piiswrong commented on a change in pull request #7570: Gluon InstanceNorm and
ReflectancePadding
URL: https://github.com/apache/incubator-mxnet/pull/7570#discussion_r137341278
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File path: python/mxnet/gluon/nn/basic_layers.py
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@@ -442,3 +442,92 @@ 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)
+ x = mx.nd.array([[[ 1.1, 2.2]],
+ [[ 3.3, 4.4]]])
+ // Instance normalization is calculated with the above formula
+ layer = InstanceNorm()
+ layer.initialize(ctx=mx.cpu(0))
+ layer(x)
+ [[[-0.99998355 0.99998331]]
+ [[-0.99998319 0.99998361]]]
+ <NDArray 2x1x2 @cpu(0)>
+ """
+ def __init__(self, axis=1, momentum=0.9, epsilon=1e-5, center=True,
scale=False,
+ beta_initializer='zeros', gamma_initializer='ones',
+ in_channels=0, **kwargs):
+ super(InstanceNorm, self).__init__(**kwargs)
+ self._kwargs = {'eps': epsilon}
+ if in_channels != 0:
+ self.in_channels = in_channels
+ self.gamma = self.params.get('gamma', grad_req='write' if scale else
'null',
+ shape=(in_channels,),
init=gamma_initializer,
+ allow_deferred_init=True)
+ self.beta = self.params.get('beta', grad_req='write' if center else
'null',
+ shape=(in_channels,),
init=beta_initializer,
+ allow_deferred_init=True)
+
+ def hybrid_forward(self, F, x, gamma, beta):
+ return F.InstanceNorm(x, gamma, beta,
+ name='fwd', **self._kwargs)
+
+ def __repr__(self):
+ s = '{name}({content}'
+ if hasattr(self, 'in_channels'):
+ s += ', in_channels={0}'.format(self.in_channels)
+ s += ')'
+ return s.format(name=self.__class__.__name__,
+ content=', '.join(['='.join([k, v.__repr__()])
+ for k, v in self._kwargs.items()]))
+
+
+class ReflectionPad(HybridBlock):
Review comment:
Should be named ReflectionPad2D and put in convolution layers
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