Refactor: 비동기 run_in_threadpool() 모두 삭제
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김진현
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@ -1,5 +1,4 @@
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from fastapi import APIRouter, HTTPException
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from fastapi.concurrency import run_in_threadpool
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from api.yolo.detection import run_predictions, get_random_color, split_data
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from schemas.predict_request import PredictRequest
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from schemas.train_request import TrainRequest, TrainDataInfo
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@ -26,7 +25,7 @@ async def classification_predict(request: PredictRequest):
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url_list = list(map(lambda x:x.image_url, request.image_list))
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# 추론
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results = await run_predictions(model, url_list, request, classes=[]) # classification은 classes를 무시함
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results = run_predictions(model, url_list, request, classes=[]) # classification은 classes를 무시함
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# 추론 결과 변환
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response = [process_prediction_result(result, image, request.label_map) for result, image in zip(results,request.image_list)]
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@ -105,7 +104,7 @@ async def classification_train(request: TrainRequest):
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download_data(train_data, test_data, dataset_root_path)
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# 학습
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results = await run_train(request, model,dataset_root_path)
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results = run_train(request, model,dataset_root_path)
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# best 모델 저장
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model_key = save_model(project_id=request.project_id, path=join_path(dataset_root_path, "result", "weights", "best.pt"))
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@ -137,7 +136,7 @@ def download_data(train_data:list[TrainDataInfo], test_data:list[TrainDataInfo],
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raise HTTPException(status_code=500, detail="exception in download_data(): " + str(e))
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async def run_train(request, model, dataset_root_path):
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def run_train(request, model, dataset_root_path):
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try:
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# 데이터 전송 콜백함수
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def send_data(trainer):
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@ -166,13 +165,14 @@ async def run_train(request, model, dataset_root_path):
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# 데이터 전송
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send_data_call_api(request.project_id, request.m_id, data)
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except Exception as e:
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raise HTTPException(status_code=500, detail="exception in send_data: "+ str(e))
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print(f"Exception in send_data(): {e}")
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# 콜백 등록
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model.add_callback("on_train_epoch_start", send_data)
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# 학습 실행
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results = await run_in_threadpool(model.train,
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try:
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results = model.train(
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data=dataset_root_path,
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name=join_path(dataset_root_path, "result"),
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epochs=request.epochs,
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@ -181,6 +181,9 @@ async def run_train(request, model, dataset_root_path):
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lrf=request.lrf,
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optimizer=request.optimizer
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)
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finally:
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# 콜백 해제 및 자원 해제
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model.reset_callbacks()
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# 마지막 에포크 전송
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model.trainer.epoch += 1
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send_data(model.trainer)
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@ -1,5 +1,4 @@
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from fastapi import APIRouter, HTTPException
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from fastapi.concurrency import run_in_threadpool
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from schemas.predict_request import PredictRequest
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from schemas.train_request import TrainRequest, TrainDataInfo
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from schemas.predict_response import PredictResponse, LabelData, Shape
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@ -10,14 +9,13 @@ from services.create_model import save_model
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from utils.file_utils import get_dataset_root_path, process_directories, join_path, process_image_and_label
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from utils.slackMessage import send_slack_message
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from utils.api_utils import send_data_call_api
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import random
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import random, torch
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router = APIRouter()
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@router.post("/predict")
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async def detection_predict(request: PredictRequest):
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send_slack_message(f"predict 요청: {request}", status="success")
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# 모델 로드
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@ -30,7 +28,7 @@ async def detection_predict(request: PredictRequest):
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classes = get_classes(request.label_map, model.names)
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# 추론
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results = await run_predictions(model, url_list, request, classes)
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results = run_predictions(model, url_list, request, classes)
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# 추론 결과 변환
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response = [process_prediction_result(result, image, request.label_map) for result, image in zip(results,request.image_list)]
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@ -52,10 +50,10 @@ def get_classes(label_map:dict[str: int], model_names: dict[int, str]):
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raise HTTPException(status_code=500, detail="exception in get_classes(): " + str(e))
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# 추론 실행 함수
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async def run_predictions(model, image, request, classes):
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def run_predictions(model, image, request, classes):
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try:
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result = await run_in_threadpool(
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model.predict,
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with torch.no_grad():
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result = model.predict(
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source=image,
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iou=request.iou_threshold,
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conf=request.conf_threshold,
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@ -65,7 +63,6 @@ async def run_predictions(model, image, request, classes):
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except Exception as e:
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raise HTTPException(status_code=500, detail="exception in run_predictions: " + str(e))
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# 추론 결과 처리 함수
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def process_prediction_result(result, image, label_map):
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try:
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@ -135,7 +132,7 @@ async def detection_train(request: TrainRequest):
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download_data(train_data, val_data, dataset_root_path, label_converter)
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# 학습
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results = await run_train(request, model,dataset_root_path)
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results = run_train(request, model,dataset_root_path)
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# best 모델 저장
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model_key = save_model(project_id=request.project_id, path=join_path(dataset_root_path, "result", "weights", "best.pt"))
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@ -178,9 +175,9 @@ def download_data(train_data:list[TrainDataInfo], val_data:list[TrainDataInfo],
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except Exception as e:
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raise HTTPException(status_code=500, detail="exception in download_data(): " + str(e))
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async def run_train(request, model, dataset_root_path):
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def run_train(request, model, dataset_root_path):
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try:
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# 데이터 전송 콜백함수
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# 콜백 함수 정의
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def send_data(trainer):
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try:
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# 첫번째 epoch는 스킵
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@ -207,13 +204,15 @@ async def run_train(request, model, dataset_root_path):
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# 데이터 전송
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send_data_call_api(request.project_id, request.m_id, data)
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"exception in send_data(): {e}")
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# 예외 처리
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print(f"Exception in send_data(): {e}")
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# 콜백 등록
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model.add_callback("on_train_epoch_start", send_data)
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# 학습 실행
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results = await run_in_threadpool(model.train,
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try:
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# 비동기 함수로 학습 실행
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results = model.train(
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data=join_path(dataset_root_path, "dataset.yaml"),
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name=join_path(dataset_root_path, "result"),
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epochs=request.epochs,
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@ -222,16 +221,16 @@ async def run_train(request, model, dataset_root_path):
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lrf=request.lrf,
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optimizer=request.optimizer
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)
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finally:
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# 콜백 해제 및 자원 해제
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model.reset_callbacks()
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torch.cuda.empty_cache()
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# 마지막 에포크 전송
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model.trainer.epoch += 1
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send_data(model.trainer)
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return results
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except HTTPException as e:
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raise e # HTTP 예외를 다시 발생
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raise e
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"exception in run_train(): {e}")
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@ -1,5 +1,4 @@
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from fastapi import APIRouter, HTTPException
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from fastapi.concurrency import run_in_threadpool
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from api.yolo.detection import get_classes, run_predictions, get_random_color, split_data, download_data
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from schemas.predict_request import PredictRequest
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from schemas.train_request import TrainRequest
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@ -28,7 +27,7 @@ async def segmentation_predict(request: PredictRequest):
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classes = get_classes(request.label_map, model.names)
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# 추론
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results = await run_predictions(model, url_list, request, classes)
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results = run_predictions(model, url_list, request, classes)
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# 추론 결과 변환
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response = [process_prediction_result(result, image, request.label_map) for result, image in zip(results,request.image_list)]
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@ -102,7 +101,7 @@ async def segmentation_train(request: TrainRequest):
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download_data(train_data, val_data, dataset_root_path, label_converter)
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# 학습
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results = await run_train(request, model,dataset_root_path)
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results = run_train(request, model,dataset_root_path)
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# best 모델 저장
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model_key = save_model(project_id=request.project_id, path=join_path(dataset_root_path, "result", "weights", "best.pt"))
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@ -122,7 +121,7 @@ async def segmentation_train(request: TrainRequest):
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return response
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async def run_train(request, model, dataset_root_path):
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def run_train(request, model, dataset_root_path):
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try:
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# 데이터 전송 콜백함수
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def send_data(trainer):
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@ -151,14 +150,14 @@ async def run_train(request, model, dataset_root_path):
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# 데이터 전송
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send_data_call_api(request.project_id, request.m_id, data)
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"send_data exception: {e}")
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print(f"Exception in send_data(): {e}")
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# 콜백 등록
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model.add_callback("on_train_epoch_start", send_data)
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# 학습 실행
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results = await run_in_threadpool(model.train,
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try:
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# 비동기 함수로 학습 실행
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results = model.train(
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data=join_path(dataset_root_path, "dataset.yaml"),
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name=join_path(dataset_root_path, "result"),
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epochs=request.epochs,
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@ -167,6 +166,9 @@ async def run_train(request, model, dataset_root_path):
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lrf=request.lrf,
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optimizer=request.optimizer
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)
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finally:
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# 콜백 해제 및 자원 해제
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model.reset_callbacks()
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# 마지막 에포크 전송
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model.trainer.epoch += 1
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