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pytorch-image-models/clean_checkpoint.py

53 lines
2.0 KiB

import torch
import argparse
import os
import hashlib
from collections import OrderedDict
parser = argparse.ArgumentParser(description='PyTorch ImageNet Validation')
parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--output', default='./cleaned.pth', type=str, metavar='PATH',
help='output path')
parser.add_argument('--use-ema', dest='use_ema', action='store_true',
help='use ema version of weights if present')
def main():
args = parser.parse_args()
if os.path.exists(args.output):
print("Error: Output filename ({}) already exists.".format(args.output))
exit(1)
# Load an existing checkpoint to CPU, strip everything but the state_dict and re-save
if args.checkpoint and os.path.isfile(args.checkpoint):
print("=> Loading checkpoint '{}'".format(args.checkpoint))
checkpoint = torch.load(args.checkpoint, map_location='cpu')
new_state_dict = OrderedDict()
if isinstance(checkpoint, dict):
state_dict_key = 'state_dict_ema' if args.use_ema else 'state_dict'
if state_dict_key in checkpoint:
state_dict = checkpoint[state_dict_key]
else:
print("Error: No state_dict found in checkpoint {}.".format(args.checkpoint))
exit(1)
else:
state_dict = checkpoint
for k, v in state_dict.items():
name = k[7:] if k.startswith('module') else k
new_state_dict[name] = v
print("=> Loaded state_dict from '{}'".format(args.checkpoint))
torch.save(new_state_dict, args.output)
with open(args.output, 'rb') as f:
sha_hash = hashlib.sha256(f.read()).hexdigest()
print("=> Saved state_dict to '{}, SHA256: {}'".format(args.output, sha_hash))
else:
print("Error: Checkpoint ({}) doesn't exist".format(args.checkpoint))
if __name__ == '__main__':
main()