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| # Copyright (c) OpenMMLab. All rights reserved. | |
| # This script consists of several convert functions which | |
| # can modify the weights of model in original repo to be | |
| # pre-trained weights. | |
| from collections import OrderedDict | |
| import torch | |
| def pvt_convert(ckpt): | |
| new_ckpt = OrderedDict() | |
| # Process the concat between q linear weights and kv linear weights | |
| use_abs_pos_embed = False | |
| use_conv_ffn = False | |
| for k in ckpt.keys(): | |
| if k.startswith('pos_embed'): | |
| use_abs_pos_embed = True | |
| if k.find('dwconv') >= 0: | |
| use_conv_ffn = True | |
| for k, v in ckpt.items(): | |
| if k.startswith('head'): | |
| continue | |
| if k.startswith('norm.'): | |
| continue | |
| if k.startswith('cls_token'): | |
| continue | |
| if k.startswith('pos_embed'): | |
| stage_i = int(k.replace('pos_embed', '')) | |
| new_k = k.replace(f'pos_embed{stage_i}', | |
| f'layers.{stage_i - 1}.1.0.pos_embed') | |
| if stage_i == 4 and v.size(1) == 50: # 1 (cls token) + 7 * 7 | |
| new_v = v[:, 1:, :] # remove cls token | |
| else: | |
| new_v = v | |
| 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.') | |
| elif k.startswith('block'): | |
| stage_i = int(k.split('.')[0].replace('block', '')) | |
| layer_i = int(k.split('.')[1]) | |
| new_layer_i = layer_i + use_abs_pos_embed | |
| new_k = k.replace(f'block{stage_i}.{layer_i}', | |
| f'layers.{stage_i - 1}.1.{new_layer_i}') | |
| 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.') | |
| if use_conv_ffn: | |
| new_k = new_k.replace('fc2.', '4.') | |
| else: | |
| new_k = new_k.replace('fc2.', '3.') | |
| string += f'{new_k} {v.shape}-{new_v.shape}' | |
| elif k.startswith('norm'): | |
| stage_i = int(k[4]) | |
| 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 tcformer_convert(ckpt): | |
| new_ckpt = OrderedDict() | |
| # Process the concat between q linear weights and kv linear weights | |
| for k, v in ckpt.items(): | |
| if 'patch_embed' in k: | |
| new_k = k.replace('.proj.', '.projection.') | |
| else: | |
| new_k = k | |
| new_ckpt[new_k] = v | |
| return new_ckpt | |