diff options
Diffstat (limited to 'src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp')
| -rw-r--r-- | src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp | 388 |
1 files changed, 388 insertions, 0 deletions
diff --git a/src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp b/src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp new file mode 100644 index 0000000000..2997b593c6 --- /dev/null +++ b/src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp @@ -0,0 +1,388 @@ +/* + * Copyright (c) 2019 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 "support/ToolchainSupport.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); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_box_encoding, input_class_score, 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->dimension(2) != kBatchSize, "The third dimension of the input box_encoding tensor should be equal to %d.", kBatchSize); + } + ARM_COMPUTE_RETURN_ERROR_ON_MSG(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(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{}; +} + +/** 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); + + const float half_factor = 0.5f; + + execute_window_loop(win, [&](const Coordinates &) + { + if(is_data_type_quantized(input_box_encoding->info()->data_type())) + { + 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) + }); + } + else + { + 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) }); + } + + // 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 + }, + 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) +{ + // ymin,xmin,ymax,xmax -> xmin,ymin,xmax,ymax + 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(), _decoded_boxes(), _decoded_scores(), _selected_indices(), + _class_scores(), _input_scores_to_use(nullptr), _result_idx_boxes_after_nms(), _result_classes_after_nms(), _result_scores_after_nms(), _sorted_indices(), _box_scores() +{ +} + +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); + _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); + + 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.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 = is_data_type_quantized(input_box_encoding->info()->data_type()) ? &_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(); + + if(info.use_regular_nms()) + { + _result_idx_boxes_after_nms.reserve(_info.detection_per_class() * _info.num_classes()); + _result_classes_after_nms.reserve(_info.detection_per_class() * _info.num_classes()); + _result_scores_after_nms.reserve(_info.detection_per_class() * _info.num_classes()); + } + else + { + _result_scores_after_nms.reserve(num_classes_per_box * _num_boxes); + _result_classes_after_nms.reserve(num_classes_per_box * _num_boxes); + _result_scores_after_nms.reserve(num_classes_per_box * _num_boxes); + _box_scores.reserve(_num_boxes); + } + _sorted_indices.resize(info.use_regular_nms() ? info.max_detections() : info.num_classes()); +} + +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(is_data_type_quantized(_input_box_encoding->info()->data_type())) + { + 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()); + } + } + } + // Regular NMS + if(_info.use_regular_nms()) + { + 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 + } + _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 + continue; + } + _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_idx_boxes_after_nms.size(); + const auto num_output = std::min<unsigned int>(max_detections, num_selected); + + // Sort selected indices based on result scores + 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()); + for(unsigned int b = 0, index = 0; b < _num_boxes; ++b) + { + _box_scores.clear(); + _sorted_indices.clear(); + 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))))); + _sorted_indices.push_back(c); + } + std::partial_sort(_sorted_indices.data(), + _sorted_indices.data() + num_classes_per_box, + _sorted_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, ++index) + { + const float score_to_add = _box_scores[_sorted_indices[i]]; + *(reinterpret_cast<float *>(_class_scores.ptr_to_element(Coordinates(index)))) = score_to_add; + _result_scores_after_nms.emplace_back(score_to_add); + _result_idx_boxes_after_nms.emplace_back(b); + _result_classes_after_nms.emplace_back(_sorted_indices[i]); + } + } + + // Run NMS + _nms.run(); + + _sorted_indices.clear(); + 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; + } + _sorted_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(), _sorted_indices.size()); + + 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); + } +} +} // namespace arm_compute
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