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*/

#ifndef CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_
#define CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_

#include <vector>

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

#include "caffe/layers/loss_layer.hpp"

namespace caffe {

/**
 * @brief Computes the contrastive loss @f$
 *          E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 +
 *              \left(1-y\right) \max \left(margin-d, 0\right)^2
 *          @f$ where @f$
 *          d = \left| \left| a_n - b_n \right| \right|_2 @f$. This can be
 *          used to train siamese networks.
 *
 * @param bottom input Blob vector (length 3)
 *   -# @f$ (N \times C \times 1 \times 1) @f$
 *      the features @f$ a \in [-\infty, +\infty]@f$
 *   -# @f$ (N \times C \times 1 \times 1) @f$
 *      the features @f$ b \in [-\infty, +\infty]@f$
 *   -# @f$ (N \times 1 \times 1 \times 1) @f$
 *      the binary similarity @f$ s \in [0, 1]@f$
 * @param top output Blob vector (length 1)
 *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
 *      the computed contrastive loss: @f$ E =
 *          \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 +
 *          \left(1-y\right) \max \left(margin-d, 0\right)^2
 *          @f$ where @f$
 *          d = \left| \left| a_n - b_n \right| \right|_2 @f$.
 * This can be used to train siamese networks.
 */
template <typename Dtype>
class ContrastiveLossLayer : public LossLayer<Dtype> {
 public:
  explicit ContrastiveLossLayer(const LayerParameter& param)
      : LossLayer<Dtype>(param), diff_() {}
  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);

  virtual inline int ExactNumBottomBlobs() const { return 3; }
  virtual inline const char* type() const { return "ContrastiveLoss"; }
  /**
   * Unlike most loss layers, in the ContrastiveLossLayer we can backpropagate
   * to the first two inputs.
   */
  virtual inline bool AllowForceBackward(const int bottom_index) const {
    return bottom_index != 2;
  }

 protected:
  /// @copydoc ContrastiveLossLayer
  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);

  /**
   * @brief Computes the Contrastive error gradient w.r.t. the inputs.
   *
   * Computes the gradients with respect to the two input vectors (bottom[0] and
   * bottom[1]), but not the similarity label (bottom[2]).
   *
   * @param top output Blob vector (length 1), providing the error gradient with
   *      respect to the outputs
   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
   *      as @f$ \lambda @f$ is the coefficient of this layer's output
   *      @f$\ell_i@f$ in the overall Net loss
   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
   *      (*Assuming that this top Blob is not used as a bottom (input) by any
   *      other layer of the Net.)
   * @param propagate_down see Layer::Backward.
   * @param bottom input Blob vector (length 2)
   *   -# @f$ (N \times C \times 1 \times 1) @f$
   *      the features @f$a@f$; Backward fills their diff with
   *      gradients if propagate_down[0]
   *   -# @f$ (N \times C \times 1 \times 1) @f$
   *      the features @f$b@f$; Backward fills their diff with gradients if
   *      propagate_down[1]
   */
  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);

  Blob<Dtype> diff_;  // cached for backward pass
  Blob<Dtype> dist_sq_;  // cached for backward pass
  Blob<Dtype> diff_sq_;  // tmp storage for gpu forward pass
  Blob<Dtype> summer_vec_;  // tmp storage for gpu forward pass
};

}  // namespace caffe

#endif  // CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_
