COMPMID-1766: Implemented CPP Non Max Suppression
Change-Id: I1dcd5fb3d9ad6c6c750415bf8074698b800dfbc1
Reviewed-on: https://review.mlplatform.org/494
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giuseppe Rossini <giuseppe.rossini@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/arm_compute/runtime/CPP/functions/CPPDetectionOutputLayer.h b/arm_compute/runtime/CPP/functions/CPPDetectionOutputLayer.h
index 7f80948..8c610f3 100644
--- a/arm_compute/runtime/CPP/functions/CPPDetectionOutputLayer.h
+++ b/arm_compute/runtime/CPP/functions/CPPDetectionOutputLayer.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -39,6 +39,56 @@
// LabelBBox used for map label and bounding box
using LabelBBox = std::map<int, std::vector<NormalizedBBox>>;
+/** CPP Function to perform non maximum suppression on the bounding boxes and scores
+ *
+ */
+class CPPNonMaximumSuppression : public IFunction
+{
+public:
+ /** Default constructor */
+ CPPNonMaximumSuppression();
+ /** Configure the function to perform non maximal suppression
+ *
+ * @param[in] bboxes The input bounding boxes. Data types supported: F32.
+ * @param[in] scores The corresponding input confidence. Same as @p scores.
+ * @param[out] indices The kept indices of bboxes after nms. Data types supported: S32.
+ * @param[in] max_output_size An integer tensor representing the maximum number of boxes to be selected by non max suppression.
+ * @param[in] score_threshold The threshold used to filter detection results.
+ * @param[in] nms_threshold The threshold used in non maximum suppression.
+ *
+ */
+ void configure(const ITensor *bboxes, const ITensor *scores, ITensor *indices, unsigned int max_output_size, const float score_threshold, const float nms_threshold);
+
+ /** Static function to check if given arguments will lead to a valid configuration of @ref CPPNonMaximumSuppression
+ *
+ * @param[in] bboxes The input bounding boxes. Data types supported: F32.
+ * @param[in] scores The corresponding input confidence. Same as @p scores.
+ * @param[out] indices The kept indices of bboxes after nms. Data types supported: S32.
+ * @param[in] max_output_size An integer tensor representing the maximum number of boxes to be selected by non max suppression.
+ * @param[in] score_threshold The threshold used to filter detection results.
+ * @param[in] nms_threshold The threshold used in non maximum suppression.
+ *
+ */
+ static Status validate(const ITensorInfo *bboxes, const ITensorInfo *scores, const ITensorInfo *indices, unsigned int max_output_size,
+ const float score_threshold, const float nms_threshold);
+
+ // Inherited methods overridden:
+ void run() override;
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ CPPNonMaximumSuppression(const CPPNonMaximumSuppression &) = delete;
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ CPPNonMaximumSuppression &operator=(const CPPNonMaximumSuppression &) = delete;
+
+private:
+ const ITensor *_bboxes;
+ const ITensor *_scores;
+ ITensor *_indices;
+ unsigned int _max_output_size;
+
+ float _score_threshold;
+ float _nms_threshold;
+};
+
/** CPP Function to generate the detection output based on location and confidence
* predictions by doing non maximum suppression.
*
diff --git a/src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp b/src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp
index 61005ab..34a7294 100644
--- a/src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp
+++ b/src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -34,7 +34,7 @@
{
namespace
{
-Status validate_arguments(const ITensorInfo *input_loc, const ITensorInfo *input_conf, const ITensorInfo *input_priorbox, const ITensorInfo *output, DetectionOutputLayerInfo info)
+Status detection_layer_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);
@@ -366,14 +366,103 @@
indices.push_back(idx);
}
score_index_vec.erase(score_index_vec.begin());
- if(keep && eta < 1 && adaptive_threshold > 0.5)
+ if(keep && eta < 1.f && adaptive_threshold > 0.5f)
{
adaptive_threshold *= eta;
}
}
}
+
+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<NormalizedBBox> &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<const float *>(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<float> &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<const float *>(it.ptr());
+ scores_vector.push_back(*input_ptr);
+ };
+ execute_window_loop(window, f, it);
+}
+
+void CPPNonMaximumSuppression::run()
+{
+ std::vector<NormalizedBBox> bboxes_vector;
+ std::vector<float> scores_vector;
+ std::vector<int> 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<int *>(_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()
@@ -391,7 +480,7 @@
auto_init_if_empty(*output->info(), input_loc->info()->clone()->set_tensor_shape(TensorShape(7U, max_size)));
// Perform validation step
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input_loc->info(), input_conf->info(), input_priorbox->info(), output->info(), info));
+ ARM_COMPUTE_ERROR_THROW_ON(detection_layer_validate_arguments(input_loc->info(), input_conf->info(), input_priorbox->info(), output->info(), info));
_input_loc = input_loc;
_input_conf = input_conf;
@@ -429,7 +518,7 @@
Status CPPDetectionOutputLayer::validate(const ITensorInfo *input_loc, const ITensorInfo *input_conf, const ITensorInfo *input_priorbox, const ITensorInfo *output, DetectionOutputLayerInfo info)
{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input_loc, input_conf, input_priorbox, output, info));
+ ARM_COMPUTE_RETURN_ON_ERROR(detection_layer_validate_arguments(input_loc, input_conf, input_priorbox, output, info));
return Status{};
}
@@ -582,4 +671,4 @@
}
}
}
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
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 d7ffc12..bd29fe6 100644
--- a/tests/datasets/ShapeDatasets.h
+++ b/tests/datasets/ShapeDatasets.h
@@ -949,6 +949,37 @@
{
}
};
+
+/** 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..013a26f
--- /dev/null
+++ b/tests/validation/reference/NonMaxSuppression.cpp
@@ -0,0 +1,126 @@
+/*
+ * 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 "Permute.h"
+
+#include "arm_compute/core/Types.h"
+#include "tests/validation/Helpers.h"
+#include <queue>
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+namespace
+{
+inline float get_elem_by_coordinate(const SimpleTensor<float> &tensor, Coordinates coord)
+{
+ return *static_cast<const float *>(tensor(coord));
+}
+
+// Return intersection-over-union overlap between boxes i and j
+inline bool iou_greater_than_threshold(const SimpleTensor<float> &boxes, size_t i, size_t j, float iou_threshold)
+{
+ const float ymin_i = std::min<float>(get_elem_by_coordinate(boxes, Coordinates(0, i)), get_elem_by_coordinate(boxes, Coordinates(2, i)));
+ const float xmin_i = std::min<float>(get_elem_by_coordinate(boxes, Coordinates(1, i)), get_elem_by_coordinate(boxes, Coordinates(3, i)));
+ const float ymax_i = std::max<float>(get_elem_by_coordinate(boxes, Coordinates(0, i)), get_elem_by_coordinate(boxes, Coordinates(2, i)));
+ const float xmax_i = std::max<float>(get_elem_by_coordinate(boxes, Coordinates(1, i)), get_elem_by_coordinate(boxes, Coordinates(3, i)));
+ const float ymin_j = std::min<float>(get_elem_by_coordinate(boxes, Coordinates(0, j)), get_elem_by_coordinate(boxes, Coordinates(2, j)));
+ const float xmin_j = std::min<float>(get_elem_by_coordinate(boxes, Coordinates(1, j)), get_elem_by_coordinate(boxes, Coordinates(3, j)));
+ const float ymax_j = std::max<float>(get_elem_by_coordinate(boxes, Coordinates(0, j)), get_elem_by_coordinate(boxes, Coordinates(2, j)));
+ const float xmax_j = std::max<float>(get_elem_by_coordinate(boxes, Coordinates(1, j)), get_elem_by_coordinate(boxes, Coordinates(3, j)));
+ const float area_i = (ymax_i - ymin_i) * (xmax_i - xmin_i);
+ const float area_j = (ymax_j - ymin_j) * (xmax_j - xmin_j);
+ if(area_i <= 0 || area_j <= 0)
+ {
+ return false;
+ }
+ const float intersection_ymin = std::max<float>(ymin_i, ymin_j);
+ const float intersection_xmin = std::max<float>(xmin_i, xmin_j);
+ const float intersection_ymax = std::min<float>(ymax_i, ymax_j);
+ const float intersection_xmax = std::min<float>(xmax_i, xmax_j);
+ const float intersection_area = std::max<float>(intersection_ymax - intersection_ymin, 0.0) * std::max<float>(intersection_xmax - intersection_xmin, 0.0);
+ const float iou = intersection_area / (area_i + area_j - intersection_area);
+ return iou > iou_threshold;
+}
+
+} // 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);
+ std::vector<float> scores_data(num_boxes);
+ std::copy_n(scores.data(), num_boxes, scores_data.begin());
+
+ using CandidateBox = std::pair<int /* index */, float /* score */>;
+ auto cmp = [](const CandidateBox bb0, const CandidateBox bb1)
+ {
+ return bb0.second < bb1.second;
+ };
+
+ std::priority_queue<CandidateBox, std::deque<CandidateBox>, decltype(cmp)> candidate_priority_queue(cmp);
+ for(size_t i = 0; i < scores_data.size(); ++i)
+ {
+ if(scores_data[i] > score_threshold)
+ {
+ candidate_priority_queue.emplace(CandidateBox({ i, scores_data[i] }));
+ }
+ }
+
+ std::vector<int> selected;
+ std::vector<float> selected_scores;
+ CandidateBox next_candidate;
+
+ while(selected.size() < output_size && !candidate_priority_queue.empty())
+ {
+ next_candidate = candidate_priority_queue.top();
+ candidate_priority_queue.pop();
+ bool should_select = true;
+ for(int j = selected.size() - 1; j >= 0; --j)
+ {
+ if(iou_greater_than_threshold(bboxes, next_candidate.first, selected[j], nms_threshold))
+ {
+ should_select = false;
+ break;
+ }
+ }
+ if(should_select)
+ {
+ selected.push_back(next_candidate.first);
+ selected_scores.push_back(next_candidate.second);
+ }
+ }
+ 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__ */