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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import subprocess
import torch
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 'message_hub' in checkpoint:
del checkpoint['message_hub']
if 'ema_state_dict' in checkpoint:
del checkpoint['ema_state_dict']
for key in list(checkpoint['state_dict']):
if key.startswith('data_preprocessor'):
checkpoint['state_dict'].pop(key)
elif 'priors_base_sizes' in key:
checkpoint['state_dict'].pop(key)
elif 'grid_offset' in key:
checkpoint['state_dict'].pop(key)
elif 'prior_inds' in key:
checkpoint['state_dict'].pop(key)
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])
def main():
args = parse_args()
process_checkpoint(args.in_file, args.out_file)
if __name__ == '__main__':
main()
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