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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_vitlayer(paras):
new_para_name = ''
if paras[0] == 'ln_1':
new_para_name = '.'.join(['ln1'] + paras[1:])
elif paras[0] == 'attn':
new_para_name = '.'.join(['attn.attn'] + paras[1:])
elif paras[0] == 'ln_2':
new_para_name = '.'.join(['ln2'] + paras[1:])
elif paras[0] == 'mlp':
if paras[1] == 'c_fc':
new_para_name = '.'.join(['ffn.layers.0.0'] + paras[-1:])
else:
new_para_name = '.'.join(['ffn.layers.1'] + paras[-1:])
else:
print(f'Wrong for {paras}')
return new_para_name
def convert_translayer(paras):
new_para_name = ''
if paras[0] == 'attn':
new_para_name = '.'.join(['attentions.0.attn'] + paras[1:])
elif paras[0] == 'ln_1':
new_para_name = '.'.join(['norms.0'] + paras[1:])
elif paras[0] == 'ln_2':
new_para_name = '.'.join(['norms.1'] + paras[1:])
elif paras[0] == 'mlp':
if paras[1] == 'c_fc':
new_para_name = '.'.join(['ffns.0.layers.0.0'] + paras[2:])
elif paras[1] == 'c_proj':
new_para_name = '.'.join(['ffns.0.layers.1'] + paras[2:])
else:
print(f'Wrong for {paras}')
else:
print(f'Wrong for {paras}')
return new_para_name
def convert_key_name(ckpt, visual_split):
new_ckpt = OrderedDict()
for k, v in ckpt.items():
key_list = k.split('.')
if key_list[0] == 'visual':
new_transform_name = 'image_encoder'
if key_list[1] == 'class_embedding':
new_name = '.'.join([new_transform_name, 'cls_token'])
elif key_list[1] == 'positional_embedding':
new_name = '.'.join([new_transform_name, 'pos_embed'])
elif key_list[1] == 'conv1':
new_name = '.'.join([
new_transform_name, 'patch_embed.projection', key_list[2]
])
elif key_list[1] == 'ln_pre':
new_name = '.'.join(
[new_transform_name, key_list[1], key_list[2]])
elif key_list[1] == 'transformer':
new_layer_name = 'layers'
layer_index = key_list[3]
paras = key_list[4:]
if int(layer_index) < visual_split:
new_para_name = convert_vitlayer(paras)
new_name = '.'.join([
new_transform_name, new_layer_name, layer_index,
new_para_name
])
else:
new_para_name = convert_translayer(paras)
new_transform_name = 'decode_head.rec_with_attnbias'
new_layer_name = 'layers'
layer_index = str(int(layer_index) - visual_split)
new_name = '.'.join([
new_transform_name, new_layer_name, layer_index,
new_para_name
])
elif key_list[1] == 'proj':
new_name = 'decode_head.rec_with_attnbias.proj.weight'
elif key_list[1] == 'ln_post':
new_name = k.replace('visual', 'decode_head.rec_with_attnbias')
else:
print(f'pop parameter: {k}')
continue
else:
text_encoder_name = 'text_encoder'
if key_list[0] == 'transformer':
layer_name = 'transformer'
layer_index = key_list[2]
paras = key_list[3:]
new_para_name = convert_translayer(paras)
new_name = '.'.join([
text_encoder_name, layer_name, layer_index, new_para_name
])
elif key_list[0] in [
'positional_embedding', 'text_projection', 'bg_embed',
'attn_mask', 'logit_scale', 'token_embedding', 'ln_final'
]:
new_name = 'text_encoder.' + k
else:
print(f'pop parameter: {k}')
continue
new_ckpt[new_name] = v
return new_ckpt
def convert_tensor(ckpt):
cls_token = ckpt['image_encoder.cls_token']
new_cls_token = cls_token.unsqueeze(0).unsqueeze(0)
ckpt['image_encoder.cls_token'] = new_cls_token
pos_embed = ckpt['image_encoder.pos_embed']
new_pos_embed = pos_embed.unsqueeze(0)
ckpt['image_encoder.pos_embed'] = new_pos_embed
proj_weight = ckpt['decode_head.rec_with_attnbias.proj.weight']
new_proj_weight = proj_weight.transpose(1, 0)
ckpt['decode_head.rec_with_attnbias.proj.weight'] = new_proj_weight
return ckpt
def main():
parser = argparse.ArgumentParser(
description='Convert keys in timm pretrained vit models 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()
if any([s in args.src for s in ['B-16', 'b16', 'base_patch16']]):
visual_split = 9
elif any([s in args.src for s in ['L-14', 'l14', 'large_patch14']]):
visual_split = 18
else:
print('Make sure the clip model is ViT-B/16 or ViT-L/14!')
visual_split = -1
checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')
if isinstance(checkpoint, torch.jit.RecursiveScriptModule):
state_dict = checkpoint.state_dict()
else:
if 'state_dict' in checkpoint:
# timm checkpoint
state_dict = checkpoint['state_dict']
elif 'model' in checkpoint:
# deit checkpoint
state_dict = checkpoint['model']
else:
state_dict = checkpoint
weight = convert_key_name(state_dict, visual_split)
weight = convert_tensor(weight)
mmengine.mkdir_or_exist(osp.dirname(args.dst))
torch.save(weight, args.dst)
if __name__ == '__main__':
main()
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