contrastive_loss_layer.hpp
#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 {
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"; }
virtual inline bool AllowForceBackward(const int bottom_index) const {
return bottom_index != 2;
}
protected:
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);
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_
caffe::ContrastiveLossLayer::Forward_gpu
virtual void Forward_gpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
caffe::ContrastiveLossLayer::LayerSetUp
virtual void LayerSetUp(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: contrastive_loss_layer.cpp:10
caffe::ContrastiveLossLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Computes the contrastive loss where . This can be used to train siamese networks.
Definition: contrastive_loss_layer.cpp:31
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers,...
Definition: blob.hpp:24
caffe::LossLayer
An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth l...
Definition: loss_layer.hpp:23
caffe::ContrastiveLossLayer::AllowForceBackward
virtual bool AllowForceBackward(const int bottom_index) const
Definition: contrastive_loss_layer.hpp:52
caffe::ContrastiveLossLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Computes the Contrastive error gradient w.r.t. the inputs.
Definition: contrastive_loss_layer.cpp:65
caffe::ContrastiveLossLayer::Backward_gpu
virtual void Backward_gpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
caffe::ContrastiveLossLayer::ExactNumBottomBlobs
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.
Definition: contrastive_loss_layer.hpp:46
caffe::ContrastiveLossLayer::type
virtual const char * type() const
Returns the layer type.
Definition: contrastive_loss_layer.hpp:47
caffe::ContrastiveLossLayer
Computes the contrastive loss where . This can be used to train siamese networks.
Definition: contrastive_loss_layer.hpp:39
caffe
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14