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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_beit(ckpt):
new_ckpt = OrderedDict()
for k, v in ckpt.items():
if k.startswith('patch_embed'):
new_key = k.replace('patch_embed.proj', 'patch_embed.projection')
new_ckpt[new_key] = v
if k.startswith('blocks'):
new_key = k.replace('blocks', 'layers')
if 'norm' in new_key:
new_key = new_key.replace('norm', 'ln')
elif 'mlp.fc1' in new_key:
new_key = new_key.replace('mlp.fc1', 'ffn.layers.0.0')
elif 'mlp.fc2' in new_key:
new_key = new_key.replace('mlp.fc2', 'ffn.layers.1')
new_ckpt[new_key] = v
else:
new_key = k
new_ckpt[new_key] = v
return new_ckpt
def main():
parser = argparse.ArgumentParser(
description='Convert keys in official pretrained beit 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()
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_beit(state_dict)
mmengine.mkdir_or_exist(osp.dirname(args.dst))
torch.save(weight, args.dst)
if __name__ == '__main__':
main()
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