diff --git a/.gitignore b/.gitignore index 0af5316..5529439 100644 --- a/.gitignore +++ b/.gitignore @@ -11,6 +11,41 @@ ipython_config.py # Remove previous ipynb_checkpoints # git rm -r .ipynb_checkpoints/ +dataset/ +pts/ +wandb/ +*.ipynb +*.png +*.pt +datapreprocess/code_prac.ipynb +datapreprocess/rt_code_test.ipynb +datapreprocess/csv/abnormal/train/*.csv +datapreprocess/csv/abnormal/val/*.csv +datapreprocess/csv/normal/train/*.csv +datapreprocess/csv/normal/val/*.csv +datapreprocess/json/abnormal/train/ +datapreprocess/json/abnormal/val/ +datapreprocess/json/abnormal/TS_03.이상행동_14.교통약자_train/ +datapreprocess/json/abnormal/TS_03.이상행동_14.교통약자_val/ +datapreprocess/json/normal/train/ +datapreprocess/json/normal/val/ +datapreprocess/*.csv +datapreprocess/*.pt +datapreprocess/*.pth +datapreprocess/npy +datapreprocess/nohup.out +model_train/code_prac.ipynb +model_train/model.h5 +model_train/pytorch_model.pth +model_train/nohup.out +model_train/*.ipynb +model_train/wandb +app/testitems/ +pths/ +app/models/pts +app/models/yolov8n-pose.pt +app/yolov8n-pose.pt +.netrc ### macOS ### # General @@ -271,4 +306,7 @@ $RECYCLE.BIN/ # Windows shortcuts *.lnk +<<<<<<< HEAD +# End of https://www.toptal.com/developers/gitignore/api/visualstudiocode,macos,windows,jupyternotebooks,virtualenv,python +======= # End of https://www.toptal.com/developers/gitignore/api/visualstudiocode,macos,windows,jupyternotebooks,virtualenv,python \ No newline at end of file diff --git a/app/README.md b/app/README.md new file mode 100644 index 0000000..b6ec7d0 --- /dev/null +++ b/app/README.md @@ -0,0 +1,57 @@ +# Project Structure + +```bash +. +├── README.md +├── __init__.py +├── api +│   ├── __init__.py +│   ├── album_router.py +│   ├── real_time_router.py +│   ├── upload_router.py +│   └── user_router.py +├── database +│   ├── __init__.py +│   ├── crud.py +│   ├── database.py +│   ├── models.py +│   └── schemas.py +├── inference +│   ├── __init__.py +│   ├── anomaly_detector.py +│   └── rt_anomaly_detector.py +├── main.py +├── templates +│   ├── album_detail.html +│   ├── album_list.html +│   ├── base.html +│   ├── frame.html +│   ├── login.html +│   ├── main.html +│   ├── real_time.html +│   ├── signup.html +│   ├── src +│   │   ├── album_detail.js +│   │   ├── album_list.js +│   │   └── video.js +│   ├── stream.html +│   ├── upload.html +│   └── video.html +└── utils +    ├── __init__.py +    ├── config.py +    ├── security.py +    └── utils.py +``` + +# Description + +- api: URL별 로직 구현 + +- database: 데이터베이스 관련 설정 및 함수 + +- inference: 모델 추론 코드(녹화영상, 실시간) + +- templates: UI 템플릿. Bootstrap 사용 + +- utils: config 및 기타 함수 \ No newline at end of file diff --git a/app/__init__.py b/app/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/app/api/__init__.py b/app/api/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/app/api/album_router.py b/app/api/album_router.py new file mode 100644 index 0000000..b7175d9 --- /dev/null +++ b/app/api/album_router.py @@ -0,0 +1,162 @@ +from typing import Optional + +from fastapi import ( + APIRouter, + Response, + Request, + HTTPException, + Form, + UploadFile, + File, + Cookie, + Query, + Depends, +) +from fastapi.responses import RedirectResponse +from fastapi.templating import Jinja2Templates +from sqlalchemy.orm import Session + +from database import crud, models +from database.database import get_db +from utils.config import settings +from utils.security import get_current_user +from utils.utils import s3 + +templates = Jinja2Templates(directory="templates") + +router = APIRouter( + prefix="/album", +) + + +@router.get("") +async def upload_get(request: Request, db: Session = Depends(get_db)): + user = get_current_user(request) + if not user: + return RedirectResponse(url="/user/login") + + album_list = crud.get_uploads(db=db, user_id=user.user_id) + print(album_list[0].completes[0].completed) + return templates.TemplateResponse("album_list.html", {'request': request, 'token': user.email, 'album_list':album_list}) + + +@router.post("") +async def modify_name(request: Request, + check_code: str = Form(...), + upload_id: Optional[int] = Form(...), + origin_name: Optional[str] = Form(None), + new_name: Optional[str] = Form(None), + is_real_time: Optional[bool] = Form(None), + db: Session = Depends(get_db)): + user = get_current_user(request) + + if check_code == "edit": + upload_info = ( + db.query(models.Upload) + .filter((models.Upload.name == origin_name) & (models.Upload.upload_id == upload_id)) + .first() + ) + upload_info.name = new_name + + db.add(upload_info) + db.commit() + db.refresh(upload_info) + elif check_code == "delete": + upload_info = crud.get_upload(db, upload_id) + if upload_info: + db.delete(upload_info) + + db.commit() + album_list = crud.get_uploads(db=db, user_id=user.user_id) + + return templates.TemplateResponse("album_list.html", {'request': request, 'token': user.email, 'album_list': album_list}) + + +@router.get("/details") +async def upload_get_one(request: Request, + user_id: int = Query(...), + upload_id: int = Query(...), + db: Session = Depends(get_db)): + + user = get_current_user(request) + + video_info = { + "user_id": user_id, + "upload_id":upload_id, + "date": None, + "upload_name": None, + "is_realtime": None, + "video_id": None, + "video_url": None, + "frame_urls": None, + "score_url": None, + "complete": None + } + + + video = crud.get_video(db=db, upload_id=upload_id) + video_info["video_id"] = video.video_id + uploaded = crud.get_upload(db=db, upload_id=video.upload_id) + video_info["upload_name"] = uploaded.name + video_info["is_realtime"] = uploaded.is_realtime + video_info["date"] = uploaded.date.strftime('%Y-%m-%d %H:%M:%S') + + #frames = crud.get_frames(db=db, video_id=video.video_id) + frames = crud.get_frames_with_highest_score(db=db, video_id=video.video_id) + frame_ids = [frame.frame_id for frame in frames] + frame_urls = [frame.frame_url for frame in frames] + frame_timestamps = [frame.time_stamp for frame in frames] + frame_objs = [] + + video_obj = s3.generate_presigned_url('get_object', + Params={'Bucket': settings.BUCKET, + 'Key': video.video_url}, + ExpiresIn=3600) + + + video_info["video_url"] = video_obj + video_info["complete"] = crud.get_complete(db=db, upload_id=upload_id).completed + if not video_info["complete"]: + return templates.TemplateResponse("album_detail.html", {'request': request, 'token': user.email, 'video_info': video_info, 'loading': True}) + + if frame_ids != []: + for frame_id, frame_url, frame_timestamp in zip(frame_ids, frame_urls, frame_timestamps): + frame_obj = s3.generate_presigned_url('get_object', + Params={'Bucket': settings.BUCKET, + 'Key': frame_url}, + ExpiresIn=3600) + frame_objs.append((frame_id, frame_obj, frame_timestamp.strftime('%H:%M:%S'))) + + score_graph_url = '/'.join(frame_urls[0].split('/')[:-1]) + '/score_graph.png' + score_obj = s3.generate_presigned_url('get_object', + Params={'Bucket': settings.BUCKET, + 'Key': score_graph_url}, + ExpiresIn=3600) + + video_info["frame_urls"] = frame_objs + video_info["score_url"] = score_obj + + #print(video_info) + return templates.TemplateResponse("album_detail.html", {'request': request, 'token': user.email, 'video_info': video_info, 'loading': False}) + + +@router.get("/details/images") +async def image_get(request: Request, + frame_id: int = Query(...), + db: Session = Depends(get_db)): + + user = get_current_user(request) + frame = crud.get_frame(db=db, frame_id=frame_id) + frame_obj = s3.generate_presigned_url( + "get_object", Params={"Bucket": settings.BUCKET, "Key": frame.frame_url}, ExpiresIn=3600 + ) + print(frame_obj) + print(frame.box_kp_json) + frame_info = { + 'frame_url': frame_obj, + 'time_stamp': frame.time_stamp, + 'frame_json': frame.box_kp_json + } + + return templates.TemplateResponse("frame.html", {'request': request, 'token': user.email, 'frame_info': frame_info}) + \ No newline at end of file diff --git a/app/api/inference_router.py b/app/api/inference_router.py new file mode 100644 index 0000000..5a0f9be --- /dev/null +++ b/app/api/inference_router.py @@ -0,0 +1,152 @@ +from datetime import timedelta, datetime, date +from fastapi import FastAPI, Depends, BackgroundTasks, WebSocket, WebSocketDisconnect +from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel +from sqlalchemy.orm import Session +import os +import sys +import json + +import pytz +import asyncio +import cv2 +import numpy as np +from cap_from_youtube import cap_from_youtube + +current_dir = os.path.dirname(os.path.abspath(__file__)) +parent_dir = os.path.dirname(current_dir) + +sys.path.append(parent_dir) + +from database.database import get_db +from database import crud + +from utils.config import settings +from utils.utils import run_model, s3 + +from inference.rt_anomaly_detector_lstmae import RT_AnomalyDetector + +app = FastAPI() + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +class ModelInfo(BaseModel): + user_id: int + upload_id: int + threshold: float + video_uuid_name: str + video_ext: str + video_id: int + video_url: str + +@app.get("/") +def root(): + return {"message": "모델이 돌아가용"} + +# 녹화영상용 +@app.post("/run_model") +async def run_model_endpoint(info: ModelInfo, + background_tasks: BackgroundTasks, + db: Session = Depends(get_db)): + + info = info.dict() + + def run_model_task(): + run_model(info["video_url"], info, settings, db) + + background_tasks.add_task(run_model_task) + + return {"message": "Model execution started."} + + +# 메일을 보내야하는지 판단하는 함수 +async def check_and_send_email(db, video_id, user_id, last_point, smtp): + global last_emailed_time + + frames = crud.get_frames_with_highest_score(db=db, video_id=video_id) + frame_timestamps = [frame.time_stamp.strftime('%H:%M:%S') for frame in frames] + + if len(frame_timestamps) < 6: + return False + + last = datetime.strptime(frame_timestamps[-2], '%H:%M:%S') + check = datetime.strptime(frame_timestamps[-6], '%H:%M:%S') + + if (last - check) == timedelta(seconds=4): # 연속적으로 5초간 지속되면 + if not check <= last_point <= last: + crud.send_email(db, frame_timestamps[-6], frame_timestamps[-2], user_id, smtp) + last_emailed_time = last + +# 과연 웹 서버와 실시간을 분리하는 것이 더 빠른가? +@app.websocket("/ws") +async def websocket_endpoint(websocket: WebSocket, + db: Session = Depends(get_db)): + + await websocket.accept() + smtp = await crud.create_smtp_server() + + try: + video_info_str = await websocket.receive_text() + print("Received video info:", video_info_str) + video_info = json.loads(video_info_str) + global detector, last_emailed_time + if detector is None: + detector = RT_AnomalyDetector(video_info, s3, settings, db, websocket) + detector.ready() + + if video_info["video_url"] == "web": + while True: + timestamp = datetime.now(pytz.timezone('Asia/Seoul')) + # Receive bytes from the websocket + bytes = await websocket.receive_bytes() + data = np.frombuffer(bytes, dtype=np.uint8) + frame = cv2.imdecode(data, cv2.IMREAD_COLOR) + await detector.run(frame, timestamp) + await check_and_send_email(db=db, video_id=video_info['video_id'], user_id=video_info['user_id'], + last_point=last_emailed_time, smtp=smtp) + + else: + if "youtube" in video_info["video_url"]: + cap = cap_from_youtube(video_info["video_url"], '240p') + + else: + cap = cv2.VideoCapture(video_info["video_url"]) + + while True: + success, frame = cap.read() + if not success: + await websocket.send_text(f'카메라 연결에 실패했습니다.') + break + else: + timestamp = datetime.now(pytz.timezone('Asia/Seoul')) + await detector.run(frame, timestamp) + await check_and_send_email(db=db, video_id=video_info['video_id'], user_id=video_info['user_id'], + last_point=last_emailed_time, smtp=smtp) + + ret, buffer = cv2.imencode('.jpg', frame) + await websocket.send_bytes(buffer.tobytes()) + + await asyncio.sleep(0.042) + + except WebSocketDisconnect: + await websocket.close() + await smtp.quit() + + except Exception as e: + # 예외 발생 시 로그 기록 및 연결 종료 + print(f"WebSocket error: {e}") + await websocket.close() + await smtp.quit() + + finally: + try: + detector.upload_score_graph_s3() + except: + pass + detector = None \ No newline at end of file diff --git a/app/api/real_time_router.py b/app/api/real_time_router.py new file mode 100644 index 0000000..4e81b9a --- /dev/null +++ b/app/api/real_time_router.py @@ -0,0 +1,249 @@ +from datetime import timedelta, datetime, date +import pytz +import asyncio +import cv2 +import numpy as np +import json + +from fastapi import APIRouter, Request, Form, Query, Depends, WebSocket, WebSocketDisconnect, status +from fastapi.responses import RedirectResponse +from fastapi.templating import Jinja2Templates +from sqlalchemy.orm import Session +import websockets +from websockets.exceptions import ConnectionClosed + + +from database import schemas, crud +from database.database import get_db +from utils.config import settings +from utils.security import get_current_user +from utils.utils import s3 +#from inference.rt_anomaly_detector import RT_AnomalyDetector +from inference.rt_anomaly_detector_lstmae import RT_AnomalyDetector + +from cap_from_youtube import cap_from_youtube + +templates = Jinja2Templates(directory="templates") +router = APIRouter( + prefix="/real_time", +) + +detector = None +last_emailed_time = datetime.strptime("0:00:00", '%H:%M:%S') + +@router.get("") +async def real_time_get(request: Request): + user = get_current_user(request) + if not user: + return RedirectResponse(url='/user/login') + + return templates.TemplateResponse("real_time.html", {'request': request, "token": user.email}) + + +@router.post("") +async def realtime_post(request: Request, + name: str = Form(...), + real_time_video: str = Form(...), + datetime: datetime = Form(...), + thr: float = Form(...), + db: Session = Depends(get_db)): + + user = get_current_user(request) + user = crud.get_user_by_email(db=db, email=user.email) + + # Form 과 user_id 를 이용하여 upload row insert + _upload_create = schemas.UploadCreate(name=name, date=datetime, is_realtime=True, thr=thr, user_id=user.user_id) + crud.create_upload(db=db, upload=_upload_create) + + # 지금 업로드된 id 획득, 클라이언트로부터 작성된 실시간 스트리밍 영상 url 획득 + uploaded = crud.get_upload_id(db=db, user_id=user.user_id, name=name, date=datetime)[-1] + + # db 에는 실시간임을 알 수 있게만 함 + video_url = f"{real_time_video}" + _video_create = schemas.VideoCreate(video_url=video_url, upload_id=uploaded.upload_id) + crud.create_video(db=db, video=_video_create) + _complete_create = schemas.Complete(completed=True, upload_id=uploaded.upload_id) + crud.create_complete(db=db, complete=_complete_create) + + # model inference 에서 사용할 정보 + info = { + "user_id": user.user_id, + "email": user.email, + "upload_id": uploaded.upload_id, + "name": uploaded.name, + "date": uploaded.date, + "threshold": uploaded.thr, + "video_url": video_url, + "video_id": crud.get_video(db=db, upload_id=uploaded.upload_id).video_id + } + + redirect_url = f"/real_time/stream?user_id={info['user_id']}&upload_id={info['upload_id']}" + + return RedirectResponse(url=redirect_url, status_code=status.HTTP_303_SEE_OTHER) + + +@router.get("/stream") +async def get_stream(request: Request, + user_id: int = Query(...), + upload_id: int = Query(...), + db: Session = Depends(get_db)): + + user = get_current_user(request) + + video = crud.get_video(db=db, upload_id=upload_id) + uploaded = crud.get_upload(db=db, upload_id=video.upload_id) + + video_info = { + "user_id": user_id, + "upload_id": upload_id, + "date": uploaded.date.strftime('%Y-%m-%d %H:%M:%S'), + "upload_name": uploaded.name, + "thr": uploaded.thr, + "video_id": video.video_id, + "video_url": video.video_url, + "is_realtime": True, + "model_server_ip": settings.STREAM_MODEL_SERVER_IP + } + + # video_info = json.dumps(video_info) + + return templates.TemplateResponse("stream.html", {'request': request, 'token': user.email, 'video_info': video_info}) + +# 메일을 보내야하는지 판단하는 함수 +async def check_and_send_email(db, video_id, user_id, last_point, smtp): + global last_emailed_time + + frames = crud.get_frames_with_highest_score(db=db, video_id=video_id) + frame_timestamps = [frame.time_stamp.strftime('%H:%M:%S') for frame in frames] + + if len(frame_timestamps) < 6: + return False + + last = datetime.strptime(frame_timestamps[-2], '%H:%M:%S') + check = datetime.strptime(frame_timestamps[-6], '%H:%M:%S') + + if (last - check) == timedelta(seconds=4): # 연속적으로 5초간 지속되면 + if not check <= last_point <= last: + crud.send_email(db, frame_timestamps[-6], frame_timestamps[-2], user_id, smtp) + last_emailed_time = last + + +@router.websocket("/ws") +async def websocket_endpoint(websocket: WebSocket, + db: Session = Depends(get_db)): + await websocket.accept() + smtp = await crud.create_smtp_server() + + try: + video_info_str = await websocket.receive_text() + print("Received video info:", video_info_str) + video_info = json.loads(video_info_str) + global detector, last_emailed_time + if detector is None: + detector = RT_AnomalyDetector(video_info, s3, settings, db, websocket) + detector.ready() + + if video_info["video_url"] == "web": + while True: + timestamp = datetime.now(pytz.timezone('Asia/Seoul')) + # Receive bytes from the websocket + bytes = await websocket.receive_bytes() + data = np.frombuffer(bytes, dtype=np.uint8) + frame = cv2.imdecode(data, cv2.IMREAD_COLOR) + await detector.run(frame, timestamp) + await check_and_send_email(db=db, video_id=video_info['video_id'], user_id=video_info['user_id'], + last_point=last_emailed_time, smtp=smtp) + + else: + if "youtube" in video_info["video_url"]: + cap = cap_from_youtube(video_info["video_url"], '240p') + + else: + cap = cv2.VideoCapture(video_info["video_url"]) + + while True: + success, frame = cap.read() + if not success: + await websocket.send_text(f'카메라 연결에 실패했습니다.') + break + else: + timestamp = datetime.now(pytz.timezone('Asia/Seoul')) + await detector.run(frame, timestamp) + await check_and_send_email(db=db, video_id=video_info['video_id'], user_id=video_info['user_id'], + last_point=last_emailed_time, smtp=smtp) + + ret, buffer = cv2.imencode('.jpg', frame) + await websocket.send_bytes(buffer.tobytes()) + + await asyncio.sleep(0.042) + + except WebSocketDisconnect: + await websocket.close() + await smtp.quit() + + except Exception as e: + # 예외 발생 시 로그 기록 및 연결 종료 + print(f"WebSocket error: {e}") + await websocket.close() + await smtp.quit() + + finally: + try: + detector.upload_score_graph_s3() + except: + pass + detector = None + + +@router.get("/stream") +async def get_stream(request: Request, + user_id: int = Query(...), + upload_id: int = Query(...), + db: Session = Depends(get_db)): + + user = get_current_user(request) + + video = crud.get_video(db=db, upload_id=upload_id) + uploaded = crud.get_upload(db=db, upload_id=video.upload_id) + + video_info = { + "user_id": user_id, + "upload_id": upload_id, + "date": uploaded.date.strftime('%Y-%m-%d %H:%M:%S'), + "upload_name": uploaded.name, + "thr": uploaded.thr, + "video_id": video.video_id, + "video_url": video.video_url, + "is_realtime": True + } + + # video_info = json.dumps(video_info) + + return templates.TemplateResponse("stream.html", {'request': request, 'token': user.email, 'video_info': video_info}) + + +# db 에서 실시간에서 저장되는 frame url 불러오는 코드 +def fetch_data(db, upload_id): + + video = crud.get_video(db=db, upload_id=upload_id) + frames = crud.get_frames_with_highest_score(db=db, video_id=video.video_id) + frame_ids = [frame.frame_id for frame in frames] + frame_urls = [frame.frame_url for frame in frames] + frame_timestamps = [frame.time_stamp for frame in frames] + frame_objs = [] + + for frame_id, frame_url, frame_timestamp in zip(frame_ids, frame_urls, frame_timestamps): + frame_obj = s3.generate_presigned_url('get_object', + Params={'Bucket': settings.BUCKET, + 'Key': frame_url}, + ExpiresIn=3600) + frame_objs.append((frame_id, frame_obj, frame_timestamp.strftime('%H:%M:%S'))) + + return {"frame_urls": frame_objs} + +@router.get("/fetch_data") +async def fetch_frame_data(upload_id: int = Query(...), + db: Session = Depends(get_db)): + frame_data = fetch_data(db, upload_id) + return frame_data + diff --git a/app/api/upload_router.py b/app/api/upload_router.py new file mode 100644 index 0000000..63d75a1 --- /dev/null +++ b/app/api/upload_router.py @@ -0,0 +1,116 @@ +from datetime import datetime +import os +import uuid +import requests + +from fastapi import APIRouter, Response, Request, Form, UploadFile, File, Depends, BackgroundTasks, status, HTTPException +from fastapi.responses import RedirectResponse +from fastapi.templating import Jinja2Templates +from sqlalchemy.orm import Session + +from utils.config import settings +from database import crud +from database import schemas +from database.database import get_db +from utils.security import get_current_user +from utils.utils import s3 + +templates = Jinja2Templates(directory="templates") + +router = APIRouter( + prefix="/upload", +) + +@router.get("") +async def upload_get(request: Request): + user = get_current_user(request) + err_msg = {"file_ext": None} + if not user: + return RedirectResponse(url='/user/login') + + return templates.TemplateResponse("upload.html", {'request': request, 'token': user.email, "err": err_msg}) + +@router.post("") +async def upload_post(request: Request, + name: str = Form(...), + upload_file: UploadFile = File(...), + datetime: datetime = Form(...), + thr: float = Form(...), + db: Session = Depends(get_db)): + + user = get_current_user(request) + err_msg = {"file_ext": None} + + if not user: + return RedirectResponse(url='/user/login') + + file_ext = os.path.splitext(upload_file.filename)[-1] + if file_ext != ".mp4": + err_msg["file_ext"] = "파일 형식이 다릅니다.(mp4만 지원 가능)" + return templates.TemplateResponse("upload.html", {'request': request, 'token': user.email, "err": err_msg}) + + _upload_create = schemas.UploadCreate(name=name, date=datetime, is_realtime=False, thr=thr, user_id=user.user_id) + crud.create_upload(db=db, upload=_upload_create) + + uploaded = crud.get_upload_id(db=db, user_id=user.user_id, name=name, date=datetime)[-1] + + video_name = uuid.uuid1() + + # model inference 에서 s3 에 올릴 주소 그대로 db 에 insert + video_url = f"video/{user.user_id}/{uploaded.upload_id}/{video_name}{file_ext}" + _video_create = schemas.VideoCreate(video_url=video_url, upload_id=uploaded.upload_id) + crud.create_video(db=db, video=_video_create) + _complete_create = schemas.Complete(completed=False, upload_id=uploaded.upload_id) + crud.create_complete(db=db, complete=_complete_create) + + s3_upload_exception = HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="video 를 s3 저장소 업로드에 실패했습니다." + ) + + try: + s3.upload_fileobj( + upload_file.file, + settings.BUCKET, + video_url, + ExtraArgs={'ContentType': 'video/mp4'} + ) + except: + raise s3_upload_exception + + info = { + "user_id": user.user_id, + "email": user.email, + "upload_id": uploaded.upload_id, + "name": name, + "date": datetime, + "threshold": uploaded.thr, + "video_name": upload_file.filename, + "video_uuid_name": video_name, + "video_ext": file_ext, + "video_id": crud.get_video(db=db, upload_id=uploaded.upload_id).video_id, + "video_url": video_url, + } + + model_data = { + "user_id": user.user_id, + "upload_id": uploaded.upload_id, + "threshold": uploaded.thr, + "video_uuid_name": str(video_name), + "video_ext": file_ext, + "video_id": crud.get_video(db=db, upload_id=uploaded.upload_id).video_id, + "video_url": video_url, + } + + model_server_url = settings.UPLOAD_MODEL_SERVER_IP + try: + response = requests.post(model_server_url, json=model_data) + response.raise_for_status() # 응답 상태 코드가 200이 아닌 경우 예외 발생 + print("Model execution started successfully.") + except requests.RequestException: + e = "모델 서버에서 오류가 발생했습니다." + raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=e) + + redirect_url = f"/album/details?user_id={info['user_id']}&upload_id={info['upload_id']}" + + return RedirectResponse(url=redirect_url, status_code=status.HTTP_303_SEE_OTHER) diff --git a/app/api/user_router.py b/app/api/user_router.py new file mode 100644 index 0000000..e20952d --- /dev/null +++ b/app/api/user_router.py @@ -0,0 +1,82 @@ +from fastapi import APIRouter, Request, Depends +from fastapi.responses import RedirectResponse +from fastapi.templating import Jinja2Templates +from sqlalchemy.orm import Session + +from database import models, crud +from database.database import get_db +from utils.security import pwd_context + +templates = Jinja2Templates(directory="templates") +router = APIRouter(prefix="/user") + +@router.get("/signup") +async def signup_get(request: Request, + db: Session=Depends(get_db)): + err_msg = {"user": None, "pw": None, "check_pw": None} + return templates.TemplateResponse("signup.html", {"request": request, "err": err_msg}) + +@router.post("/signup") +async def signup_post(request: Request, + db: Session=Depends(get_db)): + body = await request.form() + user, pw, check_pw = body["email"], body["pw"], body["check_pw"] + err_msg = {"user": None, "pw": None, "check_pw": None} + + if not user: + err_msg["user"] = "empty email" + elif not pw: + err_msg["pw"] = "empty password" + elif pw != check_pw: + err_msg["check_pw"] = "not equal password and check_password" + else: + user = db.query(models.User).filter(models.User.email == body['email']).first() + + if user: + err_msg["user"] = "invalid email" + else: + user_info = models.User(email = body['email'], + password = body['pw']) + + crud.create_user(db, user_info) + return RedirectResponse(url="/user/login") + + return templates.TemplateResponse("signup.html", {"request": request, "err": err_msg}) + +@router.get("/login") +async def login_get(request: Request): + err_msg = {"user": None, "pw": None} + return templates.TemplateResponse("login.html", {"request": request, "err": err_msg}) + +@router.post("/login") +async def login_post(request: Request, + db: Session=Depends(get_db)): + body = await request.form() + user, pw= body["email"], body["pw"] + err_msg = {"user": None, "pw": None} + + if body.get("check_pw", None): + return templates.TemplateResponse("login.html", {"request": request, "err": err_msg}) + + if not user: + err_msg["user"] = "empty email" + elif not pw: + err_msg["pw"] = "empty password" + else: + user = db.query(models.User).filter(models.User.email == body['email']).first() + if not user: + err_msg["user"] = "invalid email" + elif not pwd_context.verify(body['pw'], user.password): + err_msg["pw"] = "invalid password" + else: + return RedirectResponse(url="/") + + return templates.TemplateResponse("login.html", {"request": request, "err": err_msg}) + +@router.get("/logout") +async def logout_get(request: Request): + access_token = request.cookies.get("access_token", None) + template = RedirectResponse(url="/") + if access_token: + template.delete_cookie(key="access_token") + return template diff --git a/app/database/__init__.py b/app/database/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/app/database/crud.py b/app/database/crud.py new file mode 100644 index 0000000..1144e0c --- /dev/null +++ b/app/database/crud.py @@ -0,0 +1,210 @@ +from datetime import timedelta, datetime, date + +from sqlalchemy.orm import Session, aliased +from sqlalchemy import func +import smtplib +from email.mime.multipart import MIMEMultipart +from email.mime.text import MIMEText +# from email.mime.image import MIMEImage +# from passlib.context import CryptContext + +from database.schemas import UserBase, UserCreate, UploadCreate, VideoCreate, FrameCreate, Complete +from database import models +from utils.security import get_password_hash, verify_password +from utils.config import settings + +## User +def create_user(db: Session, user: UserCreate): + hashed_password = get_password_hash(user.password) + db_user = models.User(email=user.email, password=hashed_password) + db.add(db_user) + db.commit() + db.refresh(db_user) + return db_user + + +def get_user(db: Session, user_id: int): + return db.query(models.User).filter(models.User.user_id == user_id).first() + + +def get_user_by_email(db: Session, email: str): + return db.query(models.User).filter(models.User.email == email).first() + + +def get_existing_user(db: Session, user_create: UserCreate): + return db.query(models.User).filter((models.User.email == user_create.email)).first() + + +def authenticate(db: Session, *, email: str, password: str): + user = get_user_by_email(db, email=email) + if not user: + return None + if not verify_password(password, user.password): + return None + return user + +def is_active(user: UserBase) -> bool: + return user.is_active + + +## Upload +def create_upload(db: Session, upload: UploadCreate): + db_upload = models.Upload(**upload.dict()) + db.add(db_upload) + db.commit() + db.refresh(db_upload) + return db_upload + + +def delete_upload(db: Session, upload_id: int): + db_upload = db.query(models.Upload).filter(models.Upload.upload_id == upload_id).first() + + if db_upload: + db.delete(db_upload) + db.commit() + return True + return False + + +def get_upload(db: Session, upload_id: int): + return db.query(models.Upload).filter(models.Upload.upload_id == upload_id).first() + + +def get_upload_id( + db: Session, + user_id: int, + name: str, + date: datetime, +): + return ( + db.query(models.Upload) + .filter( + (models.Upload.user_id == user_id) & (models.Upload.name == name) & (models.Upload.date == date) + ) + .all() + ) + + +def get_uploads(db: Session, user_id: int): + return ( + db.query(models.Upload) + .filter(models.Upload.user_id == user_id) + .order_by(models.Upload.upload_id.desc()) + .all() + ) + + +def get_upload_by_name(db: Session, name: str): + return db.query(models.Upload).filter(models.Upload.name == name).first() + + +## Video +def create_video(db: Session, video: VideoCreate): + db_video = models.Video(**video.dict()) + db.add(db_video) + db.commit() + db.refresh(db_video) + return db_video + + +def get_video(db: Session, upload_id: int): + return db.query(models.Video).filter(models.Video.upload_id == upload_id).first() + + +## Frame +def create_frame(db: Session, frame: FrameCreate): + db_frame = models.Frame(**frame.dict()) + db.add(db_frame) + db.commit() + db.refresh(db_frame) + return db_frame + + +def get_frame(db: Session, frame_id: int): + return db.query(models.Frame).filter(models.Frame.frame_id == frame_id).first() + + +def get_frames(db: Session, video_id: int): + return db.query(models.Frame).filter(models.Frame.video_id == video_id).all() + + +def get_frames_with_highest_score(db: Session, video_id: int): + + subquery = ( + db.query( + models.Frame.video_id, models.Frame.time_stamp, func.max(models.Frame.score).label("max_score") + ) + .group_by(models.Frame.video_id, models.Frame.time_stamp) + .subquery() + ) + + subq_alias = aliased(subquery) + + frames = ( + db.query(models.Frame) + .join( + subq_alias, + (models.Frame.video_id == subq_alias.c.video_id) + & (models.Frame.time_stamp == subq_alias.c.time_stamp) + & (models.Frame.score == subq_alias.c.max_score), + ) + .filter(models.Frame.video_id == video_id) + .all() + ) + + return frames + + +def create_complete(db: Session, complete: Complete): + db_complete = models.Complete(**complete.dict()) + db.add(db_complete) + db.commit() + db.refresh(db_complete) + return db_complete + + +def get_complete(db: Session, upload_id: int): + return db.query(models.Complete).filter(models.Complete.upload_id == upload_id).first() + + +def update_complete_status(db: Session, upload_id: int): + + complete_record = db.query(models.Complete).filter(models.Complete.upload_id == upload_id).first() + + if complete_record and not complete_record.completed: + complete_record.completed = True + db.commit() + + +# email feat +async def create_smtp_server(): + + smtp = smtplib.SMTP_SSL(settings.SMTP_ADDRESS, settings.SMTP_PORT) # smtp 서버와 연결 + smtp.login(settings.MAIL_ACCOUNT, settings.MAIL_PASSWORD) # 프로젝트 계정으로 로그인 + + return smtp + + +def send_email(db, check, last, user_id, smtp): + user = get_user(db, user_id) + + # 메일 기본 정보 설정 + msg = MIMEMultipart() + msg["subject"] = f"[IVT] 이상행동 분석 중 결과 전달 메일입니다." + msg["from"] = settings.MAIL_ACCOUNT + msg["To"] = user.email + + # 메일 본문 내용 + content = f"""안녕하세요. Naver AI Tech 6기 '혁신비전테크' 팀 입니다. + +실시간 이상행동 탐지 중 이상행동이 발견되어 해당 시간대를 전달 드립니다. + +{check} ~ {last} 시간을 확인해주세요. +""" + + content_part = MIMEText(content, "plain") + msg.attach(content_part) + + smtp.sendmail(settings.MAIL_ACCOUNT, user.email, msg.as_string()) + + # 탐지 이미지 첨부 (향후 업데이트 예정) diff --git a/app/database/database.py b/app/database/database.py new file mode 100644 index 0000000..758080c --- /dev/null +++ b/app/database/database.py @@ -0,0 +1,26 @@ +from sqlalchemy import create_engine +from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy.orm import sessionmaker + +from utils.config import settings + +SQLALCHEMY_DATABASE_URL = "mysql+pymysql://{}:{}@{}:{}/{}".format( + settings.MYSQL_SERVER_USER, + settings.MYSQL_SERVER_PASSWORD, + settings.MYSQL_SERVER_IP, + settings.MYSQL_SERVER_PORT, + settings.MYSQL_DATABASE +) + +engine = create_engine(SQLALCHEMY_DATABASE_URL, echo=True) +SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) + +Base = declarative_base() + +# Dependency +def get_db(): + db = SessionLocal() + try: + yield db + finally: + db.close() \ No newline at end of file diff --git a/app/database/models.py b/app/database/models.py new file mode 100644 index 0000000..017e099 --- /dev/null +++ b/app/database/models.py @@ -0,0 +1,63 @@ +from sqlalchemy import Column, Integer, String, DateTime, Boolean, Float, ForeignKey, Time, JSON +from sqlalchemy.orm import relationship + +from database.database import Base + +class User(Base): + __tablename__ = "user" + + user_id = Column(Integer, primary_key=True, index=True, autoincrement=True) + email = Column(String(50), unique=True, nullable=False) + password = Column(String(200), nullable=False) + is_active = Column(Boolean, default=True) + + uploads = relationship("Upload", back_populates="user") + + +class Upload(Base): + __tablename__ = "upload" + + upload_id = Column(Integer, primary_key=True, index=True, autoincrement=True) + name = Column(String(50), nullable=False) + date = Column(DateTime, nullable=False) + is_realtime = Column(Boolean, default=False) + thr = Column(Float, nullable=False) + user_id = Column(Integer, ForeignKey("user.user_id"), nullable=False) + + user = relationship("User", back_populates="uploads") + videos = relationship("Video", back_populates="upload", cascade="all, delete-orphan") + completes = relationship("Complete", back_populates="upload", cascade="all, delete-orphan") + + +class Video(Base): + __tablename__ = "video" + + video_id = Column(Integer, primary_key=True, index=True, autoincrement=True) + video_url = Column(String(255), nullable=False) + upload_id = Column(Integer, ForeignKey("upload.upload_id"), nullable=False) + + upload = relationship("Upload", back_populates="videos") + frames = relationship("Frame", back_populates="video", cascade="all, delete-orphan") + + +class Frame(Base): + __tablename__ = "frame" + + frame_id = Column(Integer, primary_key=True, index=True, autoincrement=True) + frame_url = Column(String(255), nullable=False) + time_stamp = Column(Time, nullable=False) + box_kp_json = Column(JSON, nullable=False) + score = Column(Float, nullable=False) + video_id = Column(Integer, ForeignKey("video.video_id"), nullable=False) + + video = relationship("Video", back_populates="frames") + + +class Complete(Base): + __tablename__ = "complete" + + complete_id = Column(Integer, primary_key=True, index=True, autoincrement=True) + completed = Column(Boolean, default=False) + upload_id = Column(Integer, ForeignKey("upload.upload_id"), nullable=False) + + upload = relationship("Upload", back_populates="completes") \ No newline at end of file diff --git a/app/database/schemas.py b/app/database/schemas.py new file mode 100644 index 0000000..23d6e2a --- /dev/null +++ b/app/database/schemas.py @@ -0,0 +1,56 @@ +from typing import List, Optional, Dict +from datetime import datetime, time + +from pydantic import BaseModel, field_validator, EmailStr +from pydantic_core.core_schema import FieldValidationInfo + + +class UserBase(BaseModel): + email: EmailStr + is_active: Optional[bool] = True + +class UserCreate(UserBase): + password: str + +# Upload post 스키마 +class UploadCreate(BaseModel): + name: str + date: datetime + is_realtime: Optional[bool] = None + thr: float + user_id: int + + +# Video post 스키마 +class VideoCreate(BaseModel): + video_url: str + upload_id: int + + +class Video(VideoCreate): + video_id: int + frames: List["Frame"] = [] + + class Config: + orm_mode = True + + +# Frame Post 스키마 +class FrameCreate(BaseModel): + frame_url: str + time_stamp: time + box_kp_json: Dict + score: float + video_id: int + + +class Frame(FrameCreate): + frame_id: int + + class Config: + orm_mode = True + + +class Complete(BaseModel): + completed: bool + upload_id: int diff --git a/app/inference/__init__.py b/app/inference/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/app/inference/anomaly_detector.py b/app/inference/anomaly_detector.py new file mode 100644 index 0000000..48b8684 --- /dev/null +++ b/app/inference/anomaly_detector.py @@ -0,0 +1,419 @@ +from collections import defaultdict +import cv2 +import numpy as np +import pandas as pd +from ultralytics import YOLO +import torch +from sklearn.preprocessing import MinMaxScaler + +from fastapi import HTTPException +from starlette import status +from database import crud +from database import schemas + +import os +import uuid +import json +from datetime import datetime, time +import matplotlib.pyplot as plt +from io import BytesIO + +import torch +import torch.nn as nn + +import albumentations as A + +import sys + +current_dir = os.path.dirname(os.path.abspath(__file__)) +parent_dir = os.path.dirname(os.path.dirname(current_dir)) + +sys.path.append(os.path.join(parent_dir, "model")) +import vmae + +# @@ timm은 0.4.12 버전 사용 필수 +from timm.models import create_model + +from copy import deepcopy + +class AnomalyDetector: + def __init__(self, video_file, info, s3_client, settings, db): + self.video = s3_client.generate_presigned_url( + ClientMethod="get_object", Params={"Bucket": settings.BUCKET, "Key": video_file}, ExpiresIn=3600 + ) + # print(self.video) + self.info = info + self.s3 = s3_client + self.settings = settings + self.thr = info["threshold"] + self.video_url = ( + f"video/{info['user_id']}/{info['upload_id']}/{info['video_uuid_name']}{info['video_ext']}" + ) + self.frame_url_base = f"frame/{info['user_id']}/{info['upload_id']}/" + self.db = db + + def display_text(self, frame, text, position): + font = cv2.FONT_HERSHEY_SIMPLEX + font_scale = 1 + font_color = (0, 255, 0) # Green color + font_thickness = 2 + cv2.putText(frame, text, position, font, font_scale, font_color, font_thickness, cv2.LINE_AA) + + def upload_frame_db(self, db, temp_for_db, frame_url): + + temp_json_path = "./temp.json" + + with open(temp_json_path, "w") as f: + json.dump(temp_for_db, f) + + with open(temp_json_path, "r") as f: + box_kp_json = json.load(f) + + _frame_create = schemas.FrameCreate( + frame_url=frame_url, + time_stamp=temp_for_db["timestamp"], + box_kp_json=box_kp_json, + score=temp_for_db["score"], + video_id=self.info["video_id"], + ) + + crud.create_frame(db=db, frame=_frame_create) + + os.remove(temp_json_path) + + def upload_frame_s3(self, s3, frame): + s3_upload_exception = HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, detail="Frame 을 s3 저장소 업로드에 실패했습니다." + ) + frame_name = uuid.uuid1() + frame_url = self.frame_url_base + f"{frame_name}" + ".png" + # print(frame_url) + + try: + s3.upload_fileobj( + BytesIO(cv2.imencode(".png", frame)[1].tobytes()), + self.settings.BUCKET, + frame_url, + ExtraArgs={"ContentType": "image/png"}, + ) + except Exception as e: + # print(e) + raise s3_upload_exception + + return frame_url + + def upload_video_s3(self, s3, video): + s3_upload_exception = HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, detail="video 를 s3 저장소 업로드에 실패했습니다." + ) + video_url = self.video_url + + video_change_codec = "./temp_video_path_change_codec.mp4" + + os.system('ffmpeg -i "%s" -vcodec libx264 "%s"' % (video, video_change_codec)) + + try: + with open(video_change_codec, "rb") as video_file: + s3.upload_fileobj( + video_file, self.settings.BUCKET, video_url, ExtraArgs={"ContentType": "video/mp4"} + ) + except Exception as e: + # print(e) + raise s3_upload_exception + + os.remove(video_change_codec) + + def upload_score_graph_s3(self, s3, scores): + plt.plot(scores, color="red") + plt.title("Anomaly Scores Over Time") + plt.xlabel(" ") + plt.ylabel(" ") + + plt.xticks([]) + plt.yticks([]) + + save_path = "./model_scores_plot.png" + plt.savefig(save_path) + + with open(save_path, "rb") as image_file: + graph = image_file.read() + + s3_upload_exception = HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, detail="score Graph 를 s3 저장소 업로드에 실패했습니다." + ) + score_graph_name = "score_graph.png" + score_graph_url = self.frame_url_base + score_graph_name + + try: + s3.upload_fileobj( + BytesIO(graph), self.settings.BUCKET, score_graph_url, ExtraArgs={"ContentType": "image/png"} + ) + except: + raise s3_upload_exception + + os.remove(save_path) + + def run(self): + # YOLO + tracker_model = YOLO("yolov8n-pose.pt") + + # VMAE v2 + backbone = model = create_model( + "vit_small_patch16_224", + img_size=224, + pretrained=False, + num_classes=710, + all_frames=16, + ) + + load_dict = torch.load( + "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/model/pts/vit_s_k710_dl_from_giant.pth" + ) + + backbone.load_state_dict(load_dict["module"]) + + tf = A.Resize(224, 224) + + # Define sequence_length, prediction_time, and n_features + # sequence_length = 20 + # prediction_time = 1 + # n_features = 38 + + # LSTM autoencoder + checkpoint = torch.load( + "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/model/pts/MIL_20240325_202019_best_auc.pth" + ) + classifier = vmae.MILClassifier(input_dim=710, drop_p=0.3) + classifier.load_state_dict(checkpoint["model_state_dict"]) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + tracker_model.to(device) + backbone.to(device) + backbone.eval() + classifier.to(device) + classifier.eval() + + # Define the standard frame size + standard_width = 640 + standard_height = 480 + + # Open the video file + cap = cv2.VideoCapture(self.video) + temp_name = None + if not cap.isOpened(): + temp_name = f"{uuid.uuid4()}.mp4" + self.s3.download_file(self.settings.BUCKET, self.video_url, temp_name) + cap = cv2.VideoCapture(temp_name) + fps = cap.get(cv2.CAP_PROP_FPS) + + frame_inteval = fps // 3 + + # Store the track history + track_history = defaultdict(lambda: []) + + # Initialize a dictionary to store separate buffers for each ID + # id_buffers = defaultdict(lambda: []) + + # HTML -> H.264 codec + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + + # video writer -> ID 별 score, bbox 가 나온 영상을 s3 에 업로드 + output_video_path = "./temp_video_path.mp4" + output_video = cv2.VideoWriter(output_video_path, fourcc, fps, (standard_width, standard_height)) + + # Define a function to calculate MSE between two sequences + def calculate_mse(seq1, seq2): + return np.mean(np.power(seq1 - seq2, 2)) + + # anomaly threshold (default 0.02) + # threshold = self.thr + threshold = 0.3 + + # Loop through the video frames + frame_count = 0 + # net_mse = 0 + # avg_mse = 0 + # score graph 를 위한 score list + scores = [] + # vmae에 입력할 frame들을 저장할 list + v_frames = [] + + # 영상 frame 임시 저장 list + frame_list = [] + # yolo results 임시 저장 list + results_list = [] + # temp_for_db 임시 저장 list + tfdb_list = [] + + while cap.isOpened(): + # Read a frame from the video + success, frame = cap.read() + frame_count += 1 # Increment frame count + + if success: + frame = cv2.resize(frame, (standard_width, standard_height)) + + temp_for_db = {"timestamp": None, "bbox": {}, "keypoints": {}, "score": None} + + # track id (사람) 별로 mse 점수가 나오기 때문에 한 frame 에 여러 mse 점수가 나옴. 이를 frame 별 점수로 구하기 위해서 변수 설정 + # mse_unit = 0 + + s_timestamp = round(cap.get(cv2.CAP_PROP_POS_MSEC) / 1000, 2) + datetime_object = datetime.utcfromtimestamp(s_timestamp) + timestamp = datetime_object.strftime("%H:%M:%S") + temp_for_db["timestamp"] = timestamp + + # Run YOLOv8 tracking on the frame, persisting tracks between frames + results = tracker_model.track(frame, persist=True) + + frame_list.append(frame.copy()) + results_list.append(deepcopy(results)) + tfdb_list.append(deepcopy(temp_for_db)) + + # 1초에 3 frame만 저장해서 vmae+MIL에 사용 + if (frame_count - 1) % frame_inteval == 0: + v_frame = tf(image=frame)["image"] + # (224, 224, 3) + v_frame = np.expand_dims(v_frame, axis=0) + # (1, 224, 224, 3) + v_frames.append(v_frame.copy()) + + # 16 frame이 모이면 vmae+MIL 계산 + if len(v_frames) == 16: + in_frames = np.concatenate(v_frames) + # (16, 224, 224, 3) + in_frames = in_frames.transpose(3, 0, 1, 2) + # (RGB 3, frame T=16, H=224, W=224) + in_frames = np.expand_dims(in_frames, axis=0) + # (1, 3, 16 * segments_num, 224, 224) + in_frames = torch.from_numpy(in_frames).float() + # torch.Size([1, 3, 16, 224, 224]) + + in_frames = in_frames.to(device) + + with torch.no_grad(): + v_output = backbone(in_frames) + # torch.Size([1, 710]) + v_score = classifier(v_output) + # torch.Size([1, 1]) + scores.append(v_score.cpu().item()) + + v_frames = [] + + if len(frame_list) == 16 * frame_inteval: + for f_step, (frame_i, results_i, temp_for_db_i) in enumerate( + zip(frame_list, results_list, tfdb_list) + ): + if scores[-1] > threshold: + anomaly_text = f"Anomaly detected, score: {scores[-1]}" + if results_i[0].boxes is not None: # Check if there are results and boxes + + # Get the boxes + boxes = results_i[0].boxes.xywh.cpu() + + if results_i[0].boxes.id is not None: + # If 'int' attribute exists (there are detections), get the track IDs + + track_ids = results_i[0].boxes.id.int().cpu().tolist() + + # Loop through the detections and add data to the DataFrame + # anomaly_text = "" # Initialize the anomaly text + + # vmae에서 보는 frame들만 db에 저장 + if f_step % frame_inteval == 0: + # 한 프레임에서 검출된 사람만큼 돌아가는 반복문. 2명이면 각 id 별로 아래 연산들이 진행됨. + for i, box in zip( + range(0, len(track_ids)), results_i[0].boxes.xywhn.cpu() + ): + + x, y, w, h = box + keypoints = ( + results_i[0].keypoints.xyn[i].cpu().numpy().flatten().tolist() + ) + + xywhk = np.array( + [float(x), float(y), float(w), float(h)] + keypoints + ) + + xywhk = list(map(lambda x: str(round(x, 4)), xywhk)) + + temp_for_db_i["bbox"][f"id {i}"] = " ".join(xywhk[:4]) + + temp_for_db_i["keypoints"][f"id {i}"] = " ".join(xywhk[4:]) + + else: + # If 'int' attribute doesn't exist (no detections), set track_ids to an empty list + track_ids = [] + + # Visualize the results on the frame + annotated_frame = results_i[0].plot() + self.display_text( + annotated_frame, anomaly_text, (10, 30) + ) # Display the anomaly text + + # Plot the tracks + # for box, track_id in zip(boxes, track_ids): + # x, y, w, h = box + # track = track_history[track_id] + # track.append((float(x), float(y))) # x, y center point + # if len(track) > 30: # retain 90 tracks for 90 frames + # track.pop(0) + + # # Draw the tracking lines + # points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2)) + # cv2.polylines( + # annotated_frame, + # [points], + # isClosed=False, + # color=(230, 230, 230), + # thickness=10, + # ) + + # Display the annotated frame + output_video.write(annotated_frame) + # cv2.imshow("YOLOv8 Tracking", annotated_frame) + else: + self.display_text(frame_i, anomaly_text, (10, 30)) # Display the anomaly text + output_video.write(frame_i) + + # vmae에서 보는 frame들만 db에 저장 + if f_step % frame_inteval == 0: + temp_for_db_i["score"] = scores[-1] + + # upload frame to s3 + frame_url = self.upload_frame_s3(self.s3, frame_i) + + # upload frame, ts, bbox, kp to db + self.upload_frame_db(self.db, temp_for_db_i, frame_url) + + else: + anomaly_text = "" + output_video.write(frame_i) + # cv2.imshow("YOLOv8 Tracking", frame) + + # 16 * frame_interval개 frame 표시 후 초기화 + frame_list = [] + results_list = [] + tfdb_list = [] + + else: + if len(frame_list) != 0: + for f in frame_list: + output_video.write(f) + + # Break the loop if the end of the video is reached + break + + # Release the video capture, video writer object + cap.release() + output_video.release() + # cv2.destroyAllWindows() + + # upload video to s3 + self.upload_video_s3(self.s3, output_video_path) + + # upload score graph to s3 + self.upload_score_graph_s3(self.s3, scores) + if temp_name: + os.remove(temp_name) + os.remove(output_video_path) diff --git a/app/inference/anomaly_detector_lstmae.py b/app/inference/anomaly_detector_lstmae.py new file mode 100644 index 0000000..f91ee05 --- /dev/null +++ b/app/inference/anomaly_detector_lstmae.py @@ -0,0 +1,367 @@ +from collections import defaultdict +import cv2 +import numpy as np +import pandas as pd +from ultralytics import YOLO +import torch +from sklearn.preprocessing import MinMaxScaler + +from fastapi import HTTPException +from starlette import status +from database import crud +from database import schemas + +import os +import sys +import uuid +import json +from datetime import datetime, time +import matplotlib.pyplot as plt +from io import BytesIO + +import torch + +current_dir = os.path.dirname(os.path.abspath(__file__)) +parent_dir = os.path.dirname(os.path.dirname(current_dir)) + +sys.path.append(os.path.join(parent_dir, "model")) +from lstmae.lstm_ae import LSTMAutoEncoder + +class AnomalyDetector: + def __init__(self, video_file, info, s3_client, settings, db): + self.video = s3_client.generate_presigned_url( + ClientMethod='get_object', + Params={'Bucket': settings.BUCKET, + 'Key': video_file}, + ExpiresIn=3600) + #print(self.video) + self.info = info + self.s3 = s3_client + self.settings = settings + self.thr = info['threshold'] + self.video_url = f"video/{info['user_id']}/{info['upload_id']}/{info['video_uuid_name']}{info['video_ext']}" + self.frame_url_base = f"frame/{info['user_id']}/{info['upload_id']}/" + self.db = db + + + def display_text(self, frame, text, position): + font = cv2.FONT_HERSHEY_SIMPLEX + font_scale = 1 + font_color = (0, 255, 0) # Green color + font_thickness = 2 + cv2.putText(frame, text, position, font, font_scale, font_color, font_thickness, cv2.LINE_AA) + + + def upload_frame_db(self, db, temp_for_db, frame_url): + + temp_json_path = './temp.json' + + with open(temp_json_path, 'w') as f: + json.dump(temp_for_db, f) + + with open(temp_json_path, 'r') as f: + box_kp_json = json.load(f) + + _frame_create = schemas.FrameCreate(frame_url=frame_url, + time_stamp=temp_for_db['timestamp'], + box_kp_json=box_kp_json, + score=temp_for_db['score'], + video_id=self.info['video_id']) + + crud.create_frame(db=db, frame=_frame_create) + + os.remove(temp_json_path) + + + def upload_frame_s3(self, s3, frame): + s3_upload_exception = HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Frame 을 s3 저장소 업로드에 실패했습니다." + ) + frame_name = uuid.uuid1() + frame_url = self.frame_url_base + f'{frame_name}' + '.png' + #print(frame_url) + + try: + s3.upload_fileobj( + BytesIO(cv2.imencode('.png', frame)[1].tobytes()), + self.settings.BUCKET, + frame_url, + ExtraArgs={'ContentType': 'image/png'} + ) + except Exception as e: + #print(e) + raise s3_upload_exception + + return frame_url + + def upload_video_s3(self, s3, video): + s3_upload_exception = HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="video 를 s3 저장소 업로드에 실패했습니다." + ) + video_url = self.video_url + + video_change_codec = './temp_video_path_change_codec.mp4' + + os.system('ffmpeg -i "%s" -vcodec libx264 "%s"'%(video, video_change_codec)) + + try: + with open(video_change_codec, 'rb') as video_file: + s3.upload_fileobj( + video_file, + self.settings.BUCKET, + video_url, + ExtraArgs={'ContentType': 'video/mp4'} + ) + except Exception as e: + #print(e) + raise s3_upload_exception + + os.remove(video_change_codec) + + + def upload_score_graph_s3(self, s3, scores): + plt.plot(scores, color='red') + plt.title('Anomaly Scores Over Time') + plt.xlabel(' ') + plt.ylabel(' ') + + plt.xticks([]) + plt.yticks([]) + + save_path = './model_scores_plot.png' + plt.savefig(save_path) + + with open(save_path, 'rb') as image_file: + graph = image_file.read() + + s3_upload_exception = HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="score Graph 를 s3 저장소 업로드에 실패했습니다." + ) + score_graph_name = 'score_graph.png' + score_graph_url = self.frame_url_base + score_graph_name + + try: + s3.upload_fileobj( + BytesIO(graph), + self.settings.BUCKET, + score_graph_url, + ExtraArgs={'ContentType': 'image/png'} + ) + except: + raise s3_upload_exception + + os.remove(save_path) + + def run(self): + # YOLO + tracker_model = YOLO('yolov8n-pose.pt') + + # Define sequence_length, prediction_time, and n_features + sequence_length = 20 + prediction_time = 1 + n_features = 38 + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + # LSTM autoencoder + checkpoint = torch.load('/data/ephemeral/home/level2-3-cv-finalproject-cv-06/model/pts/LSTM_20240324_222238_best.pth') + autoencoder_model = LSTMAutoEncoder(num_layers=2, hidden_size=50, n_features=n_features, + device=device) + autoencoder_model.load_state_dict(checkpoint['model_state_dict']) + tracker_model.to(device) + autoencoder_model.to(device) + + # Define the standard frame size + standard_width = 640 + standard_height = 480 + + # Open the video file + cap = cv2.VideoCapture(self.video) + if not cap.isOpened(): + temp_name = f'{uuid.uuid4()}.mp4' + self.s3.download_file(self.settings.BUCKET, self.video_url, temp_name) + cap = cv2.VideoCapture(temp_name) + fps = cap.get(cv2.CAP_PROP_FPS) + + # Store the track history + track_history = defaultdict(lambda: []) + + # Initialize a dictionary to store separate buffers for each ID + id_buffers = defaultdict(lambda: []) + + # HTML -> H.264 codec + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + + # video writer -> ID 별 score, bbox 가 나온 영상을 s3 에 업로드 + output_video_path = './temp_video_path.mp4' + output_video = cv2.VideoWriter(output_video_path, fourcc, fps, (standard_width, standard_height)) + + # Define a function to calculate MSE between two sequences + def calculate_mse(seq1, seq2): + return np.mean(np.power(seq1 - seq2, 2)) + + # anomaly threshold (default 0.02) + threshold = self.thr + + # Loop through the video frames + frame_count = 0 + net_mse = 0 + avg_mse = 0 + # score graph 를 위한 score list + scores = [] + + while cap.isOpened(): + # Read a frame from the video + success, frame = cap.read() + frame_count += 1 # Increment frame count + + if success: + frame = cv2.resize(frame, (standard_width, standard_height)) + + temp_for_db = {'timestamp':None, 'bbox':{}, 'keypoints':{}, 'score':None} + + # track id (사람) 별로 mse 점수가 나오기 때문에 한 frame 에 여러 mse 점수가 나옴. 이를 frame 별 점수로 구하기 위해서 변수 설정 + mse_unit = 0 + + s_timestamp = round(cap.get(cv2.CAP_PROP_POS_MSEC)/1000, 2) + datetime_object = datetime.utcfromtimestamp(s_timestamp) + timestamp = datetime_object.strftime('%H:%M:%S') + temp_for_db['timestamp'] = timestamp + + # Run YOLOv8 tracking on the frame, persisting tracks between frames + results = tracker_model.track(frame, persist=True) + + if results[0].boxes is not None: # Check if there are results and boxes + + # Get the boxes + boxes = results[0].boxes.xywh.cpu() + + if results[0].boxes.id is not None: + # If 'int' attribute exists (there are detections), get the track IDs + + track_ids = results[0].boxes.id.int().cpu().tolist() + + # Loop through the detections and add data to the DataFrame + anomaly_text = "" # Initialize the anomaly text + + # 한 프레임에서 검출된 사람만큼 돌아가는 반복문. 2명이면 각 id 별로 아래 연산들이 진행됨. + for i, box in zip(range(0, len(track_ids)), results[0].boxes.xywhn.cpu()): + + x, y, w, h = box + keypoints = results[0].keypoints.xyn[i].cpu().numpy().flatten().tolist() + + # Append the keypoints to the corresponding ID's buffer + # bbox(4), keypoints per id(34) + id_buffers[track_ids[i]].append([float(x),float(y),float(w),float(h)]+keypoints) + + # If the buffer size reaches the threshold (e.g., 20 data points), perform anomaly detection + # track_id 별 20프레임이 쌓이면 아래 연산 진행 + if len(id_buffers[track_ids[i]]) >= 20: + # Convert the buffer to a NumPy array + buffer_array = np.array(id_buffers[track_ids[i]]) + + # Scale the data (you can use the same scaler you used during training) + scaler = MinMaxScaler() + buffer_scaled = scaler.fit_transform(buffer_array) + + # Create sequences for prediction + # x_pred: [1,20,38] + x_pred = buffer_scaled[-sequence_length:].reshape(1, sequence_length, n_features) + + # Predict the next values using the autoencoder model + x_pred = torch.tensor(x_pred, dtype=torch.float32).to(device) + x_pred = autoencoder_model.forward(x_pred) + + # Inverse transform the predicted data to the original scale + x_pred_original = scaler.inverse_transform(x_pred.cpu().detach().numpy().reshape(-1, n_features)) + + # Calculate the MSE between the predicted and actual values + mse = calculate_mse(buffer_array[-prediction_time:], x_pred_original) + + #print(mse) + + net_mse = mse+net_mse + avg_mse= net_mse/frame_count + + mse_unit += mse + + # Check if the MSE exceeds the threshold to detect an anomaly + if mse > 1.5*avg_mse*0.25 + 0.75*threshold: + if(anomaly_text==""): + anomaly_text = f"Focus ID {track_ids[i]}" + else: + anomaly_text = f"{anomaly_text}, {track_ids[i]}" + + #print(anomaly_text) + + temp_for_db['bbox'][f'id {i}'] = ' '.join(map(lambda x: str(round(x,4)), buffer_array[-prediction_time:][0, :4])) + + temp_for_db['keypoints'][f'id {i}'] = ' '.join(map(lambda x: str(round(x,4)), buffer_array[-prediction_time:][0, 4:])) + + # Remove the oldest data point from the buffer to maintain its size + id_buffers[track_ids[i]].pop(0) + + if temp_for_db['bbox'] != {}: + + temp_for_db['score'] = mse_unit + + # upload frame to s3 + frame_url = self.upload_frame_s3(self.s3, frame) + + # upload frame, ts, bbox, kp to db + self.upload_frame_db(self.db, temp_for_db, frame_url) + + else: + anomaly_text = "" + # If 'int' attribute doesn't exist (no detections), set track_ids to an empty list + track_ids = [] + + # Visualize the results on the frame + annotated_frame = results[0].plot() + self.display_text(annotated_frame, anomaly_text, (10, 30)) # Display the anomaly text + + # Plot the tracks + for box, track_id in zip(boxes, track_ids): + x, y, w, h = box + track = track_history[track_id] + track.append((float(x), float(y))) # x, y center point + if len(track) > 30: # retain 90 tracks for 90 frames + track.pop(0) + + # Draw the tracking lines + points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2)) + cv2.polylines(annotated_frame, [points], isClosed=False, color=(230, 230, 230), thickness=10) + + scores.append(mse_unit) + + # Display the annotated frame + output_video.write(annotated_frame) + #cv2.imshow("YOLOv8 Tracking", annotated_frame) + + else: + # If no detections, display the original frame without annotations + scores.append(mse_unit) + output_video.write(frame) + #cv2.imshow("YOLOv8 Tracking", frame) + + else: + # Break the loop if the end of the video is reached + break + + # Release the video capture, video writer object + cap.release() + output_video.release() + #cv2.destroyAllWindows() + + # upload video to s3 + self.upload_video_s3(self.s3, output_video_path) + + # upload score graph to s3 + self.upload_score_graph_s3(self.s3, scores) + + try: + os.remove(temp_name) + except: + pass + os.remove(output_video_path) \ No newline at end of file diff --git a/app/inference/rt_anomaly_detector.py b/app/inference/rt_anomaly_detector.py new file mode 100644 index 0000000..552db41 --- /dev/null +++ b/app/inference/rt_anomaly_detector.py @@ -0,0 +1,340 @@ +from collections import defaultdict +import cv2 +import numpy as np +import pandas as pd +from ultralytics import YOLO +import torch +from sklearn.preprocessing import MinMaxScaler + +from fastapi import HTTPException +from starlette import status +from database import crud +from database import schemas + +import os +import uuid +import json +from datetime import datetime, time, timedelta +import matplotlib.pyplot as plt +from io import BytesIO + +import torch +import torch.nn as nn + +import albumentations as A + +import sys + +current_dir = os.path.dirname(os.path.abspath(__file__)) +parent_dir = os.path.dirname(os.path.dirname(current_dir)) + +sys.path.append(os.path.join(parent_dir, "model")) +import vmae + +# @@ timm은 0.4.12 버전 사용 필수 +from timm.models import create_model +from copy import deepcopy + +class RT_AnomalyDetector: + def __init__(self, info, s3_client, settings, db, websocket): + self.info = info + self.s3 = s3_client + self.settings = settings + self.frame_url_base = f"frame/{info['user_id']}/{info['upload_id']}/" + self.db = db + self.websocket = websocket + + async def upload_frame_db(self, db, temp_for_db, frame_url): + + temp_json_path = "./temp.json" + + with open(temp_json_path, "w") as f: + json.dump(temp_for_db, f) + + with open(temp_json_path, "r") as f: + box_kp_json = json.load(f) + + _frame_create = schemas.FrameCreate( + frame_url=frame_url, + time_stamp=temp_for_db["timestamp"], + box_kp_json=box_kp_json, + score=temp_for_db["score"], + video_id=self.info["video_id"], + ) + + crud.create_frame(db=db, frame=_frame_create) + + os.remove(temp_json_path) + + async def upload_frame_s3(self, s3, frame): + s3_upload_exception = HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, detail="Frame 을 s3 저장소 업로드에 실패했습니다." + ) + frame_name = uuid.uuid1() + frame_url = self.frame_url_base + f"{frame_name}" + ".png" + # print(frame_url) + + try: + s3.upload_fileobj( + BytesIO(cv2.imencode(".png", frame)[1].tobytes()), + self.settings.BUCKET, + frame_url, + ExtraArgs={"ContentType": "image/png"}, + ) + except Exception as e: + # print(e) + raise s3_upload_exception + + return frame_url + + def upload_score_graph_s3(self): + plt.plot(self.scores, color="red") + plt.title("Anomaly Scores Over Time") + plt.xlabel(" ") + plt.ylabel(" ") + + plt.xticks([]) + plt.yticks([]) + + save_path = "./model_scores_plot.png" + plt.savefig(save_path) + + with open(save_path, "rb") as image_file: + graph = image_file.read() + + s3_upload_exception = HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, detail="score Graph 를 s3 저장소 업로드에 실패했습니다." + ) + score_graph_name = "score_graph.png" + score_graph_url = self.frame_url_base + score_graph_name + + try: + self.s3.upload_fileobj( + BytesIO(graph), self.settings.BUCKET, score_graph_url, ExtraArgs={"ContentType": "image/png"} + ) + except: + raise s3_upload_exception + + os.remove(save_path) + + def ready(self): + # YOLO + self.tracker_model = YOLO("yolov8n-pose.pt") + + # VMAE v2 + self.backbone = model = create_model( + "vit_small_patch16_224", + img_size=224, + pretrained=False, + num_classes=710, + all_frames=16, + ) + + load_dict = torch.load( + "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/model/pts/vit_s_k710_dl_from_giant.pth" + ) + + self.backbone.load_state_dict(load_dict["module"]) + + self.tf = A.Resize(224, 224) + + # Define sequence_length, prediction_time, and n_features + # sequence_length = 20 + # prediction_time = 1 + # n_features = 38 + + # classifier + checkpoint = torch.load( + "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/model/pts/MIL_20240325_202019_best_auc.pth" + ) + self.classifier = vmae.MILClassifier(input_dim=710, drop_p=0.3) + self.classifier.load_state_dict(checkpoint["model_state_dict"]) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.tracker_model.to(device) + self.backbone.to(device) + self.backbone.eval() + self.classifier.to(device) + self.classifier.eval() + + # Store the track history + self.track_history = defaultdict(lambda: []) + + # Initialize a dictionary to store separate buffers for each ID + self.id_buffers = defaultdict(lambda: []) + + # Loop through the video frames + self.frame_count = 0 + # self.net_mse = 0 + # self.avg_mse = 0 + + # score graph 를 위한 score list + self.scores = [] + + # vmae에 입력할 frame들을 저장할 list + self.v_frames = [] + + # 영상 frame 임시 저장 list + self.frame_list = [] + # yolo results 임시 저장 list + self.results_list = [] + # temp_for_db 임시 저장 list + self.tfdb_list = [] + + # timestamp 저장 + self.prv_timestamp = 0 + self.fps3_delta = timedelta(seconds=1 / 3) + + async def run(self, frame, timestamp): + + # Define the standard frame size + standard_width = 640 + standard_height = 480 + + # Define sequence_length, prediction_time, and n_features + # sequence_length = 20 + # prediction_time = 1 + # n_features = 38 + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Define a function to calculate MSE between two sequences + def calculate_mse(seq1, seq2): + return np.mean(np.power(seq1 - seq2, 2)) + + # anomaly threshold (default 0.02) + # threshold = self.info["thr"] + threshold = 0.1 + + self.frame_count += 1 # Increment frame count + + frame = cv2.resize(frame, (standard_width, standard_height)) + + temp_for_db = {"timestamp": None, "bbox": {}, "keypoints": {}, "score": None} + + # track id (사람) 별로 mse 점수가 나오기 때문에 한 frame 에 여러 mse 점수가 나옴. 이를 frame 별 점수로 구하기 위해서 변수 설정 + # mse_unit = 0 + + frame_checker = False + + # 이전에 frame을 저장한 시점에서 0.3333.. 초 이상 경과했는지 확인 + if self.prv_timestamp == 0: + frame_checker = True + self.prv_timestamp = timestamp + else: + time_delta = timestamp - self.prv_timestamp + if time_delta > self.fps3_delta: + frame_checker = True + self.prv_timestamp = timestamp + + timestamp = timestamp.strftime("%H:%M:%S") + temp_for_db["timestamp"] = timestamp + + # Run YOLOv8 tracking on the frame, persisting tracks between frames + + # print(f"==>> frame_checker: {frame_checker}") + # frame_checker = True + # 1초에 3 frame만 저장해서 vmae+MIL에 사용 + if frame_checker: + results = self.tracker_model.track(frame, persist=True, verbose=False) + # print("yolo 1frame inference") + self.frame_list.append(frame.copy()) + self.results_list.append(deepcopy(results)) + self.tfdb_list.append(deepcopy(temp_for_db)) + + v_frame = self.tf(image=frame)["image"] + # (224, 224, 3) + v_frame = np.expand_dims(v_frame, axis=0) + # (1, 224, 224, 3) + self.v_frames.append(v_frame.copy()) + print(f"==>> len(self.v_frames): {len(self.v_frames)}") + + # 16 frame이 모이면 vmae+MIL 계산 + if len(self.v_frames) == 176: + print("VMAE 176frame inference") + in_frames = np.concatenate(self.v_frames) + # (176, 224, 224, 3) + in_frames = in_frames.reshape(11, 16, 224, 224, 3) + in_frames = in_frames.transpose(0, 4, 1, 2, 3) + # (11, RGB 3, frame T=16, H=224, W=224) + in_frames = torch.from_numpy(in_frames).float() + # torch.Size([11, 3, 16, 224, 224]) + + in_frames = in_frames.to(device) + + with torch.no_grad(): + v_output = self.backbone(in_frames) + # torch.Size([11, 710]) + v_output = v_output.view(1, 11, -1) + v_score = self.classifier(v_output) + v_score = v_score.view(1, 11) + print(f"==>> v_score: {v_score}") + print(f"==>> v_score.shape: {v_score.shape}") + # torch.Size([1, 11]) + s_list = [v_score[0, i].cpu().item() for i in range(11)] + + self.v_frames = [] + for f_step, (frame_i, results_i, temp_for_db_i) in enumerate( + zip(self.frame_list, self.results_list, self.tfdb_list) + ): + if s_list[f_step // 16] > threshold: + # if True: + anomaly_text = f"Anomaly detected, score: {s_list[f_step // 16]}" + + if results_i[0].boxes is not None: # Check if there are results and boxes + + # Get the boxes + boxes = results_i[0].boxes.xywh.cpu() + + if results_i[0].boxes.id is not None: + # If 'int' attribute exists (there are detections), get the track IDs + + track_ids = results_i[0].boxes.id.int().cpu().tolist() + + # Loop through the detections and add data to the DataFrame + # anomaly_text = "" # Initialize the anomaly text + + # 한 프레임에서 검출된 사람만큼 돌아가는 반복문. 2명이면 각 id 별로 아래 연산들이 진행됨. + for i, box in zip(range(0, len(track_ids)), results_i[0].boxes.xywhn.cpu()): + + x, y, w, h = box + keypoints = results_i[0].keypoints.xyn[i].cpu().numpy().flatten().tolist() + + xywhk = np.array([float(x), float(y), float(w), float(h)] + keypoints) + + xywhk = list(map(lambda x: str(round(x, 4)), xywhk)) + + temp_for_db_i["bbox"][f"id {i}"] = " ".join(xywhk[:4]) + + temp_for_db_i["keypoints"][f"id {i}"] = " ".join(xywhk[4:]) + + else: + # If 'int' attribute doesn't exist (no detections), set track_ids to an empty list + track_ids = [] + + # self.scores.append(mse_unit) + + # Display the annotated frame + # cv2.imshow("YOLOv8 Tracking", annotated_frame) + + # else: + # If no detections, display the original frame without annotations + # self.scores.append(mse_unit) + # cv2.imshow("YOLOv8 Tracking", frame) + + temp_for_db_i["score"] = s_list[f_step // 16] + + # upload frame to s3 + frame_url = await self.upload_frame_s3(self.s3, frame_i) + + # upload frame, ts, bbox, kp to db + await self.upload_frame_db(self.db, temp_for_db_i, frame_url) + + await self.websocket.send_text(f"{timestamp}: {anomaly_text}") + + # 초기화 + self.scores.extend(deepcopy(s_list)) + s_list = [] + self.frame_list = [] + self.results_list = [] + self.tfdb_list = [] \ No newline at end of file diff --git a/app/inference/rt_anomaly_detector_lstmae.py b/app/inference/rt_anomaly_detector_lstmae.py new file mode 100644 index 0000000..c0b03ae --- /dev/null +++ b/app/inference/rt_anomaly_detector_lstmae.py @@ -0,0 +1,280 @@ +from collections import defaultdict +import cv2 +import numpy as np +import pandas as pd +from ultralytics import YOLO +import torch +from sklearn.preprocessing import MinMaxScaler + +from fastapi import HTTPException +from starlette import status +from database import crud +from database import schemas + +import os +import sys +import uuid +import json +from datetime import datetime, time +import matplotlib.pyplot as plt +from io import BytesIO + +import torch +import torch.nn as nn + +current_dir = os.path.dirname(os.path.abspath(__file__)) +parent_dir = os.path.dirname(os.path.dirname(current_dir)) + +sys.path.append(os.path.join(parent_dir, "model")) + +from lstmae.lstm_ae import LSTMAutoEncoder + +class RT_AnomalyDetector: + def __init__(self, info, s3_client, settings, db, websocket): + self.info = info + self.s3 = s3_client + self.settings = settings + self.frame_url_base = f"frame/{info['user_id']}/{info['upload_id']}/" + self.db = db + self.websocket = websocket + + async def upload_frame_db(self, db, temp_for_db, frame_url): + + temp_json_path = './temp.json' + + with open(temp_json_path, 'w') as f: + json.dump(temp_for_db, f) + + with open(temp_json_path, 'r') as f: + box_kp_json = json.load(f) + + _frame_create = schemas.FrameCreate(frame_url=frame_url, + time_stamp=temp_for_db['timestamp'], + box_kp_json=box_kp_json, + score=temp_for_db['score'], + video_id=self.info['video_id']) + + crud.create_frame(db=db, frame=_frame_create) + + os.remove(temp_json_path) + + async def upload_frame_s3(self, s3, frame): + s3_upload_exception = HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Frame 을 s3 저장소 업로드에 실패했습니다." + ) + frame_name = uuid.uuid1() + frame_url = self.frame_url_base + f'{frame_name}' + '.png' + #print(frame_url) + + try: + s3.upload_fileobj( + BytesIO(cv2.imencode('.png', frame)[1].tobytes()), + self.settings.BUCKET, + frame_url, + ExtraArgs={'ContentType': 'image/png'} + ) + except Exception as e: + #print(e) + raise s3_upload_exception + + return frame_url + + def upload_score_graph_s3(self): + plt.plot(self.scores, color='red') + plt.title('Anomaly Scores Over Time') + plt.xlabel(' ') + plt.ylabel(' ') + + plt.xticks([]) + plt.yticks([]) + + save_path = './model_scores_plot.png' + plt.savefig(save_path) + + with open(save_path, 'rb') as image_file: + graph = image_file.read() + + s3_upload_exception = HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="score Graph 를 s3 저장소 업로드에 실패했습니다." + ) + score_graph_name = 'score_graph.png' + score_graph_url = self.frame_url_base + score_graph_name + + try: + self.s3.upload_fileobj( + BytesIO(graph), + self.settings.BUCKET, + score_graph_url, + ExtraArgs={'ContentType': 'image/png'} + ) + except: + raise s3_upload_exception + + os.remove(save_path) + + def ready(self): + # YOLO + self.tracker_model = YOLO('yolov8n-pose.pt') + + # Define sequence_length, prediction_time, and n_features + sequence_length = 20 + prediction_time = 1 + n_features = 38 + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # LSTM autoencoder + # LSTM autoencoder + checkpoint = torch.load('/data/ephemeral/home/level2-3-cv-finalproject-cv-06/model/pts/LSTM_20240324_222238_best.pth') + self.autoencoder_model = LSTMAutoEncoder(num_layers=2, hidden_size=50, n_features=n_features, device=device) + self.autoencoder_model.load_state_dict(checkpoint['model_state_dict']) + + self.tracker_model.to(device) + self.autoencoder_model.to(device) + + # Store the track history + self.track_history = defaultdict(lambda: []) + + # Initialize a dictionary to store separate buffers for each ID + self.id_buffers = defaultdict(lambda: []) + + # Loop through the video frames + self.frame_count = 0 + self.net_mse = 0 + self.avg_mse = 0 + # score graph 를 위한 score list + self.scores = [] + + async def run(self, frame, timestamp): + + # Define the standard frame size + standard_width = 640 + standard_height = 480 + + # Define sequence_length, prediction_time, and n_features + sequence_length = 20 + prediction_time = 1 + n_features = 38 + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Define a function to calculate MSE between two sequences + def calculate_mse(seq1, seq2): + return np.mean(np.power(seq1 - seq2, 2)) + + # anomaly threshold (default 0.02) + threshold = self.info['thr'] + + self.frame_count += 1 # Increment frame count + + frame = cv2.resize(frame, (standard_width, standard_height)) + + temp_for_db = {'timestamp':None, 'bbox':{}, 'keypoints':{}, 'score':None} + + # track id (사람) 별로 mse 점수가 나오기 때문에 한 frame 에 여러 mse 점수가 나옴. 이를 frame 별 점수로 구하기 위해서 변수 설정 + mse_unit = 0 + + timestamp = timestamp.strftime('%H:%M:%S') + temp_for_db['timestamp'] = timestamp + + # Run YOLOv8 tracking on the frame, persisting tracks between frames + results = self.tracker_model.track(frame, persist=True) + + if results[0].boxes is not None: # Check if there are results and boxes + + # Get the boxes + boxes = results[0].boxes.xywh.cpu() + + if results[0].boxes.id is not None: + # If 'int' attribute exists (there are detections), get the track IDs + + track_ids = results[0].boxes.id.int().cpu().tolist() + + # Loop through the detections and add data to the DataFrame + anomaly_text = "" # Initialize the anomaly text + + # 한 프레임에서 검출된 사람만큼 돌아가는 반복문. 2명이면 각 id 별로 아래 연산들이 진행됨. + for i, box in zip(range(0, len(track_ids)), results[0].boxes.xywhn.cpu()): + + x, y, w, h = box + keypoints = results[0].keypoints.xyn[i].cpu().numpy().flatten().tolist() + + # Append the keypoints to the corresponding ID's buffer + # bbox(4), keypoints per id(34) + self.id_buffers[track_ids[i]].append([float(x),float(y),float(w),float(h)]+keypoints) + + # If the buffer size reaches the threshold (e.g., 20 data points), perform anomaly detection + # track_id 별 20프레임이 쌓이면 아래 연산 진행 + if len(self.id_buffers[track_ids[i]]) >= 20: + # Convert the buffer to a NumPy array + buffer_array = np.array(self.id_buffers[track_ids[i]]) + + # Scale the data (you can use the same scaler you used during training) + scaler = MinMaxScaler() + buffer_scaled = scaler.fit_transform(buffer_array) + + # Create sequences for prediction + x_pred = buffer_scaled[-sequence_length:].reshape(1, sequence_length, n_features) + + # Predict the next values using the autoencoder model + x_pred = torch.tensor(x_pred, dtype=torch.float32).to(device) + x_pred = self.autoencoder_model.forward(x_pred) + + # Inverse transform the predicted data to the original scale + x_pred_original = scaler.inverse_transform(x_pred.cpu().detach().numpy().reshape(-1, n_features)) + + # Calculate the MSE between the predicted and actual values + mse = calculate_mse(buffer_array[-prediction_time:], x_pred_original) + + #print(mse) + + self.net_mse = mse+self.net_mse + self.avg_mse= self.net_mse/self.frame_count + + mse_unit += mse + + # Check if the MSE exceeds the threshold to detect an anomaly + if mse > 1.5*(self.avg_mse)*0.25 + 0.75*threshold: + + if(anomaly_text==""): + anomaly_text = f"이상행동이 감지되었습니다." + else: + anomaly_text = f"이상행동이 감지되었습니다." + + #print(anomaly_text) + + temp_for_db['bbox'][f'id {i}'] = ' '.join(map(lambda x: str(round(x,4)), buffer_array[-prediction_time:][0, :4])) + + temp_for_db['keypoints'][f'id {i}'] = ' '.join(map(lambda x: str(round(x,4)), buffer_array[-prediction_time:][0, 4:])) + + # Remove the oldest data point from the buffer to maintain its size + self.id_buffers[track_ids[i]].pop(0) + + if temp_for_db['bbox'] != {}: + + temp_for_db['score'] = mse_unit + + # upload frame to s3 + frame_url = await self.upload_frame_s3(self.s3, frame) + + # upload frame, ts, bbox, kp to db + await self.upload_frame_db(self.db, temp_for_db, frame_url) + + await self.websocket.send_text(f'{timestamp}: {anomaly_text}') + + + else: + anomaly_text = "" + # If 'int' attribute doesn't exist (no detections), set track_ids to an empty list + track_ids = [] + + self.scores.append(mse_unit) + + # Display the annotated frame + #cv2.imshow("YOLOv8 Tracking", annotated_frame) + + else: + # If no detections, display the original frame without annotations + self.scores.append(mse_unit) + #cv2.imshow("YOLOv8 Tracking", frame) \ No newline at end of file diff --git a/app/main.py b/app/main.py new file mode 100644 index 0000000..cdf880a --- /dev/null +++ b/app/main.py @@ -0,0 +1,68 @@ +from datetime import timedelta, datetime +import os + +from fastapi import FastAPI, Request, Response, Form +from fastapi.templating import Jinja2Templates +from fastapi.middleware.cors import CORSMiddleware +from fastapi.staticfiles import StaticFiles +import uvicorn +from jose import jwt + +from api import user_router, upload_router, album_router, real_time_router +from utils.config import settings +from utils.security import get_current_user + +templates = Jinja2Templates(directory="templates") + +app = FastAPI() +static_dir = os.path.join(os.path.dirname(__file__), "templates", "src") +app.mount("/src", StaticFiles(directory=static_dir), name="src") + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +@app.get("/") +async def main_get(request: Request): + user = get_current_user(request) + if user: + return templates.TemplateResponse("main.html", {"request": request, "token": user.email}) + else: + return templates.TemplateResponse("main.html", {"request": request, "token": None}) + + +@app.post("/") +async def main_post(request: Request): + body = await request.form() + email = body["email"] + data = { + "sub": email, + "exp": datetime.utcnow() + timedelta(minutes=settings.ACCESS_TOKEN_EXPIRE_MINUTES) + } + token = jwt.encode(data, settings.SECRET_KEY, algorithm=settings.ALGORITHM) + + template_response = templates.TemplateResponse('main.html', {'request': request, 'token': email}) + + # 쿠키 저장 + template_response.set_cookie( + key="access_token", + value=token, + expires=timedelta(minutes=settings.ACCESS_TOKEN_EXPIRE_MINUTES), + httponly=True, + ) + + return template_response + + +app.include_router(user_router.router) +app.include_router(upload_router.router) +app.include_router(album_router.router) +app.include_router(real_time_router.router) + +if __name__ == '__main__': + uvicorn.run("main:app", host='0.0.0.0', port=30011, reload=True) \ No newline at end of file diff --git a/app/templates/album_detail.html b/app/templates/album_detail.html new file mode 100644 index 0000000..03f06d5 --- /dev/null +++ b/app/templates/album_detail.html @@ -0,0 +1,100 @@ +{% extends 'base.html' %} + +{% block title %} +Album_Detail +{% endblock %} + +{% block style %} +{{ super() }} +{% endblock %} + +{% block header %} +{{ super() }} +{% endblock %} + +{% block main %} + {% if loading %} + + + + {% endif %} + +
+
+

Video

+ {% if video_info.is_realtime %} +
실시간 서비스는 스크린샷만 제공됩니다.
+ {% else %} + + {% endif %} +
+
+
+
+

+ +

+
+
+
+
+
+

사용자 ID: {{ video_info.user_id }}

+

업로드 ID: {{ video_info.upload_id }}

+

업로드 이름: {{ video_info.upload_name }}

+

날짜: {{ video_info.date }}

+ {% if video_info.is_realtime %} +

종류: 실시간
실시간 서비스는 영상을 녹화하지 않습니다. 따라서 앨범에서 전체 영상은 보이지 않습니다.

+ {% else %} +

종류: 녹화 영상

+ {% endif %} +
+
+
+
+
+
+
+
+
+
+

Screenshot

+
+
+
+
+{% endblock %} + +{% block script %} + + +{% endblock %} \ No newline at end of file diff --git a/app/templates/album_list.html b/app/templates/album_list.html new file mode 100644 index 0000000..be5747c --- /dev/null +++ b/app/templates/album_list.html @@ -0,0 +1,121 @@ +{% extends 'base.html' %} + +{% block title %} +Album List +{% endblock %} + +{% block header %} +{{ super() }} +{% endblock %} + +{% block style %} +{% endblock %} + +{% block main %} +
+
+ + + + + + + + + + + + + {% for upload in album_list %} + + + + + + + + + {% endfor %} + +
#이름날짜&시간이상탐지분석 여부Button
{{ loop.index }} + + + {{ upload.name }} + + + {{ upload.date }} + + {% if upload.is_realtime %} + 실시간 + {% else %} + 녹화 영상 + {% endif %} + + {% if upload.completes[0].completed %} +
분석 완료
+ {% else %} +
분석중
+ {% endif %} + 		+ + +
+
+
+ + + + + +{% endblock %} + +{% block script %} + +{% endblock %} + \ No newline at end of file diff --git a/app/templates/base.html b/app/templates/base.html new file mode 100644 index 0000000..4763758 --- /dev/null +++ b/app/templates/base.html @@ -0,0 +1,130 @@ + + + + + + {% block title %} {% endblock %} + + {% block style %} + + + {% endblock %} + + + + {% block header %} + + {% endblock %} + {% block main %} {% endblock %} + {% block footer %} {% endblock %} + {% block script %} {% endblock %} + + + diff --git a/app/templates/frame.html b/app/templates/frame.html new file mode 100644 index 0000000..295ae4d --- /dev/null +++ b/app/templates/frame.html @@ -0,0 +1,76 @@ + + + + + + + Screenshot Details + + + +
+ +
+ Large Image +
+ + +
+

{{ frame_info.time_stamp }} 이미지

+ + + + + +
+
+ + + + diff --git a/app/templates/login.html b/app/templates/login.html new file mode 100644 index 0000000..b0171de --- /dev/null +++ b/app/templates/login.html @@ -0,0 +1,52 @@ +{% extends 'base.html' %} + +{% block title%} +Login +{% endblock %} + +{% block style %} +{% endblock %} + +{% block header %} + +{% endblock %} + + + +{% block main %} +
+
+ +
+ +
+ + + {% if err.user %} +

{{ err.user }}

+ {% endif %} +
+ +
+ + + {% if err.pw %} +

{{ err.pw }}

+ {% endif %} +
+ +
+ +
+ +
+ + Sign up + +
+
+{% endblock %} diff --git a/app/templates/main.html b/app/templates/main.html new file mode 100644 index 0000000..7732860 --- /dev/null +++ b/app/templates/main.html @@ -0,0 +1,70 @@ +{% extends 'base.html' %} + +{% block title %} +Main Page +{% endblock %} + +{% block style %} +{% endblock %} + +{% block header %} +{{ super() }} +{% endblock %} + +{% block main %} +
+
+
+

무인매장 이상행동 탐지

+

동영상 파일과 실시간 영상에서 이상행동을 탐지해 드립니다.

+
+ {% if not token %} +

로그인 후 무료로 이용해보세요

+ {% endif %} + 지금 시작하기 +
+
+ +
+
+ +
+
+ Image 1 +

녹화 영상

+

녹화된 영상을 업로드하여
이상행동을 탐지합니다.

+ {% if token %} +

바로가기 »

+ {% else %} +

바로가기 »

+ {% endif %} +
+
+ Image 1 +

실시간

+

웹캠, CCTV 등을 연결하여
실시간으로 이상행동을 탐지합니다.

+ {% if token %} +

바로가기 »

+ {% else %} +

바로가기 »

+ {% endif %} +
+
+ Image 1 +

앨범 기능

+

녹화 영상과 실시간에서 탐지된 이상행동을
스크린샷으로 저장하여 보관합니다.

+ {% if token %} +

바로가기 »

+ {% else %} +

바로가기 »

+ {% endif %} +
+
+
+
+ +
+{% endblock %} \ No newline at end of file diff --git a/app/templates/real_time.html b/app/templates/real_time.html new file mode 100644 index 0000000..8e92354 --- /dev/null +++ b/app/templates/real_time.html @@ -0,0 +1,43 @@ +{% extends 'base.html' %} + +{% block title %} +Real Time +{% endblock %} + +{% block style %} +{% endblock %} + +{% block header %} +{{ super() }} +{% endblock %} + +{% block main %} +
+

이상행동을 탐지할 cctv를 연결 해주세요.

+
+
+ + +
+
+ + +
+
+ +

이상행동 판단 정도입니다. 느슨할수록 정확한 이상행동이 아니더라도 잡아냅니다. 엄격할수록 정확한 이상행동일 때만 잡아냅니다.

+ +
+

느슨하게

+

보통

+

엄격하게

+
+
+
+ + +
+ +
+
+{% endblock %} diff --git a/app/templates/signup.html b/app/templates/signup.html new file mode 100644 index 0000000..b023df1 --- /dev/null +++ b/app/templates/signup.html @@ -0,0 +1,58 @@ +{% extends 'base.html' %} + +{% block title%} +Sign Up +{% endblock %} + +{% block style %} +{% endblock %} + +{% block header %} + +{% endblock %} + +{% block main %} +
+
+ +
+ +
+ + + {% if err.user %} +

{{ err.user }}

+ {% endif %} +
+ +
+ + + {% if err.pw %} +

{{ err.pw }}

+ {% endif %} +
+ +
+ + + {% if err.check_pw %} +

{{ err.check_pw }}

+ {% endif %} +
+ +
+ +
+ +
+ + Log In + +
+
+{% endblock %} diff --git a/app/templates/src/album_detail.js b/app/templates/src/album_detail.js new file mode 100644 index 0000000..26c654c --- /dev/null +++ b/app/templates/src/album_detail.js @@ -0,0 +1,72 @@ +if (videoInfo.frame_urls === "Nothing") { + const messageDiv = document.createElement('div'); + messageDiv.classList.add('container-xl', 'text-center', 'mt-2', 'py-4'); + const messageHeader = document.createElement('h4'); + messageHeader.classList.add('text-start'); + messageHeader.innerText = '아무런 이상행동이 탐지되지 않았습니다.'; + messageDiv.appendChild(messageHeader); + screenshotContainer.parentNode.insertBefore(messageDiv, screenshotContainer.nextSibling); +} else { + videoInfo.frame_urls.forEach(frameInfo => { + const col = document.createElement('div'); + col.classList.add('col'); + + const image = document.createElement('img'); + image.classList.add('img-thumbnail'); + image.src = frameInfo[1]; + + const timestampLink = document.createElement('a'); + timestampLink.classList.add('link-underline-light', 'text-reset'); + timestampLink.href = '#'; + timestampLink.innerText = frameInfo[2]; + + col.appendChild(image); + col.appendChild(timestampLink); + screenshotContainer.appendChild(col); + + // 이미지를 누르면 확대된 이미지와 버튼이 있는 팝업창을 띄우기 + image.onclick = () => showPopup(frameInfo[0]); + + // 타임스탬프를 누르면 비디오가 해당 타임스탬프로 이동 + timestampLink.onclick = () => seekVideo(frameInfo[2]); + }); +} + +function showPopup(imageUrl) { + // 팝업창 가운데 정렬을 위한 스크린 가로, 세로 크기 계산 + const screenWidth = window.screen.width; + const screenHeight = window.screen.height; + + // 팝업창 크기 조정 + const popupWidth = Math.min(screenWidth * 0.8, 800); // 최대 80% 화면 크기 또는 800px + const popupHeight = Math.min(screenHeight * 0.8, 600); // 최대 80% 화면 크기 또는 600px + + // 팝업창 가운데 정렬을 위한 위치 계산 + const left = (screenWidth - popupWidth) / 2; + const top = (screenHeight - popupHeight) / 2; + + // 팝업창 열기 + const popupWindow = window.open(`/album/details/images?frame_id=${imageUrl}`, 'ImagePopup', `width=${popupWidth}, height=${popupHeight}, top=${top}, left=${left}`); + if (popupWindow) { + popupWindow.focus(); + } +} + +function seekVideo(timestamp) { + // timestamp 문자열을 시, 분, 초로 분해 + const timeParts = timestamp.split(':'); + + // 각 부분을 정수로 변환 + const hours = parseInt(timeParts[0], 10); + const minutes = parseInt(timeParts[1], 10); + const seconds = parseInt(timeParts[2], 10); + + // 비디오의 currentTime 설정 + const video = document.getElementById('vid'); + video.currentTime = hours * 3600 + minutes * 60 + seconds; +} + +var loading = '{{ loading|tojson|safe }}'; +if (loading) { + document.body.classList.add('loading'); +} \ No newline at end of file diff --git a/app/templates/src/album_list.js b/app/templates/src/album_list.js new file mode 100644 index 0000000..43c7bb2 --- /dev/null +++ b/app/templates/src/album_list.js @@ -0,0 +1,33 @@ +function redirectToDetails(user_id, upload_id) { + // URL 생성 + let url = `/album/details?user_id=${user_id}&upload_id=${upload_id}`; + + // 페이지 리디렉션 + window.location.href = url; +} + +document.addEventListener('DOMContentLoaded', function() { + let editButtons = document.querySelectorAll('.edit-btn'); + let deleteButtons = document.querySelectorAll('.delete-btn'); + + editButtons.forEach(function(button) { + button.addEventListener('click', function() { + let uploadId = this.getAttribute('data-uploadid'); + let origin_name = this.getAttribute('data-name'); + + document.getElementById('modifyUploadID').value = uploadId; + document.getElementById('originName').value = origin_name; + }); + }); + + + deleteButtons.forEach(function(button) { + button.addEventListener('click', function() { + let uploadId = this.getAttribute('data-uploadid'); + let isRealTime = this.getAttribute('data-is-real-time'); + + document.getElementById('deleteUploadID').value = uploadId; + document.getElementById('isRealTime').value = isRealTime; + }); + }); +}); \ No newline at end of file diff --git a/app/templates/src/video.js b/app/templates/src/video.js new file mode 100644 index 0000000..26c654c --- /dev/null +++ b/app/templates/src/video.js @@ -0,0 +1,72 @@ +if (videoInfo.frame_urls === "Nothing") { + const messageDiv = document.createElement('div'); + messageDiv.classList.add('container-xl', 'text-center', 'mt-2', 'py-4'); + const messageHeader = document.createElement('h4'); + messageHeader.classList.add('text-start'); + messageHeader.innerText = '아무런 이상행동이 탐지되지 않았습니다.'; + messageDiv.appendChild(messageHeader); + screenshotContainer.parentNode.insertBefore(messageDiv, screenshotContainer.nextSibling); +} else { + videoInfo.frame_urls.forEach(frameInfo => { + const col = document.createElement('div'); + col.classList.add('col'); + + const image = document.createElement('img'); + image.classList.add('img-thumbnail'); + image.src = frameInfo[1]; + + const timestampLink = document.createElement('a'); + timestampLink.classList.add('link-underline-light', 'text-reset'); + timestampLink.href = '#'; + timestampLink.innerText = frameInfo[2]; + + col.appendChild(image); + col.appendChild(timestampLink); + screenshotContainer.appendChild(col); + + // 이미지를 누르면 확대된 이미지와 버튼이 있는 팝업창을 띄우기 + image.onclick = () => showPopup(frameInfo[0]); + + // 타임스탬프를 누르면 비디오가 해당 타임스탬프로 이동 + timestampLink.onclick = () => seekVideo(frameInfo[2]); + }); +} + +function showPopup(imageUrl) { + // 팝업창 가운데 정렬을 위한 스크린 가로, 세로 크기 계산 + const screenWidth = window.screen.width; + const screenHeight = window.screen.height; + + // 팝업창 크기 조정 + const popupWidth = Math.min(screenWidth * 0.8, 800); // 최대 80% 화면 크기 또는 800px + const popupHeight = Math.min(screenHeight * 0.8, 600); // 최대 80% 화면 크기 또는 600px + + // 팝업창 가운데 정렬을 위한 위치 계산 + const left = (screenWidth - popupWidth) / 2; + const top = (screenHeight - popupHeight) / 2; + + // 팝업창 열기 + const popupWindow = window.open(`/album/details/images?frame_id=${imageUrl}`, 'ImagePopup', `width=${popupWidth}, height=${popupHeight}, top=${top}, left=${left}`); + if (popupWindow) { + popupWindow.focus(); + } +} + +function seekVideo(timestamp) { + // timestamp 문자열을 시, 분, 초로 분해 + const timeParts = timestamp.split(':'); + + // 각 부분을 정수로 변환 + const hours = parseInt(timeParts[0], 10); + const minutes = parseInt(timeParts[1], 10); + const seconds = parseInt(timeParts[2], 10); + + // 비디오의 currentTime 설정 + const video = document.getElementById('vid'); + video.currentTime = hours * 3600 + minutes * 60 + seconds; +} + +var loading = '{{ loading|tojson|safe }}'; +if (loading) { + document.body.classList.add('loading'); +} \ No newline at end of file diff --git a/app/templates/stream.html b/app/templates/stream.html new file mode 100644 index 0000000..d1761cb --- /dev/null +++ b/app/templates/stream.html @@ -0,0 +1,374 @@ + + + + + + IVT Main Page(Login) + + + + + + + + + +
+
+

Video

+
+ {% if video_info.video_url == "web" %} + + + {% else %} +
+ 연결 중... +
+ + {% endif %} +
+
실시간 서비스는 영상이 저장되지 않습니다.
+
+ + + +
+

Log

+
+
+
+
+
+
+
+
+

+ +

+
+
+
+
+
+

사용자 ID: {{ video_info.user_id }}

+

업로드 ID: {{ video_info.upload_id }}

+

업로드 이름: {{ video_info.upload_name }}

+

날짜: {{ video_info.date }}

+ {% if video_info.is_realtime %} +

종류: 실시간
실시간 서비스는 영상을 녹화하지 않습니다. 따라서 앨범에서 접근할 시 전체 영상은 + 보이지 않습니다.

+ {% else %} +

종류: 녹화 영상

+ {% endif %} +
+ +
+
+
+
+
+
+
+
+
+

Screenshot

+
+ +
+
+ + + + + + \ No newline at end of file diff --git a/app/templates/upload.html b/app/templates/upload.html new file mode 100644 index 0000000..02bcda3 --- /dev/null +++ b/app/templates/upload.html @@ -0,0 +1,60 @@ +{% extends 'base.html' %} + +{% block title %} +Upload +{% endblock %} + +{% block style %} +{% endblock %} + +{% block header %} +{{ super() }} +{% endblock %} + +{% block main %} +
+

이상행동을 탐지할 동영상을 업로드 해주세요.

+
+
+ + +
+
+ + +
+
+ +

이상행동 판단 정도입니다. 느슨할수록 정확한 이상행동이 아니더라도 잡아냅니다. 엄격할수록 정확한 이상행동일 때만 잡아냅니다.

+ +
+

느슨하게

+

보통

+

엄격하게

+
+
+
+ + + {% if err.file_ext %} +

{{ err.file_ext }}

+ {% endif %} +
+ +
+ +
+
+ +
+{% endblock %} diff --git a/app/templates/video.html b/app/templates/video.html new file mode 100644 index 0000000..d4bb92d --- /dev/null +++ b/app/templates/video.html @@ -0,0 +1,109 @@ +{% extends 'base.html' %} + +{% block title %} +Video +{% endblock %} + +{% block style %} +{{ super() }} +{% endblock %} + +{% block header %} +{{ super() }} +{% endblock %} + +{% block main %} + {% if loading %} + + + + {% endif %} + +
+
+

Video

+ {% if video_info.is_realtime %} +
실시간 서비스는 스크린샷만 제공됩니다.
+ {% else %} + + {% endif %} +
+
+
+
+

+ +

+
+
+
+
+
+

사용자 ID: {{ video_info.user_id }}

+

업로드 ID: {{ video_info.upload_id }}

+

업로드 이름: {{ video_info.upload_name }}

+

날짜: {{ video_info.date }}

+ {% if video_info.is_realtime %} +

종류: 실시간
실시간 서비스는 영상을 녹화하지 않습니다. 따라서 앨범에서 전체 영상은 보이지 않습니다.

+ {% else %} +

종류: 녹화 영상

+ {% endif %} +
+ +
+
+
+
+
+
+
+
+
+

Screenshot

+
+ +
+
+
+{% endblock %} + +{% block script %} + + +{% endblock %} \ No newline at end of file diff --git a/app/utils/__init__.py b/app/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/app/utils/config.py b/app/utils/config.py new file mode 100644 index 0000000..eb240a8 --- /dev/null +++ b/app/utils/config.py @@ -0,0 +1,34 @@ +from pydantic_settings import BaseSettings +from dotenv import load_dotenv + +load_dotenv() + +class Settings(BaseSettings): + API_V1_STR: str = "/api/v1" + SECRET_KEY: str = "55c84cbfa7f9e183da2179cb34cc45526bea05ee80b5bef66ed950534730bf5d" + ALGORITHM: str = "HS256" + # 60 minutes * 24 hours * 7 days = 7 days + ACCESS_TOKEN_EXPIRE_MINUTES: int = 60 * 24 * 7 + + MYSQL_SERVER_IP : str + MYSQL_SERVER_PORT : int + MYSQL_SERVER_USER : str + MYSQL_SERVER_PASSWORD : str + MYSQL_DATABASE : str + + AWS_ACCESS_KEY : str + AWS_SECRET_KEY : str + BUCKET : str + + SMTP_ADDRESS : str + SMTP_PORT : int + MAIL_ACCOUNT : str + MAIL_PASSWORD : str + + UPLOAD_MODEL_SERVER_IP: str + STREAM_MODEL_SERVER_IP: str + + class Config: + env_file = ".env" + +settings = Settings() diff --git a/app/utils/security.py b/app/utils/security.py new file mode 100644 index 0000000..5bd9cd8 --- /dev/null +++ b/app/utils/security.py @@ -0,0 +1,38 @@ +from datetime import datetime, timedelta +from typing import Any, Union + +from fastapi import Request +from sqlalchemy.orm import Session +from jose import jwt, JWTError +from passlib.context import CryptContext + +from utils.config import settings +from database import models +from database.database import engine + +pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto") + +def verify_password(plain_password: str, hashed_password: str) -> bool: + return pwd_context.verify(plain_password, hashed_password) + +def get_password_hash(password: str) -> str: + return pwd_context.hash(password) + +def get_current_user(request:Request): + token = request.cookies.get("access_token", None) + + try: + if token: + payload = jwt.decode(token, settings.SECRET_KEY, algorithms=[settings.ALGORITHM]) + email = payload.get("sub", None) + session = Session(engine) + user = session.query(models.User).filter(models.User.email == email).first() + session.close() + if user: + return user + else: + return None + else: + return None + except: + return JWTError() \ No newline at end of file diff --git a/app/utils/utils.py b/app/utils/utils.py new file mode 100644 index 0000000..532e420 --- /dev/null +++ b/app/utils/utils.py @@ -0,0 +1,31 @@ +from fastapi import HTTPException, status +import boto3 +from botocore.config import Config + +from utils.config import settings +from database import crud +from inference.anomaly_detector import AnomalyDetector +#from inference.anomaly_detector_lstmae import AnomalyDetector + +boto_config = Config( + signature_version = 'v4', +) + +s3 = boto3.client("s3", + config=boto_config, + region_name='ap-northeast-2', + aws_access_key_id=settings.AWS_ACCESS_KEY, + aws_secret_access_key=settings.AWS_SECRET_KEY) + + +def run_model(video_url, info, settings, db, s3=s3): + + model = AnomalyDetector(video_file=video_url, + info=info, + s3_client=s3, + settings=settings, + db=db) + model.run() + + crud.update_complete_status(db=db, upload_id=info['upload_id']) + return \ No newline at end of file diff --git a/datapreprocess/feature_extraction.ipynb b/datapreprocess/feature_extraction.ipynb new file mode 100644 index 0000000..37b26eb --- /dev/null +++ b/datapreprocess/feature_extraction.ipynb @@ -0,0 +1,423 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import defaultdict\n", + "import cv2\n", + "import numpy as np\n", + "from ultralytics import YOLO\n", + "import csv\n", + "import os\n", + "from copy import deepcopy\n", + "\n", + "from datetime import datetime" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "root = \"/data/ephemeral/home/datasets/UCFCrime/normal/\"\n", + "\n", + "\n", + "header = [\n", + " \"Filename\",\n", + " \"Frame\",\n", + " \"ID\",\n", + " \"X\",\n", + " \"Y\",\n", + " \"Width\",\n", + " \"Height\",\n", + " \"Keypoint_0\",\n", + " \"Keypoint_1\",\n", + " \"Keypoint_2\",\n", + " \"Keypoint_3\",\n", + " \"Keypoint_4\",\n", + " \"Keypoint_5\",\n", + " \"Keypoint_6\",\n", + " \"Keypoint_7\",\n", + " \"Keypoint_8\",\n", + " \"Keypoint_9\",\n", + " \"Keypoint_10\",\n", + " \"Keypoint_11\",\n", + " \"Keypoint_12\",\n", + " \"Keypoint_13\",\n", + " \"Keypoint_14\",\n", + " \"Keypoint_15\",\n", + " \"Keypoint_16\",\n", + " \"Keypoint_17\",\n", + " \"Keypoint_18\",\n", + " \"Keypoint_19\",\n", + " \"Keypoint_20\",\n", + " \"Keypoint_21\",\n", + " \"Keypoint_22\",\n", + " \"Keypoint_23\",\n", + " \"Keypoint_24\",\n", + " \"Keypoint_25\",\n", + " \"Keypoint_26\",\n", + " \"Keypoint_27\",\n", + " \"Keypoint_28\",\n", + " \"Keypoint_29\",\n", + " \"Keypoint_30\",\n", + " \"Keypoint_31\",\n", + " \"Keypoint_32\",\n", + " \"Keypoint_33\",\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the YOLOv8 model\n", + "model = YOLO(\"yolov8n-pose.pt\")\n", + "\n", + "# Define the standard frame size (change these values as needed)\n", + "standard_width = 320\n", + "standard_height = 240" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==>> folder_list: ['Abuse', 'Arrest', 'Arson', 'Assault', 'Burglary', 'Explosion', 'Fighting', 'RoadAccidents', 'Robbery', 'Shooting', 'Shoplifting', 'Stealing', 'Vandalism']\n" + ] + } + ], + "source": [ + "folder_list = os.listdir(root)\n", + "folder_list.sort()\n", + "print(f\"==>> folder_list: {folder_list}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Abuse feature extracting starts\n", + "==>> file_list: ['Abuse001_x264.mp4', 'Abuse002_x264.mp4', 'Abuse003_x264.mp4', 'Abuse004_x264.mp4', 'Abuse005_x264.mp4', 'Abuse006_x264.mp4', 'Abuse007_x264.mp4', 'Abuse008_x264.mp4', 'Abuse009_x264.mp4', 'Abuse010_x264.mp4', 'Abuse011_x264.mp4', 'Abuse012_x264.mp4', 'Abuse013_x264.mp4', 'Abuse014_x264.mp4', 'Abuse015_x264.mp4', 'Abuse016_x264.mp4', 'Abuse017_x264.mp4', 'Abuse018_x264.mp4', 'Abuse019_x264.mp4', 'Abuse020_x264.mp4', 'Abuse021_x264.mp4', 'Abuse022_x264.mp4', 'Abuse023_x264.mp4', 'Abuse024_x264.mp4', 'Abuse025_x264.mp4', 'Abuse026_x264.mp4', 'Abuse027_x264.mp4', 'Abuse028_x264.mp4', 'Abuse029_x264.mp4', 'Abuse030_x264.mp4', 'Abuse031_x264.mp4', 'Abuse032_x264.mp4', 'Abuse033_x264.mp4', 'Abuse034_x264.mp4', 'Abuse035_x264.mp4', 'Abuse036_x264.mp4', 'Abuse037_x264.mp4', 'Abuse038_x264.mp4', 'Abuse039_x264.mp4', 'Abuse040_x264.mp4', 'Abuse041_x264.mp4', 'Abuse042_x264.mp4', 'Abuse043_x264.mp4', 'Abuse044_x264.mp4', 'Abuse045_x264.mp4', 'Abuse046_x264.mp4', 'Abuse047_x264.mp4', 'Abuse048_x264.mp4', 'Abuse049_x264.mp4', 'Abuse050_x264.mp4']\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "Abuse feature extracting ended. Elapsed time: 0:49:53\n", + "Arrest feature extracting starts\n", + "==>> file_list: ['Arrest001_x264.mp4', 'Arrest002_x264.mp4', 'Arrest003_x264.mp4', 'Arrest004_x264.mp4', 'Arrest005_x264.mp4', 'Arrest006_x264.mp4', 'Arrest007_x264.mp4', 'Arrest008_x264.mp4', 'Arrest009_x264.mp4', 'Arrest010_x264.mp4', 'Arrest011_x264.mp4', 'Arrest012_x264.mp4', 'Arrest013_x264.mp4', 'Arrest014_x264.mp4', 'Arrest015_x264.mp4', 'Arrest016_x264.mp4', 'Arrest017_x264.mp4', 'Arrest018_x264.mp4', 'Arrest019_x264.mp4', 'Arrest020_x264.mp4', 'Arrest021_x264.mp4', 'Arrest022_x264.mp4', 'Arrest023_x264.mp4', 'Arrest024_x264.mp4', 'Arrest025_x264.mp4', 'Arrest026_x264.mp4', 'Arrest027_x264.mp4', 'Arrest028_x264.mp4', 'Arrest029_x264.mp4', 'Arrest030_x264.mp4', 'Arrest031_x264.mp4', 'Arrest032_x264.mp4', 'Arrest033_x264.mp4', 'Arrest034_x264.mp4', 'Arrest035_x264.mp4', 'Arrest036_x264.mp4', 'Arrest037_x264.mp4', 'Arrest038_x264.mp4', 'Arrest039_x264.mp4', 'Arrest040_x264.mp4', 'Arrest041_x264.mp4', 'Arrest042_x264.mp4', 'Arrest043_x264.mp4', 'Arrest044_x264.mp4', 'Arrest046_x264.mp4', 'Arrest047_x264.mp4', 'Arrest048_x264.mp4', 'Arrest049_x264.mp4', 'Arrest050_x264.mp4', 'Arrest051_x264.mp4']\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "Arrest feature extracting ended. Elapsed time: 1:14:14\n", + "Arson feature extracting starts\n", + "==>> file_list: ['Arson001_x264.mp4', 'Arson002_x264.mp4', 'Arson003_x264.mp4', 'Arson005_x264.mp4', 'Arson006_x264.mp4', 'Arson007_x264.mp4', 'Arson008_x264.mp4', 'Arson009_x264.mp4', 'Arson010_x264.mp4', 'Arson011_x264.mp4', 'Arson012_x264.mp4', 'Arson013_x264.mp4', 'Arson014_x264.mp4', 'Arson015_x264.mp4', 'Arson016_x264.mp4', 'Arson017_x264.mp4', 'Arson018_x264.mp4', 'Arson019_x264.mp4', 'Arson020_x264.mp4', 'Arson021_x264.mp4', 'Arson022_x264.mp4', 'Arson023_x264.mp4', 'Arson024_x264.mp4', 'Arson025_x264.mp4', 'Arson026_x264.mp4', 'Arson027_x264.mp4', 'Arson028_x264.mp4', 'Arson029_x264.mp4', 'Arson030_x264.mp4', 'Arson031_x264.mp4', 'Arson032_x264.mp4', 'Arson034_x264.mp4', 'Arson035_x264.mp4', 'Arson036_x264.mp4', 'Arson037_x264.mp4', 'Arson038_x264.mp4', 'Arson039_x264.mp4', 'Arson040_x264.mp4', 'Arson041_x264.mp4', 'Arson042_x264.mp4', 'Arson044_x264.mp4', 'Arson045_x264.mp4', 'Arson046_x264.mp4', 'Arson047_x264.mp4', 'Arson048_x264.mp4', 'Arson049_x264.mp4', 'Arson050_x264.mp4', 'Arson051_x264.mp4', 'Arson052_x264.mp4', 'Arson053_x264.mp4']\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "Arson feature extracting ended. Elapsed time: 1:00:21\n", + "Assault feature extracting starts\n", + "==>> file_list: ['Assault001_x264.mp4', 'Assault002_x264.mp4', 'Assault003_x264.mp4', 'Assault004_x264.mp4', 'Assault005_x264.mp4', 'Assault006_x264.mp4', 'Assault007_x264.mp4', 'Assault008_x264.mp4', 'Assault009_x264.mp4', 'Assault010_x264.mp4', 'Assault011_x264.mp4', 'Assault012_x264.mp4', 'Assault013_x264.mp4', 'Assault014_x264.mp4', 'Assault015_x264.mp4', 'Assault016_x264.mp4', 'Assault017_x264.mp4', 'Assault018_x264.mp4', 'Assault019_x264.mp4', 'Assault020_x264.mp4', 'Assault022_x264.mp4', 'Assault023_x264.mp4', 'Assault024_x264.mp4', 'Assault025_x264.mp4', 'Assault026_x264.mp4', 'Assault027_x264.mp4', 'Assault028_x264.mp4', 'Assault029_x264.mp4', 'Assault030_x264.mp4', 'Assault031_x264.mp4', 'Assault032_x264.mp4', 'Assault033_x264.mp4', 'Assault034_x264.mp4', 'Assault035_x264.mp4', 'Assault036_x264.mp4', 'Assault037_x264.mp4', 'Assault038_x264.mp4', 'Assault039_x264.mp4', 'Assault040_x264.mp4', 'Assault041_x264.mp4', 'Assault042_x264.mp4', 'Assault044_x264.mp4', 'Assault045_x264.mp4', 'Assault046_x264.mp4', 'Assault047_x264.mp4', 'Assault048_x264.mp4', 'Assault049_x264.mp4', 'Assault050_x264.mp4', 'Assault051_x264.mp4', 'Assault052_x264.mp4']\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "Assault feature extracting ended. Elapsed time: 0:35:45\n", + "Burglary feature extracting starts\n", + "==>> file_list: ['Burglary001_x264.mp4', 'Burglary002_x264.mp4', 'Burglary003_x264.mp4', 'Burglary004_x264.mp4', 'Burglary005_x264.mp4', 'Burglary006_x264.mp4', 'Burglary007_x264.mp4', 'Burglary008_x264.mp4', 'Burglary009_x264.mp4', 'Burglary010_x264.mp4', 'Burglary011_x264.mp4', 'Burglary012_x264.mp4', 'Burglary013_x264.mp4', 'Burglary014_x264.mp4', 'Burglary015_x264.mp4', 'Burglary016_x264.mp4', 'Burglary017_x264.mp4', 'Burglary018_x264.mp4', 'Burglary019_x264.mp4', 'Burglary020_x264.mp4', 'Burglary021_x264.mp4', 'Burglary022_x264.mp4', 'Burglary023_x264.mp4', 'Burglary024_x264.mp4', 'Burglary025_x264.mp4', 'Burglary026_x264.mp4', 'Burglary027_x264.mp4', 'Burglary028_x264.mp4', 'Burglary029_x264.mp4', 'Burglary030_x264.mp4', 'Burglary031_x264.mp4', 'Burglary032_x264.mp4', 'Burglary033_x264.mp4', 'Burglary034_x264.mp4', 'Burglary035_x264.mp4', 'Burglary036_x264.mp4', 'Burglary037_x264.mp4', 'Burglary038_x264.mp4', 'Burglary039_x264.mp4', 'Burglary040_x264.mp4', 'Burglary041_x264.mp4', 'Burglary042_x264.mp4', 'Burglary043_x264.mp4', 'Burglary044_x264.mp4', 'Burglary045_x264.mp4', 'Burglary046_x264.mp4', 'Burglary047_x264.mp4', 'Burglary048_x264.mp4', 'Burglary049_x264.mp4', 'Burglary050_x264.mp4', 'Burglary051_x264.mp4', 'Burglary052_x264.mp4', 'Burglary053_x264.mp4', 'Burglary054_x264.mp4', 'Burglary055_x264.mp4', 'Burglary056_x264.mp4', 'Burglary057_x264.mp4', 'Burglary058_x264.mp4', 'Burglary059_x264.mp4', 'Burglary060_x264.mp4', 'Burglary061_x264.mp4', 'Burglary062_x264.mp4', 'Burglary063_x264.mp4', 'Burglary064_x264.mp4', 'Burglary065_x264.mp4', 'Burglary066_x264.mp4', 'Burglary067_x264.mp4', 'Burglary068_x264.mp4', 'Burglary069_x264.mp4', 'Burglary070_x264.mp4', 'Burglary071_x264.mp4', 'Burglary072_x264.mp4', 'Burglary073_x264.mp4', 'Burglary074_x264.mp4', 'Burglary075_x264.mp4', 'Burglary076_x264.mp4', 'Burglary077_x264.mp4', 'Burglary078_x264.mp4', 'Burglary079_x264.mp4', 'Burglary080_x264.mp4', 'Burglary081_x264.mp4', 'Burglary082_x264.mp4', 'Burglary083_x264.mp4', 'Burglary084_x264.mp4', 'Burglary085_x264.mp4', 'Burglary086_x264.mp4', 'Burglary087_x264.mp4', 'Burglary088_x264.mp4', 'Burglary089_x264.mp4', 'Burglary090_x264.mp4', 'Burglary091_x264.mp4', 'Burglary092_x264.mp4', 'Burglary093_x264.mp4', 'Burglary094_x264.mp4', 'Burglary095_x264.mp4', 'Burglary096_x264.mp4', 'Burglary097_x264.mp4', 'Burglary098_x264.mp4', 'Burglary099_x264.mp4', 'Burglary100_x264.mp4']\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "Burglary feature extracting ended. Elapsed time: 1:46:44\n", + "Explosion feature extracting starts\n", + "==>> file_list: ['Explosion001_x264.mp4', 'Explosion002_x264.mp4', 'Explosion003_x264.mp4', 'Explosion004_x264.mp4', 'Explosion005_x264.mp4', 'Explosion006_x264.mp4', 'Explosion007_x264.mp4', 'Explosion008_x264.mp4', 'Explosion009_x264.mp4', 'Explosion010_x264.mp4', 'Explosion011_x264.mp4', 'Explosion012_x264.mp4', 'Explosion013_x264.mp4', 'Explosion014_x264.mp4', 'Explosion015_x264.mp4', 'Explosion016_x264.mp4', 'Explosion017_x264.mp4', 'Explosion018_x264.mp4', 'Explosion019_x264.mp4', 'Explosion020_x264.mp4', 'Explosion021_x264.mp4', 'Explosion022_x264.mp4', 'Explosion023_x264.mp4', 'Explosion024_x264.mp4', 'Explosion025_x264.mp4', 'Explosion026_x264.mp4', 'Explosion027_x264.mp4', 'Explosion028_x264.mp4', 'Explosion029_x264.mp4', 'Explosion030_x264.mp4', 'Explosion032_x264.mp4', 'Explosion033_x264.mp4', 'Explosion034_x264.mp4', 'Explosion035_x264.mp4', 'Explosion036_x264.mp4', 'Explosion037_x264.mp4', 'Explosion038_x264.mp4', 'Explosion039_x264.mp4', 'Explosion040_x264.mp4', 'Explosion041_x264.mp4', 'Explosion042_x264.mp4', 'Explosion043_x264.mp4', 'Explosion044_x264.mp4', 'Explosion045_x264.mp4', 'Explosion046_x264.mp4', 'Explosion047_x264.mp4', 'Explosion048_x264.mp4', 'Explosion050_x264.mp4', 'Explosion051_x264.mp4', 'Explosion052_x264.mp4']\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "Explosion feature extracting ended. Elapsed time: 0:55:08\n", + "Fighting feature extracting starts\n", + "==>> file_list: ['Fighting002_x264.mp4', 'Fighting003_x264.mp4', 'Fighting004_x264.mp4', 'Fighting005_x264.mp4', 'Fighting006_x264.mp4', 'Fighting007_x264.mp4', 'Fighting008_x264.mp4', 'Fighting009_x264.mp4', 'Fighting010_x264.mp4', 'Fighting011_x264.mp4', 'Fighting012_x264.mp4', 'Fighting013_x264.mp4', 'Fighting014_x264.mp4', 'Fighting015_x264.mp4', 'Fighting016_x264.mp4', 'Fighting017_x264.mp4', 'Fighting018_x264.mp4', 'Fighting019_x264.mp4', 'Fighting020_x264.mp4', 'Fighting021_x264.mp4', 'Fighting022_x264.mp4', 'Fighting023_x264.mp4', 'Fighting024_x264.mp4', 'Fighting025_x264.mp4', 'Fighting026_x264.mp4', 'Fighting027_x264.mp4', 'Fighting028_x264.mp4', 'Fighting029_x264.mp4', 'Fighting030_x264.mp4', 'Fighting031_x264.mp4', 'Fighting032_x264.mp4', 'Fighting033_x264.mp4', 'Fighting034_x264.mp4', 'Fighting035_x264.mp4', 'Fighting036_x264.mp4', 'Fighting037_x264.mp4', 'Fighting038_x264.mp4', 'Fighting039_x264.mp4', 'Fighting040_x264.mp4', 'Fighting041_x264.mp4', 'Fighting042_x264.mp4', 'Fighting043_x264.mp4', 'Fighting044_x264.mp4', 'Fighting045_x264.mp4', 'Fighting046_x264.mp4', 'Fighting047_x264.mp4', 'Fighting048_x264.mp4', 'Fighting049_x264.mp4', 'Fighting050_x264.mp4', 'Fighting051_x264.mp4']\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "Fighting feature extracting ended. Elapsed time: 1:12:11\n", + "RoadAccidents feature extracting starts\n", + "==>> file_list: ['RoadAccidents001_x264.mp4', 'RoadAccidents002_x264.mp4', 'RoadAccidents003_x264.mp4', 'RoadAccidents004_x264.mp4', 'RoadAccidents005_x264.mp4', 'RoadAccidents006_x264.mp4', 'RoadAccidents007_x264.mp4', 'RoadAccidents008_x264.mp4', 'RoadAccidents009_x264.mp4', 'RoadAccidents010_x264.mp4', 'RoadAccidents011_x264.mp4', 'RoadAccidents012_x264.mp4', 'RoadAccidents013_x264.mp4', 'RoadAccidents014_x264.mp4', 'RoadAccidents015_x264.mp4', 'RoadAccidents016_x264.mp4', 'RoadAccidents017_x264.mp4', 'RoadAccidents018_x264.mp4', 'RoadAccidents019_x264.mp4', 'RoadAccidents020_x264.mp4', 'RoadAccidents021_x264.mp4', 'RoadAccidents022_x264.mp4', 'RoadAccidents023_x264.mp4', 'RoadAccidents024_x264.mp4', 'RoadAccidents025_x264.mp4', 'RoadAccidents026_x264.mp4', 'RoadAccidents027_x264.mp4', 'RoadAccidents028_x264.mp4', 'RoadAccidents029_x264.mp4', 'RoadAccidents030_x264.mp4', 'RoadAccidents031_x264.mp4', 'RoadAccidents032_x264.mp4', 'RoadAccidents033_x264.mp4', 'RoadAccidents034_x264.mp4', 'RoadAccidents035_x264.mp4', 'RoadAccidents036_x264.mp4', 'RoadAccidents037_x264.mp4', 'RoadAccidents038_x264.mp4', 'RoadAccidents039_x264.mp4', 'RoadAccidents040_x264.mp4', 'RoadAccidents041_x264.mp4', 'RoadAccidents042_x264.mp4', 'RoadAccidents043_x264.mp4', 'RoadAccidents044_x264.mp4', 'RoadAccidents046_x264.mp4', 'RoadAccidents047_x264.mp4', 'RoadAccidents048_x264.mp4', 'RoadAccidents049_x264.mp4', 'RoadAccidents050_x264.mp4', 'RoadAccidents051_x264.mp4', 'RoadAccidents052_x264.mp4', 'RoadAccidents053_x264.mp4', 'RoadAccidents054_x264.mp4', 'RoadAccidents055_x264.mp4', 'RoadAccidents056_x264.mp4', 'RoadAccidents057_x264.mp4', 'RoadAccidents058_x264.mp4', 'RoadAccidents059_x264.mp4', 'RoadAccidents060_x264.mp4', 'RoadAccidents061_x264.mp4', 'RoadAccidents062_x264.mp4', 'RoadAccidents063_x264.mp4', 'RoadAccidents064_x264.mp4', 'RoadAccidents065_x264.mp4', 'RoadAccidents066_x264.mp4', 'RoadAccidents067_x264.mp4', 'RoadAccidents068_x264.mp4', 'RoadAccidents069_x264.mp4', 'RoadAccidents070_x264.mp4', 'RoadAccidents071_x264.mp4', 'RoadAccidents072_x264.mp4', 'RoadAccidents073_x264.mp4', 'RoadAccidents074_x264.mp4', 'RoadAccidents075_x264.mp4', 'RoadAccidents076_x264.mp4', 'RoadAccidents077_x264.mp4', 'RoadAccidents078_x264.mp4', 'RoadAccidents079_x264.mp4', 'RoadAccidents080_x264.mp4', 'RoadAccidents081_x264.mp4', 'RoadAccidents082_x264.mp4', 'RoadAccidents083_x264.mp4', 'RoadAccidents084_x264.mp4', 'RoadAccidents085_x264.mp4', 'RoadAccidents086_x264.mp4', 'RoadAccidents087_x264.mp4', 'RoadAccidents088_x264.mp4', 'RoadAccidents089_x264.mp4', 'RoadAccidents090_x264.mp4', 'RoadAccidents091_x264.mp4', 'RoadAccidents092_x264.mp4', 'RoadAccidents093_x264.mp4', 'RoadAccidents094_x264.mp4', 'RoadAccidents095_x264.mp4', 'RoadAccidents096_x264.mp4', 'RoadAccidents097_x264.mp4', 'RoadAccidents098_x264.mp4', 'RoadAccidents099_x264.mp4', 'RoadAccidents100_x264.mp4', 'RoadAccidents101_x264.mp4', 'RoadAccidents102_x264.mp4', 'RoadAccidents103_x264.mp4', 'RoadAccidents104_x264.mp4', 'RoadAccidents105_x264.mp4', 'RoadAccidents106_x264.mp4', 'RoadAccidents107_x264.mp4', 'RoadAccidents108_x264.mp4', 'RoadAccidents109_x264.mp4', 'RoadAccidents110_x264.mp4', 'RoadAccidents111_x264.mp4', 'RoadAccidents112_x264.mp4', 'RoadAccidents113_x264.mp4', 'RoadAccidents114_x264.mp4', 'RoadAccidents115_x264.mp4', 'RoadAccidents116_x264.mp4', 'RoadAccidents117_x264.mp4', 'RoadAccidents118_x264.mp4', 'RoadAccidents119_x264.mp4', 'RoadAccidents120_x264.mp4', 'RoadAccidents121_x264.mp4', 'RoadAccidents122_x264.mp4', 'RoadAccidents123_x264.mp4', 'RoadAccidents124_x264.mp4', 'RoadAccidents125_x264.mp4', 'RoadAccidents126_x264.mp4', 'RoadAccidents127_x264.mp4', 'RoadAccidents128_x264.mp4', 'RoadAccidents129_x264.mp4', 'RoadAccidents130_x264.mp4', 'RoadAccidents131_x264.mp4', 'RoadAccidents132_x264.mp4', 'RoadAccidents133_x264.mp4', 'RoadAccidents134_x264.mp4', 'RoadAccidents135_x264.mp4', 'RoadAccidents136_x264.mp4', 'RoadAccidents137_x264.mp4', 'RoadAccidents138_x264.mp4', 'RoadAccidents139_x264.mp4', 'RoadAccidents140_x264.mp4', 'RoadAccidents141_x264.mp4', 'RoadAccidents142_x264.mp4', 'RoadAccidents143_x264.mp4', 'RoadAccidents144_x264.mp4', 'RoadAccidents145_x264.mp4', 'RoadAccidents146_x264.mp4', 'RoadAccidents147_x264.mp4', 'RoadAccidents148_x264.mp4', 'RoadAccidents149_x264.mp4', 'RoadAccidents150_x264.mp4', 'RoadAccidents151_x264.mp4']\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "RoadAccidents feature extracting ended. Elapsed time: 0:55:41\n", + "Robbery feature extracting starts\n", + "==>> file_list: ['Robbery001_x264.mp4', 'Robbery002_x264.mp4', 'Robbery003_x264.mp4', 'Robbery004_x264.mp4', 'Robbery005_x264.mp4', 'Robbery006_x264.mp4', 'Robbery007_x264.mp4', 'Robbery008_x264.mp4', 'Robbery009_x264.mp4', 'Robbery010_x264.mp4', 'Robbery011_x264.mp4', 'Robbery012_x264.mp4', 'Robbery013_x264.mp4', 'Robbery014_x264.mp4', 'Robbery015_x264.mp4', 'Robbery016_x264.mp4', 'Robbery017_x264.mp4', 'Robbery018_x264.mp4', 'Robbery019_x264.mp4', 'Robbery020_x264.mp4', 'Robbery021_x264.mp4', 'Robbery022_x264.mp4', 'Robbery023_x264.mp4', 'Robbery024_x264.mp4', 'Robbery025_x264.mp4', 'Robbery026_x264.mp4', 'Robbery027_x264.mp4', 'Robbery028_x264.mp4', 'Robbery029_x264.mp4', 'Robbery030_x264.mp4', 'Robbery031_x264.mp4', 'Robbery032_x264.mp4', 'Robbery033_x264.mp4', 'Robbery034_x264.mp4', 'Robbery035_x264.mp4', 'Robbery036_x264.mp4', 'Robbery037_x264.mp4', 'Robbery038_x264.mp4', 'Robbery039_x264.mp4', 'Robbery040_x264.mp4', 'Robbery041_x264.mp4', 'Robbery042_x264.mp4', 'Robbery043_x264.mp4', 'Robbery044_x264.mp4', 'Robbery045_x264.mp4', 'Robbery046_x264.mp4', 'Robbery047_x264.mp4', 'Robbery048_x264.mp4', 'Robbery049_x264.mp4', 'Robbery050_x264.mp4', 'Robbery051_x264.mp4', 'Robbery052_x264.mp4', 'Robbery053_x264.mp4', 'Robbery054_x264.mp4', 'Robbery055_x264.mp4', 'Robbery056_x264.mp4', 'Robbery057_x264.mp4', 'Robbery058_x264.mp4', 'Robbery059_x264.mp4', 'Robbery060_x264.mp4', 'Robbery061_x264.mp4', 'Robbery062_x264.mp4', 'Robbery063_x264.mp4', 'Robbery064_x264.mp4', 'Robbery065_x264.mp4', 'Robbery066_x264.mp4', 'Robbery067_x264.mp4', 'Robbery068_x264.mp4', 'Robbery069_x264.mp4', 'Robbery070_x264.mp4', 'Robbery071_x264.mp4', 'Robbery072_x264.mp4', 'Robbery073_x264.mp4', 'Robbery074_x264.mp4', 'Robbery075_x264.mp4', 'Robbery076_x264.mp4', 'Robbery077_x264.mp4', 'Robbery078_x264.mp4', 'Robbery079_x264.mp4', 'Robbery080_x264.mp4', 'Robbery081_x264.mp4', 'Robbery082_x264.mp4', 'Robbery083_x264.mp4', 'Robbery084_x264.mp4', 'Robbery085_x264.mp4', 'Robbery086_x264.mp4', 'Robbery087_x264.mp4', 'Robbery088_x264.mp4', 'Robbery089_x264.mp4', 'Robbery090_x264.mp4', 'Robbery091_x264.mp4', 'Robbery092_x264.mp4', 'Robbery093_x264.mp4', 'Robbery094_x264.mp4', 'Robbery095_x264.mp4', 'Robbery096_x264.mp4', 'Robbery097_x264.mp4', 'Robbery098_x264.mp4', 'Robbery099_x264.mp4', 'Robbery100_x264.mp4', 'Robbery101_x264.mp4', 'Robbery102_x264.mp4', 'Robbery103_x264.mp4', 'Robbery104_x264.mp4', 'Robbery105_x264.mp4', 'Robbery106_x264.mp4', 'Robbery107_x264.mp4', 'Robbery108_x264.mp4', 'Robbery109_x264.mp4', 'Robbery110_x264.mp4', 'Robbery111_x264.mp4', 'Robbery112_x264.mp4', 'Robbery113_x264.mp4', 'Robbery114_x264.mp4', 'Robbery115_x264.mp4', 'Robbery116_x264.mp4', 'Robbery117_x264.mp4', 'Robbery118_x264.mp4', 'Robbery119_x264.mp4', 'Robbery120_x264.mp4', 'Robbery121_x264.mp4', 'Robbery122_x264.mp4', 'Robbery123_x264.mp4', 'Robbery124_x264.mp4', 'Robbery125_x264.mp4', 'Robbery126_x264.mp4', 'Robbery127_x264.mp4', 'Robbery128_x264.mp4', 'Robbery129_x264.mp4', 'Robbery130_x264.mp4', 'Robbery131_x264.mp4', 'Robbery132_x264.mp4', 'Robbery133_x264.mp4', 'Robbery134_x264.mp4', 'Robbery135_x264.mp4', 'Robbery136_x264.mp4', 'Robbery137_x264.mp4', 'Robbery138_x264.mp4', 'Robbery139_x264.mp4', 'Robbery140_x264.mp4', 'Robbery141_x264.mp4', 'Robbery142_x264.mp4', 'Robbery143_x264.mp4', 'Robbery144_x264.mp4', 'Robbery145_x264.mp4', 'Robbery146_x264.mp4', 'Robbery147_x264.mp4', 'Robbery148_x264.mp4', 'Robbery149_x264.mp4', 'Robbery150_x264.mp4']\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "Robbery feature extracting ended. Elapsed time: 1:55:40\n", + "Shooting feature extracting starts\n", + "==>> file_list: ['Shooting001_x264.mp4', 'Shooting002_x264.mp4', 'Shooting003_x264.mp4', 'Shooting004_x264.mp4', 'Shooting005_x264.mp4', 'Shooting006_x264.mp4', 'Shooting007_x264.mp4', 'Shooting008_x264.mp4', 'Shooting009_x264.mp4', 'Shooting010_x264.mp4', 'Shooting011_x264.mp4', 'Shooting012_x264.mp4', 'Shooting013_x264.mp4', 'Shooting014_x264.mp4', 'Shooting015_x264.mp4', 'Shooting017_x264.mp4', 'Shooting018_x264.mp4', 'Shooting019_x264.mp4', 'Shooting020_x264.mp4', 'Shooting021_x264.mp4', 'Shooting022_x264.mp4', 'Shooting023_x264.mp4', 'Shooting024_x264.mp4', 'Shooting025_x264.mp4', 'Shooting026_x264.mp4', 'Shooting027_x264.mp4', 'Shooting028_x264.mp4', 'Shooting029_x264.mp4', 'Shooting030_x264.mp4', 'Shooting031_x264.mp4', 'Shooting032_x264.mp4', 'Shooting033_x264.mp4', 'Shooting034_x264.mp4', 'Shooting036_x264.mp4', 'Shooting037_x264.mp4', 'Shooting038_x264.mp4', 'Shooting039_x264.mp4', 'Shooting040_x264.mp4', 'Shooting041_x264.mp4', 'Shooting042_x264.mp4', 'Shooting043_x264.mp4', 'Shooting044_x264.mp4', 'Shooting046_x264.mp4', 'Shooting047_x264.mp4', 'Shooting048_x264.mp4', 'Shooting050_x264.mp4', 'Shooting051_x264.mp4', 'Shooting052_x264.mp4', 'Shooting053_x264.mp4', 'Shooting054_x264.mp4']\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "Shooting feature extracting ended. Elapsed time: 0:35:42\n", + "Shoplifting feature extracting starts\n", + "==>> file_list: ['Shoplifting001_x264.mp4', 'Shoplifting003_x264.mp4', 'Shoplifting004_x264.mp4', 'Shoplifting005_x264.mp4', 'Shoplifting006_x264.mp4', 'Shoplifting007_x264.mp4', 'Shoplifting008_x264.mp4', 'Shoplifting009_x264.mp4', 'Shoplifting010_x264.mp4', 'Shoplifting012_x264.mp4', 'Shoplifting013_x264.mp4', 'Shoplifting014_x264.mp4', 'Shoplifting015_x264.mp4', 'Shoplifting016_x264.mp4', 'Shoplifting017_x264.mp4', 'Shoplifting018_x264.mp4', 'Shoplifting019_x264.mp4', 'Shoplifting020_x264.mp4', 'Shoplifting021_x264.mp4', 'Shoplifting022_x264.mp4', 'Shoplifting024_x264.mp4', 'Shoplifting025_x264.mp4', 'Shoplifting026_x264.mp4', 'Shoplifting027_x264.mp4', 'Shoplifting028_x264.mp4', 'Shoplifting029_x264.mp4', 'Shoplifting030_x264.mp4', 'Shoplifting031_x264.mp4', 'Shoplifting032_x264.mp4', 'Shoplifting033_x264.mp4', 'Shoplifting034_x264.mp4', 'Shoplifting036_x264.mp4', 'Shoplifting037_x264.mp4', 'Shoplifting038_x264.mp4', 'Shoplifting039_x264.mp4', 'Shoplifting040_x264.mp4', 'Shoplifting041_x264.mp4', 'Shoplifting042_x264.mp4', 'Shoplifting043_x264.mp4', 'Shoplifting044_x264.mp4', 'Shoplifting045_x264.mp4', 'Shoplifting047_x264.mp4', 'Shoplifting048_x264.mp4', 'Shoplifting049_x264.mp4', 'Shoplifting050_x264.mp4', 'Shoplifting051_x264.mp4', 'Shoplifting052_x264.mp4', 'Shoplifting053_x264.mp4', 'Shoplifting054_x264.mp4', 'Shoplifting055_x264.mp4']\n", + "Shoplifting feature extracting ended. Elapsed time: 1:42:18\n", + "Stealing feature extracting starts\n", + "==>> file_list: ['Stealing002_x264.mp4', 'Stealing003_x264.mp4', 'Stealing004_x264.mp4', 'Stealing006_x264.mp4', 'Stealing007_x264.mp4', 'Stealing008_x264.mp4', 'Stealing009_x264.mp4', 'Stealing010_x264.mp4', 'Stealing011_x264.mp4', 'Stealing012_x264.mp4', 'Stealing013_x264.mp4', 'Stealing014_x264.mp4', 'Stealing015_x264.mp4', 'Stealing016_x264.mp4', 'Stealing017_x264.mp4', 'Stealing018_x264.mp4', 'Stealing019_x264.mp4', 'Stealing020_x264.mp4', 'Stealing021_x264.mp4', 'Stealing022_x264.mp4', 'Stealing023_x264.mp4', 'Stealing024_x264.mp4', 'Stealing025_x264.mp4', 'Stealing026_x264.mp4', 'Stealing027_x264.mp4', 'Stealing028_x264.mp4', 'Stealing029_x264.mp4', 'Stealing030_x264.mp4', 'Stealing031_x264.mp4', 'Stealing032_x264.mp4', 'Stealing035_x264.mp4', 'Stealing036_x264.mp4', 'Stealing037_x264.mp4', 'Stealing042_x264.mp4', 'Stealing043_x264.mp4', 'Stealing044_x264.mp4', 'Stealing045_x264.mp4', 'Stealing046_x264.mp4', 'Stealing047_x264.mp4', 'Stealing048_x264.mp4', 'Stealing049_x264.mp4', 'Stealing050_x264.mp4', 'Stealing051_x264.mp4', 'Stealing052_x264.mp4', 'Stealing053_x264.mp4', 'Stealing054_x264.mp4', 'Stealing055_x264.mp4', 'Stealing057_x264.mp4', 'Stealing058_x264.mp4', 'Stealing059_x264.mp4', 'Stealing060_x264.mp4', 'Stealing061_x264.mp4', 'Stealing062_x264.mp4', 'Stealing063_x264.mp4', 'Stealing065_x264.mp4', 'Stealing066_x264.mp4', 'Stealing067_x264.mp4', 'Stealing068_x264.mp4', 'Stealing069_x264.mp4', 'Stealing070_x264.mp4', 'Stealing071_x264.mp4', 'Stealing072_x264.mp4', 'Stealing073_x264.mp4', 'Stealing074_x264.mp4', 'Stealing075_x264.mp4', 'Stealing077_x264.mp4', 'Stealing078_x264.mp4', 'Stealing079_x264.mp4', 'Stealing080_x264.mp4', 'Stealing081_x264.mp4', 'Stealing082_x264.mp4', 'Stealing083_x264.mp4', 'Stealing084_x264.mp4', 'Stealing086_x264.mp4', 'Stealing087_x264.mp4', 'Stealing088_x264.mp4', 'Stealing089_x264.mp4', 'Stealing091_x264.mp4', 'Stealing092_x264.mp4', 'Stealing093_x264.mp4', 'Stealing094_x264.mp4', 'Stealing095_x264.mp4', 'Stealing096_x264.mp4', 'Stealing097_x264.mp4', 'Stealing098_x264.mp4', 'Stealing100_x264.mp4', 'Stealing101_x264.mp4', 'Stealing102_x264.mp4', 'Stealing103_x264.mp4', 'Stealing104_x264.mp4', 'Stealing105_x264.mp4', 'Stealing106_x264.mp4', 'Stealing107_x264.mp4', 'Stealing108_x264.mp4', 'Stealing109_x264.mp4', 'Stealing110_x264.mp4', 'Stealing111_x264.mp4', 'Stealing112_x264.mp4', 'Stealing113_x264.mp4', 'Stealing114_x264.mp4']\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "Stealing feature extracting ended. Elapsed time: 1:44:04\n", + "Vandalism feature extracting starts\n", + "==>> file_list: ['Vandalism001_x264.mp4', 'Vandalism002_x264.mp4', 'Vandalism003_x264.mp4', 'Vandalism004_x264.mp4', 'Vandalism005_x264.mp4', 'Vandalism006_x264.mp4', 'Vandalism007_x264.mp4', 'Vandalism008_x264.mp4', 'Vandalism009_x264.mp4', 'Vandalism010_x264.mp4', 'Vandalism011_x264.mp4', 'Vandalism012_x264.mp4', 'Vandalism013_x264.mp4', 'Vandalism014_x264.mp4', 'Vandalism015_x264.mp4', 'Vandalism016_x264.mp4', 'Vandalism017_x264.mp4', 'Vandalism018_x264.mp4', 'Vandalism019_x264.mp4', 'Vandalism020_x264.mp4', 'Vandalism021_x264.mp4', 'Vandalism022_x264.mp4', 'Vandalism023_x264.mp4', 'Vandalism024_x264.mp4', 'Vandalism025_x264.mp4', 'Vandalism026_x264.mp4', 'Vandalism027_x264.mp4', 'Vandalism028_x264.mp4', 'Vandalism029_x264.mp4', 'Vandalism030_x264.mp4', 'Vandalism031_x264.mp4', 'Vandalism032_x264.mp4', 'Vandalism033_x264.mp4', 'Vandalism034_x264.mp4', 'Vandalism035_x264.mp4', 'Vandalism036_x264.mp4', 'Vandalism037_x264.mp4', 'Vandalism038_x264.mp4', 'Vandalism039_x264.mp4', 'Vandalism040_x264.mp4', 'Vandalism041_x264.mp4', 'Vandalism042_x264.mp4', 'Vandalism043_x264.mp4', 'Vandalism044_x264.mp4', 'Vandalism045_x264.mp4', 'Vandalism046_x264.mp4', 'Vandalism047_x264.mp4', 'Vandalism048_x264.mp4', 'Vandalism049_x264.mp4', 'Vandalism050_x264.mp4']\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "WARNING: not enough matching points\n", + "Vandalism feature extracting ended. Elapsed time: 0:36:05\n" + ] + } + ], + "source": [ + "for folder_name in folder_list:\n", + " time_start = datetime.now()\n", + "\n", + " print(f\"{folder_name} feature extracting starts\")\n", + "\n", + " with open(f\"{folder_name}.csv\", \"w\") as c_file:\n", + " writer = csv.writer(c_file, delimiter=\",\")\n", + "\n", + " writer.writerow(header)\n", + "\n", + " folder_path = root + folder_name + \"/\"\n", + "\n", + " file_list = os.listdir(root+folder_name)\n", + " file_list.sort()\n", + " print(f\"==>> file_list: {file_list}\")\n", + "\n", + " id_count = 0\n", + "\n", + " for file_name in file_list:\n", + " path = folder_path + file_name\n", + "\n", + " cap = cv2.VideoCapture(path)\n", + "\n", + " # Loop through the video frames\n", + " frame_count = 0\n", + "\n", + " # Store the track history\n", + " track_history = defaultdict(lambda: [])\n", + "\n", + " while cap.isOpened():\n", + " # Read a frame from the video\n", + " success, frame = cap.read()\n", + "\n", + " frame_count += 1 # Increment frame count\n", + "\n", + " if success:\n", + " frame = cv2.resize(frame, (standard_width, standard_height))\n", + "\n", + " # Run YOLOv8 tracking on the frame, persisting tracks between frames\n", + " results = model.track(frame, persist=True, verbose=False)\n", + "\n", + " if results[0].boxes is not None: # Check if there are results and boxes\n", + " # Get the boxes\n", + " # boxes = results[0].boxes.xywh.cpu()\n", + "\n", + " if results[0].boxes.id is not None:\n", + " # If 'int' attribute exists (there are detections), get the track IDs\n", + " track_ids = results[0].boxes.id.int().cpu().tolist()\n", + "\n", + " for i, box in zip(range(0, len(track_ids)), results[0].boxes.xywhn.cpu()):\n", + " keypoints = results[0].keypoints.xyn[i].cpu().numpy().flatten().tolist()\n", + " box_list = box.numpy().flatten().tolist()\n", + " if type(box_list) == \"float\" or type(keypoints) == \"float\":\n", + " print(f\"==>> box_list: {box_list}\")\n", + " print(f\"==>> keypoints: {keypoints}\")\n", + " box_and_keypoints = box_list + keypoints\n", + " track_history[track_ids[i]].append([[frame_count], deepcopy(box_and_keypoints)])\n", + " else:\n", + " # Break the loop if the end of the video is reached\n", + " break\n", + "\n", + " with open(f\"{folder_name}.csv\", \"a\") as c_file:\n", + " writer = csv.writer(c_file, delimiter=\",\")\n", + " for key in track_history.keys():\n", + " for f_count, b_and_k in track_history[key]:\n", + " row = [file_name] + f_count + [id_count + key] + b_and_k\n", + "\n", + " writer.writerow(row)\n", + "\n", + " id_count = id_count + len(track_history.keys())\n", + "\n", + " cap.release()\n", + "\n", + " time_end = datetime.now()\n", + " total_time = time_end - time_start\n", + " total_time = str(total_time).split(\".\")[0]\n", + "\n", + " print(f\"{folder_name} feature extracting ended. Elapsed time: {total_time}\")\n", + "\n", + "# cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/datapreprocess/feature_extraction.py b/datapreprocess/feature_extraction.py new file mode 100644 index 0000000..5bf3306 --- /dev/null +++ b/datapreprocess/feature_extraction.py @@ -0,0 +1,164 @@ +from collections import defaultdict +import cv2 +import numpy as np +from ultralytics import YOLO +import csv +import os +from copy import deepcopy + +from datetime import datetime +import argparse +from tqdm import tqdm + +parser = argparse.ArgumentParser(description="Feature Extraction") + +parser.add_argument( + "--root", type=str, help="root folder path", default="/data/ephemeral/home/datasets/UCFCrime/normal/" +) + +args = parser.parse_args() + +# root = args.root + +root = "/data/ephemeral/home/datasets/UCFCrime/normal/" + +header = [ + "Filename", + "Frame", + "ID", + "X", + "Y", + "Width", + "Height", + "Keypoint_0", + "Keypoint_1", + "Keypoint_2", + "Keypoint_3", + "Keypoint_4", + "Keypoint_5", + "Keypoint_6", + "Keypoint_7", + "Keypoint_8", + "Keypoint_9", + "Keypoint_10", + "Keypoint_11", + "Keypoint_12", + "Keypoint_13", + "Keypoint_14", + "Keypoint_15", + "Keypoint_16", + "Keypoint_17", + "Keypoint_18", + "Keypoint_19", + "Keypoint_20", + "Keypoint_21", + "Keypoint_22", + "Keypoint_23", + "Keypoint_24", + "Keypoint_25", + "Keypoint_26", + "Keypoint_27", + "Keypoint_28", + "Keypoint_29", + "Keypoint_30", + "Keypoint_31", + "Keypoint_32", + "Keypoint_33", +] + + +def feat_extraction(): + # Load the YOLOv8 model + model = YOLO("yolov8n-pose.pt") + + # Define the standard frame size (change these values as needed) + standard_width = 320 + standard_height = 240 + + folder_list = os.listdir(root) + folder_list.sort() + print(f"==>> folder_list: {folder_list}") + + for folder_name in folder_list: + time_start = datetime.now() + + print(f"{folder_name} feature extracting starts") + + with open(f"{folder_name}.csv", "w") as c_file: + writer = csv.writer(c_file, delimiter=",") + + writer.writerow(header) + + folder_path = root + folder_name + "/" + + file_list = os.listdir(root + folder_name) + file_list.sort() + print(f"==>> file_list: {file_list}") + + id_count = 0 + + for file_name in tqdm(file_list, total=len(file_list)): + path = folder_path + file_name + + cap = cv2.VideoCapture(path) + + # Loop through the video frames + frame_count = 0 + + # Store the track history + track_history = defaultdict(lambda: []) + + while cap.isOpened(): + # Read a frame from the video + success, frame = cap.read() + + frame_count += 1 # Increment frame count + + if success: + frame = cv2.resize(frame, (standard_width, standard_height)) + + # Run YOLOv8 tracking on the frame, persisting tracks between frames + results = model.track(frame, persist=True, verbose=False) + + if results[0].boxes is not None: # Check if there are results and boxes + # Get the boxes + # boxes = results[0].boxes.xywh.cpu() + + if results[0].boxes.id is not None: + # If 'int' attribute exists (there are detections), get the track IDs + track_ids = results[0].boxes.id.int().cpu().tolist() + + for i, box in zip(range(0, len(track_ids)), results[0].boxes.xywhn.cpu()): + keypoints = results[0].keypoints.xyn[i].cpu().numpy().flatten().tolist() + box_list = box.numpy().flatten().tolist() + if type(box_list) == "float" or type(keypoints) == "float": + print(f"==>> box_list: {box_list}") + print(f"==>> keypoints: {keypoints}") + box_and_keypoints = box_list + keypoints + track_history[track_ids[i]].append( + [[frame_count], deepcopy(box_and_keypoints)] + ) + else: + # Break the loop if the end of the video is reached + break + + with open(f"{folder_name}.csv", "a") as c_file: + writer = csv.writer(c_file, delimiter=",") + for key in track_history.keys(): + for f_count, b_and_k in track_history[key]: + row = [file_name] + f_count + [id_count + key] + b_and_k + + writer.writerow(row) + + id_count = id_count + len(track_history.keys()) + + cap.release() + + time_end = datetime.now() + total_time = time_end - time_start + total_time = str(total_time).split(".")[0] + + print(f"{folder_name} feature extracting ended. Elapsed time: {total_time}") + + +feat_extraction() diff --git a/datapreprocess/feature_extraction_onebyone.py b/datapreprocess/feature_extraction_onebyone.py new file mode 100644 index 0000000..b48aeb0 --- /dev/null +++ b/datapreprocess/feature_extraction_onebyone.py @@ -0,0 +1,177 @@ +from collections import defaultdict +import cv2 +import numpy as np +from ultralytics import YOLO +import csv +import os +from copy import deepcopy + +from datetime import datetime +import argparse +from tqdm import tqdm + +parser = argparse.ArgumentParser(description="Feature Extraction") + +parser.add_argument( + "--root", type=str, help="root folder path", default="/data/ephemeral/home/datasets/UCFCrime/normal/" +) + +args = parser.parse_args() + +# root = args.root + +root = "/data/ephemeral/home/datasets/UCFCrime/normal/" + +header = [ + "Filename", + "Frame", + "ID", + "X", + "Y", + "Width", + "Height", + "Keypoint_0", + "Keypoint_1", + "Keypoint_2", + "Keypoint_3", + "Keypoint_4", + "Keypoint_5", + "Keypoint_6", + "Keypoint_7", + "Keypoint_8", + "Keypoint_9", + "Keypoint_10", + "Keypoint_11", + "Keypoint_12", + "Keypoint_13", + "Keypoint_14", + "Keypoint_15", + "Keypoint_16", + "Keypoint_17", + "Keypoint_18", + "Keypoint_19", + "Keypoint_20", + "Keypoint_21", + "Keypoint_22", + "Keypoint_23", + "Keypoint_24", + "Keypoint_25", + "Keypoint_26", + "Keypoint_27", + "Keypoint_28", + "Keypoint_29", + "Keypoint_30", + "Keypoint_31", + "Keypoint_32", + "Keypoint_33", +] + +csv_root = "./UCF_csv/" + +if not os.path.exists(csv_root): + os.makedirs(csv_root) + + +def feat_extraction(): + # Load the YOLOv8 model + model = YOLO("yolov8n-pose.pt") + + # Define the standard frame size (change these values as needed) + standard_width = 320 + standard_height = 240 + + folder_list = os.listdir(root) + folder_list.sort() + print(f"==>> folder_list: {folder_list}") + + for folder_name in folder_list: + time_start = datetime.now() + + print(f"{folder_name} feature extracting starts") + + if not os.path.exists(csv_root + folder_name): + os.makedirs(csv_root + folder_name) + + # with open(f"{folder_name}.csv", "w") as c_file: + # writer = csv.writer(c_file, delimiter=",") + + # writer.writerow(header) + + folder_path = root + folder_name + "/" + + file_list = os.listdir(root + folder_name) + file_list.sort() + print(f"==>> file_list: {file_list}") + + id_count = 0 + + for file_name in tqdm(file_list, total=len(file_list)): + path = folder_path + file_name + + with open(csv_root + folder_name + "/" + f"{file_name}.csv", "w") as c_file: + writer = csv.writer(c_file, delimiter=",") + + writer.writerow(header) + + cap = cv2.VideoCapture(path) + + # Loop through the video frames + frame_count = 0 + + # Store the track history + track_history = defaultdict(lambda: []) + + while cap.isOpened(): + # Read a frame from the video + success, frame = cap.read() + + frame_count += 1 # Increment frame count + + if success: + frame = cv2.resize(frame, (standard_width, standard_height)) + + # Run YOLOv8 tracking on the frame, persisting tracks between frames + results = model.track(frame, persist=True, verbose=False) + + if results[0].boxes is not None: # Check if there are results and boxes + # Get the boxes + # boxes = results[0].boxes.xywh.cpu() + + if results[0].boxes.id is not None: + # If 'int' attribute exists (there are detections), get the track IDs + track_ids = results[0].boxes.id.int().cpu().tolist() + + for i, box in zip(range(0, len(track_ids)), results[0].boxes.xywhn.cpu()): + keypoints = results[0].keypoints.xyn[i].cpu().numpy().flatten().tolist() + box_list = box.numpy().flatten().tolist() + if type(box_list) == "float" or type(keypoints) == "float": + print(f"==>> box_list: {box_list}") + print(f"==>> keypoints: {keypoints}") + box_and_keypoints = box_list + keypoints + track_history[track_ids[i]].append( + [[frame_count], deepcopy(box_and_keypoints)] + ) + else: + # Break the loop if the end of the video is reached + break + + with open(csv_root + folder_name + "/" + f"{file_name}.csv", "a") as c_file: + writer = csv.writer(c_file, delimiter=",") + for key in track_history.keys(): + for f_count, b_and_k in track_history[key]: + row = [file_name] + f_count + [id_count + key] + b_and_k + + writer.writerow(row) + + id_count = id_count + len(track_history.keys()) + + cap.release() + + time_end = datetime.now() + total_time = time_end - time_start + total_time = str(total_time).split(".")[0] + + print(f"{folder_name} feature extracting ended. Elapsed time: {total_time}") + + +feat_extraction() diff --git a/datapreprocess/feature_extraction_videoMAEv2.ipynb b/datapreprocess/feature_extraction_videoMAEv2.ipynb new file mode 100644 index 0000000..89f355a --- /dev/null +++ b/datapreprocess/feature_extraction_videoMAEv2.ipynb @@ -0,0 +1,553 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# from collections import defaultdict\n", + "import cv2\n", + "import numpy as np\n", + "import os\n", + "from copy import deepcopy\n", + "\n", + "from datetime import datetime\n", + "\n", + "import torch\n", + "# import torch.nn as nn\n", + "# import torch.nn.functional as F\n", + "\n", + "import albumentations as A\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# from datetime import datetime\n", + "\n", + "import models\n", + "from timm.models import create_model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "root = \"/data/ephemeral/home/datasets/UCFCrime/normal/\"\n", + "\n", + "\n", + "npy_root = \"./npy/\"\n", + "\n", + "if not os.path.exists(npy_root):\n", + " os.makedirs(npy_root)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==>> folder_list: ['VS_03.이상행동_07.전도', 'VS_03.이상행동_08.파손', 'VS_03.이상행동_09.방화', 'VS_03.이상행동_10.흡연', 'VS_03.이상행동_11.유기', 'VS_03.이상행동_12.절도', 'VS_03.이상행동_13.폭행', 'VS_03.이상행동_14.교통약자']\n" + ] + } + ], + "source": [ + "folder_list = os.listdir(root)\n", + "folder_list.sort()\n", + "print(f\"==>> folder_list: {folder_list}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "segments_num = 1\n", + "# 모델에 들어갈 frame수는 16 * segments_num\n", + "\n", + "model = create_model(\n", + " \"vit_small_patch16_224\",\n", + " # \"vit_base_patch16_224\",\n", + " img_size=224,\n", + " pretrained=False,\n", + " num_classes=710,\n", + " all_frames=16 * segments_num,\n", + " # tubelet_size=args.tubelet_size,\n", + " # drop_rate=args.drop,\n", + " # drop_path_rate=args.drop_path,\n", + " # attn_drop_rate=args.attn_drop_rate,\n", + " # head_drop_rate=args.head_drop_rate,\n", + " # drop_block_rate=None,\n", + " # use_mean_pooling=args.use_mean_pooling,\n", + " # init_scale=args.init_scale,\n", + " # with_cp=args.with_checkpoint,\n", + ")\n", + "\n", + "load_dict = torch.load(\n", + " \"/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/vit_s_k710_dl_from_giant.pth\"\n", + ")\n", + "# load_dict = torch.load(\n", + "# \"/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/vit_b_k710_dl_from_giant.pth\"\n", + "# )\n", + "# backbone pth 경로\n", + "\n", + "model.load_state_dict(load_dict[\"module\"])\n", + "\n", + "model.to(\"cuda\")\n", + "model.eval()\n", + "\n", + "tf = A.Resize(224, 224)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VS_03.이상행동_07.전도 feature extracting starts\n", + "==>> file_list: ['C_3_7_48_BU_DYB_10-17_10-23-53_CB_RGB_DF2_M2.mp4', 'C_3_7_48_BU_DYB_10-17_10-23-53_CC_RGB_DF2_M2.mp4', 'C_3_7_48_BU_DYB_10-17_10-23-53_CD_RGB_DF2_M2.mp4', 'C_3_7_48_BU_DYB_10-17_10-23-53_CE_RGB_DF2_M2.mp4', 'C_3_7_48_BU_SMC_10-14_09-52-07_CB_RGB_DF2_M2.mp4', 'C_3_7_48_BU_SMC_10-14_09-52-07_CD_RGB_DF2_M2.mp4', 'C_3_7_48_BU_SMC_10-14_09-52-07_CE_RGB_DF2_M2.mp4', 'C_3_7_49_BU_DYB_10-17_10-25-31_CB_RGB_DF2_M2.mp4', 'C_3_7_49_BU_DYB_10-17_10-25-31_CC_RGB_DF2_M2.mp4', 'C_3_7_49_BU_DYB_10-17_10-25-31_CD_RGB_DF2_M2.mp4', 'C_3_7_49_BU_DYB_10-17_10-25-31_CE_RGB_DF2_M2.mp4', 'C_3_7_49_BU_SMC_10-14_09-59-53_CA_RGB_DF2_F2.mp4', 'C_3_7_49_BU_SMC_10-14_09-59-53_CC_RGB_DF2_F2.mp4', 'C_3_7_49_BU_SMC_10-14_09-59-53_CD_RGB_DF2_F2.mp4', 'C_3_7_50_BU_DYB_10-17_10-28-37_CA_RGB_DF2_F2.mp4', 'C_3_7_50_BU_DYB_10-17_10-28-37_CB_RGB_DF2_F2.mp4', 'C_3_7_50_BU_DYB_10-17_10-28-37_CC_RGB_DF2_F2.mp4', 'C_3_7_50_BU_DYB_10-17_10-28-37_CD_RGB_DF2_F2.mp4', 'C_3_7_50_BU_DYB_10-17_10-28-37_CE_RGB_DF2_F2.mp4', 'C_3_7_50_BU_SMC_10-14_10-16-50_CA_RGB_DF2_F2.mp4', 'C_3_7_50_BU_SMC_10-14_10-16-50_CB_RGB_DF2_F2.mp4', 'C_3_7_50_BU_SMC_10-14_10-16-50_CC_RGB_DF2_F2.mp4', 'C_3_7_50_BU_SMC_10-14_10-16-50_CD_RGB_DF2_F2.mp4', 'C_3_7_50_BU_SMC_10-14_10-16-50_CE_RGB_DF2_F2.mp4', 'C_3_7_51_BU_DYB_10-17_10-30-20_CA_RGB_DF2_F2.mp4', 'C_3_7_51_BU_DYB_10-17_10-30-20_CB_RGB_DF2_F2.mp4', 'C_3_7_51_BU_DYB_10-17_10-30-20_CC_RGB_DF2_F2.mp4', 'C_3_7_51_BU_DYB_10-17_10-30-20_CD_RGB_DF2_F2.mp4', 'C_3_7_51_BU_DYB_10-17_10-30-20_CE_RGB_DF2_F2.mp4', 'C_3_7_51_BU_SMC_10-14_11-51-24_CA_RGB_DF2_F2.mp4', 'C_3_7_51_BU_SMC_10-14_11-51-24_CB_RGB_DF2_F2.mp4', 'C_3_7_51_BU_SMC_10-14_11-51-24_CC_RGB_DF2_F2.mp4', 'C_3_7_51_BU_SMC_10-14_11-51-24_CD_RGB_DF2_F2.mp4', 'C_3_7_52_BU_DYB_10-17_10-31-52_CA_RGB_DF2_F2.mp4', 'C_3_7_52_BU_DYB_10-17_10-31-52_CB_RGB_DF2_F2.mp4', 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Elapsed time: 0:01:34\n", + "VS_03.이상행동_08.파손 feature extracting starts\n", + "==>> file_list: ['C_3_8_53_BU_SMC_10-14_13-30-48_CB_RGB_DF2_F3.mp4', 'C_3_8_54_BU_SMC_10-14_13-32-37_CA_RGB_DF2_F3.mp4', 'C_3_8_54_BU_SMC_10-14_13-32-37_CC_RGB_DF2_F3.mp4', 'C_3_8_54_BU_SMC_10-14_13-32-37_CD_RGB_DF2_F3.mp4', 'C_3_8_54_BU_SMC_10-14_13-32-37_CE_RGB_DF2_F3.mp4', 'C_3_8_55_BU_DYB_10-17_14-19-01_CA_RGB_DF2_M3.mp4', 'C_3_8_55_BU_DYB_10-17_14-19-01_CB_RGB_DF2_M3.mp4', 'C_3_8_55_BU_DYB_10-17_14-19-01_CC_RGB_DF2_M3.mp4', 'C_3_8_55_BU_DYB_10-17_14-19-01_CD_RGB_DF2_M3.mp4', 'C_3_8_55_BU_DYB_10-17_14-19-01_CE_RGB_DF2_M3.mp4', 'C_3_8_55_BU_SMC_10-14_13-35-06_CA_RGB_DF2_F3.mp4', 'C_3_8_55_BU_SMC_10-14_13-35-06_CB_RGB_DF2_F3.mp4', 'C_3_8_55_BU_SMC_10-14_13-35-06_CC_RGB_DF2_F3.mp4', 'C_3_8_55_BU_SMC_10-14_13-35-06_CD_RGB_DF2_F3.mp4', 'C_3_8_55_BU_SMC_10-14_13-35-06_CE_RGB_DF2_F3.mp4', 'C_3_8_56_BU_DYB_10-17_13-16-04_CA_RGB_DF2_M3.mp4', 'C_3_8_56_BU_DYB_10-17_13-16-04_CB_RGB_DF2_M3.mp4', 'C_3_8_56_BU_DYB_10-17_13-16-04_CC_RGB_DF2_M3.mp4', 'C_3_8_56_BU_DYB_10-17_13-16-04_CD_RGB_DF2_M3.mp4', 'C_3_8_56_BU_DYB_10-17_13-16-04_CE_RGB_DF2_M3.mp4', 'C_3_8_56_BU_SMC_10-14_13-39-12_CA_RGB_DF2_F3.mp4', 'C_3_8_56_BU_SMC_10-14_13-39-12_CB_RGB_DF2_F3.mp4', 'C_3_8_56_BU_SMC_10-14_13-39-12_CC_RGB_DF2_F3.mp4', 'C_3_8_56_BU_SMC_10-14_13-39-12_CD_RGB_DF2_F3.mp4', 'C_3_8_56_BU_SMC_10-14_13-39-12_CE_RGB_DF2_F3.mp4', 'C_3_8_57_BU_DYA_08-23_15-06-11_CD_RGB_DF2_M3.mp4', 'C_3_8_57_BU_DYA_08-23_15-06-11_CE_RGB_DF2_M3.mp4', 'C_3_8_57_BU_DYA_08-23_15-06-11_CF_RGB_DF2_M3.mp4', 'C_3_8_57_BU_DYB_10-17_13-18-24_CA_RGB_DF2_M3.mp4', 'C_3_8_57_BU_DYB_10-17_13-18-24_CB_RGB_DF2_M3.mp4', 'C_3_8_57_BU_DYB_10-17_13-18-24_CC_RGB_DF2_M3.mp4', 'C_3_8_57_BU_DYB_10-17_13-18-24_CD_RGB_DF2_M3.mp4', 'C_3_8_57_BU_DYB_10-17_13-18-24_CE_RGB_DF2_M3.mp4', 'C_3_8_57_BU_SMC_10-14_13-40-47_CA_RGB_DF2_F3.mp4', 'C_3_8_57_BU_SMC_10-14_13-40-47_CB_RGB_DF2_F3.mp4', 'C_3_8_57_BU_SMC_10-14_13-40-47_CC_RGB_DF2_F3.mp4', 'C_3_8_57_BU_SMC_10-14_13-40-47_CD_RGB_DF2_F3.mp4', 'C_3_8_57_BU_SMC_10-14_13-40-47_CE_RGB_DF2_F3.mp4', 'C_3_8_58_BU_DYB_10-17_13-20-28_CA_RGB_DF2_M3.mp4', 'C_3_8_58_BU_DYB_10-17_13-20-28_CB_RGB_DF2_M3.mp4', 'C_3_8_58_BU_DYB_10-17_13-20-28_CC_RGB_DF2_M3.mp4', 'C_3_8_58_BU_DYB_10-17_13-20-28_CD_RGB_DF2_M3.mp4', 'C_3_8_58_BU_DYB_10-17_13-20-28_CE_RGB_DF2_M3.mp4', 'C_3_8_58_BU_SMC_10-14_13-44-22_CA_RGB_DF2_F3.mp4', 'C_3_8_58_BU_SMC_10-14_13-44-22_CB_RGB_DF2_F3.mp4', 'C_3_8_58_BU_SMC_10-14_13-44-22_CC_RGB_DF2_F3.mp4', 'C_3_8_58_BU_SMC_10-14_13-44-22_CD_RGB_DF2_F3.mp4', 'C_3_8_58_BU_SMC_10-14_13-44-22_CE_RGB_DF2_F3.mp4', 'C_3_8_59_BU_DYB_10-17_13-22-05_CB_RGB_DF2_M3.mp4', 'C_3_8_59_BU_DYB_10-17_13-22-05_CC_RGB_DF2_M3.mp4', 'C_3_8_59_BU_DYB_10-17_13-22-05_CD_RGB_DF2_M3.mp4', 'C_3_8_59_BU_DYB_10-17_13-22-05_CE_RGB_DF2_M3.mp4', 'C_3_8_59_BU_SMC_10-13_16-36-18_CC_RGB_DF2_M4.mp4', 'C_3_8_59_BU_SMC_10-13_16-36-18_CD_RGB_DF2_M4.mp4', 'C_3_8_59_BU_SMC_10-13_16-36-18_CE_RGB_DF2_M4.mp4', 'C_3_8_60_BU_SMC_10-13_16-39-42_CA_RGB_DF2_M4.mp4', 'C_3_8_60_BU_SMC_10-13_16-39-42_CB_RGB_DF2_M4.mp4', 'C_3_8_60_BU_SMC_10-13_16-39-42_CC_RGB_DF2_M4.mp4', 'C_3_8_60_BU_SMC_10-13_16-39-42_CD_RGB_DF2_M4.mp4', 'C_3_8_60_BU_SMC_10-13_16-39-42_CE_RGB_DF2_M4.mp4', 'C_3_8_61_BU_DYB_10-17_13-26-05_CA_RGB_DF2_F3.mp4', 'C_3_8_61_BU_DYB_10-17_13-26-05_CB_RGB_DF2_F3.mp4', 'C_3_8_61_BU_DYB_10-17_13-26-05_CC_RGB_DF2_F3.mp4', 'C_3_8_61_BU_DYB_10-17_13-26-05_CD_RGB_DF2_F3.mp4', 'C_3_8_61_BU_DYB_10-17_13-26-05_CE_RGB_DF2_F3.mp4', 'C_3_8_61_BU_SMC_10-13_16-41-21_CB_RGB_DF2_M4.mp4', 'C_3_8_61_BU_SMC_10-13_16-41-21_CC_RGB_DF2_M4.mp4', 'C_3_8_61_BU_SMC_10-13_16-41-21_CD_RGB_DF2_M4.mp4', 'C_3_8_61_BU_SMC_10-13_16-41-21_CE_RGB_DF2_M4.mp4', 'C_3_8_62_BU_DYB_10-17_13-27-37_CA_RGB_DF2_F3.mp4', 'C_3_8_62_BU_DYB_10-17_13-27-37_CB_RGB_DF2_F3.mp4', 'C_3_8_62_BU_DYB_10-17_13-27-37_CC_RGB_DF2_F3.mp4', 'C_3_8_62_BU_DYB_10-17_13-27-37_CD_RGB_DF2_F3.mp4', 'C_3_8_62_BU_DYB_10-17_13-27-37_CE_RGB_DF2_F3.mp4', 'C_3_8_62_BU_SMC_10-13_16-43-09_CB_RGB_DF2_M4.mp4', 'C_3_8_62_BU_SMC_10-13_16-43-09_CC_RGB_DF2_M4.mp4', 'C_3_8_62_BU_SMC_10-13_16-43-09_CD_RGB_DF2_M4.mp4', 'C_3_8_62_BU_SMC_10-13_16-43-09_CE_RGB_DF2_M4.mp4', 'C_3_8_63_BU_DYB_10-17_13-29-39_CA_RGB_DF2_F3.mp4', 'C_3_8_63_BU_DYB_10-17_13-29-39_CB_RGB_DF2_F3.mp4']\n", + "VS_03.이상행동_08.파손 feature extracting ended. Elapsed time: 0:01:30\n", + "VS_03.이상행동_09.방화 feature extracting starts\n", + "==>> file_list: ['C_3_9_34_BU_SYB_10-04_12-44-35_CC_RGB_DF2_F3.mp4', 'C_3_9_34_BU_SYB_10-04_12-44-35_CD_RGB_DF2_F3.mp4', 'C_3_9_35_BU_DYA_08-02_16-49-33_CA_RGB_DF2_F2.mp4', 'C_3_9_35_BU_DYA_08-02_16-49-34_CB_RGB_DF2_F2.mp4', 'C_3_9_35_BU_DYA_08-02_16-49-35_CC_RGB_DF2_F2.mp4', 'C_3_9_35_BU_SMA_09-27_14-10-34_CA_RGB_DF2_F3.mp4', 'C_3_9_35_BU_SMA_09-27_14-10-34_CB_RGB_DF2_F3.mp4', 'C_3_9_35_BU_SMA_09-27_14-10-34_CC_RGB_DF2_F3.mp4', 'C_3_9_35_BU_SMA_09-27_14-10-34_CD_RGB_DF2_F3.mp4', 'C_3_9_35_BU_SMB_09-02_16-06-31_CA_RGB_DF2_F3.mp4', 'C_3_9_35_BU_SMB_09-02_16-06-31_CB_RGB_DF2_F3.mp4', 'C_3_9_35_BU_SMB_09-02_16-06-31_CC_RGB_DF2_F3.mp4', 'C_3_9_35_BU_SMB_09-02_16-06-31_CD_RGB_DF2_F3.mp4', 'C_3_9_35_BU_SYA_10-06_13-15-10_CA_RGB_DF2_F3.mp4', 'C_3_9_35_BU_SYA_10-06_13-15-10_CB_RGB_DF2_F3.mp4', 'C_3_9_35_BU_SYA_10-06_13-15-10_CC_RGB_DF2_F3.mp4', 'C_3_9_35_BU_SYA_10-06_13-15-10_CD_RGB_DF2_F3.mp4', 'C_3_9_35_BU_SYB_10-04_12-46-25_CB_RGB_DF2_F3.mp4', 'C_3_9_35_BU_SYB_10-04_12-46-25_CD_RGB_DF2_F3.mp4', 'C_3_9_36_BU_DYA_08-02_16-51-36_CA_RGB_DF2_F2.mp4', 'C_3_9_36_BU_DYA_08-02_16-51-37_CB_RGB_DF2_F2.mp4', 'C_3_9_36_BU_DYA_08-02_16-51-37_CC_RGB_DF2_F2.mp4', 'C_3_9_36_BU_SMA_09-27_14-12-28_CA_RGB_DF2_F3.mp4', 'C_3_9_36_BU_SMA_09-27_14-12-28_CB_RGB_DF2_F3.mp4', 'C_3_9_36_BU_SMA_09-27_14-12-28_CC_RGB_DF2_F3.mp4', 'C_3_9_36_BU_SMA_09-27_14-12-28_CD_RGB_DF2_F3.mp4', 'C_3_9_36_BU_SMB_09-02_10-43-45_CA_RGB_DF2_F3.mp4', 'C_3_9_36_BU_SMB_09-02_10-43-45_CB_RGB_DF2_F3.mp4', 'C_3_9_36_BU_SMB_09-02_10-43-45_CC_RGB_DF2_F3.mp4', 'C_3_9_36_BU_SMB_09-02_10-43-45_CD_RGB_DF2_F3.mp4', 'C_3_9_36_BU_SYA_10-06_13-16-51_CA_RGB_DF2_F3.mp4', 'C_3_9_36_BU_SYA_10-06_13-16-51_CB_RGB_DF2_F3.mp4', 'C_3_9_36_BU_SYA_10-06_13-16-51_CC_RGB_DF2_F3.mp4', 'C_3_9_36_BU_SYA_10-06_13-16-51_CD_RGB_DF2_F3.mp4', 'C_3_9_36_BU_SYB_10-04_12-48-32_CA_RGB_DF2_F3.mp4', 'C_3_9_36_BU_SYB_10-04_12-48-32_CB_RGB_DF2_F3.mp4', 'C_3_9_37_BU_DYA_08-02_16-32-26_CB_RGB_DF2_F2.mp4', 'C_3_9_37_BU_DYA_08-02_16-32-27_CC_RGB_DF2_F2.mp4', 'C_3_9_37_BU_DYA_08-02_16-49-35_CA_RGB_DF2_F2.mp4', 'C_3_9_37_BU_SMA_09-27_14-12-28_CA_RGB_DF2_F3.mp4', 'C_3_9_37_BU_SMA_09-27_14-12-28_CB_RGB_DF2_F3.mp4', 'C_3_9_37_BU_SMA_09-27_14-12-28_CC_RGB_DF2_F3.mp4', 'C_3_9_37_BU_SMA_09-27_14-12-28_CD_RGB_DF2_F3.mp4', 'C_3_9_37_BU_SMB_09-02_16-17-08_CA_RGB_DF2_F3.mp4', 'C_3_9_37_BU_SMB_09-02_16-17-08_CB_RGB_DF2_F3.mp4', 'C_3_9_37_BU_SMB_09-02_16-17-08_CC_RGB_DF2_F3.mp4', 'C_3_9_37_BU_SMB_09-02_16-17-08_CD_RGB_DF2_F3.mp4', 'C_3_9_37_BU_SYA_10-06_13-18-41_CA_RGB_DF2_F3.mp4', 'C_3_9_37_BU_SYA_10-06_13-18-41_CB_RGB_DF2_F3.mp4', 'C_3_9_37_BU_SYA_10-06_13-18-41_CC_RGB_DF2_F3.mp4', 'C_3_9_37_BU_SYA_10-06_13-18-41_CD_RGB_DF2_F3.mp4', 'C_3_9_37_BU_SYB_10-04_12-50-14_CB_RGB_DF2_F3.mp4', 'C_3_9_38_BU_DYA_08-02_16-40-27_CA_RGB_DF2_F2.mp4', 'C_3_9_38_BU_DYA_08-02_16-40-28_CB_RGB_DF2_F2.mp4', 'C_3_9_38_BU_DYA_08-02_16-40-28_CC_RGB_DF2_F2.mp4', 'C_3_9_38_BU_SMA_09-27_14-16-00_CA_RGB_DF2_F3.mp4', 'C_3_9_38_BU_SMA_09-27_14-16-00_CB_RGB_DF2_F3.mp4', 'C_3_9_38_BU_SMA_09-27_14-16-00_CC_RGB_DF2_F3.mp4', 'C_3_9_38_BU_SMA_09-27_14-16-00_CD_RGB_DF2_F3.mp4', 'C_3_9_38_BU_SMB_09-02_16-19-06_CA_RGB_DF2_F3.mp4', 'C_3_9_38_BU_SMB_09-02_16-19-06_CB_RGB_DF2_F3.mp4', 'C_3_9_38_BU_SMB_09-02_16-19-06_CC_RGB_DF2_F3.mp4', 'C_3_9_38_BU_SMB_09-02_16-19-06_CD_RGB_DF2_F3.mp4', 'C_3_9_38_BU_SYA_10-06_13-20-24_CA_RGB_DF2_F3.mp4', 'C_3_9_38_BU_SYA_10-06_13-20-24_CB_RGB_DF2_F3.mp4', 'C_3_9_38_BU_SYA_10-06_13-20-24_CC_RGB_DF2_F3.mp4', 'C_3_9_38_BU_SYA_10-06_13-20-24_CD_RGB_DF2_F3.mp4', 'C_3_9_38_BU_SYB_10-04_12-52-11_CA_RGB_DF2_F3.mp4', 'C_3_9_38_BU_SYB_10-04_12-52-11_CB_RGB_DF2_F3.mp4', 'C_3_9_39_BU_DYA_08-02_16-40-27_CA_RGB_DF2_F2.mp4', 'C_3_9_39_BU_DYA_08-02_16-43-11_CB_RGB_DF2_F2.mp4', 'C_3_9_39_BU_DYA_08-02_16-43-11_CC_RGB_DF2_F2.mp4', 'C_3_9_39_BU_SMA_09-27_14-17-52_CA_RGB_DF2_F3.mp4', 'C_3_9_39_BU_SMA_09-27_14-17-52_CB_RGB_DF2_F3.mp4', 'C_3_9_39_BU_SMA_09-27_14-17-52_CC_RGB_DF2_F3.mp4', 'C_3_9_39_BU_SMA_09-27_14-17-52_CD_RGB_DF2_F3.mp4', 'C_3_9_39_BU_SMB_09-02_16-21-15_CA_RGB_DF2_F3.mp4', 'C_3_9_39_BU_SMB_09-02_16-21-15_CB_RGB_DF2_F3.mp4', 'C_3_9_39_BU_SMB_09-02_16-21-15_CC_RGB_DF2_F3.mp4', 'C_3_9_39_BU_SMB_09-02_16-21-15_CD_RGB_DF2_F3.mp4', 'C_3_9_39_BU_SYA_10-06_13-26-50_CA_RGB_DF2_F3.mp4', 'C_3_9_39_BU_SYA_10-06_13-26-50_CB_RGB_DF2_F3.mp4']\n", + "VS_03.이상행동_09.방화 feature extracting ended. Elapsed time: 0:01:32\n", + "VS_03.이상행동_10.흡연 feature extracting starts\n", + "==>> file_list: ['C_3_10_33_BU_SYB_10-04_13-56-16_CD_RGB_DF2_F3.mp4', 'C_3_10_34_BU_DYA_08-04_14-52-39_CA_RGB_DF2_F2.mp4', 'C_3_10_34_BU_DYA_08-04_14-52-40_CC_RGB_DF2_F2.mp4', 'C_3_10_34_BU_DYA_08-04_14-52-41_CB_RGB_DF2_F2.mp4', 'C_3_10_34_BU_SMA_09-27_14-59-25_CA_RGB_DF2_F3.mp4', 'C_3_10_34_BU_SMA_09-27_14-59-25_CB_RGB_DF2_F3.mp4', 'C_3_10_34_BU_SMA_09-27_14-59-25_CC_RGB_DF2_F3.mp4', 'C_3_10_34_BU_SMA_09-27_14-59-25_CD_RGB_DF2_F3.mp4', 'C_3_10_34_BU_SMB_09-02_16-58-39_CA_RGB_DF2_F3.mp4', 'C_3_10_34_BU_SMB_09-02_16-58-39_CB_RGB_DF2_F3.mp4', 'C_3_10_34_BU_SMB_09-02_16-58-39_CC_RGB_DF2_F3.mp4', 'C_3_10_34_BU_SMB_09-02_16-58-39_CD_RGB_DF2_F3.mp4', 'C_3_10_34_BU_SYA_10-06_13-40-39_CA_RGB_DF2_F3.mp4', 'C_3_10_34_BU_SYA_10-06_13-40-39_CB_RGB_DF2_F3.mp4', 'C_3_10_34_BU_SYB_10-04_13-59-24_CA_RGB_DF2_F3.mp4', 'C_3_10_34_BU_SYB_10-04_13-59-24_CB_RGB_DF2_F3.mp4', 'C_3_10_34_BU_SYB_10-04_13-59-24_CC_RGB_DF2_F3.mp4', 'C_3_10_34_BU_SYB_10-04_13-59-24_CD_RGB_DF2_F3.mp4', 'C_3_10_35_BU_DYA_08-04_14-54-45_CA_RGB_DF2_F2.mp4', 'C_3_10_35_BU_DYA_08-04_14-54-47_CB_RGB_DF2_F2.mp4', 'C_3_10_35_BU_DYA_08-04_14-54-47_CC_RGB_DF2_F2.mp4', 'C_3_10_35_BU_SMA_09-27_15-00-58_CA_RGB_DF2_F3.mp4', 'C_3_10_35_BU_SMA_09-27_15-00-58_CB_RGB_DF2_F3.mp4', 'C_3_10_35_BU_SMA_09-27_15-00-58_CC_RGB_DF2_F3.mp4', 'C_3_10_35_BU_SMA_09-27_15-00-58_CD_RGB_DF2_F3.mp4', 'C_3_10_35_BU_SMB_09-02_17-00-45_CA_RGB_DF2_F3.mp4', 'C_3_10_35_BU_SMB_09-02_17-00-45_CB_RGB_DF2_F3.mp4', 'C_3_10_35_BU_SMB_09-02_17-00-45_CC_RGB_DF2_F3.mp4', 'C_3_10_35_BU_SMB_09-02_17-00-45_CD_RGB_DF2_F3.mp4', 'C_3_10_35_BU_SYA_10-06_13-42-26_CA_RGB_DF2_F3.mp4', 'C_3_10_35_BU_SYA_10-06_13-42-26_CB_RGB_DF2_F3.mp4', 'C_3_10_35_BU_SYA_10-06_13-42-26_CD_RGB_DF2_F3.mp4', 'C_3_10_35_BU_SYB_10-04_14-01-10_CA_RGB_DF2_F3.mp4', 'C_3_10_35_BU_SYB_10-04_14-01-10_CB_RGB_DF2_F3.mp4', 'C_3_10_35_BU_SYB_10-04_14-01-10_CC_RGB_DF2_F3.mp4', 'C_3_10_35_BU_SYB_10-04_14-01-10_CD_RGB_DF2_F3.mp4', 'C_3_10_36_BU_DYA_08-04_14-56-41_CA_RGB_DF2_F2.mp4', 'C_3_10_36_BU_DYA_08-04_14-56-43_CB_RGB_DF2_F2.mp4', 'C_3_10_36_BU_DYA_08-04_14-56-43_CC_RGB_DF2_F2.mp4', 'C_3_10_36_BU_SMA_09-27_15-03-01_CA_RGB_DF2_F3.mp4', 'C_3_10_36_BU_SMA_09-27_15-03-01_CB_RGB_DF2_F3.mp4', 'C_3_10_36_BU_SMA_09-27_15-03-01_CC_RGB_DF2_F3.mp4', 'C_3_10_36_BU_SMA_09-27_15-03-01_CD_RGB_DF2_F3.mp4', 'C_3_10_36_BU_SMB_09-02_17-04-01_CA_RGB_DF2_F3.mp4', 'C_3_10_36_BU_SMB_09-02_17-04-01_CB_RGB_DF2_F3.mp4', 'C_3_10_36_BU_SMB_09-02_17-04-01_CC_RGB_DF2_F3.mp4', 'C_3_10_36_BU_SMB_09-02_17-04-01_CD_RGB_DF2_F3.mp4', 'C_3_10_36_BU_SYA_10-06_13-44-23_CA_RGB_DF2_F3.mp4', 'C_3_10_36_BU_SYA_10-06_13-44-23_CB_RGB_DF2_F3.mp4', 'C_3_10_36_BU_SYB_10-04_14-04-23_CA_RGB_DF2_F3.mp4', 'C_3_10_36_BU_SYB_10-04_14-04-23_CB_RGB_DF2_F3.mp4', 'C_3_10_36_BU_SYB_10-04_14-04-23_CC_RGB_DF2_F3.mp4', 'C_3_10_36_BU_SYB_10-04_14-04-23_CD_RGB_DF2_F3.mp4', 'C_3_10_37_BU_DYA_08-04_14-40-17_CA_RGB_DF2_F2.mp4', 'C_3_10_37_BU_DYA_08-04_14-40-18_CB_RGB_DF2_F2.mp4', 'C_3_10_37_BU_DYA_08-04_14-40-18_CC_RGB_DF2_F2.mp4', 'C_3_10_37_BU_SMA_09-27_15-04-41_CB_RGB_DF2_F3.mp4', 'C_3_10_37_BU_SMA_09-27_15-04-41_CC_RGB_DF2_F3.mp4', 'C_3_10_37_BU_SMB_09-02_17-05-41_CA_RGB_DF2_F3.mp4', 'C_3_10_37_BU_SMB_09-02_17-05-41_CB_RGB_DF2_F3.mp4', 'C_3_10_37_BU_SMB_09-02_17-05-41_CC_RGB_DF2_F3.mp4', 'C_3_10_37_BU_SMB_09-02_17-05-41_CD_RGB_DF2_F3.mp4', 'C_3_10_37_BU_SYA_10-06_13-45-43_CA_RGB_DF2_F3.mp4', 'C_3_10_37_BU_SYA_10-06_13-45-43_CC_RGB_DF2_F3.mp4', 'C_3_10_37_BU_SYA_10-06_13-45-43_CD_RGB_DF2_F3.mp4', 'C_3_10_37_BU_SYB_10-04_14-02-41_CA_RGB_DF2_F3.mp4', 'C_3_10_37_BU_SYB_10-04_14-02-41_CB_RGB_DF2_F3.mp4', 'C_3_10_37_BU_SYB_10-04_14-02-41_CC_RGB_DF2_F3.mp4', 'C_3_10_37_BU_SYB_10-04_14-02-41_CD_RGB_DF2_F3.mp4', 'C_3_10_38_BU_DYA_08-04_14-46-22_CA_RGB_DF2_F2.mp4', 'C_3_10_38_BU_DYA_08-04_14-46-24_CB_RGB_DF2_F2.mp4', 'C_3_10_38_BU_DYA_08-04_14-46-24_CC_RGB_DF2_F2.mp4', 'C_3_10_38_BU_SMA_09-27_14-35-50_CA_RGB_DF2_F3.mp4', 'C_3_10_38_BU_SMA_09-27_14-35-50_CB_RGB_DF2_F3.mp4', 'C_3_10_38_BU_SMA_09-27_14-35-50_CC_RGB_DF2_F3.mp4', 'C_3_10_38_BU_SMA_09-27_14-35-50_CD_RGB_DF2_F3.mp4', 'C_3_10_38_BU_SMB_09-02_17-07-45_CA_RGB_DF2_F3.mp4', 'C_3_10_38_BU_SMB_09-02_17-07-45_CB_RGB_DF2_F3.mp4', 'C_3_10_38_BU_SYA_10-06_13-56-00_CA_RGB_DF2_F3.mp4', 'C_3_10_38_BU_SYA_10-06_13-56-00_CB_RGB_DF2_F3.mp4', 'C_3_10_38_BU_SYB_10-04_13-33-33_CA_RGB_DF2_F3.mp4', 'C_3_10_38_BU_SYB_10-04_13-33-33_CB_RGB_DF2_F3.mp4', 'C_3_10_38_BU_SYB_10-04_13-33-33_CC_RGB_DF2_F3.mp4', 'C_3_10_38_BU_SYB_10-04_13-33-33_CD_RGB_DF2_F3.mp4', 'C_3_10_39_BU_DYA_08-04_14-48-32_CA_RGB_DF2_F2.mp4', 'C_3_10_39_BU_DYA_08-04_14-48-34_CB_RGB_DF2_F2.mp4']\n", + "VS_03.이상행동_10.흡연 feature extracting ended. Elapsed time: 0:01:36\n", + "VS_03.이상행동_11.유기 feature extracting starts\n", + "==>> file_list: ['C_3_11_34_BU_DYA_08-10_16-33-05_CA_RGB_DF2_M2_F2.mp4', 'C_3_11_34_BU_DYA_08-10_16-33-10_CB_RGB_DF2_M2_F2.mp4', 'C_3_11_34_BU_DYA_08-10_16-33-10_CC_RGB_DF2_M2_F2.mp4', 'C_3_11_34_BU_SMA_09-05_15-28-52_CA_RGB_DF2_M4_F4.mp4', 'C_3_11_34_BU_SMA_09-05_15-28-52_CB_RGB_DF2_M4_F4.mp4', 'C_3_11_34_BU_SMA_09-05_15-28-55_CC_RGB_DF2_M4_F4.mp4', 'C_3_11_34_BU_SMA_09-05_15-28-55_CD_RGB_DF2_M4_F4.mp4', 'C_3_11_34_BU_SMB_09-05_13-36-37_CA_RGB_DF2_M4_F4.mp4', 'C_3_11_34_BU_SMB_09-05_13-36-37_CC_RGB_DF2_M4_F4.mp4', 'C_3_11_34_BU_SMB_09-05_13-36-40_CB_RGB_DF2_M4_F4.mp4', 'C_3_11_34_BU_SMB_09-05_13-36-40_CD_RGB_DF2_M4_F4.mp4', 'C_3_11_34_BU_SMC_10-16_10-56-45_CA_RGB_DF2_M1_F1.mp4', 'C_3_11_34_BU_SMC_10-16_10-56-45_CB_RGB_DF2_M1_F1.mp4', 'C_3_11_34_BU_SMC_10-16_10-56-45_CD_RGB_DF2_M1_F1.mp4', 'C_3_11_35_BU_DYA_07-29_11-49-41_CD_RGB_DF2_M2.mp4', 'C_3_11_35_BU_DYA_07-29_11-49-41_CE_RGB_DF2_M2.mp4', 'C_3_11_35_BU_DYA_07-29_11-49-41_CF_RGB_DF2_M2.mp4', 'C_3_11_35_BU_DYA_08-10_16-35-44_CA_RGB_DF2_M2_F2.mp4', 'C_3_11_35_BU_DYA_08-10_16-35-49_CB_RGB_DF2_M2_F2.mp4', 'C_3_11_35_BU_DYA_08-10_16-35-49_CC_RGB_DF2_M2_F2.mp4', 'C_3_11_35_BU_SMA_09-05_15-30-21_CA_RGB_DF2_M4_F4.mp4', 'C_3_11_35_BU_SMA_09-05_15-30-21_CB_RGB_DF2_M4_F4.mp4', 'C_3_11_35_BU_SMA_09-05_15-30-24_CC_RGB_DF2_M4_F4.mp4', 'C_3_11_35_BU_SMA_09-05_15-30-25_CD_RGB_DF2_M4_F4.mp4', 'C_3_11_35_BU_SMB_09-05_13-38-08_CC_RGB_DF2_M4_F4.mp4', 'C_3_11_35_BU_SMB_09-05_13-38-11_CD_RGB_DF2_M4_F4.mp4', 'C_3_11_35_BU_SMC_10-14_10-20-53_CA_RGB_DF2_M2_F2.mp4', 'C_3_11_35_BU_SMC_10-14_10-20-53_CB_RGB_DF2_M2_F2.mp4', 'C_3_11_35_BU_SMC_10-14_10-20-53_CC_RGB_DF2_M2_F2.mp4', 'C_3_11_35_BU_SMC_10-14_10-20-53_CD_RGB_DF2_M2_F2.mp4', 'C_3_11_35_BU_SMC_10-14_10-20-53_CE_RGB_DF2_M2_F2.mp4', 'C_3_11_36_BU_DYA_07-29_11-52-11_CD_RGB_DF2_M2.mp4', 'C_3_11_36_BU_DYA_07-29_11-52-11_CE_RGB_DF2_M2.mp4', 'C_3_11_36_BU_DYA_07-29_11-52-11_CF_RGB_DF2_M2.mp4', 'C_3_11_36_BU_DYA_08-10_16-42-21_CA_RGB_DF2_M2_F2.mp4', 'C_3_11_36_BU_DYA_08-10_16-42-26_CB_RGB_DF2_M2_F2.mp4', 'C_3_11_36_BU_DYA_08-10_16-42-26_CC_RGB_DF2_M2_F2.mp4', 'C_3_11_36_BU_SMA_09-05_15-33-23_CA_RGB_DF2_M4_F4.mp4', 'C_3_11_36_BU_SMA_09-05_15-33-23_CB_RGB_DF2_M4_F4.mp4', 'C_3_11_36_BU_SMA_09-05_15-33-26_CC_RGB_DF2_M4_F4.mp4', 'C_3_11_36_BU_SMA_09-05_15-33-27_CD_RGB_DF2_M4_F4.mp4', 'C_3_11_36_BU_SMB_09-05_13-42-40_CA_RGB_DF2_M4_F4.mp4', 'C_3_11_36_BU_SMB_09-05_13-42-43_CB_RGB_DF2_M4_F4.mp4', 'C_3_11_36_BU_SMC_10-14_10-24-36_CA_RGB_DF2_M2_F2.mp4', 'C_3_11_36_BU_SMC_10-14_10-24-36_CB_RGB_DF2_M2_F2.mp4', 'C_3_11_36_BU_SMC_10-14_10-24-36_CC_RGB_DF2_M2_F2.mp4', 'C_3_11_36_BU_SMC_10-14_10-24-36_CD_RGB_DF2_M2_F2.mp4', 'C_3_11_36_BU_SMC_10-14_10-24-36_CE_RGB_DF2_M2_F2.mp4', 'C_3_11_37_BU_DYA_07-29_11-53-57_CD_RGB_DF2_M2.mp4', 'C_3_11_37_BU_DYA_07-29_11-53-57_CE_RGB_DF2_M2.mp4', 'C_3_11_37_BU_DYA_07-29_11-53-57_CF_RGB_DF2_M2.mp4', 'C_3_11_37_BU_DYB_10-16_14-39-23_CB_RGB_DF2_F1_M1.mp4', 'C_3_11_37_BU_DYB_10-16_14-39-23_CD_RGB_DF2_F1_M1.mp4', 'C_3_11_37_BU_SMC_10-14_11-19-25_CA_RGB_DF2_M2_F2.mp4', 'C_3_11_37_BU_SMC_10-14_11-19-25_CB_RGB_DF2_M2_F2.mp4', 'C_3_11_37_BU_SMC_10-14_11-19-25_CC_RGB_DF2_M2_F2.mp4', 'C_3_11_37_BU_SMC_10-14_11-19-25_CD_RGB_DF2_M2_F2.mp4', 'C_3_11_37_BU_SMC_10-14_11-19-25_CE_RGB_DF2_M2_F2.mp4', 'C_3_11_38_BU_DYA_07-29_15-53-58_CD_RGB_DF2_F2.mp4', 'C_3_11_38_BU_DYA_07-29_15-53-58_CE_RGB_DF2_F2.mp4', 'C_3_11_38_BU_DYA_07-29_15-53-58_CF_RGB_DF2_F2.mp4', 'C_3_11_38_BU_DYB_10-16_14-41-46_CA_RGB_DF2_M1_F1.mp4', 'C_3_11_38_BU_DYB_10-16_14-41-46_CB_RGB_DF2_M1_F1.mp4', 'C_3_11_38_BU_DYB_10-16_14-41-46_CC_RGB_DF2_M1_F1.mp4', 'C_3_11_38_BU_DYB_10-16_14-41-46_CD_RGB_DF2_M1_F1.mp4', 'C_3_11_38_BU_DYB_10-16_14-41-46_CE_RGB_DF2_M1_F1.mp4', 'C_3_11_38_BU_SMC_10-14_15-22-40_CA_RGB_DF2_F3_M3.mp4', 'C_3_11_38_BU_SMC_10-14_15-22-40_CB_RGB_DF2_F3_M3.mp4', 'C_3_11_38_BU_SMC_10-14_15-22-40_CC_RGB_DF2_F3_M3.mp4', 'C_3_11_38_BU_SMC_10-14_15-22-40_CD_RGB_DF2_F3_M3.mp4', 'C_3_11_38_BU_SMC_10-14_15-22-40_CE_RGB_DF2_F3_M3.mp4', 'C_3_11_39_BU_DYA_07-29_15-56-22_CD_RGB_DF2_F2.mp4', 'C_3_11_39_BU_DYA_07-29_15-56-22_CE_RGB_DF2_F2.mp4', 'C_3_11_39_BU_DYA_07-29_15-56-22_CF_RGB_DF2_F2.mp4', 'C_3_11_39_BU_DYB_10-16_14-43-25_CD_RGB_DF2_F1_M1.mp4', 'C_3_11_39_BU_DYB_10-16_14-43-25_CE_RGB_DF2_F1_M1.mp4', 'C_3_11_39_BU_SMC_10-14_15-26-36_CA_RGB_DF2_F3_M3.mp4', 'C_3_11_39_BU_SMC_10-14_15-26-36_CB_RGB_DF2_F3_M3.mp4', 'C_3_11_39_BU_SMC_10-14_15-26-36_CC_RGB_DF2_F3_M3.mp4', 'C_3_11_39_BU_SMC_10-14_15-26-36_CD_RGB_DF2_F3_M3.mp4']\n", + "VS_03.이상행동_11.유기 feature extracting ended. Elapsed time: 0:01:46\n", + "VS_03.이상행동_12.절도 feature extracting starts\n", + "==>> file_list: ['C_3_12_40_BU_SMC_10-14_11-43-44_CD_RGB_DF2_M2.mp4', 'C_3_12_40_BU_SMC_10-14_11-43-44_CE_RGB_DF2_M2.mp4', 'C_3_12_41_BU_DYA_07-29_15-20-29_CD_RGB_DF2_F2.mp4', 'C_3_12_41_BU_DYA_07-29_15-20-29_CE_RGB_DF2_F2.mp4', 'C_3_12_41_BU_DYA_07-29_15-20-29_CF_RGB_DF2_F2.mp4', 'C_3_12_41_BU_DYB_10-16_14-50-58_CA_RGB_DF2_M1.mp4', 'C_3_12_41_BU_DYB_10-16_14-50-58_CB_RGB_DF2_M1.mp4', 'C_3_12_41_BU_DYB_10-16_14-50-58_CD_RGB_DF2_M1.mp4', 'C_3_12_41_BU_SMC_10-14_11-45-31_CA_RGB_DF2_M2.mp4', 'C_3_12_41_BU_SMC_10-14_11-45-31_CB_RGB_DF2_M2.mp4', 'C_3_12_41_BU_SMC_10-14_11-45-31_CC_RGB_DF2_M2.mp4', 'C_3_12_41_BU_SMC_10-14_11-45-31_CD_RGB_DF2_M2.mp4', 'C_3_12_41_BU_SMC_10-14_11-45-31_CE_RGB_DF2_M2.mp4', 'C_3_12_42_BU_DYA_07-29_15-23-55_CD_RGB_DF2_F2.mp4', 'C_3_12_42_BU_DYA_07-29_15-23-55_CE_RGB_DF2_F2.mp4', 'C_3_12_42_BU_DYA_07-29_15-23-55_CF_RGB_DF2_F2.mp4', 'C_3_12_42_BU_SMC_10-14_12-15-24_CA_RGB_DF2_F2.mp4', 'C_3_12_42_BU_SMC_10-14_12-15-24_CB_RGB_DF2_F2.mp4', 'C_3_12_42_BU_SMC_10-14_12-15-24_CC_RGB_DF2_F2.mp4', 'C_3_12_42_BU_SMC_10-14_12-15-24_CD_RGB_DF2_F2.mp4', 'C_3_12_42_BU_SMC_10-14_12-15-24_CE_RGB_DF2_F2.mp4', 'C_3_12_43_BU_DYB_10-16_14-55-05_CA_RGB_DF2_F1.mp4', 'C_3_12_43_BU_DYB_10-16_14-55-05_CB_RGB_DF2_F1.mp4', 'C_3_12_43_BU_DYB_10-16_14-55-05_CC_RGB_DF2_F1.mp4', 'C_3_12_43_BU_DYB_10-16_14-55-05_CD_RGB_DF2_F1.mp4', 'C_3_12_43_BU_DYB_10-16_14-55-05_CE_RGB_DF2_F1.mp4', 'C_3_12_43_BU_SMC_10-14_12-17-14_CC_RGB_DF2_F2.mp4', 'C_3_12_44_BU_DYB_10-16_14-56-44_CA_RGB_DF2_F1.mp4', 'C_3_12_44_BU_DYB_10-16_14-56-44_CB_RGB_DF2_F1.mp4', 'C_3_12_44_BU_DYB_10-16_14-56-44_CC_RGB_DF2_F1.mp4', 'C_3_12_44_BU_DYB_10-16_14-56-44_CD_RGB_DF2_F1.mp4', 'C_3_12_44_BU_DYB_10-16_14-56-44_CE_RGB_DF2_F1.mp4', 'C_3_12_44_BU_SMC_10-14_12-19-55_CA_RGB_DF2_F2.mp4', 'C_3_12_44_BU_SMC_10-14_12-19-55_CB_RGB_DF2_F2.mp4', 'C_3_12_44_BU_SMC_10-14_12-19-55_CC_RGB_DF2_F2.mp4', 'C_3_12_44_BU_SMC_10-14_12-19-55_CD_RGB_DF2_F2.mp4', 'C_3_12_44_BU_SMC_10-14_12-19-55_CE_RGB_DF2_F2.mp4', 'C_3_12_45_BU_DYB_10-16_15-02-02_CA_RGB_DF2_F1.mp4', 'C_3_12_45_BU_DYB_10-16_15-02-02_CB_RGB_DF2_F1.mp4', 'C_3_12_45_BU_DYB_10-16_15-02-02_CD_RGB_DF2_F1.mp4', 'C_3_12_45_BU_DYB_10-16_15-02-02_CE_RGB_DF2_F1.mp4', 'C_3_12_45_BU_SMC_10-14_16-07-18_CA_RGB_DF2_M3.mp4', 'C_3_12_45_BU_SMC_10-14_16-07-18_CB_RGB_DF2_M3.mp4', 'C_3_12_45_BU_SMC_10-14_16-07-18_CC_RGB_DF2_M3.mp4', 'C_3_12_45_BU_SMC_10-14_16-07-18_CD_RGB_DF2_M3.mp4', 'C_3_12_45_BU_SMC_10-14_16-07-18_CE_RGB_DF2_M3.mp4', 'C_3_12_46_BU_SMC_10-14_16-09-57_CB_RGB_DF2_M3.mp4', 'C_3_12_46_BU_SMC_10-14_16-09-57_CC_RGB_DF2_M3.mp4', 'C_3_12_46_BU_SMC_10-14_16-09-57_CE_RGB_DF2_M3.mp4', 'C_3_12_47_BU_DYB_10-17_13-44-21_CA_RGB_DF2_M3.mp4', 'C_3_12_47_BU_DYB_10-17_13-44-21_CB_RGB_DF2_M3.mp4', 'C_3_12_47_BU_DYB_10-17_13-44-21_CC_RGB_DF2_M3.mp4', 'C_3_12_47_BU_DYB_10-17_13-44-21_CD_RGB_DF2_M3.mp4', 'C_3_12_47_BU_DYB_10-17_13-44-21_CE_RGB_DF2_M3.mp4', 'C_3_12_47_BU_SMC_10-14_16-15-55_CA_RGB_DF2_M3.mp4', 'C_3_12_47_BU_SMC_10-14_16-15-55_CB_RGB_DF2_M3.mp4', 'C_3_12_47_BU_SMC_10-14_16-15-55_CC_RGB_DF2_M3.mp4', 'C_3_12_47_BU_SMC_10-14_16-15-55_CD_RGB_DF2_M3.mp4', 'C_3_12_47_BU_SMC_10-14_16-15-55_CE_RGB_DF2_M3.mp4', 'C_3_12_48_BU_DYA_07-29_14-11-06_CD_RGB_DF2_F2.mp4', 'C_3_12_48_BU_DYA_07-29_14-11-06_CE_RGB_DF2_F2.mp4', 'C_3_12_48_BU_DYA_07-29_14-11-06_CF_RGB_DF2_F2.mp4', 'C_3_12_48_BU_DYB_10-17_13-46-19_CA_RGB_DF2_M3.mp4', 'C_3_12_48_BU_DYB_10-17_13-46-19_CB_RGB_DF2_M3.mp4', 'C_3_12_48_BU_DYB_10-17_13-46-19_CC_RGB_DF2_M3.mp4', 'C_3_12_48_BU_DYB_10-17_13-46-19_CD_RGB_DF2_M3.mp4', 'C_3_12_48_BU_DYB_10-17_13-46-19_CE_RGB_DF2_M3.mp4', 'C_3_12_48_BU_SMC_10-14_13-46-19_CA_RGB_DF2_F3.mp4', 'C_3_12_48_BU_SMC_10-14_13-46-19_CB_RGB_DF2_F3.mp4', 'C_3_12_48_BU_SMC_10-14_13-46-19_CC_RGB_DF2_F3.mp4', 'C_3_12_48_BU_SMC_10-14_13-46-19_CD_RGB_DF2_F3.mp4', 'C_3_12_48_BU_SMC_10-14_13-46-19_CE_RGB_DF2_F3.mp4', 'C_3_12_49_BU_DYA_07-29_14-13-29_CD_RGB_DF2_F3.mp4', 'C_3_12_49_BU_DYA_07-29_14-13-29_CE_RGB_DF2_F3.mp4', 'C_3_12_49_BU_DYA_07-29_14-13-29_CF_RGB_DF2_F3.mp4', 'C_3_12_49_BU_DYB_10-17_13-47-59_CA_RGB_DF2_M3.mp4', 'C_3_12_49_BU_DYB_10-17_13-47-59_CB_RGB_DF2_M3.mp4', 'C_3_12_49_BU_DYB_10-17_13-47-59_CC_RGB_DF2_M3.mp4', 'C_3_12_49_BU_DYB_10-17_13-47-59_CD_RGB_DF2_M3.mp4', 'C_3_12_49_BU_DYB_10-17_13-47-59_CE_RGB_DF2_M3.mp4']\n", + "VS_03.이상행동_12.절도 feature extracting ended. Elapsed time: 0:01:48\n", + "VS_03.이상행동_13.폭행 feature extracting starts\n", + "==>> file_list: ['C_3_13_31_BU_SMA_09-05_15-48-20_CD_RGB_DF2_F4.mp4', 'C_3_13_31_BU_SMB_09-05_14-08-01_CA_RGB_DF2_F4.mp4', 'C_3_13_31_BU_SMB_09-05_14-08-01_CC_RGB_DF2_F4.mp4', 'C_3_13_31_BU_SMB_09-05_14-08-04_CB_RGB_DF2_F4.mp4', 'C_3_13_31_BU_SMB_09-05_14-08-04_CD_RGB_DF2_F4.mp4', 'C_3_13_31_BU_SYA_09-14_14-05-04_CA_RGB_DF2_F4.mp4', 'C_3_13_31_BU_SYA_09-14_14-05-04_CB_RGB_DF2_F4.mp4', 'C_3_13_31_BU_SYA_09-14_14-05-07_CC_RGB_DF2_F4.mp4', 'C_3_13_31_BU_SYA_09-14_14-05-07_CD_RGB_DF2_F4.mp4', 'C_3_13_31_BU_SYB_09-14_15-31-44_CA_RGB_DF2_F4.mp4', 'C_3_13_31_BU_SYB_09-14_15-31-44_CB_RGB_DF2_F4.mp4', 'C_3_13_31_BU_SYB_09-14_15-31-44_CC_RGB_DF2_F4.mp4', 'C_3_13_31_BU_SYB_09-14_15-31-44_CD_RGB_DF2_F4.mp4', 'C_3_13_32_BU_DYA_08-23_17-10-37_CD_RGB_DF2_F2_M2.mp4', 'C_3_13_32_BU_DYA_08-23_17-10-37_CE_RGB_DF2_F2_M2.mp4', 'C_3_13_32_BU_SMA_09-05_15-51-19_CA_RGB_DF2_F4.mp4', 'C_3_13_32_BU_SMA_09-05_15-51-20_CB_RGB_DF2_F4.mp4', 'C_3_13_32_BU_SMA_09-05_15-51-22_CC_RGB_DF2_F4.mp4', 'C_3_13_32_BU_SMA_09-05_15-51-23_CD_RGB_DF2_F4.mp4', 'C_3_13_32_BU_SMB_09-05_14-10-31_CA_RGB_DF2_F4.mp4', 'C_3_13_32_BU_SMB_09-05_14-10-31_CC_RGB_DF2_F4.mp4', 'C_3_13_32_BU_SMB_09-05_14-10-34_CB_RGB_DF2_F4.mp4', 'C_3_13_32_BU_SMB_09-05_14-10-34_CD_RGB_DF2_F4.mp4', 'C_3_13_32_BU_SYA_09-14_14-06-33_CA_RGB_DF2_F4.mp4', 'C_3_13_32_BU_SYA_09-14_14-06-33_CB_RGB_DF2_F4.mp4', 'C_3_13_32_BU_SYA_09-14_14-06-36_CC_RGB_DF2_F4.mp4', 'C_3_13_32_BU_SYA_09-14_14-06-36_CD_RGB_DF2_F4.mp4', 'C_3_13_32_BU_SYB_09-14_15-33-04_CA_RGB_DF2_F4.mp4', 'C_3_13_32_BU_SYB_09-14_15-33-04_CB_RGB_DF2_F4.mp4', 'C_3_13_32_BU_SYB_09-14_15-33-04_CC_RGB_DF2_F4.mp4', 'C_3_13_32_BU_SYB_09-14_15-33-04_CD_RGB_DF2_F4.mp4', 'C_3_13_33_BU_SMA_09-05_15-53-00_CA_RGB_DF2_F4.mp4', 'C_3_13_33_BU_SMA_09-05_15-53-00_CB_RGB_DF2_F4.mp4', 'C_3_13_33_BU_SMA_09-05_15-53-03_CC_RGB_DF2_F4.mp4', 'C_3_13_33_BU_SMA_09-05_15-53-03_CD_RGB_DF2_F4.mp4', 'C_3_13_33_BU_SMB_09-05_14-12-29_CA_RGB_DF2_F4.mp4', 'C_3_13_33_BU_SMB_09-05_14-12-29_CC_RGB_DF2_F4.mp4', 'C_3_13_33_BU_SMB_09-05_14-12-32_CB_RGB_DF2_F4.mp4', 'C_3_13_33_BU_SMB_09-05_14-12-32_CD_RGB_DF2_F4.mp4', 'C_3_13_33_BU_SYA_09-14_14-08-41_CA_RGB_DF2_F4.mp4', 'C_3_13_33_BU_SYA_09-14_14-08-41_CB_RGB_DF2_F4.mp4', 'C_3_13_33_BU_SYA_09-14_14-08-44_CC_RGB_DF2_F4.mp4', 'C_3_13_33_BU_SYA_09-14_14-08-44_CD_RGB_DF2_F4.mp4', 'C_3_13_33_BU_SYB_09-14_15-37-07_CB_RGB_DF2_F4.mp4', 'C_3_13_33_BU_SYB_09-14_15-37-07_CC_RGB_DF2_F4.mp4', 'C_3_13_33_BU_SYB_09-14_15-37-07_CD_RGB_DF2_F4.mp4', 'C_3_13_34_BU_SMA_09-05_15-56-55_CA_RGB_DF2_M4.mp4', 'C_3_13_34_BU_SMA_09-05_15-56-56_CB_RGB_DF2_M4.mp4', 'C_3_13_34_BU_SMA_09-05_15-56-59_CC_RGB_DF2_M4.mp4', 'C_3_13_34_BU_SMA_09-05_15-56-59_CD_RGB_DF2_M4.mp4', 'C_3_13_34_BU_SMB_09-05_14-18-04_CA_RGB_DF2_M4.mp4', 'C_3_13_34_BU_SMB_09-05_14-18-04_CC_RGB_DF2_M4.mp4', 'C_3_13_34_BU_SMB_09-05_14-18-07_CD_RGB_DF2_M4.mp4', 'C_3_13_34_BU_SMB_09-05_14-18-08_CB_RGB_DF2_M4.mp4', 'C_3_13_34_BU_SYB_09-14_15-53-50_CA_RGB_DF2_M4.mp4', 'C_3_13_34_BU_SYB_09-14_15-53-50_CB_RGB_DF2_M4.mp4', 'C_3_13_34_BU_SYB_09-14_15-53-50_CC_RGB_DF2_M4.mp4', 'C_3_13_34_BU_SYB_09-14_15-53-50_CD_RGB_DF2_M4.mp4', 'C_3_13_35_BU_SMA_09-05_15-58-26_CA_RGB_DF2_M4.mp4', 'C_3_13_35_BU_SMA_09-05_15-58-26_CB_RGB_DF2_M4.mp4', 'C_3_13_35_BU_SMA_09-05_15-58-29_CC_RGB_DF2_M4.mp4', 'C_3_13_35_BU_SMA_09-05_15-58-29_CD_RGB_DF2_M4.mp4', 'C_3_13_35_BU_SMB_09-05_14-19-43_CA_RGB_DF2_M4.mp4', 'C_3_13_35_BU_SMB_09-05_14-19-43_CC_RGB_DF2_M4.mp4', 'C_3_13_35_BU_SMB_09-05_14-19-46_CD_RGB_DF2_M4.mp4', 'C_3_13_35_BU_SMB_09-05_14-19-47_CB_RGB_DF2_M4.mp4', 'C_3_13_35_BU_SYB_09-14_15-58-55_CA_RGB_DF2_M4.mp4', 'C_3_13_35_BU_SYB_09-14_15-58-55_CB_RGB_DF2_M4.mp4', 'C_3_13_35_BU_SYB_09-14_15-58-55_CC_RGB_DF2_M4.mp4', 'C_3_13_35_BU_SYB_09-14_15-58-55_CD_RGB_DF2_M4.mp4', 'C_3_13_36_BU_SMA_09-05_16-00-06_CA_RGB_DF2_M4.mp4', 'C_3_13_36_BU_SMA_09-05_16-00-07_CB_RGB_DF2_M4.mp4', 'C_3_13_36_BU_SMA_09-05_16-00-09_CC_RGB_DF2_M4.mp4', 'C_3_13_36_BU_SMA_09-05_16-00-10_CD_RGB_DF2_M4.mp4', 'C_3_13_36_BU_SMB_09-05_14-21-13_CC_RGB_DF2_M4.mp4', 'C_3_13_36_BU_SMB_09-05_14-21-14_CA_RGB_DF2_M4.mp4', 'C_3_13_36_BU_SMB_09-05_14-21-16_CD_RGB_DF2_M4.mp4', 'C_3_13_36_BU_SMB_09-05_14-21-17_CB_RGB_DF2_M4.mp4', 'C_3_13_36_BU_SYB_09-14_16-02-20_CA_RGB_DF2_M4.mp4', 'C_3_13_36_BU_SYB_09-14_16-02-20_CB_RGB_DF2_M4.mp4']\n", + "VS_03.이상행동_13.폭행 feature extracting ended. Elapsed time: 0:01:47\n", + "VS_03.이상행동_14.교통약자 feature extracting starts\n", + "==>> file_list: ['C_3_14_65_BU_DYA_07-19_12-30-50_d_DF6_F4.mp4', 'C_3_14_65_BU_DYA_07-19_12-30-50_e_DF6_F4.mp4', 'C_3_14_65_BU_DYA_07-19_12-30-50_f_DF6_F4.mp4', 'C_3_14_65_BU_DYB_10-13_12-03-56_CA_DF2_F4.mp4', 'C_3_14_65_BU_DYB_10-13_12-03-56_CB_DF2_F4.mp4', 'C_3_14_65_BU_DYB_10-13_12-03-56_CC_DF2_F4.mp4', 'C_3_14_65_BU_DYB_10-13_12-03-56_CD_DF2_F4.mp4', 'C_3_14_65_BU_DYB_10-13_12-03-56_CE_DF2_F4.mp4', 'C_3_14_65_BU_DYB_10-13_12-03-56_CF_DF2_F4.mp4', 'C_3_14_66_BU_DYA_07-19_11-46-00_a_DF6_F4.mp4', 'C_3_14_66_BU_DYA_07-19_11-46-00_b_DF6_F4.mp4', 'C_3_14_66_BU_DYA_07-19_11-46-00_c_DF6_F4.mp4', 'C_3_14_66_BU_DYA_07-19_11-46-00_d_DF6_F4.mp4', 'C_3_14_66_BU_DYA_07-19_11-46-00_e_DF6_F4.mp4', 'C_3_14_66_BU_DYA_07-19_11-46-00_f_DF6_F4.mp4', 'C_3_14_66_BU_DYB_10-13_12-06-02_CA_DF2_F4.mp4', 'C_3_14_66_BU_DYB_10-13_12-06-02_CB_DF2_F4.mp4', 'C_3_14_66_BU_DYB_10-13_12-06-02_CC_DF2_F4.mp4', 'C_3_14_66_BU_DYB_10-13_12-06-02_CD_DF2_F4.mp4', 'C_3_14_66_BU_DYB_10-13_12-06-02_CE_DF2_F4.mp4', 'C_3_14_66_BU_DYB_10-13_12-06-02_CF_DF2_F4.mp4', 'C_3_14_67_BU_DYA_07-19_11-54-57_a_DF6_F4.mp4', 'C_3_14_67_BU_DYA_07-19_11-54-57_b_DF6_F4.mp4', 'C_3_14_67_BU_DYA_07-19_11-54-57_c_DF6_F4.mp4', 'C_3_14_67_BU_DYA_07-19_11-54-57_d_DF6_F4.mp4', 'C_3_14_67_BU_DYA_07-19_11-54-57_e_DF6_F4.mp4', 'C_3_14_67_BU_DYA_07-19_11-54-57_f_DF6_F4.mp4', 'C_3_14_67_BU_DYB_10-13_10-14-49_CA_DF2_M4.mp4', 'C_3_14_67_BU_DYB_10-13_10-14-49_CB_DF2_M4.mp4', 'C_3_14_67_BU_DYB_10-13_10-14-49_CC_DF2_M4.mp4', 'C_3_14_67_BU_DYB_10-13_10-14-49_CD_DF2_M4.mp4', 'C_3_14_67_BU_DYB_10-13_10-14-49_CE_DF2_M4.mp4', 'C_3_14_67_BU_DYB_10-13_10-14-49_CF_DF2_M4.mp4', 'C_3_14_68_BU_DYA_07-19_12-04-44_a_DF6_F4.mp4', 'C_3_14_68_BU_DYA_07-19_12-04-44_b_DF6_F4.mp4', 'C_3_14_68_BU_DYA_07-19_12-04-44_c_DF6_F4.mp4', 'C_3_14_68_BU_DYA_07-19_12-04-44_d_DF6_F4.mp4', 'C_3_14_68_BU_DYA_07-19_12-04-44_e_DF6_F4.mp4', 'C_3_14_68_BU_DYA_07-19_12-04-44_f_DF6_F4.mp4', 'C_3_14_68_BU_DYB_10-13_10-17-17_CA_DF2_M4.mp4', 'C_3_14_68_BU_DYB_10-13_10-17-17_CB_DF2_M4.mp4', 'C_3_14_68_BU_DYB_10-13_10-17-17_CC_DF2_M4.mp4', 'C_3_14_68_BU_DYB_10-13_10-17-17_CD_DF2_M4.mp4', 'C_3_14_68_BU_DYB_10-13_10-17-17_CE_DF2_M4.mp4', 'C_3_14_68_BU_DYB_10-13_10-17-17_CF_DF2_M4.mp4', 'C_3_14_69_BU_DYA_07-20_14-59-57_a_DF6_M4.mp4', 'C_3_14_69_BU_DYA_07-20_14-59-57_b_DF6_M4.mp4', 'C_3_14_69_BU_DYA_07-20_14-59-57_c_DF6_M4.mp4', 'C_3_14_69_BU_DYA_07-20_14-59-57_d_DF6_M4.mp4', 'C_3_14_69_BU_DYA_07-20_14-59-57_e_DF6_M4.mp4', 'C_3_14_69_BU_DYA_07-20_14-59-57_f_DF6_M4.mp4', 'C_3_14_69_BU_DYB_10-13_10-19-42_CA_DF2_M4.mp4', 'C_3_14_69_BU_DYB_10-13_10-19-42_CB_DF2_M4.mp4', 'C_3_14_69_BU_DYB_10-13_10-19-42_CC_DF2_M4.mp4', 'C_3_14_69_BU_DYB_10-13_10-19-42_CD_DF2_M4.mp4', 'C_3_14_69_BU_DYB_10-13_10-19-42_CE_DF2_M4.mp4', 'C_3_14_69_BU_DYB_10-13_10-19-42_CF_DF2_M4.mp4', 'C_3_14_70_BU_DYA_07-20_15-01-52_a_DF6_M4.mp4', 'C_3_14_70_BU_DYA_07-20_15-01-52_b_DF6_M4.mp4', 'C_3_14_70_BU_DYA_07-20_15-01-52_c_DF6_M4.mp4', 'C_3_14_70_BU_DYA_07-20_15-01-52_d_DF6_M4.mp4', 'C_3_14_70_BU_DYA_07-20_15-01-52_e_DF6_M4.mp4', 'C_3_14_70_BU_DYA_07-20_15-01-52_f_DF6_M4.mp4', 'C_3_14_70_BU_DYB_10-13_10-21-44_CA_DF2_M4.mp4', 'C_3_14_70_BU_DYB_10-13_10-21-44_CB_DF2_M4.mp4', 'C_3_14_70_BU_DYB_10-13_10-21-44_CC_DF2_M4.mp4', 'C_3_14_70_BU_DYB_10-13_10-21-44_CD_DF2_M4.mp4', 'C_3_14_70_BU_DYB_10-13_10-21-44_CE_DF2_M4.mp4', 'C_3_14_70_BU_DYB_10-13_10-21-44_CF_DF2_M4.mp4', 'C_3_14_71_BU_DYA_07-20_15-04-31_a_DF6_M4.mp4', 'C_3_14_71_BU_DYA_07-20_15-04-31_b_DF6_M4.mp4', 'C_3_14_71_BU_DYA_07-20_15-04-31_c_DF6_M4.mp4', 'C_3_14_71_BU_DYB_10-13_10-45-53_CA_DF2_M4.mp4', 'C_3_14_71_BU_DYB_10-13_10-45-53_CB_DF2_M4.mp4', 'C_3_14_71_BU_DYB_10-13_10-45-53_CC_DF2_M4.mp4', 'C_3_14_71_BU_DYB_10-13_10-45-53_CD_DF2_M4.mp4', 'C_3_14_71_BU_DYB_10-13_10-45-53_CE_DF2_M4.mp4', 'C_3_14_71_BU_DYB_10-13_10-45-53_CF_DF2_M4.mp4', 'C_3_14_72_BU_DYA_07-20_15-07-07_a_DF6_M4.mp4', 'C_3_14_72_BU_DYA_07-20_15-07-07_b_DF6_M4.mp4', 'C_3_14_72_BU_DYA_07-20_15-07-07_c_DF6_M4.mp4']\n", + "VS_03.이상행동_14.교통약자 feature extracting ended. Elapsed time: 0:01:52\n" + ] + } + ], + "source": [ + "for folder_name in folder_list:\n", + " \n", + " time_start = datetime.now()\n", + "\n", + " print(f\"{folder_name} feature extracting starts\")\n", + "\n", + " if not os.path.exists(npy_root+folder_name):\n", + " os.makedirs(npy_root+folder_name)\n", + "\n", + " folder_path = root + folder_name + \"/\"\n", + "\n", + " file_list = os.listdir(root + folder_name)\n", + " file_list.sort()\n", + " print(f\"==>> file_list: {file_list}\")\n", + "\n", + " batch_size = 16\n", + " # Loop through the video frames\n", + " for file_name in file_list:\n", + " path = folder_path + file_name\n", + "\n", + " cap = cv2.VideoCapture(path)\n", + "\n", + " # 710차원 feature array 저장할 list\n", + " np_list = []\n", + "\n", + " # 16 * segments_num 프레임씩 저장할 list\n", + " frames = []\n", + " frame_count = 0\n", + "\n", + " # input tensor 저장할 list\n", + " input_list = []\n", + " input_count = 0\n", + "\n", + " while cap.isOpened():\n", + " # Read a frame from the video\n", + " success, frame = cap.read()\n", + " # frame.shape = (height, width, 3)\n", + "\n", + " frame_count += 1 # Increment frame count\n", + "\n", + " if success:\n", + " frame = tf(image=frame)[\"image\"]\n", + " # frame.shape = (224, 224, 3)\n", + "\n", + " frame = np.expand_dims(frame, axis=0)\n", + " # frame.shape = (1, 224, 224, 3)\n", + " frames.append(frame.copy())\n", + "\n", + " if frame_count == 16 * segments_num:\n", + " assert len(frames) == 16 * segments_num\n", + " frames = np.concatenate(frames)\n", + " # in_frames.shape = (16 * segments_num, 224, 224, 3)\n", + " in_frames = frames.transpose(3, 0, 1, 2)\n", + " # # in_frames.shape = (RGB 3, frame T=16 * segments_num, H=224, W=224)\n", + " in_frames = np.expand_dims(in_frames, axis=0)\n", + " # in_frames.shape = (1, 3, 16 * segments_num, 224, 224)\n", + " in_frames = torch.from_numpy(in_frames).float()\n", + " # in_frames.shape == torch.Size([1, 3, 16 * segments_num, 224, 224])\n", + "\n", + " input_list.append(in_frames.detach().clone())\n", + "\n", + " frame_count = 0\n", + " frames = []\n", + "\n", + " input_count += 1\n", + "\n", + " if input_count == batch_size:\n", + " # input_batch.shape == torch.Size([batch_size, 3, 16 * segments_num, 224, 224])\n", + " input_batch = torch.cat(input_list, dim=0).to(\"cuda\")\n", + "\n", + " with torch.no_grad():\n", + " output = model(input_batch)\n", + " # output.shape == torch.Size([batch_size, 710])\n", + "\n", + " np_list.append(output.cpu().numpy())\n", + "\n", + " input_count = 0\n", + " input_list = []\n", + " else:\n", + " # 남은 프레임, input_list가 지정 개수에서 모자를 때 예외 처리\n", + " if frame_count != 0 and len(frames) != 0:\n", + " # @@ success가 false 일때도 frame_count는 +1이 된다\n", + " # @@ => frames = []로 초기화 된 바로 다음 frame에 success가 false가 되면\n", + " # @@ => frame_count == 1 이지만 len(frames) == 0\n", + " len_frames_left = 16 * segments_num - len(frames)\n", + " # len_input_list_left = batch_size - len(input_list)\n", + "\n", + " # assert len(frames) != 0\n", + "\n", + " for i in range(len_frames_left):\n", + " try:\n", + " frames.append(frames[-1].copy())\n", + " except IndexError:\n", + " print(f\"==>> len(frames): {len(frames)}\")\n", + " print(f\"==>> len_frames_left: {len_frames_left}\")\n", + "\n", + " assert len(frames) == 16 * segments_num\n", + "\n", + " frames = np.concatenate(frames)\n", + " # in_frames.shape = (16 * segments_num, 224, 224, 3)\n", + " in_frames = frames.transpose(3, 0, 1, 2)\n", + " # # in_frames.shape = (RGB 3, frame T=16 * segments_num, H=224, W=224)\n", + " in_frames = np.expand_dims(in_frames, axis=0)\n", + " # in_frames.shape = (1, 3, 16 * segments_num, 224, 224)\n", + " in_frames = torch.from_numpy(in_frames).float()\n", + " # in_frames.shape == torch.Size([1, 3, 16 * segments_num, 224, 224])\n", + "\n", + " input_list.append(in_frames.detach().clone())\n", + "\n", + " # assert len(input_list) == batch_size\n", + "\n", + " # input_batch.shape == torch.Size([batch_size, 3, 16 * segments_num, 224, 224])\n", + " input_batch = torch.cat(input_list, dim=0).to(\"cuda\")\n", + "\n", + " with torch.no_grad():\n", + " output = model(input_batch)\n", + " # output.shape == torch.Size([len(input_list), 710])\n", + "\n", + " np_list.append(output.cpu().numpy())\n", + "\n", + " frame_count = 0\n", + " frames = []\n", + " input_count = 0\n", + " input_list = []\n", + "\n", + " # Break the loop if the end of the video is reached\n", + " break\n", + " try:\n", + " file_outputs = np.concatenate(np_list)\n", + " # print(f\"==>> file_outputs.shape: {file_outputs.shape}\")\n", + " np.save((npy_root + folder_name + \"/\" + file_name), file_outputs)\n", + " except ValueError:\n", + " print(f\"{file_name} ValueError: need at least one array to concatenate\")\n", + "\n", + " cap.release()\n", + "\n", + " time_end = datetime.now()\n", + " total_time = time_end - time_start\n", + " total_time = str(total_time).split(\".\")[0]\n", + "\n", + " print(f\"{folder_name} feature extracting ended. Elapsed time: {total_time}\")\n", + "\n", + " # cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "segments_num = 1\n", + "# 모델에 들어갈 frame수는 16 * segments_num\n", + "\n", + "model = create_model(\n", + " # \"vit_small_patch16_224\",\n", + " \"vit_base_patch16_224\",\n", + " img_size=224,\n", + " pretrained=False,\n", + " num_classes=710,\n", + " all_frames=16 * segments_num,\n", + " # tubelet_size=args.tubelet_size,\n", + " # drop_rate=args.drop,\n", + " # drop_path_rate=args.drop_path,\n", + " # attn_drop_rate=args.attn_drop_rate,\n", + " # head_drop_rate=args.head_drop_rate,\n", + " # drop_block_rate=None,\n", + " # use_mean_pooling=args.use_mean_pooling,\n", + " # init_scale=args.init_scale,\n", + " # with_cp=args.with_checkpoint,\n", + ")\n", + "\n", + "# load_dict = torch.load(\n", + "# \"/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/vit_s_k710_dl_from_giant.pth\"\n", + "# )\n", + "load_dict = torch.load(\n", + " \"/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/vit_b_k710_dl_from_giant.pth\"\n", + ")\n", + "# backbone pth 경로\n", + "\n", + "model.load_state_dict(load_dict[\"module\"])\n", + "\n", + "model.to(\"cuda\")\n", + "model.eval()\n", + "\n", + "tf = A.Resize(224, 224)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Abuse feature extracting starts\n", + "==>> file_list: ['Abuse001_x264.mp4', 'Abuse002_x264.mp4', 'Abuse003_x264.mp4', 'Abuse004_x264.mp4', 'Abuse005_x264.mp4', 'Abuse006_x264.mp4', 'Abuse007_x264.mp4', 'Abuse008_x264.mp4', 'Abuse009_x264.mp4', 'Abuse010_x264.mp4', 'Abuse011_x264.mp4', 'Abuse012_x264.mp4', 'Abuse013_x264.mp4', 'Abuse014_x264.mp4', 'Abuse015_x264.mp4', 'Abuse016_x264.mp4', 'Abuse017_x264.mp4', 'Abuse018_x264.mp4', 'Abuse019_x264.mp4', 'Abuse020_x264.mp4', 'Abuse021_x264.mp4', 'Abuse022_x264.mp4', 'Abuse023_x264.mp4', 'Abuse024_x264.mp4', 'Abuse025_x264.mp4', 'Abuse026_x264.mp4', 'Abuse027_x264.mp4', 'Abuse028_x264.mp4', 'Abuse029_x264.mp4', 'Abuse030_x264.mp4', 'Abuse031_x264.mp4', 'Abuse032_x264.mp4', 'Abuse033_x264.mp4', 'Abuse034_x264.mp4', 'Abuse035_x264.mp4', 'Abuse036_x264.mp4', 'Abuse037_x264.mp4', 'Abuse038_x264.mp4', 'Abuse039_x264.mp4', 'Abuse040_x264.mp4', 'Abuse041_x264.mp4', 'Abuse042_x264.mp4', 'Abuse043_x264.mp4', 'Abuse044_x264.mp4', 'Abuse045_x264.mp4', 'Abuse046_x264.mp4', 'Abuse047_x264.mp4', 'Abuse048_x264.mp4', 'Abuse049_x264.mp4', 'Abuse050_x264.mp4']\n", + "Abuse feature extracting ended. Elapsed time: 0:12:04\n", + "Arrest feature extracting starts\n", + "==>> file_list: ['Arrest001_x264.mp4', 'Arrest002_x264.mp4', 'Arrest003_x264.mp4', 'Arrest004_x264.mp4', 'Arrest005_x264.mp4', 'Arrest006_x264.mp4', 'Arrest007_x264.mp4', 'Arrest008_x264.mp4', 'Arrest009_x264.mp4', 'Arrest010_x264.mp4', 'Arrest011_x264.mp4', 'Arrest012_x264.mp4', 'Arrest013_x264.mp4', 'Arrest014_x264.mp4', 'Arrest015_x264.mp4', 'Arrest016_x264.mp4', 'Arrest017_x264.mp4', 'Arrest018_x264.mp4', 'Arrest019_x264.mp4', 'Arrest020_x264.mp4', 'Arrest021_x264.mp4', 'Arrest022_x264.mp4', 'Arrest023_x264.mp4', 'Arrest024_x264.mp4', 'Arrest025_x264.mp4', 'Arrest026_x264.mp4', 'Arrest027_x264.mp4', 'Arrest028_x264.mp4', 'Arrest029_x264.mp4', 'Arrest030_x264.mp4', 'Arrest031_x264.mp4', 'Arrest032_x264.mp4', 'Arrest033_x264.mp4', 'Arrest034_x264.mp4', 'Arrest035_x264.mp4', 'Arrest036_x264.mp4', 'Arrest037_x264.mp4', 'Arrest038_x264.mp4', 'Arrest039_x264.mp4', 'Arrest040_x264.mp4', 'Arrest041_x264.mp4', 'Arrest042_x264.mp4', 'Arrest043_x264.mp4', 'Arrest044_x264.mp4', 'Arrest046_x264.mp4', 'Arrest047_x264.mp4', 'Arrest048_x264.mp4', 'Arrest049_x264.mp4', 'Arrest050_x264.mp4', 'Arrest051_x264.mp4']\n", + "Arrest feature extracting ended. Elapsed time: 0:18:42\n", + "Arson feature extracting starts\n", + "==>> file_list: ['Arson001_x264.mp4', 'Arson002_x264.mp4', 'Arson003_x264.mp4', 'Arson005_x264.mp4', 'Arson006_x264.mp4', 'Arson007_x264.mp4', 'Arson008_x264.mp4', 'Arson009_x264.mp4', 'Arson010_x264.mp4', 'Arson011_x264.mp4', 'Arson012_x264.mp4', 'Arson013_x264.mp4', 'Arson014_x264.mp4', 'Arson015_x264.mp4', 'Arson016_x264.mp4', 'Arson017_x264.mp4', 'Arson018_x264.mp4', 'Arson019_x264.mp4', 'Arson020_x264.mp4', 'Arson021_x264.mp4', 'Arson022_x264.mp4', 'Arson023_x264.mp4', 'Arson024_x264.mp4', 'Arson025_x264.mp4', 'Arson026_x264.mp4', 'Arson027_x264.mp4', 'Arson028_x264.mp4', 'Arson029_x264.mp4', 'Arson030_x264.mp4', 'Arson031_x264.mp4', 'Arson032_x264.mp4', 'Arson034_x264.mp4', 'Arson035_x264.mp4', 'Arson036_x264.mp4', 'Arson037_x264.mp4', 'Arson038_x264.mp4', 'Arson039_x264.mp4', 'Arson040_x264.mp4', 'Arson041_x264.mp4', 'Arson042_x264.mp4', 'Arson044_x264.mp4', 'Arson045_x264.mp4', 'Arson046_x264.mp4', 'Arson047_x264.mp4', 'Arson048_x264.mp4', 'Arson049_x264.mp4', 'Arson050_x264.mp4', 'Arson051_x264.mp4', 'Arson052_x264.mp4', 'Arson053_x264.mp4']\n", + "Arson feature extracting ended. Elapsed time: 0:17:06\n", + "Assault feature extracting starts\n", + "==>> file_list: ['Assault001_x264.mp4', 'Assault002_x264.mp4', 'Assault003_x264.mp4', 'Assault004_x264.mp4', 'Assault005_x264.mp4', 'Assault006_x264.mp4', 'Assault007_x264.mp4', 'Assault008_x264.mp4', 'Assault009_x264.mp4', 'Assault010_x264.mp4', 'Assault011_x264.mp4', 'Assault012_x264.mp4', 'Assault013_x264.mp4', 'Assault014_x264.mp4', 'Assault015_x264.mp4', 'Assault016_x264.mp4', 'Assault017_x264.mp4', 'Assault018_x264.mp4', 'Assault019_x264.mp4', 'Assault020_x264.mp4', 'Assault022_x264.mp4', 'Assault023_x264.mp4', 'Assault024_x264.mp4', 'Assault025_x264.mp4', 'Assault026_x264.mp4', 'Assault027_x264.mp4', 'Assault028_x264.mp4', 'Assault029_x264.mp4', 'Assault030_x264.mp4', 'Assault031_x264.mp4', 'Assault032_x264.mp4', 'Assault033_x264.mp4', 'Assault034_x264.mp4', 'Assault035_x264.mp4', 'Assault036_x264.mp4', 'Assault037_x264.mp4', 'Assault038_x264.mp4', 'Assault039_x264.mp4', 'Assault040_x264.mp4', 'Assault041_x264.mp4', 'Assault042_x264.mp4', 'Assault044_x264.mp4', 'Assault045_x264.mp4', 'Assault046_x264.mp4', 'Assault047_x264.mp4', 'Assault048_x264.mp4', 'Assault049_x264.mp4', 'Assault050_x264.mp4', 'Assault051_x264.mp4', 'Assault052_x264.mp4']\n", + "Assault feature extracting ended. Elapsed time: 0:08:08\n", + "Burglary feature extracting starts\n", + "==>> file_list: ['Burglary001_x264.mp4', 'Burglary002_x264.mp4', 'Burglary003_x264.mp4', 'Burglary004_x264.mp4', 'Burglary005_x264.mp4', 'Burglary006_x264.mp4', 'Burglary007_x264.mp4', 'Burglary008_x264.mp4', 'Burglary009_x264.mp4', 'Burglary010_x264.mp4', 'Burglary011_x264.mp4', 'Burglary012_x264.mp4', 'Burglary013_x264.mp4', 'Burglary014_x264.mp4', 'Burglary015_x264.mp4', 'Burglary016_x264.mp4', 'Burglary017_x264.mp4', 'Burglary018_x264.mp4', 'Burglary019_x264.mp4', 'Burglary020_x264.mp4', 'Burglary021_x264.mp4', 'Burglary022_x264.mp4', 'Burglary023_x264.mp4', 'Burglary024_x264.mp4', 'Burglary025_x264.mp4', 'Burglary026_x264.mp4', 'Burglary027_x264.mp4', 'Burglary028_x264.mp4', 'Burglary029_x264.mp4', 'Burglary030_x264.mp4', 'Burglary031_x264.mp4', 'Burglary032_x264.mp4', 'Burglary033_x264.mp4', 'Burglary034_x264.mp4', 'Burglary035_x264.mp4', 'Burglary036_x264.mp4', 'Burglary037_x264.mp4', 'Burglary038_x264.mp4', 'Burglary039_x264.mp4', 'Burglary040_x264.mp4', 'Burglary041_x264.mp4', 'Burglary042_x264.mp4', 'Burglary043_x264.mp4', 'Burglary044_x264.mp4', 'Burglary045_x264.mp4', 'Burglary046_x264.mp4', 'Burglary047_x264.mp4', 'Burglary048_x264.mp4', 'Burglary049_x264.mp4', 'Burglary050_x264.mp4', 'Burglary051_x264.mp4', 'Burglary052_x264.mp4', 'Burglary053_x264.mp4', 'Burglary054_x264.mp4', 'Burglary055_x264.mp4', 'Burglary056_x264.mp4', 'Burglary057_x264.mp4', 'Burglary058_x264.mp4', 'Burglary059_x264.mp4', 'Burglary060_x264.mp4', 'Burglary061_x264.mp4', 'Burglary062_x264.mp4', 'Burglary063_x264.mp4', 'Burglary064_x264.mp4', 'Burglary065_x264.mp4', 'Burglary066_x264.mp4', 'Burglary067_x264.mp4', 'Burglary068_x264.mp4', 'Burglary069_x264.mp4', 'Burglary070_x264.mp4', 'Burglary071_x264.mp4', 'Burglary072_x264.mp4', 'Burglary073_x264.mp4', 'Burglary074_x264.mp4', 'Burglary075_x264.mp4', 'Burglary076_x264.mp4', 'Burglary077_x264.mp4', 'Burglary078_x264.mp4', 'Burglary079_x264.mp4', 'Burglary080_x264.mp4', 'Burglary081_x264.mp4', 'Burglary082_x264.mp4', 'Burglary083_x264.mp4', 'Burglary084_x264.mp4', 'Burglary085_x264.mp4', 'Burglary086_x264.mp4', 'Burglary087_x264.mp4', 'Burglary088_x264.mp4', 'Burglary089_x264.mp4', 'Burglary090_x264.mp4', 'Burglary091_x264.mp4', 'Burglary092_x264.mp4', 'Burglary093_x264.mp4', 'Burglary094_x264.mp4', 'Burglary095_x264.mp4', 'Burglary096_x264.mp4', 'Burglary097_x264.mp4', 'Burglary098_x264.mp4', 'Burglary099_x264.mp4', 'Burglary100_x264.mp4']\n", + "Burglary feature extracting ended. Elapsed time: 0:29:33\n", + "Explosion feature extracting starts\n", + "==>> file_list: ['Explosion001_x264.mp4', 'Explosion002_x264.mp4', 'Explosion003_x264.mp4', 'Explosion004_x264.mp4', 'Explosion005_x264.mp4', 'Explosion006_x264.mp4', 'Explosion007_x264.mp4', 'Explosion008_x264.mp4', 'Explosion009_x264.mp4', 'Explosion010_x264.mp4', 'Explosion011_x264.mp4', 'Explosion012_x264.mp4', 'Explosion013_x264.mp4', 'Explosion014_x264.mp4', 'Explosion015_x264.mp4', 'Explosion016_x264.mp4', 'Explosion017_x264.mp4', 'Explosion018_x264.mp4', 'Explosion019_x264.mp4', 'Explosion020_x264.mp4', 'Explosion021_x264.mp4', 'Explosion022_x264.mp4', 'Explosion023_x264.mp4', 'Explosion024_x264.mp4', 'Explosion025_x264.mp4', 'Explosion026_x264.mp4', 'Explosion027_x264.mp4', 'Explosion028_x264.mp4', 'Explosion029_x264.mp4', 'Explosion030_x264.mp4', 'Explosion032_x264.mp4', 'Explosion033_x264.mp4', 'Explosion034_x264.mp4', 'Explosion035_x264.mp4', 'Explosion036_x264.mp4', 'Explosion037_x264.mp4', 'Explosion038_x264.mp4', 'Explosion039_x264.mp4', 'Explosion040_x264.mp4', 'Explosion041_x264.mp4', 'Explosion042_x264.mp4', 'Explosion043_x264.mp4', 'Explosion044_x264.mp4', 'Explosion045_x264.mp4', 'Explosion046_x264.mp4', 'Explosion047_x264.mp4', 'Explosion048_x264.mp4', 'Explosion050_x264.mp4', 'Explosion051_x264.mp4', 'Explosion052_x264.mp4']\n", + "Explosion feature extracting ended. Elapsed time: 0:15:57\n", + "Fighting feature extracting starts\n", + "==>> file_list: ['Fighting002_x264.mp4', 'Fighting003_x264.mp4', 'Fighting004_x264.mp4', 'Fighting005_x264.mp4', 'Fighting006_x264.mp4', 'Fighting007_x264.mp4', 'Fighting008_x264.mp4', 'Fighting009_x264.mp4', 'Fighting010_x264.mp4', 'Fighting011_x264.mp4', 'Fighting012_x264.mp4', 'Fighting013_x264.mp4', 'Fighting014_x264.mp4', 'Fighting015_x264.mp4', 'Fighting016_x264.mp4', 'Fighting017_x264.mp4', 'Fighting018_x264.mp4', 'Fighting019_x264.mp4', 'Fighting020_x264.mp4', 'Fighting021_x264.mp4', 'Fighting022_x264.mp4', 'Fighting023_x264.mp4', 'Fighting024_x264.mp4', 'Fighting025_x264.mp4', 'Fighting026_x264.mp4', 'Fighting027_x264.mp4', 'Fighting028_x264.mp4', 'Fighting029_x264.mp4', 'Fighting030_x264.mp4', 'Fighting031_x264.mp4', 'Fighting032_x264.mp4', 'Fighting033_x264.mp4', 'Fighting034_x264.mp4', 'Fighting035_x264.mp4', 'Fighting036_x264.mp4', 'Fighting037_x264.mp4', 'Fighting038_x264.mp4', 'Fighting039_x264.mp4', 'Fighting040_x264.mp4', 'Fighting041_x264.mp4', 'Fighting042_x264.mp4', 'Fighting043_x264.mp4', 'Fighting044_x264.mp4', 'Fighting045_x264.mp4', 'Fighting046_x264.mp4', 'Fighting047_x264.mp4', 'Fighting048_x264.mp4', 'Fighting049_x264.mp4', 'Fighting050_x264.mp4', 'Fighting051_x264.mp4']\n", + "Fighting feature extracting ended. Elapsed time: 0:16:24\n", + "RoadAccidents feature extracting starts\n", + "==>> file_list: ['RoadAccidents001_x264.mp4', 'RoadAccidents002_x264.mp4', 'RoadAccidents003_x264.mp4', 'RoadAccidents004_x264.mp4', 'RoadAccidents005_x264.mp4', 'RoadAccidents006_x264.mp4', 'RoadAccidents007_x264.mp4', 'RoadAccidents008_x264.mp4', 'RoadAccidents009_x264.mp4', 'RoadAccidents010_x264.mp4', 'RoadAccidents011_x264.mp4', 'RoadAccidents012_x264.mp4', 'RoadAccidents013_x264.mp4', 'RoadAccidents014_x264.mp4', 'RoadAccidents015_x264.mp4', 'RoadAccidents016_x264.mp4', 'RoadAccidents017_x264.mp4', 'RoadAccidents018_x264.mp4', 'RoadAccidents019_x264.mp4', 'RoadAccidents020_x264.mp4', 'RoadAccidents021_x264.mp4', 'RoadAccidents022_x264.mp4', 'RoadAccidents023_x264.mp4', 'RoadAccidents024_x264.mp4', 'RoadAccidents025_x264.mp4', 'RoadAccidents026_x264.mp4', 'RoadAccidents027_x264.mp4', 'RoadAccidents028_x264.mp4', 'RoadAccidents029_x264.mp4', 'RoadAccidents030_x264.mp4', 'RoadAccidents031_x264.mp4', 'RoadAccidents032_x264.mp4', 'RoadAccidents033_x264.mp4', 'RoadAccidents034_x264.mp4', 'RoadAccidents035_x264.mp4', 'RoadAccidents036_x264.mp4', 'RoadAccidents037_x264.mp4', 'RoadAccidents038_x264.mp4', 'RoadAccidents039_x264.mp4', 'RoadAccidents040_x264.mp4', 'RoadAccidents041_x264.mp4', 'RoadAccidents042_x264.mp4', 'RoadAccidents043_x264.mp4', 'RoadAccidents044_x264.mp4', 'RoadAccidents046_x264.mp4', 'RoadAccidents047_x264.mp4', 'RoadAccidents048_x264.mp4', 'RoadAccidents049_x264.mp4', 'RoadAccidents050_x264.mp4', 'RoadAccidents051_x264.mp4', 'RoadAccidents052_x264.mp4', 'RoadAccidents053_x264.mp4', 'RoadAccidents054_x264.mp4', 'RoadAccidents055_x264.mp4', 'RoadAccidents056_x264.mp4', 'RoadAccidents057_x264.mp4', 'RoadAccidents058_x264.mp4', 'RoadAccidents059_x264.mp4', 'RoadAccidents060_x264.mp4', 'RoadAccidents061_x264.mp4', 'RoadAccidents062_x264.mp4', 'RoadAccidents063_x264.mp4', 'RoadAccidents064_x264.mp4', 'RoadAccidents065_x264.mp4', 'RoadAccidents066_x264.mp4', 'RoadAccidents067_x264.mp4', 'RoadAccidents068_x264.mp4', 'RoadAccidents069_x264.mp4', 'RoadAccidents070_x264.mp4', 'RoadAccidents071_x264.mp4', 'RoadAccidents072_x264.mp4', 'RoadAccidents073_x264.mp4', 'RoadAccidents074_x264.mp4', 'RoadAccidents075_x264.mp4', 'RoadAccidents076_x264.mp4', 'RoadAccidents077_x264.mp4', 'RoadAccidents078_x264.mp4', 'RoadAccidents079_x264.mp4', 'RoadAccidents080_x264.mp4', 'RoadAccidents081_x264.mp4', 'RoadAccidents082_x264.mp4', 'RoadAccidents083_x264.mp4', 'RoadAccidents084_x264.mp4', 'RoadAccidents085_x264.mp4', 'RoadAccidents086_x264.mp4', 'RoadAccidents087_x264.mp4', 'RoadAccidents088_x264.mp4', 'RoadAccidents089_x264.mp4', 'RoadAccidents090_x264.mp4', 'RoadAccidents091_x264.mp4', 'RoadAccidents092_x264.mp4', 'RoadAccidents093_x264.mp4', 'RoadAccidents094_x264.mp4', 'RoadAccidents095_x264.mp4', 'RoadAccidents096_x264.mp4', 'RoadAccidents097_x264.mp4', 'RoadAccidents098_x264.mp4', 'RoadAccidents099_x264.mp4', 'RoadAccidents100_x264.mp4', 'RoadAccidents101_x264.mp4', 'RoadAccidents102_x264.mp4', 'RoadAccidents103_x264.mp4', 'RoadAccidents104_x264.mp4', 'RoadAccidents105_x264.mp4', 'RoadAccidents106_x264.mp4', 'RoadAccidents107_x264.mp4', 'RoadAccidents108_x264.mp4', 'RoadAccidents109_x264.mp4', 'RoadAccidents110_x264.mp4', 'RoadAccidents111_x264.mp4', 'RoadAccidents112_x264.mp4', 'RoadAccidents113_x264.mp4', 'RoadAccidents114_x264.mp4', 'RoadAccidents115_x264.mp4', 'RoadAccidents116_x264.mp4', 'RoadAccidents117_x264.mp4', 'RoadAccidents118_x264.mp4', 'RoadAccidents119_x264.mp4', 'RoadAccidents120_x264.mp4', 'RoadAccidents121_x264.mp4', 'RoadAccidents122_x264.mp4', 'RoadAccidents123_x264.mp4', 'RoadAccidents124_x264.mp4', 'RoadAccidents125_x264.mp4', 'RoadAccidents126_x264.mp4', 'RoadAccidents127_x264.mp4', 'RoadAccidents128_x264.mp4', 'RoadAccidents129_x264.mp4', 'RoadAccidents130_x264.mp4', 'RoadAccidents131_x264.mp4', 'RoadAccidents132_x264.mp4', 'RoadAccidents133_x264.mp4', 'RoadAccidents134_x264.mp4', 'RoadAccidents135_x264.mp4', 'RoadAccidents136_x264.mp4', 'RoadAccidents137_x264.mp4', 'RoadAccidents138_x264.mp4', 'RoadAccidents139_x264.mp4', 'RoadAccidents140_x264.mp4', 'RoadAccidents141_x264.mp4', 'RoadAccidents142_x264.mp4', 'RoadAccidents143_x264.mp4', 'RoadAccidents144_x264.mp4', 'RoadAccidents145_x264.mp4', 'RoadAccidents146_x264.mp4', 'RoadAccidents147_x264.mp4', 'RoadAccidents148_x264.mp4', 'RoadAccidents149_x264.mp4', 'RoadAccidents150_x264.mp4', 'RoadAccidents151_x264.mp4']\n", + "RoadAccidents005_x264.mp4 ValueError: need at least one array to concatenate\n", + "RoadAccidents feature extracting ended. Elapsed time: 0:16:31\n", + "Robbery feature extracting starts\n", + "==>> file_list: ['Robbery001_x264.mp4', 'Robbery002_x264.mp4', 'Robbery003_x264.mp4', 'Robbery004_x264.mp4', 'Robbery005_x264.mp4', 'Robbery006_x264.mp4', 'Robbery007_x264.mp4', 'Robbery008_x264.mp4', 'Robbery009_x264.mp4', 'Robbery010_x264.mp4', 'Robbery011_x264.mp4', 'Robbery012_x264.mp4', 'Robbery013_x264.mp4', 'Robbery014_x264.mp4', 'Robbery015_x264.mp4', 'Robbery016_x264.mp4', 'Robbery017_x264.mp4', 'Robbery018_x264.mp4', 'Robbery019_x264.mp4', 'Robbery020_x264.mp4', 'Robbery021_x264.mp4', 'Robbery022_x264.mp4', 'Robbery023_x264.mp4', 'Robbery024_x264.mp4', 'Robbery025_x264.mp4', 'Robbery026_x264.mp4', 'Robbery027_x264.mp4', 'Robbery028_x264.mp4', 'Robbery029_x264.mp4', 'Robbery030_x264.mp4', 'Robbery031_x264.mp4', 'Robbery032_x264.mp4', 'Robbery033_x264.mp4', 'Robbery034_x264.mp4', 'Robbery035_x264.mp4', 'Robbery036_x264.mp4', 'Robbery037_x264.mp4', 'Robbery038_x264.mp4', 'Robbery039_x264.mp4', 'Robbery040_x264.mp4', 'Robbery041_x264.mp4', 'Robbery042_x264.mp4', 'Robbery043_x264.mp4', 'Robbery044_x264.mp4', 'Robbery045_x264.mp4', 'Robbery046_x264.mp4', 'Robbery047_x264.mp4', 'Robbery048_x264.mp4', 'Robbery049_x264.mp4', 'Robbery050_x264.mp4', 'Robbery051_x264.mp4', 'Robbery052_x264.mp4', 'Robbery053_x264.mp4', 'Robbery054_x264.mp4', 'Robbery055_x264.mp4', 'Robbery056_x264.mp4', 'Robbery057_x264.mp4', 'Robbery058_x264.mp4', 'Robbery059_x264.mp4', 'Robbery060_x264.mp4', 'Robbery061_x264.mp4', 'Robbery062_x264.mp4', 'Robbery063_x264.mp4', 'Robbery064_x264.mp4', 'Robbery065_x264.mp4', 'Robbery066_x264.mp4', 'Robbery067_x264.mp4', 'Robbery068_x264.mp4', 'Robbery069_x264.mp4', 'Robbery070_x264.mp4', 'Robbery071_x264.mp4', 'Robbery072_x264.mp4', 'Robbery073_x264.mp4', 'Robbery074_x264.mp4', 'Robbery075_x264.mp4', 'Robbery076_x264.mp4', 'Robbery077_x264.mp4', 'Robbery078_x264.mp4', 'Robbery079_x264.mp4', 'Robbery080_x264.mp4', 'Robbery081_x264.mp4', 'Robbery082_x264.mp4', 'Robbery083_x264.mp4', 'Robbery084_x264.mp4', 'Robbery085_x264.mp4', 'Robbery086_x264.mp4', 'Robbery087_x264.mp4', 'Robbery088_x264.mp4', 'Robbery089_x264.mp4', 'Robbery090_x264.mp4', 'Robbery091_x264.mp4', 'Robbery092_x264.mp4', 'Robbery093_x264.mp4', 'Robbery094_x264.mp4', 'Robbery095_x264.mp4', 'Robbery096_x264.mp4', 'Robbery097_x264.mp4', 'Robbery098_x264.mp4', 'Robbery099_x264.mp4', 'Robbery100_x264.mp4', 'Robbery101_x264.mp4', 'Robbery102_x264.mp4', 'Robbery103_x264.mp4', 'Robbery104_x264.mp4', 'Robbery105_x264.mp4', 'Robbery106_x264.mp4', 'Robbery107_x264.mp4', 'Robbery108_x264.mp4', 'Robbery109_x264.mp4', 'Robbery110_x264.mp4', 'Robbery111_x264.mp4', 'Robbery112_x264.mp4', 'Robbery113_x264.mp4', 'Robbery114_x264.mp4', 'Robbery115_x264.mp4', 'Robbery116_x264.mp4', 'Robbery117_x264.mp4', 'Robbery118_x264.mp4', 'Robbery119_x264.mp4', 'Robbery120_x264.mp4', 'Robbery121_x264.mp4', 'Robbery122_x264.mp4', 'Robbery123_x264.mp4', 'Robbery124_x264.mp4', 'Robbery125_x264.mp4', 'Robbery126_x264.mp4', 'Robbery127_x264.mp4', 'Robbery128_x264.mp4', 'Robbery129_x264.mp4', 'Robbery130_x264.mp4', 'Robbery131_x264.mp4', 'Robbery132_x264.mp4', 'Robbery133_x264.mp4', 'Robbery134_x264.mp4', 'Robbery135_x264.mp4', 'Robbery136_x264.mp4', 'Robbery137_x264.mp4', 'Robbery138_x264.mp4', 'Robbery139_x264.mp4', 'Robbery140_x264.mp4', 'Robbery141_x264.mp4', 'Robbery142_x264.mp4', 'Robbery143_x264.mp4', 'Robbery144_x264.mp4', 'Robbery145_x264.mp4', 'Robbery146_x264.mp4', 'Robbery147_x264.mp4', 'Robbery148_x264.mp4', 'Robbery149_x264.mp4', 'Robbery150_x264.mp4']\n", + "Robbery feature extracting ended. Elapsed time: 0:26:40\n", + "Shooting feature extracting starts\n", + "==>> file_list: ['Shooting001_x264.mp4', 'Shooting002_x264.mp4', 'Shooting003_x264.mp4', 'Shooting004_x264.mp4', 'Shooting005_x264.mp4', 'Shooting006_x264.mp4', 'Shooting007_x264.mp4', 'Shooting008_x264.mp4', 'Shooting009_x264.mp4', 'Shooting010_x264.mp4', 'Shooting011_x264.mp4', 'Shooting012_x264.mp4', 'Shooting013_x264.mp4', 'Shooting014_x264.mp4', 'Shooting015_x264.mp4', 'Shooting017_x264.mp4', 'Shooting018_x264.mp4', 'Shooting019_x264.mp4', 'Shooting020_x264.mp4', 'Shooting021_x264.mp4', 'Shooting022_x264.mp4', 'Shooting023_x264.mp4', 'Shooting024_x264.mp4', 'Shooting025_x264.mp4', 'Shooting026_x264.mp4', 'Shooting027_x264.mp4', 'Shooting028_x264.mp4', 'Shooting029_x264.mp4', 'Shooting030_x264.mp4', 'Shooting031_x264.mp4', 'Shooting032_x264.mp4', 'Shooting033_x264.mp4', 'Shooting034_x264.mp4', 'Shooting036_x264.mp4', 'Shooting037_x264.mp4', 'Shooting038_x264.mp4', 'Shooting039_x264.mp4', 'Shooting040_x264.mp4', 'Shooting041_x264.mp4', 'Shooting042_x264.mp4', 'Shooting043_x264.mp4', 'Shooting044_x264.mp4', 'Shooting046_x264.mp4', 'Shooting047_x264.mp4', 'Shooting048_x264.mp4', 'Shooting050_x264.mp4', 'Shooting051_x264.mp4', 'Shooting052_x264.mp4', 'Shooting053_x264.mp4', 'Shooting054_x264.mp4']\n", + "Shooting feature extracting ended. Elapsed time: 0:09:18\n", + "Shoplifting feature extracting starts\n", + "==>> file_list: ['Shoplifting001_x264.mp4', 'Shoplifting003_x264.mp4', 'Shoplifting004_x264.mp4', 'Shoplifting005_x264.mp4', 'Shoplifting006_x264.mp4', 'Shoplifting007_x264.mp4', 'Shoplifting008_x264.mp4', 'Shoplifting009_x264.mp4', 'Shoplifting010_x264.mp4', 'Shoplifting012_x264.mp4', 'Shoplifting013_x264.mp4', 'Shoplifting014_x264.mp4', 'Shoplifting015_x264.mp4', 'Shoplifting016_x264.mp4', 'Shoplifting017_x264.mp4', 'Shoplifting018_x264.mp4', 'Shoplifting019_x264.mp4', 'Shoplifting020_x264.mp4', 'Shoplifting021_x264.mp4', 'Shoplifting022_x264.mp4', 'Shoplifting024_x264.mp4', 'Shoplifting025_x264.mp4', 'Shoplifting026_x264.mp4', 'Shoplifting027_x264.mp4', 'Shoplifting028_x264.mp4', 'Shoplifting029_x264.mp4', 'Shoplifting030_x264.mp4', 'Shoplifting031_x264.mp4', 'Shoplifting032_x264.mp4', 'Shoplifting033_x264.mp4', 'Shoplifting034_x264.mp4', 'Shoplifting036_x264.mp4', 'Shoplifting037_x264.mp4', 'Shoplifting038_x264.mp4', 'Shoplifting039_x264.mp4', 'Shoplifting040_x264.mp4', 'Shoplifting041_x264.mp4', 'Shoplifting042_x264.mp4', 'Shoplifting043_x264.mp4', 'Shoplifting044_x264.mp4', 'Shoplifting045_x264.mp4', 'Shoplifting047_x264.mp4', 'Shoplifting048_x264.mp4', 'Shoplifting049_x264.mp4', 'Shoplifting050_x264.mp4', 'Shoplifting051_x264.mp4', 'Shoplifting052_x264.mp4', 'Shoplifting053_x264.mp4', 'Shoplifting054_x264.mp4', 'Shoplifting055_x264.mp4']\n", + "Shoplifting feature extracting ended. Elapsed time: 0:20:30\n", + "Stealing feature extracting starts\n", + "==>> file_list: ['Stealing002_x264.mp4', 'Stealing003_x264.mp4', 'Stealing004_x264.mp4', 'Stealing006_x264.mp4', 'Stealing007_x264.mp4', 'Stealing008_x264.mp4', 'Stealing009_x264.mp4', 'Stealing010_x264.mp4', 'Stealing011_x264.mp4', 'Stealing012_x264.mp4', 'Stealing013_x264.mp4', 'Stealing014_x264.mp4', 'Stealing015_x264.mp4', 'Stealing016_x264.mp4', 'Stealing017_x264.mp4', 'Stealing018_x264.mp4', 'Stealing019_x264.mp4', 'Stealing020_x264.mp4', 'Stealing021_x264.mp4', 'Stealing022_x264.mp4', 'Stealing023_x264.mp4', 'Stealing024_x264.mp4', 'Stealing025_x264.mp4', 'Stealing026_x264.mp4', 'Stealing027_x264.mp4', 'Stealing028_x264.mp4', 'Stealing029_x264.mp4', 'Stealing030_x264.mp4', 'Stealing031_x264.mp4', 'Stealing032_x264.mp4', 'Stealing035_x264.mp4', 'Stealing036_x264.mp4', 'Stealing037_x264.mp4', 'Stealing042_x264.mp4', 'Stealing043_x264.mp4', 'Stealing044_x264.mp4', 'Stealing045_x264.mp4', 'Stealing046_x264.mp4', 'Stealing047_x264.mp4', 'Stealing048_x264.mp4', 'Stealing049_x264.mp4', 'Stealing050_x264.mp4', 'Stealing051_x264.mp4', 'Stealing052_x264.mp4', 'Stealing053_x264.mp4', 'Stealing054_x264.mp4', 'Stealing055_x264.mp4', 'Stealing057_x264.mp4', 'Stealing058_x264.mp4', 'Stealing059_x264.mp4', 'Stealing060_x264.mp4', 'Stealing061_x264.mp4', 'Stealing062_x264.mp4', 'Stealing063_x264.mp4', 'Stealing065_x264.mp4', 'Stealing066_x264.mp4', 'Stealing067_x264.mp4', 'Stealing068_x264.mp4', 'Stealing069_x264.mp4', 'Stealing070_x264.mp4', 'Stealing071_x264.mp4', 'Stealing072_x264.mp4', 'Stealing073_x264.mp4', 'Stealing074_x264.mp4', 'Stealing075_x264.mp4', 'Stealing077_x264.mp4', 'Stealing078_x264.mp4', 'Stealing079_x264.mp4', 'Stealing080_x264.mp4', 'Stealing081_x264.mp4', 'Stealing082_x264.mp4', 'Stealing083_x264.mp4', 'Stealing084_x264.mp4', 'Stealing086_x264.mp4', 'Stealing087_x264.mp4', 'Stealing088_x264.mp4', 'Stealing089_x264.mp4', 'Stealing091_x264.mp4', 'Stealing092_x264.mp4', 'Stealing093_x264.mp4', 'Stealing094_x264.mp4', 'Stealing095_x264.mp4', 'Stealing096_x264.mp4', 'Stealing097_x264.mp4', 'Stealing098_x264.mp4', 'Stealing100_x264.mp4', 'Stealing101_x264.mp4', 'Stealing102_x264.mp4', 'Stealing103_x264.mp4', 'Stealing104_x264.mp4', 'Stealing105_x264.mp4', 'Stealing106_x264.mp4', 'Stealing107_x264.mp4', 'Stealing108_x264.mp4', 'Stealing109_x264.mp4', 'Stealing110_x264.mp4', 'Stealing111_x264.mp4', 'Stealing112_x264.mp4', 'Stealing113_x264.mp4', 'Stealing114_x264.mp4']\n", + "Stealing feature extracting ended. Elapsed time: 0:29:19\n", + "Vandalism feature extracting starts\n", + "==>> file_list: ['Vandalism001_x264.mp4', 'Vandalism002_x264.mp4', 'Vandalism003_x264.mp4', 'Vandalism004_x264.mp4', 'Vandalism005_x264.mp4', 'Vandalism006_x264.mp4', 'Vandalism007_x264.mp4', 'Vandalism008_x264.mp4', 'Vandalism009_x264.mp4', 'Vandalism010_x264.mp4', 'Vandalism011_x264.mp4', 'Vandalism012_x264.mp4', 'Vandalism013_x264.mp4', 'Vandalism014_x264.mp4', 'Vandalism015_x264.mp4', 'Vandalism016_x264.mp4', 'Vandalism017_x264.mp4', 'Vandalism018_x264.mp4', 'Vandalism019_x264.mp4', 'Vandalism020_x264.mp4', 'Vandalism021_x264.mp4', 'Vandalism022_x264.mp4', 'Vandalism023_x264.mp4', 'Vandalism024_x264.mp4', 'Vandalism025_x264.mp4', 'Vandalism026_x264.mp4', 'Vandalism027_x264.mp4', 'Vandalism028_x264.mp4', 'Vandalism029_x264.mp4', 'Vandalism030_x264.mp4', 'Vandalism031_x264.mp4', 'Vandalism032_x264.mp4', 'Vandalism033_x264.mp4', 'Vandalism034_x264.mp4', 'Vandalism035_x264.mp4', 'Vandalism036_x264.mp4', 'Vandalism037_x264.mp4', 'Vandalism038_x264.mp4', 'Vandalism039_x264.mp4', 'Vandalism040_x264.mp4', 'Vandalism041_x264.mp4', 'Vandalism042_x264.mp4', 'Vandalism043_x264.mp4', 'Vandalism044_x264.mp4', 'Vandalism045_x264.mp4', 'Vandalism046_x264.mp4', 'Vandalism047_x264.mp4', 'Vandalism048_x264.mp4', 'Vandalism049_x264.mp4', 'Vandalism050_x264.mp4']\n", + "Vandalism feature extracting ended. Elapsed time: 0:09:15\n" + ] + } + ], + "source": [ + "for folder_name in folder_list:\n", + " time_start = datetime.now()\n", + "\n", + " print(f\"{folder_name} feature extracting starts\")\n", + "\n", + " if not os.path.exists(npy_root + folder_name + \"_base\"):\n", + " os.makedirs(npy_root + folder_name + \"_base\")\n", + "\n", + " folder_path = root + folder_name + \"/\"\n", + "\n", + " file_list = os.listdir(root + folder_name)\n", + " file_list.sort()\n", + " print(f\"==>> file_list: {file_list}\")\n", + "\n", + " batch_size = 16\n", + " # Loop through the video frames\n", + " for file_name in file_list:\n", + " path = folder_path + file_name\n", + "\n", + " cap = cv2.VideoCapture(path)\n", + "\n", + " # 710차원 feature array 저장할 list\n", + " np_list = []\n", + "\n", + " # 16 * segments_num 프레임씩 저장할 list\n", + " frames = []\n", + " frame_count = 0\n", + "\n", + " # input tensor 저장할 list\n", + " input_list = []\n", + " input_count = 0\n", + "\n", + " while cap.isOpened():\n", + " # Read a frame from the video\n", + " success, frame = cap.read()\n", + " # frame.shape = (height, width, 3)\n", + "\n", + " frame_count += 1 # Increment frame count\n", + "\n", + " if success:\n", + " frame = tf(image=frame)[\"image\"]\n", + " # frame.shape = (224, 224, 3)\n", + "\n", + " frame = np.expand_dims(frame, axis=0)\n", + " # frame.shape = (1, 224, 224, 3)\n", + " frames.append(frame.copy())\n", + "\n", + " if frame_count == 16 * segments_num:\n", + " assert len(frames) == 16 * segments_num\n", + " frames = np.concatenate(frames)\n", + " # in_frames.shape = (16 * segments_num, 224, 224, 3)\n", + " in_frames = frames.transpose(3, 0, 1, 2)\n", + " # # in_frames.shape = (RGB 3, frame T=16 * segments_num, H=224, W=224)\n", + " in_frames = np.expand_dims(in_frames, axis=0)\n", + " # in_frames.shape = (1, 3, 16 * segments_num, 224, 224)\n", + " in_frames = torch.from_numpy(in_frames).float()\n", + " # in_frames.shape == torch.Size([1, 3, 16 * segments_num, 224, 224])\n", + "\n", + " input_list.append(in_frames.detach().clone())\n", + "\n", + " frame_count = 0\n", + " frames = []\n", + "\n", + " input_count += 1\n", + "\n", + " if input_count == batch_size:\n", + " # input_batch.shape == torch.Size([batch_size, 3, 16 * segments_num, 224, 224])\n", + " input_batch = torch.cat(input_list, dim=0).to(\"cuda\")\n", + "\n", + " with torch.no_grad():\n", + " output = model(input_batch)\n", + " # output.shape == torch.Size([batch_size, 710])\n", + "\n", + " np_list.append(output.cpu().numpy())\n", + "\n", + " input_count = 0\n", + " input_list = []\n", + " else:\n", + " # 남은 프레임, input_list가 지정 개수에서 모자를 때 예외 처리\n", + " if frame_count != 0 and len(frames) != 0:\n", + " # @@ success가 false 일때도 frame_count는 +1이 된다\n", + " # @@ => frames = []로 초기화 된 바로 다음 frame에 success가 false가 되면\n", + " # @@ => frame_count == 1 이지만 len(frames) == 0\n", + " len_frames_left = 16 * segments_num - len(frames)\n", + " # len_input_list_left = batch_size - len(input_list)\n", + " for i in range(len_frames_left):\n", + " frames.append(frames[-1].copy())\n", + "\n", + " assert len(frames) == 16 * segments_num\n", + "\n", + " frames = np.concatenate(frames)\n", + " # in_frames.shape = (16 * segments_num, 224, 224, 3)\n", + " in_frames = frames.transpose(3, 0, 1, 2)\n", + " # # in_frames.shape = (RGB 3, frame T=16 * segments_num, H=224, W=224)\n", + " in_frames = np.expand_dims(in_frames, axis=0)\n", + " # in_frames.shape = (1, 3, 16 * segments_num, 224, 224)\n", + " in_frames = torch.from_numpy(in_frames).float()\n", + " # in_frames.shape == torch.Size([1, 3, 16 * segments_num, 224, 224])\n", + "\n", + " input_list.append(in_frames.detach().clone())\n", + "\n", + " # assert len(input_list) == batch_size\n", + "\n", + " # input_batch.shape == torch.Size([batch_size, 3, 16 * segments_num, 224, 224])\n", + " input_batch = torch.cat(input_list, dim=0).to(\"cuda\")\n", + "\n", + " with torch.no_grad():\n", + " output = model(input_batch)\n", + " # output.shape == torch.Size([len(input_list), 710])\n", + "\n", + " np_list.append(output.cpu().numpy())\n", + "\n", + " frame_count = 0\n", + " frames = []\n", + " input_count = 0\n", + " input_list = []\n", + "\n", + " # Break the loop if the end of the video is reached\n", + " break\n", + "\n", + " try:\n", + " file_outputs = np.concatenate(np_list)\n", + " # print(f\"==>> file_outputs.shape: {file_outputs.shape}\")\n", + " np.save((npy_root + folder_name + \"_base/\" + file_name), file_outputs)\n", + " except ValueError:\n", + " print(f\"{file_name} ValueError: need at least one array to concatenate\")\n", + "\n", + " cap.release()\n", + "\n", + " time_end = datetime.now()\n", + " total_time = time_end - time_start\n", + " total_time = str(total_time).split(\".\")[0]\n", + "\n", + " print(f\"{folder_name} feature extracting ended. Elapsed time: {total_time}\")\n", + "\n", + " # cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/datapreprocess/feature_extraction_videoMAEv2.py b/datapreprocess/feature_extraction_videoMAEv2.py new file mode 100644 index 0000000..1f68daf --- /dev/null +++ b/datapreprocess/feature_extraction_videoMAEv2.py @@ -0,0 +1,365 @@ +# from collections import defaultdict +import argparse +import cv2 +import numpy as np +import os +from copy import deepcopy +from datetime import datetime +import torch + +# import torch.nn as nn +# import torch.nn.functional as F +import albumentations as A +import matplotlib.pyplot as plt + +# from datetime import datetime +import models +from timm.models import create_model +from tqdm import tqdm + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +print(torch.cuda.is_available()) + + +parser = argparse.ArgumentParser(description="Feature Extraction") + +parser.add_argument( + "--root", type=str, help="root folder path", default="/data/ephemeral/home/datasets/UCFCrime/normal/" +) + +args = parser.parse_args() + + +root = "/data/ephemeral/home/datasets/UCFCrime/normal/" + +npy_root = "./npy/" + +if not os.path.exists(npy_root): + os.makedirs(npy_root) + + +folder_list = os.listdir(root) +folder_list.sort() +print(f"==>> folder_list: {folder_list}") + +segments_num = 1 +# 모델에 들어갈 frame수는 16 * segments_num + +model = create_model( + "vit_small_patch16_224", + img_size=224, + pretrained=False, + num_classes=710, + all_frames=16 * segments_num, +) + +load_dict = torch.load( + "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/vit_s_k710_dl_from_giant.pth" +) + +model.load_state_dict(load_dict["module"]) + +model.to("cuda") +model.eval() + +tf = A.Resize(224, 224) + +for folder_name in folder_list: + + time_start = datetime.now() + + print(f"{folder_name} feature extracting starts") + + if not os.path.exists(npy_root + folder_name): + os.makedirs(npy_root + folder_name) + + folder_path = root + folder_name + "/" + + file_list = os.listdir(root + folder_name) + file_list.sort() + print(f"==>> file_list: {file_list}") + + batch_size = 16 + # Loop through the video frames + for file_name in tqdm(file_list, total=len(file_list)): + path = folder_path + file_name + + cap = cv2.VideoCapture(path) + + # 710차원 feature array 저장할 list + np_list = [] + + # 16 * segments_num 프레임씩 저장할 list + frames = [] + frame_count = 0 + + # input tensor 저장할 list + input_list = [] + input_count = 0 + + while cap.isOpened(): + # Read a frame from the video + success, frame = cap.read() + # frame.shape = (height, width, 3) + + frame_count += 1 # Increment frame count + + if success: + frame = tf(image=frame)["image"] + # frame.shape = (224, 224, 3) + + frame = np.expand_dims(frame, axis=0) + # frame.shape = (1, 224, 224, 3) + frames.append(frame.copy()) + + if frame_count == 16 * segments_num: + assert len(frames) == 16 * segments_num + frames = np.concatenate(frames) + # in_frames.shape = (16 * segments_num, 224, 224, 3) + in_frames = frames.transpose(3, 0, 1, 2) + # # in_frames.shape = (RGB 3, frame T=16 * segments_num, H=224, W=224) + in_frames = np.expand_dims(in_frames, axis=0) + # in_frames.shape = (1, 3, 16 * segments_num, 224, 224) + in_frames = torch.from_numpy(in_frames).float() + # in_frames.shape == torch.Size([1, 3, 16 * segments_num, 224, 224]) + + input_list.append(in_frames.detach().clone()) + + frame_count = 0 + frames = [] + + input_count += 1 + + if input_count == batch_size: + # input_batch.shape == torch.Size([batch_size, 3, 16 * segments_num, 224, 224]) + input_batch = torch.cat(input_list, dim=0).to("cuda") + + with torch.no_grad(): + output = model(input_batch) + # output.shape == torch.Size([batch_size, 710]) + + np_list.append(output.cpu().numpy()) + + input_count = 0 + input_list = [] + else: + # 남은 프레임, input_list가 지정 개수에서 모자를 때 예외 처리 + if frame_count != 0 and len(frames) != 0: + # @@ success가 false 일때도 frame_count는 +1이 된다 + # @@ => frames = []로 초기화 된 바로 다음 frame에 success가 false가 되면 + # @@ => frame_count == 1 이지만 len(frames) == 0 + len_frames_left = 16 * segments_num - len(frames) + # len_input_list_left = batch_size - len(input_list) + + # assert len(frames) != 0 + + for i in range(len_frames_left): + try: + frames.append(frames[-1].copy()) + except IndexError: + print(f"==>> len(frames): {len(frames)}") + print(f"==>> len_frames_left: {len_frames_left}") + + assert len(frames) == 16 * segments_num + + frames = np.concatenate(frames) + # in_frames.shape = (16 * segments_num, 224, 224, 3) + in_frames = frames.transpose(3, 0, 1, 2) + # # in_frames.shape = (RGB 3, frame T=16 * segments_num, H=224, W=224) + in_frames = np.expand_dims(in_frames, axis=0) + # in_frames.shape = (1, 3, 16 * segments_num, 224, 224) + in_frames = torch.from_numpy(in_frames).float() + # in_frames.shape == torch.Size([1, 3, 16 * segments_num, 224, 224]) + + input_list.append(in_frames.detach().clone()) + + # assert len(input_list) == batch_size + + # input_batch.shape == torch.Size([batch_size, 3, 16 * segments_num, 224, 224]) + input_batch = torch.cat(input_list, dim=0).to("cuda") + + with torch.no_grad(): + output = model(input_batch) + # output.shape == torch.Size([len(input_list), 710]) + + np_list.append(output.cpu().numpy()) + + frame_count = 0 + frames = [] + input_count = 0 + input_list = [] + + # Break the loop if the end of the video is reached + break + try: + file_outputs = np.concatenate(np_list) + # print(f"==>> file_outputs.shape: {file_outputs.shape}") + np.save((npy_root + folder_name + "/" + file_name), file_outputs) + except ValueError: + print(f"{file_name} ValueError: need at least one array to concatenate") + + cap.release() + + time_end = datetime.now() + total_time = time_end - time_start + total_time = str(total_time).split(".")[0] + + print(f"{folder_name} feature extracting ended. Elapsed time: {total_time}") + +# segments_num = 1 +# 모델에 들어갈 frame수는 16 * segments_num + +model = create_model( + "vit_base_patch16_224", + img_size=224, + pretrained=False, + num_classes=710, + all_frames=16 * segments_num, +) + +load_dict = torch.load( + "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/vit_b_k710_dl_from_giant.pth" +) +# backbone pth 경로 + +model.load_state_dict(load_dict["module"]) + +model.to("cuda") +model.eval() + +tf = A.Resize(224, 224) + +for folder_name in folder_list: + time_start = datetime.now() + + print(f"{folder_name} feature extracting starts") + + if not os.path.exists(npy_root + folder_name + "_base"): + os.makedirs(npy_root + folder_name + "_base") + + folder_path = root + folder_name + "/" + + file_list = os.listdir(root + folder_name) + file_list.sort() + print(f"==>> file_list: {file_list}") + + batch_size = 16 + # Loop through the video frames + for file_name in tqdm(file_list, total=len(file_list)): + path = folder_path + file_name + + cap = cv2.VideoCapture(path) + + # 710차원 feature array 저장할 list + np_list = [] + + # 16 * segments_num 프레임씩 저장할 list + frames = [] + frame_count = 0 + + # input tensor 저장할 list + input_list = [] + input_count = 0 + + while cap.isOpened(): + # Read a frame from the video + success, frame = cap.read() + # frame.shape = (height, width, 3) + + frame_count += 1 # Increment frame count + + if success: + frame = tf(image=frame)["image"] + # frame.shape = (224, 224, 3) + + frame = np.expand_dims(frame, axis=0) + # frame.shape = (1, 224, 224, 3) + frames.append(frame.copy()) + + if frame_count == 16 * segments_num: + assert len(frames) == 16 * segments_num + frames = np.concatenate(frames) + # in_frames.shape = (16 * segments_num, 224, 224, 3) + in_frames = frames.transpose(3, 0, 1, 2) + # # in_frames.shape = (RGB 3, frame T=16 * segments_num, H=224, W=224) + in_frames = np.expand_dims(in_frames, axis=0) + # in_frames.shape = (1, 3, 16 * segments_num, 224, 224) + in_frames = torch.from_numpy(in_frames).float() + # in_frames.shape == torch.Size([1, 3, 16 * segments_num, 224, 224]) + + input_list.append(in_frames.detach().clone()) + + frame_count = 0 + frames = [] + + input_count += 1 + + if input_count == batch_size: + # input_batch.shape == torch.Size([batch_size, 3, 16 * segments_num, 224, 224]) + input_batch = torch.cat(input_list, dim=0).to("cuda") + + with torch.no_grad(): + output = model(input_batch) + # output.shape == torch.Size([batch_size, 710]) + + np_list.append(output.cpu().numpy()) + + input_count = 0 + input_list = [] + else: + # 남은 프레임, input_list가 지정 개수에서 모자를 때 예외 처리 + if frame_count != 0 and len(frames) != 0: + # @@ success가 false 일때도 frame_count는 +1이 된다 + # @@ => frames = []로 초기화 된 바로 다음 frame에 success가 false가 되면 + # @@ => frame_count == 1 이지만 len(frames) == 0 + len_frames_left = 16 * segments_num - len(frames) + # len_input_list_left = batch_size - len(input_list) + for i in range(len_frames_left): + frames.append(frames[-1].copy()) + + assert len(frames) == 16 * segments_num + + frames = np.concatenate(frames) + # in_frames.shape = (16 * segments_num, 224, 224, 3) + in_frames = frames.transpose(3, 0, 1, 2) + # # in_frames.shape = (RGB 3, frame T=16 * segments_num, H=224, W=224) + in_frames = np.expand_dims(in_frames, axis=0) + # in_frames.shape = (1, 3, 16 * segments_num, 224, 224) + in_frames = torch.from_numpy(in_frames).float() + # in_frames.shape == torch.Size([1, 3, 16 * segments_num, 224, 224]) + + input_list.append(in_frames.detach().clone()) + + # assert len(input_list) == batch_size + + # input_batch.shape == torch.Size([batch_size, 3, 16 * segments_num, 224, 224]) + input_batch = torch.cat(input_list, dim=0).to("cuda") + + with torch.no_grad(): + output = model(input_batch) + # output.shape == torch.Size([len(input_list), 710]) + + np_list.append(output.cpu().numpy()) + + frame_count = 0 + frames = [] + input_count = 0 + input_list = [] + + # Break the loop if the end of the video is reached + break + + try: + file_outputs = np.concatenate(np_list) + # print(f"==>> file_outputs.shape: {file_outputs.shape}") + np.save((npy_root + folder_name + "_base/" + file_name), file_outputs) + except ValueError: + print(f"{file_name} ValueError: need at least one array to concatenate") + + cap.release() + + time_end = datetime.now() + total_time = time_end - time_start + total_time = str(total_time).split(".")[0] + + print(f"{folder_name} feature extracting ended. Elapsed time: {total_time}") diff --git a/datapreprocess/models/__init__.py b/datapreprocess/models/__init__.py new file mode 100644 index 0000000..2b7069a --- /dev/null +++ b/datapreprocess/models/__init__.py @@ -0,0 +1,27 @@ +from .modeling_finetune import ( + vit_base_patch16_224, + vit_giant_patch14_224, + vit_huge_patch16_224, + vit_large_patch16_224, + vit_small_patch16_224, +) +from .modeling_pretrain import ( + pretrain_videomae_base_patch16_224, + pretrain_videomae_giant_patch14_224, + pretrain_videomae_huge_patch16_224, + pretrain_videomae_large_patch16_224, + pretrain_videomae_small_patch16_224, +) + +__all__ = [ + 'pretrain_videomae_small_patch16_224', + 'pretrain_videomae_base_patch16_224', + 'pretrain_videomae_large_patch16_224', + 'pretrain_videomae_huge_patch16_224', + 'pretrain_videomae_giant_patch14_224', + 'vit_small_patch16_224', + 'vit_base_patch16_224', + 'vit_large_patch16_224', + 'vit_huge_patch16_224', + 'vit_giant_patch14_224', +] \ No newline at end of file diff --git a/datapreprocess/models/modeling_finetune.py b/datapreprocess/models/modeling_finetune.py new file mode 100644 index 0000000..b63b475 --- /dev/null +++ b/datapreprocess/models/modeling_finetune.py @@ -0,0 +1,529 @@ +# -------------------------------------------------------- +# Based on BEiT, timm, DINO and DeiT code bases +# https://github.com/microsoft/unilm/tree/master/beit +# https://github.com/rwightman/pytorch-image-models/tree/master/timm +# https://github.com/facebookresearch/deit +# https://github.com/facebookresearch/dino +# --------------------------------------------------------' +from functools import partial + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from timm.models.layers import drop_path, to_2tuple, trunc_normal_ +from timm.models.registry import register_model + + +def _cfg(url='', **kwargs): + return { + 'url': url, + 'num_classes': 400, + 'input_size': (3, 224, 224), + 'pool_size': None, + 'crop_pct': .9, + 'interpolation': 'bicubic', + 'mean': (0.5, 0.5, 0.5), + 'std': (0.5, 0.5, 0.5), + **kwargs + } + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return 'p={}'.format(self.drop_prob) + + +class Mlp(nn.Module): + + def __init__(self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + # x = self.drop(x) + # commit this for the orignal BERT implement + x = self.fc2(x) + x = self.drop(x) + return x + + +class CosAttention(nn.Module): + + def __init__(self, + dim, + num_heads=8, + qkv_bias=False, + qk_scale=None, + attn_drop=0., + proj_drop=0., + attn_head_dim=None): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + # self.scale = qk_scale or head_dim**-0.5 + # DO NOT RENAME [self.scale] (for no weight decay) + if qk_scale is None: + self.scale = nn.Parameter( + torch.log(10 * torch.ones((num_heads, 1, 1))), + requires_grad=True) + else: + self.scale = qk_scale + + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat( + (self.q_bias, + torch.zeros_like(self.v_bias, + requires_grad=False), self.v_bias)) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[ + 2] # make torchscript happy (cannot use tensor as tuple) + + attn = ( + F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) + + # torch.log(torch.tensor(1. / 0.01)) = 4.6052 + logit_scale = torch.clamp(self.scale, max=4.6052).exp() + + attn = attn * logit_scale + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Attention(nn.Module): + + def __init__(self, + dim, + num_heads=8, + qkv_bias=False, + qk_scale=None, + attn_drop=0., + proj_drop=0., + attn_head_dim=None): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat( + (self.q_bias, + torch.zeros_like(self.v_bias, + requires_grad=False), self.v_bias)) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[ + 2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__(self, + dim, + num_heads, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop=0., + attn_drop=0., + drop_path=0., + init_values=None, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + attn_head_dim=None, + cos_attn=False): + super().__init__() + self.norm1 = norm_layer(dim) + if cos_attn: + self.attn = CosAttention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + attn_head_dim=attn_head_dim) + else: + self.attn = Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + attn_head_dim=attn_head_dim) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath( + drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop) + + if init_values > 0: + self.gamma_1 = nn.Parameter( + init_values * torch.ones((dim)), requires_grad=True) + self.gamma_2 = nn.Parameter( + init_values * torch.ones((dim)), requires_grad=True) + else: + self.gamma_1, self.gamma_2 = None, None + + def forward(self, x): + if self.gamma_1 is None: + x = x + self.drop_path(self.attn(self.norm1(x))) + x = x + self.drop_path(self.mlp(self.norm2(x))) + else: + x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x))) + x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + + def __init__(self, + img_size=224, + patch_size=16, + in_chans=3, + embed_dim=768, + num_frames=16, + tubelet_size=2): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + num_spatial_patches = (img_size[0] // patch_size[0]) * ( + img_size[1] // patch_size[1]) + num_patches = num_spatial_patches * (num_frames // tubelet_size) + + self.img_size = img_size + self.tubelet_size = tubelet_size + self.patch_size = patch_size + self.num_patches = num_patches + self.proj = nn.Conv3d( + in_channels=in_chans, + out_channels=embed_dim, + kernel_size=(self.tubelet_size, patch_size[0], patch_size[1]), + stride=(self.tubelet_size, patch_size[0], patch_size[1])) + + def forward(self, x, **kwargs): + B, C, T, H, W = x.shape + assert H == self.img_size[0] and W == self.img_size[ + 1], f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + # b, c, l -> b, l, c + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +# sin-cos position encoding +# https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31 +def get_sinusoid_encoding_table(n_position, d_hid): + ''' Sinusoid position encoding table ''' + + # TODO: make it with torch instead of numpy + def get_position_angle_vec(position): + return [ + position / np.power(10000, 2 * (hid_j // 2) / d_hid) + for hid_j in range(d_hid) + ] + + sinusoid_table = np.array( + [get_position_angle_vec(pos_i) for pos_i in range(n_position)]) + sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i + sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 + + return torch.tensor( + sinusoid_table, dtype=torch.float, requires_grad=False).unsqueeze(0) + + +class VisionTransformer(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + + def __init__(self, + img_size=224, + patch_size=16, + in_chans=3, + num_classes=1000, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + head_drop_rate=0., + norm_layer=nn.LayerNorm, + init_values=0., + use_learnable_pos_emb=False, + init_scale=0., + all_frames=16, + tubelet_size=2, + use_mean_pooling=True, + with_cp=False, + cos_attn=False): + super().__init__() + self.num_classes = num_classes + # num_features for consistency with other models + self.num_features = self.embed_dim = embed_dim + self.tubelet_size = tubelet_size + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + num_frames=all_frames, + tubelet_size=tubelet_size) + num_patches = self.patch_embed.num_patches + self.with_cp = with_cp + + if use_learnable_pos_emb: + self.pos_embed = nn.Parameter( + torch.zeros(1, num_patches, embed_dim)) + else: + # sine-cosine positional embeddings is on the way + self.pos_embed = get_sinusoid_encoding_table( + num_patches, embed_dim) + + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth) + ] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer, + init_values=init_values, + cos_attn=cos_attn) for i in range(depth) + ]) + self.norm = nn.Identity() if use_mean_pooling else norm_layer( + embed_dim) + self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None + self.head_dropout = nn.Dropout(head_drop_rate) + self.head = nn.Linear( + embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + if use_learnable_pos_emb: + trunc_normal_(self.pos_embed, std=.02) + + self.apply(self._init_weights) + + self.head.weight.data.mul_(init_scale) + self.head.bias.data.mul_(init_scale) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear( + self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x): + B = x.size(0) + + x = self.patch_embed(x) + + if self.pos_embed is not None: + x = x + self.pos_embed.expand(B, -1, -1).type_as(x).to( + x.device).clone().detach() + x = self.pos_drop(x) + + for blk in self.blocks: + if self.with_cp: + x = cp.checkpoint(blk, x) + else: + x = blk(x) + + if self.fc_norm is not None: + return self.fc_norm(x.mean(1)) + else: + return self.norm(x[:, 0]) + + def forward(self, x): + x = self.forward_features(x) + x = self.head_dropout(x) + x = self.head(x) + return x + + +@register_model +def vit_small_patch16_224(pretrained=False, **kwargs): + model = VisionTransformer( + patch_size=16, + embed_dim=384, + depth=12, + num_heads=6, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + return model + + +@register_model +def vit_base_patch16_224(pretrained=False, **kwargs): + model = VisionTransformer( + patch_size=16, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + return model + + +@register_model +def vit_large_patch16_224(pretrained=False, **kwargs): + model = VisionTransformer( + patch_size=16, + embed_dim=1024, + depth=24, + num_heads=16, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + return model + + +@register_model +def vit_huge_patch16_224(pretrained=False, **kwargs): + model = VisionTransformer( + patch_size=16, + embed_dim=1280, + depth=32, + num_heads=16, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + return model + + +@register_model +def vit_giant_patch14_224(pretrained=False, **kwargs): + model = VisionTransformer( + patch_size=14, + embed_dim=1408, + depth=40, + num_heads=16, + mlp_ratio=48 / 11, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + return model diff --git a/datapreprocess/models/modeling_pretrain.py b/datapreprocess/models/modeling_pretrain.py new file mode 100644 index 0000000..d2081ec --- /dev/null +++ b/datapreprocess/models/modeling_pretrain.py @@ -0,0 +1,475 @@ +# -------------------------------------------------------- +# Based on BEiT, timm, DINO and DeiT code bases +# https://github.com/microsoft/unilm/tree/master/beit +# https://github.com/rwightman/pytorch-image-models/tree/master/timm +# https://github.com/facebookresearch/deit +# https://github.com/facebookresearch/dino +# --------------------------------------------------------' +from functools import partial + +import torch +import torch.nn as nn +import torch.utils.checkpoint as cp +from timm.models.layers import trunc_normal_ as __call_trunc_normal_ +from timm.models.registry import register_model + +from .modeling_finetune import ( + Block, + PatchEmbed, + _cfg, + get_sinusoid_encoding_table, +) + + +def trunc_normal_(tensor, mean=0., std=1.): + __call_trunc_normal_(tensor, mean=mean, std=std, a=-std, b=std) + + +class PretrainVisionTransformerEncoder(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + + def __init__(self, + img_size=224, + patch_size=16, + in_chans=3, + num_classes=0, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + norm_layer=nn.LayerNorm, + init_values=None, + tubelet_size=2, + use_learnable_pos_emb=False, + with_cp=False, + all_frames=16, + cos_attn=False): + super().__init__() + self.num_classes = num_classes + # num_features for consistency with other models + self.num_features = self.embed_dim = embed_dim + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + num_frames=all_frames, + tubelet_size=tubelet_size) + num_patches = self.patch_embed.num_patches + self.with_cp = with_cp + + if use_learnable_pos_emb: + self.pos_embed = nn.Parameter( + torch.zeros(1, num_patches + 1, embed_dim)) + else: + # sine-cosine positional embeddings + self.pos_embed = get_sinusoid_encoding_table( + num_patches, embed_dim) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth) + ] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer, + init_values=init_values, + cos_attn=cos_attn) for i in range(depth) + ]) + self.norm = norm_layer(embed_dim) + self.head = nn.Linear( + embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + if use_learnable_pos_emb: + trunc_normal_(self.pos_embed, std=.02) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear( + self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x, mask): + x = self.patch_embed(x) + + x = x + self.pos_embed.type_as(x).to(x.device).clone().detach() + + B, _, C = x.shape + x_vis = x[~mask].reshape(B, -1, C) # ~mask means visible + + for blk in self.blocks: + if self.with_cp: + x_vis = cp.checkpoint(blk, x_vis) + else: + x_vis = blk(x_vis) + + x_vis = self.norm(x_vis) + return x_vis + + def forward(self, x, mask): + x = self.forward_features(x, mask) + x = self.head(x) + return x + + +class PretrainVisionTransformerDecoder(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + + def __init__(self, + patch_size=16, + num_classes=768, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + norm_layer=nn.LayerNorm, + init_values=None, + num_patches=196, + tubelet_size=2, + with_cp=False, + cos_attn=False): + super().__init__() + self.num_classes = num_classes + assert num_classes == 3 * tubelet_size * patch_size**2 + # num_features for consistency with other models + self.num_features = self.embed_dim = embed_dim + self.patch_size = patch_size + self.with_cp = with_cp + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth) + ] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer, + init_values=init_values, + cos_attn=cos_attn) for i in range(depth) + ]) + self.norm = norm_layer(embed_dim) + self.head = nn.Linear( + embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear( + self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward(self, x, return_token_num): + for blk in self.blocks: + if self.with_cp: + x = cp.checkpoint(blk, x) + else: + x = blk(x) + + if return_token_num > 0: + # only return the mask tokens predict pixels + x = self.head(self.norm(x[:, -return_token_num:])) + else: + # [B, N, 3*16^2] + x = self.head(self.norm(x)) + return x + + +class PretrainVisionTransformer(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + + def __init__( + self, + img_size=224, + patch_size=16, + encoder_in_chans=3, + encoder_num_classes=0, + encoder_embed_dim=768, + encoder_depth=12, + encoder_num_heads=12, + decoder_num_classes=1536, # decoder_num_classes=768 + decoder_embed_dim=512, + decoder_depth=8, + decoder_num_heads=8, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + norm_layer=nn.LayerNorm, + init_values=0., + use_learnable_pos_emb=False, + tubelet_size=2, + num_classes=0, # avoid the error from create_fn in timm + in_chans=0, # avoid the error from create_fn in timm + with_cp=False, + all_frames=16, + cos_attn=False, + ): + super().__init__() + self.encoder = PretrainVisionTransformerEncoder( + img_size=img_size, + patch_size=patch_size, + in_chans=encoder_in_chans, + num_classes=encoder_num_classes, + embed_dim=encoder_embed_dim, + depth=encoder_depth, + num_heads=encoder_num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop_rate=drop_rate, + attn_drop_rate=attn_drop_rate, + drop_path_rate=drop_path_rate, + norm_layer=norm_layer, + init_values=init_values, + tubelet_size=tubelet_size, + use_learnable_pos_emb=use_learnable_pos_emb, + with_cp=with_cp, + all_frames=all_frames, + cos_attn=cos_attn) + + self.decoder = PretrainVisionTransformerDecoder( + patch_size=patch_size, + num_patches=self.encoder.patch_embed.num_patches, + num_classes=decoder_num_classes, + embed_dim=decoder_embed_dim, + depth=decoder_depth, + num_heads=decoder_num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop_rate=drop_rate, + attn_drop_rate=attn_drop_rate, + drop_path_rate=drop_path_rate, + norm_layer=norm_layer, + init_values=init_values, + tubelet_size=tubelet_size, + with_cp=with_cp, + cos_attn=cos_attn) + + self.encoder_to_decoder = nn.Linear( + encoder_embed_dim, decoder_embed_dim, bias=False) + + self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim)) + + self.pos_embed = get_sinusoid_encoding_table( + self.encoder.patch_embed.num_patches, decoder_embed_dim) + + trunc_normal_(self.mask_token, std=.02) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token', 'mask_token'} + + def forward(self, x, mask, decode_mask=None): + decode_vis = mask if decode_mask is None else ~decode_mask + + x_vis = self.encoder(x, mask) # [B, N_vis, C_e] + x_vis = self.encoder_to_decoder(x_vis) # [B, N_vis, C_d] + B, N_vis, C = x_vis.shape + + # we don't unshuffle the correct visible token order, + # but shuffle the pos embedding accorddingly. + expand_pos_embed = self.pos_embed.expand(B, -1, -1).type_as(x).to( + x.device).clone().detach() + pos_emd_vis = expand_pos_embed[~mask].reshape(B, -1, C) + pos_emd_mask = expand_pos_embed[decode_vis].reshape(B, -1, C) + + # [B, N, C_d] + x_full = torch.cat( + [x_vis + pos_emd_vis, self.mask_token + pos_emd_mask], dim=1) + # NOTE: if N_mask==0, the shape of x is [B, N_mask, 3 * 16 * 16] + x = self.decoder(x_full, pos_emd_mask.shape[1]) + + return x + + +@register_model +def pretrain_videomae_small_patch16_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=16, + encoder_embed_dim=384, + encoder_depth=12, + encoder_num_heads=6, + encoder_num_classes=0, + decoder_num_classes=1536, # 16 * 16 * 3 * 2 + decoder_embed_dim=192, + decoder_num_heads=3, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model + + +@register_model +def pretrain_videomae_base_patch16_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=16, + encoder_embed_dim=768, + encoder_depth=12, + encoder_num_heads=12, + encoder_num_classes=0, + decoder_num_classes=1536, # 16 * 16 * 3 * 2 + decoder_embed_dim=384, + decoder_num_heads=6, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model + + +@register_model +def pretrain_videomae_large_patch16_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=16, + encoder_embed_dim=1024, + encoder_depth=24, + encoder_num_heads=16, + encoder_num_classes=0, + decoder_num_classes=1536, # 16 * 16 * 3 * 2 + decoder_embed_dim=512, + decoder_num_heads=8, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model + + +@register_model +def pretrain_videomae_huge_patch16_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=16, + encoder_embed_dim=1280, + encoder_depth=32, + encoder_num_heads=16, + encoder_num_classes=0, + decoder_num_classes=1536, # 16 * 16 * 3 * 2 + decoder_embed_dim=512, + decoder_num_heads=8, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model + + +@register_model +def pretrain_videomae_giant_patch14_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=14, + encoder_embed_dim=1408, + encoder_depth=40, + encoder_num_heads=16, + encoder_num_classes=0, + decoder_num_classes=1176, # 14 * 14 * 3 * 2, + decoder_embed_dim=512, + decoder_num_heads=8, + mlp_ratio=48 / 11, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model diff --git a/datapreprocess/run.sh b/datapreprocess/run.sh new file mode 100755 index 0000000..0423a39 --- /dev/null +++ b/datapreprocess/run.sh @@ -0,0 +1,2 @@ +python feature_extraction_videoMAEv2.py +python feature_extraction.py \ No newline at end of file diff --git a/model/lstmae/__init__.py b/model/lstmae/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/model/lstmae/calc_abnormal_thr.py b/model/lstmae/calc_abnormal_thr.py new file mode 100644 index 0000000..f8c2f93 --- /dev/null +++ b/model/lstmae/calc_abnormal_thr.py @@ -0,0 +1,159 @@ +from tqdm import tqdm +import random +import matplotlib.pyplot as plt + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DataLoader, random_split +from sklearn.preprocessing import MinMaxScaler +from sklearn.metrics import roc_auc_score, precision_recall_curve + +from dataset import AbnormalDataset + +from lstm_ae import LSTMAutoEncoder +''' +MSE loss +평균과 공분산 이용 +# 평균[0.000071] +# 중간[0.000050] +# 최소[0.000002] +# 최대[0.001509] + +loss 그대로 이용 +# 평균[0.026619] +# 중간[0.026682] +# 최소[0.022427] +# 최대[0.030678] + +MAE loss +# 평균[0.002338] +# 중간[0.001776] +# 최소[0.000088] +# 최대[0.025161] + +=> MSE 는 너무 작아서 MAE 로 실시 +''' + +def set_seed(seed): + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # if use multi-GPU + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + np.random.seed(seed) + random.seed(seed) + +set_seed(666) + +def calculate_mse(seq1, seq2): + return np.mean(np.power(seq1 - seq2, 2)) + +def main(): + root_dir = "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/lstmae/dataset/abnormal" + json_dir = "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/lstmae/dataset/label" + abnormal_dataset = AbnormalDataset( + root=root_dir, + label_root=json_dir, + ) + abnormal_loader = DataLoader( + dataset=abnormal_dataset, batch_size=1, shuffle=False, num_workers=0 + ) + + sequence_length = 20 + n_features = 38 + + device = "cuda" if torch.cuda.is_available() else "cpu" + model = LSTMAutoEncoder(num_layers=2, hidden_size=50, n_features=38, + device=device) + load_dict = torch.load("/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/pts/LSTM_20240324_222238_best.pth", map_location="cpu") + + model.load_state_dict( + load_dict['model_state_dict'] + ) + model.to(device) + val_criterion = nn.MSELoss(reduction="none") + + label_list = [] + mse_list = [] + + model.eval() + + with torch.no_grad(): + + for i, (data, label) in tqdm(enumerate(abnormal_loader), total=len(abnormal_loader)): + scaler = MinMaxScaler() + + label = label.reshape(-1).cpu().numpy() + + if sum(label) >= 1: + label_list.append(1) + else: + label_list.append(0) + + data = data.cpu().detach().numpy() + data = data.reshape(sequence_length, n_features) + data = scaler.fit_transform(data) + scaled_data = data.reshape(1, sequence_length, n_features) + scaled_data = torch.from_numpy(scaled_data).float().to(device) + + pred = model(scaled_data) + pred = pred.cpu().detach().numpy().reshape(-1, n_features) + #pred_original = scaler.inverse_transform(pred.cpu().detach().numpy().reshape(-1, n_features)) + + mse = calculate_mse(data, pred) + mse_list.append(mse) + + precision_rt, recall_rt, threshold_rt = precision_recall_curve(label_list, mse_list) + + plt.figure(figsize=(8,5)) + plt.plot(threshold_rt, precision_rt[1:], label='Precision') + plt.plot(threshold_rt, recall_rt[1:], label='Recall') + plt.xlabel('Threshold'); plt.ylabel('Precision/Recall') + plt.legend() + plt.savefig("/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/lstmae/pr_curve.png") + + # best position of threshold + index_cnt = [cnt for cnt, (p, r) in enumerate(zip(precision_rt, recall_rt)) if p==r][0] + print('precision: ',precision_rt[index_cnt],', recall: ',recall_rt[index_cnt]) + + # fixed Threshold + threshold_fixed = threshold_rt[index_cnt] + print('threshold: ',threshold_fixed) + + print('mse mean: ',np.mean(mse)) + +if __name__ == "__main__": + + main() + +# 이상행동 20 중 5프레임 이상 +# precision: 0.591683741111245 , recall: 0.591683741111245 +# threshold: 0.02313113 + +# 이상행동 20 중 1프레임 이상 +# precision: 0.6168715461824743 , recall: 0.6168715461824743 +# threshold: 0.02235273 + +# 이상행동 20 중 20프레임 모두 +# precision: 0.4549022511848341 , recall: 0.4549022511848341 +# threshold: 0.02781844 + +# 이상행동 20 중 3프레임 이상 +# precision: 0.6046858260748056 , recall: 0.6046858260748056 +# threshold: 0.022715755 + +# min_max_scaler inverse 후 + +# 이상행동 20 중 1프레임 이상 +# precision: 0.46159918800045113 , recall: 0.46159918800045113 +# threshold: 0.0026742023210807914 + +# 이상행동 20 중 10프레임 이상 +# precision: 0.37000934704232874 , recall: 0.37000934704232874 +# threshold: 0.003047690912489323 + +# 이상행동 20 중 3프레임 이상 +# precision: 0.44426848013414694 , recall: 0.44426848013414694 +# threshold: 0.002750229995391417 \ No newline at end of file diff --git a/model/lstmae/calc_metric.py b/model/lstmae/calc_metric.py new file mode 100644 index 0000000..e489f09 --- /dev/null +++ b/model/lstmae/calc_metric.py @@ -0,0 +1,248 @@ +import pandas as pd +import numpy as np +import seaborn as sns +import random +import matplotlib.pyplot as plt + +from datetime import datetime + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from torch.utils.data import Dataset, DataLoader, random_split + +from sklearn.metrics import roc_curve, auc, confusion_matrix +from sklearn.preprocessing import MinMaxScaler + +from argparse import ArgumentParser + +import os +import os.path as osp + +from tqdm import tqdm + +import wandb +from PIL import Image + +from dataset import NormalDataset, AbnormalDataset +from lstm_ae import LSTMAutoEncoder + + +def parse_args(): + parser = ArgumentParser() + + parser.add_argument( + "--abnormal_root_dir", + type=str, + default=os.environ.get( + "SM_CHANNEL_ABNORMAL_CSV", + "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/lstmae/dataset/abnormal", + ), + ) + parser.add_argument( + "--json_dir", + type=str, + default=os.environ.get( + "SM_CHANNEL_ABNORMAL_VAL_JSON", + "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/lstmae/dataset/label", + ), + ) + + parser.add_argument( + "--model_dir", type=str, default=os.environ.get("SM_MODEL_DIR", "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/pts") + ) + + parser.add_argument("--model_name", type=str, default="LSTM") + parser.add_argument("--pth_name", type=str, default="LSTM_20240324_222238_best") + parser.add_argument("--seed", type=int, default=666) + + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") + parser.add_argument("--num_workers", type=int, default=8) + + parser.add_argument("--batch_size", type=int, default=64) + parser.add_argument("--thr", type=float, default=0.02) + + parser.add_argument("--wandb_mode", type=str, default="online") + parser.add_argument("--wandb_run_name", type=str, default="LSTM_auc") + + args = parser.parse_args() + + return args + + +def set_seed(seed): + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # if use multi-GPU + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + np.random.seed(seed) + random.seed(seed) + +def save_image_to_wandb(image_path): + with open(image_path, "rb") as file: + img = Image.open(file) + wandb.log({image_path.split('/')[-1].split('.')[0]: [wandb.Image(img, caption=f"{image_path.split('/')[-1]}")]}) + +def calculate_mse(seq1, seq2): + return np.mean(np.power(seq1 - seq2, 2)) + +def train( + abnormal_root_dir, + json_dir, + model_dir, + model_name, + pth_name, + device, + num_workers, + batch_size, + thr, + seed, + wandb_mode, + wandb_run_name, +): + + time_start = datetime.now() + + train_start = time_start.strftime("%Y%m%d_%H%M%S") + + set_seed(seed) + + if not osp.exists(model_dir): + os.makedirs(model_dir) + + # Define parameters + n_features = 38 # Number of features to predict + sequence_length = 20 + + batch_size = batch_size + + abnormal_dataset = AbnormalDataset( + root=abnormal_root_dir, + label_root=json_dir, + ) + + abnormal_loader = DataLoader( + dataset=abnormal_dataset, batch_size=1, shuffle=False, num_workers=0 + ) + + data_load_end = datetime.now() + data_load_time = data_load_end - time_start + data_load_time = str(data_load_time).split(".")[0] + print(f"==>> data_load_time: {data_load_time}") + + # Initialize the LSTM autoencoder model + model = LSTMAutoEncoder(num_layers=2, hidden_size=50, n_features=n_features, device=device) + + load_dict = torch.load(osp.join(model_dir, f"{pth_name}.pth"), map_location="cpu") + + model.load_state_dict( + load_dict['model_state_dict'] + ) + model.to(device) + + val_criterion = nn.MSELoss(reduction="none") + + print(f"Start calculation auc..") + + wandb.init( + project="VAD", + config={ + "dataset": "무인매장", + "loss": "MSE", + "notes": "LSTM auc 구하기", + }, + name=wandb_run_name + "_" + train_start, + mode=wandb_mode, + ) + + wandb.watch((model,)) + model.eval() + + label_list = [] + mse_list = [] + pred_list = [] + + with torch.no_grad(): + for step, (data, label) in tqdm(enumerate(abnormal_loader), total=len(abnormal_loader)): + scaler = MinMaxScaler() + + label = label.reshape(-1).cpu().numpy() + if sum(label) >= 1: + label_list.append(1) + else: + label_list.append(0) + + data = data.cpu().detach().numpy() + data = data.reshape(sequence_length, n_features) + data = scaler.fit_transform(data) + scaled_data = data.reshape(1, sequence_length, n_features) + scaled_data = torch.from_numpy(scaled_data).float().to(device) + + pred = model(scaled_data) + pred = pred.cpu().detach().numpy().reshape(-1, n_features) + + #pred_original = scaler.inverse_transform(pred.cpu().detach().numpy().reshape(-1, n_features)) + + mse = calculate_mse(data, pred) + mse_list.append(mse) + + pred_list.append(1 if mse > thr else 0) + + conf_matrix_path = "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/lstmae/confusion_matrix.png" + roc_curve_path = "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/lstmae/roc_curve.png" + pr_curve_path = "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/lstmae/pr_curve.png" + + conf_matrix = confusion_matrix(label_list, pred_list) + plt.figure(figsize=(7, 7)) + sns.heatmap(conf_matrix, xticklabels=['Normal', 'Abnormal'], yticklabels=['Normal', 'Abnormal'], annot=True, fmt='d') + plt.title('Confusion Matrix') + plt.xlabel('Predicted Class') + plt.ylabel('True Class') + plt.savefig(conf_matrix_path) + + false_pos_rate, true_pos_rate, thresholds = roc_curve(label_list, mse_list) + roc_auc = auc(false_pos_rate, true_pos_rate,) + + plt.plot(false_pos_rate, true_pos_rate, linewidth=5, label='AUC = %0.3f'% roc_auc) + plt.plot([0,1],[0,1], linewidth=5) + + plt.xlim([-0.01, 1]) + plt.ylim([0, 1.01]) + plt.legend(loc='lower right') + plt.title('ROC curve') + plt.ylabel('True Positive Rate') + plt.xlabel('False Positive Rate') + plt.savefig(roc_curve_path) + + pred_list = np.array(pred_list) + label_list = np.array(label_list) + acc = sum((pred_list==label_list)) / len(pred_list) + print('accuracy 점수: {}'.format(acc)) + print('roc_auc 점수: {}'.format(roc_auc)) + + new_wandb_metric_dict = { + "thr": thr, + "auc": roc_auc, + "accuracy": acc, + } + + wandb.log(new_wandb_metric_dict) + + save_image_to_wandb(conf_matrix_path) + save_image_to_wandb(roc_curve_path) + save_image_to_wandb(pr_curve_path) + + os.remove(conf_matrix_path) + os.remove(roc_curve_path) + os.remove(pr_curve_path) + +def main(args): + train(**args.__dict__) + + +if __name__ == "__main__": + args = parse_args() + print(args) + main(args) \ No newline at end of file diff --git a/model/lstmae/calc_normal_thr.py b/model/lstmae/calc_normal_thr.py new file mode 100644 index 0000000..55159f0 --- /dev/null +++ b/model/lstmae/calc_normal_thr.py @@ -0,0 +1,109 @@ +from tqdm import tqdm +import random + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DataLoader, random_split + +from dataset import NormalDataset + +from lstm_ae import LSTMAutoEncoder +''' +MSE loss +평균과 공분산 이용 +# 평균[0.000071] +# 중간[0.000050] +# 최소[0.000002] +# 최대[0.001509] + +loss 그대로 이용 +# 평균[0.026619] +# 중간[0.026682] +# 최소[0.022427] +# 최대[0.030678] + +MAE loss +# 평균[0.002338] +# 중간[0.001776] +# 최소[0.000088] +# 최대[0.025161] + +=> MSE 는 너무 작아서 MAE 로 실시 +''' + +def set_seed(seed): + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # if use multi-GPU + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + np.random.seed(seed) + random.seed(seed) + +set_seed(666) + +def main(): + root_dir = "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/lstmae/dataset/normal" + dataset = NormalDataset( + root=root_dir, + ) + valid_data_size = len(dataset) // 10 + train_data_size = len(dataset) - valid_data_size + + train_dataset, valid_dataset = random_split(dataset, lengths=[train_data_size, valid_data_size]) + + valid_loader = DataLoader( + dataset=valid_dataset, batch_size=64, shuffle=False, num_workers=8 + ) + + device = "cuda" if torch.cuda.is_available() else "cpu" + model = LSTMAutoEncoder(num_layers=2, hidden_size=50, n_features=38, + device=device) + load_dict = torch.load("/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/pts/LSTM_20240324_222238_best.pth", map_location="cpu") + + model.load_state_dict( + load_dict['model_state_dict'] + ) + model.to(device) + val_criterion = nn.MSELoss(reduction="none") + + loss_list = [] + loss_m_list = [] + model.eval() + + with torch.no_grad(): + total_loss = 0 + for i, batch_data in tqdm(enumerate(valid_loader), total=len(valid_loader)): + + batch_data = batch_data.to(device) + predict_values = model(batch_data) + + loss = val_criterion(predict_values, batch_data) + loss_m = torch.mean(loss) + loss_m_list.append(loss_m.cpu().numpy()) + + loss_mae = F.l1_loss(predict_values, batch_data, reduction="none") + loss_mae = loss_mae.mean() + loss_list.append(loss_mae.cpu().numpy()) + + total_loss += loss_m + + loss_list = np.array(loss_list) + loss_m_list = np.array(loss_m_list) + + val_abnormal_mean_loss = (total_loss / len(valid_loader)).item() + + ## 정상구간에서 mse 점수 분포 + print('mae') + print("평균[{:.6f}]\n중간[{:.6f}]\n최소[{:.6f}]\n최대[{:.6f}]".format(np.mean(loss_list), np.median(loss_list), np.min(loss_list), np.max(loss_list))) + print('=' * 40) + print('mse') + print("평균[{:.6f}]\n중간[{:.6f}]\n최소[{:.6f}]\n최대[{:.6f}]".format(np.mean(loss_m_list), np.median(loss_m_list), np.min(loss_m_list), np.max(loss_m_list))) + print('=' * 40) + print("total_loss: {}".format(val_abnormal_mean_loss)) + +if __name__ == "__main__": + + main() diff --git a/model/lstmae/dataset.py b/model/lstmae/dataset.py new file mode 100644 index 0000000..73f4b36 --- /dev/null +++ b/model/lstmae/dataset.py @@ -0,0 +1,221 @@ +import os +import os.path as osp + +from torch.utils.data import Dataset + +from sklearn.preprocessing import MinMaxScaler + +import pandas as pd +import numpy as np +import torch + +import json +from collections import defaultdict as dd + + +class NormalDataset(Dataset): + + def __init__( + self, + sequence_length=20, + root="/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/lstmae/dataset", + ): + super().__init__() + self.sequence_length = sequence_length + + self.scaler = MinMaxScaler() + + # Load the dataset + file_list = os.listdir(root) + + df_list = [] + + self.length = 0 + self.range_table = [] + + self.real_length = 0 + self.real_idx_table = [] + + for i, file_name in enumerate(file_list): + dat = pd.read_csv(root + "/" + file_name) + dat.drop(columns=["Frame"], inplace=True) + + print(f"==>>{i}번째 dat.shape: {dat.shape}") + + id_counter = pd.Series(dat["ID"]).value_counts(sort=False) + + for id_to_del in id_counter[id_counter < sequence_length].index: + dat.drop(dat[dat["ID"] == id_to_del].index, inplace=True) + + id_counter = pd.Series(dat["ID"]).value_counts(sort=False) + + print(f"==>>{i}번째 처리 후 dat.shape: {dat.shape}") + assert len(id_counter[id_counter < sequence_length].index) == 0 + + for count in id_counter: + cur_id_length = count - sequence_length + 1 + self.range_table.append(self.length + cur_id_length) + self.real_idx_table.append(self.real_length + count) + self.length += cur_id_length + self.real_length += count + + dat["ID"] = dat["ID"].astype("str") + f"_{i}" + df_list.append(dat.copy()) + + self.dat = pd.concat(df_list, ignore_index=True) + + def __len__(self): + return self.length + + def __getitem__(self, idx): + real_idx = self.find_real_idx(idx) + + sequence = self.dat[real_idx : real_idx + self.sequence_length].copy() + sequence.drop(columns=["ID"], inplace=True) + sequence = self.scaler.fit_transform(sequence.values) + sequence = np.array(sequence) + + return torch.from_numpy(sequence).float() + + def find_real_idx(self, idx): + + start = 0 + end = len(self.range_table) - 1 + while start <= end: + mid = (start + end) // 2 + if self.range_table[mid] == idx: + real_idx = idx + ((mid + 1) * (self.sequence_length - 1)) + return real_idx + + if self.range_table[mid] > idx: + end = mid - 1 + else: + start = mid + 1 + + real_idx = idx + (start * (self.sequence_length - 1)) + + return real_idx + + +class AbnormalDataset(Dataset): + + def __init__( + self, + sequence_length=20, + root="/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/lstmae/dataset/abnormal", + label_root="/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/lstmae/dataset/label", + ): + super().__init__() + self.sequence_length = sequence_length + + self.scaler = MinMaxScaler() + # 데이터 값 [0,1] 범위로 scaling할때 사용 + + # Load the dataset + file_list = os.listdir(root) + + df_list = [] + + self.length = 0 + self.range_table = [] + + self.real_length = 0 + self.real_idx_table = [] + + for i, file_name in enumerate(file_list): + dat = pd.read_csv(root + "/" + file_name) + # dat.drop(columns=["Frame"], inplace=True) # Remove the 'Frame' column + + print(f"==>>{i}번째 dat.shape: {dat.shape}") + + id_counter = pd.Series(dat["ID"]).value_counts(sort=False) + + for id_to_del in id_counter[id_counter < sequence_length].index: + dat.drop(dat[dat["ID"] == id_to_del].index, inplace=True) + + id_counter = pd.Series(dat["ID"]).value_counts(sort=False) + + print(f"==>>{i}번째 처리 후 dat.shape: {dat.shape}") + assert len(id_counter[id_counter < sequence_length].index) == 0 + + for count in id_counter: + cur_id_length = count - sequence_length + 1 + self.range_table.append(self.length + cur_id_length) + self.real_idx_table.append(self.real_length + count) + self.length += cur_id_length + self.real_length += count + + dat["ID"] = dat["ID"].astype("str") + f"_{i}" + df_list.append(dat.copy()) + + self.dat = pd.concat(df_list, ignore_index=True) + + # 정답 frame 담은 dict 만들기 + self.frame_label = dd(lambda: dd(lambda: [-1, -1])) + + folder_list = os.listdir(label_root) + + for folder in folder_list: + json_list = os.listdir(label_root + "/" + folder) + + for js in json_list: + with open(label_root + "/" + folder + "/" + js, "r") as j: + json_dict = json.load(j) + + for dict in json_dict["annotations"]["track"]: + if dict["@label"].endswith("_start"): + cur_id = dict["@id"] + self.frame_label[js[:-5]][cur_id][0] = dict["box"][0]["@frame"] + elif dict["@label"].endswith("_end"): + cur_id = dict["@id"] + self.frame_label[js[:-5]][cur_id][1] = dict["box"][0]["@frame"] + + def __len__(self): + return self.length + + def __getitem__(self, idx): + real_idx = self.find_real_idx(idx) + + sequence = self.dat[real_idx : real_idx + self.sequence_length].copy() + target_frames = sequence["Frame"].values + target_filename = sequence["Filename"].unique()[0].split(".")[0] + sequence.drop(columns=["ID"], inplace=True) + sequence.drop(columns=["Frame"], inplace=True) + sequence.drop(columns=["Filename"], inplace=True) + #sequence = self.scaler.fit_transform(sequence.values) + sequence = np.array(sequence) + + target_labels = [] + + for target_frame in target_frames: + temp = 0 + for cur_id in range(0,len(self.frame_label[target_filename].keys()),2): + if int(target_frame) >= int(self.frame_label[target_filename][str(int(cur_id))][0]) and int( + target_frame + ) <= int(self.frame_label[target_filename][str(int(cur_id)+1)][1]): + temp = 1 + + target_labels.append(temp) + + target_labels = torch.LongTensor(target_labels) + + return (sequence, target_labels) + + def find_real_idx(self, idx): + + start = 0 + end = len(self.range_table) - 1 + while start <= end: + mid = (start + end) // 2 + if self.range_table[mid] == idx: + real_idx = idx + ((mid + 1) * (self.sequence_length - 1)) + return real_idx + + if self.range_table[mid] > idx: + end = mid - 1 + else: + start = mid + 1 + + real_idx = idx + (start * (self.sequence_length - 1)) + + return real_idx \ No newline at end of file diff --git a/model/lstmae/lstm_ae.py b/model/lstmae/lstm_ae.py new file mode 100644 index 0000000..72456ff --- /dev/null +++ b/model/lstmae/lstm_ae.py @@ -0,0 +1,99 @@ +import torch +import torch.nn as nn + +''' +LSTM output +- N : number of batches +- L : sequence lengh +- Q : input dim +- K : number of layers +- D : LSTM feature dimension + +Y,(hn,cn) = LSTM(X) + +- X : [N x L x Q] - `N` input sequnce of length `L` with `Q` dim. +- Y : [N x L x D] - `N` output sequnce of length `L` with `D` feature dim. +- hn : [K x N x D] - `K` (per each layer) of `N` final hidden state with `D` feature dim. +- cn : [K x N x D] - `K` (per each layer) of `N` final hidden state with `D` cell dim. +''' + +class Encoder(nn.Module): + ''' + input: input_seq: (batch_size, seq_len, n_features) -> (1, 20, 38) + output: hidden_cell -> (hn, cn) + -> ((num_layers, batch_size, hidden_size), (num_layers, batch_size, hidden_size)) + ''' + def __init__(self, num_layers, hidden_size, n_features, device): + super(Encoder, self).__init__() + + self.input_size = n_features + self.hidden_size = hidden_size + self.num_layers = num_layers + self.device = device + + self.lstm = nn.LSTM(input_size=n_features, hidden_size=hidden_size, + num_layers=num_layers, batch_first=True) + + def initHidden(self, batch_size): + ''' + intialize hn, cn + ''' + self.hidden_cell = ( + torch.randn((self.num_layers, batch_size, self.hidden_size), dtype=torch.float).to(self.device), + torch.randn((self.num_layers, batch_size, self.hidden_size), dtype=torch.float).to(self.device) + ) + + def forward(self, input_seq): + self.initHidden(input_seq.shape[0]) + _, self.hidden_cell = self.lstm(input_seq, self.hidden_cell) + return self.hidden_cell + + +class Decoder(nn.Module): + ''' + input: (input_seq, hidden_cell) + input_seq: + hidden_cell: encoder 에서 넘어온 hidden_cell (hn, cn) + output: + decoder output: (batch_size, seq_len, n_features) -> (1, 1, 38) + linear output: (batch_size, n_features) -> (1, 38) + ''' + def __init__(self, num_layers, hidden_size, n_features, device): + super(Decoder, self).__init__() + + self.input_size = n_features + self.hidden_size = hidden_size + self.num_layers = num_layers + self.device = device + + self.lstm = nn.LSTM(input_size=n_features, hidden_size=hidden_size, + num_layers=num_layers, batch_first=True) + self.linear = nn.Linear(in_features=hidden_size, out_features=n_features) + + def forward(self, input_seq, hidden_cell): + output, hidden_cell = self.lstm(input_seq, hidden_cell) + output = self.linear(output) + return output, hidden_cell + + +class LSTMAutoEncoder(nn.Module): + ''' + output: input seq_len(20) 모두 복원 + reconstruction 순서는 입력의 반대. + ''' + def __init__(self, num_layers, hidden_size, n_features, device): + super(LSTMAutoEncoder, self).__init__() + self.device = device + self.encoder = Encoder(num_layers, hidden_size, n_features, device) + self.decoder = Decoder(num_layers, hidden_size, n_features, device) + + def forward(self, input_seq): + output = torch.zeros(size=input_seq.shape, dtype=torch.float) + hidden_cell = self.encoder(input_seq) + input_decoder = torch.zeros((input_seq.shape[0], 1, input_seq.shape[2]), dtype=torch.float).to(self.device) + for i in range(input_seq.shape[1] - 1, -1, -1): + output_decoder, hidden_cell = self.decoder(input_decoder, hidden_cell) + input_decoder = output_decoder + output[:, i, :] = output_decoder[:, 0, :] + + return output.to(self.device) \ No newline at end of file diff --git a/model/lstmae/lstm_ae_old.py b/model/lstmae/lstm_ae_old.py new file mode 100644 index 0000000..6c76bec --- /dev/null +++ b/model/lstmae/lstm_ae_old.py @@ -0,0 +1,128 @@ +import torch +import torch.nn as nn + +''' +LSTM output +- N : number of batches +- L : sequence lengh +- Q : input dim +- K : number of layers +- D : LSTM feature dimension + +Y,(hn,cn) = LSTM(X) + +- X : [N x L x Q] - `N` input sequnce of length `L` with `Q` dim. +- Y : [N x L x D] - `N` output sequnce of length `L` with `D` feature dim. +- hn : [K x N x D] - `K` (per each layer) of `N` final hidden state with `D` feature dim. +- cn : [K x N x D] - `K` (per each layer) of `N` final hidden state with `D` cell dim. +''' + +class Encoder(nn.Module): + def __init__(self, seq_len, n_features, embedding_dim=64): + super(Encoder, self).__init__() + self.seq_len, self.n_features = seq_len, n_features + self.embedding_dim, self.hidden_dim = ( + embedding_dim, 2 * embedding_dim + ) + + self.rnn1 = nn.LSTM( + input_size=n_features, + hidden_size=self.hidden_dim, + num_layers=1, + batch_first=True + ) + + self.rnn2 = nn.LSTM( + input_size=self.hidden_dim, + hidden_size=embedding_dim, + num_layers=1, + batch_first=True + ) + + def forward(self, x): + ''' + input(x): [batch_size, seq_len, n_features] - [1,20,38] + rnn1 output(x1): [batch_size, seq_len, hidden_dim] - [1,20,128] + rnn2 output(x2): [batch_size, seq_len, embedding_dim] - [1,20,64] + decoder input: 마지막 sequence hidden state + ''' + x1, (hidden_n, cell_n) = self.rnn1(x) + x2, (hidden_n, cell_n) = self.rnn2(x1) + return x2[:,-1,:] + #return (hidden_n, cell_n) + + +class TimeDistributed(nn.Module): + def __init__(self, module, batch_first=True): + super(TimeDistributed, self).__init__() + self.module = module + self.batch_first = batch_first + + def forward(self, x): + if len(x.size()) <= 2: + return self.module(x) + # Squash batch_size and seq_len into a single axis + x_reshape = x.contiguous().view(-1, x.size(-1)) # (batch_size * seq_len, input_size) => (1, 128) + y = self.module(x_reshape) # linear layer, output: batch_siz, n_features(38) + # We have to reshape Y + if self.batch_first: + y = y.contiguous().view(x.size(0), -1, y.size(-1)) # (batch_size, seq_len, n_features) + else: + y = y.view(-1, x.size(1), y.size(-1)) # (seq_len, batch_size, n_features) + return y + + +class Decoder(nn.Module): + def __init__(self, prediction_time=1, input_dim=64, n_features=38): + super(Decoder, self).__init__() + # input 은 encoder 에서 나온 embedding_dim + self.prediction_time, self.input_dim = prediction_time, input_dim + self.hidden_dim, self.n_features = 2 * input_dim, n_features + + self.rnn1 = nn.LSTM( + input_size=input_dim, + hidden_size=input_dim, + num_layers=1, + batch_first=True + ) + + self.rnn2 = nn.LSTM( + input_size=input_dim, + hidden_size=self.hidden_dim, + num_layers=1, + batch_first=True + ) + + # time_distributed + # linear 로 1차원으로 복원 후 각 time 에서 출력된 아웃풋을 linear layer 와 연결 + self.output_layer = torch.nn.Linear(self.hidden_dim, n_features) + self.timedist = TimeDistributed(self.output_layer) + + def forward(self, x): + ''' + repeat_x: prediction_time 만큼 encoder에서 온 input(hidden_n) 을 늘림. prediction_time이 1일 때 (1,1,64) -> (1,1,64). 입력 그대로 복원할 때는 seq_len. 그러면 (1,20,64). + 즉, + rnn1 output(x1): [batch_size, prediction_time, input_dim] - [1,1,64] + rnn2 output(x2): [batch_size, prediction_time, hidden_dim] - [1,1,128] + timedist output(return output): [batch_size, prediction_time, n_features] - [1,1,38] + ''' + repeat_x=x.reshape(-1,1,self.input_dim).repeat(1,self.prediction_time,1) + x1, (hidden_n, cell_n) = self.rnn1(repeat_x) + x2, (hidden_n, cell_n) = self.rnn2(x1) + return self.timedist(x2) + + +class LSTMAutoencoder(nn.Module): + def __init__(self, seq_len, prediction_time, n_features, embedding_dim=64): + super(LSTMAutoencoder, self).__init__() + self.encoder = Encoder(seq_len, n_features, embedding_dim) + self.decoder = Decoder(prediction_time, embedding_dim, n_features) + def forward(self, x): + ''' + input(x): [1, seq_len, n_features] + encoder output(x_e): [batch_size, last seq_len, embedding_dim] + final output(x_d): [1, predict_time, n_features] + ''' + x_e = self.encoder(x) + x_d = self.decoder(x_e) + return x_d \ No newline at end of file diff --git a/model/lstmae/train.py b/model/lstmae/train.py new file mode 100644 index 0000000..e15de20 --- /dev/null +++ b/model/lstmae/train.py @@ -0,0 +1,309 @@ +import numpy as np +import random + +from datetime import datetime + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from torch.utils.data import DataLoader, random_split + +from argparse import ArgumentParser + +import os +import os.path as osp + +from tqdm import tqdm +from sklearn.preprocessing import MinMaxScaler + +import wandb + +from dataset import NormalDataset +from lstm_ae_old import LSTMAutoencoder +from lstm_ae import LSTMAutoEncoder + +def parse_args(): + parser = ArgumentParser() + + # 학습 데이터 경로 + parser.add_argument( + "--root_dir", + type=str, + default=os.environ.get( + "SM_CHANNEL_TRAIN_CSV", + "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/lstmae/dataset/normal", + ), + ) + + # pth 파일 저장 경로 + parser.add_argument( + "--model_dir", type=str, default=os.environ.get("SM_MODEL_DIR", "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/pts") + ) + + # import_module로 불러올 model name + parser.add_argument("--model_name", type=str, default="LSTM") + # resume 파일 이름 + parser.add_argument("--resume_name", type=str, default="") + # random seed + parser.add_argument("--seed", type=int, default=666) + + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") + parser.add_argument("--num_workers", type=int, default=8) + parser.add_argument("--batch_size", type=int, default=64) + parser.add_argument("--val_batch_size", type=int, default=64) + parser.add_argument("--val_num_workers", type=int, default=8) + parser.add_argument("--learning_rate", type=float, default=0.001) + parser.add_argument("--max_epoch", type=int, default=50) + + parser.add_argument("--save_interval", type=int, default=1) + parser.add_argument("--val_interval", type=int, default=1) + parser.add_argument("--thr", type=float, default=0.02) + + parser.add_argument("--patience", type=int, default=10) + + parser.add_argument("--wandb_mode", type=str, default="online") + parser.add_argument("--wandb_run_name", type=str, default="LSTM-AE") + + args = parser.parse_args() + + return args + + +def set_seed(seed): + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # if use multi-GPU + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + np.random.seed(seed) + random.seed(seed) + + +def train( + root_dir, + model_dir, + model_name, + device, + num_workers, + batch_size, + val_num_workers, + val_batch_size, + learning_rate, + max_epoch, + val_interval, + save_interval, + thr, + patience, + resume_name, + seed, + wandb_mode, + wandb_run_name, +): + + time_start = datetime.now() + + train_start = time_start.strftime("%Y%m%d_%H%M%S") + + set_seed(seed) + + if not osp.exists(model_dir): + os.makedirs(model_dir) + + # Define parameters + sequence_length = 20 + prediction_time = 1 + n_features = 38 + + batch_size = batch_size + val_batch_size = val_batch_size + + # -- early stopping flag + patience = patience + counter = 0 + + # 데이터셋 + dataset = NormalDataset( + root=root_dir, + ) + + valid_data_size = len(dataset) // 10 + + train_data_size = len(dataset) - valid_data_size + + train_dataset, valid_dataset = random_split(dataset, lengths=[train_data_size, valid_data_size]) + + train_loader = DataLoader( + dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers + ) + + valid_loader = DataLoader( + dataset=valid_dataset, batch_size=val_batch_size, shuffle=False, num_workers=val_num_workers + ) + + data_load_end = datetime.now() + data_load_time = data_load_end - time_start + data_load_time = str(data_load_time).split(".")[0] + print(f"==>> data_load_time: {data_load_time}") + + # Initialize the LSTM autoencoder model + # model = LSTMAutoencoder(sequence_length, prediction_time, n_features, 50) + # model.to(device) + + model = LSTMAutoEncoder(num_layers=2, hidden_size=50, n_features=n_features, + device=device) + model.to(device) + + optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-6) + + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[15, 40], gamma=0.1) + + # if resume_name: + # optimizer.load_state_dict(load_dict["optimizer_state_dict"]) + # scheduler.load_state_dict(load_dict["scheduler_state_dict"]) + # scaler.load_state_dict(load_dict["scaler_state_dict"]) + + criterion = nn.MSELoss() + val_criterion = nn.MSELoss(reduction="none") + + print(f"Start training..") + + wandb.init( + project="VAD", + config={ + "lr": learning_rate, + "dataset": "무인매장", + "n_epochs": max_epoch, + "loss": "MSE", + "notes": "VAD 실험", + }, + name=wandb_run_name + "_" + train_start, + mode=wandb_mode, + ) + + wandb.watch((model,)) + + best_loss = np.inf + total_batches = len(train_loader) + + for epoch in range(max_epoch): + model.train() + + epoch_start = datetime.now() + + epoch_loss = 0 + + for step, data in tqdm(enumerate(train_loader), total=total_batches): + + data = data.to(device) + optimizer.zero_grad() + + pred = model(data) + + loss = criterion(pred, data) + + loss.backward() + optimizer.step() + + epoch_loss += loss + + epoch_mean_loss = (epoch_loss / total_batches).item() + + train_end = datetime.now() + train_time = train_end - epoch_start + train_time = str(train_time).split(".")[0] + print(f"==>> epoch {epoch+1} train_time: {train_time}\nloss: {round(epoch_mean_loss,4)}") + + if (epoch + 1) % save_interval == 0: + + ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_latest.pth") + + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + "optimizer_state_dict": optimizer.state_dict(), + "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + } + + torch.save(states, ckpt_fpath) + + # validation 주기에 따라 loss를 출력하고 best model을 저장합니다. + if (epoch + 1) % val_interval == 0: + + print(f"Start validation #{epoch+1:2d}") + model.eval() + + with torch.no_grad(): + total_loss = 0 + + for step, data in tqdm(enumerate(valid_loader), total=len(valid_loader)): + + data = data.to(device) + + pred = model(data) + + val_loss = val_criterion(pred, data) + val_loss = torch.mean(val_loss) + + total_loss += val_loss + + val_mean_loss = (total_loss / len(valid_loader)).item() + + + if best_loss > val_mean_loss: + print(f"Best performance at epoch: {epoch + 1}, {best_loss:.4f} -> {val_mean_loss:.4f}") + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best.pth") + torch.save(states, best_ckpt_fpath) + best_loss = val_mean_loss + counter = 0 + else: + counter += 1 + + new_wandb_metric_dict = { + "train_loss": epoch_mean_loss, + "valid_loss": val_mean_loss, + "learning_rate": scheduler.get_lr()[0], + } + + wandb.log(new_wandb_metric_dict) + + scheduler.step() + + epoch_end = datetime.now() + epoch_time = epoch_end - epoch_start + epoch_time = str(epoch_time).split(".")[0] + print( + f"==>> epoch {epoch+1} time: {epoch_time}\nvalid_loss: {round(val_mean_loss,4)}" + ) + + if counter > patience: + print("Early Stopping...") + break + + time_end = datetime.now() + total_time = time_end - time_start + total_time = str(total_time).split(".")[0] + print(f"==>> total time: {total_time}") + + +def main(args): + train(**args.__dict__) + + +if __name__ == "__main__": + args = parse_args() + + main(args) \ No newline at end of file diff --git a/model/train/anomaly_detector_LSTM.py b/model/train/anomaly_detector_LSTM.py new file mode 100644 index 0000000..8a25aff --- /dev/null +++ b/model/train/anomaly_detector_LSTM.py @@ -0,0 +1,171 @@ +from collections import defaultdict +import cv2 +import numpy as np +import pandas as pd +from ultralytics import YOLO +import torch +from sklearn.preprocessing import MinMaxScaler +import argparse + +# Create an argument parser +parser = argparse.ArgumentParser() + +# Define command-line arguments +parser.add_argument( + "--video_path", type=str, default="../videos/anomaly_1.mp4", help="Path to the video file" +) +parser.add_argument("--threshold", type=float, default=0.02, help="Anomaly detection threshold") + +# Parse the command-line arguments +args = parser.parse_args() + + +def display_text(frame, text, position): + font = cv2.FONT_HERSHEY_SIMPLEX + font_scale = 1 + font_color = (0, 255, 0) # Green color + font_thickness = 2 + cv2.putText(frame, text, position, font, font_scale, font_color, font_thickness, cv2.LINE_AA) + + +# Load the YOLOv8 model +model = YOLO("yolov8n-pose.pt") + +# Load the LSTM autoencoder model +autoencoder_model = torch.load("model.h5") # Replace 'model.h5' with your model's path + +# Define the standard frame size (change these values as needed) +standard_width = 640 +standard_height = 480 + +# Open the video file +if args.video_path == "0": + cap = cv2.VideoCapture(0) +else: + cap = cv2.VideoCapture(args.video_path) + +# Store the track history +track_history = defaultdict(lambda: []) + +# Define sequence_length, prediction_time, and n_features based on your model's configuration +sequence_length = 20 +prediction_time = 1 +n_features = 38 + +# Initialize a dictionary to store separate buffers for each ID +id_buffers = defaultdict(lambda: []) + + +# Define a function to calculate MSE between two sequences +def calculate_mse(seq1, seq2): + return np.mean(np.power(seq1 - seq2, 2)) + + +# Define the anomaly threshold +threshold = args.threshold # Adjust as needed + +# Loop through the video frames +frame_count = 0 +net_mse = 0 +avg_mse = 0 +while cap.isOpened(): + # Read a frame from the video + success, frame = cap.read() + frame_count += 1 # Increment frame count + + if success: + frame = cv2.resize(frame, (standard_width, standard_height)) + + # Run YOLOv8 tracking on the frame, persisting tracks between frames + results = model.track(frame, persist=True) + + if results[0].boxes is not None: # Check if there are results and boxes + # Get the boxes + boxes = results[0].boxes.xywh.cpu() + + if results[0].boxes.id is not None: + # If 'int' attribute exists (there are detections), get the track IDs + track_ids = results[0].boxes.id.int().cpu().tolist() + + # Loop through the detections and add data to the DataFrame + anomaly_text = "" # Initialize the anomaly text + for i, box in zip(range(0, len(track_ids)), results[0].boxes.xywhn.cpu()): + x, y, w, h = box + keypoints = results[0].keypoints.xyn[i].cpu().numpy().flatten().tolist() + + # Create a dictionary with named columns for keypoints + keypoints_dict = {f"Keypoint_{j}": float(val) for j, val in enumerate(keypoints, 0)} + + # Append the keypoints to the corresponding ID's buffer + id_buffers[track_ids[i]].append([float(x), float(y), float(w), float(h)] + keypoints) + + # If the buffer size reaches the threshold (e.g., 20 data points), perform anomaly detection + if len(id_buffers[track_ids[i]]) >= 20: + # Convert the buffer to a NumPy array + buffer_array = np.array(id_buffers[track_ids[i]]) + + # Scale the data (you can use the same scaler you used during training) + scaler = MinMaxScaler() + buffer_scaled = scaler.fit_transform(buffer_array) + + # Create sequences for prediction + x_pred = buffer_scaled[-sequence_length:].reshape(1, sequence_length, n_features) + + # Predict the next values using the autoencoder model + x_pred = autoencoder_model.predict(x_pred) + + # Inverse transform the predicted data to the original scale + x_pred_original = scaler.inverse_transform(x_pred.reshape(-1, n_features)) + + # Calculate the MSE between the predicted and actual values + mse = calculate_mse(buffer_array[-prediction_time:], x_pred_original) + print(mse) + net_mse = mse + net_mse + avg_mse = net_mse / frame_count + # Check if the MSE exceeds the threshold to detect an anomaly + if mse > 1.5 * avg_mse * 0.25 + 0.75 * threshold: + if anomaly_text == "": + anomaly_text = f"Anomaly detected for ID {track_ids[i]}" + else: + anomaly_text = f"{anomaly_text}, {track_ids[i]}" + print(anomaly_text) + + # Remove the oldest data point from the buffer to maintain its size + id_buffers[track_ids[i]].pop(0) + else: + anomaly_text = "" + # If 'int' attribute doesn't exist (no detections), set track_ids to an empty list + track_ids = [] + + # Visualize the results on the frame + annotated_frame = results[0].plot() + display_text(annotated_frame, anomaly_text, (10, 30)) # Display the anomaly text + # Plot the tracks + for box, track_id in zip(boxes, track_ids): + x, y, w, h = box + track = track_history[track_id] + track.append((float(x), float(y))) # x, y center point + if len(track) > 30: # retain 90 tracks for 90 frames + track.pop(0) + + # Draw the tracking lines + points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2)) + cv2.polylines(annotated_frame, [points], isClosed=False, color=(230, 230, 230), thickness=10) + + # Display the annotated frame + cv2.imshow("YOLOv8 Tracking", annotated_frame) + + else: + # If no detections, display the original frame without annotations + cv2.imshow("YOLOv8 Tracking", frame) + + # Break the loop if 'q' is pressed + if cv2.waitKey(1) & 0xFF == ord("q"): + break + else: + # Break the loop if the end of the video is reached + break + +# Release the video capture object and close the display window +cap.release() +cv2.destroyAllWindows() diff --git a/model/train/classifier.py b/model/train/classifier.py new file mode 100644 index 0000000..fd3a6af --- /dev/null +++ b/model/train/classifier.py @@ -0,0 +1,525 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from einops import rearrange + + +class LSTMAutoencoder(nn.Module): + def __init__(self, sequence_length, n_features, prediction_time): + super().__init__() + + self.sequence_length = sequence_length + self.n_features = n_features + self.prediction_time = prediction_time + + # Encoder + self.encoder = nn.LSTM(input_size=n_features, hidden_size=100, batch_first=True) + self.encoder2 = nn.LSTM(input_size=100, hidden_size=50, batch_first=True) + + # Repeat vector for prediction_time + self.repeat_vector = nn.Sequential( + nn.ReplicationPad1d(padding=(0, prediction_time - 1)), + nn.ReplicationPad1d(padding=(0, 0)), # Adjusted padding + ) + + # Decoder + self.decoder = nn.LSTM(input_size=50 + prediction_time - 1, hidden_size=100, batch_first=True) + self.decoder2 = nn.LSTM(input_size=100, hidden_size=n_features, batch_first=True) + + def forward(self, x): + # Encoder + # _, (x, _) = self.encoder(x) + x, (_, _) = self.encoder(x) + # output, (hn, cn) = rnn(x) + x, (_, _) = self.encoder2(x) + + # Repeat vector for prediction_time + x = self.repeat_vector(x) + + # Decoder + x, (_, _) = self.decoder(x) + x, (_, _) = self.decoder2(x) + + if self.prediction_time == 1: + return x[:, -1, :].unsqueeze(dim=1) + else: + return x[:, -(self.prediction_time) :, :] + + +class MILClassifier(nn.Module): + def __init__(self, input_dim=710, drop_p=0.0): + super().__init__() + # self.embedding = Temporal(input_dim, 512) + # self.selfatt = Transformer(512, 2, 4, 128, 512, dropout=0) + self.classifier = nn.Sequential( + nn.Linear(input_dim, 512), + # nn.Linear(512, 512), + # nn.BatchNorm1d(512), + nn.ReLU(), + nn.Dropout(drop_p), + nn.Linear(512, 512), + # nn.BatchNorm1d(1024), + nn.ReLU(), + nn.Dropout(drop_p), + # nn.Linear(1024, 512), + # # nn.BatchNorm1d(512), + # nn.ReLU(), + # nn.Dropout(drop_p), + nn.Linear(512, 32), + # nn.BatchNorm1d(32), + nn.ReLU(), + nn.Dropout(drop_p), + nn.Linear(32, 1), + nn.Sigmoid(), + ) + + self.drop_p = drop_p + self.weight_init() + + def weight_init(self): + # for layer in self.classifier: + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_normal_(m.weight) + elif isinstance(m, nn.BatchNorm1d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def forward(self, x): + + # x = self.embedding(x) + # x = self.selfatt(x) + + x = x.view(-1, x.size(-1)) + + x = self.classifier(x) + + return x + + +class NormalHead(nn.Module): + def __init__(self, in_channel=512, ratios=[16, 32], kernel_sizes=[1, 1, 1]): + super(NormalHead, self).__init__() + self.ratios = ratios + # 기본값 [16, 32] + self.kernel_sizes = kernel_sizes + # 기본값 [1, 1, 1] + + self.build_layers(in_channel) + + def build_layers(self, in_channel): + ratio_1, ratio_2 = self.ratios + self.conv1 = nn.Conv1d( + in_channel, in_channel // ratio_1, self.kernel_sizes[0], 1, self.kernel_sizes[0] // 2 + ) + # stride는 1, padding은 kernel_size // 2로 두면 + # (input_length - kernel_size + 2 * (kernel_size // 2)) + 1 == input_length + # => 길이 유지 + self.bn1 = nn.BatchNorm1d(in_channel // ratio_1) + self.conv2 = nn.Conv1d( + in_channel // ratio_1, in_channel // ratio_2, self.kernel_sizes[1], 1, self.kernel_sizes[1] // 2 + ) + self.bn2 = nn.BatchNorm1d(in_channel // ratio_2) + self.conv3 = nn.Conv1d(in_channel // ratio_2, 1, self.kernel_sizes[2], 1, self.kernel_sizes[2] // 2) + self.act = nn.ReLU() + self.sigmoid = nn.Sigmoid() + + self.bns = [self.bn1, self.bn2] + + def forward(self, x): + """ + x: BN * C * T + return BN * C // 64 * T and BN * 1 * T + """ + outputs = [] + x = self.conv1(x) + outputs.append(x) + x = self.conv2(self.act(self.bn1(x))) + outputs.append(x) + x = self.sigmoid(self.conv3(self.act(self.bn2(x)))) + outputs.append(x) + return outputs + + +def pair(t): + return t if isinstance(t, tuple) else (t, t) + + +class PreNorm(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.fn = fn + + def forward(self, x, **kwargs): + return self.fn(self.norm(x), **kwargs) + + +class FeedForward(nn.Module): + def __init__(self, dim, hidden_dim, dropout=0.0): + super().__init__() + self.net = nn.Sequential( + nn.Linear(dim, hidden_dim), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(hidden_dim, dim), + nn.Dropout(dropout), + ) + + def forward(self, x): + return self.net(x) + + +class Attention(nn.Module): + def __init__(self, dim, heads=8, dim_head=64, dropout=0.0): + super().__init__() + inner_dim = dim_head * heads + project_out = not (heads == 1 and dim_head == dim) + + self.heads = heads + self.scale = dim_head**-0.5 + + self.attend = nn.Softmax(dim=-1) + self.to_qkv = nn.Linear(dim, inner_dim * 4, bias=False) + + self.to_out = ( + nn.Sequential(nn.Linear(2 * inner_dim, dim), nn.Dropout(dropout)) + if project_out + else nn.Identity() + ) + + def forward(self, x): + b, n, d = x.size() + qkvt = self.to_qkv(x).chunk(4, dim=-1) + q, k, v, t = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.heads), qkvt) + + dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale + + attn1 = self.attend(dots) + + tmp_ones = torch.ones(n).cuda() + tmp_n = torch.linspace(1, n, n).cuda() + tg_tmp = torch.abs(tmp_n * tmp_ones - tmp_n.view(-1, 1)) + attn2 = torch.exp(-tg_tmp / torch.exp(torch.tensor(1.0))) + attn2 = (attn2 / attn2.sum(-1)).unsqueeze(0).unsqueeze(1).repeat(b, self.heads, 1, 1) + + out = torch.cat([torch.matmul(attn1, v), torch.matmul(attn2, t)], dim=-1) + out = rearrange(out, "b h n d -> b n (h d)") + return self.to_out(out) + + +class Transformer(nn.Module): + def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.0): + super().__init__() + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + PreNorm(dim, Attention(dim, heads=heads, dim_head=dim_head, dropout=dropout)), + PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout)), + ] + ) + ) + + def forward(self, x): + for attn, ff in self.layers: + x = attn(x) + x + x = ff(x) + x + return x + + +class Temporal(nn.Module): + # Temporal convolutional network + def __init__(self, input_size, out_size): + super(Temporal, self).__init__() + self.conv_1 = nn.Sequential( + nn.Conv1d(in_channels=input_size, out_channels=out_size, kernel_size=3, stride=1, padding=1), + nn.ReLU(), + ) + + def forward(self, x): + # x는 (batch * n crops, t snippets, d feature dim) + # 영상 1개를 t개의 snippet(토막)으로 나누고 각 snippet은 d 차원 feature 벡터 + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # permute를 하지 않고 conv1d를 하면 t가 채널축 + # => conv 특성상 1 entry 계산할 때 (t, 3)사이즈 필터를 곱해서 계산 + # => 이 entry는 각 snippet의 feature는 3개만 보지만 시간축(t)으로는 영상 전체를 보게 된다 + # => 마치 과거, 현재, 미래 정보를 다보고 계산하는 것과 마찬가지 + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + x = x.permute(0, 2, 1) + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # permute를 하게 되면 (batch * n crops, d feature dim, t snippets) + # 이제 conv1d를 하면 (d, 3) 사이즈 필터를 곱해서 계산한다 + # => 바로 전, 현재, 바로 다음(또는 전전, 전, 현재) 시간의 영상 snippet 3개만 보고 각 snippet의 feature들은 전부 보게 된다 + # +@ 영상을 나누는 snippet(segment) 개수 유지(kernel_size=3, stride=1, padding=1) + # +@ 이제 각 snippet의 feature dimension수를 조절 가능(필터 개수로 조절) + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + x = self.conv_1(x) + x = x.permute(0, 2, 1) + return x + + +class WSAD(nn.Module): + def __init__( + self, input_size, ratio_sample=0.2, ratio_batch=0.4, ratios=[16, 32], kernel_sizes=[1, 1, 1] + ): + super().__init__() + # self.flag = flag + + self.ratio_sample = ratio_sample + # 기본값 0.2 + self.ratio_batch = ratio_batch + # 기본값 0.4 + + self.ratios = ratios + # 기본값 [16, 32] + self.kernel_sizes = kernel_sizes + # 기본값 [1, 1, 1] + + self.normal_head = NormalHead(in_channel=512, ratios=ratios, kernel_sizes=kernel_sizes) + self.embedding = Temporal(input_size, 512) + self.selfatt = Transformer(512, 2, 4, 128, 512, dropout=0) + # embedding + selfatt은 논문의 feature enhancer + # embedding은 feature 차원을 permute + conv1d를 이용해 512로 변경 + # selfatt는 transformer계열 enhancer + self.step = 0 + + def get_normal_scores(self, x, ncrops=None): + # x는 (batch * n crops, segment 개수, feature 차원 = 512(논문)) + new_x = x.permute(0, 2, 1) + # conv1d에 넣기전에 (batch * n crops, feature 차원, segment 개수)로 변경 + + outputs = self.normal_head(new_x) + # normal_head는 conv1d - bn - relu - conv1d - bn - relu - conv1d - sig 3층 구조 + # outputs는 normal_head 안의 3개의 conv1d output을 담은 list (마지막 output은 conv1d + sig output) + normal_scores = outputs[-1] + xhs = outputs[:-1] + + if ncrops: + b = normal_scores.shape[0] // ncrops + normal_scores = normal_scores.view(b, ncrops, -1).mean(1) + # (batch_size, t snippets) + + return xhs, normal_scores + + def get_mahalanobis_distance(self, feats, anchor, var, ncrops=None): + # 첫번째는 feat는 (batch_size * n crops, 512 // 16, t snippets) + # 두번째는 (batch_size * n crops, 512 // 32, t snippets) + # BN은 각 feature(채널 축)별 batch*h*w개 평균, 분산 계산 + # => (b, c, h*w) -> (c) + # => None으로 unsqueeze해서 (1, c, 1)로 변경 + distance = torch.sqrt(torch.sum((feats - anchor[None, :, None]) ** 2 / var[None, :, None], dim=1)) + # (x - m)^2/var -> torch.sum(dim=1)로 각배치 안의 각 토막(segment)별로 값 존재 (b, t) + # sqrt후에도 사이즈 그대로 (b, t) + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # var가 전부 1이면 distance는 BN running mean vector와 각 토막의 feature vector 간의 차이 벡터의 L2 norm 길이가 된다 + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + + if ncrops: + bs = distance.shape[0] // ncrops + # b x t + distance = distance.view(bs, ncrops, -1).mean(dim=1) + # (batch_size, n crops, t snippets)을 dim=1로 평균 => 동일 영상 10개 crop들의 결과를 평균 + # => (batch_size, t snippets) + return distance + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # 배치 내의 각 영상의 각 토막 feature 벡터가 + # 데이터셋 분포 내 모든 영상의 모든 토막의 feature(512 // 16 또는 512 // 32 차원) 벡터들의 평균인 벡터(running_mean으로 추정)와 + # 얼마나 다른지 알려주는 mahalanobis 거리 계산 + # 데이터 분포내의 모든 토막 feature 벡터의 평균이고 정상토막의 비중이 이상토막의 비중보다 압도적으로 크기 때문에 + # 이 평균 벡터는 정상 토막의 기준처럼 사용 가능(anchor) + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + + def pos_neg_select(self, feats, distance, ncrops): + batch_select_ratio = self.ratio_batch + # 기본값 0.4 + sample_select_ratio = self.ratio_sample + # 기본값 0.2 + bs, c, t = feats.shape + # 첫번째는 (batch_size * n crops, 512 // 16, t snippets) + # 두번째는 (batch_size * n crops, 512 // 32, t snippets) + select_num_sample = int(t * sample_select_ratio) + # sample-level selection(SLS)은 20% + select_num_batch = int(bs // 2 * t * batch_select_ratio) + # 데이터는 torch.cat((정상영상, 이상영상), dim=0)으로 정상영상 배치 뒤에 이상영상 배치가 붙어있음 + # => bs // 2가 실제 batch_size * n crops 개수 + # => batch-level selection(BLS)은 (bs // 2) * t개 중 40% + # ==> 40 // 2 해서 사실상 SLS와 동일 비율로 배치 하나당 20% + + feats = feats.view(bs, ncrops, c, t).mean(dim=1) # b x c x t + # 동일 영상에서 나온 10개 crop들 결과 평균 + # => (batch_size, c features, t snippets) + nor_distance = distance[: bs // 2] # b x t + # distance는 10개 crop들을 이미 평균내고 (batch_size, t snippets) + # 그리고 배치 앞 절반은 정상영상 배치 => (n_batch_size = batch_size // 2, t snippets) + nor_feats = feats[: bs // 2].permute(0, 2, 1) # b x t x c + # 정상부분 앞 절반만 가져와 permute => (n_batch_size, t snippets, c features) + abn_distance = distance[bs // 2 :] # b x t + # 배치 뒤 절반은 이상영상 배치 (a_batch_size = batch_size // 2, t snippets) + abn_feats = feats[bs // 2 :].permute(0, 2, 1) # b x t x c + # (a_batch_size, t snippets, c features) + abn_distance_flatten = abn_distance.reshape(-1) + # (a_batch_size * t snippets) + abn_feats_flatten = abn_feats.reshape(-1, c) + # (a_batch_size * t snippets, c features) + + mask_select_abnormal_sample = torch.zeros_like(abn_distance, dtype=torch.bool) + # (a_batch_size, t snippets) + topk_abnormal_sample = torch.topk(abn_distance, select_num_sample, dim=-1)[1] + # torch.topk(abn_distance, select_num_sample, dim=-1)는 top k개 value와 그 value들 indices를 담고 있다 + # value와 indices 둘 다 (a_batch_size, top K = select_num_sample) 형태 + # => [1]로 indices만 가져오기 + mask_select_abnormal_sample.scatter_( + dim=1, + index=topk_abnormal_sample, + src=torch.full_like(topk_abnormal_sample, True, dtype=torch.bool), + ) + # (a_batch_size, t snippets) 형태이고 True는 a_batch_size * select_num_sample개이고 나머지는 False + # (top k에 속하는 index 자리만 True, 나머지는 False) + # scatter는 gather의 reverse operation + + mask_select_abnormal_batch = torch.zeros_like(abn_distance_flatten, dtype=torch.bool) + # (a_batch_size * t snippets) + topk_abnormal_batch = torch.topk(abn_distance_flatten, select_num_batch, dim=-1)[1] + # (a_batch_size * select_num_batch) + # top K = select_num_batch 개 indices + mask_select_abnormal_batch.scatter_( + dim=0, + index=topk_abnormal_batch, + src=torch.full_like(topk_abnormal_batch, True, dtype=torch.bool), + ) + # (a_batch_size * t snippets) + + mask_select_abnormal = mask_select_abnormal_batch | mask_select_abnormal_sample.reshape(-1) + # SLS와 BLS를 or 연산 | 으로 합쳐서 논문의 Sample-Batch Selection(SBS) + select_abn_feats = abn_feats_flatten[mask_select_abnormal] + # mask_select_abnormal는 (a_batch_size * t snippets)개 중 num_select_abnormal개만 True고 나머진 False + # abn_feats_flatten의 (a_batch_size * t snippets, c features)에서 mask_select_abnormal를 indices로 쓰면 + # (num_select_abnormal, c feature) 형태가 된다 + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # select_abn_feats는 SLS와 BLS를 합쳐 SBS를 만드는 과정에서 상위 ~%에 들었다는 정보만 남고 distance 상위 몇번째인지 순서 정보가 날아간다 + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + + num_select_abnormal = torch.sum(mask_select_abnormal) + # SBS 추출 개수 + + k_nor = int(num_select_abnormal / (bs // 2)) + 1 + # 이상영상 배치에서 SBS로 선택한 개수 / 배치내 영상 개수 == 1 배치 당 평균 선택 개수 + # + 1을 해주어서 정상 영상에서 선택된 토막(snippets)개수가 이상 영상 선택 토막 개수보다 크게 설정 + topk_normal_sample = torch.topk(nor_distance, k_nor, dim=-1)[1] + # nor_distance는 (n_batch_size, t snippets) + # topk_normal_sample는 각 영상의 t개 토막 중 상위 k_nor개의 indices + # => (n_batch_size, k_nor) + select_nor_feats = torch.gather(nor_feats, 1, topk_normal_sample[..., None].expand(-1, -1, c)) + # nor_feats는 (n_batch_size, t snippets, c features) + # gather의 index는 input과 차원수가 같아야하므로 None으로 (n_batch_size, k_nor, 1), expand로 (n_batch_size, k_nor, c) 형태로 변경 + # expand : Returns a new view of the self tensor with singleton dimensions expanded to a larger size. + # gather dimension이 1 => select_nor_feats[i][j][k] = nor_feats[i][topk_normal_sample[i][j][k]][k] + # select_nor_feats는 (n_batch_size, k_nor, c) 형태 (gather는 index와 output 형태가 동일) + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # select_abn_feats와 다르게 select_nor_feats는 크기 순서를 지우지 않고 gather를 써서 dim=1 방향으로 nor_distance 값 내림차순 + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + select_nor_feats = select_nor_feats.permute(1, 0, 2).reshape(-1, c) + # (k_nor, n_batch_size, c)로 바꾼 후 reshape로 (k_nor * n_batch_size, c) 형태 + select_nor_feats = select_nor_feats[:num_select_abnormal] + # k_nor * n_batch_size는 num_select_abnormal보다 크다 => out of index 에러 안 일어남 + # select_nor_feats는 최종적으로 (num_select_abnormal, c feature) 형태 + + return select_nor_feats, select_abn_feats + + def forward(self, x, flag="Eval"): + if len(x.size()) == 4: + b, n, t, d = x.size() + # 실험에 사용한 I3D UCF-Crime feature는 하나의 영상을 중앙, 4코너 + 중앙, 4코너 거울상 = 10개 crop으로 증강해서 계산 + # => batch 개수, n crop 개수, t 토막(snippet, segment) 개수, d snippet당 feature 차원수 + + x = x.reshape(b * n, t, d) + else: + b, t, d = x.size() + n = 1 + x = self.embedding(x) + x = self.selfatt(x) + # feature enhancer를 지난 feature의 차원수 d == 512(논문) + + normal_feats, normal_scores = self.get_normal_scores(x, n) + # normal_head는 conv1d - bn - relu - conv1d - bn - relu - conv1d - sig 3층 구조 + # normal_feats는 [첫 conv1d output, 두번째 conv1d output] + # => (batch_size * n crops, 512 // 16, t snippets), (batch_size * n crops, 512 // 32, t snippets) 형태 + # normal_scores는 마지막 conv1d - sig output => (batch_size, t snippets) 형태 (n crops는 평균-> 1) + + anchors = [bn.running_mean for bn in self.normal_head.bns] + variances = [bn.running_var for bn in self.normal_head.bns] + # conv1d output 바로 뒤 bn은 conv1d output 전체 분포 추정 평균, 분산을 담고 있다 + # 두개의 bn => 첫 conv1d output, 두번째 conv1d output 추정 평균, 분산 + + distances = [ + self.get_mahalanobis_distance(normal_feat, anchor, var, ncrops=n) + for normal_feat, anchor, var in zip(normal_feats, anchors, variances) + ] + # list안의 각 distance는 (batch_size, t snippets) 형태 + + if flag == "Train": + + select_normals = [] + select_abnormals = [] + for feat, distance in zip(normal_feats, distances): + select_feat_normal, select_feat_abnormal = self.pos_neg_select(feat, distance, n) + # select_feat_normal, select_feat_abnormal 둘다 (num_select_abnormal, c feature) 형태 + select_normals.append(select_feat_normal[..., None]) + select_abnormals.append(select_feat_abnormal[..., None]) + # 두 정상, 이상 리스트 모두 feature 두개씩 + # 첫번째는 (num_select_abnormal, 512 // 16 feature, 1) + # 두번째는 (num_select_abnormal, 512 // 32 feature, 1) + + bn_results = dict( + anchors=anchors, + variances=variances, + select_normals=select_normals, + select_abnormals=select_abnormals, + ) + # breakpoint() + distance_sum = sum(distances) + + return { + "pre_normal_scores": normal_scores[0 : b // 2], + # classifier 학습에 사용되는 normal loss 계산에는 label 노이즈가 없는 normal 영상만 사용 + # (label noise: MIL은 비디오 단위 라벨링만 있음 + # => 이상 영상안의 normal snippet을 abnormal snippet으로 판단 하는 등의 noise 발생 가능) + # 정상 영상의 snippet들은 무조건 정상 => 정상 영상 하나의 t snippets의 scores => t 차원 score 벡터 + # ==> 이 t 차원 score 벡터의 L2 norm 값 * n_batch_size 개 정상 영상 == normal loss + # ==> L2 norm인 normal loss가 작아지기 위해서 정상 영상 snippet들의 예측 score가 작아지는 방향으로 학습 + # 논문 3.4 확인 + "bn_results": bn_results, + # mpp loss 계산에 사용 + # 논문 3.2 확인 + # @@@@@@@@@@@@@@@@@@@@@@@@@ + # bce loss를 위해 추가 + "normal_scores": normal_scores, + "scores": distance_sum * normal_scores, + } + elif flag == "Train_extra": + distance_sum = sum(distances) + # (batch_size, t snippets) 형태인 distance들 sum + + return { + "normal_scores": normal_scores, + "scores": distance_sum * normal_scores, + } + elif flag == "Eval_MPP": + + distance_sum = sum(distances) + # (batch_size, t snippets) 형태인 distance들 sum + + return { + "normal_scores": normal_scores, + "scores": distance_sum * normal_scores, + } + else: + + distance_sum = sum(distances) + # (batch_size, t snippets) 형태인 distance들 sum + + return distance_sum * normal_scores + # normal_scores도 (batch_size, t snippets) 형태 diff --git a/model/train/loss.py b/model/train/loss.py new file mode 100644 index 0000000..c735c7b --- /dev/null +++ b/model/train/loss.py @@ -0,0 +1,126 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from functools import partial + +import pdb + + +def MIL(y_pred, batch_size, feature_length, is_transformer=0): + loss = torch.tensor(0.0).cuda() + loss_intra = torch.tensor(0.0).cuda() + sparsity = torch.tensor(0.0).cuda() + smooth = torch.tensor(0.0).cuda() + if is_transformer == 0: + y_pred = y_pred.view(batch_size, -1) + # (30*24, 1)을 (30, 24)로 다시 변경 + # dim=1 24 = 이상12 + 정상12 + else: + y_pred = torch.sigmoid(y_pred) + + # print(f"==>> y_pred.shape: {y_pred.shape}") + + for i in range(batch_size): + # anomaly_index = torch.randperm(12).cuda() + # print(f"==>> anomaly_index: {anomaly_index}") + # normal_index = torch.randperm(12).cuda() + # print(f"==>> normal_index: {normal_index}") + + # print(f"==>> y_pred[i, :12].shape: {y_pred[i, :12].shape}") + # print(f"==>> y_pred[i, 12:].shape: {y_pred[i, 12:].shape}") + + y_anomaly = y_pred[i, :feature_length] + # y_anomaly = y_pred[i, :12][anomaly_index] + # print(f"==>> y_anomaly.shape: {y_anomaly.shape}") + # MIL 논문의 segment 개수 32와 다르게 무인매장 데이터셋 feature는 12 segment + y_normal = y_pred[i, feature_length:] + # y_normal = y_pred[i, 12:][normal_index] + # print(f"==>> y_normal.shape: {y_normal.shape}") + + y_anomaly_max = torch.max(y_anomaly) # anomaly + y_anomaly_min = torch.min(y_anomaly) + + y_normal_max = torch.max(y_normal) # normal + y_normal_min = torch.min(y_normal) + + loss += F.relu(1.0 - y_anomaly_max + y_normal_max) + + sparsity += torch.sum(y_anomaly) * 0.00008 + smooth += torch.sum((y_pred[i, : feature_length - 1] - y_pred[i, 1:feature_length]) ** 2) * 0.00008 + loss = (loss + sparsity + smooth) / batch_size + + return loss + + +class NormalLoss(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, normal_scores): + """ + normal_scores: [bs, pre_k] + """ + loss_normal = torch.norm(normal_scores, dim=1, p=2) + # normal_scores는 정상영상의 snippet score만 있는 상태 => (n_batch_size, t snippets) + + return loss_normal.mean() + + +class MPPLoss(nn.Module): + def __init__(self): + super().__init__() + self.w_triplet = [5, 20] + + def forward(self, anchors, variances, select_normals, select_abnormals): + losses_triplet = [] + + def mahalanobis_distance(mu, x, var): + return torch.sqrt(torch.sum((x - mu) ** 2 / var, dim=-1)) + + for anchor, var, pos, neg, wt in zip( + anchors, variances, select_normals, select_abnormals, self.w_triplet + ): + triplet_loss = nn.TripletMarginWithDistanceLoss( + margin=1, distance_function=partial(mahalanobis_distance, var=var) + ) + + B, C, k = pos.shape + pos = pos.permute(0, 2, 1).reshape(B * k, -1) + neg = neg.permute(0, 2, 1).reshape(B * k, -1) + loss_triplet = triplet_loss(anchor[None, ...].repeat(B * k, 1), pos, neg) + # pos, neg, anchor[None, ...].repeat(B * k, 1) 모두 (B * k, -1) 형태 동일 + losses_triplet.append(loss_triplet * wt) + + return sum(losses_triplet) + + +class LossComputer(nn.Module): + def __init__(self, w_normal=1.0, w_mpp=1.0): + super().__init__() + self.w_normal = w_normal + self.w_mpp = w_mpp + self.mppLoss = MPPLoss() + self.normalLoss = NormalLoss() + + def forward(self, result): + loss = {} + # breakpoint() + pre_normal_scores = result["pre_normal_scores"] + # (n_batch_size, t snippets) 형태 + normal_loss = self.normalLoss(pre_normal_scores) + # normal_loss 계산에는 정상영상의 정상 snippet들 score만 사용 + # 논문 3.4 확인 + loss["normal_loss"] = normal_loss + + anchors = result["bn_results"]["anchors"] + variances = result["bn_results"]["variances"] + select_normals = result["bn_results"]["select_normals"] + select_abnormals = result["bn_results"]["select_abnormals"] + + mpp_loss = self.mppLoss(anchors, variances, select_normals, select_abnormals) + loss["mpp_loss"] = mpp_loss + + loss["total_loss"] = self.w_normal * normal_loss + self.w_mpp * mpp_loss + + return loss["total_loss"], loss diff --git a/model/train/model_convert.py b/model/train/model_convert.py new file mode 100644 index 0000000..f6e24c7 --- /dev/null +++ b/model/train/model_convert.py @@ -0,0 +1,80 @@ +import torch +import torch.nn as nn + +class LSTMAutoencoder(nn.Module): + def __init__(self, sequence_length, n_features, prediction_time): + super(LSTMAutoencoder, self).__init__() + + self.sequence_length = sequence_length + self.n_features = n_features + self.prediction_time = prediction_time + + # Encoder + self.encoder = nn.LSTM(input_size=n_features, hidden_size=100, batch_first=True) + self.encoder2 = nn.LSTM(input_size=100, hidden_size=50, batch_first=True) + + # Repeat vector for prediction_time + self.repeat_vector = nn.Sequential( + nn.ReplicationPad1d(padding=(0, prediction_time - 1)), + nn.ReplicationPad1d(padding=(0, 0)) # Adjusted padding + ) + + # Decoder + self.decoder = nn.LSTM(input_size=50, hidden_size=100, batch_first=True) + self.decoder2 = nn.LSTM(input_size=100, hidden_size=n_features, batch_first=True) + + def forward(self, x): + # Encoder + _, (x, _) = self.encoder(x) + _, (x, _) = self.encoder2(x) + + # Repeat vector for prediction_time + x = self.repeat_vector(x) + + # Decoder + _, (x, _) = self.decoder(x) + _, (x, _) = self.decoder2(x) + + return x + +# Instantiate the model +sequence_length = 20 # Adjust as needed +prediction_time = 1 # Adjust as needed +n_features = 38 # Number of features to predict + +# Create a sample input tensor +x2 = torch.rand((1, sequence_length, n_features)) + +model = LSTMAutoencoder(sequence_length, n_features, prediction_time) +output = model(x2) + +import torch +import torch.nn as nn +import h5py + +# Instantiate the PyTorch model +sequence_length = 20 # Adjust as needed +prediction_time = 1 # Adjust as needed +n_features = 38 # Number of features to predict +pytorch_model = LSTMAutoencoder(sequence_length, n_features, prediction_time) + +# Load weights from Keras h5 file +keras_weights_file = 'model.h5' +keras_weights = {} +def extract_weights(name, obj): + if isinstance(obj, h5py.Dataset): + print(f"Dataset: {name}") + keras_weights[name] = torch.tensor(obj[()]) + +with h5py.File(keras_weights_file, 'r') as hf: + hf.visititems(extract_weights) + +# Set PyTorch model weights +state_dict = pytorch_model.state_dict() +for name, param in state_dict.items(): + if name in keras_weights: + print(keras_weights[name]) + param.data.copy_(keras_weights[name]) + +# Save PyTorch model +torch.save(pytorch_model.state_dict(), 'pytorch_model.pth') \ No newline at end of file diff --git a/model/train/shop_dataset.py b/model/train/shop_dataset.py new file mode 100644 index 0000000..02957a5 --- /dev/null +++ b/model/train/shop_dataset.py @@ -0,0 +1,672 @@ +import os +import os.path as osp + +from torch.utils.data import Dataset + +# from sklearn.preprocessing import MinMaxScaler +from sklearn.preprocessing import normalize + +import pandas as pd +import numpy as np +import torch + +import json +from collections import defaultdict as dd + + +class NormalDataset(Dataset): + + def __init__( + self, + sequence_length=20, + prediction_time=1, + root="/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/csv/normal/val", + ): + super().__init__() + self.sequence_length = sequence_length + self.prediction_time = prediction_time + + # self.scaler = MinMaxScaler() + # 데이터 값 [0,1] 범위로 scaling할때 사용 + + # Load the dataset + file_list = os.listdir(root) + + df_list = [] + + self.length = 0 + self.range_table = [] + + self.real_length = 0 + self.real_idx_table = [] + + for i, file_name in enumerate(file_list): + dat = pd.read_csv(root + "/" + file_name) + dat.drop(columns=["Frame"], inplace=True) # Remove the 'Frame' column + + print(f"==>>{i}번째 dat.shape: {dat.shape}") + + id_counter = pd.Series(dat["ID"]).value_counts(sort=False) + + for id_to_del in id_counter[id_counter < sequence_length + prediction_time].index: + dat.drop(dat[dat["ID"] == id_to_del].index, inplace=True) + + id_counter = pd.Series(dat["ID"]).value_counts(sort=False) + + print(f"==>>{i}번째 처리 후 dat.shape: {dat.shape}") + assert len(id_counter[id_counter < sequence_length + prediction_time].index) == 0 + + for count in id_counter: + cur_id_length = count - sequence_length - prediction_time + 1 + self.range_table.append(self.length + cur_id_length) + self.real_idx_table.append(self.real_length + count) + self.length += cur_id_length + self.real_length += count + + dat["ID"] = dat["ID"].astype("str") + f"_{i}" + df_list.append(dat.copy()) + + self.dat = pd.concat(df_list, ignore_index=True) + # self.dat.drop(columns=["Frame"], inplace=True) # Remove the 'Frame' column + + id_counter = pd.Series(self.dat["ID"]).value_counts(sort=False) + + # # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # DF를 다 합치고 나서 ID를 거르면 데이터셋 초기화에 1분정도 걸리지만 + # DF 조각마다 ID를 거르고나서 합치면 6초 밖에 안 걸린다 + # self.checker = [] + + # for id_to_del in id_counter[id_counter < sequence_length + prediction_time].index: + # self.checker.append((id_to_del, id_counter[id_to_del])) + + # self.dat.drop(self.dat[self.dat["ID"] == id_to_del].index, inplace=True) + + # # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # # sequence_length + prediction_time 보다 짧은 ID를 지우는 것을 한번만 하면 + # # 13개 ID가 sequence_length + prediction_time보다 짧은데도 남아 있다??? + # id_counter = pd.Series(self.dat["ID"]).value_counts(sort=False) + + # if len(id_counter[id_counter < sequence_length + prediction_time].index) != 0: + # for id_to_del in id_counter[id_counter < sequence_length + prediction_time].index: + # self.checker.append((id_to_del, id_counter[id_to_del])) + + # self.dat.drop(self.dat[self.dat["ID"] == id_to_del].index, inplace=True) + + # id_counter = pd.Series(self.dat["ID"]).value_counts(sort=False) + # # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + + assert len(id_counter[id_counter < sequence_length + prediction_time].index) == 0 + + # self.length = 0 + + # self.range_table = [] + + # for count in id_counter: + # cur_id_length = count - sequence_length - prediction_time + 1 + # self.range_table.append(self.length + cur_id_length) + # self.length += cur_id_length + + # self.dat.drop(columns=["ID"], inplace=True) + + def __len__(self): + return self.length + + def __getitem__(self, idx): + real_idx = self.find_real_idx(idx) + + sequence = self.dat[real_idx : real_idx + self.sequence_length].copy() + sequence.drop(columns=["ID"], inplace=True) + sequence = np.array(sequence) + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # sequence = self.scaler.fit_transform(sequence.values) + # # 데이터 값 [min, max] -> [0,1] 범위로 scaling + # scale 된 후에는 numpy array로 변환된다 + # sequence나 target은 이미 yolo v8에서 xywhn, xyn으로 0~1 범위인데 scaling을 한번 더 할 필요가 있을지? + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # (self.sequence_length, 38) + target = self.dat[ + real_idx + self.sequence_length : real_idx + self.sequence_length + self.prediction_time + ].copy() + target.drop(columns=["ID"], inplace=True) + target = np.array(target) + # target = self.scaler.fit_transform(target.values) + # (self.prediction_time, 38) + + label = torch.LongTensor([0 for i in range(self.prediction_time)]) + + return ( + torch.from_numpy(sequence).float(), + torch.from_numpy(target).float(), + label, + ) + + def find_real_idx(self, idx): + + start = 0 + end = len(self.range_table) - 1 + while start <= end: + mid = (start + end) // 2 + if self.range_table[mid] == idx: + real_idx = idx + ((mid + 1) * (self.sequence_length + self.prediction_time - 1)) + return real_idx + + if self.range_table[mid] > idx: + end = mid - 1 + else: + start = mid + 1 + + real_idx = idx + (start * (self.sequence_length + self.prediction_time - 1)) + + return real_idx + + +class AbnormalDataset(Dataset): + + def __init__( + self, + sequence_length=20, + prediction_time=1, + root="/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/csv/abnormal/val", + label_root="/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/json/abnormal/val", + ): + super().__init__() + self.sequence_length = sequence_length + self.prediction_time = prediction_time + + # self.scaler = MinMaxScaler() + # 데이터 값 [0,1] 범위로 scaling할때 사용 + + # Load the dataset + file_list = os.listdir(root) + + df_list = [] + + self.length = 0 + self.range_table = [] + + self.real_length = 0 + self.real_idx_table = [] + + for i, file_name in enumerate(file_list): + dat = pd.read_csv(root + "/" + file_name) + # dat.drop(columns=["Frame"], inplace=True) # Remove the 'Frame' column + + print(f"==>>{i}번째 dat.shape: {dat.shape}") + + id_counter = pd.Series(dat["ID"]).value_counts(sort=False) + + for id_to_del in id_counter[id_counter < sequence_length + prediction_time].index: + dat.drop(dat[dat["ID"] == id_to_del].index, inplace=True) + + id_counter = pd.Series(dat["ID"]).value_counts(sort=False) + + print(f"==>>{i}번째 처리 후 dat.shape: {dat.shape}") + assert len(id_counter[id_counter < sequence_length + prediction_time].index) == 0 + + for count in id_counter: + cur_id_length = count - sequence_length - prediction_time + 1 + self.range_table.append(self.length + cur_id_length) + self.real_idx_table.append(self.real_length + count) + self.length += cur_id_length + self.real_length += count + + dat["ID"] = dat["ID"].astype("str") + f"_{i}" + df_list.append(dat.copy()) + + self.dat = pd.concat(df_list, ignore_index=True) + # self.dat.drop(columns=["Frame"], inplace=True) # Remove the 'Frame' column + + id_counter = pd.Series(self.dat["ID"]).value_counts(sort=False) + + assert len(id_counter[id_counter < sequence_length + prediction_time].index) == 0 + + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # TODO: 한 영상에 start end 여러번 있는 경우 고려해서 코드 수정하기 + # 정답 frame 담은 dict 만들기 + self.frame_label = dd(lambda: dd(lambda: [-1, -1])) + + folder_list = os.listdir(label_root) + + for folder in folder_list: + json_list = os.listdir(label_root + "/" + folder) + + for js in json_list: + with open(label_root + "/" + folder + "/" + js, "r") as j: + json_dict = json.load(j) + + for dict in json_dict["annotations"]["track"]: + if dict["@label"].endswith("_start"): + cur_id = dict["@id"] + self.frame_label[js[:-5]][cur_id][0] = dict["box"][0]["@frame"] + elif dict["@label"].endswith("_end"): + cur_id = dict["@id"] + self.frame_label[js[:-5]][cur_id][1] = dict["box"][0]["@frame"] + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + + def __len__(self): + return self.length + + def __getitem__(self, idx): + real_idx = self.find_real_idx(idx) + + sequence = self.dat[real_idx : real_idx + self.sequence_length].copy() + sequence.drop(columns=["ID"], inplace=True) + sequence.drop(columns=["Frame"], inplace=True) + sequence.drop(columns=["Filename"], inplace=True) + sequence = np.array(sequence) + # (self.sequence_length, 38) + target = self.dat[ + real_idx + self.sequence_length : real_idx + self.sequence_length + self.prediction_time + ].copy() + target_frames = target["Frame"].unique() + target_filename = target["Filename"].unique()[0].split(".")[0] + + target.drop(columns=["ID"], inplace=True) + target.drop(columns=["Frame"], inplace=True) + target.drop(columns=["Filename"], inplace=True) + target = np.array(target) + # target = self.scaler.fit_transform(target.values) + # (self.prediction_time, 38) + + target_labels = [] + + for target_frame in target_frames: + temp = 0 + for cur_id in self.frame_label[target_filename].keys(): + if int(target_frame) >= int(self.frame_label[target_filename][cur_id][0]) and int( + target_frame + ) <= int(self.frame_label[target_filename][cur_id][1]): + temp = 1 + + target_labels.append(temp) + + target_labels = torch.LongTensor(target_labels) + + return torch.from_numpy(sequence).float(), torch.from_numpy(target).float(), target_labels + + def find_real_idx(self, idx): + + start = 0 + end = len(self.range_table) - 1 + while start <= end: + mid = (start + end) // 2 + if self.range_table[mid] == idx: + real_idx = idx + ((mid + 1) * (self.sequence_length + self.prediction_time - 1)) + return real_idx + + if self.range_table[mid] > idx: + end = mid - 1 + else: + start = mid + 1 + + real_idx = idx + (start * (self.sequence_length + self.prediction_time - 1)) + + return real_idx + + +class NormalVMAE(Dataset): + """ + is_train = 1 <- train, 0 <- test + """ + + def __init__( + self, + # is_train=1, + model_size="small", + root="/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/npy/normal", + # label_root="/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/json/abnormal", + ): + super().__init__() + # self.is_train = is_train + # normal의 경우 torch.utils.data.random_split 함수로 train/val 나눔 + + self.path = root + + folder_list = os.listdir(self.path) + folder_list.sort() + + self.data_list = [] + + for folder_name in folder_list: + if folder_name.endswith("_base") and model_size == "small": + continue + elif not folder_name.endswith("_base") and model_size != "small": + continue + print(f"==>> {folder_name} 폴더 데이터 로딩 시작") + + folder_path = folder_name + "/" + data_list = os.listdir(self.path + "/" + folder_path) + data_list.sort() + data_list = [folder_path + name for name in data_list] + self.data_list.extend(data_list) + print(f"==>> {folder_name} 폴더 데이터 로딩 완료") + + def __len__(self): + return len(self.data_list) + + def __getitem__(self, idx): + file_name = self.data_list[idx] + + feature = np.load(self.path + "/" + file_name) + # 정상 영상 feature는 (57,710) 또는 (38,710) + if feature.shape[0] % 12 == 0: + feature_npy = feature + else: + count = (feature.shape[0] // 12) + 1 + + feature_npy = np.zeros((count * 12, 710)) + # 12로 나눌 수 있도록 (60, 710) or (48, 710) 준비 + + feature_npy[: feature.shape[0]] = feature + # np.load로 불러온 정상영상 feature는 (57, 710) 또는 (38,710) + + feature_npy[feature.shape[0] :] = [feature_npy[-1]] * (count * 12 - feature.shape[0]) + # 정상영상 feature의 마지막 부분으로 빈 자리 채우기 + + feature_npy = feature_npy.reshape(12, -1, 710) + # (12, 5, 710) or (12, 4, 710) + feature_npy = np.mean(feature_npy, axis=1) + # 이상행동 영상 feature의 (12,710)과 같아지도록 평균으로 조절 + + gts = np.zeros(11) + # 정상영상은 전부 정답이 0 + + return torch.from_numpy(feature_npy[:-1, :]).float(), torch.from_numpy(gts).float() + + +class AbnormalVMAE(Dataset): + """ + is_train = 1 <- train, 0 <- test + """ + + def __init__( + self, + is_train=1, + model_size="small", + root="/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/npy/abnormal", + label_root="/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/json/abnormal", + ): + print(f"==>> abnormal 데이터 로딩 시작") + super().__init__() + self.is_train = is_train + + if self.is_train == 1: + self.path = root + "/train/" + if model_size == "small": + self.label_path = label_root + "/train/abnormal_train.json" + else: + self.label_path = label_root + "/train/abnormal_train_base.json" + else: + self.path = root + "/val/" + if model_size == "small": + self.label_path = label_root + "/val/abnormal_val.json" + else: + self.label_path = label_root + "/val/abnormal_val_base.json" + + with open(self.label_path, "r", encoding="utf-8") as j: + self.data_list = json.load(j) + print(f"==>> abnormal 데이터 로딩 완료") + + def __len__(self): + return len(self.data_list) + + def __getitem__(self, idx): + file_info = self.data_list[str(idx)] + + feature_npy = np.load(self.path + file_info["filename"]) + # feature_npy.shape: (12, 710) + + # file_name = file_info["filename"].split("/")[-1].split(".")[0] + + gts = np.zeros(176) + # 이상행동 영상 180 프레임 => 12 * 16 = 192 가 되도록 길이 연장 + + for start, end in zip(file_info["frames_start"], file_info["frames_end"]): + gts[int(start) - 1 : min(int(end), 176)] = 1 + + # for i in range(12): + # gts[180 + i] = gts[179] + # @@ feature extraction할때 마지막 조각에서 frame 개수가 16개가 안되면 마지막 frame을 복사해서 추가함 + + if self.is_train: + gts = gts.reshape(11, 16) + # (192) => (12, 16)로 변경 + # gts = np.mean(gts, axis=1) + # 평균 내서 (12)로 변경 + gts = np.max(gts, axis=1) + + # @@ validation일때는 평균내지 않고 (192) numpy array 그대로 반환 + + return torch.from_numpy(feature_npy[:-1, :]).float(), torch.from_numpy(gts).float() + + +class NewNormalVMAE(Dataset): + """ + is_train = 1 <- train, 0 <- test + """ + + def __init__( + self, + is_train=1, + model_size="small", + root="/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/npy/UCFCrime/normal", + # label_root="/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/json/abnormal", + num_segments=200, + l2_norm=False, + ): + set_type = "학습" if is_train == 1 else "검증" + print(f"==>> normal {set_type} 데이터 로딩 시작") + super().__init__() + self.is_train = is_train + self.l2_norm = l2_norm + + if self.is_train == 1: + self.path = root + "/train/" + else: + self.path = root + "/val/" + self.num_segments = num_segments + + folder_list = os.listdir(self.path) + folder_list.sort() + + self.data_list = [] + + for folder_name in folder_list: + if folder_name.endswith("_base") and model_size == "small": + continue + elif not folder_name.endswith("_base") and model_size != "small": + continue + print(f"==>> {folder_name} 폴더 데이터 로딩 시작") + + folder_path = folder_name + "/" + data_list = os.listdir(self.path + "/" + folder_path) + data_list.sort() + data_list = [folder_path + name for name in data_list] + self.data_list.extend(data_list) + print(f"==>> {folder_name} 폴더 데이터 로딩 완료") + + print(f"==>> normal {set_type} 데이터 로딩 완료") + + def __len__(self): + return len(self.data_list) + + def __getitem__(self, idx): + file_name = self.data_list[idx] + + feature = np.load(self.path + "/" + file_name).astype(np.float32) + # (원본영상 frame 수 // 16,710) + + if self.l2_norm: + feature = normalize(feature, norm="l2") + + feature_npy = np.zeros((self.num_segments, 710)).astype(np.float32) + + sample_index = np.linspace(0, feature.shape[0], self.num_segments + 1, dtype=np.uint16) + # ex: feature.shape[0]이 62이고, self.num_segments이 200이면 + # sample_index == [ 0 0 0 0 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 6 6 6 7 + # 7 7 8 8 8 8 9 9 9 10 10 10 11 11 11 12 12 12 13 13 13 13 14 14 + # 14 15 15 15 16 16 16 17 17 17 17 18 18 18 19 19 19 20 20 20 21 21 21 22 + # 22 22 22 23 23 23 24 24 24 25 25 25 26 26 26 26 27 27 27 28 28 28 29 29 + # 29 30 30 30 31 31 31 31 32 32 32 33 33 33 34 34 34 35 35 35 35 36 36 36 + # 37 37 37 38 38 38 39 39 39 39 40 40 40 41 41 41 42 42 42 43 43 43 44 44 + # 44 44 45 45 45 46 46 46 47 47 47 48 48 48 48 49 49 49 50 50 50 51 51 51 + # 52 52 52 53 53 53 53 54 54 54 55 55 55 56 56 56 57 57 57 57 58 58 58 59 + # 59 59 60 60 60 61 61 61 62] 꼴 + # ex2: feature.shape[0]이 62이고, self.num_segments이 11이면 + # sample_index == [ 0, 6, 12, 18, 24, 31, 37, 43, 49, 55, 62] + + for i in range(len(sample_index) - 1): + if sample_index[i] == sample_index[i + 1]: + feature_npy[i, :] = feature[sample_index[i], :] + else: + feature_npy[i, :] = feature[sample_index[i] : sample_index[i + 1], :].mean(0) + # ex2의 0과 6 => [0:6] => 0~5 feature 6개 평균 + # ex1의 0과 1 => [0:1] => 0~0 feature 1개 평균 => 0번 feature 그대로 + + # feature.shape[0]이 self.num_segments보다 짧으면 같은 feature 반복 + # feature.shape[0]이 self.num_segments보다 길면 평균 내서 self.num_segments개로 줄인다 + + if self.is_train != 1: + gts = np.zeros(self.num_segments).astype(np.float32) + # 정상영상은 전부 정답이 0 + + return torch.from_numpy(feature_npy), torch.from_numpy(gts) + else: + return torch.from_numpy(feature_npy) + + +class NewAbnormalVMAE(Dataset): + """ + is_train = 1 <- train, 0 <- test + """ + + def __init__( + self, + is_train=1, + model_size="small", + root="/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/npy/UCFCrime/abnormal", + label_root="/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/npy/UCFCrime/test_anomalyv2.txt", + num_segments=200, + gt_thr=0.25, + l2_norm=False, + ): + set_type = "학습" if is_train == 1 else "검증" + print(f"==>> abnormal {set_type} 데이터 로딩 시작") + super().__init__() + self.is_train = is_train + self.l2_norm = l2_norm + + if self.is_train == 1: + self.path = root + "/train/" + else: + self.path = root + "/val/" + self.label_dict = {} + with open(label_root, "r", encoding="utf-8") as f: + for line in f: + # line.split()은 ['Arrest/Arrest039_x264.mp4', '15836', '[7215, 10335]\n'] 이런 형태 + temp = line.split("|") + self.label_dict[temp[0].split("/")[1] + ".npy"] = { + "frame_counts": int(temp[1]), + "frames_gt": temp[2][1:-2].split(","), + } + + self.num_segments = num_segments + self.gt_thr = gt_thr + + folder_list = os.listdir(self.path) + folder_list.sort() + + self.data_list = [] + + for folder_name in folder_list: + if folder_name.endswith("_base") and model_size == "small": + continue + elif not folder_name.endswith("_base") and model_size != "small": + continue + print(f"==>> {folder_name} 폴더 데이터 로딩 시작") + + folder_path = folder_name + "/" + data_list = os.listdir(self.path + "/" + folder_path) + data_list.sort() + data_list = [folder_path + name for name in data_list] + self.data_list.extend(data_list) + print(f"==>> {folder_name} 폴더 데이터 로딩 완료") + + print(f"==>> abnormal {set_type} 데이터 로딩 완료") + + def __len__(self): + return len(self.data_list) + + def __getitem__(self, idx): + file_name = self.data_list[idx] + + feature = np.load(self.path + "/" + file_name).astype(np.float32) + # (원본영상 frame 수 // 16,710) + + if self.l2_norm: + feature = normalize(feature, norm="l2") + + feature_npy = np.zeros((self.num_segments, 710)).astype(np.float32) + + sample_index = np.linspace(0, feature.shape[0], self.num_segments + 1, dtype=np.uint16) + # ex: feature.shape[0]이 62이고, self.num_segments이 11이면 + # sample_index == [ 0, 6, 12, 18, 24, 31, 37, 43, 49, 55, 62] + + for i in range(len(sample_index) - 1): + if sample_index[i] == sample_index[i + 1]: + feature_npy[i, :] = feature[sample_index[i], :] + else: + feature_npy[i, :] = feature[sample_index[i] : sample_index[i + 1], :].mean(0) + # ex의 0과 6 => [0:6] => 0~5 feature 6개 평균 + + # feature.shape[0]이 self.num_segments보다 짧으면 같은 feature 반복 + # feature.shape[0]이 self.num_segments보다 길면 평균 내서 self.num_segments개로 줄인다 + + if self.is_train != 1: + label_info = self.label_dict[file_name.split("/")[1]] + frame_counts = label_info["frame_counts"] + frames_gt = label_info["frames_gt"] + + # if frame_counts % 16 == 0: + # gts = np.zeros(frame_counts) + # else: + # gts = np.zeros(frame_counts + (16 - (frame_counts % 16))) + gts = np.zeros(feature.shape[0] * 16) + + # @@@@@@TODO 토막 단위 또는 프레임 단위로 gt 만들기 @@@@@@@@@@@@@@@@@@@@ + + gts[int(frames_gt[0]) - 1 : min(int(frames_gt[1]), frame_counts)] = 1 + + if len(frames_gt) == 4: + gts[int(frames_gt[2]) - 1 : min(int(frames_gt[3]), frame_counts)] = 1 + + # # for i in range(12): + # # gts[180 + i] = gts[179] + # # @@ feature extraction할때 마지막 조각에서 frame 개수가 16개가 안되면 마지막 frame을 복사해서 추가함 + + gts = gts.reshape(-1, 16) + + # assert feature.shape[0] == gts.shape[0] + + # gts = np.max(gts, axis=1) + # 16프레임중 1개라도 1이 있으면 True로 취급 + + gts = np.mean(gts, axis=1) + # 마지막에 self.gt_thr 넘는 값만 True로 취급 + # 기본값 0.25 + + gts_npy = np.zeros(self.num_segments).astype(np.float32) + + for i in range(len(sample_index) - 1): + if sample_index[i] == sample_index[i + 1]: + gts_npy[i] = gts[sample_index[i]] + else: + gts_npy[i] = gts[sample_index[i] : sample_index[i + 1]].mean(0) + # ex의 0과 6 => [0:6] => 0~5 gts 6개 평균 + + gts_npy = gts_npy > self.gt_thr + gts_npy = gts_npy.astype(np.float32) + + return torch.from_numpy(feature_npy), torch.from_numpy(gts_npy) + else: + return torch.from_numpy(feature_npy) diff --git a/model/train/train.sh b/model/train/train.sh new file mode 100755 index 0000000..40694e5 --- /dev/null +++ b/model/train/train.sh @@ -0,0 +1,2 @@ +python train_MIL.py --model_size="small" --num_workers=8 --drop_rate=0.3 --thr=0.4 --wandb_mode="online" --wandb_run_name="MIL_nl_onlyMIL_no_extra_small" --patience=1000 --use_extra +python train_MIL.py --model_size="small" --num_workers=8 --drop_rate=0.3 --thr=0.4 --wandb_mode="online" --wandb_run_name="MIL_nl_onlyMIL_in_t1loop_small" --patience=1000 diff --git a/model/train/train_BNWVAD.py b/model/train/train_BNWVAD.py new file mode 100644 index 0000000..a2c5afb --- /dev/null +++ b/model/train/train_BNWVAD.py @@ -0,0 +1,2285 @@ +import pandas as pd +import numpy as np +import random +import matplotlib.pyplot as plt + +from datetime import datetime + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from torch.utils.data import Dataset, DataLoader, random_split, ConcatDataset + +from sklearn.metrics import roc_auc_score, roc_curve, precision_recall_curve +import sklearn.metrics +from sklearn.preprocessing import MinMaxScaler + +from argparse import ArgumentParser + +import os +import os.path as osp + +from tqdm import tqdm + +import wandb + +from shop_dataset import NormalVMAE, AbnormalVMAE +from classifier import WSAD +from loss import MIL, LossComputer + + +def parse_args(): + parser = ArgumentParser() + + # Conventional args + parser.add_argument( + "--normal_root_dir", + type=str, + default=os.environ.get( + "SM_CHANNEL_NORMAL_NPY", + "../datapreprocess/npy/normal", + ), + ) + # 학습 데이터 경로 + parser.add_argument( + "--abnormal_root_dir", + type=str, + default=os.environ.get( + "SM_CHANNEL_ABNORMAL_NPY", + "../datapreprocess/npy/abnormal", + ), + ) + parser.add_argument( + "--json_dir", + type=str, + default=os.environ.get( + "SM_CHANNEL_ABNORMAL_JSON", + "../datapreprocess/json/abnormal", + ), + ) + # abnormal 검증셋 npy, json파일 경로 + parser.add_argument("--model_dir", type=str, default=os.environ.get("SM_MODEL_DIR", "../pths")) + # pth 파일 저장 경로 + + parser.add_argument("--model_name", type=str, default="BNWVAD") + # import_module로 불러올 model name + + parser.add_argument("--len_feature", type=int, default=710) + # npy파일 feature length + parser.add_argument("--num_segments", type=int, default=11) + # 영상 segment 개수 + + parser.add_argument("--resume_name", type=str, default="") + # resume 파일 이름 + + parser.add_argument("--model_size", type=str, default="small") + # VideoMAEv2 backbone 사이즈 = "small" or "base" + + parser.add_argument("--seed", type=int, default=666) + # random seed + + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") + parser.add_argument("--num_workers", type=int, default=0) + + parser.add_argument("--batch_size", type=int, default=30) + # parser.add_argument("--val_batch_size", type=int, default=1) + # parser.add_argument("--val_num_workers", type=int, default=0) + parser.add_argument("--learning_rate", type=float, default=0.0001) + parser.add_argument("--weight_decay", type=float, default=0.00005) + parser.add_argument("--max_epoch", type=int, default=1000) + + parser.add_argument("--save_interval", type=int, default=1) + parser.add_argument("--val_interval", type=int, default=1) + parser.add_argument("--w_normal", type=float, default=1.0) + parser.add_argument("--w_mpp", type=float, default=1.0) + parser.add_argument("--thr", type=float, default=0.25) + + parser.add_argument("--ratio_sample", type=float, default=0.2) + parser.add_argument("--ratio_batch", type=float, default=0.4) + + parser.add_argument("--ratios", type=int, nargs="+", default=[16, 32]) + parser.add_argument("--kernel_sizes", type=int, nargs="+", default=[1, 1, 1]) + + parser.add_argument("--patience", type=int, default=100) + + # parser.add_argument("--mp", action="store_false") + # https://stackoverflow.com/questions/60999816/argparse-not-parsing-boolean-arguments + # mixed precision 사용할 지 여부 + + parser.add_argument("--use_extra", action="store_false") + + # parser.add_argument("--wandb_mode", type=str, default="online") + parser.add_argument("--wandb_mode", type=str, default="disabled") + # wandb mode + parser.add_argument("--wandb_run_name", type=str, default="BNWVAD") + # wandb run name + + args = parser.parse_args() + + return args + + +def set_seed(seed): + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # if use multi-GPU + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + np.random.seed(seed) + random.seed(seed) + + +def train( + normal_root_dir, + abnormal_root_dir, + json_dir, + model_dir, + model_name, + model_size, + device, + num_workers, + batch_size, + # val_num_workers, + # val_batch_size, + learning_rate, + weight_decay, + max_epoch, + val_interval, + save_interval, + w_normal, + w_mpp, + thr, + len_feature, + num_segments, + ratio_sample, + ratio_batch, + ratios, + kernel_sizes, + patience, + resume_name, + seed, + # mp, + use_extra, + wandb_mode, + wandb_run_name, +): + + time_start = datetime.now() + + train_start = time_start.strftime("%Y%m%d_%H%M%S") + + set_seed(seed) + + if not osp.exists(model_dir): + os.makedirs(model_dir) + + batch_size = batch_size + + val_batch_size = 1 + val_num_workers = 0 + + # -- early stopping flag + patience = patience + counter = 0 + + # 데이터셋 + dataset = NormalVMAE( + model_size=model_size, + root=normal_root_dir, + ) + + valid_data_size = len(dataset) // 10 + + train_data_size = len(dataset) - valid_data_size + + train_dataset, valid_dataset = random_split(dataset, lengths=[train_data_size, valid_data_size]) + + normal_train_loader = DataLoader( + dataset=train_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=num_workers + ) + + normal_valid_loader = DataLoader( + dataset=valid_dataset, + batch_size=val_batch_size, + shuffle=False, + drop_last=True, + num_workers=val_num_workers, + ) + + abnormal_train_dataset = AbnormalVMAE( + model_size=model_size, + root=abnormal_root_dir, + label_root=json_dir, + ) + abnormal_valid_dataset = AbnormalVMAE( + is_train=0, + model_size=model_size, + root=abnormal_root_dir, + label_root=json_dir, + ) + + abnormal_train_loader = DataLoader( + dataset=abnormal_train_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True, + num_workers=num_workers, + ) + + abnormal_valid_loader = DataLoader( + dataset=abnormal_valid_dataset, batch_size=val_batch_size, shuffle=False, num_workers=val_num_workers + ) + + data_load_end = datetime.now() + data_load_time = data_load_end - time_start + data_load_time = str(data_load_time).split(".")[0] + print(f"==>> {model_size} data_load_time: {data_load_time}") + + # Initialize the model + model = WSAD( + input_size=len_feature, + ratio_sample=ratio_sample, + ratio_batch=ratio_batch, + ratios=ratios, + kernel_sizes=kernel_sizes, + ) + + load_dict = None + + if resume_name: + load_dict = torch.load(osp.join(model_dir, f"{resume_name}.pth"), map_location="cpu") + model.load_state_dict(load_dict["model_state_dict"]) + + model.to(device) + + # optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + # 1e-6 => 0.0010000000474974513 + optimizer = torch.optim.Adam( + model.parameters(), lr=learning_rate, betas=(0.9, 0.999), weight_decay=weight_decay + ) + # optimizer = torch.optim.Adagrad(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[1000, 1500], gamma=0.5) + + if resume_name: + optimizer.load_state_dict(load_dict["optimizer_state_dict"]) + scheduler.load_state_dict(load_dict["scheduler_state_dict"]) + # scaler.load_state_dict(load_dict["scaler_state_dict"]) + + criterion = nn.BCELoss() + MPP_criterion = LossComputer(w_normal=w_normal, w_mpp=w_mpp) + + print(f"Start training..") + + wandb.init( + project="VAD", + entity="pao-kim-si-woong", + config={ + "lr": learning_rate, + "dataset": "무인매장", + "n_epochs": max_epoch, + "loss": "MPP", + "notes": "VAD 실험", + }, + name=wandb_run_name + "_" + train_start, + mode=wandb_mode, + ) + + wandb.watch((model,)) + + best_loss = np.inf + best_auc = 0 + + total_batches = len(abnormal_train_loader) + + for epoch in range(max_epoch): + model.train() + + epoch_start = datetime.now() + + epoch_loss = 0 + epoch_n_corrects = 0 + epoch_n_MPP_loss = 0 + epoch_n_norm_loss = 0 + epoch_n_MPP_and_norm_loss = 0 + epoch_n_loss = 0 + epoch_n_n_corrects = 0 + + epoch_abnormal_max = 0 + epoch_abnormal_mean = 0 + epoch_normal_max = 0 + epoch_normal_mean = 0 + + norm_train_iter = iter(normal_train_loader) + # iterator를 여기서 매번 새로 할당해줘야 iterator가 다시 처음부터 작동 + + for step, abnormal_inputs in tqdm( + enumerate(abnormal_train_loader), + total=total_batches, + ): + try: + normal_inputs = next(norm_train_iter) + + normal_input, normal_gt = normal_inputs + # (batch_size, 11, 710), (batch_size, 11) + abnormal_input, abnormal_gt = abnormal_inputs + # (batch_size, 11, 710), (batch_size, 11) + + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + inputs, gts = torch.cat((normal_input, abnormal_input), dim=1), torch.cat( + (normal_gt, abnormal_gt), dim=1 + ) + # @@@@ BN-WVAD는 정상 영상 먼저 @@@@ + # inputs는 (batch_size, 22, 710), gts는 (batch_size, 22) + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + + # batch_size = inputs.shape[0] + + inputs = inputs.to(device) + gts = gts.view(-1, 1).to(device) + # (batch_size * 22, 1) + + optimizer.zero_grad() + + pred_result = model(inputs, flag="Train") + # pred_result["pre_normal_scores"]: normal_scores[0 : b // 2], + # pred_result["bn_results"]: bn_results, + # pred_result["normal_scores"]: normal_scores, + # pred_result["scores"]: distance_sum * normal_scores, + + pred_acc = pred_result["normal_scores"].view(-1, 1) + pred = pred_result["scores"].view(-1, 1) + # pred_result["normal_scores"]는 (batch_size, 22) + # => pred는 (batch_size * 22, 1) + + loss = criterion(pred_acc, gts) + MPP_and_norm_loss, loss_dict = MPP_criterion(pred_result) + # sum_loss = loss + MPP_and_norm_loss + sum_loss = MPP_and_norm_loss + sum_loss.backward() + + # loss.backward() + optimizer.step() + with torch.no_grad(): + pred_n = pred.view(batch_size, 2, abnormal_input.size(1))[:, 0, :] + pred_a = pred.view(batch_size, 2, abnormal_input.size(1))[:, 1, :] + + pred_n_max = torch.mean(torch.max(pred_n, dim=-1)[0]) + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + + pred_n_mean = torch.mean(pred_n) + pred_a_mean = torch.mean(pred_a) + + pred_correct = pred_acc > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + epoch_n_loss += loss.item() + epoch_n_MPP_loss += loss_dict["mpp_loss"].item() + epoch_n_norm_loss += loss_dict["normal_loss"].item() + epoch_n_MPP_and_norm_loss += MPP_and_norm_loss.item() + + epoch_n_n_corrects += corrects / (abnormal_input.size(1) * 2) + + epoch_normal_max += pred_n_max.item() + epoch_normal_mean += pred_n_mean.item() + epoch_abnormal_max += pred_a_max.item() + epoch_abnormal_mean += pred_a_mean.item() + + except StopIteration: + if not use_extra: + break + + abnormal_input, abnormal_gt = abnormal_inputs + # (batch_size, 11, 710), (batch_size, 11) + + inputs = abnormal_input.to(device) + # (batch_size, 11, 710) + gts = abnormal_gt.view(-1, 1).to(device) + # (batch_size * 11, 1) + + optimizer.zero_grad() + + # pred_result = model(inputs, flag="Train_extra") + pred_result = model(inputs, flag="Train_extra") + # pred_result는 (batch_size, 11) + + pred_acc = pred_result["normal_scores"].view(-1, 1) + pred = pred_result["scores"].view(-1, 1) + # pred는(batch_size * 11, 1) + + loss = criterion(pred_acc, gts) + + loss.backward() + optimizer.step() + with torch.no_grad(): + # print(f"==>> pred.shape: {pred.shape}") + pred_a = pred.view(batch_size, abnormal_input.size(1)) + + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + + pred_a_mean = torch.mean(pred_a) + + pred_correct = pred_acc > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + epoch_loss += loss.item() + epoch_n_corrects += corrects / abnormal_input.size(1) + + epoch_abnormal_max += pred_a_max.item() + epoch_abnormal_mean += pred_a_mean.item() + + epoch_n_mean_loss = epoch_n_loss / len(normal_train_loader) + epoch_n_mean_MPP_loss = epoch_n_MPP_loss / len(normal_train_loader) + epoch_n_mean_norm_loss = epoch_n_norm_loss / len(normal_train_loader) + epoch_n_mean_MPP_and_norm_loss = epoch_n_MPP_and_norm_loss / len(normal_train_loader) + epoch_n_accuracy = epoch_n_n_corrects / (batch_size * (len(normal_train_loader))) + + epoch_mean_normal_max = epoch_normal_max / len(normal_train_loader) + epoch_mean_normal_mean = epoch_normal_mean / len(normal_train_loader) + if use_extra: + epoch_mean_loss = (epoch_loss + epoch_n_loss) / total_batches + epoch_accuracy = (epoch_n_corrects + epoch_n_n_corrects) / (batch_size * total_batches) + epoch_mean_abnormal_max = epoch_abnormal_max / total_batches + epoch_mean_abnormal_mean = epoch_abnormal_mean / total_batches + else: + epoch_mean_loss = (epoch_loss + epoch_n_loss) / len(normal_train_loader) + epoch_accuracy = (epoch_n_corrects + epoch_n_n_corrects) / ( + batch_size * (len(normal_train_loader)) + ) + epoch_mean_abnormal_max = epoch_abnormal_max / len(normal_train_loader) + epoch_mean_abnormal_mean = epoch_abnormal_mean / len(normal_train_loader) + + train_end = datetime.now() + train_time = train_end - epoch_start + train_time = str(train_time).split(".")[0] + print( + f"==>> epoch {epoch+1} train_time: {train_time}\nloss: {round(epoch_mean_loss,4)} n_loss: {round(epoch_n_mean_loss,4)}" + ) + print( + f"MPP_loss: {round(epoch_n_mean_MPP_loss,4)} norm_loss: {round(epoch_n_mean_norm_loss,4)} MPP+norm_loss: {round(epoch_n_mean_MPP_and_norm_loss,4)}" + ) + print(f"accuracy: {epoch_accuracy:.2f} n_accuracy: {epoch_n_accuracy:.2f}") + print(f"==>> abnormal_max_mean: {epoch_mean_abnormal_max} abnormal_mean: {epoch_mean_abnormal_mean}") + print(f"==>> normal_max_mean: {epoch_mean_normal_max} normal_mean: {epoch_mean_normal_mean}") + + if (epoch + 1) % save_interval == 0: + + ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_latest.pth") + + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + "optimizer_state_dict": optimizer.state_dict(), + "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + } + + torch.save(states, ckpt_fpath) + + # validation 주기에 따라 loss를 출력하고 best model을 저장합니다. + if (epoch + 1) % val_interval == 0: + + print(f"Start validation #{epoch+1:2d}") + model.eval() + + with torch.no_grad(): + total_n_loss = 0 + # total_n_MPP_loss = 0 + # total_n_norm_loss = 0 + # total_n_MPP_and_norm_loss = 0 + total_n_n_corrects = 0 + + total_n_fpr = 0 + total_n_tpr = 0 + total_n_bthr = 0 + total_n_auc = 0 + total_n_ap = 0 + + total_loss = 0 + total_n_corrects = 0 + + total_fpr = 0 + total_tpr = 0 + total_bthr = 0 + total_auc = 0 + total_ap = 0 + + error_n_count = 0 + error_count = 0 + + total_abnormal_max = 0 + total_abnormal_mean = 0 + total_normal_max = 0 + total_normal_mean = 0 + + norm_valid_iter = iter(normal_valid_loader) + # iterator를 여기서 매번 새로 할당해줘야 iterator가 다시 처음부터 작동 + + for step, abnormal_inputs in tqdm( + enumerate(abnormal_valid_loader), total=len(abnormal_valid_loader) + ): + try: + normal_inputs = next(norm_valid_iter) + + normal_input, normal_gt = normal_inputs + # (val_batch_size, 11, 710), (val_batch_size, 11) + abnormal_input, abnormal_gt = abnormal_inputs + # (val_batch_size, 11, 710), (val_batch_size, 176) + + abnormal_gt2 = torch.max(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2)[0] + # abnormal_gt2 = torch.mean(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2) + # (val_batch_size, 11) + + inputs = torch.cat((normal_input, abnormal_input), dim=1) + gts = torch.cat((normal_gt, abnormal_gt2), dim=1) + # inputs는 (val_batch_size, 22, 710), gts는 (val_batch_size, 22) + + inputs = inputs.to(device) + # (val_batch_size, 22, 710) + gts = gts.view(-1, 1).to(device) + # (val_batch_size * 22, 1) + + pred_result = model(inputs, flag="Eval_MPP") + # pred_result["pre_normal_scores"]: normal_scores[0 : b // 2], + # pred_result["bn_results"]: bn_results, + # pred_result["scores"]: distance_sum * normal_scores, + # breakpoint() + pred_acc = pred_result["normal_scores"].view(-1, 1) + pred = pred_result["scores"].view(-1, 1) + # pred는(batch_size * 11, 1) + + val_loss = criterion(pred_acc, gts) + # if val_loss > 2: + # print(f"==>> pred: {pred}") + # print(f"==>> gts: {gts}") + # counter = patience + 1 + + # val_MPP_and_norm_loss, val_loss_dict = MPP_criterion(pred_result) + + pred_n = pred.view(val_batch_size, 2, abnormal_input.size(1))[:, 0, :] + pred_a = pred.view(val_batch_size, 2, abnormal_input.size(1))[:, 1, :] + + pred_n_max = torch.mean(torch.max(pred_n, dim=-1)[0]) + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + + pred_n_mean = torch.mean(pred_n) + pred_a_mean = torch.mean(pred_a) + + pred_correct = pred_acc > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + pred = (pred.squeeze()).detach().cpu().numpy() + + pred_normal_np = np.zeros(abnormal_gt.size(1)) + pred_abnormal_np = np.zeros(abnormal_gt.size(1)) + + step = np.array([i for i in range(abnormal_input.size(1) + 1)]) + + for j in range(abnormal_input.size(1)): + pred_normal_np[step[j] * 16 : step[j + 1] * 16] = pred[j] + pred_abnormal_np[step[j] * 16 : step[j + 1] * 16] = pred[ + abnormal_input.size(1) + j + ] + + pred_np = np.concatenate((pred_normal_np, pred_abnormal_np), axis=0) + + abnormal_gt = abnormal_gt.squeeze().detach().cpu().numpy() + # abnormal_gt2 = abnormal_gt2.squeeze().detach().cpu().numpy() + # normal_gt = np.zeros_like(abnormal_gt2) + normal_gt = np.zeros_like(abnormal_gt) + # gt_np = np.concatenate((abnormal_gt2, normal_gt), axis=0) + gt_np = np.concatenate((normal_gt, abnormal_gt), axis=0) + + try: + # auc = roc_auc_score(y_true=gt_np, y_score=pred_np) + # auc = roc_auc_score(y_true=gt_np, y_score=pred) + + fpr, tpr, cut = roc_curve(y_true=gt_np, y_score=pred_np) + precision, recall, cut2 = precision_recall_curve(gt_np, pred_np) + + auc = sklearn.metrics.auc(fpr, tpr) + ap = sklearn.metrics.auc(recall, precision) + + diff = tpr - fpr + diff_idx = np.argmax(diff) + best_thr = cut[diff_idx] + + pred_positive = pred_np > thr + TP_and_FN = pred_positive[gt_np > 0.9] + FP_and_TN = pred_positive[gt_np < 0.1] + + total_n_fpr += np.sum(FP_and_TN) / len(FP_and_TN) + total_n_tpr += np.sum(TP_and_FN) / len(TP_and_FN) + total_n_bthr += best_thr if diff_idx != 0 else 1 + + total_n_auc += auc + total_n_ap += ap + total_n_n_corrects += corrects / (abnormal_input.size(1) * 2) + total_n_loss += val_loss.item() + # total_n_MPP_loss += val_loss_dict["mpp_loss"].item() + # total_n_norm_loss += val_loss_dict["normal_loss"].item() + # total_n_MPP_and_norm_loss += val_MPP_and_norm_loss.item() + + total_normal_max += pred_n_max.item() + total_normal_mean += pred_n_mean.item() + total_abnormal_max += pred_a_max.item() + total_abnormal_mean += pred_a_mean.item() + + except ValueError: + # print( + # "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." + # ) + # total_auc += 0 + error_n_count += 1 + # print("0~180 전부 0인 abnormal 영상 있음") + except StopIteration: + # if not use_extra: + # break + abnormal_input, abnormal_gt = abnormal_inputs + # (val_batch_size, 11, 710), (val_batch_size, 176) + + abnormal_gt2 = torch.max(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2)[0] + # abnormal_gt2 = torch.mean(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2) + # (val_batch_size, 11) + + inputs = abnormal_input.to(device) + # (val_batch_size, 11, 710) + gts = abnormal_gt2.view(-1, 1).to(device) + # (val_batch_size * 11, 1) + + pred_result = model(inputs, flag="Eval_MPP") + # pred_result는 (batch_size, 11) + + pred_acc = pred_result["normal_scores"].view(-1, 1) + pred = pred_result["scores"].view(-1, 1) + # pred = pred_result.view(-1, 1) + # pred는(batch_size * 11, 1) + + val_loss = criterion(pred_acc, gts) + # if val_loss > 2: + # print(f"==>> pred: {pred}") + # print(f"==>> gts: {gts}") + # counter = patience + 1 + + pred_a = pred.view(val_batch_size, abnormal_input.size(1)) + + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + + pred_a_mean = torch.mean(pred_a) + + pred_correct = pred_acc > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + pred = (pred.squeeze()).detach().cpu().numpy() + pred_abnormal_np = np.zeros(abnormal_gt.size(1)) + + step = np.array([i for i in range(abnormal_input.size(1) + 1)]) + + for j in range(abnormal_input.size(1)): + pred_abnormal_np[step[j] * 16 : step[j + 1] * 16] = pred[j] + + # abnormal_gt2 = abnormal_gt2.squeeze().detach().cpu().numpy() + abnormal_gt = abnormal_gt.squeeze().detach().cpu().numpy() + + try: + # auc = roc_auc_score(y_true=abnormal_gt, y_score=pred_abnormal_np) + # auc = roc_auc_score(y_true=abnormal_gt2, y_score=pred) + + fpr, tpr, cut = roc_curve(y_true=abnormal_gt, y_score=pred_abnormal_np) + precision, recall, cut2 = precision_recall_curve(abnormal_gt, pred_abnormal_np) + + auc = sklearn.metrics.auc(fpr, tpr) + ap = sklearn.metrics.auc(recall, precision) + + diff = tpr - fpr + diff_idx = np.argmax(diff) + best_thr = cut[diff_idx] + + pred_positive = pred_abnormal_np > thr + TP_and_FN = pred_positive[abnormal_gt > 0.9] + FP_and_TN = pred_positive[abnormal_gt < 0.1] + + total_fpr += np.sum(FP_and_TN) / len(FP_and_TN) + total_tpr += np.sum(TP_and_FN) / len(TP_and_FN) + total_bthr += best_thr if diff_idx != 0 else 1 + + total_auc += auc + total_ap += ap + total_n_corrects += corrects / abnormal_input.size(1) + # normal + abnormal 24개와 다르게 abnormal 12개만 있음 -> /12 => 2/24 + total_loss += val_loss.item() + + total_abnormal_max += pred_a_max.item() + total_abnormal_mean += pred_a_mean.item() + except ValueError: + # print( + # "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." + # ) + # total_auc += 0 + error_count += 1 + # print("0~180 전부 0인 abnormal 영상 있음") + + val_n_mean_loss = total_n_loss / (len(normal_valid_loader) - error_n_count) + # val_n_mean_MPP_loss = total_n_MPP_loss / (len(normal_valid_loader) - error_n_count) + # val_n_mean_norm_loss = total_n_norm_loss / (len(normal_valid_loader) - error_n_count) + # val_n_mean_MPP_and_norm_loss = total_n_MPP_and_norm_loss / ( + # len(normal_valid_loader) - error_n_count + # ) + + val_n_fpr = total_n_fpr / ((len(normal_valid_loader) - error_n_count)) + val_n_tpr = total_n_tpr / ((len(normal_valid_loader) - error_n_count)) + val_n_bthr = total_n_bthr / ((len(normal_valid_loader) - error_n_count)) + val_n_auc = total_n_auc / (len(normal_valid_loader) - error_n_count) + val_n_ap = total_n_ap / (len(normal_valid_loader) - error_n_count) + + val_n_accuracy = total_n_n_corrects / ((len(normal_valid_loader) - error_n_count)) + + val_mean_loss = (total_loss + total_n_loss) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + + val_fpr = (total_fpr + total_n_fpr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_tpr = (total_tpr + total_n_tpr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_bthr = (total_bthr + total_n_bthr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_auc = (total_auc + total_n_auc) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_ap = (total_ap + total_n_ap) / (len(abnormal_valid_loader) - error_n_count - error_count) + val_accuracy = (total_n_corrects + total_n_n_corrects) / ( + (len(abnormal_valid_loader) - error_n_count - error_count) + ) + # for loop 한번에 abnormal 12, normal 12해서 24개 정답 확인 + + val_mean_normal_max = total_normal_max / (len(normal_valid_loader) - error_n_count) + val_mean_normal_mean = total_normal_mean / (len(normal_valid_loader) - error_n_count) + val_mean_abnormal_max = total_abnormal_max / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_mean_abnormal_mean = total_abnormal_mean / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + + if best_loss > val_mean_loss: + print(f"Best performance at epoch: {epoch + 1}, {best_loss:.4f} -> {val_mean_loss:.4f}") + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best.pth") + torch.save(states, best_ckpt_fpath) + best_loss = val_mean_loss + # counter = 0 + # else: + # counter += 1 + + if best_auc < val_auc: + print(f"Best auc performance at epoch: {epoch + 1}, {best_auc:.4f} -> {val_auc:.4f}") + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best_auc.pth") + torch.save(states, best_ckpt_fpath) + best_auc = val_auc + counter = 0 + else: + counter += 1 + + new_wandb_metric_dict = { + "train_loss": epoch_mean_loss, + "train_accuracy": epoch_accuracy, + "train_n_loss": epoch_n_mean_loss, + "train_n_MPP_loss": epoch_n_mean_MPP_loss, + "train_n_norm_loss": epoch_n_mean_norm_loss, + "train_n_MPP+norm_loss": epoch_n_mean_MPP_and_norm_loss, + "train_n_accuracy": epoch_n_accuracy, + "valid_loss": val_mean_loss, + "valid_fpr": val_fpr, + "valid_tpr": val_tpr, + "valid_bthr": val_bthr, + "valid_auc": val_auc, + "valid_ap": val_ap, + "valid_accuracy": val_accuracy, + "valid_n_loss": val_n_mean_loss, + # "valid_n_MPP_loss": val_n_mean_MPP_loss, + # "valid_n_norm_loss": val_n_mean_norm_loss, + # "valid_n_MPP+norm_loss": val_n_mean_MPP_and_norm_loss, + "valid_n_fpr": val_n_fpr, + "valid_n_tpr": val_n_tpr, + "valid_n_bthr": val_n_bthr, + "valid_n_auc": val_n_auc, + "valid_n_ap": val_n_ap, + "valid_n_accuracy": val_n_accuracy, + "learning_rate": scheduler.get_last_lr()[0], + "train_abnormal_max_mean": epoch_mean_abnormal_max, + "train_abnormal_mean": epoch_mean_abnormal_mean, + "train_normal_max_mean": epoch_mean_normal_max, + "train_normal_mean": epoch_mean_normal_mean, + "valid_abnormal_max_mean": val_mean_abnormal_max, + "valid_abnormal_mean": val_mean_abnormal_mean, + "valid_normal_max_mean": val_mean_normal_max, + "valid_normal_mean": val_mean_normal_mean, + } + + wandb.log(new_wandb_metric_dict) + + scheduler.step() + + epoch_end = datetime.now() + epoch_time = epoch_end - epoch_start + epoch_time = str(epoch_time).split(".")[0] + print( + f"==>> epoch {epoch+1} time: {epoch_time}\nvalid_loss: {round(val_mean_loss,4)} valid_n_loss: {round(val_n_mean_loss,4)}" + ) + # print( + # f"valid_n_MPP_loss: {round(val_n_mean_MPP_loss,4)} valid_n_norm_loss: {round(val_n_mean_norm_loss,4)} valid_n_MPP+norm_loss: {round(val_n_mean_MPP_and_norm_loss,4)}" + # ) + print(f"valid_fpr: {val_fpr} valid_n_fpr: {val_n_fpr}") + print(f"valid_tpr: {val_tpr} valid_n_tpr: {val_n_tpr}") + print(f"valid_bthr: {val_bthr} valid_n_bthr: {val_n_bthr}") + print( + f"valid_auc: {val_auc:.4f} valid_n_auc: {val_n_auc:.4f}\nvalid_ap: {val_ap:.4f} valid_n_ap: {val_n_ap:.4f}\nvalid_accuracy: {val_accuracy:.2f} valid_n_accuracy: {val_n_accuracy:.2f}" + ) + print( + f"==>> val_abnormal_max_mean: {val_mean_abnormal_max} val_abnormal_mean: {val_mean_abnormal_mean}" + ) + print(f"==>> val_normal_max_mean: {val_mean_normal_max} val_normal_mean: {val_mean_normal_mean}") + print(f"==>> error_count: {error_count}") + + if counter > patience: + print("Early Stopping...") + break + + time_end = datetime.now() + total_time = time_end - time_start + total_time = str(total_time).split(".")[0] + print(f"==>> total time: {total_time}") + + +# @@TODO: BNWVAD에 맞게 수정 필요 +def train2( + normal_root_dir, + abnormal_root_dir, + json_dir, + model_dir, + model_name, + model_size, + device, + num_workers, + batch_size, + # val_num_workers, + # val_batch_size, + learning_rate, + weight_decay, + max_epoch, + val_interval, + save_interval, + w_normal, + w_mpp, + thr, + len_feature, + num_segments, + ratio_sample, + ratio_batch, + ratios, + kernel_sizes, + patience, + resume_name, + seed, + # mp, + use_extra, + wandb_mode, + wandb_run_name, +): + + time_start = datetime.now() + + train_start = time_start.strftime("%Y%m%d_%H%M%S") + + set_seed(seed) + + if not osp.exists(model_dir): + os.makedirs(model_dir) + + batch_size = batch_size + + val_batch_size = 1 + val_num_workers = 0 + + # -- early stopping flag + patience = patience + counter = 0 + + # 데이터셋 + dataset = NormalVMAE( + model_size=model_size, + root=normal_root_dir, + ) + + valid_data_size = len(dataset) // 10 + + train_data_size = len(dataset) - valid_data_size + + train_dataset, valid_dataset = random_split(dataset, lengths=[train_data_size, valid_data_size]) + + # normal_train_loader = DataLoader( + # dataset=train_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=num_workers + # ) + + normal_valid_loader = DataLoader( + dataset=valid_dataset, + batch_size=val_batch_size, + shuffle=False, + drop_last=True, + num_workers=val_num_workers, + ) + + abnormal_train_dataset = AbnormalVMAE( + model_size=model_size, + root=abnormal_root_dir, + label_root=json_dir, + ) + abnormal_valid_dataset = AbnormalVMAE( + is_train=0, + model_size=model_size, + root=abnormal_root_dir, + label_root=json_dir, + ) + + # abnormal_train_loader = DataLoader( + # dataset=abnormal_train_dataset, + # batch_size=batch_size, + # shuffle=True, + # drop_last=True, + # num_workers=num_workers, + # ) + + concat_trainset = ConcatDataset([train_dataset, abnormal_train_dataset]) + + concat_train_loader = DataLoader( + dataset=concat_trainset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=num_workers + ) + + abnormal_valid_loader = DataLoader( + dataset=abnormal_valid_dataset, batch_size=val_batch_size, shuffle=False, num_workers=val_num_workers + ) + + data_load_end = datetime.now() + data_load_time = data_load_end - time_start + data_load_time = str(data_load_time).split(".")[0] + print(f"==>> {model_size} data_load_time: {data_load_time}") + + # Initialize the LSTM autoencoder model + model = WSAD( + input_size=len_feature, + ratio_sample=ratio_sample, + ratio_batch=ratio_batch, + ratios=ratios, + kernel_sizes=kernel_sizes, + ) + + load_dict = None + + if resume_name: + load_dict = torch.load(osp.join(model_dir, f"{resume_name}.pth"), map_location="cpu") + model.load_state_dict(load_dict["model_state_dict"]) + + model.to(device) + + # optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + # 1e-6 => 0.0010000000474974513 + # optimizer = torch.optim.Adagrad(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + # optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + + optimizer = torch.optim.Adam( + model.parameters(), lr=learning_rate, betas=(0.9, 0.999), weight_decay=weight_decay + ) + + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[1000, 1500], gamma=0.5) + + if resume_name: + optimizer.load_state_dict(load_dict["optimizer_state_dict"]) + scheduler.load_state_dict(load_dict["scheduler_state_dict"]) + # scaler.load_state_dict(load_dict["scaler_state_dict"]) + + criterion = nn.BCELoss() + MPP_criterion = LossComputer(w_normal=w_normal, w_mpp=w_mpp) + + print(f"Start training..") + + wandb.init( + project="VAD", + entity="pao-kim-si-woong", + config={ + "lr": learning_rate, + "dataset": "무인매장", + "n_epochs": max_epoch, + "loss": "MPP", + "notes": "VAD 실험", + }, + name=wandb_run_name + "_" + train_start, + mode=wandb_mode, + ) + wandb.watch((model,)) + + best_loss = np.inf + best_auc = 0 + + total_batches = len(concat_train_loader) + + for epoch in range(max_epoch): + model.train() + + epoch_start = datetime.now() + + epoch_loss = 0 + epoch_n_corrects = 0 + + epoch_abnormal_max = 0 + epoch_abnormal_mean = 0 + epoch_normal_max = 0 + epoch_normal_mean = 0 + + nan_count = 0 + + for step, inputs in tqdm( + enumerate(concat_train_loader), + total=total_batches, + ): + inp, gts = inputs + # (batch_size, 11, 710), (batch_size, 11) + + num_segs = inp.size(1) + + inp = inp.to(device) + # (batch_size, 11, 710) + gts = gts.view(-1, 1).to(device) + # (batch_size * 11, 1) + + optimizer.zero_grad() + + pred_result = model(inp, flag="Train_extra") + + pred_acc = pred_result["normal_scores"].view(-1, 1) + # pred = pred_result["scores"].view(-1, 1) + pred = pred_acc + # pred는 (batch_size * 11, 1) + + loss = criterion(pred_acc, gts) + + loss.backward() + optimizer.step() + with torch.no_grad(): + # print(f"==>> pred.shape: {pred.shape}") + + pred_correct = pred_acc > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + epoch_loss += loss.item() + epoch_n_corrects += corrects / (num_segs * batch_size) + + check = gts.view(batch_size, num_segs) != 0 + check = torch.sum(check, dim=1) + + check_a = check != 0 + check_n = check_a == False + + pred_reshape = pred.view(batch_size, num_segs) + + pred_a_max = torch.mean(torch.max(pred_reshape[check_a], dim=-1)[0]) + + pred_a_mean = torch.mean(pred_reshape[check_a]) + + epoch_abnormal_max += pred_a_max.item() + epoch_abnormal_mean += pred_a_mean.item() + + if torch.sum(check_n) != 0: + pred_n_max = torch.mean(torch.max(pred_reshape[check_n], dim=-1)[0]) + + pred_n_mean = torch.mean(pred_reshape[check_n]) + + epoch_normal_max += pred_n_max.item() + epoch_normal_mean += pred_n_mean.item() + else: + nan_count += 1 + + epoch_mean_loss = epoch_loss / total_batches + epoch_accuracy = epoch_n_corrects / total_batches + + epoch_mean_normal_max = epoch_normal_max / (total_batches - nan_count) + epoch_mean_normal_mean = epoch_normal_mean / (total_batches - nan_count) + epoch_mean_abnormal_max = epoch_abnormal_max / total_batches + epoch_mean_abnormal_mean = epoch_abnormal_mean / total_batches + + train_end = datetime.now() + train_time = train_end - epoch_start + train_time = str(train_time).split(".")[0] + print(f"==>> epoch {epoch+1} train_time: {train_time}\nloss: {round(epoch_mean_loss,4)}") + print(f"accuracy: {epoch_accuracy:.2f}") + print(f"==>> abnormal_max_mean: {epoch_mean_abnormal_max} abnormal_mean: {epoch_mean_abnormal_mean}") + print(f"==>> normal_max_mean: {epoch_mean_normal_max} normal_mean: {epoch_mean_normal_mean}") + + if (epoch + 1) % save_interval == 0: + + ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_latest.pth") + + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + "optimizer_state_dict": optimizer.state_dict(), + "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + } + + torch.save(states, ckpt_fpath) + + # validation 주기에 따라 loss를 출력하고 best model을 저장합니다. + if (epoch + 1) % val_interval == 0: + + print(f"Start validation #{epoch+1:2d}") + model.eval() + + with torch.no_grad(): + total_n_loss = 0 + # total_n_MPP_loss = 0 + # total_n_norm_loss = 0 + # total_n_MPP_and_norm_loss = 0 + total_n_n_corrects = 0 + + total_n_fpr = 0 + total_n_tpr = 0 + total_n_bthr = 0 + total_n_auc = 0 + total_n_ap = 0 + + total_loss = 0 + total_n_corrects = 0 + + total_fpr = 0 + total_tpr = 0 + total_bthr = 0 + total_auc = 0 + total_ap = 0 + + error_n_count = 0 + error_count = 0 + + total_abnormal_max = 0 + total_abnormal_mean = 0 + total_normal_max = 0 + total_normal_mean = 0 + + norm_valid_iter = iter(normal_valid_loader) + # iterator를 여기서 매번 새로 할당해줘야 iterator가 다시 처음부터 작동 + + for step, abnormal_inputs in tqdm( + enumerate(abnormal_valid_loader), total=len(abnormal_valid_loader) + ): + try: + normal_inputs = next(norm_valid_iter) + + normal_input, normal_gt = normal_inputs + # (val_batch_size, 11, 710), (val_batch_size, 11) + abnormal_input, abnormal_gt = abnormal_inputs + # (val_batch_size, 11, 710), (val_batch_size, 176) + + abnormal_gt2 = torch.max(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2)[0] + # abnormal_gt2 = torch.mean(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2) + # (val_batch_size, 11) + + inputs = torch.cat((normal_input, abnormal_input), dim=1) + gts = torch.cat((normal_gt, abnormal_gt2), dim=1) + # inputs는 (val_batch_size, 22, 710), gts는 (val_batch_size, 22) + + inputs = inputs.to(device) + # (val_batch_size, 22, 710) + gts = gts.view(-1, 1).to(device) + # (val_batch_size * 22, 1) + + pred_result = model(inputs, flag="Eval_MPP") + # pred_result["pre_normal_scores"]: normal_scores[0 : b // 2], + # pred_result["bn_results"]: bn_results, + # pred_result["scores"]: distance_sum * normal_scores, + # breakpoint() + pred_acc = pred_result["normal_scores"].view(-1, 1) + # pred = pred_result["scores"].view(-1, 1) + pred = pred_acc + # pred는(batch_size * 11, 1) + + val_loss = criterion(pred_acc, gts) + # if val_loss > 2: + # print(f"==>> pred: {pred}") + # print(f"==>> gts: {gts}") + # counter = patience + 1 + + # val_MPP_and_norm_loss, val_loss_dict = MPP_criterion(pred_result) + + pred_n = pred.view(val_batch_size, 2, abnormal_input.size(1))[:, 0, :] + pred_a = pred.view(val_batch_size, 2, abnormal_input.size(1))[:, 1, :] + + pred_n_max = torch.mean(torch.max(pred_n, dim=-1)[0]) + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + + pred_n_mean = torch.mean(pred_n) + pred_a_mean = torch.mean(pred_a) + + pred_correct = pred_acc > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + pred = (pred.squeeze()).detach().cpu().numpy() + + pred_normal_np = np.zeros(abnormal_gt.size(1)) + pred_abnormal_np = np.zeros(abnormal_gt.size(1)) + + step = np.array([i for i in range(abnormal_input.size(1) + 1)]) + + for j in range(abnormal_input.size(1)): + pred_normal_np[step[j] * 16 : step[j + 1] * 16] = pred[j] + pred_abnormal_np[step[j] * 16 : step[j + 1] * 16] = pred[ + abnormal_input.size(1) + j + ] + + pred_np = np.concatenate((pred_normal_np, pred_abnormal_np), axis=0) + + abnormal_gt = abnormal_gt.squeeze().detach().cpu().numpy() + # abnormal_gt2 = abnormal_gt2.squeeze().detach().cpu().numpy() + # normal_gt = np.zeros_like(abnormal_gt2) + normal_gt = np.zeros_like(abnormal_gt) + # gt_np = np.concatenate((abnormal_gt2, normal_gt), axis=0) + gt_np = np.concatenate((normal_gt, abnormal_gt), axis=0) + + try: + # auc = roc_auc_score(y_true=gt_np, y_score=pred_np) + # auc = roc_auc_score(y_true=gt_np, y_score=pred) + + fpr, tpr, cut = roc_curve(y_true=gt_np, y_score=pred_np) + precision, recall, cut2 = precision_recall_curve(gt_np, pred_np) + + auc = sklearn.metrics.auc(fpr, tpr) + ap = sklearn.metrics.auc(recall, precision) + + diff = tpr - fpr + diff_idx = np.argmax(diff) + best_thr = cut[diff_idx] + + pred_positive = pred_np > thr + TP_and_FN = pred_positive[gt_np > 0.9] + FP_and_TN = pred_positive[gt_np < 0.1] + + total_n_fpr += np.sum(FP_and_TN) / len(FP_and_TN) + total_n_tpr += np.sum(TP_and_FN) / len(TP_and_FN) + total_n_bthr += best_thr if diff_idx != 0 else 1 + + total_n_auc += auc + total_n_ap += ap + total_n_n_corrects += corrects / (abnormal_input.size(1) * 2) + total_n_loss += val_loss.item() + # total_n_MPP_loss += val_loss_dict["mpp_loss"].item() + # total_n_norm_loss += val_loss_dict["normal_loss"].item() + # total_n_MPP_and_norm_loss += val_MPP_and_norm_loss.item() + + total_normal_max += pred_n_max.item() + total_normal_mean += pred_n_mean.item() + total_abnormal_max += pred_a_max.item() + total_abnormal_mean += pred_a_mean.item() + + except ValueError: + # print( + # "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." + # ) + # total_auc += 0 + error_n_count += 1 + # print("0~180 전부 0인 abnormal 영상 있음") + except StopIteration: + # if not use_extra: + # break + abnormal_input, abnormal_gt = abnormal_inputs + # (val_batch_size, 11, 710), (val_batch_size, 176) + + abnormal_gt2 = torch.max(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2)[0] + # abnormal_gt2 = torch.mean(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2) + # (val_batch_size, 11) + + inputs = abnormal_input.to(device) + # (val_batch_size, 11, 710) + gts = abnormal_gt2.view(-1, 1).to(device) + # (val_batch_size * 11, 1) + + pred_result = model(inputs, flag="Eval_MPP") + # pred_result는 (batch_size, 11) + + pred_acc = pred_result["normal_scores"].view(-1, 1) + pred = pred_result["scores"].view(-1, 1) + pred = pred_acc + # pred = pred_result.view(-1, 1) + # pred는(batch_size * 11, 1) + + val_loss = criterion(pred_acc, gts) + # if val_loss > 2: + # print(f"==>> pred: {pred}") + # print(f"==>> gts: {gts}") + # counter = patience + 1 + + pred_a = pred.view(val_batch_size, abnormal_input.size(1)) + + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + + pred_a_mean = torch.mean(pred_a) + + pred_correct = pred_acc > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + pred = (pred.squeeze()).detach().cpu().numpy() + pred_abnormal_np = np.zeros(abnormal_gt.size(1)) + + step = np.array([i for i in range(abnormal_input.size(1) + 1)]) + + for j in range(abnormal_input.size(1)): + pred_abnormal_np[step[j] * 16 : step[j + 1] * 16] = pred[j] + + # abnormal_gt2 = abnormal_gt2.squeeze().detach().cpu().numpy() + abnormal_gt = abnormal_gt.squeeze().detach().cpu().numpy() + + try: + # auc = roc_auc_score(y_true=abnormal_gt, y_score=pred_abnormal_np) + # auc = roc_auc_score(y_true=abnormal_gt2, y_score=pred) + + fpr, tpr, cut = roc_curve(y_true=abnormal_gt, y_score=pred_abnormal_np) + precision, recall, cut2 = precision_recall_curve(abnormal_gt, pred_abnormal_np) + + auc = sklearn.metrics.auc(fpr, tpr) + ap = sklearn.metrics.auc(recall, precision) + + diff = tpr - fpr + diff_idx = np.argmax(diff) + best_thr = cut[diff_idx] + + pred_positive = pred_abnormal_np > thr + TP_and_FN = pred_positive[abnormal_gt > 0.9] + FP_and_TN = pred_positive[abnormal_gt < 0.1] + + total_fpr += np.sum(FP_and_TN) / len(FP_and_TN) + total_tpr += np.sum(TP_and_FN) / len(TP_and_FN) + total_bthr += best_thr if diff_idx != 0 else 1 + + total_auc += auc + total_ap += ap + total_n_corrects += corrects / abnormal_input.size(1) + # normal + abnormal 24개와 다르게 abnormal 12개만 있음 -> /12 => 2/24 + total_loss += val_loss.item() + + total_abnormal_max += pred_a_max.item() + total_abnormal_mean += pred_a_mean.item() + except ValueError: + # print( + # "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." + # ) + # total_auc += 0 + error_count += 1 + # print("0~180 전부 0인 abnormal 영상 있음") + + val_n_mean_loss = total_n_loss / (len(normal_valid_loader) - error_n_count) + # val_n_mean_MPP_loss = total_n_MPP_loss / (len(normal_valid_loader) - error_n_count) + # val_n_mean_norm_loss = total_n_norm_loss / (len(normal_valid_loader) - error_n_count) + # val_n_mean_MPP_and_norm_loss = total_n_MPP_and_norm_loss / ( + # len(normal_valid_loader) - error_n_count + # ) + + val_n_fpr = total_n_fpr / ((len(normal_valid_loader) - error_n_count)) + val_n_tpr = total_n_tpr / ((len(normal_valid_loader) - error_n_count)) + val_n_bthr = total_n_bthr / ((len(normal_valid_loader) - error_n_count)) + val_n_auc = total_n_auc / (len(normal_valid_loader) - error_n_count) + val_n_ap = total_n_ap / (len(normal_valid_loader) - error_n_count) + + val_n_accuracy = total_n_n_corrects / ((len(normal_valid_loader) - error_n_count)) + + val_mean_loss = (total_loss + total_n_loss) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + + val_fpr = (total_fpr + total_n_fpr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_tpr = (total_tpr + total_n_tpr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_bthr = (total_bthr + total_n_bthr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_auc = (total_auc + total_n_auc) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_ap = (total_ap + total_n_ap) / (len(abnormal_valid_loader) - error_n_count - error_count) + val_accuracy = (total_n_corrects + total_n_n_corrects) / ( + (len(abnormal_valid_loader) - error_n_count - error_count) + ) + # for loop 한번에 abnormal 12, normal 12해서 24개 정답 확인 + + val_mean_normal_max = total_normal_max / (len(normal_valid_loader) - error_n_count) + val_mean_normal_mean = total_normal_mean / (len(normal_valid_loader) - error_n_count) + val_mean_abnormal_max = total_abnormal_max / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_mean_abnormal_mean = total_abnormal_mean / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + + if best_loss > val_mean_loss: + print(f"Best performance at epoch: {epoch + 1}, {best_loss:.4f} -> {val_mean_loss:.4f}") + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best.pth") + torch.save(states, best_ckpt_fpath) + best_loss = val_mean_loss + # counter = 0 + # else: + # counter += 1 + + if best_auc < val_auc: + print(f"Best auc performance at epoch: {epoch + 1}, {best_auc:.4f} -> {val_auc:.4f}") + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best_auc.pth") + torch.save(states, best_ckpt_fpath) + best_auc = val_auc + counter = 0 + else: + counter += 1 + + new_wandb_metric_dict = { + "train_loss": epoch_mean_loss, + "train_accuracy": epoch_accuracy, + "valid_loss": val_mean_loss, + "valid_fpr": val_fpr, + "valid_tpr": val_tpr, + "valid_bthr": val_bthr, + "valid_auc": val_auc, + "valid_ap": val_ap, + "valid_accuracy": val_accuracy, + "valid_n_loss": val_n_mean_loss, + # "valid_n_MPP_loss": val_n_mean_MPP_loss, + # "valid_n_norm_loss": val_n_mean_norm_loss, + # "valid_n_MPP+norm_loss": val_n_mean_MPP_and_norm_loss, + "valid_n_fpr": val_n_fpr, + "valid_n_tpr": val_n_tpr, + "valid_n_bthr": val_n_bthr, + "valid_n_auc": val_n_auc, + "valid_n_ap": val_n_ap, + "valid_n_accuracy": val_n_accuracy, + "learning_rate": scheduler.get_last_lr()[0], + "train_abnormal_max_mean": epoch_mean_abnormal_max, + "train_abnormal_mean": epoch_mean_abnormal_mean, + "train_normal_max_mean": epoch_mean_normal_max, + "train_normal_mean": epoch_mean_normal_mean, + "valid_abnormal_max_mean": val_mean_abnormal_max, + "valid_abnormal_mean": val_mean_abnormal_mean, + "valid_normal_max_mean": val_mean_normal_max, + "valid_normal_mean": val_mean_normal_mean, + } + + wandb.log(new_wandb_metric_dict) + + scheduler.step() + + epoch_end = datetime.now() + epoch_time = epoch_end - epoch_start + epoch_time = str(epoch_time).split(".")[0] + print( + f"==>> epoch {epoch+1} time: {epoch_time}\nvalid_loss: {round(val_mean_loss,4)} valid_n_loss: {round(val_n_mean_loss,4)}" + ) + # print( + # f"valid_n_MPP_loss: {round(val_n_mean_MPP_loss,4)} valid_n_norm_loss: {round(val_n_mean_norm_loss,4)} valid_n_MPP+norm_loss: {round(val_n_mean_MPP_and_norm_loss,4)}" + # ) + print(f"valid_fpr: {val_fpr} valid_n_fpr: {val_n_fpr}") + print(f"valid_tpr: {val_tpr} valid_n_tpr: {val_n_tpr}") + print(f"valid_bthr: {val_bthr} valid_n_bthr: {val_n_bthr}") + print( + f"valid_auc: {val_auc:.4f} valid_n_auc: {val_n_auc:.4f}\nvalid_ap: {val_ap:.4f} valid_n_ap: {val_n_ap:.4f}\nvalid_accuracy: {val_accuracy:.2f} valid_n_accuracy: {val_n_accuracy:.2f}" + ) + print( + f"==>> val_abnormal_max_mean: {val_mean_abnormal_max} val_abnormal_mean: {val_mean_abnormal_mean}" + ) + print(f"==>> val_normal_max_mean: {val_mean_normal_max} val_normal_mean: {val_mean_normal_mean}") + print(f"==>> error_count: {error_count}") + + if counter > patience: + print("Early Stopping...") + break + + time_end = datetime.now() + total_time = time_end - time_start + total_time = str(total_time).split(".")[0] + print(f"==>> total time: {total_time}") + + +def train3( + normal_root_dir, + abnormal_root_dir, + json_dir, + model_dir, + model_name, + model_size, + device, + num_workers, + batch_size, + # val_num_workers, + # val_batch_size, + learning_rate, + weight_decay, + max_epoch, + val_interval, + save_interval, + w_normal, + w_mpp, + thr, + len_feature, + num_segments, + ratio_sample, + ratio_batch, + ratios, + kernel_sizes, + patience, + resume_name, + seed, + # mp, + use_extra, + wandb_mode, + wandb_run_name, +): + + time_start = datetime.now() + + train_start = time_start.strftime("%Y%m%d_%H%M%S") + + set_seed(seed) + + if not osp.exists(model_dir): + os.makedirs(model_dir) + + batch_size = batch_size + + val_batch_size = 1 + val_num_workers = 0 + + # -- early stopping flag + patience = patience + counter = 0 + + # 데이터셋 + dataset = NormalVMAE( + model_size=model_size, + root=normal_root_dir, + ) + + valid_data_size = len(dataset) // 10 + + train_data_size = len(dataset) - valid_data_size + + train_dataset, valid_dataset = random_split(dataset, lengths=[train_data_size, valid_data_size]) + + normal_train_loader = DataLoader( + dataset=train_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=num_workers + ) + + normal_valid_loader = DataLoader( + dataset=valid_dataset, + batch_size=val_batch_size, + shuffle=False, + drop_last=True, + num_workers=val_num_workers, + ) + + abnormal_train_dataset = AbnormalVMAE( + model_size=model_size, + root=abnormal_root_dir, + label_root=json_dir, + ) + abnormal_valid_dataset = AbnormalVMAE( + is_train=0, + model_size=model_size, + root=abnormal_root_dir, + label_root=json_dir, + ) + + abnormal_train_loader = DataLoader( + dataset=abnormal_train_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True, + num_workers=num_workers, + ) + + abnormal_valid_loader = DataLoader( + dataset=abnormal_valid_dataset, batch_size=val_batch_size, shuffle=False, num_workers=val_num_workers + ) + + data_load_end = datetime.now() + data_load_time = data_load_end - time_start + data_load_time = str(data_load_time).split(".")[0] + print(f"==>> {model_size} data_load_time: {data_load_time}") + + # Initialize the model + model = WSAD( + input_size=len_feature, + ratio_sample=ratio_sample, + ratio_batch=ratio_batch, + ratios=ratios, + kernel_sizes=kernel_sizes, + ) + + load_dict = None + + if resume_name: + load_dict = torch.load(osp.join(model_dir, f"{resume_name}.pth"), map_location="cpu") + model.load_state_dict(load_dict["model_state_dict"]) + + model.to(device) + + # optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + # 1e-6 => 0.0010000000474974513 + optimizer = torch.optim.Adam( + model.parameters(), lr=learning_rate, betas=(0.9, 0.999), weight_decay=weight_decay + ) + # optimizer = torch.optim.AdamW( + # model.parameters(), lr=learning_rate, betas=(0.9, 0.999), weight_decay=weight_decay + # ) + # optimizer = torch.optim.Adagrad(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[1000, 1500], gamma=0.5) + + if resume_name: + optimizer.load_state_dict(load_dict["optimizer_state_dict"]) + scheduler.load_state_dict(load_dict["scheduler_state_dict"]) + # scaler.load_state_dict(load_dict["scaler_state_dict"]) + + criterion = nn.BCELoss() + MPP_criterion = LossComputer(w_normal=w_normal, w_mpp=w_mpp) + + print(f"Start training..") + + wandb.init( + project="VAD", + entity="pao-kim-si-woong", + config={ + "lr": learning_rate, + "dataset": "무인매장", + "n_epochs": max_epoch, + "loss": "MPP", + "notes": "VAD 실험", + }, + name=wandb_run_name + "_" + train_start, + mode=wandb_mode, + ) + + wandb.watch((model,)) + + best_loss = np.inf + best_auc = 0 + + total_batches = len(abnormal_train_loader) + + for epoch in range(max_epoch): + model.train() + + epoch_start = datetime.now() + + epoch_loss = 0 + epoch_n_corrects = 0 + epoch_MPP_loss = 0 + epoch_norm_loss = 0 + epoch_MPP_and_norm_loss = 0 + + epoch_abnormal_max = 0 + epoch_abnormal_mean = 0 + epoch_normal_max = 0 + epoch_normal_mean = 0 + + for step, abnormal_inputs in tqdm( + enumerate(abnormal_train_loader), + total=total_batches, + ): + if step % len(normal_train_loader) == 0: + norm_train_iter = iter(normal_train_loader) + # 중복 추출하더라도 정상, 이상 영상 1대1 대응 loop 끝까지 유지 + + normal_inputs = next(norm_train_iter) + + normal_input, normal_gt = normal_inputs + # (batch_size, 11, 710), (batch_size, 11) + abnormal_input, abnormal_gt = abnormal_inputs + # (batch_size, 11, 710), (batch_size, 11) + + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + inputs, gts = torch.cat((normal_input, abnormal_input), dim=1), torch.cat( + (normal_gt, abnormal_gt), dim=1 + ) + # @@@@ BN-WVAD는 정상 영상 먼저 @@@@ + # inputs는 (batch_size, 22, 710), gts는 (batch_size, 22) + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + + # batch_size = inputs.shape[0] + + inputs = inputs.to(device) + gts = gts.view(-1, 1).to(device) + # (batch_size * 22, 1) + + optimizer.zero_grad() + + pred_result = model(inputs, flag="Train") + # pred_result["pre_normal_scores"]: normal_scores[0 : b // 2], + # pred_result["bn_results"]: bn_results, + # pred_result["normal_scores"]: normal_scores, + # pred_result["scores"]: distance_sum * normal_scores, + + pred_acc = pred_result["normal_scores"].view(-1, 1) + pred = pred_result["scores"].view(-1, 1) + # pred_result["normal_scores"]는 (batch_size, 22) + # => pred는 (batch_size * 22, 1) + + loss = criterion(pred_acc, gts) + MPP_and_norm_loss, loss_dict = MPP_criterion(pred_result) + # sum_loss = loss + MPP_and_norm_loss + sum_loss = MPP_and_norm_loss + sum_loss.backward() + + # loss.backward() + optimizer.step() + with torch.no_grad(): + pred_n = pred.view(batch_size, 2, abnormal_input.size(1))[:, 0, :] + pred_a = pred.view(batch_size, 2, abnormal_input.size(1))[:, 1, :] + + pred_n_max = torch.mean(torch.max(pred_n, dim=-1)[0]) + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + + pred_n_mean = torch.mean(pred_n) + pred_a_mean = torch.mean(pred_a) + + pred_correct = pred_acc > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + epoch_loss += loss.item() + epoch_MPP_loss += loss_dict["mpp_loss"].item() + epoch_norm_loss += loss_dict["normal_loss"].item() + epoch_MPP_and_norm_loss += MPP_and_norm_loss.item() + + epoch_n_corrects += corrects / (abnormal_input.size(1) * 2) + + epoch_normal_max += pred_n_max.item() + epoch_normal_mean += pred_n_mean.item() + epoch_abnormal_max += pred_a_max.item() + epoch_abnormal_mean += pred_a_mean.item() + + epoch_mean_loss = epoch_loss / total_batches + epoch_mean_MPP_loss = epoch_MPP_loss / total_batches + epoch_mean_norm_loss = epoch_norm_loss / total_batches + epoch_mean_MPP_and_norm_loss = epoch_MPP_and_norm_loss / total_batches + epoch_accuracy = epoch_n_corrects / (batch_size * total_batches) + + epoch_mean_normal_max = epoch_normal_max / total_batches + epoch_mean_normal_mean = epoch_normal_mean / total_batches + epoch_mean_abnormal_max = epoch_abnormal_max / total_batches + epoch_mean_abnormal_mean = epoch_abnormal_mean / total_batches + + train_end = datetime.now() + train_time = train_end - epoch_start + train_time = str(train_time).split(".")[0] + print(f"==>> epoch {epoch+1} train_time: {train_time}\nloss: {round(epoch_mean_loss,4)}") + print( + f"MPP_loss: {round(epoch_mean_MPP_loss,4)} norm_loss: {round(epoch_mean_norm_loss,4)} MPP+norm_loss: {round(epoch_mean_MPP_and_norm_loss,4)}" + ) + print(f"accuracy: {epoch_accuracy:.2f}") + print(f"==>> abnormal_max_mean: {epoch_mean_abnormal_max} abnormal_mean: {epoch_mean_abnormal_mean}") + print(f"==>> normal_max_mean: {epoch_mean_normal_max} normal_mean: {epoch_mean_normal_mean}") + + if (epoch + 1) % save_interval == 0: + + ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_latest.pth") + + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + "optimizer_state_dict": optimizer.state_dict(), + "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + } + + torch.save(states, ckpt_fpath) + + # validation 주기에 따라 loss를 출력하고 best model을 저장합니다. + if (epoch + 1) % val_interval == 0: + + print(f"Start validation #{epoch+1:2d}") + model.eval() + + with torch.no_grad(): + total_n_loss = 0 + # total_n_MPP_loss = 0 + # total_n_norm_loss = 0 + # total_n_MPP_and_norm_loss = 0 + total_n_n_corrects = 0 + + total_n_fpr = 0 + total_n_tpr = 0 + total_n_bthr = 0 + total_n_auc = 0 + total_n_ap = 0 + + total_loss = 0 + total_n_corrects = 0 + + total_fpr = 0 + total_tpr = 0 + total_bthr = 0 + total_auc = 0 + total_ap = 0 + + error_n_count = 0 + error_count = 0 + + total_abnormal_max = 0 + total_abnormal_mean = 0 + total_normal_max = 0 + total_normal_mean = 0 + + norm_valid_iter = iter(normal_valid_loader) + # iterator를 여기서 매번 새로 할당해줘야 iterator가 다시 처음부터 작동 + + for step, abnormal_inputs in tqdm( + enumerate(abnormal_valid_loader), total=len(abnormal_valid_loader) + ): + try: + normal_inputs = next(norm_valid_iter) + + normal_input, normal_gt = normal_inputs + # (val_batch_size, 11, 710), (val_batch_size, 11) + abnormal_input, abnormal_gt = abnormal_inputs + # (val_batch_size, 11, 710), (val_batch_size, 176) + + abnormal_gt2 = torch.max(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2)[0] + # abnormal_gt2 = torch.mean(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2) + # (val_batch_size, 11) + + inputs = torch.cat((normal_input, abnormal_input), dim=1) + gts = torch.cat((normal_gt, abnormal_gt2), dim=1) + # inputs는 (val_batch_size, 22, 710), gts는 (val_batch_size, 22) + + inputs = inputs.to(device) + # (val_batch_size, 22, 710) + gts = gts.view(-1, 1).to(device) + # (val_batch_size * 22, 1) + + pred_result = model(inputs, flag="Eval_MPP") + # pred_result["pre_normal_scores"]: normal_scores[0 : b // 2], + # pred_result["bn_results"]: bn_results, + # pred_result["scores"]: distance_sum * normal_scores, + # breakpoint() + pred_acc = pred_result["normal_scores"].view(-1, 1) + pred = pred_result["scores"].view(-1, 1) + # pred는(batch_size * 11, 1) + + val_loss = criterion(pred_acc, gts) + # if val_loss > 2: + # print(f"==>> pred: {pred}") + # print(f"==>> gts: {gts}") + # counter = patience + 1 + + # val_MPP_and_norm_loss, val_loss_dict = MPP_criterion(pred_result) + + pred_n = pred.view(val_batch_size, 2, abnormal_input.size(1))[:, 0, :] + pred_a = pred.view(val_batch_size, 2, abnormal_input.size(1))[:, 1, :] + + pred_n_max = torch.mean(torch.max(pred_n, dim=-1)[0]) + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + + pred_n_mean = torch.mean(pred_n) + pred_a_mean = torch.mean(pred_a) + + pred_correct = pred_acc > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + pred = (pred.squeeze()).detach().cpu().numpy() + + pred_normal_np = np.zeros(abnormal_gt.size(1)) + pred_abnormal_np = np.zeros(abnormal_gt.size(1)) + + step = np.array([i for i in range(abnormal_input.size(1) + 1)]) + + for j in range(abnormal_input.size(1)): + pred_normal_np[step[j] * 16 : step[j + 1] * 16] = pred[j] + pred_abnormal_np[step[j] * 16 : step[j + 1] * 16] = pred[ + abnormal_input.size(1) + j + ] + + pred_np = np.concatenate((pred_normal_np, pred_abnormal_np), axis=0) + + abnormal_gt = abnormal_gt.squeeze().detach().cpu().numpy() + # abnormal_gt2 = abnormal_gt2.squeeze().detach().cpu().numpy() + # normal_gt = np.zeros_like(abnormal_gt2) + normal_gt = np.zeros_like(abnormal_gt) + # gt_np = np.concatenate((abnormal_gt2, normal_gt), axis=0) + gt_np = np.concatenate((normal_gt, abnormal_gt), axis=0) + + try: + # auc = roc_auc_score(y_true=gt_np, y_score=pred_np) + # auc = roc_auc_score(y_true=gt_np, y_score=pred) + + fpr, tpr, cut = roc_curve(y_true=gt_np, y_score=pred_np) + precision, recall, cut2 = precision_recall_curve(gt_np, pred_np) + + auc = sklearn.metrics.auc(fpr, tpr) + ap = sklearn.metrics.auc(recall, precision) + + diff = tpr - fpr + diff_idx = np.argmax(diff) + best_thr = cut[diff_idx] + + pred_positive = pred_np > thr + TP_and_FN = pred_positive[gt_np > 0.9] + FP_and_TN = pred_positive[gt_np < 0.1] + + total_n_fpr += np.sum(FP_and_TN) / len(FP_and_TN) + total_n_tpr += np.sum(TP_and_FN) / len(TP_and_FN) + total_n_bthr += best_thr if diff_idx != 0 else 1 + + total_n_auc += auc + total_n_ap += ap + total_n_n_corrects += corrects / (abnormal_input.size(1) * 2) + total_n_loss += val_loss.item() + # total_n_MPP_loss += val_loss_dict["mpp_loss"].item() + # total_n_norm_loss += val_loss_dict["normal_loss"].item() + # total_n_MPP_and_norm_loss += val_MPP_and_norm_loss.item() + + total_normal_max += pred_n_max.item() + total_normal_mean += pred_n_mean.item() + total_abnormal_max += pred_a_max.item() + total_abnormal_mean += pred_a_mean.item() + + except ValueError: + # print( + # "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." + # ) + # total_auc += 0 + error_n_count += 1 + # print("0~180 전부 0인 abnormal 영상 있음") + except StopIteration: + # if not use_extra: + # break + abnormal_input, abnormal_gt = abnormal_inputs + # (val_batch_size, 11, 710), (val_batch_size, 176) + + abnormal_gt2 = torch.max(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2)[0] + # abnormal_gt2 = torch.mean(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2) + # (val_batch_size, 11) + + inputs = abnormal_input.to(device) + # (val_batch_size, 11, 710) + gts = abnormal_gt2.view(-1, 1).to(device) + # (val_batch_size * 11, 1) + + pred_result = model(inputs, flag="Eval_MPP") + # pred_result는 (batch_size, 11) + + pred_acc = pred_result["normal_scores"].view(-1, 1) + pred = pred_result["scores"].view(-1, 1) + # pred = pred_result.view(-1, 1) + # pred는(batch_size * 11, 1) + + val_loss = criterion(pred_acc, gts) + # if val_loss > 2: + # print(f"==>> pred: {pred}") + # print(f"==>> gts: {gts}") + # counter = patience + 1 + + pred_a = pred.view(val_batch_size, abnormal_input.size(1)) + + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + + pred_a_mean = torch.mean(pred_a) + + pred_correct = pred_acc > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + pred = (pred.squeeze()).detach().cpu().numpy() + pred_abnormal_np = np.zeros(abnormal_gt.size(1)) + + step = np.array([i for i in range(abnormal_input.size(1) + 1)]) + + for j in range(abnormal_input.size(1)): + pred_abnormal_np[step[j] * 16 : step[j + 1] * 16] = pred[j] + + # abnormal_gt2 = abnormal_gt2.squeeze().detach().cpu().numpy() + abnormal_gt = abnormal_gt.squeeze().detach().cpu().numpy() + + try: + # auc = roc_auc_score(y_true=abnormal_gt, y_score=pred_abnormal_np) + # auc = roc_auc_score(y_true=abnormal_gt2, y_score=pred) + + fpr, tpr, cut = roc_curve(y_true=abnormal_gt, y_score=pred_abnormal_np) + precision, recall, cut2 = precision_recall_curve(abnormal_gt, pred_abnormal_np) + + auc = sklearn.metrics.auc(fpr, tpr) + ap = sklearn.metrics.auc(recall, precision) + + diff = tpr - fpr + diff_idx = np.argmax(diff) + best_thr = cut[diff_idx] + + pred_positive = pred_abnormal_np > thr + TP_and_FN = pred_positive[abnormal_gt > 0.9] + FP_and_TN = pred_positive[abnormal_gt < 0.1] + + total_fpr += np.sum(FP_and_TN) / len(FP_and_TN) + total_tpr += np.sum(TP_and_FN) / len(TP_and_FN) + total_bthr += best_thr if diff_idx != 0 else 1 + + total_auc += auc + total_ap += ap + total_n_corrects += corrects / abnormal_input.size(1) + # normal + abnormal 24개와 다르게 abnormal 12개만 있음 -> /12 => 2/24 + total_loss += val_loss.item() + + total_abnormal_max += pred_a_max.item() + total_abnormal_mean += pred_a_mean.item() + except ValueError: + # print( + # "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." + # ) + # total_auc += 0 + error_count += 1 + # print("0~180 전부 0인 abnormal 영상 있음") + + val_n_mean_loss = total_n_loss / (len(normal_valid_loader) - error_n_count) + # val_n_mean_MPP_loss = total_n_MPP_loss / (len(normal_valid_loader) - error_n_count) + # val_n_mean_norm_loss = total_n_norm_loss / (len(normal_valid_loader) - error_n_count) + # val_n_mean_MPP_and_norm_loss = total_n_MPP_and_norm_loss / ( + # len(normal_valid_loader) - error_n_count + # ) + + val_n_fpr = total_n_fpr / ((len(normal_valid_loader) - error_n_count)) + val_n_tpr = total_n_tpr / ((len(normal_valid_loader) - error_n_count)) + val_n_bthr = total_n_bthr / ((len(normal_valid_loader) - error_n_count)) + val_n_auc = total_n_auc / (len(normal_valid_loader) - error_n_count) + val_n_ap = total_n_ap / (len(normal_valid_loader) - error_n_count) + + val_n_accuracy = total_n_n_corrects / ((len(normal_valid_loader) - error_n_count)) + + val_mean_loss = (total_loss + total_n_loss) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + + val_fpr = (total_fpr + total_n_fpr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_tpr = (total_tpr + total_n_tpr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_bthr = (total_bthr + total_n_bthr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_auc = (total_auc + total_n_auc) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_ap = (total_ap + total_n_ap) / (len(abnormal_valid_loader) - error_n_count - error_count) + val_accuracy = (total_n_corrects + total_n_n_corrects) / ( + (len(abnormal_valid_loader) - error_n_count - error_count) + ) + # for loop 한번에 abnormal 12, normal 12해서 24개 정답 확인 + + val_mean_normal_max = total_normal_max / (len(normal_valid_loader) - error_n_count) + val_mean_normal_mean = total_normal_mean / (len(normal_valid_loader) - error_n_count) + val_mean_abnormal_max = total_abnormal_max / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_mean_abnormal_mean = total_abnormal_mean / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + + if best_loss > val_mean_loss: + print(f"Best performance at epoch: {epoch + 1}, {best_loss:.4f} -> {val_mean_loss:.4f}") + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best.pth") + torch.save(states, best_ckpt_fpath) + best_loss = val_mean_loss + # counter = 0 + # else: + # counter += 1 + + if best_auc < val_auc: + print(f"Best auc performance at epoch: {epoch + 1}, {best_auc:.4f} -> {val_auc:.4f}") + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best_auc.pth") + torch.save(states, best_ckpt_fpath) + best_auc = val_auc + counter = 0 + else: + counter += 1 + + new_wandb_metric_dict = { + "train_loss": epoch_mean_loss, + "train_accuracy": epoch_accuracy, + "train_MPP_loss": epoch_mean_MPP_loss, + "train_norm_loss": epoch_mean_norm_loss, + "train_MPP+norm_loss": epoch_mean_MPP_and_norm_loss, + "valid_loss": val_mean_loss, + "valid_fpr": val_fpr, + "valid_tpr": val_tpr, + "valid_bthr": val_bthr, + "valid_auc": val_auc, + "valid_ap": val_ap, + "valid_accuracy": val_accuracy, + "valid_n_loss": val_n_mean_loss, + # "valid_n_MPP_loss": val_n_mean_MPP_loss, + # "valid_n_norm_loss": val_n_mean_norm_loss, + # "valid_n_MPP+norm_loss": val_n_mean_MPP_and_norm_loss, + "valid_n_fpr": val_n_fpr, + "valid_n_tpr": val_n_tpr, + "valid_n_bthr": val_n_bthr, + "valid_n_auc": val_n_auc, + "valid_n_ap": val_n_ap, + "valid_n_accuracy": val_n_accuracy, + "learning_rate": scheduler.get_last_lr()[0], + "train_abnormal_max_mean": epoch_mean_abnormal_max, + "train_abnormal_mean": epoch_mean_abnormal_mean, + "train_normal_max_mean": epoch_mean_normal_max, + "train_normal_mean": epoch_mean_normal_mean, + "valid_abnormal_max_mean": val_mean_abnormal_max, + "valid_abnormal_mean": val_mean_abnormal_mean, + "valid_normal_max_mean": val_mean_normal_max, + "valid_normal_mean": val_mean_normal_mean, + } + + wandb.log(new_wandb_metric_dict) + + scheduler.step() + + epoch_end = datetime.now() + epoch_time = epoch_end - epoch_start + epoch_time = str(epoch_time).split(".")[0] + print( + f"==>> epoch {epoch+1} time: {epoch_time}\nvalid_loss: {round(val_mean_loss,4)} valid_n_loss: {round(val_n_mean_loss,4)}" + ) + # print( + # f"valid_n_MPP_loss: {round(val_n_mean_MPP_loss,4)} valid_n_norm_loss: {round(val_n_mean_norm_loss,4)} valid_n_MPP+norm_loss: {round(val_n_mean_MPP_and_norm_loss,4)}" + # ) + print(f"valid_fpr: {val_fpr} valid_n_fpr: {val_n_fpr}") + print(f"valid_tpr: {val_tpr} valid_n_tpr: {val_n_tpr}") + print(f"valid_bthr: {val_bthr} valid_n_bthr: {val_n_bthr}") + print( + f"valid_auc: {val_auc:.4f} valid_n_auc: {val_n_auc:.4f}\nvalid_ap: {val_ap:.4f} valid_n_ap: {val_n_ap:.4f}\nvalid_accuracy: {val_accuracy:.2f} valid_n_accuracy: {val_n_accuracy:.2f}" + ) + print( + f"==>> val_abnormal_max_mean: {val_mean_abnormal_max} val_abnormal_mean: {val_mean_abnormal_mean}" + ) + print(f"==>> val_normal_max_mean: {val_mean_normal_max} val_normal_mean: {val_mean_normal_mean}") + print(f"==>> error_count: {error_count}") + + if counter > patience: + print("Early Stopping...") + break + + time_end = datetime.now() + total_time = time_end - time_start + total_time = str(total_time).split(".")[0] + print(f"==>> total time: {total_time}") + + +def main(args): + train3(**args.__dict__) + + +if __name__ == "__main__": + args = parse_args() + + main(args) diff --git a/model/train/train_LSTM.py b/model/train/train_LSTM.py new file mode 100644 index 0000000..4ab0f90 --- /dev/null +++ b/model/train/train_LSTM.py @@ -0,0 +1,456 @@ +import pandas as pd +import numpy as np +import random +import matplotlib.pyplot as plt + +from datetime import datetime + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from torch.utils.data import Dataset, DataLoader, random_split + +from sklearn.metrics import roc_auc_score +from sklearn.preprocessing import MinMaxScaler + +from argparse import ArgumentParser + +import os +import os.path as osp + +from tqdm import tqdm + +import wandb + +from shop_dataset import NormalDataset, AbnormalDataset +from classifier import LSTMAutoencoder + + +def parse_args(): + parser = ArgumentParser() + + # Conventional args + parser.add_argument( + "--root_dir", + type=str, + default=os.environ.get( + "SM_CHANNEL_TRAIN_CSV", + "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/csv/normal/val", + ), + ) + # 학습 데이터 경로 + parser.add_argument( + "--abnormal_root_dir", + type=str, + default=os.environ.get( + "SM_CHANNEL_ABNORMAL_CSV", + "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/csv/abnormal/val", + ), + ) + parser.add_argument( + "--json_dir", + type=str, + default=os.environ.get( + "SM_CHANNEL_ABNORMAL_VAL_JSON", + "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/datapreprocess/json/abnormal/val", + ), + ) + # abnormal 검증셋 csv, json파일 경로 + parser.add_argument( + "--model_dir", type=str, default=os.environ.get("SM_MODEL_DIR", "/data/ephemeral/home/pths") + ) + # pth 파일 저장 경로 + + parser.add_argument("--model_name", type=str, default="LSTM") + # import_module로 불러올 model name + + parser.add_argument("--resume_name", type=str, default="") + # resume 파일 이름 + + parser.add_argument("--seed", type=int, default=666) + # random seed + + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") + parser.add_argument("--num_workers", type=int, default=8) + + parser.add_argument("--batch_size", type=int, default=64) + parser.add_argument("--val_batch_size", type=int, default=64) + parser.add_argument("--val_num_workers", type=int, default=8) + parser.add_argument("--learning_rate", type=float, default=0.001) + parser.add_argument("--max_epoch", type=int, default=50) + + parser.add_argument("--save_interval", type=int, default=1) + parser.add_argument("--val_interval", type=int, default=1) + parser.add_argument("--thr", type=float, default=0.02) + + parser.add_argument("--patience", type=int, default=10) + + # parser.add_argument("--mp", action="store_false") + # https://stackoverflow.com/questions/60999816/argparse-not-parsing-boolean-arguments + # mixed precision 사용할 지 여부 + + parser.add_argument("--wandb_mode", type=str, default="online") + # parser.add_argument("--wandb_mode", type=str, default="disabled") + # wandb mode + parser.add_argument("--wandb_run_name", type=str, default="LSTM") + # wandb run name + + args = parser.parse_args() + + return args + + +def set_seed(seed): + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # if use multi-GPU + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + np.random.seed(seed) + random.seed(seed) + + +def train( + root_dir, + abnormal_root_dir, + json_dir, + model_dir, + model_name, + device, + num_workers, + batch_size, + val_num_workers, + val_batch_size, + learning_rate, + max_epoch, + val_interval, + save_interval, + thr, + patience, + resume_name, + seed, + # mp, + wandb_mode, + wandb_run_name, +): + + time_start = datetime.now() + + train_start = time_start.strftime("%Y%m%d_%H%M%S") + + set_seed(seed) + + if not osp.exists(model_dir): + os.makedirs(model_dir) + + # Define parameters + sequence_length = 20 # Adjust as needed + prediction_time = 1 # Adjust as needed + n_features = 38 # Number of features to predict + + batch_size = batch_size + val_batch_size = val_batch_size + + # -- early stopping flag + patience = patience + counter = 0 + + # 데이터셋 + dataset = NormalDataset( + root=root_dir, + ) + + valid_data_size = len(dataset) // 10 + + train_data_size = len(dataset) - valid_data_size + + train_dataset, valid_dataset = random_split(dataset, lengths=[train_data_size, valid_data_size]) + + train_loader = DataLoader( + dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers + ) + + valid_loader = DataLoader( + dataset=valid_dataset, batch_size=val_batch_size, shuffle=False, num_workers=val_num_workers + ) + + abnormal_dataset = AbnormalDataset( + root=abnormal_root_dir, + label_root=json_dir, + ) + + abnormal_loader = DataLoader( + dataset=abnormal_dataset, batch_size=val_batch_size, shuffle=True, num_workers=val_num_workers + ) + + data_load_end = datetime.now() + data_load_time = data_load_end - time_start + data_load_time = str(data_load_time).split(".")[0] + print(f"==>> data_load_time: {data_load_time}") + + # Initialize the LSTM autoencoder model + model = LSTMAutoencoder(sequence_length, n_features, prediction_time) + + # load_dict = None + + # if resume_name: + # load_dict = torch.load(osp.join(model_dir, f"{resume_name}.pth"), map_location="cpu") + # model.load_state_dict(load_dict["model_state_dict"]) + + # model.load_state_dict( + # torch.load( + # "/data/ephemeral/home/level2-3-cv-finalproject-cv-06/app/models/pytorch_model.pth", + # map_location="cpu", + # ) + # ) + model.to(device) + + optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=1e-6) + + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[15, 40], gamma=0.1) + + # if resume_name: + # optimizer.load_state_dict(load_dict["optimizer_state_dict"]) + # scheduler.load_state_dict(load_dict["scheduler_state_dict"]) + # scaler.load_state_dict(load_dict["scaler_state_dict"]) + + criterion = nn.MSELoss() + val_criterion = nn.MSELoss(reduction="none") + + print(f"Start training..") + + wandb.init( + project="VAD", + entity="pao-kim-si-woong", + config={ + "lr": learning_rate, + "dataset": "무인매장", + "n_epochs": max_epoch, + "loss": "MSE", + "notes": "VAD 실험", + }, + name=wandb_run_name + "_" + train_start, + mode=wandb_mode, + ) + + wandb.watch((model,)) + + best_loss = np.inf + + total_batches = len(train_loader) + + for epoch in range(max_epoch): + model.train() + + epoch_start = datetime.now() + + epoch_loss = 0 + + for step, (x, y, _) in tqdm(enumerate(train_loader), total=total_batches): + + x, y = x.to(device), y.to(device) + + optimizer.zero_grad() + + pred = model(x) + + loss = criterion(pred, y) + + loss.backward() + optimizer.step() + + epoch_loss += loss + + epoch_mean_loss = (epoch_loss / total_batches).item() + + train_end = datetime.now() + train_time = train_end - epoch_start + train_time = str(train_time).split(".")[0] + print(f"==>> epoch {epoch+1} train_time: {train_time}\nloss: {round(epoch_mean_loss,4)}") + + if (epoch + 1) % save_interval == 0: + + ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_latest.pth") + + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + "optimizer_state_dict": optimizer.state_dict(), + "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + } + + torch.save(states, ckpt_fpath) + + # validation 주기에 따라 loss를 출력하고 best model을 저장합니다. + if (epoch + 1) % val_interval == 0: + + print(f"Start validation #{epoch+1:2d}") + model.eval() + + with torch.no_grad(): + total_loss = 0 + total_abnormal_loss = 0 + total_n_corrects = 0 + total_abnormal_n_corrects = 0 + total_auc = 0 + total_abnormal_auc = 0 + error_count = 0 + error_count_abnormal = 0 + + for step, (x, y, label) in tqdm(enumerate(valid_loader), total=len(valid_loader)): + x, y, label = x.to(device), y.to(device), label.to(device) + + pred = model(x) + + val_loss = val_criterion(pred, y) + val_loss_rdim = torch.mean(val_loss, dim=2) + pred_label = val_loss_rdim > thr + # pred_sig = F.sigmoid(val_loss_rdim-thr) + label = label.view(-1, 1) + + try: + auc = roc_auc_score(label.cpu(), pred_label.cpu()) + # auc = roc_auc_score(label.cpu(), pred_sig.cpu()) + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # 정상상황인 경우 label이 항상 전부 0 + # => 무조건 "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." 발생 + total_auc += auc + except ValueError: + # print( + # "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." + # ) + total_auc += 0 + error_count += 1 + + pred_correct = pred_label == label + corrects = torch.sum(pred_correct).item() + + total_n_corrects += corrects + + val_loss = torch.mean(val_loss) + + total_loss += val_loss + + val_mean_loss = (total_loss / len(valid_loader)).item() + if error_count < len(valid_loader): + val_auc = total_auc / (len(valid_loader) - error_count) + else: + # 정상영상은 roc_auc_score 함수 사용 불가 => error_count == len(valid_loader) + val_auc = 0 + # ==> vaild_auc는 항상 0 + val_accuracy = total_n_corrects / valid_data_size + + for step, (x, y, label) in tqdm(enumerate(abnormal_loader), total=len(abnormal_loader)): + x, y, label = x.to(device), y.to(device), label.to(device) + + pred = model(x) + + val_loss = val_criterion(pred, y) + val_loss_rdim = torch.mean(val_loss, dim=2) + pred_label = val_loss_rdim > thr + # pred_sig = F.sigmoid(val_loss_rdim - thr) + label = label.view(-1, 1) + + try: + auc = roc_auc_score(label.cpu(), pred_label.cpu()) + # auc = roc_auc_score(label.cpu(), pred_sig.cpu()) + total_abnormal_auc += auc + except ValueError: + # print( + # "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." + # ) + total_abnormal_auc += 0 + error_count_abnormal += 1 + + pred_correct = pred_label == label + corrects = torch.sum(pred_correct).item() + + total_abnormal_n_corrects += corrects + + val_loss = torch.mean(val_loss) + + total_abnormal_loss += val_loss + + val_abnormal_mean_loss = (total_abnormal_loss / len(abnormal_loader)).item() + val_abnormal_auc = total_abnormal_auc / (len(abnormal_loader) - error_count_abnormal) + val_abnormal_accuracy = total_abnormal_n_corrects / len(abnormal_dataset) + + val_total_auc = (total_auc + total_abnormal_auc) / ( + len(valid_loader) + len(abnormal_loader) - error_count - error_count_abnormal + ) + val_total_accuracy = (total_n_corrects + total_abnormal_n_corrects) / ( + valid_data_size + len(abnormal_dataset) + ) + + if best_loss > val_mean_loss: + print(f"Best performance at epoch: {epoch + 1}, {best_loss:.4f} -> {val_mean_loss:.4f}") + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best.pth") + torch.save(states, best_ckpt_fpath) + best_loss = val_mean_loss + counter = 0 + else: + counter += 1 + + new_wandb_metric_dict = { + "train_loss": epoch_mean_loss, + "valid_loss": val_mean_loss, + "valid_abnormal_loss": val_abnormal_mean_loss, + "valid_auc": val_auc, + "valid_abnormal_auc": val_abnormal_auc, + "valid_normal+abnormal_auc": val_total_auc, + "valid_accuracy": val_accuracy, + "valid_abnormal_accuracy": val_abnormal_accuracy, + "valid_normal+abnormal_accuracy": val_total_accuracy, + "learning_rate": scheduler.get_lr()[0], + } + + wandb.log(new_wandb_metric_dict) + + scheduler.step() + + epoch_end = datetime.now() + epoch_time = epoch_end - epoch_start + epoch_time = str(epoch_time).split(".")[0] + print( + f"==>> epoch {epoch+1} time: {epoch_time}\nvalid_loss: {round(val_mean_loss,4)}\nvalid_auc: {val_auc:.4f}\nvalid_accuracy: {val_accuracy:.2f}" + ) + print( + f"valid_abnormal_loss: {round(val_abnormal_mean_loss,4)}\nvalid_abnormal_auc: {val_abnormal_auc:.4f}\nvalid_abnormal_accuracy: {val_abnormal_accuracy:.2f}" + ) + print( + f"valid_normal+abnormal_auc: {val_total_auc:.4f}\nvalid_normal+abnormal_accuracy: {val_total_accuracy:.2f}" + ) + print(f"auc_roc_error_count: {error_count+error_count_abnormal}") + + if counter > patience: + print("Early Stopping...") + break + + time_end = datetime.now() + total_time = time_end - time_start + total_time = str(total_time).split(".")[0] + print(f"==>> total time: {total_time}") + + +def main(args): + train(**args.__dict__) + + +if __name__ == "__main__": + args = parse_args() + + main(args) diff --git a/model/train/train_MIL.py b/model/train/train_MIL.py new file mode 100644 index 0000000..016656a --- /dev/null +++ b/model/train/train_MIL.py @@ -0,0 +1,2112 @@ +import pandas as pd +import numpy as np +import random +import matplotlib.pyplot as plt + +from datetime import datetime + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from torch.utils.data import Dataset, DataLoader, random_split, ConcatDataset + +from sklearn.metrics import roc_auc_score, roc_curve, precision_recall_curve +import sklearn.metrics +from sklearn.preprocessing import MinMaxScaler + +from argparse import ArgumentParser + +import os +import os.path as osp + +from tqdm import tqdm + +import wandb + +from shop_dataset import NormalVMAE, AbnormalVMAE +from classifier import MILClassifier +from loss import MIL + + +def parse_args(): + parser = ArgumentParser() + + # Conventional args + parser.add_argument( + "--normal_root_dir", + type=str, + default=os.environ.get( + "SM_CHANNEL_NORMAL_NPY", + "../datapreprocess/npy/normal", + ), + ) + # 학습 데이터 경로 + parser.add_argument( + "--abnormal_root_dir", + type=str, + default=os.environ.get( + "SM_CHANNEL_ABNORMAL_NPY", + "../datapreprocess/npy/abnormal", + ), + ) + parser.add_argument( + "--json_dir", + type=str, + default=os.environ.get( + "SM_CHANNEL_ABNORMAL_JSON", + "../datapreprocess/json/abnormal", + ), + ) + # abnormal 검증셋 npy, json파일 경로 + parser.add_argument("--model_dir", type=str, default=os.environ.get("SM_MODEL_DIR", "../pths")) + # pth 파일 저장 경로 + + parser.add_argument("--model_name", type=str, default="MIL") + # import_module로 불러올 model name + + parser.add_argument("--resume_name", type=str, default="") + # resume 파일 이름 + + parser.add_argument("--model_size", type=str, default="small") + # VideoMAEv2 backbone 사이즈 = "small" or "base" + + parser.add_argument("--seed", type=int, default=666) + # random seed + + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") + parser.add_argument("--num_workers", type=int, default=0) + + parser.add_argument("--batch_size", type=int, default=30) + # parser.add_argument("--val_batch_size", type=int, default=1) + # parser.add_argument("--val_num_workers", type=int, default=0) + parser.add_argument("--learning_rate", type=float, default=0.001) + parser.add_argument("--max_epoch", type=int, default=1000) + + parser.add_argument("--save_interval", type=int, default=1) + parser.add_argument("--val_interval", type=int, default=1) + parser.add_argument("--thr", type=float, default=0.25) + parser.add_argument("--drop_rate", type=float, default=0.3) + + parser.add_argument("--patience", type=int, default=100) + + # parser.add_argument("--mp", action="store_false") + # https://stackoverflow.com/questions/60999816/argparse-not-parsing-boolean-arguments + # mixed precision 사용할 지 여부 + + parser.add_argument("--use_extra", action="store_false") + + # parser.add_argument("--wandb_mode", type=str, default="online") + parser.add_argument("--wandb_mode", type=str, default="disabled") + # wandb mode + parser.add_argument("--wandb_run_name", type=str, default="MIL") + # wandb run name + + args = parser.parse_args() + + return args + + +def set_seed(seed): + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # if use multi-GPU + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + np.random.seed(seed) + random.seed(seed) + + +def train( + normal_root_dir, + abnormal_root_dir, + json_dir, + model_dir, + model_name, + model_size, + device, + num_workers, + batch_size, + # val_num_workers, + # val_batch_size, + learning_rate, + max_epoch, + val_interval, + save_interval, + thr, + drop_rate, + patience, + resume_name, + seed, + # mp, + use_extra, + wandb_mode, + wandb_run_name, +): + + time_start = datetime.now() + + train_start = time_start.strftime("%Y%m%d_%H%M%S") + + set_seed(seed) + + if not osp.exists(model_dir): + os.makedirs(model_dir) + + batch_size = batch_size + + val_batch_size = 1 + val_num_workers = 0 + + # -- early stopping flag + patience = patience + counter = 0 + + # 데이터셋 + dataset = NormalVMAE( + model_size=model_size, + root=normal_root_dir, + ) + + valid_data_size = len(dataset) // 10 + + train_data_size = len(dataset) - valid_data_size + + train_dataset, valid_dataset = random_split(dataset, lengths=[train_data_size, valid_data_size]) + + normal_train_loader = DataLoader( + dataset=train_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=num_workers + ) + + normal_valid_loader = DataLoader( + dataset=valid_dataset, + batch_size=val_batch_size, + shuffle=False, + drop_last=True, + num_workers=val_num_workers, + ) + + abnormal_train_dataset = AbnormalVMAE( + model_size=model_size, + root=abnormal_root_dir, + label_root=json_dir, + ) + abnormal_valid_dataset = AbnormalVMAE( + is_train=0, + model_size=model_size, + root=abnormal_root_dir, + label_root=json_dir, + ) + + abnormal_train_loader = DataLoader( + dataset=abnormal_train_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True, + num_workers=num_workers, + ) + + abnormal_valid_loader = DataLoader( + dataset=abnormal_valid_dataset, batch_size=val_batch_size, shuffle=False, num_workers=val_num_workers + ) + + data_load_end = datetime.now() + data_load_time = data_load_end - time_start + data_load_time = str(data_load_time).split(".")[0] + print(f"==>> {model_size} data_load_time: {data_load_time}") + + # Initialize the model + model = MILClassifier(drop_p=drop_rate) + + load_dict = None + + if resume_name: + load_dict = torch.load(osp.join(model_dir, f"{resume_name}.pth"), map_location="cpu") + model.load_state_dict(load_dict["model_state_dict"]) + + model.to(device) + + # optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + # 1e-6 => 0.0010000000474974513 + optimizer = torch.optim.Adagrad(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + # optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[1000, 1500], gamma=0.5) + + if resume_name: + optimizer.load_state_dict(load_dict["optimizer_state_dict"]) + scheduler.load_state_dict(load_dict["scheduler_state_dict"]) + # scaler.load_state_dict(load_dict["scaler_state_dict"]) + + criterion = nn.BCELoss() + MIL_criterion = MIL + + print(f"Start training..") + + wandb.init( + project="VAD", + entity="pao-kim-si-woong", + config={ + "lr": learning_rate, + "dataset": "무인매장", + "n_epochs": max_epoch, + "loss": "BCE+MIL", + "notes": "VAD 실험", + }, + name=wandb_run_name + "_" + train_start, + mode=wandb_mode, + ) + + wandb.watch((model,)) + + best_loss = np.inf + best_auc = 0 + + total_batches = len(abnormal_train_loader) + + for epoch in range(max_epoch): + model.train() + + epoch_start = datetime.now() + + epoch_loss = 0 + epoch_n_corrects = 0 + epoch_n_MIL_loss = 0 + epoch_n_loss = 0 + epoch_n_n_corrects = 0 + + epoch_abnormal_max = 0 + epoch_abnormal_mean = 0 + epoch_normal_max = 0 + epoch_normal_mean = 0 + + norm_train_iter = iter(normal_train_loader) + # iterator를 여기서 매번 새로 할당해줘야 iterator가 다시 처음부터 작동 + + for step, abnormal_inputs in tqdm( + enumerate(abnormal_train_loader), + total=total_batches, + ): + try: + normal_inputs = next(norm_train_iter) + + abnormal_input, abnormal_gt = abnormal_inputs + # (batch_size, 12, 710), (batch_size, 12) + normal_input, normal_gt = normal_inputs + # (batch_size, 12, 710), (batch_size, 12) + + inputs, gts = torch.cat((abnormal_input, normal_input), dim=1), torch.cat( + (abnormal_gt, normal_gt), dim=1 + ) + # inputs는 (batch_size, 24, 710), gts는 (batch_size, 24) + + # batch_size = inputs.shape[0] + + # inputs = inputs.view(-1, inputs.size(-1)).to(device) + # (batch_size * 24, 710) + inputs = inputs.to(device) + gts = gts.view(-1, 1).to(device) + # (batch_size * 24, 1) + + optimizer.zero_grad() + + pred = model(inputs) + # pred는 (batch_size * 24, 1) + + loss = criterion(pred, gts) + MIL_loss = MIL_criterion(pred, batch_size, abnormal_input.size(1)) + sum_loss = loss + MIL_loss + # sum_loss = MIL_loss + sum_loss.backward() + + # loss.backward() + optimizer.step() + with torch.no_grad(): + pred_a = pred.view(batch_size, 2, abnormal_input.size(1))[:, 0, :] + pred_n = pred.view(batch_size, 2, abnormal_input.size(1))[:, 1, :] + + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + pred_n_max = torch.mean(torch.max(pred_n, dim=-1)[0]) + + pred_a_mean = torch.mean(pred_a) + pred_n_mean = torch.mean(pred_n) + + pred_correct = pred > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + epoch_n_loss += loss.item() + epoch_n_MIL_loss += MIL_loss.item() + + epoch_n_n_corrects += corrects / (abnormal_input.size(1) * 2) + + epoch_abnormal_max += pred_a_max.item() + epoch_abnormal_mean += pred_a_mean.item() + epoch_normal_max += pred_n_max.item() + epoch_normal_mean += pred_n_mean.item() + + except StopIteration: + if not use_extra: + + break + abnormal_input, abnormal_gt = abnormal_inputs + # (batch_size, 12, 710), (batch_size, 12) + + # inputs = abnormal_input.view(-1, inputs.size(-1)).to(device) + # (batch_size * 12, 710) + inputs = abnormal_input.to(device) + gts = abnormal_gt.view(-1, 1).to(device) + # (batch_size * 12, 1) + + optimizer.zero_grad() + + pred = model(inputs) + # pred는 (batch_size * 12, 1) + + loss = criterion(pred, gts) + + loss.backward() + optimizer.step() + with torch.no_grad(): + # print(f"==>> pred.shape: {pred.shape}") + pred_a = pred.view(batch_size, abnormal_input.size(1)) + + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + + pred_a_mean = torch.mean(pred_a) + + pred_correct = pred > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + epoch_loss += loss.item() + epoch_n_corrects += corrects / abnormal_input.size(1) + + epoch_abnormal_max += pred_a_max.item() + epoch_abnormal_mean += pred_a_mean.item() + + epoch_n_mean_loss = epoch_n_loss / len(normal_train_loader) + epoch_n_mean_MIL_loss = epoch_n_MIL_loss / len(normal_train_loader) + epoch_n_accuracy = epoch_n_n_corrects / (batch_size * (len(normal_train_loader))) + + epoch_mean_normal_max = epoch_normal_max / len(normal_train_loader) + epoch_mean_normal_mean = epoch_normal_mean / len(normal_train_loader) + if use_extra: + epoch_mean_loss = (epoch_loss + epoch_n_loss) / total_batches + epoch_accuracy = (epoch_n_corrects + epoch_n_n_corrects) / ( + batch_size * (len(abnormal_train_loader)) + ) + epoch_mean_abnormal_max = epoch_abnormal_max / total_batches + epoch_mean_abnormal_mean = epoch_abnormal_mean / total_batches + else: + epoch_mean_loss = (epoch_loss + epoch_n_loss) / len(normal_train_loader) + epoch_accuracy = (epoch_n_corrects + epoch_n_n_corrects) / ( + batch_size * (len(normal_train_loader)) + ) + epoch_mean_abnormal_max = epoch_abnormal_max / len(normal_train_loader) + epoch_mean_abnormal_mean = epoch_abnormal_mean / len(normal_train_loader) + + train_end = datetime.now() + train_time = train_end - epoch_start + train_time = str(train_time).split(".")[0] + print( + f"==>> epoch {epoch+1} train_time: {train_time}\nloss: {round(epoch_mean_loss,4)} n_loss: {round(epoch_n_mean_loss,4)} MIL_loss: {round(epoch_n_mean_MIL_loss,4)}" + ) + print(f"accuracy: {epoch_accuracy:.2f} n_accuracy: {epoch_n_accuracy:.2f}") + print(f"==>> abnormal_max_mean: {epoch_mean_abnormal_max} abnormal_mean: {epoch_mean_abnormal_mean}") + print(f"==>> normal_max_mean: {epoch_mean_normal_max} normal_mean: {epoch_mean_normal_mean}") + + if (epoch + 1) % save_interval == 0: + + ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_latest.pth") + + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + "optimizer_state_dict": optimizer.state_dict(), + "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + } + + torch.save(states, ckpt_fpath) + + # validation 주기에 따라 loss를 출력하고 best model을 저장합니다. + if (epoch + 1) % val_interval == 0: + + print(f"Start validation #{epoch+1:2d}") + model.eval() + + with torch.no_grad(): + total_n_loss = 0 + total_n_MIL_loss = 0 + total_n_n_corrects = 0 + + total_n_fpr = 0 + total_n_tpr = 0 + total_n_bthr = 0 + total_n_auc = 0 + total_n_ap = 0 + + total_loss = 0 + total_n_corrects = 0 + + total_fpr = 0 + total_tpr = 0 + total_bthr = 0 + total_auc = 0 + total_ap = 0 + + error_n_count = 0 + error_count = 0 + + total_abnormal_max = 0 + total_abnormal_mean = 0 + total_normal_max = 0 + total_normal_mean = 0 + + norm_valid_iter = iter(normal_valid_loader) + # iterator를 여기서 매번 새로 할당해줘야 iterator가 다시 처음부터 작동 + + for step, abnormal_inputs in tqdm( + enumerate(abnormal_valid_loader), total=len(abnormal_valid_loader) + ): + try: + normal_inputs = next(norm_valid_iter) + + abnormal_input, abnormal_gt = abnormal_inputs + # (val_batch_size, 12, 710), (val_batch_size, 192) + normal_input, normal_gt = normal_inputs + # (val_batch_size, 12, 710), (val_batch_size, 12) + + abnormal_gt2 = torch.max(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2)[0] + # abnormal_gt2 = torch.mean(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2) + # (val_batch_size, 12) + + inputs = torch.cat((abnormal_input, normal_input), dim=1) + gts = torch.cat((abnormal_gt2, normal_gt), dim=1) + # inputs는 (val_batch_size, 24, 710), gts는 (val_batch_size, 24) + + # inputs = inputs.view(-1, inputs.size(-1)).to(device) + # (val_batch_size * 24, 710) + inputs = inputs.to(device) + gts = gts.view(-1, 1).to(device) + # (val_batch_size * 24, 1) + + pred = model(inputs) + # pred는 (val_batch_size * 24, 1) + + val_loss = criterion(pred, gts) + # if val_loss > 2: + # print(f"==>> pred: {pred}") + # print(f"==>> gts: {gts}") + # counter = patience + 1 + + val_MIL_loss = MIL_criterion(pred, val_batch_size, abnormal_input.size(1)) + + pred_a = pred.view(val_batch_size, 2, abnormal_input.size(1))[:, 0, :] + pred_n = pred.view(val_batch_size, 2, abnormal_input.size(1))[:, 1, :] + + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + pred_n_max = torch.mean(torch.max(pred_n, dim=-1)[0]) + + pred_a_mean = torch.mean(pred_a) + pred_n_mean = torch.mean(pred_n) + + pred_correct = pred > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + pred = (pred.squeeze()).detach().cpu().numpy() + + pred_abnormal_np = np.zeros(abnormal_gt.size(1)) + pred_normal_np = np.zeros(abnormal_gt.size(1)) + + step = np.array([i for i in range(abnormal_input.size(1) + 1)]) + + for j in range(abnormal_input.size(1)): + pred_abnormal_np[step[j] * 16 : step[j + 1] * 16] = pred[j] + pred_normal_np[step[j] * 16 : step[j + 1] * 16] = pred[abnormal_input.size(1) + j] + + pred_np = np.concatenate((pred_abnormal_np, pred_normal_np), axis=0) + + abnormal_gt = abnormal_gt.squeeze().detach().cpu().numpy() + # abnormal_gt2 = abnormal_gt2.squeeze().detach().cpu().numpy() + # normal_gt = np.zeros_like(abnormal_gt2) + normal_gt = np.zeros_like(abnormal_gt) + # gt_np = np.concatenate((abnormal_gt2, normal_gt), axis=0) + gt_np = np.concatenate((abnormal_gt, normal_gt), axis=0) + + try: + # auc = roc_auc_score(y_true=gt_np, y_score=pred_np) + # auc = roc_auc_score(y_true=gt_np, y_score=pred) + + fpr, tpr, cut = roc_curve(y_true=gt_np, y_score=pred_np) + precision, recall, cut2 = precision_recall_curve(gt_np, pred_np) + + auc = sklearn.metrics.auc(fpr, tpr) + ap = sklearn.metrics.auc(recall, precision) + + diff = tpr - fpr + diff_idx = np.argmax(diff) + best_thr = cut[diff_idx] + + pred_positive = pred_np > thr + TP_and_FN = pred_positive[gt_np > 0.9] + FP_and_TN = pred_positive[gt_np < 0.1] + + total_n_fpr += np.sum(FP_and_TN) / len(FP_and_TN) + total_n_tpr += np.sum(TP_and_FN) / len(TP_and_FN) + total_n_bthr += best_thr if diff_idx != 0 else 1 + + total_n_auc += auc + total_n_ap += ap + total_n_n_corrects += corrects / (abnormal_input.size(1) * 2) + total_n_loss += val_loss.item() + total_n_MIL_loss += val_MIL_loss.item() + + total_abnormal_max += pred_a_max.item() + total_abnormal_mean += pred_a_mean.item() + total_normal_max += pred_n_max.item() + total_normal_mean += pred_n_mean.item() + + except ValueError: + # print( + # "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." + # ) + # total_auc += 0 + error_n_count += 1 + # print("0~180 전부 0인 abnormal 영상 있음") + except StopIteration: + # if not use_extra: + # break + abnormal_input, abnormal_gt = abnormal_inputs + # (val_batch_size, 12, 710), (val_batch_size, 192) + + abnormal_gt2 = torch.max(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2)[0] + # abnormal_gt2 = torch.mean(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2) + # (val_batch_size, 12) + + # inputs = abnormal_input.view(-1, inputs.size(-1)).to(device) + # (val_batch_size * 12, 710) + inputs = abnormal_input.to(device) + gts = abnormal_gt2.view(-1, 1).to(device) + # (val_batch_size * 12, 1) + + pred = model(inputs) + # pred는 (val_batch_size * 12, 1) + + val_loss = criterion(pred, gts) + # if val_loss > 2: + # print(f"==>> pred: {pred}") + # print(f"==>> gts: {gts}") + # counter = patience + 1 + + pred_a = pred.view(val_batch_size, abnormal_input.size(1)) + + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + + pred_a_mean = torch.mean(pred_a) + + pred_correct = pred > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + pred = (pred.squeeze()).detach().cpu().numpy() + pred_abnormal_np = np.zeros(abnormal_gt.size(1)) + + step = np.array([i for i in range(abnormal_input.size(1) + 1)]) + + for j in range(abnormal_input.size(1)): + pred_abnormal_np[step[j] * 16 : step[j + 1] * 16] = pred[j] + + # abnormal_gt2 = abnormal_gt2.squeeze().detach().cpu().numpy() + abnormal_gt = abnormal_gt.squeeze().detach().cpu().numpy() + + try: + # auc = roc_auc_score(y_true=abnormal_gt, y_score=pred_abnormal_np) + # auc = roc_auc_score(y_true=abnormal_gt2, y_score=pred) + + fpr, tpr, cut = roc_curve(y_true=abnormal_gt, y_score=pred_abnormal_np) + precision, recall, cut2 = precision_recall_curve(abnormal_gt, pred_abnormal_np) + + auc = sklearn.metrics.auc(fpr, tpr) + ap = sklearn.metrics.auc(recall, precision) + + diff = tpr - fpr + diff_idx = np.argmax(diff) + best_thr = cut[diff_idx] + + pred_positive = pred_abnormal_np > thr + TP_and_FN = pred_positive[abnormal_gt > 0.9] + FP_and_TN = pred_positive[abnormal_gt < 0.1] + + total_fpr += np.sum(FP_and_TN) / len(FP_and_TN) + total_tpr += np.sum(TP_and_FN) / len(TP_and_FN) + total_bthr += best_thr if diff_idx != 0 else 1 + + total_auc += auc + total_ap += ap + total_n_corrects += corrects / abnormal_input.size(1) + # normal + abnormal 24개와 다르게 abnormal 12개만 있음 -> /12 => 2/24 + total_loss += val_loss.item() + + total_abnormal_max += pred_a_max.item() + total_abnormal_mean += pred_a_mean.item() + except ValueError: + # print( + # "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." + # ) + # total_auc += 0 + error_count += 1 + # print("0~180 전부 0인 abnormal 영상 있음") + + val_n_mean_loss = total_n_loss / (len(normal_valid_loader) - error_n_count) + val_n_mean_MIL_loss = total_n_MIL_loss / (len(normal_valid_loader) - error_n_count) + + val_n_fpr = total_n_fpr / ((len(normal_valid_loader) - error_n_count)) + val_n_tpr = total_n_tpr / ((len(normal_valid_loader) - error_n_count)) + val_n_bthr = total_n_bthr / ((len(normal_valid_loader) - error_n_count)) + val_n_auc = total_n_auc / (len(normal_valid_loader) - error_n_count) + val_n_ap = total_n_ap / (len(normal_valid_loader) - error_n_count) + + val_n_accuracy = total_n_n_corrects / ((len(normal_valid_loader) - error_n_count)) + + val_mean_loss = (total_loss + total_n_loss) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + + val_fpr = (total_fpr + total_n_fpr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_tpr = (total_tpr + total_n_tpr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_bthr = (total_bthr + total_n_bthr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_auc = (total_auc + total_n_auc) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_ap = (total_ap + total_n_ap) / (len(abnormal_valid_loader) - error_n_count - error_count) + val_accuracy = (total_n_corrects + total_n_n_corrects) / ( + (len(abnormal_valid_loader) - error_n_count - error_count) + ) + # for loop 한번에 abnormal 12, normal 12해서 24개 정답 확인 + + val_mean_normal_max = total_normal_max / (len(normal_valid_loader) - error_n_count) + val_mean_normal_mean = total_normal_mean / (len(normal_valid_loader) - error_n_count) + val_mean_abnormal_max = total_abnormal_max / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_mean_abnormal_mean = total_abnormal_mean / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + + if best_loss > val_mean_loss: + print(f"Best performance at epoch: {epoch + 1}, {best_loss:.4f} -> {val_mean_loss:.4f}") + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best.pth") + torch.save(states, best_ckpt_fpath) + best_loss = val_mean_loss + # counter = 0 + # else: + # counter += 1 + + if best_auc < val_auc: + print(f"Best auc performance at epoch: {epoch + 1}, {best_auc:.4f} -> {val_auc:.4f}") + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best_auc.pth") + torch.save(states, best_ckpt_fpath) + best_auc = val_auc + counter = 0 + else: + counter += 1 + + new_wandb_metric_dict = { + "train_loss": epoch_mean_loss, + "train_accuracy": epoch_accuracy, + "train_n_loss": epoch_n_mean_loss, + "train_n_MIL_loss": epoch_n_mean_MIL_loss, + "train_n_accuracy": epoch_n_accuracy, + "valid_loss": val_mean_loss, + "valid_fpr": val_fpr, + "valid_tpr": val_tpr, + "valid_bthr": val_bthr, + "valid_auc": val_auc, + "valid_ap": val_ap, + "valid_accuracy": val_accuracy, + "valid_n_loss": val_n_mean_loss, + "valid_n_MIL_loss": val_n_mean_MIL_loss, + "valid_n_fpr": val_n_fpr, + "valid_n_tpr": val_n_tpr, + "valid_n_bthr": val_n_bthr, + "valid_n_auc": val_n_auc, + "valid_n_ap": val_n_ap, + "valid_n_accuracy": val_n_accuracy, + "learning_rate": scheduler.get_last_lr()[0], + "train_abnormal_max_mean": epoch_mean_abnormal_max, + "train_abnormal_mean": epoch_mean_abnormal_mean, + "train_normal_max_mean": epoch_mean_normal_max, + "train_normal_mean": epoch_mean_normal_mean, + "valid_abnormal_max_mean": val_mean_abnormal_max, + "valid_abnormal_mean": val_mean_abnormal_mean, + "valid_normal_max_mean": val_mean_normal_max, + "valid_normal_mean": val_mean_normal_mean, + } + + wandb.log(new_wandb_metric_dict) + + scheduler.step() + + epoch_end = datetime.now() + epoch_time = epoch_end - epoch_start + epoch_time = str(epoch_time).split(".")[0] + print( + f"==>> epoch {epoch+1} time: {epoch_time}\nvalid_loss: {round(val_mean_loss,4)} valid_n_loss: {round(val_n_mean_loss,4)} valid_n_MIL_loss: {round(val_n_mean_MIL_loss,4)}" + ) + print(f"valid_fpr: {val_fpr} valid_n_fpr: {val_n_fpr}") + print(f"valid_tpr: {val_tpr} valid_n_tpr: {val_n_tpr}") + print(f"valid_bthr: {val_bthr} valid_n_bthr: {val_n_bthr}") + print( + f"valid_auc: {val_auc:.4f} valid_n_auc: {val_n_auc:.4f}\nvalid_ap: {val_ap:.4f} valid_n_ap: {val_n_ap:.4f}\nvalid_accuracy: {val_accuracy:.2f} valid_n_accuracy: {val_n_accuracy:.2f}" + ) + print( + f"==>> val_abnormal_max_mean: {val_mean_abnormal_max} val_abnormal_mean: {val_mean_abnormal_mean}" + ) + print(f"==>> val_normal_max_mean: {val_mean_normal_max} val_normal_mean: {val_mean_normal_mean}") + print(f"==>> error_count: {error_count}") + + if counter > patience: + print("Early Stopping...") + break + + time_end = datetime.now() + total_time = time_end - time_start + total_time = str(total_time).split(".")[0] + print(f"==>> total time: {total_time}") + + +def train2( + normal_root_dir, + abnormal_root_dir, + json_dir, + model_dir, + model_name, + model_size, + device, + num_workers, + batch_size, + # val_num_workers, + # val_batch_size, + learning_rate, + max_epoch, + val_interval, + save_interval, + thr, + drop_rate, + patience, + resume_name, + seed, + # mp, + use_extra, + wandb_mode, + wandb_run_name, +): + + time_start = datetime.now() + + train_start = time_start.strftime("%Y%m%d_%H%M%S") + + set_seed(seed) + + if not osp.exists(model_dir): + os.makedirs(model_dir) + + batch_size = batch_size + + val_batch_size = 1 + val_num_workers = 0 + + # -- early stopping flag + patience = patience + counter = 0 + + # 데이터셋 + dataset = NormalVMAE( + model_size=model_size, + root=normal_root_dir, + ) + + valid_data_size = len(dataset) // 10 + + train_data_size = len(dataset) - valid_data_size + + train_dataset, valid_dataset = random_split(dataset, lengths=[train_data_size, valid_data_size]) + + # normal_train_loader = DataLoader( + # dataset=train_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=num_workers + # ) + + normal_valid_loader = DataLoader( + dataset=valid_dataset, + batch_size=val_batch_size, + shuffle=False, + drop_last=True, + num_workers=val_num_workers, + ) + + abnormal_train_dataset = AbnormalVMAE( + model_size=model_size, + root=abnormal_root_dir, + label_root=json_dir, + ) + abnormal_valid_dataset = AbnormalVMAE( + is_train=0, + model_size=model_size, + root=abnormal_root_dir, + label_root=json_dir, + ) + + # abnormal_train_loader = DataLoader( + # dataset=abnormal_train_dataset, + # batch_size=batch_size, + # shuffle=True, + # drop_last=True, + # num_workers=num_workers, + # ) + + concat_trainset = ConcatDataset([train_dataset, abnormal_train_dataset]) + + concat_train_loader = DataLoader( + dataset=concat_trainset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=num_workers + ) + + abnormal_valid_loader = DataLoader( + dataset=abnormal_valid_dataset, batch_size=val_batch_size, shuffle=False, num_workers=val_num_workers + ) + + data_load_end = datetime.now() + data_load_time = data_load_end - time_start + data_load_time = str(data_load_time).split(".")[0] + print(f"==>> {model_size} data_load_time: {data_load_time}") + + # Initialize the LSTM autoencoder model + model = MILClassifier(drop_p=drop_rate) + + load_dict = None + + if resume_name: + load_dict = torch.load(osp.join(model_dir, f"{resume_name}.pth"), map_location="cpu") + model.load_state_dict(load_dict["model_state_dict"]) + + model.to(device) + + # optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + # 1e-6 => 0.0010000000474974513 + optimizer = torch.optim.Adagrad(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + # optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[1000, 1500], gamma=0.5) + + if resume_name: + optimizer.load_state_dict(load_dict["optimizer_state_dict"]) + scheduler.load_state_dict(load_dict["scheduler_state_dict"]) + # scaler.load_state_dict(load_dict["scaler_state_dict"]) + + criterion = nn.BCELoss() + MIL_criterion = MIL + + print(f"Start training..") + + wandb.init( + project="VAD", + entity="pao-kim-si-woong", + config={ + "lr": learning_rate, + "dataset": "무인매장", + "n_epochs": max_epoch, + "loss": "BCE", + "notes": "VAD 실험", + }, + name=wandb_run_name + "_" + train_start, + mode=wandb_mode, + ) + + wandb.watch((model,)) + + best_loss = np.inf + best_auc = 0 + + total_batches = len(concat_train_loader) + + for epoch in range(max_epoch): + model.train() + + epoch_start = datetime.now() + + epoch_loss = 0 + epoch_n_corrects = 0 + + epoch_abnormal_max = 0 + epoch_abnormal_mean = 0 + epoch_normal_max = 0 + epoch_normal_mean = 0 + + nan_count = 0 + + for step, inputs in tqdm( + enumerate(concat_train_loader), + total=total_batches, + ): + inp, gts = inputs + # (batch_size, 11, 710), (batch_size, 11) + + num_segs = inp.size(1) + + # inp = inp.view(-1, inp.size(-1)).to(device) + # (batch_size * 11, 710) + inp = inp.to(device) + gts = gts.view(-1, 1).to(device) + # (batch_size * 11, 1) + + optimizer.zero_grad() + + pred = model(inp) + # pred는 (batch_size * 11, 1) + + loss = criterion(pred, gts) + + loss.backward() + optimizer.step() + with torch.no_grad(): + # print(f"==>> pred.shape: {pred.shape}") + + pred_correct = pred > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + epoch_loss += loss.item() + epoch_n_corrects += corrects / (num_segs * batch_size) + + check = gts.view(batch_size, num_segs) != 0 + check = torch.sum(check, dim=1) + + check_a = check != 0 + check_n = check_a == False + + pred_reshape = pred.view(batch_size, num_segs) + + pred_a_max = torch.mean(torch.max(pred_reshape[check_a], dim=-1)[0]) + + pred_a_mean = torch.mean(pred_reshape[check_a]) + + epoch_abnormal_max += pred_a_max.item() + epoch_abnormal_mean += pred_a_mean.item() + + if torch.sum(check_n) != 0: + pred_n_max = torch.mean(torch.max(pred_reshape[check_n], dim=-1)[0]) + + pred_n_mean = torch.mean(pred_reshape[check_n]) + + epoch_normal_max += pred_n_max.item() + epoch_normal_mean += pred_n_mean.item() + else: + nan_count += 1 + + epoch_mean_loss = epoch_loss / total_batches + epoch_accuracy = epoch_n_corrects / total_batches + + epoch_mean_normal_max = epoch_normal_max / (total_batches - nan_count) + epoch_mean_normal_mean = epoch_normal_mean / (total_batches - nan_count) + epoch_mean_abnormal_max = epoch_abnormal_max / total_batches + epoch_mean_abnormal_mean = epoch_abnormal_mean / total_batches + + train_end = datetime.now() + train_time = train_end - epoch_start + train_time = str(train_time).split(".")[0] + print(f"==>> epoch {epoch+1} train_time: {train_time}\nloss: {round(epoch_mean_loss,4)}") + print(f"accuracy: {epoch_accuracy:.2f}") + print(f"==>> abnormal_max_mean: {epoch_mean_abnormal_max} abnormal_mean: {epoch_mean_abnormal_mean}") + print(f"==>> normal_max_mean: {epoch_mean_normal_max} normal_mean: {epoch_mean_normal_mean}") + + if (epoch + 1) % save_interval == 0: + + ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_latest.pth") + + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + "optimizer_state_dict": optimizer.state_dict(), + "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + } + + torch.save(states, ckpt_fpath) + + # validation 주기에 따라 loss를 출력하고 best model을 저장합니다. + if (epoch + 1) % val_interval == 0: + + print(f"Start validation #{epoch+1:2d}") + model.eval() + + with torch.no_grad(): + total_n_loss = 0 + total_n_MIL_loss = 0 + total_n_n_corrects = 0 + + total_n_fpr = 0 + total_n_tpr = 0 + total_n_bthr = 0 + total_n_auc = 0 + total_n_ap = 0 + + total_loss = 0 + total_n_corrects = 0 + + total_fpr = 0 + total_tpr = 0 + total_bthr = 0 + total_auc = 0 + total_ap = 0 + + error_n_count = 0 + error_count = 0 + + total_abnormal_max = 0 + total_abnormal_mean = 0 + total_normal_max = 0 + total_normal_mean = 0 + + norm_valid_iter = iter(normal_valid_loader) + # iterator를 여기서 매번 새로 할당해줘야 iterator가 다시 처음부터 작동 + + for step, abnormal_inputs in tqdm( + enumerate(abnormal_valid_loader), total=len(abnormal_valid_loader) + ): + try: + normal_inputs = next(norm_valid_iter) + + abnormal_input, abnormal_gt = abnormal_inputs + # (val_batch_size, 12, 710), (val_batch_size, 192) + normal_input, normal_gt = normal_inputs + # (val_batch_size, 12, 710), (val_batch_size, 12) + + abnormal_gt2 = torch.max(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2)[0] + # abnormal_gt2 = torch.mean(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2) + # (val_batch_size, 12) + + inputs = torch.cat((abnormal_input, normal_input), dim=1) + gts = torch.cat((abnormal_gt2, normal_gt), dim=1) + # inputs는 (val_batch_size, 24, 710), gts는 (val_batch_size, 24) + + # inputs = inputs.view(-1, inputs.size(-1)).to(device) + # (val_batch_size * 24, 710) + inputs = inputs.to(device) + gts = gts.view(-1, 1).to(device) + # (val_batch_size * 24, 1) + + pred = model(inputs) + # pred는 (val_batch_size * 24, 1) + + val_loss = criterion(pred, gts) + # if val_loss > 2: + # print(f"==>> pred: {pred}") + # print(f"==>> gts: {gts}") + # counter = patience + 1 + + val_MIL_loss = MIL_criterion(pred, val_batch_size, abnormal_input.size(1)) + + pred_a = pred.view(val_batch_size, 2, abnormal_input.size(1))[:, 0, :] + pred_n = pred.view(val_batch_size, 2, abnormal_input.size(1))[:, 1, :] + + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + pred_n_max = torch.mean(torch.max(pred_n, dim=-1)[0]) + + pred_a_mean = torch.mean(pred_a) + pred_n_mean = torch.mean(pred_n) + + pred_correct = pred > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + pred = (pred.squeeze()).detach().cpu().numpy() + + pred_abnormal_np = np.zeros(abnormal_gt.size(1)) + pred_normal_np = np.zeros(abnormal_gt.size(1)) + + step = np.array([i for i in range(abnormal_input.size(1) + 1)]) + + for j in range(abnormal_input.size(1)): + pred_abnormal_np[step[j] * 16 : step[j + 1] * 16] = pred[j] + pred_normal_np[step[j] * 16 : step[j + 1] * 16] = pred[abnormal_input.size(1) + j] + + pred_np = np.concatenate((pred_abnormal_np, pred_normal_np), axis=0) + + abnormal_gt = abnormal_gt.squeeze().detach().cpu().numpy() + # abnormal_gt2 = abnormal_gt2.squeeze().detach().cpu().numpy() + # normal_gt = np.zeros_like(abnormal_gt2) + normal_gt = np.zeros_like(abnormal_gt) + # gt_np = np.concatenate((abnormal_gt2, normal_gt), axis=0) + gt_np = np.concatenate((abnormal_gt, normal_gt), axis=0) + + try: + # auc = roc_auc_score(y_true=gt_np, y_score=pred_np) + # auc = roc_auc_score(y_true=gt_np, y_score=pred) + + fpr, tpr, cut = roc_curve(y_true=gt_np, y_score=pred_np) + precision, recall, cut2 = precision_recall_curve(gt_np, pred_np) + + auc = sklearn.metrics.auc(fpr, tpr) + ap = sklearn.metrics.auc(recall, precision) + + diff = tpr - fpr + diff_idx = np.argmax(diff) + best_thr = cut[diff_idx] + + pred_positive = pred_np > thr + TP_and_FN = pred_positive[gt_np > 0.9] + FP_and_TN = pred_positive[gt_np < 0.1] + + total_n_fpr += np.sum(FP_and_TN) / len(FP_and_TN) + total_n_tpr += np.sum(TP_and_FN) / len(TP_and_FN) + total_n_bthr += best_thr if diff_idx != 0 else 1 + + total_n_auc += auc + total_n_ap += ap + total_n_n_corrects += corrects / (abnormal_input.size(1) * 2) + total_n_loss += val_loss.item() + total_n_MIL_loss += val_MIL_loss.item() + + total_abnormal_max += pred_a_max.item() + total_abnormal_mean += pred_a_mean.item() + total_normal_max += pred_n_max.item() + total_normal_mean += pred_n_mean.item() + + except ValueError: + # print( + # "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." + # ) + # total_auc += 0 + error_n_count += 1 + # print("0~180 전부 0인 abnormal 영상 있음") + except StopIteration: + # if not use_extra: + # break + abnormal_input, abnormal_gt = abnormal_inputs + # (val_batch_size, 12, 710), (val_batch_size, 192) + + abnormal_gt2 = torch.max(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2)[0] + # abnormal_gt2 = torch.mean(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2) + # (val_batch_size, 12) + + # inputs = abnormal_input.view(-1, inputs.size(-1)).to(device) + # (val_batch_size * 12, 710) + inputs = abnormal_input.to(device) + gts = abnormal_gt2.view(-1, 1).to(device) + # (val_batch_size * 12, 1) + + pred = model(inputs) + # pred는 (val_batch_size * 12, 1) + + val_loss = criterion(pred, gts) + # if val_loss > 2: + # print(f"==>> pred: {pred}") + # print(f"==>> gts: {gts}") + # counter = patience + 1 + + pred_a = pred.view(val_batch_size, abnormal_input.size(1)) + + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + + pred_a_mean = torch.mean(pred_a) + + pred_correct = pred > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + pred = (pred.squeeze()).detach().cpu().numpy() + pred_abnormal_np = np.zeros(abnormal_gt.size(1)) + + step = np.array([i for i in range(abnormal_input.size(1) + 1)]) + + for j in range(abnormal_input.size(1)): + pred_abnormal_np[step[j] * 16 : step[j + 1] * 16] = pred[j] + + # abnormal_gt2 = abnormal_gt2.squeeze().detach().cpu().numpy() + abnormal_gt = abnormal_gt.squeeze().detach().cpu().numpy() + + try: + # auc = roc_auc_score(y_true=abnormal_gt, y_score=pred_abnormal_np) + # auc = roc_auc_score(y_true=abnormal_gt2, y_score=pred) + + fpr, tpr, cut = roc_curve(y_true=abnormal_gt, y_score=pred_abnormal_np) + precision, recall, cut2 = precision_recall_curve(abnormal_gt, pred_abnormal_np) + + auc = sklearn.metrics.auc(fpr, tpr) + ap = sklearn.metrics.auc(recall, precision) + + diff = tpr - fpr + diff_idx = np.argmax(diff) + best_thr = cut[diff_idx] + + pred_positive = pred_abnormal_np > thr + TP_and_FN = pred_positive[abnormal_gt > 0.9] + FP_and_TN = pred_positive[abnormal_gt < 0.1] + + total_fpr += np.sum(FP_and_TN) / len(FP_and_TN) + total_tpr += np.sum(TP_and_FN) / len(TP_and_FN) + total_bthr += best_thr if diff_idx != 0 else 1 + + total_auc += auc + total_ap += ap + total_n_corrects += corrects / abnormal_input.size(1) + # normal + abnormal 24개와 다르게 abnormal 12개만 있음 -> /12 => 2/24 + total_loss += val_loss.item() + + total_abnormal_max += pred_a_max.item() + total_abnormal_mean += pred_a_mean.item() + except ValueError: + # print( + # "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." + # ) + # total_auc += 0 + error_count += 1 + # print("0~180 전부 0인 abnormal 영상 있음") + + val_n_mean_loss = total_n_loss / (len(normal_valid_loader) - error_n_count) + val_n_mean_MIL_loss = total_n_MIL_loss / (len(normal_valid_loader) - error_n_count) + + val_n_fpr = total_n_fpr / ((len(normal_valid_loader) - error_n_count)) + val_n_tpr = total_n_tpr / ((len(normal_valid_loader) - error_n_count)) + val_n_bthr = total_n_bthr / ((len(normal_valid_loader) - error_n_count)) + val_n_auc = total_n_auc / (len(normal_valid_loader) - error_n_count) + val_n_ap = total_n_ap / (len(normal_valid_loader) - error_n_count) + + val_n_accuracy = total_n_n_corrects / ((len(normal_valid_loader) - error_n_count)) + + val_mean_loss = (total_loss + total_n_loss) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + + val_fpr = (total_fpr + total_n_fpr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_tpr = (total_tpr + total_n_tpr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_bthr = (total_bthr + total_n_bthr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_auc = (total_auc + total_n_auc) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_ap = (total_ap + total_n_ap) / (len(abnormal_valid_loader) - error_n_count - error_count) + val_accuracy = (total_n_corrects + total_n_n_corrects) / ( + (len(abnormal_valid_loader) - error_n_count - error_count) + ) + # for loop 한번에 abnormal 12, normal 12해서 24개 정답 확인 + + val_mean_normal_max = total_normal_max / (len(normal_valid_loader) - error_n_count) + val_mean_normal_mean = total_normal_mean / (len(normal_valid_loader) - error_n_count) + val_mean_abnormal_max = total_abnormal_max / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_mean_abnormal_mean = total_abnormal_mean / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + + if best_loss > val_mean_loss: + print(f"Best performance at epoch: {epoch + 1}, {best_loss:.4f} -> {val_mean_loss:.4f}") + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best.pth") + torch.save(states, best_ckpt_fpath) + best_loss = val_mean_loss + # counter = 0 + # else: + # counter += 1 + + if best_auc < val_auc: + print(f"Best auc performance at epoch: {epoch + 1}, {best_auc:.4f} -> {val_auc:.4f}") + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best_auc.pth") + torch.save(states, best_ckpt_fpath) + best_auc = val_auc + counter = 0 + else: + counter += 1 + + new_wandb_metric_dict = { + "train_loss": epoch_mean_loss, + "train_accuracy": epoch_accuracy, + "valid_loss": val_mean_loss, + "valid_fpr": val_fpr, + "valid_tpr": val_tpr, + "valid_bthr": val_bthr, + "valid_auc": val_auc, + "valid_ap": val_ap, + "valid_accuracy": val_accuracy, + "valid_n_loss": val_n_mean_loss, + "valid_n_MIL_loss": val_n_mean_MIL_loss, + "valid_n_fpr": val_n_fpr, + "valid_n_tpr": val_n_tpr, + "valid_n_bthr": val_n_bthr, + "valid_n_auc": val_n_auc, + "valid_n_ap": val_n_ap, + "valid_n_accuracy": val_n_accuracy, + "learning_rate": scheduler.get_last_lr()[0], + "train_abnormal_max_mean": epoch_mean_abnormal_max, + "train_abnormal_mean": epoch_mean_abnormal_mean, + "train_normal_max_mean": epoch_mean_normal_max, + "train_normal_mean": epoch_mean_normal_mean, + "valid_abnormal_max_mean": val_mean_abnormal_max, + "valid_abnormal_mean": val_mean_abnormal_mean, + "valid_normal_max_mean": val_mean_normal_max, + "valid_normal_mean": val_mean_normal_mean, + } + + wandb.log(new_wandb_metric_dict) + + scheduler.step() + + epoch_end = datetime.now() + epoch_time = epoch_end - epoch_start + epoch_time = str(epoch_time).split(".")[0] + print( + f"==>> epoch {epoch+1} time: {epoch_time}\nvalid_loss: {round(val_mean_loss,4)} valid_n_loss: {round(val_n_mean_loss,4)} valid_n_MIL_loss: {round(val_n_mean_MIL_loss,4)}" + ) + print(f"valid_fpr: {val_fpr} valid_n_fpr: {val_n_fpr}") + print(f"valid_tpr: {val_tpr} valid_n_tpr: {val_n_tpr}") + print(f"valid_bthr: {val_bthr} valid_n_bthr: {val_n_bthr}") + print( + f"valid_auc: {val_auc:.4f} valid_n_auc: {val_n_auc:.4f}\nvalid_ap: {val_ap:.4f} valid_n_ap: {val_n_ap:.4f}\nvalid_accuracy: {val_accuracy:.2f} valid_n_accuracy: {val_n_accuracy:.2f}" + ) + print( + f"==>> val_abnormal_max_mean: {val_mean_abnormal_max} val_abnormal_mean: {val_mean_abnormal_mean}" + ) + print(f"==>> val_normal_max_mean: {val_mean_normal_max} val_normal_mean: {val_mean_normal_mean}") + print(f"==>> error_count: {error_count}") + + if counter > patience: + print("Early Stopping...") + break + + time_end = datetime.now() + total_time = time_end - time_start + total_time = str(total_time).split(".")[0] + print(f"==>> total time: {total_time}") + + +def train3( + normal_root_dir, + abnormal_root_dir, + json_dir, + model_dir, + model_name, + model_size, + device, + num_workers, + batch_size, + # val_num_workers, + # val_batch_size, + learning_rate, + max_epoch, + val_interval, + save_interval, + thr, + drop_rate, + patience, + resume_name, + seed, + # mp, + use_extra, + wandb_mode, + wandb_run_name, +): + + time_start = datetime.now() + + train_start = time_start.strftime("%Y%m%d_%H%M%S") + + set_seed(seed) + + if not osp.exists(model_dir): + os.makedirs(model_dir) + + batch_size = batch_size + + val_batch_size = 1 + val_num_workers = 0 + + # -- early stopping flag + patience = patience + counter = 0 + + # 데이터셋 + dataset = NormalVMAE( + model_size=model_size, + root=normal_root_dir, + ) + + valid_data_size = len(dataset) // 10 + + train_data_size = len(dataset) - valid_data_size + + train_dataset, valid_dataset = random_split(dataset, lengths=[train_data_size, valid_data_size]) + + normal_train_loader = DataLoader( + dataset=train_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=num_workers + ) + + normal_valid_loader = DataLoader( + dataset=valid_dataset, + batch_size=val_batch_size, + shuffle=False, + drop_last=True, + num_workers=val_num_workers, + ) + + abnormal_train_dataset = AbnormalVMAE( + model_size=model_size, + root=abnormal_root_dir, + label_root=json_dir, + ) + abnormal_valid_dataset = AbnormalVMAE( + is_train=0, + model_size=model_size, + root=abnormal_root_dir, + label_root=json_dir, + ) + + abnormal_train_loader = DataLoader( + dataset=abnormal_train_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True, + num_workers=num_workers, + ) + + abnormal_valid_loader = DataLoader( + dataset=abnormal_valid_dataset, batch_size=val_batch_size, shuffle=False, num_workers=val_num_workers + ) + + data_load_end = datetime.now() + data_load_time = data_load_end - time_start + data_load_time = str(data_load_time).split(".")[0] + print(f"==>> {model_size} data_load_time: {data_load_time}") + + # Initialize the model + model = MILClassifier(drop_p=drop_rate) + + load_dict = None + + if resume_name: + load_dict = torch.load(osp.join(model_dir, f"{resume_name}.pth"), map_location="cpu") + model.load_state_dict(load_dict["model_state_dict"]) + + model.to(device) + + # optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + # 1e-6 => 0.0010000000474974513 + optimizer = torch.optim.Adagrad(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + # optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[1000, 1500], gamma=0.5) + + if resume_name: + optimizer.load_state_dict(load_dict["optimizer_state_dict"]) + scheduler.load_state_dict(load_dict["scheduler_state_dict"]) + # scaler.load_state_dict(load_dict["scaler_state_dict"]) + + criterion = nn.BCELoss() + MIL_criterion = MIL + + print(f"Start training..") + + wandb.init( + project="VAD", + entity="pao-kim-si-woong", + config={ + "lr": learning_rate, + "dataset": "무인매장", + "n_epochs": max_epoch, + "loss": "BCE+MIL", + "notes": "VAD 실험", + }, + name=wandb_run_name + "_" + train_start, + mode=wandb_mode, + ) + + wandb.watch((model,)) + + best_loss = np.inf + best_auc = 0 + + total_batches = len(abnormal_train_loader) + + for epoch in range(max_epoch): + model.train() + + epoch_start = datetime.now() + + epoch_loss = 0 + epoch_n_corrects = 0 + epoch_MIL_loss = 0 + + epoch_abnormal_max = 0 + epoch_abnormal_mean = 0 + epoch_normal_max = 0 + epoch_normal_mean = 0 + + for step, abnormal_inputs in tqdm( + enumerate(abnormal_train_loader), + total=total_batches, + ): + if step % len(normal_train_loader) == 0: + norm_train_iter = iter(normal_train_loader) + # 중복 추출하더라도 정상, 이상 영상 1대1 대응 loop 끝까지 유지 + + normal_inputs = next(norm_train_iter) + + abnormal_input, abnormal_gt = abnormal_inputs + # (batch_size, 12, 710), (batch_size, 12) + normal_input, normal_gt = normal_inputs + # (batch_size, 12, 710), (batch_size, 12) + + inputs, gts = torch.cat((abnormal_input, normal_input), dim=1), torch.cat( + (abnormal_gt, normal_gt), dim=1 + ) + # inputs는 (batch_size, 24, 710), gts는 (batch_size, 24) + + # batch_size = inputs.shape[0] + + # inputs = inputs.view(-1, inputs.size(-1)).to(device) + # (batch_size * 24, 710) + inputs = inputs.to(device) + gts = gts.view(-1, 1).to(device) + # (batch_size * 24, 1) + + optimizer.zero_grad() + + pred = model(inputs) + # pred는 (batch_size * 24, 1) + + loss = criterion(pred, gts) + MIL_loss = MIL_criterion(pred, batch_size, abnormal_input.size(1)) + sum_loss = loss + MIL_loss + # sum_loss = MIL_loss + sum_loss.backward() + + # loss.backward() + optimizer.step() + with torch.no_grad(): + pred_a = pred.view(batch_size, 2, abnormal_input.size(1))[:, 0, :] + pred_n = pred.view(batch_size, 2, abnormal_input.size(1))[:, 1, :] + + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + pred_n_max = torch.mean(torch.max(pred_n, dim=-1)[0]) + + pred_a_mean = torch.mean(pred_a) + pred_n_mean = torch.mean(pred_n) + + pred_correct = pred > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + epoch_loss += loss.item() + epoch_MIL_loss += MIL_loss.item() + + epoch_n_corrects += corrects / (abnormal_input.size(1) * 2) + + epoch_abnormal_max += pred_a_max.item() + epoch_abnormal_mean += pred_a_mean.item() + epoch_normal_max += pred_n_max.item() + epoch_normal_mean += pred_n_mean.item() + + epoch_mean_loss = epoch_loss / total_batches + epoch_mean_MIL_loss = epoch_MIL_loss / total_batches + epoch_accuracy = epoch_n_corrects / (batch_size * (total_batches)) + + epoch_mean_normal_max = epoch_normal_max / total_batches + epoch_mean_normal_mean = epoch_normal_mean / total_batches + epoch_mean_abnormal_max = epoch_abnormal_max / total_batches + epoch_mean_abnormal_mean = epoch_abnormal_mean / total_batches + + train_end = datetime.now() + train_time = train_end - epoch_start + train_time = str(train_time).split(".")[0] + print( + f"==>> epoch {epoch+1} train_time: {train_time}\nloss: {round(epoch_mean_loss,4)} MIL_loss: {round(epoch_mean_MIL_loss,4)}" + ) + print(f"accuracy: {epoch_accuracy:.2f}") + print(f"==>> abnormal_max_mean: {epoch_mean_abnormal_max} abnormal_mean: {epoch_mean_abnormal_mean}") + print(f"==>> normal_max_mean: {epoch_mean_normal_max} normal_mean: {epoch_mean_normal_mean}") + + if (epoch + 1) % save_interval == 0: + + ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_latest.pth") + + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + "optimizer_state_dict": optimizer.state_dict(), + "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + } + + torch.save(states, ckpt_fpath) + + # validation 주기에 따라 loss를 출력하고 best model을 저장합니다. + if (epoch + 1) % val_interval == 0: + + print(f"Start validation #{epoch+1:2d}") + model.eval() + + with torch.no_grad(): + total_n_loss = 0 + total_n_MIL_loss = 0 + total_n_n_corrects = 0 + + total_n_fpr = 0 + total_n_tpr = 0 + total_n_bthr = 0 + total_n_auc = 0 + total_n_ap = 0 + + total_loss = 0 + total_n_corrects = 0 + + total_fpr = 0 + total_tpr = 0 + total_bthr = 0 + total_auc = 0 + total_ap = 0 + + error_n_count = 0 + error_count = 0 + + total_abnormal_max = 0 + total_abnormal_mean = 0 + total_normal_max = 0 + total_normal_mean = 0 + + norm_valid_iter = iter(normal_valid_loader) + # iterator를 여기서 매번 새로 할당해줘야 iterator가 다시 처음부터 작동 + + for step, abnormal_inputs in tqdm( + enumerate(abnormal_valid_loader), total=len(abnormal_valid_loader) + ): + try: + normal_inputs = next(norm_valid_iter) + + abnormal_input, abnormal_gt = abnormal_inputs + # (val_batch_size, 12, 710), (val_batch_size, 192) + normal_input, normal_gt = normal_inputs + # (val_batch_size, 12, 710), (val_batch_size, 12) + + abnormal_gt2 = torch.max(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2)[0] + # abnormal_gt2 = torch.mean(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2) + # (val_batch_size, 12) + + inputs = torch.cat((abnormal_input, normal_input), dim=1) + gts = torch.cat((abnormal_gt2, normal_gt), dim=1) + # inputs는 (val_batch_size, 24, 710), gts는 (val_batch_size, 24) + + # inputs = inputs.view(-1, inputs.size(-1)).to(device) + # (val_batch_size * 24, 710) + inputs = inputs.to(device) + gts = gts.view(-1, 1).to(device) + # (val_batch_size * 24, 1) + + pred = model(inputs) + # pred는 (val_batch_size * 24, 1) + + val_loss = criterion(pred, gts) + # if val_loss > 2: + # print(f"==>> pred: {pred}") + # print(f"==>> gts: {gts}") + # counter = patience + 1 + + val_MIL_loss = MIL_criterion(pred, val_batch_size, abnormal_input.size(1)) + + pred_a = pred.view(val_batch_size, 2, abnormal_input.size(1))[:, 0, :] + pred_n = pred.view(val_batch_size, 2, abnormal_input.size(1))[:, 1, :] + + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + pred_n_max = torch.mean(torch.max(pred_n, dim=-1)[0]) + + pred_a_mean = torch.mean(pred_a) + pred_n_mean = torch.mean(pred_n) + + pred_correct = pred > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + pred = (pred.squeeze()).detach().cpu().numpy() + + pred_abnormal_np = np.zeros(abnormal_gt.size(1)) + pred_normal_np = np.zeros(abnormal_gt.size(1)) + + step = np.array([i for i in range(abnormal_input.size(1) + 1)]) + + for j in range(abnormal_input.size(1)): + pred_abnormal_np[step[j] * 16 : step[j + 1] * 16] = pred[j] + pred_normal_np[step[j] * 16 : step[j + 1] * 16] = pred[abnormal_input.size(1) + j] + + pred_np = np.concatenate((pred_abnormal_np, pred_normal_np), axis=0) + + abnormal_gt = abnormal_gt.squeeze().detach().cpu().numpy() + # abnormal_gt2 = abnormal_gt2.squeeze().detach().cpu().numpy() + # normal_gt = np.zeros_like(abnormal_gt2) + normal_gt = np.zeros_like(abnormal_gt) + # gt_np = np.concatenate((abnormal_gt2, normal_gt), axis=0) + gt_np = np.concatenate((abnormal_gt, normal_gt), axis=0) + + try: + # auc = roc_auc_score(y_true=gt_np, y_score=pred_np) + # auc = roc_auc_score(y_true=gt_np, y_score=pred) + + fpr, tpr, cut = roc_curve(y_true=gt_np, y_score=pred_np) + precision, recall, cut2 = precision_recall_curve(gt_np, pred_np) + + auc = sklearn.metrics.auc(fpr, tpr) + ap = sklearn.metrics.auc(recall, precision) + + diff = tpr - fpr + diff_idx = np.argmax(diff) + best_thr = cut[diff_idx] + + pred_positive = pred_np > thr + TP_and_FN = pred_positive[gt_np > 0.9] + FP_and_TN = pred_positive[gt_np < 0.1] + + total_n_fpr += np.sum(FP_and_TN) / len(FP_and_TN) + total_n_tpr += np.sum(TP_and_FN) / len(TP_and_FN) + total_n_bthr += best_thr if diff_idx != 0 else 1 + + total_n_auc += auc + total_n_ap += ap + total_n_n_corrects += corrects / (abnormal_input.size(1) * 2) + total_n_loss += val_loss.item() + total_n_MIL_loss += val_MIL_loss.item() + + total_abnormal_max += pred_a_max.item() + total_abnormal_mean += pred_a_mean.item() + total_normal_max += pred_n_max.item() + total_normal_mean += pred_n_mean.item() + + except ValueError: + # print( + # "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." + # ) + # total_auc += 0 + error_n_count += 1 + # print("0~180 전부 0인 abnormal 영상 있음") + except StopIteration: + # if not use_extra: + # break + abnormal_input, abnormal_gt = abnormal_inputs + # (val_batch_size, 12, 710), (val_batch_size, 192) + + abnormal_gt2 = torch.max(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2)[0] + # abnormal_gt2 = torch.mean(abnormal_gt.view(-1, abnormal_input.size(1), 16), dim=2) + # (val_batch_size, 12) + + # inputs = abnormal_input.view(-1, inputs.size(-1)).to(device) + # (val_batch_size * 12, 710) + inputs = abnormal_input.to(device) + gts = abnormal_gt2.view(-1, 1).to(device) + # (val_batch_size * 12, 1) + + pred = model(inputs) + # pred는 (val_batch_size * 12, 1) + + val_loss = criterion(pred, gts) + # if val_loss > 2: + # print(f"==>> pred: {pred}") + # print(f"==>> gts: {gts}") + # counter = patience + 1 + + pred_a = pred.view(val_batch_size, abnormal_input.size(1)) + + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + + pred_a_mean = torch.mean(pred_a) + + pred_correct = pred > thr + gts_correct = gts > thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + + pred = (pred.squeeze()).detach().cpu().numpy() + pred_abnormal_np = np.zeros(abnormal_gt.size(1)) + + step = np.array([i for i in range(abnormal_input.size(1) + 1)]) + + for j in range(abnormal_input.size(1)): + pred_abnormal_np[step[j] * 16 : step[j + 1] * 16] = pred[j] + + # abnormal_gt2 = abnormal_gt2.squeeze().detach().cpu().numpy() + abnormal_gt = abnormal_gt.squeeze().detach().cpu().numpy() + + try: + # auc = roc_auc_score(y_true=abnormal_gt, y_score=pred_abnormal_np) + # auc = roc_auc_score(y_true=abnormal_gt2, y_score=pred) + + fpr, tpr, cut = roc_curve(y_true=abnormal_gt, y_score=pred_abnormal_np) + precision, recall, cut2 = precision_recall_curve(abnormal_gt, pred_abnormal_np) + + auc = sklearn.metrics.auc(fpr, tpr) + ap = sklearn.metrics.auc(recall, precision) + + diff = tpr - fpr + diff_idx = np.argmax(diff) + best_thr = cut[diff_idx] + + pred_positive = pred_abnormal_np > thr + TP_and_FN = pred_positive[abnormal_gt > 0.9] + FP_and_TN = pred_positive[abnormal_gt < 0.1] + + total_fpr += np.sum(FP_and_TN) / len(FP_and_TN) + total_tpr += np.sum(TP_and_FN) / len(TP_and_FN) + total_bthr += best_thr if diff_idx != 0 else 1 + + total_auc += auc + total_ap += ap + total_n_corrects += corrects / abnormal_input.size(1) + # normal + abnormal 24개와 다르게 abnormal 12개만 있음 -> /12 => 2/24 + total_loss += val_loss.item() + + total_abnormal_max += pred_a_max.item() + total_abnormal_mean += pred_a_mean.item() + except ValueError: + # print( + # "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." + # ) + # total_auc += 0 + error_count += 1 + # print("0~180 전부 0인 abnormal 영상 있음") + + val_n_mean_loss = total_n_loss / (len(normal_valid_loader) - error_n_count) + val_n_mean_MIL_loss = total_n_MIL_loss / (len(normal_valid_loader) - error_n_count) + + val_n_fpr = total_n_fpr / ((len(normal_valid_loader) - error_n_count)) + val_n_tpr = total_n_tpr / ((len(normal_valid_loader) - error_n_count)) + val_n_bthr = total_n_bthr / ((len(normal_valid_loader) - error_n_count)) + val_n_auc = total_n_auc / (len(normal_valid_loader) - error_n_count) + val_n_ap = total_n_ap / (len(normal_valid_loader) - error_n_count) + + val_n_accuracy = total_n_n_corrects / ((len(normal_valid_loader) - error_n_count)) + + val_mean_loss = (total_loss + total_n_loss) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + + val_fpr = (total_fpr + total_n_fpr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_tpr = (total_tpr + total_n_tpr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_bthr = (total_bthr + total_n_bthr) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_auc = (total_auc + total_n_auc) / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_ap = (total_ap + total_n_ap) / (len(abnormal_valid_loader) - error_n_count - error_count) + val_accuracy = (total_n_corrects + total_n_n_corrects) / ( + (len(abnormal_valid_loader) - error_n_count - error_count) + ) + # for loop 한번에 abnormal 12, normal 12해서 24개 정답 확인 + + val_mean_normal_max = total_normal_max / (len(normal_valid_loader) - error_n_count) + val_mean_normal_mean = total_normal_mean / (len(normal_valid_loader) - error_n_count) + val_mean_abnormal_max = total_abnormal_max / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + val_mean_abnormal_mean = total_abnormal_mean / ( + len(abnormal_valid_loader) - error_n_count - error_count + ) + + if best_loss > val_mean_loss: + print(f"Best performance at epoch: {epoch + 1}, {best_loss:.4f} -> {val_mean_loss:.4f}") + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best.pth") + torch.save(states, best_ckpt_fpath) + best_loss = val_mean_loss + # counter = 0 + # else: + # counter += 1 + + if best_auc < val_auc: + str_to_keep = f"Best auc performance at epoch: {epoch + 1}, {best_auc:.4f} -> {val_auc:.4f}" + print(str_to_keep) + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best_auc.pth") + torch.save(states, best_ckpt_fpath) + best_auc = val_auc + counter = 0 + else: + counter += 1 + + new_wandb_metric_dict = { + "train_loss": epoch_mean_loss, + "train_accuracy": epoch_accuracy, + "train_MIL_loss": epoch_mean_MIL_loss, + "valid_loss": val_mean_loss, + "valid_fpr": val_fpr, + "valid_tpr": val_tpr, + "valid_bthr": val_bthr, + "valid_auc": val_auc, + "valid_ap": val_ap, + "valid_accuracy": val_accuracy, + "valid_n_loss": val_n_mean_loss, + "valid_n_MIL_loss": val_n_mean_MIL_loss, + "valid_n_fpr": val_n_fpr, + "valid_n_tpr": val_n_tpr, + "valid_n_bthr": val_n_bthr, + "valid_n_auc": val_n_auc, + "valid_n_ap": val_n_ap, + "valid_n_accuracy": val_n_accuracy, + "learning_rate": scheduler.get_last_lr()[0], + "train_abnormal_max_mean": epoch_mean_abnormal_max, + "train_abnormal_mean": epoch_mean_abnormal_mean, + "train_normal_max_mean": epoch_mean_normal_max, + "train_normal_mean": epoch_mean_normal_mean, + "valid_abnormal_max_mean": val_mean_abnormal_max, + "valid_abnormal_mean": val_mean_abnormal_mean, + "valid_normal_max_mean": val_mean_normal_max, + "valid_normal_mean": val_mean_normal_mean, + } + + wandb.log(new_wandb_metric_dict) + + scheduler.step() + + epoch_end = datetime.now() + epoch_time = epoch_end - epoch_start + epoch_time = str(epoch_time).split(".")[0] + print( + f"==>> epoch {epoch+1} time: {epoch_time}\nvalid_loss: {round(val_mean_loss,4)} valid_n_loss: {round(val_n_mean_loss,4)} valid_n_MIL_loss: {round(val_n_mean_MIL_loss,4)}" + ) + print(f"valid_fpr: {val_fpr} valid_n_fpr: {val_n_fpr}") + print(f"valid_tpr: {val_tpr} valid_n_tpr: {val_n_tpr}") + print(f"valid_bthr: {val_bthr} valid_n_bthr: {val_n_bthr}") + print( + f"valid_auc: {val_auc:.4f} valid_n_auc: {val_n_auc:.4f}\nvalid_ap: {val_ap:.4f} valid_n_ap: {val_n_ap:.4f}\nvalid_accuracy: {val_accuracy:.2f} valid_n_accuracy: {val_n_accuracy:.2f}" + ) + print( + f"==>> val_abnormal_max_mean: {val_mean_abnormal_max} val_abnormal_mean: {val_mean_abnormal_mean}" + ) + print(f"==>> val_normal_max_mean: {val_mean_normal_max} val_normal_mean: {val_mean_normal_mean}") + print(f"==>> error_count: {error_count}") + + if counter > patience: + print("Early Stopping...") + break + + time_end = datetime.now() + total_time = time_end - time_start + total_time = str(total_time).split(".")[0] + print(str_to_keep) + print(f"==>> total time: {total_time}") + + +def main(args): + if (args.wandb_run_name).startswith("MIL_nl_feat_enhancer_"): + print("train 1") + train(**args.__dict__) + elif (args.wandb_run_name).startswith("MIL_nl_BCEonly_feat_enhancer_"): + print("train 2") + train2(**args.__dict__) + else: + print("train 3") + train3(**args.__dict__) + + +if __name__ == "__main__": + args = parse_args() + + main(args) diff --git a/model/train/train_UCFC.py b/model/train/train_UCFC.py new file mode 100644 index 0000000..c069b84 --- /dev/null +++ b/model/train/train_UCFC.py @@ -0,0 +1,1251 @@ +import pandas as pd +import numpy as np +import random +import matplotlib.pyplot as plt + +from datetime import datetime + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from torch.utils.data import Dataset, DataLoader, random_split, ConcatDataset + +from sklearn.metrics import roc_auc_score, roc_curve, precision_recall_curve +import sklearn.metrics + +# from sklearn.preprocessing import MinMaxScaler + +from argparse import ArgumentParser + +import os +import os.path as osp + +from tqdm import tqdm + +import wandb + +from shop_dataset import NewNormalVMAE, NewAbnormalVMAE +from classifier import WSAD, MILClassifier +from loss import MIL, LossComputer + + +def parse_args(): + parser = ArgumentParser() + + # Conventional args + parser.add_argument( + "--normal_root_dir", + type=str, + default=os.environ.get( + "SM_CHANNEL_NORMAL_NPY", + "../datapreprocess/npy/UCFCrime/normal", + ), + ) + # 학습 데이터 경로 + parser.add_argument( + "--abnormal_root_dir", + type=str, + default=os.environ.get( + "SM_CHANNEL_ABNORMAL_NPY", + "../datapreprocess/npy/UCFCrime/abnormal", + ), + ) + parser.add_argument( + "--label_dir", + type=str, + default=os.environ.get( + "SM_CHANNEL_ABNORMAL_LABEL", + "../datapreprocess/npy/UCFCrime/test_anomalyv2.txt", + ), + ) + # abnormal 검증셋 npy, json파일 경로 + parser.add_argument("--model_dir", type=str, default=os.environ.get("SM_MODEL_DIR", "../pths")) + # pth 파일 저장 경로 + + parser.add_argument("--model_name", type=str, default="BNWVAD") + # import_module로 불러올 model name + + parser.add_argument("--len_feature", type=int, default=710) + # npy파일 feature length + parser.add_argument("--use_l2norm", action="store_true") + # npy feature l2 normalization 여부 + parser.add_argument("--num_segments", type=int, default=200) + # 영상 segment 개수 + + parser.add_argument("--resume_name", type=str, default="") + # resume 파일 이름 + + parser.add_argument("--model_size", type=str, default="small") + # VideoMAEv2 backbone 사이즈 = "small" or "base" + + parser.add_argument("--seed", type=int, default=666) + # random seed + + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") + parser.add_argument("--num_workers", type=int, default=0) + + parser.add_argument("--batch_size", type=int, default=30) + # parser.add_argument("--val_batch_size", type=int, default=1) + # parser.add_argument("--val_num_workers", type=int, default=0) + parser.add_argument("--learning_rate", type=float, default=0.0001) + parser.add_argument("--weight_decay", type=float, default=0.00005) + parser.add_argument("--max_epoch", type=int, default=1000) + + parser.add_argument("--save_interval", type=int, default=1) + parser.add_argument("--val_interval", type=int, default=1) + parser.add_argument("--w_normal", type=float, default=1.0) + parser.add_argument("--w_mpp", type=float, default=1.0) + parser.add_argument("--gt_thr", type=float, default=0.25) + parser.add_argument("--dist_thr", type=float, default=10) + + parser.add_argument("--ratio_sample", type=float, default=0.2) + parser.add_argument("--ratio_batch", type=float, default=0.4) + + parser.add_argument("--ratios", type=int, nargs="+", default=[16, 32]) + parser.add_argument("--kernel_sizes", type=int, nargs="+", default=[1, 1, 1]) + + parser.add_argument("--patience", type=int, default=100) + + # parser.add_argument("--mp", action="store_false") + # https://stackoverflow.com/questions/60999816/argparse-not-parsing-boolean-arguments + # mixed precision 사용할 지 여부 + + # parser.add_argument("--use_extra", action="store_false") + + # parser.add_argument("--wandb_mode", type=str, default="online") + parser.add_argument("--wandb_mode", type=str, default="disabled") + # wandb mode + parser.add_argument("--wandb_run_name", type=str, default="BNWVAD") + # wandb run name + + args = parser.parse_args() + + return args + + +def set_seed(seed): + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # if use multi-GPU + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + np.random.seed(seed) + random.seed(seed) + + +def train_BNWVAD( + normal_root_dir, + abnormal_root_dir, + label_dir, + model_dir, + model_name, + model_size, + device, + num_workers, + batch_size, + # val_num_workers, + # val_batch_size, + learning_rate, + weight_decay, + max_epoch, + val_interval, + save_interval, + w_normal, + w_mpp, + gt_thr, + dist_thr, + len_feature, + use_l2norm, + num_segments, + ratio_sample, + ratio_batch, + ratios, + kernel_sizes, + patience, + resume_name, + seed, + # mp, + # use_extra, + wandb_mode, + wandb_run_name, +): + + time_start = datetime.now() + + train_start = time_start.strftime("%Y%m%d_%H%M%S") + + set_seed(seed) + + if not osp.exists(model_dir): + os.makedirs(model_dir) + + batch_size = batch_size + + val_batch_size = 1 + val_num_workers = 0 + + # -- early stopping flag + patience = patience + counter = 0 + + # 데이터셋 + normal_train_dataset = NewNormalVMAE( + is_train=1, + model_size=model_size, + root=normal_root_dir, + num_segments=num_segments, + l2_norm=use_l2norm, + ) + # 800개 + normal_valid_dataset = NewNormalVMAE( + is_train=0, + model_size=model_size, + root=normal_root_dir, + num_segments=num_segments, + l2_norm=use_l2norm, + ) + # 149개 + + normal_train_loader = DataLoader( + dataset=normal_train_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True, + num_workers=num_workers, + ) + + normal_valid_loader = DataLoader( + dataset=normal_valid_dataset, + batch_size=val_batch_size, + shuffle=False, + num_workers=val_num_workers, + ) + + abnormal_train_dataset = NewAbnormalVMAE( + is_train=1, + model_size=model_size, + root=abnormal_root_dir, + label_root=label_dir, + num_segments=num_segments, + l2_norm=use_l2norm, + ) + # 809개 + abnormal_valid_dataset = NewAbnormalVMAE( + is_train=0, + model_size=model_size, + root=abnormal_root_dir, + label_root=label_dir, + num_segments=num_segments, + gt_thr=gt_thr, + l2_norm=use_l2norm, + ) + # 140개 + + abnormal_train_loader = DataLoader( + dataset=abnormal_train_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True, + num_workers=num_workers, + ) + + abnormal_valid_loader = DataLoader( + dataset=abnormal_valid_dataset, batch_size=val_batch_size, shuffle=False, num_workers=val_num_workers + ) + + data_load_end = datetime.now() + data_load_time = data_load_end - time_start + data_load_time = str(data_load_time).split(".")[0] + print(f"==>> {model_size} data_load_time: {data_load_time}") + + # Initialize the model + model = WSAD( + input_size=len_feature, + ratio_sample=ratio_sample, + ratio_batch=ratio_batch, + ratios=ratios, + kernel_sizes=kernel_sizes, + ) + + load_dict = None + + if resume_name: + load_dict = torch.load(osp.join(model_dir, f"{resume_name}.pth"), map_location="cpu") + model.load_state_dict(load_dict["model_state_dict"]) + + model.to(device) + + # optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + # 1e-6 => 0.0010000000474974513 + optimizer = torch.optim.Adam( + model.parameters(), lr=learning_rate, betas=(0.9, 0.999), weight_decay=weight_decay + ) + # optimizer = torch.optim.AdamW( + # model.parameters(), lr=learning_rate, betas=(0.9, 0.999), weight_decay=weight_decay + # ) + # optimizer = torch.optim.Adagrad(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[1000, 1500], gamma=0.5) + + if resume_name: + optimizer.load_state_dict(load_dict["optimizer_state_dict"]) + scheduler.load_state_dict(load_dict["scheduler_state_dict"]) + # scaler.load_state_dict(load_dict["scaler_state_dict"]) + + criterion = nn.BCELoss() + MPP_criterion = LossComputer(w_normal=w_normal, w_mpp=w_mpp) + + print(f"Start training..") + + wandb.init( + project="VAD", + entity="pao-kim-si-woong", + config={ + "lr": learning_rate, + "dataset": "무인매장", + "n_epochs": max_epoch, + "loss": "MPP", + "notes": "VAD 실험", + }, + name=wandb_run_name + "_" + train_start, + mode=wandb_mode, + ) + + wandb.watch((model,)) + + best_loss = np.inf + best_auc = 0 + + total_batches = len(abnormal_train_loader) + + for epoch in range(max_epoch): + model.train() + + epoch_start = datetime.now() + + epoch_MPP_loss = 0 + epoch_norm_loss = 0 + epoch_MPP_and_norm_loss = 0 + + epoch_abnormal_max = 0 + epoch_abnormal_mean = 0 + epoch_normal_max = 0 + epoch_normal_mean = 0 + + for step, abnormal_input in tqdm( + enumerate(abnormal_train_loader), + total=total_batches, + ): + if step % len(normal_train_loader) == 0: + norm_train_iter = iter(normal_train_loader) + # 중복 추출하더라도 정상, 이상 영상 1대1 대응 loop 끝까지 유지 + + normal_input = next(norm_train_iter) + + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + input = torch.cat((normal_input, abnormal_input), dim=1) + # @@@@ BN-WVAD는 정상 영상 먼저 @@@@ + # inputs는 (batch_size, 2 * num_segments, 710) + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + + # batch_size = input.shape[0] + + input = input.to(device) + + optimizer.zero_grad() + + pred_result = model(input, flag="Train") + # pred_result["pre_normal_scores"]: normal_scores[0 : b // 2], + # pred_result["bn_results"]: bn_results, + # pred_result["normal_scores"]: normal_scores, + # pred_result["scores"]: distance_sum * normal_scores, + + pred = pred_result["scores"].view(-1, 1) + # => pred는 (batch_size * 2 * num_segments, 1) + + MPP_and_norm_loss, loss_dict = MPP_criterion(pred_result) + + MPP_and_norm_loss.backward() + + # loss.backward() + optimizer.step() + with torch.no_grad(): + pred_n = pred.view(batch_size, 2, num_segments)[:, 0, :] + pred_a = pred.view(batch_size, 2, num_segments)[:, 1, :] + + pred_n_max = torch.mean(torch.max(pred_n, dim=-1)[0]) + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + + pred_n_mean = torch.mean(pred_n) + pred_a_mean = torch.mean(pred_a) + + epoch_MPP_loss += loss_dict["mpp_loss"].item() + epoch_norm_loss += loss_dict["normal_loss"].item() + epoch_MPP_and_norm_loss += MPP_and_norm_loss.item() + + epoch_normal_max += pred_n_max.item() + epoch_normal_mean += pred_n_mean.item() + epoch_abnormal_max += pred_a_max.item() + epoch_abnormal_mean += pred_a_mean.item() + + epoch_mean_MPP_loss = epoch_MPP_loss / total_batches + epoch_mean_norm_loss = epoch_norm_loss / total_batches + epoch_mean_MPP_and_norm_loss = epoch_MPP_and_norm_loss / total_batches + + epoch_mean_normal_max = epoch_normal_max / total_batches + epoch_mean_normal_mean = epoch_normal_mean / total_batches + epoch_mean_abnormal_max = epoch_abnormal_max / total_batches + epoch_mean_abnormal_mean = epoch_abnormal_mean / total_batches + + train_end = datetime.now() + train_time = train_end - epoch_start + train_time = str(train_time).split(".")[0] + print(f"==>> epoch {epoch+1} train_time: {train_time}") + print( + f"MPP_loss: {round(epoch_mean_MPP_loss,4)} norm_loss: {round(epoch_mean_norm_loss,4)} MPP+norm_loss: {round(epoch_mean_MPP_and_norm_loss,4)}" + ) + print(f"==>> abnormal_max_mean: {epoch_mean_abnormal_max} abnormal_mean: {epoch_mean_abnormal_mean}") + print(f"==>> normal_max_mean: {epoch_mean_normal_max} normal_mean: {epoch_mean_normal_mean}") + + if (epoch + 1) % save_interval == 0: + + ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_latest.pth") + + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + "optimizer_state_dict": optimizer.state_dict(), + "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + } + + torch.save(states, ckpt_fpath) + + # validation 주기에 따라 loss를 출력하고 best model을 저장합니다. + if (epoch + 1) % val_interval == 0: + + print(f"Start validation #{epoch+1:2d}") + model.eval() + + with torch.no_grad(): + total_loss = 0 + + total_n_corrects = 0 + + total_ab_n_corrects = 0 + + total_fpr = 0 + total_tpr = 0 + total_bthr = 0 + total_auc = 0 + total_ap = 0 + + total_ab_fpr = 0 + total_ab_tpr = 0 + total_ab_bthr = 0 + total_ab_auc = 0 + total_ab_ap = 0 + + error_count = 0 + + total_abnormal_max = 0 + total_abnormal_mean = 0 + total_normal_max = 0 + total_normal_mean = 0 + + norm_valid_iter = iter(normal_valid_loader) + # iterator를 여기서 매번 새로 할당해줘야 iterator가 다시 처음부터 작동 + + for step, abnormal_inputs in tqdm( + enumerate(abnormal_valid_loader), total=len(abnormal_valid_loader) + ): + normal_inputs = next(norm_valid_iter) + + normal_input, normal_gt = normal_inputs + # (val_batch_size, num_segments, 710), (val_batch_size, num_segments) + abnormal_input, abnormal_gt = abnormal_inputs + # (val_batch_size, num_segments, 710), (val_batch_size, num_segments) + + inputs = torch.cat((normal_input, abnormal_input), dim=1) + gts = torch.cat((normal_gt, abnormal_gt), dim=1) + # inputs는 (val_batch_size, 2 * num_segments, 710), gts는 (val_batch_size, 2 * num_segments) + + inputs = inputs.to(device) + # (val_batch_size, 2 * num_segments, 710) + gts = gts.view(-1, 1).to(device) + # (val_batch_size * 2 * num_segments, 1) + + pred_result = model(inputs, flag="Eval_MPP") + # pred_result["normal_scores"]: normal_scores, + # pred_result["scores"]: distance_sum * normal_scores, + # breakpoint() + pred_acc = pred_result["normal_scores"].view(-1, 1) + pred = pred_result["scores"].view(-1, 1) + # pred는(batch_size * 2 * num_segments, 1) + + val_loss = criterion(pred_acc, gts) + + pred_n = pred.view(val_batch_size, 2, num_segments)[:, 0, :] + pred_a = pred.view(val_batch_size, 2, num_segments)[:, 1, :] + + pred_n_max = torch.mean(torch.max(pred_n, dim=-1)[0]) + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + + pred_n_mean = torch.mean(pred_n) + pred_a_mean = torch.mean(pred_a) + + pred_correct = pred > dist_thr + gts_correct = gts # > gt_thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + ab_corrects = torch.sum(pred_correct[num_segments:]).item() + + pred_np = (pred.squeeze()).detach().cpu().numpy() + gts_np = (gts.squeeze()).detach().cpu().numpy() + # pred_np, gts_np 둘다 (batch_size * 2 * num_segments) + + try: + # auc = roc_auc_score(y_true=gt_np, y_score=pred_np) + # auc = roc_auc_score(y_true=gt_np, y_score=pred) + + fpr, tpr, cut = roc_curve(y_true=gts_np, y_score=pred_np) + precision, recall, cut2 = precision_recall_curve(gts_np, pred_np) + + auc = sklearn.metrics.auc(fpr, tpr) + ap = sklearn.metrics.auc(recall, precision) + + diff = tpr - fpr + diff_idx = np.argmax(diff) + best_thr = cut[diff_idx] + + pred_positive = pred_np > dist_thr + TP_and_FN = pred_positive[gts_np > 0.9] + FP_and_TN = pred_positive[gts_np < 0.1] + + total_fpr += np.sum(FP_and_TN) / len(FP_and_TN) + total_tpr += np.sum(TP_and_FN) / len(TP_and_FN) + total_bthr += best_thr if diff_idx != 0 else 1 + + total_auc += auc + total_ap += ap + total_n_corrects += corrects / (num_segments * 2) + + ab_fpr, ab_tpr, ab_cut = roc_curve( + y_true=gts_np[num_segments:], y_score=pred_np[num_segments:] + ) + ab_precision, ab_recall, ab_cut2 = precision_recall_curve( + gts_np[num_segments:], pred_np[num_segments:] + ) + + ab_auc = sklearn.metrics.auc(ab_fpr, ab_tpr) + ab_ap = sklearn.metrics.auc(ab_recall, ab_precision) + + ab_diff = ab_tpr - ab_fpr + ab_diff_idx = np.argmax(ab_diff) + ab_best_thr = ab_cut[ab_diff_idx] + + ab_pred_positive = pred_positive[num_segments:] + ab_TP_and_FN = ab_pred_positive[gts_np[num_segments:] > 0.9] + ab_FP_and_TN = ab_pred_positive[gts_np[num_segments:] < 0.1] + + total_ab_fpr += np.sum(ab_FP_and_TN) / len(ab_FP_and_TN) + total_ab_tpr += np.sum(ab_TP_and_FN) / len(ab_TP_and_FN) + total_ab_bthr += ab_best_thr if ab_diff_idx != 0 else 1 + + total_ab_auc += ab_auc + total_ab_ap += ab_ap + total_ab_n_corrects += ab_corrects / (num_segments) + + total_loss += val_loss.item() + + total_normal_max += pred_n_max.item() + total_normal_mean += pred_n_mean.item() + total_abnormal_max += pred_a_max.item() + total_abnormal_mean += pred_a_mean.item() + + except ValueError: + # print( + # "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." + # ) + # total_auc += 0 + error_count += 1 + # print("gt가 전부 0인 abnormal 영상 있음") + + val_mean_loss = total_loss / (len(abnormal_valid_loader) - error_count) + + val_fpr = total_fpr / (len(abnormal_valid_loader) - error_count) + val_tpr = total_tpr / (len(abnormal_valid_loader) - error_count) + val_bthr = total_bthr / (len(abnormal_valid_loader) - error_count) + val_auc = total_auc / (len(abnormal_valid_loader) - error_count) + val_ap = total_ap / (len(abnormal_valid_loader) - error_count) + val_accuracy = total_n_corrects / ((len(abnormal_valid_loader) - error_count)) + + val_ab_fpr = total_ab_fpr / (len(abnormal_valid_loader) - error_count) + val_ab_tpr = total_ab_tpr / (len(abnormal_valid_loader) - error_count) + val_ab_bthr = total_ab_bthr / (len(abnormal_valid_loader) - error_count) + val_ab_auc = total_ab_auc / (len(abnormal_valid_loader) - error_count) + val_ab_ap = total_ab_ap / (len(abnormal_valid_loader) - error_count) + val_ab_accuracy = total_ab_n_corrects / ((len(abnormal_valid_loader) - error_count)) + + val_mean_normal_max = total_normal_max / (len(abnormal_valid_loader) - error_count) + val_mean_normal_mean = total_normal_mean / (len(abnormal_valid_loader) - error_count) + val_mean_abnormal_max = total_abnormal_max / (len(abnormal_valid_loader) - error_count) + val_mean_abnormal_mean = total_abnormal_mean / (len(abnormal_valid_loader) - error_count) + + if best_loss > val_mean_loss: + print(f"Best loss performance at epoch: {epoch + 1}, {best_loss:.4f} -> {val_mean_loss:.4f}") + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best.pth") + torch.save(states, best_ckpt_fpath) + best_loss = val_mean_loss + # counter = 0 + # else: + # counter += 1 + + if best_auc < val_auc: + str_to_keep = f"Best auc performance at epoch: {epoch + 1}, {best_auc:.4f} -> {val_auc:.4f}" + print(str_to_keep) + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best_auc.pth") + torch.save(states, best_ckpt_fpath) + best_auc = val_auc + counter = 0 + else: + counter += 1 + + new_wandb_metric_dict = { + "train_MPP_loss": epoch_mean_MPP_loss, + "train_norm_loss": epoch_mean_norm_loss, + "train_MPP+norm_loss": epoch_mean_MPP_and_norm_loss, + "valid_loss": val_mean_loss, + "valid_fpr": val_fpr, + "valid_tpr": val_tpr, + "valid_bthr": val_bthr, + "valid_auc": val_auc, + "valid_ap": val_ap, + "valid_accuracy": val_accuracy, + "valid_ab_fpr": val_ab_fpr, + "valid_ab_tpr": val_ab_tpr, + "valid_ab_bthr": val_ab_bthr, + "valid_ab_auc": val_ab_auc, + "valid_ab_ap": val_ab_ap, + "valid_ab_accuracy": val_ab_accuracy, + "learning_rate": scheduler.get_last_lr()[0], + "train_abnormal_max_mean": epoch_mean_abnormal_max, + "train_abnormal_mean": epoch_mean_abnormal_mean, + "train_normal_max_mean": epoch_mean_normal_max, + "train_normal_mean": epoch_mean_normal_mean, + "valid_abnormal_max_mean": val_mean_abnormal_max, + "valid_abnormal_mean": val_mean_abnormal_mean, + "valid_normal_max_mean": val_mean_normal_max, + "valid_normal_mean": val_mean_normal_mean, + } + + wandb.log(new_wandb_metric_dict) + + scheduler.step() + + epoch_end = datetime.now() + epoch_time = epoch_end - epoch_start + epoch_time = str(epoch_time).split(".")[0] + print(f"==>> epoch {epoch+1} time: {epoch_time}\nvalid_loss: {round(val_mean_loss,4)}") + # print( + # f"valid_n_MPP_loss: {round(val_n_mean_MPP_loss,4)} valid_n_norm_loss: {round(val_n_mean_norm_loss,4)} valid_n_MPP+norm_loss: {round(val_n_mean_MPP_and_norm_loss,4)}" + # ) + print(f"valid_fpr: {val_fpr} valid_tpr: {val_tpr} valid_bthr: {val_bthr}") + print(f"valid_auc: {val_auc:.4f} valid_ap: {val_ap:.4f} valid_accuracy: {val_accuracy:.2f}") + print(f"valid_ab_fpr: {val_ab_fpr} valid_ab_tpr: {val_ab_tpr} valid_ab_bthr: {val_ab_bthr}") + print( + f"valid_ab_auc: {val_ab_auc:.4f} valid_ab_ap: {val_ab_ap:.4f} valid_ab_accuracy: {val_ab_accuracy:.2f}" + ) + print( + f"==>> val_abnormal_max_mean: {val_mean_abnormal_max} val_abnormal_mean: {val_mean_abnormal_mean}" + ) + print(f"==>> val_normal_max_mean: {val_mean_normal_max} val_normal_mean: {val_mean_normal_mean}") + print(f"==>> error_count: {error_count}") + + if counter > patience: + print("Early Stopping...") + break + + time_end = datetime.now() + total_time = time_end - time_start + total_time = str(total_time).split(".")[0] + print(str_to_keep) + print(f"==>> total time: {total_time}") + + +def train_MIL( + normal_root_dir, + abnormal_root_dir, + label_dir, + model_dir, + model_name, + model_size, + device, + num_workers, + batch_size, + # val_num_workers, + # val_batch_size, + learning_rate, + weight_decay, + max_epoch, + val_interval, + save_interval, + w_normal, + w_mpp, + gt_thr, + dist_thr, + len_feature, + use_l2norm, + num_segments, + ratio_sample, + ratio_batch, + ratios, + kernel_sizes, + patience, + resume_name, + seed, + # mp, + # use_extra, + wandb_mode, + wandb_run_name, +): + + time_start = datetime.now() + + train_start = time_start.strftime("%Y%m%d_%H%M%S") + + set_seed(seed) + + if not osp.exists(model_dir): + os.makedirs(model_dir) + + batch_size = batch_size + + val_batch_size = 1 + val_num_workers = 0 + + # -- early stopping flag + patience = patience + counter = 0 + + # 데이터셋 + normal_train_dataset = NewNormalVMAE( + is_train=1, + model_size=model_size, + root=normal_root_dir, + num_segments=num_segments, + l2_norm=use_l2norm, + ) + # 800개 + normal_valid_dataset = NewNormalVMAE( + is_train=0, + model_size=model_size, + root=normal_root_dir, + num_segments=num_segments, + l2_norm=use_l2norm, + ) + # 149개 + + normal_train_loader = DataLoader( + dataset=normal_train_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True, + num_workers=num_workers, + ) + + normal_valid_loader = DataLoader( + dataset=normal_valid_dataset, + batch_size=val_batch_size, + shuffle=False, + num_workers=val_num_workers, + ) + + abnormal_train_dataset = NewAbnormalVMAE( + is_train=1, + model_size=model_size, + root=abnormal_root_dir, + label_root=label_dir, + num_segments=num_segments, + l2_norm=use_l2norm, + ) + # 809개 + abnormal_valid_dataset = NewAbnormalVMAE( + is_train=0, + model_size=model_size, + root=abnormal_root_dir, + label_root=label_dir, + num_segments=num_segments, + gt_thr=gt_thr, + l2_norm=use_l2norm, + ) + # 140개 + + abnormal_train_loader = DataLoader( + dataset=abnormal_train_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True, + num_workers=num_workers, + ) + + abnormal_valid_loader = DataLoader( + dataset=abnormal_valid_dataset, batch_size=val_batch_size, shuffle=False, num_workers=val_num_workers + ) + + data_load_end = datetime.now() + data_load_time = data_load_end - time_start + data_load_time = str(data_load_time).split(".")[0] + print(f"==>> {model_size} data_load_time: {data_load_time}") + + # Initialize the model + model = MILClassifier(drop_p=0.3) + + load_dict = None + + if resume_name: + load_dict = torch.load(osp.join(model_dir, f"{resume_name}.pth"), map_location="cpu") + model.load_state_dict(load_dict["model_state_dict"]) + + model.to(device) + + # optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + # 1e-6 => 0.0010000000474974513 + optimizer = torch.optim.Adagrad(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + # optimizer = torch.optim.AdamW( + # model.parameters(), lr=learning_rate, betas=(0.9, 0.999), weight_decay=weight_decay + # ) + # optimizer = torch.optim.Adagrad(model.parameters(), lr=learning_rate, weight_decay=0.0010000000474974513) + + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[1000, 1500], gamma=0.5) + + if resume_name: + optimizer.load_state_dict(load_dict["optimizer_state_dict"]) + scheduler.load_state_dict(load_dict["scheduler_state_dict"]) + # scaler.load_state_dict(load_dict["scaler_state_dict"]) + + criterion = nn.BCELoss() + MIL_criterion = MIL + + print(f"Start training..") + + wandb.init( + project="VAD", + entity="pao-kim-si-woong", + config={ + "lr": learning_rate, + "dataset": "무인매장", + "n_epochs": max_epoch, + "loss": "MIL", + "notes": "VAD 실험", + }, + name=wandb_run_name + "_" + train_start, + mode=wandb_mode, + ) + + wandb.watch((model,)) + + best_loss = np.inf + best_auc = 0 + + total_batches = len(abnormal_train_loader) + + for epoch in range(max_epoch): + model.train() + + epoch_start = datetime.now() + + epoch_MIL_loss = 0 + + epoch_abnormal_max = 0 + epoch_abnormal_mean = 0 + epoch_normal_max = 0 + epoch_normal_mean = 0 + + for step, abnormal_input in tqdm( + enumerate(abnormal_train_loader), + total=total_batches, + ): + if step % len(normal_train_loader) == 0: + norm_train_iter = iter(normal_train_loader) + # 중복 추출하더라도 정상, 이상 영상 1대1 대응 loop 끝까지 유지 + + normal_input = next(norm_train_iter) + + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + input = torch.cat((abnormal_input, normal_input), dim=1) + # @@@@ MIL은 이상 영상 먼저 @@@@ + # inputs는 (batch_size, 2 * num_segments, 710) + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + + # batch_size = input.shape[0] + + input = input.to(device) + + optimizer.zero_grad() + + pred = model(input) + # pred는 (batch_size * 2 * num_segments, 1) + + MIL_loss = MIL_criterion(pred, batch_size, num_segments) + + MIL_loss.backward() + + # loss.backward() + optimizer.step() + with torch.no_grad(): + pred_a = pred.view(batch_size, 2, num_segments)[:, 0, :] + pred_n = pred.view(batch_size, 2, num_segments)[:, 1, :] + + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + pred_n_max = torch.mean(torch.max(pred_n, dim=-1)[0]) + + pred_a_mean = torch.mean(pred_a) + pred_n_mean = torch.mean(pred_n) + + epoch_MIL_loss += MIL_loss.item() + + epoch_normal_max += pred_n_max.item() + epoch_normal_mean += pred_n_mean.item() + epoch_abnormal_max += pred_a_max.item() + epoch_abnormal_mean += pred_a_mean.item() + + epoch_mean_MIL_loss = epoch_MIL_loss / total_batches + + epoch_mean_normal_max = epoch_normal_max / total_batches + epoch_mean_normal_mean = epoch_normal_mean / total_batches + epoch_mean_abnormal_max = epoch_abnormal_max / total_batches + epoch_mean_abnormal_mean = epoch_abnormal_mean / total_batches + + train_end = datetime.now() + train_time = train_end - epoch_start + train_time = str(train_time).split(".")[0] + print(f"==>> epoch {epoch+1} train_time: {train_time}") + print(f"MIL_loss: {round(epoch_mean_MIL_loss,4)}") + print(f"==>> abnormal_max_mean: {epoch_mean_abnormal_max} abnormal_mean: {epoch_mean_abnormal_mean}") + print(f"==>> normal_max_mean: {epoch_mean_normal_max} normal_mean: {epoch_mean_normal_mean}") + + if (epoch + 1) % save_interval == 0: + + ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_latest.pth") + + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + "optimizer_state_dict": optimizer.state_dict(), + "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + } + + torch.save(states, ckpt_fpath) + + # validation 주기에 따라 loss를 출력하고 best model을 저장합니다. + if (epoch + 1) % val_interval == 0: + + print(f"Start validation #{epoch+1:2d}") + model.eval() + + with torch.no_grad(): + total_loss = 0 + + total_n_corrects = 0 + + total_ab_n_corrects = 0 + + total_fpr = 0 + total_tpr = 0 + total_bthr = 0 + total_auc = 0 + total_ap = 0 + + total_ab_fpr = 0 + total_ab_tpr = 0 + total_ab_bthr = 0 + total_ab_auc = 0 + total_ab_ap = 0 + + error_count = 0 + + total_abnormal_max = 0 + total_abnormal_mean = 0 + total_normal_max = 0 + total_normal_mean = 0 + + norm_valid_iter = iter(normal_valid_loader) + # iterator를 여기서 매번 새로 할당해줘야 iterator가 다시 처음부터 작동 + + for step, abnormal_inputs in tqdm( + enumerate(abnormal_valid_loader), total=len(abnormal_valid_loader) + ): + normal_inputs = next(norm_valid_iter) + + normal_input, normal_gt = normal_inputs + # (val_batch_size, num_segments, 710), (val_batch_size, num_segments) + abnormal_input, abnormal_gt = abnormal_inputs + # (val_batch_size, num_segments, 710), (val_batch_size, num_segments) + + inputs = torch.cat((abnormal_input, normal_input), dim=1) + gts = torch.cat((abnormal_gt, normal_gt), dim=1) + # @@@@ MIL은 이상 영상 먼저 @@@@ + # inputs는 (val_batch_size, 2 * num_segments, 710), gts는 (val_batch_size, 2 * num_segments) + + inputs = inputs.to(device) + # (val_batch_size, 2 * num_segments, 710) + gts = gts.view(-1, 1).to(device) + # (val_batch_size * 2 * num_segments, 1) + + pred = model(inputs) + # pred는 (val_batch_size * 2 * num_segments, 1) + + val_loss = criterion(pred, gts) + + pred_a = pred.view(val_batch_size, 2, num_segments)[:, 0, :] + pred_n = pred.view(val_batch_size, 2, num_segments)[:, 1, :] + + pred_a_max = torch.mean(torch.max(pred_a, dim=-1)[0]) + pred_n_max = torch.mean(torch.max(pred_n, dim=-1)[0]) + + pred_a_mean = torch.mean(pred_a) + pred_n_mean = torch.mean(pred_n) + + pred_correct = pred > dist_thr + gts_correct = gts # > gt_thr + + pred_correct = pred_correct == gts_correct + corrects = torch.sum(pred_correct).item() + ab_corrects = torch.sum(pred_correct[:num_segments]).item() + # @@@@ MIL은 이상 영상 먼저 @@@@ + + pred_np = (pred.squeeze()).detach().cpu().numpy() + gts_np = (gts.squeeze()).detach().cpu().numpy() + # pred_np, gts_np 둘다 (batch_size * 2 * num_segments) + + try: + # auc = roc_auc_score(y_true=gt_np, y_score=pred_np) + # auc = roc_auc_score(y_true=gt_np, y_score=pred) + + fpr, tpr, cut = roc_curve(y_true=gts_np, y_score=pred_np) + precision, recall, cut2 = precision_recall_curve(gts_np, pred_np) + + auc = sklearn.metrics.auc(fpr, tpr) + ap = sklearn.metrics.auc(recall, precision) + + diff = tpr - fpr + diff_idx = np.argmax(diff) + best_thr = cut[diff_idx] + + pred_positive = pred_np > dist_thr + TP_and_FN = pred_positive[gts_np > 0.9] + FP_and_TN = pred_positive[gts_np < 0.1] + + total_fpr += np.sum(FP_and_TN) / len(FP_and_TN) + total_tpr += np.sum(TP_and_FN) / len(TP_and_FN) + total_bthr += best_thr if diff_idx != 0 else 1 + + total_auc += auc + total_ap += ap + total_n_corrects += corrects / (num_segments * 2) + + ab_fpr, ab_tpr, ab_cut = roc_curve( + y_true=gts_np[:num_segments], y_score=pred_np[:num_segments] + ) + # @@@@ MIL은 이상 영상 먼저 @@@@ + ab_precision, ab_recall, ab_cut2 = precision_recall_curve( + gts_np[:num_segments], pred_np[:num_segments] + ) + # @@@@ MIL은 이상 영상 먼저 @@@@ + + ab_auc = sklearn.metrics.auc(ab_fpr, ab_tpr) + ab_ap = sklearn.metrics.auc(ab_recall, ab_precision) + + ab_diff = ab_tpr - ab_fpr + ab_diff_idx = np.argmax(ab_diff) + ab_best_thr = ab_cut[ab_diff_idx] + + ab_pred_positive = pred_positive[:num_segments] + ab_TP_and_FN = ab_pred_positive[gts_np[:num_segments] > 0.9] + ab_FP_and_TN = ab_pred_positive[gts_np[:num_segments] < 0.1] + # @@@@ MIL은 이상 영상 먼저 @@@@ + + total_ab_fpr += np.sum(ab_FP_and_TN) / len(ab_FP_and_TN) + total_ab_tpr += np.sum(ab_TP_and_FN) / len(ab_TP_and_FN) + total_ab_bthr += ab_best_thr if ab_diff_idx != 0 else 1 + + total_ab_auc += ab_auc + total_ab_ap += ab_ap + total_ab_n_corrects += ab_corrects / (num_segments) + + total_loss += val_loss.item() + + total_normal_max += pred_n_max.item() + total_normal_mean += pred_n_mean.item() + total_abnormal_max += pred_a_max.item() + total_abnormal_mean += pred_a_mean.item() + + except ValueError: + # print( + # "ValueError: Only one class present in y_true. ROC AUC score is not defined in that case." + # ) + # total_auc += 0 + error_count += 1 + # print("gt가 전부 0인 abnormal 영상 있음") + + val_mean_loss = total_loss / (len(abnormal_valid_loader) - error_count) + + val_fpr = total_fpr / (len(abnormal_valid_loader) - error_count) + val_tpr = total_tpr / (len(abnormal_valid_loader) - error_count) + val_bthr = total_bthr / (len(abnormal_valid_loader) - error_count) + val_auc = total_auc / (len(abnormal_valid_loader) - error_count) + val_ap = total_ap / (len(abnormal_valid_loader) - error_count) + val_accuracy = total_n_corrects / ((len(abnormal_valid_loader) - error_count)) + + val_ab_fpr = total_ab_fpr / (len(abnormal_valid_loader) - error_count) + val_ab_tpr = total_ab_tpr / (len(abnormal_valid_loader) - error_count) + val_ab_bthr = total_ab_bthr / (len(abnormal_valid_loader) - error_count) + val_ab_auc = total_ab_auc / (len(abnormal_valid_loader) - error_count) + val_ab_ap = total_ab_ap / (len(abnormal_valid_loader) - error_count) + val_ab_accuracy = total_ab_n_corrects / ((len(abnormal_valid_loader) - error_count)) + + val_mean_normal_max = total_normal_max / (len(abnormal_valid_loader) - error_count) + val_mean_normal_mean = total_normal_mean / (len(abnormal_valid_loader) - error_count) + val_mean_abnormal_max = total_abnormal_max / (len(abnormal_valid_loader) - error_count) + val_mean_abnormal_mean = total_abnormal_mean / (len(abnormal_valid_loader) - error_count) + + if best_loss > val_mean_loss: + print(f"Best loss performance at epoch: {epoch + 1}, {best_loss:.4f} -> {val_mean_loss:.4f}") + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best.pth") + torch.save(states, best_ckpt_fpath) + best_loss = val_mean_loss + # counter = 0 + # else: + # counter += 1 + + if best_auc < val_auc: + str_to_keep = f"Best auc performance at epoch: {epoch + 1}, {best_auc:.4f} -> {val_auc:.4f}" + print(str_to_keep) + print(f"Save model in {model_dir}") + states = { + "epoch": epoch, + "model_name": model_name, + "model_state_dict": model.state_dict(), # 모델의 state_dict 저장 + # "optimizer_state_dict": optimizer.state_dict(), + # "scheduler_state_dict": scheduler.state_dict(), + # "scaler_state_dict": scaler.state_dict(), + # best.pth는 inference에서만 쓰기? + } + + best_ckpt_fpath = osp.join(model_dir, f"{model_name}_{train_start}_best_auc.pth") + torch.save(states, best_ckpt_fpath) + best_auc = val_auc + counter = 0 + else: + counter += 1 + + new_wandb_metric_dict = { + "train_MIL_loss": epoch_mean_MIL_loss, + "valid_loss": val_mean_loss, + "valid_fpr": val_fpr, + "valid_tpr": val_tpr, + "valid_bthr": val_bthr, + "valid_auc": val_auc, + "valid_ap": val_ap, + "valid_accuracy": val_accuracy, + "valid_ab_fpr": val_ab_fpr, + "valid_ab_tpr": val_ab_tpr, + "valid_ab_bthr": val_ab_bthr, + "valid_ab_auc": val_ab_auc, + "valid_ab_ap": val_ab_ap, + "valid_ab_accuracy": val_ab_accuracy, + "learning_rate": scheduler.get_last_lr()[0], + "train_abnormal_max_mean": epoch_mean_abnormal_max, + "train_abnormal_mean": epoch_mean_abnormal_mean, + "train_normal_max_mean": epoch_mean_normal_max, + "train_normal_mean": epoch_mean_normal_mean, + "valid_abnormal_max_mean": val_mean_abnormal_max, + "valid_abnormal_mean": val_mean_abnormal_mean, + "valid_normal_max_mean": val_mean_normal_max, + "valid_normal_mean": val_mean_normal_mean, + } + + wandb.log(new_wandb_metric_dict) + + scheduler.step() + + epoch_end = datetime.now() + epoch_time = epoch_end - epoch_start + epoch_time = str(epoch_time).split(".")[0] + print(f"==>> epoch {epoch+1} time: {epoch_time}\nvalid_loss: {round(val_mean_loss,4)}") + # print( + # f"valid_n_MPP_loss: {round(val_n_mean_MPP_loss,4)} valid_n_norm_loss: {round(val_n_mean_norm_loss,4)} valid_n_MPP+norm_loss: {round(val_n_mean_MPP_and_norm_loss,4)}" + # ) + print(f"valid_fpr: {val_fpr} valid_tpr: {val_tpr} valid_bthr: {val_bthr}") + print(f"valid_auc: {val_auc:.4f} valid_ap: {val_ap:.4f} valid_accuracy: {val_accuracy:.2f}") + print(f"valid_ab_fpr: {val_ab_fpr} valid_ab_tpr: {val_ab_tpr} valid_ab_bthr: {val_ab_bthr}") + print( + f"valid_ab_auc: {val_ab_auc:.4f} valid_ab_ap: {val_ab_ap:.4f} valid_ab_accuracy: {val_ab_accuracy:.2f}" + ) + print( + f"==>> val_abnormal_max_mean: {val_mean_abnormal_max} val_abnormal_mean: {val_mean_abnormal_mean}" + ) + print(f"==>> val_normal_max_mean: {val_mean_normal_max} val_normal_mean: {val_mean_normal_mean}") + print(f"==>> error_count: {error_count}") + + if counter > patience: + print("Early Stopping...") + break + + time_end = datetime.now() + total_time = time_end - time_start + total_time = str(total_time).split(".")[0] + print(str_to_keep) + print(f"==>> total time: {total_time}") + + +def main(args): + if (args.wandb_run_name).split("_")[0] == "BNWVAD": + train_BNWVAD(**args.__dict__) + else: + train_MIL(**args.__dict__) + + +if __name__ == "__main__": + args = parse_args() + + main(args) diff --git a/model/vmae/__init__.py b/model/vmae/__init__.py new file mode 100644 index 0000000..192cec4 --- /dev/null +++ b/model/vmae/__init__.py @@ -0,0 +1,31 @@ +from .modeling_finetune import ( + vit_base_patch16_224, + vit_giant_patch14_224, + vit_huge_patch16_224, + vit_large_patch16_224, + vit_small_patch16_224, +) +from .modeling_pretrain import ( + pretrain_videomae_base_patch16_224, + pretrain_videomae_giant_patch14_224, + pretrain_videomae_huge_patch16_224, + pretrain_videomae_large_patch16_224, + pretrain_videomae_small_patch16_224, +) + +from .classifier import MILClassifier +from .classifier import WSAD + +__all__ = [ + "pretrain_videomae_small_patch16_224", + "pretrain_videomae_base_patch16_224", + "pretrain_videomae_large_patch16_224", + "pretrain_videomae_huge_patch16_224", + "pretrain_videomae_giant_patch14_224", + "vit_small_patch16_224", + "vit_base_patch16_224", + "vit_large_patch16_224", + "vit_huge_patch16_224", + "vit_giant_patch14_224", + "MILClassifier", +] diff --git a/model/vmae/classifier.py b/model/vmae/classifier.py new file mode 100644 index 0000000..5ea01ec --- /dev/null +++ b/model/vmae/classifier.py @@ -0,0 +1,483 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from einops import rearrange + + +class MILClassifier(nn.Module): + def __init__(self, input_dim=710, drop_p=0.0): + super().__init__() + # self.embedding = Temporal(input_dim, 512) + # self.selfatt = Transformer(512, 2, 4, 128, 512, dropout=0) + self.classifier = nn.Sequential( + nn.Linear(input_dim, 512), + # nn.Linear(512, 512), + # nn.BatchNorm1d(512), + nn.ReLU(), + nn.Dropout(drop_p), + nn.Linear(512, 512), + # nn.BatchNorm1d(1024), + nn.ReLU(), + nn.Dropout(drop_p), + # nn.Linear(1024, 512), + # # nn.BatchNorm1d(512), + # nn.ReLU(), + # nn.Dropout(drop_p), + nn.Linear(512, 32), + # nn.BatchNorm1d(32), + nn.ReLU(), + nn.Dropout(drop_p), + nn.Linear(32, 1), + nn.Sigmoid(), + ) + + self.drop_p = drop_p + self.weight_init() + + def weight_init(self): + # for layer in self.classifier: + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_normal_(m.weight) + elif isinstance(m, nn.BatchNorm1d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def forward(self, x): + + # x = self.embedding(x) + # x = self.selfatt(x) + + x = x.view(-1, x.size(-1)) + + x = self.classifier(x) + + return x + + +class NormalHead(nn.Module): + def __init__(self, in_channel=512, ratios=[16, 32], kernel_sizes=[1, 1, 1]): + super(NormalHead, self).__init__() + self.ratios = ratios + # 기본값 [16, 32] + self.kernel_sizes = kernel_sizes + # 기본값 [1, 1, 1] + + self.build_layers(in_channel) + + def build_layers(self, in_channel): + ratio_1, ratio_2 = self.ratios + self.conv1 = nn.Conv1d( + in_channel, in_channel // ratio_1, self.kernel_sizes[0], 1, self.kernel_sizes[0] // 2 + ) + # stride는 1, padding은 kernel_size // 2로 두면 + # (input_length - kernel_size + 2 * (kernel_size // 2)) + 1 == input_length + # => 길이 유지 + self.bn1 = nn.BatchNorm1d(in_channel // ratio_1) + self.conv2 = nn.Conv1d( + in_channel // ratio_1, in_channel // ratio_2, self.kernel_sizes[1], 1, self.kernel_sizes[1] // 2 + ) + self.bn2 = nn.BatchNorm1d(in_channel // ratio_2) + self.conv3 = nn.Conv1d(in_channel // ratio_2, 1, self.kernel_sizes[2], 1, self.kernel_sizes[2] // 2) + self.act = nn.ReLU() + self.sigmoid = nn.Sigmoid() + + self.bns = [self.bn1, self.bn2] + + def forward(self, x): + """ + x: BN * C * T + return BN * C // 64 * T and BN * 1 * T + """ + outputs = [] + x = self.conv1(x) + outputs.append(x) + x = self.conv2(self.act(self.bn1(x))) + outputs.append(x) + x = self.sigmoid(self.conv3(self.act(self.bn2(x)))) + outputs.append(x) + return outputs + + +def pair(t): + return t if isinstance(t, tuple) else (t, t) + + +class PreNorm(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.fn = fn + + def forward(self, x, **kwargs): + return self.fn(self.norm(x), **kwargs) + + +class FeedForward(nn.Module): + def __init__(self, dim, hidden_dim, dropout=0.0): + super().__init__() + self.net = nn.Sequential( + nn.Linear(dim, hidden_dim), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(hidden_dim, dim), + nn.Dropout(dropout), + ) + + def forward(self, x): + return self.net(x) + + +class Attention(nn.Module): + def __init__(self, dim, heads=8, dim_head=64, dropout=0.0): + super().__init__() + inner_dim = dim_head * heads + project_out = not (heads == 1 and dim_head == dim) + + self.heads = heads + self.scale = dim_head**-0.5 + + self.attend = nn.Softmax(dim=-1) + self.to_qkv = nn.Linear(dim, inner_dim * 4, bias=False) + + self.to_out = ( + nn.Sequential(nn.Linear(2 * inner_dim, dim), nn.Dropout(dropout)) + if project_out + else nn.Identity() + ) + + def forward(self, x): + b, n, d = x.size() + qkvt = self.to_qkv(x).chunk(4, dim=-1) + q, k, v, t = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.heads), qkvt) + + dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale + + attn1 = self.attend(dots) + + tmp_ones = torch.ones(n).cuda() + tmp_n = torch.linspace(1, n, n).cuda() + tg_tmp = torch.abs(tmp_n * tmp_ones - tmp_n.view(-1, 1)) + attn2 = torch.exp(-tg_tmp / torch.exp(torch.tensor(1.0))) + attn2 = (attn2 / attn2.sum(-1)).unsqueeze(0).unsqueeze(1).repeat(b, self.heads, 1, 1) + + out = torch.cat([torch.matmul(attn1, v), torch.matmul(attn2, t)], dim=-1) + out = rearrange(out, "b h n d -> b n (h d)") + return self.to_out(out) + + +class Transformer(nn.Module): + def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.0): + super().__init__() + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + PreNorm(dim, Attention(dim, heads=heads, dim_head=dim_head, dropout=dropout)), + PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout)), + ] + ) + ) + + def forward(self, x): + for attn, ff in self.layers: + x = attn(x) + x + x = ff(x) + x + return x + + +class Temporal(nn.Module): + # Temporal convolutional network + def __init__(self, input_size, out_size): + super(Temporal, self).__init__() + self.conv_1 = nn.Sequential( + nn.Conv1d(in_channels=input_size, out_channels=out_size, kernel_size=3, stride=1, padding=1), + nn.ReLU(), + ) + + def forward(self, x): + # x는 (batch * n crops, t snippets, d feature dim) + # 영상 1개를 t개의 snippet(토막)으로 나누고 각 snippet은 d 차원 feature 벡터 + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # permute를 하지 않고 conv1d를 하면 t가 채널축 + # => conv 특성상 1 entry 계산할 때 (t, 3)사이즈 필터를 곱해서 계산 + # => 이 entry는 각 snippet의 feature는 3개만 보지만 시간축(t)으로는 영상 전체를 보게 된다 + # => 마치 과거, 현재, 미래 정보를 다보고 계산하는 것과 마찬가지 + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + x = x.permute(0, 2, 1) + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # permute를 하게 되면 (batch * n crops, d feature dim, t snippets) + # 이제 conv1d를 하면 (d, 3) 사이즈 필터를 곱해서 계산한다 + # => 바로 전, 현재, 바로 다음(또는 전전, 전, 현재) 시간의 영상 snippet 3개만 보고 각 snippet의 feature들은 전부 보게 된다 + # +@ 영상을 나누는 snippet(segment) 개수 유지(kernel_size=3, stride=1, padding=1) + # +@ 이제 각 snippet의 feature dimension수를 조절 가능(필터 개수로 조절) + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + x = self.conv_1(x) + x = x.permute(0, 2, 1) + return x + + +class WSAD(nn.Module): + def __init__( + self, input_size, ratio_sample=0.2, ratio_batch=0.4, ratios=[16, 32], kernel_sizes=[1, 1, 1] + ): + super().__init__() + # self.flag = flag + + self.ratio_sample = ratio_sample + # 기본값 0.2 + self.ratio_batch = ratio_batch + # 기본값 0.4 + + self.ratios = ratios + # 기본값 [16, 32] + self.kernel_sizes = kernel_sizes + # 기본값 [1, 1, 1] + + self.normal_head = NormalHead(in_channel=512, ratios=ratios, kernel_sizes=kernel_sizes) + self.embedding = Temporal(input_size, 512) + self.selfatt = Transformer(512, 2, 4, 128, 512, dropout=0) + # embedding + selfatt은 논문의 feature enhancer + # embedding은 feature 차원을 permute + conv1d를 이용해 512로 변경 + # selfatt는 transformer계열 enhancer + self.step = 0 + + def get_normal_scores(self, x, ncrops=None): + # x는 (batch * n crops, segment 개수, feature 차원 = 512(논문)) + new_x = x.permute(0, 2, 1) + # conv1d에 넣기전에 (batch * n crops, feature 차원, segment 개수)로 변경 + + outputs = self.normal_head(new_x) + # normal_head는 conv1d - bn - relu - conv1d - bn - relu - conv1d - sig 3층 구조 + # outputs는 normal_head 안의 3개의 conv1d output을 담은 list (마지막 output은 conv1d + sig output) + normal_scores = outputs[-1] + xhs = outputs[:-1] + + if ncrops: + b = normal_scores.shape[0] // ncrops + normal_scores = normal_scores.view(b, ncrops, -1).mean(1) + # (batch_size, t snippets) + + return xhs, normal_scores + + def get_mahalanobis_distance(self, feats, anchor, var, ncrops=None): + # 첫번째는 feat는 (batch_size * n crops, 512 // 16, t snippets) + # 두번째는 (batch_size * n crops, 512 // 32, t snippets) + # BN은 각 feature(채널 축)별 batch*h*w개 평균, 분산 계산 + # => (b, c, h*w) -> (c) + # => None으로 unsqueeze해서 (1, c, 1)로 변경 + distance = torch.sqrt(torch.sum((feats - anchor[None, :, None]) ** 2 / var[None, :, None], dim=1)) + # (x - m)^2/var -> torch.sum(dim=1)로 각배치 안의 각 토막(segment)별로 값 존재 (b, t) + # sqrt후에도 사이즈 그대로 (b, t) + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # var가 전부 1이면 distance는 BN running mean vector와 각 토막의 feature vector 간의 차이 벡터의 L2 norm 길이가 된다 + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + + if ncrops: + bs = distance.shape[0] // ncrops + # b x t + distance = distance.view(bs, ncrops, -1).mean(dim=1) + # (batch_size, n crops, t snippets)을 dim=1로 평균 => 동일 영상 10개 crop들의 결과를 평균 + # => (batch_size, t snippets) + return distance + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # 배치 내의 각 영상의 각 토막 feature 벡터가 + # 데이터셋 분포 내 모든 영상의 모든 토막의 feature(512 // 16 또는 512 // 32 차원) 벡터들의 평균인 벡터(running_mean으로 추정)와 + # 얼마나 다른지 알려주는 mahalanobis 거리 계산 + # 데이터 분포내의 모든 토막 feature 벡터의 평균이고 정상토막의 비중이 이상토막의 비중보다 압도적으로 크기 때문에 + # 이 평균 벡터는 정상 토막의 기준처럼 사용 가능(anchor) + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + + def pos_neg_select(self, feats, distance, ncrops): + batch_select_ratio = self.ratio_batch + # 기본값 0.4 + sample_select_ratio = self.ratio_sample + # 기본값 0.2 + bs, c, t = feats.shape + # 첫번째는 (batch_size * n crops, 512 // 16, t snippets) + # 두번째는 (batch_size * n crops, 512 // 32, t snippets) + select_num_sample = int(t * sample_select_ratio) + # sample-level selection(SLS)은 20% + select_num_batch = int(bs // 2 * t * batch_select_ratio) + # 데이터는 torch.cat((정상영상, 이상영상), dim=0)으로 정상영상 배치 뒤에 이상영상 배치가 붙어있음 + # => bs // 2가 실제 batch_size * n crops 개수 + # => batch-level selection(BLS)은 (bs // 2) * t개 중 40% + # ==> 40 // 2 해서 사실상 SLS와 동일 비율로 배치 하나당 20% + + feats = feats.view(bs, ncrops, c, t).mean(dim=1) # b x c x t + # 동일 영상에서 나온 10개 crop들 결과 평균 + # => (batch_size, c features, t snippets) + nor_distance = distance[: bs // 2] # b x t + # distance는 10개 crop들을 이미 평균내고 (batch_size, t snippets) + # 그리고 배치 앞 절반은 정상영상 배치 => (n_batch_size = batch_size // 2, t snippets) + nor_feats = feats[: bs // 2].permute(0, 2, 1) # b x t x c + # 정상부분 앞 절반만 가져와 permute => (n_batch_size, t snippets, c features) + abn_distance = distance[bs // 2 :] # b x t + # 배치 뒤 절반은 이상영상 배치 (a_batch_size = batch_size // 2, t snippets) + abn_feats = feats[bs // 2 :].permute(0, 2, 1) # b x t x c + # (a_batch_size, t snippets, c features) + abn_distance_flatten = abn_distance.reshape(-1) + # (a_batch_size * t snippets) + abn_feats_flatten = abn_feats.reshape(-1, c) + # (a_batch_size * t snippets, c features) + + mask_select_abnormal_sample = torch.zeros_like(abn_distance, dtype=torch.bool) + # (a_batch_size, t snippets) + topk_abnormal_sample = torch.topk(abn_distance, select_num_sample, dim=-1)[1] + # torch.topk(abn_distance, select_num_sample, dim=-1)는 top k개 value와 그 value들 indices를 담고 있다 + # value와 indices 둘 다 (a_batch_size, top K = select_num_sample) 형태 + # => [1]로 indices만 가져오기 + mask_select_abnormal_sample.scatter_( + dim=1, + index=topk_abnormal_sample, + src=torch.full_like(topk_abnormal_sample, True, dtype=torch.bool), + ) + # (a_batch_size, t snippets) 형태이고 True는 a_batch_size * select_num_sample개이고 나머지는 False + # (top k에 속하는 index 자리만 True, 나머지는 False) + # scatter는 gather의 reverse operation + + mask_select_abnormal_batch = torch.zeros_like(abn_distance_flatten, dtype=torch.bool) + # (a_batch_size * t snippets) + topk_abnormal_batch = torch.topk(abn_distance_flatten, select_num_batch, dim=-1)[1] + # (a_batch_size * select_num_batch) + # top K = select_num_batch 개 indices + mask_select_abnormal_batch.scatter_( + dim=0, + index=topk_abnormal_batch, + src=torch.full_like(topk_abnormal_batch, True, dtype=torch.bool), + ) + # (a_batch_size * t snippets) + + mask_select_abnormal = mask_select_abnormal_batch | mask_select_abnormal_sample.reshape(-1) + # SLS와 BLS를 or 연산 | 으로 합쳐서 논문의 Sample-Batch Selection(SBS) + select_abn_feats = abn_feats_flatten[mask_select_abnormal] + # mask_select_abnormal는 (a_batch_size * t snippets)개 중 num_select_abnormal개만 True고 나머진 False + # abn_feats_flatten의 (a_batch_size * t snippets, c features)에서 mask_select_abnormal를 indices로 쓰면 + # (num_select_abnormal, c feature) 형태가 된다 + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # select_abn_feats는 SLS와 BLS를 합쳐 SBS를 만드는 과정에서 상위 ~%에 들었다는 정보만 남고 distance 상위 몇번째인지 순서 정보가 날아간다 + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + + num_select_abnormal = torch.sum(mask_select_abnormal) + # SBS 추출 개수 + + k_nor = int(num_select_abnormal / (bs // 2)) + 1 + # 이상영상 배치에서 SBS로 선택한 개수 / 배치내 영상 개수 == 1 배치 당 평균 선택 개수 + # + 1을 해주어서 정상 영상에서 선택된 토막(snippets)개수가 이상 영상 선택 토막 개수보다 크게 설정 + topk_normal_sample = torch.topk(nor_distance, k_nor, dim=-1)[1] + # nor_distance는 (n_batch_size, t snippets) + # topk_normal_sample는 각 영상의 t개 토막 중 상위 k_nor개의 indices + # => (n_batch_size, k_nor) + select_nor_feats = torch.gather(nor_feats, 1, topk_normal_sample[..., None].expand(-1, -1, c)) + # nor_feats는 (n_batch_size, t snippets, c features) + # gather의 index는 input과 차원수가 같아야하므로 None으로 (n_batch_size, k_nor, 1), expand로 (n_batch_size, k_nor, c) 형태로 변경 + # expand : Returns a new view of the self tensor with singleton dimensions expanded to a larger size. + # gather dimension이 1 => select_nor_feats[i][j][k] = nor_feats[i][topk_normal_sample[i][j][k]][k] + # select_nor_feats는 (n_batch_size, k_nor, c) 형태 (gather는 index와 output 형태가 동일) + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # select_abn_feats와 다르게 select_nor_feats는 크기 순서를 지우지 않고 gather를 써서 dim=1 방향으로 nor_distance 값 내림차순 + # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + select_nor_feats = select_nor_feats.permute(1, 0, 2).reshape(-1, c) + # (k_nor, n_batch_size, c)로 바꾼 후 reshape로 (k_nor * n_batch_size, c) 형태 + select_nor_feats = select_nor_feats[:num_select_abnormal] + # k_nor * n_batch_size는 num_select_abnormal보다 크다 => out of index 에러 안 일어남 + # select_nor_feats는 최종적으로 (num_select_abnormal, c feature) 형태 + + return select_nor_feats, select_abn_feats + + def forward(self, x, flag="Eval"): + if len(x.size()) == 4: + b, n, t, d = x.size() + # 실험에 사용한 I3D UCF-Crime feature는 하나의 영상을 중앙, 4코너 + 중앙, 4코너 거울상 = 10개 crop으로 증강해서 계산 + # => batch 개수, n crop 개수, t 토막(snippet, segment) 개수, d snippet당 feature 차원수 + + x = x.reshape(b * n, t, d) + else: + b, t, d = x.size() + n = 1 + x = self.embedding(x) + x = self.selfatt(x) + # feature enhancer를 지난 feature의 차원수 d == 512(논문) + + normal_feats, normal_scores = self.get_normal_scores(x, n) + # normal_head는 conv1d - bn - relu - conv1d - bn - relu - conv1d - sig 3층 구조 + # normal_feats는 [첫 conv1d output, 두번째 conv1d output] + # => (batch_size * n crops, 512 // 16, t snippets), (batch_size * n crops, 512 // 32, t snippets) 형태 + # normal_scores는 마지막 conv1d - sig output => (batch_size, t snippets) 형태 (n crops는 평균-> 1) + + anchors = [bn.running_mean for bn in self.normal_head.bns] + variances = [bn.running_var for bn in self.normal_head.bns] + # conv1d output 바로 뒤 bn은 conv1d output 전체 분포 추정 평균, 분산을 담고 있다 + # 두개의 bn => 첫 conv1d output, 두번째 conv1d output 추정 평균, 분산 + + distances = [ + self.get_mahalanobis_distance(normal_feat, anchor, var, ncrops=n) + for normal_feat, anchor, var in zip(normal_feats, anchors, variances) + ] + # list안의 각 distance는 (batch_size, t snippets) 형태 + + if flag == "Train": + + select_normals = [] + select_abnormals = [] + for feat, distance in zip(normal_feats, distances): + select_feat_normal, select_feat_abnormal = self.pos_neg_select(feat, distance, n) + # select_feat_normal, select_feat_abnormal 둘다 (num_select_abnormal, c feature) 형태 + select_normals.append(select_feat_normal[..., None]) + select_abnormals.append(select_feat_abnormal[..., None]) + # 두 정상, 이상 리스트 모두 feature 두개씩 + # 첫번째는 (num_select_abnormal, 512 // 16 feature, 1) + # 두번째는 (num_select_abnormal, 512 // 32 feature, 1) + + bn_results = dict( + anchors=anchors, + variances=variances, + select_normals=select_normals, + select_abnormals=select_abnormals, + ) + # breakpoint() + distance_sum = sum(distances) + + return { + "pre_normal_scores": normal_scores[0 : b // 2], + # classifier 학습에 사용되는 normal loss 계산에는 label 노이즈가 없는 normal 영상만 사용 + # (label noise: MIL은 비디오 단위 라벨링만 있음 + # => 이상 영상안의 normal snippet을 abnormal snippet으로 판단 하는 등의 noise 발생 가능) + # 정상 영상의 snippet들은 무조건 정상 => 정상 영상 하나의 t snippets의 scores => t 차원 score 벡터 + # ==> 이 t 차원 score 벡터의 L2 norm 값 * n_batch_size 개 정상 영상 == normal loss + # ==> L2 norm인 normal loss가 작아지기 위해서 정상 영상 snippet들의 예측 score가 작아지는 방향으로 학습 + # 논문 3.4 확인 + "bn_results": bn_results, + # mpp loss 계산에 사용 + # 논문 3.2 확인 + # @@@@@@@@@@@@@@@@@@@@@@@@@ + # bce loss를 위해 추가 + "normal_scores": normal_scores, + "scores": distance_sum * normal_scores, + } + elif flag == "Train_extra": + distance_sum = sum(distances) + # (batch_size, t snippets) 형태인 distance들 sum + + return { + "normal_scores": normal_scores, + "scores": distance_sum * normal_scores, + } + elif flag == "Eval_MPP": + + distance_sum = sum(distances) + # (batch_size, t snippets) 형태인 distance들 sum + + return { + "normal_scores": normal_scores, + "scores": distance_sum * normal_scores, + } + else: + + distance_sum = sum(distances) + # (batch_size, t snippets) 형태인 distance들 sum + + return distance_sum * normal_scores + # normal_scores도 (batch_size, t snippets) 형태 diff --git a/model/vmae/modeling_finetune.py b/model/vmae/modeling_finetune.py new file mode 100644 index 0000000..b63b475 --- /dev/null +++ b/model/vmae/modeling_finetune.py @@ -0,0 +1,529 @@ +# -------------------------------------------------------- +# Based on BEiT, timm, DINO and DeiT code bases +# https://github.com/microsoft/unilm/tree/master/beit +# https://github.com/rwightman/pytorch-image-models/tree/master/timm +# https://github.com/facebookresearch/deit +# https://github.com/facebookresearch/dino +# --------------------------------------------------------' +from functools import partial + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from timm.models.layers import drop_path, to_2tuple, trunc_normal_ +from timm.models.registry import register_model + + +def _cfg(url='', **kwargs): + return { + 'url': url, + 'num_classes': 400, + 'input_size': (3, 224, 224), + 'pool_size': None, + 'crop_pct': .9, + 'interpolation': 'bicubic', + 'mean': (0.5, 0.5, 0.5), + 'std': (0.5, 0.5, 0.5), + **kwargs + } + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return 'p={}'.format(self.drop_prob) + + +class Mlp(nn.Module): + + def __init__(self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + # x = self.drop(x) + # commit this for the orignal BERT implement + x = self.fc2(x) + x = self.drop(x) + return x + + +class CosAttention(nn.Module): + + def __init__(self, + dim, + num_heads=8, + qkv_bias=False, + qk_scale=None, + attn_drop=0., + proj_drop=0., + attn_head_dim=None): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + # self.scale = qk_scale or head_dim**-0.5 + # DO NOT RENAME [self.scale] (for no weight decay) + if qk_scale is None: + self.scale = nn.Parameter( + torch.log(10 * torch.ones((num_heads, 1, 1))), + requires_grad=True) + else: + self.scale = qk_scale + + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat( + (self.q_bias, + torch.zeros_like(self.v_bias, + requires_grad=False), self.v_bias)) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[ + 2] # make torchscript happy (cannot use tensor as tuple) + + attn = ( + F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) + + # torch.log(torch.tensor(1. / 0.01)) = 4.6052 + logit_scale = torch.clamp(self.scale, max=4.6052).exp() + + attn = attn * logit_scale + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Attention(nn.Module): + + def __init__(self, + dim, + num_heads=8, + qkv_bias=False, + qk_scale=None, + attn_drop=0., + proj_drop=0., + attn_head_dim=None): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat( + (self.q_bias, + torch.zeros_like(self.v_bias, + requires_grad=False), self.v_bias)) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[ + 2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__(self, + dim, + num_heads, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop=0., + attn_drop=0., + drop_path=0., + init_values=None, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + attn_head_dim=None, + cos_attn=False): + super().__init__() + self.norm1 = norm_layer(dim) + if cos_attn: + self.attn = CosAttention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + attn_head_dim=attn_head_dim) + else: + self.attn = Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + attn_head_dim=attn_head_dim) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath( + drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop) + + if init_values > 0: + self.gamma_1 = nn.Parameter( + init_values * torch.ones((dim)), requires_grad=True) + self.gamma_2 = nn.Parameter( + init_values * torch.ones((dim)), requires_grad=True) + else: + self.gamma_1, self.gamma_2 = None, None + + def forward(self, x): + if self.gamma_1 is None: + x = x + self.drop_path(self.attn(self.norm1(x))) + x = x + self.drop_path(self.mlp(self.norm2(x))) + else: + x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x))) + x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + + def __init__(self, + img_size=224, + patch_size=16, + in_chans=3, + embed_dim=768, + num_frames=16, + tubelet_size=2): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + num_spatial_patches = (img_size[0] // patch_size[0]) * ( + img_size[1] // patch_size[1]) + num_patches = num_spatial_patches * (num_frames // tubelet_size) + + self.img_size = img_size + self.tubelet_size = tubelet_size + self.patch_size = patch_size + self.num_patches = num_patches + self.proj = nn.Conv3d( + in_channels=in_chans, + out_channels=embed_dim, + kernel_size=(self.tubelet_size, patch_size[0], patch_size[1]), + stride=(self.tubelet_size, patch_size[0], patch_size[1])) + + def forward(self, x, **kwargs): + B, C, T, H, W = x.shape + assert H == self.img_size[0] and W == self.img_size[ + 1], f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + # b, c, l -> b, l, c + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +# sin-cos position encoding +# https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31 +def get_sinusoid_encoding_table(n_position, d_hid): + ''' Sinusoid position encoding table ''' + + # TODO: make it with torch instead of numpy + def get_position_angle_vec(position): + return [ + position / np.power(10000, 2 * (hid_j // 2) / d_hid) + for hid_j in range(d_hid) + ] + + sinusoid_table = np.array( + [get_position_angle_vec(pos_i) for pos_i in range(n_position)]) + sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i + sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 + + return torch.tensor( + sinusoid_table, dtype=torch.float, requires_grad=False).unsqueeze(0) + + +class VisionTransformer(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + + def __init__(self, + img_size=224, + patch_size=16, + in_chans=3, + num_classes=1000, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + head_drop_rate=0., + norm_layer=nn.LayerNorm, + init_values=0., + use_learnable_pos_emb=False, + init_scale=0., + all_frames=16, + tubelet_size=2, + use_mean_pooling=True, + with_cp=False, + cos_attn=False): + super().__init__() + self.num_classes = num_classes + # num_features for consistency with other models + self.num_features = self.embed_dim = embed_dim + self.tubelet_size = tubelet_size + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + num_frames=all_frames, + tubelet_size=tubelet_size) + num_patches = self.patch_embed.num_patches + self.with_cp = with_cp + + if use_learnable_pos_emb: + self.pos_embed = nn.Parameter( + torch.zeros(1, num_patches, embed_dim)) + else: + # sine-cosine positional embeddings is on the way + self.pos_embed = get_sinusoid_encoding_table( + num_patches, embed_dim) + + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth) + ] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer, + init_values=init_values, + cos_attn=cos_attn) for i in range(depth) + ]) + self.norm = nn.Identity() if use_mean_pooling else norm_layer( + embed_dim) + self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None + self.head_dropout = nn.Dropout(head_drop_rate) + self.head = nn.Linear( + embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + if use_learnable_pos_emb: + trunc_normal_(self.pos_embed, std=.02) + + self.apply(self._init_weights) + + self.head.weight.data.mul_(init_scale) + self.head.bias.data.mul_(init_scale) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear( + self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x): + B = x.size(0) + + x = self.patch_embed(x) + + if self.pos_embed is not None: + x = x + self.pos_embed.expand(B, -1, -1).type_as(x).to( + x.device).clone().detach() + x = self.pos_drop(x) + + for blk in self.blocks: + if self.with_cp: + x = cp.checkpoint(blk, x) + else: + x = blk(x) + + if self.fc_norm is not None: + return self.fc_norm(x.mean(1)) + else: + return self.norm(x[:, 0]) + + def forward(self, x): + x = self.forward_features(x) + x = self.head_dropout(x) + x = self.head(x) + return x + + +@register_model +def vit_small_patch16_224(pretrained=False, **kwargs): + model = VisionTransformer( + patch_size=16, + embed_dim=384, + depth=12, + num_heads=6, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + return model + + +@register_model +def vit_base_patch16_224(pretrained=False, **kwargs): + model = VisionTransformer( + patch_size=16, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + return model + + +@register_model +def vit_large_patch16_224(pretrained=False, **kwargs): + model = VisionTransformer( + patch_size=16, + embed_dim=1024, + depth=24, + num_heads=16, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + return model + + +@register_model +def vit_huge_patch16_224(pretrained=False, **kwargs): + model = VisionTransformer( + patch_size=16, + embed_dim=1280, + depth=32, + num_heads=16, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + return model + + +@register_model +def vit_giant_patch14_224(pretrained=False, **kwargs): + model = VisionTransformer( + patch_size=14, + embed_dim=1408, + depth=40, + num_heads=16, + mlp_ratio=48 / 11, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + return model diff --git a/model/vmae/modeling_pretrain.py b/model/vmae/modeling_pretrain.py new file mode 100644 index 0000000..d2081ec --- /dev/null +++ b/model/vmae/modeling_pretrain.py @@ -0,0 +1,475 @@ +# -------------------------------------------------------- +# Based on BEiT, timm, DINO and DeiT code bases +# https://github.com/microsoft/unilm/tree/master/beit +# https://github.com/rwightman/pytorch-image-models/tree/master/timm +# https://github.com/facebookresearch/deit +# https://github.com/facebookresearch/dino +# --------------------------------------------------------' +from functools import partial + +import torch +import torch.nn as nn +import torch.utils.checkpoint as cp +from timm.models.layers import trunc_normal_ as __call_trunc_normal_ +from timm.models.registry import register_model + +from .modeling_finetune import ( + Block, + PatchEmbed, + _cfg, + get_sinusoid_encoding_table, +) + + +def trunc_normal_(tensor, mean=0., std=1.): + __call_trunc_normal_(tensor, mean=mean, std=std, a=-std, b=std) + + +class PretrainVisionTransformerEncoder(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + + def __init__(self, + img_size=224, + patch_size=16, + in_chans=3, + num_classes=0, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + norm_layer=nn.LayerNorm, + init_values=None, + tubelet_size=2, + use_learnable_pos_emb=False, + with_cp=False, + all_frames=16, + cos_attn=False): + super().__init__() + self.num_classes = num_classes + # num_features for consistency with other models + self.num_features = self.embed_dim = embed_dim + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + num_frames=all_frames, + tubelet_size=tubelet_size) + num_patches = self.patch_embed.num_patches + self.with_cp = with_cp + + if use_learnable_pos_emb: + self.pos_embed = nn.Parameter( + torch.zeros(1, num_patches + 1, embed_dim)) + else: + # sine-cosine positional embeddings + self.pos_embed = get_sinusoid_encoding_table( + num_patches, embed_dim) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth) + ] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer, + init_values=init_values, + cos_attn=cos_attn) for i in range(depth) + ]) + self.norm = norm_layer(embed_dim) + self.head = nn.Linear( + embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + if use_learnable_pos_emb: + trunc_normal_(self.pos_embed, std=.02) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear( + self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x, mask): + x = self.patch_embed(x) + + x = x + self.pos_embed.type_as(x).to(x.device).clone().detach() + + B, _, C = x.shape + x_vis = x[~mask].reshape(B, -1, C) # ~mask means visible + + for blk in self.blocks: + if self.with_cp: + x_vis = cp.checkpoint(blk, x_vis) + else: + x_vis = blk(x_vis) + + x_vis = self.norm(x_vis) + return x_vis + + def forward(self, x, mask): + x = self.forward_features(x, mask) + x = self.head(x) + return x + + +class PretrainVisionTransformerDecoder(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + + def __init__(self, + patch_size=16, + num_classes=768, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + norm_layer=nn.LayerNorm, + init_values=None, + num_patches=196, + tubelet_size=2, + with_cp=False, + cos_attn=False): + super().__init__() + self.num_classes = num_classes + assert num_classes == 3 * tubelet_size * patch_size**2 + # num_features for consistency with other models + self.num_features = self.embed_dim = embed_dim + self.patch_size = patch_size + self.with_cp = with_cp + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth) + ] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer, + init_values=init_values, + cos_attn=cos_attn) for i in range(depth) + ]) + self.norm = norm_layer(embed_dim) + self.head = nn.Linear( + embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear( + self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward(self, x, return_token_num): + for blk in self.blocks: + if self.with_cp: + x = cp.checkpoint(blk, x) + else: + x = blk(x) + + if return_token_num > 0: + # only return the mask tokens predict pixels + x = self.head(self.norm(x[:, -return_token_num:])) + else: + # [B, N, 3*16^2] + x = self.head(self.norm(x)) + return x + + +class PretrainVisionTransformer(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + + def __init__( + self, + img_size=224, + patch_size=16, + encoder_in_chans=3, + encoder_num_classes=0, + encoder_embed_dim=768, + encoder_depth=12, + encoder_num_heads=12, + decoder_num_classes=1536, # decoder_num_classes=768 + decoder_embed_dim=512, + decoder_depth=8, + decoder_num_heads=8, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + norm_layer=nn.LayerNorm, + init_values=0., + use_learnable_pos_emb=False, + tubelet_size=2, + num_classes=0, # avoid the error from create_fn in timm + in_chans=0, # avoid the error from create_fn in timm + with_cp=False, + all_frames=16, + cos_attn=False, + ): + super().__init__() + self.encoder = PretrainVisionTransformerEncoder( + img_size=img_size, + patch_size=patch_size, + in_chans=encoder_in_chans, + num_classes=encoder_num_classes, + embed_dim=encoder_embed_dim, + depth=encoder_depth, + num_heads=encoder_num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop_rate=drop_rate, + attn_drop_rate=attn_drop_rate, + drop_path_rate=drop_path_rate, + norm_layer=norm_layer, + init_values=init_values, + tubelet_size=tubelet_size, + use_learnable_pos_emb=use_learnable_pos_emb, + with_cp=with_cp, + all_frames=all_frames, + cos_attn=cos_attn) + + self.decoder = PretrainVisionTransformerDecoder( + patch_size=patch_size, + num_patches=self.encoder.patch_embed.num_patches, + num_classes=decoder_num_classes, + embed_dim=decoder_embed_dim, + depth=decoder_depth, + num_heads=decoder_num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop_rate=drop_rate, + attn_drop_rate=attn_drop_rate, + drop_path_rate=drop_path_rate, + norm_layer=norm_layer, + init_values=init_values, + tubelet_size=tubelet_size, + with_cp=with_cp, + cos_attn=cos_attn) + + self.encoder_to_decoder = nn.Linear( + encoder_embed_dim, decoder_embed_dim, bias=False) + + self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim)) + + self.pos_embed = get_sinusoid_encoding_table( + self.encoder.patch_embed.num_patches, decoder_embed_dim) + + trunc_normal_(self.mask_token, std=.02) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token', 'mask_token'} + + def forward(self, x, mask, decode_mask=None): + decode_vis = mask if decode_mask is None else ~decode_mask + + x_vis = self.encoder(x, mask) # [B, N_vis, C_e] + x_vis = self.encoder_to_decoder(x_vis) # [B, N_vis, C_d] + B, N_vis, C = x_vis.shape + + # we don't unshuffle the correct visible token order, + # but shuffle the pos embedding accorddingly. + expand_pos_embed = self.pos_embed.expand(B, -1, -1).type_as(x).to( + x.device).clone().detach() + pos_emd_vis = expand_pos_embed[~mask].reshape(B, -1, C) + pos_emd_mask = expand_pos_embed[decode_vis].reshape(B, -1, C) + + # [B, N, C_d] + x_full = torch.cat( + [x_vis + pos_emd_vis, self.mask_token + pos_emd_mask], dim=1) + # NOTE: if N_mask==0, the shape of x is [B, N_mask, 3 * 16 * 16] + x = self.decoder(x_full, pos_emd_mask.shape[1]) + + return x + + +@register_model +def pretrain_videomae_small_patch16_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=16, + encoder_embed_dim=384, + encoder_depth=12, + encoder_num_heads=6, + encoder_num_classes=0, + decoder_num_classes=1536, # 16 * 16 * 3 * 2 + decoder_embed_dim=192, + decoder_num_heads=3, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model + + +@register_model +def pretrain_videomae_base_patch16_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=16, + encoder_embed_dim=768, + encoder_depth=12, + encoder_num_heads=12, + encoder_num_classes=0, + decoder_num_classes=1536, # 16 * 16 * 3 * 2 + decoder_embed_dim=384, + decoder_num_heads=6, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model + + +@register_model +def pretrain_videomae_large_patch16_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=16, + encoder_embed_dim=1024, + encoder_depth=24, + encoder_num_heads=16, + encoder_num_classes=0, + decoder_num_classes=1536, # 16 * 16 * 3 * 2 + decoder_embed_dim=512, + decoder_num_heads=8, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model + + +@register_model +def pretrain_videomae_huge_patch16_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=16, + encoder_embed_dim=1280, + encoder_depth=32, + encoder_num_heads=16, + encoder_num_classes=0, + decoder_num_classes=1536, # 16 * 16 * 3 * 2 + decoder_embed_dim=512, + decoder_num_heads=8, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model + + +@register_model +def pretrain_videomae_giant_patch14_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=14, + encoder_embed_dim=1408, + encoder_depth=40, + encoder_num_heads=16, + encoder_num_classes=0, + decoder_num_classes=1176, # 14 * 14 * 3 * 2, + decoder_embed_dim=512, + decoder_num_heads=8, + mlp_ratio=48 / 11, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..ba814e1 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,5050 @@ 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["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..e358bd6 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,178 @@ +absl-py==2.1.0 ; python_version >= "3.11" and python_version < "3.12" +albumentations==1.4.1 ; python_version >= "3.11" and python_version < "3.12" +annotated-types==0.6.0 ; python_version >= "3.11" and python_version < "3.12" +anyio==3.7.1 ; python_version >= "3.11" and python_version < "3.12" +appdirs==1.4.4 ; python_version >= "3.11" and python_version < "3.12" +appnope==0.1.4 ; python_version >= "3.11" and python_version < "3.12" and platform_system == "Darwin" +asttokens==2.4.1 ; python_version >= "3.11" and python_version < "3.12" +astunparse==1.6.3 ; python_version >= "3.11" and python_version < "3.12" +boto3==1.34.53 ; python_version >= "3.11" and python_version < "3.12" +botocore==1.34.53 ; python_version >= "3.11" and python_version < "3.12" +brotli==1.1.0 ; python_version >= "3.11" and python_version < "3.12" and implementation_name == "cpython" +brotlicffi==1.1.0.0 ; python_version >= "3.11" and python_version < "3.12" and implementation_name != "cpython" +cap-from-youtube @ git+https://github.com/ibaiGorordo/cap_from_youtube@d34d7d9f3ce3853d3c31ac393a091e4a723a03d1 ; python_version >= "3.11" and python_version < "3.12" +certifi==2024.2.2 ; python_version >= "3.11" and python_version < "3.12" +cffi==1.16.0 ; python_version >= "3.11" and python_version < "3.12" and implementation_name != "cpython" +charset-normalizer==3.3.2 ; python_version >= "3.11" and python_version < "3.12" +click==8.1.7 ; python_version >= "3.11" and python_version < "3.12" +cmake==3.28.3 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.11" and python_version < "3.12" +colorama==0.4.6 ; python_version >= "3.11" and python_version < "3.12" and (sys_platform == "win32" or platform_system == "Windows") +coloredlogs==15.0.1 ; python_version >= "3.11" and python_version < "3.12" +comm==0.2.1 ; python_version >= "3.11" and python_version < "3.12" +contourpy==1.2.0 ; python_version >= "3.11" and python_version < "3.12" +cycler==0.12.1 ; python_version >= "3.11" and python_version < "3.12" +debugpy==1.8.1 ; python_version >= "3.11" and python_version < "3.12" +decorator==5.1.1 ; python_version >= "3.11" and python_version < "3.12" +dm-tree==0.1.8 ; python_version >= "3.11" and python_version < "3.12" +dnspython==2.6.1 ; python_version >= "3.11" and python_version < "3.12" +docker-pycreds==0.4.0 ; python_version >= "3.11" and python_version < "3.12" +ecdsa==0.18.0 ; python_version >= "3.11" and python_version < "3.12" +einops==0.7.0 ; python_version >= "3.11" and python_version < "3.12" +email-validator==2.1.1 ; python_version >= "3.11" and python_version < "3.12" +executing==2.0.1 ; python_version >= "3.11" and python_version < "3.12" +fastapi[all]==0.105.0 ; python_version >= "3.11" and python_version < "3.12" 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python_version < "3.12" +h5py==3.10.0 ; python_version >= "3.11" and python_version < "3.12" +httpcore==1.0.4 ; python_version >= "3.11" and python_version < "3.12" +httptools==0.6.1 ; python_version >= "3.11" and python_version < "3.12" +httpx==0.27.0 ; python_version >= "3.11" and python_version < "3.12" +humanfriendly==10.0 ; python_version >= "3.11" and python_version < "3.12" +idna==3.6 ; python_version >= "3.11" and python_version < "3.12" +imageio==2.34.0 ; python_version >= "3.11" and python_version < "3.12" +ipykernel==6.29.3 ; python_version >= "3.11" and python_version < "3.12" +ipython==8.22.2 ; python_version >= "3.11" and python_version < "3.12" +itsdangerous==2.1.2 ; python_version >= "3.11" and python_version < "3.12" +jedi==0.19.1 ; python_version >= "3.11" and python_version < "3.12" +jinja2==3.1.3 ; python_version >= "3.11" and python_version < "3.12" +jmespath==1.0.1 ; python_version >= "3.11" and python_version < "3.12" +joblib==1.3.2 ; python_version >= "3.11" and python_version < "3.12" +jupyter-client==8.6.0 ; python_version >= "3.11" and python_version < "3.12" +jupyter-core==5.7.1 ; python_version >= "3.11" and python_version < "3.12" +keras2onnx==1.7.0 ; python_version >= "3.11" and python_version < "3.12" +keras==3.0.5 ; python_version >= "3.11" and python_version < "3.12" +kiwisolver==1.4.5 ; python_version >= "3.11" and python_version < "3.12" +lazy-loader==0.3 ; python_version >= "3.11" and python_version < "3.12" +libclang==16.0.6 ; python_version >= "3.11" and python_version < "3.12" +lit==17.0.6 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.11" and python_version < "3.12" +loguru==0.7.2 ; python_version >= "3.11" and python_version < "3.12" +markdown-it-py==3.0.0 ; python_version >= "3.11" and python_version < "3.12" +markdown==3.5.2 ; python_version >= "3.11" and python_version < "3.12" +markupsafe==2.1.5 ; python_version >= "3.11" and python_version < "3.12" +matplotlib-inline==0.1.6 ; python_version >= "3.11" and python_version < "3.12" +matplotlib==3.8.3 ; python_version >= "3.11" and python_version < "3.12" +mdurl==0.1.2 ; python_version >= "3.11" and python_version < "3.12" +ml-dtypes==0.3.2 ; python_version >= "3.11" and python_version < "3.12" +mpmath==1.3.0 ; python_version >= "3.11" and python_version < "3.12" +mutagen==1.47.0 ; python_version >= "3.11" and python_version < "3.12" +namex==0.0.7 ; python_version >= "3.11" and python_version < "3.12" +nest-asyncio==1.6.0 ; python_version >= "3.11" and python_version < "3.12" +networkx==3.2.1 ; python_version >= "3.11" and python_version < "3.12" +numpy==1.26.4 ; python_version >= "3.11" and python_version < "3.12" +onnx2pytorch==0.4.1 ; python_version >= "3.11" and python_version < "3.12" +onnx==1.15.0 ; python_version >= "3.11" and python_version < "3.12" +onnxconverter-common==1.13.0 ; python_version >= "3.11" and python_version < "3.12" +onnxruntime==1.17.1 ; python_version >= "3.11" and python_version < 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python_version < "3.12" +pyzmq==25.1.2 ; python_version >= "3.11" and python_version < "3.12" +requests==2.31.0 ; python_version >= "3.11" and python_version < "3.12" +rich==13.7.1 ; python_version >= "3.11" and python_version < "3.12" +rsa==4.9 ; python_version >= "3.11" and python_version < "3.12" +s3transfer==0.10.0 ; python_version >= "3.11" and python_version < "3.12" +scikit-image==0.22.0 ; python_version >= "3.11" and python_version < "3.12" +scikit-learn==1.4.1.post1 ; python_version >= "3.11" and python_version < "3.12" +scipy==1.12.0 ; python_version >= "3.11" and python_version < "3.12" +seaborn==0.13.2 ; python_version >= "3.11" and python_version < "3.12" +sentry-sdk==1.43.0 ; python_version >= "3.11" and python_version < "3.12" +setproctitle==1.3.3 ; python_version >= "3.11" and python_version < "3.12" +setuptools==69.1.1 ; python_version >= "3.11" and python_version < "3.12" +six==1.16.0 ; python_version >= "3.11" and python_version < "3.12" +smmap==5.0.1 ; python_version >= "3.11" and python_version < "3.12" +sniffio==1.3.1 ; python_version >= "3.11" and python_version < "3.12" +sqlalchemy==2.0.23 ; python_version >= "3.11" and python_version < "3.12" +sqlmodel==0.0.14 ; python_version >= "3.11" and python_version < "3.12" +sse-starlette==2.0.0 ; python_version >= "3.11" and python_version < "3.12" +stack-data==0.6.3 ; python_version >= "3.11" and python_version < "3.12" +starlette==0.27.0 ; python_version >= "3.11" and python_version < "3.12" +sympy==1.12 ; python_version >= "3.11" and python_version < "3.12" +tensorboard-data-server==0.7.2 ; python_version >= "3.11" and python_version < "3.12" +tensorboard==2.16.2 ; python_version >= "3.11" and python_version < "3.12" +tensorflow-io-gcs-filesystem==0.36.0 ; python_version >= "3.11" and python_version < "3.12" +tensorflow==2.16.0rc0 ; python_version >= "3.11" and python_version < "3.12" +termcolor==2.4.0 ; python_version >= "3.11" and python_version < "3.12" +tf2onnx @ git+https://github.com/onnx/tensorflow-onnx@9538da86d8e932d0eea8f0999672ea1a6a203b57 ; python_version >= "3.11" and python_version < "3.12" +thop==0.1.1.post2209072238 ; python_version >= "3.11" and python_version < "3.12" +threadpoolctl==3.3.0 ; python_version >= "3.11" and python_version < "3.12" +tifffile==2024.2.12 ; python_version >= "3.11" and python_version < "3.12" +timm==0.4.12 ; python_version >= "3.11" and python_version < "3.12" +torch @ https://download.pytorch.org/whl/cu118/torch-2.0.1%2Bcu118-cp311-cp311-linux_x86_64.whl ; python_version >= "3.11" and python_version < "3.12" +torchinfo==1.8.0 ; python_version >= "3.11" and python_version < "3.12" +torchvision @ https://download.pytorch.org/whl/cu118/torchvision-0.15.2%2Bcu118-cp311-cp311-linux_x86_64.whl ; python_version >= "3.11" and python_version < "3.12" +tornado==6.4 ; python_version >= "3.11" and python_version < "3.12" +tqdm==4.66.2 ; python_version >= "3.11" and python_version < "3.12" +traitlets==5.14.1 ; python_version >= "3.11" and python_version < "3.12" +triton==2.0.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.11" and python_version < "3.12" +typing-extensions==4.10.0 ; python_version >= "3.11" and python_version < "3.12" +tzdata==2024.1 ; python_version >= "3.11" and python_version < "3.12" +ujson==5.9.0 ; python_version >= "3.11" and python_version < "3.12" +ultralytics==8.1.24 ; python_version >= "3.11" and python_version < "3.12" +urllib3==2.0.7 ; python_version >= "3.11" and python_version < "3.12" +uvicorn==0.27.1 ; python_version >= "3.11" and python_version < "3.12" +uvicorn[standard]==0.27.1 ; python_version >= "3.11" and python_version < "3.12" +uvloop==0.19.0 ; (sys_platform != "win32" and sys_platform != "cygwin") and platform_python_implementation != "PyPy" and python_version >= "3.11" and python_version < "3.12" +wandb==0.16.4 ; python_version >= "3.11" and python_version < "3.12" +watchfiles==0.21.0 ; python_version >= "3.11" and python_version < "3.12" +wcwidth==0.2.13 ; python_version >= "3.11" and python_version < "3.12" +websockets==12.0 ; python_version >= "3.11" and python_version < "3.12" +werkzeug==3.0.1 ; python_version >= "3.11" and python_version < "3.12" +wheel==0.42.0 ; python_version >= "3.11" and python_version < "3.12" +win32-setctime==1.1.0 ; python_version >= "3.11" and python_version < "3.12" and sys_platform == "win32" +wrapt==1.16.0 ; python_version >= "3.11" and python_version < "3.12" +xmltodict==0.13.0 ; python_version >= "3.11" and python_version < "3.12" +youtube-dl==2021.12.17 ; python_version >= "3.11" and python_version < "3.12" +yt-dlp==2024.3.10 ; python_version >= "3.11" and python_version < "3.12" diff --git a/server_setting.sh b/server_setting.sh new file mode 100644 index 0000000..f03b8ed --- /dev/null +++ b/server_setting.sh @@ -0,0 +1,45 @@ +#!/bin/bash + +echo "--------------------------" +echo "install packages for build" +echo "--------------------------" +apt-get upgrade -y +apt-get update -y +apt-get install git -y +apt-get install curl -y +apt-get install gcc make -y +apt-get install -y net-tools tree vim telnet netcat +apt-get install -y make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev liblzma-dev wget llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev +apt-get install -y mysql-server + +echo "-------------------------------" +echo "install pyenv and bashrc update" +echo "-------------------------------" +git clone https://github.com/pyenv/pyenv.git ~/.pyenv +if !(grep -qc "PYENV_ROOT" ~/.bashrc); then + echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.bashrc + echo 'export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.bashrc + echo 'eval "$(pyenv init -)"' >> ~/.bashrc +fi + +sleep 5 +. ~/.bashrc + +echo "--------------------" +echo "install python3.11.4" +echo "--------------------" +pyenv install 3.11.4 +pyenv global 3.11.4 + +echo "--------------------------------" +echo "install poetry and bashrc update" +echo "--------------------------------" +curl -sSL https://install.python-poetry.org | python3 - +if !(grep -qc "$HOME/.local/bin" ~/.bashrc); then + echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc +fi + +echo "Executing ~/.bashrc" >> ~/.bashrc +. ~/.bashrc + +poetry config virtualenvs.in-project true \ No newline at end of file