COMPMID-1766: Implemented CPP Non Max Suppression
Change-Id: I2d2b684d464f7b3bb1f91cfd29952f612d65f11f
Signed-off-by: Pablo Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/708
Reviewed-by: VidhyaSudhan Loganathan <vidhyasudhan.loganathan@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/tests/AssetsLibrary.h b/tests/AssetsLibrary.h
index d09e227..366c145 100644
--- a/tests/AssetsLibrary.h
+++ b/tests/AssetsLibrary.h
@@ -207,6 +207,9 @@
template <typename T, typename D>
void fill(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const;
+ template <typename T, typename D>
+ void fill_boxes(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const;
+
/** Fills the specified @p raw tensor with random values drawn from @p
* distribution.
*
@@ -482,6 +485,40 @@
}
template <typename T, typename D>
+void AssetsLibrary::fill_boxes(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const
+{
+ using ResultType = typename std::remove_reference<D>::type::result_type;
+ std::mt19937 gen(_seed + seed_offset);
+ TensorShape shape(tensor.shape());
+ const int num_boxes = tensor.num_elements() / 4;
+ // Iterate over all elements
+ std::uniform_real_distribution<> size_dist(0.f, 1.f);
+ for(int element_idx = 0; element_idx < num_boxes * 4; element_idx += 4)
+ {
+ const ResultType delta = size_dist(gen);
+ const ResultType epsilon = size_dist(gen);
+ const ResultType left = distribution(gen);
+ const ResultType top = distribution(gen);
+ const ResultType right = left + delta;
+ const ResultType bottom = top + epsilon;
+ const std::tuple<ResultType, ResultType, ResultType, ResultType> box(left, top, right, bottom);
+ Coordinates x1 = index2coord(shape, element_idx);
+ Coordinates y1 = index2coord(shape, element_idx + 1);
+ Coordinates x2 = index2coord(shape, element_idx + 2);
+ Coordinates y2 = index2coord(shape, element_idx + 3);
+ ResultType &target_value_x1 = reinterpret_cast<ResultType *>(tensor(x1))[0];
+ ResultType &target_value_y1 = reinterpret_cast<ResultType *>(tensor(y1))[0];
+ ResultType &target_value_x2 = reinterpret_cast<ResultType *>(tensor(x2))[0];
+ ResultType &target_value_y2 = reinterpret_cast<ResultType *>(tensor(y2))[0];
+ store_value_with_data_type(&target_value_x1, std::get<0>(box), tensor.data_type());
+ store_value_with_data_type(&target_value_y1, std::get<1>(box), tensor.data_type());
+ store_value_with_data_type(&target_value_x2, std::get<2>(box), tensor.data_type());
+ store_value_with_data_type(&target_value_y2, std::get<3>(box), tensor.data_type());
+ }
+ fill_borders_with_garbage(tensor, distribution, seed_offset);
+}
+
+template <typename T, typename D>
void AssetsLibrary::fill(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const
{
using ResultType = typename std::remove_reference<D>::type::result_type;
diff --git a/tests/datasets/ShapeDatasets.h b/tests/datasets/ShapeDatasets.h
index 480df3c..f461d7f 100644
--- a/tests/datasets/ShapeDatasets.h
+++ b/tests/datasets/ShapeDatasets.h
@@ -946,6 +946,37 @@
{
}
};
+
+/** Data set containing small 2D tensor shapes. */
+class Small2DNonMaxSuppressionShapes final : public ShapeDataset
+{
+public:
+ Small2DNonMaxSuppressionShapes()
+ : ShapeDataset("Shape",
+ {
+ TensorShape{ 4U, 7U },
+ TensorShape{ 4U, 13U },
+ TensorShape{ 4U, 64U }
+ })
+ {
+ }
+};
+
+/** Data set containing large 2D tensor shapes. */
+class Large2DNonMaxSuppressionShapes final : public ShapeDataset
+{
+public:
+ Large2DNonMaxSuppressionShapes()
+ : ShapeDataset("Shape",
+ {
+ TensorShape{ 4U, 207U },
+ TensorShape{ 4U, 113U },
+ TensorShape{ 4U, 264U }
+ })
+ {
+ }
+};
+
} // namespace datasets
} // namespace test
} // namespace arm_compute
diff --git a/tests/validation/CPP/NonMaximalSuppression.cpp b/tests/validation/CPP/NonMaximalSuppression.cpp
new file mode 100644
index 0000000..6cd7b52
--- /dev/null
+++ b/tests/validation/CPP/NonMaximalSuppression.cpp
@@ -0,0 +1,144 @@
+/*
+ * 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/Types.h"
+#include "arm_compute/runtime/CPP/functions/CPPDetectionOutputLayer.h"
+#include "arm_compute/runtime/Tensor.h"
+#include "arm_compute/runtime/TensorAllocator.h"
+#include "tests/NEON/Accessor.h"
+#include "tests/PaddingCalculator.h"
+#include "tests/datasets/ShapeDatasets.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/fixtures/NonMaxSuppressionFixture.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+const auto max_output_boxes_dataset = framework::dataset::make("MaxOutputBoxes", 1, 10);
+const auto score_threshold_dataset = framework::dataset::make("ScoreThreshold", { 0.1f, 0.5f, 0.f, 1.f });
+const auto nms_threshold_dataset = framework::dataset::make("NMSThreshold", { 0.1f, 0.5f, 0.f, 1.f });
+const auto NMSParametersSmall = datasets::Small2DNonMaxSuppressionShapes() * max_output_boxes_dataset * score_threshold_dataset * nms_threshold_dataset;
+const auto NMSParametersBig = datasets::Large2DNonMaxSuppressionShapes() * max_output_boxes_dataset * score_threshold_dataset * nms_threshold_dataset;
+
+} // namespace
+
+TEST_SUITE(CPP)
+TEST_SUITE(NMS)
+
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
+ framework::dataset::make("BoundingBox",{
+ TensorInfo(TensorShape(4U, 100U), 1, DataType::F32),
+ TensorInfo(TensorShape(1U, 4U, 2U), 1, DataType::F32), // invalid shape
+ TensorInfo(TensorShape(4U, 2U), 1, DataType::S32), // invalid data type
+ TensorInfo(TensorShape(4U, 3U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U, 66U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U, 100U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U, 100U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U, 100U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U, 100U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U, 100U), 1, DataType::F32),
+ }),
+ framework::dataset::make("Scores", {
+ TensorInfo(TensorShape(100U), 1, DataType::F32),
+ TensorInfo(TensorShape(37U, 2U, 13U, 27U), 1, DataType::F32), // invalid shape
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U), 1, DataType::U8), // invalid data type
+ TensorInfo(TensorShape(66U), 1, DataType::F32), // invalid data type
+ TensorInfo(TensorShape(100U), 1, DataType::F32),
+ TensorInfo(TensorShape(100U), 1, DataType::F32),
+ TensorInfo(TensorShape(100U), 1, DataType::F32),
+ TensorInfo(TensorShape(100U), 1, DataType::F32),
+ TensorInfo(TensorShape(100U), 1, DataType::F32),
+ })),
+ framework::dataset::make("Indices", {
+ TensorInfo(TensorShape(100U), 1, DataType::S32),
+ TensorInfo(TensorShape(100U), 1, DataType::S32),
+ TensorInfo(TensorShape(4U), 1, DataType::S32),
+ TensorInfo(TensorShape(3U), 1, DataType::S32),
+ TensorInfo(TensorShape(200U), 1, DataType::S32), // indices bigger than max bbs, OK because max_output is 66
+ TensorInfo(TensorShape(100U), 1, DataType::F32), // invalid data type
+ TensorInfo(TensorShape(100U), 1, DataType::S32),
+ TensorInfo(TensorShape(100U), 1, DataType::S32),
+ TensorInfo(TensorShape(100U), 1, DataType::S32),
+ TensorInfo(TensorShape(100U), 1, DataType::S32),
+
+ })),
+ framework::dataset::make("max_output", {
+ 10U, 2U,4U, 3U,66U, 1U,
+ 0U, /* invalid, must be greater than 0 */
+ 10000U, /* OK, clamped to indices' size */
+ 100U,
+ 10U,
+ })),
+ framework::dataset::make("score_threshold", {
+ 0.1f, 0.4f, 0.2f,0.8f,0.3f, 0.01f, 0.5f, 0.45f,
+ -1.f, /* invalid value, must be in [0,1] */
+ 0.5f,
+ })),
+ framework::dataset::make("nms_threshold", {
+ 0.3f, 0.7f, 0.1f,0.13f,0.2f, 0.97f, 0.76f, 0.87f, 0.1f,
+ 10.f, /* invalid value, must be in [0,1]*/
+ })),
+ framework::dataset::make("Expected", {
+ true, false, false, false, true, false, false,true, false, false
+ })),
+
+ bbox_info, scores_info, indices_info, max_out, score_threshold, nms_threshold, expected)
+{
+ ARM_COMPUTE_EXPECT(bool(CPPNonMaximumSuppression::validate(&bbox_info.clone()->set_is_resizable(false),
+ &scores_info.clone()->set_is_resizable(false),
+ &indices_info.clone()->set_is_resizable(false),
+ max_out,score_threshold,nms_threshold)) == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
+using CPPNonMaxSuppressionFixture = NMSValidationFixture<Tensor, Accessor, CPPNonMaximumSuppression>;
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CPPNonMaxSuppressionFixture, framework::DatasetMode::PRECOMMIT, NMSParametersSmall)
+{
+ // Validate output
+ validate(Accessor(_target), _reference);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CPPNonMaxSuppressionFixture, framework::DatasetMode::NIGHTLY, NMSParametersBig)
+{
+ // Validate output
+ validate(Accessor(_target), _reference);
+}
+
+TEST_SUITE_END() // CPP
+TEST_SUITE_END() // NMS
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/fixtures/NonMaxSuppressionFixture.h b/tests/validation/fixtures/NonMaxSuppressionFixture.h
new file mode 100644
index 0000000..9299ed6
--- /dev/null
+++ b/tests/validation/fixtures/NonMaxSuppressionFixture.h
@@ -0,0 +1,124 @@
+/*
+ * 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.
+ */
+#ifndef ARM_COMPUTE_TEST_NON_MAX_SUPPRESSION_FIXTURE
+#define ARM_COMPUTE_TEST_NON_MAX_SUPPRESSION_FIXTURE
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/Tensor.h"
+#include "tests/AssetsLibrary.h"
+#include "tests/Globals.h"
+#include "tests/IAccessor.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Fixture.h"
+#include "tests/validation/reference/NonMaxSuppression.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+template <typename TensorType, typename AccessorType, typename FunctionType>
+
+class NMSValidationFixture : public framework::Fixture
+{
+public:
+ template <typename...>
+ void setup(TensorShape input_shape, unsigned int max_output_size, float score_threshold, float nms_threshold)
+ {
+ ARM_COMPUTE_ERROR_ON(max_output_size == 0);
+ ARM_COMPUTE_ERROR_ON(input_shape.num_dimensions() != 2);
+ const TensorShape output_shape(max_output_size);
+ const TensorShape scores_shape(input_shape[1]);
+ _target = compute_target(input_shape, scores_shape, output_shape, max_output_size, score_threshold, nms_threshold);
+ _reference = compute_reference(input_shape, scores_shape, output_shape, max_output_size, score_threshold, nms_threshold);
+ }
+
+protected:
+ template <typename U>
+ void fill(U &&tensor, int i, int lo, int hi)
+ {
+ std::uniform_real_distribution<> distribution(lo, hi);
+ library->fill_boxes(tensor, distribution, i);
+ }
+
+ TensorType compute_target(const TensorShape input_shape, const TensorShape scores_shape, const TensorShape output_shape,
+ unsigned int max_output_size, float score_threshold, float nms_threshold)
+ {
+ // Create tensors
+ TensorType bboxes = create_tensor<TensorType>(input_shape, DataType::F32);
+ TensorType scores = create_tensor<TensorType>(scores_shape, DataType::F32);
+ TensorType indices = create_tensor<TensorType>(output_shape, DataType::S32);
+
+ // Create and configure function
+ FunctionType nms_func;
+ nms_func.configure(&bboxes, &scores, &indices, max_output_size, score_threshold, nms_threshold);
+
+ ARM_COMPUTE_EXPECT(bboxes.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(indices.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(scores.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate tensors
+ bboxes.allocator()->allocate();
+ indices.allocator()->allocate();
+ scores.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!bboxes.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!indices.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!scores.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensors
+ fill(AccessorType(bboxes), 0, 0.f, 1.f);
+ fill(AccessorType(scores), 1, 0.f, 1.f);
+
+ // Compute function
+ nms_func.run();
+ return indices;
+ }
+
+ SimpleTensor<int> compute_reference(const TensorShape input_shape, const TensorShape scores_shape, const TensorShape output_shape,
+ unsigned int max_output_size, float score_threshold, float nms_threshold)
+ {
+ // Create reference
+ SimpleTensor<float> bboxes{ input_shape, DataType::F32 };
+ SimpleTensor<float> scores{ scores_shape, DataType::F32 };
+ SimpleTensor<int> indices{ output_shape, DataType::S32 };
+
+ // Fill reference
+ fill(bboxes, 0, 0.f, 1.f);
+ fill(scores, 1, 0.f, 1.f);
+
+ return reference::non_max_suppression(bboxes, scores, indices, max_output_size, score_threshold, nms_threshold);
+ }
+
+ TensorType _target{};
+ SimpleTensor<int> _reference{};
+};
+
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_NON_MAX_SUPPRESSION_FIXTURE */
diff --git a/tests/validation/reference/NonMaxSuppression.cpp b/tests/validation/reference/NonMaxSuppression.cpp
new file mode 100644
index 0000000..7592908
--- /dev/null
+++ b/tests/validation/reference/NonMaxSuppression.cpp
@@ -0,0 +1,157 @@
+/*
+ * 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 "NonMaxSuppression.h"
+
+#include "arm_compute/core/Types.h"
+#include "tests/validation/Helpers.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+namespace
+{
+using CandidateBox = std::pair<int /* index */, float /* score */>;
+using Box = std::tuple<float, float, float, float>;
+
+inline float get_elem_by_coordinate(const SimpleTensor<float> &tensor, Coordinates coord)
+{
+ return *static_cast<const float *>(tensor(coord));
+}
+
+inline Box get_box(const SimpleTensor<float> &boxes, size_t id)
+{
+ return std::make_tuple(
+ get_elem_by_coordinate(boxes, Coordinates(0, id)),
+ get_elem_by_coordinate(boxes, Coordinates(1, id)),
+ get_elem_by_coordinate(boxes, Coordinates(2, id)),
+ get_elem_by_coordinate(boxes, Coordinates(3, id)));
+}
+
+inline std::pair<float, float> get_min_yx(Box b)
+{
+ return std::make_pair(
+ std::min<float>(std::get<0>(b), std::get<2>(b)),
+ std::min<float>(std::get<1>(b), std::get<3>(b)));
+}
+
+inline std::pair<float, float> get_max_yx(Box b)
+{
+ return std::make_pair(
+ std::max<float>(std::get<0>(b), std::get<2>(b)),
+ std::max<float>(std::get<1>(b), std::get<3>(b)));
+}
+
+inline float compute_size(const std::pair<float, float> &min, const std::pair<float, float> &max)
+{
+ return (max.first - min.first) * (max.second - min.second);
+}
+
+inline float compute_intersection(const std::pair<float, float> &b0_min, const std::pair<float, float> &b0_max,
+ const std::pair<float, float> &b1_min, const std::pair<float, float> &b1_max, float b0_size, float b1_size)
+{
+ const float inter = std::max<float>(std::min<float>(b0_max.first, b1_max.first) - std::max<float>(b0_min.first, b1_min.first), 0.0) * std::max<float>(std::min<float>(b0_max.second,
+ b1_max.second)
+ - std::max<float>(b0_min.second, b1_min.second),
+ 0.0);
+ return inter / (b0_size + b1_size - inter);
+}
+
+inline bool reject_box(Box b0, Box b1, float threshold)
+{
+ const auto b0_min = get_min_yx(b0);
+ const auto b0_max = get_max_yx(b0);
+ const auto b1_min = get_min_yx(b1);
+ const auto b1_max = get_max_yx(b1);
+ const float b0_size = compute_size(b0_min, b0_max);
+ const float b1_size = compute_size(b1_min, b1_max);
+ if(b0_size <= 0.f || b1_size <= 0.f)
+ {
+ return false;
+ }
+ else
+ {
+ return compute_intersection(b0_min, b0_max, b1_min, b1_max, b0_size, b1_size) > threshold;
+ }
+}
+
+inline std::vector<CandidateBox> get_candidates(const SimpleTensor<float> &scores, float threshold)
+{
+ std::vector<CandidateBox> candidates_vector;
+ for(int i = 0; i < scores.num_elements(); ++i)
+ {
+ if(scores[i] > threshold)
+ {
+ const auto cb = CandidateBox({ i, scores[i] });
+ candidates_vector.push_back(cb);
+ }
+ }
+ std::sort(candidates_vector.begin(), candidates_vector.end(), [](const CandidateBox bb0, const CandidateBox bb1)
+ {
+ return bb0.second >= bb1.second;
+ });
+ return candidates_vector;
+}
+
+inline bool is_box_selected(const CandidateBox &cb, const SimpleTensor<float> &bboxes, std::vector<int> &selected_boxes, float threshold)
+{
+ for(int j = selected_boxes.size() - 1; j >= 0; --j)
+ {
+ if(reject_box(get_box(bboxes, cb.first), get_box(bboxes, selected_boxes[j]), threshold))
+ {
+ return false;
+ }
+ }
+ return true;
+}
+} // namespace
+
+SimpleTensor<int> non_max_suppression(const SimpleTensor<float> &bboxes, const SimpleTensor<float> &scores, SimpleTensor<int> &indices,
+ unsigned int max_output_size, float score_threshold, float nms_threshold)
+{
+ const size_t num_boxes = bboxes.shape().y();
+ const size_t output_size = std::min(static_cast<size_t>(max_output_size), num_boxes);
+ const std::vector<CandidateBox> candidates_vector = get_candidates(scores, score_threshold);
+ std::vector<int> selected;
+ size_t p(0);
+ while(selected.size() < output_size && p < candidates_vector.size() && selected.size() < candidates_vector.size())
+ {
+ const auto nc = candidates_vector[p++];
+ if(is_box_selected(nc, bboxes, selected, nms_threshold))
+ {
+ selected.push_back(nc.first);
+ }
+ }
+ std::copy_n(selected.begin(), selected.size(), indices.data());
+ return indices;
+}
+
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/reference/NonMaxSuppression.h b/tests/validation/reference/NonMaxSuppression.h
new file mode 100644
index 0000000..0418412
--- /dev/null
+++ b/tests/validation/reference/NonMaxSuppression.h
@@ -0,0 +1,44 @@
+/*
+ * 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.
+ */
+#ifndef __ARM_COMPUTE_TEST_NON_MAX_SUPPRESION_H__
+#define __ARM_COMPUTE_TEST_NON_MAX_SUPPRESION_H__
+
+#include "tests/SimpleTensor.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+SimpleTensor<int> non_max_suppression(const SimpleTensor<float> &bboxes, const SimpleTensor<float> &scores, SimpleTensor<int> &indices,
+ unsigned int max_output_size, float score_threshold, float nms_threshold);
+
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_TEST_NON_MAX_SUPPRESION_H__ */