From 883bad7ef34c3429b3338d5544a8cdf3b60cd1e8 Mon Sep 17 00:00:00 2001 From: Isabella Gottardi Date: Mon, 15 Jul 2019 17:33:07 +0100 Subject: COMPMID-1849: Add DetectorPostProcess operator Part1 - Rework of CPPNonMaximumSuppression Change-Id: I2b34fbd12188db49b0ac050a12312494eeefd819 Signed-off-by: Isabella Gottardi Reviewed-on: https://review.mlplatform.org/c/1585 Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Michele Di Giorgio --- .../CPP/kernels/CPPNonMaximumSuppressionKernel.cpp | 205 +++++++++++++++++++++ .../CPP/functions/CPPDetectionOutputLayer.cpp | 95 +--------- .../CPP/functions/CPPNonMaximumSuppression.cpp | 46 +++++ 3 files changed, 254 insertions(+), 92 deletions(-) create mode 100644 src/core/CPP/kernels/CPPNonMaximumSuppressionKernel.cpp create mode 100644 src/runtime/CPP/functions/CPPNonMaximumSuppression.cpp (limited to 'src') diff --git a/src/core/CPP/kernels/CPPNonMaximumSuppressionKernel.cpp b/src/core/CPP/kernels/CPPNonMaximumSuppressionKernel.cpp new file mode 100644 index 0000000000..fb38bdcf94 --- /dev/null +++ b/src/core/CPP/kernels/CPPNonMaximumSuppressionKernel.cpp @@ -0,0 +1,205 @@ +/* + * 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/core/CPP/kernels/CPPNonMaximumSuppressionKernel.h" + +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/Validate.h" +#include "support/ToolchainSupport.h" + +#include + +namespace arm_compute +{ +namespace +{ +Status validate_arguments(const ITensorInfo *bboxes, const ITensorInfo *scores, const ITensorInfo *output_indices, unsigned int max_output_size, + const float score_threshold, const float iou_threshold) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(bboxes, scores, output_indices); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bboxes, 1, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_indices, 1, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(bboxes->num_dimensions() > 2, "The bboxes tensor must be a 2-D float tensor of shape [4, num_boxes]."); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(scores->num_dimensions() > 1, "The scores tensor must be a 1-D float tensor of shape [num_boxes]."); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(output_indices->num_dimensions() > 1, "The indices must be 1-D integer tensor of shape [M], where max_output_size <= M"); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(bboxes, scores); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(output_indices->dimension(0) == 0, "Indices tensor must be bigger than 0"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(max_output_size == 0, "Max size cannot be 0"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(iou_threshold < 0.f || iou_threshold > 1.f, "IOU threshold must be in [0,1]"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(score_threshold < 0.f || score_threshold > 1.f, "Score threshold must be in [0,1]"); + + return Status{}; +} +} // namespace + +CPPNonMaximumSuppressionKernel::CPPNonMaximumSuppressionKernel() + : _input_bboxes(nullptr), _input_scores(nullptr), _output_indices(nullptr), _max_output_size(0), _score_threshold(0.f), _iou_threshold(0.f), _num_boxes(0), _scores_above_thd_vector(), + _indices_above_thd_vector(), _visited(), _sorted_indices() +{ +} + +void CPPNonMaximumSuppressionKernel::configure( + const ITensor *input_bboxes, const ITensor *input_scores, ITensor *output_indices, unsigned int max_output_size, + const float score_threshold, const float iou_threshold) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input_bboxes, input_scores, output_indices); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input_bboxes->info(), input_scores->info(), output_indices->info(), max_output_size, score_threshold, iou_threshold)); + + auto_init_if_empty(*output_indices->info(), TensorShape(max_output_size), 1, DataType::U8, QuantizationInfo()); + + _input_bboxes = input_bboxes; + _input_scores = input_scores; + _output_indices = output_indices; + _score_threshold = score_threshold; + _iou_threshold = iou_threshold; + _max_output_size = max_output_size; + _num_boxes = input_scores->info()->dimension(0); + + _scores_above_thd_vector.reserve(_num_boxes); + _indices_above_thd_vector.reserve(_num_boxes); + + // Visited and sorted_indices are preallocated as num_boxes size, which is the maximum size possible + // Will be used only N elements where N is the number of score above the threshold + _visited.reserve(_num_boxes); + _sorted_indices.reserve(_num_boxes); + + // Configure kernel window + Window win = calculate_max_window(*output_indices->info(), Steps()); + + // The CPPNonMaximumSuppressionKernel doesn't need padding so update_window_and_padding() can be skipped + ICPPKernel::configure(win); +} + +Status CPPNonMaximumSuppressionKernel::validate( + const ITensorInfo *bboxes, const ITensorInfo *scores, const ITensorInfo *output_indices, unsigned int max_output_size, + const float score_threshold, const float iou_threshold) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(bboxes, scores, output_indices, max_output_size, score_threshold, iou_threshold)); + return Status{}; +} + +void CPPNonMaximumSuppressionKernel::run(const Window &window, const ThreadInfo &info) +{ + ARM_COMPUTE_UNUSED(info); + ARM_COMPUTE_UNUSED(window); + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICPPKernel::window(), window); + + unsigned int num_above_thd = 0; + for(unsigned int i = 0; i < _num_boxes; ++i) + { + const float score_i = *(reinterpret_cast(_input_scores->ptr_to_element(Coordinates(i)))); + if(score_i >= _score_threshold) + { + _indices_above_thd_vector.emplace_back(i); + _scores_above_thd_vector.emplace_back(score_i); + // Initialize respective index and visited + _sorted_indices.emplace_back(num_above_thd); + _visited.emplace_back(false); + ++num_above_thd; + } + } + + // Sort selected indices based on scores + std::sort(_sorted_indices.begin(), + _sorted_indices.end(), + [&](unsigned int first, unsigned int second) + { + return _scores_above_thd_vector[first] > _scores_above_thd_vector[second]; + }); + + // Number of output is the minimum between max_detection and the scores above the threshold + const unsigned int num_output = std::min(_max_output_size, num_above_thd); + unsigned int output_idx = 0; + + for(unsigned int i = 0; i < num_above_thd; ++i) + { + // Check if the output is full + if(output_idx >= num_output) + { + break; + } + + // Check if it was already visited, if not add it to the output and update the indices counter + if(!_visited[_sorted_indices[i]]) + { + *(reinterpret_cast(_output_indices->ptr_to_element(Coordinates(output_idx)))) = _indices_above_thd_vector[_sorted_indices[i]]; + ++output_idx; + } + else + { + continue; + } + + // Once added one element at the output check if the next ones overlap and can be skipped + for(unsigned int j = i + 1; j < num_above_thd; ++j) + { + if(!_visited[_sorted_indices[j]]) + { + // Calculate IoU + const unsigned int i_index = _indices_above_thd_vector[_sorted_indices[i]]; + const unsigned int j_index = _indices_above_thd_vector[_sorted_indices[j]]; + // Box-corner format: xmin, ymin, xmax, ymax + const auto box_i_xmin = *(reinterpret_cast(_input_bboxes->ptr_to_element(Coordinates(0, i_index)))); + const auto box_i_ymin = *(reinterpret_cast(_input_bboxes->ptr_to_element(Coordinates(1, i_index)))); + const auto box_i_xmax = *(reinterpret_cast(_input_bboxes->ptr_to_element(Coordinates(2, i_index)))); + const auto box_i_ymax = *(reinterpret_cast(_input_bboxes->ptr_to_element(Coordinates(3, i_index)))); + + const auto box_j_xmin = *(reinterpret_cast(_input_bboxes->ptr_to_element(Coordinates(0, j_index)))); + const auto box_j_ymin = *(reinterpret_cast(_input_bboxes->ptr_to_element(Coordinates(1, j_index)))); + const auto box_j_xmax = *(reinterpret_cast(_input_bboxes->ptr_to_element(Coordinates(2, j_index)))); + const auto box_j_ymax = *(reinterpret_cast(_input_bboxes->ptr_to_element(Coordinates(3, j_index)))); + + const float area_i = (box_i_xmax - box_i_xmin) * (box_i_ymax - box_i_ymin); + const float area_j = (box_j_xmax - box_j_xmin) * (box_j_ymax - box_j_ymin); + float overlap; + if(area_i <= 0 || area_j <= 0) + { + overlap = 0.0f; + } + else + { + const auto y_min_intersection = std::max(box_i_ymin, box_j_ymin); + const auto x_min_intersection = std::max(box_i_xmin, box_j_xmin); + const auto y_max_intersection = std::min(box_i_ymax, box_j_ymax); + const auto x_max_intersection = std::min(box_i_xmax, box_j_xmax); + const auto area_intersection = std::max(y_max_intersection - y_min_intersection, 0.0f) * std::max(x_max_intersection - x_min_intersection, 0.0f); + overlap = area_intersection / (area_i + area_j - area_intersection); + } + + if(overlap > _iou_threshold) + { + _visited[_sorted_indices[j]] = true; + } + } + } + } + // The output could be full but not the output indices tensor + // Instead return values not valid we put -1 + for(; output_idx < _max_output_size; ++output_idx) + { + *(reinterpret_cast(_output_indices->ptr_to_element(Coordinates(output_idx)))) = -1; + } +} +} // namespace arm_compute diff --git a/src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp b/src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp index 9a141cb73a..a1f4e6e89c 100644 --- a/src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp +++ b/src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp @@ -34,7 +34,7 @@ namespace arm_compute { namespace { -Status detection_layer_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); @@ -380,97 +380,8 @@ void ApplyNMSFast(const std::vector &bboxes, } } } - -Status non_max_suppression_validate_arguments(const ITensorInfo *bboxes, const ITensorInfo *scores, const ITensorInfo *indices, unsigned int max_output_size, - const float score_threshold, const float nms_threshold) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(bboxes, scores, indices); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bboxes, 1, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(scores, 1, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(indices, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(bboxes->num_dimensions() > 2, "The bboxes tensor must be a 2-D float tensor of shape [4, num_boxes]."); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(scores->num_dimensions() > 1, "The scores tensor must be a 1-D float tensor of shape [num_boxes]."); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(indices->num_dimensions() > 1, "The indices must be 1-D integer tensor of shape [M], where max_output_size <= M"); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(bboxes, scores); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(scores->num_dimensions() > 1, "Scores must be a 1D float tensor"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(indices->dimension(0) == 0, "Indices tensor must be bigger than 0"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(max_output_size == 0, "Max size cannot be 0"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(nms_threshold < 0.f || nms_threshold > 1.f, "Threshould must be in [0,1]"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(score_threshold < 0.f || score_threshold > 1.f, "Threshould must be in [0,1]"); - - return Status{}; -} } // namespace -CPPNonMaximumSuppression::CPPNonMaximumSuppression() - : _bboxes(nullptr), _scores(nullptr), _indices(nullptr), _max_output_size(0), _score_threshold(0.f), _nms_threshold(0.f) -{ -} - -void CPPNonMaximumSuppression::configure( - const ITensor *bboxes, const ITensor *scores, ITensor *indices, unsigned int max_output_size, - const float score_threshold, const float nms_threshold) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(bboxes, scores, indices); - ARM_COMPUTE_ERROR_THROW_ON(non_max_suppression_validate_arguments(bboxes->info(), scores->info(), indices->info(), max_output_size, score_threshold, nms_threshold)); - - // copy scores also to a vector - _bboxes = bboxes; - _scores = scores; - _indices = indices; - - _nms_threshold = nms_threshold; - _max_output_size = max_output_size; - _score_threshold = score_threshold; -} - -Status CPPNonMaximumSuppression::validate( - const ITensorInfo *bboxes, const ITensorInfo *scores, const ITensorInfo *indices, unsigned int max_output_size, - const float score_threshold, const float nms_threshold) -{ - ARM_COMPUTE_RETURN_ON_ERROR(non_max_suppression_validate_arguments(bboxes, scores, indices, max_output_size, score_threshold, nms_threshold)); - return Status{}; -} - -void extract_bounding_boxes_from_tensor(const ITensor *bboxes, std::vector &bboxes_vector) -{ - Window input_win; - input_win.use_tensor_dimensions(bboxes->info()->tensor_shape()); - input_win.set_dimension_step(0U, 4U); - input_win.set_dimension_step(1U, 1U); - Iterator input(bboxes, input_win); - auto f = [&bboxes_vector, &input](const Coordinates &) - { - const auto input_ptr = reinterpret_cast(input.ptr()); - bboxes_vector.push_back(NormalizedBBox({ { *input_ptr, *(input_ptr + 1), *(2 + input_ptr), *(3 + input_ptr) } })); - }; - execute_window_loop(input_win, f, input); -} - -void extract_scores_from_tensor(const ITensor *scores, std::vector &scores_vector) -{ - Window window; - window.use_tensor_dimensions(scores->info()->tensor_shape()); - Iterator it(scores, window); - auto f = [&it, &scores_vector](const Coordinates &) - { - const auto input_ptr = reinterpret_cast(it.ptr()); - scores_vector.push_back(*input_ptr); - }; - execute_window_loop(window, f, it); -} - -void CPPNonMaximumSuppression::run() -{ - std::vector bboxes_vector; - std::vector scores_vector; - std::vector indices_vector; - extract_bounding_boxes_from_tensor(_bboxes, bboxes_vector); - extract_scores_from_tensor(_scores, scores_vector); - ApplyNMSFast(bboxes_vector, scores_vector, _score_threshold, _nms_threshold, 1, -1 /* disable top_k */, indices_vector); - std::copy_n(indices_vector.begin(), std::min(indices_vector.size(), _indices->info()->dimension(0)), reinterpret_cast(_indices->ptr_to_element(Coordinates(0)))); -} - 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() @@ -488,7 +399,7 @@ void CPPDetectionOutputLayer::configure(const ITensor *input_loc, const ITensor 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(detection_layer_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; @@ -526,7 +437,7 @@ void CPPDetectionOutputLayer::configure(const ITensor *input_loc, const ITensor 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(detection_layer_validate_arguments(input_loc, input_conf, input_priorbox, output, info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input_loc, input_conf, input_priorbox, output, info)); return Status{}; } diff --git a/src/runtime/CPP/functions/CPPNonMaximumSuppression.cpp b/src/runtime/CPP/functions/CPPNonMaximumSuppression.cpp new file mode 100644 index 0000000000..f13674a42f --- /dev/null +++ b/src/runtime/CPP/functions/CPPNonMaximumSuppression.cpp @@ -0,0 +1,46 @@ +/* + * 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/CPPNonMaximumSuppression.h" + +#include "arm_compute/core/CPP/kernels/CPPNonMaximumSuppressionKernel.h" +#include "support/ToolchainSupport.h" + +namespace arm_compute +{ +void CPPNonMaximumSuppression::configure( + const ITensor *bboxes, const ITensor *scores, ITensor *indices, unsigned int max_output_size, + const float score_threshold, const float nms_threshold) +{ + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(bboxes, scores, indices, max_output_size, score_threshold, nms_threshold); + _kernel = std::move(k); +} + +Status CPPNonMaximumSuppression::validate( + const ITensorInfo *bboxes, const ITensorInfo *scores, const ITensorInfo *indices, unsigned int max_output_size, + const float score_threshold, const float nms_threshold) +{ + return CPPNonMaximumSuppressionKernel::validate(bboxes, scores, indices, max_output_size, score_threshold, nms_threshold); +} +} // namespace arm_compute -- cgit v1.2.1