diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index fc08744ccdc..5d4380e4a68 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -45,7 +45,7 @@ class _BatchNorm(Cell):
                  moving_var_init='ones',
                  use_batch_statistics=None,
                  device_num_each_group=1,
-                 input_dims='1d'):
+                 input_dims='2d'):
         super(_BatchNorm, self).__init__()
         if num_features < 1:
             raise ValueError("num_features must be at least 1")
@@ -151,6 +151,8 @@ class _BatchNorm(Cell):
             _shape_check(self.shape(x))
         if self.input_dims == '1d':
             _shape_check_2d(self.shape(x))
+        if self.input_dims == 'both':
+            _shape_check_2d_or_4d(self.shape(x))
         if self.use_batch_statistics is None:
             flag = self.training
         else:
@@ -211,7 +213,13 @@ def _shape_check_2d(input_shape):
 @constexpr
 def _shape_check(in_shape):
     if len(in_shape) != 4:
-        raise ValueError("The input must has 4 dims")
+        raise ValueError("The input must has 4 dims.")
+
+
+@constexpr
+def _shape_check_2d_or_4d(in_shape):
+    if len(in_shape) != 2 and len(in_shape) != 4:
+        raise ValueError("The input must has 2 dims or 4 dims.")
 
 
 @constexpr
@@ -449,7 +457,8 @@ class GlobalBatchNorm(_BatchNorm):
                                               moving_mean_init,
                                               moving_var_init,
                                               use_batch_statistics,
-                                              device_num_each_group)
+                                              device_num_each_group,
+                                              input_dims='both')
         self.group = check_int_positive(device_num_each_group)
         if self.group <= 1:
             raise ValueError("the number of group must be greater than 1.")