| /* |
| * Copyright (c) 2019 ARM Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "NonMaxSuppression.h" |
| |
| #include "arm_compute/core/Types.h" |
| #include "tests/validation/Helpers.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace reference |
| { |
| namespace |
| { |
| using CandidateBox = std::pair<int /* index */, float /* score */>; |
| using Box = std::tuple<float, float, float, float>; |
| |
| inline float get_elem_by_coordinate(const SimpleTensor<float> &tensor, Coordinates coord) |
| { |
| return *static_cast<const float *>(tensor(coord)); |
| } |
| |
| inline Box get_box(const SimpleTensor<float> &boxes, size_t id) |
| { |
| return std::make_tuple( |
| get_elem_by_coordinate(boxes, Coordinates(0, id)), |
| get_elem_by_coordinate(boxes, Coordinates(1, id)), |
| get_elem_by_coordinate(boxes, Coordinates(2, id)), |
| get_elem_by_coordinate(boxes, Coordinates(3, id))); |
| } |
| |
| // returns a pair (minX, minY) |
| inline std::pair<float, float> get_min_yx(Box b) |
| { |
| return std::make_pair( |
| std::min<float>(std::get<0>(b), std::get<2>(b)), |
| std::min<float>(std::get<1>(b), std::get<3>(b))); |
| } |
| // returns a pair (maxX, maxY) |
| inline std::pair<float, float> get_max_yx(Box b) |
| { |
| return std::make_pair( |
| std::max<float>(std::get<0>(b), std::get<2>(b)), |
| std::max<float>(std::get<1>(b), std::get<3>(b))); |
| } |
| |
| inline float compute_size(const std::pair<float, float> &min, const std::pair<float, float> &max) |
| { |
| return (max.first - min.first) * (max.second - min.second); |
| } |
| |
| inline float compute_intersection(const std::pair<float, float> &b0_min, const std::pair<float, float> &b0_max, |
| const std::pair<float, float> &b1_min, const std::pair<float, float> &b1_max, float b0_size, float b1_size) |
| { |
| const float inter = std::max<float>(std::min<float>(b0_max.first, b1_max.first) - std::max<float>(b0_min.first, b1_min.first), 0.0f) * std::max<float>(std::min<float>(b0_max.second, |
| b1_max.second) |
| - std::max<float>(b0_min.second, b1_min.second), |
| 0.0f); |
| return inter / (b0_size + b1_size - inter); |
| } |
| |
| inline bool reject_box(Box b0, Box b1, float threshold) |
| { |
| const auto b0_min = get_min_yx(b0); |
| const auto b0_max = get_max_yx(b0); |
| const auto b1_min = get_min_yx(b1); |
| const auto b1_max = get_max_yx(b1); |
| const float b0_size = compute_size(b0_min, b0_max); |
| const float b1_size = compute_size(b1_min, b1_max); |
| if(b0_size <= 0.f || b1_size <= 0.f) |
| { |
| return false; |
| } |
| else |
| { |
| const float box_weight = compute_intersection(b0_min, b0_max, b1_min, b1_max, b0_size, b1_size); |
| return box_weight > threshold; |
| } |
| } |
| |
| inline std::vector<CandidateBox> get_candidates(const SimpleTensor<float> &scores, float threshold) |
| { |
| std::vector<CandidateBox> candidates_vector; |
| for(int i = 0; i < scores.num_elements(); ++i) |
| { |
| if(scores[i] >= threshold) |
| { |
| const auto cb = CandidateBox({ i, scores[i] }); |
| candidates_vector.push_back(cb); |
| } |
| } |
| std::stable_sort(candidates_vector.begin(), candidates_vector.end(), [](const CandidateBox bb0, const CandidateBox bb1) |
| { |
| return bb0.second > bb1.second; |
| }); |
| return candidates_vector; |
| } |
| |
| inline bool is_box_selected(const CandidateBox &cb, const SimpleTensor<float> &bboxes, std::vector<int> &selected_boxes, float threshold) |
| { |
| for(int j = selected_boxes.size() - 1; j >= 0; --j) |
| { |
| const auto selected_box_jth = get_box(bboxes, selected_boxes[j]); |
| const auto candidate_box = get_box(bboxes, cb.first); |
| const bool candidate_rejected = reject_box(candidate_box, selected_box_jth, threshold); |
| if(candidate_rejected) |
| { |
| return false; |
| } |
| } |
| return true; |
| } |
| } // namespace |
| |
| SimpleTensor<int> non_max_suppression(const SimpleTensor<float> &bboxes, const SimpleTensor<float> &scores, SimpleTensor<int> &indices, |
| unsigned int max_output_size, float score_threshold, float nms_threshold) |
| { |
| const size_t num_boxes = bboxes.shape().y(); |
| const size_t output_size = std::min(static_cast<size_t>(max_output_size), num_boxes); |
| const std::vector<CandidateBox> candidates_vector = get_candidates(scores, score_threshold); |
| std::vector<int> selected; |
| for(const auto c : candidates_vector) |
| { |
| if(selected.size() == output_size) |
| { |
| break; |
| } |
| if(is_box_selected(c, bboxes, selected, nms_threshold)) |
| { |
| selected.push_back(c.first); |
| } |
| } |
| std::copy_n(selected.begin(), selected.size(), indices.data()); |
| |
| for(unsigned int i = selected.size(); i < max_output_size; ++i) |
| { |
| indices[i] = -1; |
| } |
| |
| return indices; |
| } |
| } // namespace reference |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |