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Feat: classification API 구현

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김진현 2024-09-26 22:14:16 +09:00
parent 4d49b925dc
commit cae5fb5ae4
5 changed files with 79 additions and 52 deletions
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82
ai/app/api/yolo/classfication.py
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@ -6,7 +6,7 @@ from schemas.train_report_data import ReportData
from services.load_model import load_classification_model
from services.create_model import save_model
from utils.dataset_utils import split_data
from utils.file_utils import get_dataset_root_path, process_directories, process_image_and_label, join_path
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
@ -17,7 +17,7 @@ router = APIRouter()
@router.post("/predict")
async def classification_predict(request: PredictRequest):
send_slack_message(f"predict 요청: {request}", status="success")
send_slack_message(f"cls predict 요청: {request}", status="success")
# 모델 로드
model = get_model(request)
@ -61,17 +61,16 @@ def process_prediction_result(result, image, label_map):
try:
label_data = LabelData(
version="0.0.0",
task_type="det",
task_type="cls",
shapes=[
{
"label": summary['name'],
"color": get_random_color(),
"points": [
[summary['box']['x1'], summary['box']['y1']],
[summary['box']['x2'], summary['box']['y2']]
[0, 0]
],
"group_id": label_map[summary['class']] if label_map else summary['class'],
"shape_type": "rectangle",
"shape_type": "point",
"flags": {}
}
for summary in result.summary()
@ -96,16 +95,9 @@ def get_random_color():
@router.post("/train")
async def classification_train(request: TrainRequest, http_request: Request):
async def classification_train(request: TrainRequest):
send_slack_message(f"train 요청{request}", status="success")
# Authorization 헤더에서 Bearer 토큰 추출
auth_header = http_request.headers.get("Authorization")
token = auth_header.split(" ")[1] if auth_header and auth_header.startswith("Bearer ") else None
# 레이블 맵
inverted_label_map = {value: key for key, value in request.label_map.items()} if request.label_map else None
send_slack_message(f"cls train 요청{request}", status="success")
# 데이터셋 루트 경로 얻기
dataset_root_path = get_dataset_root_path(request.project_id)
@ -117,10 +109,10 @@ async def classification_train(request: TrainRequest, http_request: Request):
model_categories = model.names
# 데이터 전처리
preprocess_dataset(dataset_root_path, model_categories, request.data, request.ratio, inverted_label_map)
preprocess_dataset(dataset_root_path, model_categories, request.data, request.ratio)
# 학습
results = run_train(request,token,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"))
@ -132,30 +124,30 @@ async def classification_train(request: TrainRequest, http_request: Request):
return response
def preprocess_dataset(dataset_root_path, model_categories, data, ratio, label_map):
def preprocess_dataset(dataset_root_path, model_categories, data, ratio):
try:
# 디렉토리 생성 및 초기화
process_directories(dataset_root_path, model_categories)
process_directories_in_cls(dataset_root_path, model_categories)
# 학습 데이터 분류
train_data, val_data = split_data(data, ratio)
if not train_data or not val_data:
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(data, dataset_root_path, "train", label_map)
process_image_and_label_in_cls(data, dataset_root_path, "train")
# 검증 데이터 처리
for data in val_data:
process_image_and_label(data, dataset_root_path, "val", label_map)
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))
def run_train(request, token, model, dataset_root_path):
def run_train(request, model, dataset_root_path):
try:
# 데이터 전송 콜백함수
def send_data(trainer):
@ -171,17 +163,17 @@ def run_train(request, token, model, dataset_root_path):
# 로스 box_loss, cls_loss, dfl_loss
loss = trainer.label_loss_items(loss_items=trainer.loss_items)
data = ReportData(
epoch=trainer.epoch, # 현재 에포크
total_epochs=trainer.epochs, # 전체 에포크
box_loss=loss["train/box_loss"], # box loss
cls_loss=loss["train/cls_loss"], # cls loss
dfl_loss=loss["train/dfl_loss"], # dfl loss
fitness=trainer.fitness, # 적합도
epoch_time=trainer.epoch_time, # 지난 에포크 걸린 시간 (에포크 시작 기준으로 결정)
left_seconds=left_seconds # 남은 시간(초)
epoch=trainer.epoch, # 현재 에포크
total_epochs=trainer.epochs, # 전체 에포크
box_loss=0, # box loss
cls_loss=loss["train/loss"], # cls loss
dfl_loss=0, # dfl loss
fitness=trainer.fitness, # 적합도
epoch_time=trainer.epoch_time, # 지난 에포크 걸린 시간 (에포크 시작 기준으로 결정)
left_seconds=left_seconds # 남은 시간(초)
)
# 데이터 전송
send_data_call_api(request.project_id, request.m_id, data, token)
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}")
@ -189,19 +181,15 @@ def run_train(request, token, model, dataset_root_path):
model.add_callback("on_train_epoch_start", send_data)
# 학습 실행
try:
results = 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
)
except Exception as e:
raise HTTPException(status_code=500, detail=f"model train exception: {e}")
results = model.train(
data=dataset_root_path,
name=join_path(dataset_root_path, "result"),
epochs=request.epochs,
batch=request.batch,
lr0=request.lr0,
lrf=request.lrf,
optimizer=request.optimizer
)
# 마지막 에포크 전송
model.trainer.epoch += 1
send_data(model.trainer)

1
ai/app/api/yolo/detection.py
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@ -101,7 +101,6 @@ async def detection_train(request: TrainRequest):
send_slack_message(f"train 요청{request}", status="success")
# Authorization 헤더에서 Bearer 토큰 추출
try:
# 레이블 맵
inverted_label_map = {value: key for key, value in request.label_map.items()} if request.label_map else None

2
ai/app/main.py
View File

@ -4,6 +4,7 @@ from fastapi.exceptions import RequestValidationError
from starlette.exceptions import HTTPException
from api.yolo.detection import router as yolo_detection_router
from api.yolo.segmentation import router as yolo_segmentation_router
from api.yolo.classfication import router as yolo_classification_router
from api.yolo.model import router as yolo_model_router
from utils.slackMessage import send_slack_message
@ -12,6 +13,7 @@ app = FastAPI()
# 각 기능별 라우터를 애플리케이션에 등록
app.include_router(yolo_detection_router, prefix="/api/detection", tags=["Detection"])
app.include_router(yolo_segmentation_router, prefix="/api/segmentation", tags=["Segmentation"])
app.include_router(yolo_classification_router, prefix="/api/classification", tags=["Classification"])
app.include_router(yolo_model_router, prefix="/api/model", tags=["Model"])

17
ai/app/services/load_model.py
View File

@ -10,7 +10,7 @@ def load_detection_model(project_id:int, model_key:str):
if model_key in default_model_map:
model = YOLO(default_model_map[model_key])
else:
model = load_model(model_path=os.path.join("projects",str(project_id),"models", model_key))
model = load_model(model_path=os.path.join("resources", "projects",str(project_id),"models", model_key))
# Detection 모델인지 검증
if model.task != "detect":
@ -23,13 +23,26 @@ def load_segmentation_model(project_id:int, model_key:str):
if model_key in default_model_map:
model = YOLO(default_model_map[model_key])
else:
model = load_model(model_path=os.path.join("projects",str(project_id),"models",model_key))
model = load_model(model_path=os.path.join("resources", "projects",str(project_id),"models",model_key))
# Segmentation 모델인지 검증
if model.task != "segment":
raise TypeError(f"Invalid model type: {model.task}. Expected a SegmentationModel.")
return model
def load_classification_model(project_id:int, model_key:str):
default_model_map = {"yolo8": os.path.join("resources","models","yolov8n-cls.pt")}
# 디폴트 모델 확인
if model_key in default_model_map:
model = YOLO(default_model_map[model_key])
else:
model = load_model(model_path=os.path.join("resources", "projects",str(project_id),"models",model_key))
# Segmentation 모델인지 검증
if model.task != "classify":
raise TypeError(f"Invalid model type: {model.task}. Expected a ClassificationModel.")
return model
def load_model(model_path: str):
if not os.path.exists(model_path):
raise FileNotFoundError(f"Model file not found at path: {model_path}")

27
ai/app/utils/file_utils.py
View File

@ -24,7 +24,7 @@ def make_yml(path:str, model_categories):
data = {
"train": f"{path}/train",
"val": f"{path}/val",
"nc": 80,
"nc": len(model_categories),
"names": model_categories
}
with open(os.path.join(path, "dataset.yaml"), 'w') as f:
@ -117,3 +117,28 @@ def get_file_name(path):
if not os.path.exists(path):
raise FileNotFoundError()
return os.path.basename(path)
def process_directories_in_cls(dataset_root_path:str, model_categories:dict[int,str]):
"""classification 학습을 위한 디렉토리 생성"""
make_dir(dataset_root_path, init=False)
for category in model_categories.values():
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")):
shutil.rmtree(os.path.join(dataset_root_path, "result"))
def process_image_and_label_in_cls(data:TrainDataInfo, dataset_root_path:str, child_path:str):
"""이미지 저장 및 레이블 파일 생성"""
# 이미지 url로부터 파일명 분리
img_name = data.image_url.split('/')[-1]
# 레이블 객체 불러오기
label = json.loads(urllib.request.urlopen(data.data_url).read())
label_name = label["shapes"][0]["label"]
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))