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>
diff --git a/tests/validation/CPP/DetectionPostProcessLayer.cpp b/tests/validation/CPP/DetectionPostProcessLayer.cpp
index f4528fb..934ffea 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 @@
{
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(uint8_t) * quantized.size());
+ std::memcpy(tensor.data(), quantized.data(), sizeof(D) * quantized.size());
}
-inline QuantizationInfo qinfo_scaleoffset_from_minmax(const float min, const float max)
+template <typename T>
+inline int calc_qinfo(const float min, const float max, float &scale)
{
- 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;
+ 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));
+ }
+ return static_cast<int>(tmp_offset);
+}
+inline QuantizationInfo qinfo_scaleoffset_from_minmax(DataType data_type, const float min, const float max)
+{
+ 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)
+ if(data_type == DataType::QASYMM8_SIGNED)
{
- uint8_offset = qmin;
- }
- else if(f_offset > f_qmax)
- {
- 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 @@
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 @@
};
// 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 @@
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_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