fix batchnorm bug

mindspore/nn/layer/normalization.py

@@ -33,7 +33,6 @@ class _BatchNorm(Cell):
     @cell_attr_register
     def __init__(self,
                  num_features,
-                 group=1,
                  eps=1e-5,
                  momentum=0.9,
                  affine=True,
@@ -41,7 +40,8 @@ class _BatchNorm(Cell):
                  beta_init='zeros',
                  moving_mean_init='zeros',
                  moving_var_init='ones',
-                 use_batch_statistics=True):
+                 use_batch_statistics=True,
+                 group=1):
         super(_BatchNorm, self).__init__()
         if num_features < 1:
             raise ValueError("num_features must be at least 1")
@@ -214,6 +214,25 @@ class BatchNorm1d(_BatchNorm):
         >>> input = Tensor(np.random.randint(0, 255, [3, 16]), mindspore.float32)
         >>> net(input)
     """
+    def __init__(self,
+                 num_features,
+                 eps=1e-5,
+                 momentum=0.9,
+                 affine=True,
+                 gamma_init='ones',
+                 beta_init='zeros',
+                 moving_mean_init='zeros',
+                 moving_var_init='ones',
+                 use_batch_statistics=True):
+        super(BatchNorm1d, self).__init__(num_features,
+                                          eps,
+                                          momentum,
+                                          affine,
+                                          gamma_init,
+                                          beta_init,
+                                          moving_mean_init,
+                                          moving_var_init,
+                                          use_batch_statistics)
     def _check_data_dim(self, x):
         if x.dim() != 2:
             pass
@@ -266,6 +285,25 @@ class BatchNorm2d(_BatchNorm):
         >>> input = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]), mindspore.float32)
         >>> net(input)
     """
+    def __init__(self,
+                 num_features,
+                 eps=1e-5,
+                 momentum=0.9,
+                 affine=True,
+                 gamma_init='ones',
+                 beta_init='zeros',
+                 moving_mean_init='zeros',
+                 moving_var_init='ones',
+                 use_batch_statistics=True):
+        super(BatchNorm2d, self).__init__(num_features,
+                                          eps,
+                                          momentum,
+                                          affine,
+                                          gamma_init,
+                                          beta_init,
+                                          moving_mean_init,
+                                          moving_var_init,
+                                          use_batch_statistics)
     def _check_data_dim(self, x):
         if x.dim() != 4:
             pass
@@ -316,6 +354,30 @@ class GlobalBatchNorm(_BatchNorm):
         >>> input = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]), mindspore.float32)
         >>> global_bn_op(input)
     """
+    def __init__(self,
+                 num_features,
+                 eps=1e-5,
+                 momentum=0.9,
+                 affine=True,
+                 gamma_init='ones',
+                 beta_init='zeros',
+                 moving_mean_init='zeros',
+                 moving_var_init='ones',
+                 use_batch_statistics=True,
+                 group=1):
+        super(GlobalBatchNorm, self).__init__(num_features,
+                                          eps,
+                                          momentum,
+                                          affine,
+                                          gamma_init,
+                                          beta_init,
+                                          moving_mean_init,
+                                          moving_var_init,
+                                          use_batch_statistics,
+                                          group)
+        self.group = check_int_positive(group)
+        if self.group <=1:
+            raise ValueError("the number of group must be greater than 1.")
     def _check_data_dim(self, x):
         if x.dim == 0:
             pass