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
88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import argparse
|
|
import os.path as osp
|
|
from collections import OrderedDict
|
|
|
|
import mmcv
|
|
import torch
|
|
from mmcv.runner import CheckpointLoader
|
|
|
|
|
|
def convert_twins(args, ckpt):
|
|
|
|
new_ckpt = OrderedDict()
|
|
|
|
for k, v in list(ckpt.items()):
|
|
new_v = v
|
|
if k.startswith('head'):
|
|
continue
|
|
elif k.startswith('patch_embeds'):
|
|
if 'proj.' in k:
|
|
new_k = k.replace('proj.', 'projection.')
|
|
else:
|
|
new_k = k
|
|
elif k.startswith('blocks'):
|
|
# Union
|
|
if 'attn.q.' in k:
|
|
new_k = k.replace('q.', 'attn.in_proj_')
|
|
new_v = torch.cat([v, ckpt[k.replace('attn.q.', 'attn.kv.')]],
|
|
dim=0)
|
|
elif 'mlp.fc1' in k:
|
|
new_k = k.replace('mlp.fc1', 'ffn.layers.0.0')
|
|
elif 'mlp.fc2' in k:
|
|
new_k = k.replace('mlp.fc2', 'ffn.layers.1')
|
|
# Only pcpvt
|
|
elif args.model == 'pcpvt':
|
|
if 'attn.proj.' in k:
|
|
new_k = k.replace('proj.', 'attn.out_proj.')
|
|
else:
|
|
new_k = k
|
|
|
|
# Only svt
|
|
else:
|
|
if 'attn.proj.' in k:
|
|
k_lst = k.split('.')
|
|
if int(k_lst[2]) % 2 == 1:
|
|
new_k = k.replace('proj.', 'attn.out_proj.')
|
|
else:
|
|
new_k = k
|
|
else:
|
|
new_k = k
|
|
new_k = new_k.replace('blocks.', 'layers.')
|
|
elif k.startswith('pos_block'):
|
|
new_k = k.replace('pos_block', 'position_encodings')
|
|
if 'proj.0.' in new_k:
|
|
new_k = new_k.replace('proj.0.', 'proj.')
|
|
else:
|
|
new_k = k
|
|
if 'attn.kv.' not in k:
|
|
new_ckpt[new_k] = new_v
|
|
return new_ckpt
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser(
|
|
description='Convert keys in timm pretrained vit models to '
|
|
'MMSegmentation style.')
|
|
parser.add_argument('src', help='src model path or url')
|
|
# The dst path must be a full path of the new checkpoint.
|
|
parser.add_argument('dst', help='save path')
|
|
parser.add_argument('model', help='model: pcpvt or svt')
|
|
args = parser.parse_args()
|
|
|
|
checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')
|
|
|
|
if 'state_dict' in checkpoint:
|
|
# timm checkpoint
|
|
state_dict = checkpoint['state_dict']
|
|
else:
|
|
state_dict = checkpoint
|
|
|
|
weight = convert_twins(args, state_dict)
|
|
mmcv.mkdir_or_exist(osp.dirname(args.dst))
|
|
torch.save(weight, args.dst)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|