jeonghyo.k
3946ff6a25
- prediction/image/ FastAPI 서버 Docker 환경 구성 - Dockerfile: PyTorch 2.1 + CUDA 12.1 기반 GPU 이미지 - docker-compose.yml: GPU 할당 + 데이터 볼륨 마운트 - requirements.txt: 서버 의존성 목록 - .env.example: 환경변수 템플릿 - DOCKER_USAGE.md: 빌드/실행/API 사용법 문서 - Dockerfile에 .dockerignore 제외 폴더 mkdir -p 추가 - .gitignore: prediction/image 결과물 및 모델 가중치(.pth) 제외 추가 - dbInsert_csv.py, dbInsert_shp.py 삭제 (미사용 DB 로직) - api.py: dbInsert import 및 주석 처리된 DB 호출 코드 제거 - aerialRouter.ts: req.params 타입 오류 수정
53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import collections
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from mmcv.utils import build_from_cfg
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from ..builder import PIPELINES
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@PIPELINES.register_module()
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class Compose(object):
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"""Compose multiple transforms sequentially.
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Args:
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transforms (Sequence[dict | callable]): Sequence of transform object or
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config dict to be composed.
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"""
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def __init__(self, transforms):
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assert isinstance(transforms, collections.abc.Sequence)
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self.transforms = []
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for transform in transforms:
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if isinstance(transform, dict):
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transform = build_from_cfg(transform, PIPELINES)
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self.transforms.append(transform)
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elif callable(transform):
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self.transforms.append(transform)
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else:
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raise TypeError('transform must be callable or a dict')
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def __call__(self, data):
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"""Call function to apply transforms sequentially.
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Args:
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data (dict): A result dict contains the data to transform.
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Returns:
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dict: Transformed data.
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"""
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for t in self.transforms:
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data = t(data)
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if data is None:
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return None
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return data
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def __repr__(self):
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format_string = self.__class__.__name__ + '('
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for t in self.transforms:
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format_string += '\n'
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format_string += f' {t}'
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format_string += '\n)'
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return format_string
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