COMPMID-3273: Add support for QASYMM8_SIGNED in CPPDetectionPostProcessLayer

Signed-off-by: Sheri Zhang <sheri.zhang@arm.com>
Change-Id: I8dad529892caf7389efb311e810c8a80ca3d03c2
Signed-off-by: Sheri Zhang <sheri.zhang@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2888
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

3 files changed, 216 insertions, 79 deletions

diff --git a/arm_compute/runtime/CPP/functions/CPPDetectionPostProcessLayer.h b/arm_compute/runtime/CPP/functions/CPPDetectionPostProcessLayer.h
index 44ebf9d7f2..cb74ca9077 100644
--- a/arm_compute/runtime/CPP/functions/CPPDetectionPostProcessLayer.h
+++ b/arm_compute/runtime/CPP/functions/CPPDetectionPostProcessLayer.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019 ARM Limited.
+ * Copyright (c) 2019-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -54,7 +54,7 @@ public:
     CPPDetectionPostProcessLayer &operator=(const CPPDetectionPostProcessLayer &) = delete;
     /** Configure the detection output layer CPP function
      *
-     * @param[in]  input_box_encoding The bounding box input tensor. Data types supported: F32, QASYMM8.
+     * @param[in]  input_box_encoding The bounding box input tensor. Data types supported: F32/QASYMM8/QASYMM8_SIGNED.
      * @param[in]  input_score        The class prediction input tensor. Data types supported: Same as @p input_box_encoding.
      * @param[in]  input_anchors      The anchors input tensor. Data types supported: Same as @p input_box_encoding.
      * @param[out] output_boxes       The boxes output tensor. Data types supported: F32.
@@ -69,8 +69,8 @@ public:
                    ITensor *output_boxes, ITensor *output_classes, ITensor *output_scores, ITensor *num_detection, DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo());
     /** Static function to check if given info will lead to a valid configuration of @ref CPPDetectionPostProcessLayer
      *
-     * @param[in]  input_box_encoding The bounding box input tensor info. Data types supported: F32, QASYMM8.
-     * @param[in]  input_class_score  The class prediction input tensor info. Data types supported: F32, QASYMM8.
+     * @param[in]  input_box_encoding The bounding box input tensor info. Data types supported: F32/QASYMM8/QASYMM8_SIGNED.
+     * @param[in]  input_class_score  The class prediction input tensor info. Data types supported: Same as @p input_box_encoding.
      * @param[in]  input_anchors      The anchors input tensor. Data types supported: F32, QASYMM8.
      * @param[out] output_boxes       The output tensor. Data types supported: F32.
      * @param[out] output_classes     The output tensor. Data types supported: Same as @p output_boxes.
diff --git a/src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp b/src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp
index 9856f527ee..b3fc9c776d 100644
--- a/src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp
+++ b/src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp
@@ -40,7 +40,7 @@ Status validate_arguments(const ITensorInfo *input_box_encoding, const ITensorIn
                           DetectionPostProcessLayerInfo info, const unsigned int kBatchSize, const unsigned int kNumCoordBox)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input_box_encoding, input_class_score, input_anchors);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_box_encoding, 1, DataType::F32, DataType::QASYMM8);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_box_encoding, 1, DataType::F32, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_box_encoding, input_anchors);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(input_box_encoding->num_dimensions() > 3, "The location input tensor shape should be [4, N, kBatchSize].");
     if(input_box_encoding->num_dimensions() > 2)
@@ -90,6 +90,24 @@ Status validate_arguments(const ITensorInfo *input_box_encoding, const ITensorIn
     return Status{};
 }
 
+inline void DecodeBoxCorner(BBox &box_centersize, BBox &anchor, Iterator &decoded_it, DetectionPostProcessLayerInfo info)
+{
+    const float half_factor = 0.5f;
+
+    // BBox is equavalent to CenterSizeEncoding [y,x,h,w]
+    const float y_center = box_centersize[0] / info.scale_value_y() * anchor[2] + anchor[0];
+    const float x_center = box_centersize[1] / info.scale_value_x() * anchor[3] + anchor[1];
+    const float half_h   = half_factor * static_cast<float>(std::exp(box_centersize[2] / info.scale_value_h())) * anchor[2];
+    const float half_w   = half_factor * static_cast<float>(std::exp(box_centersize[3] / info.scale_value_w())) * anchor[3];
+
+    // Box Corner encoding boxes are saved as [xmin, ymin, xmax, ymax]
+    auto decoded_ptr   = reinterpret_cast<float *>(decoded_it.ptr());
+    *(decoded_ptr)     = x_center - half_w; // xmin
+    *(1 + decoded_ptr) = y_center - half_h; // ymin
+    *(2 + decoded_ptr) = x_center + half_w; // xmax
+    *(3 + decoded_ptr) = y_center + half_h; // ymax
+}
+
 /** Decode a bbox according to a anchors and scale info.
  *
  * @param[in]  input_box_encoding The input prior bounding boxes.
@@ -101,8 +119,8 @@ void DecodeCenterSizeBoxes(const ITensor *input_box_encoding, const ITensor *inp
 {
     const QuantizationInfo &qi_box     = input_box_encoding->info()->quantization_info();
     const QuantizationInfo &qi_anchors = input_anchors->info()->quantization_info();
-    BBox                    box_centersize;
-    BBox                    anchor;
+    BBox                    box_centersize{ {} };
+    BBox                    anchor{ {} };
 
     Window win;
     win.use_tensor_dimensions(input_box_encoding->info()->tensor_shape());
@@ -112,11 +130,9 @@ void DecodeCenterSizeBoxes(const ITensor *input_box_encoding, const ITensor *inp
     Iterator anchor_it(input_anchors, win);
     Iterator decoded_it(decoded_boxes, win);
 
-    const float half_factor = 0.5f;
-
-    execute_window_loop(win, [&](const Coordinates &)
+    if(input_box_encoding->info()->data_type() == DataType::QASYMM8)
     {
-        if(is_data_type_quantized(input_box_encoding->info()->data_type()))
+        execute_window_loop(win, [&](const Coordinates &)
         {
             const auto box_ptr    = reinterpret_cast<const qasymm8_t *>(box_it.ptr());
             const auto anchor_ptr = reinterpret_cast<const qasymm8_t *>(anchor_it.ptr());
@@ -126,29 +142,38 @@ void DecodeCenterSizeBoxes(const ITensor *input_box_encoding, const ITensor *inp
             anchor = BBox({ dequantize_qasymm8(*anchor_ptr, qi_anchors), dequantize_qasymm8(*(anchor_ptr + 1), qi_anchors),
                             dequantize_qasymm8(*(2 + anchor_ptr), qi_anchors), dequantize_qasymm8(*(3 + anchor_ptr), qi_anchors)
                           });
-        }
-        else
+            DecodeBoxCorner(box_centersize, anchor, decoded_it, info);
+        },
+        box_it, anchor_it, decoded_it);
+    }
+    else if(input_box_encoding->info()->data_type() == DataType::QASYMM8_SIGNED)
+    {
+        execute_window_loop(win, [&](const Coordinates &)
+        {
+            const auto box_ptr    = reinterpret_cast<const qasymm8_signed_t *>(box_it.ptr());
+            const auto anchor_ptr = reinterpret_cast<const qasymm8_signed_t *>(anchor_it.ptr());
+            box_centersize        = BBox({ dequantize_qasymm8_signed(*box_ptr, qi_box), dequantize_qasymm8_signed(*(box_ptr + 1), qi_box),
+                                           dequantize_qasymm8_signed(*(2 + box_ptr), qi_box), dequantize_qasymm8_signed(*(3 + box_ptr), qi_box)
+                                         });
+            anchor = BBox({ dequantize_qasymm8_signed(*anchor_ptr, qi_anchors), dequantize_qasymm8_signed(*(anchor_ptr + 1), qi_anchors),
+                            dequantize_qasymm8_signed(*(2 + anchor_ptr), qi_anchors), dequantize_qasymm8_signed(*(3 + anchor_ptr), qi_anchors)
+                          });
+            DecodeBoxCorner(box_centersize, anchor, decoded_it, info);
+        },
+        box_it, anchor_it, decoded_it);
+    }
+    else
+    {
+        execute_window_loop(win, [&](const Coordinates &)
         {
             const auto box_ptr    = reinterpret_cast<const float *>(box_it.ptr());
             const auto anchor_ptr = reinterpret_cast<const float *>(anchor_it.ptr());
             box_centersize        = BBox({ *box_ptr, *(box_ptr + 1), *(2 + box_ptr), *(3 + box_ptr) });
             anchor                = BBox({ *anchor_ptr, *(anchor_ptr + 1), *(2 + anchor_ptr), *(3 + anchor_ptr) });
-        }
-
-        // BBox is equavalent to CenterSizeEncoding [y,x,h,w]
-        const float y_center = box_centersize[0] / info.scale_value_y() * anchor[2] + anchor[0];
-        const float x_center = box_centersize[1] / info.scale_value_x() * anchor[3] + anchor[1];
-        const float half_h   = half_factor * static_cast<float>(std::exp(box_centersize[2] / info.scale_value_h())) * anchor[2];
-        const float half_w   = half_factor * static_cast<float>(std::exp(box_centersize[3] / info.scale_value_w())) * anchor[3];
-
-        // Box Corner encoding boxes are saved as [xmin, ymin, xmax, ymax]
-        auto decoded_ptr   = reinterpret_cast<float *>(decoded_it.ptr());
-        *(decoded_ptr)     = x_center - half_w; // xmin
-        *(1 + decoded_ptr) = y_center - half_h; // ymin
-        *(2 + decoded_ptr) = x_center + half_w; // xmax
-        *(3 + decoded_ptr) = y_center + half_h; // ymax
-    },
-    box_it, anchor_it, decoded_it);
+            DecodeBoxCorner(box_centersize, anchor, decoded_it, info);
+        },
+        box_it, anchor_it, decoded_it);
+    }
 }
 
 void SaveOutputs(const Tensor *decoded_boxes, const std::vector<int> &result_idx_boxes_after_nms, const std::vector<float> &result_scores_after_nms, const std::vector<int> &result_classes_after_nms,
@@ -263,12 +288,26 @@ void CPPDetectionPostProcessLayer::run()
     // Decode scores if necessary
     if(_dequantize_scores)
     {
-        for(unsigned int idx_c = 0; idx_c < _num_classes_with_background; ++idx_c)
+        if(_input_box_encoding->info()->data_type() == DataType::QASYMM8)
         {
-            for(unsigned int idx_b = 0; idx_b < _num_boxes; ++idx_b)
+            for(unsigned int idx_c = 0; idx_c < _num_classes_with_background; ++idx_c)
             {
-                *(reinterpret_cast<float *>(_decoded_scores.ptr_to_element(Coordinates(idx_c, idx_b)))) =
-                    dequantize_qasymm8(*(reinterpret_cast<qasymm8_t *>(_input_scores->ptr_to_element(Coordinates(idx_c, idx_b)))), _input_scores->info()->quantization_info());
+                for(unsigned int idx_b = 0; idx_b < _num_boxes; ++idx_b)
+                {
+                    *(reinterpret_cast<float *>(_decoded_scores.ptr_to_element(Coordinates(idx_c, idx_b)))) =
+                        dequantize_qasymm8(*(reinterpret_cast<qasymm8_t *>(_input_scores->ptr_to_element(Coordinates(idx_c, idx_b)))), _input_scores->info()->quantization_info());
+                }
+            }
+        }
+        else if(_input_box_encoding->info()->data_type() == DataType::QASYMM8_SIGNED)
+        {
+            for(unsigned int idx_c = 0; idx_c < _num_classes_with_background; ++idx_c)
+            {
+                for(unsigned int idx_b = 0; idx_b < _num_boxes; ++idx_b)
+                {
+                    *(reinterpret_cast<float *>(_decoded_scores.ptr_to_element(Coordinates(idx_c, idx_b)))) =
+                        dequantize_qasymm8_signed(*(reinterpret_cast<qasymm8_signed_t *>(_input_scores->ptr_to_element(Coordinates(idx_c, idx_b)))), _input_scores->info()->quantization_info());
+                }
             }
         }
     }
diff --git a/tests/validation/CPP/DetectionPostProcessLayer.cpp b/tests/validation/CPP/DetectionPostProcessLayer.cpp
index f4528fb593..934ffea545 100644
--- a/tests/validation/CPP/DetectionPostProcessLayer.cpp
+++ b/tests/validation/CPP/DetectionPostProcessLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019 ARM Limited.
+ * Copyright (c) 2019-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -46,52 +46,64 @@ 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>
+template <typename D, 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;
+    QuantizationInfo qi = tensor.quantization_info();
+    std::vector<D>   quantized;
     quantized.reserve(v.size());
     for(auto elem : v)
     {
-        quantized.emplace_back(quantize_qasymm8(elem, qi));
+        quantized.emplace_back(Qasymm8QuantizationHelper<D>::quantize(elem, qi));
+    }
+    std::memcpy(tensor.data(), quantized.data(), sizeof(D) * quantized.size());
+}
+template <typename T>
+inline int calc_qinfo(const float min, const float max, float &scale)
+{
+    const auto  qmin   = std::numeric_limits<T>::min();
+    const auto  qmax   = std::numeric_limits<T>::max();
+    const float f_qmin = qmin;
+    const float f_qmax = qmax;
+
+    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;
+    T           tmp_offset;
+    if(f_offset < f_qmin)
+    {
+        tmp_offset = qmin;
+    }
+    else if(f_offset > f_qmax)
+    {
+        tmp_offset = qmax;
+    }
+    else
+    {
+        tmp_offset = static_cast<T>(arm_compute::support::cpp11::round(f_offset));
     }
-    std::memcpy(tensor.data(), quantized.data(), sizeof(uint8_t) * quantized.size());
+    return static_cast<int>(tmp_offset);
 }
-inline QuantizationInfo qinfo_scaleoffset_from_minmax(const float min, const float max)
+inline QuantizationInfo qinfo_scaleoffset_from_minmax(DataType data_type, 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;
+    int   offset = 0;
+    float scale  = 0;
 
     // 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)
+        if(data_type == DataType::QASYMM8_SIGNED)
         {
-            uint8_offset = qmax;
+            offset = calc_qinfo<int8_t>(min, max, scale);
         }
         else
         {
-            uint8_offset = static_cast<uint8_t>(arm_compute::support::cpp11::round(f_offset));
+            offset = calc_qinfo<uint8_t>(min, max, scale);
         }
-        offset = uint8_offset;
     }
     return QuantizationInfo(scale, offset);
 }
@@ -100,9 +112,9 @@ inline void base_test_case(DetectionPostProcessLayerInfo info, DataType data_typ
                            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));
+    Tensor box_encoding     = create_tensor<Tensor>(TensorShape(4U, 6U, 1U), data_type, 1, qinfo_scaleoffset_from_minmax(data_type, -1.0f, 1.0f));
+    Tensor class_prediction = create_tensor<Tensor>(TensorShape(3U, 6U, 1U), data_type, 1, qinfo_scaleoffset_from_minmax(data_type, 0.0f, 1.0f));
+    Tensor anchors          = create_tensor<Tensor>(TensorShape(4U, 6U), data_type, 1, qinfo_scaleoffset_from_minmax(data_type, 0.0f, 100.5f));
 
     box_encoding.allocator()->allocate();
     class_prediction.allocator()->allocate();
@@ -137,17 +149,31 @@ inline void base_test_case(DetectionPostProcessLayerInfo info, DataType data_typ
     };
 
     // Fill the tensors with random pre-generated values
-    if(data_type == DataType::F32)
+    switch(data_type)
     {
-        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);
+        case DataType::F32:
+        {
+            fill_tensor(Accessor(box_encoding), box_encoding_vector);
+            fill_tensor(Accessor(class_prediction), class_prediction_vector);
+            fill_tensor(Accessor(anchors), anchors_vector);
+        }
+        break;
+        case DataType::QASYMM8:
+        {
+            quantize_and_fill_tensor<uint8_t>(Accessor(box_encoding), box_encoding_vector);
+            quantize_and_fill_tensor<uint8_t>(Accessor(class_prediction), class_prediction_vector);
+            quantize_and_fill_tensor<uint8_t>(Accessor(anchors), anchors_vector);
+        }
+        break;
+        case DataType::QASYMM8_SIGNED:
+        {
+            quantize_and_fill_tensor<int8_t>(Accessor(box_encoding), box_encoding_vector);
+            quantize_and_fill_tensor<int8_t>(Accessor(class_prediction), class_prediction_vector);
+            quantize_and_fill_tensor<int8_t>(Accessor(anchors), anchors_vector);
+        }
+        break;
+        default:
+            return;
     }
 
     // Determine the output through the CPP kernel
@@ -189,19 +215,22 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zi
                                                 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 
+                                                TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::QASYMM8), // Wrong score dimension
+                                                TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::QASYMM8_SIGNED)}), // 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)})),
+                                                TensorInfo(TensorShape(3U ,10U), 1, DataType::QASYMM8),
+                                                TensorInfo(TensorShape(3U ,10U), 1, DataType::QASYMM8_SIGNED)})),
         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)})),
+                                                TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::QASYMM8),
+                                                TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::QASYMM8_SIGNED)})),
         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),
@@ -383,6 +412,75 @@ TEST_CASE(Quantized_regular, framework::DatasetMode::ALL)
 
 TEST_SUITE_END() // QASYMM8
 
+TEST_SUITE(QASYMM8_SIGNED)
+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_SIGNED, 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_SIGNED, 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_SIGNED, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection, AbsoluteTolerance<float>(0.3f));
+}
+
+TEST_SUITE_END() // QASYMM8_SIGNED
+
 TEST_SUITE_END() // DetectionPostProcessLayer
 TEST_SUITE_END() // CPP
 } // namespace validation