Feat: classification 프로젝트 명세 변경에 따른 수정

ai/app/api/yolo/classfication.py

@@ -1,32 +1,32 @@
-from fastapi import APIRouter, HTTPException, Request
+from fastapi import APIRouter, HTTPException
+from api.yolo.detection import get_classes, run_predictions, get_random_color, split_data
 from schemas.predict_request import PredictRequest
-from schemas.train_request import TrainRequest
+from schemas.train_request import TrainRequest, TrainDataInfo
 from schemas.predict_response import PredictResponse, LabelData
 from schemas.train_report_data import ReportData
+from schemas.train_response import ClassificationTrainResponse
 from services.load_model import load_classification_model
 from services.create_model import save_model
 from utils.file_utils import get_dataset_root_path, process_directories_in_cls, process_image_and_label_in_cls, 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"cls predict 요청: {request}", status="success")
+    send_slack_message(f"predict 요청: {request}", status="success")
 
     # 모델 로드
-    model = get_model(request)
+    model = get_model(request.project_id, request.m_key)
-    
-    # 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))
 
+    # 이 값을 모델에 입력하면 해당하는 클래스 id만 출력됨
+    classes = get_classes(request.label_map, model.names)
+
     # 추론
     results = run_predictions(model, url_list, request, classes)
 
@@ -40,20 +40,7 @@ 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))
+        raise HTTPException(status_code=500, detail="exception in get_model(): " + 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):
@@ -68,7 +55,7 @@ def process_prediction_result(result, image, label_map):
                     "points": [
                         [0, 0]
                     ],
-                    "group_id": label_map[summary['class']] if label_map else summary['class'],
+                    "group_id": label_map[summary['name']],
                     "shape_type": "point",
                     "flags": {}
                 }
@@ -80,71 +67,63 @@ def process_prediction_result(result, image, label_map):
             imageDepth=result.orig_img.shape[2]
         )
     except Exception as e:
-        raise HTTPException(status_code=500, detail="model predict exception: " + str(e))
+        raise HTTPException(status_code=500, detail="exception in process_prediction_result(): " + 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):
 
-    send_slack_message(f"cls train 요청{request}", status="success")
+    send_slack_message(f"train 요청{request}", status="success")
 
-    # 데이터셋 루트 경로 얻기
+    # 데이터셋 루트 경로 얻기 (프로젝트 id 기반)
     dataset_root_path = get_dataset_root_path(request.project_id)
 
     # 모델 로드
-    model = get_model(request)
+    model = get_model(request.project_id, request.m_key)
 
-    # 학습할 모델 카테고리, 카테고리가 추가되는 경우 추가 작업 필요
+    # 이 값을 학습할때 넣으면 이 카테고리들이 학습됨
-    model_categories = model.names
+    names = list(request.label_map)
     
-    # 데이터 전처리
+    # 데이터 전처리: 학습할 디렉토리 & 데이터셋 설정 파일을 생성
-    preprocess_dataset(dataset_root_path, model_categories, request.data, request.ratio)
+    process_directories_in_cls(dataset_root_path, names)
+
+    # 데이터 전처리: 데이터를 학습데이터와 테스트 데이터로 분류
+    train_data, test_data = split_data(request.data, request.ratio)
+
+    # 데이터 전처리: 데이터 이미지 및 레이블 다운로드
+    download_data(train_data, test_data, dataset_root_path)
 
     # 학습
-    results = run_train(request,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 = ClassificationTrainResponse(
+        modelKey=model_key,
+        precision= result["accuracy_top1"],
+        fitness= result["fitness"]
+    )
     send_slack_message(f"train 성공{response}", status="success")
             
     return response
 
-
+def download_data(train_data:list[TrainDataInfo], test_data:list[TrainDataInfo], dataset_root_path:str):
-def preprocess_dataset(dataset_root_path, model_categories, data, ratio):
     try:
-        # 디렉토리 생성 및 초기화
-        process_directories_in_cls(dataset_root_path, model_categories)
-
-        # 학습 데이터 분류
-        train_data, test_data = split_data(data, ratio)
-        if not train_data or not test_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_in_cls(data, dataset_root_path, "train")
 
-        # 검증 데이터 처리
         for data in test_data:
             process_image_and_label_in_cls(data, dataset_root_path, "test")
-
-    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))
+        raise HTTPException(status_code=500, detail="exception in download_data(): " + str(e))
+
     
 def run_train(request, model, dataset_root_path):
     try:
@@ -164,6 +143,7 @@ def run_train(request, model, dataset_root_path):
                 data = ReportData(
                     epoch=trainer.epoch,            # 현재 에포크
                     total_epochs=trainer.epochs,    # 전체 에포크
+                    seg_loss=0,                     # seg loss
                     box_loss=0,                     # box loss
                     cls_loss=loss["train/loss"],    # cls loss
                     dfl_loss=0,                     # dfl loss
@@ -174,7 +154,7 @@ def run_train(request, model, dataset_root_path):
                 # 데이터 전송
                 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}")
+                raise HTTPException(status_code=500, detail="exception in send_data: "+ str(e))
 
         # 콜백 등록
         model.add_callback("on_train_epoch_start", send_data)
@@ -198,6 +178,6 @@ def run_train(request, model, dataset_root_path):
     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}")
+        raise HTTPException(status_code=500, detail="exception in run_train(): "+str(e))
 
 

ai/app/schemas/train_response.py

@@ -7,3 +7,8 @@ class TrainResponse(BaseModel):
     mAP50: float
     mAP5095: float
     fitness: float
+
+class ClassificationTrainResponse(BaseModel):
+    modelKey: str
+    accuracy: float
+    fitness: float

ai/app/utils/file_utils.py

@@ -118,10 +118,10 @@ def get_file_name(path):
         raise FileNotFoundError()
     return os.path.basename(path)
 
-def process_directories_in_cls(dataset_root_path:str, model_categories:dict[int,str]):
+def process_directories_in_cls(dataset_root_path:str, model_categories:list[str]):
     """classification 학습을 위한 디렉토리 생성"""
     make_dir(dataset_root_path, init=False)
-    for category in model_categories.values():
+    for category in model_categories:
         make_dir(os.path.join(dataset_root_path, "train", category), init=True)
         make_dir(os.path.join(dataset_root_path, "test", category), init=True)
     if os.path.exists(os.path.join(dataset_root_path, "result")):
@@ -140,4 +140,11 @@ def process_image_and_label_in_cls(data:TrainDataInfo, dataset_root_path:str, ch
     label_path = os.path.join(dataset_root_path,child_path,label_name)
 
     # url로부터 이미지 다운로드
-    urllib.request.urlretrieve(data.image_url, os.path.join(label_path, img_name))
+    if os.path.exists(label_path):
+        urllib.request.urlretrieve(data.image_url, os.path.join(label_path, img_name))
+    else:
+        # raise FileNotFoundError("failed download")
+        print("Not Found Label Category. Failed Download")
+    # 레이블 데이터 중에서 프로젝트 카테고리에 해당되지않는 데이터가 있는 경우 처리 1. 에러 raise 2. 무시(+ warning)
+
+