// Copyright 2013 Yangqing Jia

#include <vector>

#include "caffe/layer.hpp"
#include "caffe/vision_layers.hpp"
#include "caffe/util/math_functions.hpp"

namespace caffe {

template <typename Dtype>
void LRNLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
      vector<Blob<Dtype>*>* top) {
  CHECK_EQ(bottom.size(), 1) <<
      "Local Response Normalization Layer takes a single blob as input.";
  CHECK_EQ(top->size(), 1) <<
      "Local Response Normalization Layer takes a single blob as output.";
  num_ = bottom[0]->num();
  channels_ = bottom[0]->channels();
  height_ = bottom[0]->height();
  width_ = bottom[0]->width();
  (*top)[0]->Reshape(num_, channels_, height_, width_);
  scale_.Reshape(num_, channels_, height_, width_);
  size_ = this->layer_param_.local_size();
  pre_pad_ = (size_ - 1) / 2;
  alpha_ = this->layer_param_.alpha();
  beta_ = this->layer_param_.beta();
};

template <typename Dtype>
void LRNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    vector<Blob<Dtype>*>* top) {
  const Dtype* bottom_data = bottom[0]->cpu_data();
  Dtype* top_data = (*top)[0]->mutable_cpu_data();
  Dtype* scale_data = scale_.mutable_cpu_data();
  // start with the constant value
  for (int i = 0; i < scale_.count(); ++i) {
    scale_data[i] = 1.;
  }
  Blob<Dtype> padded_square(1, channels_ + size_ - 1, height_, width_);
  Dtype* padded_square_data = padded_square.mutable_cpu_data();
  memset(padded_square_data, 0, sizeof(Dtype) * padded_square.count());
  Dtype alpha_over_size = alpha_ / size_;
  // go through the images
  for (int n = 0; n < num_; ++n) {
    // compute the padded square
    caffe_sqr(channels_ * height_ * width_,
        bottom_data + bottom[0]->offset(n),
        padded_square_data + padded_square.offset(0, pre_pad_));
    // Create the first channel scale
    for (int c = 0; c < size_; ++c) {
      caffe_axpy<Dtype>(height_ * width_, alpha_over_size,
          padded_square_data + padded_square.offset(0, c),
          scale_data + scale_.offset(n, 0));
    }
    for (int c = 1; c < channels_; ++c) {
      // copy previous scale
      caffe_copy<Dtype>(height_ * width_,
          scale_data + scale_.offset(n, c - 1),
          scale_data + scale_.offset(n, c));
      // add head
      caffe_axpy<Dtype>(height_ * width_, alpha_over_size,
          padded_square_data + padded_square.offset(0, c + size_ - 1),
          scale_data + scale_.offset(n, c));
      // subtract tail
      caffe_axpy<Dtype>(height_ * width_, -alpha_over_size,
          padded_square_data + padded_square.offset(0, c - 1),
          scale_data + scale_.offset(n, c));
    }
  }

  // In the end, compute output
  caffe_powx<Dtype>(scale_.count(), scale_data, -beta_, top_data);
  caffe_mul<Dtype>(scale_.count(), top_data, bottom_data, top_data);
}

template <typename Dtype>
Dtype LRNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
  const Dtype* top_diff = top[0]->cpu_diff();
  const Dtype* top_data = top[0]->cpu_data();
  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
  const Dtype* scale_data = scale_.cpu_data();
  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
  Blob<Dtype> padded_ratio(1, channels_ + size_ - 1, height_, width_);
  Blob<Dtype> accum_ratio(1, 1, height_, width_);
  Dtype* padded_ratio_data = padded_ratio.mutable_cpu_data();
  Dtype* accum_ratio_data = accum_ratio.mutable_cpu_data();
  // We hack a little bit by using the diff() to store an additional result
  Dtype* accum_ratio_times_bottom = accum_ratio.mutable_cpu_diff();
  memset(padded_ratio_data, 0, sizeof(Dtype) * padded_ratio.count());
  Dtype cache_ratio_value = 2. * alpha_ * beta_ / size_;

  caffe_powx<Dtype>(scale_.count(), scale_data, -beta_, bottom_diff);
  caffe_mul<Dtype>(scale_.count(), top_diff, bottom_diff, bottom_diff);

  // go through individual data
  int inverse_pre_pad = size_ - (size_ + 1) / 2;
  for (int n = 0; n < num_; ++n) {
    int block_offset = scale_.offset(n);
    // first, compute diff_i * y_i / s_i
    caffe_mul<Dtype>(channels_ * height_ * width_,
        top_diff + block_offset, top_data + block_offset,
        padded_ratio_data + padded_ratio.offset(0, inverse_pre_pad));
    caffe_div<Dtype>(channels_ * height_ * width_,
        padded_ratio_data + padded_ratio.offset(0, inverse_pre_pad),
        scale_data + block_offset,
        padded_ratio_data + padded_ratio.offset(0, inverse_pre_pad));
    // Now, compute the accumulated ratios and the bottom diff
    memset(accum_ratio_data, 0, sizeof(Dtype) * accum_ratio.count());
    for (int c = 0; c < size_ - 1; ++c) {
      caffe_axpy<Dtype>(height_ * width_, 1.,
          padded_ratio_data + padded_ratio.offset(0, c), accum_ratio_data);
    }
    for (int c = 0; c < channels_; ++c) {
      caffe_axpy<Dtype>(height_ * width_, 1.,
          padded_ratio_data + padded_ratio.offset(0, c + size_ - 1),
          accum_ratio_data);
      // compute bottom diff
      caffe_mul<Dtype>(height_ * width_,
          bottom_data + top[0]->offset(n, c),
          accum_ratio_data, accum_ratio_times_bottom);
      caffe_axpy<Dtype>(height_ * width_, -cache_ratio_value,
          accum_ratio_times_bottom, bottom_diff + top[0]->offset(n, c));
      caffe_axpy<Dtype>(height_ * width_, -1.,
          padded_ratio_data + padded_ratio.offset(0, c), accum_ratio_data);
    }
  }
  return Dtype(0.);
}

INSTANTIATE_CLASS(LRNLayer);


}  // namespace caffe
