Modify eval_mm for MiniCPM-o 2.6

eval_mm/vlmevalkit/vlmeval/dataset/utils/hrbench.py

@@ -0,0 +1,54 @@
+from ...smp import *
+import os
+
+
+def report_acc_hrbench(df):
+    cycle_group = df.groupby('cycle_category')
+    result_dic = defaultdict(list)
+    avg_dic = defaultdict(int)
+
+    count = 0
+    for key, data_value in cycle_group:
+        count += 1
+        _, resp_dic = hrbench_score(data_value)
+
+        for task_type, accuracy in resp_dic.items():
+            result_dic['cycle'].append(key)
+            result_dic['type'].append(task_type)
+            result_dic['accuracy'].append(accuracy)
+
+            avg_dic[task_type] += accuracy
+    for task_type, accuracy in avg_dic.items():
+        result_dic['cycle'].append('Average')
+        result_dic['type'].append(task_type)
+        result_dic['accuracy'].append(accuracy / count)
+    result_pd = pd.DataFrame(result_dic)
+
+    return result_pd
+
+
+def hrbench_score(data):
+    ret = defaultdict(list)
+    resp_dic = {}
+    category_list = set(data['category'])
+    score_dict = defaultdict(list)
+
+    for i in range(len(data)):
+        d = data.iloc[i]
+        category = d['category']
+        gpt_score = d['hit']
+        score_dict[category].append(gpt_score)
+        score_dict['all'].append(gpt_score)
+
+    all_acc = np.mean(score_dict['all'])
+    ret['type'].append('all')
+    ret['acc'].append(all_acc)
+    resp_dic['all'] = all_acc
+    for cate in category_list:
+        acc = np.mean(score_dict[cate])
+        ret['type'].append(cate)
+        ret['acc'].append(acc)
+
+        resp_dic[cate] = acc
+
+    return pd.DataFrame(ret), resp_dic