidden_layer = \
+                upBlock(self.mid_block_channel + extra_channel,
+                        self.high_block_channel)
+        else:
+            self.high_hidden_layer = \
+                DeConv2DBLK(self.mid_block_channel + extra_channel,
+                            self.high_block_channel,
+                            kernel_size=4,
+                            stride=2,
+                            padding=1,
+                            activation='relu',
+                            normalization=self.normalization)
+        self.high_output_layer = \
+            DeConv2DBLK(self.high_block_channel,
+                        self.output_channel,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        activation='none',
+                        normalization='none')
+
+    def forward(self, x):
+        low_code = self.base_block(x)
+        low_imgs = self.low_output_layer(low_code)
+        #print('low_code:', low_code.shape)
+        if self.extra_feature_map:
+            low_code = self.mid_ef_block(x, low_code)
+            #print('low_code:', low_code.shape)
+        mid_code = self.mid_hidden_layer(low_code)
+        mid_imgs = self.mid_output_layer(mid_code)
+        if self.build_high_img:
+            #print('mid_code:', mid_code.shape)
+            if self.extra_feature_map:
+                mid_code = self.high_ef_block(x, mid_code)
+                #print('mid_code:', mid_code.shape)
+            high_code = self.high_hidden_layer(mid_code)
+            high_imgs = self.high_output_layer(high_code)
+            return (low_imgs, mid_imgs, high_imgs)
+        else:
+            return (low_imgs, mid_imgs)
+
+
+class AttnV2FDecoder(nn.Module):
+    def __init__(self,
+                 input_channel,
+                 cnn_channels,
+                 attn_channels,
+                 output_channel,
+                 seq_in_dim,
+                 normalization='none'):
+        super(AttnV2FDecoder, self).__init__()
+        output_size = 2 ** (len(cnn_channels) + 1)
+        print('V2FDecoder: According to your channels list, ' + \
+            'the output will be generated with shape ({}, {})'.format(
+                output_size, output_size))
+        #torch.nn.ConvTranspose2d(
+        #in_channels, out_channels, kernel_size, stride=1, padding=0,
+        #output_padding=0, groups=1, bias=True,
+        #dilation=1, padding_mode='zeros')
+        self.layers = []
+        self.layer_names = []
+        # First Layer
+        layer_id = 0
+        self.layers.append(
+            DeConv2DBLK(input_channel,
+                        cnn_channels[0],
+                        kernel_size=4,
+                        stride=1,
+                        padding=0,
+                        activation='relu',
+                        normalization=normalization))
+        self.layer_names.append('DeConv2DBLK{}'.format(layer_id))
+        # Hidden Layers
+        for layer_id in range(1, len(cnn_channels)):
+            self.layers.append(
+                DeConv2DBLK(
+                    cnn_channels[layer_id - 1]+attn_channels[layer_id - 1],
+                    cnn_channels[layer_id],
+                    kernel_size=4,
+                    stride=2,
+                    padding=1,
+                    activation='relu',
+                    normalization=normalization))
+            self.layer_names.append('DeConv2DBLK{}'.format(layer_id))
+            attn_channel = attn_channels[layer_id]
+            if attn_channel > 0:
+                self.layers.append(
+                    AttnBlock(attn_dim_in=seq_in_dim,
+                              attn_dim_out=attn_channel,
+                              attn_num_query=16,
+                              normalization=normalization))
+                self.layer_names.append('AttnBlock{}'.format(layer_id))
+        # Output Layer
+        layer_id = layer_id + 1
+        self.layers.append(
+            DeConv2DBLK(cnn_channels[layer_id - 1]+attn_channels[layer_id - 1],
+                        output_channel,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        activation='none',
+                        normalization='none'))
+        self.layer_names.append('DeConv2DBLK{}'.format(layer_id))
+
+        for layer_id, layer in enumerate(self.layers):
+            self.add_module(self.layer_names[layer_id], layer)
+
+    def forward(self, x, seq_feat):
+        for layer, name in zip(self.layers, self.layer_names):
+            if 'AttnBlock' in name:
+                x = layer(x, seq_feat)
+            else:
+                x = layer(x)
+            #print(x.shape)
+        return x
+
+    def return_attn_weights(self):
+        for layer, name in zip(self.layers, self.layer_names):
+            if 'AttnBlock' in name:
+                return layer.return_attn_weights()
+
+
+class FaceGanDecoder(nn.Module):
+    def __init__(self,
+                 image_size=(64, 64),
+                 normalization='batch',
+                 activation='leakyrelu-0.2',
+                 mlp_normalization='none',
+                 noise_dim=128,
+                 **kwargs):
+
+        super(FaceGanDecoder, self).__init__()
+        if len(kwargs) > 0:
+            print('WARNING: Model got unexpected kwargs ', kwargs)
+
+        self.dim = noise_dim
+        self.image_size = image_size
+
+        self.fc = nn.Sequential(
+            nn.Linear(self.dim, self.dim * 4 * 4 * 2, bias=False),
+            nn.BatchNorm1d(self.dim * 4 * 4 * 2),
+            GLU())
+
+        up_num, up_layers = [], []
+        num_in, num_out = 1, 1
+        up_num = range(1, int(np.log2(image_size[0]))-1)
+
+        for i, num in enumerate(up_num):
+            if i == 0:
+                num_in = num
+            num_out = 2**num
+            up_layers.append(upBlock(self.dim//num_in, self.dim//num_out))
+            num_in = num_out
+
+        self.up_layers = nn.Sequential(*up_layers)
+
+        gnet_layers = [
+            nn.Conv2d(self.dim//num_out, self.dim//num_out, kernel_size=3, padding=1),
+            get_activation('leakyrelu'),
+            nn.Conv2d(self.dim//num_out, 3, kernel_size=1, padding=0),
+        ]
+        self.gnet = nn.Sequential(*gnet_layers)
+
+    def forward(self, x):
+        """
+        Required Inputs:
+        - placeholder: This is a placeholder.
+
+        Optional Inputs:
+        - placeholder: This is a placeholder.
+        """
+
+        vecs = self.fc(x.view(-1, 512))
+        vecs = vecs.view(x.size()[0], -1, 4, 4)
+
+        img_code = self.up_layers(vecs)
+        img_pred = self.gnet(img_code)
+
+        return img_pred
+
+
+class FaceGanDecoder_v2(nn.Module):
+    def __init__(self,
+                 image_size=(64, 64),
+                 normalization='batch',
+                 activation='leakyrelu-0.2',
+                 mlp_normalization='none',
+                 noise_dim=128,
+                 **kwargs):
+
+        super(FaceGanDecoder_v2, self).__init__()
+        if len(kwargs) > 0:
+            print('WARNING: Model got unexpected kwargs ', kwargs)
+
+        self.dim = noise_dim
+        self.image_size = image_size
+
+        self.fc = nn.Sequential(
+            nn.Linear(self.dim, self.dim * 4 * 4 * 2, bias=False),
+            nn.BatchNorm1d(self.dim * 4 * 4 * 2),
+            GLU())
+
+        up_num, up_layers = [], []
+        num_in, num_out = 1, 1
+        up_num = range(1, int(np.log2(image_size[0]))-1)
+
+        for i, num in enumerate(up_num):
+            if i == 0:
+                num_in = num
+            num_out = 2**num
+            up_layers.append(upBlock(self.dim//num_in, self.dim//num_out))
+            num_in = num_out
+
+        self.up_low_layers = nn.Sequential(*up_layers)
+        self.up_mid_layer = upBlock(self.dim//num_out, self.dim//num_out//2)
+
+        gnet_layers = [
+            nn.Conv2d(self.dim//num_out, self.dim//num_out, kernel_size=3, padding=1),
+            get_activation('leakyrelu'),
+            nn.Conv2d(self.dim//num_out, 3, kernel_size=1, padding=0),
+        ]
+        self.gnet_low = nn.Sequential(*gnet_layers)
+
+        gnet_layers = [
+            nn.Conv2d(self.dim//num_out//2, self.dim//num_out//2, kernel_size=3, padding=1),
+            get_activation('leakyrelu'),
+            nn.Conv2d(self.dim//num_out//2, 3, kernel_size=1, padding=0),
+        ]
+        self.gnet_mid = nn.Sequential(*gnet_layers)
+
+    def forward(self, x):
+        """
+        Required Inputs:
+        - placeholder: This is a placeholder.
+
+        Optional Inputs:
+        - placeholder: This is a placeholder.
+        """
+
+        vecs = self.fc(x.view(-1, 512))
+        vecs = vecs.view(x.size()[0], -1, 4, 4)
+
+        img_code_low = self.up_low_layers(vecs)
+        img_pred_low = self.gnet_low(img_code_low)
+
+        img_code_mid = self.up_mid_layer(img_code_low)
+        img_pred_mid = self.gnet_mid(img_code_mid)
+
+        return (img_pred_low, img_pred_mid)
+
+
+if __name__ == '__main__':
+    # Demo Input
+    voice_embeddings = torch.ones((16, 512, 1, 1))
+    seq_voice_embeddings = torch.ones((16, 512, 270))
+
+    # Test V2F 1D CNN
+    mr_decoder_kwargs = {
+        'input_channel': 512,
+        'base_block_channels': [1024, 512, 256, 128, 64],
+        'mid_block_channel': 32,
+        'high_block_channel': 32,
+        'output_channel': 3,
+        'normalization': 'batch',
+        'use_up_block': True,
+        'extra_feature_map': True,
+        }
+    mr_decoder = MRDecoder(**mr_decoder_kwargs)
+    print(mr_decoder)
+    for i, imgs in enumerate(mr_decoder(voice_embeddings)):
+        print('MRDecoder Output {} shape:'.format(i), imgs.shape)
+
+    # Test V2F Decoder
+    v2f_decoder_kwargs = {
+        'input_channel': 512,
+        'channels': [1024, 512, 256, 128, 64],
+        'output_channel': 3,
+        'normalization': 'batch',
+        }
+    v2f_decoder = V2FDecoder(**v2f_decoder_kwargs)
+    print(v2f_decoder)
+    print('V2FDecoder Output shape:', v2f_decoder(voice_embeddings).shape)
+    print('V2F Sequential:')
+    print(v2f_decoder.model[5])
+    for name, param in v2f_decoder.model[5].named_parameters():
+        print(name)
+    #for module in v2f_decoder.model:
+    #    print(module)
+
+    v2f_decoder_kwargs = {
+        'input_channel': 512,
+        'cnn_channels': [1024, 512, 256, 128, 64],
+        'attn_channels': [0, 0, 128, 0, 32],
+        'output_channel': 3,
+        'seq_in_dim': 512,
+        'normalization': 'none',
+        }
+    attn_v2f_decoder = AttnV2FDecoder(**v2f_decoder_kwargs)
+    print(attn_v2f_decoder)
+    print('AttnV2FDecoder Output shape:', \
+        attn_v2f_decoder(voice_embeddings, seq_voice_embeddings).shape)
+    print('Return Attention Weights:',
+          attn_v2f_decoder.return_attn_weights().shape,
+          attn_v2f_decoder.return_attn_weights()[0][0][0:10])
+
+    dimLatentVector = 512
+    depthScale0 = 512
+
+    pgandecoder = PganDecoder(dimLatent=dimLatentVector,
+                          depthScale0=depthScale0,
+                          initBiasToZero=True,
+                          leakyReluLeak=0.2,
+                          normalization=True,
+                          generationActivation=None,
+                          dimOutput=3,
+                          equalizedlR=True,
+                          depthOtherScales=[512,512,512,256])
+
+    img = pgandecoder(voice_embeddings)
+    
diff --git a/modeling/sf2f/models/fusers.py b/modeling/sf2f/models/fusers.py
new file mode 100644
index 0000000..ecc8043
--- /dev/null
+++ b/modeling/sf2f/models/fusers.py
@@ -0,0 +1,136 @@
+'''
+Implementations of different Fusers
+'''
+
+
+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+import numpy as np
+import math
+
+try:
+    from attention import Attention
+except:
+    from .attention import Attention
+
+
+class AttentionFuserV1(nn.Module):
+    def __init__(self,
+                 dimensions,
+                 dim_out,
+                 attention_type='general',
+                 linear_out_layer=True,
+                 ignore_tanh=False):
+        '''
+        1 Layer Attnetion Fuser
+        '''
+        nn.Module.__init__(self)
+        self.attention = Attention(
+            dimensions,
+            dim_out,
+            attention_type=attention_type,
+            linear_out_layer=linear_out_layer,
+            ignore_tanh=ignore_tanh)
+
+    def forward(self, embeddings):
+        '''
+        seq_in: tensor with shape [N, D, L]
+        '''
+        # Embeddings: [B, N, C]
+        embeddings, attention_weights = \
+            self.attention(embeddings, embeddings)
+        # [B, C, N]
+        embeddings = embeddings.transpose(1, 2)
+        #print('Transposed Shape:', embeddings.shape)
+        embeddings = F.avg_pool1d(
+            embeddings, embeddings.size()[2], stride=1)
+        embeddings = embeddings.view(embeddings.size()[0], -1, 1, 1)
+        #print('Attention Fuser Output Shape:', embeddings.shape)
+        return embeddings
+
+
+class AttentionFuserV2(nn.Module):
+    def __init__(self,
+                 dimensions,
+                 attention_type='general'):
+        '''
+        2-Layer Attention
+        '''
+        nn.Module.__init__(self)
+        self.attention_1 = Attention(
+            dimensions,
+            dimensions,
+            attention_type=attention_type,
+            linear_out_layer=True,
+            ignore_tanh=False)
+        self.attention_2 = Attention(
+            dimensions,
+            dimensions,
+            attention_type=attention_type,
+            linear_out_layer=True,
+            ignore_tanh=True)
+
+    def forward(self, embeddings):
+        '''
+        seq_in: tensor with shape [N, D, L]
+        '''
+        # Embeddings: [B, N, C]
+        embeddings, attention_weights = \
+            self.attention_1(embeddings, embeddings)
+        embeddings, attention_weights = \
+            self.attention_2(embeddings, embeddings)
+        # [B, C, N]
+        embeddings = embeddings.transpose(1, 2)
+        #print('Transposed Shape:', embeddings.shape)
+        embeddings = F.avg_pool1d(
+            embeddings, embeddings.size()[2], stride=1)
+        embeddings = embeddings.view(embeddings.size()[0], -1, 1, 1)
+        #print('Attention Fuser Output Shape:', embeddings.shape)
+        return embeddings
+
+
+class AttentionFuserV3(nn.Module):
+    def __init__(self,
+                 dimensions,
+                 attention_type='general'):
+        '''
+        2-Layer Attention with Residual Connection
+        '''
+        nn.Module.__init__(self)
+        self.attention_1 = Attention(
+            dimensions,
+            dimensions,
+            attention_type=attention_type,
+            linear_out_layer=True,
+            ignore_tanh=False)
+        self.attention_2 = Attention(
+            dimensions*2,
+            dimensions,
+            attention_type=attention_type,
+            linear_out_layer=True,
+            ignore_tanh=True)
+
+    def forward(self, input):
+        '''
+        seq_in: tensor with shape [N, D, L]
+        '''
+        # Embeddings: [B, N, C]
+        hidden, attention_weights = \
+            self.attention_1(input, input)
+        hidden = F.normalize(hidden, dim=2)
+        # Residual Connection
+        combined = torch.cat((hidden, input), dim=2)
+        # 2nd Attention
+        embeddings, attention_weights = \
+            self.attention_2(combined, combined)
+        # [B, C, N]
+        embeddings = embeddings.transpose(1, 2)
+        #print('Transposed Shape:', embeddings.shape)
+        embeddings = F.avg_pool1d(
+            embeddings, embeddings.size()[2], stride=1)
+        embeddings = embeddings.view(embeddings.size()[0], -1, 1, 1)
+        #print('Attention Fuser Output Shape:', embeddings.shape)
+        return embeddings
diff --git a/modeling/sf2f/models/inception_resnet_v1.py b/modeling/sf2f/models/inception_resnet_v1.py
new file mode 100644
index 0000000..f95a1f8
--- /dev/null
+++ b/modeling/sf2f/models/inception_resnet_v1.py
@@ -0,0 +1,439 @@
+'''
+Reference: https://github.com/timesler/facenet-pytorch/ \
+    blob/master/models/inception_resnet_v1.py
+
+Both pretrained models were trained on 160x160 px images,
+so will perform best if applied to images resized to this shape.
+For best results, images should also be cropped to the face using
+MTCNN (see below).
+'''
+
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+import requests
+from requests.adapters import HTTPAdapter
+import os
+
+
+class BasicConv2d(nn.Module):
+
+    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
+        super().__init__()
+        self.conv = nn.Conv2d(
+            in_planes, out_planes,
+            kernel_size=kernel_size, stride=stride,
+            padding=padding, bias=False
+        ) # verify bias false
+        self.bn = nn.BatchNorm2d(
+            out_planes,
+            eps=0.001, # value found in tensorflow
+            momentum=0.1, # default pytorch value
+            affine=True
+        )
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Block35(nn.Module):
+
+    def __init__(self, scale=1.0):
+        super().__init__()
+
+        self.scale = scale
+
+        self.branch0 = BasicConv2d(256, 32, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(256, 32, kernel_size=1, stride=1),
+            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(256, 32, kernel_size=1, stride=1),
+            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.conv2d = nn.Conv2d(96, 256, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        out = self.relu(out)
+        return out
+
+
+class Block17(nn.Module):
+
+    def __init__(self, scale=1.0):
+        super().__init__()
+
+        self.scale = scale
+
+        self.branch0 = BasicConv2d(896, 128, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(896, 128, kernel_size=1, stride=1),
+            BasicConv2d(128, 128, kernel_size=(1,7), stride=1, padding=(0,3)),
+            BasicConv2d(128, 128, kernel_size=(7,1), stride=1, padding=(3,0))
+        )
+
+        self.conv2d = nn.Conv2d(256, 896, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        out = self.relu(out)
+        return out
+
+
+class Block8(nn.Module):
+
+    def __init__(self, scale=1.0, noReLU=False):
+        super().__init__()
+
+        self.scale = scale
+        self.noReLU = noReLU
+
+        self.branch0 = BasicConv2d(1792, 192, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1792, 192, kernel_size=1, stride=1),
+            BasicConv2d(192, 192, kernel_size=(1,3), stride=1, padding=(0,1)),
+            BasicConv2d(192, 192, kernel_size=(3,1), stride=1, padding=(1,0))
+        )
+
+        self.conv2d = nn.Conv2d(384, 1792, kernel_size=1, stride=1)
+        if not self.noReLU:
+            self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        if not self.noReLU:
+            out = self.relu(out)
+        return out
+
+
+class Mixed_6a(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+        self.branch0 = BasicConv2d(256, 384, kernel_size=3, stride=2)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(256, 192, kernel_size=1, stride=1),
+            BasicConv2d(192, 192, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(192, 256, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        return out
+
+
+class Mixed_7a(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+        self.branch0 = nn.Sequential(
+            BasicConv2d(896, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 384, kernel_size=3, stride=2)
+        )
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(896, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 256, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(896, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(256, 256, kernel_size=3, stride=2)
+        )
+
+        self.branch3 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class InceptionResnetV1(nn.Module):
+    """Inception Resnet V1 model with optional loading of pretrained weights.
+
+    Model parameters can be loaded based on pretraining on the VGGFace2 or CASIA-Webface
+    datasets. Pretrained state_dicts are automatically downloaded on model instantiation if
+    requested and cached in the torch cache. Subsequent instantiations use the cache rather than
+    redownloading.
+
+    Keyword Arguments:
+        pretrained {str} -- Optional pretraining dataset. Either 'vggface2' or 'casia-webface'.
+            (default: {None})
+        classify {bool} -- Whether the model should output classification probabilities or feature
+            embeddings. (default: {False})
+        num_classes {int} -- Number of output classes. If 'pretrained' is set and num_classes not
+            equal to that used for the pretrained model, the final linear layer will be randomly
+            initialized. (default: {None})
+        dropout_prob {float} -- Dropout probability. (default: {0.6})
+    """
+    def __init__(self,
+                 pretrained=None,
+                 classify=False,
+                 num_classes=None,
+                 dropout_prob=0.6,
+                 device=None,
+                 auto_input_resize=True,
+                 return_pooling=False):
+        super().__init__()
+
+        # Set simple attributes
+        self.pretrained = pretrained
+        self.classify = classify
+        self.num_classes = num_classes
+        self.auto_input_resize = auto_input_resize
+
+        if pretrained == 'vggface2':
+            tmp_classes = 8631
+        elif pretrained == 'casia-webface':
+            tmp_classes = 10575
+        elif pretrained is None and self.num_classes is None:
+            raise Exception('At least one of "pretrained" or "num_classes" must be specified')
+        else:
+            tmp_classes = self.num_classes
+
+        if self.auto_input_resize:
+            self.input_resize = nn.UpsamplingBilinear2d(size=(140, 140))
+        self.return_pooling = return_pooling
+        # Define layers
+        self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
+        self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
+        self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.maxpool_3a = nn.MaxPool2d(3, stride=2)
+        self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
+        self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
+        self.conv2d_4b = BasicConv2d(192, 256, kernel_size=3, stride=2)
+        self.repeat_1 = nn.Sequential(
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+        )
+        self.mixed_6a = Mixed_6a()
+        self.repeat_2 = nn.Sequential(
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+        )
+        self.mixed_7a = Mixed_7a()
+        self.repeat_3 = nn.Sequential(
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+        )
+        self.block8 = Block8(noReLU=True)
+        self.avgpool_1a = nn.AdaptiveAvgPool2d(1)
+        self.dropout = nn.Dropout(dropout_prob)
+        self.last_linear = nn.Linear(1792, 512, bias=False)
+        self.last_bn = nn.BatchNorm1d(512, eps=0.001, momentum=0.1, affine=True)
+        self.logits = nn.Linear(512, tmp_classes)
+
+        if pretrained is not None:
+            load_weights(self, pretrained)
+
+        if self.num_classes is not None:
+            self.logits = nn.Linear(512, self.num_classes)
+
+        self.device = torch.device('cpu')
+        if device is not None:
+            self.device = device
+            self.to(device)
+
+    def forward(self, x):
+        """
+        Calculate embeddings or probabilities given a batch of input image
+        tensors.
+
+        Arguments:
+            x {torch.tensor} -- Batch of image tensors representing faces.
+
+        Returns:
+            torch.tensor -- Batch of embeddings or softmax probabilities.
+        """
+        if self.auto_input_resize:
+            x = self.input_resize(x)
+        x = self.conv2d_1a(x)
+        x = self.conv2d_2a(x)
+        x = self.conv2d_2b(x)
+        x = self.maxpool_3a(x)
+        x = self.conv2d_3b(x)
+        x = self.conv2d_4a(x)
+        x = self.conv2d_4b(x)
+        x = self.repeat_1(x)
+        x = self.mixed_6a(x)
+        x = self.repeat_2(x)
+        x = self.mixed_7a(x)
+        x = self.repeat_3(x)
+        x = self.block8(x)
+        x = self.avgpool_1a(x)
+        if self.return_pooling:
+            pooled = x.view(x.shape[0], -1)
+            pooled = F.normalize(pooled, p=2, dim=1)
+            return pooled
+        x = self.dropout(x)
+        x = self.last_linear(x.view(x.shape[0], -1))
+        x = self.last_bn(x)
+        x = F.normalize(x, p=2, dim=1)
+        if self.classify:
+            x = self.logits(x)
+        return x
+
+    def get_hidden_states(self, x):
+        """
+        Calculate embeddings or probabilities given a batch of input image
+        tensors.
+
+        Arguments:
+            x {torch.tensor} -- Batch of image tensors representing faces.
+
+        Returns:
+            torch.tensor -- Batch of embeddings or softmax probabilities.
+        """
+        if self.auto_input_resize:
+            x = self.input_resize(x)
+        conv2d_1a = self.conv2d_1a(x)
+        conv2d_2a = self.conv2d_2a(conv2d_1a)
+        conv2d_2b = self.conv2d_2b(conv2d_2a)
+        maxpool_3a = self.maxpool_3a(conv2d_2b)
+        conv2d_3b = self.conv2d_3b(maxpool_3a)
+        conv2d_4a = self.conv2d_4a(conv2d_3b)
+        conv2d_4b = self.conv2d_4b(conv2d_4a)
+        repeat_1 = self.repeat_1(conv2d_4b)
+        mixed_6a = self.mixed_6a(repeat_1)
+        repeat_2 = self.repeat_2(mixed_6a)
+        mixed_7a = self.mixed_7a(repeat_2)
+        repeat_3 = self.repeat_3(mixed_7a)
+        block8 = self.block8(repeat_3)
+        avgpool_1a = self.avgpool_1a(block8)
+        dropout = self.dropout(avgpool_1a)
+        last_linear = self.last_linear(dropout.view(x.shape[0], -1))
+        last_bn = self.last_bn(last_linear)
+        x = F.normalize(last_bn, p=2, dim=1)
+        if self.classify:
+            x = self.logits(x)
+        out = [repeat_1, repeat_2, repeat_3, x]
+        return out
+
+
+def load_weights(mdl, name):
+    """Download pretrained state_dict and load into model.
+
+    Arguments:
+        mdl {torch.nn.Module} -- Pytorch model.
+        name {str} -- Name of dataset that was used to generate pretrained state_dict.
+
+    Raises:
+        ValueError: If 'pretrained' not equal to 'vggface2' or 'casia-webface'.
+    """
+    if name == 'vggface2':
+        features_path = \
+            'https://drive.google.com/uc?export=download&' + \
+                'id=1cWLH_hPns8kSfMz9kKl9PsG5aNV2VSMn'
+        logits_path = \
+            'https://drive.google.com/uc?export=download' + \
+                '&id=1mAie3nzZeno9UIzFXvmVZrDG3kwML46X'
+    elif name == 'casia-webface':
+        features_path = 'https://drive.google.com/uc?export=download&id=1LSHHee_IQj5W3vjBcRyVaALv4py1XaGy'
+        logits_path = 'https://drive.google.com/uc?export=download&id=1QrhPgn1bGlDxAil2uc07ctunCQoDnCzT'
+    else:
+        raise ValueError('Pretrained models only exist for "vggface2" and "casia-webface"')
+
+    model_dir = os.path.join(get_torch_home(), 'checkpoints')
+    os.makedirs(model_dir, exist_ok=True)
+
+    state_dict = {}
+    for i, path in enumerate([features_path, logits_path]):
+        cached_file = os.path.join(
+            model_dir, '{}_{}.pt'.format(name, path[-10:]))
+        #print('Cashed File Location:', cached_file)
+        if not os.path.exists(cached_file):
+            print('Downloading parameters ({}/2)'.format(i+1))
+            s = requests.Session()
+            s.mount('https://', HTTPAdapter(max_retries=10))
+            r = s.get(path, allow_redirects=True)
+            with open(cached_file, 'wb') as f:
+                f.write(r.content)
+        state_dict.update(torch.load(cached_file))
+
+    mdl.load_state_dict(state_dict)
+
+
+def get_torch_home():
+    torch_home = os.path.expanduser(
+        os.getenv(
+            'TORCH_HOME',
+            os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch')
+        )
+    )
+    return torch_home
+
+
+def fixed_image_standardization(image_tensor):
+    '''
+    This is the default preprocess function for this pretrained model
+    '''
+    processed_tensor = (image_tensor - 127.5) / 128.0
+    return processed_tensor
+
+
+if __name__ == '__main__':
+    model = InceptionResnetV1(pretrained='vggface2').eval()
+    print(model)
+    demo_imgs = torch.zeros(16, 3, 64, 64)
+    print('Output size 1:', model(demo_imgs).shape, model(demo_imgs)[0])
+
+    model_2 = InceptionResnetV1(
+        pretrained='vggface2',
+        auto_input_resize=False).eval()
+    demo_imgs = torch.zeros(16, 3, 140, 140)
+    print('Output size 2:', model(demo_imgs).shape, model_2(demo_imgs)[0])
diff --git a/modeling/sf2f/models/layers.py b/modeling/sf2f/models/layers.py
new file mode 100644
index 0000000..001e3eb
--- /dev/null
+++ b/modeling/sf2f/models/layers.py
@@ -0,0 +1,257 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+## We use the batchnorm in pytorch 0.3.x to avoid unexpected errors
+## Otherwise, some in-place modification will bring some errors
+# from models.utils.batchnorm import BatchNorm1d, BatchNorm2d
+
+
+
+def get_normalization_2d(channels, normalization):
+    if normalization == 'instance':
+        return nn.InstanceNorm2d(channels)
+    elif normalization == 'batch':
+        return nn.BatchNorm2d(channels)
+    elif normalization == 'none':
+        return None
+    else:
+        raise ValueError('Unrecognized normalization type "%s"' %
+                         normalization)
+
+
+def get_activation(name):
+    kwargs = {}
+    if name.lower().startswith('leakyrelu'):
+        if '-' in name:
+            slope = float(name.split('-')[1])
+            kwargs = {'negative_slope': slope}
+            name = 'leakyrelu'
+    activations = {
+        'relu': nn.ReLU,
+        'leakyrelu': nn.LeakyReLU,
+        'none': None,
+    }
+    if name.lower() not in activations:
+        raise ValueError('Invalid activation "%s"' % name)
+    if activations[name.lower()] is not None:
+        return activations[name.lower()](**kwargs)
+
+
+def weights_init(m):
+    raise NotImplementedError
+    classname = m.__class__.__name__
+    if classname.find('Conv') != -1:
+        m.weight.data.normal_(0.0, 0.02)
+        m.bias.data.fill_(0)
+    elif classname.find('BatchNorm2d') != -1:
+        m.weight.data.normal_(1.0, 0.02)
+        m.bias.data.fill_(0)
+    elif classname.find('BatchNorm1d') != -1:
+        m.weight.data.normal_(1.0, 0.02)
+        m.bias.data.fill_(0)
+    elif classname.find('Linear') != -1:
+        m.weight.data.normal_(0.0, 0.02)
+        m.bias.data.fill_(0)
+
+
+class Flatten(nn.Module):
+    def forward(self, x):
+        return x.view(x.size(0), -1)
+
+    def __repr__(self):
+        return 'Flatten()'
+
+
+class Unflatten(nn.Module):
+    def __init__(self, size):
+        super(Unflatten, self).__init__()
+        self.size = size
+
+    def forward(self, x):
+        return x.view(*self.size)
+
+    def __repr__(self):
+        size_str = ', '.join('%d' % d for d in self.size)
+        return 'Unflatten(%s)' % size_str
+
+
+class GlobalAvgPool(nn.Module):
+    def __init__(self, pooling="avg"):
+        super(GlobalAvgPool, self).__init__()
+        self.pooling = pooling
+    def forward(self, x):
+        N, C = x.size(0), x.size(1)
+        if self.pooling == "avg":
+            return x.view(N, C, -1).mean(dim=2)
+        elif self.pooling == "max":
+            return x.view(N, C, -1).max(dim=2)[0]
+        else:
+            assert False, "Unrecognized pooling: {}".format(self.pooling)
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, channels, normalization='batch', activation='relu',
+                 padding='same', kernel_size=3, init='default'):
+        super(ResidualBlock, self).__init__()
+        K = kernel_size
+        P = _get_padding(K, "same")
+        C = channels
+        self.padding = P
+        layers = [
+            get_normalization_2d(C, normalization),
+            get_activation(activation),
+            nn.Conv2d(C, C, kernel_size=K, padding=P),
+            get_normalization_2d(C, normalization),
+            get_activation(activation),
+            nn.Conv2d(C, C, kernel_size=K, padding=P),
+        ]
+        layers = [layer for layer in layers if layer is not None]
+        # for layer in layers:
+        #     _init_conv(layer, method=init)
+        self.net = nn.Sequential(*layers)
+
+    def forward(self, x):
+        P = self.padding
+        shortcut = x
+        y = self.net(x)
+        return shortcut + self.net(x)
+
+
+def _get_padding(K, mode):
+    """ Helper method to compute padding size """
+    if mode == 'valid':
+        return 0
+    elif mode == 'same':
+        assert K % 2 == 1, 'Invalid kernel size %d for "same" padding' % K
+        return (K - 1) // 2
+
+
+def build_cnn(arch, normalization='batch', activation='relu', padding='same',
+              pooling='max', init='default', non_linear_activated=False):
+    """
+    Build a CNN from an architecture string, which is a list of layer
+    specification strings. The overall architecture can be given as a list or as
+    a comma-separated string.
+
+    All convolutions *except for the first* are preceeded by normalization and
+    nonlinearity.
+
+    non_linear_activated: whether to add [non-linear activation / normalization]
+        Default is [False] (do not add these layers)
+
+    All other layers support the following:
+    - IX: Indicates that the number of input channels to the network is X.
+          Can only be used at the first layer; if not present then we assume
+          3 input channels.
+    - CK-X: KxK convolution with X output channels
+    - CK-X-S: KxK convolution with X output channels and stride S
+    - R: Residual block keeping the same number of channels
+    - UX: Nearest-neighbor upsampling with factor X
+    - PX: Spatial pooling with factor X
+    - FC-X-Y: Flatten followed by fully-connected layer
+
+    Returns a tuple of:
+    - cnn: An nn.Sequential
+    - channels: Number of output channels
+    """
+    if isinstance(arch, str):
+        arch = arch.split(',')
+    cur_C = 3
+    if len(arch) > 0 and arch[0][0] == 'I':
+        cur_C = int(arch[0][1:])
+        arch = arch[1:]
+
+    first_conv = True
+    flat = False
+    layers = []
+    for i, s in enumerate(arch):
+        if s[0] == 'C':
+            if not first_conv:
+                layers.append(get_normalization_2d(cur_C, normalization))
+                layers.append(get_activation(activation))
+            first_conv = False
+            vals = [int(i) for i in s[1:].split('-')]
+            P = None
+            if len(vals) == 2:
+                K, next_C = vals
+                stride = 1
+            elif len(vals) == 3:
+                K, next_C, stride = vals
+            elif len(vals) == 4:
+                # Add padding
+                K, next_C, stride, P = vals
+            # K, next_C = (int(i) for i in s[1:].split('-'))
+            if P is None:
+                P = _get_padding(K, padding)
+            conv = nn.Conv2d(cur_C, next_C, kernel_size=K,
+                             padding=P, stride=stride)
+            layers.append(conv)
+            # _init_conv(layers[-1], init)
+            cur_C = next_C
+        elif s[0] == 'R':
+            norm = 'none' if first_conv else normalization
+            res = ResidualBlock(cur_C, normalization=norm, activation=activation,
+                                padding=padding, init=init)
+            layers.append(res)
+            first_conv = False
+        elif s[0] == 'U':
+            factor = int(s[1:])
+            layers.append(nn.Upsample(scale_factor=factor, mode='nearest'))
+        elif s[0] == 'P':
+            factor = int(s[1:])
+            if pooling == 'max':
+                pool = nn.MaxPool2d(kernel_size=factor, stride=factor)
+            elif pooling == 'avg':
+                pool = nn.AvgPool2d(kernel_size=factor, stride=factor)
+            layers.append(pool)
+        elif s[:2] == 'FC':
+            _, Din, Dout = s.split('-')
+            Din, Dout = int(Din), int(Dout)
+            if not flat:
+                layers.append(Flatten())
+            flat = True
+            layers.append(nn.Linear(Din, Dout))
+            if i + 1 < len(arch):
+                layers.append(get_activation(activation))
+            cur_C = Dout
+        else:
+            raise ValueError('Invalid layer "%s"' % s)
+    if non_linear_activated:
+        layers.append(get_normalization_2d(cur_C, normalization))
+        layers.append(get_activation(activation))
+    layers = [layer for layer in layers if layer is not None]
+    #for layer in layers:
+    #    print(layer)
+
+    model = nn.Sequential(*layers)
+    return model, cur_C
+
+
+def build_mlp(dim_list, activation='relu', batch_norm='none',
+              dropout=0, final_nonlinearity=True,
+              start_nonlinearity=False,):
+    layers = []
+    for i in range(len(dim_list) - 1):
+        dim_in, dim_out = dim_list[i], dim_list[i + 1]
+        if start_nonlinearity and i == 0:
+            if batch_norm == 'batch':
+                layers.append(nn.BatchNorm1d(dim_in))
+            # elif batch_norm == 'instance':
+            #     layers.append(InstanceNorm1d(dim_in))
+            if activation == 'relu':
+                layers.append(nn.ReLU())
+            elif activation == 'leakyrelu':
+                layers.append(nn.LeakyReLU())
+        layers.append(nn.Linear(dim_in, dim_out))
+        final_layer = (i == len(dim_list) - 2)
+        if not final_layer or final_nonlinearity:
+            if batch_norm == 'batch':
+                layers.append(nn.BatchNorm1d(dim_out))
+            if activation == 'relu':
+                layers.append(nn.ReLU())
+            elif activation == 'leakyrelu':
+                layers.append(nn.LeakyReLU())
+        if dropout > 0:
+            layers.append(nn.Dropout(p=dropout))
+    model = nn.Sequential(*layers)
+    return model
diff --git a/modeling/sf2f/models/model_collection.py b/modeling/sf2f/models/model_collection.py
new file mode 100644
index 0000000..bf3e4ba
--- /dev/null
+++ b/modeling/sf2f/models/model_collection.py
@@ -0,0 +1,38 @@
+'''
+A collection of all the models, for the convenience of internal calling
+'''
+
+
+# Lower level encoder decoders
+try:
+    from .voice_encoders import V2F1DCNN, TransEncoder
+except:
+    from voice_encoders import V2F1DCNN, TransEncoder
+
+try:
+    from .face_decoders import V2FDecoder, AttnV2FDecoder, MRDecoder, \
+        FaceGanDecoder, FaceGanDecoder_v2
+except:
+    from face_decoders import V2FDecoder, AttnV2FDecoder, MRDecoder, \
+        FaceGanDecoder, FaceGanDecoder_v2
+
+try:
+    from .attention import Attention
+except:
+    from .attention import Attention
+
+
+class ModelCollection():
+    def __init__(self):
+        self.V2F1DCNN = V2F1DCNN
+        self.V2FDecoder = V2FDecoder
+        self.AttnV2FDecoder = AttnV2FDecoder
+        self.TransEncoder = TransEncoder
+        self.MRDecoder = MRDecoder
+        self.FaceGanDecoder = FaceGanDecoder
+        self.FaceGanDecoder_v2 = FaceGanDecoder_v2
+        # self.PganDecoder = PganDecoder
+        self.Attention = Attention
+
+
+model_collection = ModelCollection()
diff --git a/modeling/sf2f/models/model_setup.py b/modeling/sf2f/models/model_setup.py
new file mode 100644
index 0000000..7f3d8f3
--- /dev/null
+++ b/modeling/sf2f/models/model_setup.py
@@ -0,0 +1,99 @@
+import torch
+import models
+from copy import deepcopy
+from utils import update_values, load_model_state
+import glog as log
+
+def build_model(opts, image_size, checkpoint_start_from=None):
+    if checkpoint_start_from is not None:
+        log.info("Load checkpoint as initialization: {}".format(
+            checkpoint_start_from))
+        checkpoint = torch.load(checkpoint_start_from)
+        # kwarg aka keyword arguments
+        #kwargs = checkpoint['model_kwargs']
+        kwargs = deepcopy(opts["options"])
+        kwargs["image_size"] = image_size
+        model = getattr(models, opts["arch"])(**kwargs)
+        raw_state_dict = checkpoint['model_state']
+        state_dict = {}
+        for k, v in raw_state_dict.items():
+            if k.startswith('module.'):
+                k = k[7:]
+            state_dict[k] = v
+        #print("state_dict: ", state_dict.keys())
+        model = load_model_state(model, state_dict, strict=False)
+    else:
+        kwargs = deepcopy(opts["options"])
+        kwargs["image_size"] = image_size
+        model = getattr(models, opts["arch"])(**kwargs)
+    return model, kwargs
+
+def build_ac_discriminator(opts):
+    d_kwargs = deepcopy(opts["generic"])
+    discriminator = []
+    if "identity" in opts.keys():
+        d_kwargs = update_values(opts["identity"], d_kwargs)
+        dis = models.AcDiscriminator(**d_kwargs)
+        discriminator.append(dis)
+    if "identity_low" in opts.keys():
+        d_kwargs = update_values(opts["identity_low"], d_kwargs)
+        dis_low = models.AcDiscriminator(**d_kwargs)
+        discriminator.append(dis_low)
+    if "identity_mid" in opts.keys():
+        d_kwargs = update_values(opts["identity_mid"], d_kwargs)
+        dis_mid = models.AcDiscriminator(**d_kwargs)
+        discriminator.append(dis_mid)
+    if "identity_high" in opts.keys():
+        d_kwargs = update_values(opts["identity_high"], d_kwargs)
+        dis_high = models.AcDiscriminator(**d_kwargs)
+        discriminator.append(dis_high)
+    return discriminator, d_kwargs
+
+def build_img_discriminator(opts):
+    discriminator = []
+    if opts.get("pgan_dis") is not None:
+        d_kwargs = deepcopy(opts["pgan_dis"])
+        pgan_dis = models.PGAN_Discriminator(**d_kwargs)
+        discriminator.append(pgan_dis)
+    else:
+        d_kwargs = deepcopy(opts["generic"])
+        if "image" in opts.keys():
+            d_kwargs = update_values(opts["image"], d_kwargs)
+            dis = models.PatchDiscriminator(**d_kwargs)
+            discriminator.append(dis)
+        else:
+            if "image_low" in opts.keys():
+                d_kwargs = update_values(opts["image_low"], d_kwargs)
+                discriminator_low = models.PatchDiscriminator(**d_kwargs)
+                discriminator.append(discriminator_low)
+            if "image_mid" in opts.keys():
+                d_kwargs = update_values(opts["image_mid"], d_kwargs)
+                discriminator_mid = models.PatchDiscriminator(**d_kwargs)
+                discriminator.append(discriminator_mid)
+            if "image_high" in opts.keys():
+                d_kwargs = update_values(opts["image_high"], d_kwargs)
+                discriminator_high = models.PatchDiscriminator(**d_kwargs)
+                discriminator.append(discriminator_high)
+    return discriminator, d_kwargs
+
+def build_cond_discriminator(opts):
+    discriminator = []
+    d_kwargs = deepcopy(opts["generic"])
+    if "condition" in opts.keys():
+        d_kwargs = update_values(opts["condition"], d_kwargs)
+        dis = models.CondPatchDiscriminator(**d_kwargs)
+        discriminator.append(dis)
+    else:
+        if "cond_low" in opts.keys():
+            d_kwargs = update_values(opts["cond_low"], d_kwargs)
+            discriminator_low = models.CondPatchDiscriminator(**d_kwargs)
+            discriminator.append(discriminator_low)
+        if "cond_mid" in opts.keys():
+            d_kwargs = update_values(opts["cond_mid"], d_kwargs)
+            discriminator_mid = models.CondPatchDiscriminator(**d_kwargs)
+            discriminator.append(discriminator_mid)
+        if "cond_high" in opts.keys():
+            d_kwargs = update_values(opts["cond_high"], d_kwargs)
+            discriminator_high = models.CondPatchDiscriminator(**d_kwargs)
+            discriminator.append(discriminator_high)
+    return discriminator, d_kwargs
diff --git a/modeling/sf2f/models/networks.py b/modeling/sf2f/models/networks.py
new file mode 100644
index 0000000..28f3333
--- /dev/null
+++ b/modeling/sf2f/models/networks.py
@@ -0,0 +1,203 @@
+import torch
+import torch.nn as nn
+import functools
+from torch.autograd import Variable
+import numpy as np
+import torch.nn.functional as F
+import math
+from numpy import prod
+
+
+
+class VGGLoss(nn.Module):
+    def __init__(self):
+        super(VGGLoss, self).__init__()
+        self.vgg = Vgg19().cuda()
+        self.criterion = nn.L1Loss()
+        self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]
+
+    def forward(self, x, y):
+        x_vgg = self.vgg(x)
+        # we do not store gradients for y
+        with torch.no_grad():
+            y_vgg = self.vgg(y)
+        loss = 0
+        for i in range(len(x_vgg)):
+            loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i])
+        return loss
+
+from torchvision import models
+class Vgg19(torch.nn.Module):
+    def __init__(self, requires_grad=False, ignore_last=False):
+        super(Vgg19, self).__init__()
+        vgg_pretrained_features = models.vgg19(pretrained=True).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        if ignore_last:
+            self.slice5 = None
+        else:
+            self.slice5 = torch.nn.Sequential()
+        for x in range(2):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(2, 7):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(7, 12):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(12, 21):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        if self.slice5 is not None:
+            for x in range(21, 30):
+                self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h_relu1 = self.slice1(X)
+        h_relu2 = self.slice2(h_relu1)
+        h_relu3 = self.slice3(h_relu2)
+        h_relu4 = self.slice4(h_relu3)
+        if self.slice5 is None or h_relu4.size(3) < 3:
+            return [h_relu1, h_relu2, h_relu3, h_relu4,]
+        h_relu5 = self.slice5(h_relu4)
+        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
+        return out
+
+
+class GLU(nn.Module):
+    def __init__(self):
+        super(GLU, self).__init__()
+
+    def forward(self, x):
+        nc = x.size(1)
+        assert nc % 2 == 0, 'channels dont divide 2!'
+        nc = int(nc/2)
+        return x[:, :nc] * F.sigmoid(x[:, nc:])
+
+
+def upBlock(in_planes, out_planes):
+    block = nn.Sequential(
+        nn.Upsample(scale_factor=2, mode='nearest'),
+        nn.Conv2d(in_planes, out_planes * 2, kernel_size=3,
+           stride=1, padding=1, bias=False),
+        nn.BatchNorm2d(out_planes * 2),
+        GLU())
+    return block
+
+# The defination of Progressive GAN
+
+class NormalizationLayer(nn.Module):
+
+    def __init__(self):
+        super(NormalizationLayer, self).__init__()
+
+    def forward(self, x, epsilon=1e-8):
+        return x * (((x**2).mean(dim=1, keepdim=True) + epsilon).rsqrt())
+
+
+def Upscale2d(x, factor=2):
+    assert isinstance(factor, int) and factor >= 1
+    if factor == 1:
+        return x
+    s = x.size()
+    x = x.view(-1, s[1], s[2], 1, s[3], 1)
+    x = x.expand(-1, s[1], s[2], factor, s[3], factor)
+    x = x.contiguous().view(-1, s[1], s[2] * factor, s[3] * factor)
+    return x
+
+
+def getLayerNormalizationFactor(x):
+    r"""
+    Get He's constant for the given layer
+    https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
+    """
+    size = x.weight.size()
+    fan_in = prod(size[1:])
+
+    return math.sqrt(2.0 / fan_in)
+
+
+class ConstrainedLayer(nn.Module):
+    r"""
+    A handy refactor that allows the user to:
+    - initialize one layer's bias to zero
+    - apply He's initialization at runtime
+    """
+
+    def __init__(self,
+                 module,
+                 equalized=True,
+                 lrMul=1.0,
+                 initBiasToZero=True):
+        r"""
+        equalized (bool): if true, the layer's weight should evolve within
+                         the range (-1, 1)
+        initBiasToZero (bool): if true, bias will be initialized to zero
+        """
+
+        super(ConstrainedLayer, self).__init__()
+
+        self.module = module
+        self.equalized = equalized
+
+        if initBiasToZero:
+            self.module.bias.data.fill_(0)
+        if self.equalized:
+            self.module.weight.data.normal_(0, 1)
+            self.module.weight.data /= lrMul
+            self.weight = getLayerNormalizationFactor(self.module) * lrMul
+
+    def forward(self, x):
+
+        x = self.module(x)
+        if self.equalized:
+            x *= self.weight
+        return x
+
+
+class EqualizedConv2d(ConstrainedLayer):
+
+    def __init__(self,
+                 nChannelsPrevious,
+                 nChannels,
+                 kernelSize,
+                 padding=0,
+                 bias=True,
+                 **kwargs):
+        r"""
+        A nn.Conv2d module with specific constraints
+        Args:
+            nChannelsPrevious (int): number of channels in the previous layer
+            nChannels (int): number of channels of the current layer
+            kernelSize (int): size of the convolutional kernel
+            padding (int): convolution's padding
+            bias (bool): with bias ?
+        """
+
+        ConstrainedLayer.__init__(self,
+                                  nn.Conv2d(nChannelsPrevious, nChannels,
+                                            kernelSize, padding=padding,
+                                            bias=bias),
+                                  **kwargs)
+
+
+class EqualizedLinear(ConstrainedLayer):
+
+    def __init__(self,
+                 nChannelsPrevious,
+                 nChannels,
+                 bias=True,
+                 **kwargs):
+        r"""
+        A nn.Linear module with specific constraints
+        Args:
+            nChannelsPrevious (int): number of channels in the previous layer
+            nChannels (int): number of channels of the current layer
+            bias (bool): with bias ?
+        """
+
+        ConstrainedLayer.__init__(self,
+                                  nn.Linear(nChannelsPrevious, nChannels,
+                                  bias=bias), **kwargs)
diff --git a/modeling/sf2f/models/perceptual.py b/modeling/sf2f/models/perceptual.py
new file mode 100644
index 0000000..44fb6aa
--- /dev/null
+++ b/modeling/sf2f/models/perceptual.py
@@ -0,0 +1,111 @@
+import torch
+import torch.nn as nn
+import functools
+from torch.autograd import Variable
+import numpy as np
+from torchvision import models
+try:
+    from .inception_resnet_v1 import InceptionResnetV1
+except:
+    from inception_resnet_v1 import InceptionResnetV1
+
+
+class FaceNetLoss(nn.Module):
+    def __init__(self, cos_loss_weight=0.0):
+        super(FaceNetLoss, self).__init__()
+        self.facenet = InceptionResnetV1(pretrained='vggface2').eval().cuda()
+        self.criterion = nn.L1Loss()
+        self.weights = [1.0/16, 1.0/8, 1.0/4, 1.0]
+        self.cos_loss_weight = cos_loss_weight
+        if self.cos_loss_weight > 0.0:
+            self.cos_emb_loss = torch.nn.CosineEmbeddingLoss()
+            self.cos_target = torch.ones((1,), dtype=torch.int8).cuda()
+
+    def forward(self, x, y):
+        x_facenet = self.facenet.get_hidden_states(x)
+        # we do not store gradients for y
+        with torch.no_grad():
+            y_facenet = self.facenet.get_hidden_states(y)
+        loss = 0
+        for i in range(len(x_facenet)):
+            #print(x_facenet[i].shape)
+            #print(y_facenet[i].shape)
+            loss += self.weights[i] * \
+                self.criterion(x_facenet[i], y_facenet[i])
+        if self.cos_loss_weight > 0.0:
+            loss += self.cos_loss_weight * \
+                self.cos_emb_loss(
+                    x_facenet[-1],
+                    y_facenet[-1],
+                    self.cos_target)
+        return loss
+
+
+class VGGLoss(nn.Module):
+    def __init__(self):
+        super(VGGLoss, self).__init__()
+        self.vgg = Vgg19().cuda()
+        self.criterion = nn.L1Loss()
+        self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]
+
+    def forward(self, x, y):
+        x_vgg = self.vgg(x)
+        # we do not store gradients for y
+        with torch.no_grad():
+            y_vgg = self.vgg(y)
+        loss = 0
+        for i in range(len(x_vgg)):
+            loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i])
+        return loss
+
+
+class Vgg19(torch.nn.Module):
+    def __init__(self, requires_grad=False, ignore_last=False):
+        super(Vgg19, self).__init__()
+        vgg_pretrained_features = models.vgg19(pretrained=True).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        if ignore_last:
+            self.slice5 = None
+        else:
+            self.slice5 = torch.nn.Sequential()
+        for x in range(2):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(2, 7):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(7, 12):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(12, 21):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        if self.slice5 is not None:
+            for x in range(21, 30):
+                self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h_relu1 = self.slice1(X)
+        h_relu2 = self.slice2(h_relu1)
+        h_relu3 = self.slice3(h_relu2)
+        h_relu4 = self.slice4(h_relu3)
+        if self.slice5 is None or h_relu4.size(3) < 3:
+            return [h_relu1, h_relu2, h_relu3, h_relu4,]
+        h_relu5 = self.slice5(h_relu4)
+        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
+        return out
+
+
+if __name__ == '__main__':
+    demo_imgs = torch.ones((4, 3, 224, 224))
+    facenet = InceptionResnetV1(pretrained='vggface2').eval()
+    facenet_out = facenet.get_hidden_states(demo_imgs)
+    for i, feature in enumerate(facenet_out):
+        print('FaceNet Feature Shape {}:'.format(i), feature.shape)
+
+    vgg_19 = Vgg19()
+    vgg_out = vgg_19(demo_imgs)
+    for i, feature in enumerate(vgg_out):
+        print('VGG Feature Shape {}:'.format(i), feature.shape)
diff --git a/modeling/sf2f/models/voice_encoders.py b/modeling/sf2f/models/voice_encoders.py
new file mode 100644
index 0000000..f26acfb
--- /dev/null
+++ b/modeling/sf2f/models/voice_encoders.py
@@ -0,0 +1,474 @@
+'''
+Implementations of different Voice Encoders
+'''
+
+
+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+import numpy as np
+import math
+
+try:
+    from fusers import AttentionFuserV1, AttentionFuserV2, AttentionFuserV3
+except:
+    from .fusers import AttentionFuserV1, AttentionFuserV2, AttentionFuserV3
+
+
+class EncoderModelCollection():
+    def __init__(self):
+        self.AttentionFuserV1 = AttentionFuserV1
+        self.AttentionFuserV2 = AttentionFuserV2
+        self.AttentionFuserV3 = AttentionFuserV3
+
+encoder_model_collection = EncoderModelCollection()
+
+
+class TransposeLayer(nn.Module):
+    def __init__(self, dim_1, dim_2):
+        '''
+        Transpose Layer
+        '''
+        nn.Module.__init__(self)
+        self.dim_1 = dim_1
+        self.dim_2 = dim_2
+
+    def forward(self, x):
+        '''
+        seq_in: tensor with shape [N, D, L]
+        '''
+        x = x.transpose(self.dim_1, self.dim_2)
+        return x
+
+
+class CNN1DBlock(nn.Module):
+    def __init__(self,
+                 channel_in,
+                 channel_out,
+                 kernel=3,
+                 stride=2,
+                 padding=1):
+        '''
+        1D CNN block
+        '''
+        nn.Module.__init__(self)
+        self.layers = []
+        self.layers.append(
+            nn.Conv1d(channel_in,
+                      channel_out,
+                      kernel,
+                      stride,
+                      padding,
+                      bias=False))
+        self.layers.append(
+            nn.BatchNorm1d(channel_out, affine=True))
+        self.layers.append(
+            nn.ReLU(inplace=True))
+        self.model = nn.Sequential(*self.layers)
+
+    def forward(self, seq_in):
+        '''
+        seq_in: tensor with shape [N, D, L]
+        '''
+        seq_out = self.model(seq_in)
+        return seq_out
+
+
+class Inception1DBlock(nn.Module):
+    def __init__(self,
+                 channel_in,
+                 channel_k2,
+                 channel_k3,
+                 channel_k5,
+                 channel_k7):
+        '''
+        Basic building block of 1D Inception Encoder
+        '''
+        nn.Module.__init__(self)
+        self.conv_k2 = None
+        self.conv_k3 = None
+        self.conv_k5 = None
+
+        if channel_k2 > 0:
+            self.conv_k2 = CNN1DBlock(
+                channel_in,
+                channel_k2,
+                2, 2, 1)
+        if channel_k3 > 0:
+            self.conv_k3 = CNN1DBlock(
+                channel_in,
+                channel_k3,
+                3, 2, 1)
+        if channel_k5 > 0:
+            self.conv_k5 = CNN1DBlock(
+                channel_in,
+                channel_k3,
+                5, 2, 2)
+        if channel_k7 > 0:
+            self.conv_k7 = CNN1DBlock(
+                channel_in,
+                channel_k3,
+                7, 2, 3)
+
+    def forward(self, input):
+        output = []
+        if self.conv_k2 is not None:
+            c2_out = self.conv_k2(input)
+            output.append(c2_out)
+        if self.conv_k3 is not None:
+            c3_out = self.conv_k3(input)
+            output.append(c3_out)
+        if self.conv_k5 is not None:
+            c5_out = self.conv_k5(input)
+            output.append(c5_out)
+        if self.conv_k7 is not None:
+            c7_out = self.conv_k7(input)
+            output.append(c7_out)
+        #print(c2_out.shape, c3_out.shape, c5_out.shape, c7_out.shape)
+        if output[0].shape[-1] > output[1].shape[-1]:
+            output[0] = output[0][:, :, 0:-1]
+        output = torch.cat(output, 1)
+        #print(output.shape)
+        return output
+
+
+class PositionalEncoding(nn.Module):
+
+    def __init__(self, d_model, dropout=0.1, max_len=10000):
+        super(PositionalEncoding, self).__init__()
+        self.dropout = nn.Dropout(p=dropout)
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * \
+            (-math.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0).transpose(0, 1)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        x = x + self.pe[:x.size(0), :]
+        return self.dropout(x)
+
+
+class PosEmbLayer(nn.Module):
+    def __init__(self, max_length=2000, pos_embedding_dim=5):
+        '''
+        Postional embedding block
+        '''
+        nn.Module.__init__(self)
+
+        self.max_length = max_length
+        self.pos_embedding_dim = pos_embedding_dim
+        # Position Embedding
+        # Each position index in range (0, 2 * max_length - 1) have its own embedding
+        self.pos_embedding = nn.Embedding(
+            max_length, pos_embedding_dim, padding_idx=0)
+
+    def forward(self, seq_in):
+        '''
+        seq_in: tensor with shape [N, L, D]
+        '''
+        N, L, D = seq_in.shape
+        # (L, )
+        pos_idx = torch.tensor(np.arange(0, L))
+        # (1, L)
+        pos_idx = pos_idx.unsqueeze(0)
+        # (N, L)
+        pos_idx = pos_idx.repeat(N, 1).cuda()
+        # (N, L, D_pos)
+        pos_emb = self.pos_embedding(pos_idx).type(seq_in.type())
+        #pos_emb = torch.transpose(pos_emb, 1, 2)
+        seq_out = torch.cat([seq_in, pos_emb], 2)
+        # (N, L, D + D_pos)
+        return seq_out
+
+
+class TransEncoder(nn.Module):
+    def __init__(self,
+                 input_channel=40,
+                 cnn_channels=[512, ],
+                 transformer_dim=512,
+                 transformer_depth=3,
+                 return_seq=True,
+                 sin_pos_encoding=False,
+                 pos_embedding_dim=8,
+                 add_noise=False,
+                 normalize_embedding=False):
+        super(TransEncoder, self).__init__()
+        self.layers = []
+
+        if pos_embedding_dim > 0:
+            # Postional Embedding Layer
+            # (N, L, D)
+            self.layers.append(TransposeLayer(1, 2))
+            # (N, D, L)
+            self.layers.append(
+                PosEmbLayer(2000, pos_embedding_dim))
+            # (N, L, D)
+            self.layers.append(TransposeLayer(1, 2))
+
+        # CNN Layer for dimension conversion
+        self.layers.append(
+            CNN1DBlock(input_channel+pos_embedding_dim, cnn_channels[0]))
+        for cnn_layer_id in range(1, len(cnn_channels)):
+            self.layers.append(CNN1DBlock(
+                cnn_channels[cnn_layer_id - 1],
+                cnn_channels[cnn_layer_id]))
+
+        self.layers.append(TransposeLayer(1, 2))
+        # Sin Positional Encoding
+        if sin_pos_encoding:
+            self.layers.append(PositionalEncoding(transformer_dim))
+
+        # Transformer
+        trans_layer = nn.TransformerEncoderLayer(
+            transformer_dim,
+            8,
+            transformer_dim)
+        transformer_encoder = nn.TransformerEncoder(
+            trans_layer, transformer_depth)
+        self.layers.append(transformer_encoder)
+        self.layers.append(TransposeLayer(1, 2))
+
+        self.model = nn.Sequential(*self.layers)
+        self.add_noise = add_noise
+        self.normalize_embedding = normalize_embedding
+        self.return_seq = return_seq
+
+    def forward(self, seq_in):
+        seq_out = self.model(seq_in)
+        #for layer in self.layers:
+        #    print(layer)
+        #    x = layer(x)
+        #    print(x.shape)
+        embeddings = F.avg_pool1d(seq_out, seq_out.size()[2], stride=1)
+        embeddings = embeddings.view(embeddings.size()[0], -1, 1, 1)
+
+        if self.normalize_embedding:
+            embeddings = F.normalize(embeddings)
+        if self.add_noise:
+            noise = 0.05 * torch.randn(
+                embeddings.shape[0], embeddings.shape[1], 1, 1)
+            noise = noise.type(embeddings.type())
+            embeddings = embeddings + noise
+            if self.normalize_embedding:
+                embeddings = F.normalize(embeddings)
+
+        if self.return_seq:
+            return embeddings, seq_out
+        else:
+            return embeddings
+
+
+class V2F1DCNN(nn.Module):
+    def __init__(self,
+                 input_channel,
+                 channels,
+                 output_channel,
+                 add_noise=False,
+                 normalize_embedding=False,
+                 return_seq=False,
+                 inception_mode=False,
+                 segments_fusion=False,
+                 normalize_fusion=False,
+                 fuser_arch='Attention',
+                 fuser_kwargs=None):
+        super(V2F1DCNN, self).__init__()
+        if inception_mode:
+            self.model = nn.Sequential(
+                Inception1DBlock(
+                    channel_in=input_channel,
+                    channel_k2=channels[0]//4,
+                    channel_k3=channels[0]//4,
+                    channel_k5=channels[0]//4,
+                    channel_k7=channels[0]//4),
+                Inception1DBlock(
+                    channel_in=channels[0],
+                    channel_k2=channels[1]//4,
+                    channel_k3=channels[1]//4,
+                    channel_k5=channels[1]//4,
+                    channel_k7=channels[1]//4),
+                Inception1DBlock(
+                    channel_in=channels[1],
+                    channel_k2=channels[2]//4,
+                    channel_k3=channels[2]//4,
+                    channel_k5=channels[2]//4,
+                    channel_k7=channels[2]//4),
+                Inception1DBlock(
+                    channel_in=channels[2],
+                    channel_k2=channels[3]//4,
+                    channel_k3=channels[3]//4,
+                    channel_k5=channels[3]//4,
+                    channel_k7=channels[3]//4),
+                nn.Conv1d(channels[3], output_channel, 3, 2, 1, bias=True),
+            )
+        else:
+            self.model = nn.Sequential(
+                nn.Conv1d(input_channel, channels[0], 3, 2, 1, bias=False),
+                nn.BatchNorm1d(channels[0], affine=True),
+                nn.ReLU(inplace=True),
+                nn.Conv1d(channels[0], channels[1], 3, 2, 1, bias=False),
+                nn.BatchNorm1d(channels[1], affine=True),
+                nn.ReLU(inplace=True),
+                nn.Conv1d(channels[1], channels[2], 3, 2, 1, bias=False),
+                nn.BatchNorm1d(channels[2], affine=True),
+                nn.ReLU(inplace=True),
+                nn.Conv1d(channels[2], channels[3], 3, 2, 1, bias=False),
+                nn.BatchNorm1d(channels[3], affine=True),
+                nn.ReLU(inplace=True),
+                nn.Conv1d(channels[3], output_channel, 3, 2, 1, bias=True),
+            )
+        self.add_noise = add_noise
+        self.normalize_embedding = normalize_embedding
+        self.return_seq = return_seq
+        self.output_channel = output_channel
+
+        self.segments_fusion = segments_fusion
+        self.normalize_fusion = normalize_fusion
+        if segments_fusion:
+            #self.attn_fuser = Attention(
+            #    output_channel,
+            #    output_channel,
+            #    ignore_tanh=True)
+            self.attn_fuser = \
+                getattr(encoder_model_collection, fuser_arch)(**fuser_kwargs)
+
+    def forward(self, x):
+        # In case more than one mel segment per person is passed
+        if len(x.shape) == 4:
+            fusion_mode = True
+            B, N, C, L = x.shape
+            #print('Fusion Mode On! Input Shape:', x.shape)
+            x = x.view(B*N, C, L)
+        else:
+            fusion_mode = False
+            B, C, L = x.shape
+        x = self.model(x)
+        embeddings = F.avg_pool1d(x, x.size()[2], stride=1)
+        embeddings = embeddings.view(embeddings.size()[0], -1, 1, 1)
+
+        if self.normalize_embedding:
+            embeddings = F.normalize(embeddings)
+        if self.add_noise:
+            noise = 0.05 * torch.randn(x.shape[0], x.shape[1], 1, 1)
+            noise = noise.type(embeddings.type())
+            embeddings = embeddings + noise
+            if self.normalize_embedding:
+                embeddings = F.normalize(embeddings)
+
+        # Restore Tensor shape
+        if fusion_mode:
+            #print(embeddings.shape)
+            C_emb = embeddings.shape[1]
+            embeddings = embeddings.view(B, N, C_emb)
+            # Attention fusion
+            embeddings = self.attn_fuser(embeddings)
+            if self.normalize_fusion:
+                embeddings = F.normalize(embeddings)
+
+        if self.return_seq:
+            return embeddings, x
+        else:
+            return embeddings
+
+    def print_param(self):
+        print('All parameters:')
+        for name, param in self.named_parameters():
+            print(name)
+
+    def print_trainable_param(model):
+        print('Trainable Parameters:')
+        for name, param in model.named_parameters():
+            if param.requires_grad:
+                print(name)
+
+    def train_fuser_only(self):
+        print('Training Attention Fuser Only')
+        for name, param in self.named_parameters():
+            if 'attn_fuser' in name:
+                param.requires_grad = True
+            else:
+                param.requires_grad = False
+
+    def init_attn_fusion(self):
+        self.attn_fuser = Attention(
+            self.output_channel,
+            self.output_channel,
+            ignore_tanh=True)
+
+if __name__ == '__main__':
+    import time
+    # Demo Input
+    log_mels = torch.ones((16, 40, 151))
+    pos_log_mel = torch.ones((16, 48, 150)).transpose(1, 2)
+
+    # Test Inception1DBlock
+    incept_block = Inception1DBlock(
+        channel_in=40,
+        channel_k2=64,
+        channel_k3=64,
+        channel_k5=64,
+        channel_k7=64)
+    incept_block(log_mels)
+
+    # Test V2F 1D CNN
+    log_mel_segs = torch.ones((16, 20, 40, 150))
+    v2f_id_cnn_kwargs = {
+        'input_channel': 40,
+        'channels': [256, 384, 576, 864],
+        'output_channel': 64,
+        'segments_fusion': True,
+        'inception_mode': True,
+        }
+    v2f_id_cnn_fuse = V2F1DCNN(**v2f_id_cnn_kwargs)
+    print(v2f_id_cnn_fuse)
+    print('V2F1DCNN Output shape:', v2f_id_cnn_fuse(log_mel_segs).shape)
+    v2f_id_cnn_fuse.print_param()
+    v2f_id_cnn_fuse.train_fuser_only()
+    v2f_id_cnn_fuse.print_trainable_param()
+
+    # test a simple transformer layer
+    trans_layer = nn.TransformerEncoderLayer(48, 8,
+        dim_feedforward=48, dropout=0.1, activation='relu')
+    print(trans_layer(pos_log_mel).shape)
+
+
+    # Transformer
+    trans_kwargs = {
+        'input_channel': 40,
+        'cnn_channels': [512, 512],
+        'transformer_dim': 512,
+        'transformer_depth': 2,
+        'return_seq': True,
+        'pos_embedding_dim': 0, #8
+        'sin_pos_encoding': True, #False
+    }
+    trans_encoder = TransEncoder(**trans_kwargs).cuda()
+    print(trans_encoder)
+    emb, seq_emb = trans_encoder(log_mels.cuda())
+    print('TransEncoder Output shape:', emb.shape, seq_emb.shape)
+
+    # Test V2F 1D CNN
+    v2f_id_cnn_kwargs = {
+        'input_channel': 40,
+        'channels': [256, 384, 576, 864],
+        'output_channel': 64,
+        }
+    v2f_id_cnn = V2F1DCNN(**v2f_id_cnn_kwargs)
+    print(v2f_id_cnn)
+    print('V2F1DCNN Output shape:', v2f_id_cnn(log_mels).shape)
+
+    # Test V2F 1D CNN Sequential Return
+    v2f_id_cnn_kwargs = {
+        'input_channel': 40,
+        'channels': [256, 384, 576, 864],
+        'output_channel': 64,
+        'return_seq': True,
+        }
+    v2f_id_cnn = V2F1DCNN(**v2f_id_cnn_kwargs)
+    print(v2f_id_cnn)
+    emb, seq_emb = v2f_id_cnn(log_mels)
+    print('V2F1DCNN Output shape:', emb.shape, seq_emb.shape)
diff --git a/modeling/sf2f/options/__init__.py b/modeling/sf2f/options/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/modeling/sf2f/options/data_opts/vox.yaml b/modeling/sf2f/options/data_opts/vox.yaml
new file mode 100644
index 0000000..7ff28ec
--- /dev/null
+++ b/modeling/sf2f/options/data_opts/vox.yaml
@@ -0,0 +1,7 @@
+root_dir: sf2f/data/VoxCeleb
+face_type: masked
+image_normalize_method: imagenet #standard
+mel_normalize_method: vox_mel
+nframe_range: (100, 150)
+split_set: train
+split_json: ./sf2f/data/VoxCeleb/split.json
diff --git a/modeling/sf2f/options/opts.py b/modeling/sf2f/options/opts.py
new file mode 100644
index 0000000..e9a3828
--- /dev/null
+++ b/modeling/sf2f/options/opts.py
@@ -0,0 +1,93 @@
+import os
+import os.path as osp
+import argparse
+import yaml
+from utils import update_values
+from utils import int_tuple, float_tuple, str_tuple, bool_flag
+
+parser = argparse.ArgumentParser()
+
+# Optimization hyperparameters
+parser.add_argument('--batch_size', default=16, type=int)
+parser.add_argument('--epochs', default=90, type=int)
+parser.add_argument('--learning_rate', default=5e-4, type=float)
+# by default, it is disabled
+parser.add_argument('--decay_lr_epochs', default=10000, type=float)
+parser.add_argument('--beta1', type=float, default=0.9,
+                    help='momentum term of adam')
+parser.add_argument('--eval_epochs', default=1, type=int)
+parser.add_argument('--eval_mode_after', default=10000, type=int)
+parser.add_argument('--disable_l1_loss_after', default=10000000, type=int)
+parser.add_argument('--path_opts', type=str,
+                    default='sf2f/options/vox/sf2f/sf2f_fuser.yaml', help="Options.")
+
+parser.add_argument('--workers', default=8, type=int)
+
+# Output options
+parser.add_argument('--print_every', default=100, type=int)
+parser.add_argument('--visualize_every', type=int,
+                    default=100, help="visualize to visdom.")
+parser.add_argument('--timing', action="store_true")
+parser.add_argument('--output_dir', type=str)
+parser.add_argument('--log_suffix', type=str)
+parser.add_argument('--checkpoint_name', default='checkpoint')
+parser.add_argument('--checkpoint_start_from', default=None)
+parser.add_argument('--checkpoint', nargs='+')
+parser.add_argument('--resume', type=str)
+parser.add_argument('--evaluate', action='store_true',
+    help="Set to evaluate the model.")
+parser.add_argument('--evaluate_train', action='store_true',
+    help="Set to evaluate the training set.")
+
+# For inference in run_model.py
+parser.add_argument('--output_demo_dir', default='output/results')
+parser.add_argument('--samples_path', default='samples')
+
+# For evaluation
+parser.add_argument('--recall_method', default='cos_sim')
+parser.add_argument('--facenet_return_pooling', default=False)
+# parser.add_argument('--face_gen_mode', default='average_facenet_embedding')
+parser.add_argument('--face_gen_mode', nargs='+')
+
+# For test.py
+parser.add_argument('--get_faces_from_different_segments', default=False)
+
+# Attention Fuser Mode
+parser.add_argument('--train_fuser_only', default=False)
+# Train the fuser and decoder
+parser.add_argument('--train_fuser_decoder', default=False)
+parser.add_argument('--pretrained_path', default=None)
+parser.add_argument('--freeze_discriminators', default=False)
+
+# For Inference
+parser.add_argument('--input_wav_dir', type=str, default='data/example_audio')
+parser.add_argument('--fuser_infer', default=False)
+parser.add_argument('--seed', type=int, default=311)
+parser.add_argument('--input_wav_file', type=str, default='data/example_audio/exp.wav')
+args = parser.parse_args()
+
+options = {
+    "data": {
+        "batch_size": args.batch_size,
+        "workers": args.workers,
+        "data_opts": {},
+    },
+    "optim": {
+        "lr": args.learning_rate,
+        "epochs": args.epochs,
+        "eval_epochs": args.eval_epochs,
+    },
+    "logs": {
+        "output_dir": args.output_dir,
+    },
+}
+
+with open(args.path_opts, "r") as f:
+    options_yaml = yaml.full_load(f)
+with open(options_yaml["data"]["data_opts_path"], "r") as f:
+    data_opts = yaml.full_load(f)
+    options_yaml["data"]["data_opts"] = data_opts
+
+options = update_values(options, options_yaml)
+if args.log_suffix:
+    options["logs"]["name"] += "-" + args.log_suffix
diff --git a/modeling/sf2f/options/vox/baseline/v2f.yaml b/modeling/sf2f/options/vox/baseline/v2f.yaml
new file mode 100644
index 0000000..cd406bf
--- /dev/null
+++ b/modeling/sf2f/options/vox/baseline/v2f.yaml
@@ -0,0 +1,50 @@
+# The implementation of NeurIPS 19 V2F baseline model
+# Log-related settings
+logs:
+  name: v2f
+  output_dir: output/
+# data-related settings
+data:
+  dataset: vox
+  data_opts_path: options/data_opts/vox.yaml
+  image_size: [64, 64]
+# model related settings
+generator:
+  arch: EncoderDecoder
+  options:
+    encoder_arch: V2F1DCNN
+    encoder_kwargs:
+      input_channel: 40
+      channels: [256, 384, 576, 864]
+      output_channel: 512
+      add_noise: True
+      normalize_embedding: True
+    decoder_arch: V2FDecoder
+    decoder_kwargs:
+      input_channel: 512
+      channels: [1024, 512, 256, 128, 64]
+      output_channel: 3
+discriminator:
+  generic:
+    normalization: none
+    # padding mode is set to unused because we are specifying padding value
+    padding: unused
+    activation: leakyrelu-0.2
+  # image discrimintor is implemented in AC Discriminator
+  identity:
+    # 'C{kernel_size}-{channel_out}-{stride}-{padding}'
+    arch: 'C1-32-1-0,C4-64-2-1,C4-128-2-1,C4-256-2-1,C4-512-1-0'
+    num_id: 0 # will be updated in train.py
+optim:
+  # Discriminator Loss Weights
+  d_loss_weight: 1.0
+  d_img_weight: -1
+  ac_loss_weight: 1.0
+  # Generator Loss Weights
+  gan_loss_type: 'gan'
+  l1_pixel_loss_weight: 100.0
+  # Perceptual Loss
+eval:
+  facenet:
+    deprocess_and_preprocess: True
+    crop_faces: True
diff --git a/modeling/sf2f/options/vox/sf2f/sf2f_1st_stage.yaml b/modeling/sf2f/options/vox/sf2f/sf2f_1st_stage.yaml
new file mode 100644
index 0000000..5308887
--- /dev/null
+++ b/modeling/sf2f/options/vox/sf2f/sf2f_1st_stage.yaml
@@ -0,0 +1,52 @@
+# Optimal final version of encoder-decoder and Loss Function
+# Encoder: Inceptional 1D CNN
+# Decoder: Upsampling + CNN
+logs:
+  name: sf2f_1st_stage
+  output_dir: output/
+# data-related settings
+data:
+  dataset: vox
+  data_opts_path: sf2f/options/data_opts/vox.yaml
+  image_size: [64, 64]
+# model related settings
+generator:
+  arch: EncoderDecoder
+  options:
+    encoder_arch: V2F1DCNN
+    encoder_kwargs:
+      input_channel: 40
+      channels: [256, 384, 576, 864]
+      output_channel: 512
+      normalize_embedding: True
+      inception_mode: True
+    decoder_arch: FaceGanDecoder
+    decoder_kwargs:
+      noise_dim: 512
+      mlp_normalization: none
+      normalization: batch
+      activation: leakyrelu-0.1
+discriminator:
+  generic:
+    normalization: batch
+    padding: valid
+    activation: leakyrelu-0.1
+  image:
+    arch: 'C4-64-2,C4-128-2,C4-256-2'
+  identity:
+    arch: 'C4-64-2,C4-128-2,C4-256-2'
+    num_id: 0 # will be updated in train.py
+optim:
+  # Discriminator Loss Weights
+  d_loss_weight: 1.0
+  d_img_weight: 1.0 #0.5
+  ac_loss_weight: 0.05
+  # Generator Loss Weights
+  gan_loss_type: 'gan'
+  l1_pixel_loss_weight: 10.0
+  # Perceptual Loss
+  perceptual_loss_weight: 100.0
+eval:
+  facenet:
+    deprocess_and_preprocess: True
+    crop_faces: True
diff --git a/modeling/sf2f/options/vox/sf2f/sf2f_fuser.yaml b/modeling/sf2f/options/vox/sf2f/sf2f_fuser.yaml
new file mode 100644
index 0000000..0949e33
--- /dev/null
+++ b/modeling/sf2f/options/vox/sf2f/sf2f_fuser.yaml
@@ -0,0 +1,62 @@
+# Optimal final version of encoder-decoder and Loss Function
+# Encoder: Inceptional 1D CNN
+# Decoder: Upsampling + CNN
+# Fuser: Vanilla 1-layer attention 'general'
+logs:
+  name: sf2f_fuser
+  output_dir: sf2f/output/
+# data-related settings
+data:
+  dataset: vox
+  data_opts_path: sf2f/options/data_opts/vox.yaml
+  image_size: [64, 64]
+# model related settings
+generator:
+  arch: EncoderDecoder
+  options:
+    encoder_arch: V2F1DCNN
+    encoder_kwargs:
+      input_channel: 40
+      channels: [256, 384, 576, 864]
+      output_channel: 512
+      normalize_embedding: True
+      inception_mode: True
+      segments_fusion: True
+      normalize_fusion: True
+      # Initialize Attention Fuser as a submodule of encoder
+      fuser_arch: AttentionFuserV1
+      fuser_kwargs:
+        dimensions: 512
+        dim_out: 512
+        attention_type: general
+        ignore_tanh: True
+    decoder_arch: FaceGanDecoder
+    decoder_kwargs:
+      noise_dim: 512
+      mlp_normalization: none
+      normalization: batch
+      activation: leakyrelu-0.1
+discriminator:
+  generic:
+    normalization: batch
+    padding: valid
+    activation: leakyrelu-0.1
+  image:
+    arch: 'C4-64-2,C4-128-2,C4-256-2'
+  identity:
+    arch: 'C4-64-2,C4-128-2,C4-256-2'
+    num_id: 0 # will be updated in train.py
+optim:
+  # Discriminator Loss Weights
+  d_loss_weight: 1.0
+  d_img_weight: 1.0 #0.5
+  ac_loss_weight: 0.05
+  # Generator Loss Weights
+  gan_loss_type: 'gan'
+  l1_pixel_loss_weight: 10.0
+  # Perceptual Loss
+  perceptual_loss_weight: 100.0
+eval:
+  facenet:
+    deprocess_and_preprocess: True
+    crop_faces: True
diff --git a/modeling/sf2f/options/vox/sf2f/sf2f_mid_1st_stage.yaml b/modeling/sf2f/options/vox/sf2f/sf2f_mid_1st_stage.yaml
new file mode 100644
index 0000000..6f0e407
--- /dev/null
+++ b/modeling/sf2f/options/vox/sf2f/sf2f_mid_1st_stage.yaml
@@ -0,0 +1,58 @@
+# Optimal final version of encoder-decoder and Loss Function
+# Encoder: Inceptional 1D CNN
+# Decoder: Upsampling + CNN
+# Mid resolution: with multi-reso training
+logs:
+  name: sf2f_mid_1st_stage
+  output_dir: output/
+# data-related settings
+data:
+  dataset: vox
+  data_opts_path: sf2f/options/data_opts/vox.yaml
+  image_size: [128, 128]
+# model related settings
+generator:
+  arch: EncoderDecoder
+  options:
+    encoder_arch: V2F1DCNN
+    encoder_kwargs:
+      input_channel: 40
+      channels: [256, 384, 576, 864]
+      output_channel: 512
+      normalize_embedding: True
+      inception_mode: True
+    decoder_arch: FaceGanDecoder_v2
+    decoder_kwargs:
+      noise_dim: 512
+      mlp_normalization: none
+      normalization: batch
+      activation: leakyrelu-0.1
+discriminator:
+  generic:
+    normalization: batch
+    padding: valid
+    activation: leakyrelu-0.1
+  image_low:
+    arch: 'C4-64-2,C4-128-2,C4-256-2'
+  image_mid:
+    arch: 'C4-64-2,C4-128-2,C4-256-2,C4-512-2'
+  identity_low:
+    arch: 'C4-64-2,C4-128-2,C4-256-2'
+    num_id: 0 # will be updated in train.py
+  identity_mid:
+    arch: 'C4-64-2,C4-128-2,C4-256-2,C4-512-2'
+    num_id: 0 # will be updated in train.py
+optim:
+  # Discriminator Loss Weights
+  d_loss_weight: 1.0
+  d_img_weight: 1.0 #0.5
+  ac_loss_weight: 0.05
+  # Generator Loss Weights
+  gan_loss_type: 'gan'
+  l1_pixel_loss_weight: 100.0
+  # Perceptual Loss
+  perceptual_loss_weight: 100.0
+eval:
+  facenet:
+    deprocess_and_preprocess: True
+    crop_faces: True
diff --git a/modeling/sf2f/options/vox/sf2f/sf2f_mid_fuser.yaml b/modeling/sf2f/options/vox/sf2f/sf2f_mid_fuser.yaml
new file mode 100644
index 0000000..ed021dc
--- /dev/null
+++ b/modeling/sf2f/options/vox/sf2f/sf2f_mid_fuser.yaml
@@ -0,0 +1,67 @@
+# Optimal final version of encoder-decoder and Loss Function
+# Encoder: Inceptional 1D CNN
+# Decoder: Upsampling + CNN
+# Mid resolution: with multi-reso training
+logs:
+  name: sf2f_mid_fuser
+  output_dir: output/
+# data-related settings
+data:
+  dataset: vox
+  data_opts_path: sf2f/options/data_opts/vox.yaml
+  image_size: [128, 128]
+# model related settings
+generator:
+  arch: EncoderDecoder
+  options:
+    encoder_arch: V2F1DCNN
+    encoder_kwargs:
+      input_channel: 40
+      channels: [256, 384, 576, 864]
+      output_channel: 512
+      normalize_embedding: True
+      inception_mode: True
+      segments_fusion: True
+      normalize_fusion: True
+      # Initialize Attention Fuser as a submodule of encoder
+      fuser_arch: AttentionFuserV1
+      fuser_kwargs:
+        dimensions: 512
+        dim_out: 512
+        attention_type: general
+        ignore_tanh: True
+    decoder_arch: FaceGanDecoder_v2
+    decoder_kwargs:
+      noise_dim: 512
+      mlp_normalization: none
+      normalization: batch
+      activation: leakyrelu-0.1
+discriminator:
+  generic:
+    normalization: batch
+    padding: valid
+    activation: leakyrelu-0.1
+  image_low:
+    arch: 'C4-64-2,C4-128-2,C4-256-2'
+  image_mid:
+    arch: 'C4-64-2,C4-128-2,C4-256-2,C4-512-2'
+  identity_low:
+    arch: 'C4-64-2,C4-128-2,C4-256-2'
+    num_id: 0 # will be updated in train.py
+  identity_mid:
+    arch: 'C4-64-2,C4-128-2,C4-256-2,C4-512-2'
+    num_id: 0 # will be updated in train.py
+optim:
+  # Discriminator Loss Weights
+  d_loss_weight: 1.0
+  d_img_weight: 1.0 #0.5
+  ac_loss_weight: 0.05
+  # Generator Loss Weights
+  gan_loss_type: 'gan'
+  l1_pixel_loss_weight: 100.0
+  # Perceptual Loss
+  perceptual_loss_weight: 100.0
+eval:
+  facenet:
+    deprocess_and_preprocess: True
+    crop_faces: True
diff --git a/modeling/sf2f/requirements.txt b/modeling/sf2f/requirements.txt
new file mode 100644
index 0000000..f4aab45
--- /dev/null
+++ b/modeling/sf2f/requirements.txt
@@ -0,0 +1,36 @@
+# Training and testing
+cloudpickle==0.5.3
+cycler==0.10.0
+#Cython==0.28.3
+dask==0.17.5
+decorator==4.3.0
+h5py==2.8.0
+imageio==2.3.0
+kiwisolver==1.0.1
+matplotlib>=2.2.2
+networkx==2.1
+numpy>=1.14.4
+Pillow==5.1.0
+pyparsing==2.2.0
+python-dateutil==2.7.3
+pytz==2018.4
+PyWavelets==0.5.2
+scikit-image==0.14.0
+scipy==1.2.1
+six==1.11.0
+toolz==0.9.0
+pyyaml>=3.13.0
+tensorflow==1.14.0
+pandas==0.23.4
+jupyter
+pyprind
+glog
+sklearn
+requests
+openpyxl
+
+# Audio based inference
+webrtcvad==2.0.10
+python-speech-features==0.6
+pydub
+librosa==0.6.1
diff --git a/modeling/sf2f/scripts/DScore/__init__.py b/modeling/sf2f/scripts/DScore/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/modeling/sf2f/scripts/DScore/models/__init__.py b/modeling/sf2f/scripts/DScore/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/modeling/sf2f/scripts/DScore/models/base_model.py b/modeling/sf2f/scripts/DScore/models/base_model.py
new file mode 100644
index 0000000..ff9e937
--- /dev/null
+++ b/modeling/sf2f/scripts/DScore/models/base_model.py
@@ -0,0 +1,60 @@
+import os
+import torch
+import scripts.DScore.util.util as util
+from torch.autograd import Variable
+from pdb import set_trace as st
+from IPython import embed
+
+class BaseModel():
+    def __init__(self):
+        pass;
+
+    def name(self):
+        return 'BaseModel'
+
+    def initialize(self, use_gpu=True):
+        self.use_gpu = use_gpu
+        self.Tensor = torch.cuda.FloatTensor if self.use_gpu else torch.Tensor
+        # self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)
+
+    def forward(self):
+        pass
+
+    def get_image_paths(self):
+        pass
+
+    def optimize_parameters(self):
+        pass
+
+    def get_current_visuals(self):
+        return self.input
+
+    def get_current_errors(self):
+        return {}
+
+    def save(self, label):
+        pass
+
+    # helper saving function that can be used by subclasses
+    def save_network(self, network, path, network_label, epoch_label):
+        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
+        save_path = os.path.join(path, save_filename)
+        torch.save(network.state_dict(), save_path)
+
+    # helper loading function that can be used by subclasses
+    def load_network(self, network, network_label, epoch_label):
+        # embed()
+        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
+        save_path = os.path.join(self.save_dir, save_filename)
+        print('Loading network from %s'%save_path)
+        network.load_state_dict(torch.load(save_path))
+
+    def update_learning_rate():
+        pass
+
+    def get_image_paths(self):
+        return self.image_paths
+
+    def save_done(self, flag=False):
+        np.save(os.path.join(self.save_dir, 'done_flag'),flag)
+        np.savetxt(os.path.join(self.save_dir, 'done_flag'),[flag,],fmt='%i')
diff --git a/modeling/sf2f/scripts/DScore/models/dist_model.py b/modeling/sf2f/scripts/DScore/models/dist_model.py
new file mode 100644
index 0000000..4c14a88
--- /dev/null
+++ b/modeling/sf2f/scripts/DScore/models/dist_model.py
@@ -0,0 +1,326 @@
+from __future__ import absolute_import
+
+import sys
+sys.path.append('..')
+sys.path.append('.')
+import numpy as np
+import torch
+from torch import nn
+import os
+from collections import OrderedDict
+from torch.autograd import Variable
+import itertools
+from .base_model import BaseModel
+from scipy.ndimage import zoom
+import fractions
+import functools
+import skimage.transform
+from IPython import embed
+
+from . import networks_basic as networks
+# from PerceptualSimilarity.util import util
+from scripts.DScore.util import util
+
+class DistModel(BaseModel):
+    def name(self):
+        return self.model_name
+
+    def initialize(self, model='net-lin', net='alex', pnet_rand=False, pnet_tune=False, model_path=None, colorspace='Lab', use_gpu=True, printNet=False, spatial=False, spatial_shape=None, spatial_order=1, spatial_factor=None, is_train=False, lr=.0001, beta1=0.5, version='0.1'):
+        '''
+        INPUTS
+            model - ['net-lin'] for linearly calibrated network
+                    ['net'] for off-the-shelf network
+                    ['L2'] for L2 distance in Lab colorspace
+                    ['SSIM'] for ssim in RGB colorspace
+            net - ['squeeze','alex','vgg']
+            model_path - if None, will look in weights/[NET_NAME].pth
+            colorspace - ['Lab','RGB'] colorspace to use for L2 and SSIM
+            use_gpu - bool - whether or not to use a GPU
+            printNet - bool - whether or not to print network architecture out
+            spatial - bool - whether to output an array containing varying distances across spatial dimensions
+            spatial_shape - if given, output spatial shape. if None then spatial shape is determined automatically via spatial_factor (see below).
+            spatial_factor - if given, specifies upsampling factor relative to the largest spatial extent of a convolutional layer. if None then resized to size of input images.
+            spatial_order - spline order of filter for upsampling in spatial mode, by default 1 (bilinear).
+            is_train - bool - [True] for training mode
+            lr - float - initial learning rate
+            beta1 - float - initial momentum term for adam
+            version - 0.1 for latest, 0.0 was original
+        '''
+        BaseModel.initialize(self, use_gpu=use_gpu)
+
+        self.model = model
+        self.net = net
+        self.use_gpu = use_gpu
+        self.is_train = is_train
+        self.spatial = spatial
+        self.spatial_shape = spatial_shape
+        self.spatial_order = spatial_order
+        self.spatial_factor = spatial_factor
+
+        self.model_name = '%s [%s]'%(model,net)
+        if(self.model == 'net-lin'): # pretrained net + linear layer
+            self.net = networks.PNetLin(use_gpu=use_gpu,pnet_rand=pnet_rand, pnet_tune=pnet_tune, pnet_type=net,use_dropout=True,spatial=spatial,version=version)
+            kw = {}
+            if not use_gpu:
+                kw['map_location'] = 'cpu'
+            if(model_path is None):
+                import inspect
+                # model_path = './PerceptualSimilarity/weights/v%s/%s.pth'%(version,net)
+                model_path = os.path.abspath(os.path.join(inspect.getfile(self.initialize), '..', '..', 'weights/v%s/%s.pth'%(version,net)))
+
+            if(not is_train):
+                # print('Loading model from: %s'%model_path)
+                self.net.load_state_dict(torch.load(model_path, **kw))
+
+        elif(self.model=='net'): # pretrained network
+            assert not self.spatial, 'spatial argument not supported yet for uncalibrated networks'
+            self.net = networks.PNet(use_gpu=use_gpu,pnet_type=net)
+            self.is_fake_net = True
+        elif(self.model in ['L2','l2']):
+            self.net = networks.L2(use_gpu=use_gpu,colorspace=colorspace) # not really a network, only for testing
+            self.model_name = 'L2'
+        elif(self.model in ['DSSIM','dssim','SSIM','ssim']):
+            self.net = networks.DSSIM(use_gpu=use_gpu,colorspace=colorspace)
+            self.model_name = 'SSIM'
+        else:
+            raise ValueError("Model [%s] not recognized." % self.model)
+
+        self.parameters = list(self.net.parameters())
+
+        if self.is_train: # training mode
+            # extra network on top to go from distances (d0,d1) => predicted human judgment (h*)
+            self.rankLoss = networks.BCERankingLoss(use_gpu=use_gpu)
+            self.parameters+=self.rankLoss.parameters
+            self.lr = lr
+            self.old_lr = lr
+            self.optimizer_net = torch.optim.Adam(self.parameters, lr=lr, betas=(beta1, 0.999))
+        else: # test mode
+            self.net.eval()
+
+        if(printNet):
+            print('---------- Networks initialized -------------')
+            networks.print_network(self.net)
+            print('-----------------------------------------------')
+
+    def forward_pair(self,in1,in2,retPerLayer=False):
+        if(retPerLayer):
+            return self.net.forward(in1,in2, retPerLayer=True)
+        else:
+            return self.net.forward(in1,in2)
+
+    def forward(self, in0, in1, retNumpy=True):
+        ''' Function computes the distance between image patches in0 and in1
+        INPUTS
+            in0, in1 - torch.Tensor object of shape Nx3xXxY - image patch scaled to [-1,1]
+            retNumpy - [False] to return as torch.Tensor, [True] to return as numpy array
+        OUTPUT
+            computed distances between in0 and in1
+        '''
+
+        self.input_ref = in0
+        self.input_p0 = in1
+
+        if(self.use_gpu):
+            self.input_ref = self.input_ref.cuda()
+            self.input_p0 = self.input_p0.cuda()
+
+        self.var_ref = Variable(self.input_ref,requires_grad=True)
+        self.var_p0 = Variable(self.input_p0,requires_grad=True)
+
+        self.d0 = self.forward_pair(self.var_ref, self.var_p0)
+        self.loss_total = self.d0
+
+        def convert_output(d0):
+            if(retNumpy):
+                ans = d0.cpu().data.numpy()
+                if not self.spatial:
+                    ans = ans.flatten()
+                else:
+                    assert(ans.shape[0] == 1 and len(ans.shape) == 4)
+                    return ans[0,...].transpose([1, 2, 0])                  # Reshape to usual numpy image format: (height, width, channels)
+                return ans
+            else:
+                return d0
+
+        if self.spatial:
+            L = [convert_output(x) for x in self.d0]
+            spatial_shape = self.spatial_shape
+            if spatial_shape is None:
+                if(self.spatial_factor is None):
+                    spatial_shape = (in0.size()[2],in0.size()[3])
+                else:
+                    spatial_shape = (max([x.shape[0] for x in L])*self.spatial_factor, max([x.shape[1] for x in L])*self.spatial_factor)
+
+            L = [skimage.transform.resize(x, spatial_shape, order=self.spatial_order, mode='edge') for x in L]
+
+            L = np.mean(np.concatenate(L, 2) * len(L), 2)
+            return L
+        else:
+            return convert_output(self.d0)
+
+    # ***** TRAINING FUNCTIONS *****
+    def optimize_parameters(self):
+        self.forward_train()
+        self.optimizer_net.zero_grad()
+        self.backward_train()
+        self.optimizer_net.step()
+        self.clamp_weights()
+
+    def clamp_weights(self):
+        for module in self.net.modules():
+            if(hasattr(module, 'weight') and module.kernel_size==(1,1)):
+                module.weight.data = torch.clamp(module.weight.data,min=0)
+
+    def set_input(self, data):
+        self.input_ref = data['ref']
+        self.input_p0 = data['p0']
+        self.input_p1 = data['p1']
+        self.input_judge = data['judge']
+
+        if(self.use_gpu):
+            self.input_ref = self.input_ref.cuda()
+            self.input_p0 = self.input_p0.cuda()
+            self.input_p1 = self.input_p1.cuda()
+            self.input_judge = self.input_judge.cuda()
+
+        self.var_ref = Variable(self.input_ref,requires_grad=True)
+        self.var_p0 = Variable(self.input_p0,requires_grad=True)
+        self.var_p1 = Variable(self.input_p1,requires_grad=True)
+
+    def forward_train(self): # run forward pass
+        self.d0 = self.forward_pair(self.var_ref, self.var_p0)
+        self.d1 = self.forward_pair(self.var_ref, self.var_p1)
+        self.acc_r = self.compute_accuracy(self.d0,self.d1,self.input_judge)
+
+        # var_judge
+        self.var_judge = Variable(1.*self.input_judge).view(self.d0.size())
+
+        self.loss_total = self.rankLoss.forward(self.d0, self.d1, self.var_judge*2.-1.)
+        return self.loss_total
+
+    def backward_train(self):
+        torch.mean(self.loss_total).backward()
+
+    def compute_accuracy(self,d0,d1,judge):
+        ''' d0, d1 are Variables, judge is a Tensor '''
+        d1_lt_d0 = (d1<d0).cpu().data.numpy().flatten()
+        judge_per = judge.cpu().numpy().flatten()
+        return d1_lt_d0*judge_per + (1-d1_lt_d0)*(1-judge_per)
+
+    def get_current_errors(self):
+        retDict = OrderedDict([('loss_total', self.loss_total.data.cpu().numpy()),
+                            ('acc_r', self.acc_r)])
+
+        for key in retDict.keys():
+            retDict[key] = np.mean(retDict[key])
+
+        return retDict
+
+    def get_current_visuals(self):
+        zoom_factor = 256/self.var_ref.data.size()[2]
+
+        ref_img = util.tensor2im(self.var_ref.data)
+        p0_img = util.tensor2im(self.var_p0.data)
+        p1_img = util.tensor2im(self.var_p1.data)
+
+        ref_img_vis = zoom(ref_img,[zoom_factor, zoom_factor, 1],order=0)
+        p0_img_vis = zoom(p0_img,[zoom_factor, zoom_factor, 1],order=0)
+        p1_img_vis = zoom(p1_img,[zoom_factor, zoom_factor, 1],order=0)
+
+        return OrderedDict([('ref', ref_img_vis),
+                            ('p0', p0_img_vis),
+                            ('p1', p1_img_vis)])
+
+    def save(self, path, label):
+        self.save_network(self.net, path, '', label)
+        self.save_network(self.rankLoss.net, path, 'rank', label)
+
+    def update_learning_rate(self,nepoch_decay):
+        lrd = self.lr / nepoch_decay
+        lr = self.old_lr - lrd
+
+        for param_group in self.optimizer_net.param_groups:
+            param_group['lr'] = lr
+
+        print('update lr [%s] decay: %f -> %f' % (type,self.old_lr, lr))
+        self.old_lr = lr
+
+
+
+def score_2afc_dataset(data_loader,func):
+    ''' Function computes Two Alternative Forced Choice (2AFC) score using
+        distance function 'func' in dataset 'data_loader'
+    INPUTS
+        data_loader - CustomDatasetDataLoader object - contains a TwoAFCDataset inside
+        func - callable distance function - calling d=func(in0,in1) should take 2
+            pytorch tensors with shape Nx3xXxY, and return numpy array of length N
+    OUTPUTS
+        [0] - 2AFC score in [0,1], fraction of time func agrees with human evaluators
+        [1] - dictionary with following elements
+            d0s,d1s - N arrays containing distances between reference patch to perturbed patches
+            gts - N array in [0,1], preferred patch selected by human evaluators
+                (closer to "0" for left patch p0, "1" for right patch p1,
+                "0.6" means 60pct people preferred right patch, 40pct preferred left)
+            scores - N array in [0,1], corresponding to what percentage function agreed with humans
+    CONSTS
+        N - number of test triplets in data_loader
+    '''
+
+    d0s = []
+    d1s = []
+    gts = []
+
+    # bar = pb.ProgressBar(max_value=data_loader.load_data().__len__())
+    for (i,data) in enumerate(data_loader.load_data()):
+        d0s+=func(data['ref'],data['p0']).tolist()
+        d1s+=func(data['ref'],data['p1']).tolist()
+        gts+=data['judge'].cpu().numpy().flatten().tolist()
+        # bar.update(i)
+
+    d0s = np.array(d0s)
+    d1s = np.array(d1s)
+    gts = np.array(gts)
+    scores = (d0s<d1s)*(1.-gts) + (d1s<d0s)*gts + (d1s==d0s)*.5
+
+    return(np.mean(scores), dict(d0s=d0s,d1s=d1s,gts=gts,scores=scores))
+
+def score_jnd_dataset(data_loader,func):
+    ''' Function computes JND score using distance function 'func' in dataset 'data_loader'
+    INPUTS
+        data_loader - CustomDatasetDataLoader object - contains a JNDDataset inside
+        func - callable distance function - calling d=func(in0,in1) should take 2
+            pytorch tensors with shape Nx3xXxY, and return numpy array of length N
+    OUTPUTS
+        [0] - JND score in [0,1], mAP score (area under precision-recall curve)
+        [1] - dictionary with following elements
+            ds - N array containing distances between two patches shown to human evaluator
+            sames - N array containing fraction of people who thought the two patches were identical
+    CONSTS
+        N - number of test triplets in data_loader
+    '''
+
+    ds = []
+    gts = []
+
+    # bar = pb.ProgressBar(max_value=data_loader.load_data().__len__())
+    for (i,data) in enumerate(data_loader.load_data()):
+        ds+=func(data['p0'],data['p1']).tolist()
+        gts+=data['same'].cpu().numpy().flatten().tolist()
+        # bar.update(i)
+
+    sames = np.array(gts)
+    ds = np.array(ds)
+
+    sorted_inds = np.argsort(ds)
+    ds_sorted = ds[sorted_inds]
+    sames_sorted = sames[sorted_inds]
+
+    TPs = np.cumsum(sames_sorted)
+    FPs = np.cumsum(1-sames_sorted)
+    FNs = np.sum(sames_sorted)-TPs
+
+    precs = TPs/(TPs+FPs)
+    recs = TPs/(TPs+FNs)
+    score = util.voc_ap(recs,precs)
+
+    return(score, dict(ds=ds,sames=sames))
diff --git a/modeling/sf2f/scripts/DScore/models/models.py b/modeling/sf2f/scripts/DScore/models/models.py
new file mode 100644
index 0000000..1595d55
--- /dev/null
+++ b/modeling/sf2f/scripts/DScore/models/models.py
@@ -0,0 +1,11 @@
+from __future__ import absolute_import
+
+def create_model(opt):
+    model = None
+    print(opt.model)
+    from .siam_model import *
+    model = DistModel()
+    model.initialize(opt, opt.batchSize, )
+    print("model [%s] was created" % (model.name()))
+    return model
+
diff --git a/modeling/sf2f/scripts/DScore/models/networks_basic.py b/modeling/sf2f/scripts/DScore/models/networks_basic.py
new file mode 100644
index 0000000..1889d6a
--- /dev/null
+++ b/modeling/sf2f/scripts/DScore/models/networks_basic.py
@@ -0,0 +1,268 @@
+from __future__ import absolute_import
+
+import sys
+sys.path.append('..')
+sys.path.append('.')
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+import numpy as np
+from pdb import set_trace as st
+from skimage import color
+from IPython import embed
+from . import pretrained_networks as pn
+
+# from PerceptualSimilarity.util import util
+from scripts.DScore.util import util
+
+# Off-the-shelf deep network
+class PNet(nn.Module):
+    '''Pre-trained network with all channels equally weighted by default'''
+    def __init__(self, pnet_type='vgg', pnet_rand=False, use_gpu=True):
+        super(PNet, self).__init__()
+
+        self.use_gpu = use_gpu
+
+        self.pnet_type = pnet_type
+        self.pnet_rand = pnet_rand
+
+        self.shift = torch.autograd.Variable(torch.Tensor([-.030, -.088, -.188]).view(1,3,1,1))
+        self.scale = torch.autograd.Variable(torch.Tensor([.458, .448, .450]).view(1,3,1,1))
+
+        if(self.pnet_type in ['vgg','vgg16']):
+            self.net = pn.vgg16(pretrained=not self.pnet_rand,requires_grad=False)
+        elif(self.pnet_type=='alex'):
+            self.net = pn.alexnet(pretrained=not self.pnet_rand,requires_grad=False)
+        elif(self.pnet_type[:-2]=='resnet'):
+            self.net = pn.resnet(pretrained=not self.pnet_rand,requires_grad=False, num=int(self.pnet_type[-2:]))
+        elif(self.pnet_type=='squeeze'):
+            self.net = pn.squeezenet(pretrained=not self.pnet_rand,requires_grad=False)
+
+        self.L = self.net.N_slices
+
+        if(use_gpu):
+            self.net.cuda()
+            self.shift = self.shift.cuda()
+            self.scale = self.scale.cuda()
+
+    def forward(self, in0, in1, retPerLayer=False):
+        in0_sc = (in0 - self.shift.expand_as(in0))/self.scale.expand_as(in0)
+        in1_sc = (in1 - self.shift.expand_as(in0))/self.scale.expand_as(in0)
+
+        outs0 = self.net.forward(in0_sc)
+        outs1 = self.net.forward(in1_sc)
+
+        if(retPerLayer):
+            all_scores = []
+        for (kk,out0) in enumerate(outs0):
+            cur_score = (1.-util.cos_sim(outs0[kk],outs1[kk]))
+            if(kk==0):
+                val = 1.*cur_score
+            else:
+                # val = val + self.lambda_feat_layers[kk]*cur_score
+                val = val + cur_score
+            if(retPerLayer):
+                all_scores+=[cur_score]
+
+        if(retPerLayer):
+            return (val, all_scores)
+        else:
+            return val
+
+# Learned perceptual metric
+class PNetLin(nn.Module):
+    def __init__(self, pnet_type='vgg', pnet_rand=False, pnet_tune=False, use_dropout=True, use_gpu=True, spatial=False, version='0.1'):
+        super(PNetLin, self).__init__()
+
+        self.use_gpu = use_gpu
+        self.pnet_type = pnet_type
+        self.pnet_tune = pnet_tune
+        self.pnet_rand = pnet_rand
+        self.spatial = spatial
+        self.version = version
+
+        if(self.pnet_type in ['vgg','vgg16']):
+            net_type = pn.vgg16
+            self.chns = [64,128,256,512,512]
+        elif(self.pnet_type=='alex'):
+            net_type = pn.alexnet
+            self.chns = [64,192,384,256,256]
+        elif(self.pnet_type=='squeeze'):
+            net_type = pn.squeezenet
+            self.chns = [64,128,256,384,384,512,512]
+
+        if(self.pnet_tune):
+            self.net = net_type(pretrained=not self.pnet_rand,requires_grad=True)
+        else:
+            self.net = [net_type(pretrained=not self.pnet_rand,requires_grad=False),]
+
+        self.lin0 = NetLinLayer(self.chns[0],use_dropout=use_dropout)
+        self.lin1 = NetLinLayer(self.chns[1],use_dropout=use_dropout)
+        self.lin2 = NetLinLayer(self.chns[2],use_dropout=use_dropout)
+        self.lin3 = NetLinLayer(self.chns[3],use_dropout=use_dropout)
+        self.lin4 = NetLinLayer(self.chns[4],use_dropout=use_dropout)
+        self.lins = [self.lin0,self.lin1,self.lin2,self.lin3,self.lin4]
+        if(self.pnet_type=='squeeze'): # 7 layers for squeezenet
+            self.lin5 = NetLinLayer(self.chns[5],use_dropout=use_dropout)
+            self.lin6 = NetLinLayer(self.chns[6],use_dropout=use_dropout)
+            self.lins+=[self.lin5,self.lin6]
+
+        self.shift = torch.autograd.Variable(torch.Tensor([-.030, -.088, -.188]).view(1,3,1,1))
+        self.scale = torch.autograd.Variable(torch.Tensor([.458, .448, .450]).view(1,3,1,1))
+
+        if(use_gpu):
+            if(self.pnet_tune):
+                self.net.cuda()
+            else:
+                self.net[0].cuda()
+            self.shift = self.shift.cuda()
+            self.scale = self.scale.cuda()
+            self.lin0.cuda()
+            self.lin1.cuda()
+            self.lin2.cuda()
+            self.lin3.cuda()
+            self.lin4.cuda()
+            if(self.pnet_type=='squeeze'):
+                self.lin5.cuda()
+                self.lin6.cuda()
+
+    def forward(self, in0, in1):
+        in0_sc = (in0 - self.shift.expand_as(in0))/self.scale.expand_as(in0)
+        in1_sc = (in1 - self.shift.expand_as(in0))/self.scale.expand_as(in0)
+
+        if(self.version=='0.0'):
+            # v0.0 - original release had a bug, where input was not scaled
+            in0_input = in0
+            in1_input = in1
+        else:
+            # v0.1
+            in0_input = in0_sc
+            in1_input = in1_sc
+
+        if(self.pnet_tune):
+            outs0 = self.net.forward(in0_input)
+            outs1 = self.net.forward(in1_input)
+        else:
+            outs0 = self.net[0].forward(in0_input)
+            outs1 = self.net[0].forward(in1_input)
+
+        feats0 = {}
+        feats1 = {}
+        diffs = [0]*len(outs0)
+
+        for (kk,out0) in enumerate(outs0):
+            feats0[kk] = util.normalize_tensor(outs0[kk])
+            feats1[kk] = util.normalize_tensor(outs1[kk])
+            diffs[kk] = (feats0[kk]-feats1[kk])**2
+
+        if self.spatial:
+            lin_models = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
+            if(self.pnet_type=='squeeze'):
+                lin_models.extend([self.lin5, self.lin6])
+            res = [lin_models[kk].model(diffs[kk]) for kk in range(len(diffs))]
+            return res
+
+        val = torch.mean(torch.mean(self.lin0.model(diffs[0]),dim=3),dim=2)
+        val = val + torch.mean(torch.mean(self.lin1.model(diffs[1]),dim=3),dim=2)
+        val = val + torch.mean(torch.mean(self.lin2.model(diffs[2]),dim=3),dim=2)
+        val = val + torch.mean(torch.mean(self.lin3.model(diffs[3]),dim=3),dim=2)
+        val = val + torch.mean(torch.mean(self.lin4.model(diffs[4]),dim=3),dim=2)
+        if(self.pnet_type=='squeeze'):
+            val = val + torch.mean(torch.mean(self.lin5.model(diffs[5]),dim=3),dim=2)
+            val = val + torch.mean(torch.mean(self.lin6.model(diffs[6]),dim=3),dim=2)
+
+        val = val.view(val.size()[0],val.size()[1],1,1)
+
+        return val
+
+class Dist2LogitLayer(nn.Module):
+    ''' takes 2 distances, puts through fc layers, spits out value between [0,1] (if use_sigmoid is True) '''
+    def __init__(self, chn_mid=32,use_sigmoid=True):
+        super(Dist2LogitLayer, self).__init__()
+        layers = [nn.Conv2d(5, chn_mid, 1, stride=1, padding=0, bias=True),]
+        layers += [nn.LeakyReLU(0.2,True),]
+        layers += [nn.Conv2d(chn_mid, chn_mid, 1, stride=1, padding=0, bias=True),]
+        layers += [nn.LeakyReLU(0.2,True),]
+        layers += [nn.Conv2d(chn_mid, 1, 1, stride=1, padding=0, bias=True),]
+        if(use_sigmoid):
+            layers += [nn.Sigmoid(),]
+        self.model = nn.Sequential(*layers)
+
+    def forward(self,d0,d1,eps=0.1):
+        return self.model.forward(torch.cat((d0,d1,d0-d1,d0/(d1+eps),d1/(d0+eps)),dim=1))
+
+class BCERankingLoss(nn.Module):
+    def __init__(self, use_gpu=True, chn_mid=32):
+        super(BCERankingLoss, self).__init__()
+        self.use_gpu = use_gpu
+        self.net = Dist2LogitLayer(chn_mid=chn_mid)
+        self.parameters = list(self.net.parameters())
+        self.loss = torch.nn.BCELoss()
+        self.model = nn.Sequential(*[self.net])
+
+        if(self.use_gpu):
+            self.net.cuda()
+
+    def forward(self, d0, d1, judge):
+        per = (judge+1.)/2.
+        if(self.use_gpu):
+            per = per.cuda()
+        self.logit = self.net.forward(d0,d1)
+        return self.loss(self.logit, per)
+
+class NetLinLayer(nn.Module):
+    ''' A single linear layer which does a 1x1 conv '''
+    def __init__(self, chn_in, chn_out=1, use_dropout=False):
+        super(NetLinLayer, self).__init__()
+
+        layers = [nn.Dropout(),] if(use_dropout) else []
+        layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),]
+        self.model = nn.Sequential(*layers)
+
+
+# L2, DSSIM metrics
+class FakeNet(nn.Module):
+    def __init__(self, use_gpu=True, colorspace='Lab'):
+        super(FakeNet, self).__init__()
+        self.use_gpu = use_gpu
+        self.colorspace=colorspace
+
+class L2(FakeNet):
+
+    def forward(self, in0, in1):
+        assert(in0.size()[0]==1) # currently only supports batchSize 1
+
+        if(self.colorspace=='RGB'):
+            (N,C,X,Y) = in0.size()
+            value = torch.mean(torch.mean(torch.mean((in0-in1)**2,dim=1).view(N,1,X,Y),dim=2).view(N,1,1,Y),dim=3).view(N)
+            return value
+        elif(self.colorspace=='Lab'):
+            value = util.l2(util.tensor2np(util.tensor2tensorlab(in0.data,to_norm=False)),
+                util.tensor2np(util.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float')
+            ret_var = Variable( torch.Tensor((value,) ) )
+            if(self.use_gpu):
+                ret_var = ret_var.cuda()
+            return ret_var
+
+class DSSIM(FakeNet):
+
+    def forward(self, in0, in1):
+        assert(in0.size()[0]==1) # currently only supports batchSize 1
+
+        if(self.colorspace=='RGB'):
+            value = util.dssim(1.*util.tensor2im(in0.data), 1.*util.tensor2im(in1.data), range=255.).astype('float')
+        elif(self.colorspace=='Lab'):
+            value = util.dssim(util.tensor2np(util.tensor2tensorlab(in0.data,to_norm=False)),
+                util.tensor2np(util.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float')
+        ret_var = Variable( torch.Tensor((value,) ) )
+        if(self.use_gpu):
+            ret_var = ret_var.cuda()
+        return ret_var
+
+def print_network(net):
+    num_params = 0
+    for param in net.parameters():
+        num_params += param.numel()
+    print('Network',net)
+    print('Total number of parameters: %d' % num_params)
diff --git a/modeling/sf2f/scripts/DScore/models/pretrained_networks.py b/modeling/sf2f/scripts/DScore/models/pretrained_networks.py
new file mode 100644
index 0000000..b1329d6
--- /dev/null
+++ b/modeling/sf2f/scripts/DScore/models/pretrained_networks.py
@@ -0,0 +1,181 @@
+from collections import namedtuple
+import torch
+from torchvision import models
+from IPython import embed
+
+class squeezenet(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True):
+        super(squeezenet, self).__init__()
+        pretrained_features = models.squeezenet1_1(pretrained=pretrained).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        self.slice6 = torch.nn.Sequential()
+        self.slice7 = torch.nn.Sequential()
+        self.N_slices = 7
+        for x in range(2):
+            self.slice1.add_module(str(x), pretrained_features[x])
+        for x in range(2,5):
+            self.slice2.add_module(str(x), pretrained_features[x])
+        for x in range(5, 8):
+            self.slice3.add_module(str(x), pretrained_features[x])
+        for x in range(8, 10):
+            self.slice4.add_module(str(x), pretrained_features[x])
+        for x in range(10, 11):
+            self.slice5.add_module(str(x), pretrained_features[x])
+        for x in range(11, 12):
+            self.slice6.add_module(str(x), pretrained_features[x])
+        for x in range(12, 13):
+            self.slice7.add_module(str(x), pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h = self.slice1(X)
+        h_relu1 = h
+        h = self.slice2(h)
+        h_relu2 = h
+        h = self.slice3(h)
+        h_relu3 = h
+        h = self.slice4(h)
+        h_relu4 = h
+        h = self.slice5(h)
+        h_relu5 = h
+        h = self.slice6(h)
+        h_relu6 = h
+        h = self.slice7(h)
+        h_relu7 = h
+        vgg_outputs = namedtuple("SqueezeOutputs", ['relu1','relu2','relu3','relu4','relu5','relu6','relu7'])
+        out = vgg_outputs(h_relu1,h_relu2,h_relu3,h_relu4,h_relu5,h_relu6,h_relu7)
+
+        return out
+
+
+class alexnet(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True):
+        super(alexnet, self).__init__()
+        alexnet_pretrained_features = models.alexnet(pretrained=pretrained).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        self.N_slices = 5
+        for x in range(2):
+            self.slice1.add_module(str(x), alexnet_pretrained_features[x])
+        for x in range(2, 5):
+            self.slice2.add_module(str(x), alexnet_pretrained_features[x])
+        for x in range(5, 8):
+            self.slice3.add_module(str(x), alexnet_pretrained_features[x])
+        for x in range(8, 10):
+            self.slice4.add_module(str(x), alexnet_pretrained_features[x])
+        for x in range(10, 12):
+            self.slice5.add_module(str(x), alexnet_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h = self.slice1(X)
+        h_relu1 = h
+        h = self.slice2(h)
+        h_relu2 = h
+        h = self.slice3(h)
+        h_relu3 = h
+        h = self.slice4(h)
+        h_relu4 = h
+        h = self.slice5(h)
+        h_relu5 = h
+        alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5'])
+        out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)
+
+        return out
+
+class vgg16(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True):
+        super(vgg16, self).__init__()
+        vgg_pretrained_features = models.vgg16(pretrained=pretrained).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        self.N_slices = 5
+        for x in range(4):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(4, 9):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(9, 16):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(16, 23):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(23, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h = self.slice1(X)
+        h_relu1_2 = h
+        h = self.slice2(h)
+        h_relu2_2 = h
+        h = self.slice3(h)
+        h_relu3_3 = h
+        h = self.slice4(h)
+        h_relu4_3 = h
+        h = self.slice5(h)
+        h_relu5_3 = h
+        vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
+        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
+
+        return out
+
+
+
+class resnet(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True, num=18):
+        super(resnet, self).__init__()
+        if(num==18):
+            self.net = models.resnet18(pretrained=pretrained)
+        elif(num==34):
+            self.net = models.resnet34(pretrained=pretrained)
+        elif(num==50):
+            self.net = models.resnet50(pretrained=pretrained)
+        elif(num==101):
+            self.net = models.resnet101(pretrained=pretrained)
+        elif(num==152):
+            self.net = models.resnet152(pretrained=pretrained)
+        self.N_slices = 5
+
+        self.conv1 = self.net.conv1
+        self.bn1 = self.net.bn1
+        self.relu = self.net.relu
+        self.maxpool = self.net.maxpool
+        self.layer1 = self.net.layer1
+        self.layer2 = self.net.layer2
+        self.layer3 = self.net.layer3
+        self.layer4 = self.net.layer4
+
+    def forward(self, X):
+        h = self.conv1(X)
+        h = self.bn1(h)
+        h = self.relu(h)
+        h_relu1 = h
+        h = self.maxpool(h)
+        h = self.layer1(h)
+        h_conv2 = h
+        h = self.layer2(h)
+        h_conv3 = h
+        h = self.layer3(h)
+        h_conv4 = h
+        h = self.layer4(h)
+        h_conv5 = h
+
+        outputs = namedtuple("Outputs", ['relu1','conv2','conv3','conv4','conv5'])
+        out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5)
+
+        return out
diff --git a/modeling/sf2f/scripts/DScore/util/__init__.py b/modeling/sf2f/scripts/DScore/util/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/modeling/sf2f/scripts/DScore/util/html.py b/modeling/sf2f/scripts/DScore/util/html.py
new file mode 100644
index 0000000..ebec4fb
--- /dev/null
+++ b/modeling/sf2f/scripts/DScore/util/html.py
@@ -0,0 +1,66 @@
+import dominate
+from dominate.tags import *
+import os
+
+
+class HTML:
+    def __init__(self, web_dir, title, image_subdir='', reflesh=0):
+        self.title = title
+        self.web_dir = web_dir
+        # self.img_dir = os.path.join(self.web_dir, )
+        self.img_subdir = image_subdir
+        self.img_dir = os.path.join(self.web_dir, image_subdir)
+        if not os.path.exists(self.web_dir):
+            os.makedirs(self.web_dir)
+        if not os.path.exists(self.img_dir):
+            os.makedirs(self.img_dir)
+        # print(self.img_dir)
+
+        self.doc = dominate.document(title=title)
+        if reflesh > 0:
+            with self.doc.head:
+                meta(http_equiv="reflesh", content=str(reflesh))
+
+    def get_image_dir(self):
+        return self.img_dir
+
+    def add_header(self, str):
+        with self.doc:
+            h3(str)
+
+    def add_table(self, border=1):
+        self.t = table(border=border, style="table-layout: fixed;")
+        self.doc.add(self.t)
+
+    def add_images(self, ims, txts, links, width=400):
+        self.add_table()
+        with self.t:
+            with tr():
+                for im, txt, link in zip(ims, txts, links):
+                    with td(style="word-wrap: break-word;", halign="center", valign="top"):
+                        with p():
+                            with a(href=os.path.join(link)):
+                                img(style="width:%dpx" % width, src=os.path.join(im))
+                            br()
+                            p(txt)
+
+    def save(self,file='index'):
+        html_file = '%s/%s.html' % (self.web_dir,file)
+        f = open(html_file, 'wt')
+        f.write(self.doc.render())
+        f.close()
+
+
+if __name__ == '__main__':
+    html = HTML('web/', 'test_html')
+    html.add_header('hello world')
+
+    ims = []
+    txts = []
+    links = []
+    for n in range(4):
+        ims.append('image_%d.png' % n)
+        txts.append('text_%d' % n)
+        links.append('image_%d.png' % n)
+    html.add_images(ims, txts, links)
+    html.save()
diff --git a/modeling/sf2f/scripts/DScore/util/util.py b/modeling/sf2f/scripts/DScore/util/util.py
new file mode 100644
index 0000000..93a07aa
--- /dev/null
+++ b/modeling/sf2f/scripts/DScore/util/util.py
@@ -0,0 +1,452 @@
+from __future__ import print_function
+
+import numpy as np
+from PIL import Image
+import inspect
+import re
+import numpy as np
+import os
+import collections
+import matplotlib.pyplot as plt
+from scipy.ndimage.interpolation import zoom
+from skimage.measure import compare_ssim
+import torch
+from IPython import embed
+# import cv2
+from datetime import datetime
+
+def datetime_str():
+    now = datetime.now()
+    return '%04d-%02d-%02d-%02d-%02d-%02d'%(now.year,now.month,now.day,now.hour,now.minute,now.second)
+
+def read_text_file(in_path):
+    fid = open(in_path,'r')
+
+    vals = []
+    cur_line = fid.readline()
+    while(cur_line!=''):
+        vals.append(float(cur_line))
+        cur_line = fid.readline()
+
+    fid.close()
+    return np.array(vals)
+
+def bootstrap(in_vec,num_samples=100,bootfunc=np.mean):
+    from astropy import stats
+    return stats.bootstrap(np.array(in_vec),bootnum=num_samples,bootfunc=bootfunc)
+
+def rand_flip(input1,input2):
+    if(np.random.binomial(1,.5)==1):
+        return (input1,input2)
+    else:
+        return (input2,input1)
+
+def l2(p0, p1, range=255.):
+    return .5*np.mean((p0 / range - p1 / range)**2)
+
+def psnr(p0, p1, peak=255.):
+    return 10*np.log10(peak**2/np.mean((1.*p0-1.*p1)**2))
+
+def dssim(p0, p1, range=255.):
+    # embed()
+    return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2.
+
+def rgb2lab(in_img,mean_cent=False):
+    from skimage import color
+    img_lab = color.rgb2lab(in_img)
+    if(mean_cent):
+        img_lab[:,:,0] = img_lab[:,:,0]-50
+    return img_lab
+
+def normalize_blob(in_feat,eps=1e-10):
+    norm_factor = np.sqrt(np.sum(in_feat**2,axis=1,keepdims=True))
+    return in_feat/(norm_factor+eps)
+
+def cos_sim_blob(in0,in1):
+    in0_norm = normalize_blob(in0)
+    in1_norm = normalize_blob(in1)
+    (N,C,X,Y) = in0_norm.shape
+
+    return np.mean(np.mean(np.sum(in0_norm*in1_norm,axis=1),axis=1),axis=1)
+
+def normalize_tensor(in_feat,eps=1e-10):
+    # norm_factor = torch.sqrt(torch.sum(in_feat**2,dim=1)).view(in_feat.size()[0],1,in_feat.size()[2],in_feat.size()[3]).repeat(1,in_feat.size()[1],1,1)
+    norm_factor = torch.sqrt(torch.sum(in_feat**2,dim=1)).view(in_feat.size()[0],1,in_feat.size()[2],in_feat.size()[3])
+    return in_feat/(norm_factor.expand_as(in_feat)+eps)
+
+def cos_sim(in0,in1):
+    in0_norm = normalize_tensor(in0)
+    in1_norm = normalize_tensor(in1)
+    N = in0.size()[0]
+    X = in0.size()[2]
+    Y = in0.size()[3]
+
+    return torch.mean(torch.mean(torch.sum(in0_norm*in1_norm,dim=1).view(N,1,X,Y),dim=2).view(N,1,1,Y),dim=3).view(N)
+
+# Converts a Tensor into a Numpy array
+# |imtype|: the desired type of the conve
+
+def tensor2np(tensor_obj):
+    # change dimension of a tensor object into a numpy array
+    return tensor_obj[0].cpu().float().numpy().transpose((1,2,0))
+
+def np2tensor(np_obj):
+     # change dimenion of np array into tensor array
+    return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
+
+def tensor2tensorlab(image_tensor,to_norm=True,mc_only=False):
+    # image tensor to lab tensor
+    from skimage import color
+
+    img = tensor2im(image_tensor)
+    # print('img_rgb',img.flatten())
+    img_lab = color.rgb2lab(img)
+    # print('img_lab',img_lab.flatten())
+    if(mc_only):
+        img_lab[:,:,0] = img_lab[:,:,0]-50
+    if(to_norm and not mc_only):
+        img_lab[:,:,0] = img_lab[:,:,0]-50
+        img_lab = img_lab/100.
+
+    return np2tensor(img_lab)
+
+def tensorlab2tensor(lab_tensor,return_inbnd=False):
+    from skimage import color
+    import warnings
+    warnings.filterwarnings("ignore")
+
+    lab = tensor2np(lab_tensor)*100.
+    lab[:,:,0] = lab[:,:,0]+50
+    # print('lab',lab)
+
+    rgb_back = 255.*np.clip(color.lab2rgb(lab.astype('float')),0,1)
+    # print('rgb',rgb_back)
+    if(return_inbnd):
+        # convert back to lab, see if we match
+        lab_back = color.rgb2lab(rgb_back.astype('uint8'))
+        # print('lab_back',lab_back)
+        # print('lab==lab_back',np.isclose(lab_back,lab,atol=1.))
+        # print('lab-lab_back',np.abs(lab-lab_back))
+        mask = 1.*np.isclose(lab_back,lab,atol=2.)
+        mask = np2tensor(np.prod(mask,axis=2)[:,:,np.newaxis])
+        return (im2tensor(rgb_back),mask)
+    else:
+        return im2tensor(rgb_back)
+
+def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
+# def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.):
+    image_numpy = image_tensor[0].cpu().float().numpy()
+    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
+    return image_numpy.astype(imtype)
+
+def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.):
+# def im2tensor(image, imtype=np.uint8, cent=1., factor=1.):
+    return torch.Tensor((image / factor - cent)
+                        [:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
+
+def tensor2vec(vector_tensor):
+    return vector_tensor.data.cpu().numpy()[:, :, 0, 0]
+
+def diagnose_network(net, name='network'):
+    mean = 0.0
+    count = 0
+    for param in net.parameters():
+        if param.grad is not None:
+            mean += torch.mean(torch.abs(param.grad.data))
+            count += 1
+    if count > 0:
+        mean = mean / count
+    print(name)
+    print(mean)
+
+def grab_patch(img_in, P, yy, xx):
+    return img_in[yy:yy+P,xx:xx+P,:]
+
+# def load_image(path):
+#     if(path[-3:] == 'dng'):
+#         import rawpy
+#         with rawpy.imread(path) as raw:
+#             img = raw.postprocess()
+#         # img = plt.imread(path)
+#     elif(path[-3:]=='bmp' or path[-3:]=='jpg' or path[-3:]=='png'):
+#         import cv2
+#         return cv2.imread(path)[:,:,::-1]
+#     else:
+#         img = (255*plt.imread(path)[:,:,:3]).astype('uint8')
+#
+#     return img
+
+
+def resize_image(img, max_size=256):
+    [Y, X] = img.shape[:2]
+
+    # resize
+    max_dim = max([Y, X])
+    zoom_factor = 1. * max_size / max_dim
+    img = zoom(img, [zoom_factor, zoom_factor, 1])
+
+    return img
+
+def resize_image_zoom(img, zoom_factor=1., order=3):
+    if(zoom_factor==1):
+        return img
+    else:
+        return zoom(img, [zoom_factor, zoom_factor, 1], order=order)
+
+def save_image(image_numpy, image_path, ):
+    image_pil = Image.fromarray(image_numpy)
+    image_pil.save(image_path)
+
+
+def prep_display_image(img, dtype='uint8'):
+    if(dtype == 'uint8'):
+        return np.clip(img, 0, 255).astype('uint8')
+    else:
+        return np.clip(img, 0, 1.)
+
+
+def info(object, spacing=10, collapse=1):
+    """Print methods and doc strings.
+    Takes module, class, list, dictionary, or string."""
+    methodList = [
+        e for e in dir(object) if isinstance(
+            getattr(
+                object,
+                e),
+            collections.Callable)]
+    processFunc = collapse and (lambda s: " ".join(s.split())) or (lambda s: s)
+    print("\n".join(["%s %s" %
+                     (method.ljust(spacing),
+                      processFunc(str(getattr(object, method).__doc__)))
+                     for method in methodList]))
+
+
+def varname(p):
+    for line in inspect.getframeinfo(inspect.currentframe().f_back)[3]:
+        m = re.search(r'\bvarname\s*\(\s*([A-Za-z_][A-Za-z0-9_]*)\s*\)', line)
+        if m:
+            return m.group(1)
+
+
+def print_numpy(x, val=True, shp=False):
+    x = x.astype(np.float64)
+    if shp:
+        print('shape,', x.shape)
+    if val:
+        x = x.flatten()
+        print(
+            'mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' %
+            (np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))
+
+
+def mkdirs(paths):
+    if isinstance(paths, list) and not isinstance(paths, str):
+        for path in paths:
+            mkdir(path)
+    else:
+        mkdir(paths)
+
+
+def mkdir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+
+def rgb2lab(input):
+    from skimage import color
+    return color.rgb2lab(input / 255.)
+
+
+def montage(
+    imgs,
+    PAD=5,
+    RATIO=16 / 9.,
+    EXTRA_PAD=(
+        False,
+        False),
+        MM=-1,
+        NN=-1,
+        primeDir=0,
+        verbose=False,
+        returnGridPos=False,
+        backClr=np.array(
+            (0,
+             0,
+             0))):
+    # INPUTS
+    #   imgs        YxXxMxN or YxXxN
+    #   PAD         scalar              number of pixels in between
+    #   RATIO       scalar              target ratio of cols/rows
+    #   MM          scalar              # rows, if specified, overrides RATIO
+    #   NN          scalar              # columns, if specified, overrides RATIO
+    #   primeDir    scalar              0 for top-to-bottom, 1 for left-to-right
+    # OUTPUTS
+    #   mont_imgs   MM*Y x NN*X x M     big image with everything montaged
+    # def montage(imgs, PAD=5, RATIO=16/9., MM=-1, NN=-1, primeDir=0,
+    # verbose=False, forceFloat=False):
+    if(imgs.ndim == 3):
+        toExp = True
+        imgs = imgs[:, :, np.newaxis, :]
+    else:
+        toExp = False
+
+    Y = imgs.shape[0]
+    X = imgs.shape[1]
+    M = imgs.shape[2]
+    N = imgs.shape[3]
+
+    PADS = np.array((PAD))
+    if(PADS.flatten().size == 1):
+        PADY = PADS
+        PADX = PADS
+    else:
+        PADY = PADS[0]
+        PADX = PADS[1]
+
+    if(MM == -1 and NN == -1):
+        NN = np.ceil(np.sqrt(1.0 * N * RATIO))
+        MM = np.ceil(1.0 * N / NN)
+        NN = np.ceil(1.0 * N / MM)
+    elif(MM == -1):
+        MM = np.ceil(1.0 * N / NN)
+    elif(NN == -1):
+        NN = np.ceil(1.0 * N / MM)
+
+    if(primeDir == 0):  # write top-to-bottom
+        [grid_mm, grid_nn] = np.meshgrid(
+            np.arange(MM, dtype='uint'), np.arange(NN, dtype='uint'))
+    elif(primeDir == 1):  # write left-to-right
+        [grid_nn, grid_mm] = np.meshgrid(
+            np.arange(NN, dtype='uint'), np.arange(MM, dtype='uint'))
+
+    grid_mm = np.uint(grid_mm.flatten()[0:N])
+    grid_nn = np.uint(grid_nn.flatten()[0:N])
+
+    EXTRA_PADY = EXTRA_PAD[0] * PADY
+    EXTRA_PADX = EXTRA_PAD[0] * PADX
+
+    # mont_imgs = np.zeros(((Y+PAD)*MM-PAD, (X+PAD)*NN-PAD, M), dtype=use_dtype)
+    mont_imgs = np.zeros(
+        (np.uint(
+            (Y + PADY) * MM - PADY + EXTRA_PADY),
+            np.uint(
+            (X + PADX) * NN - PADX + EXTRA_PADX),
+            M),
+        dtype=imgs.dtype)
+    mont_imgs = mont_imgs + \
+        backClr.flatten()[np.newaxis, np.newaxis, :].astype(mont_imgs.dtype)
+
+    for ii in np.random.permutation(N):
+        # print imgs[:,:,:,ii].shape
+        # mont_imgs[grid_mm[ii]*(Y+PAD):(grid_mm[ii]*(Y+PAD)+Y), grid_nn[ii]*(X+PAD):(grid_nn[ii]*(X+PAD)+X),:]
+        mont_imgs[np.uint(grid_mm[ii] *
+                          (Y +
+                           PADY)):np.uint((grid_mm[ii] *
+                                           (Y +
+                                            PADY) +
+                                           Y)), np.uint(grid_nn[ii] *
+                                                        (X +
+                                                         PADX)):np.uint((grid_nn[ii] *
+                                                                         (X +
+                                                                          PADX) +
+                                                                         X)), :] = imgs[:, :, :, ii]
+
+    if(M == 1):
+        imgs = imgs.reshape(imgs.shape[0], imgs.shape[1], imgs.shape[3])
+
+    if(toExp):
+        mont_imgs = mont_imgs[:, :, 0]
+
+    if(returnGridPos):
+        # return (mont_imgs,np.concatenate((grid_mm[:,:,np.newaxis]*(Y+PAD),
+        # grid_nn[:,:,np.newaxis]*(X+PAD)),axis=2))
+        return (mont_imgs, np.concatenate(
+            (grid_mm[:, np.newaxis] * (Y + PADY), grid_nn[:, np.newaxis] * (X + PADX)), axis=1))
+        # return (mont_imgs, (grid_mm,grid_nn))
+    else:
+        return mont_imgs
+
+class zeroClipper(object):
+    def __init__(self, frequency=1):
+        self.frequency = frequency
+
+    def __call__(self, module):
+        embed()
+        if hasattr(module, 'weight'):
+            # module.weight.data = torch.max(module.weight.data, 0)
+            module.weight.data = torch.max(module.weight.data, 0) + 100
+
+def flatten_nested_list(nested_list):
+    # only works for list of list
+    accum = []
+    for sublist in nested_list:
+        for item in sublist:
+            accum.append(item)
+    return accum
+
+def read_file(in_path,list_lines=False):
+    agg_str = ''
+    f = open(in_path,'r')
+    cur_line = f.readline()
+    while(cur_line!=''):
+        agg_str+=cur_line
+        cur_line = f.readline()
+    f.close()
+    if(list_lines==False):
+        return agg_str.replace('\n','')
+    else:
+        line_list = agg_str.split('\n')
+        ret_list = []
+        for item in line_list:
+            if(item!=''):
+                ret_list.append(item)
+        return ret_list
+
+def read_csv_file_as_text(in_path):
+    agg_str = []
+    f = open(in_path,'r')
+    cur_line = f.readline()
+    while(cur_line!=''):
+        agg_str.append(cur_line)
+        cur_line = f.readline()
+    f.close()
+    return agg_str
+
+def random_swap(obj0,obj1):
+    if(np.random.rand() < .5):
+        return (obj0,obj1,0)
+    else:
+        return (obj1,obj0,1)
+
+def voc_ap(rec, prec, use_07_metric=False):
+    """ ap = voc_ap(rec, prec, [use_07_metric])
+    Compute VOC AP given precision and recall.
+    If use_07_metric is true, uses the
+    VOC 07 11 point method (default:False).
+    """
+    if use_07_metric:
+        # 11 point metric
+        ap = 0.
+        for t in np.arange(0., 1.1, 0.1):
+            if np.sum(rec >= t) == 0:
+                p = 0
+            else:
+                p = np.max(prec[rec >= t])
+            ap = ap + p / 11.
+    else:
+        # correct AP calculation
+        # first append sentinel values at the end
+        mrec = np.concatenate(([0.], rec, [1.]))
+        mpre = np.concatenate(([0.], prec, [0.]))
+
+        # compute the precision envelope
+        for i in range(mpre.size - 1, 0, -1):
+            mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
+
+        # to calculate area under PR curve, look for points
+        # where X axis (recall) changes value
+        i = np.where(mrec[1:] != mrec[:-1])[0]
+
+        # and sum (\Delta recall) * prec
+        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
+    return ap
diff --git a/modeling/sf2f/scripts/DScore/util/visualizer.py b/modeling/sf2f/scripts/DScore/util/visualizer.py
new file mode 100644
index 0000000..1509c8a
--- /dev/null
+++ b/modeling/sf2f/scripts/DScore/util/visualizer.py
@@ -0,0 +1,216 @@
+import numpy as np
+import os
+import time
+from . import util
+from . import html
+# from pdb import set_trace as st
+import matplotlib.pyplot as plt
+import math
+# from IPython import embed
+
+def zoom_to_res(img,res=256,order=0,axis=0):
+    # img   3xXxX
+    from scipy.ndimage import zoom
+    zoom_factor = res/img.shape[1]
+    if(axis==0):
+        return zoom(img,[1,zoom_factor,zoom_factor],order=order)
+    elif(axis==2):
+        return zoom(img,[zoom_factor,zoom_factor,1],order=order)
+
+class Visualizer():
+    def __init__(self, opt):
+        # self.opt = opt
+        self.display_id = opt.display_id
+        # self.use_html = opt.is_train and not opt.no_html
+        self.win_size = opt.display_winsize
+        self.name = opt.name
+        self.display_cnt = 0 # display_current_results counter
+        self.display_cnt_high = 0
+        self.use_html = opt.use_html
+
+        if self.display_id > 0:
+            import visdom
+            self.vis = visdom.Visdom(port = opt.display_port)
+
+        self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
+        util.mkdirs([self.web_dir,])
+        if self.use_html:
+            self.img_dir = os.path.join(self.web_dir, 'images')
+            print('create web directory %s...' % self.web_dir)
+            util.mkdirs([self.img_dir,])
+
+    # |visuals|: dictionary of images to display or save
+    def display_current_results(self, visuals, epoch, nrows=None, res=256):
+        if self.display_id > 0: # show images in the browser
+            title = self.name
+            if(nrows is None):
+                nrows = int(math.ceil(len(visuals.items()) / 2.0))
+            images = []
+            idx = 0
+            for label, image_numpy in visuals.items():
+                title += " | " if idx % nrows == 0 else ", "
+                title += label
+                img = image_numpy.transpose([2, 0, 1])
+                img = zoom_to_res(img,res=res,order=0)
+                images.append(img)
+                idx += 1
+            if len(visuals.items()) % 2 != 0:
+                white_image = np.ones_like(image_numpy.transpose([2, 0, 1]))*255
+                white_image = zoom_to_res(white_image,res=res,order=0)
+                images.append(white_image)
+            self.vis.images(images, nrow=nrows, win=self.display_id + 1,
+                            opts=dict(title=title))
+
+        if self.use_html: # save images to a html file
+            for label, image_numpy in visuals.items():
+                img_path = os.path.join(self.img_dir, 'epoch%.3d_cnt%.6d_%s.png' % (epoch, self.display_cnt, label))
+                util.save_image(zoom_to_res(image_numpy, res=res, axis=2), img_path)
+
+            self.display_cnt += 1
+            self.display_cnt_high = np.maximum(self.display_cnt_high, self.display_cnt)
+
+            # update website
+            webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, reflesh=1)
+            for n in range(epoch, 0, -1):
+                webpage.add_header('epoch [%d]' % n)
+                if(n==epoch):
+                    high = self.display_cnt
+                else:
+                    high = self.display_cnt_high
+                for c in range(high-1,-1,-1):
+                    ims = []
+                    txts = []
+                    links = []
+
+                    for label, image_numpy in visuals.items():
+                        img_path = 'epoch%.3d_cnt%.6d_%s.png' % (n, c, label)
+                        ims.append(os.path.join('images',img_path))
+                        txts.append(label)
+                        links.append(os.path.join('images',img_path))
+                    webpage.add_images(ims, txts, links, width=self.win_size)
+            webpage.save()
+
+    # save errors into a directory
+    def plot_current_errors_save(self, epoch, counter_ratio, opt, errors,keys='+ALL',name='loss', to_plot=False):
+        if not hasattr(self, 'plot_data'):
+            self.plot_data = {'X':[],'Y':[], 'legend':list(errors.keys())}
+        self.plot_data['X'].append(epoch + counter_ratio)
+        self.plot_data['Y'].append([errors[k] for k in self.plot_data['legend']])
+
+        # embed()
+        if(keys=='+ALL'):
+            plot_keys = self.plot_data['legend']
+        else:
+            plot_keys = keys
+
+        if(to_plot):
+            (f,ax) = plt.subplots(1,1)
+        for (k,kname) in enumerate(plot_keys):
+            kk = np.where(np.array(self.plot_data['legend'])==kname)[0][0]
+            x = self.plot_data['X']
+            y = np.array(self.plot_data['Y'])[:,kk]
+            if(to_plot):
+                ax.plot(x, y, 'o-', label=kname)
+            np.save(os.path.join(self.web_dir,'%s_x')%kname,x)
+            np.save(os.path.join(self.web_dir,'%s_y')%kname,y)
+
+        if(to_plot):
+            plt.legend(loc=0,fontsize='small')
+            plt.xlabel('epoch')
+            plt.ylabel('Value')
+            f.savefig(os.path.join(self.web_dir,'%s.png'%name))
+            f.clf()
+            plt.close()
+
+    # errors: dictionary of error labels and values
+    def plot_current_errors(self, epoch, counter_ratio, opt, errors):
+        if not hasattr(self, 'plot_data'):
+            self.plot_data = {'X':[],'Y':[], 'legend':list(errors.keys())}
+        self.plot_data['X'].append(epoch + counter_ratio)
+        self.plot_data['Y'].append([errors[k] for k in self.plot_data['legend']])
+        self.vis.line(
+            X=np.stack([np.array(self.plot_data['X'])]*len(self.plot_data['legend']),1),
+            Y=np.array(self.plot_data['Y']),
+            opts={
+                'title': self.name + ' loss over time',
+                'legend': self.plot_data['legend'],
+                'xlabel': 'epoch',
+                'ylabel': 'loss'},
+            win=self.display_id)
+
+    # errors: same format as |errors| of plotCurrentErrors
+    def print_current_errors(self, epoch, i, errors, t, t2=-1, t2o=-1, fid=None):
+        message = '(ep: %d, it: %d, t: %.3f[s], ept: %.2f/%.2f[h]) ' % (epoch, i, t, t2o, t2)
+        message += (', ').join(['%s: %.3f' % (k, v) for k, v in errors.items()])
+
+        print(message)
+        if(fid is not None):
+            fid.write('%s\n'%message)
+
+
+    # save image to the disk
+    def save_images_simple(self, webpage, images, names, in_txts, prefix='', res=256):
+        image_dir = webpage.get_image_dir()
+        ims = []
+        txts = []
+        links = []
+
+        for name, image_numpy, txt in zip(names, images, in_txts):
+            image_name = '%s_%s.png' % (prefix, name)
+            save_path = os.path.join(image_dir, image_name)
+            if(res is not None):
+                util.save_image(zoom_to_res(image_numpy,res=res,axis=2), save_path)
+            else:
+                util.save_image(image_numpy, save_path)
+
+            ims.append(os.path.join(webpage.img_subdir,image_name))
+            # txts.append(name)
+            txts.append(txt)
+            links.append(os.path.join(webpage.img_subdir,image_name))
+        # embed()
+        webpage.add_images(ims, txts, links, width=self.win_size)
+
+    # save image to the disk
+    def save_images(self, webpage, images, names, image_path, title=''):
+        image_dir = webpage.get_image_dir()
+        # short_path = ntpath.basename(image_path)
+        # name = os.path.splitext(short_path)[0]
+        # name = short_path
+        # webpage.add_header('%s, %s' % (name, title))
+        ims = []
+        txts = []
+        links = []
+
+        for label, image_numpy in zip(names, images):
+            image_name = '%s.jpg' % (label,)
+            save_path = os.path.join(image_dir, image_name)
+            util.save_image(image_numpy, save_path)
+
+            ims.append(image_name)
+            txts.append(label)
+            links.append(image_name)
+        webpage.add_images(ims, txts, links, width=self.win_size)
+
+    # save image to the disk
+    # def save_images(self, webpage, visuals, image_path, short=False):
+    #     image_dir = webpage.get_image_dir()
+    #     if short:
+    #         short_path = ntpath.basename(image_path)
+    #         name = os.path.splitext(short_path)[0]
+    #     else:
+    #         name = image_path
+
+    #     webpage.add_header(name)
+    #     ims = []
+    #     txts = []
+    #     links = []
+
+    #     for label, image_numpy in visuals.items():
+    #         image_name = '%s_%s.png' % (name, label)
+    #         save_path = os.path.join(image_dir, image_name)
+    #         util.save_image(image_numpy, save_path)
+
+    #         ims.append(image_name)
+    #         txts.append(label)
+    #         links.append(image_name)
+    #     webpage.add_images(ims, txts, links, width=self.win_size)
diff --git a/modeling/sf2f/scripts/build_demo_set.py b/modeling/sf2f/scripts/build_demo_set.py
new file mode 100644
index 0000000..127a76e
--- /dev/null
+++ b/modeling/sf2f/scripts/build_demo_set.py
@@ -0,0 +1,85 @@
+'''
+Build a demo inference dataset
+'''
+
+
+import os
+import shutil
+import json
+import pandas as pd
+from PIL import Image
+from pydub import AudioSegment
+
+
+DATA_DIR = './data'
+vox2_face_dir = os.path.join(DATA_DIR, 'VoxCeleb', 'vox2', 'masked_faces')
+vox2_wav_dir = os.path.join(DATA_DIR, 'VoxCeleb', 'raw_wav', 'vox2')
+vox2_meta_csv = os.path.join(DATA_DIR, 'VoxCeleb', 'vox2', 'full_vox2_meta.csv')
+vox_split_json = os.path.join(DATA_DIR, 'VoxCeleb', 'split.json')
+demo_dir = os.path.join(DATA_DIR, 'VoxCeleb', 'demo_data')
+
+def main():
+    try:
+        shutil.rmtree(demo_dir)
+    except:
+        pass
+    os.makedirs(demo_dir, exist_ok=True)
+    #print(os.listdir(vox2_face_dir))
+    #print(os.listdir(vox2_wav_dir))
+    with open(vox_split_json) as json_file:
+        split_dict = json.load(json_file)
+    # A list of names in the vox2 test set (in out splition)
+    test_list = split_dict['vox2']['test']
+    # meta_csv
+    meta_df = pd.read_csv(vox2_meta_csv)
+    print(meta_df.head(5))
+    name2id = {}
+    name2set = {}
+    for i in range(len(meta_df)):
+        name = meta_df['Name'][i]
+        id = meta_df['VoxCeleb2ID'][i]
+        split_set = meta_df['Set'][i]
+        name2id[name] = id
+        name2set[name] = split_set
+    #print(name2id)
+    available_names = set(test_list).intersection(
+        set(os.listdir(vox2_face_dir)))
+    available_names = list(available_names)
+    for name in available_names[:50]:
+        id = name2id[name]
+        split_set = name2set[name]
+        face_dir = os.path.join(vox2_face_dir, name)
+        #print(os.listdir(face_dir))
+        wav_dir = os.path.join(vox2_wav_dir, split_set, id)
+        # Avoid PermissionError
+        try:
+            #print(os.listdir(wav_dir))
+            wav_sub_dir_list = os.listdir(wav_dir)
+        except PermissionError:
+            continue
+        save_dir = os.path.join(demo_dir, name)
+        os.makedirs(save_dir, exist_ok=True)
+        for i, jpg in enumerate(os.listdir(face_dir)):
+            face_jpg = os.path.join(face_dir, jpg)
+            img = Image.open(face_jpg)
+            for reso, w in [('low', 64), ('mid', 128), ('high', 256)]:
+                save_path = os.path.join(save_dir, '{}_{}.jpg'.format(i, reso))
+                cur_img = img.resize((w, w))
+                cur_img.save(save_path)
+        # select 2 wavs
+        for j, wav_sub_dir in enumerate(wav_sub_dir_list[:2]):
+            #print(wav_sub_dir)
+            wav_sub_dir = os.path.join(wav_dir, wav_sub_dir)
+            wav_path = os.path.join(wav_sub_dir, os.listdir(wav_sub_dir)[0])
+            wav_save_dir = os.path.join(save_dir, 'audio_{}'.format(j))
+            os.makedirs(wav_save_dir, exist_ok=True)
+            try:
+                track = AudioSegment.from_file(wav_path, 'm4a')
+                wav_save_path = os.path.join(wav_save_dir, '{}.wav'.format(j))
+                file_handle = track.export(wav_save_path, format='wav')
+            except:
+                print("ERROR CONVERTING " + str(wav_path))
+        print(name)
+
+if __name__ == '__main__':
+    main()
diff --git a/modeling/sf2f/scripts/compute_diversity_score.py b/modeling/sf2f/scripts/compute_diversity_score.py
new file mode 100644
index 0000000..1ffda79
--- /dev/null
+++ b/modeling/sf2f/scripts/compute_diversity_score.py
@@ -0,0 +1,28 @@
+from scripts.DScore.models import dist_model as dm
+from scripts.DScore.util import util
+import numpy as np
+from pyprind import prog_bar
+
+def compute_diversity_score(images_A, images_B, use_gpu):
+    assert(type(images_A) == list)
+    assert(type(images_B) == list)
+    assert(len(images_A) == len(images_B))
+
+    img_pair_num = len(images_A)
+
+    ## Initializing the model
+    model = dm.DistModel()
+    model.initialize(model='net-lin', net='alex', use_gpu=use_gpu)
+
+    distance = []
+    for i in prog_bar(range(img_pair_num), title="Calculating Diversity Scores", width=50):
+        img_A = util.im2tensor(images_A[i])
+        img_B = util.im2tensor(images_B[i])
+        dist = model.forward(img_A, img_B)
+        distance.append(dist)
+
+    return np.mean(distance), np.std(distance)
+
+
+if __name__ == '__main__':
+    print("please Call from other file.")
diff --git a/modeling/sf2f/scripts/compute_fid_score.py b/modeling/sf2f/scripts/compute_fid_score.py
new file mode 100644
index 0000000..d7a7212
--- /dev/null
+++ b/modeling/sf2f/scripts/compute_fid_score.py
@@ -0,0 +1,230 @@
+# Code derived from https://github.com/openai/improved-gan/tree/master/inception_score
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os.path
+import sys
+import tarfile
+
+import numpy as np
+from six.moves import urllib
+import tensorflow as tf
+import glob
+import scipy.misc
+from scipy import linalg
+import math
+import sys
+from pyprind import prog_bar
+
+tf.compat.v1.disable_v2_behavior()
+
+
+try:
+    MODEL_DIR = '/mnt/.tensorflow/imagenet'#'/mnt/imagenet'
+    os.listdir(MODEL_DIR)
+except:
+    MODEL_DIR = './.cache/.tensorflow/imagenet'
+DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
+softmax = None
+last_layer = None
+
+config = tf.compat.v1.ConfigProto()
+config.gpu_options.per_process_gpu_memory_fraction = 0.3
+
+
+def inception_forward(images, layer):
+    assert (type(images[0]) == np.ndarray)
+    assert (len(images[0].shape) == 3)
+    assert (np.max(images[0]) > 10)
+    assert (np.min(images[0]) >= 0.0)
+    # bs = 100
+    bs = 1
+    # images = images.transpose(0, 2, 3, 1)
+    #with tf.Session(config=config) as sess:
+    preds = []
+    n_batches = int(math.ceil(float(len(images)) / float(bs)))
+    # for i in range(n_batches):
+    for i in prog_bar(range(n_batches), title="Calculating FID Scores", width=50):
+        # sys.stdout.write(".")
+        # sys.stdout.flush()
+        inps = images[(i * bs):min((i + 1) * bs, len(images))]
+        for inp in inps:
+            pred = fw_sess.run(layer, {'ExpandDims:0': [inp]})
+            preds.append(pred)
+    preds = np.array(preds)
+        # preds = np.concatenate(preds, 0)
+    return preds
+
+
+def get_mean_and_cov(images):
+    before_preds = inception_forward(images, last_layer)
+    m = np.mean(before_preds, 0)
+    cov = np.cov(before_preds, rowvar=False)
+    return m, cov
+
+
+def get_fid(images, ref_stats=None, images_ref=None, splits=10):
+    before_preds = inception_forward(images, last_layer)
+    if ref_stats is None:
+        if images_ref is None:
+            raise ValueError('images_ref should be provided if ref_stats is None')
+        m_ref, cov_ref = get_mean_and_cov(images_ref)
+    fids = []
+    for i in range(splits):
+        part = before_preds[(i * before_preds.shape[0] // splits):((i + 1) * before_preds.shape[0] // splits), :]
+        m_gen = np.mean(part, 0)
+        cov_gen = np.cov(part, rowvar=False)
+        fid = np.sum((m_ref - m_gen) ** 2) + np.trace(
+            cov_ref + cov_gen - 2 * scipy.linalg.sqrtm(np.dot(cov_ref, cov_gen)))
+        fids.append(fid)
+    return np.mean(fids), np.std(fids)
+
+
+# Call this function with list of images. Each of elements should be a
+# numpy array with values ranging from 0 to 255.
+def get_inception_score(images, splits=10):
+    preds = inception_forward(images, softmax)
+    scores = []
+    for i in range(splits):
+        part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :]
+        kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
+        kl = np.mean(np.sum(kl, 1))
+        scores.append(np.exp(kl))
+    return np.mean(scores), np.std(scores)
+
+
+def get_inception_pred(images):
+    preds = inception_forward(images, softmax)
+    return preds
+
+
+def get_fid_pred(images):
+    preds = inception_forward(images, last_layer)
+    return preds
+
+def close_sess():
+    fw_sess.close()
+
+
+# This function is called automatically.
+def _init_inception():
+    global softmax
+    global last_layer
+    if not os.path.exists(MODEL_DIR):
+        os.makedirs(MODEL_DIR)
+    filename = DATA_URL.split('/')[-1]
+    filepath = os.path.join(MODEL_DIR, filename)
+    if not os.path.exists(filepath):
+        def _progress(count, block_size, total_size):
+            sys.stdout.write('\r>> Downloading %s %.1f%%' % (
+                filename, float(count * block_size) / float(total_size) * 100.0))
+            sys.stdout.flush()
+
+        filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
+        print()
+        statinfo = os.stat(filepath)
+        print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
+    tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR)
+    with tf.io.gfile.GFile(os.path.join(
+            MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:
+        graph_def = tf.compat.v1.GraphDef()
+        graph_def.ParseFromString(f.read())
+        _ = tf.import_graph_def(graph_def, name='')
+    # Works with an arbitrary minibatch size.
+    with tf.compat.v1.Session(config=config) as sess:
+        pool3 = sess.graph.get_tensor_by_name('pool_3:0')
+        ops = pool3.graph.get_operations()
+        for op_idx, op in enumerate(ops):
+            for o in op.outputs:
+                shape = o.get_shape()
+                shape = [s.value for s in shape]
+                new_shape = []
+                for j, s in enumerate(shape):
+                    if s == 1 and j == 0:
+                        new_shape.append(None)
+                    else:
+                        new_shape.append(s)
+                # o.set_shape(tf.TensorShape(new_shape))
+                o.set_shape = tf.TensorShape(new_shape)
+        w = sess.graph.get_operation_by_name("softmax/logits/MatMul").inputs[1]
+        last_layer = tf.squeeze(pool3)
+        logits = tf.matmul(tf.expand_dims(last_layer, 0), w)
+        softmax = tf.nn.softmax(logits[0])
+
+    global fw_sess
+    fw_sess = tf.compat.v1.Session(config=config)
+
+
+if softmax is None:
+    _init_inception()
+
+
+
+def calculate_activation_statistics(act):
+    """Calculation of the statistics used by the FID.
+    Params:
+    -- act      : Numpy array of dimension (n_images, dim (e.g. 2048)).
+    Returns:
+    -- mu    : The mean over samples of the activations of the pool_3 layer of
+               the inception model.
+    -- sigma : The covariance matrix of the activations of the pool_3 layer of
+               the inception model.
+    """
+    mu = np.mean(act, axis=0)
+    sigma = np.cov(act, rowvar=False)
+    return mu, sigma
+
+
+def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
+    """Numpy implementation of the Frechet Distance.
+    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
+    and X_2 ~ N(mu_2, C_2) is
+            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
+    Stable version by Dougal J. Sutherland.
+    Params:
+    -- mu1   : Numpy array containing the activations of a layer of the
+               inception net (like returned by the function 'get_predictions')
+               for generated samples.
+    -- mu2   : The sample mean over activations, precalculated on an
+               representive data set.
+    -- sigma1: The covariance matrix over activations for generated samples.
+    -- sigma2: The covariance matrix over activations, precalculated on an
+               representive data set.
+    Returns:
+    --   : The Frechet Distance.
+    """
+
+    mu1 = np.atleast_1d(mu1)
+    mu2 = np.atleast_1d(mu2)
+
+    sigma1 = np.atleast_2d(sigma1)
+    sigma2 = np.atleast_2d(sigma2)
+
+    assert mu1.shape == mu2.shape, \
+        'Training and test mean vectors have different lengths'
+    assert sigma1.shape == sigma2.shape, \
+        'Training and test covariances have different dimensions'
+
+    diff = mu1 - mu2
+
+    # Product might be almost singular
+    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+    if not np.isfinite(covmean).all():
+        msg = ('fid calculation produces singular product; '
+               'adding %s to diagonal of cov estimates') % eps
+        print(msg)
+        offset = np.eye(sigma1.shape[0]) * eps
+        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+    # Numerical error might give slight imaginary component
+    if np.iscomplexobj(covmean):
+        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+            m = np.max(np.abs(covmean.imag))
+            raise ValueError('Imaginary component {}'.format(m))
+        covmean = covmean.real
+
+    tr_covmean = np.trace(covmean)
+
+    return (diff.dot(diff) + np.trace(sigma1) +
+            np.trace(sigma2) - 2 * tr_covmean)
diff --git a/modeling/sf2f/scripts/compute_inception_score.py b/modeling/sf2f/scripts/compute_inception_score.py
new file mode 100644
index 0000000..13f9738
--- /dev/null
+++ b/modeling/sf2f/scripts/compute_inception_score.py
@@ -0,0 +1,229 @@
+# Code adapted from
+# https://github.com/openai/improved-gan/blob/master/inception_score/model.py
+# which was in turn derived from
+# tensorflow/tensorflow/models/image/imagenet/classify_image.py
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import os
+import sys
+import tarfile
+import h5py
+
+import numpy as np
+from six.moves import urllib
+import tensorflow as tf
+import glob
+# from scipy.misc import imread, imresize
+from cv2 import resize as imresize
+from imageio import imread
+
+from imageio import imwrite
+import math
+from pyprind import prog_bar
+
+tf.compat.v1.disable_v2_behavior()
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--input_npy_file', default=None)
+parser.add_argument('--input_image_dir', default=None)
+parser.add_argument('--input_image_dir_list', default=None)
+parser.add_argument('--input_image_superdir', default=None)
+parser.add_argument('--image_size', default=None, type=int)
+
+# Most papers use 50k samples and 10 splits but I don't have that much
+# data so I'll use 3 splits for everything
+parser.add_argument('--num_splits', default=3, type=int)
+parser.add_argument('--tensor_layout', default='NHWC', choices=['NHWC', 'NCHW'])
+
+IMAGE_EXTS = ['.png', '.jpg', '.jpeg']
+
+def main(args):
+    got_npy_file = args.input_npy_file is not None
+    got_image_dir = args.input_image_dir is not None
+    got_image_dir_list = args.input_image_dir_list is not None
+    got_image_superdir = args.input_image_superdir is not None
+    inputs = [got_npy_file, got_image_dir, got_image_dir_list, got_image_superdir]
+    if sum(inputs) != 1:
+        # raise ValueError('Must give exactly one input type')
+        images = read_images()
+        mean, std = get_inception_score(images)
+        print('Inception mean: ', mean)
+        print('Inception std: ', std)
+
+
+    if args.input_npy_file is not None:
+        images = np.load(args.input_npy_file)
+        images = np.split(image, images.shape[0], axis=0)
+        images = [img[0] for img in images]
+        mean, std = get_inception_score(args, images)
+        print('Inception mean: ', mean)
+        print('Inception std: ', std)
+    elif args.input_image_dir is not None:
+        images = load_images(args, args.input_image_dir)
+        import pdb; pdb.set_trace()
+        print(type(images))
+        import pdb; pdb.set_trace()
+        exit()
+        mean, std = get_inception_score(args, images)
+        print('Inception mean: ', mean)
+        print('Inception std: ', std)
+    elif got_image_dir_list:
+        with open(args.input_image_dir_list, 'r') as f:
+            dir_list = [line.strip() for line in f]
+        for image_dir in dir_list:
+            images = load_images(args, image_dir)
+            mean, std = get_inception_score(args, images)
+            print('Inception mean: ', mean)
+            print('Inception std: ', std)
+            print()
+    elif got_image_superdir:
+        for fn in sorted(os.listdir(args.input_image_superdir)):
+            print(fn)
+            if not fn.startswith('result'): continue
+            image_dir = os.path.join(args.input_image_superdir, fn, 'images')
+            if not os.path.isdir(image_dir): continue
+            images = load_images(args, image_dir)
+            mean, std = get_inception_score(args, images)
+            print('Inception mean: ', mean)
+            print('Inception std: ', std)
+            print()
+
+def load_images(args, image_dir):
+    print('Loading images from ', image_dir)
+    images = []
+    args.tensor_layout = 'NHWC'
+    for fn in os.listdir(image_dir):
+        ext = os.path.splitext(fn)[1].lower()
+        if ext not in IMAGE_EXTS:
+            continue
+        img_path = os.path.join(image_dir, fn)
+        img = imread(img_path)
+        if args.image_size is not None:
+            img = imresize(img, (args.image_size, args.image_size))
+        images.append(img)
+    print('Found %d images' % len(images))
+    return images
+
+def read_images():
+    img_dir = './data/vg/images/'
+    vg_dataset = []
+    print("Begin to load test...")
+    test_file = "./data/visual_genome/val.h5"
+    test_set = h5py.File(test_file, 'r')
+    test_set_name = np.array(test_set['image_paths'])
+    for iter, img_name in enumerate(test_set_name):
+        img_path = os.path.join(img_dir, img_name)
+        img = imread(img_path)
+        if len(img.shape) != 3:
+            continue
+        vg_dataset.append(img)
+    print('Found %d images' % len(vg_dataset))
+    return vg_dataset
+try:
+    MODEL_DIR = '/mnt/.tensorflow/imagenet'#'/mnt/imagenet'
+    os.listdir(MODEL_DIR)
+except:
+    MODEL_DIR = './.cache/.tensorflow/imagenet'
+DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
+softmax = None
+
+# Call this function with list of images. Each of elements should be a
+# numpy array with values ranging from 0 to 255.
+def get_inception_score(images):
+    splits = 3
+    layout = 'NHWC'
+
+    assert(type(images) == list)
+    assert(type(images[0]) == np.ndarray)
+    assert(len(images[0].shape) == 3)
+    # assert(np.max(images[0]) > 10)
+    # assert(np.min(images[0]) >= 0.0)
+    inps = []
+    for img in images:
+        img = img.astype(np.float32)
+        inps.append(np.expand_dims(img, 0))
+    bs = 1
+    # bs = 100
+    with tf.compat.v1.Session() as sess:
+        preds = []
+        n_batches = int(math.ceil(float(len(inps)) / float(bs)))
+        n_preds = 0
+        for i in prog_bar(range(n_batches), title="Calculating Inception Scores", width=50):
+            # import pdb; pdb.set_trace()
+            inp = inps[(i * bs):min((i + 1) * bs, len(inps))]
+
+            inp = np.concatenate(inp, 0)
+            if layout == 'NCHW':
+                inp = inp.transpose(0, 2, 3, 1)
+            # print(inp.shape)
+            pred = sess.run(softmax, {'ExpandDims:0': inp})
+            preds.append(pred)
+            n_preds += pred.shape[0]
+            # print('Ran %d / %d images' % (n_preds, len(images)))
+            # import pdb; pdb.set_trace()
+        preds = np.concatenate(preds, 0)
+        scores = []
+        for i in range(splits):
+            part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :]
+            kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
+            kl = np.mean(np.sum(kl, 1))
+            scores.append(np.exp(kl))
+        return np.mean(scores), np.std(scores)
+
+# This function is called automatically.
+def _init_inception():
+    global softmax
+    if not os.path.exists(MODEL_DIR):
+        os.makedirs(MODEL_DIR)
+    filename = DATA_URL.split('/')[-1]
+    filepath = os.path.join(MODEL_DIR, filename)
+    if not os.path.exists(filepath):
+        def _progress(count, block_size, total_size):
+            sys.stdout.write('\r>> Downloading %s %.1f%%' % (
+                filename, float(count * block_size) / float(total_size) * 100.0))
+            sys.stdout.flush()
+        filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
+        print()
+        statinfo = os.stat(filepath)
+        print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
+    tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR)
+    with tf.io.gfile.GFile(os.path.join(
+        MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:
+        #
+        graph_def = tf.compat.v1.GraphDef()
+        graph_def.ParseFromString(f.read())
+        _ = tf.import_graph_def(graph_def, name='')
+    # Works with an arbitrary minibatch size.
+    with tf.compat.v1.Session() as sess:
+        pool3 = sess.graph.get_tensor_by_name('pool_3:0')
+        ops = pool3.graph.get_operations()
+        for op_idx, op in enumerate(ops):
+            for o in op.outputs:
+                shape = o.get_shape()
+                shape = [s.value for s in shape]
+                new_shape = []
+                for j, s in enumerate(shape):
+                    if s == 1 and j == 0:
+                        new_shape.append(None)
+                    else:
+                        new_shape.append(s)
+                # o._shape = tf.TensorShape(new_shape)
+                o.set_shape = tf.TensorShape(new_shape)
+        w = sess.graph.get_operation_by_name("softmax/logits/MatMul").inputs[1]
+        pool3 = tf.squeeze(pool3)
+        pool3 = tf.reshape(pool3, (1, 2048))
+        # logits = tf.matmul(tf.squeeze(pool3), w)
+        logits = tf.matmul(pool3, w)
+        softmax = tf.nn.softmax(logits)
+
+if softmax is None:
+    _init_inception()
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    main(args)
diff --git a/modeling/sf2f/scripts/compute_mel_mean_var.py b/modeling/sf2f/scripts/compute_mel_mean_var.py
new file mode 100644
index 0000000..d730e17
--- /dev/null
+++ b/modeling/sf2f/scripts/compute_mel_mean_var.py
@@ -0,0 +1,55 @@
+# -*- coding: utf-8 -*-
+'''
+Compute the mean and variance of VoxCeleb dataset's mel spectrograms
+'''
+
+
+import os
+import numpy as np
+import sys
+import torch
+from torch.utils.data import DataLoader
+sys.path.append('./')
+print(sys.path)
+from datasets.vox_dataset import VoxDataset
+
+
+def mean():
+    epoch_num = 100
+    vox_dataset = VoxDataset(
+        data_dir='./data/VoxCeleb',
+        image_size=(64, 64),
+        image_normalize_method=None,
+        mel_normalize_method=None)
+    loader_kwargs = {
+        'batch_size': 128,
+        'num_workers': 8,
+        'shuffle': False,
+        "drop_last": True,
+    }
+    print(vox_dataset)
+    vox_loader = DataLoader(vox_dataset, **loader_kwargs)
+
+    total_mean = torch.tensor(0.0)
+    total_std = torch.tensor(0.0)
+    min_length = 10000000
+    for epoch in range(epoch_num):
+        for iter, batch in enumerate(vox_loader):
+            #images, log_mels, mel_length = batch
+            images, log_mels = batch
+            total_mean = total_mean + torch.mean(log_mels)
+            total_std = total_std + torch.std(log_mels)
+            #cur_min_length = torch.min(mel_length)
+            #if cur_min_length < min_length:
+            #    min_length = cur_min_length
+        print(epoch)
+
+    avg_mean = (total_mean / epoch_num) / (iter + 1)
+    avg_std = (total_std / epoch_num) / (iter + 1)
+
+    print('Dataset Mel Spectrogram Mean:', avg_mean)
+    print('Dataset Mel Spectrogram Standard Deviation:', avg_std)
+    print('Dataset minimum mel spectrogram length:', min_length)
+
+if __name__ == '__main__':
+    mean()
diff --git a/modeling/sf2f/scripts/compute_vggface_score.py b/modeling/sf2f/scripts/compute_vggface_score.py
new file mode 100644
index 0000000..d6fbd24
--- /dev/null
+++ b/modeling/sf2f/scripts/compute_vggface_score.py
@@ -0,0 +1,291 @@
+import argparse
+import os
+import sys
+import torch
+import torch.nn as nn
+import math
+import pickle
+import numpy as np
+import torch.nn.functional as F
+import requests
+from requests.adapters import HTTPAdapter
+
+
+__all__ = ['ResNet', 'resnet50']
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, num_classes=1000, include_top=True):
+        self.inplanes = 64
+        super(ResNet, self).__init__()
+        self.include_top = include_top
+
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0, ceil_mode=True)
+
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AvgPool2d(7, stride=1)
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+
+        if not self.include_top:
+            return x
+
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+
+    def resnet50(**kwargs):
+        """Constructs a ResNet-50 model.
+        """
+        model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
+        return model
+
+
+def load_state_dict(model, fname):
+    """
+    Set parameters converted from Caffe models authors of VGGFace2 provide.
+    See https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/.
+    Arguments:
+        model: model
+        fname: file name of parameters converted from a Caffe model, assuming the file format is Pickle.
+    """
+    with open(fname, 'rb') as f:
+        weights = pickle.load(f, encoding='latin1')
+
+    own_state = model.state_dict()
+    for name, param in weights.items():
+        if name in own_state:
+            try:
+                own_state[name].copy_(torch.from_numpy(param))
+            except Exception:
+                raise RuntimeError('While copying the parameter named {}, whose dimensions in the model are {} and whose '\
+                                   'dimensions in the checkpoint are {}.'.format(name, own_state[name].size(), param.size()))
+        else:
+            raise KeyError('unexpected key "{}" in state_dict'.format(name))
+
+
+def transform(img):
+    img = img[:, :, ::-1]  # RGB -> BGR
+    img = img.astype(np.float32)
+    img -= self.mean_bgr
+    img = img.transpose(2, 0, 1)  # C x H x W
+    img = torch.from_numpy(img).float()
+    return img
+
+def download_weight_for_small_file(weight_file):
+    '''
+    Not working for large files
+    '''
+    weight_url = 'https://drive.google.com/uc?export=download' + \
+        '&id=1A94PAAnwk6L7hXdBXLFosB_s0SzEhAFU'
+    if not os.path.exists(weight_file):
+        print('Downloading VGGFace resnet50_ft_weight.pkl (1/1)')
+        s = requests.Session()
+        s.mount('https://', HTTPAdapter(max_retries=10))
+        r = s.get(weight_url, allow_redirects=True)
+        with open(weight_file, 'wb') as f:
+            f.write(r.content)
+
+def download_weight(weight_file):
+    '''
+    Reference:
+        https://medium.com/@acpanjan/
+            download-google-drive-files-using-wget-3c2c025a8b99
+    '''
+    if not os.path.exists(weight_file):
+        cmd = "bash scripts/download_vggface_weights.sh"
+        print(cmd)
+        os.system(cmd)
+
+def get_vggface_score(imgs):
+    N_IDENTITY = 8631
+    # include_top = True if args.cmd != 'extract' else False
+    include_top = True
+    weight_file = 'sf2f/scripts/weights/resnet50_ft_weight.pkl'
+    # mean_bgr = np.array([91.4953, 103.8827, 131.0912])  # from resnet50_ft.prototxt
+    model = ResNet.resnet50(num_classes=N_IDENTITY, include_top=include_top)
+    download_weight(weight_file)
+    load_state_dict(model, weight_file)
+    model = model.cuda()
+    model.eval()
+    mean_rgb = np.array([131.0912, 103.8827, 91.4953])
+    # mean_rgb = torch.Tensor([131.0912, 103.8827, 91.4953]).cuda()
+    preds = []
+    for img in imgs:
+        # img = img.permute(1, 2, 0) - mean_rgb
+        img = img - mean_rgb
+        img = torch.Tensor(img).cuda().permute(2, 0, 1)
+        img = nn.UpsamplingBilinear2d(size=(224, 224))(img.view(1, 3, img.size(1), img.size(2)))
+        pred = model(img)
+        pred = F.softmax(pred, dim=1)
+        preds.append(pred.data.cpu().numpy())
+    preds = np.concatenate(preds, 0)
+    # preds = model(imgs)
+
+    np.random.shuffle(preds)
+    splits = 5
+    scores = []
+    for i in range(splits):
+        part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :]
+        kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
+        kl = np.mean(np.sum(kl, 1))
+        scores.append(np.exp(kl))
+
+    return np.mean(scores), np.std(scores)
+
+
+def get_vggface_act(imgs):
+    N_IDENTITY = 8631
+    # include_top = True if args.cmd != 'extract' else False
+    include_top = True
+    weight_file = 'sf2f/scripts/weights/resnet50_ft_weight.pkl'
+    # mean_bgr = np.array([91.4953, 103.8827, 131.0912])  # from resnet50_ft.prototxt
+    model = ResNet.resnet50(num_classes=N_IDENTITY, include_top=include_top)
+    load_state_dict(model, weight_file)
+    model = model.cuda()
+    model.eval()
+    mean_rgb = np.array([131.0912, 103.8827, 91.4953])
+    # mean_rgb = torch.Tensor([131.0912, 103.8827, 91.4953]).cuda()
+    preds = []
+    for img in imgs:
+        # img = img.permute(1, 2, 0) - mean_rgb
+        img = img - mean_rgb
+        img = torch.Tensor(img).cuda().permute(2, 0, 1)
+        img = nn.UpsamplingBilinear2d(size=(224, 224))(img.view(1, 3, img.size(1), img.size(2)))
+        pred = model(img)
+        pred = F.softmax(pred, dim=1)
+        preds.append(pred.data.cpu().numpy())
+    preds = np.concatenate(preds, 0)
+    return preds
+
+
+def main():
+    N_IDENTITY = 8631
+    # include_top = True if args.cmd != 'extract' else False
+    include_top = False
+    weight_file = './weights/resnet50_ft_weights.pkl'
+    model = ResNet.resnet50(num_classes=N_IDENTITY, include_top=include_top)
+    load_state_dict(model, weight_file)
+    model = model.cuda()
+
+if __name__ == '__main__':
+    main()
diff --git a/modeling/sf2f/scripts/convert_wav_to_mel.py b/modeling/sf2f/scripts/convert_wav_to_mel.py
new file mode 100644
index 0000000..ad2796f
--- /dev/null
+++ b/modeling/sf2f/scripts/convert_wav_to_mel.py
@@ -0,0 +1,216 @@
+'''
+Convert raw_wavs in VoxCeleb dataSet to mel_gram
+
+    1. Build two id2name mapping for vox1 & vox2
+    2. Create a folder for each identity with his/her name
+    3. for each raw wav file, convert it to mel_gram,
+        save it under the identity folder
+'''
+
+
+import warnings
+warnings.filterwarnings("ignore")
+import argparse
+import os
+import shutil
+import gc
+import time
+import pickle
+import numpy as np
+import pandas as pd
+import multiprocessing as mp
+from tensorflow.io import gfile
+import sys
+sys.path.append('./')
+from utils.wav2mel import wav_to_mel
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from utils.filter_pickle import filtering_pickle
+
+
+VOX_DIR = os.path.join('./data/VoxCeleb')
+vox1_raw = os.path.join(VOX_DIR, 'raw_wav', 'vox1')
+# vox2_raw = os.path.join(VOX_DIR, 'raw_wav', 'vox2')
+vox1_meta_csv = os.path.join(VOX_DIR, 'vox1', 'vox1_meta.csv')
+# vox2_meta_csv = os.path.join(VOX_DIR, 'vox2', 'full_vox2_meta.csv')
+
+parser = argparse.ArgumentParser()
+
+# arguments:
+#parser.add_argument('--output_folder',
+#    default=os.path.join(PROC_DIR, 'VoxCeleb1', 'faces'),
+#    help='folder that contains the outputs')
+
+args = parser.parse_args()
+
+class WavConvertor:
+    def __init__(self):
+        self.load_metadata()
+        self.get_id2name()
+        self.get_wav_dirs()
+        self.create_output_dirs()
+
+    def load_metadata(self):
+        self.vox1_df = pd.read_csv(vox1_meta_csv, sep='\t')
+        # self.vox2_df = pd.read_csv(vox2_meta_csv, sep=',')
+        #print(self.vox1_df.head())
+        #print(self.vox1_df.columns)
+        #print(self.vox2_df.head())
+        #print(self.vox2_df.columns)
+
+    def get_id2name(self):
+        self.vox1_id2name = dict(
+            zip(self.vox1_df['VoxCeleb1 ID'], self.vox1_df['VGGFace1 ID']))
+        # self.vox2_id2name = dict(
+        #     zip(self.vox2_df['VoxCeleb2ID'], self.vox2_df['Name']))
+        #print((self.vox2_id2name))
+
+    def convert_identity(self, wav_dir, mel_home_dir, dataset):
+        '''
+        Create a mel_gram folder for the given identity
+
+        Arguments:
+            1. wav_dir (str): path to the identity's raw_wav folder
+            2. mel_home_dir (str): path to the identity's raw_wav folder
+            3. dataset (str): 'vox1' or 'vox2'
+        '''
+        spkid = wav_dir.split('/')[-1]
+        # In case the input path ends with '/'
+        if spkid == '':
+            spkid = wav_dir.split('/')[-2]
+        if dataset == 'vox1':
+            name = self.vox1_id2name[spkid]
+        # elif dataset == 'vox2':
+        #     name = self.vox2_id2name[spkid]
+        else:
+            raise ValueError("Invalid dataset argument")
+        #print(name)
+
+        # Create mel_gram directory of the speaker
+        mel_dir = os.path.join(mel_home_dir, name)
+        gfile.mkdir(mel_dir)
+
+        clipids = os.listdir(wav_dir)
+        for clipid in clipids:
+            clip_dir = os.path.join(wav_dir, clipid)
+            wav_files = os.listdir(clip_dir)
+            for wav_file in wav_files:
+                # Read and process the wav
+                wav_path = os.path.join(clip_dir, wav_file)
+                try:
+                    wavid = wav_file.replace('.wav', '').replace('.m4a', '')
+                    pickle_name = "{}_{}_{}.pickle".format(
+                        spkid, clipid, wavid)
+                    pickle_path = os.path.join(
+                        mel_dir, pickle_name)
+                    if os.path.exists(pickle_path):
+                        # Skip if exists
+                        continue
+                    # Vox1 use .wav format, Vox2 use .m4a format
+                    log_mel = wav_to_mel(wav_path)
+                    pickle_dict = {
+                        'LogMel_Features': log_mel,
+                        'spkid': spkid,
+                        'clipid': clipid,
+                        'wavid': wavid
+                    }
+                    pickle.dump(
+                        pickle_dict,
+                        open(pickle_path, "wb")
+                    )
+                except IndexError:
+                    pass
+        gc.collect()
+
+    def get_wav_dirs(self):
+        '''
+        Generate a list containing paths to the wav_dir of all the speakers
+        '''
+        # The original VoxCeleb dataset comes with test set and dev set
+        vox1_dev = os.path.join(vox1_raw, 'dev')
+        vox1_test = os.path.join(vox1_raw, 'test')
+        # vox2_dev = os.path.join(vox2_raw, 'dev')
+        # vox2_test = os.path.join(vox2_raw, 'test')
+        # Get Vox1 wav_dir paths
+        vox1_wav_dirs = []
+        for wav_dir in os.listdir(vox1_dev):
+            vox1_wav_dirs.append(os.path.join(vox1_dev, wav_dir))
+        for wav_dir in os.listdir(vox1_test):
+            vox1_wav_dirs.append(os.path.join(vox1_test, wav_dir))
+        # Get Vox2 wav_dir paths
+        # vox2_wav_dirs = []
+        # for wav_dir in os.listdir(vox2_dev):
+        #     vox2_wav_dirs.append(os.path.join(vox2_dev, wav_dir))
+        # for wav_dir in os.listdir(vox2_test):
+        #     vox2_wav_dirs.append(os.path.join(vox2_test, wav_dir))
+        self.vox1_wav_dirs = vox1_wav_dirs
+        # self.vox2_wav_dirs = vox2_wav_dirs
+        return 0
+
+    def create_output_dirs(self):
+        self.vox1_mel = os.path.join(VOX_DIR, 'vox1', 'mel_spectrograms')
+        # self.vox2_mel = os.path.join(VOX_DIR, 'vox2', 'mel_spectrograms')
+        if not gfile.exists(self.vox1_mel):
+            gfile.mkdir(self.vox1_mel)
+        # gfile.mkdir(self.vox2_mel)
+
+    def _worker(self, job_id, infos):
+            for i, info in enumerate(infos):
+                self.convert_identity(info[0], info[1], info[2])
+                print('job #{} prcess {} {} done'.format(job_id, i, info[0]))
+                shutil.rmtree(info[0]) # remove complete file
+                
+    def convert_wav_to_mel(self, n_jobs=1):
+
+        infos = []
+        for wav_dir in self.vox1_wav_dirs:
+            infos.append((wav_dir, self.vox1_mel, 'vox1'))
+        print(len(infos))
+        # l1 = len(infos)
+        # for wav_dir in self.vox2_wav_dirs:
+        #     infos.append((wav_dir, self.vox2_mel, 'vox2'))
+        # print(len(infos) - l1)
+        
+        n_wav_dirs = len(infos)
+        n_jobs = n_jobs if n_jobs <= n_wav_dirs else n_wav_dirs
+        n_wav_dirs_per_job = n_wav_dirs // n_jobs
+        process_index = []
+        for ii in range(n_jobs):
+            process_index.append([ii*n_wav_dirs_per_job, (ii+1)*n_wav_dirs_per_job])
+        if n_jobs * n_wav_dirs_per_job != n_wav_dirs:
+            process_index[-1][-1] = n_wav_dirs
+        
+        futures = set()
+        with ProcessPoolExecutor() as executor:
+            for job_id in range(n_jobs):
+                # future = executor.submit(_worker, process_index[job_id][0], process_index[job_id][1])
+                future = executor.submit(self._worker, job_id, infos[process_index[job_id][0]:process_index[job_id][1]])
+                futures.add(future)
+                print('submit job {}, {}-{}'.format(job_id, process_index[job_id][0], process_index[job_id][1]))
+            for future in as_completed(futures):
+                pass
+        
+        print("Done.")
+
+
+
+def main():
+    
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--n_jobs', '-n_jobs', type=int, default=5)
+    args = parser.parse_args()
+    
+    wav_convertor = WavConvertor()
+    wav_convertor.convert_wav_to_mel(args.n_jobs)
+    #gfile.mkdir('./data/test')
+    #wav_convertor.convert_identity(
+    #    'data/VoxCeleb/raw_wav/vox1/dev/id10001/',
+    #    './data/test', 'vox1')
+
+    dir_path = './sf2f/data/VoxCeleb/vox1/mel_spectrograms' # wav_convertor.vox1_mel
+    filtering_pickle(dir_path)
+
+
+if __name__ == '__main__':
+    print(os.listdir(VOX_DIR))
+    main()
diff --git a/modeling/sf2f/scripts/create_split_json.py b/modeling/sf2f/scripts/create_split_json.py
new file mode 100644
index 0000000..a90a71a
--- /dev/null
+++ b/modeling/sf2f/scripts/create_split_json.py
@@ -0,0 +1,88 @@
+'''
+This script generates a train/test/val split json for VoxCeleb dataset
+
+vox1_meta.csv could be download from VoxCeleb official website:
+    https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/vox1_meta.csv
+'''
+
+
+import os
+import json
+import pandas as pd
+
+VOX_DIR = os.path.join('./data/VoxCeleb')
+vox1_meta_csv = os.path.join(VOX_DIR, 'vox1', 'vox1_meta.csv')
+# vox2_meta_csv = os.path.join(VOX_DIR, 'vox2', 'full_vox2_meta.csv')
+# output:
+split_json = os.path.join(VOX_DIR, 'split.json')
+
+
+def main():
+    vox1_df = pd.read_csv(vox1_meta_csv, sep='\t')
+    # vox2_df = pd.read_csv(vox2_meta_csv, sep=',')
+
+    print(vox1_df.head())
+    # print(vox2_df.head())
+
+    split_dict = {
+        'vox1': {'train':[], 'val':[], 'test':[]},
+        # 'vox2': {'train':[], 'val':[], 'test':[]}
+        }
+
+    for i in range(len(vox1_df)):
+        name = vox1_df['VGGFace1 ID'].iloc[i]
+        if i % 10 == 8:
+            split_dict['vox1']['test'].append(name)
+        elif i % 10 == 9:
+            split_dict['vox1']['val'].append(name)
+        else:
+            split_dict['vox1']['train'].append(name)
+
+    # for i in range(len(vox2_df)):
+    #     name = vox2_df['Name'].iloc[i]
+    #     if i % 10 == 8:
+    #         split_dict['vox2']['test'].append(name)
+    #     elif i % 10 == 9:
+    #         split_dict['vox2']['val'].append(name)
+    #     else:
+    #         split_dict['vox2']['train'].append(name)
+
+    '''
+    for i in range(len(vox1_df)):
+        name = vox1_df['VGGFace1 ID'].iloc[i]
+        set = vox1_df['Set'].iloc[i]
+        if set == 'test':
+            split_dict['vox1']['test'].append(name)
+        if set == 'dev':
+            if i % 10 == 9:
+                split_dict['vox1']['val'].append(name)
+            else:
+                split_dict['vox1']['train'].append(name)
+
+    for i in range(len(vox2_df)):
+        name = vox2_df['Name'].iloc[i]
+        set = vox2_df['Set'].iloc[i]
+        if set == 'test':
+            split_dict['vox2']['test'].append(name)
+        if set == 'dev':
+            if i % 10 == 9:
+                split_dict['vox2']['val'].append(name)
+            else:
+                split_dict['vox2']['train'].append(name)
+    '''
+
+    print(len(split_dict['vox1']['train']),
+          len(split_dict['vox1']['val']),
+          len(split_dict['vox1']['test']))
+    # print(len(split_dict['vox2']['train']),
+    #       len(split_dict['vox2']['val']),
+    #       len(split_dict['vox2']['test']))
+
+    with open(split_json, 'w') as outfile:
+        json.dump(split_dict, outfile)
+
+    return 0
+
+
+if __name__ == '__main__':
+    main()
diff --git a/modeling/sf2f/scripts/download_vggface_weights.sh b/modeling/sf2f/scripts/download_vggface_weights.sh
new file mode 100644
index 0000000..2d59ada
--- /dev/null
+++ b/modeling/sf2f/scripts/download_vggface_weights.sh
@@ -0,0 +1,4 @@
+mkdir './scripts/weights/'
+wget --load-cookies /tmp/cookies.txt \
+    "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1A94PAAnwk6L7hXdBXLFosB_s0SzEhAFU' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1A94PAAnwk6L7hXdBXLFosB_s0SzEhAFU" \
+        -O 'scripts/weights/resnet50_ft_weight.pkl' && rm -rf /tmp/cookies.txt
\ No newline at end of file
diff --git a/modeling/sf2f/scripts/install_requirements.py b/modeling/sf2f/scripts/install_requirements.py
new file mode 100644
index 0000000..b531402
--- /dev/null
+++ b/modeling/sf2f/scripts/install_requirements.py
@@ -0,0 +1,50 @@
+import os
+from multiprocessing import Process
+
+
+def pip_install(package):
+    cmd = 'pip install {}'.format(package)
+    print(cmd)
+    try:
+        os.system(cmd)
+        return 0
+    except:
+        print('###########################')
+        print('Exception')
+        print('###########################')
+        pip_install(package)
+
+
+def txt2list(txt_path):
+    '''
+    Read a txt file and return a list
+    Arguments:
+        txt_path (str): path to save the text file
+    '''
+    output = []
+    f = open(txt_path, 'r')
+    for line in f.readlines():
+        output.append(line.replace('\n', ''))
+    f.close()
+    print('{} loaded.'.format(txt_path))
+    return output
+
+
+def install_once():
+    packages = txt2list('./requirements.txt')
+    procs = []
+
+    # instantiating process with arguments
+    for package in packages:
+        proc = Process(target=pip_install, args=(package,))
+        procs.append(proc)
+        proc.start()
+
+    # complete the processes
+    for proc in procs:
+        proc.join()
+
+
+if __name__ == "__main__":  # confirms that the code is under main function
+    for i in range(100):
+        install_once()
diff --git a/modeling/sf2f/scripts/print_args.py b/modeling/sf2f/scripts/print_args.py
new file mode 100644
index 0000000..5ccaa26
--- /dev/null
+++ b/modeling/sf2f/scripts/print_args.py
@@ -0,0 +1,22 @@
+"""
+Tiny utility to print the command-line args used for a checkpoint
+"""
+
+
+import argparse
+import torch
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('checkpoint')
+
+
+def main(args):
+    checkpoint = torch.load(args.checkpoint, map_location='cpu')
+    for k, v in checkpoint['args'].items():
+        print(k, v)
+
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    main(args)
diff --git a/modeling/sf2f/scripts/sample_mel_spectrograms.py b/modeling/sf2f/scripts/sample_mel_spectrograms.py
new file mode 100644
index 0000000..deb63a8
--- /dev/null
+++ b/modeling/sf2f/scripts/sample_mel_spectrograms.py
@@ -0,0 +1,52 @@
+'''
+This file samples several mel spectrograms from the VoxCeleb Dataset, and
+    visualize them as png images
+'''
+
+
+import os
+import PIL
+
+import sys
+sys.path.append('./')
+#print(sys.path)
+from datasets import VoxDataset, fast_mel_deprocess_batch
+from utils.visualization.plot import plot_mel_spectrogram #get_np_plot,
+from tensorflow.io.gfile import mkdir
+
+
+VOX_DIR = os.path.join('./data', 'VoxCeleb')
+output_dir = os.path.join('./output', 'sampled_mel_spectrograms')
+mkdir(output_dir)
+
+
+def main():
+    vox_dataset = VoxDataset(
+        data_dir=VOX_DIR,
+        image_size=(64, 64),
+        nframe_range=(300, 600),
+        face_type='masked',
+        image_normalize_method='imagenet',
+        mel_normalize_method='vox_mel',
+        mel_seg_window_stride=(125, 63),
+        split_set='test',
+        split_json=os.path.join(VOX_DIR, 'split.json'))
+
+    log_mels = vox_dataset.get_all_mel_segments_of_id(5)
+
+    log_mels_de = fast_mel_deprocess_batch(log_mels, 'vox_mel')
+
+    for i in range(len(log_mels_de)):
+        log_mel = log_mels_de[i].cpu().detach().numpy()
+        buf = plot_mel_spectrogram(
+            log_mel,
+            colorbar=False,
+            label=False,
+            coordinate=False,
+            remove_boarder=True)
+        log_mel_img = PIL.Image.open(buf)
+        log_mel_img.save(os.path.join(output_dir, '{}.png'.format(i)))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/modeling/sf2f/scripts/strip_checkpoint.py b/modeling/sf2f/scripts/strip_checkpoint.py
new file mode 100644
index 0000000..f91831b
--- /dev/null
+++ b/modeling/sf2f/scripts/strip_checkpoint.py
@@ -0,0 +1,53 @@
+"""
+Checkpoints saved by train.py contain not only model parameters but also
+optimizer states, losses, a history of generated images, and other statistics.
+This information is very useful for development and debugging models, but makes
+the saved checkpoints very large. This utility script strips away all extra
+information from saved checkpoints, keeping only the saved models.
+"""
+
+
+import argparse
+import os
+import torch
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--input_checkpoint', default=None)
+parser.add_argument('--output_checkpoint', default=None)
+parser.add_argument('--input_dir', default=None)
+parser.add_argument('--output_dir', default=None)
+parser.add_argument('--keep_discriminators', type=int, default=1)
+
+
+def main(args):
+    if args.input_checkpoint is not None:
+        handle_checkpoint(args, args.input_checkpoint, args.output_checkpoint)
+    if args.input_dir is not None:
+        handle_dir(args, args.input_dir, args.output_dir)
+
+
+def handle_dir(args, input_dir, output_dir):
+    for fn in os.listdir(input_dir):
+        if not fn.endswith('.pt'):
+            continue
+        input_path = os.path.join(input_dir, fn)
+        output_path = os.path.join(output_dir, fn)
+        handle_checkpoint(args, input_path, output_path)
+
+
+def handle_checkpoint(args, input_path, output_path):
+    input_checkpoint = torch.load(input_path)
+    keep = ['args', 'model_state', 'model_kwargs']
+    if args.keep_discriminators == 1:
+        keep += ['d_img_state', 'd_img_kwargs', 'd_obj_state', 'd_obj_kwargs']
+    output_checkpoint = {}
+    for k, v in input_checkpoint.items():
+        if k in keep:
+            output_checkpoint[k] = v
+    torch.save(output_checkpoint, output_path)
+
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    main(args)
diff --git a/modeling/sf2f/scripts/strip_old_args.py b/modeling/sf2f/scripts/strip_old_args.py
new file mode 100644
index 0000000..578cece
--- /dev/null
+++ b/modeling/sf2f/scripts/strip_old_args.py
@@ -0,0 +1,55 @@
+"""
+This utility script removes deprecated kwargs in checkpoints.
+"""
+
+
+import argparse
+import os
+import torch
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--input_checkpoint', default=None)
+parser.add_argument('--input_dir', default=None)
+
+
+DEPRECATED_KWARGS = {
+    'model_kwargs': [
+        'vec_noise_dim', 'gconv_mode', 'box_anchor', 'decouple_obj_predictions',
+    ],
+}
+
+
+def main(args):
+    got_checkpoint = (args.input_checkpoint is not None)
+    got_dir = (args.input_dir is not None)
+    assert got_checkpoint != got_dir, "Must give exactly one of checkpoint or dir"
+    if got_checkpoint:
+        handle_checkpoint(args.input_checkpoint)
+    elif got_dir:
+        handle_dir(args.input_dir)
+
+
+def handle_dir(dir_path):
+    for fn in os.listdir(dir_path):
+        if not fn.endswith('.pt'):
+            continue
+        checkpoint_path = os.path.join(dir_path, fn)
+        handle_checkpoint(checkpoint_path)
+
+
+def handle_checkpoint(checkpoint_path):
+    print('Stripping old args from checkpoint "%s"' % checkpoint_path)
+    checkpoint = torch.load(checkpoint_path)
+    for group, deprecated in DEPRECATED_KWARGS.items():
+        assert group in checkpoint
+        for k in deprecated:
+            if k in checkpoint[group]:
+                print('Removing key "%s" from "%s"' % (k, group))
+                del checkpoint[group][k]
+    torch.save(checkpoint, checkpoint_path)
+
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    main(args)
diff --git a/modeling/sf2f/scripts/watch_data.py b/modeling/sf2f/scripts/watch_data.py
new file mode 100644
index 0000000..8cd319c
--- /dev/null
+++ b/modeling/sf2f/scripts/watch_data.py
@@ -0,0 +1,111 @@
+import os
+import numpy as np
+import random
+import pickle
+import json
+import PIL
+from PIL import Image
+
+DIR = './data/VoxCeleb/'
+
+
+def main():
+    dir_1 = os.path.join(DIR, 'vox1/masked_faces')
+    names = os.listdir(dir_1)
+    print("Length of names: ", len(names))  # 1187
+    img_num_1 = 0
+    all_W1, all_H1 = 0, 0
+    num1_50, num1_100, num1_200, num1_300, num1_400, num1_600 = 0, 0, 0, 0, 0, 0
+    for i, name in enumerate(names):
+        path = os.path.join(dir_1, name)
+        images = os.listdir(path)
+        for j, image in enumerate(images):
+            image_path = os.path.join(path, image)
+            with open(image_path, 'rb') as f:
+                with PIL.Image.open(f) as image:
+                    WW, HH = image.size
+                    img_num_1 += 1
+                    all_W1 += WW
+                    all_H1 += HH
+                    # print("PIL Image:", np.array(image))
+                    if (WW >= 50 and WW < 100) or (HH >= 50 and HH < 100):
+                        num1_50 += 1
+                    elif (WW >= 100 and WW < 200) or (HH >= 100 and HH < 200):
+                        num1_100 += 1
+                    elif (WW >= 200 and WW < 300) or (HH >= 200 and HH < 300):
+                        num1_200 += 1
+                    elif (WW >= 300 and WW < 400) or (HH >= 300 and HH < 400):
+                        num1_300 += 1
+                    elif (WW >= 400 and WW < 600) or (HH >= 400 and HH < 600):
+                        num1_400 += 1
+                    elif (WW >= 600) or (HH >= 600):
+                        num1_600 += 1
+
+    print("======== Vox1 =========")
+    print("Number of vox1: ", img_num_1)
+    print("Average W: ", all_W1/img_num_1)
+    print("Average H: ", all_H1/img_num_1)
+    print("50~100: ", num1_50)
+    print("100~200: ", num1_100)
+    print("200~300: ", num1_200)
+    print("300~400: ", num1_300)
+    print("400~600: ", num1_400)
+    print("600+: ", num1_600)
+    print("\n")
+
+    dir_2 = os.path.join(DIR, 'vox2/masked_faces')
+    names = os.listdir(dir_2)
+    print("Length of names: ", len(names))  # 2850
+    img_num_2 = 0
+    all_W2, all_H2 = 0, 0
+    num2_50, num2_100, num2_200, num2_300, num2_400, num2_600 = 0, 0, 0, 0, 0, 0
+    for i, name in enumerate(names):
+        path = os.path.join(dir_2, name)
+        images = os.listdir(path)
+        for j, image in enumerate(images):
+            image_path = os.path.join(path, image)
+            with open(image_path, 'rb') as f:
+                with PIL.Image.open(f) as image:
+                    WW, HH = image.size
+                    img_num_2 += 1
+                    all_W2 += WW
+                    all_H2 += HH
+                    # print("PIL Image:", np.array(image))
+                    if (WW >= 50 and WW < 100) or (HH >= 50 and HH < 100):
+                        num2_50 += 1
+                    elif (WW >= 100 and WW < 200) or (HH >= 100 and HH < 200):
+                        num2_100 += 1
+                    elif (WW >= 200 and WW < 300) or (HH >= 200 and HH < 300):
+                        num2_200 += 1
+                    elif (WW >= 300 and WW < 400) or (HH >= 300 and HH < 400):
+                        num2_300 += 1
+                    elif (WW >= 400 and WW < 600) or (HH >= 400 and HH < 600):
+                        num2_400 += 1
+                    elif (WW >= 600) or (HH >= 600):
+                        num2_600 += 1
+
+    print("======== Vox2 =========")
+    print("Number of vox2: ", img_num_2)
+    print("Average W: ", all_W2/img_num_2)
+    print("Average H: ", all_H2/img_num_2)
+    print("50~100: ", num2_50)
+    print("100~200: ", num2_100)
+    print("200~300: ", num2_200)
+    print("300~400: ", num2_300)
+    print("400~600: ", num2_400)
+    print("600+: ", num2_600)
+    print("\n")
+
+    print("======== ALL DATASET ========")
+    print("Number of images: ", img_num_1 + img_num_2)
+    print("Average W: ", (all_W1 + all_W2) / (img_num_1 + img_num_2))
+    print("Average H: ", (all_H1 + all_H2) / (img_num_1 + img_num_2))
+    print("50~100: ", num1_50 + num2_50)
+    print("100~200: ", num1_100 + num2_100)
+    print("200~300: ", num1_200 + num2_200)
+    print("300~400: ", num1_300 + num2_300)
+    print("400~600: ", num1_400 + num2_400)
+    print("600+: ", num1_600 + num2_600)
+
+if __name__ == '__main__':
+    main()
diff --git a/modeling/sf2f/test.py b/modeling/sf2f/test.py
new file mode 100644
index 0000000..d5b63e7
--- /dev/null
+++ b/modeling/sf2f/test.py
@@ -0,0 +1,265 @@
+import os
+import pyprind
+import glog as log
+
+import torch
+from torch.utils.data.dataloader import default_collate
+#from scipy.misc import imresize
+import pandas as pd
+
+from datasets import imagenet_deprocess_batch
+import datasets
+import models
+import models.perceptual
+from options.opts import args, options
+from utils.s2f_evaluator import S2fEvaluator
+
+from scripts.compute_inception_score import get_inception_score
+from scripts.compute_vggface_score import get_vggface_score
+from scripts.compute_fid_score import calculate_activation_statistics, \
+    calculate_frechet_distance
+import scripts.compute_fid_score as inception_score
+
+
+torch.backends.cudnn.benchmark = True
+
+
+VGG_BOX = [0.235, 0.195, 0.765, 0.915]
+def crop_vgg_box(imgs):
+    # with correct cropping & correct processing
+    left, top, right, bottom = VGG_BOX
+    # = [0.235015, 0.19505739, 0.76817876, 0.9154963]
+    N, C, H, W = imgs.shape
+    left = int(left * W)
+    right = int(right * W)
+    top = int(top * H)
+    bottom = int(bottom * H)
+    imgs = imgs[:, :, top:bottom+1, left:right+1]
+    return imgs
+
+def load_model(options, checkpoint_start_from, checkpoint):
+    output_model, output_name = [], []
+    if os.path.isfile(checkpoint_start_from):
+        model, _ = models.build_model(
+            options["generator"],
+            image_size=options["data"]["image_size"],
+            checkpoint_start_from=checkpoint_start_from)
+        output_model.append(model)
+        output_name.append(checkpoint_start_from.split('/')[-1].split('_')[1])
+    else:
+        assert checkpoint
+        for name in checkpoint:
+            if 'epoch' in name:
+                ckpt_name = '%s_model.pt' % name
+            else:
+                ckpt_name = 'best_%s_with_model.pt' % name
+            checkpoint_path = os.path.join(checkpoint_start_from, ckpt_name)
+            model, _ = models.build_model(
+                options["generator"],
+                image_size=options["data"]["image_size"],
+                checkpoint_start_from=checkpoint_path)
+            output_model.append(model)
+            output_name.append(name)
+    return output_model, output_name
+
+def load_s2f(loader, options, extraction_size,
+             hq_emb_dict, face_gen_mode, facenet_return_pooling):
+    output, mode = [], []
+    for fgm in face_gen_mode:
+        evaluator = S2fEvaluator(
+            loader,
+            options,
+            extraction_size=extraction_size,
+            hq_emb_dict=hq_emb_dict,
+            face_gen_mode=fgm,
+            facenet_return_pooling=facenet_return_pooling)
+        output.append(evaluator)
+        mode.append(fgm)
+    return output, mode
+
+def main():
+    global args, options
+    print(args)
+
+    device = torch.device('cuda')
+    float_dtype = torch.cuda.FloatTensor
+    long_dtype = torch.cuda.LongTensor
+
+    log.info("Building loader...")
+    train_loader, val_loader, test_loader = \
+        datasets.build_loaders(options["data"])
+    if args.train_fuser_only:
+        train_loader.collate_fn = default_collate
+        train_loader.dataset.return_mel_segments = True
+        val_loader.collate_fn = default_collate
+        val_loader.dataset.return_mel_segments = True
+        test_loader.collate_fn = default_collate
+        test_loader.dataset.return_mel_segments = True
+
+    normalize_method = options["data"]["data_opts"].get(
+        'normalize_method', 'imagenet')
+
+    log.info("Building Generative Model...")
+    print(options["generator"])
+    model, model_name = load_model(options,
+                                   args.checkpoint_start_from,
+                                   args.checkpoint)
+
+    s2f_val_evaluator, face_gen_mode = load_s2f(test_loader,
+        options,
+        extraction_size=100, # [100,200,300],
+        hq_emb_dict=True,
+        face_gen_mode=args.face_gen_mode,
+        facenet_return_pooling=args.facenet_return_pooling)
+
+    # init the output result
+    result = {}
+    result['Model'] = []
+    result['Name'] = []
+    result['Mode'] = []
+    result['L2'] = []
+    result['L1'] = []
+    result['Cos'] = []
+    result['Human'] = []
+    result['R@1'] = []
+    result['R@2'] = []
+    result['R@5'] = []
+    result['R@10'] = []
+    result['R@20'] = []
+    result['R@50'] = []
+    result['IS mean'] = []
+    result['IS std'] = []
+    result['FID'] = []
+    result['VFS mean'] = []
+    result['VFS std'] = []
+    exp_dir = args.checkpoint_start_from.split('/')[1]
+
+    # Loop the face_gen_mode
+    for i, mode in enumerate(face_gen_mode):
+        # Loop the different model
+        for j, name in enumerate(model_name):
+            # print('segments_fusion', model[j].encoder.segments_fusion)
+            model[j].type(float_dtype)
+            # print(model[j])
+            model[j].eval()
+            model[j].to(device)
+
+            if args.get_faces_from_different_segments:
+                diff_seg_faces_dir = os.path.join(
+                    './output', exp_dir, mode, name, 'diff_seg_faces_dir')
+                s2f_val_evaluator[i].get_faces_from_different_segments(
+                    model[j], diff_seg_faces_dir)
+            val_facenet_L2_dist, val_facenet_L1_dist, val_facenet_cos_sim, \
+                val_recall_tuple, ih_sim = \
+                    s2f_val_evaluator[i].get_metrics(
+                        model[j],
+                        recall_method=args.recall_method,
+                        get_ih_sim=True)
+            val_recall_at_1, val_recall_at_2, val_recall_at_5, \
+                val_recall_at_10, val_recall_at_20, \
+                    val_recall_at_50 = val_recall_tuple
+
+            print('-'*80)
+            print('-'*30, 'Model Type: ', name, mode, '-'*30)
+            print('val L2 Distance: ', val_facenet_L2_dist)
+            print('val L1 Distance: ', val_facenet_L1_dist)
+            print('val Cosine Similarity: ', val_facenet_cos_sim)
+            print('val Inter-human (Cosine) Similarity: ', ih_sim)
+            print('val Recall@1: ', val_recall_at_1)
+            print('val Recall@2: ', val_recall_at_2)
+            print('val Recall@5: ', val_recall_at_5)
+            print('val Recall@10: ', val_recall_at_10)
+            print('val Recall@20: ', val_recall_at_20)
+            print('val Recall@50: ', val_recall_at_50)
+
+            images = []
+            images_gt = []
+            for iter, batch in enumerate(pyprind.prog_bar(test_loader,
+                                          title="[Generating Images]",
+                                          width=50)):
+                ######### unpack the data #########
+                imgs, log_mels, human_ids = batch
+                imgs = imgs.cuda()
+                log_mels = log_mels.type(float_dtype) #cuda()
+                human_ids = human_ids.type(long_dtype)
+                ###################################
+                with torch.no_grad():
+                    model_out = model[j](log_mels)
+                imgs_pred, _ = model_out
+
+                if isinstance(imgs_pred, tuple):
+                    imgs_pred = imgs_pred[-1]
+
+                img = imagenet_deprocess_batch(
+                    imgs_pred, normalize_method=normalize_method)
+                # Crop the face
+                #img = crop_vgg_box(img)
+                for i in range(img.shape[0]):
+                    img_np = img[i].numpy().transpose(1, 2, 0)
+                    images.append(img_np)
+
+                img = imagenet_deprocess_batch(
+                    imgs, normalize_method=normalize_method)
+                # Crop the face
+                #img = crop_vgg_box(img)
+                for i in range(img.shape[0]):
+                    img_np = img[i].numpy().transpose(1, 2, 0)
+                    images_gt.append(img_np)
+
+            log.info("Start to compute Inception Score...")
+            is_mean, is_std = get_inception_score(images)
+            print('Inception Score mean: ', is_mean)
+            print('Inception Score std: ', is_std)
+
+            log.info("Start to compute FID Score...")
+            acts_set = inception_score.get_fid_pred(images_gt)
+            fake_acts_set = inception_score.get_fid_pred(images)
+            real_mu, real_sigma = calculate_activation_statistics(acts_set)
+            fake_mu, fake_sigma = calculate_activation_statistics(fake_acts_set)
+            fid_score = calculate_frechet_distance(
+                real_mu, real_sigma, fake_mu, fake_sigma)
+            print('FID Score: ', fid_score)
+
+            log.info("Start to compute VggFace Score...")
+            vfs_mean, vfs_std = get_vggface_score(images)
+            print('VggFace Score mean: ', vfs_mean)
+            print('VggFace Score std: ', vfs_std)
+
+            # log.info("Start to compute FVFD Score...")
+            # acts_set = get_vggface_act(images_gt)
+            # fake_acts_set = get_vggface_act(images)
+            # real_mu, real_sigma = calculate_activation_statistics(acts_set)
+            # fake_mu, fake_sigma = calculate_activation_statistics(fake_acts_set)
+            # fvfd_score = calculate_frechet_distance(real_mu, real_sigma, fake_mu, fake_sigma)
+            # print('FVFD Score: ', fvfd_score)
+
+            print('-'*80)
+
+            # Save the result
+            result['Model'].append(exp_dir)
+            result['Name'].append(name)
+            result['Mode'].append(mode)
+            result['L2'].append('%.3f' % val_facenet_L2_dist)
+            result['L1'].append('%.3f' % val_facenet_L1_dist)
+            result['Cos'].append('%.3f' % val_facenet_cos_sim)
+            result['Human'].append('%.3f' % ih_sim)
+            result['R@1'].append('%.3f' % val_recall_at_1)
+            result['R@2'].append('%.3f' % val_recall_at_2)
+            result['R@5'].append('%.3f' % val_recall_at_5)
+            result['R@10'].append('%.3f' % val_recall_at_10)
+            result['R@20'].append('%.3f' % val_recall_at_20)
+            result['R@50'].append('%.3f' % val_recall_at_50)
+            result['IS mean'].append('%.3f' % is_mean)
+            result['IS std'].append('%.3f' % is_std)
+            result['FID'].append('%.3f' % fid_score)
+            result['VFS mean'].append('%.3f' % vfs_mean)
+            result['VFS std'].append('%.3f' % vfs_std)
+
+    # Save to Excel
+    result_df = pd.DataFrame(result)
+    sv_path = os.path.join('./output', exp_dir, '%s.xlsx' % exp_dir)
+    result_df.to_excel(sv_path)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/modeling/sf2f/train.py b/modeling/sf2f/train.py
new file mode 100644
index 0000000..f60415b
--- /dev/null
+++ b/modeling/sf2f/train.py
@@ -0,0 +1,853 @@
+import functools
+import os
+import json
+import math
+from collections import defaultdict
+import random
+import time
+import pyprind
+import glog as log
+from shutil import copyfile
+
+import numpy as np
+import torch
+import torch.optim as optim
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data.dataloader import default_collate
+
+from datasets import imagenet_deprocess_batch
+import datasets
+import models
+import models.perceptual
+from utils.losses import get_gan_losses
+from utils import timeit, LossManager
+from options.opts import args, options
+from utils.logger import Logger
+from utils import tensor2im
+from utils.utils import load_my_state_dict
+# losseds need to be modified
+from utils.training_utils import add_loss, check_model, calculate_model_losses
+from utils.training_utils import visualize_sample
+from utils.evaluate import evaluate
+from utils.evaluate_fid import evaluate_fid
+from utils.s2f_evaluator import S2fEvaluator
+torch.backends.cudnn.benchmark = True
+
+
+def main():
+    global args, options
+    print(args)
+    print(options['data'])
+    # Set random seed, deterministic
+    torch.cuda.manual_seed(args.seed)
+    torch.manual_seed(args.seed)
+    np.random.seed(args.seed)
+    random.seed(args.seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    #visualize_attn=options['eval'].get('visualize_attn', False)
+    #print('########### visualize_attn: {} ###########'.format(visualize_attn))
+    float_dtype = torch.cuda.FloatTensor
+    long_dtype = torch.cuda.LongTensor
+    log.info("Building loader...")
+    train_loader, val_loader, test_loader = \
+        datasets.build_loaders(options["data"])
+    # Fuser Logic
+    if args.train_fuser_only:
+        train_loader.collate_fn = default_collate
+        train_loader.dataset.return_mel_segments = True
+        train_loader.dataset.mel_segments_rand_start = True
+        val_loader.collate_fn = default_collate
+        val_loader.dataset.return_mel_segments = True
+        s2f_face_gen_mode = 'naive'
+    else:
+        s2f_face_gen_mode = 'average_facenet_embedding'
+    # End of fuser logic
+    #s2f_val_evaluator = S2fEvaluator(val_loader, options)
+    s2f_val_evaluator = S2fEvaluator(
+        val_loader,
+        options,
+        extraction_size=100, #[100,200,300],
+        hq_emb_dict=True,
+        face_gen_mode=s2f_face_gen_mode)
+
+    # [:len(checkpoint[term_name]['id_classifier.bias'])] class 맞추기
+    # if restore_path is not None and os.path.isfile(restore_path):
+    train_loader.dataset.available_names = train_loader.dataset.available_names[:729]
+   
+    # Get total number of people in training set
+    num_train_id = len(train_loader.dataset)
+    # Initialize softmax neuron value
+    for ac_net in ['identity', 'identity_low', 'identity_mid', 'identity_high']:
+        if options['discriminator'].get(ac_net) is not None:
+            options['discriminator'][ac_net]['num_id'] = num_train_id
+
+    log.info("Building Generative Model...")
+    model, model_kwargs = models.build_model(
+        options["generator"],
+        image_size=options["data"]["image_size"],
+        checkpoint_start_from=args.checkpoint_start_from)
+    model.type(float_dtype)
+    # Fuser logic
+    if args.train_fuser_only:
+        # Hardcode to freeze batchnorm layers
+        model.eval()
+        if args.train_fuser_decoder:
+            model.encoder.train_fuser_only()
+        else:
+            model.train_fuser_only()
+    # End of fuser logic
+    print(model)
+
+    optimizer = torch.optim.Adam(
+            filter(lambda x: x.requires_grad, model.parameters()),
+            lr=args.learning_rate,
+            betas=(args.beta1, 0.999),)
+
+    if (options["optim"]["d_loss_weight"] < 0 or \
+        options["optim"]["d_img_weight"] < 0):
+        # Ignore image discriminator
+        img_discriminator = None
+        d_img_kwargs = {}
+        log.info("Ignoring Image Discriminator.")
+    else:
+        img_discriminator, d_img_kwargs = models.build_img_discriminator(
+            options["discriminator"])
+        log.info("Done Building Image Discriminator.")
+
+    if (options["optim"]["d_loss_weight"] < 0 or \
+        options["optim"]["ac_loss_weight"] < 0):
+        # Ignore AC discriminator
+        ac_discriminator = None
+        ac_img_kwargs = {}
+        log.info("Ignoring Auxilary Classifier Discriminator.")
+    else:
+        ac_discriminator, ac_img_kwargs = models.build_ac_discriminator(
+            options["discriminator"])
+        #print('AC Discriminator:', ac_discriminator)
+        log.info("Done Building Auxilary Classifier Discriminator.")
+
+    if (options["optim"]["d_loss_weight"] < 0 or \
+        options["optim"].get("cond_loss_weight", -1) < 0):
+        # Ignore Conditional discriminator
+        cond_discriminator = None
+        cond_d_kwargs = {}
+        log.info("Ignoring Conditional Discriminator.")
+    else:
+        cond_discriminator, cond_d_kwargs = models.build_cond_discriminator(
+            options["discriminator"])
+        #print('Conditional Discriminator:', cond_discriminator)
+        log.info("Done Building Conditional Discriminator.")
+
+    perceptual_module = None
+    if options["optim"].get("perceptual_loss_weight", -1) > 0:
+        ploss_name = options.get("perceptual", {}).get("arch", "FaceNetLoss")
+        ploss_cos_weight = options["optim"].get("cos_percept_loss_weight", -1)
+        perceptual_module = getattr(
+            models.perceptual,
+            ploss_name)(cos_loss_weight=ploss_cos_weight)
+        log.info("Done Building Perceptual {} Module.".format(ploss_name))
+        if ploss_cos_weight > 0:
+            log.info("Perceptual Cos Loss Weight: {}".format(ploss_cos_weight))
+    else:
+        log.info("Ignoring Perceptual Module.")
+
+    gan_g_loss, gan_d_loss = get_gan_losses(options["optim"]["gan_loss_type"])
+
+    optimizer_d_img = []
+    if img_discriminator is not None:
+        for i in range(len(img_discriminator)):
+            img_discriminator[i].type(float_dtype)
+            img_discriminator[i].train()
+            print(img_discriminator[i])
+            optimizer_d_img.append(torch.optim.Adam(
+                    filter(lambda x: x.requires_grad,
+                           img_discriminator[i].parameters()),
+                    lr=args.learning_rate,
+                    betas=(args.beta1, 0.999),))
+
+    optimizer_d_ac = []
+    if ac_discriminator is not None:
+        for i in range(len(ac_discriminator)):
+            ac_discriminator[i].type(float_dtype)
+            ac_discriminator[i].train()
+            print(ac_discriminator[i])
+            optimizer_d_ac.append(torch.optim.Adam(
+                    filter(lambda x: x.requires_grad,
+                           ac_discriminator[i].parameters()),
+                    lr=args.learning_rate,
+                    betas=(args.beta1, 0.999),))
+
+    optimizer_cond_d = []
+    if cond_discriminator is not None:
+        for i in range(len(cond_discriminator)):
+            cond_discriminator[i].type(float_dtype)
+            cond_discriminator[i].train()
+            print(cond_discriminator[i])
+            optimizer_cond_d.append(torch.optim.Adam(
+                    filter(lambda x: x.requires_grad,
+                           cond_discriminator[i].parameters()),
+                    lr=args.learning_rate,
+                    betas=(args.beta1, 0.999),))
+
+    restore_path = None
+    if args.resume is not None:
+        restore_path = '%s_with_model.pt' % args.checkpoint_name
+        restore_path = os.path.join(
+            options["logs"]["output_dir"], args.resume, restore_path)
+
+    if restore_path is not None and os.path.isfile(restore_path):
+        log.info('Restoring from checkpoint: {}'.format(restore_path))
+        checkpoint = torch.load(restore_path)
+        model.load_state_dict(checkpoint['model_state'])
+        optimizer.load_state_dict(checkpoint['optim_state'])
+
+        if img_discriminator is not None:
+            for i in range(len(img_discriminator)):
+                term_name = 'd_img_state_%d' % i
+                img_discriminator[i].load_state_dict(checkpoint[term_name])
+                term_name = 'd_img_optim_state_%d' % i
+                optimizer_d_img[i].load_state_dict(checkpoint[term_name])
+
+        if ac_discriminator is not None:
+            for i in range(len(ac_discriminator)):
+                term_name = 'd_ac_state_%d' % i
+                ac_discriminator[i].load_state_dict(checkpoint[term_name])
+                term_name = 'd_ac_optim_state_%d' % i
+                optimizer_d_ac[i].load_state_dict(checkpoint[term_name])
+
+        if cond_discriminator is not None:
+            for i in range(len(cond_discriminator)):
+                term_name = 'd_img_state_%d' % i
+                cond_discriminator[i].load_state_dict(checkpoint[term_name])
+                term_name = 'd_img_optim_state_%d' % i
+                optimizer_cond_d[i].load_state_dict(checkpoint[term_name])
+
+        t = checkpoint['counters']['t'] + 1
+        if 0 <= args.eval_mode_after <= t:
+            model.eval()
+        else:
+            model.train()
+        # Reset epoch here later
+        start_epoch = checkpoint['counters']['epoch'] + 1
+        log_path = os.path.join(options["logs"]["output_dir"], args.resume,)
+        lr = checkpoint.get('learning_rate', args.learning_rate)
+        best_inception = checkpoint["counters"].get("best_inception", (0., 0.))
+        best_vfs = checkpoint["counters"].get("best_vfs", (0., 0.))
+        best_recall_1 = checkpoint["counters"].get("best_recall_1", 0.)
+        best_recall_5 = checkpoint["counters"].get("best_recall_5", 0.)
+        best_recall_10 = checkpoint["counters"].get("best_recall_10", 0.)
+        best_cos = checkpoint["counters"].get("best_cos", 0.)
+        best_L1 = checkpoint["counters"].get("best_L1", 100000.0)
+        options = checkpoint.get("options", options)
+    else:
+        t, start_epoch, best_inception, best_vfs = 0, 0, (0., 0.), (0., 0.)
+        best_recall_1, best_recall_5, best_recall_10 = 0.0, 0.0, 0.0
+        best_cos, best_L1 = 0.0, 100000.0
+        lr = args.learning_rate
+        checkpoint = {
+            'args': args.__dict__,
+            'options': options,
+            'model_kwargs': model_kwargs,
+            'd_img_kwargs': d_img_kwargs,
+            'train_losses': defaultdict(list),
+            'checkpoint_ts': [],
+            'train_batch_data': [],
+            'train_samples': [],
+            'train_iou': [],
+            'train_inception': [],
+            'lr': [],
+            'val_batch_data': [],
+            'val_samples': [],
+            'val_losses': defaultdict(list),
+            'val_iou': [],
+            'val_inception': [],
+            'norm_d': [],
+            'norm_g': [],
+            'counters': {
+                't': None,
+                'epoch': None,
+                'best_inception': None,
+                'best_vfs': None,
+                'best_recall_1': None,
+                'best_recall_5': None,
+                'best_recall_10': None,
+                'best_cos': None,
+                'best_L1': None,
+            },
+            'model_state': None,
+            'model_best_state': None,
+            'optim_state': None,
+            'd_img_state': None,
+            'd_img_best_state': None,
+            'd_img_optim_state': None,
+            'd_ac_state': None,
+            'd_ac_optim_state': None,
+        }
+
+        log_path = os.path.join(
+            options["logs"]["output_dir"],
+            options["logs"]["name"] + "-" + time.strftime("%Y%m%d-%H%M%S")
+        )
+
+    ### Fuser Logic
+    if args.pretrained_path is not None and \
+        os.path.isfile(args.pretrained_path):
+        # Load
+        log.info('Loading Pretrained Model: {}'.format(args.pretrained_path))
+        pre_checkpoint = torch.load(args.pretrained_path)
+        #model.load_state_dict(pre_checkpoint['model_state'])
+        load_my_state_dict(model, pre_checkpoint['model_state'])
+        #optimizer.load_state_dict(pre_checkpoint['optim_state'])
+
+        if img_discriminator is not None:
+            for i in range(len(img_discriminator)):
+                term_name = 'd_img_state_%d' % i
+                img_discriminator[i].load_state_dict(pre_checkpoint[term_name])
+                #term_name = 'd_img_optim_state_%d' % i
+                #optimizer_d_img[i].load_state_dict(pre_checkpoint[term_name])
+
+        if ac_discriminator is not None:
+            for i in range(len(ac_discriminator)):
+                term_name = 'd_ac_state_%d' % i
+                ac_discriminator[i].load_state_dict(pre_checkpoint[term_name])
+                #term_name = 'd_ac_optim_state_%d' % i
+                #optimizer_d_ac[i].load_state_dict(pre_checkpoint[term_name])
+    ### End of fuser logic
+    logger = Logger(log_path)
+    log.info("Logging to: {}".format(log_path))
+    # save the current config yaml
+    copyfile(args.path_opts,
+             os.path.join(log_path, options["logs"]["name"] + '.yaml'))
+
+    model = nn.DataParallel(model.cuda())
+    if ac_discriminator is not None:
+        for i in range(len(ac_discriminator)):
+            ac_discriminator[i] = nn.DataParallel(ac_discriminator[i].cuda())
+    if img_discriminator is not None:
+        for i in range(len(img_discriminator)):
+            img_discriminator[i] = nn.DataParallel(img_discriminator[i].cuda())
+    if cond_discriminator is not None:
+        for i in range(len(cond_discriminator)):
+            cond_discriminator[i] = nn.DataParallel(cond_discriminator[i].cuda())
+    perceptual_module = nn.DataParallel(perceptual_module.cuda()) if \
+        perceptual_module else None
+
+    if args.evaluate:
+        assert args.resume is not None
+        if args.evaluate_train:
+            log.info("Evaluting the training set.")
+            train_mean, train_std, train_vfs_mean, train_vfs_std = \
+                evaluate(model, train_loader, options)
+            log.info("Inception score: {} ({})".format(train_mean, train_std))
+            log.info("VggFace score: {} ({})".format(
+                train_vfs_mean, train_vfs_std))
+        log.info("Evaluting the validation set.")
+        val_mean, val_std, vfs_mean, vfs_std = evaluate(
+            model, val_loader, options)
+        log.info("Inception score: {} ({})".format(val_mean, val_std))
+        log.info("VggFace score: {} ({})".format(vfs_mean, vfs_std))
+        fid_score = evaluate_fid(model, val_loader, options)
+        log.info("FID score: {}".format(fid_score))
+        return 0
+
+
+    got_best_IS = True
+    got_best_VFS = True
+    got_best_R1 = True
+    got_best_R5 = True
+    got_best_R10 = True
+    got_best_cos = True
+    got_best_L1 = True
+    others = None
+    for epoch in range(start_epoch, args.epochs):
+        if epoch >= args.eval_mode_after and model.training:
+            log.info('[Epoch {}/{}] switching to eval mode'.format(
+                epoch, args.epochs))
+            model.eval()
+            if epoch == args.eval_mode_after:
+                optimizer = optim.Adam(
+                        filter(lambda x: x.requires_grad, model.parameters()),
+                        lr=lr,
+                        betas=(args.beta1, 0.999),)
+        if epoch >= args.disable_l1_loss_after and \
+            options["optim"]["l1_pixel_loss_weight"] > 1e-10:
+            #
+            log.info('[Epoch {}/{}] Disable L1 Loss'.format(epoch, args.epochs))
+            options["optim"]["l1_pixel_loss_weight"] = 0
+        start_time = time.time()
+        for iter, batch in enumerate(pyprind.prog_bar(
+            train_loader,
+            title="[Epoch {}/{}]".format(epoch, args.epochs),
+            width=50)):
+            # An iteration
+            if args.timing:
+                print("Loading Time: {} ms".format(
+                    (time.time() - start_time) * 1000))
+            t += 1
+            ######### unpack the data #########
+            imgs, log_mels, human_ids = batch
+            imgs = imgs.cuda()
+            log_mels = log_mels.type(float_dtype)
+            human_ids = human_ids.type(long_dtype)
+            ###################################
+            with timeit('forward', args.timing):
+                #model_out = model(imgs)
+                model_out = model(log_mels)
+                """
+                imgs_pred: generated images
+                others: placeholder for other output
+                """
+                imgs_pred, others = model_out
+
+            if t % args.visualize_every == 0:
+                #print('Performing Visualization...')
+                training_status = model.training
+                model.eval()
+                samples = visualize_sample(
+                    model,
+                    imgs,
+                    log_mels,
+                    options["data"]["data_opts"]["image_normalize_method"],
+                    visualize_attn=options['eval'].get('visualize_attn', False))
+                model.train(mode=training_status)
+                logger.image_summary(samples, t, tag="vis")
+
+            with timeit('G_loss', args.timing):
+                #model_boxes = None
+                # Skip the pixel loss if not using GT boxes
+                skip_pixel_loss = False
+
+                # calculate L1 loss between imgs and imgs_self
+                total_loss, losses = calculate_model_losses(
+                    options["optim"], skip_pixel_loss, imgs, imgs_pred,)
+
+                if img_discriminator is not None:
+                    for i in range(len(img_discriminator)):
+                        # TODO: Here we need to choose the two methods:
+                        # 1. determine whether imgs_pred is tuple or not
+                        # 2. change the return (imgs_pred) of model to be tuple
+                        if isinstance(imgs_pred, tuple):
+                            scores_fake = img_discriminator[i](imgs_pred[i])
+                        else:
+                            scores_fake = img_discriminator[i](imgs_pred)
+                        weight = options["optim"]["d_loss_weight"] * \
+                            options["optim"]["d_img_weight"]
+                        loss_name = 'g_gan_img_loss_%d' % i
+                        total_loss = add_loss(total_loss,
+                            gan_g_loss(scores_fake), losses, loss_name, weight)
+
+                if ac_discriminator is not None:
+                    for i in range(len(ac_discriminator)):
+                        if isinstance(imgs_pred, tuple):
+                            scores_fake, ac_loss = ac_discriminator[i](
+                                imgs_pred[i], human_ids)
+                        else:
+                            scores_fake, ac_loss = ac_discriminator[i](
+                                imgs_pred, human_ids)
+                        weight = options["optim"]["d_loss_weight"] * \
+                            options["optim"]["ac_loss_weight"]
+                        loss_name = 'ac_loss_%d' % i
+                        total_loss = add_loss(total_loss,
+                                              ac_loss.mean(),
+                                              losses,
+                                              loss_name,
+                                              weight)
+                        loss_name = 'g_gan_ac_loss_%d' % i
+                        total_loss = add_loss(total_loss,
+                                              gan_g_loss(scores_fake),
+                                              losses,
+                                              loss_name,
+                                              weight)
+
+                if cond_discriminator is not None:
+                    for i in range(len(cond_discriminator)):
+                        # TODO: check whether condition is in others or not ?
+                        cond_vecs = others['cond']
+                        if isinstance(imgs_pred, tuple):
+                            scores_fake = cond_discriminator[i](
+                                imgs_pred[i], cond_vecs)
+                        else:
+                            scores_fake = cond_discriminator[i](
+                                imgs_pred, cond_vecs)
+                        weight = options["optim"]["d_loss_weight"] * \
+                            options["optim"]["cond_loss_weight"]
+                        loss_name = 'g_cond_loss_%d' % i
+                        total_loss = add_loss(total_loss,
+                            gan_g_loss(scores_fake), losses, loss_name, weight)
+
+                if options["optim"].get("perceptual_loss_weight", -1) > 0:
+                    if isinstance(imgs_pred, tuple):
+                        # Multi-Resolution Pixel Loss
+                        for i, img_pred in enumerate(imgs_pred):
+                            loss_name = 'img_perceptual_loss_%d' % i
+                            img = imgs
+                            while img.size()[2] != img_pred.size()[2]:
+                                img = F.interpolate(
+                                    img, scale_factor=0.5, mode='nearest')
+                            if s2f_val_evaluator.do_deprocess_and_preprocess:
+                                img_pred_in = \
+                                    s2f_val_evaluator.deprocess_and_preprocess(
+                                        img_pred)
+                                img_in = \
+                                    s2f_val_evaluator.deprocess_and_preprocess(
+                                        img)
+                            if s2f_val_evaluator.crop_faces:
+                                img_pred_in = \
+                                    s2f_val_evaluator.crop_vgg_box(img_pred_in)
+                                img_in = s2f_val_evaluator.crop_vgg_box(img_in)
+                            perceptual_loss = perceptual_module(
+                                img_pred_in, img_in)
+                            perceptual_loss = perceptual_loss.mean()
+                            weight = options["optim"]["perceptual_loss_weight"]
+                            total_loss = add_loss(total_loss, perceptual_loss,
+                                                  losses, loss_name,
+                                                  weight)
+                    else:
+                        if s2f_val_evaluator.do_deprocess_and_preprocess:
+                            imgs_pred_in = \
+                                s2f_val_evaluator.deprocess_and_preprocess(
+                                    imgs_pred)
+                            imgs_in = \
+                                s2f_val_evaluator.deprocess_and_preprocess(imgs)
+                        if s2f_val_evaluator.crop_faces:
+                            imgs_pred_in = \
+                                s2f_val_evaluator.crop_vgg_box(imgs_pred_in)
+                            imgs_in = s2f_val_evaluator.crop_vgg_box(imgs_in)
+                        perceptual_loss = perceptual_module(
+                            imgs_pred_in, imgs_in)
+                        perceptual_loss = perceptual_loss.mean()
+                        weight = options["optim"]["perceptual_loss_weight"]
+                        total_loss = add_loss(total_loss, perceptual_loss,
+                                              losses, "img_perceptual_loss",
+                                              weight)
+
+            losses['total_loss'] = total_loss.item()
+            if not math.isfinite(losses['total_loss']):
+                log.warn('WARNING: Got loss = NaN, not backpropping')
+                continue
+
+            optimizer.zero_grad()
+            with timeit('backward', args.timing):
+                total_loss.backward()
+            optimizer.step()
+
+            total_loss_d = None
+            ac_loss_real = None
+            ac_loss_fake = None
+            d_losses = {}
+
+            with timeit('D_loss', args.timing):
+                if img_discriminator is not None:
+                    d_img_losses = LossManager()
+                    for i in range(len(img_discriminator)):
+                        if isinstance(imgs_pred, tuple):
+                            imgs_fake = imgs_pred[i].detach()
+                        else:
+                            imgs_fake = imgs_pred.detach()
+                        imgs_real = imgs.detach()
+                        while imgs_real.size()[2] != imgs_fake.size()[2]:
+                            imgs_real = F.interpolate(
+                                imgs_real, scale_factor=0.5, mode='nearest')
+
+                        scores_fake = img_discriminator[i](imgs_fake)
+                        scores_real = img_discriminator[i](imgs_real)
+
+                        d_img_gan_loss = gan_d_loss(scores_real, scores_fake)
+                        d_img_losses.add_loss(
+                            d_img_gan_loss, 'd_img_gan_loss_%d' % i)
+
+                    for i in range(len(img_discriminator)):
+                        optimizer_d_img[i].zero_grad()
+                    d_img_losses.total_loss.backward()
+                    for i in range(len(img_discriminator)):
+                        optimizer_d_img[i].step()
+
+                if ac_discriminator is not None:
+                    d_ac_losses = LossManager()
+                    for i in range(len(ac_discriminator)):
+                        if isinstance(imgs_pred, tuple):
+                            imgs_fake = imgs_pred[i].detach()
+                        else:
+                            imgs_fake = imgs_pred.detach()
+
+                        imgs_real = imgs.detach()
+                        while imgs_real.size()[2] != imgs_fake.size()[2]:
+                            imgs_real = F.interpolate(
+                                imgs_real, scale_factor=0.5, mode='nearest')
+
+                        scores_real, ac_loss_real= ac_discriminator[i](
+                            imgs_real, human_ids)
+                        scores_fake, ac_loss_fake = ac_discriminator[i](
+                            imgs_fake, human_ids)
+
+                        d_ac_gan_loss = gan_d_loss(scores_real, scores_fake)
+                        d_ac_losses.add_loss(
+                            d_ac_gan_loss, 'd_ac_gan_loss_%d' % i)
+                        d_ac_losses.add_loss(
+                            ac_loss_real.mean(), 'd_ac_loss_real_%d' % i)
+
+                    for i in range(len(ac_discriminator)):
+                        optimizer_d_ac[i].zero_grad()
+                    d_ac_losses.total_loss.backward()
+                    for i in range(len(ac_discriminator)):
+                        optimizer_d_ac[i].step()
+
+                if cond_discriminator is not None:
+                    cond_d_losses = LossManager()
+                    for i in range(len(cond_discriminator)):
+                        if isinstance(imgs_pred, tuple):
+                            imgs_fake = imgs_pred[i].detach()
+                        else:
+                            imgs_fake = imgs_pred.detach()
+                        imgs_real = imgs.detach()
+                        cond_vecs = others['cond'].detach()
+                        while imgs_real.size()[2] != imgs_fake.size()[2]:
+                            imgs_real = F.interpolate(
+                                imgs_real, scale_factor=0.5, mode='nearest')
+
+                        scores_fake = cond_discriminator[i](
+                            imgs_fake, cond_vecs)
+                        scores_real = cond_discriminator[i](
+                            imgs_real, cond_vecs)
+
+                        cond_d_gan_loss = gan_d_loss(scores_real, scores_fake)
+                        cond_d_losses.add_loss(
+                            cond_d_gan_loss, 'cond_d_gan_loss_%d' % i)
+
+                    for i in range(len(cond_discriminator)):
+                        optimizer_cond_d[i].zero_grad()
+                    cond_d_losses.total_loss.backward()
+                    for i in range(len(cond_discriminator)):
+                        optimizer_cond_d[i].step()
+
+            # Logging generative model losses
+            for name, val in losses.items():
+                logger.scalar_summary("loss/{}".format(name), val, t)
+            if img_discriminator is not None:
+                for name, val in d_img_losses.items():
+                    logger.scalar_summary("d_loss/{}".format(name), val, t)
+            if ac_discriminator is not None:
+                for name, val in d_ac_losses.items():
+                    logger.scalar_summary("d_loss/{}".format(name), val, t)
+            if cond_discriminator is not None:
+                for name, val in cond_d_losses.items():
+                    logger.scalar_summary("d_loss/{}".format(name), val, t)
+            start_time = time.time()
+
+        if epoch % args.eval_epochs == 0:
+            log.info('[Epoch {}/{}] checking on val'.format(
+                epoch, args.epochs)
+            )
+            val_results = check_model(
+                args, options, epoch, val_loader, model)
+            val_losses, val_samples, val_inception, val_vfs = val_results
+            # call evaluate_s2f_metrics() here
+            val_facenet_L2_dist, val_facenet_L1_dist, val_facenet_cos_sim, \
+                val_recall_tuple, val_ih_sim  = \
+                    s2f_val_evaluator.get_metrics(
+                        model, recall_method='cos_sim', get_ih_sim=True)
+            val_recall_at_1, val_recall_at_2, val_recall_at_5, \
+                val_recall_at_10, val_recall_at_20, \
+                    val_recall_at_50 = val_recall_tuple
+            # Update the best of metrics
+            if val_inception[0] > best_inception[0]:
+                got_best_IS = True
+                best_inception = val_inception
+            if val_vfs[0] > best_vfs[0]:
+                got_best_VFS = True
+                best_vfs = val_vfs
+            if val_recall_at_1 > best_recall_1:
+                got_best_R1 = True
+                best_recall_1 = val_recall_at_1
+            if val_recall_at_5 > best_recall_5:
+                got_best_R5 = True
+                best_recall_5 = val_recall_at_5
+            if val_recall_at_10 > best_recall_10:
+                got_best_R10 = True
+                best_recall_10 = val_recall_at_10
+            if val_facenet_cos_sim > best_cos:
+                got_best_cos = True
+                best_cos = val_facenet_cos_sim
+            if val_facenet_L1_dist < best_L1:
+                got_best_L1 = True
+                best_L1 = val_facenet_L1_dist
+            checkpoint['counters']['best_inception'] = best_inception
+            checkpoint['counters']['best_vfs'] = best_vfs
+            checkpoint['val_samples'].append(val_samples)
+            # checkpoint['val_batch_data'].append(val_batch_data)
+            for k, v in val_losses.items():
+                checkpoint['val_losses'][k].append(v)
+                logger.scalar_summary("ckpt/val_{}".format(k), v, epoch)
+            logger.scalar_summary("ckpt/val_inception", val_inception[0], epoch)
+            logger.scalar_summary("ckpt/val_facenet_L2_dist",
+                val_facenet_L2_dist, epoch)
+            logger.scalar_summary("ckpt/val_facenet_L1_dist",
+                val_facenet_L1_dist, epoch)
+            logger.scalar_summary("ckpt/val_facenet_cos_sim",
+                val_facenet_cos_sim, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_1",
+                val_recall_at_1, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_2",
+                val_recall_at_2, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_5",
+                val_recall_at_5, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_10",
+                val_recall_at_10, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_20",
+                val_recall_at_20, epoch)
+            logger.scalar_summary("ckpt/val_recall_at_50",
+                val_recall_at_50, epoch)
+            logger.scalar_summary("ckpt/val_ih_sim",
+                val_ih_sim, epoch)
+            logger.scalar_summary("ckpt/val_vfs",
+                val_vfs[0], epoch)
+            # Add speech2face metrics here..
+            #
+            logger.image_summary(val_samples, epoch, tag="ckpt_val")
+            # log.info('[Epoch {}/{}] val iou: {}'.format(
+            # epoch, args.epochs, val_avg_iou))
+            log.info('[Epoch {}/{}] val inception score: {} ({})'.format(
+                    epoch, args.epochs, val_inception[0], val_inception[1]))
+            log.info('[Epoch {}/{}] best inception scores: {} ({})'.format(
+                    epoch, args.epochs, best_inception[0], best_inception[1]))
+            log.info('[Epoch {}/{}] val vfs scores: {} ({})'.format(
+                    epoch, args.epochs, val_vfs[0], val_vfs[1]))
+            log.info('[Epoch {}/{}] best vfs scores: {} ({})'.format(
+                    epoch, args.epochs, best_vfs[0], best_vfs[1]))
+            log.info('[Epoch {}/{}] val recall at 5: {}, '.format(
+                     epoch, args.epochs, val_recall_at_5) + \
+                        'best recall at 5: {}'.format(best_recall_5))
+            log.info('[Epoch {}/{}] val recall at 10: {}, '.format(
+                     epoch, args.epochs, val_recall_at_10) + \
+                        'best recall at 10: {}'.format(best_recall_10))
+            log.info('[Epoch {}/{}] val cosine similarity: {}, '.format(
+                     epoch, args.epochs, val_facenet_cos_sim) + \
+                        'best cosine similarity: {}'.format(best_cos))
+            log.info('[Epoch {}/{}] val L1 distance: {}, '.format(
+                     epoch, args.epochs, val_facenet_L1_dist) + \
+                            'best L1 distance: {}'.format(best_L1))
+
+            checkpoint['model_state'] = model.module.state_dict()
+
+            if img_discriminator is not None:
+                for i in range(len(img_discriminator)):
+                    term_name = 'd_img_state_%d' % i
+                    checkpoint[term_name] = \
+                        img_discriminator[i].module.state_dict()
+                    term_name = 'd_img_optim_state_%d' % i
+                    checkpoint[term_name] = \
+                        optimizer_d_img[i].state_dict()
+
+            if ac_discriminator is not None:
+                for i in range(len(ac_discriminator)):
+                    term_name = 'd_ac_state_%d' % i
+                    checkpoint[term_name] = \
+                        ac_discriminator[i].module.state_dict()
+                    term_name = 'd_ac_optim_state_%d' % i
+                    checkpoint[term_name] = \
+                        optimizer_d_ac[i].state_dict()
+
+            if cond_discriminator is not None:
+                for i in range(len(cond_discriminator)):
+                    term_name = 'cond_d_state_%d' % i
+                    checkpoint[term_name] = \
+                        cond_discriminator[i].module.state_dict()
+                    term_name = 'cond_d_optim_state_%d' % i
+                    checkpoint[term_name] = \
+                        optimizer_cond_d[i].state_dict()
+
+            checkpoint['optim_state'] = optimizer.state_dict()
+            checkpoint['counters']['epoch'] = epoch
+            checkpoint['counters']['t'] = t
+            checkpoint['counters']['best_inception'] = best_inception
+            checkpoint['counters']['best_vfs'] = best_vfs
+            checkpoint['counters']['best_recall_1'] = best_recall_1
+            checkpoint['counters']['best_recall_5'] = best_recall_5
+            checkpoint['counters']['best_recall_10'] = best_recall_10
+            checkpoint['counters']['best_cos'] = best_cos
+            checkpoint['counters']['best_L1'] = best_L1
+            checkpoint['lr'] = lr
+            checkpoint_path = os.path.join(
+                log_path,
+                '%s_with_model.pt' % args.checkpoint_name)
+            log.info('[Epoch {}/{}] Saving checkpoint: {}'.format(
+                epoch, args.epochs, checkpoint_path))
+            torch.save(checkpoint, checkpoint_path)
+            if got_best_IS:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_IS_with_model.pt'))
+                got_best_IS = False
+            if got_best_VFS:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_VFS_with_model.pt'))
+                got_best_VFS = False
+            if got_best_R1:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_R1_with_model.pt'))
+                got_best_R1 = False
+            if got_best_R5:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_R5_with_model.pt'))
+                got_best_R5 = False
+            if got_best_R10:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_R10_with_model.pt'))
+                got_best_R10 = False
+            if got_best_L1:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_L1_with_model.pt'))
+                got_best_L1 = False
+            if got_best_cos:
+                copyfile(
+                    checkpoint_path,
+                    os.path.join(log_path, 'best_cos_with_model.pt'))
+                got_best_cos = False
+
+        if epoch > 0 and epoch % 1000 == 0:
+            print('Saving checkpoint for Epoch {}.'.format(epoch))
+            copyfile(
+                checkpoint_path,
+                os.path.join(log_path, 'epoch_{}_model.pt'.format(epoch)))
+        # Fuser Logic
+        elif args.train_fuser_only and epoch > 0 and epoch % 1 == 0:
+            print('Saving checkpoint for Epoch {}.'.format(epoch))
+            copyfile(
+                checkpoint_path,
+                os.path.join(log_path, 'epoch_{}_model.pt'.format(epoch)))
+        # End of fuser logic
+
+        if epoch >= args.decay_lr_epochs:
+            lr_end = args.learning_rate * 1e-3
+            decay_frac = (epoch - args.decay_lr_epochs + 1) / \
+                (args.epochs - args.decay_lr_epochs + 1e-5)
+            lr = args.learning_rate - decay_frac * (args.learning_rate - lr_end)
+            for param_group in optimizer.param_groups:
+                param_group["lr"] = lr
+            if img_discriminator is not None:
+                for i in range(len(optimizer_d_img)):
+                    for param_group in optimizer_d_img[i].param_groups:
+                        param_group["lr"] = lr
+                # for param_group in optimizer_d_img.param_groups:
+                #     param_group["lr"] = lr
+            log.info('[Epoch {}/{}] learning rate: {}'.format(
+                epoch+1, args.epochs, lr))
+
+        logger.scalar_summary("ckpt/learning_rate", lr, epoch)
+
+    # Evaluating after the whole training process.
+    log.info("Evaluting the validation set.")
+    is_mean, is_std, vfs_mean, vfs_std = evaluate(model, val_loader, options)
+    log.info("Inception score: {} ({})".format(is_mean, is_std))
+    log.info("VggFace score: {} ({})".format(vfs_mean, vfs_std))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/modeling/sf2f/utils/__init__.py b/modeling/sf2f/utils/__init__.py
new file mode 100644
index 0000000..55e5f84
--- /dev/null
+++ b/modeling/sf2f/utils/__init__.py
@@ -0,0 +1 @@
+from .common import *
diff --git a/modeling/sf2f/utils/bilinear.py b/modeling/sf2f/utils/bilinear.py
new file mode 100644
index 0000000..7a12100
--- /dev/null
+++ b/modeling/sf2f/utils/bilinear.py
@@ -0,0 +1,296 @@
+import numpy as np
+import torch
+import torch.nn.functional as F
+from .common import timeit
+
+
+"""
+Functions for performing differentiable bilinear cropping of images, for use in
+the object discriminator
+"""
+
+def crop_bbox_batch(feats, bbox, bbox_to_feats, HH, WW=None, backend='cudnn'):
+    """
+    Inputs:
+    - feats: FloatTensor of shape (N, C, H, W)
+    - bbox: FloatTensor of shape (B, 4) giving bounding box coordinates
+    - bbox_to_feats: LongTensor of shape (B,) mapping boxes to feature maps;
+      each element is in the range [0, N) and bbox_to_feats[b] = i means that
+      bbox[b] will be cropped from feats[i].
+    - HH, WW: Size of the output crops
+
+    Returns:
+    - crops: FloatTensor of shape (B, C, HH, WW) where crops[i] uses bbox[i] to
+      crop from feats[bbox_to_feats[i]].
+    """
+    if backend == 'cudnn':
+        return crop_bbox_batch_cudnn(feats, bbox, bbox_to_feats, HH, WW)
+    N, C, H, W = feats.size()
+    B = bbox.size(0)
+    if WW is None:
+        WW = HH
+    dtype, device = feats.dtype, feats.device
+    crops = torch.zeros(B, C, HH, WW, dtype=dtype, device=device)
+    for i in range(N):
+        idx = (bbox_to_feats == i).nonzero()
+        if idx.dim() == 0:
+            continue
+        idx = idx.view(-1)
+        n = idx.size(0)
+        cur_feats = feats[i].view(1, C, H, W).expand(n, C, H, W).contiguous()
+        cur_bbox = bbox[idx]
+        cur_crops = crop_bbox(cur_feats, cur_bbox, HH, WW)
+        crops[idx] = cur_crops
+    return crops
+
+
+def _invperm(p):
+    N = p.size(0)
+    eye = torch.arange(0, N).type_as(p)
+    pp = (eye[:, None] == p).nonzero()[:, 1]
+    return pp
+
+
+def crop_bbox_batch_cudnn(feats, bbox, bbox_to_feats, HH, WW=None):
+    N, C, H, W = feats.size()
+    B = bbox.size(0)
+    if WW is None:
+        WW = HH
+    dtype = feats.type()
+
+    feats_flat, bbox_flat, all_idx = [], [], []
+    for i in range(N):
+        idx = (bbox_to_feats == i).nonzero()
+        if idx.dim() == 0:
+            continue
+        idx = idx.view(-1)
+        n = idx.size(0)
+        cur_feats = feats[i].view(1, C, H, W).expand(n, C, H, W).contiguous()
+        cur_bbox = bbox[idx]
+
+        feats_flat.append(cur_feats)
+        bbox_flat.append(cur_bbox)
+        all_idx.append(idx)
+
+    feats_flat = torch.cat(feats_flat, dim=0)
+    bbox_flat = torch.cat(bbox_flat, dim=0)
+    crops = crop_bbox(feats_flat, bbox_flat, HH, WW, backend='cudnn')
+
+    # If the crops were sequential (all_idx is identity permutation) then we can
+    # simply return them; otherwise we need to permute crops by the inverse
+    # permutation from all_idx.
+    all_idx = torch.cat(all_idx, dim=0)
+    eye = torch.arange(0, B).type_as(all_idx)
+    if (all_idx == eye).all():
+        return crops
+    return crops[_invperm(all_idx)]
+
+
+def crop_bbox(feats, bbox, HH, WW=None, backend='cudnn'):
+    """
+    Take differentiable crops of feats specified by bbox.
+
+    Inputs:
+    - feats: Tensor of shape (N, C, H, W)
+    - bbox: Bounding box coordinates of shape (N, 4) in the format
+      [x0, y0, x1, y1] in the [0, 1] coordinate space.
+    - HH, WW: Size of the output crops.
+
+    Returns:
+    - crops: Tensor of shape (N, C, HH, WW) where crops[i] is the portion of
+      feats[i] specified by bbox[i], reshaped to (HH, WW) using bilinear sampling.
+    """
+    N = feats.size(0)
+    assert bbox.size(0) == N
+    assert bbox.size(1) == 4
+    if WW is None:
+        WW = HH
+    if backend == 'cudnn':
+        # Change box from [0, 1] to [-1, 1] coordinate system
+        bbox = 2 * bbox - 1
+    x0, y0 = bbox[:, 0], bbox[:, 1]
+    x1, y1 = bbox[:, 2], bbox[:, 3]
+    X = tensor_linspace(x0, x1, steps=WW).view(N, 1, WW).expand(N, HH, WW)
+    Y = tensor_linspace(y0, y1, steps=HH).view(N, HH, 1).expand(N, HH, WW)
+    if backend == 'jj':
+        return bilinear_sample(feats, X, Y)
+    elif backend == 'cudnn':
+        grid = torch.stack([X, Y], dim=3)
+        return F.grid_sample(feats, grid)
+
+
+def uncrop_bbox(feats, bbox, H, W=None, fill_value=0):
+    """
+    Inverse operation to crop_bbox; construct output images where the feature maps
+    from feats have been reshaped and placed into the positions specified by bbox.
+
+    Inputs:
+    - feats: Tensor of shape (N, C, HH, WW)
+    - bbox: Bounding box coordinates of shape (N, 4) in the format
+      [x0, y0, x1, y1] in the [0, 1] coordinate space.
+    - H, W: Size of output.
+    - fill_value: Portions of the output image that are outside the bounding box
+      will be filled with this value.
+
+    Returns:
+    - out: Tensor of shape (N, C, H, W) where the portion of out[i] given by
+      bbox[i] contains feats[i], reshaped using bilinear sampling.
+    """
+    N, C = feats.size(0), feats.size(1)
+    assert bbox.size(0) == N
+    assert bbox.size(1) == 4
+    if W is None:
+        W = H
+
+    x0, y0 = bbox[:, 0], bbox[:, 1]
+    x1, y1 = bbox[:, 2], bbox[:, 3]
+    ww = x1 - x0
+    hh = y1 - y0
+
+    x0 = x0.contiguous().view(N, 1).expand(N, H)
+    x1 = x1.contiguous().view(N, 1).expand(N, H)
+    ww = ww.view(N, 1).expand(N, H)
+
+    y0 = y0.contiguous().view(N, 1).expand(N, W)
+    y1 = y1.contiguous().view(N, 1).expand(N, W)
+    hh = hh.view(N, 1).expand(N, W)
+
+    X = torch.linspace(0, 1, steps=W).view(1, W).expand(N, W).to(feats)
+    Y = torch.linspace(0, 1, steps=H).view(1, H).expand(N, H).to(feats)
+
+    X = (X - x0) / ww
+    Y = (Y - y0) / hh
+
+    # For ByteTensors, (x + y).clamp(max=1) gives logical_or
+    X_out_mask = ((X < 0) + (X > 1)).view(N, 1, W).expand(N, H, W)
+    Y_out_mask = ((Y < 0) + (Y > 1)).view(N, H, 1).expand(N, H, W)
+    out_mask = (X_out_mask + Y_out_mask).clamp(max=1)
+    out_mask = out_mask.view(N, 1, H, W).expand(N, C, H, W)
+
+    X = X.view(N, 1, W).expand(N, H, W)
+    Y = Y.view(N, H, 1).expand(N, H, W)
+
+    grid = torch.stack([X, Y], dim=3)
+    out = F.grid_sample(feats, grid)
+    out[out_mask] = fill_value
+    return out
+
+
+def bilinear_sample(feats, X, Y):
+    """
+    Perform bilinear sampling on the features in feats using the sampling grid
+    given by X and Y.
+
+    Inputs:
+    - feats: Tensor holding input feature map, of shape (N, C, H, W)
+    - X, Y: Tensors holding x and y coordinates of the sampling
+      grids; both have shape shape (N, HH, WW) and have elements in the range [0, 1].
+    Returns:
+    - out: Tensor of shape (B, C, HH, WW) where out[i] is computed
+      by sampling from feats[idx[i]] using the sampling grid (X[i], Y[i]).
+    """
+    N, C, H, W = feats.size()
+    assert X.size() == Y.size()
+    assert X.size(0) == N
+    _, HH, WW = X.size()
+
+    X = X.mul(W)
+    Y = Y.mul(H)
+
+    # Get the x and y coordinates for the four samples
+    x0 = X.floor().clamp(min=0, max=W-1)
+    x1 = (x0 + 1).clamp(min=0, max=W-1)
+    y0 = Y.floor().clamp(min=0, max=H-1)
+    y1 = (y0 + 1).clamp(min=0, max=H-1)
+
+    # In numpy we could do something like feats[i, :, y0, x0] to pull out
+    # the elements of feats at coordinates y0 and x0, but PyTorch doesn't
+    # yet support this style of indexing. Instead we have to use the gather
+    # method, which only allows us to index along one dimension at a time;
+    # therefore we will collapse the features (BB, C, H, W) into (BB, C, H * W)
+    # and index along the last dimension. Below we generate linear indices into
+    # the collapsed last dimension for each of the four combinations we need.
+    y0x0_idx = (W * y0 + x0).view(N, 1, HH * WW).expand(N, C, HH * WW)
+    y1x0_idx = (W * y1 + x0).view(N, 1, HH * WW).expand(N, C, HH * WW)
+    y0x1_idx = (W * y0 + x1).view(N, 1, HH * WW).expand(N, C, HH * WW)
+    y1x1_idx = (W * y1 + x1).view(N, 1, HH * WW).expand(N, C, HH * WW)
+
+    # Actually use gather to pull out the values from feats corresponding
+    # to our four samples, then reshape them to (BB, C, HH, WW)
+    feats_flat = feats.view(N, C, H * W)
+    v1 = feats_flat.gather(2, y0x0_idx.long()).view(N, C, HH, WW)
+    v2 = feats_flat.gather(2, y1x0_idx.long()).view(N, C, HH, WW)
+    v3 = feats_flat.gather(2, y0x1_idx.long()).view(N, C, HH, WW)
+    v4 = feats_flat.gather(2, y1x1_idx.long()).view(N, C, HH, WW)
+
+    # Compute the weights for the four samples
+    w1 = ((x1 - X) * (y1 - Y)).view(N, 1, HH, WW).expand(N, C, HH, WW)
+    w2 = ((x1 - X) * (Y - y0)).view(N, 1, HH, WW).expand(N, C, HH, WW)
+    w3 = ((X - x0) * (y1 - Y)).view(N, 1, HH, WW).expand(N, C, HH, WW)
+    w4 = ((X - x0) * (Y - y0)).view(N, 1, HH, WW).expand(N, C, HH, WW)
+
+    # Multiply the samples by the weights to give our interpolated results.
+    out = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4
+    return out
+
+
+def tensor_linspace(start, end, steps=10):
+    """
+    Vectorized version of torch.linspace.
+
+    Inputs:
+    - start: Tensor of any shape
+    - end: Tensor of the same shape as start
+    - steps: Integer
+
+    Returns:
+    - out: Tensor of shape start.size() + (steps,), such that
+      out.select(-1, 0) == start, out.select(-1, -1) == end,
+      and the other elements of out linearly interpolate between
+      start and end.
+    """
+    assert start.size() == end.size()
+    view_size = start.size() + (1,)
+    w_size = (1,) * start.dim() + (steps,)
+    out_size = start.size() + (steps,)
+
+    start_w = torch.linspace(1, 0, steps=steps).to(start)
+    start_w = start_w.view(w_size).expand(out_size)
+    end_w = torch.linspace(0, 1, steps=steps).to(start)
+    end_w = end_w.view(w_size).expand(out_size)
+
+    start = start.contiguous().view(view_size).expand(out_size)
+    end = end.contiguous().view(view_size).expand(out_size)
+
+    out = start_w * start + end_w * end
+    return out
+
+
+if __name__ == '__main__':
+    import numpy as np
+    # from scipy.misc import imread, imsave, imresize
+    from cv2 import imresize
+    from imageio import imread
+    from imageio import imwrite as imsave
+
+    cat = imresize(imread('cat.jpg'), (256, 256))
+    dog = imresize(imread('dog.jpg'), (256, 256))
+    feats = torch.stack([
+        torch.from_numpy(cat.transpose(2, 0, 1).astype(np.float32)),
+        torch.from_numpy(dog.transpose(2, 0, 1).astype(np.float32))],
+        dim=0)
+
+    boxes = torch.FloatTensor([
+        [0, 0, 1, 1],
+        [0.25, 0.25, 0.75, 0.75],
+        [0, 0, 0.5, 0.5],
+    ])
+
+    box_to_feats = torch.LongTensor([1, 0, 1]).cuda()
+
+    feats, boxes = feats.cuda(), boxes.cuda()
+    crops = crop_bbox_batch_cudnn(feats, boxes, box_to_feats, 128)
+    for i in range(crops.size(0)):
+        crop_np = crops.data[i].cpu().numpy().transpose(
+            1, 2, 0).astype(np.uint8)
+        imsave('out%d.png' % i, crop_np)
diff --git a/modeling/sf2f/utils/box_utils.py b/modeling/sf2f/utils/box_utils.py
new file mode 100644
index 0000000..898e543
--- /dev/null
+++ b/modeling/sf2f/utils/box_utils.py
@@ -0,0 +1,133 @@
+import torch
+import numpy as np
+"""
+Utilities for dealing with bounding boxes
+"""
+
+
+def box_union(box1, box2):
+    assert isinstance(box1, torch.Tensor)
+    return torch.cat(
+            [torch.min(
+                torch.stack([box1[:, :2], box2[:, :2],], dim=0),
+                dim=0,
+                keepdim=False,
+            )[0],
+            torch.max(
+                torch.stack([box1[:, 2:], box2[:, 2:],], dim=0),
+                dim=0,
+                keepdim=False,
+            )[0],]
+        , dim=1)
+
+def box_in_region(boxes, regions):
+    region_w = regions[:, 2] - regions[:, 0]
+    region_h = regions[:, 3] - regions[:, 1]
+    assert (region_w > 0).all() and (region_h > 0).all()
+    result = torch.zeros_like(regions)
+    result[:, 0::2] = (boxes[:, 0::2] - regions[:, 0::2]) / region_w
+    result[:, 1::2] = (boxes[:, 1::2] - regions[:, 1::2]) / region_h
+    return result
+
+
+def apply_box_transform(anchors, transforms):
+    """
+    Apply box transforms to a set of anchor boxes.
+
+    Inputs:
+    - anchors: Anchor boxes of shape (N, 4), where each anchor is specified
+      in the form [xc, yc, w, h]
+    - transforms: Box transforms of shape (N, 4) where each transform is
+      specified as [tx, ty, tw, th]
+
+    Returns:
+    - boxes: Transformed boxes of shape (N, 4) where each box is in the
+      format [xc, yc, w, h]
+    """
+    # Unpack anchors
+    xa, ya = anchors[:, 0], anchors[:, 1]
+    wa, ha = anchors[:, 2], anchors[:, 3]
+
+    # Unpack transforms
+    tx, ty = transforms[:, 0], transforms[:, 1]
+    tw, th = transforms[:, 2], transforms[:, 3]
+
+    x = xa + tx * wa
+    y = ya + ty * ha
+    w = wa * tw.exp()
+    h = ha * th.exp()
+
+    boxes = torch.stack([x, y, w, h], dim=1)
+    return boxes
+
+
+def invert_box_transform(anchors, boxes):
+    """
+    Compute the box transform that, when applied to anchors, would give boxes.
+
+    Inputs:
+    - anchors: Box anchors of shape (N, 4) in the format [xc, yc, w, h]
+    - boxes: Target boxes of shape (N, 4) in the format [xc, yc, w, h]
+
+    Returns:
+    - transforms: Box transforms of shape (N, 4) in the format [tx, ty, tw, th]
+    """
+    # Unpack anchors
+    xa, ya = anchors[:, 0], anchors[:, 1]
+    wa, ha = anchors[:, 2], anchors[:, 3]
+
+    # Unpack boxes
+    x, y = boxes[:, 0], boxes[:, 1]
+    w, h = boxes[:, 2], boxes[:, 3]
+
+    tx = (x - xa) / wa
+    ty = (y - ya) / ha
+    tw = w.log() - wa.log()
+    th = h.log() - ha.log()
+
+    transforms = torch.stack([tx, ty, tw, th], dim=1)
+    return transforms
+
+
+def centers_to_extents(boxes):
+    """
+    Convert boxes from [xc, yc, w, h] format to [x0, y0, x1, y1] format
+
+    Input:
+    - boxes: Input boxes of shape (N, 4) in [xc, yc, w, h] format
+
+    Returns:
+    - boxes: Output boxes of shape (N, 4) in [x0, y0, x1, y1] format
+    """
+    xc, yc = boxes[:, 0], boxes[:, 1]
+    w, h = boxes[:, 2], boxes[:, 3]
+
+    x0 = xc - w / 2
+    x1 = x0 + w
+    y0 = yc - h / 2
+    y1 = y0 + h
+
+    boxes_out = torch.stack([x0, y0, x1, y1], dim=1)
+    return boxes_out
+
+
+def extents_to_centers(boxes):
+    """
+    Convert boxes from [x0, y0, x1, y1] format to [xc, yc, w, h] format
+
+    Input:
+    - boxes: Input boxes of shape (N, 4) in [x0, y0, x1, y1] format
+
+    Returns:
+    - boxes: Output boxes of shape (N, 4) in [xc, yc, w, h] format
+    """
+    x0, y0 = boxes[:, 0], boxes[:, 1]
+    x1, y1 = boxes[:, 2], boxes[:, 3]
+
+    xc = 0.5 * (x0 + x1)
+    yc = 0.5 * (y0 + y1)
+    w = x1 - x0
+    h = y1 - y0
+
+    boxes_out = torch.stack([xc, yc, w, h], dim=1)
+    return boxes_out
diff --git a/modeling/sf2f/utils/common.py b/modeling/sf2f/utils/common.py
new file mode 100644
index 0000000..2248793
--- /dev/null
+++ b/modeling/sf2f/utils/common.py
@@ -0,0 +1,192 @@
+import os
+import time
+import inspect
+import subprocess
+from contextlib import contextmanager
+
+import torch
+from PIL import Image
+import numpy as np
+from collections import OrderedDict
+
+def int_tuple(s):
+    return tuple(int(i) for i in s.split(','))
+
+
+def float_tuple(s):
+    return tuple(float(i) for i in s.split(','))
+
+
+def str_tuple(s):
+    return tuple(s.split(','))
+
+
+def set_trainable(model, requires_grad):
+    set_trainable_param(model.parameters(), requires_grad)
+
+def set_trainable_param(parameters, requires_grad):
+    for param in parameters:
+        param.requires_grad = requires_grad
+
+
+def update_values(dict_from, dict_to):
+    for key, value in dict_from.items():
+        if isinstance(value, dict) and key in dict_to.keys():
+            update_values(dict_from[key], dict_to[key])
+        elif value is not None or key not in dict_to.keys():
+            dict_to[key] = dict_from[key]
+    return dict_to
+
+
+def params_count(model):
+    count = 0
+    for p in model.parameters():
+        c = 1
+        for i in range(p.dim()):
+            c *= p.size(i)
+        count += c
+    return count
+
+
+def bool_flag(s):
+    if s == '1':
+        return True
+    elif s == '0':
+        return False
+    msg = 'Invalid value "%s" for bool flag (should be 0 or 1)'
+    raise ValueError(msg % s)
+
+
+def lineno():
+    return inspect.currentframe().f_back.f_lineno
+
+
+def get_gpu_memory():
+    torch.cuda.synchronize()
+    opts = [
+        'nvidia-smi', '-q', '--gpu=' + str(0), '|', 'grep', '"Used GPU Memory"'
+    ]
+    cmd = str.join(' ', opts)
+    ps = subprocess.Popen(
+        cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+    output = ps.communicate()[0].decode('utf-8')
+    output = output.split("\n")[1].split(":")
+    consumed_mem = int(output[1].strip().split(" ")[0])
+    return consumed_mem
+
+# Converts a Tensor into an image array (numpy)
+# |imtype|: the desired type of the converted numpy array
+
+
+def tensor2im(input_image, imtype=np.uint8):
+    if not isinstance(input_image, torch.Tensor):
+        return input_image
+    image_numpy = input_image.numpy()
+    if image_numpy.ndim == 3:
+        if image_numpy.shape[0] == 1:
+            image_numpy = np.tile(image_numpy, (3, 1, 1))
+        elif image_numpy.shape[0] == 3:
+            image_numpy = np.transpose(image_numpy, (1, 2, 0))
+    elif image_numpy.ndim == 4:
+        if image_numpy.shape[1] == 1:
+            image_numpy = np.tile(image_numpy, (1, 3, 1, 1))
+        elif image_numpy.shape[1] == 3:
+            image_numpy = np.transpose(image_numpy, (0, 2, 3, 1))
+    else:
+        raise ValueError("Only support images or batches")
+    return image_numpy.astype(imtype)
+
+
+def save_image(image_numpy, image_path):
+    image_pil = Image.fromarray(image_numpy)
+    image_pil.save(image_path)
+
+
+def mkdirs(paths):
+    if isinstance(paths, list) and not isinstance(paths, str):
+        for path in paths:
+            mkdir(path)
+    else:
+        mkdir(paths)
+
+
+def mkdir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+
+@contextmanager
+def timeit(msg, should_time=True):
+    if should_time:
+        torch.cuda.synchronize()
+        t0 = time.time()
+    yield
+    if should_time:
+        torch.cuda.synchronize()
+        t1 = time.time()
+        duration = (t1 - t0) * 1000.0
+        print('%s: %.2f ms' % (msg, duration))
+
+
+class LossManager(object):
+    def __init__(self):
+        self.total_loss = None
+        self.all_losses = {}
+
+    def add_loss(self, loss, name, weight=1.0):
+        cur_loss = loss * weight
+        if self.total_loss is not None:
+            self.total_loss += cur_loss
+        else:
+            self.total_loss = cur_loss
+
+        self.all_losses[name] = cur_loss.data.cpu().item()
+
+    def items(self):
+        return self.all_losses.items()
+
+
+def load_model_state(model, state_dict, strict=True):
+    # sometimes if the state_dict is saved from DataParallel model, the keys
+    # has a prefix "module.", so we remove them before Loading
+    new_state_dict = OrderedDict()
+    if not isinstance(model, torch.nn.DataParallel):
+        for k, v in state_dict.items():
+            k = k[7:] if k.startswith("module") else k
+            new_state_dict[k] = v
+    else:
+        new_state_dict = state_dict
+    if strict:
+        # requires the model's state_dict to be exactly same as the new_state_dict
+        model.load_state_dict(new_state_dict)
+    else:
+        # only load parameters that are same size and type, and print warnings
+        # for parameters that don't match
+        own_state = model.state_dict()
+        for name, param in new_state_dict.items():
+            if name in own_state:
+                if isinstance(param, torch.nn.Parameter):
+                    # backwards compatibility for serialized parameters
+                    param = param.data
+                if own_state[name].size() == param.size():
+                    own_state[name].copy_(param)
+                else:
+                    print(
+                        "Warning: While copying the parameter named {}, "
+                        "whose dimensions in the model are {} and "
+                        "whose dimensions in the checkpoint are {}.".format(
+                            name, own_state[name].size(), param.size()
+                        )
+                    )
+            else:
+                print(
+                    "Warning: Parameter named {} is not used "
+                    "by this model.".format(name)
+                )
+        for name, _ in own_state.items():
+            if name not in new_state_dict:
+                print(
+                    "Warning: Parameter named {} in the model "
+                    "is not initialized.".format(name)
+                )
+    return model
diff --git a/modeling/sf2f/utils/evaluate.py b/modeling/sf2f/utils/evaluate.py
new file mode 100644
index 0000000..fc30393
--- /dev/null
+++ b/modeling/sf2f/utils/evaluate.py
@@ -0,0 +1,67 @@
+import pyprind
+import numpy as np
+import torch
+import torch.nn.functional as F
+import glog as log
+
+from datasets import imagenet_deprocess_batch
+from scripts.compute_inception_score import get_inception_score
+from scripts.compute_vggface_score import get_vggface_score
+
+
+VGG_BOX = [0.235, 0.195, 0.765, 0.915]
+def crop_vgg_box(imgs):
+    # with correct cropping & correct processing
+    left, top, right, bottom = VGG_BOX
+    # = [0.235015, 0.19505739, 0.76817876, 0.9154963]
+    N, C, H, W = imgs.shape
+    left = int(left * W)
+    right = int(right * W)
+    top = int(top * H)
+    bottom = int(bottom * H)
+    imgs = imgs[:, :, top:bottom+1, left:right+1]
+    return imgs
+
+def evaluate(model, data_loader, options):
+    '''
+    Evaluate the current model...
+    '''
+    normalize_method = options["data"]["data_opts"].get(
+        'normalize_method', 'imagenet')
+    model.eval()
+    log.info("Evaluating with Inception Scores.")
+    images = []
+    imgs_gt = []
+    for iter, batch in enumerate(pyprind.prog_bar(data_loader,
+                                  title="[Generating Images]",
+                                  width=50)):
+        ######### unpack the data #########
+        imgs, log_mels, human_ids = batch
+        imgs = imgs.cuda()
+        imgs_gt.append(imgs[0])
+        ###################################
+        with torch.no_grad():
+            float_dtype = torch.cuda.FloatTensor
+            log_mels = log_mels.type(float_dtype)
+            model_out = model(log_mels)
+            """
+            imgs_pred: generated images
+            others: placeholder for other output
+            """
+        imgs_pred, others = model_out
+        img = imagenet_deprocess_batch(
+            imgs_pred, normalize_method=normalize_method)
+        img = crop_vgg_box(img)
+        for i in range(img.shape[0]):
+            img_np = img[i].numpy().transpose(1, 2, 0)
+            images.append(img_np)
+    log.info("Computing inception scores...")
+    is_mean, is_std = get_inception_score(images)
+    log.info("Computing VF scores...")
+    vfs_mean, vfs_std = get_vggface_score(images)
+
+    return is_mean, is_std, vfs_mean, vfs_std
+
+
+if __name__ == '__main__':
+    evaluate()
diff --git a/modeling/sf2f/utils/evaluate_fid.py b/modeling/sf2f/utils/evaluate_fid.py
new file mode 100644
index 0000000..90da53d
--- /dev/null
+++ b/modeling/sf2f/utils/evaluate_fid.py
@@ -0,0 +1,82 @@
+import pyprind
+import numpy as np
+import torch
+import torch.nn.functional as F
+import glog as log
+
+from datasets import imagenet_deprocess_batch
+from scripts.compute_fid_score import calculate_activation_statistics, calculate_frechet_distance
+import scripts.compute_fid_score as inception_score
+from scripts.compute_vggface_score import get_vggface_act
+
+VGG_BOX = [0.235, 0.195, 0.765, 0.915]
+def crop_vgg_box(imgs):
+    # with correct cropping & correct processing
+    left, top, right, bottom = VGG_BOX
+    # = [0.235015, 0.19505739, 0.76817876, 0.9154963]
+    N, C, H, W = imgs.shape
+    left = int(left * W)
+    right = int(right * W)
+    top = int(top * H)
+    bottom = int(bottom * H)
+    imgs = imgs[:, :, top:bottom+1, left:right+1]
+    return imgs
+
+def evaluate_fid(model, data_loader, options):
+    '''
+    Evaluate the current model...
+    '''
+    normalize_method = options["data"]["data_opts"].get(
+        'normalize_method', 'imagenet')
+    model.eval()
+    log.info("Evaluating with Inception Scores.")
+    images = []
+    imgs_gt = []
+    for iter, batch in enumerate(pyprind.prog_bar(data_loader,
+                                  title="[Generating Images]",
+                                  width=50)):
+        ######### unpack the data #########
+        imgs, log_mels, human_ids = batch
+        imgs = imgs.cuda()
+        ###################################
+        with torch.no_grad():
+            float_dtype = torch.cuda.FloatTensor
+            log_mels = log_mels.type(float_dtype)
+            model_out = model(log_mels)
+            """
+            imgs_pred: generated images
+            others: placeholder for other output
+            """
+        imgs_pred, others = model_out
+        img = imagenet_deprocess_batch(
+            imgs_pred, normalize_method=normalize_method)
+        img = crop_vgg_box(img)
+        for i in range(img.shape[0]):
+            img_np = img[i].numpy().transpose(1, 2, 0)
+            images.append(img_np)
+
+        img = imagenet_deprocess_batch(
+            imgs, normalize_method=normalize_method)
+        img = crop_vgg_box(img)
+        for i in range(img.shape[0]):
+            img_np = img[i].numpy().transpose(1, 2, 0)
+            imgs_gt.append(img_np)
+
+    log.info("Computing FID scores...")
+    acts_set = inception_score.get_fid_pred(imgs_gt)
+    fake_acts_set = inception_score.get_fid_pred(images)
+    real_mu, real_sigma = calculate_activation_statistics(acts_set)
+    fake_mu, fake_sigma = calculate_activation_statistics(fake_acts_set)
+    fid_score = calculate_frechet_distance(real_mu, real_sigma, fake_mu, fake_sigma)
+
+    acts_set = get_vggface_act(imgs_gt)
+    fake_acts_set = get_vggface_act(images)
+    real_mu, real_sigma = calculate_activation_statistics(acts_set)
+    fake_mu, fake_sigma = calculate_activation_statistics(fake_acts_set)
+    fvfd_score = calculate_frechet_distance(real_mu, real_sigma, fake_mu, fake_sigma)
+
+    return fid_score
+
+
+if __name__ == '__main__':
+    evaluate()
diff --git a/modeling/sf2f/utils/filter_pickle.py b/modeling/sf2f/utils/filter_pickle.py
new file mode 100644
index 0000000..ccf5646
--- /dev/null
+++ b/modeling/sf2f/utils/filter_pickle.py
@@ -0,0 +1,35 @@
+import pickle
+import os
+
+
+def check_pickle(mel_pickle):
+    """
+    Load a mel pickle file and remove the file if it is empty
+
+    Args:
+        mel_pickle: str, path to the mel pickle file
+    Returns:
+        None
+    """
+    file = open(mel_pickle, 'rb')
+    try:
+        data = pickle.load(file)
+    except:
+        print('Error loading:', mel_pickle)
+        os.remove(mel_pickle)
+        
+    file.close()
+
+def filtering_pickle(dir_path):
+    for dir in os.listdir(dir_path):
+        data_path = os.path.join(dir_path, dir)
+        for id in os.listdir(data_path):
+            data = os.path.join(data_path, id)
+            check_pickle(data)
+    print("Filtering Done.")
+
+
+# if __name__=="__main__":
+#     dir_path = './data/VoxCeleb/vox1/mel_spectrograms' # wav_convertor.vox1_mel
+#     filtering_pickle(dir_path)
+
diff --git a/modeling/sf2f/utils/logger.py b/modeling/sf2f/utils/logger.py
new file mode 100644
index 0000000..fa47f64
--- /dev/null
+++ b/modeling/sf2f/utils/logger.py
@@ -0,0 +1,93 @@
+# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
+import tensorflow as tf
+import numpy as np
+# import scipy.misc
+from PIL.Image import fromarray
+
+try:
+    from StringIO import StringIO  # Python 2.7
+except ImportError:
+    from io import BytesIO         # Python 3.x
+    
+tf.compat.v1.disable_v2_behavior()
+
+
+class Logger(object):
+
+    def __init__(self, log_dir):
+        """Create a summary writer logging to log_dir."""
+        self.writer = tf.compat.v1.summary.FileWriter(log_dir)
+        # self.writer = tf.compat.v1.summary.create_file_writer(log_dir)
+
+    def scalar_summary(self, tag, value, step):
+        """Log a scalar variable."""
+        summary = tf.compat.v1.Summary(
+            value=[tf.compat.v1.Summary.Value(tag=tag, simple_value=value)])
+        self.writer.add_summary(summary, step)
+
+    def image_summary(self, images, step, tag="vis"):
+        """Log a list of images."""
+
+        img_summaries = []
+        if isinstance(images, list):
+            for i, img in enumerate(images):
+                _tag = '%s/%d' % (tag, i)
+                # Create a Summary value
+                img_summaries.append(self.add_image(img, _tag))
+        elif isinstance(images, dict):
+            for key, img in images.items():
+                _tag = '%s/%s' % (tag, key)
+                # Create a Summary value
+                img_summaries.append(self.add_image(img, _tag))
+        else:
+            raise ValueError("Either a list or a dict is supported.")
+
+        # Create and write Summary
+        summary = tf.compat.v1.Summary(value=img_summaries)
+        self.writer.add_summary(summary, step)
+
+    def histo_summary(self, tag, values, step, bins=1000):
+        """Log a histogram of the tensor of values."""
+
+        # Create a histogram using numpy
+        counts, bin_edges = np.histogram(values, bins=bins)
+
+        # Fill the fields of the histogram proto
+        hist = tf.HistogramProto()
+        hist.min = float(np.min(values))
+        hist.max = float(np.max(values))
+        hist.num = int(np.prod(values.shape))
+        hist.sum = float(np.sum(values))
+        hist.sum_squares = float(np.sum(values**2))
+
+        # Drop the start of the first bin
+        bin_edges = bin_edges[1:]
+
+        # Add bin edges and counts
+        for edge in bin_edges:
+            hist.bucket_limit.append(edge)
+        for c in counts:
+            hist.bucket.append(c)
+
+        # Create and write Summary
+        summary = tf.compat.v1.Summary(
+            value=[tf.compat.v1.Summary.Value(tag=tag, histo=hist)])
+        self.writer.add_summary(summary, step)
+        self.writer.flush()
+
+    def add_image(self, img, tag):
+        # Write the image to a string
+        try:
+            s = StringIO()
+        except:
+            s = BytesIO()
+        # scipy.misc.toimage(img).save(s, format="png")
+        fromarray(img).save(s, format="png")
+
+
+        # Create an Image object
+        img_sum = tf.compat.v1.Summary.Image(encoded_image_string=s.getvalue(),
+                                   height=img.shape[0],
+                                   width=img.shape[1])
+
+        return tf.compat.v1.Summary.Value(tag=tag, image=img_sum)
diff --git a/modeling/sf2f/utils/losses.py b/modeling/sf2f/utils/losses.py
new file mode 100644
index 0000000..55fcb70
--- /dev/null
+++ b/modeling/sf2f/utils/losses.py
@@ -0,0 +1,144 @@
+import torch
+import torch.nn.functional as F
+
+
+def get_gan_losses(gan_type):
+    """
+    Returns the generator and discriminator loss for a particular GAN type.
+
+    The returned functions have the following API:
+    loss_g = g_loss(scores_fake)
+    loss_d = d_loss(scores_real, scores_fake)
+    """
+    if gan_type == 'gan':
+        return gan_g_loss, gan_d_loss
+    elif gan_type == 'wgan':
+        return wgan_g_loss, wgan_d_loss
+    elif gan_type == 'lsgan':
+        return lsgan_g_loss, lsgan_d_loss
+    else:
+        raise ValueError('Unrecognized GAN type "%s"' % gan_type)
+
+
+def bce_loss(input, target):
+    """
+    Numerically stable version of the binary cross-entropy loss function.
+
+    As per https://github.com/pytorch/pytorch/issues/751
+    See the TensorFlow docs for a derivation of this formula:
+    https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits
+
+    Inputs:
+    - input: PyTorch Tensor of shape (N, ) giving scores.
+    - target: PyTorch Tensor of shape (N,) containing 0 and 1 giving targets.
+
+    Returns:
+    - A PyTorch Tensor containing the mean BCE loss over the minibatch of
+      input data.
+    """
+    neg_abs = -input.abs()
+    loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
+    return loss.mean()
+
+
+def _make_targets(x, y):
+    """
+    Inputs:
+    - x: PyTorch Tensor
+    - y: Python scalar
+
+    Outputs:
+    - out: PyTorch Variable with same shape and dtype as x, but filled with y
+    """
+    return torch.full_like(x, y)
+
+
+def gan_g_loss(scores_fake):
+    """
+    Input:
+    - scores_fake: Tensor of shape (N,) containing scores for fake samples
+
+    Output:
+    - loss: Variable of shape (,) giving GAN generator loss
+    """
+    if scores_fake.dim() > 1:
+        scores_fake = scores_fake.view(-1)
+    y_fake = _make_targets(scores_fake, 1)
+    return bce_loss(scores_fake, y_fake)
+
+
+def gan_d_loss(scores_real, scores_fake):
+    """
+    Input:
+    - scores_real: Tensor of shape (N,) giving scores for real samples
+    - scores_fake: Tensor of shape (N,) giving scores for fake samples
+
+    Output:
+    - loss: Tensor of shape (,) giving GAN discriminator loss
+    """
+    assert scores_real.size() == scores_fake.size()
+    if scores_real.dim() > 1:
+        scores_real = scores_real.view(-1)
+        scores_fake = scores_fake.view(-1)
+    y_real = _make_targets(scores_real, 1)
+    y_fake = _make_targets(scores_fake, 0)
+    loss_real = bce_loss(scores_real, y_real)
+    loss_fake = bce_loss(scores_fake, y_fake)
+    return loss_real + loss_fake
+
+
+def wgan_g_loss(scores_fake):
+    """
+    Input:
+    - scores_fake: Tensor of shape (N,) containing scores for fake samples
+
+    Output:
+    - loss: Tensor of shape (,) giving WGAN generator loss
+    """
+    return -scores_fake.mean()
+
+
+def wgan_d_loss(scores_real, scores_fake):
+    """
+    Input:
+    - scores_real: Tensor of shape (N,) giving scores for real samples
+    - scores_fake: Tensor of shape (N,) giving scores for fake samples
+
+    Output:
+    - loss: Tensor of shape (,) giving WGAN discriminator loss
+    """
+    return scores_fake.mean() - scores_real.mean()
+
+
+def lsgan_g_loss(scores_fake):
+    if scores_fake.dim() > 1:
+        scores_fake = scores_fake.view(-1)
+    y_fake = _make_targets(scores_fake, 1)
+    return F.mse_loss(scores_fake.sigmoid(), y_fake)
+
+
+def lsgan_d_loss(scores_real, scores_fake):
+    assert scores_real.size() == scores_fake.size()
+    if scores_real.dim() > 1:
+        scores_real = scores_real.view(-1)
+        scores_fake = scores_fake.view(-1)
+    y_real = _make_targets(scores_real, 1)
+    y_fake = _make_targets(scores_fake, 0)
+    loss_real = F.mse_loss(scores_real.sigmoid(), y_real)
+    loss_fake = F.mse_loss(scores_fake.sigmoid(), y_fake)
+    return loss_real + loss_fake
+
+
+def gradient_penalty(x_real, x_fake, f, gamma=1.0):
+    N = x_real.size(0)
+    device, dtype = x_real.device, x_real.dtype
+    eps = torch.randn(N, 1, 1, 1, device=device, dtype=dtype)
+    x_hat = eps * x_real + (1 - eps) * x_fake
+    x_hat_score = f(x_hat)
+    if x_hat_score.dim() > 1:
+        x_hat_score = x_hat_score.view(x_hat_score.size(0), -1).mean(dim=1)
+    x_hat_score = x_hat_score.sum()
+    grad_x_hat, = torch.autograd.grad(x_hat_score, x_hat, create_graph=True)
+    grad_x_hat_norm = grad_x_hat.contiguous().view(N, -1).norm(p=2, dim=1)
+    gp_loss = (grad_x_hat_norm - gamma).pow(2).div(gamma * gamma).mean()
+    return gp_loss
diff --git a/modeling/sf2f/utils/metrics.py b/modeling/sf2f/utils/metrics.py
new file mode 100644
index 0000000..f337aaf
--- /dev/null
+++ b/modeling/sf2f/utils/metrics.py
@@ -0,0 +1,50 @@
+import torch
+
+def accuracy(output, target, topk=(1,)):
+    """Computes the accuracy over the k top predictions for the specified values of k"""
+    with torch.no_grad():
+        maxk = max(topk)
+        batch_size = target.size(0)
+
+        _, pred = output.topk(maxk, 1, True, True)
+        pred = pred.t()
+        correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+        res = []
+        for k in topk:
+            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+            res.append(correct_k.mul_(100.0 / batch_size).item())
+        return res
+
+
+def intersection(bbox_pred, bbox_gt):
+    max_xy = torch.min(bbox_pred[:, 2:], bbox_gt[:, 2:])
+    min_xy = torch.max(bbox_pred[:, :2], bbox_gt[:, :2])
+    inter = torch.clamp((max_xy - min_xy), min=0)
+    return inter[:, 0] * inter[:, 1]
+
+
+def jaccard(bbox_pred, bbox_gt):
+    inter = intersection(bbox_pred, bbox_gt)
+    area_pred = (bbox_pred[:, 2] - bbox_pred[:, 0]) * (bbox_pred[:, 3] -
+                                                       bbox_pred[:, 1])
+    area_gt = (bbox_gt[:, 2] - bbox_gt[:, 0]) * (bbox_gt[:, 3] -
+                                                 bbox_gt[:, 1])
+    union = area_pred + area_gt - inter
+    iou = torch.div(inter, union)
+    return torch.sum(iou)
+
+
+def get_total_norm(parameters, norm_type=2):
+    if norm_type == float('inf'):
+        total_norm = max(p.grad.data.abs().max() for p in parameters)
+    else:
+        total_norm = 0
+        for p in parameters:
+            try:
+                param_norm = p.grad.data.norm(norm_type)
+                total_norm += param_norm ** norm_type
+                total_norm = total_norm ** (1. / norm_type)
+            except:
+                continue
+    return total_norm
diff --git a/modeling/sf2f/utils/pgan_utils.py b/modeling/sf2f/utils/pgan_utils.py
new file mode 100644
index 0000000..efb4e52
--- /dev/null
+++ b/modeling/sf2f/utils/pgan_utils.py
@@ -0,0 +1,45 @@
+import os
+import time
+import json
+import math
+import torch
+
+
+def num_flat_features(x):
+    size = x.size()[1:]  # all dimensions except the batch dimension
+    num_features = 1
+    for s in size:
+        num_features *= s
+    return num_features
+
+
+def miniBatchStdDev(x, subGroupSize=4):
+    r"""
+    Add a minibatch standard deviation channel to the current layer.
+    In other words:
+        1) Compute the standard deviation of the feature map over the minibatch
+        2) Get the mean, over all pixels and all channels of thsi ValueError
+        3) expand the layer and cocatenate it with the input
+    Args:
+        - x (tensor): previous layer
+        - subGroupSize (int): size of the mini-batches on which the standard deviation
+        should be computed
+    """
+    size = x.size()
+    subGroupSize = min(size[0], subGroupSize)
+    if size[0] % subGroupSize != 0:
+        subGroupSize = size[0]
+    G = int(size[0] / subGroupSize)
+    if subGroupSize > 1:
+        y = x.view(-1, subGroupSize, size[1], size[2], size[3])
+        y = torch.var(y, 1)
+        y = torch.sqrt(y + 1e-8)
+        y = y.view(G, -1)
+        y = torch.mean(y, 1).view(G, 1)
+        y = y.expand(G, size[2]*size[3]).view((G, 1, 1, size[2], size[3]))
+        y = y.expand(G, subGroupSize, -1, -1, -1)
+        y = y.contiguous().view((-1, 1, size[2], size[3]))
+    else:
+        y = torch.zeros(x.size(0), 1, x.size(2), x.size(3), device=x.device)
+
+    return torch.cat([x, y], dim=1)
diff --git a/modeling/sf2f/utils/s2f_evaluator.py b/modeling/sf2f/utils/s2f_evaluator.py
new file mode 100644
index 0000000..9341cc0
--- /dev/null
+++ b/modeling/sf2f/utils/s2f_evaluator.py
@@ -0,0 +1,449 @@
+'''
+This file contains the utils for evaluation, and is used to cooperate with \
+    training_utils.py and test.py
+'''
+
+
+import os
+import json
+import math
+from collections import defaultdict
+import time
+from copy import deepcopy
+
+import numpy as np
+import torch
+import torch.optim as optim
+import torch.nn as nn
+import torch.nn.functional as F
+from scipy.stats import entropy
+from torchvision.models import inception_v3
+from pyprind import prog_bar
+from tensorflow.io import gfile
+from imageio import imwrite
+
+from datasets import fast_imagenet_deprocess_batch, imagenet_deprocess_batch
+import datasets
+import models
+from models import InceptionResnetV1, fixed_image_standardization
+
+
+# left, top, right, bottom
+VGG_BOX = [0.235, 0.195, 0.765, 0.915]
+
+
+class S2fEvaluator:
+    def __init__(self,
+                 loader,
+                 options,
+                 nframe_range=None,
+                 extraction_size=100,
+                 hq_emb_dict=True,
+                 face_gen_mode='naive',
+                 facenet_return_pooling=False):
+        '''
+        Inputs:
+            loader
+            options
+        '''
+        self.loader = deepcopy(loader)
+        # This makes sure same group of meg_spectrograms is used for fuser
+        self.loader.dataset.shuffle_mel_segments = False
+        if nframe_range is not None:
+            self.loader.dataset.nframe_range = nframe_range
+        self.facenet = InceptionResnetV1(
+            pretrained='vggface2',
+            auto_input_resize=True,
+            return_pooling=facenet_return_pooling).cuda().eval()
+        self.float_dtype = torch.cuda.FloatTensor
+        self.long_dtype = torch.cuda.LongTensor
+        self.options = options
+        self.image_normalize_method= \
+                self.options["data"]["data_opts"]["image_normalize_method"]
+        self.do_deprocess_and_preprocess = \
+            self.options["eval"]["facenet"]["deprocess_and_preprocess"]
+        # crop faces according to VGG average bounding box
+        self.crop_faces = \
+            self.options["eval"]["facenet"]["crop_faces"]
+        self.extraction_size = extraction_size
+        self.hq_emb_dict = hq_emb_dict
+        self.face_gen_mode = face_gen_mode
+
+        self.get_dataset_embeddings()
+
+        # Implement Evaluation Metrics
+        self.L2_dist = \
+            nn.PairwiseDistance(p=2.0, eps=1e-06, keepdim=False)
+        self.L1_dist = \
+            nn.PairwiseDistance(p=1.0, eps=1e-06, keepdim=False)
+        self.cos_sim = nn.CosineSimilarity(dim=-1, eps=1e-08)
+
+    def deprocess_and_preprocess(self, imgs):
+        '''
+        For a batch of real / generated image, perform 'imagenet' or 'standard'
+        deprocess, and FaceNet Preprocess
+        '''
+        #print('Begin:', imgs[0])
+        imgs = fast_imagenet_deprocess_batch(
+            imgs,
+            normalize_method=self.image_normalize_method)
+        #print('Our distribution:', imgs[0])
+        imgs = fixed_image_standardization(imgs)
+        #print('fixed_image_standardization:', imgs[0])
+        return imgs
+
+    def crop_vgg_box(self, imgs):
+        # with correct cropping & correct processing
+        left, top, right, bottom = VGG_BOX
+        # = [0.235015, 0.19505739, 0.76817876, 0.9154963]
+        N, C, H, W = imgs.shape
+        left = int(left * W)
+        right = int(right * W)
+        top = int(top * H)
+        bottom = int(bottom * H)
+        imgs = imgs[:, :, top:bottom+1, left:right+1]
+        return imgs
+
+    def get_dataset_embeddings(self):
+        with torch.no_grad():
+            # To avoid influence to the running_mean/var of BatchNorm Layers
+            embedding_batches = []
+            if self.hq_emb_dict:
+                for i in prog_bar(
+                    range(len(self.loader.dataset)),
+                    title="[S2fEvaluator: " + \
+                        "Preparing FaceNet Embedding Dictionary]",
+                    width=50):
+                    # Loop logic
+                    ######### unpack the data #########
+                    imgs = self.loader.dataset.get_all_faces_of_id(i)
+                    imgs = imgs.cuda()
+                    ###################################
+                    if self.do_deprocess_and_preprocess:
+                        imgs = self.deprocess_and_preprocess(imgs)
+                    if self.crop_faces:
+                        imgs = self.crop_vgg_box(imgs)
+                    embeddings = self.facenet(imgs)
+                    embeddings = torch.mean(embeddings, 0, keepdim=True)
+                    embedding_batches.append(embeddings)
+            else:
+                for batch in prog_bar(
+                    self.loader,
+                    title="[S2fEvaluator: " + \
+                        "Preparing FaceNet Embedding Dictionary]",
+                    width=50):
+                    # Loop logic
+                    ######### unpack the data #########
+                    imgs, log_mels, human_ids = batch
+                    imgs = imgs.cuda()
+                    ###################################
+                    if self.do_deprocess_and_preprocess:
+                        imgs = self.deprocess_and_preprocess(imgs)
+                    if self.crop_faces:
+                        imgs = self.crop_vgg_box(imgs)
+                    embeddings = self.facenet(imgs)
+                    embedding_batches.append(embeddings)
+            self.dataset_embedding = torch.cat(embedding_batches, 0)
+        print("S2fEvaluator: dataset_embedding shape:",
+              self.dataset_embedding.shape)
+
+    def get_pred_img_embeddings(self, model):
+        '''
+        This function focus on evaluating the speech-to-face specific metrics,
+        Including:
+            1. Deep feature (FaceNet Feature) Similarity
+            2. Extraction Recall@K
+            3. Landmark Correlation
+        '''
+        training_status = model.training
+        model.eval()
+        pred_img_embedding_batches = []
+        with torch.no_grad():
+            # To avoid influence to the running_mean/var of BatchNorm Layers
+            if self.face_gen_mode == 'naive':
+                for batch in prog_bar(
+                    self.loader,
+                    title="[S2fEvaluator: " + \
+                        "Getting FaceNet Embedding for Predicted Images]",
+                    width=50):
+                    # Loop logic
+                    ######### unpack the data #########
+                    imgs, log_mels, human_ids = batch
+                    imgs = imgs.cuda()
+                    log_mels = log_mels.type(self.float_dtype)
+                    human_ids = human_ids.type(self.long_dtype)
+                    ###################################
+                    # Run the model as it has been run during training
+                    model_out = model(log_mels)
+                    imgs_pred, others = model_out
+                    # Multi-Reso Case
+                    if isinstance(imgs_pred, tuple):
+                        imgs_pred = imgs_pred[-1]
+                    if self.do_deprocess_and_preprocess:
+                        imgs_pred = self.deprocess_and_preprocess(imgs_pred)
+                    if self.crop_faces:
+                        imgs_pred = self.crop_vgg_box(imgs_pred)
+                    pred_img_embeddings = self.facenet(imgs_pred)
+                    pred_img_embedding_batches.append(pred_img_embeddings)
+            elif self.face_gen_mode == 'average_facenet_embedding':
+                for i in prog_bar(
+                    range(len(self.loader.dataset)),
+                    title="[S2fEvaluator: " + \
+                        "Getting FaceNet Embedding for Predicted Images, " + \
+                            "with average Facenet embedding policy]",
+                    width=50):
+                    # Loop logic
+                    ######### unpack the data #########
+                    log_mels = self.loader.dataset.get_all_mel_segments_of_id(i)
+                    log_mels = log_mels.type(self.float_dtype)
+                    ###################################
+                    # Run the model as it has been run during training
+                    model_out = model(log_mels)
+                    imgs_pred, others = model_out
+                    if isinstance(imgs_pred, tuple):
+                        imgs_pred = imgs_pred[-1]
+                    if self.do_deprocess_and_preprocess:
+                        imgs_pred = self.deprocess_and_preprocess(imgs_pred)
+                    if self.crop_faces:
+                        imgs_pred = self.crop_vgg_box(imgs_pred)
+                    pred_img_embeddings = self.facenet(imgs_pred)
+                    pred_img_embeddings = torch.mean(
+                        pred_img_embeddings, 0, keepdim=True)
+                    pred_img_embedding_batches.append(pred_img_embeddings)
+            elif self.face_gen_mode == 'average_voice_embedding':
+                for i in prog_bar(
+                    range(len(self.loader.dataset)),
+                    title="[S2fEvaluator: " + \
+                        "Getting FaceNet Embedding for Predicted Images, " + \
+                            "with average voice embedding policy]",
+                    width=50):
+                    # Loop logic
+                    ######### unpack the data #########
+                    log_mels = self.loader.dataset.get_all_mel_segments_of_id(i)
+                    log_mels = log_mels.type(self.float_dtype)
+                    ###################################
+                    # Run the model as it has been run during training
+                    model_out = model(log_mels, average_voice_embedding=True)
+                    imgs_pred, others = model_out
+                    if isinstance(imgs_pred, tuple):
+                        imgs_pred = imgs_pred[-1]
+                    if self.do_deprocess_and_preprocess:
+                        imgs_pred = self.deprocess_and_preprocess(imgs_pred)
+                    if self.crop_faces:
+                        imgs_pred = self.crop_vgg_box(imgs_pred)
+                    pred_img_embeddings = self.facenet(imgs_pred)
+                    #pred_img_embeddings = torch.mean(
+                    #    pred_img_embeddings, 0, keepdim=True)
+                    pred_img_embedding_batches.append(pred_img_embeddings)
+            pred_img_embedding = torch.cat(pred_img_embedding_batches, 0)
+        #print("S2fEvaluator: pred_img_embedding shape:",
+        #      pred_img_embedding.shape)
+        model.train(mode=training_status)
+
+        return pred_img_embedding
+
+    def L1_query(self, x, y):
+        '''
+        Given x, extract top K similar features from y, based on L1 distance
+        x in shape (N_x, D)
+        y in shape (N_y, D)
+        '''
+        # (N_x, D) --> (N_x, 1, D)
+        x = x.unsqueeze(1)
+        # Initialize: (N_x, )
+        x_ids = torch.tensor(np.arange(x.shape[0])).cpu()
+        # (N_y, D) --> (1, N_y, D)
+        y = y.unsqueeze(0)
+        # Output: (N_x, N_y, D)
+        L1_table = torch.abs(x - y)
+        # (N_x, N_y, D) --> (N_x, N_y)
+        L1_table = torch.mean(L1_table, dim=-1)
+        L1_table = torch.neg(L1_table)
+        # Top K: (N_x, K)
+        top_1_vals, top_1_indices = torch.topk(L1_table, 1, dim=-1)
+        top_5_vals, top_5_indices = torch.topk(L1_table, 5, dim=-1)
+        top_10_vals, top_10_indices = torch.topk(L1_table, 10, dim=-1)
+        top_50_vals, top_50_indices = torch.topk(L1_table, 50, dim=-1)
+
+        recall_at_1 = self.in_top_k(top_1_indices.cpu(), x_ids)
+        recall_at_5 = self.in_top_k(top_5_indices.cpu(), x_ids)
+        recall_at_10 = self.in_top_k(top_10_indices.cpu(), x_ids)
+        recall_at_50 = self.in_top_k(top_50_indices.cpu(), x_ids)
+
+        return recall_at_1, recall_at_5, recall_at_10, recall_at_50
+
+    def cos_query(self, x, y):
+        '''
+        Given x, extract top K similar features from y, based on L1 distance
+        x in shape (N_x, D)
+        y in shape (N_y, D)
+        '''
+        # (N_x, D) --> (N_x, 1, D)
+        x = x.unsqueeze(1)
+        # Initialize: (N_x, )
+        x_ids = torch.tensor(np.arange(x.shape[0])).cpu()
+        # (N_y, D) --> (1, N_y, D)
+        y = y.unsqueeze(0)
+        # Output: (N_x, N_y)
+        cos_table = self.cos_sim(x, y)
+
+        # Top K: (N_x, K)
+        top_1_vals, top_1_indices = torch.topk(cos_table, 1, dim=-1)
+        top_2_vals, top_2_indices = torch.topk(cos_table, 2, dim=-1)
+        top_5_vals, top_5_indices = torch.topk(cos_table, 5, dim=-1)
+        top_10_vals, top_10_indices = torch.topk(cos_table, 10, dim=-1)
+        top_20_vals, top_20_indices = torch.topk(cos_table, 20, dim=-1)
+        top_50_vals, top_50_indices = torch.topk(cos_table, 50, dim=-1)
+
+        recall_at_1 = self.in_top_k(top_1_indices.cpu(), x_ids)
+        recall_at_2 = self.in_top_k(top_2_indices.cpu(), x_ids)
+        recall_at_5 = self.in_top_k(top_5_indices.cpu(), x_ids)
+        recall_at_10 = self.in_top_k(top_10_indices.cpu(), x_ids)
+        recall_at_20 = self.in_top_k(top_20_indices.cpu(), x_ids)
+        recall_at_50 = self.in_top_k(top_50_indices.cpu(), x_ids)
+
+        recall_tuple = (recall_at_1, recall_at_2, recall_at_5, recall_at_10, \
+            recall_at_20, recall_at_50)
+
+        return recall_tuple
+
+    def cal_ih_sim(self, x, y):
+        '''
+        For an existing face embedding distribution, calculate the inter-human
+        similarity
+
+        Arguments:
+            x in shape (N_x, D)
+            x in shape (N_y, D)
+        '''
+        # (N_x, D) --> (N_x, 1, D)
+        y = y.unsqueeze(0)
+        x = x.unsqueeze(1)
+        # Output: (N_x, N_y)
+        cos_table = self.cos_sim(x, y)
+        cos_table = cos_table.detach().cpu().numpy()
+        ih_sum = 0.0
+        for i in range(cos_table.shape[0]):
+            for j in range(cos_table.shape[1]):
+                if i != j:
+                    ih_sum = ih_sum + cos_table[i, j]
+        ih_sim = ih_sum / float(cos_table.shape[0] * (cos_table.shape[1] - 1))
+        return ih_sim
+
+    def in_top_k(self, top_k_indices, gt_labels):
+        results = []
+        for i, top_k_id in enumerate(top_k_indices):
+            gt_label = gt_labels[i]
+            #print(gt_label, top_k_id)
+            if gt_label in top_k_id:
+                results.append(1.0)
+            else:
+                results.append(0.0)
+            #print('results:', results)
+        return np.mean(results)
+
+    def get_metrics(self, model, recall_method='cos_sim', get_ih_sim=False):
+        '''
+        Arguments:
+            recall_method: the similarity metric to use for retrival
+            get_ih_sim: get the inter-human similarity
+        '''
+        pred_img_embedding = self.get_pred_img_embeddings(model)
+        L2_dist = self.L2_dist(self.dataset_embedding, pred_img_embedding)
+        L2_dist = torch.mean(L2_dist).item()
+        L1_dist = self.L1_dist(self.dataset_embedding, pred_img_embedding)
+        L1_dist = torch.mean(L1_dist).item()
+        cos_sim = self.cos_sim(self.dataset_embedding, pred_img_embedding)
+        cos_sim = torch.mean(cos_sim).item()
+
+        # We might use only 100 id for extraction
+        if self.extraction_size is None:
+            pred_emb_to_use = pred_img_embedding
+            data_emb_to_use = self.dataset_embedding
+        elif isinstance(self.extraction_size, list):
+            pred_emb_to_use = [pred_img_embedding[0:self.extraction_size[0]], \
+                pred_img_embedding[self.extraction_size[0]:self.extraction_size[1]], \
+                pred_img_embedding[self.extraction_size[1]:self.extraction_size[2]]]
+            data_emb_to_use = [self.dataset_embedding[0:self.extraction_size[0]], \
+                self.dataset_embedding[self.extraction_size[0]:self.extraction_size[1]], \
+                self.dataset_embedding[self.extraction_size[1]:self.extraction_size[2]]]
+        else:
+            pred_emb_to_use = pred_img_embedding[0:self.extraction_size]
+            data_emb_to_use = self.dataset_embedding[0:self.extraction_size]
+
+        if recall_method == 'L1':
+            if isinstance(pred_emb_to_use, list):
+                recall_temp = []
+                for i, pred_emb in enumerate(pred_emb_to_use):
+                    recall_temp.append(self.L1_query(pred_emb, data_emb_to_use[i]))
+                recall_tuple = tuple(np.mean(np.array(recall_temp), axis=0))
+            else:
+                recall_tuple = self.L1_query(pred_emb_to_use, data_emb_to_use)
+        elif recall_method == 'cos_sim':
+            if isinstance(pred_emb_to_use, list):
+                recall_temp = []
+                for i, pred_emb in enumerate(pred_emb_to_use):
+                    recall_temp.append(self.cos_query(pred_emb, data_emb_to_use[i]))
+                recall_tuple = tuple(np.mean(np.array(recall_temp), axis=0))
+            else:
+                recall_tuple = self.cos_query(pred_emb_to_use, data_emb_to_use)
+
+        if get_ih_sim:
+            ih_sim = self.cal_ih_sim(pred_img_embedding, self.dataset_embedding)
+            return L2_dist, L1_dist, cos_sim, recall_tuple, ih_sim
+        else:
+            return L2_dist, L1_dist, cos_sim, recall_tuple
+
+    def get_faces_from_different_segments(self, model, output_dir):
+        #gfile.MkDir(output_dir)
+        os.makedirs(output_dir, exist_ok=True)
+        temp_loader = deepcopy(self.loader)
+        for i in prog_bar(
+            range(len(self.loader.dataset)),
+            title="[S2fEvaluator: " + \
+                "Generating Faces from Different Speech Segments]",
+            width=50):
+            ######### unpack the data #########
+            imgs = temp_loader.dataset.get_all_faces_of_id(i)
+            log_mels = temp_loader.dataset.get_all_mel_segments_of_id(i)
+            imgs = imgs.cuda()
+            log_mels = log_mels.type(self.float_dtype) #cuda()
+            #human_ids = human_ids.type(long_dtype)
+            ###################################
+            with torch.no_grad():
+                model_out = model(log_mels)
+            imgs_pred, _ = model_out
+            if isinstance(imgs_pred, tuple):
+                imgs_pred = imgs_pred[-1]
+            imgs = imagenet_deprocess_batch(
+                imgs, normalize_method=self.image_normalize_method)
+            imgs_pred = imagenet_deprocess_batch(
+                imgs_pred, normalize_method=self.image_normalize_method)
+            #print(imgs.shape, imgs_pred.shape)
+            identity_dir = os.path.join(output_dir, str(i))
+            gfile.MkDir(identity_dir)
+
+            for j in range(imgs.shape[0]):
+                img_np = imgs[j].numpy().transpose(1, 2, 0)
+                img_path = os.path.join(identity_dir, 'origin_%d.png' % j)
+                imwrite(img_path, img_np)
+
+            for k in range(imgs_pred.shape[0]):
+                img_np = imgs_pred[k].numpy().transpose(1, 2, 0)
+                img_path = os.path.join(identity_dir, 'pred_%d.png' % k)
+                imwrite(img_path, img_np)
+
+    def L2_distances(self, x, y=None):
+        '''
+        Input: x is a Nxd matrix
+               y is an optional Mxd matirx
+        Output: dist is a NxM matrix where dist[i,j] is the square norm between x[i,:] and y[j,:]
+                if y is not given then use 'y=x'.
+        i.e. dist[i,j] = ||x[i,:]-y[j,:]||^2
+        '''
+        if y is not None:
+             differences = x.unsqueeze(1) - y.unsqueeze(0)
+        else:
+            differences = x.unsqueeze(1) - x.unsqueeze(0)
+        distances = torch.sum(differences * differences, -1)
+        return distances
diff --git a/modeling/sf2f/utils/training_utils.py b/modeling/sf2f/utils/training_utils.py
new file mode 100644
index 0000000..fd1fd76
--- /dev/null
+++ b/modeling/sf2f/utils/training_utils.py
@@ -0,0 +1,250 @@
+import os
+import json
+import math
+from collections import defaultdict
+import time
+
+import numpy as np
+import torch
+import torch.optim as optim
+import torch.nn as nn
+import torch.nn.functional as F
+from scipy.stats import entropy
+from torchvision.models import inception_v3
+from pyprind import prog_bar
+
+from datasets import imagenet_deprocess_batch, fast_mel_deprocess_batch
+import datasets
+import models
+from utils.metrics import jaccard
+from utils import tensor2im
+from utils.visualization.plot import get_np_plot, plot_mel_spectrogram, \
+    plot_attention
+
+from scripts.compute_vggface_score import get_vggface_score
+
+
+def add_loss_with_tensor(total_loss,
+                         curr_loss,
+                         loss_dict,
+                         loss_name,
+                         weight=1):
+    curr_loss = curr_loss * weight
+    loss_dict[loss_name] = curr_loss
+    if total_loss is not None:
+        total_loss += curr_loss
+    else:
+        total_loss = curr_loss
+    return total_loss
+
+def add_loss(total_loss, curr_loss, loss_dict, loss_name, weight=1):
+    curr_loss = curr_loss * weight
+    loss_dict[loss_name] = curr_loss.item()
+    if total_loss is not None:
+        total_loss += curr_loss
+    else:
+        total_loss = curr_loss
+    return total_loss
+
+
+def visualize_sample(model,
+                     imgs,
+                     log_mels,
+                     image_normalize_method,
+                     mel_normalize_method='vox_mel',
+                     visualize_attn=False):
+    '''
+    Prepare data for tensorboard
+    '''
+    samples = []
+    # add the ground-truth images
+    samples.append(imgs[:1])
+
+    with torch.no_grad():
+        model_out = model(log_mels)
+        imgs_pred, others = model_out
+        # add the reconstructed images
+        if isinstance(imgs_pred, tuple):
+            if len(imgs_pred) >= 2:
+                low_imgs_pred = imgs_pred[0]
+                # Rescale
+                while low_imgs_pred.size()[2] != imgs_pred[-1].size()[2]:
+                    low_imgs_pred = F.interpolate(
+                        low_imgs_pred, scale_factor=2, mode='nearest')
+                samples.append(low_imgs_pred[:1])
+            if len(imgs_pred) == 3:
+                mid_imgs_pred = imgs_pred[1]
+                while mid_imgs_pred.size()[2] != imgs_pred[-1].size()[2]:
+                    mid_imgs_pred = F.interpolate(
+                        mid_imgs_pred, scale_factor=2, mode='nearest')
+                samples.append(mid_imgs_pred[:1])
+            imgs_pred = imgs_pred[-1]
+        samples.append(imgs_pred[:1])
+
+        log_mels_de = fast_mel_deprocess_batch(log_mels, mel_normalize_method)
+        log_mel = log_mels_de[0].cpu().detach().numpy()
+
+        if visualize_attn:
+            attn_weights = model.module.decoder.return_attn_weights()
+            attn_weight = attn_weights[0].cpu().detach().numpy()
+
+    samples = torch.cat(samples, dim=3)
+    samples = {
+            "samples": tensor2im(
+                imagenet_deprocess_batch(
+                    samples,
+                    rescale=False,
+                    normalize_method=image_normalize_method
+                ).squeeze(0)
+            ),
+            #"mel_spectrogram": get_np_plot(
+            #    plot_mel_spectrogram(
+            #        log_mel)
+            #)
+    }
+    if visualize_attn:
+        samples['attention_weights'] = get_np_plot(
+            plot_attention(attn_weight))
+
+    # Draw Scene Graphs
+    #sg_array = draw_scene_graph(objs[obj_to_img == 0],
+    #                 triples[triple_to_img == 0],
+    #                 vocab=vocab)
+    #samples["scene_graph"] = sg_array
+
+    return samples
+
+
+def check_model(args,
+                options,
+                t,
+                loader,
+                model):
+    training_status = model.training
+    model.eval()
+    float_dtype = torch.cuda.FloatTensor
+    long_dtype = torch.cuda.LongTensor
+    num_samples = 0
+    all_losses = defaultdict(list)
+    inception_module = nn.DataParallel(
+        inception_v3(
+            pretrained=True,
+            transform_input=False).cuda())
+    inception_module.eval()
+    preds = []
+    images = []
+    with torch.no_grad():
+        # To avoid influence to the running_mean/var of BatchNorm Layers
+        for batch in prog_bar(
+            loader,
+            title="[Validating Inception Score and Pixel Loss]",
+            width=50):
+            # Loop logic
+            ######### unpack the data #########
+            imgs, log_mels, human_ids = batch
+            imgs = imgs.cuda()
+            log_mels = log_mels.type(float_dtype)
+            human_ids = human_ids.type(long_dtype)
+            ###################################
+            # Run the model as it has been run during training
+            model_out = model(log_mels)
+            imgs_pred, others = model_out
+
+            skip_pixel_loss = False
+            total_loss, losses = calculate_model_losses(
+                options["optim"],
+                skip_pixel_loss,
+                imgs,
+                imgs_pred,
+                get_item=True)
+
+            if isinstance(imgs_pred, tuple):
+                imgs_pred = imgs_pred[-1]
+            image = imagenet_deprocess_batch(
+                imgs_pred,
+                normalize_method=options["data"]["data_opts"]["image_normalize_method"])
+            for i in range(image.shape[0]):
+                img_np = image[i].numpy().transpose(1, 2, 0)
+                images.append(img_np)
+
+            # check inception scores
+            x = F.interpolate(imgs_pred, (299, 299), mode="bilinear")
+            x = inception_module(x)
+            preds.append(F.softmax(x).cpu().numpy())
+
+            for loss_name, loss_val in losses.items():
+                all_losses[loss_name].append(loss_val)
+            num_samples += imgs.size(0)
+
+        samples = visualize_sample(
+            model,
+            imgs,
+            log_mels,
+            options["data"]["data_opts"]["image_normalize_method"],
+            visualize_attn=options['eval'].get('visualize_attn', False))
+        mean_losses = {k: np.mean(v) for k, v in all_losses.items()}
+
+        # calculate the inception scores
+        splits = 5
+        preds = np.concatenate(preds, axis=0)
+        # Now compute the mean kl-div
+        split_scores = []
+        N = preds.shape[0]
+        for k in range(splits):
+            part = preds[k * (N // splits): (k+1) * (N // splits), :]
+            py = np.mean(part, axis=0)
+            scores = []
+            for i in range(part.shape[0]):
+                pyx = part[i, :]
+                scores.append(entropy(pyx, py))
+            split_scores.append(np.exp(np.mean(scores)))
+        inception_score = (np.mean(split_scores), np.std(split_scores))
+
+    vf_score = get_vggface_score(images)
+
+    out = [mean_losses, samples, inception_score, vf_score]
+    model.train(mode=training_status)
+    return tuple(out)
+
+
+def calculate_model_losses(opts,
+                           skip_pixel_loss,
+                           imgs,
+                           imgs_pred,
+                           get_item=False):
+    if get_item:
+        add_loss_fn = add_loss
+    else:
+        add_loss_fn = add_loss_with_tensor
+    total_loss = torch.zeros(1).to(imgs)
+    losses = {}
+
+    l1_pixel_weight = opts["l1_pixel_loss_weight"]
+    if skip_pixel_loss:
+        l1_pixel_weight = 0
+
+    if isinstance(imgs_pred, tuple):
+        # Multi-Resolution Pixel Loss
+        for i, img_pred in enumerate(imgs_pred):
+            loss_name = 'L1_pixel_loss_%d' % i
+            img = imgs
+            while img.size()[2] != img_pred.size()[2]:
+                img = F.interpolate(img, scale_factor=0.5, mode='nearest')
+            l1_pixel_loss = F.l1_loss(img_pred, img)
+            total_loss = add_loss_fn(
+                total_loss,
+                l1_pixel_loss,
+                losses,
+                loss_name,
+                l1_pixel_weight)
+    else:
+        # Single Resolution
+        l1_pixel_loss = F.l1_loss(imgs_pred, imgs)
+        total_loss = add_loss_fn(
+            total_loss,
+            l1_pixel_loss,
+            losses,
+            'L1_pixel_loss',
+            l1_pixel_weight)
+
+    return total_loss, losses
diff --git a/modeling/sf2f/utils/utils.py b/modeling/sf2f/utils/utils.py
new file mode 100644
index 0000000..0157a82
--- /dev/null
+++ b/modeling/sf2f/utils/utils.py
@@ -0,0 +1,89 @@
+import time
+import inspect
+import subprocess
+from contextlib import contextmanager
+
+import torch
+from torch import nn
+
+def int_tuple(s):
+    return tuple(int(i) for i in s.split(','))
+
+
+def float_tuple(s):
+    return tuple(float(i) for i in s.split(','))
+
+
+def str_tuple(s):
+    return tuple(s.split(','))
+
+
+def bool_flag(s):
+    if s == '1':
+        return True
+    elif s == '0':
+        return False
+    msg = 'Invalid value "%s" for bool flag (should be 0 or 1)'
+    raise ValueError(msg % s)
+
+
+def lineno():
+    return inspect.currentframe().f_back.f_lineno
+
+
+def get_gpu_memory():
+    torch.cuda.synchronize()
+    opts = [
+        'nvidia-smi', '-q', '--gpu=' + str(0), '|', 'grep', '"Used GPU Memory"'
+    ]
+    cmd = str.join(' ', opts)
+    ps = subprocess.Popen(
+        cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+    output = ps.communicate()[0].decode('utf-8')
+    output = output.split("\n")[1].split(":")
+    consumed_mem = int(output[1].strip().split(" ")[0])
+    return consumed_mem
+
+
+@contextmanager
+def timeit(msg, should_time=True):
+    if should_time:
+        torch.cuda.synchronize()
+        t0 = time.time()
+    yield
+    if should_time:
+        torch.cuda.synchronize()
+        t1 = time.time()
+        duration = (t1 - t0) * 1000.0
+        print('%s: %.2f ms' % (msg, duration))
+
+
+class LossManager(object):
+    def __init__(self):
+        self.total_loss = None
+        self.all_losses = {}
+
+    def add_loss(self, loss, name, weight=1.0):
+        cur_loss = loss * weight
+        if self.total_loss is not None:
+            self.total_loss += cur_loss
+        else:
+            self.total_loss = cur_loss
+
+        self.all_losses[name] = cur_loss.data.cpu().item()
+
+    def items(self):
+        return self.all_losses.items()
+
+
+def load_my_state_dict(model, state_dict):
+    print('Loading my state_dict')
+    own_state = model.state_dict()
+    for name, param in state_dict.items():
+        if name not in own_state:
+            continue
+        if isinstance(param, nn.Parameter):
+            # backwards compatibility for serialized parameters
+            param = param.data
+        own_state[name].copy_(param)
+        print('{} Loaded!'.format(name))
diff --git a/modeling/sf2f/utils/vad_ex.py b/modeling/sf2f/utils/vad_ex.py
new file mode 100644
index 0000000..f8b489e
--- /dev/null
+++ b/modeling/sf2f/utils/vad_ex.py
@@ -0,0 +1,174 @@
+import collections
+import contextlib
+import sys
+import wave
+from pydub import AudioSegment
+import webrtcvad
+
+# Modified https://github.com/wiseman/py-webrtcvad/blob/master/example.py
+
+def read_wave(path):
+    """Reads a .wav file.
+    Takes the path, and returns (PCM audio data, sample rate).
+    """
+    with contextlib.closing(wave.open(path, 'rb')) as wf:
+        num_channels = wf.getnchannels()
+        #assert num_channels == 1
+        sample_width = wf.getsampwidth()
+        assert sample_width == 2
+        sample_rate = wf.getframerate()
+        #assert sample_rate in (8000, 16000, 32000)
+        pcm_data = wf.readframes(wf.getnframes())
+        return pcm_data, sample_rate
+
+def read_libri(path):
+    mf = AudioSegment.from_file(path, "wav")
+    sample_rate = mf.frame_rate
+    pcm_data = mf.raw_data
+    return pcm_data, sample_rate
+
+
+def read_m4a(path):
+    mf = AudioSegment.from_file(path, "m4a")
+    sample_rate = mf.frame_rate
+    pcm_data = mf.raw_data
+    return pcm_data, sample_rate
+
+
+def write_wave(path, audio, sample_rate):
+    """Writes a .wav file.
+    Takes path, PCM audio data, and sample rate.
+    """
+    with contextlib.closing(wave.open(path, 'wb')) as wf:
+        wf.setnchannels(1)
+        wf.setsampwidth(2)
+        wf.setframerate(sample_rate)
+        wf.writeframes(audio)
+
+
+class Frame(object):
+    """Represents a "frame" of audio data."""
+    def __init__(self, bytes, timestamp, duration):
+        self.bytes = bytes
+        self.timestamp = timestamp
+        self.duration = duration
+
+
+def frame_generator(frame_duration_ms, audio, sample_rate):
+    """Generates audio frames from PCM audio data.
+    Takes the desired frame duration in milliseconds, the PCM data, and
+    the sample rate.
+    Yields Frames of the requested duration.
+    """
+    n = int(sample_rate * (frame_duration_ms / 1000.0) * 2)
+    offset = 0
+    timestamp = 0.0
+    duration = (float(n) / sample_rate) / 2.0
+    while offset + n < len(audio):
+        yield Frame(audio[offset:offset + n], timestamp, duration)
+        timestamp += duration
+        offset += n
+
+
+def vad_collector(sample_rate, frame_duration_ms,
+                  padding_duration_ms, vad, frames):
+    """Filters out non-voiced audio frames.
+    Given a webrtcvad.Vad and a source of audio frames, yields only
+    the voiced audio.
+    Uses a padded, sliding window algorithm over the audio frames.
+    When more than 90% of the frames in the window are voiced (as
+    reported by the VAD), the collector triggers and begins yielding
+    audio frames. Then the collector waits until 90% of the frames in
+    the window are unvoiced to detrigger.
+    The window is padded at the front and back to provide a small
+    amount of silence or the beginnings/endings of speech around the
+    voiced frames.
+    Arguments:
+    sample_rate - The audio sample rate, in Hz.
+    frame_duration_ms - The frame duration in milliseconds.
+    padding_duration_ms - The amount to pad the window, in milliseconds.
+    vad - An instance of webrtcvad.Vad.
+    frames - a source of audio frames (sequence or generator).
+    Returns: A generator that yields PCM audio data.
+    """
+    num_padding_frames = int(padding_duration_ms / frame_duration_ms)
+    # We use a deque for our sliding window/ring buffer.
+    ring_buffer = collections.deque(maxlen=num_padding_frames)
+    # We have two states: TRIGGERED and NOTTRIGGERED. We start in the
+    # NOTTRIGGERED state.
+    triggered = False
+
+    voiced_frames = []
+    for frame in frames:
+        is_speech = vad.is_speech(frame.bytes, sample_rate)
+
+        #sys.stdout.write('1' if is_speech else '0')
+        if not triggered:
+            ring_buffer.append((frame, is_speech))
+            num_voiced = len([f for f, speech in ring_buffer if speech])
+            # If we're NOTTRIGGERED and more than 90% of the frames in
+            # the ring buffer are voiced frames, then enter the
+            # TRIGGERED state.
+            if num_voiced > 0.9 * ring_buffer.maxlen:
+                triggered = True
+                #sys.stdout.write('+(%s)' % (ring_buffer[0][0].timestamp,))
+                # We want to yield all the audio we see from now until
+                # we are NOTTRIGGERED, but we have to start with the
+                # audio that's already in the ring buffer.
+                for f, s in ring_buffer:
+                    voiced_frames.append(f)
+                ring_buffer.clear()
+        else:
+            # We're in the TRIGGERED state, so collect the audio data
+            # and add it to the ring buffer.
+            voiced_frames.append(frame)
+            ring_buffer.append((frame, is_speech))
+            num_unvoiced = len([f for f, speech in ring_buffer if not speech])
+            # If more than 90% of the frames in the ring buffer are
+            # unvoiced, then enter NOTTRIGGERED and yield whatever
+            # audio we've collected.
+            if num_unvoiced > 0.9 * ring_buffer.maxlen:
+                #sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
+                triggered = False
+                yield b''.join([f.bytes for f in voiced_frames])
+                ring_buffer.clear()
+                voiced_frames = []
+    #if triggered:
+    #    sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
+    #sys.stdout.write('\n')
+    # If we have any leftover voiced audio when we run out of input,
+    # yield it.
+    if voiced_frames:
+        yield b''.join([f.bytes for f in voiced_frames])
+
+
+def main(args):
+    if len(args) != 2:
+        sys.stderr.write(
+            'Usage: example.py <aggressiveness> <path to wav file>\n')
+        sys.exit(1)
+    audio, sample_rate = read_wave(args[1])
+    #audio, sample_rate = read_m4a(args[1])
+    #audio, sample_rate = read_libri(args[1])
+    vad = webrtcvad.Vad(int(args[0]))
+    frames = frame_generator(30, audio, sample_rate)
+    frames = list(frames)
+    segments = vad_collector(sample_rate, 30, 300, vad, frames)
+    total_wav = b""
+    for i, segment in enumerate(segments):
+    	total_wav += segment
+
+
+    path = 'test.wav'
+    write_wave(path, total_wav, sample_rate)
+
+
+if __name__ == '__main__':
+    #main(sys.argv[1:])
+    import os
+    WAV_DIR = os.path.join('./data', 'VoxCeleb', 'raw_wav')
+    wav_path = 'vox1/test/id10270/zjwijMp0Qyw/00001.wav'
+    wav_path = os.path.join(WAV_DIR, wav_path)
+
+    audio, sample_rate = read_wave(wav_path)
+    print(audio, sample_rate)
diff --git a/modeling/sf2f/utils/visualization/__init__.py b/modeling/sf2f/utils/visualization/__init__.py
new file mode 100644
index 0000000..2b54423
--- /dev/null
+++ b/modeling/sf2f/utils/visualization/__init__.py
@@ -0,0 +1,2 @@
+from .vis import *
+# from .visualizer import Visualizer
diff --git a/modeling/sf2f/utils/visualization/html.py b/modeling/sf2f/utils/visualization/html.py
new file mode 100644
index 0000000..34035ba
--- /dev/null
+++ b/modeling/sf2f/utils/visualization/html.py
@@ -0,0 +1,65 @@
+import dominate
+from dominate.tags import *
+import os
+
+
+class HTML:
+    def __init__(self, web_dir, title, reflesh=0):
+        self.title = title
+        self.web_dir = web_dir
+        self.img_dir = os.path.join(self.web_dir, 'images')
+        if not os.path.exists(self.web_dir):
+            os.makedirs(self.web_dir)
+        if not os.path.exists(self.img_dir):
+            os.makedirs(self.img_dir)
+        # print(self.img_dir)
+
+        self.doc = dominate.document(title=title)
+        if reflesh > 0:
+            with self.doc.head:
+                meta(http_equiv="reflesh", content=str(reflesh))
+
+    def get_image_dir(self):
+        return self.img_dir
+
+    def add_header(self, str):
+        with self.doc:
+            h3(str)
+
+    def add_table(self, border=1):
+        self.t = table(border=border, style="table-layout: fixed;")
+        self.doc.add(self.t)
+
+    def add_images(self, ims, txts, links, width=400):
+        self.add_table()
+        with self.t:
+            with tr():
+                for im, txt, link in zip(ims, txts, links):
+                    with td(style="word-wrap: break-word;", halign="center", valign="top"):
+                        with p():
+                            with a(href=os.path.join('images', link)):
+                                img(style="width:%dpx" %
+                                    width, src=os.path.join('images', im))
+                            br()
+                            p(txt)
+
+    def save(self):
+        html_file = '%s/index.html' % self.web_dir
+        f = open(html_file, 'wt')
+        f.write(self.doc.render())
+        f.close()
+
+
+if __name__ == '__main__':
+    html = HTML('web/', 'test_html')
+    html.add_header('hello world')
+
+    ims = []
+    txts = []
+    links = []
+    for n in range(4):
+        ims.append('image_%d.png' % n)
+        txts.append('text_%d' % n)
+        links.append('image_%d.png' % n)
+    html.add_images(ims, txts, links)
+    html.save()
diff --git a/modeling/sf2f/utils/visualization/plot.py b/modeling/sf2f/utils/visualization/plot.py
new file mode 100644
index 0000000..7f24e9d
--- /dev/null
+++ b/modeling/sf2f/utils/visualization/plot.py
@@ -0,0 +1,199 @@
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import io
+import tensorflow as tf
+import PIL
+import numpy as np
+
+
+def plot_attention(attn_mat, path=None, info=None):
+    num_query_head, seq_len = attn_mat.shape
+    fig, ax = plt.subplots()
+    fig.set_size_inches(10, 10)
+
+    querys = ['query {}'.format(i) for i in range(num_query_head)]
+    steps = ['steps {}'.format(j) for j in range(seq_len)]
+
+    im, cbar = heatmap(attn_mat, querys, steps, ax=ax,
+                       cmap="YlGn", cbarlabel="Attention Weight")
+    annotate_heatmap(im)
+
+    fig.tight_layout()
+    if path is not None:
+        plt.savefig(path, format='png')
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    plt.clf()
+    plt.cla()
+    plt.close(fig)
+    return buf
+
+
+def plot_mel_spectrogram(log_mels,
+                         path=None,
+                         info=None,
+                         colorbar=True,
+                         label=True,
+                         coordinate=True,
+                         remove_boarder=False):
+    fig, ax = plt.subplots()
+    if not coordinate:
+        #ax.format_coord = lambda x, y: ''
+        plt.axis('off')
+    fig.set_size_inches(5, 3)
+    im = ax.imshow(
+        log_mels,
+        aspect='auto',
+        origin='lower',
+        interpolation='none')
+    im.set_cmap('viridis')
+    if colorbar:
+        fig.colorbar(im, ax=ax)
+    xlabel = 'Time'
+    if info is not None:
+        xlabel += '\n\n' + info
+    if label:
+        plt.xlabel(xlabel)
+        plt.ylabel('Frequency')
+    plt.tight_layout()
+    if path is not None:
+        plt.savefig(path, format='png')
+    buf = io.BytesIO()
+    if not remove_boarder:
+        plt.savefig(buf, format='png')
+    else:
+        plt.savefig(buf, format='png', bbox_inches='tight')
+    buf.seek(0)
+    plt.clf()
+    plt.cla()
+    plt.close(fig)
+    return buf
+
+
+def get_np_plot(plot_buf):
+    image = PIL.Image.open(plot_buf)
+    WW, HH = image.size
+
+    return np.array(image)
+
+
+def heatmap(data, row_labels, col_labels, ax=None,
+            cbar_kw={}, cbarlabel="", **kwargs):
+    """
+    Create a heatmap from a numpy array and two lists of labels.
+
+    Parameters
+    ----------
+    data
+        A 2D numpy array of shape (N, M).
+    row_labels
+        A list or array of length N with the labels for the rows.
+    col_labels
+        A list or array of length M with the labels for the columns.
+    ax
+        A `matplotlib.axes.Axes` instance to which the heatmap is plotted.  If
+        not provided, use current axes or create a new one.  Optional.
+    cbar_kw
+        A dictionary with arguments to `matplotlib.Figure.colorbar`.  Optional.
+    cbarlabel
+        The label for the colorbar.  Optional.
+    **kwargs
+        All other arguments are forwarded to `imshow`.
+    """
+
+    if not ax:
+        ax = plt.gca()
+
+    # Plot the heatmap
+    im = ax.imshow(data, **kwargs)
+
+    # Create colorbar
+    cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
+    cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom")
+
+    # We want to show all ticks...
+    ax.set_xticks(np.arange(data.shape[1]))
+    ax.set_yticks(np.arange(data.shape[0]))
+    # ... and label them with the respective list entries.
+    ax.set_xticklabels(col_labels)
+    ax.set_yticklabels(row_labels)
+
+    # Let the horizontal axes labeling appear on top.
+    ax.tick_params(top=True, bottom=False,
+                   labeltop=True, labelbottom=False)
+
+    # Rotate the tick labels and set their alignment.
+    plt.setp(ax.get_xticklabels(), rotation=-30, ha="right",
+             rotation_mode="anchor")
+
+    # Turn spines off and create white grid.
+    for edge, spine in ax.spines.items():
+        spine.set_visible(False)
+
+    ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)
+    ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)
+    ax.grid(which="minor", color="w", linestyle='-', linewidth=3)
+    ax.tick_params(which="minor", bottom=False, left=False)
+
+    return im, cbar
+
+
+def annotate_heatmap(im, data=None, valfmt="{x:.2f}",
+                     textcolors=["black", "white"],
+                     threshold=None, **textkw):
+    """
+    A function to annotate a heatmap.
+
+    Parameters
+    ----------
+    im
+        The AxesImage to be labeled.
+    data
+        Data used to annotate.  If None, the image's data is used.  Optional.
+    valfmt
+        The format of the annotations inside the heatmap.  This should either
+        use the string format method, e.g. "$ {x:.2f}", or be a
+        `matplotlib.ticker.Formatter`.  Optional.
+    textcolors
+        A list or array of two color specifications.  The first is used for
+        values below a threshold, the second for those above.  Optional.
+    threshold
+        Value in data units according to which the colors from textcolors are
+        applied.  If None (the default) uses the middle of the colormap as
+        separation.  Optional.
+    **kwargs
+        All other arguments are forwarded to each call to `text` used to create
+        the text labels.
+    """
+
+    if not isinstance(data, (list, np.ndarray)):
+        data = im.get_array()
+
+    # Normalize the threshold to the images color range.
+    if threshold is not None:
+        threshold = im.norm(threshold)
+    else:
+        threshold = im.norm(data.max())/2.
+
+    # Set default alignment to center, but allow it to be
+    # overwritten by textkw.
+    kw = dict(horizontalalignment="center",
+              verticalalignment="center")
+    kw.update(textkw)
+
+    # Get the formatter in case a string is supplied
+    if isinstance(valfmt, str):
+        valfmt = matplotlib.ticker.StrMethodFormatter(valfmt)
+
+    # Loop over the data and create a `Text` for each "pixel".
+    # Change the text's color depending on the data.
+    texts = []
+    for i in range(data.shape[0]):
+        for j in range(data.shape[1]):
+            kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)])
+            text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)
+            texts.append(text)
+
+    return texts
diff --git a/modeling/sf2f/utils/visualization/tsne.py b/modeling/sf2f/utils/visualization/tsne.py
new file mode 100644
index 0000000..62f8791
--- /dev/null
+++ b/modeling/sf2f/utils/visualization/tsne.py
@@ -0,0 +1,40 @@
+'''
+Visualize embeddings via TSNE
+'''
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from sklearn.manifold import TSNE
+
+
+def visualize_distribution(
+        embeddings,
+        num_class,
+        sample_per_class,
+        class_names=None):
+    '''
+    Inputs:
+        embeddings: numpy array with shape
+            [num_class * sample_per_class, emb_dim]
+    '''
+    emb_2d = TSNE(n_components=2).fit_transform(embeddings)
+    emb_2d = emb_2d.reshape([num_class, sample_per_class, 2])
+    fig, axes = plt.subplots()
+    fig.set_size_inches(18.5, 10.5)
+    if class_names is None:
+        class_names = [' ' for i in range(num_class)]
+    color_list = ['b', 'g', 'r', 'c', 'm', 'y', 'k', \
+        'orange', 'purple', 'pink']
+    for i, class_name in enumerate(class_names):
+        x = emb_2d[i, :, 0]
+        y = emb_2d[i, :, 1]
+        axes.scatter(x=x, y=y, c=color_list[i], label=class_name)
+
+    axes.set_xlabel("Component 1")
+    axes.set_ylabel("Component 2")
+    axes.legend(loc='upper left')
+    axes.set_xlim(-300, 300)
+    axes.set_ylim(-300, 300)
+    plt.show()
diff --git a/modeling/sf2f/utils/visualization/vis.py b/modeling/sf2f/utils/visualization/vis.py
new file mode 100644
index 0000000..de412bd
--- /dev/null
+++ b/modeling/sf2f/utils/visualization/vis.py
@@ -0,0 +1,219 @@
+import tempfile
+import os
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Rectangle
+from imageio import imread
+import time
+
+
+"""
+Utilities for making visualizations.
+"""
+
+
+def draw_layout(vocab, objs, boxes, masks=None, size=256,
+                show_boxes=False, bgcolor=(0, 0, 0)):
+    if bgcolor == 'white':
+        bgcolor = (255, 255, 255)
+
+    cmap = plt.get_cmap('rainbow')
+    colors = cmap(np.linspace(0, 1, len(objs)))
+
+    with torch.no_grad():
+        objs = objs.cpu().clone()
+        boxes = boxes.cpu().clone()
+        boxes *= size
+
+        if masks is not None:
+            masks = masks.cpu().clone()
+
+        bgcolor = np.asarray(bgcolor)
+        bg = np.ones((size, size, 1)) * bgcolor
+        plt.imshow(bg.astype(np.uint8))
+
+        plt.gca().set_xlim(0, size)
+        plt.gca().set_ylim(size, 0)
+        plt.gca().set_aspect(1.0, adjustable='box')
+
+        for i, obj in enumerate(objs):
+            name = vocab['object_idx_to_name'][obj]
+            if name == '__image__':
+                continue
+            box = boxes[i]
+
+            if masks is None:
+                continue
+            mask = masks[i].numpy()
+            mask /= mask.max()
+
+            r, g, b, a = colors[i]
+            colored_mask = mask[:, :, None] * np.asarray(colors[i])
+
+            x0, y0, x1, y1 = box
+            plt.imshow(colored_mask, extent=(x0, x1, y1, y0),
+                       interpolation='bicubic', alpha=1.0)
+
+        if show_boxes:
+            for i, obj in enumerate(objs):
+                name = vocab['object_idx_to_name'][obj]
+                if name == '__image__':
+                    continue
+                box = boxes[i]
+
+                draw_box(box, colors[i], name)
+
+
+def draw_box(box, color, text=None):
+    """
+    Draw a bounding box using pyplot, optionally with a text box label.
+
+    Inputs:
+    - box: Tensor or list with 4 elements: [x0, y0, x1, y1] in [0, W] x [0, H]
+           coordinate system.
+    - color: pyplot color to use for the box.
+    - text: (Optional) String; if provided then draw a label for this box.
+    """
+    TEXT_BOX_HEIGHT = 10
+    if torch.is_tensor(box) and box.dim() == 2:
+        box = box.view(-1)
+        assert box.size(0) == 4
+    x0, y0, x1, y1 = box
+    assert y1 > y0, box
+    assert x1 > x0, box
+    w, h = x1 - x0, y1 - y0
+    rect = Rectangle((x0, y0), w, h, fc='none', lw=2, ec=color)
+    plt.gca().add_patch(rect)
+    if text is not None:
+        text_rect = Rectangle(
+            (x0, y0), w, TEXT_BOX_HEIGHT, fc=color, alpha=0.5)
+        plt.gca().add_patch(text_rect)
+        tx = 0.5 * (x0 + x1)
+        ty = y0 + TEXT_BOX_HEIGHT / 2.0
+        plt.text(tx, ty, text, va='center', ha='center')
+
+
+def draw_scene_graph(objs, triples, vocab=None, **kwargs):
+    """
+    Use GraphViz to draw a scene graph. If vocab is not passed then we assume
+    that objs and triples are python lists containing strings for object and
+    relationship names.
+
+    Using this requires that GraphViz is installed. On Ubuntu 16.04 this is easy:
+    sudo apt-get install graphviz
+    """
+    output_filename = kwargs.pop('output_filename',
+                                 'graph-{}.png'.format(time.strftime("%Y%m%d-%H%M%S")))
+    orientation = kwargs.pop('orientation', 'V')
+    edge_width = kwargs.pop('edge_width', 6)
+    arrow_size = kwargs.pop('arrow_size', 1.5)
+    binary_edge_weight = kwargs.pop('binary_edge_weight', 1.2)
+    ignore_dummies = kwargs.pop('ignore_dummies', True)
+
+    if orientation not in ['V', 'H']:
+        raise ValueError('Invalid orientation "%s"' % orientation)
+    rankdir = {'H': 'LR', 'V': 'TD'}[orientation]
+
+    if vocab is not None:
+        # Decode object and relationship names
+        assert torch.is_tensor(objs)
+        assert torch.is_tensor(triples)
+        objs_list, triples_list = [], []
+        for i in range(objs.size(0)):
+            objs_list.append(vocab['object_idx_to_name'][objs[i].item()])
+        for i in range(triples.size(0)):
+            s = triples[i, 0].item()
+            p = vocab['pred_idx_to_name'][triples[i, 1].item()]
+            # p = triples[i, 1].item()
+            o = triples[i, 2].item()
+            triples_list.append([s, p, o])
+        objs, triples = objs_list, triples_list
+
+    # General setup, and style for object nodes
+    lines = [
+        'digraph{',
+        'graph [size="5,3",ratio="compress",dpi="300",bgcolor="transparent"]',
+        'rankdir=%s' % rankdir,
+        'nodesep="0.5"',
+        'ranksep="0.5"',
+        'node [shape="box",style="rounded,filled",fontsize="48",color="none"]',
+        'node [fillcolor="lightpink1"]',
+    ]
+    # Output nodes for objects
+    for i, obj in enumerate(objs):
+        if ignore_dummies and obj == '__image__':
+            continue
+        lines.append('%d [label="%s"]' % (i, obj))
+
+    # Output relationships
+    next_node_id = len(objs)
+    lines.append('node [fillcolor="lightblue1"]')
+    for s, p, o in triples:
+        if ignore_dummies and p == '__in_image__':
+            continue
+        lines += [
+            '%d [label="%s"]' % (next_node_id, p),
+            '%d->%d [penwidth=%f,arrowsize=%f,weight=%f]' % (
+                s, next_node_id, edge_width, arrow_size, binary_edge_weight),
+            '%d->%d [penwidth=%f,arrowsize=%f,weight=%f]' % (
+                next_node_id, o, edge_width, arrow_size, binary_edge_weight)
+        ]
+        next_node_id += 1
+    lines.append('}')
+
+    # Now it gets slightly hacky. Write the graphviz spec to a temporary
+    # text file
+    ff, dot_filename = tempfile.mkstemp()
+    with open(dot_filename, 'w') as f:
+        for line in lines:
+            f.write('%s\n' % line)
+    os.close(ff)
+
+    # Shell out to invoke graphviz; this will save the resulting image to disk,
+    # so we read it, delete it, then return it.
+    output_format = os.path.splitext(output_filename)[1][1:]
+    os.system('dot -T%s %s > %s' %
+              (output_format, dot_filename, output_filename))
+    os.remove(dot_filename)
+    try:
+        img = imread(output_filename)
+        os.remove(output_filename)
+    except:
+        print("####################################")
+        print("Fail to load image: {}".format(output_filename))
+        print("####################################")
+        img = np.zeros((400, 400, 3))
+
+    return np.array(img)
+
+
+if __name__ == '__main__':
+    o_idx_to_name = ['cat', 'dog', 'hat', 'skateboard']
+    p_idx_to_name = ['riding', 'wearing', 'on', 'next to', 'above']
+    o_name_to_idx = {s: i for i, s in enumerate(o_idx_to_name)}
+    p_name_to_idx = {s: i for i, s in enumerate(p_idx_to_name)}
+    vocab = {
+        'object_idx_to_name': o_idx_to_name,
+        'object_name_to_idx': o_name_to_idx,
+        'pred_idx_to_name': p_idx_to_name,
+        'pred_name_to_idx': p_name_to_idx,
+    }
+
+    objs = [
+        'cat',
+        'cat',
+        'skateboard',
+        'hat',
+    ]
+    objs = torch.LongTensor([o_name_to_idx[o] for o in objs])
+    triples = [
+        [0, 'next to', 1],
+        [0, 'riding', 2],
+        [1, 'wearing', 3],
+        [3, 'above', 2],
+    ]
+    triples = [[s, p_name_to_idx[p], o] for s, p, o in triples]
+    triples = torch.LongTensor(triples)
+
+    draw_scene_graph(objs, triples, vocab, orientation='V')
diff --git a/modeling/sf2f/utils/wav2mel.py b/modeling/sf2f/utils/wav2mel.py
new file mode 100644
index 0000000..35a822d
--- /dev/null
+++ b/modeling/sf2f/utils/wav2mel.py
@@ -0,0 +1,104 @@
+'''
+Convert *.wav or *.m4a into mel spectrogram\
+
+for unit test, run:
+    python utils/wav2mel.py
+'''
+
+
+import random
+import numpy as np
+import argparse
+import time
+from _thread import start_new_thread
+import queue
+from python_speech_features import logfbank
+import webrtcvad
+try:
+    import vad_ex
+except:
+    from utils import vad_ex
+
+
+def vad_process(path):
+    # VAD Process
+    if path.endswith('.wav'):
+        audio, sample_rate = vad_ex.read_wave(path)
+    elif path.endswith('.m4a'):
+        audio, sample_rate = vad_ex.read_m4a(path)
+    else:
+        raise TypeError('Unsupported file type: {}'.format(path.split('.')[-1]))
+
+    vad = webrtcvad.Vad(1)
+    frames = vad_ex.frame_generator(30, audio, sample_rate)
+    frames = list(frames)
+    segments = vad_ex.vad_collector(sample_rate, 30, 300, vad, frames)
+    total_wav = b""
+    for i, segment in enumerate(segments):
+        total_wav += segment
+    # Without writing, unpack total_wav into numpy [N,1] array
+    # 16bit PCM 기준 dtype=np.int16
+    wav_arr = np.frombuffer(total_wav, dtype=np.int16)
+    #print("read audio data from byte string. np array of shape:" + \
+    #    str(wav_arr.shape))
+    return wav_arr, sample_rate
+
+
+def wav_to_mel(path, nfilt=40):
+    '''
+    Output shape: (nfilt, length)
+    '''
+    wav_arr, sample_rate = vad_process(path)
+    #print("sample_rate:", sample_rate)
+    logmel_feats = logfbank(
+        wav_arr,
+        samplerate=sample_rate,
+        nfilt=nfilt)
+    #print("created logmel feats from audio data. np array of shape:" \
+    #    + str(logmel_feats.shape))
+    return np.transpose(logmel_feats)
+
+
+if __name__ == '__main__':
+    import os
+    VOX_DIR = os.path.join('./data', 'VoxCeleb')
+    WAV_DIR = os.path.join('./data', 'VoxCeleb', 'raw_wav')
+    #wav_path = 'vox1/test/id10270/zjwijMp0Qyw/00001.wav'
+    wav_path = 'vox1/dev/id10001/J9lHsKG98U8/00026.wav'
+    wav_path = os.path.join(WAV_DIR, wav_path)
+    # m4a_path = 'vox2/test/id04253/dfbCPe2xOPA/00257.m4a'
+    # m4a_path = os.path.join(WAV_DIR, m4a_path)
+
+    #wav_arr, sample_rate = vad_process(wav_path)
+    #print(wav_arr, sample_rate)
+    #wav_arr, sample_rate = vad_process(m4a_path)
+    #print(wav_arr, sample_rate)
+
+    print(wav_to_mel(wav_path).shape)
+    # print(wav_to_mel(m4a_path).shape)
+
+    # Compare with preprocessed pickles
+    import sys
+    sys.path.append('./')
+    from datasets.vox_dataset import VoxDataset
+    # Config
+    image_size = (256, 256)
+    #image_normalize_method = 'imagenet'
+    image_normalize_method = 'standard'
+    mel_normalize_method = 'vox_mel'
+    test_case_dir = os.path.join('./data', 'test_cases')
+    os.makedirs(test_case_dir, exist_ok=True)
+
+    # Dataset
+    vox_dataset = VoxDataset(
+        data_dir=VOX_DIR,
+        image_size=image_size,
+        image_normalize_method=image_normalize_method,
+        mel_normalize_method=mel_normalize_method)
+    np_mel = vox_dataset.load_mel_gram(mel_pickle= \
+        './data/VoxCeleb/vox1/mel_spectrograms/A.J._Buckley' + \
+            '/id10001_J9lHsKG98U8_00026.pickle')
+    print('Load from pickle:', np_mel.shape)
+    print('Calculated by program:', wav_to_mel(wav_path).shape)
+
+    print(np_mel - wav_to_mel(wav_path))
diff --git a/modeling/train.sh b/modeling/train.sh
new file mode 100644
index 0000000..5fc67d6
--- /dev/null
+++ b/modeling/train.sh
@@ -0,0 +1,3 @@
+python sf2f/train.py --path_opt sf2f/options/vox/sf2f/sf2f_1st_stage.yaml --batch_size 512 --visualize_every 10 --epochs 12000 --eval_epochs 15 --workers 0 --resume sf2f_1st_stage-20240221-150348 --checkpoint_name checkpoint \
+
+python sf2f/train.py --path_opt sf2f/options/vox/sf2f/sf2f_fuser.yaml --batch_size 512 --visualize_every 10 --epochs 50 --eval_epochs 1 --eval_mode_after 100 --train_fuser_only True --pretrained_path output/{sf2f_1st_stage_experiment_pretrained_model}.pt --learning_rate 1e-4
\ No newline at end of file
diff --git a/modeling/wcgan-gp/dataset.py b/modeling/wcgan-gp/dataset.py
new file mode 100644
index 0000000..70d2c3b
--- /dev/null
+++ b/modeling/wcgan-gp/dataset.py
@@ -0,0 +1,71 @@
+import os
+from torch.utils.data import Dataset
+
+import pandas as pd
+from PIL import Image
+import random
+from natsort import natsorted
+import os
+
+DATA_PATH = "/workspace/VoxCeleb/vox1"
+FILE_NAME = "vox1_age_meta.csv"
+CELEBA_PATH = 'data/50k'
+FILE_PATH = os.path.join(DATA_PATH, FILE_NAME)
+
+class HQVoxceleb(Dataset):
+    def __init__(self, root_path="/workspace/VoxCeleb/vox1", file_name="vox1_age_meta.csv", data_folder="masked_faces", transform=None):
+        super(HQVoxceleb, self).__init__()
+        self.root_path = root_path
+        self.data_folder_path = os.path.join(self.root_path, data_folder)
+        self.file_path = os.path.join(root_path, file_name)
+        self.df = pd.read_csv(self.file_path, sep='\t', index_col=False)
+        self.transform = transform
+
+    def __len__(self):
+        return self.df.shape[0]
+
+    def __getitem__(self, index):
+        data_path = os.path.join(self.data_folder_path, self.df["VGGFace1 ID"][index])
+        image_name = random.choice(os.listdir(data_path))
+        image_path = os.path.join(data_path, image_name)
+        image = Image.open(image_path).convert('RGB')
+        gender_label_str = self.df["Gender"][index]
+        if gender_label_str == None:
+            print(self.df["VGGFace1 ID"])
+        age_label = int(self.df["age"][index])        # 문자열을 정수로 변환
+        
+        # 성별을 숫자로 매핑
+        gender_label = 0 if gender_label_str == "m" else 1
+
+        if self.transform:
+            image = self.transform(image)
+        return image, gender_label, age_label
+
+
+class CelebADataset(Dataset):
+  def __init__(self, root_dir="data", data_folder='50k', transform=None):
+    """
+    Args:
+      root_dir (string): Directory with all the images
+      transform (callable, optional): transform to be applied to each image sample
+    """
+    self.root_dir = root_dir
+    self.data_folder_path = os.path.join(self.root_dir, data_folder)
+    
+    image_names = os.listdir(self.data_folder_path)
+    self.transform = transform 
+    self.image_names = natsorted(image_names)
+
+  def __len__(self): 
+    return len(self.image_names)
+
+  def __getitem__(self, idx):
+    # Get the path to the image 
+    img_path = os.path.join(self.root_dir, self.image_names[idx])
+    # Load image and convert it to RGB
+    img = Image.open(img_path).convert('RGB')
+    # Apply transformations to the image
+    if self.transform:
+      img = self.transform(img)
+
+    return img
diff --git a/modeling/wcgan-gp/inference.py b/modeling/wcgan-gp/inference.py
new file mode 100644
index 0000000..4c3cbcc
--- /dev/null
+++ b/modeling/wcgan-gp/inference.py
@@ -0,0 +1,94 @@
+import os
+import torch
+import torch.nn as nn
+from datetime import datetime
+
+from model import ConditionLinearFinetuneGAN, init_net
+from inference_options import InferenceOptions
+
+from utils import model_load, gender_processing, age_processing, save_image
+
+class Inference:
+    def __init__(self, args):
+        self.checkpoint_path = args.checkpoint_path
+        self.checkpoint_name = args.checkpoint_name
+        self.checkpoint_fine_tune_name = args.checkpoint_fine_tune_name
+
+        self.output_path = args.output_path
+        
+        self.nch_ker = args.nch_ker
+
+        self.fine_tune = args.fine_tune
+
+        self.input_gender = args.input_gender
+        self.input_age = args.input_age
+
+        self.use_gpu = args.use_gpu
+        self.gpu_ids = args.gpu_ids
+        if self.use_gpu and torch.cuda.is_available():
+            self.device = torch.device("cuda:%d" % self.gpu_ids[0])
+            torch.cuda.set_device(self.gpu_ids[0])
+        else:
+            self.device = torch.device("cpu")
+
+        self.start_time = datetime.now().strftime("%Y%m%d_%H%M%S")
+        
+    def inference(self):
+        noise = 100
+        device = self.device
+
+        output_path = os.path.join(self.output_path, 'images')
+        if not os.path.exists(self.output_path):
+            os.makedirs(self.output_path)
+            os.makedirs(output_path)
+
+        netG = ConditionLinearFinetuneGAN(nch_in=noise)
+
+        init_net(netG, init_type='normal', init_gain=0.02, gpu_ids=self.gpu_ids)
+        netG.set_fine_tune(self.fine_tune)
+        if self.fine_tune:
+            new_sequence = nn.Sequential(
+                nn.Linear(in_features=(noise+self.nch_ker//4+self.nch_ker//4), out_features=128),
+                nn.ReLU(0.2),
+                nn.Dropout(0.1)
+            )
+            netG.fc1 = new_sequence
+            netG = netG.to(device)
+            dir_ckpt = os.path.join(self.checkpoint_path, self.checkpoint_fine_tune_name)
+        else:
+            dir_ckpt = os.path.join(self.checkpoint_path, self.checkpoint_name)
+        ckpt = os.listdir(dir_ckpt)
+        ckpt.sort()
+
+        model_path = os.path.join(dir_ckpt, ckpt[-1])
+        netG = model_load(model_path, netG, mode=self.mode)
+        with torch.no_grad():
+
+            netG.eval()
+            ## test phase
+
+            # input = torch.randn(1, nch_in).to(device)
+            noise = torch.randn(1, noise, device=device)
+            gender = gender_processing(self.input_gender, device=device)
+            # age = age_processing(self.input_age)
+            age_conditions = torch.zeros(1, 8)
+            age_conditions[0][self.input_age] = torch.Tensor(1)
+            age_conditions = age_conditions.to(device)
+
+            # output = netG(input, condition)
+            output = netG((noise, gender, age_conditions))
+
+            min_ = output.min()
+            max_ = output.max()
+            clipping = ((output-min_)/(max_-min_))
+
+            save_image(clipping, output_path, self.start_time)
+        
+        return output_path
+    
+if __name__ == '__main__':
+    opt = InferenceOptions().parse()
+
+    INFERENCER = Inference(opt)
+    output_path = INFERENCER.inference()
+    print(output_path)
\ No newline at end of file
diff --git a/modeling/wcgan-gp/inference_options.py b/modeling/wcgan-gp/inference_options.py
new file mode 100644
index 0000000..e36c3c3
--- /dev/null
+++ b/modeling/wcgan-gp/inference_options.py
@@ -0,0 +1,84 @@
+import argparse
+import torch.backends.cudnn as cudnn
+import os
+import torch
+
+class InferenceOptions():
+    def __init__(self):
+        self.parser = argparse.ArgumentParser()
+        self.initialized = False
+
+    def initialize(self):
+        # experiment specifics
+        self.parser.add_argument('--gpu_ids', default='0', dest='gpu_ids')
+        self.parser.add_argument('--use_gpu', default=True, dest="use_gpu")
+        self.parser.add_argument('--checkpoint_path', default='./checkpoints', dest='checkpoint_path')
+        self.parser.add_argument('--checkpoint_name', default="", dest="checkpoint_name")
+        self.parser.add_argument('--checkpoint_fine_tune_name', default="fine_tune", dest="checkpoint_fine_tune_name")
+        self.parser.add_argument('--output_path', default='./results', dest='dir_result')
+
+        self.parser.add_argument('--nch_ker', type=int, default=64, dest='nch_ker')
+
+        self.parser.add_argument('--fine_tune', type=bool, default=False, dest='fine_tune')
+
+        self.parser.add_argument('--input_gender', default='m', choices=['m', 'man', 'male', 'f', 'female', 'woman'], dest='input_gender')
+        self.parser.add_argument('--input_age', type=int, default=25, dest='input_age')
+
+        self.initialized = True
+
+    def parse(self, save=True):
+        os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
+
+        cudnn.benchmark = True
+        cudnn.fastest = True
+        if not self.initialized:
+            self.initialize()
+        self.opt = self.parser.parse_args()
+        self.opt.isTrain = self.isTrain   # train or test
+
+        str_ids = self.opt.gpu_ids.split(',')
+        self.opt.gpu_ids = []
+        for str_id in str_ids:
+            id = int(str_id)
+            if id >= 0:
+                self.opt.gpu_ids.append(id)
+        
+        # set gpu ids
+        if len(self.opt.gpu_ids) > 0:
+            torch.cuda.set_device(self.opt.gpu_ids[0])
+
+        args = vars(self.opt)
+
+        print('------------ Options -------------')
+        for k, v in sorted(args.items()):
+            print('%s: %s' % (str(k), str(v)))
+        print('-------------- End ----------------')
+
+        log_dir = os.path.join(args['dir_log'], args['scope'], args['name_data'])
+        args_name = os.path.join(log_dir, 'args.txt')
+
+        if not os.path.exists(log_dir):
+            os.makedirs(log_dir)
+
+        with open(args_name, 'wt') as args_fid:
+            args_fid.write('----' * 10 + '\n')
+            args_fid.write('{0:^40}'.format('PARAMETER TABLES') + '\n')
+            args_fid.write('----' * 10 + '\n')
+            for k, v in sorted(args.items()):
+                args_fid.write('{}'.format(str(k)) + ' : ' + ('{0:>%d}' % (35 - len(str(k)))).format(str(v)) + '\n')
+            args_fid.write('----' * 10 + '\n')
+
+
+        # save to the disk
+        if self.opt.isTrain:
+            expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name)
+            if not os.path.exists(expr_dir):
+                os.makedirs(expr_dir)
+            if save and not self.opt.continue_train:
+                file_name = os.path.join(expr_dir, 'opt.txt')
+                with open(file_name, 'wt') as opt_file:
+                    opt_file.write('------------ Options -------------\n')
+                    for k, v in sorted(args.items()):
+                        opt_file.write('%s: %s\n' % (str(k), str(v)))
+                    opt_file.write('-------------- End ----------------\n')
+        return self.opt
\ No newline at end of file
diff --git a/modeling/wcgan-gp/model.py b/modeling/wcgan-gp/model.py
new file mode 100644
index 0000000..7da552f
--- /dev/null
+++ b/modeling/wcgan-gp/model.py
@@ -0,0 +1,240 @@
+import torch
+import torch.nn as nn
+from torch.nn import init
+from torch.optim import lr_scheduler
+import numpy as np
+
+
+## finetuning gan
+class ConditionLinearFinetuneGAN(nn.Module):
+    def __init__(self, nch_in=100, nch_out=3, nch_ker=64, norm='bnorm', fine_tune=False):
+        super(ConditionLinearFinetuneGAN, self).__init__()
+
+        self.nch_in = nch_in
+        self.nch_ker = nch_ker
+        self.norm = norm
+        self.fine_tune = fine_tune
+
+        self.gender_layer = nn.Sequential(
+            nn.Linear(2, self.nch_ker),
+            nn.LeakyReLU(0.2),
+            nn.Linear(self.nch_ker, self.nch_ker),
+            nn.LeakyReLU(0.2),
+            nn.Linear(self.nch_ker, self.nch_ker//4),
+            nn.LeakyReLU(0.2)
+        )
+        self.age_layer = nn.Sequential(
+            nn.Linear(8, self.nch_ker),
+            nn.LeakyReLU(0.2),
+            nn.Linear(self.nch_ker, self.nch_ker),
+            nn.LeakyReLU(0.2),
+            nn.Linear(self.nch_ker, self.nch_ker//4),
+            nn.LeakyReLU(0.2)
+        )
+
+        self.fc1 = nn.Sequential(
+            nn.Linear(1 * self.nch_in,  2 * self.nch_ker),
+            nn.LeakyReLU(0.2),
+            nn.Dropout(0.1)
+        )
+        self.fc2 = nn.Sequential(
+            nn.Linear(2 * self.nch_ker,  4 * self.nch_ker),
+            nn.LeakyReLU(0.2),
+            nn.Dropout(0.1)
+        )
+        self.fc3 = nn.Sequential(
+            nn.Linear(4 * self.nch_ker,  16 * self.nch_ker),
+            nn.LeakyReLU(0.2),
+            nn.Dropout(0.1)
+        )
+        self.fc4 = nn.Sequential(
+            nn.Linear(16 * self.nch_ker,  32 * self.nch_ker),
+            nn.LeakyReLU(0.2),
+            nn.Dropout(0.1)
+        )
+        self.fc5 = nn.Sequential(
+            nn.Linear(32 * self.nch_ker,  self.nch_ker * self.nch_ker),
+            nn.LeakyReLU(0.2),
+        )
+        self.fc6 = nn.Linear(self.nch_ker * self.nch_ker,  3 * self.nch_ker * self.nch_ker)
+
+    def forward(self, x):
+        if self.fine_tune:
+            noize, gender, age = x
+            gender = self.gender_layer(gender)
+            age = self.age_layer(age)
+            x = torch.concat((noize,gender,age), -1)
+        x = self.fc1(x) # fine_tune : [batch_size, 132]
+        x = self.fc2(x)
+        x = self.fc3(x)
+        x = self.fc4(x)
+        x = self.fc5(x)
+        x = self.fc6(x)
+
+        x = torch.tanh(x)
+
+        x = x.reshape(-1, 3, 64, 64)
+
+        return x
+
+    # fine tune 학습 진행 시 forward 구조 변경을 위해 사용됨
+    def set_fine_tune(self, fine_tune):
+        self.fine_tune = fine_tune
+
+
+class Discriminator(nn.Module):
+    def __init__(self, nch_in=3, nch_ker=64, norm='bnorm'):
+        super(Discriminator, self).__init__()
+
+        self.nch_in = nch_in
+        self.nch_ker = nch_ker
+        self.norm = norm
+
+        if norm == 'bnorm':
+            self.bias = False
+        else:
+            self.bias = True
+
+        self.dsc1 = CNR2d(1 * self.nch_in,  1 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.2)
+        self.dsc2 = CNR2d(1 * self.nch_ker, 2 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.2)
+        self.dsc3 = CNR2d(2 * self.nch_ker, 4 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.2)
+        self.dsc4 = CNR2d(4 * self.nch_ker, 8 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.2)
+        self.dsc5 = CNR2d(8 * self.nch_ker, 1,                kernel_size=4, stride=1, padding=1, norm=[],        relu=[], bias=False)
+
+    def forward(self, x):
+
+        x = self.dsc1(x)
+        x = self.dsc2(x)
+        x = self.dsc3(x)
+        x = self.dsc4(x)
+        x = self.dsc5(x)
+
+        x = torch.sigmoid(x)
+
+        return x
+
+
+class CNR2d(nn.Module):
+    def __init__(self, nch_in, nch_out, kernel_size=4, stride=1, padding=1, norm='bnorm', relu=0.0, drop=[], bias=[]):
+        super().__init__()
+
+        if bias == []:
+            if norm == 'bnorm':
+                bias = False
+            else:
+                bias = True
+
+        layers = []
+        layers += [Conv2d(nch_in, nch_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias)]
+
+        if norm != []:
+            layers += [Norm2d(nch_out, norm)]
+
+        if relu != []:
+            layers += [ReLU(relu)]
+
+        if drop != []:
+            layers += [nn.Dropout2d(drop)]
+
+        self.cbr = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.cbr(x)
+
+
+class Conv2d(nn.Module):
+    def __init__(self, nch_in, nch_out, kernel_size=4, stride=1, padding=1, bias=True):
+        super(Conv2d, self).__init__()
+        self.conv = nn.Conv2d(nch_in, nch_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias)
+
+    def forward(self, x):
+        return self.conv(x)
+
+class Norm2d(nn.Module):
+    def __init__(self, nch, norm_mode):
+        super(Norm2d, self).__init__()
+        if norm_mode == 'bnorm':
+            self.norm = nn.BatchNorm2d(nch)
+        elif norm_mode == 'inorm':
+            self.norm = nn.InstanceNorm2d(nch)
+
+    def forward(self, x):
+        return self.norm(x)
+
+
+class ReLU(nn.Module):
+    def __init__(self, relu):
+        super(ReLU, self).__init__()
+        if relu > 0:
+            self.relu = nn.LeakyReLU(relu, True)
+        elif relu == 0:
+            self.relu = nn.ReLU(True)
+
+    def forward(self, x):
+        return self.relu(x)
+
+
+def init_weights(net, init_type='normal', init_gain=0.02):
+    """Initialize network weights.
+
+    Parameters:
+        net (network)   -- network to be initialized
+        init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
+        init_gain (float)    -- scaling factor for normal, xavier and orthogonal.
+
+    We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
+    work better for some applications. Feel free to try yourself.
+    """
+    def init_func(m):  # define the initialization function
+        classname = m.__class__.__name__
+        if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
+            if init_type == 'normal':
+                init.normal_(m.weight.data, 0.0, init_gain)
+            elif init_type == 'xavier':
+                init.xavier_normal_(m.weight.data, gain=init_gain)
+            elif init_type == 'kaiming':
+                init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
+            elif init_type == 'orthogonal':
+                init.orthogonal_(m.weight.data, gain=init_gain)
+            else:
+                raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
+            if hasattr(m, 'bias') and m.bias is not None:
+                init.constant_(m.bias.data, 0.0)
+        elif classname.find('BatchNorm2d') != -1:  # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
+            init.normal_(m.weight.data, 1.0, init_gain)
+            init.constant_(m.bias.data, 0.0)
+
+    print('initialize network with %s' % init_type)
+    net.apply(init_func)  # apply the initialization function <init_func>
+
+
+def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):
+    """Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights
+    Parameters:
+        net (network)      -- the network to be initialized
+        init_type (str)    -- the name of an initialization method: normal | xavier | kaiming | orthogonal
+        gain (float)       -- scaling factor for normal, xavier and orthogonal.
+        gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
+
+    Return an initialized network.
+    """
+    if gpu_ids:
+        assert(torch.cuda.is_available())
+        net.to(gpu_ids[0])
+        net = torch.nn.DataParallel(net, gpu_ids)  # multi-GPUs
+    init_weights(net, init_type, init_gain=init_gain)
+    return net
+
+
+def set_requires_grad(nets, requires_grad=False):
+    """Set requies_grad=Fasle for all the networks to avoid unnecessary computations
+    Parameters:
+        nets (network list)   -- a list of networks
+        requires_grad (bool)  -- whether the networks require gradients or not
+    """
+    if not isinstance(nets, list):
+        nets = [nets]
+    for net in nets:
+        if net is not None:
+            for param in net.parameters():
+                param.requires_grad = requires_grad
\ No newline at end of file
diff --git a/modeling/wcgan-gp/train.py b/modeling/wcgan-gp/train.py
new file mode 100644
index 0000000..bdf60c4
--- /dev/null
+++ b/modeling/wcgan-gp/train.py
@@ -0,0 +1,394 @@
+import os
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from torch.optim import lr_scheduler
+from torchvision import transforms
+from statistics import mean
+from datetime import datetime
+
+from model import ConditionLinearFinetuneGAN, Discriminator, init_net, set_requires_grad
+from dataset import HQVoxceleb, CelebADataset
+from utils import GradientPaneltyLoss, model_load, model_save, custom_collate_fn
+
+from train_options import TrainOptions
+
+class Train:
+    def __init__(self, args):
+        self.train_continue = args.train_continue
+
+        self.checkpoint_path = args.checkpoint_path
+        self.checkpoint_name = args.checkpoint_name
+        self.checkpoint_fine_tune_name = args.checkpoint_fine_tune_name
+
+        self.dataset_path = args.dataset_path
+        self.dataset_name = args.dataset_name
+        self.dataset_fine_tune_name = args.dataset_fine_tune_name
+
+        self.output_path = args.output_path
+
+        self.num_epoch = args.num_epoch
+        self.num_epochs_decay = args.num_epochs_decay
+        self.num_freq_save = args.num_freq_save
+        
+        self.batch_size = args.batch_size
+
+        self.lr_G = args.lr_G
+        self.lr_D = args.lr_D
+        self.lr_G_weight_decay = args.lr_G_weight_decay
+        self.lr_D_weight_decay = args.lr_D_weight_decay
+        self.lr_policy = args.lr_policy
+
+        self.nch_ker = args.nch_ker
+
+        self.fine_tune = args.fine_tune
+        self.fine_tune_num_epoch = args.fine_tune_num_epoch
+        self.fine_tune_num_freq_save = args.fine_tune_num_freq_save
+
+        self.use_gpu = args.use_gpu
+        self.gpu_ids = args.gpu_ids
+        if self.use_gpu and torch.cuda.is_available():
+            self.device = torch.device("cuda:%d" % self.gpu_ids[0])
+            torch.cuda.set_device(self.gpu_ids[0])
+        else:
+            self.device = torch.device("cpu")
+
+        self.start_time = datetime.now().strftime("%Y%m%d_%H%M%S")
+        self.transform = transforms.Compose([
+            transforms.Resize((64, 64)),
+            # transforms.RandomHorizontalFlip(p=0.5), 
+            transforms.ToTensor(),
+            transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+        ])
+        
+
+    def train(self):
+        device = self.device
+        noise = 100
+
+        dataset = CelebADataset(root_dir=self.dataset_path, data_folder=self.dataset_name, transform=self.transform)
+        loader_train = DataLoader(dataset, batch_size=self.batch_size, shuffle=True, num_workers=0, drop_last=True)
+
+        num_train = len(dataset)
+
+        num_batch_train = int((num_train / self.batch_size) + ((num_train % self.batch_size) != 0))
+
+        netG = ConditionLinearFinetuneGAN(nch_in=noise, nch_ker=self.nch_ker)
+        netD = Discriminator(nch_ker=self.nch_ker)
+
+        init_net(netG, init_type='normal', init_gain=0.02, gpu_ids=self.gpu_ids)
+        init_net(netD, init_type='normal', init_gain=0.02, gpu_ids=self.gpu_ids)
+
+        ## setup loss & optimization
+        fn_GP = GradientPaneltyLoss().to(device)
+
+        paramsG = netG.parameters()
+        paramsD = netD.parameters()
+
+        optimG = torch.optim.Adam(paramsG, lr=self.lr_G, weight_decay=self.lr_G_weight_decay, betas=(0.999, 0.999))
+        optimD = torch.optim.Adam(paramsD, lr=self.lr_D, weight_decay=self.lr_D_weight_decay, betas=(0.999, 0.999))
+
+        # schedG = get_scheduler(optimG, self.opts)
+        # schedD = get_scheduler(optimD, self.opts)
+
+        # schedG = torch.optim.lr_scheduler.ExponentialLR(optimG, gamma=0.9)
+        # schedD = torch.optim.lr_scheduler.ExponentialLR(optimD, gamma=0.9)
+
+        ## load from checkpoints
+        st_epoch = 0
+
+        if self.train_continue == 'on':
+            ckpt = os.path.join(self.checkpoint_path, self.checkpoint_name)
+            netG, netD, optimG, optimD, st_epoch = model_load(ckpt, netG, netD, optimG, optimD, mode=self.mode)
+
+        for epoch in range(st_epoch + 1, self.num_epoch + 1):
+            ## training phase
+            netG.train()
+            netD.train()
+
+            loss_G_train = []
+            loss_D_real_train = []
+            loss_D_fake_train = []
+
+            for i, data in enumerate(loader_train):
+                def should(freq):
+                    return freq > 0 and (i % freq == 0 or i == num_batch_train)
+
+                images = data.to(device)
+                input = torch.randn(self.batch_size, noise).to(device)
+                output = netG(input)
+
+                # backward netD
+                set_requires_grad(netD, True)
+                optimD.zero_grad()
+
+                pred_real = netD(images)
+                pred_fake = netD(output.detach())
+
+                alpha = torch.rand(self.batch_size, 1, 1, 1).to(self.device)
+                output_ = (alpha * images + (1 - alpha) * output.detach()).requires_grad_(True)
+                src_out_ = netD(output_)
+
+                # WGAN Loss
+                loss_D_real = torch.mean(pred_real)
+                loss_D_fake = -torch.mean(pred_fake)
+
+                # Gradient penalty Loss
+                loss_D_gp = fn_GP(src_out_, output_)
+                loss_D = 0.5 * (loss_D_real + loss_D_fake) + loss_D_gp
+                loss_D.backward(retain_graph=True)
+                optimD.step()
+
+
+                set_requires_grad(netD, False)
+                # backward netG
+                optimG.zero_grad()
+                
+                pred_fake = netD(output)
+
+                loss_G = torch.mean(pred_fake)
+                loss_G.backward()
+                optimG.step()
+
+                # get losses
+                loss_G_train += [loss_G.item()]
+                loss_D_real_train += [loss_D_real.item()]
+                loss_D_fake_train += [loss_D_fake.item()]
+
+                print('TRAIN: EPOCH %d: BATCH %04d/%04d: '
+                      'GEN GAN: %.4f DISC FAKE: %.4f DISC REAL: %.4f' %
+                      (epoch, i, num_batch_train,
+                       mean(loss_G_train), mean(loss_D_fake_train), mean(loss_D_real_train)))
+
+
+            ## save
+            if (epoch % self.num_freq_save) == 0:
+                self.save(dir_chck, netG, netD, optimG, optimD, epoch, self.start_time)
+
+        if self.fine_tune:
+            voxceleb_path = os.path.join(self.dataset_path, self.dataset_fine_tune_name)
+            dataset = HQVoxceleb(root_path=voxceleb_path, transform=self.transform)
+            loader_train = DataLoader(dataset, batch_size=self.batch_size, shuffle=True, num_workers=8, drop_last=True, collate_fn=custom_collate_fn)
+
+            init_net(netG, init_type='normal', init_gain=0.02, gpu_ids=self.gpu_ids)
+            init_net(netD, init_type='normal', init_gain=0.02, gpu_ids=self.gpu_ids)
+
+            ## setup loss & optimization
+            fn_GP = GradientPaneltyLoss().to(device)
+
+            paramsG = netG.parameters()
+            paramsD = netD.parameters()
+
+            optimG = torch.optim.Adam(paramsG, lr=1e-5, weight_decay=self.lr_G_weight_decay, betas=(0.999, 0.999))
+            optimD = torch.optim.Adam(paramsD, lr=1e-5, weight_decay=self.lr_D_weight_decay, betas=(0.999, 0.999))
+
+            new_sequence = nn.Sequential(
+                nn.Linear(in_features=(noise+self.nch_ker//4+self.nch_ker//4), out_features=2*self.nch_ker),
+                nn.LeakyReLU(0.2),
+                nn.Dropout(0.1)
+            )
+            netG.fc1 = new_sequence
+            netG.set_fine_tune(True)
+            netG = netG.to(device)
+
+            dir_chck = os.path.join(dir_chck, self.checkpoint_fine_tune_name)
+            for epoch in range(self.fine_tune_num_epoch + 1):
+                ## training phase
+                netG.train()
+                netD.train()
+
+                loss_G_train = []
+                loss_D_real_train = []
+                loss_D_fake_train = []
+
+                for i, data in enumerate(loader_train):
+                    def should(freq):
+                        return freq > 0 and (i % freq == 0 or i == num_batch_train)
+
+                    images, gender_labels, age_labels = data
+                    images = images.to(device)
+
+                    gender_labels = gender_labels.unsqueeze(1)
+                    gender_conditions = torch.zeros(self.batch_size, 2)
+                    gender_conditions = gender_conditions.scatter_(1, gender_labels, 1).to(device)
+
+                    age_labels = age_labels.unsqueeze(1)
+                    age_conditions = torch.zeros(self.batch_size, 8)
+                    age_conditions = age_conditions.scatter_(1, age_labels, 1).to(device)
+                    
+                    input = torch.randn(self.batch_size, noise).to(device)
+                    input_G = (input, gender_conditions, age_conditions)
+
+                    output = netG(input_G)
+                    
+                    # backward netD
+                    set_requires_grad(netD, True)
+                    optimD.zero_grad()
+
+                    pred_real = netD(images)
+                    pred_fake = netD(output.detach())
+
+                    alpha = torch.rand(self.atch_size, 1, 1, 1).to(self.device)
+                    output_ = (alpha * images + (1 - alpha) * output.detach()).requires_grad_(True)
+                    src_out_ = netD(output_)
+
+                    # WGAN Loss
+                    loss_D_real = torch.mean(pred_real)
+                    loss_D_fake = -torch.mean(pred_fake)
+
+                    # Gradient penalty Loss
+                    loss_D_gp = fn_GP(src_out_, output_)
+
+                    loss_D = 0.5 * (loss_D_real + loss_D_fake) + loss_D_gp
+
+                    loss_D.backward(retain_graph=True)
+                    optimD.step()
+
+
+                    set_requires_grad(netD, False)
+                    # backward netG
+                    optimG.zero_grad()
+                    
+                    loss_G = torch.mean(pred_fake)
+                    loss_G.backward()
+                    optimG.step()
+                    
+                    # get losses
+                    loss_G_train += [loss_G.item()]
+                    loss_D_real_train += [loss_D_real.item()]
+                    loss_D_fake_train += [loss_D_fake.item()]
+
+                    print('TRAIN: EPOCH %d: BATCH %04d/%04d: '
+                        'GEN GAN: %.4f DISC FAKE: %.4f DISC REAL: %.4f' %
+                        (epoch, i, num_batch_train,
+                        mean(loss_G_train), mean(loss_D_fake_train), mean(loss_D_real_train)))
+                
+                ## save
+                if (epoch % self.fine_tune_num_freq_save) == 0:
+                    model_save(dir_chck, netG, netD, optimG, optimD, epoch, start_time=self.start_time)
+
+    def fine_tuning(self):
+        device = self.device
+        noise = 100
+        self.fine_tune = True
+
+        dataset = HQVoxceleb(root_dir=self.dataset_path, data_folder=self.dataset_fine_tune_name, transform=self.transform)
+        loader_train = DataLoader(dataset, batch_size=self.batch_size, shuffle=True, num_workers=0, drop_last=True)
+
+        num_train = len(dataset)
+
+        num_batch_train = int((num_train / self.batch_size) + ((num_train % self.batch_size) != 0))
+
+        netG = ConditionLinearFinetuneGAN(nch_in=noise, nch_ker=self.nch_ker)
+        netD = Discriminator(nch_ker=self.nch_ker)
+
+
+        ## setup loss & optimization
+        fn_GP = GradientPaneltyLoss().to(device)
+        
+        new_sequence = nn.Sequential(
+            nn.Linear(in_features=(noise+self.nch_ker//4+self.nch_ker//4), out_features=2*self.nch_ker),
+            nn.LeakyReLU(0.2),
+            nn.Dropout(0.1)
+        )
+
+        netG.fc1 = new_sequence
+        ckpt = os.path.join(self.checkpoint_path, self.checkpoint_fine_tune_name)
+        netG, netD, optimG, optimD, st_epoch = model_load(ckpt, netG, netD, optimG, optimD, mode=self.mode)
+
+        netG.set_fine_tune(True)
+
+        netG = netG.to(device)
+
+        paramsG = netG.parameters()
+        paramsD = netD.parameters()
+
+        optimG = torch.optim.Adam(paramsG, lr=self.lr_G, weight_decay=self.lr_G_weight_decay, betas=(0.999, 0.999))
+        optimD = torch.optim.Adam(paramsD, lr=self.lr_D, weight_decay=self.lr_D_weight_decay, betas=(0.999, 0.999))
+
+        for epoch in range(self.fine_tune_num_epoch + 1):
+            ## training phase
+            netG.train()
+            netD.train()
+
+            loss_G_train = []
+            loss_D_real_train = []
+            loss_D_fake_train = []
+
+            for i, data in enumerate(loader_train):
+                def should(freq):
+                    return freq > 0 and (i % freq == 0 or i == num_batch_train)
+
+                images, gender_labels, age_labels = data
+                images = images.to(device)
+
+                gender_labels = gender_labels.unsqueeze(1)
+                gender_conditions = torch.zeros(self.batch_size, 2)
+                gender_conditions = gender_conditions.scatter_(1, gender_labels, 1).to(device)
+
+                age_labels = age_labels.unsqueeze(1)
+                age_conditions = torch.zeros(self.batch_size, 8)
+                age_conditions = age_conditions.scatter_(1, age_labels, 1).to(device)
+                
+                input = torch.randn(self.batch_size, noise).to(device)
+                input_G = (input, gender_conditions, age_conditions)
+
+                output = netG(input_G)
+                
+                # backward netD
+                set_requires_grad(netD, True)
+                optimD.zero_grad()
+
+                pred_real = netD(images)
+                pred_fake = netD(output.detach())
+
+                alpha = torch.rand(self.atch_size, 1, 1, 1).to(self.device)
+                output_ = (alpha * images + (1 - alpha) * output.detach()).requires_grad_(True)
+                src_out_ = netD(output_)
+
+                # WGAN Loss
+                loss_D_real = torch.mean(pred_real)
+                loss_D_fake = -torch.mean(pred_fake)
+
+                # Gradient penalty Loss
+                loss_D_gp = fn_GP(src_out_, output_)
+
+                loss_D = 0.5 * (loss_D_real + loss_D_fake) + loss_D_gp
+
+                loss_D.backward(retain_graph=True)
+                optimD.step()
+
+                set_requires_grad(netD, False)
+                # backward netG
+                optimG.zero_grad()
+                
+                # pred_fake = netD(output, condition)
+                pred_fake = netD(output)
+
+                loss_G = torch.mean(pred_fake)
+                loss_G.backward()
+                optimG.step()
+                
+                # get losses
+                loss_G_train += [loss_G.item()]
+                loss_D_real_train += [loss_D_real.item()]
+                loss_D_fake_train += [loss_D_fake.item()]
+
+                print('TRAIN: EPOCH %d: BATCH %04d/%04d: '
+                    'GEN GAN: %.4f DISC FAKE: %.4f DISC REAL: %.4f' %
+                    (epoch, i, num_batch_train,
+                    mean(loss_G_train), mean(loss_D_fake_train), mean(loss_D_real_train)))
+            
+            ## save
+            if (epoch % self.fine_tune_num_freq_save) == 0:
+                model_save(ckpt, netG, netD, optimG, optimD, epoch, start_time=self.start_time)
+                
+if __name__ == '__main__':
+    opt = TrainOptions().parse()
+
+    TRAINER = Train(opt)
+    
+    if opt.mode == 'train':
+        TRAINER.train()
+    elif opt.mode == 'finetune':
+        TRAINER.fine_tuning()
\ No newline at end of file
diff --git a/modeling/wcgan-gp/train_options.py b/modeling/wcgan-gp/train_options.py
new file mode 100644
index 0000000..349be40
--- /dev/null
+++ b/modeling/wcgan-gp/train_options.py
@@ -0,0 +1,100 @@
+import argparse
+import torch.backends.cudnn as cudnn
+import os
+import torch
+
+class TrainOptions():
+    def __init__(self):
+        self.parser = argparse.ArgumentParser()
+        self.initialized = False
+
+    def initialize(self):
+        # experiment specifics
+        
+        self.parser.add_argument('--gpu_ids', default='0', dest='gpu_ids')
+        self.parser.add_argument('--use_gpu', default=True, dest="use_gpu")
+        self.parser.add_argument('--mode', default='finetune', choices=['train', 'finetune', 'inference'], dest='mode')
+        self.parser.add_argument('--train_continue', default='off', choices=['on', 'off'], dest='train_continue')
+        self.parser.add_argument('--checkpoint_path', default='./checkpoints', dest='checkpoint_path')
+        self.parser.add_argument('--checkpoint_name', default="", dest="checkpoint_name")
+        self.parser.add_argument('--checkpoint_fine_tune_name', default="fine_tune", dest="checkpoint_fine_tune_name")
+        self.parser.add_argument('--dataset_path', type=str, default='/workspace/data', dest='dataset_path')
+        self.parser.add_argument('--dataset_name', default='celeba', dest='dataset_name')
+        self.parser.add_argument('--dataset_fine_tune_name', default='Voxceleb', dest='dataset_fine_tune_name')
+        self.parser.add_argument('--output_path', default='./results', dest='dir_result')
+
+        self.parser.add_argument('--num_epoch', type=int,  default=100, dest='num_epoch')
+        self.parser.add_argument('--num_epochs_decay', type=int, default=100, dest='n_epochs_decay')
+        self.parser.add_argument('--num_freq_save', type=int,  default=5, dest='num_freq_save')
+        self.parser.add_argument('--batch_size', type=int, default=128, dest='batch_size')
+
+        self.parser.add_argument('--lr_G', type=float, default=2e-4, dest='lr_G')
+        self.parser.add_argument('--lr_D', type=float, default=2e-4, dest='lr_D')
+        self.parser.add_argument('--lr_policy', type=str, default='linear', choices=['linear', 'step', 'plateau', 'cosine'], dest='lr_policy')
+        self.parser.add_argument('--lr_G_weight_decay', type=float, default=1e-5, dest='lr_G_weight_decay')
+        self.parser.add_argument('--lr_D_weight_decay', type=float, default=1e-5, dest='lr_D_weight_decay')
+
+        self.parser.add_argument('--nch_ker', type=int, default=64, dest='nch_ker')
+
+        self.parser.add_argument('--fine_tune', type=bool, default=False, dest='fine_tune')
+        self.parser.add_argument('--fine_tune_num_epoch', type=int, default=100, dest='fine_tune_num_epoch')
+        self.parser.add_argument('--fine_tune_num_freq_save', type=int, default=1, dest='fine_tune_num_freq_save')
+
+        self.initialized = True
+
+    def parse(self, save=True):
+        os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
+
+        cudnn.benchmark = True
+        cudnn.fastest = True
+        if not self.initialized:
+            self.initialize()
+        self.opt = self.parser.parse_args()
+        self.opt.isTrain = self.isTrain   # train or test
+
+        str_ids = self.opt.gpu_ids.split(',')
+        self.opt.gpu_ids = []
+        for str_id in str_ids:
+            id = int(str_id)
+            if id >= 0:
+                self.opt.gpu_ids.append(id)
+        
+        # set gpu ids
+        if len(self.opt.gpu_ids) > 0:
+            torch.cuda.set_device(self.opt.gpu_ids[0])
+
+        args = vars(self.opt)
+
+        print('------------ Options -------------')
+        for k, v in sorted(args.items()):
+            print('%s: %s' % (str(k), str(v)))
+        print('-------------- End ----------------')
+
+        log_dir = os.path.join(args['dir_log'], args['scope'], args['name_data'])
+        args_name = os.path.join(log_dir, 'args.txt')
+
+        if not os.path.exists(log_dir):
+            os.makedirs(log_dir)
+
+        with open(args_name, 'wt') as args_fid:
+            args_fid.write('----' * 10 + '\n')
+            args_fid.write('{0:^40}'.format('PARAMETER TABLES') + '\n')
+            args_fid.write('----' * 10 + '\n')
+            for k, v in sorted(args.items()):
+                args_fid.write('{}'.format(str(k)) + ' : ' + ('{0:>%d}' % (35 - len(str(k)))).format(str(v)) + '\n')
+            args_fid.write('----' * 10 + '\n')
+
+
+        # save to the disk
+        if self.opt.isTrain:
+            expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name)
+            if not os.path.exists(expr_dir):
+                os.makedirs(expr_dir)
+            if save and not self.opt.continue_train:
+                file_name = os.path.join(expr_dir, 'opt.txt')
+                with open(file_name, 'wt') as opt_file:
+                    opt_file.write('------------ Options -------------\n')
+                    for k, v in sorted(args.items()):
+                        opt_file.write('%s: %s\n' % (str(k), str(v)))
+                    opt_file.write('-------------- End ----------------\n')
+        return self.opt
\ No newline at end of file
diff --git a/modeling/wcgan-gp/utils.py b/modeling/wcgan-gp/utils.py
new file mode 100644
index 0000000..196fce7
--- /dev/null
+++ b/modeling/wcgan-gp/utils.py
@@ -0,0 +1,221 @@
+import os
+import torch
+import torch.nn as nn
+from torch.optim import lr_scheduler
+import torch.nn.functional as F
+import logging
+import numpy as np
+
+import matplotlib.pyplot as plt
+from torchvision.transforms import Normalize
+from torchvision.transforms.functional import to_pil_image
+from torchmetrics.image.fid import FrechetInceptionDistance
+
+    
+class GradientPaneltyLoss(nn.Module):
+    def __init__(self):
+        super(GradientPaneltyLoss, self).__init__()
+
+    def forward(self, y, x):
+        """Compute gradient penalty: (L2_norm(dy/dx) - 1)**2."""
+        weight = torch.ones_like(y)
+        dydx = torch.autograd.grad(outputs=y,
+                                   inputs=x,
+                                   grad_outputs=weight,
+                                   retain_graph=True,
+                                   create_graph=True,
+                                   only_inputs=True)[0]
+
+        dydx = dydx.view(dydx.size(0), -1)
+        dydx_l2norm = torch.sqrt(torch.sum(dydx ** 2, dim=1))
+        return torch.mean((dydx_l2norm - 1) ** 2)
+    
+
+def calculate_fid(generator, val_loader, batch_size=32):
+
+    values = []
+    fid = FrechetInceptionDistance(feature=64)
+
+    with torch.no_grad():
+        for images, gender_labels, age_labels in val_loader:
+            age_labels = age_labels.unsqueeze(1)
+            gender_labels = gender_labels.unsqueeze(1)
+            age_condition = torch.zeros(batch_size, 8)
+            age_condition.scatter_(1, age_labels, 1)
+            condition = torch.cat([gender_labels, age_condition], dim=1).to('cuda')
+            noise = torch.randn(images.size(0), 100).to('cuda')
+            fake_images = generator(noise, condition)
+            fake_feature = extract_features(fake_images).to('cpu')
+            real_feature = extract_features(images)
+
+            fid.update(real_feature.to(torch.uint8), real=True)
+            fid.update(fake_feature.to(torch.uint8), real=False)
+            values.append(fid.compute())
+            fid.reset()
+    
+    return np.mean(values)
+
+def extract_features(images):
+    # Inception v3 모델에 이미지 전처리 및 특징 추출
+    up = nn.Upsample(size=(299, 299), mode='bilinear', align_corners=False)
+    norm = Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+    features = norm(up(images))
+    features = F.adaptive_avg_pool2d(features, output_size=64)
+    features = F.sigmoid(features)
+    return features
+
+def model_save(dir_chck, netG, netD, optimG, optimD, epoch, start_time):
+    if not os.path.exists(os.path.join(dir_chck, start_time)):
+        os.makedirs(os.path.join(dir_chck, start_time))
+
+    torch.save({'netG': netG.state_dict(), 'netD': netD.state_dict(),
+                'optimG': optimG.state_dict(), 'optimD': optimD.state_dict()},
+                '%s/%s/model_epoch%04d.pth' % (dir_chck, start_time, epoch))
+
+
+def model_load(ckpt, netG, netD=[], optimG=[], optimD=[], epoch=[], mode='train'):
+
+    dict_net = torch.load(ckpt)
+
+    print(f'Loaded {ckpt}')
+
+    if mode == 'train' or mode == 'finetune':
+        netG.load_state_dict(dict_net['netG'])
+        netD.load_state_dict(dict_net['netD'])
+        optimG.load_state_dict(dict_net['optimG'])
+        optimD.load_state_dict(dict_net['optimD'])
+        st_epoch = ckpt.split("epoch")[1]
+        st_epoch = int(st_epoch.split(".pth")[0])
+        return netG, netD, optimG, optimD, st_epoch
+
+    elif mode == 'test' or mode == 'inference':
+        netG.load_state_dict(dict_net['netG'])
+
+        return netG
+    
+
+def custom_collate_fn(batch):
+    images, gender_labels, age_labels = zip(*batch)
+    images = torch.stack(images, dim=0).float()  # 이미지를 torch.Tensor로 변환하고 float로 변환
+    gender_labels = torch.tensor(gender_labels)
+    age_labels = torch.tensor(age_labels)
+    return images, gender_labels, age_labels
+
+
+def set_requires_grad(nets, requires_grad=False):
+    """Set requies_grad=Fasle for all the networks to avoid unnecessary computations
+    Parameters:
+        nets (network list)   -- a list of networks
+        requires_grad (bool)  -- whether the networks require gradients or not
+    """
+    if not isinstance(nets, list):
+        nets = [nets]
+    for net in nets:
+        if net is not None:
+            for param in net.parameters():
+                param.requires_grad = requires_grad
+
+
+def get_scheduler(optimizer, opt):
+    """Return a learning rate scheduler
+
+    Parameters:
+        optimizer          -- the optimizer of the network
+        opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions.
+                            opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine
+
+    For 'linear', we keep the same learning rate for the first <opt.n_epochs> epochs
+    and linearly decay the rate to zero over the next <opt.n_epochs_decay> epochs.
+    For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers.
+    See https://pytorch.org/docs/stable/optim.html for more details.
+    """
+    if opt.lr_policy == 'linear':
+        def lambda_rule(epoch):
+            lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1)
+            return lr_l
+        scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
+    elif opt.lr_policy == 'step':
+        scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1)
+    elif opt.lr_policy == 'plateau':
+        scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
+    elif opt.lr_policy == 'cosine':
+        scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0)
+    else:
+        return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)
+    return scheduler
+
+
+def append_index(dir_result, fileset, step=False):
+    index_path = os.path.join(dir_result, "index.html")
+    if os.path.exists(index_path):
+        index = open(index_path, "a")
+    else:
+        index = open(index_path, "w")
+        index.write("<html><body><table><tr>")
+        if step:
+            index.write("<th>step</th>")
+        for key, value in fileset.items():
+            index.write("<th>%s</th>" % key)
+        index.write('</tr>')
+
+    # for fileset in filesets:
+    index.write("<tr>")
+
+    if step:
+        index.write("<td>%d</td>" % fileset["step"])
+    index.write("<td>%s</td>" % fileset["name"])
+
+    del fileset['name']
+
+    for key, value in fileset.items():
+        index.write("<td><img src='images/%s'></td>" % value)
+
+    index.write("</tr>")
+    return index_path
+
+
+def add_plot(output, label, writer, epoch=[], ylabel='Density', xlabel='Radius', namescope=[]):
+    fig, ax = plt.subplots()
+
+    ax.plot(output.transpose(1, 0).detach().numpy(), '-')
+    ax.plot(label.transpose(1, 0).detach().numpy(), '--')
+
+    ax.set_xlim(0, 400)
+
+    ax.grid(True)
+    ax.set_ylabel(ylabel)
+    ax.set_xlabel(xlabel)
+
+    writer.add_figure(namescope, fig, epoch)
+
+def gender_processing(input_genders, device='cpu') -> torch.Tensor:
+    gender_tensors = torch.zeros((1, 2))
+    for i, input_gender in enumerate(input_genders):
+        gender = 0
+        if input_gender in ['m', 'male', 'man'] or input_gender == 0 :
+            gender = 0
+        elif input_gender in ['f', 'female', 'woman'] or input_gender == 1:
+            gender = 1
+        gender_tensors[i][gender] = torch.tensor(1)
+    return gender_tensors.to(device)
+
+def age_processing(input_age):
+    if type(input_age) == str:
+        input_age = int(input_age)
+    age_boundary = [3, 7, 14, 23, 36, 46, 58, 121]
+    for i in range(len(age_boundary)):
+        if input_age < age_boundary[i]:
+            age = i
+            break
+    
+    if age >= 8:
+        age = 7
+    
+    return age
+
+def save_image(image, path, start_time):
+    img = image.cpu().squeeze(0)
+    img = to_pil_image(img)
+    if not os.path.exists(path):
+        os.mkdir(path)
+    img.save(os.path.join(path, f'{start_time}.jpeg'), "JPEG")
\ No newline at end of file
diff --git a/readme.md b/readme.md
new file mode 100644
index 0000000..4be91ee
--- /dev/null
+++ b/readme.md
@@ -0,0 +1,124 @@
+
+
+# 🔊 목소리로 가상 얼굴 생성 서비스 [너의 목소리가 보여]
+<img src="https://img.shields.io/badge/PyTorch-EE4C2C?style=for-the-badge&logo=PyTorch&logoColor=white"> <img src="https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=Python&logoColor=white"> 
+ <img src="https://img.shields.io/badge/opencv-5C3EE8?style=for-the-badge&logo=opencv&logoColor=white"> 
+ <img src="https://img.shields.io/badge/mysql-4479A1?style=for-the-badge&logo=mysql&logoColor=white"> 
+ <img src="https://img.shields.io/badge/github-181717?style=for-the-badge&logo=github&logoColor=white"> 
+ <img src="https://img.shields.io/badge/git-F05032?style=for-the-badge&logo=git&logoColor=white">
+ <img src="https://img.shields.io/badge/Docker-2496ED?style=for-the-badge&logo=Docker&logoColor=white">
+  <img src="https://img.shields.io/badge/Flask-000000?style=for-the-badge&logo=Flask&logoColor=white">
+  <img src="https://img.shields.io/badge/Prometheus-E6522C?style=for-the-badge&logo=Prometheus&logoColor=white">
+  <img src="https://img.shields.io/badge/Grafana-F46800?style=for-the-badge&logo=Grafana&logoColor=white">
+  <img src="https://img.shields.io/badge/NCP-03C75A?style=for-the-badge&logo=Naver&logoColor=white">
+ <img src="https://img.shields.io/badge/RabbitMQ-FF6600?style=for-the-badge&logo=RabbitMQ&logoColor=white">
+ <img src="https://img.shields.io/badge/Celery-37814A?style=for-the-badge&logo=Celery&logoColor=white">
+<img src="https://img.shields.io/badge/minio-C72E49?style=for-the-badge&logo=minio&logoColor=white">
+ <img src="https://img.shields.io/badge/amazonrds-527FFF?style=for-the-badge&logo=amazonrds&logoColor=white">
+ <img src="https://img.shields.io/badge/svelte-FF3E00?style=for-the-badge&logo=svelte&logoColor=white">
+ <img src="https://img.shields.io/badge/Linux-FCC624?style=for-the-badge&logo=Linux&logoColor=white">
+  <img src="https://img.shields.io/badge/JavaScript-F7DF1E?style=for-the-badge&logo=JavaScript&logoColor=white">
+ 
+# ✅ Project Overview
+<p align="center"><img src="https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/assets/46400961/73b51b54-e7af-4268-a7e5-84458ecc3056" style="max-width: 100%; height: auto;"></p>
+
+사용자의 목소리를 입력 받아 얼굴을 생성하고,  
+생성된 얼굴을 다른 영상 속 얼굴에 합성하여 결과물 제공하는 서비스 제작
+###  ✔️ 기획의도
+
+<p align="center"><img src="https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/assets/46400961/af1a03ba-1bdd-4b3b-b2f7-502e29f82c58" style="max-width: 100%; height: auto;"></p>
+
+- 사생활 보호와 사용자 경험을 향상
+- Multi-modal 기술에 대한 경험
+- 실제 서비스를 개발하고 개선하는 것
+
+<p align="center"><img src="https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/assets/46400961/48ba55f8-ea8b-4850-b555-5c5f5335395c" style="max-width: 100%; height: auto;"></p>
+
+1. 요즘 얼굴을 드러내지 않고 활동하는 사람이 많아졌다는 점
+2. 사생활이나 목소리와 어울리지 않아 이질감을 주는 문제
+
+이를 해결하기 위하여 **성대와 울림통에 의해 목소리가 결정되고, 사람의 목소리에서 추출한 특성으로 여러 생리학적 구조와 특성을 구별할 수 있음** 을 이용합니다.
+
+이런 ***목소리와 얼굴 간의 상관 관계*** 를 학습하도록 하였습니다.
+
+###  ✔️ 기대효과
+<p align="center"><img src="https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/assets/46400961/dc734e1a-4867-4e28-b266-76daf1241c85" style="max-width: 100%; height: auto;"></p>
+
+### ✔️ 차별점
+⭐ 성장과 공유, 그리고 **함께**
+- 각자가 학습한 내용을 팀 내에 발표를 통해 공유하여 `함께 성장`하기
+- 캠퍼 모두와 함께 성장하기
+    - `MLOps` 에 대해 팀원들과 공부한 내용을 정리하고 외부 세션에서 그 내용을 공유하기
+    - `부캠 논문 읽기 모임 세션`(aka 부논모)을 운영
+- 팀원 간의 친목 도모 및 개발 효율 극대화하기
+    - 매주 1회 이상 오프라인 회의 및 `모각코` 진행
+    - `Workcation` 2회 진행
+
+⭐ **도파민(중독)** 개발
+- 최종 프로젝트를 구현하기 위해 `애자일하게 문제를 접근` 하여 **Bottom Up** 방식으로 개발할 부분을 정리하여 단계적으로 서비스를 고도화 해나갈 수 있도록 하였습니다.
+- 모든 프로젝트에 사용했던 코드를 **`모듈화`** 을 통해 재사용 가능하도록 하였으며, 이를 활용하여 서비스 고도화 작업을 이뤄나갔습니다.
+- `실 서비스`까지 프로젝트로 제공하겠다는 마음과, 순간 순간 집중하는 팀원들간의 `단합력`으로 도파민 풀충전 개발을 항상 함께 해나갔습니다.
+### ➡️ 그 결과 **2일 만에 사용 횟수 🌟 100회** 달성 🌟
+
+# ✅ 팀원 소개
+**우리는 `조화`와 `지속 가능한 성장`을 추구하는 `팀 MakeZenerator` 입니다.**
+
+**각자의 위치에서 `오너십`을 가지고 `적극적으로 참여`함으로써, 우리는 협업의 힘을 최대화합니다. `치열한 고민`과 `끊임없는 학습`으로, 자신의 역량을 키우고 새로운 아이디어를 탐구합니다.** **우리의 모토는 `함께 성장한다`입니다. 서로를 뒷받침하며 동료들과의 소통을 통해 더 나은 팀을 만들어갑니다.**
+
+***열정과 도전 정신으로, 우리는 항상 앞으로 나아가고 있습니다 !***
+
+### 👥 팀원 
+| [신호준] | [김태양]| [김혜지] |
+|--|--|--|
+|<p align="center"><img src="https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/assets/46400961/46462e68-25ee-4b9e-ba55-c1d47be9d965" height="150px" width="150px"></p>|<p align="center"><img src="https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/assets/46400961/e300980a-b7e7-4d3d-96d6-680804a1554d" height="150px" width="150px"></p>|<p align="center"><img src="https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/assets/46400961/7392ccf4-afc4-4936-8003-621f277ec158" height="150px" width="150px"></p>|
+|<p align='center'>[<img src="https://img.shields.io/badge/github-181717?style=for-the-badge&logo=github&logoColor=white">](https://github.com/internationalwe)</p>|<p align='center'>[<img src="https://img.shields.io/badge/github-181717?style=for-the-badge&logo=github&logoColor=white">](https://github.com/taeyang916)</p>|<p align='center'>[<img src="https://img.shields.io/badge/github-181717?style=for-the-badge&logo=github&logoColor=white">](https://github.com/carboxaminoo)</p>|
+
+| [정소윤] | [성주희] | [임서현] |
+|--|--|--|
+|<p align="center"><img src="https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/assets/46400961/223e0b57-bf61-4035-bf98-0712cb7f3dd0" height="150px" width="150px"></p>|<p align="center"><img src="https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/assets/76814748/acc92e73-2452-4a3c-b99d-abc571b2d1ed" height="150px" width="150px"></p>|<p align="center"><img src="https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/assets/46400961/21e2dcd9-c982-4695-a640-0770da6694bf" height="150px" width="150px"></p>
+|<p align='center'>[<img src="https://img.shields.io/badge/github-181717?style=for-the-badge&logo=github&logoColor=white">](https://github.com/soyoonjeong)</p>|<p align='center'>[<img src="https://img.shields.io/badge/github-181717?style=for-the-badge&logo=github&logoColor=white">](https://github.com/jouhy)</p>|<p align='center'>[<img src="https://img.shields.io/badge/github-181717?style=for-the-badge&logo=github&logoColor=white">](https://github.com/dlatjgus0612)</p>|
+
+### 👥 역할 
+<p align="center"><img src="https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/assets/46400961/f2bb09f6-53d8-46f9-9728-d271778bdd1d" style="max-width: 100%; height: auto;"></p>
+
+# ✅ 타임라인 
+팀 구성, 방향성을 설정하고 프로젝트를 설계하였습니다. 이후 `서비스 파이프라인`을 설계,  `sprint 주기`에 맞춰 회의를 진행하고, `기능명세서`를 작성하여 `task`를 진행하였습니다.
+
+원활한 서비스 완성을 위해 `Version`을 나누었고, 
+서비스를  실습해보기  위해  간단한 v0.1,
+이후  데이터 파이프라인을 구축하고, 시스템 아키텍처와 서비스 전반을 고도화하여 현재의 **`v1.0 최종 프로젝트 배포`** 가 완료된 상태입니다.
+
+✔️ 타임라인 표 
+<p align="center"><img src="https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/assets/46400961/fcde2215-042c-48ea-983b-0e570fffa8d4" style="max-width: 100%; height: auto;"></p>
+
+# ✅ 시스템 아키텍처 & 파이프라인
+
+✔️ 아키텍처 사진
+<p align="center"><img src="https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/assets/46400961/1722531e-4ebe-4f57-bf0d-08b6dc80c0ea" style="max-width: 100%; height: auto;"></p>
+
+✔️ 파이프라인 사진
+<p align="center"><img src="https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/assets/46400961/538e368d-83ef-43a6-8aca-87826dee824e" style="max-width: 100%; height: auto;"></p>
+
+# ✅ Getting Started
+> 아래 Readme를 통해 직접 프로젝트에 구현된 코드를 살펴볼 수 있습니다.
+
+### 💫 Model
+-   [Model](https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/tree/main/modeling)  /  [README.md](https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/tree/main/modeling/README.md)
+
+### 📊 Data
+-   [Data Pipeline](https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/tree/main/data)  /  [README.md](https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/tree/main/data/README.md)
+
+### 🖼️ Frontend
+-   [Frontend](https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/tree/main/frontend)  /  [README.md](https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/tree/main/frontend/README.md)
+
+### 💻 Backend
+-   [Backend](https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/tree/main/backend)  /  [README.md](https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/tree/main/backend/README.md)
+
+### 🌐 MLOps
+-   [MLOps](https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/tree/main/mlops)  /  [README.md](https://github.com/boostcampaitech6/level2-3-cv-finalproject-cv-08/tree/main/mlops/README.md)
+
+## Links
+### [YOUTUBE 발표자료](https://youtu.be/VJL6UQUTKrY?si=ZEs1L4BqvAnHlwvh) 
+updated. 2024.04.02
+### [NOTION LINK](https://truealex.notion.site/CV-08-d7987f38c09f470aababdedd822e96ca?pvs=4)
+updated. 2024.03.31