Merge branch 'ai/feat/segmentation' into 'ai/develop'
Feat: segmentation API 구현 See merge request s11-s-project/S11P21S002!187
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@ -122,7 +122,7 @@ async def detection_train(request: TrainRequest, http_request: Request):
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# 학습
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results = run_train(request,token,model,dataset_root_path)
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# last 모델 저장
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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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response = {"model_key": model_key, "results": results.results_dict}
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@ -1,62 +1,213 @@
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from fastapi import APIRouter, HTTPException
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from fastapi import APIRouter, HTTPException, Request
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from schemas.predict_request import PredictRequest
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from schemas.train_request import TrainRequest
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from schemas.predict_response import PredictResponse, LabelData
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from schemas.train_report_data import ReportData
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from services.load_model import load_segmentation_model
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from typing import List
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from services.create_model import save_model
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from utils.dataset_utils import split_data
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from utils.file_utils import get_dataset_root_path, process_directories, process_image_and_label, join_path
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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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router = APIRouter()
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@router.post("/predict", response_model=List[PredictResponse])
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def predict(request: PredictRequest):
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@router.post("/predict")
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async def segmentation_predict(request: PredictRequest):
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send_slack_message(f"seg predict 요청: {request}", status="success")
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# 모델 로드
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model = get_model(request)
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# 모델 레이블 카테고리 연결
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classes = list(request.label_map) if request.label_map else None
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# 이미지 데이터 정리
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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 = 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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send_slack_message(f"predict 성공{response}", status="success")
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return response
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# 모델 로드
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def get_model(request: PredictRequest):
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try:
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model = load_segmentation_model(request.project_id, request.m_key)
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return load_segmentation_model(request.project_id, request.m_key)
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except Exception as e:
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raise HTTPException(status_code=500, detail="load model exception: " + str(e))
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# 추론
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results = []
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# 추론 실행 함수
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def run_predictions(model, image, request, classes):
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try:
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for image in request.image_list:
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predict_results = model.predict(
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source=image.image_url,
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return 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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classes=request.classes
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classes=classes
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)
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results.append(predict_results[0])
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except Exception as e:
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raise HTTPException(status_code=500, detail="model predict exception: " + str(e))
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# 추론 결과 -> 레이블 객체 파싱
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response = []
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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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for (image, result) in zip(request.image_list, results):
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label_data:LabelData = {
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"version": "0.0.0",
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"task_type": "seg",
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"shapes": [
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label_data = LabelData(
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version="0.0.0",
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task_type="seg",
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shapes=[
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{
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"label": summary['name'],
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"color": "#ff0000",
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"color": get_random_color(),
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"points": list(zip(summary['segments']['x'], summary['segments']['y'])),
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"group_id": summary['class'],
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"group_id": label_map[summary['class']] if label_map else summary['class'],
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"shape_type": "polygon",
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"flags": {}
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}
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for summary in result.summary()
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],
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"split": "none",
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"imageHeight": result.orig_img.shape[0],
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"imageWidth": result.orig_img.shape[1],
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"imageDepth": result.orig_img.shape[2]
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}
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response.append({
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"image_id":image.image_id,
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"image_url":image.image_url,
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"data":label_data
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})
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split="none",
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imageHeight=result.orig_img.shape[0],
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imageWidth=result.orig_img.shape[1],
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imageDepth=result.orig_img.shape[2]
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)
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except Exception as e:
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raise HTTPException(status_code=500, detail="label parsing exception: "+str(e))
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raise HTTPException(status_code=500, detail="model predict exception: " + str(e))
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return PredictResponse(
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image_id=image.image_id,
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data=label_data.model_dump_json()
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)
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def get_random_color():
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random_number = random.randint(0, 0xFFFFFF)
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return f"#{random_number:06X}"
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@router.post("/train")
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async def segmentation_train(request: TrainRequest, http_request: Request):
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send_slack_message(f"train 요청{request}", status="success")
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# Authorization 헤더에서 Bearer 토큰 추출
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auth_header = http_request.headers.get("Authorization")
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token = auth_header.split(" ")[1] if auth_header and auth_header.startswith("Bearer ") else None
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# 레이블 맵
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inverted_label_map = {value: key for key, value in request.label_map.items()} if request.label_map else None
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# 데이터셋 루트 경로 얻기
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dataset_root_path = get_dataset_root_path(request.project_id)
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# 모델 로드
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model = get_model(request)
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# 학습할 모델 카테고리, 카테고리가 추가되는 경우 추가 작업 필요
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model_categories = model.names
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# 데이터 전처리
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preprocess_dataset(dataset_root_path, model_categories, request.data, request.ratio, inverted_label_map)
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# 학습
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results = run_train(request,token,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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response = {"model_key": model_key, "results": results.results_dict}
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send_slack_message(f"train 성공{response}", status="success")
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return response
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def preprocess_dataset(dataset_root_path, model_categories, data, ratio, label_map):
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try:
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# 디렉토리 생성 및 초기화
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process_directories(dataset_root_path, model_categories)
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# 학습 데이터 분류
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train_data, val_data = split_data(data, ratio)
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if not train_data or not val_data:
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raise HTTPException(status_code=400, detail="data split exception: data size is too small or \"ratio\" has invalid value")
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# 학습 데이터 처리
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for data in train_data:
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process_image_and_label(data, dataset_root_path, "train", label_map)
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# 검증 데이터 처리
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for data in val_data:
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process_image_and_label(data, dataset_root_path, "val", label_map)
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except HTTPException as e:
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raise e # HTTP 예외를 다시 발생
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except Exception as e:
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raise HTTPException(status_code=500, detail="preprocess dataset exception: " + str(e))
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def run_train(request, token, 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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try:
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# 첫번째 epoch는 스킵
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if trainer.epoch == 0:
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return
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# 남은 시간 계산(초)
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left_epochs = trainer.epochs - trainer.epoch
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left_seconds = left_epochs * trainer.epoch_time
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# 로스 box_loss, cls_loss, dfl_loss
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loss = trainer.label_loss_items(loss_items=trainer.loss_items)
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data = ReportData(
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epoch=trainer.epoch, # 현재 에포크
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total_epochs=trainer.epochs, # 전체 에포크
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seg_loss=loss["train/seg_loss"], # seg loss
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cls_loss=loss["train/cls_loss"], # cls loss
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dfl_loss=loss["train/dfl_loss"], # dfl loss
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fitness=trainer.fitness, # 적합도
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epoch_time=trainer.epoch_time, # 지난 에포크 걸린 시간 (에포크 시작 기준으로 결정)
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left_seconds=left_seconds # 남은 시간(초)
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)
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# 데이터 전송
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send_data_call_api(request.project_id, request.m_id, data, token)
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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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# 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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try:
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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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batch=request.batch,
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lr0=request.lr0,
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lrf=request.lrf,
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optimizer=request.optimizer
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)
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"model train exception: {e}")
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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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except Exception as e:
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raise HTTPException(status_code=500, detail=f"run_train exception: {e}")
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@ -20,24 +20,24 @@ def make_dir(path:str, init: bool):
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shutil.rmtree(path)
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os.makedirs(path, exist_ok=True)
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def make_yml(path:str, names):
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def make_yml(path:str, model_categories):
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data = {
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"train": f"{path}/train",
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"val": f"{path}/val",
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"nc": 80,
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"names": names
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"names": model_categories
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}
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with open(os.path.join(path, "dataset.yaml"), 'w') as f:
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yaml.dump(data, f)
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def process_directories(dataset_root_path:str, names:list[str]):
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def process_directories(dataset_root_path:str, model_categories:list[str]):
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"""학습을 위한 디렉토리 생성"""
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make_dir(dataset_root_path, init=False)
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make_dir(os.path.join(dataset_root_path, "train"), init=True)
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make_dir(os.path.join(dataset_root_path, "val"), init=True)
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if os.path.exists(os.path.join(dataset_root_path, "result")):
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shutil.rmtree(os.path.join(dataset_root_path, "result"))
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make_yml(dataset_root_path, names)
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make_yml(dataset_root_path, model_categories)
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def process_image_and_label(data:TrainDataInfo, dataset_root_path:str, child_path:str, label_map:dict[int, int]|None):
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"""이미지 저장 및 레이블 파일 생성"""
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@ -59,9 +59,12 @@ def process_image_and_label(data:TrainDataInfo, dataset_root_path:str, child_pat
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label = json.loads(urllib.request.urlopen(data.data_url).read())
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# 레이블 -> 학습용 레이블 데이터 파싱 후 생성
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if label['task_type'] == "det":
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create_detection_train_label(label, label_path, label_map)
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elif label["task_type"] == "seg":
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create_segmentation_train_label(label, label_path, label_map)
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def create_detection_train_label(label:LabelData, label_path:str, label_map:dict[int, int]|None):
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def create_detection_train_label(label:dict, label_path:str, label_map:dict[int, int]|None):
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with open(label_path, "w") as train_label_txt:
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for shape in label["shapes"]:
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train_label = []
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@ -76,6 +79,16 @@ def create_detection_train_label(label:LabelData, label_path:str, label_map:dict
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train_label.append(str((y2 - y1) / label["imageHeight"] )) # 높이
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train_label_txt.write(" ".join(train_label)+"\n")
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def create_segmentation_train_label(label:dict, label_path:str, label_map:dict[int, int]|None):
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with open(label_path, "w") as train_label_txt:
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for shape in label["shapes"]:
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train_label = []
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train_label.append(str(label_map[shape["group_id"]]) if label_map else str(shape["group_id"])) # label Id
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for x, y in shape["points"]:
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train_label.append(str(x / label["imageWidth"]))
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train_label.append(str(y / label["imageHeight"]))
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train_label_txt.write(" ".join(train_label)+"\n")
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def join_path(path, *paths):
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"""os.path.join()과 같은 기능, os import 하기 싫어서 만듦"""
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return os.path.join(path, *paths)
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