99 lines
2.7 KiB
Python
99 lines
2.7 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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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 correct_unfold_reduction_order(x: torch.Tensor):
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out_channel, in_channel = x.shape
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x = x.reshape(out_channel, 4, in_channel // 4)
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x = x[:, [0, 2, 1, 3], :].transpose(1, 2).reshape(out_channel, in_channel)
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return x
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def correct_unfold_norm_order(x):
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in_channel = x.shape[0]
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x = x.reshape(4, in_channel // 4)
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x = x[[0, 2, 1, 3], :].transpose(0, 1).reshape(in_channel)
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return x
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def convert_mixmim(ckpt):
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new_ckpt = OrderedDict()
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for k, v in list(ckpt.items()):
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new_v = v
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if k.startswith('patch_embed'):
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new_k = k.replace('proj', 'projection')
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elif k.startswith('layers'):
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if 'norm1' in k:
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new_k = k.replace('norm1', 'ln1')
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elif 'norm2' in k:
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new_k = k.replace('norm2', 'ln2')
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elif 'mlp.fc1' in k:
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new_k = k.replace('mlp.fc1', 'ffn.layers.0.0')
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elif 'mlp.fc2' in k:
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new_k = k.replace('mlp.fc2', 'ffn.layers.1')
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else:
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new_k = k
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elif k.startswith('norm') or k.startswith('absolute_pos_embed'):
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new_k = k
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elif k.startswith('head'):
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new_k = k.replace('head.', 'head.fc.')
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else:
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raise ValueError
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# print(new_k)
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if not new_k.startswith('head'):
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new_k = 'backbone.' + new_k
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if 'downsample' in new_k:
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print('Covert {} in PatchMerging from timm to mmcv format!'.format(
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new_k))
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if 'reduction' in new_k:
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new_v = correct_unfold_reduction_order(new_v)
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elif 'norm' in new_k:
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new_v = correct_unfold_norm_order(new_v)
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new_ckpt[new_k] = new_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 pretrained van models to mmcls 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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weight = convert_mixmim(state_dict)
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# weight = convert_official_mixmim(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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