diff --git a/ppocr/losses/rec_srn_loss.py b/ppocr/losses/rec_srn_loss.py
index d722ee0f22b7692e4c4c9bbc8d2f1f583d754dac..7d5b65ebaf1ee135d1fefe8d93ddc3f77985b132 100644
--- a/ppocr/losses/rec_srn_loss.py
+++ b/ppocr/losses/rec_srn_loss.py
@@ -42,6 +42,6 @@ class SRNLoss(nn.Layer):
         cost_gsrm = paddle.reshape(x=paddle.sum(cost_gsrm), shape=[1])
         cost_vsfd = paddle.reshape(x=paddle.sum(cost_vsfd), shape=[1])
 
-        sum_cost = cost_word + cost_vsfd * 2.0 + cost_gsrm * 0.15
+        sum_cost = cost_word * 3.0 + cost_vsfd + cost_gsrm * 0.15
 
         return {'loss': sum_cost, 'word_loss': cost_word, 'img_loss': cost_vsfd}
diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py
index 867f920a3ca5aedf4c32ab8ed80e7cf35b4781c2..8c972a143ba506d9cd13f960802efdde5df3e54f 100644
--- a/ppocr/postprocess/rec_postprocess.py
+++ b/ppocr/postprocess/rec_postprocess.py
@@ -182,12 +182,12 @@ class SRNLabelDecode(BaseRecLabelDecode):
 
         preds_prob = np.reshape(preds_prob, [-1, 25])
 
-        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
+        text = self.decode(preds_idx, preds_prob)
 
         if label is None:
             text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
             return text
-        label = self.decode(label, is_remove_duplicate=True)
+        label = self.decode(label)
         return text, label
 
     def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
diff --git a/tools/program.py b/tools/program.py
index 885d45f5e91f364910057dbcd97753c1f2125b6c..f329dcd5782b21cc76d0537eb8a65d41adead19a 100755
--- a/tools/program.py
+++ b/tools/program.py
@@ -174,6 +174,7 @@ def train(config,
     best_model_dict = {main_indicator: 0}
     best_model_dict.update(pre_best_model_dict)
     train_stats = TrainingStats(log_smooth_window, ['lr'])
+    model_average = False
     model.train()
 
     if 'start_epoch' in best_model_dict:
@@ -197,6 +198,7 @@ def train(config,
             if config['Architecture']['algorithm'] == "SRN":
                 others = batch[-4:]
                 preds = model(images, others)
+                model_average = True
             else:
                 preds = model(images)
             loss = loss_class(preds, batch)
@@ -242,12 +244,13 @@ def train(config,
             # eval
             if global_step > start_eval_step and \
                     (global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
-                model_average = paddle.optimizer.ModelAverage(
-                    0.15,
-                    parameters=model.parameters(),
-                    min_average_window=10000,
-                    max_average_window=15625)
-                model_average.apply()
+                if model_average:
+                    Model_Average = paddle.incubate.optimizer.ModelAverage(
+                        0.15,
+                        parameters=model.parameters(),
+                        min_average_window=10000,
+                        max_average_window=15625)
+                    Model_Average.apply()
                 cur_metirc = eval(model, valid_dataloader, post_process_class,
                                   eval_class)
                 cur_metirc_str = 'cur metirc, {}'.format(', '.join(