Refactor: train response 수정

ai/app/api/yolo/classfication.py

@@ -0,0 +1,216 @@
+from fastapi import APIRouter, HTTPException, Request
+from schemas.predict_request import PredictRequest
+from schemas.train_request import TrainRequest
+from schemas.predict_response import PredictResponse, LabelData
+from schemas.train_report_data import ReportData
+from services.load_model import load_classification_model
+from services.create_model import save_model
+from utils.dataset_utils import split_data
+from utils.file_utils import get_dataset_root_path, process_directories, process_image_and_label, join_path
+from utils.slackMessage import send_slack_message
+from utils.api_utils import send_data_call_api
+import random
+
+
+router = APIRouter()
+
+@router.post("/predict")
+async def classification_predict(request: PredictRequest):
+
+    send_slack_message(f"predict 요청: {request}", status="success")
+
+    # 모델 로드
+    model = get_model(request)
+    
+    # 모델 레이블 카테고리 연결
+    classes = list(request.label_map) if request.label_map else None
+
+    # 이미지 데이터 정리
+    url_list = list(map(lambda x:x.image_url, request.image_list))
+
+    # 추론
+    results = run_predictions(model, url_list, request, classes)
+
+    # 추론 결과 변환
+    response = [process_prediction_result(result, image, request.label_map) for result, image in zip(results,request.image_list)]
+    send_slack_message(f"predict 성공{response}", status="success")
+    return response
+
+# 모델 로드
+def get_model(request: PredictRequest):
+    try:
+        return load_classification_model(request.project_id, request.m_key)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail="load model exception: " + str(e))
+
+# 추론 실행 함수
+def run_predictions(model, image, request, classes):
+    try:
+        return model.predict(
+            source=image,
+            iou=request.iou_threshold,
+            conf=request.conf_threshold,
+            classes=classes
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail="model predict exception: " + str(e))
+    
+
+# 추론 결과 처리 함수
+def process_prediction_result(result, image, label_map):
+    try:
+        label_data = LabelData(
+            version="0.0.0",
+            task_type="det",
+            shapes=[
+                {
+                    "label": summary['name'],
+                    "color": get_random_color(),
+                    "points": [
+                        [summary['box']['x1'], summary['box']['y1']],
+                        [summary['box']['x2'], summary['box']['y2']]
+                    ],
+                    "group_id": label_map[summary['class']] if label_map else summary['class'],
+                    "shape_type": "rectangle",
+                    "flags": {}
+                }
+                for summary in result.summary()
+            ],
+            split="none",
+            imageHeight=result.orig_img.shape[0],
+            imageWidth=result.orig_img.shape[1],
+            imageDepth=result.orig_img.shape[2]
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail="model predict exception: " + str(e))
+
+    return PredictResponse(
+        image_id=image.image_id,
+        data=label_data.model_dump_json()
+    )
+
+def get_random_color():
+    random_number = random.randint(0, 0xFFFFFF)
+    return f"#{random_number:06X}"
+
+
+
+@router.post("/train")
+async def classification_train(request: TrainRequest, http_request: Request):
+
+    send_slack_message(f"train 요청{request}", status="success")
+
+    # Authorization 헤더에서 Bearer 토큰 추출
+    auth_header = http_request.headers.get("Authorization")
+    token = auth_header.split(" ")[1] if auth_header and auth_header.startswith("Bearer ") else None
+
+    # 레이블 맵
+    inverted_label_map = {value: key for key, value in request.label_map.items()} if request.label_map else None
+
+    # 데이터셋 루트 경로 얻기
+    dataset_root_path = get_dataset_root_path(request.project_id)
+
+    # 모델 로드
+    model = get_model(request)
+
+    # 학습할 모델 카테고리, 카테고리가 추가되는 경우 추가 작업 필요
+    model_categories = model.names
+    
+    # 데이터 전처리
+    preprocess_dataset(dataset_root_path, model_categories, request.data, request.ratio, inverted_label_map)
+
+    # 학습
+    results = run_train(request,token,model,dataset_root_path)
+
+    # best 모델 저장
+    model_key = save_model(project_id=request.project_id, path=join_path(dataset_root_path, "result", "weights", "best.pt"))
+
+    response = {"model_key": model_key, "results": results.results_dict}
+
+    send_slack_message(f"train 성공{response}", status="success")
+    
+    return response
+
+
+def preprocess_dataset(dataset_root_path, model_categories, data, ratio, label_map):
+    try:
+        # 디렉토리 생성 및 초기화
+        process_directories(dataset_root_path, model_categories)
+
+        # 학습 데이터 분류
+        train_data, val_data = split_data(data, ratio)
+        if not train_data or not val_data:
+            raise HTTPException(status_code=400, detail="data split exception: data size is too small or \"ratio\" has invalid value")
+
+        # 학습 데이터 처리
+        for data in train_data:
+            process_image_and_label(data, dataset_root_path, "train", label_map)
+
+        # 검증 데이터 처리
+        for data in val_data:
+            process_image_and_label(data, dataset_root_path, "val", label_map)
+
+    except HTTPException as e:
+        raise e  # HTTP 예외를 다시 발생
+    except Exception as e:
+        raise HTTPException(status_code=500, detail="preprocess dataset exception: " + str(e))
+    
+def run_train(request, token, model, dataset_root_path):
+    try:
+        # 데이터 전송 콜백함수
+        def send_data(trainer):
+            try:
+                # 첫번째 epoch는 스킵
+                if trainer.epoch == 0:
+                    return
+
+                # 남은 시간 계산(초)
+                left_epochs = trainer.epochs - trainer.epoch
+                left_seconds = left_epochs * trainer.epoch_time
+
+                # 로스 box_loss, cls_loss, dfl_loss
+                loss = trainer.label_loss_items(loss_items=trainer.loss_items)
+                data = ReportData(
+                    epoch=trainer.epoch,             # 현재 에포크
+                    total_epochs=trainer.epochs,     # 전체 에포크
+                    box_loss=loss["train/box_loss"], # box loss
+                    cls_loss=loss["train/cls_loss"], # cls loss
+                    dfl_loss=loss["train/dfl_loss"], # dfl loss
+                    fitness=trainer.fitness,         # 적합도
+                    epoch_time=trainer.epoch_time,   # 지난 에포크 걸린 시간 (에포크 시작 기준으로 결정)
+                    left_seconds=left_seconds        # 남은 시간(초)
+                )
+                # 데이터 전송
+                send_data_call_api(request.project_id, request.m_id, data, token)
+            except Exception as e:
+                raise HTTPException(status_code=500, detail=f"send_data exception: {e}")
+
+        # 콜백 등록
+        model.add_callback("on_train_epoch_start", send_data)
+
+        # 학습 실행
+        try:
+            results = model.train(
+                data=join_path(dataset_root_path, "dataset.yaml"),
+                name=join_path(dataset_root_path, "result"),
+                epochs=request.epochs,
+                batch=request.batch,
+                lr0=request.lr0,
+                lrf=request.lrf,
+                optimizer=request.optimizer
+            )
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"model train exception: {e}")
+
+        # 마지막 에포크 전송
+        model.trainer.epoch += 1
+        send_data(model.trainer)
+
+        return results
+
+    except HTTPException as e:
+        raise e # HTTP 예외를 다시 발생
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"run_train exception: {e}")
+
+

ai/app/api/yolo/detection.py

@@ -3,6 +3,7 @@ from schemas.predict_request import PredictRequest
 from schemas.train_request import TrainRequest
 from schemas.predict_response import PredictResponse, LabelData
 from schemas.train_report_data import ReportData
+from schemas.train_response import TrainResponse
 from services.load_model import load_detection_model
 from services.create_model import save_model
 from utils.dataset_utils import split_data
@@ -101,6 +102,7 @@ async def detection_train(request: TrainRequest, http_request: Request):
     send_slack_message(f"train 요청{request}", status="success")
     
     # Authorization 헤더에서 Bearer 토큰 추출
+    try:
         auth_header = http_request.headers.get("Authorization")
         token = auth_header.split(" ")[1] if auth_header and auth_header.startswith("Bearer ") else None
 
@@ -125,8 +127,21 @@ async def detection_train(request: TrainRequest, http_request: Request):
         # best 모델 저장
         model_key = save_model(project_id=request.project_id, path=join_path(dataset_root_path, "result", "weights", "best.pt"))
         
-    response = {"model_key": model_key, "results": results.results_dict}
+        result = results.result_dict
 
+        response = TrainResponse(
+            modelKey=model_key,
+            precision= result["metrics/precision(B)"],
+            recall= result["metrics/recall(B)"],
+            mAP50= result["metrics/mAP50(B)"],
+            mAP5095= result["metrics/mAP50-95(B)"],
+            fitness= result["fitness"]
+        )
+
+    except HTTPException as e:
+        raise e
+    except Exception as e:
+        HTTPException(status_code=500, detail=str(e))
     send_slack_message(f"train 성공{response}", status="success")
         
     return response

ai/app/schemas/train_response.py

@@ -0,0 +1,9 @@
+from pydantic import BaseModel
+
+class TrainResponse(BaseModel):
+    modelKey:str
+    precision: float
+    recall: float
+    mAP50: float
+    mAP5095: float
+    fitness: float