| /* |
| * Copyright (c) 2019-2020 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 "arm_compute/core/CPP/kernels/CPPNonMaximumSuppressionKernel.h" |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/Validate.h" |
| |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| |
| #include <algorithm> |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *bboxes, const ITensorInfo *scores, const ITensorInfo *output_indices, unsigned int max_output_size, |
| const float score_threshold, const float iou_threshold) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(bboxes, scores, output_indices); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bboxes, 1, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_indices, 1, DataType::S32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(bboxes->num_dimensions() > 2, "The bboxes tensor must be a 2-D float tensor of shape [4, num_boxes]."); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(scores->num_dimensions() > 1, "The scores tensor must be a 1-D float tensor of shape [num_boxes]."); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(output_indices->num_dimensions() > 1, "The indices must be 1-D integer tensor of shape [M], where max_output_size <= M"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(bboxes, scores); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(output_indices->dimension(0) == 0, "Indices tensor must be bigger than 0"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(max_output_size == 0, "Max size cannot be 0"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(iou_threshold < 0.f || iou_threshold > 1.f, "IOU threshold must be in [0,1]"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(score_threshold < 0.f || score_threshold > 1.f, "Score threshold must be in [0,1]"); |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| CPPNonMaximumSuppressionKernel::CPPNonMaximumSuppressionKernel() |
| : _input_bboxes(nullptr), _input_scores(nullptr), _output_indices(nullptr), _max_output_size(0), _score_threshold(0.f), _iou_threshold(0.f), _num_boxes(0) |
| { |
| } |
| |
| void CPPNonMaximumSuppressionKernel::configure(const ITensor *input_bboxes, const ITensor *input_scores, ITensor *output_indices, |
| unsigned int max_output_size, const float score_threshold, const float iou_threshold) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input_bboxes, input_scores, output_indices); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input_bboxes->info(), input_scores->info(), output_indices->info(), max_output_size, score_threshold, iou_threshold)); |
| |
| auto_init_if_empty(*output_indices->info(), TensorShape(max_output_size), 1, DataType::U8, QuantizationInfo()); |
| |
| _input_bboxes = input_bboxes; |
| _input_scores = input_scores; |
| _output_indices = output_indices; |
| _score_threshold = score_threshold; |
| _iou_threshold = iou_threshold; |
| _max_output_size = max_output_size; |
| _num_boxes = input_scores->info()->dimension(0); |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*output_indices->info(), Steps()); |
| |
| // The CPPNonMaximumSuppressionKernel doesn't need padding so update_window_and_padding() can be skipped |
| ICPPKernel::configure(win); |
| } |
| |
| Status CPPNonMaximumSuppressionKernel::validate(const ITensorInfo *bboxes, const ITensorInfo *scores, const ITensorInfo *output_indices, |
| unsigned int max_output_size, const float score_threshold, const float iou_threshold) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(bboxes, scores, output_indices, max_output_size, score_threshold, iou_threshold)); |
| return Status{}; |
| } |
| |
| void CPPNonMaximumSuppressionKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_UNUSED(window); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICPPKernel::window(), window); |
| |
| // Auxiliary tensors |
| std::vector<int> indices_above_thd; |
| std::vector<float> scores_above_thd; |
| for(unsigned int i = 0; i < _num_boxes; ++i) |
| { |
| const float score_i = *(reinterpret_cast<float *>(_input_scores->ptr_to_element(Coordinates(i)))); |
| if(score_i >= _score_threshold) |
| { |
| scores_above_thd.emplace_back(score_i); |
| indices_above_thd.emplace_back(i); |
| } |
| } |
| |
| // Sort selected indices based on scores |
| const unsigned int num_above_thd = indices_above_thd.size(); |
| std::vector<unsigned int> sorted_indices; |
| sorted_indices.resize(num_above_thd); |
| std::iota(sorted_indices.data(), sorted_indices.data() + num_above_thd, 0); |
| std::sort(std::begin(sorted_indices), |
| std::end(sorted_indices), |
| [&](unsigned int first, unsigned int second) |
| { |
| return scores_above_thd[first] > scores_above_thd[second]; |
| }); |
| |
| // Number of output is the minimum between max_detection and the scores above the threshold |
| const unsigned int num_output = std::min(_max_output_size, num_above_thd); |
| unsigned int output_idx = 0; |
| std::vector<bool> visited(num_above_thd, false); |
| |
| // Keep only boxes with small IoU |
| for(unsigned int i = 0; i < num_above_thd; ++i) |
| { |
| // Check if the output is full |
| if(output_idx >= num_output) |
| { |
| break; |
| } |
| |
| // Check if it was already visited, if not add it to the output and update the indices counter |
| if(!visited[sorted_indices[i]]) |
| { |
| *(reinterpret_cast<int *>(_output_indices->ptr_to_element(Coordinates(output_idx)))) = indices_above_thd[sorted_indices[i]]; |
| visited[sorted_indices[i]] = true; |
| ++output_idx; |
| } |
| else |
| { |
| continue; |
| } |
| |
| // Once added one element at the output check if the next ones overlap and can be skipped |
| for(unsigned int j = i + 1; j < num_above_thd; ++j) |
| { |
| if(!visited[sorted_indices[j]]) |
| { |
| // Calculate IoU |
| const unsigned int i_index = indices_above_thd[sorted_indices[i]]; |
| const unsigned int j_index = indices_above_thd[sorted_indices[j]]; |
| // Box-corner format: xmin, ymin, xmax, ymax |
| const auto box_i_xmin = *(reinterpret_cast<float *>(_input_bboxes->ptr_to_element(Coordinates(0, i_index)))); |
| const auto box_i_ymin = *(reinterpret_cast<float *>(_input_bboxes->ptr_to_element(Coordinates(1, i_index)))); |
| const auto box_i_xmax = *(reinterpret_cast<float *>(_input_bboxes->ptr_to_element(Coordinates(2, i_index)))); |
| const auto box_i_ymax = *(reinterpret_cast<float *>(_input_bboxes->ptr_to_element(Coordinates(3, i_index)))); |
| |
| const auto box_j_xmin = *(reinterpret_cast<float *>(_input_bboxes->ptr_to_element(Coordinates(0, j_index)))); |
| const auto box_j_ymin = *(reinterpret_cast<float *>(_input_bboxes->ptr_to_element(Coordinates(1, j_index)))); |
| const auto box_j_xmax = *(reinterpret_cast<float *>(_input_bboxes->ptr_to_element(Coordinates(2, j_index)))); |
| const auto box_j_ymax = *(reinterpret_cast<float *>(_input_bboxes->ptr_to_element(Coordinates(3, j_index)))); |
| |
| const float area_i = (box_i_xmax - box_i_xmin) * (box_i_ymax - box_i_ymin); |
| const float area_j = (box_j_xmax - box_j_xmin) * (box_j_ymax - box_j_ymin); |
| float overlap; |
| if(area_i <= 0 || area_j <= 0) |
| { |
| overlap = 0.0f; |
| } |
| else |
| { |
| const auto y_min_intersection = std::max<float>(box_i_ymin, box_j_ymin); |
| const auto x_min_intersection = std::max<float>(box_i_xmin, box_j_xmin); |
| const auto y_max_intersection = std::min<float>(box_i_ymax, box_j_ymax); |
| const auto x_max_intersection = std::min<float>(box_i_xmax, box_j_xmax); |
| const auto area_intersection = std::max<float>(y_max_intersection - y_min_intersection, 0.0f) * std::max<float>(x_max_intersection - x_min_intersection, 0.0f); |
| overlap = area_intersection / (area_i + area_j - area_intersection); |
| } |
| |
| if(overlap > _iou_threshold) |
| { |
| visited[sorted_indices[j]] = true; |
| } |
| } |
| } |
| } |
| // The output could be full but not the output indices tensor |
| // Instead return values not valid we put -1 |
| for(; output_idx < _max_output_size; ++output_idx) |
| { |
| *(reinterpret_cast<int *>(_output_indices->ptr_to_element(Coordinates(output_idx)))) = -1; |
| } |
| } |
| } // namespace arm_compute |