Method ReduceWindow3DAdd

tensorflow/compiler/xla/reference_util.cc:187–229

static */

}

/* static  */ std::unique_ptr<Array3D<float>> ReferenceUtil::ReduceWindow3DAdd(
    const Array3D<float>& operand, float init, absl::Span<const int64> window,
    absl::Span<const int64> stride, Padding padding) {
  std::vector<int64> dim_lengths{operand.n1(), operand.n2(), operand.n3()};
  auto padding_both = xla::MakePadding(dim_lengths, window, stride, padding);

  std::vector<int64> window_counts(window.size(), 0);
  std::vector<int64> pad_low(window.size(), 0);
  for (int64 i = 0; i < window.size(); ++i) {
    window_counts[i] =
        WindowCount(dim_lengths[i], window[i], stride[i], padding);
    pad_low[i] = padding_both[i].first;
  }
  auto result = absl::make_unique<Array3D<float>>(
      window_counts[0], window_counts[1], window_counts[2]);

  for (int64 i0 = 0; i0 < window_counts[0]; ++i0) {
    for (int64 i1 = 0; i1 < window_counts[1]; ++i1) {
      for (int64 i2 = 0; i2 < window_counts[2]; ++i2) {
        int64 i0_base = i0 * stride[0] - pad_low[0];
        int64 i1_base = i1 * stride[1] - pad_low[1];
        int64 i2_base = i2 * stride[2] - pad_low[2];

        float val = init;
        for (int64 i0_win = 0; i0_win < window[0]; ++i0_win) {
          for (int64 i1_win = 0; i1_win < window[1]; ++i1_win) {
            for (int64 i2_win = 0; i2_win < window[2]; ++i2_win) {
              if (i0_base + i0_win >= 0 && i1_base + i1_win >= 0 &&
                  i2_base + i2_win >= 0 && i0_base + i0_win < operand.n1() &&
                  i1_base + i1_win < operand.n2() &&
                  i2_base + i2_win < operand.n3()) {
                val += operand(i0_base + i0_win, i1_base + i1_win,
                               i2_base + i2_win);
              }
            }
          }
        }
        (*result)(i0, i1, i2) = val;
      }
    }
  }
  return result;
}

/* static */ std::unique_ptr<Array4D<float>>
ReferenceUtil::ReduceWindow4DGeneric(