src/operator/instance_norm.cc

/*
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 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 * KIND, either express or implied.  See the License for the
 * specific language governing permissions and limitations
 * under the License.
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/*!
 * Copyright (c) 2015 by Contributors
 * \file instance_norm.cc
 * \brief
 * \author Sebastian Bodenstein
*/

#include "./instance_norm-inl.h"

namespace mxnet {
namespace op {

DMLC_REGISTER_PARAMETER(InstanceNormParam);

struct InstanceNormGrad {
  const char *op_name;
  std::vector<nnvm::NodeEntry> operator()(const nnvm::ObjectPtr& n,
                                          const std::vector<nnvm::NodeEntry>& ograds) const {
    std::vector<nnvm::NodeEntry> out_data;
    out_data.reserve(n->num_outputs());
    for (size_t i = 0; i < n->num_outputs(); ++i)
      out_data.emplace_back(n, i, 0);
    std::vector<nnvm::NodeEntry> heads;
    heads.reserve(5);
    heads.emplace_back(ograds.at(instance_norm::kOut));
    heads.emplace_back(out_data.at(instance_norm::kMean));
    heads.emplace_back(out_data.at(instance_norm::kVar));
    heads.emplace_back(n->inputs.at(instance_norm::kData));
    heads.emplace_back(n->inputs.at(instance_norm::kGamma));

    return MakeGradNode(op_name, n, heads, n->attrs.dict);
  }
};


NNVM_REGISTER_OP(InstanceNorm)
.add_alias("_npx_instance_norm")
.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 this paper [1]_

.. [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)
.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__())
.set_num_inputs(3)
.set_num_outputs(3)
.set_attr<nnvm::FListInputNames>("FListInputNames", [](const NodeAttrs& attrs) {
  return std::vector<std::string>{"data", "gamma", "beta"};
})
.set_attr<nnvm::FListOutputNames>("FListOutputNames",
    [](const NodeAttrs& attrs) {
    return std::vector<std::string>{"output"};
})
.set_attr<nnvm::FNumVisibleOutputs>("FNumVisibleOutputs",
  [](const NodeAttrs& attrs) { return 1; })
.set_attr_parser(ParamParser<InstanceNormParam>)
.set_attr<mxnet::FInferShape>("FInferShape", InstanceNormShape)
.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
.set_attr<nnvm::FGradient>("FGradient", InstanceNormGrad{"_backward_instance_norm"})
.set_attr<FCompute>("FCompute<cpu>", InstanceNormForward<cpu>);

NNVM_REGISTER_OP(_backward_instance_norm)
.set_num_inputs(5)
.set_num_outputs(3)
.set_attr_parser(ParamParser<InstanceNormParam>)
.set_attr<FResourceRequest>("FResourceRequest",
  [](const NodeAttrs& attrs) {
    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
  })
.set_attr<nnvm::TIsBackward>("TIsBackward", true)
.set_attr<FCompute>("FCompute<cpu>", InstanceNormBackward<cpu>);

}  // namespace op
}  // namespace mxnet