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Merge branch 'ai/refactor/exception-handle' into 'ai/develop'

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Refactor: request 검증 classification 레이블 없을때 에러 처리

See merge request s11-s-project/S11P21S002!248
This commit is contained in:
김용수 2024-09-30 15:39:30 +09:00
commit 354676d867
8 changed files with 77 additions and 41 deletions
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3
ai/app/api/yolo/model.py
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@ -32,9 +32,6 @@ def get_model_list(project_id:int):
@router.post("/projects/{project_id}", status_code=201)
def create_model(project_id: int, request: ModelCreateRequest):
if request.project_type not in ["segmentation", "detection", "classification"]:
raise HTTPException(status_code=400,
detail= f"Invalid type '{request.type}'. Must be one of \"segmentation\", \"detection\", \"classification\".")
model_key = create_new_model(project_id, request.project_type, request.pretrained)
return {"model_key": model_key}

3
ai/app/schemas/model_create_request.py
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@ -1,5 +1,6 @@
from pydantic import BaseModel
from typing import Literal
class ModelCreateRequest(BaseModel):
project_type: str
project_type: Literal["segmentation", "detection", "classification"]
pretrained:bool = True

7
ai/app/schemas/predict_request.py
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@ -1,5 +1,4 @@
from pydantic import BaseModel, Field
from typing import Optional, Union
class ImageInfo(BaseModel):
image_id: int
@ -9,7 +8,7 @@ class ImageInfo(BaseModel):
class PredictRequest(BaseModel):
project_id: int
m_key: str = Field("yolo8", alias="model_key") # model_ 로 시작하는 변수를 BaseModel의 변수로 만들경우 Warning 떠서 m_key로 대체
label_map: dict[str, int] = Field(..., description="프로젝트 레이블 이름: 프로젝트 레이블 pk , None일 경우 모델 레이블 카테고리 idx로 레이블링")
label_map: dict[str, int] = Field(..., description="프로젝트 레이블 이름: 프로젝트 레이블 pk")
image_list: list[ImageInfo] # 이미지 리스트
conf_threshold: float = 0.25 #
iou_threshold: float = 0.45
conf_threshold: float = Field(0.25, gt=0, lt= 1)
iou_threshold: float = Field(0.45, gt=0, lt= 1)

28
ai/app/schemas/train_label_data.py Normal file
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@ -0,0 +1,28 @@
from pydantic import BaseModel, Field
class Segment(BaseModel):
x: float = Field(..., ge=0, le=1)
y: float = Field(..., ge=0, le=1)
def to_string(self) -> str:
return f"{self.x} {self.y}"
class DetectionLabelData(BaseModel):
label_id: int = Field(..., ge=0)
center_x: float = Field(..., ge=0, le=1)
center_y: float = Field(..., ge=0, le=1)
width: float = Field(..., ge=0, le=1)
height: float = Field(..., ge=0, le=1)
def to_string(self) -> str:
return f"{self.label_id} {self.center_x} {self.center_y} {self.width} {self.height}"
class SegmentationLabelData(BaseModel):
label_id: int
segments: list[Segment]
def to_string(self) -> str:
points_str = " ".join([segment.to_string() for segment in self.segments])
return f"{self.label_id} {points_str}"

1
ai/app/schemas/train_report_data.py
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@ -1,7 +1,6 @@
from pydantic import BaseModel
class ReportData(BaseModel):
epoch: int # 현재 에포크
total_epochs: int # 전체 에포크
seg_loss: float # seg_loss

22
ai/app/schemas/train_request.py
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@ -1,22 +1,22 @@
from pydantic import BaseModel, Field
from typing import List, Optional, Union, Literal
from schemas.predict_response import LabelData
from typing import Literal
class TrainDataInfo(BaseModel):
image_url: str
data_url: str
class TrainRequest(BaseModel):
project_id: int
project_id: int = Field(..., gt= 0)
m_key: str = Field("yolo8", alias="model_key")
m_id: int = Field(..., alias="model_id") # 학습 중 에포크 결과를 보낼때 model_id를 보냄
label_map: dict[str, int] = Field(..., description="프로젝트 레이블 이름: 프로젝트 레이블 pk , None일 경우 모델 레이블 카테고리 idx로 레이블링")
data: List[TrainDataInfo]
ratio: float = 0.8 # 훈련/검증 분할 비율
m_id: int = Field(..., alias="model_id", gt= 0) # 학습 중 에포크 결과를 보낼때 model_id를 보냄
label_map: dict[str, int] = Field(..., description="프로젝트 레이블 이름: 프로젝트 레이블 pk")
data: list[TrainDataInfo]
ratio: float = Field(0.8, gt=0, lt=1) # 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)
epochs: int = Field(50, gt= 0, lt = 1000) # 훈련 반복 횟수
batch: int = Field(16, gt=0, le = 10000) # 훈련 batch 수[int] or GPU의 사용률 자동[float] default(-1): gpu의 60% 사용 유지
lr0: float = Field(0.01, gt= 0, lt= 1) # 초기 학습 가중치
lrf: float = Field(0.01, gt= 0, lt= 1) # lr0 기준으로 학습 가중치의 최종 수렴치 (ex lr0의 0.01배)
optimizer: Literal['auto', 'SGD', 'Adam', 'AdamW', 'NAdam', 'RAdam', 'RMSProp'] = 'auto'

10
ai/app/utils/api_utils.py
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@ -5,14 +5,15 @@ import os, httpx
def send_data_call_api(project_id:int, model_id:int, data:ReportData):
try:
# load_dotenv()
# base_url = os.getenv("API_BASE_URL")
load_dotenv()
base_url = os.getenv("API_BASE_URL")
# main.py와 같은 디렉토리에 .env 파일 생성해서 따옴표 없이 아래 데이터를 입력
# API_BASE_URL = {url}
# API_KEY = {key}
# 하드코딩으로 대체
base_url = "http://127.0.0.1:8080"
if not base_url:
base_url = "http://127.0.0.1:8080"
headers = {
"Content-Type": "application/json"
@ -22,7 +23,8 @@ def send_data_call_api(project_id:int, model_id:int, data:ReportData):
method="POST",
url=base_url+f"/api/projects/{project_id}/reports/models/{model_id}",
json=data.model_dump(),
headers=headers
headers=headers,
timeout=10
)
# status에 따라 예외 발생
response.raise_for_status()

44
ai/app/utils/file_utils.py
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@ -3,7 +3,7 @@ import shutil
import yaml
from PIL import Image
from schemas.train_request import TrainDataInfo
from schemas.predict_response import LabelData
from schemas.train_label_data import DetectionLabelData, SegmentationLabelData, Segment
import urllib
import json
@ -67,28 +67,34 @@ def process_image_and_label(data:TrainDataInfo, dataset_root_path:str, child_pat
def create_detection_train_label(label:dict, label_path:str, label_converter:dict[int, int]):
with open(label_path, "w") as train_label_txt:
for shape in label["shapes"]:
train_label = []
x1 = shape["points"][0][0]
y1 = shape["points"][0][1]
x2 = shape["points"][1][0]
y2 = shape["points"][1][1]
train_label.append(str(label_converter[shape["group_id"]])) # label Id
train_label.append(str((x1 + x2) / 2 / label["imageWidth"])) # 중심 x 좌표
train_label.append(str((y1 + y2) / 2 / label["imageHeight"])) # 중심 y 좌표
train_label.append(str((x2 - x1) / label["imageWidth"])) # 너비
train_label.append(str((y2 - y1) / label["imageHeight"] )) # 높이
train_label_txt.write(" ".join(train_label)+"\n")
detection_label = DetectionLabelData(
label_id= label_converter[shape["group_id"]], # 모델의 id (converter : pjt category pk -> model category id)
center_x= (x1 + x2) / 2 / label["imageWidth"], # 중심 x 좌표
center_y= (y1 + y2) / 2 / label["imageHeight"], # 중심 y 좌표
width= (x2 - x1) / label["imageWidth"], # 너비
height= (y2 - y1) / label["imageHeight"] # 높이
)
train_label_txt.write(detection_label.to_string()+"\n") # str변환 후 txt에 쓰기
def create_segmentation_train_label(label:dict, label_path:str, label_converter:dict[int, int]):
with open(label_path, "w") as train_label_txt:
for shape in label["shapes"]:
train_label = []
train_label.append(str(label_converter[shape["group_id"]])) # label Id
for x, y in shape["points"]:
train_label.append(str(x / label["imageWidth"]))
train_label.append(str(y / label["imageHeight"]))
train_label_txt.write(" ".join(train_label)+"\n")
segmentation_label = SegmentationLabelData(
label_id = label_converter[shape["group_id"]], # label Id
segments = [
Segment(
x=x / label["imageWidth"], # shapes의 points 갯수만큼 x, y 반복
y=y / label["imageHeight"]
) for x, y in shape["points"]
]
)
train_label_txt.write(segmentation_label.to_string()+"\n")
def join_path(path, *paths):
"""os.path.join()과 같은 기능, os import 하기 싫어서 만듦"""
return os.path.join(path, *paths)
@ -135,6 +141,10 @@ def process_image_and_label_in_cls(data:TrainDataInfo, dataset_root_path:str, ch
# 레이블 객체 불러오기
label = json.loads(urllib.request.urlopen(data.data_url).read())
if not label["shapes"]:
# assert label["shapes"], No Label. Failed Download" # AssertionError 발생
print("No Label. Failed Download")
return
label_name = label["shapes"][0]["label"]
label_path = os.path.join(dataset_root_path,child_path,label_name)
@ -143,8 +153,8 @@ def process_image_and_label_in_cls(data:TrainDataInfo, dataset_root_path:str, ch
if os.path.exists(label_path):
urllib.request.urlretrieve(data.image_url, os.path.join(label_path, img_name))
else:
# raise FileNotFoundError("failed download")
print("Not Found Label Category. Failed Download")
# raise FileNotFoundError("No Label Category. Failed Download")
print("No Label Category. Failed Download")
# 레이블 데이터 중에서 프로젝트 카테고리에 해당되지않는 데이터가 있는 경우 처리 1. 에러 raise 2. 무시(+ warning)