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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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