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import argparse |
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import os.path as osp |
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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_beit(ckpt): |
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new_ckpt = OrderedDict() |
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for k, v in ckpt.items(): |
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if k.startswith('patch_embed'): |
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new_key = k.replace('patch_embed.proj', 'patch_embed.projection') |
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new_ckpt[new_key] = v |
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if k.startswith('blocks'): |
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new_key = k.replace('blocks', 'layers') |
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if 'norm' in new_key: |
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new_key = new_key.replace('norm', 'ln') |
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elif 'mlp.fc1' in new_key: |
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new_key = new_key.replace('mlp.fc1', 'ffn.layers.0.0') |
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elif 'mlp.fc2' in new_key: |
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new_key = new_key.replace('mlp.fc2', 'ffn.layers.1') |
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new_ckpt[new_key] = v |
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else: |
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new_key = k |
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new_ckpt[new_key] = v |
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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 official pretrained beit models to' |
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'MMSegmentation style.') |
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parser.add_argument('src', help='src model path or url') |
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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 'state_dict' in checkpoint: |
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state_dict = checkpoint['state_dict'] |
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elif '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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weight = convert_beit(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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if __name__ == '__main__': |
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main() |
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