diff --git a/paddleseg/utils/metrics.py b/paddleseg/utils/metrics.py
index ec2ef26c57..cbc4aec592 100644
--- a/paddleseg/utils/metrics.py
+++ b/paddleseg/utils/metrics.py
@@ -74,29 +74,33 @@ def auc_roc(logits, label, num_classes, ignore_index=None):
     Returns:
         auc_roc(float): The area under roc curve
     """
-    if ignore_index or len(np.unique(label))>num_classes:
+    if ignore_index or len(np.unique(label)) > num_classes:
         raise RuntimeError('labels with ignore_index is not supported yet.')
-    
+
     if len(label.shape) != 4:
-        raise ValueError('The shape of label is not 4 dimension as (N, C, H, W), it is {}'.format(label.shape))
+        raise ValueError(
+            'The shape of label is not 4 dimension as (N, C, H, W), it is {}'.
+            format(label.shape))
 
     if len(logits.shape) != 4:
-        raise ValueError('The shape of logits is not 4 dimension as (N, C, H, W), it is {}'.format(logits.shape))
-    
-    N, C, H, W =  logits.shape
+        raise ValueError(
+            'The shape of logits is not 4 dimension as (N, C, H, W), it is {}'.
+            format(logits.shape))
+
+    N, C, H, W = logits.shape
     logits = np.transpose(logits, (1, 0, 2, 3))
-    logits = logits.reshape([C, N*H*W]).transpose([1,0])
+    logits = logits.reshape([C, N * H * W]).transpose([1, 0])
 
     label = np.transpose(label, (1, 0, 2, 3))
-    label = label.reshape([1, N*H*W]).squeeze()
+    label = label.reshape([1, N * H * W]).squeeze()
 
     if not logits.shape[0] == label.shape[0]:
         raise ValueError('length of `logit` and `label` should be equal, '
                          'but they are {} and {}.'.format(
-                             pred.shape[0], label.shape[0]))
-                            
+                             logits.shape[0], label.shape[0]))
+
     if num_classes == 2:
-        auc = skmetrics.roc_auc_score(label, logits[:,1])
+        auc = skmetrics.roc_auc_score(label, logits[:, 1])
     else:
         auc = skmetrics.roc_auc_score(label, logits, multi_class='ovr')
 
@@ -156,7 +160,7 @@ def dice(intersect_area, pred_area, label_area):
             dice = (2 * intersect_area[i]) / union[i]
         class_dice.append(dice)
     mdice = np.mean(class_dice)
-    return np.array(class_dice), mdice  
+    return np.array(class_dice), mdice
 
 
 def accuracy(intersect_area, pred_area):