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Merge branch 'ai/refactor/detection' into 'ai/develop'

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refactor: detection_train 리팩토링

See merge request s11-s-project/S11P21S002!184
This commit is contained in:
김용수 2024-09-26 01:24:35 +09:00
commit 342795d7a3
2 changed files with 91 additions and 50 deletions
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89
ai/app/api/yolo/detection.py
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@ -8,7 +8,7 @@ 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.slackMessage import send_slack_message
from utils.api_utils import report_data
from utils.api_utils import send_data_call_api
import random
@ -59,16 +59,13 @@ def run_predictions(model, image, request, classes):
# 추론 결과 처리 함수
def process_prediction_result(result, image, label_map):
try:
random_number = random.randint(0, 0xFFFFFF)
color = f"#{random_number:06X}"
label_data = LabelData(
version="0.0.0",
task_type="det",
shapes=[
{
"label": summary['name'],
"color": color,
"color": get_random_color(),
"points": [
[summary['box']['x1'], summary['box']['y1']],
[summary['box']['x2'], summary['box']['y2']]
@ -92,6 +89,10 @@ def process_prediction_result(result, image, label_map):
data=label_data.model_dump_json()
)
def get_random_color():
random_number = random.randint(0, 0xFFFFFF)
return f"#{random_number:06X}"
@router.post("/train")
@ -103,42 +104,71 @@ async def detection_train(request: TrainRequest, http_request: Request):
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
# 데이터셋 루트 경로 얻기
dataset_root_path = get_dataset_root_path(request.project_id)
# 모델 로드
model = get_model(request)
# 학습할 모델 카테고리 정리 카테고리가 추가되는 경우에 추가할 수 있게
names = model.names
# 학습할 모델 카테고리, 카테고리가 추가되는 경우 추가 작업 필요
model_categories = model.names
# 데이터 전처리
preprocess_dataset(dataset_root_path, model_categories, request.data, request.ratio, inverted_label_map)
# 학습
results = run_train(request,token,model,dataset_root_path)
# last 모델 저장
model_key = save_model(project_id=request.project_id, path=join_path(dataset_root_path, "result", "weights", "best.pt"))
response = {"model_key": model_key, "results": results.results_dict}
send_slack_message(f"train 성공{response}", status="success")
return response
def preprocess_dataset(dataset_root_path, model_categories, data, ratio, label_map):
try:
# 디렉토리 생성 및 초기화
process_directories(dataset_root_path, names)
# 레이블 맵
inverted_label_map = {value: key for key, value in request.label_map.items()} if request.label_map else None
process_directories(dataset_root_path, model_categories)
# 학습 데이터 분류
train_data, val_data = split_data(request.data, request.ratio)
train_data, val_data = split_data(data, ratio)
if not train_data or not val_data:
raise HTTPException(status_code=400, detail="data split exception: data size is too small or \"ratio\" has invalid value")
try:
# 학습 데이터 처리
for data in train_data:
process_image_and_label(data, dataset_root_path, "train", inverted_label_map)
process_image_and_label(data, dataset_root_path, "train", label_map)
# 검증 데이터 처리
for data in val_data:
process_image_and_label(data, dataset_root_path, "val", inverted_label_map)
process_image_and_label(data, dataset_root_path, "val", label_map)
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):
try:
# 데이터 전송 콜백함수
def send_data(trainer):
try:
# 첫번째 epoch는 스킵
if trainer.epoch == 0:
return
## 남은 시간 계산(초)
# 남은 시간 계산(초)
left_epochs = trainer.epochs - trainer.epoch
left_seconds = left_epochs * trainer.epoch_time
## 로스 box_loss, cls_loss, dfl_loss
# 로스 box_loss, cls_loss, dfl_loss
loss = trainer.label_loss_items(loss_items=trainer.loss_items)
data = ReportData(
epoch=trainer.epoch, # 현재 에포크
@ -151,10 +181,15 @@ async def detection_train(request: TrainRequest, http_request: Request):
left_seconds=left_seconds # 남은 시간(초)
)
# 데이터 전송
report_data(request.project_id, request.m_id, data, token)
send_data_call_api(request.project_id, request.m_id, data, token)
except Exception as e:
raise HTTPException(status_code=500, detail=f"send_data exception: {e}")
# 콜백 등록
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"),
@ -164,12 +199,18 @@ async def detection_train(request: TrainRequest, http_request: Request):
lrf=request.lrf,
optimizer=request.optimizer
)
model_key = save_model(project_id=request.project_id, path=join_path(dataset_root_path, "result", "weights", "best.pt"))
response = {"model_key": model_key, "results": results.results_dict}
send_slack_message(f"train 성공{response}", status="success")
return response
except Exception as e:
raise HTTPException(status_code=500, detail=f"model train exception: {e}")
# 마지막 에포크 전송
model.trainer.epoch += 1
send_data(model.trainer)
return results
except HTTPException as e:
raise e # HTTP 예외를 다시 발생
except Exception as e:
raise HTTPException(status_code=500, detail=f"run_train exception: {e}")
raise HTTPException(status_code=500, detail="model train exception: " + str(e))

2
ai/app/utils/api_utils.py
View File

@ -3,7 +3,7 @@ from dotenv import load_dotenv
import os, httpx
def report_data(project_id:int, model_id:int, data:ReportData, token):
def send_data_call_api(project_id:int, model_id:int, data:ReportData, token):
try:
load_dotenv()
# main.py와 같은 디렉토리에 .env 파일 생성해서 따옴표 없이 입력