text: import argparse
  
  import torch
  from mmcv import Config, DictAction
  
  from mmdet.models import build_detector
  
  try:
      from flops_counter import get_model_complexity_info
  except ImportError:
      raise ImportError('Please upgrade mmcv to >0.6.2')
  
  
  def parse_args():
      parser = argparse.ArgumentParser(description='Train a detector')
      parser.add_argument('config', help='train config file path')
      parser.add_argument(
          '--shape',
          type=int,
          nargs='+',
          default=[1280, 800],
          help='input image size')
      parser.add_argument(
          '--cfg-options',
          nargs='+',
          action=DictAction,
          help='override some settings in the used config, the key-value pair '
          'in xxx=yyy format will be merged into config file. If the value to '
          'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
          'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
          'Note that the quotation marks are necessary and that no white space '
          'is allowed.')
      args = parser.parse_args()
      return args
  
  
  def main():
  
      args = parse_args()
  
      if len(args.shape) == 1:
          input_shape = (3, args.shape[0], args.shape[0])
      elif len(args.shape) == 2:
          input_shape = (3, ) + tuple(args.shape)
      else:
          raise ValueError('invalid input shape')
  
      cfg = Config.fromfile(args.config)
      if args.cfg_options is not None:
          cfg.merge_from_dict(args.cfg_options)
      # import modules from string list.
      if cfg.get('custom_imports', None):
          from mmcv.utils import import_modules_from_strings
          import_modules_from_strings(**cfg['custom_imports'])
  
      model = build_detector(
          cfg.model,
          train_cfg=cfg.get('train_cfg'),
          test_cfg=cfg.get('test_cfg'))
      if torch.cuda.is_available():
          model.cuda()
      model.eval()
  
      if hasattr(model, 'forward_dummy'):
          model.forward = model.forward_dummy
      else:
          raise NotImplementedError(
              'FLOPs counter is currently not currently supported with {}'.
              format(model.__class__.__name__))
  
      flops, params = get_model_complexity_info(model, input_shape)
      split_line = '=' * 30
      print(f'{split_line}\nInput shape: {input_shape}\n'
            f'Flops: {flops}\nParams: {params}\n{split_line}')
      print('!!!Please be cautious if you use the results in papers. '
            'You may need to check if all ops are supported and verify that the '
            'flops computation is correct.')
  
  
  if __name__ == '__main__':
      main()
  