mmpretrain/tools/model_converters/clip_to_mmpretrain.py

73 lines
2.1 KiB
Python

# 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_clip(ckpt):
new_ckpt = OrderedDict()
for k, v in list(ckpt.items()):
new_v = v
if k.startswith('head'):
new_k = k.replace('head.', 'head.layers.head.')
new_ckpt[new_k] = new_v
continue
elif k.startswith('patch_embed'):
if 'proj.' in k:
new_k = k.replace('proj.', 'projection.')
else:
new_k = k
elif k.startswith('norm_pre'):
new_k = k.replace('norm_pre', 'pre_norm')
elif k.startswith('blocks'):
new_k = k.replace('blocks.', 'layers.')
if 'norm1' in k:
new_k = new_k.replace('norm1', 'ln1')
elif 'norm2' in k:
new_k = new_k.replace('norm2', 'ln2')
elif 'mlp.fc1' in k:
new_k = new_k.replace('mlp.fc1', 'ffn.layers.0.0')
elif 'mlp.fc2' in k:
new_k = new_k.replace('mlp.fc2', 'ffn.layers.1')
elif k.startswith('norm'):
new_k = k.replace('norm', 'ln1')
else:
new_k = k
if not new_k.startswith('head'):
new_k = 'backbone.' + new_k
new_ckpt[new_k] = new_v
return new_ckpt
def main():
parser = argparse.ArgumentParser(
description='Convert keys in pretrained clip '
'models to mmpretrain 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']
else:
state_dict = checkpoint
weight = convert_clip(state_dict)
mmengine.mkdir_or_exist(osp.dirname(args.dst))
torch.save(weight, args.dst)
print('Done!!')
if __name__ == '__main__':
main()