pooling.cpp

/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
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==============================================================================*/

#include "pooling.h"
#include <common/types.h>

namespace paddle_mobile {
namespace operators {
namespace math {

/*
 * All tensors are in NCHW format.
 * Ksize, strides, paddings are two elements. These two elements represent
 * height and width, respectively.
 */
template <typename PoolProcess, typename T>
class PoolFunctor<CPU, PoolProcess, T> {
public:
  void operator()(const framework::Tensor &input, const std::vector<int> &ksize,
                  const std::vector<int> &strides,
                  const std::vector<int> &paddings, PoolProcess pool_process,
                  framework::Tensor *output) {

    const int batch_size = input.dims()[0];

    const int input_height = input.dims()[2];

    const int input_width = input.dims()[3];
    if (output == nullptr) {
      DLOG << "output tensor is null";
    }
    const int output_channels = output->dims()[1];

    const int output_height = output->dims()[2];
    const int output_width = output->dims()[3];
    const int ksize_height = ksize[0];
    const int ksize_width = ksize[1];
    const int stride_height = strides[0];
    const int stride_width = strides[1];
    const int padding_height = paddings[0];
    const int padding_width = paddings[1];

    const int input_stride = input_height * input_width;
    const int output_stride = output_height * output_width;

    const T *input_data = input.data<T>();
    T *output_data = output->mutable_data<T>();

    for (int i = 0; i < batch_size; i++) {
#pragma omp parallel for
      for (int c = 0; c < output_channels; ++c) {
        for (int ph = 0; ph < output_height; ++ph) {
          int hstart = ph * stride_height - padding_height;
          int hend = std::min(hstart + ksize_height, input_height);
          hstart = std::max(hstart, 0);
          for (int pw = 0; pw < output_width; ++pw) {
            int wstart = pw * stride_width - padding_width;
            int wend = std::min(wstart + ksize_width, input_width);
            wstart = std::max(wstart, 0);

            T ele = pool_process.initial();
            for (int h = hstart; h < hend; ++h) {
              for (int w = wstart; w < wend; ++w) {
                pool_process.compute(input_data[h * input_width + w], &ele);
              }
            }
            int pool_size = (hend - hstart) * (wend - wstart);
            pool_process.finalize(static_cast<T>(pool_size), &ele);
            output_data[ph * output_width + pw] = ele;
          }
        }
        input_data += input_stride;
        output_data += output_stride;
      }
    }
  }
};

template class PoolFunctor<CPU, math::AvgPool<float>, float>;
template class PoolFunctor<CPU, math::MaxPool<float>, float>;
} // namespace math
} // namespace operators
} // namespace paddle_mobile