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
83 lines
3.0 KiB
Python
83 lines
3.0 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_mit(ckpt):
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new_ckpt = OrderedDict()
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# Process the concat between q linear weights and kv linear weights
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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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# patch embedding conversion
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elif k.startswith('patch_embed'):
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stage_i = int(k.split('.')[0].replace('patch_embed', ''))
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new_k = k.replace(f'patch_embed{stage_i}', f'layers.{stage_i-1}.0')
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new_v = v
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if 'proj.' in new_k:
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new_k = new_k.replace('proj.', 'projection.')
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# transformer encoder layer conversion
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elif k.startswith('block'):
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stage_i = int(k.split('.')[0].replace('block', ''))
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new_k = k.replace(f'block{stage_i}', f'layers.{stage_i-1}.1')
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new_v = v
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if 'attn.q.' in new_k:
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sub_item_k = k.replace('q.', 'kv.')
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new_k = new_k.replace('q.', 'attn.in_proj_')
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new_v = torch.cat([v, ckpt[sub_item_k]], dim=0)
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elif 'attn.kv.' in new_k:
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continue
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elif 'attn.proj.' in new_k:
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new_k = new_k.replace('proj.', 'attn.out_proj.')
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elif 'attn.sr.' in new_k:
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new_k = new_k.replace('sr.', 'sr.')
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elif 'mlp.' in new_k:
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string = f'{new_k}-'
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new_k = new_k.replace('mlp.', 'ffn.layers.')
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if 'fc1.weight' in new_k or 'fc2.weight' in new_k:
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new_v = v.reshape((*v.shape, 1, 1))
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new_k = new_k.replace('fc1.', '0.')
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new_k = new_k.replace('dwconv.dwconv.', '1.')
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new_k = new_k.replace('fc2.', '4.')
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string += f'{new_k} {v.shape}-{new_v.shape}'
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# norm layer conversion
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elif k.startswith('norm'):
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stage_i = int(k.split('.')[0].replace('norm', ''))
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new_k = k.replace(f'norm{stage_i}', f'layers.{stage_i-1}.2')
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new_v = v
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else:
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new_k = k
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new_v = v
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new_ckpt[new_k] = new_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 official pretrained segformer 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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state_dict = checkpoint['state_dict']
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elif 'model' in 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_mit(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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