#ifndef CAFFE_CONCAT_LAYER_HPP_
#define CAFFE_CONCAT_LAYER_HPP_

#include <vector>

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

namespace caffe {

/**
 * @brief Takes at least two Blob%s and concatenates them along either the num
 *        or channel dimension, outputting the result.
 */
template <typename Dtype>
class ConcatLayer : public Layer<Dtype> {
 public:
  explicit ConcatLayer(const LayerParameter& param)
      : Layer<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 "Concat"; }
  virtual inline int MinBottomBlobs() const { return 1; }
  virtual inline int ExactNumTopBlobs() const { return 1; }

 protected:
  /**
   * @param bottom input Blob vector (length 2+)
   *   -# @f$ (N \times C \times H \times W) @f$
   *      the inputs @f$ x_1 @f$
   *   -# @f$ (N \times C \times H \times W) @f$
   *      the inputs @f$ x_2 @f$
   *   -# ...
   *   - K @f$ (N \times C \times H \times W) @f$
   *      the inputs @f$ x_K @f$
   * @param top output Blob vector (length 1)
   *   -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
   *      @f$ (N \times KC \times H \times W) @f$ if axis == 1:
   *      the concatenated output @f$
   *        y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}]
   *      @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);

  /**
   * @brief Computes the error gradient w.r.t. the concatenate inputs.
   *
   * @param top output Blob vector (length 1), providing the error gradient with
   *        respect to the outputs
   *   -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
   *      @f$ (N \times KC \times H \times W) @f$ if axis == 1:
   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
   *      with respect to concatenated outputs @f$ y @f$
   * @param propagate_down see Layer::Backward.
   * @param bottom input Blob vector (length K), into which the top gradient
   *        @f$ \frac{\partial E}{\partial y} @f$ is deconcatenated back to the
   *        inputs @f$
   *        \left[ \begin{array}{cccc}
   *          \frac{\partial E}{\partial x_1} &
   *          \frac{\partial E}{\partial x_2} &
   *          ... &
   *          \frac{\partial E}{\partial x_K}
   *        \end{array} \right] =
   *        \frac{\partial E}{\partial y}
   *        @f$
   */
  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);

  int count_;
  int num_concats_;
  int concat_input_size_;
  int concat_axis_;
};

}  // namespace caffe

#endif  // CAFFE_CONCAT_LAYER_HPP_