Function ReplicateDepthRamp

tensorflow/lite/kernels/pooling_test.cc:102–115

Replicate each entry in a vector n times along depth (innermost dimension). The values are incremented by delta, creating ramps offset by each input value. This is used to create simple and predicatable variation.

// The values are incremented by delta, creating ramps offset by each input
// value. This is used to create simple and predicatable variation.
std::vector<float> ReplicateDepthRamp(const std::vector<float>& image_plane,
                                      int n, float delta) {
  const int size = image_plane.size();
  std::vector<float> ramped_data(n * size);
  // The input is treated as a 1-D even if logically it is multi-dimensional.
  for (int input_index = 0; input_index < size; ++input_index) {
    for (int depth = 0; depth < n; ++depth) {
      ramped_data[n * input_index + depth] =
          image_plane[input_index] + depth * delta;
    }
  }

  return ramped_data;
}

TEST(FloatPoolingOpTest, AveragePool) {
  FloatPoolingOpModel m(BuiltinOperator_AVERAGE_POOL_2D,