mmpretrain/tools/model_converters/repvgg_to_mmcls.py

61 lines
1.9 KiB
Python

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
def convert(src, dst):
print('Converting...')
blobs = torch.load(src, map_location='cpu')
converted_state_dict = OrderedDict()
for key in blobs:
splited_key = key.split('.')
splited_key = ['norm' if i == 'bn' else i for i in splited_key]
splited_key = [
'branch_norm' if i == 'rbr_identity' else i for i in splited_key
]
splited_key = [
'branch_1x1' if i == 'rbr_1x1' else i for i in splited_key
]
splited_key = [
'branch_3x3' if i == 'rbr_dense' else i for i in splited_key
]
splited_key = [
'backbone.stem' if i[:6] == 'stage0' else i for i in splited_key
]
splited_key = [
'backbone.stage_' + i[5] if i[:5] == 'stage' else i
for i in splited_key
]
splited_key = ['se_layer' if i == 'se' else i for i in splited_key]
splited_key = ['conv1.conv' if i == 'down' else i for i in splited_key]
splited_key = ['conv2.conv' if i == 'up' else i for i in splited_key]
splited_key = ['head.fc' if i == 'linear' else i for i in splited_key]
new_key = '.'.join(splited_key)
converted_state_dict[new_key] = blobs[key]
torch.save(converted_state_dict, dst)
print('Done!')
def main():
parser = argparse.ArgumentParser(description='Convert model keys')
parser.add_argument('src', help='src detectron model path')
parser.add_argument('dst', help='save path')
args = parser.parse_args()
dst = Path(args.dst)
if dst.suffix != '.pth':
print('The path should contain the name of the pth format file.')
exit(1)
dst.parent.mkdir(parents=True, exist_ok=True)
convert(args.src, args.dst)
if __name__ == '__main__':
main()