COMPMID-1849: Implement CPPDetectionPostProcessLayer

* Add DetectionPostProcessLayer
* Add DetectionPostProcessLayer at the graph

Change-Id: I7e56f6cffc26f112d26dfe74853085bb8ec7d849
Signed-off-by: Isabella Gottardi <isabella.gottardi@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1639
Reviewed-by: Giuseppe Rossini <giuseppe.rossini@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 390 insertions, 0 deletions

diff --git a/tests/validation/CPP/DetectionPostProcessLayer.cpp b/tests/validation/CPP/DetectionPostProcessLayer.cpp
new file mode 100644
index 0000000000..51f3452b3d
--- /dev/null
+++ b/tests/validation/CPP/DetectionPostProcessLayer.cpp
@@ -0,0 +1,390 @@
+/*
+ * 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/CPPDetectionPostProcessLayer.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"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+template <typename U, typename T>
+inline void fill_tensor(U &&tensor, const std::vector<T> &v)
+{
+    std::memcpy(tensor.data(), v.data(), sizeof(T) * v.size());
+}
+template <typename U, typename T>
+inline void quantize_and_fill_tensor(U &&tensor, const std::vector<T> &v)
+{
+    QuantizationInfo     qi = tensor.quantization_info();
+    std::vector<uint8_t> quantized;
+    quantized.reserve(v.size());
+    for(auto elem : v)
+    {
+        quantized.emplace_back(quantize_qasymm8(elem, qi));
+    }
+    std::memcpy(tensor.data(), quantized.data(), sizeof(uint8_t) * quantized.size());
+}
+inline QuantizationInfo qinfo_scaleoffset_from_minmax(const float min, const float max)
+{
+    int           offset = 0;
+    float         scale  = 0;
+    const uint8_t qmin   = std::numeric_limits<uint8_t>::min();
+    const uint8_t qmax   = std::numeric_limits<uint8_t>::max();
+    const float   f_qmin = qmin;
+    const float   f_qmax = qmax;
+
+    // Continue only if [min,max] is a valid range and not a point
+    if(min != max)
+    {
+        scale                       = (max - min) / (f_qmax - f_qmin);
+        const float offset_from_min = f_qmin - min / scale;
+        const float offset_from_max = f_qmax - max / scale;
+
+        const float offset_from_min_error = std::abs(f_qmin) + std::abs(min / scale);
+        const float offset_from_max_error = std::abs(f_qmax) + std::abs(max / scale);
+        const float f_offset              = offset_from_min_error < offset_from_max_error ? offset_from_min : offset_from_max;
+
+        uint8_t uint8_offset = 0;
+        if(f_offset < f_qmin)
+        {
+            uint8_offset = qmin;
+        }
+        else if(f_offset > f_qmax)
+        {
+            uint8_offset = qmax;
+        }
+        else
+        {
+            uint8_offset = static_cast<uint8_t>(std::round(f_offset));
+        }
+        offset = uint8_offset;
+    }
+    return QuantizationInfo(scale, offset);
+}
+
+inline void base_test_case(DetectionPostProcessLayerInfo info, DataType data_type, const SimpleTensor<float> &expected_output_boxes,
+                           const SimpleTensor<float> &expected_output_classes, const SimpleTensor<float> &expected_output_scores, const SimpleTensor<float> &expected_num_detection,
+                           AbsoluteTolerance<float> tolerance_boxes = AbsoluteTolerance<float>(0.1f), AbsoluteTolerance<float> tolerance_others = AbsoluteTolerance<float>(0.1f))
+{
+    Tensor box_encoding     = create_tensor<Tensor>(TensorShape(4U, 6U, 1U), data_type, 1, qinfo_scaleoffset_from_minmax(-1.0f, 1.0f));
+    Tensor class_prediction = create_tensor<Tensor>(TensorShape(3U, 6U, 1U), data_type, 1, qinfo_scaleoffset_from_minmax(0.0f, 1.0f));
+    Tensor anchors          = create_tensor<Tensor>(TensorShape(4U, 6U), data_type, 1, qinfo_scaleoffset_from_minmax(0.0f, 100.5f));
+
+    box_encoding.allocator()->allocate();
+    class_prediction.allocator()->allocate();
+    anchors.allocator()->allocate();
+
+    std::vector<float> box_encoding_vector =
+    {
+        0.0f, 1.0f, 0.0f, 0.0f,
+        0.0f, -1.0f, 0.0f, 0.0f,
+        0.0f, 0.0f, 0.0f, 0.0f,
+        0.0f, 0.0f, 0.0f, 0.0f,
+        0.0f, 1.0f, 0.0f, 0.0f,
+        0.0f, 0.0f, 0.0f, 0.0f
+    };
+    std::vector<float> class_prediction_vector =
+    {
+        0.0f, 0.7f, 0.68f,
+        0.0f, 0.6f, 0.5f,
+        0.0f, 0.9f, 0.83f,
+        0.0f, 0.91f, 0.97f,
+        0.0f, 0.5f, 0.4f,
+        0.0f, 0.31f, 0.22f
+    };
+    std::vector<float> anchors_vector =
+    {
+        0.4f, 0.4f, 1.1f, 1.1f,
+        0.4f, 0.4f, 1.1f, 1.1f,
+        0.4f, 0.4f, 1.1f, 1.1f,
+        0.4f, 10.4f, 1.1f, 1.1f,
+        0.4f, 10.4f, 1.1f, 1.1f,
+        0.4f, 100.4f, 1.1f, 1.1f
+    };
+
+    // Fill the tensors with random pre-generated values
+    if(data_type == DataType::F32)
+    {
+        fill_tensor(Accessor(box_encoding), box_encoding_vector);
+        fill_tensor(Accessor(class_prediction), class_prediction_vector);
+        fill_tensor(Accessor(anchors), anchors_vector);
+    }
+    else
+    {
+        quantize_and_fill_tensor(Accessor(box_encoding), box_encoding_vector);
+        quantize_and_fill_tensor(Accessor(class_prediction), class_prediction_vector);
+        quantize_and_fill_tensor(Accessor(anchors), anchors_vector);
+    }
+
+    // Determine the output through the CPP kernel
+    Tensor                       output_boxes;
+    Tensor                       output_classes;
+    Tensor                       output_scores;
+    Tensor                       num_detection;
+    CPPDetectionPostProcessLayer detection;
+    detection.configure(&box_encoding, &class_prediction, &anchors, &output_boxes, &output_classes, &output_scores, &num_detection, info);
+
+    output_boxes.allocator()->allocate();
+    output_classes.allocator()->allocate();
+    output_scores.allocator()->allocate();
+    num_detection.allocator()->allocate();
+
+    // Run the kernel
+    detection.run();
+
+    // Validate against the expected output
+    // Validate output boxes
+    validate(Accessor(output_boxes), expected_output_boxes, tolerance_boxes);
+    // Validate detection classes
+    validate(Accessor(output_classes), expected_output_classes, tolerance_others);
+    // Validate detection scores
+    validate(Accessor(output_scores), expected_output_scores, tolerance_others);
+    // Validate num detections
+    validate(Accessor(num_detection), expected_num_detection, tolerance_others);
+}
+} // namespace
+
+TEST_SUITE(CPP)
+TEST_SUITE(DetectionPostProcessLayer)
+
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(zip(
+        framework::dataset::make("BoxEncodingsInfo", { TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(4U, 10U, 3U), 1, DataType::F32),  // Mismatching batch_size
+                                                TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::S8), // Unsupported data type
+                                                TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32), // Wrong Detection Info
+                                                TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32), // Wrong boxes dimensions
+                                                TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::QASYMM8)}), // Wrong score dimension 
+        framework::dataset::make("ClassPredsInfo",{ TensorInfo(TensorShape(3U ,10U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(3U ,10U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(3U ,10U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(3U ,10U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(3U ,10U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(3U ,10U), 1, DataType::QASYMM8)})),
+        framework::dataset::make("AnchorsInfo",{ TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::QASYMM8)})),
+        framework::dataset::make("OutputBoxInfo", { TensorInfo(TensorShape(4U, 3U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(4U, 3U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(4U, 3U, 1U), 1, DataType::S8),
+                                                TensorInfo(TensorShape(4U, 3U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(1U, 5U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(4U, 3U, 1U), 1, DataType::F32)})),
+        framework::dataset::make("OuputClassesInfo",{ TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(6U, 1U), 1, DataType::F32)})),
+        framework::dataset::make("OutputScoresInfo",{ TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(6U, 1U), 1, DataType::F32)})),
+        framework::dataset::make("NumDetectionsInfo",{ TensorInfo(TensorShape(1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(1U), 1, DataType::F32),
+                                                TensorInfo(TensorShape(1U), 1, DataType::F32)})),
+        framework::dataset::make("DetectionPostProcessLayerInfo",{ DetectionPostProcessLayerInfo(3, 1, 0.0f, 0.5f, 2, {0.1f,0.1f,0.1f,0.1f}),
+                                                DetectionPostProcessLayerInfo(3, 1, 0.0f, 0.5f, 2, {0.1f,0.1f,0.1f,0.1f}),
+                                                DetectionPostProcessLayerInfo(3, 1, 0.0f, 0.5f, 2, {0.1f,0.1f,0.1f,0.1f}),
+                                                DetectionPostProcessLayerInfo(3, 1, 0.0f, 1.5f, 2, {0.0f,0.1f,0.1f,0.1f}),
+                                                DetectionPostProcessLayerInfo(3, 1, 0.0f, 0.5f, 2, {0.1f,0.1f,0.1f,0.1f}),
+                                                DetectionPostProcessLayerInfo(3, 1, 0.0f, 0.5f, 2, {0.1f,0.1f,0.1f,0.1f})})),
+        framework::dataset::make("Expected", {true, false, false, false, false, false })),
+        box_encodings_info, classes_info, anchors_info, output_boxes_info, output_classes_info,output_scores_info, num_detection_info, detect_info, expected)
+{
+    const Status status = CPPDetectionPostProcessLayer::validate(&box_encodings_info.clone()->set_is_resizable(false),
+            &classes_info.clone()->set_is_resizable(false),
+            &anchors_info.clone()->set_is_resizable(false),
+            &output_boxes_info.clone()->set_is_resizable(false),
+            &output_classes_info.clone()->set_is_resizable(false),
+            &output_scores_info.clone()->set_is_resizable(false), &num_detection_info.clone()->set_is_resizable(false), detect_info);
+    ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
+TEST_SUITE(F32)
+TEST_CASE(Float_general, framework::DatasetMode::ALL)
+{
+    DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/,
+                                                                       0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/);
+    // Fill expected detection boxes
+    SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32);
+    fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, -0.15, 0.95, 0.95, -0.15, 99.85, 0.95, 100.95 });
+    // Fill expected detection classes
+    SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32);
+    fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f });
+    // Fill expected detection scores
+    SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32);
+    fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.95f, 0.31f });
+    // Fill expected num detections
+    SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32);
+    fill_tensor(expected_num_detection, std::vector<float> { 3.f });
+    // Run base test
+    base_test_case(info, DataType::F32, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection);
+}
+
+TEST_CASE(Float_fast, framework::DatasetMode::ALL)
+{
+    DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/,
+                                                                       0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/,
+                                                                       false /*use_regular_nms*/, 1 /*detections_per_class*/);
+
+    // Fill expected detection boxes
+    SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32);
+    fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, -0.15, 0.95, 0.95, -0.15, 99.85, 0.95, 100.95 });
+    // Fill expected detection classes
+    SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32);
+    fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f });
+    // Fill expected detection scores
+    SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32);
+    fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.95f, 0.31f });
+    // Fill expected num detections
+    SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32);
+    fill_tensor(expected_num_detection, std::vector<float> { 3.f });
+
+    // Run base test
+    base_test_case(info, DataType::F32, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection);
+}
+
+TEST_CASE(Float_regular, framework::DatasetMode::ALL)
+{
+    DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/,
+                                                                       0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/,
+                                                                       true /*use_regular_nms*/, 1 /*detections_per_class*/);
+
+    // Fill expected detection boxes
+    SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32);
+    fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, 9.85, 0.95, 10.95, 0.0f, 0.0f, 0.0f, 0.0f });
+    // Fill expected detection classes
+    SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32);
+    fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f });
+    // Fill expected detection scores
+    SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32);
+    fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.91f, 0.0f });
+    // Fill expected num detections
+    SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32);
+    fill_tensor(expected_num_detection, std::vector<float> { 2.f });
+
+    // Run test
+    base_test_case(info, DataType::F32, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection);
+}
+TEST_SUITE_END() // F32
+
+TEST_SUITE(QASYMM8)
+TEST_CASE(Quantized_general, framework::DatasetMode::ALL)
+{
+    DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/,
+                                                                       0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/);
+
+    // Fill expected detection boxes
+    SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32);
+    fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, -0.15, 0.95, 0.95, -0.15, 99.85, 0.95, 100.95 });
+    // Fill expected detection classes
+    SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32);
+    fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f });
+    // Fill expected detection scores
+    SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32);
+    fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.95f, 0.31f });
+    // Fill expected num detections
+    SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32);
+    fill_tensor(expected_num_detection, std::vector<float> { 3.f });
+    // Run test
+    base_test_case(info, DataType::QASYMM8, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection, AbsoluteTolerance<float>(0.3f));
+}
+
+TEST_CASE(Quantized_fast, framework::DatasetMode::ALL)
+{
+    DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/,
+                                                                       0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/,
+                                                                       false /*use_regular_nms*/, 1 /*detections_per_class*/);
+
+    // Fill expected detection boxes
+    SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32);
+    fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, -0.15, 0.95, 0.95, -0.15, 99.85, 0.95, 100.95 });
+    // Fill expected detection classes
+    SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32);
+    fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f });
+    // Fill expected detection scores
+    SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32);
+    fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.95f, 0.31f });
+    // Fill expected num detections
+    SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32);
+    fill_tensor(expected_num_detection, std::vector<float> { 3.f });
+
+    // Run base test
+    base_test_case(info, DataType::QASYMM8, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection, AbsoluteTolerance<float>(0.3f));
+}
+
+TEST_CASE(Quantized_regular, framework::DatasetMode::ALL)
+{
+    DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/,
+                                                                       0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/,
+                                                                       true /*use_regular_nms*/, 1 /*detections_per_class*/);
+    // Fill expected detection boxes
+    SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32);
+    fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, 9.85, 0.95, 10.95, 0.0f, 0.0f, 0.0f, 0.0f });
+    // Fill expected detection classes
+    SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32);
+    fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f });
+    // Fill expected detection scores
+    SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32);
+    fill_tensor(expected_output_scores, std::vector<float> { 0.95f, 0.91f, 0.0f });
+    // Fill expected num detections
+    SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32);
+    fill_tensor(expected_num_detection, std::vector<float> { 2.f });
+
+    // Run test
+    base_test_case(info, DataType::QASYMM8, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection, AbsoluteTolerance<float>(0.3f));
+}
+
+TEST_SUITE_END() // QASYMM8
+
+TEST_SUITE_END() // DetectionPostProcessLayer
+TEST_SUITE_END() // CPP
+} // namespace validation
+} // namespace test
+} // namespace arm_compute