fix bn train&eval loss problem

mindspore/nn/layer/normalization.py

@@ -43,7 +43,7 @@ class _BatchNorm(Cell):
                  beta_init='zeros',
                  moving_mean_init='zeros',
                  moving_var_init='ones',
-                 use_batch_statistics=True,
+                 use_batch_statistics=None,
                  device_num_each_group=1):
         super(_BatchNorm, self).__init__()
         if num_features < 1:
@@ -147,7 +147,11 @@ class _BatchNorm(Cell):
         return y
 
     def construct(self, x):
-        if self.training and self.use_batch_statistics:
+        if self.use_batch_statistics is None:
+            flag = self.training
+        else:
+            flag = self.use_batch_statistics
+        if flag:
             if self.is_ge_backend and self.is_global:
                 axes, re_shape = _shape_infer(F.shape(x), self.num_features)
                 y = self._global_sync(x, axes, re_shape)
@@ -236,8 +240,10 @@ class BatchNorm1d(_BatchNorm):
         moving_var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving variance.
             The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
             'he_uniform', etc. Default: 'ones'.
-        use_batch_statistics (bool): If true, use the mean value and variance value of current batch data, else use
-            the mean value and variance value of specified value. Default: True.
+        use_batch_statistics (bool): If true, use the mean value and variance value of current batch data. If false,
+            use the mean value and variance value of specified value. If None, training process will use the mean and
+            variance of current batch data and track the running mean and variance, eval process will use the running
+            mean and variance. Default: None.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
@@ -259,7 +265,7 @@ class BatchNorm1d(_BatchNorm):
                  beta_init='zeros',
                  moving_mean_init='zeros',
                  moving_var_init='ones',
-                 use_batch_statistics=True):
+                 use_batch_statistics=None):
         super(BatchNorm1d, self).__init__(num_features,
                                           eps,
                                           momentum,
@@ -307,8 +313,10 @@ class BatchNorm2d(_BatchNorm):
         moving_var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving variance.
             The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
             'he_uniform', etc. Default: 'ones'.
-        use_batch_statistics (bool): If true, use the mean value and variance value of current batch data, else use
-            the mean value and variance value of specified value. Default: True.
+        use_batch_statistics (bool): If true, use the mean value and variance value of current batch data. If false,
+            use the mean value and variance value of specified value. If None, training process will use the mean and
+            variance of current batch data and track the running mean and variance, eval process will use the running
+            mean and variance. Default: None.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
@@ -330,7 +338,7 @@ class BatchNorm2d(_BatchNorm):
                  beta_init='zeros',
                  moving_mean_init='zeros',
                  moving_var_init='ones',
-                 use_batch_statistics=True):
+                 use_batch_statistics=None):
         super(BatchNorm2d, self).__init__(num_features,
                                           eps,
                                           momentum,
@@ -379,8 +387,10 @@ class GlobalBatchNorm(_BatchNorm):
         moving_var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving variance.
             The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
             'he_uniform', etc. Default: 'ones'.
-        use_batch_statistics (bool): If true, use the mean value and variance value of current batch data, else use
-            the mean value and variance value of specified value. Default: True.
+        use_batch_statistics (bool): If true, use the mean value and variance value of current batch data. If false,
+            use the mean value and variance value of specified value. If None, training process will use the mean and
+            variance of current batch data and track the running mean and variance, eval process will use the running
+            mean and variance. Default: None.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
@@ -402,7 +412,7 @@ class GlobalBatchNorm(_BatchNorm):
                  beta_init='zeros',
                  moving_mean_init='zeros',
                  moving_var_init='ones',
-                 use_batch_statistics=True,
+                 use_batch_statistics=None,
                  device_num_each_group=1):
         super(GlobalBatchNorm, self).__init__(num_features,
                                               eps,