Method Forward_cpu

src/caffe/layers/mvn_layer.cpp:31–71

template <typename Dtype>
void MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top) {
  const Dtype* bottom_data = bottom[0]->cpu_data();
  Dtype* top_data = top[0]->mutable_cpu_data();
  int num;
  if (this->layer_param_.mvn_param().across_channels())
    num = bottom[0]->num();
  else
    num = bottom[0]->num() * bottom[0]->channels();

  int dim = bottom[0]->count() / num;

  // subtract mean
  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
      mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
      temp_.mutable_cpu_data());
  caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data);  // X-EX

  if (this->layer_param_.mvn_param().normalize_variance()) {
    // compute variance using var(X) = E((X-EX)^2)
    caffe_powx(bottom[0]->count(), top_data, Dtype(2),
        temp_.mutable_cpu_data());  // (X-EX)^2
    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
        sum_multiplier_.cpu_data(), 0.,
        variance_.mutable_cpu_data());  // E((X-EX)^2)

    // normalize variance
    caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
          variance_.mutable_cpu_data());

    caffe_add_scalar(variance_.count(), eps_, variance_.mutable_cpu_data());

    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
          variance_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
          temp_.mutable_cpu_data());

    caffe_div(temp_.count(), top_data, temp_.cpu_data(), top_data);
  }
}

template <typename Dtype>
void MVNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,