Merge branch 'ai/feat/segmentation' into 'ai/develop'
Fix: results_dict 오타 수정, raise 누락 수정 See merge request s11-s-project/S11P21S002!196
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commit
7f59d05a80
@ -97,15 +97,12 @@ def get_random_color():
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@router.post("/train")
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async def detection_train(request: TrainRequest, http_request: Request):
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async def detection_train(request: TrainRequest):
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send_slack_message(f"train 요청{request}", status="success")
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# Authorization 헤더에서 Bearer 토큰 추출
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try:
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auth_header = http_request.headers.get("Authorization")
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token = auth_header.split(" ")[1] if auth_header and auth_header.startswith("Bearer ") else None
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# 레이블 맵
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inverted_label_map = {value: key for key, value in request.label_map.items()} if request.label_map else None
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@ -122,12 +119,12 @@ async def detection_train(request: TrainRequest, http_request: Request):
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preprocess_dataset(dataset_root_path, model_categories, request.data, request.ratio, inverted_label_map)
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# 학습
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results = run_train(request,token,model,dataset_root_path)
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results = run_train(request, model,dataset_root_path)
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# best 모델 저장
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model_key = save_model(project_id=request.project_id, path=join_path(dataset_root_path, "result", "weights", "best.pt"))
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result = results.result_dict
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result = results.results_dict
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response = TrainResponse(
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modelKey=model_key,
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@ -137,14 +134,14 @@ async def detection_train(request: TrainRequest, http_request: Request):
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mAP5095= result["metrics/mAP50-95(B)"],
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fitness= result["fitness"]
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)
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send_slack_message(f"train 성공{response}", status="success")
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return response
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except HTTPException as e:
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raise e
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except Exception as e:
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HTTPException(status_code=500, detail=str(e))
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send_slack_message(f"train 성공{response}", status="success")
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return response
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raise HTTPException(status_code=500, detail=str(e))
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def preprocess_dataset(dataset_root_path, model_categories, data, ratio, label_map):
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@ -170,7 +167,7 @@ def preprocess_dataset(dataset_root_path, model_categories, data, ratio, label_m
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except Exception as e:
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raise HTTPException(status_code=500, detail="preprocess dataset exception: " + str(e))
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def run_train(request, token, model, dataset_root_path):
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def run_train(request, model, dataset_root_path):
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try:
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# 데이터 전송 콜백함수
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def send_data(trainer):
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@ -196,7 +193,7 @@ def run_train(request, token, model, dataset_root_path):
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left_seconds=left_seconds # 남은 시간(초)
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)
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# 데이터 전송
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send_data_call_api(request.project_id, request.m_id, data, token)
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send_data_call_api(request.project_id, request.m_id, data)
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"send_data exception: {e}")
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@ -204,7 +201,6 @@ def run_train(request, token, model, dataset_root_path):
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model.add_callback("on_train_epoch_start", send_data)
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# 학습 실행
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try:
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results = model.train(
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data=join_path(dataset_root_path, "dataset.yaml"),
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name=join_path(dataset_root_path, "result"),
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@ -214,15 +210,13 @@ def run_train(request, token, model, dataset_root_path):
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lrf=request.lrf,
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optimizer=request.optimizer
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)
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"model train exception: {e}")
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# 마지막 에포크 전송
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model.trainer.epoch += 1
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send_data(model.trainer)
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return results
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except HTTPException as e:
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raise e # HTTP 예외를 다시 발생
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except Exception as e:
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@ -3,18 +3,20 @@ from dotenv import load_dotenv
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import os, httpx
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def send_data_call_api(project_id:int, model_id:int, data:ReportData, token):
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def send_data_call_api(project_id:int, model_id:int, data:ReportData):
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try:
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load_dotenv()
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# main.py와 같은 디렉토리에 .env 파일 생성해서 따옴표 없이 입력
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# load_dotenv()
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# base_url = os.getenv("API_BASE_URL")
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# main.py와 같은 디렉토리에 .env 파일 생성해서 따옴표 없이 아래 데이터를 입력
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# API_BASE_URL = {url}
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# API_KEY = {key}
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base_url = os.getenv("API_BASE_URL")
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# 하드코딩으로 대체
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base_url = "http://127.0.0.1:8080"
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headers = {
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"Content-Type": "application/json"
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}
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if token:
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headers["Authorization"] = f"Bearer {token}"
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response = httpx.request(
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method="POST",
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