62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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from mmengine.analysis import get_model_complexity_info
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from mmpretrain import get_model
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def parse_args():
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parser = argparse.ArgumentParser(description='Get model flops and params')
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parser.add_argument('config', help='config file path')
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parser.add_argument(
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'--shape',
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type=int,
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nargs='+',
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default=[224, 224],
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help='input image size')
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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if len(args.shape) == 1:
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input_shape = (3, args.shape[0], args.shape[0])
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elif len(args.shape) == 2:
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input_shape = (3, ) + tuple(args.shape)
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else:
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raise ValueError('invalid input shape')
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model = get_model(args.config)
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model.eval()
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if hasattr(model, 'extract_feat'):
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model.forward = model.extract_feat
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else:
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raise NotImplementedError(
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'FLOPs counter is currently not currently supported with {}'.
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format(model.__class__.__name__))
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analysis_results = get_model_complexity_info(
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model,
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input_shape,
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)
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flops = analysis_results['flops_str']
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params = analysis_results['params_str']
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activations = analysis_results['activations_str']
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out_table = analysis_results['out_table']
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out_arch = analysis_results['out_arch']
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print(out_arch)
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print(out_table)
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split_line = '=' * 30
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print(f'{split_line}\nInput shape: {input_shape}\n'
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f'Flops: {flops}\nParams: {params}\n'
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f'Activation: {activations}\n{split_line}')
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print('!!!Only the backbone network is counted in FLOPs analysis.')
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print('!!!Please be cautious if you use the results in papers. '
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'You may need to check if all ops are supported and verify that the '
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'flops computation is correct.')
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if __name__ == '__main__':
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main()
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