mirror of https://github.com/open-mmlab/mmpose
143 lines
4.6 KiB
Python
143 lines
4.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import numpy as np
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import torch
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from mmengine.config import DictAction
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from mmengine.logging import MMLogger
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from mmpose.apis.inference import init_model
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try:
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from mmengine.analysis import get_model_complexity_info
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from mmengine.analysis.print_helper import _format_size
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except ImportError:
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raise ImportError('Please upgrade mmengine >= 0.6.0')
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Get complexity information from a model config')
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parser.add_argument('config', help='train config file path')
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parser.add_argument(
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'--device', default='cpu', help='Device used for model initialization')
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parser.add_argument(
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'--cfg-options',
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nargs='+',
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action=DictAction,
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default={},
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help='override some settings in the used config, the key-value pair '
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'in xxx=yyy format will be merged into config file. For example, '
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"'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'")
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parser.add_argument(
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'--input-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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'--batch-size',
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'-b',
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type=int,
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default=1,
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help='Input batch size. If specified and greater than 1, it takes a '
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'callable method that generates a batch input. Otherwise, it will '
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'generate a random tensor with input shape to calculate FLOPs.')
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parser.add_argument(
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'--show-arch-info',
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'-s',
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action='store_true',
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help='Whether to show model arch information')
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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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inputs = torch.randn(batch_size, *input_shape).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['inputs'] = inputs
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return batch
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def inference(args, input_shape, logger):
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model = init_model(
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args.config,
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checkpoint=None,
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device=args.device,
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cfg_options=args.cfg_options)
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if hasattr(model, '_forward'):
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model.forward = model._forward
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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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if args.batch_size > 1:
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outputs = {}
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avg_flops = []
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logger.info('Running get_flops with batch size specified as {}'.format(
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args.batch_size))
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batch = batch_constructor(model, args.batch_size, input_shape)
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for i in range(args.batch_size):
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result = get_model_complexity_info(
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model,
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input_shape,
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inputs=batch['inputs'],
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show_table=True,
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show_arch=args.show_arch_info)
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avg_flops.append(result['flops'])
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mean_flops = _format_size(int(np.average(avg_flops)))
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outputs['flops_str'] = mean_flops
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outputs['params_str'] = result['params_str']
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outputs['out_table'] = result['out_table']
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outputs['out_arch'] = result['out_arch']
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else:
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outputs = get_model_complexity_info(
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model,
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input_shape,
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inputs=None,
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show_table=True,
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show_arch=args.show_arch_info)
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return outputs
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def main():
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args = parse_args()
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logger = MMLogger.get_instance(name='MMLogger')
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if len(args.input_shape) == 1:
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input_shape = (3, args.input_shape[0], args.input_shape[0])
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elif len(args.input_shape) == 2:
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input_shape = (3, ) + tuple(args.input_shape)
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else:
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raise ValueError('invalid input shape')
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if args.device == 'cuda:0':
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assert torch.cuda.is_available(
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), 'No valid cuda device detected, please double check...'
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outputs = inference(args, input_shape, logger)
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flops = outputs['flops_str']
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params = outputs['params_str']
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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(outputs['out_table'])
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if args.show_arch_info:
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print(outputs['out_arch'])
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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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