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https://github.com/open-mmlab/mmsegmentation.git
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8f8abe373f
* [Refactor] Using mmcv bricks to refactor vit * Follow the vit code structure from mmclassification * Add MMCV install into CI system. * Add to 'Install MMCV' CI item * Add 'Install MMCV_CPU' and 'Install MMCV_GPU CI' items * Fix & Add 1. Fix low code coverage of vit.py; 2. Remove HybirdEmbed; 3. Fix doc string of VisionTransformer; * Add helpers unit test. * Add converter to convert vit pretrain weights from timm style to mmcls style. * Clean some rebundant code and refactor init 1. Use timm style init_weights; 2. Remove to_xtuple and trunc_norm_; * Add comments for VisionTransformer.init_weights() * Add arg: pretrain_style to choose timm or mmcls vit pretrain weights.
34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
from collections import OrderedDict
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def vit_convert(timm_dict):
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mmseg_dict = OrderedDict()
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for k, v in timm_dict.items():
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if k.startswith('head'):
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continue
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if k.startswith('norm'):
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new_k = k.replace('norm.', 'ln1.')
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elif k.startswith('patch_embed'):
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if 'proj' in k:
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new_k = k.replace('proj', 'projection')
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elif k.startswith('blocks'):
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new_k = k.replace('blocks.', 'layers.')
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if 'norm' in new_k:
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new_k = new_k.replace('norm', 'ln')
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elif 'mlp.fc1' in new_k:
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new_k = new_k.replace('mlp.fc1', 'ffn.layers.0.0')
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elif 'mlp.fc2' in new_k:
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new_k = new_k.replace('mlp.fc2', 'ffn.layers.1')
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elif 'attn.qkv' in new_k:
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new_k = new_k.replace('attn.qkv.', 'attn.attn.in_proj_')
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elif 'attn.proj' in new_k:
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new_k = new_k.replace('attn.proj', 'attn.attn.out_proj')
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else:
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new_k = k
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new_k = f'backbone.{new_k}'
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mmseg_dict[new_k] = v
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return mmseg_dict
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