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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_
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