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|
| import argparse
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| from pathlib import Path
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|
|
| import torch
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|
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| from mmaction.apis import init_recognizer
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| from mmaction.models.recognizers import BaseRecognizer
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|
|
|
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| def convert_recoginzer_to_deploy(model, checkpoint, save_path):
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| print('Converting...')
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| assert hasattr(model, 'backbone') and \
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| hasattr(model.backbone, 'switch_to_deploy'), \
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| '`model.backbone` must has method of "switch_to_deploy".' \
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| f' But {model.backbone.__class__} does not have.'
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|
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| model.backbone.switch_to_deploy()
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| checkpoint['state_dict'] = model.state_dict()
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| torch.save(checkpoint, save_path)
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|
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| print('Done! Save at path "{}"'.format(save_path))
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|
|
|
|
| def main():
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| parser = argparse.ArgumentParser(
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| description='Convert the parameters of the repvgg block '
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| 'from training mode to deployment mode.')
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| parser.add_argument(
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| 'config_path',
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| help='The path to the configuration file of the network '
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| 'containing the repvgg block.')
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| parser.add_argument(
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| 'checkpoint_path',
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| help='The path to the checkpoint file corresponding to the model.')
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| parser.add_argument(
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| 'save_path',
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| help='The path where the converted checkpoint file is stored.')
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| args = parser.parse_args()
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|
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| save_path = Path(args.save_path)
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| if save_path.suffix != '.pth' and save_path.suffix != '.tar':
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| print('The path should contain the name of the pth format file.')
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| exit()
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| save_path.parent.mkdir(parents=True, exist_ok=True)
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|
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| model = init_recognizer(
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| args.config_path, checkpoint=args.checkpoint_path, device='cpu')
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| assert isinstance(model, BaseRecognizer), \
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| '`model` must be a `mmpretrain.classifiers.ImageClassifier` instance.'
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|
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| checkpoint = torch.load(args.checkpoint_path)
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| convert_recoginzer_to_deploy(model, checkpoint, args.save_path)
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|
|
|
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| if __name__ == '__main__':
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| main()
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|
|
