mmclassification/tools/model_converters/reparameterize_repvgg.py

61 lines
1.8 KiB
Python

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import warnings
from pathlib import Path
import torch
from mmcls.apis import init_model
bright_style, reset_style = '\x1b[1m', '\x1b[0m'
red_text, blue_text = '\x1b[31m', '\x1b[34m'
white_background = '\x1b[107m'
msg = bright_style + red_text
msg += 'DeprecationWarning: This tool will be deprecated in future. '
msg += red_text + 'Welcome to use the '
msg += white_background
msg += '"tools/convert_models/reparameterize_model.py"'
msg += reset_style
warnings.warn(msg)
def convert_repvggblock_param(config_path, checkpoint_path, save_path):
model = init_model(config_path, checkpoint=checkpoint_path)
print('Converting...')
model.backbone.switch_to_deploy()
torch.save(model.state_dict(), save_path)
print('Done! Save at path "{}"'.format(save_path))
def main():
parser = argparse.ArgumentParser(
description='Convert the parameters of the repvgg block '
'from training mode to deployment mode.')
parser.add_argument(
'config_path',
help='The path to the configuration file of the network '
'containing the repvgg block.')
parser.add_argument(
'checkpoint_path',
help='The path to the checkpoint file corresponding to the model.')
parser.add_argument(
'save_path',
help='The path where the converted checkpoint file is stored.')
args = parser.parse_args()
save_path = Path(args.save_path)
if save_path.suffix != '.pth':
print('The path should contain the name of the pth format file.')
exit(1)
save_path.parent.mkdir(parents=True, exist_ok=True)
convert_repvggblock_param(args.config_path, args.checkpoint_path,
args.save_path)
if __name__ == '__main__':
main()