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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import argparse | |
| import os.path as osp | |
| from collections import OrderedDict | |
| import mmengine | |
| import torch | |
| from mmengine.runner import CheckpointLoader | |
| def convert_mit(ckpt): | |
| new_ckpt = OrderedDict() | |
| # Process the concat between q linear weights and kv linear weights | |
| for k, v in ckpt.items(): | |
| if k.startswith('head'): | |
| continue | |
| # patch embedding conversion | |
| elif k.startswith('patch_embed'): | |
| stage_i = int(k.split('.')[0].replace('patch_embed', '')) | |
| new_k = k.replace(f'patch_embed{stage_i}', f'layers.{stage_i-1}.0') | |
| new_v = v | |
| if 'proj.' in new_k: | |
| new_k = new_k.replace('proj.', 'projection.') | |
| # transformer encoder layer conversion | |
| elif k.startswith('block'): | |
| stage_i = int(k.split('.')[0].replace('block', '')) | |
| new_k = k.replace(f'block{stage_i}', f'layers.{stage_i-1}.1') | |
| new_v = v | |
| if 'attn.q.' in new_k: | |
| sub_item_k = k.replace('q.', 'kv.') | |
| new_k = new_k.replace('q.', 'attn.in_proj_') | |
| new_v = torch.cat([v, ckpt[sub_item_k]], dim=0) | |
| elif 'attn.kv.' in new_k: | |
| continue | |
| elif 'attn.proj.' in new_k: | |
| new_k = new_k.replace('proj.', 'attn.out_proj.') | |
| elif 'attn.sr.' in new_k: | |
| new_k = new_k.replace('sr.', 'sr.') | |
| elif 'mlp.' in new_k: | |
| string = f'{new_k}-' | |
| new_k = new_k.replace('mlp.', 'ffn.layers.') | |
| if 'fc1.weight' in new_k or 'fc2.weight' in new_k: | |
| new_v = v.reshape((*v.shape, 1, 1)) | |
| new_k = new_k.replace('fc1.', '0.') | |
| new_k = new_k.replace('dwconv.dwconv.', '1.') | |
| new_k = new_k.replace('fc2.', '4.') | |
| string += f'{new_k} {v.shape}-{new_v.shape}' | |
| # norm layer conversion | |
| elif k.startswith('norm'): | |
| stage_i = int(k.split('.')[0].replace('norm', '')) | |
| new_k = k.replace(f'norm{stage_i}', f'layers.{stage_i-1}.2') | |
| new_v = v | |
| else: | |
| new_k = k | |
| new_v = v | |
| new_ckpt[new_k] = new_v | |
| return new_ckpt | |
| def main(): | |
| parser = argparse.ArgumentParser( | |
| description='Convert keys in official pretrained segformer to ' | |
| 'MMSegmentation style.') | |
| parser.add_argument('src', help='src model path or url') | |
| # The dst path must be a full path of the new checkpoint. | |
| parser.add_argument('dst', help='save path') | |
| args = parser.parse_args() | |
| checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu') | |
| if 'state_dict' in checkpoint: | |
| state_dict = checkpoint['state_dict'] | |
| elif 'model' in checkpoint: | |
| state_dict = checkpoint['model'] | |
| else: | |
| state_dict = checkpoint | |
| weight = convert_mit(state_dict) | |
| mmengine.mkdir_or_exist(osp.dirname(args.dst)) | |
| torch.save(weight, args.dst) | |
| if __name__ == '__main__': | |
| main() | |