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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
from pathlib import Path
import torch
from mmaction.apis import init_recognizer
from mmaction.models.recognizers import BaseRecognizer
def convert_recoginzer_to_deploy(model, checkpoint, save_path):
print('Converting...')
assert hasattr(model, 'backbone') and \
hasattr(model.backbone, 'switch_to_deploy'), \
'`model.backbone` must has method of "switch_to_deploy".' \
f' But {model.backbone.__class__} does not have.'
model.backbone.switch_to_deploy()
checkpoint['state_dict'] = model.state_dict()
torch.save(checkpoint, 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' and save_path.suffix != '.tar':
print('The path should contain the name of the pth format file.')
exit()
save_path.parent.mkdir(parents=True, exist_ok=True)
model = init_recognizer(
args.config_path, checkpoint=args.checkpoint_path, device='cpu')
assert isinstance(model, BaseRecognizer), \
'`model` must be a `mmpretrain.classifiers.ImageClassifier` instance.'
checkpoint = torch.load(args.checkpoint_path)
convert_recoginzer_to_deploy(model, checkpoint, args.save_path)
if __name__ == '__main__':
main()
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