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86 lines
3.5 KiB
86 lines
3.5 KiB
#!/usr/bin/env python3
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""" Checkpoint Cleaning Script
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Takes training checkpoints with GPU tensors, optimizer state, extra dict keys, etc.
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and outputs a CPU tensor checkpoint with only the `state_dict` along with SHA256
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calculation for model zoo compatibility.
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Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
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"""
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import torch
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import argparse
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import os
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import hashlib
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import safetensors.torch
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import shutil
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from timm.models import load_state_dict
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parser = argparse.ArgumentParser(description='PyTorch Checkpoint Cleaner')
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parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
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help='path to latest checkpoint (default: none)')
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parser.add_argument('--output', default='', type=str, metavar='PATH',
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help='output path')
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parser.add_argument('--no-use-ema', dest='no_use_ema', action='store_true',
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help='use ema version of weights if present')
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parser.add_argument('--clean-aux-bn', dest='clean_aux_bn', action='store_true',
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help='remove auxiliary batch norm layers (from SplitBN training) from checkpoint')
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parser.add_argument('--safetensors', action='store_true',
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help='Save weights using safetensors instead of the default torch way (pickle).')
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_TEMP_NAME = './_checkpoint.pth'
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def main():
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args = parser.parse_args()
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if os.path.exists(args.output):
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print("Error: Output filename ({}) already exists.".format(args.output))
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exit(1)
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clean_checkpoint(args.checkpoint, args.output, not args.no_use_ema, args.clean_aux_bn, safe_serialization=args.safetensors)
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def clean_checkpoint(checkpoint, output='', use_ema=True, clean_aux_bn=False, safe_serialization: bool=False):
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# Load an existing checkpoint to CPU, strip everything but the state_dict and re-save
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if checkpoint and os.path.isfile(checkpoint):
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print("=> Loading checkpoint '{}'".format(checkpoint))
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state_dict = load_state_dict(checkpoint, use_ema=use_ema)
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new_state_dict = {}
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for k, v in state_dict.items():
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if clean_aux_bn and 'aux_bn' in k:
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# If all aux_bn keys are removed, the SplitBN layers will end up as normal and
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# load with the unmodified model using BatchNorm2d.
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continue
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name = k[7:] if k.startswith('module.') else k
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new_state_dict[name] = v
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print("=> Loaded state_dict from '{}'".format(checkpoint))
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if safe_serialization:
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safetensors.torch.save_file(new_state_dict, _TEMP_NAME)
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else:
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try:
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torch.save(new_state_dict, _TEMP_NAME, _use_new_zipfile_serialization=False)
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except:
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torch.save(new_state_dict, _TEMP_NAME)
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with open(_TEMP_NAME, 'rb') as f:
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sha_hash = hashlib.sha256(f.read()).hexdigest()
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if output:
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checkpoint_root, checkpoint_base = os.path.split(output)
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checkpoint_base = os.path.splitext(checkpoint_base)[0]
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else:
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checkpoint_root = ''
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checkpoint_base = os.path.splitext(checkpoint)[0]
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final_filename = '-'.join([checkpoint_base, sha_hash[:8]]) + ('.safetensors' if safe_serialization else '.pth')
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shutil.move(_TEMP_NAME, os.path.join(checkpoint_root, final_filename))
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print("=> Saved state_dict to '{}, SHA256: {}'".format(final_filename, sha_hash))
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return final_filename
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else:
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print("Error: Checkpoint ({}) doesn't exist".format(checkpoint))
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return ''
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if __name__ == '__main__':
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main()
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