diff options
Diffstat (limited to 'src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp')
| -rw-r--r-- | src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp | 418 |
1 files changed, 264 insertions, 154 deletions
diff --git a/src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp b/src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp index 31f1fafd69..2861d6cacb 100644 --- a/src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp +++ b/src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2020 Arm Limited. + * Copyright (c) 2019-2021 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 <cstddef> @@ -36,53 +38,76 @@ 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) +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_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(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(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_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_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)), + 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."); + 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) + 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_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) + 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_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) + 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_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) + 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); @@ -91,15 +116,18 @@ Status validate_arguments(const ITensorInfo *input_box_encoding, const ITensorIn return Status{}; } -inline void DecodeBoxCorner(BBox &box_centersize, BBox &anchor, Iterator &decoded_it, DetectionPostProcessLayerInfo info) +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]; + 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()); @@ -116,12 +144,15 @@ inline void DecodeBoxCorner(BBox &box_centersize, BBox &anchor, Iterator &decode * @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) +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{ {} }; + BBox box_centersize{{}}; + BBox anchor{{}}; Window win; win.use_tensor_dimensions(input_box_encoding->info()->tensor_shape()); @@ -131,103 +162,155 @@ void DecodeCenterSizeBoxes(const ITensor *input_box_encoding, const ITensor *inp Iterator anchor_it(input_anchors, win); Iterator decoded_it(decoded_boxes, win); - if(input_box_encoding->info()->data_type() == DataType::QASYMM8) + 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); + 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) + 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); + 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); + 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) +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) + 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]]; + *(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) + 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 *>(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) + : _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) +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_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(*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)); + 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; @@ -239,13 +322,24 @@ void CPPDetectionPostProcessLayer::configure(const ITensor *input_box_encoding, _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)); + _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)); + 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; @@ -254,7 +348,9 @@ void CPPDetectionPostProcessLayer::configure(const ITensor *input_box_encoding, _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()); + _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(); @@ -263,18 +359,28 @@ void CPPDetectionPostProcessLayer::configure(const ITensor *input_box_encoding, _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) +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)); + 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{}; } @@ -287,62 +393,69 @@ void CPPDetectionPostProcessLayer::run() DecodeCenterSizeBoxes(_input_box_encoding, _input_anchors, _info, &_decoded_boxes); // Decode scores if necessary - if(_dequantize_scores) + if (_dequantize_scores) { - if(_input_box_encoding->info()->data_type() == DataType::QASYMM8) + 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_c = 0; idx_c < _num_classes_with_background; ++idx_c) { - for(unsigned int idx_b = 0; idx_b < _num_boxes; ++idx_b) + 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()); + 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) + 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_c = 0; idx_c < _num_classes_with_background; ++idx_c) { - for(unsigned int idx_b = 0; idx_b < _num_boxes; ++idx_b) + 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()); + 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()) + 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 (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) + 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 + *(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) + 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) + 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_scores_after_nms.emplace_back( + (reinterpret_cast<float *>(_class_scores.buffer()))[selected_index]); result_classes_after_nms.emplace_back(c); } } @@ -354,49 +467,46 @@ void CPPDetectionPostProcessLayer::run() // 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, + 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]; - }); + { 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); + 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()); + 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) + for (unsigned int b = 0; b < _num_boxes; ++b) { std::vector<float> box_scores; - for(unsigned int c = 0; c < num_classes; ++c) + 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))))); + 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, + 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]; - }); + { return box_scores[first] > box_scores[second]; }); - for(unsigned int i = 0; i < num_classes_per_box; ++i) + 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; + 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]); @@ -406,10 +516,10 @@ void CPPDetectionPostProcessLayer::run() // Run Non-maxima Suppression _nms.run(); std::vector<unsigned int> selected_indices; - for(unsigned int i = 0; i < max_detections; ++i) + 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) + 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; @@ -419,8 +529,8 @@ void CPPDetectionPostProcessLayer::run() // 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); + 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 |