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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() | |