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
37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import subprocess
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import torch
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Process a checkpoint to be published')
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parser.add_argument('in_file', help='input checkpoint filename')
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parser.add_argument('out_file', help='output checkpoint filename')
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args = parser.parse_args()
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return args
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def process_checkpoint(in_file, out_file):
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checkpoint = torch.load(in_file, map_location='cpu')
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# remove optimizer for smaller file size
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if 'optimizer' in checkpoint:
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del checkpoint['optimizer']
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# if it is necessary to remove some sensitive data in checkpoint['meta'],
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# add the code here.
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torch.save(checkpoint, out_file)
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sha = subprocess.check_output(['sha256sum', out_file]).decode()
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final_file = out_file.rstrip('.pth') + '-{}.pth'.format(sha[:8])
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subprocess.Popen(['mv', out_file, final_file])
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def main():
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args = parse_args()
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process_checkpoint(args.in_file, args.out_file)
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if __name__ == '__main__':
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main()
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