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Cleanup in RoiAlign (#7012)

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Hariharan Seshadri 2021-03-16 07:21:57 -07:00 committed by GitHub
parent 087d96200d
commit 3f0e50f14d
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1 changed files with 11 additions and 15 deletions
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26
onnxruntime/core/providers/cpu/object_detection/roialign.cc
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@ -51,10 +51,10 @@ struct PreCalc {
};
template <typename T>
void pre_calc_for_bilinear_interpolate(const int64_t height, const int64_t width, const int64_t pooled_height,
const int64_t pooled_width, const int64_t iy_upper, const int64_t ix_upper,
T roi_start_h, T roi_start_w, T bin_size_h, T bin_size_w, int64_t roi_bin_grid_h,
int64_t roi_bin_grid_w, std::vector<PreCalc<T>>& pre_calc) {
static void PreCalcForBilinearInterpolate(const int64_t height, const int64_t width, const int64_t pooled_height,
const int64_t pooled_width, const int64_t iy_upper, const int64_t ix_upper,
T roi_start_h, T roi_start_w, T bin_size_h, T bin_size_w, int64_t roi_bin_grid_h,
int64_t roi_bin_grid_w, std::vector<PreCalc<T>>& pre_calc) {
int64_t pre_calc_index = 0;
for (int64_t ph = 0; ph < pooled_height; ph++) {
for (int64_t pw = 0; pw < pooled_width; pw++) {
@ -69,8 +69,7 @@ void pre_calc_for_bilinear_interpolate(const int64_t height, const int64_t width
T y = yy;
// deal with: inverse elements are out of feature map boundary
if (y < -1.0 || y > height || x < -1.0 || x > width) {
// empty
PreCalc<T> pc;
auto& pc = pre_calc[pre_calc_index];
pc.pos1 = 0;
pc.pos2 = 0;
pc.pos3 = 0;
@ -79,7 +78,6 @@ void pre_calc_for_bilinear_interpolate(const int64_t height, const int64_t width
pc.w2 = 0;
pc.w3 = 0;
pc.w4 = 0;
pre_calc[pre_calc_index] = pc;
pre_calc_index += 1;
continue;
}
@ -170,9 +168,7 @@ void RoiAlignForward(const TensorShape& output_shape, const T* bottom_data, floa
T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
// We use roi_bin_grid to sample the grid and mimic integral
int64_t roi_bin_grid_h = (sampling_ratio > 0) ?
sampling_ratio :
static_cast<int64_t>(std::ceil(roi_height / pooled_height)); // e.g., = 2
int64_t roi_bin_grid_h = (sampling_ratio > 0) ? sampling_ratio : static_cast<int64_t>(std::ceil(roi_height / pooled_height)); // e.g., = 2
int64_t roi_bin_grid_w =
(sampling_ratio > 0) ? sampling_ratio : static_cast<int64_t>(std::ceil(roi_width / pooled_width));
@ -182,9 +178,9 @@ void RoiAlignForward(const TensorShape& output_shape, const T* bottom_data, floa
// we want to precalculate indices and weights shared by all channels,
// this is the key point of optimization
std::vector<PreCalc<T>> pre_calc(roi_bin_grid_h * roi_bin_grid_w * pooled_width * pooled_height);
pre_calc_for_bilinear_interpolate(height, width, pooled_height, pooled_width, roi_bin_grid_h, roi_bin_grid_w,
roi_start_h, roi_start_w, bin_size_h, bin_size_w, roi_bin_grid_h,
roi_bin_grid_w, pre_calc);
PreCalcForBilinearInterpolate(height, width, pooled_height, pooled_width, roi_bin_grid_h, roi_bin_grid_w,
roi_start_h, roi_start_w, bin_size_h, bin_size_w, roi_bin_grid_h,
roi_bin_grid_w, pre_calc);
for (int64_t c = 0; c < channels; c++) {
int64_t index_n_c = index_n + c * pooled_width * pooled_height;
@ -200,7 +196,7 @@ void RoiAlignForward(const TensorShape& output_shape, const T* bottom_data, floa
if (mode == RoiAlignMode::avg) { // avg pooling
for (int64_t iy = 0; iy < roi_bin_grid_h; iy++) {
for (int64_t ix = 0; ix < roi_bin_grid_w; ix++) {
PreCalc<T> pc = pre_calc[pre_calc_index];
const auto& pc = pre_calc[pre_calc_index];
output_val += pc.w1 * offset_bottom_data[pc.pos1] + pc.w2 * offset_bottom_data[pc.pos2] +
pc.w3 * offset_bottom_data[pc.pos3] + pc.w4 * offset_bottom_data[pc.pos4];
@ -212,7 +208,7 @@ void RoiAlignForward(const TensorShape& output_shape, const T* bottom_data, floa
bool max_flag = false;
for (int64_t iy = 0; iy < roi_bin_grid_h; iy++) {
for (int64_t ix = 0; ix < roi_bin_grid_w; ix++) {
PreCalc<T> pc = pre_calc[pre_calc_index];
const auto& pc = pre_calc[pre_calc_index];
T val = std::max(
std::max(std::max(pc.w1 * offset_bottom_data[pc.pos1], pc.w2 * offset_bottom_data[pc.pos2]),
pc.w3 * offset_bottom_data[pc.pos3]),