| /* | |
| * Copyright (c) 2019-2021 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/runtime/CPP/functions/CPPDetectionPostProcessLayer.h" | |
| #include "arm_compute/core/Error.h" | |
| #include "arm_compute/core/Helpers.h" | |
| #include "arm_compute/core/Validate.h" | |
| #include "src/core/helpers/AutoConfiguration.h" | |
| #include "src/common/utils/Log.h" | |
| #include <cstddef> | |
| #include <ios> | |
| #include <list> | |
| namespace arm_compute | |
| { | |
| namespace | |
| { | |
| Status validate_arguments(const ITensorInfo *input_box_encoding, const ITensorInfo *input_class_score, const ITensorInfo *input_anchors, | |
| ITensorInfo *output_boxes, ITensorInfo *output_classes, ITensorInfo *output_scores, ITensorInfo *num_detection, | |
| DetectionPostProcessLayerInfo info, const unsigned int kBatchSize, const unsigned int kNumCoordBox) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input_box_encoding, input_class_score, input_anchors); | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_box_encoding, 1, DataType::F32, DataType::QASYMM8, DataType::QASYMM8_SIGNED); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_box_encoding, input_anchors); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input_box_encoding->num_dimensions() > 3, "The location input tensor shape should be [4, N, kBatchSize]."); | |
| if(input_box_encoding->num_dimensions() > 2) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(input_box_encoding->dimension(2) != kBatchSize, "The third dimension of the input box_encoding tensor should be equal to %d.", kBatchSize); | |
| } | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(input_box_encoding->dimension(0) != kNumCoordBox, "The first dimension of the input box_encoding tensor should be equal to %d.", kNumCoordBox); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input_class_score->dimension(0) != (info.num_classes() + 1), | |
| "The first dimension of the input class_prediction should be equal to the number of classes plus one."); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input_anchors->num_dimensions() > 3, "The anchors input tensor shape should be [4, N, kBatchSize]."); | |
| if(input_anchors->num_dimensions() > 2) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(input_anchors->dimension(0) != kNumCoordBox, "The first dimension of the input anchors tensor should be equal to %d.", kNumCoordBox); | |
| } | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((input_box_encoding->dimension(1) != input_class_score->dimension(1)) | |
| || (input_box_encoding->dimension(1) != input_anchors->dimension(1)), | |
| "The second dimension of the inputs should be the same."); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(num_detection->num_dimensions() > 1, "The num_detection output tensor shape should be [M]."); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((info.iou_threshold() <= 0.0f) || (info.iou_threshold() > 1.0f), "The intersection over union should be positive and less than 1."); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.max_classes_per_detection() <= 0, "The number of max classes per detection should be positive."); | |
| const unsigned int num_detected_boxes = info.max_detections() * info.max_classes_per_detection(); | |
| // Validate configured outputs | |
| if(output_boxes->total_size() != 0) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output_boxes->tensor_shape(), TensorShape(4U, num_detected_boxes, 1U)); | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_boxes, 1, DataType::F32); | |
| } | |
| if(output_classes->total_size() != 0) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output_classes->tensor_shape(), TensorShape(num_detected_boxes, 1U)); | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_classes, 1, DataType::F32); | |
| } | |
| if(output_scores->total_size() != 0) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output_scores->tensor_shape(), TensorShape(num_detected_boxes, 1U)); | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_scores, 1, DataType::F32); | |
| } | |
| if(num_detection->total_size() != 0) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(num_detection->tensor_shape(), TensorShape(1U)); | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(num_detection, 1, DataType::F32); | |
| } | |
| return Status{}; | |
| } | |
| inline void DecodeBoxCorner(BBox &box_centersize, BBox &anchor, Iterator &decoded_it, DetectionPostProcessLayerInfo info) | |
| { | |
| const float half_factor = 0.5f; | |
| // BBox is equavalent to CenterSizeEncoding [y,x,h,w] | |
| const float y_center = box_centersize[0] / info.scale_value_y() * anchor[2] + anchor[0]; | |
| const float x_center = box_centersize[1] / info.scale_value_x() * anchor[3] + anchor[1]; | |
| const float half_h = half_factor * static_cast<float>(std::exp(box_centersize[2] / info.scale_value_h())) * anchor[2]; | |
| const float half_w = half_factor * static_cast<float>(std::exp(box_centersize[3] / info.scale_value_w())) * anchor[3]; | |
| // Box Corner encoding boxes are saved as [xmin, ymin, xmax, ymax] | |
| auto decoded_ptr = reinterpret_cast<float *>(decoded_it.ptr()); | |
| *(decoded_ptr) = x_center - half_w; // xmin | |
| *(1 + decoded_ptr) = y_center - half_h; // ymin | |
| *(2 + decoded_ptr) = x_center + half_w; // xmax | |
| *(3 + decoded_ptr) = y_center + half_h; // ymax | |
| } | |
| /** Decode a bbox according to a anchors and scale info. | |
| * | |
| * @param[in] input_box_encoding The input prior bounding boxes. | |
| * @param[in] input_anchors The corresponding input variance. | |
| * @param[in] info The detection informations | |
| * @param[out] decoded_boxes The decoded bboxes. | |
| */ | |
| void DecodeCenterSizeBoxes(const ITensor *input_box_encoding, const ITensor *input_anchors, DetectionPostProcessLayerInfo info, Tensor *decoded_boxes) | |
| { | |
| const QuantizationInfo &qi_box = input_box_encoding->info()->quantization_info(); | |
| const QuantizationInfo &qi_anchors = input_anchors->info()->quantization_info(); | |
| BBox box_centersize{ {} }; | |
| BBox anchor{ {} }; | |
| Window win; | |
| win.use_tensor_dimensions(input_box_encoding->info()->tensor_shape()); | |
| win.set_dimension_step(0U, 4U); | |
| win.set_dimension_step(1U, 1U); | |
| Iterator box_it(input_box_encoding, win); | |
| Iterator anchor_it(input_anchors, win); | |
| Iterator decoded_it(decoded_boxes, win); | |
| if(input_box_encoding->info()->data_type() == DataType::QASYMM8) | |
| { | |
| execute_window_loop(win, [&](const Coordinates &) | |
| { | |
| const auto box_ptr = reinterpret_cast<const qasymm8_t *>(box_it.ptr()); | |
| const auto anchor_ptr = reinterpret_cast<const qasymm8_t *>(anchor_it.ptr()); | |
| box_centersize = BBox({ dequantize_qasymm8(*box_ptr, qi_box), dequantize_qasymm8(*(box_ptr + 1), qi_box), | |
| dequantize_qasymm8(*(2 + box_ptr), qi_box), dequantize_qasymm8(*(3 + box_ptr), qi_box) | |
| }); | |
| anchor = BBox({ dequantize_qasymm8(*anchor_ptr, qi_anchors), dequantize_qasymm8(*(anchor_ptr + 1), qi_anchors), | |
| dequantize_qasymm8(*(2 + anchor_ptr), qi_anchors), dequantize_qasymm8(*(3 + anchor_ptr), qi_anchors) | |
| }); | |
| DecodeBoxCorner(box_centersize, anchor, decoded_it, info); | |
| }, | |
| box_it, anchor_it, decoded_it); | |
| } | |
| else if(input_box_encoding->info()->data_type() == DataType::QASYMM8_SIGNED) | |
| { | |
| execute_window_loop(win, [&](const Coordinates &) | |
| { | |
| const auto box_ptr = reinterpret_cast<const qasymm8_signed_t *>(box_it.ptr()); | |
| const auto anchor_ptr = reinterpret_cast<const qasymm8_signed_t *>(anchor_it.ptr()); | |
| box_centersize = BBox({ dequantize_qasymm8_signed(*box_ptr, qi_box), dequantize_qasymm8_signed(*(box_ptr + 1), qi_box), | |
| dequantize_qasymm8_signed(*(2 + box_ptr), qi_box), dequantize_qasymm8_signed(*(3 + box_ptr), qi_box) | |
| }); | |
| anchor = BBox({ dequantize_qasymm8_signed(*anchor_ptr, qi_anchors), dequantize_qasymm8_signed(*(anchor_ptr + 1), qi_anchors), | |
| dequantize_qasymm8_signed(*(2 + anchor_ptr), qi_anchors), dequantize_qasymm8_signed(*(3 + anchor_ptr), qi_anchors) | |
| }); | |
| DecodeBoxCorner(box_centersize, anchor, decoded_it, info); | |
| }, | |
| box_it, anchor_it, decoded_it); | |
| } | |
| else | |
| { | |
| execute_window_loop(win, [&](const Coordinates &) | |
| { | |
| const auto box_ptr = reinterpret_cast<const float *>(box_it.ptr()); | |
| const auto anchor_ptr = reinterpret_cast<const float *>(anchor_it.ptr()); | |
| box_centersize = BBox({ *box_ptr, *(box_ptr + 1), *(2 + box_ptr), *(3 + box_ptr) }); | |
| anchor = BBox({ *anchor_ptr, *(anchor_ptr + 1), *(2 + anchor_ptr), *(3 + anchor_ptr) }); | |
| DecodeBoxCorner(box_centersize, anchor, decoded_it, info); | |
| }, | |
| box_it, anchor_it, decoded_it); | |
| } | |
| } | |
| void SaveOutputs(const Tensor *decoded_boxes, const std::vector<int> &result_idx_boxes_after_nms, const std::vector<float> &result_scores_after_nms, const std::vector<int> &result_classes_after_nms, | |
| std::vector<unsigned int> &sorted_indices, const unsigned int num_output, const unsigned int max_detections, ITensor *output_boxes, ITensor *output_classes, ITensor *output_scores, | |
| ITensor *num_detection) | |
| { | |
| // xmin,ymin,xmax,ymax -> ymin,xmin,ymax,xmax | |
| unsigned int i = 0; | |
| for(; i < num_output; ++i) | |
| { | |
| const unsigned int box_in_idx = result_idx_boxes_after_nms[sorted_indices[i]]; | |
| *(reinterpret_cast<float *>(output_boxes->ptr_to_element(Coordinates(0, i)))) = *(reinterpret_cast<float *>(decoded_boxes->ptr_to_element(Coordinates(1, box_in_idx)))); | |
| *(reinterpret_cast<float *>(output_boxes->ptr_to_element(Coordinates(1, i)))) = *(reinterpret_cast<float *>(decoded_boxes->ptr_to_element(Coordinates(0, box_in_idx)))); | |
| *(reinterpret_cast<float *>(output_boxes->ptr_to_element(Coordinates(2, i)))) = *(reinterpret_cast<float *>(decoded_boxes->ptr_to_element(Coordinates(3, box_in_idx)))); | |
| *(reinterpret_cast<float *>(output_boxes->ptr_to_element(Coordinates(3, i)))) = *(reinterpret_cast<float *>(decoded_boxes->ptr_to_element(Coordinates(2, box_in_idx)))); | |
| *(reinterpret_cast<float *>(output_classes->ptr_to_element(Coordinates(i)))) = static_cast<float>(result_classes_after_nms[sorted_indices[i]]); | |
| *(reinterpret_cast<float *>(output_scores->ptr_to_element(Coordinates(i)))) = result_scores_after_nms[sorted_indices[i]]; | |
| } | |
| for(; i < max_detections; ++i) | |
| { | |
| *(reinterpret_cast<float *>(output_boxes->ptr_to_element(Coordinates(1, i)))) = 0.0f; | |
| *(reinterpret_cast<float *>(output_boxes->ptr_to_element(Coordinates(0, i)))) = 0.0f; | |
| *(reinterpret_cast<float *>(output_boxes->ptr_to_element(Coordinates(3, i)))) = 0.0f; | |
| *(reinterpret_cast<float *>(output_boxes->ptr_to_element(Coordinates(2, i)))) = 0.0f; | |
| *(reinterpret_cast<float *>(output_classes->ptr_to_element(Coordinates(i)))) = 0.0f; | |
| *(reinterpret_cast<float *>(output_scores->ptr_to_element(Coordinates(i)))) = 0.0f; | |
| } | |
| *(reinterpret_cast<float *>(num_detection->ptr_to_element(Coordinates(0)))) = num_output; | |
| } | |
| } // namespace | |
| CPPDetectionPostProcessLayer::CPPDetectionPostProcessLayer(std::shared_ptr<IMemoryManager> memory_manager) | |
| : _memory_group(std::move(memory_manager)), _nms(), _input_box_encoding(nullptr), _input_scores(nullptr), _input_anchors(nullptr), _output_boxes(nullptr), _output_classes(nullptr), | |
| _output_scores(nullptr), _num_detection(nullptr), _info(), _num_boxes(), _num_classes_with_background(), _num_max_detected_boxes(), _dequantize_scores(false), _decoded_boxes(), _decoded_scores(), | |
| _selected_indices(), _class_scores(), _input_scores_to_use(nullptr) | |
| { | |
| } | |
| void CPPDetectionPostProcessLayer::configure(const ITensor *input_box_encoding, const ITensor *input_scores, | |
| const ITensor *input_anchors, ITensor *output_boxes, ITensor *output_classes, | |
| ITensor *output_scores, ITensor *num_detection, DetectionPostProcessLayerInfo info) | |
| { | |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input_box_encoding, input_scores, input_anchors, output_boxes, output_classes, output_scores); | |
| ARM_COMPUTE_LOG_PARAMS(input_box_encoding, input_scores, input_anchors, output_boxes, output_classes, output_scores, | |
| num_detection, info); | |
| _num_max_detected_boxes = info.max_detections() * info.max_classes_per_detection(); | |
| auto_init_if_empty(*output_boxes->info(), TensorInfo(TensorShape(_kNumCoordBox, _num_max_detected_boxes, _kBatchSize), 1, DataType::F32)); | |
| auto_init_if_empty(*output_classes->info(), TensorInfo(TensorShape(_num_max_detected_boxes, _kBatchSize), 1, DataType::F32)); | |
| auto_init_if_empty(*output_scores->info(), TensorInfo(TensorShape(_num_max_detected_boxes, _kBatchSize), 1, DataType::F32)); | |
| auto_init_if_empty(*num_detection->info(), TensorInfo(TensorShape(1U), 1, DataType::F32)); | |
| // Perform validation step | |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input_box_encoding->info(), input_scores->info(), input_anchors->info(), output_boxes->info(), output_classes->info(), output_scores->info(), | |
| num_detection->info(), | |
| info, _kBatchSize, _kNumCoordBox)); | |
| _input_box_encoding = input_box_encoding; | |
| _input_scores = input_scores; | |
| _input_anchors = input_anchors; | |
| _output_boxes = output_boxes; | |
| _output_classes = output_classes; | |
| _output_scores = output_scores; | |
| _num_detection = num_detection; | |
| _info = info; | |
| _num_boxes = input_box_encoding->info()->dimension(1); | |
| _num_classes_with_background = _input_scores->info()->dimension(0); | |
| _dequantize_scores = (info.dequantize_scores() && is_data_type_quantized(input_box_encoding->info()->data_type())); | |
| auto_init_if_empty(*_decoded_boxes.info(), TensorInfo(TensorShape(_kNumCoordBox, _input_box_encoding->info()->dimension(1), _kBatchSize), 1, DataType::F32)); | |
| auto_init_if_empty(*_decoded_scores.info(), TensorInfo(TensorShape(_input_scores->info()->dimension(0), _input_scores->info()->dimension(1), _kBatchSize), 1, DataType::F32)); | |
| auto_init_if_empty(*_selected_indices.info(), TensorInfo(TensorShape(info.use_regular_nms() ? info.detection_per_class() : info.max_detections()), 1, DataType::S32)); | |
| const unsigned int num_classes_per_box = std::min(info.max_classes_per_detection(), info.num_classes()); | |
| auto_init_if_empty(*_class_scores.info(), TensorInfo(info.use_regular_nms() ? TensorShape(_num_boxes) : TensorShape(_num_boxes * num_classes_per_box), 1, DataType::F32)); | |
| _input_scores_to_use = _dequantize_scores ? &_decoded_scores : _input_scores; | |
| // Manage intermediate buffers | |
| _memory_group.manage(&_decoded_boxes); | |
| _memory_group.manage(&_decoded_scores); | |
| _memory_group.manage(&_selected_indices); | |
| _memory_group.manage(&_class_scores); | |
| _nms.configure(&_decoded_boxes, &_class_scores, &_selected_indices, info.use_regular_nms() ? info.detection_per_class() : info.max_detections(), info.nms_score_threshold(), info.iou_threshold()); | |
| // Allocate and reserve intermediate tensors and vectors | |
| _decoded_boxes.allocator()->allocate(); | |
| _decoded_scores.allocator()->allocate(); | |
| _selected_indices.allocator()->allocate(); | |
| _class_scores.allocator()->allocate(); | |
| } | |
| Status CPPDetectionPostProcessLayer::validate(const ITensorInfo *input_box_encoding, const ITensorInfo *input_class_score, const ITensorInfo *input_anchors, | |
| ITensorInfo *output_boxes, ITensorInfo *output_classes, ITensorInfo *output_scores, ITensorInfo *num_detection, DetectionPostProcessLayerInfo info) | |
| { | |
| constexpr unsigned int kBatchSize = 1; | |
| constexpr unsigned int kNumCoordBox = 4; | |
| const TensorInfo _decoded_boxes_info = TensorInfo(TensorShape(kNumCoordBox, input_box_encoding->dimension(1)), 1, DataType::F32); | |
| const TensorInfo _decoded_scores_info = TensorInfo(TensorShape(input_box_encoding->dimension(1)), 1, DataType::F32); | |
| const TensorInfo _selected_indices_info = TensorInfo(TensorShape(info.max_detections()), 1, DataType::S32); | |
| ARM_COMPUTE_RETURN_ON_ERROR(CPPNonMaximumSuppression::validate(&_decoded_boxes_info, &_decoded_scores_info, &_selected_indices_info, info.max_detections(), info.nms_score_threshold(), | |
| info.iou_threshold())); | |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input_box_encoding, input_class_score, input_anchors, output_boxes, output_classes, output_scores, num_detection, info, kBatchSize, kNumCoordBox)); | |
| return Status{}; | |
| } | |
| void CPPDetectionPostProcessLayer::run() | |
| { | |
| const unsigned int num_classes = _info.num_classes(); | |
| const unsigned int max_detections = _info.max_detections(); | |
| DecodeCenterSizeBoxes(_input_box_encoding, _input_anchors, _info, &_decoded_boxes); | |
| // Decode scores if necessary | |
| if(_dequantize_scores) | |
| { | |
| if(_input_box_encoding->info()->data_type() == DataType::QASYMM8) | |
| { | |
| for(unsigned int idx_c = 0; idx_c < _num_classes_with_background; ++idx_c) | |
| { | |
| for(unsigned int idx_b = 0; idx_b < _num_boxes; ++idx_b) | |
| { | |
| *(reinterpret_cast<float *>(_decoded_scores.ptr_to_element(Coordinates(idx_c, idx_b)))) = | |
| dequantize_qasymm8(*(reinterpret_cast<qasymm8_t *>(_input_scores->ptr_to_element(Coordinates(idx_c, idx_b)))), _input_scores->info()->quantization_info()); | |
| } | |
| } | |
| } | |
| else if(_input_box_encoding->info()->data_type() == DataType::QASYMM8_SIGNED) | |
| { | |
| for(unsigned int idx_c = 0; idx_c < _num_classes_with_background; ++idx_c) | |
| { | |
| for(unsigned int idx_b = 0; idx_b < _num_boxes; ++idx_b) | |
| { | |
| *(reinterpret_cast<float *>(_decoded_scores.ptr_to_element(Coordinates(idx_c, idx_b)))) = | |
| dequantize_qasymm8_signed(*(reinterpret_cast<qasymm8_signed_t *>(_input_scores->ptr_to_element(Coordinates(idx_c, idx_b)))), _input_scores->info()->quantization_info()); | |
| } | |
| } | |
| } | |
| } | |
| // Regular NMS | |
| if(_info.use_regular_nms()) | |
| { | |
| std::vector<int> result_idx_boxes_after_nms; | |
| std::vector<int> result_classes_after_nms; | |
| std::vector<float> result_scores_after_nms; | |
| std::vector<unsigned int> sorted_indices; | |
| for(unsigned int c = 0; c < num_classes; ++c) | |
| { | |
| // For each boxes get scores of the boxes for the class c | |
| for(unsigned int i = 0; i < _num_boxes; ++i) | |
| { | |
| *(reinterpret_cast<float *>(_class_scores.ptr_to_element(Coordinates(i)))) = | |
| *(reinterpret_cast<float *>(_input_scores_to_use->ptr_to_element(Coordinates(c + 1, i)))); // i * _num_classes_with_background + c + 1 | |
| } | |
| // Run Non-maxima Suppression | |
| _nms.run(); | |
| for(unsigned int i = 0; i < _info.detection_per_class(); ++i) | |
| { | |
| const auto selected_index = *(reinterpret_cast<int *>(_selected_indices.ptr_to_element(Coordinates(i)))); | |
| if(selected_index == -1) | |
| { | |
| // Nms will return -1 for all the last M-elements not valid | |
| break; | |
| } | |
| result_idx_boxes_after_nms.emplace_back(selected_index); | |
| result_scores_after_nms.emplace_back((reinterpret_cast<float *>(_class_scores.buffer()))[selected_index]); | |
| result_classes_after_nms.emplace_back(c); | |
| } | |
| } | |
| // We select the max detection numbers of the highest score of all classes | |
| const auto num_selected = result_scores_after_nms.size(); | |
| const auto num_output = std::min<unsigned int>(max_detections, num_selected); | |
| // Sort selected indices based on result scores | |
| sorted_indices.resize(num_selected); | |
| std::iota(sorted_indices.begin(), sorted_indices.end(), 0); | |
| std::partial_sort(sorted_indices.data(), | |
| sorted_indices.data() + num_output, | |
| sorted_indices.data() + num_selected, | |
| [&](unsigned int first, unsigned int second) | |
| { | |
| return result_scores_after_nms[first] > result_scores_after_nms[second]; | |
| }); | |
| SaveOutputs(&_decoded_boxes, result_idx_boxes_after_nms, result_scores_after_nms, result_classes_after_nms, sorted_indices, | |
| num_output, max_detections, _output_boxes, _output_classes, _output_scores, _num_detection); | |
| } | |
| // Fast NMS | |
| else | |
| { | |
| const unsigned int num_classes_per_box = std::min<unsigned int>(_info.max_classes_per_detection(), _info.num_classes()); | |
| std::vector<float> max_scores; | |
| std::vector<int> box_indices; | |
| std::vector<int> max_score_classes; | |
| for(unsigned int b = 0; b < _num_boxes; ++b) | |
| { | |
| std::vector<float> box_scores; | |
| for(unsigned int c = 0; c < num_classes; ++c) | |
| { | |
| box_scores.emplace_back(*(reinterpret_cast<float *>(_input_scores_to_use->ptr_to_element(Coordinates(c + 1, b))))); | |
| } | |
| std::vector<unsigned int> max_score_indices; | |
| max_score_indices.resize(_info.num_classes()); | |
| std::iota(max_score_indices.data(), max_score_indices.data() + _info.num_classes(), 0); | |
| std::partial_sort(max_score_indices.data(), | |
| max_score_indices.data() + num_classes_per_box, | |
| max_score_indices.data() + num_classes, | |
| [&](unsigned int first, unsigned int second) | |
| { | |
| return box_scores[first] > box_scores[second]; | |
| }); | |
| for(unsigned int i = 0; i < num_classes_per_box; ++i) | |
| { | |
| const float score_to_add = box_scores[max_score_indices[i]]; | |
| *(reinterpret_cast<float *>(_class_scores.ptr_to_element(Coordinates(b * num_classes_per_box + i)))) = score_to_add; | |
| max_scores.emplace_back(score_to_add); | |
| box_indices.emplace_back(b); | |
| max_score_classes.emplace_back(max_score_indices[i]); | |
| } | |
| } | |
| // Run Non-maxima Suppression | |
| _nms.run(); | |
| std::vector<unsigned int> selected_indices; | |
| for(unsigned int i = 0; i < max_detections; ++i) | |
| { | |
| // NMS returns M valid indices, the not valid tail is filled with -1 | |
| if(*(reinterpret_cast<int *>(_selected_indices.ptr_to_element(Coordinates(i)))) == -1) | |
| { | |
| // Nms will return -1 for all the last M-elements not valid | |
| break; | |
| } | |
| selected_indices.emplace_back(*(reinterpret_cast<int *>(_selected_indices.ptr_to_element(Coordinates(i))))); | |
| } | |
| // We select the max detection numbers of the highest score of all classes | |
| const auto num_output = std::min<unsigned int>(_info.max_detections(), selected_indices.size()); | |
| SaveOutputs(&_decoded_boxes, box_indices, max_scores, max_score_classes, selected_indices, | |
| num_output, max_detections, _output_boxes, _output_classes, _output_scores, _num_detection); | |
| } | |
| } | |
| } // namespace arm_compute |