Feat: Detection 모델 학습 API 구현 - S11P21S002-117

ai/.gitignore

@@ -33,3 +33,8 @@ dist/
 
 # 테스트 파일
 test-data/
+
+# 리소스
+resources/
+datasets/
+*.pt

ai/app/api/yolo/detection.py

@@ -4,9 +4,9 @@ from schemas.train_request import TrainRequest
 from schemas.predict_response import PredictResponse, LabelData
 from services.ai_service import load_detection_model
 from utils.dataset_utils import split_data
-from utils.file_utils import get_dataset_root_path, process_directories, process_image_and_label
+from utils.file_utils import get_dataset_root_path, process_directories, process_image_and_label, join_path
 from typing import List
-from PIL import Image
+from fastapi.responses import FileResponse
 
 router = APIRouter()
 @router.post("/detection", response_model=List[PredictResponse])
@@ -97,3 +97,14 @@ def train(request: TrainRequest):
     # 검증 데이터 처리
     for data in val_data:
         process_image_and_label(data, dataset_root_path, "val")
+
+    model = load_detection_model("test-data/model/best.pt")
+
+    model.train(
+        data=join_path(dataset_root_path,"dataset.yaml"),
+        name=join_path(dataset_root_path,"result"), 
+        epochs= request.epochs, 
+        batch=request.batch, 
+        )
+    
+    return FileResponse(path=join_path(dataset_root_path, "result", "weights", "best.pt"), filename="best.pt", media_type="application/octet-stream")

ai/app/schemas/train_request.py

@@ -1,5 +1,5 @@
 from pydantic import BaseModel
-from typing import List, Optional
+from typing import List, Optional, Union
 from schemas.predict_response import LabelData
 
 class TrainDataInfo(BaseModel):
@@ -11,4 +11,6 @@ class TrainRequest(BaseModel):
     project_id: int
     data: List[TrainDataInfo]
     seed: Optional[int] = None # 랜덤 변수 시드
-    ratio: Optional[float] = 0.8 # 훈련/검증 분할 비율
+    ratio: float = 0.8 # 훈련/검증 분할 비율
+    epochs: int = 50 # 훈련 반복 횟수
+    batch: Union[float, int] = -1 # 훈련 batch 수[int] or GPU의 사용률 자동[float] default(-1): gpu의 60% 사용 유지

ai/app/services/ai_service.py

@@ -6,7 +6,7 @@ from ultralytics.nn.tasks import DetectionModel, SegmentationModel
 import os
 import torch
 
-def load_detection_model(model_path: str = "test-data/model/yolov8n.pt", device:str ="auto"): 
+def load_detection_model(model_path: str = os.path.join("test-data","model","initial.pt"), device:str ="auto"): 
     """
     지정된 경로에서 YOLO 모델을 로드합니다.
 

ai/app/utils/file_utils.py

@@ -5,8 +5,8 @@ from PIL import Image
 from schemas.train_request import TrainDataInfo
 
 def get_dataset_root_path(project_id):
-    """프로젝트 ID를 기반으로 데이터셋 루트 경로 반환"""
-    return os.path.join('test-data', 'projects', str(project_id), 'train_model')
+    """데이터셋 루트 절대 경로 반환"""
+    return os.path.join(os.getcwd(), 'datasets', 'train')
 
 def make_dir(path:str, init: bool):
     """
@@ -19,8 +19,8 @@ def make_dir(path:str, init: bool):
 
 def make_yml(path:str):
     data = {
-        "train": "train",
-        "val": "val",
+        "train": f"{path}/train",
+        "val": f"{path}/val",
         "nc": 80,
         "names": 
         {
@@ -106,7 +106,7 @@ def make_yml(path:str):
             79: "toothbrush"
         }
     }
-    with open(path, 'w') as f:
+    with open(os.path.join(path, "dataset.yaml"), 'w') as f:
         yaml.dump(data, f)
 
 def process_directories(dataset_root_path:str):
@@ -114,7 +114,9 @@ def process_directories(dataset_root_path:str):
     make_dir(dataset_root_path, init=False)
     make_dir(os.path.join(dataset_root_path, "train"), init=True)
     make_dir(os.path.join(dataset_root_path, "val"), init=True)
-    make_yml(os.path.join(dataset_root_path, "dataset.yaml"))
+    if os.path.exists(os.path.join(dataset_root_path, "result")):
+        shutil.rmtree(os.path.join(dataset_root_path, "result"))
+    make_yml(dataset_root_path)
 
 def process_image_and_label(data:TrainDataInfo, dataset_root_path:str, child_path:str):
     
@@ -138,8 +140,12 @@ def process_image_and_label(data:TrainDataInfo, dataset_root_path:str, child_pat
             x2 = shape.points[1][0]
             y2 = shape.points[1][1]
             train_label.append(str(shape.group_id)) # label Id
-            train_label.append(str((x1 + x2) / 2))  # 중심 x 좌표
-            train_label.append(str((y1 + y2) / 2))  # 중심 y 좌표
-            train_label.append(str(x2 - x1))        # 너비
-            train_label.append(str(y2 - y1))        # 높이
+            train_label.append(str((x1 + x2) / 2 / label.imageWidth))   # 중심 x 좌표
+            train_label.append(str((y1 + y2) / 2 / label.imageHeight))  # 중심 y 좌표
+            train_label.append(str((x2 - x1) / label.imageWidth))       # 너비
+            train_label.append(str((y2 - y1) / label.imageHeight ))     # 높이
             train_label_txt.write(" ".join(train_label)+"\n")
+
+def join_path(path, *paths):
+    """os.path.join()과 같은 기능, os import 하기 싫어서 만듦"""
+    return os.path.join(path, *paths)