opencv/sources/modules/dnn/src/opencl/lrn.cl

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__kernel void LRNComputeOutput(const int nthreads, __global T* in, __global T* scale, const T negative_beta, __global T* out) {
  int index = get_global_id(0);
  int tmp = get_global_size(0);
  for(index; index < nthreads; index += tmp)
  out[index] = in[index] * pow(scale[index], negative_beta);
}

__kernel void LRNFillScale(const int nthreads, __global T* in, const int num, const int channels, const int height, const int width, const int size, const T alpha_over_size, const T k, __global T* scale) {
  int index = get_global_id(0);
  int tmp = get_global_size(0);
  for(index; index < nthreads; index += tmp) {
    // find out the local offset
    const int w = index % width;
    const int h = (index / width) % height;
    const int n = index / width / height;
    const int offset = (n * channels * height + h) * width + w;
    const int step = height * width;
    in = in + offset;
    scale = scale + offset;
    int head = 0;
    const int pre_pad = (size - 1) / 2;
    const int post_pad = size - pre_pad - 1;
    T accum_scale = 0;
    // fill the scale at [n, :, h, w]
    // accumulate values
    while (head < post_pad && head < channels) {
      accum_scale += in[head * step] * in[head * step];
      ++head;
    }
    // both add and subtract
    while (head < channels) {
      accum_scale += in[head * step] * in[head * step];
      if (head - size >= 0) {
        accum_scale -= in[(head - size) * step]
        * in[(head - size) * step];
      }
      scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
      ++head;
    }
    // subtract only
    while (head < channels + post_pad) {
      if (head - size >= 0) {
        accum_scale -= in[(head - size) * step]
        * in[(head - size) * step];
      }
      scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
      ++head;
    }
  }
}