lrn_kernel.h

/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
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The above copyright notice and this permission notice shall be included in all
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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==============================================================================*/

#include "framework/operator.h"
#include "operators/op_param.h"
#pragma once;

namespace paddle_mobile {
namespace operators {

using namespace framework;

template <typename T> struct LRNFunctor {
  void operator()(const framework::Tensor &input, framework::Tensor *out, int N,
                  int C, int H, int W, int n, T k, T alpha, T beta) {
    auto input_ptr = input.data<T>();
    const int start = -(n - 1) / 2;
    const int end = start + n;

    const int stride0 = C * H * W;
    const int stride1 = H * W;
    const int stride2 = W;
    const int stride3 = 1;

    framework::Tensor sqr_buffer;
    auto sqr_buffer_ptr = sqr_buffer.mutable_data<T>(input.dims());
    std::fill(sqr_buffer_ptr, sqr_buffer_ptr + sqr_buffer.numel(), k);
    for (int a = 0; a < N; a++) {
      for (int b = 0; b < C; b++) {
        for (int index = start; index < end; index++) {
          int channel = b + index;
          if (channel >= 0 && channel < C) {
            for (int c = 0; c < H; c++) {
              for (int d = 0; d < W; d++) {
                int u = a * stride0 + b * stride1 + c * stride2 + d;

                int i = a * stride0 + channel * stride1 + c * stride2 + d;

                sqr_buffer_ptr[u] += alpha * input_ptr[i] * input_ptr[i];
              }
            }
          }
        }
      }
    }
    auto out_ptr = out->data<T>();
    for (int i = 0; i < input.numel(); i++) {
      out_ptr[i] = input_ptr[i] / pow(sqr_buffer_ptr[i], beta);
    }
  }
};

template <typename DeviceType, typename T>
class LrnKernel : public framework::OpKernelBase<DeviceType, LrnParam> {
public:
  void Compute(const LrnParam &param) const;
};
} // namespace operators
} // namespace paddle_mobile