diff --git a/configs/det/ch_ppocr_v2.1/ch_det_lite_train_distill_v2.1.yml b/configs/det/ch_ppocr_v2.1/ch_det_lite_train_distill_v2.1.yml
index b27eb2f9..5f0846fa 100644
--- a/configs/det/ch_ppocr_v2.1/ch_det_lite_train_distill_v2.1.yml
+++ b/configs/det/ch_ppocr_v2.1/ch_det_lite_train_distill_v2.1.yml
@@ -88,7 +88,7 @@ Loss:
   - DistillationDMLLoss:
       model_name_pairs:
       - ["Student", "Student2"]
-      maps_name: ["thrink_maps"]
+      maps_name: "thrink_maps"
       weight: 1.0
       act: "softmax"
       model_name_pairs: ["Student", "Student2"]
@@ -96,7 +96,7 @@ Loss:
   - DistillationDBLoss:
       weight: 1.0
       model_name_list: ["Student", "Student2"]
-      key: maps
+      # key: maps
       name: DBLoss
       balance_loss: true
       main_loss_type: DiceLoss
diff --git a/ppocr/losses/combined_loss.py b/ppocr/losses/combined_loss.py
index f37c4db1..f10efa31 100644
--- a/ppocr/losses/combined_loss.py
+++ b/ppocr/losses/combined_loss.py
@@ -50,11 +50,11 @@ class CombinedLoss(nn.Layer):
             if isinstance(loss, paddle.Tensor):
                 loss = {"loss_{}_{}".format(str(loss), idx): loss}
             weight = self.loss_weight[idx]
-            for key in loss:
+            for key in loss.keys():
                 if key == "loss":
                     loss_all += loss[key] * weight
                 else:
-                    loss["{}_{}".format(key, idx)] = loss[key]
+                    loss_dict["{}_{}".format(key, idx)] = loss[key]
                     # loss[f"{key}_{idx}"] = loss[key]
             loss_dict.update(loss)
         loss_dict["loss"] = loss_all
diff --git a/ppocr/losses/distillation_loss.py b/ppocr/losses/distillation_loss.py
index d4e4a8a2..43356c6f 100644
--- a/ppocr/losses/distillation_loss.py
+++ b/ppocr/losses/distillation_loss.py
@@ -24,7 +24,6 @@ from .det_db_loss import DBLoss
 from .det_basic_loss import BalanceLoss, MaskL1Loss, DiceLoss
 
 
-
 def _sum_loss(loss_dict):
     if "loss" in loss_dict.keys():
         return loss_dict
@@ -51,9 +50,17 @@ class DistillationDMLLoss(DMLLoss):
         super().__init__(act=act)
         assert isinstance(model_name_pairs, list)
         self.key = key
-        self.model_name_pairs = model_name_pairs
+        self.model_name_pairs = self._check_model_name_pairs(model_name_pairs)
         self.name = name
         self.maps_name = maps_name
+    
+    def _check_model_name_pairs(self, model_name_pairs):
+        if not isinstance(model_name_pairs, list):
+            return []
+        elif isinstance(model_name_pairs[0], list) and isinstance(model_name_pairs[0][0], str):
+            return model_name_pairs
+        else:
+            return [model_name_pairs]
 
     def _check_maps_name(self, maps_name):
         if maps_name is None:
@@ -69,13 +76,14 @@ class DistillationDMLLoss(DMLLoss):
         new_outs = {}
         for k in self.maps_name:
             if k == "thrink_maps":
-                new_outs[k] = paddle.slice(outs, axes=1, starts=0, ends=1)
+                new_outs[k] = paddle.slice(outs, axes=[1], starts=[0], ends=[1])
             elif k == "threshold_maps":
-                new_outs[k] = paddle.slice(outs, axes=1, starts=1, ends=2)
+                new_outs[k] = paddle.slice(outs, axes=[1], starts=[1], ends=[2])
             elif k == "binary_maps":
-                new_outs[k] = paddle.slice(outs, axes=1, starts=2, ends=3)
+                new_outs[k] = paddle.slice(outs, axes=[1], starts=[2], ends=[3])
             else:
                 continue
+        return new_outs
 
     def forward(self, predicts, batch):
         loss_dict = dict()
@@ -104,7 +112,7 @@ class DistillationDMLLoss(DMLLoss):
                             loss_dict["{}_{}_{}_{}_{}".format(key, pair[
                                 0], pair[1], map_name, idx)] = loss[key]
                     else:
-                        loss_dict["{}_{}_{}".format(self.name, map_name,
+                        loss_dict["{}_{}_{}".format(self.name, self.maps_name,
                                                     idx)] = loss
 
         loss_dict = _sum_loss(loss_dict)
@@ -151,7 +159,7 @@ class DistillationDBLoss(DBLoss):
         self.name = name
         self.key = None
 
-    def forward(self, preicts, batch):
+    def forward(self, predicts, batch):
         loss_dict = {}
         for idx, model_name in enumerate(self.model_name_list):
             out = predicts[model_name]
diff --git a/ppocr/postprocess/__init__.py b/ppocr/postprocess/__init__.py
index f2ac65c4..654ddf39 100644
--- a/ppocr/postprocess/__init__.py
+++ b/ppocr/postprocess/__init__.py
@@ -34,7 +34,8 @@ def build_post_process(config, global_config=None):
     support_dict = [
         'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
         'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess',
-        'DistillationCTCLabelDecode', 'TableLabelDecode', 'DistillationDBPostProcess'
+        'DistillationCTCLabelDecode', 'TableLabelDecode',
+        'DistillationDBPostProcess'
     ]
 
     config = copy.deepcopy(config)
diff --git a/ppocr/postprocess/db_postprocess.py b/ppocr/postprocess/db_postprocess.py
index 4561b464..f2b2fc69 100755
--- a/ppocr/postprocess/db_postprocess.py
+++ b/ppocr/postprocess/db_postprocess.py
@@ -200,12 +200,9 @@ class DistillationDBPostProcess(DBPostProcess):
                  use_dilation=False,
                  score_mode="fast",
                  **kwargs):
-        super(DistillationDBPostProcess, self).__init__(thresh, 
-                                                        box_thresh,
-                                                        max_candidates,
-                                                        unclip_ratio,
-                                                        use_dilation,
-                                                        score_mode)
+        super(DistillationDBPostProcess, self).__init__(
+            thresh, box_thresh, max_candidates, unclip_ratio, use_dilation,
+            score_mode)
         if not isinstance(model_name, list):
             model_name = [model_name]
         self.model_name = model_name
@@ -221,10 +218,3 @@ class DistillationDBPostProcess(DBPostProcess):
             results[name] = super().__call__(pred, shape_list=label)
 
         return results
-
-
-
-
-
-
-