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Commit 390b87ae authored by charlie's avatar charlie
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parent 2c1cdd15
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Showing with 56 additions and 50 deletions
src/include/migraphx/op/nonmaxsuppression.hpp
View file @ 390b87ae
......@@ -111,8 +111,8 @@ struct nonmaxsuppression
double half_height = start[3] / 2.0;
double x_center = start[0];
double y_center = start[1];
result.x = {x_center - half_width, x_center + half_width};
result.y = {y_center - half_height, y_center + half_height};
result.x = {x_center - half_width, x_center + half_width};
result.y = {y_center - half_height, y_center + half_height};
}
else
{
......@@ -157,11 +157,11 @@ struct nonmaxsuppression
return intersection_over_union > iou_threshold;
}
// filter boxes below score_threshold
template <class T>
std::priority_queue<std::pair<double, int64_t>> filter_boxes_by_score(
T scores_start, std::size_t num_boxes, double score_threshold) const
std::priority_queue<std::pair<double, int64_t>>
filter_boxes_by_score(T scores_start, std::size_t num_boxes, double score_threshold) const
{
std::priority_queue<std::pair<double, int64_t>> boxes_heap;
auto insert_to_boxes_heap =
......@@ -178,36 +178,34 @@ struct nonmaxsuppression
[&](auto sc) { return std::make_pair(sc, box_idx - 1); });
return boxes_heap;
}
template <class H, class S>
void select_boxes(
H& boxes_heap,
std::vector<std::pair<double, int64_t>>& selected_boxes_inside_class,
std::vector<int64_t>& selected_indices,
S batch_boxes_start,
std::size_t max_output_boxes_per_class,
double iou_threshold,
std::size_t batch_idx,
std::size_t class_idx
) const
void select_boxes(H& boxes_heap,
std::vector<std::pair<double, int64_t>>& selected_boxes_inside_class,
std::vector<int64_t>& selected_indices,
S batch_boxes_start,
std::size_t max_output_boxes_per_class,
double iou_threshold,
std::size_t batch_idx,
std::size_t class_idx) const
{
selected_boxes_inside_class.clear();
// Get the next box with top score, filter by iou_threshold
while(!boxes_heap.empty() &&
selected_boxes_inside_class.size() < max_output_boxes_per_class)
selected_boxes_inside_class.size() < max_output_boxes_per_class)
{
// Check with existing selected boxes for this class, remove box if it
// exceeds the IOU (Intersection Over Union) threshold
const auto next_top_score = boxes_heap.top();
bool not_selected = std::any_of(
selected_boxes_inside_class.begin(),
selected_boxes_inside_class.end(),
[&](auto selected_index) {
return this->suppress_by_iou(
batch_box(batch_boxes_start, next_top_score.second),
batch_box(batch_boxes_start, selected_index.second),
iou_threshold);
});
bool not_selected =
std::any_of(selected_boxes_inside_class.begin(),
selected_boxes_inside_class.end(),
[&](auto selected_index) {
return this->suppress_by_iou(
batch_box(batch_boxes_start, next_top_score.second),
batch_box(batch_boxes_start, selected_index.second),
iou_threshold);
});
if(not not_selected)
{
......@@ -219,7 +217,7 @@ struct nonmaxsuppression
boxes_heap.pop();
}
}
argument compute(const shape& output_shape, std::vector<argument> args) const
{
argument result{output_shape};
......@@ -235,29 +233,37 @@ struct nonmaxsuppression
result.visit([&](auto output) {
visit_all(args[0], args[1])([&](auto boxes, auto scores) {
std::fill(output.begin(), output.end(), 0);
const auto& lens = scores.get_shape().lens();
const auto num_batches = lens[0];
const auto num_classes = lens[1];
const auto num_boxes = lens[2];
// boxes of a class with NMS applied [score, index]
std::vector<std::pair<double, int64_t>> selected_boxes_inside_class;
std::vector<int64_t> selected_indices;
selected_boxes_inside_class.reserve(output_shape.elements());
// iterate over batches and classes
shape comp_s{shape::double_type, {num_batches, num_classes}};
shape_for_each(comp_s, [&](auto idx) {
auto batch_idx = idx[0];
auto class_idx = idx[1];
// index offset for this class
auto scores_start = scores.begin() + (batch_idx * num_classes + class_idx) * num_boxes;
// iterator to first value of this batch
auto batch_boxes_start = boxes.begin() + batch_idx * num_boxes * 4;
auto boxes_heap =
filter_boxes_by_score(scores_start, num_boxes, score_threshold);
select_boxes(boxes_heap, selected_boxes_inside_class, selected_indices, batch_boxes_start, max_output_boxes_per_class, iou_threshold, batch_idx, class_idx);
});
std::copy(selected_indices.begin(), selected_indices.end(), output.begin());
std::fill(output.begin(), output.end(), 0);
const auto& lens = scores.get_shape().lens();
const auto num_batches = lens[0];
const auto num_classes = lens[1];
const auto num_boxes = lens[2];
// boxes of a class with NMS applied [score, index]
std::vector<std::pair<double, int64_t>> selected_boxes_inside_class;
std::vector<int64_t> selected_indices;
selected_boxes_inside_class.reserve(output_shape.elements());
// iterate over batches and classes
shape comp_s{shape::double_type, {num_batches, num_classes}};
shape_for_each(comp_s, [&](auto idx) {
auto batch_idx = idx[0];
auto class_idx = idx[1];
// index offset for this class
auto scores_start =
scores.begin() + (batch_idx * num_classes + class_idx) * num_boxes;
// iterator to first value of this batch
auto batch_boxes_start = boxes.begin() + batch_idx * num_boxes * 4;
auto boxes_heap =
filter_boxes_by_score(scores_start, num_boxes, score_threshold);
select_boxes(boxes_heap,
selected_boxes_inside_class,
selected_indices,
batch_boxes_start,
max_output_boxes_per_class,
iou_threshold,
batch_idx,
class_idx);
});
std::copy(selected_indices.begin(), selected_indices.end(), output.begin());
});
});
......
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