content: import argparse
  
  from mmcv import load
  from scipy.special import softmax
  
  from mmaction.core.evaluation import (get_weighted_score, mean_class_accuracy,
                                        top_k_accuracy)
  
  
  def parse_args():
      parser = argparse.ArgumentParser(description='Fusing multiple scores')
      parser.add_argument(
          '--scores',
          nargs='+',
          help='list of scores',
          default=['demo/fuse/rgb.pkl', 'demo/fuse/flow.pkl'])
      parser.add_argument(
          '--coefficients',
          nargs='+',
          type=float,
          help='coefficients of each score file',
          default=[1.0, 1.0])
      parser.add_argument(
          '--datalist',
          help='list of testing data',
          default='demo/fuse/data_list.txt')
      parser.add_argument('--apply-softmax', action='store_true')
      args = parser.parse_args()
      return args
  
  
  def main():
      args = parse_args()
      assert len(args.scores) == len(args.coefficients)
      score_list = args.scores
      score_list = [load(f) for f in score_list]
      if args.apply_softmax:
  
          def apply_softmax(scores):
              return [softmax(score) for score in scores]
  
          score_list = [apply_softmax(scores) for scores in score_list]
  
      weighted_scores = get_weighted_score(score_list, args.coefficients)
      data = open(args.datalist).readlines()
      labels = [int(x.strip().split()[-1]) for x in data]
  
      mean_class_acc = mean_class_accuracy(weighted_scores, labels)
      top_1_acc, top_5_acc = top_k_accuracy(weighted_scores, labels, (1, 5))
      print(f'Mean Class Accuracy: {mean_class_acc:.04f}')
      print(f'Top 1 Accuracy: {top_1_acc:.04f}')
      print(f'Top 5 Accuracy: {top_5_acc:.04f}')
  
  
  if __name__ == '__main__':
      main()
  