58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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from pathlib import Path
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import torch
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from mmpretrain.apis import init_model
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from mmpretrain.models.classifiers import ImageClassifier
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def convert_classifier_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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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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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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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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model = init_model(
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args.config_path, checkpoint=args.checkpoint_path, device='cpu')
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assert isinstance(model, ImageClassifier), \
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'`model` must be a `mmpretrain.classifiers.ImageClassifier` instance.'
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checkpoint = torch.load(args.checkpoint_path)
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convert_classifier_to_deploy(model, checkpoint, args.save_path)
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if __name__ == '__main__':
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main()
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