Merge branch 'ai/feat/229-data-process-for-train-stats' into 'ai/develop'

Feat: 학습 통계 데이터 처리 구현 - S11P21S002-229

See merge request s11-s-project/S11P21S002!140

ai/app/api/yolo/detection.py

@@ -10,6 +10,7 @@ from utils.dataset_utils import split_data
 from utils.file_utils import get_dataset_root_path, process_directories, process_image_and_label, join_path, get_model_path
 from utils.websocket_utils import WebSocketClient, WebSocketConnectionException
 import asyncio
+import time
 
 
 router = APIRouter()
@@ -189,7 +190,7 @@ async def detection_train(request: TrainRequest):
         inverted_label_map =  {value: key for key, value in request.label_map.items()}
 
     # 학습 데이터 분류
-    train_data, val_data = split_data(request.data, request.ratio, request.seed)
+    train_data, val_data = split_data(request.data, request.ratio)
 
     try:
         await ws_client.connect()
@@ -218,15 +219,46 @@ async def detection_train(request: TrainRequest):
             # 웹소켓으로 메시지 전송 (필요할 경우 추가)
             await ws_client.send_message("/app/ai/val/progress", f"검증 데이터 처리 중 {request.project_id}: {progress:.2f}% 완료")
 
-        model = load_detection_model("test-data/model/best.pt")
+        from ultralytics.models.yolo.detect import DetectionTrainer
+
+        def send_data(trainer):
+            # 첫번째 epoch는 스킵
+            if trainer.epoch == 0:
+                return
+
+            ## 남은 시간 계산(초)
+            left_epochs = trainer.epochs-trainer.epoch
+            left_sec = left_epochs*trainer.epoch_time
+            ## 로스 box_loss, cls_loss, dfl_loss
+            loss = trainer.label_loss_items(loss_items=trainer.loss_items)
+            data = {
+                "epoch": trainer.epoch,             # 현재 에포크
+                "total_epochs": trainer.epochs,     # 전체 에포크
+                "box_loss": loss["box_loss"],       # box loss
+                "cls_loss": loss["cls_loss"],       # cls loss
+                "dfl_loss": loss["dfl_loss"],       # dfl loss
+                "fitness": trainer.fitness,         # 적합도
+                "epoch_time": trainer.epoch_time,   # 지난 에포크 걸린 시간 (에포크 시작 기준으로 결정)
+                "left_second": left_sec             # 남은 시간(초)
+            }
+            # 데이터 전송
+            ws_client.send_message() 
+
+        model.add_callback("on_train_epoch_start", send_data)
+
         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
         )
-        # return FileResponse(path=join_path(dataset_root_path, "result", "weights", "best.pt"), filename="best.pt", media_type="application/octet-stream")
+
-        return {"status": "Training completed successfully"}
+        model_key = save_model(project_id=request.project_id, path=join_path(dataset_root_path, "result", "weights", "best.pt"))
+
+        return {"model_key": model_key, "results": results.results_dict}
     
     except WebSocketConnectionException as e:
 
@@ -247,9 +279,12 @@ async def detection_train(request: TrainRequest):
             name=join_path(dataset_root_path, "result"),
             epochs=request.epochs,
             batch=request.batch,
+            lr0=request.lr0,
+            lrf=request.lrf,
+            optimizer=request.optimizer
         )
 
-        model_key = save_model(project_id=request.project_id, path=join_path(dataset_root_path, "result", "weights", "last.pt"))
+        model_key = save_model(project_id=request.project_id, path=join_path(dataset_root_path, "result", "weights", "best.pt"))
 
         return {"model_key": model_key, "results": results.results_dict}
 
@@ -261,3 +296,5 @@ async def detection_train(request: TrainRequest):
     finally:
         if ws_client.is_connected():
             await ws_client.close()
+
+

ai/app/schemas/train_request.py

@@ -1,5 +1,5 @@
 from pydantic import BaseModel, Field
-from typing import List, Optional, Union
+from typing import List, Optional, Union, Literal
 from schemas.predict_response import LabelData
 
 class TrainDataInfo(BaseModel):
@@ -11,7 +11,11 @@ class TrainRequest(BaseModel):
     m_key: Optional[str] = Field(None, alias="model_key")
     label_map: dict[int, int] = Field(None, description="모델 레이블 카테고리 idx: 프로젝트 레이블 카테고리 idx , None 일경우 레이블 데이터(프로젝트 레이블)의 idx로 학습")
     data: List[TrainDataInfo]
-    seed: Optional[int] = None # 랜덤 변수 시드
     ratio: float = 0.8 # 훈련/검증 분할 비율
+
+    # 학습 파라미터
     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
+    lr0: float = 0.01 # 초기 학습 가중치
+    lrf: float = 0.01 # lr0 기준으로 학습 가중치의 최종 수렴치 (ex lr0의 0.01배)
+    optimizer: Literal['auto', 'SGD', 'Adam', 'AdamW', 'NAdam', 'RAdam', 'RMSProp'] = 'auto'

ai/app/utils/dataset_utils.py

@@ -1,11 +1,8 @@
 import random
-from typing import List, Any, Optional
 
-def split_data(data:List[Any], ratio:float, seed:Optional[int] = None):
+def split_data(data:list, ratio:float):
-    random.seed(seed)
     train_size = int(ratio * len(data))
     random.shuffle(data)
-    random.seed(None)
     train_data = data[:train_size]
     val_data = data[train_size:]
     return train_data, val_data