mmpretrain/tools/model_converters/replknet_to_mmpretrain.py

59 lines
1.6 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('.')
print(splited_key)
splited_key = [
'backbone.stem' if i[:4] == 'stem' else i for i in splited_key
]
splited_key = [
'backbone.stages' if i[:6] == 'stages' else i for i in splited_key
]
splited_key = [
'backbone.transitions' if i[:11] == 'transitions' else i
for i in splited_key
]
splited_key = [
'backbone.stages.3.norm' if i[:4] == 'norm' else i
for i in splited_key
]
splited_key = [
'head.fc' if i[:4] == 'head' 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()