[GitHub] szha commented on a change in pull request #7570: Gluon InstanceNorm and ReflectancePadding

[gi...@git.apache.org]

szha commented on a change in pull request #7570: Gluon InstanceNorm and ReflectancePadding
URL: https://github.com/apache/incubator-mxnet/pull/7570#discussion_r137090552
 
 

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 File path: python/mxnet/gluon/nn/basic_layers.py
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 @@ -442,3 +442,87 @@ def hybrid_forward(self, F, x):
 
     def __repr__(self):
         return self.__class__.__name__
+
+
+class InstanceNorm(HybridBlock):
+    """
+    Applies instance normalization to the n-dimensional input array.
+    This operator takes an n-dimensional input array where (n>2) and normalizes 
+    the input using the following formula:
+    .. math::
+    out = \frac{x - mean[data]}{ \sqrt{Var[data]} + \epsilon} * gamma + beta
+    This layer is similar to batch normalization layer (`BatchNorm`)
+    with two differences: first, the normalization is
+    carried out per example (instance), not over a batch. Second, the
+    same normalization is applied both at test and train time. This
+    operation is also known as `contrast normalization`.
+    If the input data is of shape [batch, channel, spacial_dim1, spacial_dim2, ...],
+    `gamma` and `beta` parameters must be vectors of shape [channel].
+    This implementation is based on paper:
+    .. [1] Instance Normalization: The Missing Ingredient for Fast Stylization,
+       D. Ulyanov, A. Vedaldi, V. Lempitsky, 2016 (arXiv:1607.08022v2).
+    Examples::
+      // Input of shape (2,1,2)
 
 Review comment:
   add an empty line after ::
   http://www.sphinx-doc.org/en/stable/rest.html#source-code
 
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