add use_global_stats in nn.BatchNorm

python/mxnet/gluon/nn/basic_layers.py

@@ -308,6 +308,10 @@ class BatchNorm(HybridBlock):
         When the next layer is linear (also e.g. `nn.relu`),
         this can be disabled since the scaling
         will be done by the next layer.
+    use_global_stats: bool, default False
+        If True, use global moving statistics instead of local batch-norm. This will force
+        change batch-norm into a scale shift operator.
+        If False, use local batch-norm.
     beta_initializer: str or `Initializer`, default 'zeros'
         Initializer for the beta weight.
     gamma_initializer: str or `Initializer`, default 'ones'
@@ -329,12 +333,12 @@ class BatchNorm(HybridBlock):
         - **out**: output tensor with the same shape as `data`.
     """
     def __init__(self, axis=1, momentum=0.9, epsilon=1e-5, center=True, scale=True,
-                 beta_initializer='zeros', gamma_initializer='ones',
+                 use_global_stats=False, beta_initializer='zeros', gamma_initializer='ones',
                  running_mean_initializer='zeros', running_variance_initializer='ones',
                  in_channels=0, **kwargs):
         super(BatchNorm, self).__init__(**kwargs)
         self._kwargs = {'axis': axis, 'eps': epsilon, 'momentum': momentum,
-                        'fix_gamma': not scale}
+                        'fix_gamma': not scale, 'use_global_stats': use_global_stats}
         if in_channels != 0:
             self.in_channels = in_channels