Merge branch 'ai/feat/segmentation' into 'ai/develop'

Feat: 세그멘테이션 train response 수정

See merge request s11-s-project/S11P21S002!198

ai/app/api/yolo/segmentation.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_segmentation_model
 from services.create_model import save_model
 from utils.dataset_utils import split_data
@@ -91,14 +92,11 @@ def get_random_color():
 
 
 @router.post("/train")
-async def segmentation_train(request: TrainRequest, http_request: Request):
+async def segmentation_train(request: TrainRequest):
   
     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
-
+    try:
         # 레이블 맵
         inverted_label_map = {value: key for key, value in request.label_map.items()} if request.label_map else None
 
@@ -115,17 +113,30 @@ async def segmentation_train(request: TrainRequest, http_request: Request):
         preprocess_dataset(dataset_root_path, model_categories, request.data, request.ratio, inverted_label_map)
 
         # 학습
-    results = run_train(request,token,model,dataset_root_path)
+        results = run_train(request, 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}
+        result = results.results_dict
 
+        response = TrainResponse(
+            modelKey=model_key,
+            precision= result["metrics/precision(M)"],
+            recall= result["metrics/recall(M)"],
+            mAP50= result["metrics/mAP50(M)"],
+            mAP5095= result["metrics/mAP50-95(M)"],
+            fitness= result["fitness"]
+        )
         send_slack_message(f"train 성공{response}", status="success")
             
         return response
 
+    except HTTPException as e:
+        raise e
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
 
 def preprocess_dataset(dataset_root_path, model_categories, data, ratio, label_map):
     try:
@@ -150,7 +161,7 @@ def preprocess_dataset(dataset_root_path, model_categories, data, ratio, label_m
     except Exception as e:
         raise HTTPException(status_code=500, detail="preprocess dataset exception: " + str(e))
     
-def run_train(request, token, model, dataset_root_path):
+def run_train(request, model, dataset_root_path):
     try:
         # 데이터 전송 콜백함수
         def send_data(trainer):
@@ -168,7 +179,7 @@ def run_train(request, token, model, dataset_root_path):
                 data = ReportData(
                     epoch=trainer.epoch,             # 현재 에포크
                     total_epochs=trainer.epochs,     # 전체 에포크
-                    seg_loss=loss["train/seg_loss"], # seg loss
+                    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
@@ -176,7 +187,7 @@ def run_train(request, token, model, dataset_root_path):
                     left_seconds=left_seconds        # 남은 시간(초)
                 )
                 # 데이터 전송
-                send_data_call_api(request.project_id, request.m_id, data, token)
+                send_data_call_api(request.project_id, request.m_id, data)
             except Exception as e:
                 raise HTTPException(status_code=500, detail=f"send_data exception: {e}")
 
@@ -184,7 +195,6 @@ def run_train(request, token, model, dataset_root_path):
         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"),
@@ -194,13 +204,10 @@ def run_train(request, token, model, dataset_root_path):
             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:
@@ -211,3 +218,6 @@ def run_train(request, token, model, dataset_root_path):
 
 
 
+
+
+