/*
All modification made by Intel Corporation: © 2016 Intel Corporation

All contributions by the University of California:
Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
All rights reserved.

All other contributions:
Copyright (c) 2014, 2015, the respective contributors
All rights reserved.
For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md


Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

    * Redistributions of source code must retain the above copyright notice,
      this list of conditions and the following disclaimer.
    * Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      documentation and/or other materials provided with the distribution.
    * Neither the name of Intel Corporation nor the names of its contributors
      may be used to endorse or promote products derived from this software
      without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/

#ifndef CAFFE_DROPOUT_LAYER_HPP_
#define CAFFE_DROPOUT_LAYER_HPP_

#include <vector>

#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"

#include "caffe/layers/neuron_layer.hpp"

namespace caffe {

/**
 * @brief During training only, sets a random portion of @f$x@f$ to 0, adjusting
 *        the rest of the vector magnitude accordingly.
 *
 * @param bottom input Blob vector (length 1)
 *   -# @f$ (N \times C \times H \times W) @f$
 *      the inputs @f$ x @f$
 * @param top output Blob vector (length 1)
 *   -# @f$ (N \times C \times H \times W) @f$
 *      the computed outputs @f$ y = |x| @f$
 */
template <typename Dtype>
class DropoutLayer : public NeuronLayer<Dtype> {
 public:
  /**
   * @param param provides DropoutParameter dropout_param,
   *     with DropoutLayer options:
   *   - dropout_ratio (\b optional, default 0.5).
   *     Sets the probability @f$ p @f$ that any given unit is dropped.
   */
  explicit DropoutLayer(const LayerParameter& param)
      : NeuronLayer<Dtype>(param) {}
  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);

  virtual inline const char* type() const { return "Dropout"; }

 protected:
  /**
   * @param bottom input Blob vector (length 1)
   *   -# @f$ (N \times C \times H \times W) @f$
   *      the inputs @f$ x @f$
   * @param top output Blob vector (length 1)
   *   -# @f$ (N \times C \times H \times W) @f$
   *      the computed outputs. At training time, we have @f$
   *      y_{\mbox{train}} = \left\{
   *         \begin{array}{ll}
   *            \frac{x}{1 - p} & \mbox{if } u > p \\
   *            0 & \mbox{otherwise}
   *         \end{array} \right.
   *      @f$, where @f$ u \sim U(0, 1)@f$ is generated independently for each
   *      input at each iteration. At test time, we simply have
   *      @f$ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x @f$.
   */
  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);

  /// when divided by UINT_MAX, the randomly generated values @f$u\sim U(0,1)@f$
  Blob<unsigned int> rand_vec_;
  /// the probability @f$ p @f$ of dropping any input
  Dtype threshold_;
  /// the scale for undropped inputs at train time @f$ 1 / (1 - p) @f$
  Dtype scale_;
  unsigned int uint_thres_;
};

}  // namespace caffe

#endif  // CAFFE_DROPOUT_LAYER_HPP_
