99 lines
3.3 KiB
Python
99 lines
3.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os.path as osp
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import re
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from collections import OrderedDict
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import mmengine
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import torch
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from mmengine.runner import CheckpointLoader
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def convert_vig(ckpt):
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new_ckpt = OrderedDict()
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for k, v in ckpt.items():
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new_key = k
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new_value = v
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if 'pos_embed' in new_key:
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new_key = new_key.replace('pos_embed', 'backbone.pos_embed')
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elif 'stem' in new_key:
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new_key = new_key.replace('stem.convs', 'backbone.stem')
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elif 'backbone' in new_key:
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new_key = new_key.replace('backbone', 'backbone.blocks')
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elif 'prediction.0' in new_key:
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new_key = new_key.replace('prediction.0', 'head.fc1')
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new_value = v.squeeze(-1).squeeze(-1)
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elif 'prediction.1' in new_key:
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new_key = new_key.replace('prediction.1', 'head.bn')
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elif 'prediction.4' in new_key:
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new_key = new_key.replace('prediction.4', 'head.fc2')
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new_value = v.squeeze(-1).squeeze(-1)
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new_ckpt[new_key] = new_value
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return new_ckpt
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def convert_pvig(ckpt):
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new_ckpt = OrderedDict()
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stage_idx = 0
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stage_blocks = 0
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for k, v in ckpt.items():
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new_key: str = k
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new_value = v
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if 'pos_embed' in new_key:
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new_key = new_key.replace('pos_embed', 'backbone.pos_embed')
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elif 'stem' in new_key:
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new_key = new_key.replace('stem.convs', 'backbone.stem')
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elif re.match(r'^backbone\.\d+\.conv', new_key) is not None:
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if new_key.endswith('0.weight'):
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stage_idx += 1
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stage_blocks = int(new_key.split('.')[1])
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other = new_key.split('.', maxsplit=3)[-1]
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new_key = f'backbone.stages.{stage_idx}.0.' + other
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elif 'backbone' in new_key:
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block_idx = int(new_key.split('.')[1]) - stage_blocks
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other = new_key.split('.', maxsplit=2)[-1]
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new_key = f'backbone.stages.{stage_idx}.{block_idx}.' + other
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elif 'prediction.0' in new_key:
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new_key = new_key.replace('prediction.0', 'head.fc1')
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new_value = v.squeeze(-1).squeeze(-1)
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elif 'prediction.1' in new_key:
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new_key = new_key.replace('prediction.1', 'head.bn')
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elif 'prediction.4' in new_key:
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new_key = new_key.replace('prediction.4', 'head.fc2')
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new_value = v.squeeze(-1).squeeze(-1)
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new_ckpt[new_key] = new_value
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return new_ckpt
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def main():
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parser = argparse.ArgumentParser(
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description='Convert keys in pretrained vig models to '
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'mmpretrain style.')
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parser.add_argument('src', help='src model path or url')
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# The dst path must be a full path of the new checkpoint.
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parser.add_argument('dst', help='save path')
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args = parser.parse_args()
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checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')
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if 'model' in checkpoint:
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state_dict = checkpoint['model']
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else:
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state_dict = checkpoint
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if 'backbone.2.conv.0.weight' in state_dict:
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weight = convert_pvig(state_dict)
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else:
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weight = convert_vig(state_dict)
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mmengine.mkdir_or_exist(osp.dirname(args.dst))
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torch.save(weight, args.dst)
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print('Done!!')
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if __name__ == '__main__':
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main()
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