#ifndef CAFFE_BIAS_LAYER_HPP_

#define CAFFE_BIAS_LAYER_HPP_

#include <vector>

#include "caffe/blob.hpp"

#include "caffe/layer.hpp"

#include "caffe/proto/caffe.pb.h"

namespace caffe {

/**

* @brief Computes a sum of two input Blobs, with the shape of the latter Blob

* "broadcast" to match the shape of the former. Equivalent to tiling

* the latter Blob, then computing the elementwise sum.

*

* The second input may be omitted, in which case it's learned as a parameter

* of the layer. Note: in case bias and scaling are desired, both operations can

* be handled by `ScaleLayer` configured with `bias_term: true`.

*/

template <typename Dtype>

class BiasLayer : public Layer<Dtype> {

public:

explicit BiasLayer(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 "Bias"; }

virtual inline int MinBottomBlobs() const { return 1; }

virtual inline int MaxBottomBlobs() const { return 2; }

virtual inline int ExactNumTopBlobs() const { return 1; }

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);

private:

Blob<Dtype> bias_multiplier_;

int outer_dim_, bias_dim_, inner_dim_, dim_;

};

} // namespace caffe

#endif // CAFFE_BIAS_LAYER_HPP_