62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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from pathlib import Path
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import torch
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def convert_weights(weight):
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"""Weight Converter.
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Converts the weights from timm to mmpretrain
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Args:
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weight (dict): weight dict from timm
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Returns:
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Converted weight dict for mmpretrain
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"""
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result = dict()
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result['meta'] = dict()
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temp = dict()
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mapping = {
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'c.weight': 'conv2d.weight',
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'bn.weight': 'bn2d.weight',
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'bn.bias': 'bn2d.bias',
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'bn.running_mean': 'bn2d.running_mean',
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'bn.running_var': 'bn2d.running_var',
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'bn.num_batches_tracked': 'bn2d.num_batches_tracked',
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'layers': 'stages',
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'norm_head': 'norm3',
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}
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weight = weight['model']
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for k, v in weight.items():
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# keyword mapping
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for mk, mv in mapping.items():
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if mk in k:
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k = k.replace(mk, mv)
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if k.startswith('head.'):
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temp['head.fc.' + k[5:]] = v
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else:
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temp['backbone.' + k] = v
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result['state_dict'] = temp
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return result
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Convert model keys')
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parser.add_argument('src', help='src detectron model path')
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parser.add_argument('dst', help='save path')
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args = parser.parse_args()
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dst = Path(args.dst)
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if dst.suffix != '.pth':
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print('The path should contain the name of the pth format file.')
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exit(1)
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dst.parent.mkdir(parents=True, exist_ok=True)
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original_model = torch.load(args.src, map_location='cpu')
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converted_model = convert_weights(original_model)
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torch.save(converted_model, args.dst)
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