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
61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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from mmcv import Config
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from mmcv.cnn import get_model_complexity_info
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from mmseg.models import build_segmentor
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Get the FLOPs of a segmentor')
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parser.add_argument('config', help='train config file path')
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parser.add_argument(
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'--shape',
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type=int,
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nargs='+',
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default=[2048, 1024],
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help='input image size')
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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if len(args.shape) == 1:
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input_shape = (3, args.shape[0], args.shape[0])
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elif len(args.shape) == 2:
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input_shape = (3, ) + tuple(args.shape)
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else:
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raise ValueError('invalid input shape')
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cfg = Config.fromfile(args.config)
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cfg.model.pretrained = None
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model = build_segmentor(
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cfg.model,
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train_cfg=cfg.get('train_cfg'),
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test_cfg=cfg.get('test_cfg')).cuda()
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model.eval()
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if hasattr(model, 'forward_dummy'):
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model.forward = model.forward_dummy
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else:
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raise NotImplementedError(
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'FLOPs counter is currently not currently supported with {}'.
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format(model.__class__.__name__))
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flops, params = get_model_complexity_info(model, input_shape)
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split_line = '=' * 30
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print('{0}\nInput shape: {1}\nFlops: {2}\nParams: {3}\n{0}'.format(
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split_line, input_shape, flops, params))
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print('!!!Please be cautious if you use the results in papers. '
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'You may need to check if all ops are supported and verify that the '
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'flops computation is correct.')
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if __name__ == '__main__':
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main()
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