diff --git a/.gitignore b/.gitignore
index ec48707..af32c7e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -152,12 +152,17 @@ dmypy.json
 # Cython debug symbols
 cython_debug/
 
+#Model weight
+*.pt
 # 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/
+wandb/
+*.pkl
+events.out*
 grafana.db
 000000*
 lock
@@ -166,5 +171,8 @@ tombstones
 prometheus/prometheus-volume/data/*
 index
 0000*
+meta.json
+*.tar
+*.pth
 *.onnx
 *.pth
diff --git a/docker/mlflow/DockerFile.mlflow b/docker/mlflow/DockerFile.mlflow
new file mode 100644
index 0000000..4eb5baf
--- /dev/null
+++ b/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/docker/mlflow/docker-compose_mlflow.yaml b/docker/mlflow/docker-compose_mlflow.yaml
new file mode 100644
index 0000000..f284193
--- /dev/null
+++ b/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: minio
+      MINIO_ROOT_PASSWORD: miniostorage
+    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/mlflow/registry/Swimswap/checkpoints/people/iter.txt b/mlflow/registry/Swimswap/checkpoints/people/iter.txt
new file mode 100644
index 0000000..f05eb86
--- /dev/null
+++ b/mlflow/registry/Swimswap/checkpoints/people/iter.txt
@@ -0,0 +1,2 @@
+519
+4062
diff --git a/mlflow/registry/Swimswap/checkpoints/people/loss_log.txt b/mlflow/registry/Swimswap/checkpoints/people/loss_log.txt
new file mode 100644
index 0000000..0585ac8
--- /dev/null
+++ b/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 
+(epoch: 28, iters: 8384, time: 0.064) G_GAN: 0.023 G_GAN_Feat: 0.887 G_ID: 0.352 G_Rec: 0.377 D_GP: 0.067 D_real: 0.457 D_fake: 0.977 
+(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/mlflow/registry/Swimswap/checkpoints/people/opt.txt b/mlflow/registry/Swimswap/checkpoints/people/opt.txt
new file mode 100644
index 0000000..3cc4ed4
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/insightface_func/__init__.py b/mlflow/registry/Swimswap/insightface_func/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mlflow/registry/Swimswap/insightface_func/face_detect_crop_multi.py b/mlflow/registry/Swimswap/insightface_func/face_detect_crop_multi.py
new file mode 100644
index 0000000..d75c36b
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/insightface_func/face_detect_crop_single.py b/mlflow/registry/Swimswap/insightface_func/face_detect_crop_single.py
new file mode 100644
index 0000000..f3f648f
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/insightface_func/utils/face_align_ffhqandnewarc.py b/mlflow/registry/Swimswap/insightface_func/utils/face_align_ffhqandnewarc.py
new file mode 100644
index 0000000..2f5aeb7
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/model_registry.py b/mlflow/registry/Swimswap/model_registry.py
new file mode 100644
index 0000000..aa9f9b5
--- /dev/null
+++ b/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"] = "http://223.130.133.236:9000"
+os.environ["MLFLOW_TRACKING_URI"] = "http://223.130.133.236:5001"
+os.environ["AWS_ACCESS_KEY_ID"] = "minio"
+os.environ["AWS_SECRET_ACCESS_KEY"] = "miniostorage"
+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/mlflow/registry/Swimswap/models/__init__.py b/mlflow/registry/Swimswap/models/__init__.py
new file mode 100644
index 0000000..289de91
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/models/arcface_models.py b/mlflow/registry/Swimswap/models/arcface_models.py
new file mode 100644
index 0000000..39a6ac5
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/models/base_model.py b/mlflow/registry/Swimswap/models/base_model.py
new file mode 100644
index 0000000..0a6474a
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/models/config.py b/mlflow/registry/Swimswap/models/config.py
new file mode 100644
index 0000000..eb83edb
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/models/fs_model.py b/mlflow/registry/Swimswap/models/fs_model.py
new file mode 100644
index 0000000..95ac877
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/models/fs_networks.py b/mlflow/registry/Swimswap/models/fs_networks.py
new file mode 100644
index 0000000..227f6fa
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/models/fs_networks_512.py b/mlflow/registry/Swimswap/models/fs_networks_512.py
new file mode 100644
index 0000000..1b9f9c8
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/models/fs_networks_fix.py b/mlflow/registry/Swimswap/models/fs_networks_fix.py
new file mode 100644
index 0000000..c7b0525
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/models/models.py b/mlflow/registry/Swimswap/models/models.py
new file mode 100644
index 0000000..bf9e84e
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/models/networks.py b/mlflow/registry/Swimswap/models/networks.py
new file mode 100644
index 0000000..3c7eb4d
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/models/pix2pixHD_model.py b/mlflow/registry/Swimswap/models/pix2pixHD_model.py
new file mode 100644
index 0000000..fafdec0
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/models/projected_model.py b/mlflow/registry/Swimswap/models/projected_model.py
new file mode 100644
index 0000000..15d70f9
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/models/projectionhead.py b/mlflow/registry/Swimswap/models/projectionhead.py
new file mode 100644
index 0000000..a0d18f1
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/models/ui_model.py b/mlflow/registry/Swimswap/models/ui_model.py
new file mode 100644
index 0000000..c5b3433
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/options/base_options.py b/mlflow/registry/Swimswap/options/base_options.py
new file mode 100644
index 0000000..f5e5e8c
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/options/test_options.py b/mlflow/registry/Swimswap/options/test_options.py
new file mode 100644
index 0000000..e46e435
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/parsing_model/model.py b/mlflow/registry/Swimswap/parsing_model/model.py
new file mode 100644
index 0000000..d434836
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/parsing_model/resnet.py b/mlflow/registry/Swimswap/parsing_model/resnet.py
new file mode 100644
index 0000000..aa2bf95
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/pg_modules/blocks.py b/mlflow/registry/Swimswap/pg_modules/blocks.py
new file mode 100644
index 0000000..78bd113
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/pg_modules/diffaug.py b/mlflow/registry/Swimswap/pg_modules/diffaug.py
new file mode 100644
index 0000000..54020be
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/pg_modules/projected_discriminator.py b/mlflow/registry/Swimswap/pg_modules/projected_discriminator.py
new file mode 100644
index 0000000..d0c879f
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/pg_modules/projector.py b/mlflow/registry/Swimswap/pg_modules/projector.py
new file mode 100644
index 0000000..610a482
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/util/add_watermark.py b/mlflow/registry/Swimswap/util/add_watermark.py
new file mode 100644
index 0000000..a4c5d3d
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/util/gifswap.py b/mlflow/registry/Swimswap/util/gifswap.py
new file mode 100644
index 0000000..e0eaca9
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/util/html.py b/mlflow/registry/Swimswap/util/html.py
new file mode 100644
index 0000000..71c48ad
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/util/image_pool.py b/mlflow/registry/Swimswap/util/image_pool.py
new file mode 100644
index 0000000..63e1877
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/util/json_config.py b/mlflow/registry/Swimswap/util/json_config.py
new file mode 100644
index 0000000..c68fbff
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/util/logo_class.py b/mlflow/registry/Swimswap/util/logo_class.py
new file mode 100644
index 0000000..044dce3
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/util/norm.py b/mlflow/registry/Swimswap/util/norm.py
new file mode 100644
index 0000000..981adcf
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/util/plot.py b/mlflow/registry/Swimswap/util/plot.py
new file mode 100644
index 0000000..0da1c75
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/util/reverse2original.py b/mlflow/registry/Swimswap/util/reverse2original.py
new file mode 100644
index 0000000..81305b8
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/util/save_heatmap.py b/mlflow/registry/Swimswap/util/save_heatmap.py
new file mode 100644
index 0000000..71ce4c9
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/util/util.py b/mlflow/registry/Swimswap/util/util.py
new file mode 100644
index 0000000..f4f79ec
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/util/videoswap.py b/mlflow/registry/Swimswap/util/videoswap.py
new file mode 100644
index 0000000..99294d5
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/util/videoswap_multispecific.py b/mlflow/registry/Swimswap/util/videoswap_multispecific.py
new file mode 100644
index 0000000..40e9488
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/util/videoswap_specific.py b/mlflow/registry/Swimswap/util/videoswap_specific.py
new file mode 100644
index 0000000..8177efb
--- /dev/null
+++ b/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/mlflow/registry/Swimswap/util/visualizer.py b/mlflow/registry/Swimswap/util/visualizer.py
new file mode 100644
index 0000000..584ac45
--- /dev/null
+++ b/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/mlflow/train/sf2f/GETTING_STARTED.md b/mlflow/train/sf2f/GETTING_STARTED.md
new file mode 100644
index 0000000..1241f5a
--- /dev/null
+++ b/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/mlflow/train/sf2f/datasets/__init__.py b/mlflow/train/sf2f/datasets/__init__.py
new file mode 100644
index 0000000..1b99502
--- /dev/null
+++ b/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/mlflow/train/sf2f/datasets/build_dataset.py b/mlflow/train/sf2f/datasets/build_dataset.py
new file mode 100644
index 0000000..6bc91c6
--- /dev/null
+++ b/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/mlflow/train/sf2f/datasets/utils.py b/mlflow/train/sf2f/datasets/utils.py
new file mode 100644
index 0000000..c2bb4e4
--- /dev/null
+++ b/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/mlflow/train/sf2f/datasets/vox_dataset.py b/mlflow/train/sf2f/datasets/vox_dataset.py
new file mode 100644
index 0000000..d3dbc0f
--- /dev/null
+++ b/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/mlflow/train/sf2f/infer.py b/mlflow/train/sf2f/infer.py
new file mode 100644
index 0000000..a795e8e
--- /dev/null
+++ b/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/mlflow/train/sf2f/inference_fuser.py b/mlflow/train/sf2f/inference_fuser.py
new file mode 100644
index 0000000..aa7fa89
--- /dev/null
+++ b/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/mlflow/train/sf2f/model_registry.py b/mlflow/train/sf2f/model_registry.py
new file mode 100644
index 0000000..0140dfe
--- /dev/null
+++ b/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"] = "http://223.130.133.236:9000"
+os.environ["MLFLOW_TRACKING_URI"] = "http://223.130.133.236:5001"
+os.environ["AWS_ACCESS_KEY_ID"] = "minio"
+os.environ["AWS_SECRET_ACCESS_KEY"] = "miniostorage"
+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/mlflow/train/sf2f/models/__init__.py b/mlflow/train/sf2f/models/__init__.py
new file mode 100644
index 0000000..694bee5
--- /dev/null
+++ b/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/mlflow/train/sf2f/models/attention.py b/mlflow/train/sf2f/models/attention.py
new file mode 100644
index 0000000..e74b2af
--- /dev/null
+++ b/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/mlflow/train/sf2f/models/crn.py b/mlflow/train/sf2f/models/crn.py
new file mode 100644
index 0000000..33cd9a3
--- /dev/null
+++ b/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/mlflow/train/sf2f/models/discriminators.py b/mlflow/train/sf2f/models/discriminators.py
new file mode 100644
index 0000000..f0f276d
--- /dev/null
+++ b/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/mlflow/train/sf2f/models/encoder_decoder.py b/mlflow/train/sf2f/models/encoder_decoder.py
new file mode 100644
index 0000000..d564d86
--- /dev/null
+++ b/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/mlflow/train/sf2f/models/face_decoders.py b/mlflow/train/sf2f/models/face_decoders.py
new file mode 100644
index 0000000..a05a8b7
--- /dev/null
+++ b/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/mlflow/train/sf2f/models/fusers.py b/mlflow/train/sf2f/models/fusers.py
new file mode 100644
index 0000000..ecc8043
--- /dev/null
+++ b/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/mlflow/train/sf2f/models/inception_resnet_v1.py b/mlflow/train/sf2f/models/inception_resnet_v1.py
new file mode 100644
index 0000000..f95a1f8
--- /dev/null
+++ b/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/mlflow/train/sf2f/models/layers.py b/mlflow/train/sf2f/models/layers.py
new file mode 100644
index 0000000..001e3eb
--- /dev/null
+++ b/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/mlflow/train/sf2f/models/model_collection.py b/mlflow/train/sf2f/models/model_collection.py
new file mode 100644
index 0000000..bf3e4ba
--- /dev/null
+++ b/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/mlflow/train/sf2f/models/model_setup.py b/mlflow/train/sf2f/models/model_setup.py
new file mode 100644
index 0000000..94ef21a
--- /dev/null
+++ b/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/mlflow/train/sf2f/models/networks.py b/mlflow/train/sf2f/models/networks.py
new file mode 100644
index 0000000..28f3333
--- /dev/null
+++ b/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/mlflow/train/sf2f/models/perceptual.py b/mlflow/train/sf2f/models/perceptual.py
new file mode 100644
index 0000000..c22015f
--- /dev/null
+++ b/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/mlflow/train/sf2f/models/voice_encoders.py b/mlflow/train/sf2f/models/voice_encoders.py
new file mode 100644
index 0000000..f26acfb
--- /dev/null
+++ b/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/mlflow/train/sf2f/options/__init__.py b/mlflow/train/sf2f/options/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mlflow/train/sf2f/options/data_opts/vox.yaml b/mlflow/train/sf2f/options/data_opts/vox.yaml
new file mode 100644
index 0000000..d0d3207
--- /dev/null
+++ b/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/mlflow/train/sf2f/options/opts.py b/mlflow/train/sf2f/options/opts.py
new file mode 100644
index 0000000..988372f
--- /dev/null
+++ b/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/mlflow/train/sf2f/options/vox/baseline/v2f.yaml b/mlflow/train/sf2f/options/vox/baseline/v2f.yaml
new file mode 100644
index 0000000..cd406bf
--- /dev/null
+++ b/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/mlflow/train/sf2f/options/vox/sf2f/sf2f_1st_stage.yaml b/mlflow/train/sf2f/options/vox/sf2f/sf2f_1st_stage.yaml
new file mode 100644
index 0000000..c627b1d
--- /dev/null
+++ b/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/mlflow/train/sf2f/options/vox/sf2f/sf2f_fuser.yaml b/mlflow/train/sf2f/options/vox/sf2f/sf2f_fuser.yaml
new file mode 100644
index 0000000..c354661
--- /dev/null
+++ b/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/mlflow/train/sf2f/options/vox/sf2f/sf2f_mid_1st_stage.yaml b/mlflow/train/sf2f/options/vox/sf2f/sf2f_mid_1st_stage.yaml
new file mode 100644
index 0000000..6f0e407
--- /dev/null
+++ b/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/mlflow/train/sf2f/options/vox/sf2f/sf2f_mid_fuser.yaml b/mlflow/train/sf2f/options/vox/sf2f/sf2f_mid_fuser.yaml
new file mode 100644
index 0000000..ed021dc
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/DScore/__init__.py b/mlflow/train/sf2f/scripts/DScore/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mlflow/train/sf2f/scripts/DScore/models/__init__.py b/mlflow/train/sf2f/scripts/DScore/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mlflow/train/sf2f/scripts/DScore/models/base_model.py b/mlflow/train/sf2f/scripts/DScore/models/base_model.py
new file mode 100644
index 0000000..36f4d83
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/DScore/models/dist_model.py b/mlflow/train/sf2f/scripts/DScore/models/dist_model.py
new file mode 100644
index 0000000..d11eee1
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/DScore/models/models.py b/mlflow/train/sf2f/scripts/DScore/models/models.py
new file mode 100644
index 0000000..97cad61
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/DScore/models/networks_basic.py b/mlflow/train/sf2f/scripts/DScore/models/networks_basic.py
new file mode 100644
index 0000000..783ebf6
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/DScore/models/pretrained_networks.py b/mlflow/train/sf2f/scripts/DScore/models/pretrained_networks.py
new file mode 100644
index 0000000..f5c70dc
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/DScore/util/__init__.py b/mlflow/train/sf2f/scripts/DScore/util/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mlflow/train/sf2f/scripts/DScore/util/html.py b/mlflow/train/sf2f/scripts/DScore/util/html.py
new file mode 100644
index 0000000..3a113e2
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/DScore/util/util.py b/mlflow/train/sf2f/scripts/DScore/util/util.py
new file mode 100644
index 0000000..1a84327
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/DScore/util/visualizer.py b/mlflow/train/sf2f/scripts/DScore/util/visualizer.py
new file mode 100644
index 0000000..3609705
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/build_demo_set.py b/mlflow/train/sf2f/scripts/build_demo_set.py
new file mode 100644
index 0000000..127a76e
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/compute_diversity_score.py b/mlflow/train/sf2f/scripts/compute_diversity_score.py
new file mode 100644
index 0000000..52e338c
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/compute_fid_score.py b/mlflow/train/sf2f/scripts/compute_fid_score.py
new file mode 100644
index 0000000..d7a7212
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/compute_inception_score.py b/mlflow/train/sf2f/scripts/compute_inception_score.py
new file mode 100644
index 0000000..85992e2
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/compute_mel_mean_var.py b/mlflow/train/sf2f/scripts/compute_mel_mean_var.py
new file mode 100644
index 0000000..d730e17
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/compute_vggface_score.py b/mlflow/train/sf2f/scripts/compute_vggface_score.py
new file mode 100644
index 0000000..8868c2b
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/convert_wav_to_mel.py b/mlflow/train/sf2f/scripts/convert_wav_to_mel.py
new file mode 100644
index 0000000..ff334fa
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/create_split_json.py b/mlflow/train/sf2f/scripts/create_split_json.py
new file mode 100644
index 0000000..3bb5b8f
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/download_vggface_weights.sh b/mlflow/train/sf2f/scripts/download_vggface_weights.sh
new file mode 100644
index 0000000..2d59ada
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/install_requirements.py b/mlflow/train/sf2f/scripts/install_requirements.py
new file mode 100644
index 0000000..b531402
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/print_args.py b/mlflow/train/sf2f/scripts/print_args.py
new file mode 100644
index 0000000..5ccaa26
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/sample_mel_spectrograms.py b/mlflow/train/sf2f/scripts/sample_mel_spectrograms.py
new file mode 100644
index 0000000..deb63a8
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/strip_checkpoint.py b/mlflow/train/sf2f/scripts/strip_checkpoint.py
new file mode 100644
index 0000000..f91831b
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/strip_old_args.py b/mlflow/train/sf2f/scripts/strip_old_args.py
new file mode 100644
index 0000000..578cece
--- /dev/null
+++ b/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/mlflow/train/sf2f/scripts/watch_data.py b/mlflow/train/sf2f/scripts/watch_data.py
new file mode 100644
index 0000000..8cd319c
--- /dev/null
+++ b/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/mlflow/train/sf2f/train.py b/mlflow/train/sf2f/train.py
new file mode 100644
index 0000000..ab8304e
--- /dev/null
+++ b/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/mlflow/train/sf2f/train_registry.py b/mlflow/train/sf2f/train_registry.py
new file mode 100644
index 0000000..f9785b3
--- /dev/null
+++ b/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"] = "http://localhost:9000"
+    os.environ["MLFLOW_TRACKING_URI"] = "http://localhost:5001"
+    os.environ["AWS_ACCESS_KEY_ID"] = "minio"
+    os.environ["AWS_SECRET_ACCESS_KEY"] = "miniostorage"
+    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/mlflow/train/sf2f/utils/__init__.py b/mlflow/train/sf2f/utils/__init__.py
new file mode 100644
index 0000000..55e5f84
--- /dev/null
+++ b/mlflow/train/sf2f/utils/__init__.py
@@ -0,0 +1 @@
+from .common import *
diff --git a/mlflow/train/sf2f/utils/bilinear.py b/mlflow/train/sf2f/utils/bilinear.py
new file mode 100644
index 0000000..7a12100
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/box_utils.py b/mlflow/train/sf2f/utils/box_utils.py
new file mode 100644
index 0000000..898e543
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/common.py b/mlflow/train/sf2f/utils/common.py
new file mode 100644
index 0000000..2248793
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/evaluate.py b/mlflow/train/sf2f/utils/evaluate.py
new file mode 100644
index 0000000..81f27cc
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/evaluate_fid.py b/mlflow/train/sf2f/utils/evaluate_fid.py
new file mode 100644
index 0000000..90da53d
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/filter_pickle.py b/mlflow/train/sf2f/utils/filter_pickle.py
new file mode 100644
index 0000000..ccf5646
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/logger.py b/mlflow/train/sf2f/utils/logger.py
new file mode 100644
index 0000000..c21f383
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/losses.py b/mlflow/train/sf2f/utils/losses.py
new file mode 100644
index 0000000..55fcb70
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/metrics.py b/mlflow/train/sf2f/utils/metrics.py
new file mode 100644
index 0000000..f337aaf
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/mlflow_wandb.py b/mlflow/train/sf2f/utils/mlflow_wandb.py
new file mode 100644
index 0000000..5ce4f04
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/pgan_utils.py b/mlflow/train/sf2f/utils/pgan_utils.py
new file mode 100644
index 0000000..efb4e52
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/s2f_evaluator.py b/mlflow/train/sf2f/utils/s2f_evaluator.py
new file mode 100644
index 0000000..9341cc0
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/training_utils.py b/mlflow/train/sf2f/utils/training_utils.py
new file mode 100644
index 0000000..b917b7f
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/utils.py b/mlflow/train/sf2f/utils/utils.py
new file mode 100644
index 0000000..0157a82
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/vad_ex.py b/mlflow/train/sf2f/utils/vad_ex.py
new file mode 100644
index 0000000..f8b489e
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/visualization/__init__.py b/mlflow/train/sf2f/utils/visualization/__init__.py
new file mode 100644
index 0000000..2b54423
--- /dev/null
+++ b/mlflow/train/sf2f/utils/visualization/__init__.py
@@ -0,0 +1,2 @@
+from .vis import *
+# from .visualizer import Visualizer
diff --git a/mlflow/train/sf2f/utils/visualization/html.py b/mlflow/train/sf2f/utils/visualization/html.py
new file mode 100644
index 0000000..1e71687
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/visualization/plot.py b/mlflow/train/sf2f/utils/visualization/plot.py
new file mode 100644
index 0000000..7f24e9d
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/visualization/tsne.py b/mlflow/train/sf2f/utils/visualization/tsne.py
new file mode 100644
index 0000000..62f8791
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/visualization/vis.py b/mlflow/train/sf2f/utils/visualization/vis.py
new file mode 100644
index 0000000..de412bd
--- /dev/null
+++ b/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/mlflow/train/sf2f/utils/wav2mel.py b/mlflow/train/sf2f/utils/wav2mel.py
new file mode 100644
index 0000000..35a822d
--- /dev/null
+++ b/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))