diff options
Diffstat (limited to 'src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp')
| -rw-r--r-- | src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp | 315 |
1 files changed, 186 insertions, 129 deletions
diff --git a/src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp b/src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp index fdb4c9f0f6..e6291f973e 100644 --- a/src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp +++ b/src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2020 Arm Limited. + * Copyright (c) 2018-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -26,6 +26,8 @@ #include "arm_compute/core/Error.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/Validate.h" + +#include "src/common/utils/Log.h" #include "src/core/helpers/AutoConfiguration.h" #include <list> @@ -34,25 +36,35 @@ namespace arm_compute { namespace { -Status validate_arguments(const ITensorInfo *input_loc, const ITensorInfo *input_conf, const ITensorInfo *input_priorbox, const ITensorInfo *output, DetectionOutputLayerInfo info) +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(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."); + 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) + if (output->total_size() != 0) { - const unsigned int max_size = info.keep_top_k() * (input_loc->num_dimensions() > 1 ? input_loc->dimension(1) : 1); + 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); } @@ -63,8 +75,7 @@ Status validate_arguments(const ITensorInfo *input_loc, const ITensorInfo *input /** 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) +bool SortScorePairDescend(const std::pair<float, T> &pair1, const std::pair<float, T> &pair2) { return pair1.first > pair2.first; } @@ -80,16 +91,19 @@ bool SortScorePairDescend(const std::pair<float, T> &pair1, * @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) +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 i = 0; i < num; ++i) { - for(int c = 0; c < num_loc_classes; ++c) + 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()) + if (all_location_predictions[i].find(label) == all_location_predictions[i].end()) { all_location_predictions[i][label].resize(num_priors); } @@ -100,19 +114,23 @@ void retrieve_all_loc_predictions(const ITensor *input_loc, const int num, } } } - for(int i = 0; i < num; ++i) + for (int i = 0; i < num; ++i) { - for(int p = 0; p < num_priors; ++p) + for (int p = 0; p < num_priors; ++p) { - for(int c = 0; c < num_loc_classes; ++c) + 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))); + 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))); } } } @@ -128,26 +146,28 @@ void retrieve_all_loc_predictions(const ITensor *input_loc, const int num, * @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, +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 i = 0; i < num; ++i) { - for(int c = 0; c < num_classes; ++c) + for (int c = 0; c < num_classes; ++c) { - for(int p = 0; p < num_priors; ++p) + 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))); + 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 i = 0; i < num; ++i) { - for(int c = 0; c < num_classes; ++c) + 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], @@ -166,28 +186,23 @@ void retrieve_all_conf_scores(const ITensor *input_conf, const int num, * @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, +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) + 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))) - } - }; + 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) + std::array<float, 4> var({{0, 0, 0, 0}}); + for (int i = 0; i < num_priors; ++i) { - for(int j = 0; j < 4; ++j) + for (int j = 0; j < 4; ++j) { var[j] = *reinterpret_cast<float *>(input_priorbox->ptr_to_element(Coordinates((num_priors + i) * 4 + j))); } @@ -206,13 +221,17 @@ void retrieve_all_priorbox(const ITensor *input_priorbox, * @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) +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) + switch (code_type) { case DetectionOutputLayerCodeType::CORNER: { @@ -235,10 +254,14 @@ void DecodeBBox(const BBox &prior_bbox, const std::array<float, 4> &prior_varian 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; + 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); @@ -256,10 +279,14 @@ void DecodeBBox(const BBox &prior_bbox, const std::array<float, 4> &prior_varian 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; + 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; } @@ -267,9 +294,9 @@ void DecodeBBox(const BBox &prior_bbox, const std::array<float, 4> &prior_varian ARM_COMPUTE_ERROR("Unsupported Detection Output Code Type."); } - if(clip_bbox) + if (clip_bbox) { - for(auto &d_bbox : decode_bbox) + for (auto &d_bbox : decode_bbox) { d_bbox = utility::clamp(d_bbox, 0.f, 1.f); } @@ -287,10 +314,13 @@ void DecodeBBox(const BBox &prior_bbox, const std::array<float, 4> &prior_varian * @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) +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."); @@ -298,9 +328,9 @@ void ApplyNMSFast(const std::vector<BBox> &bboxes, std::list<std::pair<float, int>> score_index_vec; // Generate index score pairs. - for(size_t i = 0; i < scores.size(); ++i) + for (size_t i = 0; i < scores.size(); ++i) { - if(scores[i] > score_threshold) + if (scores[i] > score_threshold) { score_index_vec.emplace_back(std::make_pair(scores[i], i)); } @@ -311,7 +341,7 @@ void ApplyNMSFast(const std::vector<BBox> &bboxes, // 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) + if (top_k > -1 && top_k < score_index_vec_size) { score_index_vec.resize(top_k); } @@ -320,46 +350,45 @@ void ApplyNMSFast(const std::vector<BBox> &bboxes, float adaptive_threshold = nms_threshold; indices.clear(); - while(!score_index_vec.empty()) + while (!score_index_vec.empty()) { const int idx = score_index_vec.front().second; bool keep = true; - for(int kept_idx : indices) + for (int kept_idx : indices) { - if(keep) + 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]) + 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 } }); + 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]) - } - }); + 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) + 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]); + 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); @@ -369,12 +398,12 @@ void ApplyNMSFast(const std::vector<BBox> &bboxes, break; } } - if(keep) + if (keep) { indices.push_back(idx); } score_index_vec.erase(score_index_vec.begin()); - if(keep && eta < 1.f && adaptive_threshold > 0.5f) + if (keep && eta < 1.f && adaptive_threshold > 0.5f) { adaptive_threshold *= eta; } @@ -383,23 +412,42 @@ void ApplyNMSFast(const std::vector<BBox> &bboxes, } // 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() + : _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) +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); + 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)); + 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; @@ -415,12 +463,12 @@ void CPPDetectionOutputLayer::configure(const ITensor *input_loc, const ITensor _all_prior_variances.resize(_num_priors); _all_decode_bboxes.resize(_num); - for(int i = 0; i < _num; ++i) + for (int i = 0; i < _num; ++i) { - for(int c = 0; c < _info.num_loc_classes(); ++c) + for (int c = 0; c < _info.num_loc_classes(); ++c) { const int label = _info.share_location() ? -1 : c; - if(label == _info.background_label_id()) + if (label == _info.background_label_id()) { // Ignore background class. continue; @@ -435,7 +483,11 @@ void CPPDetectionOutputLayer::configure(const ITensor *input_loc, const ITensor 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) +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{}; @@ -444,7 +496,8 @@ Status CPPDetectionOutputLayer::validate(const ITensorInfo *input_loc, const ITe 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_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); @@ -454,75 +507,79 @@ void CPPDetectionOutputLayer::run() // Decode all loc predictions to bboxes const bool clip_bbox = false; - for(int i = 0; i < _num; ++i) + for (int i = 0; i < _num; ++i) { - for(int c = 0; c < _info.num_loc_classes(); ++c) + for (int c = 0; c < _info.num_loc_classes(); ++c) { const int label = _info.share_location() ? -1 : c; - if(label == _info.background_label_id()) + 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); + 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) + 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]); + 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) + 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]; + 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) + int num_det = 0; + for (int c = 0; c < _info.num_classes(); ++c) { - if(c == _info.background_label_id()) + 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()) + 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; + 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]); + 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()) + 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) + 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()) + 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) + 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))); @@ -536,7 +593,7 @@ void CPPDetectionOutputLayer::run() // Store the new indices. std::map<int, std::vector<int>> new_indices; - for(auto score_index_pair : score_index_pairs) + for (auto score_index_pair : score_index_pairs) { int label = score_index_pair.second.first; int idx = score_index_pair.second.second; @@ -557,25 +614,25 @@ void CPPDetectionOutputLayer::run() _output->info()->set_valid_region(ValidRegion(Coordinates(0, 0), TensorShape(7, num_kept))); int count = 0; - for(int i = 0; i < _num; ++i) + 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 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()) + 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; + const std::vector<int> &indices = it.second; - for(auto idx : indices) + 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; |