77 lines
2.7 KiB
Python
77 lines
2.7 KiB
Python
import argparse
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from collections import OrderedDict
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import torch
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def mit_convert(ckpt):
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new_ckpt = OrderedDict()
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# Process the concat between q linear weights and kv linear weights
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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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# patch embedding convertion
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elif k.startswith('patch_embed'):
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stage_i = int(k.split('.')[0].replace('patch_embed', ''))
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new_k = k.replace(f'patch_embed{stage_i}', f'layers.{stage_i-1}.0')
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new_v = v
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if 'proj.' in new_k:
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new_k = new_k.replace('proj.', 'projection.')
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# transformer encoder layer convertion
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elif k.startswith('block'):
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stage_i = int(k.split('.')[0].replace('block', ''))
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new_k = k.replace(f'block{stage_i}', f'layers.{stage_i-1}.1')
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new_v = v
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if 'attn.q.' in new_k:
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sub_item_k = k.replace('q.', 'kv.')
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new_k = new_k.replace('q.', 'attn.in_proj_')
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new_v = torch.cat([v, ckpt[sub_item_k]], dim=0)
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elif 'attn.kv.' in new_k:
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continue
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elif 'attn.proj.' in new_k:
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new_k = new_k.replace('proj.', 'attn.out_proj.')
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elif 'attn.sr.' in new_k:
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new_k = new_k.replace('sr.', 'sr.')
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elif 'mlp.' in new_k:
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string = f'{new_k}-'
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new_k = new_k.replace('mlp.', 'ffn.layers.')
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if 'fc1.weight' in new_k or 'fc2.weight' in new_k:
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new_v = v.reshape((*v.shape, 1, 1))
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new_k = new_k.replace('fc1.', '0.')
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new_k = new_k.replace('dwconv.dwconv.', '1.')
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new_k = new_k.replace('fc2.', '4.')
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string += f'{new_k} {v.shape}-{new_v.shape}'
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# norm layer convertion
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elif k.startswith('norm'):
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stage_i = int(k.split('.')[0].replace('norm', ''))
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new_k = k.replace(f'norm{stage_i}', f'layers.{stage_i-1}.2')
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new_v = v
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else:
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new_k = k
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new_v = v
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new_ckpt[new_k] = new_v
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return new_ckpt
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def parse_args():
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parser = argparse.ArgumentParser(
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'Convert official segformer backbone weights to mmseg style.')
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parser.add_argument(
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'src', help='Source path of official segformer backbone weights.')
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parser.add_argument(
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'dst',
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help='Destination path of converted segformer backbone weights.')
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return parser.parse_args()
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if __name__ == '__main__':
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args = parse_args()
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src_path = args.src
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dst_path = args.dst
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ckpt = torch.load(src_path, map_location='cpu')
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ckpt = mit_convert(ckpt)
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torch.save(ckpt, dst_path)
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