mirror of
https://github.com/open-mmlab/mmsegmentation.git
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c11da07e18
* delete convert function and add instruction to README.md * unified model convert and README * remove url * fix import error * fix unittest * rename pretrain * rename vit and deit pretrain * Update upernet_deit-b16_512x512_160k_ade20k.py * Update upernet_deit-b16_512x512_80k_ade20k.py * Update upernet_deit-b16_ln_mln_512x512_160k_ade20k.py * Update upernet_deit-b16_mln_512x512_160k_ade20k.py * Update upernet_deit-s16_512x512_160k_ade20k.py * Update upernet_deit-s16_512x512_80k_ade20k.py * Update upernet_deit-s16_ln_mln_512x512_160k_ade20k.py * Update upernet_deit-s16_mln_512x512_160k_ade20k.py Co-authored-by: Jiarui XU <xvjiarui0826@gmail.com> Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
88 lines
2.7 KiB
Python
88 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 mmcv
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import torch
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from mmcv.runner import CheckpointLoader
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def convert_swin(ckpt):
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new_ckpt = OrderedDict()
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def correct_unfold_reduction_order(x):
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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,
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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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for k, v in ckpt.items():
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if k.startswith('head'):
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continue
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elif k.startswith('layers'):
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new_v = v
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if 'attn.' in k:
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new_k = k.replace('attn.', 'attn.w_msa.')
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elif 'mlp.' in k:
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if '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.replace('mlp.', 'ffn.')
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elif 'downsample' in k:
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new_k = k
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if 'reduction.' in k:
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new_v = correct_unfold_reduction_order(v)
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elif 'norm.' in k:
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new_v = correct_unfold_norm_order(v)
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else:
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new_k = k
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new_k = new_k.replace('layers', 'stages', 1)
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elif k.startswith('patch_embed'):
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new_v = v
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if 'proj' in k:
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new_k = k.replace('proj', 'projection')
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else:
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new_k = k
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else:
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new_v = v
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new_k = k
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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 official pretrained swin 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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# 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 '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_swin(state_dict)
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mmcv.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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