82 lines
2.4 KiB
Python
82 lines
2.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import torch
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try:
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from fvcore.nn import (ActivationCountAnalysis, FlopCountAnalysis,
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flop_count_str, flop_count_table, parameter_count)
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except ImportError:
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print('You may need to install fvcore for flops computation, '
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'and you can use `pip install fvcore` to set up the environment')
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from fvcore.nn.print_model_statistics import _format_size
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from mmengine import Config
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from mmcls.models import build_classifier
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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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cfg = Config.fromfile(args.config)
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model = build_classifier(cfg.model)
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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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inputs = (torch.randn((1, *input_shape)), )
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flops_ = FlopCountAnalysis(model, inputs)
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activations_ = ActivationCountAnalysis(model, inputs)
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flops = _format_size(flops_.total())
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activations = _format_size(activations_.total())
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params = _format_size(parameter_count(model)[''])
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flop_table = flop_count_table(
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flops=flops_,
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activations=activations_,
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show_param_shapes=True,
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)
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flop_str = flop_count_str(flops=flops_, activations=activations_)
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print('\n' + flop_str)
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print('\n' + flop_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('!!!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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