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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()
final_file = out_file.rstrip('.pth') + 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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