diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py
index 27b4aca21dbc4877fa436a61e585be89f12ff3cb..786647f1f655dd40be1117df912f59c42108539e 100644
--- a/ppocr/data/imaug/label_ops.py
+++ b/ppocr/data/imaug/label_ops.py
@@ -833,19 +833,20 @@ class VQATokenLabelEncode(object):
         segment_offset_id = []
         gt_label_list = []
 
-        if self.contains_re:
-            # for re
-            entities = []
-            if not self.infer_mode:
-                relations = []
-                id2label = {}
-                entity_id_to_index_map = {}
-                empty_entity = set()
+        entities = []
+
+        # for re
+        train_re = self.contains_re and not self.infer_mode
+        if train_re:
+            relations = []
+            id2label = {}
+            entity_id_to_index_map = {}
+            empty_entity = set()
 
         data['ocr_info'] = copy.deepcopy(ocr_info)
 
         for info in ocr_info:
-            if self.contains_re and not self.infer_mode:
+            if train_re:
                 # for re
                 if len(info["text"]) == 0:
                     empty_entity.add(info["id"])
@@ -872,24 +873,22 @@ class VQATokenLabelEncode(object):
                 gt_label = self._parse_label(label, encode_res)
 
             # construct entities for re
-            if self.contains_re:
-                if not self.infer_mode:
-                    if gt_label[0] != self.label2id_map["O"]:
-                        entity_id_to_index_map[info["id"]] = len(entities)
-                        label = label.upper()
-                        entities.append({
-                            "start": len(input_ids_list),
-                            "end":
-                            len(input_ids_list) + len(encode_res["input_ids"]),
-                            "label": label.upper(),
-                        })
-                else:
+            if train_re:
+                if gt_label[0] != self.label2id_map["O"]:
+                    entity_id_to_index_map[info["id"]] = len(entities)
+                    label = label.upper()
                     entities.append({
                         "start": len(input_ids_list),
                         "end":
                         len(input_ids_list) + len(encode_res["input_ids"]),
-                        "label": 'O',
+                        "label": label.upper(),
                     })
+            else:
+                entities.append({
+                    "start": len(input_ids_list),
+                    "end": len(input_ids_list) + len(encode_res["input_ids"]),
+                    "label": 'O',
+                })
             input_ids_list.extend(encode_res["input_ids"])
             token_type_ids_list.extend(encode_res["token_type_ids"])
             bbox_list.extend([bbox] * len(encode_res["input_ids"]))
@@ -908,19 +907,23 @@ class VQATokenLabelEncode(object):
             padding_side=self.tokenizer.padding_side,
             pad_token_type_id=self.tokenizer.pad_token_type_id,
             pad_token_id=self.tokenizer.pad_token_id)
+        data['entities'] = entities
 
-        if self.contains_re:
-            data['entities'] = entities
-            if self.infer_mode:
-                data['ocr_info'] = ocr_info
-            else:
-                data['relations'] = relations
-                data['id2label'] = id2label
-                data['empty_entity'] = empty_entity
-                data['entity_id_to_index_map'] = entity_id_to_index_map
+        if train_re:
+            data['relations'] = relations
+            data['id2label'] = id2label
+            data['empty_entity'] = empty_entity
+            data['entity_id_to_index_map'] = entity_id_to_index_map
         return data
 
     def _load_ocr_info(self, data):
+        def trans_poly_to_bbox(poly):
+            x1 = np.min([p[0] for p in poly])
+            x2 = np.max([p[0] for p in poly])
+            y1 = np.min([p[1] for p in poly])
+            y2 = np.max([p[1] for p in poly])
+            return [x1, y1, x2, y2]
+
         if self.infer_mode:
             ocr_result = self.ocr_engine.ocr(data['image'], cls=False)
             ocr_info = []