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
71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os.path as osp
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from collections import OrderedDict
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import mmcv
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import torch
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from mmcv.runner import CheckpointLoader
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def convert_vit(ckpt):
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new_ckpt = OrderedDict()
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for k, v in ckpt.items():
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if k.startswith('head'):
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continue
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if k.startswith('norm'):
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new_k = k.replace('norm.', 'ln1.')
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elif k.startswith('patch_embed'):
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if 'proj' in k:
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new_k = k.replace('proj', 'projection')
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else:
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new_k = k
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elif k.startswith('blocks'):
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if 'norm' in k:
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new_k = k.replace('norm', 'ln')
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elif 'mlp.fc1' in k:
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new_k = k.replace('mlp.fc1', 'ffn.layers.0.0')
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elif 'mlp.fc2' in k:
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new_k = k.replace('mlp.fc2', 'ffn.layers.1')
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elif 'attn.qkv' in k:
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new_k = k.replace('attn.qkv.', 'attn.attn.in_proj_')
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elif 'attn.proj' in k:
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new_k = k.replace('attn.proj', 'attn.attn.out_proj')
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else:
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new_k = k
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new_k = new_k.replace('blocks.', 'layers.')
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else:
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new_k = k
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new_ckpt[new_k] = v
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return new_ckpt
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def main():
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parser = argparse.ArgumentParser(
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description='Convert keys in timm pretrained vit models to '
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'MMSegmentation style.')
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parser.add_argument('src', help='src model path or url')
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# The dst path must be a full path of the new checkpoint.
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parser.add_argument('dst', help='save path')
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args = parser.parse_args()
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checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')
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if 'state_dict' in checkpoint:
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# timm checkpoint
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state_dict = checkpoint['state_dict']
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elif 'model' in checkpoint:
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# deit checkpoint
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state_dict = checkpoint['model']
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else:
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state_dict = checkpoint
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weight = convert_vit(state_dict)
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mmcv.mkdir_or_exist(osp.dirname(args.dst))
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torch.save(weight, args.dst)
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if __name__ == '__main__':
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main()
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