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103 lines
3.0 KiB
103 lines
3.0 KiB
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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from functools import partial
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import torch
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from mmpose.apis.inference import init_pose_model
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try:
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from mmcv.cnn import get_model_complexity_info
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except ImportError:
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raise ImportError('Please upgrade mmcv to >0.6.2')
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def parse_args():
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parser = argparse.ArgumentParser(description='Train a recognizer')
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parser.add_argument('config', help='train 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=[256, 192],
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help='input image size')
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parser.add_argument(
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'--input-constructor',
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'-c',
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type=str,
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choices=['none', 'batch'],
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default='none',
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help='If specified, it takes a callable method that generates '
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'input. Otherwise, it will generate a random tensor with '
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'input shape to calculate FLOPs.')
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parser.add_argument(
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'--batch-size', '-b', type=int, default=1, help='input batch size')
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parser.add_argument(
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'--not-print-per-layer-stat',
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'-n',
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action='store_true',
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help='Whether to print complexity information'
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'for each layer in a model')
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args = parser.parse_args()
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return args
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def batch_constructor(flops_model, batch_size, input_shape):
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"""Generate a batch of tensors to the model."""
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batch = {}
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img = torch.ones(()).new_empty(
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(batch_size, *input_shape),
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dtype=next(flops_model.parameters()).dtype,
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device=next(flops_model.parameters()).device)
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batch['img'] = img
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return batch
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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 = init_pose_model(args.config)
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if args.input_constructor == 'batch':
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input_constructor = partial(batch_constructor, model, args.batch_size)
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else:
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input_constructor = None
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if args.input_constructor == 'batch':
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input_constructor = partial(batch_constructor, model, args.batch_size)
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else:
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input_constructor = None
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if hasattr(model, 'forward_dummy'):
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model.forward = model.forward_dummy
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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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flops, params = get_model_complexity_info(
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model,
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input_shape,
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input_constructor=input_constructor,
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print_per_layer_stat=(not args.not_print_per_layer_stat))
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split_line = '=' * 30
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input_shape = (args.batch_size, ) + input_shape
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print(f'{split_line}\nInput shape: {input_shape}\n'
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f'Flops: {flops}\nParams: {params}\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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