batch_norm_layer.hpp

#ifndef CAFFE_BATCHNORM_LAYER_HPP_
#define CAFFE_BATCHNORM_LAYER_HPP_

#include <vector>

#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"

namespace caffe {

template <typename Dtype>
class BatchNormLayer : public Layer<Dtype> {
 public:
  explicit BatchNormLayer(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 "BatchNorm"; }
  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);

  Blob<Dtype> mean_, variance_, temp_, x_norm_;
  bool use_global_stats_;
  Dtype moving_average_fraction_;
  int channels_;
  Dtype eps_;

  // extra temporarary variables is used to carry out sums/broadcasting
  // using BLAS
  Blob<Dtype> batch_sum_multiplier_;
  Blob<Dtype> num_by_chans_;
  Blob<Dtype> spatial_sum_multiplier_;
};

}  // namespace caffe

#endif  // CAFFE_BATCHNORM_LAYER_HPP_