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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 swin_converter(ckpt):
new_ckpt = OrderedDict()
def correct_unfold_reduction_order(x):
out_channel, in_channel = x.shape
x = x.reshape(out_channel, 4, in_channel // 4)
x = x[:, [0, 2, 1, 3], :].transpose(1,
2).reshape(out_channel, in_channel)
return x
def correct_unfold_norm_order(x):
in_channel = x.shape[0]
x = x.reshape(4, in_channel // 4)
x = x[[0, 2, 1, 3], :].transpose(0, 1).reshape(in_channel)
return x
for k, v in ckpt.items():
if k.startswith('head'):
continue
elif k.startswith('layers'):
new_v = v
if 'attn.' in k:
new_k = k.replace('attn.', 'attn.w_msa.')
elif 'mlp.' in k:
if 'mlp.fc1.' in k:
new_k = k.replace('mlp.fc1.', 'ffn.layers.0.0.')
elif 'mlp.fc2.' in k:
new_k = k.replace('mlp.fc2.', 'ffn.layers.1.')
else:
new_k = k.replace('mlp.', 'ffn.')
elif 'downsample' in k:
new_k = k
if 'reduction.' in k:
new_v = correct_unfold_reduction_order(v)
elif 'norm.' in k:
new_v = correct_unfold_norm_order(v)
else:
new_k = k
new_k = new_k.replace('layers', 'stages', 1)
elif k.startswith('patch_embed'):
new_v = v
if 'proj' in k:
new_k = k.replace('proj', 'projection')
else:
new_k = k
else:
new_v = v
new_k = k
new_ckpt['backbone.' + new_k] = new_v
return new_ckpt
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