content: import argparse
  
  import mmcv
  from mmcv import Config, DictAction
  
  from mmaction.datasets import build_dataset
  
  
  def parse_args():
      parser = argparse.ArgumentParser(description='Evaluate metric of the '
                                       'results saved in pkl/yaml/json format')
      parser.add_argument('config', help='Config of the model')
      parser.add_argument('results', help='Results in pkl/yaml/json format')
      parser.add_argument(
          '--eval',
          type=str,
          nargs='+',
          help='evaluation metrics, which depends on the dataset, e.g.,'
          ' "top_k_accuracy", "mean_class_accuracy" for video dataset')
      parser.add_argument(
          '--cfg-options',
          nargs='+',
          action=DictAction,
          default={},
          help='override some settings in the used config, the key-value pair '
          'in xxx=yyy format will be merged into config file. For example, '
          "'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'")
      parser.add_argument(
          '--eval-options',
          nargs='+',
          action=DictAction,
          help='custom options for evaluation, the key-value pair in xxx=yyy '
          'format will be kwargs for dataset.evaluate() function')
      args = parser.parse_args()
      return args
  
  
  def main():
      args = parse_args()
  
      cfg = Config.fromfile(args.config)
  
      assert args.eval is not None
  
      if args.cfg_options is not None:
          cfg.merge_from_dict(args.cfg_options)
      cfg.data.test.test_mode = True
  
      dataset = build_dataset(cfg.data.test)
      outputs = mmcv.load(args.results)
  
      kwargs = {} if args.eval_options is None else args.eval_options
      eval_kwargs = cfg.get('evaluation', {}).copy()
      # hard-code way to remove EpochEvalHook args
      for key in [
              'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best', 'rule',
              'key_indicator'
      ]:
          eval_kwargs.pop(key, None)
      eval_kwargs.update(dict(metrics=args.eval, **kwargs))
      print(dataset.evaluate(outputs, **eval_kwargs))
  
  
  if __name__ == '__main__':
      main()
  