| /* | |
| * Copyright (c) 2018-2021 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/CPPDetectionOutputLayer.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 <list> | |
| namespace arm_compute | |
| { | |
| namespace | |
| { | |
| Status validate_arguments(const ITensorInfo *input_loc, const ITensorInfo *input_conf, const ITensorInfo *input_priorbox, const ITensorInfo *output, DetectionOutputLayerInfo info) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input_loc, input_conf, input_priorbox, output); | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_loc, 1, DataType::F32); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_loc, input_conf, input_priorbox); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input_loc->num_dimensions() > 2, "The location input tensor should be [C1, N]."); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input_conf->num_dimensions() > 2, "The location input tensor should be [C2, N]."); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input_priorbox->num_dimensions() > 3, "The priorbox input tensor should be [C3, 2, N]."); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.eta() <= 0.f && info.eta() > 1.f, "Eta should be between 0 and 1"); | |
| const int num_priors = input_priorbox->tensor_shape()[0] / 4; | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(static_cast<size_t>((num_priors * info.num_loc_classes() * 4)) != input_loc->tensor_shape()[0], "Number of priors must match number of location predictions."); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(static_cast<size_t>((num_priors * info.num_classes())) != input_conf->tensor_shape()[0], "Number of priors must match number of confidence predictions."); | |
| // Validate configured output | |
| if(output->total_size() != 0) | |
| { | |
| const unsigned int max_size = info.keep_top_k() * (input_loc->num_dimensions() > 1 ? input_loc->dimension(1) : 1); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), TensorShape(7U, max_size)); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_loc, output); | |
| } | |
| return Status{}; | |
| } | |
| /** Function used to sort pair<float, T> in descend order based on the score (first) value. | |
| */ | |
| template <typename T> | |
| bool SortScorePairDescend(const std::pair<float, T> &pair1, | |
| const std::pair<float, T> &pair2) | |
| { | |
| return pair1.first > pair2.first; | |
| } | |
| /** Get location predictions from input_loc. | |
| * | |
| * @param[in] input_loc The input location prediction. | |
| * @param[in] num The number of images. | |
| * @param[in] num_priors number of predictions per class. | |
| * @param[in] num_loc_classes number of location classes. It is 1 if share_location is true, | |
| * and is equal to number of classes needed to predict otherwise. | |
| * @param[in] share_location If true, all classes share the same location prediction. | |
| * @param[out] all_location_predictions All the location predictions. | |
| * | |
| */ | |
| void retrieve_all_loc_predictions(const ITensor *input_loc, const int num, | |
| const int num_priors, const int num_loc_classes, | |
| const bool share_location, std::vector<LabelBBox> &all_location_predictions) | |
| { | |
| for(int i = 0; i < num; ++i) | |
| { | |
| for(int c = 0; c < num_loc_classes; ++c) | |
| { | |
| int label = share_location ? -1 : c; | |
| if(all_location_predictions[i].find(label) == all_location_predictions[i].end()) | |
| { | |
| all_location_predictions[i][label].resize(num_priors); | |
| } | |
| else | |
| { | |
| ARM_COMPUTE_ERROR_ON(all_location_predictions[i][label].size() != static_cast<size_t>(num_priors)); | |
| break; | |
| } | |
| } | |
| } | |
| for(int i = 0; i < num; ++i) | |
| { | |
| for(int p = 0; p < num_priors; ++p) | |
| { | |
| for(int c = 0; c < num_loc_classes; ++c) | |
| { | |
| const int label = share_location ? -1 : c; | |
| const int base_ptr = i * num_priors * num_loc_classes * 4 + p * num_loc_classes * 4 + c * 4; | |
| //xmin, ymin, xmax, ymax | |
| all_location_predictions[i][label][p][0] = *reinterpret_cast<float *>(input_loc->ptr_to_element(Coordinates(base_ptr))); | |
| all_location_predictions[i][label][p][1] = *reinterpret_cast<float *>(input_loc->ptr_to_element(Coordinates(base_ptr + 1))); | |
| all_location_predictions[i][label][p][2] = *reinterpret_cast<float *>(input_loc->ptr_to_element(Coordinates(base_ptr + 2))); | |
| all_location_predictions[i][label][p][3] = *reinterpret_cast<float *>(input_loc->ptr_to_element(Coordinates(base_ptr + 3))); | |
| } | |
| } | |
| } | |
| } | |
| /** Get confidence predictions from input_conf. | |
| * | |
| * @param[in] input_loc The input location prediction. | |
| * @param[in] num The number of images. | |
| * @param[in] num_priors Number of predictions per class. | |
| * @param[in] num_loc_classes Number of location classes. It is 1 if share_location is true, | |
| * and is equal to number of classes needed to predict otherwise. | |
| * @param[out] all_location_predictions All the location predictions. | |
| * | |
| */ | |
| void retrieve_all_conf_scores(const ITensor *input_conf, const int num, | |
| const int num_priors, const int num_classes, | |
| std::vector<std::map<int, std::vector<float>>> &all_confidence_scores) | |
| { | |
| std::vector<float> tmp_buffer; | |
| tmp_buffer.resize(num * num_priors * num_classes); | |
| for(int i = 0; i < num; ++i) | |
| { | |
| for(int c = 0; c < num_classes; ++c) | |
| { | |
| for(int p = 0; p < num_priors; ++p) | |
| { | |
| tmp_buffer[i * num_classes * num_priors + c * num_priors + p] = | |
| *reinterpret_cast<float *>(input_conf->ptr_to_element(Coordinates(i * num_classes * num_priors + p * num_classes + c))); | |
| } | |
| } | |
| } | |
| for(int i = 0; i < num; ++i) | |
| { | |
| for(int c = 0; c < num_classes; ++c) | |
| { | |
| all_confidence_scores[i][c].resize(num_priors); | |
| all_confidence_scores[i][c].assign(&tmp_buffer[i * num_classes * num_priors + c * num_priors], | |
| &tmp_buffer[i * num_classes * num_priors + c * num_priors + num_priors]); | |
| } | |
| } | |
| } | |
| /** Get prior boxes from input_priorbox. | |
| * | |
| * @param[in] input_priorbox The input location prediction. | |
| * @param[in] num_priors Number of priors. | |
| * @param[in] num_loc_classes number of location classes. It is 1 if share_location is true, | |
| * and is equal to number of classes needed to predict otherwise. | |
| * @param[out] all_prior_bboxes If true, all classes share the same location prediction. | |
| * @param[out] all_location_predictions All the location predictions. | |
| * | |
| */ | |
| void retrieve_all_priorbox(const ITensor *input_priorbox, | |
| const int num_priors, | |
| std::vector<BBox> &all_prior_bboxes, | |
| std::vector<std::array<float, 4>> &all_prior_variances) | |
| { | |
| for(int i = 0; i < num_priors; ++i) | |
| { | |
| all_prior_bboxes[i] = | |
| { | |
| { | |
| *reinterpret_cast<float *>(input_priorbox->ptr_to_element(Coordinates(i * 4))), | |
| *reinterpret_cast<float *>(input_priorbox->ptr_to_element(Coordinates(i * 4 + 1))), | |
| *reinterpret_cast<float *>(input_priorbox->ptr_to_element(Coordinates(i * 4 + 2))), | |
| *reinterpret_cast<float *>(input_priorbox->ptr_to_element(Coordinates(i * 4 + 3))) | |
| } | |
| }; | |
| } | |
| std::array<float, 4> var({ { 0, 0, 0, 0 } }); | |
| for(int i = 0; i < num_priors; ++i) | |
| { | |
| for(int j = 0; j < 4; ++j) | |
| { | |
| var[j] = *reinterpret_cast<float *>(input_priorbox->ptr_to_element(Coordinates((num_priors + i) * 4 + j))); | |
| } | |
| all_prior_variances[i] = var; | |
| } | |
| } | |
| /** Decode a bbox according to a prior bbox. | |
| * | |
| * @param[in] prior_bbox The input prior bounding boxes. | |
| * @param[in] prior_variance The corresponding input variance. | |
| * @param[in] code_type The detection output code type used to decode the results. | |
| * @param[in] variance_encoded_in_target If true, the variance is encoded in target. | |
| * @param[in] clip_bbox If true, the results should be between 0.f and 1.f. | |
| * @param[in] bbox The input bbox to decode | |
| * @param[out] decode_bbox The decoded bboxes. | |
| * | |
| */ | |
| void DecodeBBox(const BBox &prior_bbox, const std::array<float, 4> &prior_variance, | |
| const DetectionOutputLayerCodeType code_type, const bool variance_encoded_in_target, | |
| const bool clip_bbox, const BBox &bbox, BBox &decode_bbox) | |
| { | |
| // if the variance is encoded in target, we simply need to add the offset predictions | |
| // otherwise we need to scale the offset accordingly. | |
| switch(code_type) | |
| { | |
| case DetectionOutputLayerCodeType::CORNER: | |
| { | |
| decode_bbox[0] = prior_bbox[0] + (variance_encoded_in_target ? bbox[0] : prior_variance[0] * bbox[0]); | |
| decode_bbox[1] = prior_bbox[1] + (variance_encoded_in_target ? bbox[1] : prior_variance[1] * bbox[1]); | |
| decode_bbox[2] = prior_bbox[2] + (variance_encoded_in_target ? bbox[2] : prior_variance[2] * bbox[2]); | |
| decode_bbox[3] = prior_bbox[3] + (variance_encoded_in_target ? bbox[3] : prior_variance[3] * bbox[3]); | |
| break; | |
| } | |
| case DetectionOutputLayerCodeType::CENTER_SIZE: | |
| { | |
| const float prior_width = prior_bbox[2] - prior_bbox[0]; | |
| const float prior_height = prior_bbox[3] - prior_bbox[1]; | |
| // Check if the prior width and height are right | |
| ARM_COMPUTE_ERROR_ON(prior_width <= 0.f); | |
| ARM_COMPUTE_ERROR_ON(prior_height <= 0.f); | |
| const float prior_center_x = (prior_bbox[0] + prior_bbox[2]) / 2.; | |
| const float prior_center_y = (prior_bbox[1] + prior_bbox[3]) / 2.; | |
| const float decode_bbox_center_x = (variance_encoded_in_target ? bbox[0] : prior_variance[0] * bbox[0]) * prior_width + prior_center_x; | |
| const float decode_bbox_center_y = (variance_encoded_in_target ? bbox[1] : prior_variance[1] * bbox[1]) * prior_height + prior_center_y; | |
| const float decode_bbox_width = (variance_encoded_in_target ? std::exp(bbox[2]) : std::exp(prior_variance[2] * bbox[2])) * prior_width; | |
| const float decode_bbox_height = (variance_encoded_in_target ? std::exp(bbox[3]) : std::exp(prior_variance[3] * bbox[3])) * prior_height; | |
| decode_bbox[0] = (decode_bbox_center_x - decode_bbox_width / 2.f); | |
| decode_bbox[1] = (decode_bbox_center_y - decode_bbox_height / 2.f); | |
| decode_bbox[2] = (decode_bbox_center_x + decode_bbox_width / 2.f); | |
| decode_bbox[3] = (decode_bbox_center_y + decode_bbox_height / 2.f); | |
| break; | |
| } | |
| case DetectionOutputLayerCodeType::CORNER_SIZE: | |
| { | |
| const float prior_width = prior_bbox[2] - prior_bbox[0]; | |
| const float prior_height = prior_bbox[3] - prior_bbox[1]; | |
| // Check if the prior width and height are greater than 0 | |
| ARM_COMPUTE_ERROR_ON(prior_width <= 0.f); | |
| ARM_COMPUTE_ERROR_ON(prior_height <= 0.f); | |
| decode_bbox[0] = prior_bbox[0] + (variance_encoded_in_target ? bbox[0] : prior_variance[0] * bbox[0]) * prior_width; | |
| decode_bbox[1] = prior_bbox[1] + (variance_encoded_in_target ? bbox[1] : prior_variance[1] * bbox[1]) * prior_height; | |
| decode_bbox[2] = prior_bbox[2] + (variance_encoded_in_target ? bbox[2] : prior_variance[2] * bbox[2]) * prior_width; | |
| decode_bbox[3] = prior_bbox[3] + (variance_encoded_in_target ? bbox[3] : prior_variance[3] * bbox[3]) * prior_height; | |
| break; | |
| } | |
| default: | |
| ARM_COMPUTE_ERROR("Unsupported Detection Output Code Type."); | |
| } | |
| if(clip_bbox) | |
| { | |
| for(auto &d_bbox : decode_bbox) | |
| { | |
| d_bbox = utility::clamp(d_bbox, 0.f, 1.f); | |
| } | |
| } | |
| } | |
| /** Do non maximum suppression given bboxes and scores. | |
| * | |
| * @param[in] bboxes The input bounding boxes. | |
| * @param[in] scores The corresponding input confidence. | |
| * @param[in] score_threshold The threshold used to filter detection results. | |
| * @param[in] nms_threshold The threshold used in non maximum suppression. | |
| * @param[in] eta Adaptation rate for nms threshold. | |
| * @param[in] top_k If not -1, keep at most top_k picked indices. | |
| * @param[out] indices The kept indices of bboxes after nms. | |
| * | |
| */ | |
| void ApplyNMSFast(const std::vector<BBox> &bboxes, | |
| const std::vector<float> &scores, const float score_threshold, | |
| const float nms_threshold, const float eta, const int top_k, | |
| std::vector<int> &indices) | |
| { | |
| ARM_COMPUTE_ERROR_ON_MSG(bboxes.size() != scores.size(), "bboxes and scores have different size."); | |
| // Get top_k scores (with corresponding indices). | |
| std::list<std::pair<float, int>> score_index_vec; | |
| // Generate index score pairs. | |
| for(size_t i = 0; i < scores.size(); ++i) | |
| { | |
| if(scores[i] > score_threshold) | |
| { | |
| score_index_vec.emplace_back(std::make_pair(scores[i], i)); | |
| } | |
| } | |
| // Sort the score pair according to the scores in descending order | |
| score_index_vec.sort(SortScorePairDescend<int>); | |
| // Keep top_k scores if needed. | |
| const int score_index_vec_size = score_index_vec.size(); | |
| if(top_k > -1 && top_k < score_index_vec_size) | |
| { | |
| score_index_vec.resize(top_k); | |
| } | |
| // Do nms. | |
| float adaptive_threshold = nms_threshold; | |
| indices.clear(); | |
| while(!score_index_vec.empty()) | |
| { | |
| const int idx = score_index_vec.front().second; | |
| bool keep = true; | |
| for(int kept_idx : indices) | |
| { | |
| if(keep) | |
| { | |
| // Compute the jaccard (intersection over union IoU) overlap between two bboxes. | |
| BBox intersect_bbox = std::array<float, 4>({ 0, 0, 0, 0 }); | |
| if(bboxes[kept_idx][0] > bboxes[idx][2] || bboxes[kept_idx][2] < bboxes[idx][0] || bboxes[kept_idx][1] > bboxes[idx][3] || bboxes[kept_idx][3] < bboxes[idx][1]) | |
| { | |
| intersect_bbox = std::array<float, 4>({ { 0, 0, 0, 0 } }); | |
| } | |
| else | |
| { | |
| intersect_bbox = std::array<float, 4>({ { | |
| std::max(bboxes[idx][0], bboxes[kept_idx][0]), | |
| std::max(bboxes[idx][1], bboxes[kept_idx][1]), | |
| std::min(bboxes[idx][2], bboxes[kept_idx][2]), | |
| std::min(bboxes[idx][3], bboxes[kept_idx][3]) | |
| } | |
| }); | |
| } | |
| float intersect_width = intersect_bbox[2] - intersect_bbox[0]; | |
| float intersect_height = intersect_bbox[3] - intersect_bbox[1]; | |
| float overlap = 0.f; | |
| if(intersect_width > 0 && intersect_height > 0) | |
| { | |
| float intersect_size = intersect_width * intersect_height; | |
| float bbox1_size = (bboxes[idx][2] < bboxes[idx][0] | |
| || bboxes[idx][3] < bboxes[idx][1]) ? | |
| 0.f : | |
| (bboxes[idx][2] - bboxes[idx][0]) * (bboxes[idx][3] - bboxes[idx][1]); //BBoxSize(bboxes[idx]); | |
| float bbox2_size = (bboxes[kept_idx][2] < bboxes[kept_idx][0] | |
| || bboxes[kept_idx][3] < bboxes[kept_idx][1]) ? | |
| 0.f : | |
| (bboxes[kept_idx][2] - bboxes[kept_idx][0]) * (bboxes[kept_idx][3] - bboxes[kept_idx][1]); // BBoxSize(bboxes[kept_idx]); | |
| overlap = intersect_size / (bbox1_size + bbox2_size - intersect_size); | |
| } | |
| keep = (overlap <= adaptive_threshold); | |
| } | |
| else | |
| { | |
| break; | |
| } | |
| } | |
| if(keep) | |
| { | |
| indices.push_back(idx); | |
| } | |
| score_index_vec.erase(score_index_vec.begin()); | |
| if(keep && eta < 1.f && adaptive_threshold > 0.5f) | |
| { | |
| adaptive_threshold *= eta; | |
| } | |
| } | |
| } | |
| } // namespace | |
| CPPDetectionOutputLayer::CPPDetectionOutputLayer() | |
| : _input_loc(nullptr), _input_conf(nullptr), _input_priorbox(nullptr), _output(nullptr), _info(), _num_priors(), _num(), _all_location_predictions(), _all_confidence_scores(), _all_prior_bboxes(), | |
| _all_prior_variances(), _all_decode_bboxes(), _all_indices() | |
| { | |
| } | |
| void CPPDetectionOutputLayer::configure(const ITensor *input_loc, const ITensor *input_conf, const ITensor *input_priorbox, | |
| ITensor *output, DetectionOutputLayerInfo info) | |
| { | |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input_loc, input_conf, input_priorbox, output); | |
| ARM_COMPUTE_LOG_PARAMS(input_loc, input_conf, input_priorbox, output, info); | |
| // Output auto initialization if not yet initialized | |
| // Since the number of bboxes to kept is unknown before nms, the shape is set to the maximum | |
| // The maximum is keep_top_k * input_loc_size[1] | |
| // Each row is a 7 dimension std::vector, which stores [image_id, label, confidence, xmin, ymin, xmax, ymax] | |
| const unsigned int max_size = info.keep_top_k() * (input_loc->info()->num_dimensions() > 1 ? input_loc->info()->dimension(1) : 1); | |
| auto_init_if_empty(*output->info(), input_loc->info()->clone()->set_tensor_shape(TensorShape(7U, max_size))); | |
| // Perform validation step | |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input_loc->info(), input_conf->info(), input_priorbox->info(), output->info(), info)); | |
| _input_loc = input_loc; | |
| _input_conf = input_conf; | |
| _input_priorbox = input_priorbox; | |
| _output = output; | |
| _info = info; | |
| _num_priors = input_priorbox->info()->dimension(0) / 4; | |
| _num = (_input_loc->info()->num_dimensions() > 1 ? _input_loc->info()->dimension(1) : 1); | |
| _all_location_predictions.resize(_num); | |
| _all_confidence_scores.resize(_num); | |
| _all_prior_bboxes.resize(_num_priors); | |
| _all_prior_variances.resize(_num_priors); | |
| _all_decode_bboxes.resize(_num); | |
| for(int i = 0; i < _num; ++i) | |
| { | |
| for(int c = 0; c < _info.num_loc_classes(); ++c) | |
| { | |
| const int label = _info.share_location() ? -1 : c; | |
| if(label == _info.background_label_id()) | |
| { | |
| // Ignore background class. | |
| continue; | |
| } | |
| _all_decode_bboxes[i][label].resize(_num_priors); | |
| } | |
| } | |
| _all_indices.resize(_num); | |
| Coordinates coord; | |
| coord.set_num_dimensions(output->info()->num_dimensions()); | |
| output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); | |
| } | |
| Status CPPDetectionOutputLayer::validate(const ITensorInfo *input_loc, const ITensorInfo *input_conf, const ITensorInfo *input_priorbox, const ITensorInfo *output, DetectionOutputLayerInfo info) | |
| { | |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input_loc, input_conf, input_priorbox, output, info)); | |
| return Status{}; | |
| } | |
| void CPPDetectionOutputLayer::run() | |
| { | |
| // Retrieve all location predictions. | |
| retrieve_all_loc_predictions(_input_loc, _num, _num_priors, _info.num_loc_classes(), _info.share_location(), _all_location_predictions); | |
| // Retrieve all confidences. | |
| retrieve_all_conf_scores(_input_conf, _num, _num_priors, _info.num_classes(), _all_confidence_scores); | |
| // Retrieve all prior bboxes. | |
| retrieve_all_priorbox(_input_priorbox, _num_priors, _all_prior_bboxes, _all_prior_variances); | |
| // Decode all loc predictions to bboxes | |
| const bool clip_bbox = false; | |
| for(int i = 0; i < _num; ++i) | |
| { | |
| for(int c = 0; c < _info.num_loc_classes(); ++c) | |
| { | |
| const int label = _info.share_location() ? -1 : c; | |
| if(label == _info.background_label_id()) | |
| { | |
| // Ignore background class. | |
| continue; | |
| } | |
| ARM_COMPUTE_ERROR_ON_MSG_VAR(_all_location_predictions[i].find(label) == _all_location_predictions[i].end(), "Could not find location predictions for label %d.", label); | |
| const std::vector<BBox> &label_loc_preds = _all_location_predictions[i].find(label)->second; | |
| const int num_bboxes = _all_prior_bboxes.size(); | |
| ARM_COMPUTE_ERROR_ON(_all_prior_variances[i].size() != 4); | |
| for(int j = 0; j < num_bboxes; ++j) | |
| { | |
| DecodeBBox(_all_prior_bboxes[j], _all_prior_variances[j], _info.code_type(), _info.variance_encoded_in_target(), clip_bbox, label_loc_preds[j], _all_decode_bboxes[i][label][j]); | |
| } | |
| } | |
| } | |
| int num_kept = 0; | |
| for(int i = 0; i < _num; ++i) | |
| { | |
| const LabelBBox &decode_bboxes = _all_decode_bboxes[i]; | |
| const std::map<int, std::vector<float>> &conf_scores = _all_confidence_scores[i]; | |
| std::map<int, std::vector<int>> indices; | |
| int num_det = 0; | |
| for(int c = 0; c < _info.num_classes(); ++c) | |
| { | |
| if(c == _info.background_label_id()) | |
| { | |
| // Ignore background class | |
| continue; | |
| } | |
| const int label = _info.share_location() ? -1 : c; | |
| if(conf_scores.find(c) == conf_scores.end() || decode_bboxes.find(label) == decode_bboxes.end()) | |
| { | |
| ARM_COMPUTE_ERROR_VAR("Could not find predictions for label %d.", label); | |
| } | |
| const std::vector<float> &scores = conf_scores.find(c)->second; | |
| const std::vector<BBox> &bboxes = decode_bboxes.find(label)->second; | |
| ApplyNMSFast(bboxes, scores, _info.confidence_threshold(), _info.nms_threshold(), _info.eta(), _info.top_k(), indices[c]); | |
| num_det += indices[c].size(); | |
| } | |
| int num_to_add = 0; | |
| if(_info.keep_top_k() > -1 && num_det > _info.keep_top_k()) | |
| { | |
| std::vector<std::pair<float, std::pair<int, int>>> score_index_pairs; | |
| for(auto const &it : indices) | |
| { | |
| const int label = it.first; | |
| const std::vector<int> &label_indices = it.second; | |
| if(conf_scores.find(label) == conf_scores.end()) | |
| { | |
| ARM_COMPUTE_ERROR_VAR("Could not find predictions for label %d.", label); | |
| } | |
| const std::vector<float> &scores = conf_scores.find(label)->second; | |
| for(auto idx : label_indices) | |
| { | |
| ARM_COMPUTE_ERROR_ON(idx > static_cast<int>(scores.size())); | |
| score_index_pairs.emplace_back(std::make_pair(scores[idx], std::make_pair(label, idx))); | |
| } | |
| } | |
| // Keep top k results per image. | |
| std::sort(score_index_pairs.begin(), score_index_pairs.end(), SortScorePairDescend<std::pair<int, int>>); | |
| score_index_pairs.resize(_info.keep_top_k()); | |
| // Store the new indices. | |
| std::map<int, std::vector<int>> new_indices; | |
| for(auto score_index_pair : score_index_pairs) | |
| { | |
| int label = score_index_pair.second.first; | |
| int idx = score_index_pair.second.second; | |
| new_indices[label].push_back(idx); | |
| } | |
| _all_indices[i] = new_indices; | |
| num_to_add = _info.keep_top_k(); | |
| } | |
| else | |
| { | |
| _all_indices[i] = indices; | |
| num_to_add = num_det; | |
| } | |
| num_kept += num_to_add; | |
| } | |
| //Update the valid region of the ouput to mark the exact number of detection | |
| _output->info()->set_valid_region(ValidRegion(Coordinates(0, 0), TensorShape(7, num_kept))); | |
| int count = 0; | |
| for(int i = 0; i < _num; ++i) | |
| { | |
| const std::map<int, std::vector<float>> &conf_scores = _all_confidence_scores[i]; | |
| const LabelBBox &decode_bboxes = _all_decode_bboxes[i]; | |
| for(auto &it : _all_indices[i]) | |
| { | |
| const int label = it.first; | |
| const std::vector<float> &scores = conf_scores.find(label)->second; | |
| const int loc_label = _info.share_location() ? -1 : label; | |
| if(conf_scores.find(label) == conf_scores.end() || decode_bboxes.find(loc_label) == decode_bboxes.end()) | |
| { | |
| // Either if there are no confidence predictions | |
| // or there are no location predictions for current label. | |
| ARM_COMPUTE_ERROR_VAR("Could not find predictions for the label %d.", label); | |
| } | |
| const std::vector<BBox> &bboxes = decode_bboxes.find(loc_label)->second; | |
| const std::vector<int> &indices = it.second; | |
| for(auto idx : indices) | |
| { | |
| *(reinterpret_cast<float *>(_output->ptr_to_element(Coordinates(count * 7)))) = i; | |
| *(reinterpret_cast<float *>(_output->ptr_to_element(Coordinates(count * 7 + 1)))) = label; | |
| *(reinterpret_cast<float *>(_output->ptr_to_element(Coordinates(count * 7 + 2)))) = scores[idx]; | |
| *(reinterpret_cast<float *>(_output->ptr_to_element(Coordinates(count * 7 + 3)))) = bboxes[idx][0]; | |
| *(reinterpret_cast<float *>(_output->ptr_to_element(Coordinates(count * 7 + 4)))) = bboxes[idx][1]; | |
| *(reinterpret_cast<float *>(_output->ptr_to_element(Coordinates(count * 7 + 5)))) = bboxes[idx][2]; | |
| *(reinterpret_cast<float *>(_output->ptr_to_element(Coordinates(count * 7 + 6)))) = bboxes[idx][3]; | |
| ++count; | |
| } | |
| } | |
| } | |
| } | |
| } // namespace arm_compute |