mmclassification/tools/model_converters/publish_model.py

56 lines
1.7 KiB
Python

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import datetime
import subprocess
from pathlib import Path
import torch
from mmcv import digit_version
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')
args = parser.parse_args()
return args
def process_checkpoint(in_file, out_file):
checkpoint = torch.load(in_file, map_location='cpu')
# remove optimizer for smaller file size
if 'optimizer' in checkpoint:
del checkpoint['optimizer']
# if it is necessary to remove some sensitive data in checkpoint['meta'],
# add the code here.
if digit_version(torch.__version__) >= digit_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
current_date = datetime.datetime.now().strftime('%Y%m%d')
final_file = out_file_name + f'_{current_date}-{sha[:8]}.pth'
subprocess.Popen(['mv', out_file, final_file])
print(f'Successfully generated the publish-ckpt as {final_file}.')
def main():
args = parse_args()
out_dir = Path(args.out_file).parent
if not out_dir.exists():
raise ValueError(f'Directory {out_dir} does not exist, '
'please generate it manually.')
process_checkpoint(args.in_file, args.out_file)
if __name__ == '__main__':
main()