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- # Copyright (c) OpenMMLab. All rights reserved.
- import argparse
- import subprocess
- import torch
- from mmengine.logging import print_log
- def parse_args():
- parser = argparse.ArgumentParser(
- description='Process a checkpoint to be published')
- parser.add_argument('in_file', help='input checkpoint filename')
- parser.add_argument('out_file', help='output checkpoint filename')
- parser.add_argument(
- '--save-keys',
- nargs='+',
- type=str,
- default=['meta', 'state_dict'],
- help='keys to save in the published checkpoint')
- args = parser.parse_args()
- return args
- def process_checkpoint(in_file, out_file, save_keys=['meta', 'state_dict']):
- checkpoint = torch.load(in_file, map_location='cpu')
- # only keep `meta` and `state_dict` for smaller file size
- ckpt_keys = list(checkpoint.keys())
- for k in ckpt_keys:
- if k not in save_keys:
- print_log(
- f'Key `{k}` will be removed because it is not in '
- f'save_keys. If you want to keep it, '
- f'please set --save-keys.',
- logger='current')
- checkpoint.pop(k, None)
- # if it is necessary to remove some sensitive data in checkpoint['meta'],
- # add the code here.
- if torch.__version__ >= '1.6':
- torch.save(checkpoint, out_file, _use_new_zipfile_serialization=False)
- else:
- torch.save(checkpoint, out_file)
- sha = subprocess.check_output(['sha256sum', out_file]).decode()
- if out_file.endswith('.pth'):
- out_file_name = out_file[:-4]
- else:
- out_file_name = out_file
- final_file = out_file_name + f'-{sha[:8]}.pth'
- subprocess.Popen(['mv', out_file, final_file])
- print_log(
- f'The published model is saved at {final_file}.', logger='current')
- def main():
- args = parse_args()
- process_checkpoint(args.in_file, args.out_file, args.save_keys)
- if __name__ == '__main__':
- main()
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