src/operator/instance_norm.cc

/*!
 * Copyright (c) 2015 by Contributors
 * \file instance_norm.cc
 * \brief
 * \author Sebastian Bodenstein
*/

#include "./instance_norm-inl.h"

namespace mxnet {
namespace op {
template <>
Operator* CreateOp<cpu>(InstanceNormParam param, int dtype) {
  return new InstanceNormOp<cpu>(param);
}

// DO_BIND_DISPATCH comes from operator_common.h
Operator* InstanceNormProp::CreateOperatorEx(Context ctx,
                                             std::vector<TShape>* in_shape,
                                             std::vector<int>* in_type) const {
  std::vector<TShape> out_shape, aux_shape;
  std::vector<int> out_type, aux_type;
  CHECK(InferType(in_type, &out_type, &aux_type));
  CHECK(InferShape(in_shape, &out_shape, &aux_shape));
  DO_BIND_DISPATCH(CreateOp, param_, (*in_type)[0]);
}

DMLC_REGISTER_PARAMETER(InstanceNormParam);

MXNET_REGISTER_OP_PROPERTY(InstanceNorm, InstanceNormProp)
.add_argument("data", "NDArray-or-Symbol",
              "An n-dimensional input array (n > 2) of the form [batch, "
              "channel, spatial_dim1, spatial_dim2, ...].")
.add_argument("gamma", "NDArray-or-Symbol",
              "A vector of length \'channel\', which multiplies the "
              "normalized input.")
.add_argument("beta", "NDArray-or-Symbol",
              "A vector of length \'channel\', which is added to the "
              "product of the normalized input and the weight.")
.add_arguments(InstanceNormParam::__FIELDS__())
.describe(R"code(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 = [[[ 1.1,  2.2]],
       [[ 3.3,  4.4]]]

  // gamma parameter of length 1
  gamma = [1.5]

  // beta parameter of length 1
  beta = [0.5]

  // Instance normalization is calculated with the above formula
  InstanceNorm(x,gamma,beta) = [[[-0.997527  ,  1.99752665]],
                                [[-0.99752653,  1.99752724]]]

)code" ADD_FILELINE);
}  // namespace op
}  // namespace mxnet