--- a
+++ b/tools/analysis/report_accuracy.py
@@ -0,0 +1,57 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+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()