/usr/include/caffe/layers/lrn_layer.hpp is in libcaffe-cpu-dev 1.0.0~rc4-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | #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 {
/**
* @brief Normalize the input in a local region across or within feature maps.
*
* TODO(dox): thorough documentation for Forward, Backward, and proto params.
*/
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_
|