lrn_layer.hpp
#ifndef CAFFE_LRN_LAYER_HPP_
#define CAFFE_LRN_LAYER_HPP_
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/layers/eltwise_layer.hpp"
#include "caffe/layers/pooling_layer.hpp"
#include "caffe/layers/power_layer.hpp"
#include "caffe/layers/split_layer.hpp"
namespace caffe {
template <typename Dtype>
class LRNLayer : public Layer<Dtype> {
public:
explicit LRNLayer(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 "LRN"; }
virtual inline int ExactNumBottomBlobs() const { return 1; }
virtual inline int ExactNumTopBlobs() const { return 1; }
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);
virtual void CrossChannelForward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void CrossChannelForward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void WithinChannelForward(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void CrossChannelBackward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
virtual void CrossChannelBackward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
virtual void WithinChannelBackward(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
int size_;
int pre_pad_;
Dtype alpha_;
Dtype beta_;
Dtype k_;
int num_;
int channels_;
int height_;
int width_;
// Fields used for normalization ACROSS_CHANNELS
// scale_ stores the intermediate summing results
Blob<Dtype> scale_;
// Fields used for normalization WITHIN_CHANNEL
shared_ptr<SplitLayer<Dtype> > split_layer_;
vector<Blob<Dtype>*> split_top_vec_;
shared_ptr<PowerLayer<Dtype> > square_layer_;
Blob<Dtype> square_input_;
Blob<Dtype> square_output_;
vector<Blob<Dtype>*> square_bottom_vec_;
vector<Blob<Dtype>*> square_top_vec_;
shared_ptr<PoolingLayer<Dtype> > pool_layer_;
Blob<Dtype> pool_output_;
vector<Blob<Dtype>*> pool_top_vec_;
shared_ptr<PowerLayer<Dtype> > power_layer_;
Blob<Dtype> power_output_;
vector<Blob<Dtype>*> power_top_vec_;
shared_ptr<EltwiseLayer<Dtype> > product_layer_;
Blob<Dtype> product_input_;
vector<Blob<Dtype>*> product_bottom_vec_;
};
} // namespace caffe
#endif // CAFFE_LRN_LAYER_HPP_
caffe::LRNLayer::Backward_cpu
virtual void Backward_cpu(const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: lrn_layer.cpp:165
caffe::LRNLayer::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: lrn_layer.cpp:9
caffe::LRNLayer::Reshape
virtual void Reshape(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.
Definition: lrn_layer.cpp:69
caffe::LRNLayer::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::LRNLayer
Normalize the input in a local region across or within feature maps.
Definition: lrn_layer.hpp:23
caffe::Blob
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers,...
Definition: blob.hpp:24
caffe::Layer
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
caffe::LRNLayer::Forward_cpu
virtual void Forward_cpu(const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
Using the CPU device, compute the layer output.
Definition: lrn_layer.cpp:93
caffe::LRNLayer::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: lrn_layer.hpp:33
caffe::LRNLayer::type
virtual const char * type() const
Returns the layer type.
Definition: lrn_layer.hpp:32
caffe::LRNLayer::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
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
caffe::LRNLayer::ExactNumTopBlobs
virtual int ExactNumTopBlobs() const
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.
Definition: lrn_layer.hpp:34