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[Feature] Add tools to convert distill ckpt to student-only ckpt. #381
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# Copyright (c) OpenMMLab. All rights reserved. | ||
import argparse | ||
from pathlib import Path | ||
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import torch | ||
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def parse_args(): | ||
parser = argparse.ArgumentParser( | ||
description='Process a checkpoint to be published') | ||
parser.add_argument('checkpoint', help='input checkpoint filename') | ||
parser.add_argument( | ||
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There are five parts When the student part is cut off, it can only be used for inference, deployment, or as a pre-training weight. Therefore, there is no need to consider the situation of resuming training, you can directly delete
This script can keep only |
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'--model-only', action='store_true', help='only save model') | ||
parser.add_argument( | ||
'--inplace', action='store_true', help='replace origin ckpt') | ||
args = parser.parse_args() | ||
return args | ||
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def main(): | ||
args = parse_args() | ||
checkpoint = torch.load(args.checkpoint, map_location='cpu') | ||
new_state_dict = dict() | ||
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for key, value in checkpoint['state_dict'].items(): | ||
if key.startswith('architecture.'): | ||
new_key = key.replace('architecture.', '') | ||
new_state_dict[new_key] = value | ||
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if args.model_only: | ||
checkpoint = dict() | ||
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checkpoint['state_dict'] = new_state_dict | ||
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if args.inplace: | ||
torch.save(checkpoint, args.checkpoint) | ||
else: | ||
ckpt_path = Path(args.checkpoint) | ||
ckpt_name = ckpt_path.stem | ||
ckpt_dir = ckpt_path.parent | ||
new_ckpt_path = ckpt_dir / f'{ckpt_name}_student.pth' | ||
torch.save(checkpoint, new_ckpt_path) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Add an arg |
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if __name__ == '__main__': | ||
main() |
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Pls update the description