IVGCVSW-2971 Support QSymm16 for DetectionPostProcess workloads
Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Change-Id: I8af45afe851a9ccbf8bce54727147fcd52ac9a1f
diff --git a/src/backends/backendsCommon/WorkloadData.cpp b/src/backends/backendsCommon/WorkloadData.cpp
index a373f55..d0aaf1d 100644
--- a/src/backends/backendsCommon/WorkloadData.cpp
+++ b/src/backends/backendsCommon/WorkloadData.cpp
@@ -1459,53 +1459,63 @@
void DetectionPostProcessQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const
{
- ValidateNumInputs(workloadInfo, "DetectionPostProcessQueueDescriptor", 2);
+ const std::string& descriptorName = " DetectionPostProcessQueueDescriptor";
+ ValidateNumInputs(workloadInfo, descriptorName, 2);
if (workloadInfo.m_OutputTensorInfos.size() != 4)
{
- throw InvalidArgumentException("DetectionPostProcessQueueDescriptor: Requires exactly four outputs. " +
+ throw InvalidArgumentException(descriptorName + ": Requires exactly four outputs. " +
to_string(workloadInfo.m_OutputTensorInfos.size()) + " has been provided.");
}
if (m_Anchors == nullptr)
{
- throw InvalidArgumentException("DetectionPostProcessQueueDescriptor: Anchors tensor descriptor is missing.");
+ throw InvalidArgumentException(descriptorName + ": Anchors tensor descriptor is missing.");
}
const TensorInfo& boxEncodingsInfo = workloadInfo.m_InputTensorInfos[0];
- const TensorInfo& scoresInfo = workloadInfo.m_InputTensorInfos[1];
- const TensorInfo& anchorsInfo = m_Anchors->GetTensorInfo();
- const TensorInfo& detectionBoxesInfo = workloadInfo.m_OutputTensorInfos[0];
+ const TensorInfo& scoresInfo = workloadInfo.m_InputTensorInfos[1];
+ const TensorInfo& anchorsInfo = m_Anchors->GetTensorInfo();
+
+ const TensorInfo& detectionBoxesInfo = workloadInfo.m_OutputTensorInfos[0];
const TensorInfo& detectionClassesInfo = workloadInfo.m_OutputTensorInfos[1];
- const TensorInfo& detectionScoresInfo = workloadInfo.m_OutputTensorInfos[2];
- const TensorInfo& numDetectionsInfo = workloadInfo.m_OutputTensorInfos[3];
+ const TensorInfo& detectionScoresInfo = workloadInfo.m_OutputTensorInfos[2];
+ const TensorInfo& numDetectionsInfo = workloadInfo.m_OutputTensorInfos[3];
- ValidateTensorNumDimensions(boxEncodingsInfo, "DetectionPostProcessQueueDescriptor", 3, "box encodings");
- ValidateTensorNumDimensions(scoresInfo, "DetectionPostProcessQueueDescriptor", 3, "scores");
- ValidateTensorNumDimensions(anchorsInfo, "DetectionPostProcessQueueDescriptor", 2, "anchors");
+ ValidateTensorNumDimensions(boxEncodingsInfo, descriptorName, 3, "box encodings");
+ ValidateTensorNumDimensions(scoresInfo, descriptorName, 3, "scores");
+ ValidateTensorNumDimensions(anchorsInfo, descriptorName, 2, "anchors");
- ValidateTensorNumDimensions(detectionBoxesInfo, "DetectionPostProcessQueueDescriptor", 3, "detection boxes");
- ValidateTensorNumDimensions(detectionScoresInfo, "DetectionPostProcessQueueDescriptor", 2, "detection scores");
- ValidateTensorNumDimensions(detectionClassesInfo, "DetectionPostProcessQueueDescriptor", 2, "detection classes");
- ValidateTensorNumDimensions(numDetectionsInfo, "DetectionPostProcessQueueDescriptor", 1, "num detections");
+ const std::vector<DataType> supportedInputTypes =
+ {
+ DataType::Float32,
+ DataType::QuantisedAsymm8,
+ DataType::QuantisedSymm16
+ };
- ValidateTensorDataType(detectionBoxesInfo, DataType::Float32,
- "DetectionPostProcessQueueDescriptor", "detection boxes");
- ValidateTensorDataType(detectionScoresInfo, DataType::Float32,
- "DetectionPostProcessQueueDescriptor", "detection scores");
- ValidateTensorDataType(detectionClassesInfo, DataType::Float32,
- "DetectionPostProcessQueueDescriptor", "detection classes");
- ValidateTensorDataType(numDetectionsInfo, DataType::Float32,
- "DetectionPostProcessQueueDescriptor", "num detections");
+ ValidateDataTypes(boxEncodingsInfo, supportedInputTypes, descriptorName);
+ ValidateDataTypes(scoresInfo, supportedInputTypes, descriptorName);
+ ValidateDataTypes(anchorsInfo, supportedInputTypes, descriptorName);
+
+ ValidateTensorNumDimensions(detectionBoxesInfo, descriptorName, 3, "detection boxes");
+ ValidateTensorNumDimensions(detectionScoresInfo, descriptorName, 2, "detection scores");
+ ValidateTensorNumDimensions(detectionClassesInfo, descriptorName, 2, "detection classes");
+ ValidateTensorNumDimensions(numDetectionsInfo, descriptorName, 1, "num detections");
+
+ // NOTE: Output is always Float32 regardless of input type
+ ValidateTensorDataType(detectionBoxesInfo, DataType::Float32, descriptorName, "detection boxes");
+ ValidateTensorDataType(detectionScoresInfo, DataType::Float32, descriptorName, "detection scores");
+ ValidateTensorDataType(detectionClassesInfo, DataType::Float32, descriptorName, "detection classes");
+ ValidateTensorDataType(numDetectionsInfo, DataType::Float32, descriptorName, "num detections");
if (m_Parameters.m_NmsIouThreshold <= 0.0f || m_Parameters.m_NmsIouThreshold > 1.0f)
{
- throw InvalidArgumentException("DetectionPostProcessQueueDescriptor: Intersection over union threshold "
+ throw InvalidArgumentException(descriptorName + ": Intersection over union threshold "
"must be positive and less than or equal to 1.");
}
if (scoresInfo.GetShape()[2] != m_Parameters.m_NumClasses + 1)
{
- throw InvalidArgumentException("DetectionPostProcessQueueDescriptor: Number of classes with background "
+ throw InvalidArgumentException(descriptorName + ": Number of classes with background "
"should be equal to number of classes + 1.");
}
}
diff --git a/src/backends/backendsCommon/test/DetectionPostProcessLayerTestImpl.hpp b/src/backends/backendsCommon/test/DetectionPostProcessLayerTestImpl.hpp
index 092ce26..2726fde 100644
--- a/src/backends/backendsCommon/test/DetectionPostProcessLayerTestImpl.hpp
+++ b/src/backends/backendsCommon/test/DetectionPostProcessLayerTestImpl.hpp
@@ -15,7 +15,124 @@
#include <backendsCommon/test/WorkloadFactoryHelper.hpp>
#include <test/TensorHelpers.hpp>
-template <typename FactoryType, armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+namespace
+{
+
+using FloatData = std::vector<float>;
+using QuantData = std::pair<float, int32_t>;
+
+struct TestData
+{
+ static const armnn::TensorShape s_BoxEncodingsShape;
+ static const armnn::TensorShape s_ScoresShape;
+ static const armnn::TensorShape s_AnchorsShape;
+
+ static const QuantData s_BoxEncodingsQuantData;
+ static const QuantData s_ScoresQuantData;
+ static const QuantData s_AnchorsQuantData;
+
+ static const FloatData s_BoxEncodings;
+ static const FloatData s_Scores;
+ static const FloatData s_Anchors;
+};
+
+struct RegularNmsExpectedResults
+{
+ static const FloatData s_DetectionBoxes;
+ static const FloatData s_DetectionScores;
+ static const FloatData s_DetectionClasses;
+ static const FloatData s_NumDetections;
+};
+
+struct FastNmsExpectedResults
+{
+ static const FloatData s_DetectionBoxes;
+ static const FloatData s_DetectionScores;
+ static const FloatData s_DetectionClasses;
+ static const FloatData s_NumDetections;
+};
+
+const armnn::TensorShape TestData::s_BoxEncodingsShape = { 1, 6, 4 };
+const armnn::TensorShape TestData::s_ScoresShape = { 1, 6, 3 };
+const armnn::TensorShape TestData::s_AnchorsShape = { 6, 4 };
+
+const QuantData TestData::s_BoxEncodingsQuantData = { 1.00f, 1 };
+const QuantData TestData::s_ScoresQuantData = { 0.01f, 0 };
+const QuantData TestData::s_AnchorsQuantData = { 0.50f, 0 };
+
+const FloatData TestData::s_BoxEncodings =
+{
+ 0.0f, 0.0f, 0.0f, 0.0f,
+ 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, 1.0f, 0.0f, 0.0f,
+ 0.0f, 0.0f, 0.0f, 0.0f
+};
+
+const FloatData TestData::s_Scores =
+{
+ 0.0f, 0.90f, 0.80f,
+ 0.0f, 0.75f, 0.72f,
+ 0.0f, 0.60f, 0.50f,
+ 0.0f, 0.93f, 0.95f,
+ 0.0f, 0.50f, 0.40f,
+ 0.0f, 0.30f, 0.20f
+};
+
+const FloatData TestData::s_Anchors =
+{
+ 0.5f, 0.5f, 1.0f, 1.0f,
+ 0.5f, 0.5f, 1.0f, 1.0f,
+ 0.5f, 0.5f, 1.0f, 1.0f,
+ 0.5f, 10.5f, 1.0f, 1.0f,
+ 0.5f, 10.5f, 1.0f, 1.0f,
+ 0.5f, 100.5f, 1.0f, 1.0f
+};
+
+const FloatData RegularNmsExpectedResults::s_DetectionBoxes =
+{
+ 0.0f, 10.0f, 1.0f, 11.0f,
+ 0.0f, 10.0f, 1.0f, 11.0f,
+ 0.0f, 0.0f, 0.0f, 0.0f
+};
+
+const FloatData RegularNmsExpectedResults::s_DetectionScores =
+{
+ 0.95f, 0.93f, 0.0f
+};
+
+const FloatData RegularNmsExpectedResults::s_DetectionClasses =
+{
+ 1.0f, 0.0f, 0.0f
+};
+
+const FloatData RegularNmsExpectedResults::s_NumDetections = { 2.0f };
+
+const FloatData FastNmsExpectedResults::s_DetectionBoxes =
+{
+ 0.0f, 10.0f, 1.0f, 11.0f,
+ 0.0f, 0.0f, 1.0f, 1.0f,
+ 0.0f, 100.0f, 1.0f, 101.0f
+};
+
+const FloatData FastNmsExpectedResults::s_DetectionScores =
+{
+ 0.95f, 0.9f, 0.3f
+};
+
+const FloatData FastNmsExpectedResults::s_DetectionClasses =
+{
+ 1.0f, 0.0f, 0.0f
+};
+
+const FloatData FastNmsExpectedResults::s_NumDetections = { 3.0f };
+
+} // anonymous namespace
+
+template<typename FactoryType,
+ armnn::DataType ArmnnType,
+ typename T = armnn::ResolveType<ArmnnType>>
void DetectionPostProcessImpl(const armnn::TensorInfo& boxEncodingsInfo,
const armnn::TensorInfo& scoresInfo,
const armnn::TensorInfo& anchorsInfo,
@@ -110,254 +227,140 @@
BOOST_TEST(CompareTensors(numDetectionsResult.output, numDetectionsResult.outputExpected));
}
-inline void QuantizeData(uint8_t* quant, const float* dequant, const armnn::TensorInfo& info)
+template<armnn::DataType QuantizedType, typename RawType = armnn::ResolveType<QuantizedType>>
+void QuantizeData(RawType* quant, const float* dequant, const armnn::TensorInfo& info)
{
for (size_t i = 0; i < info.GetNumElements(); i++)
{
- quant[i] = armnn::Quantize<uint8_t>(dequant[i], info.GetQuantizationScale(), info.GetQuantizationOffset());
+ quant[i] = armnn::Quantize<RawType>(
+ dequant[i], info.GetQuantizationScale(), info.GetQuantizationOffset());
}
}
-template <typename FactoryType>
+template<typename FactoryType>
void DetectionPostProcessRegularNmsFloatTest()
{
- armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::Float32);
- armnn::TensorInfo scoresInfo({ 1, 6, 3}, armnn::DataType::Float32);
- armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32);
-
- std::vector<float> boxEncodingsData({
- 0.0f, 0.0f, 0.0f, 0.0f,
- 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, 1.0f, 0.0f, 0.0f,
- 0.0f, 0.0f, 0.0f, 0.0f
- });
- std::vector<float> scoresData({
- 0.0f, 0.9f, 0.8f,
- 0.0f, 0.75f, 0.72f,
- 0.0f, 0.6f, 0.5f,
- 0.0f, 0.93f, 0.95f,
- 0.0f, 0.5f, 0.4f,
- 0.0f, 0.3f, 0.2f
- });
- std::vector<float> anchorsData({
- 0.5f, 0.5f, 1.0f, 1.0f,
- 0.5f, 0.5f, 1.0f, 1.0f,
- 0.5f, 0.5f, 1.0f, 1.0f,
- 0.5f, 10.5f, 1.0f, 1.0f,
- 0.5f, 10.5f, 1.0f, 1.0f,
- 0.5f, 100.5f, 1.0f, 1.0f
- });
-
- std::vector<float> expectedDetectionBoxes({
- 0.0f, 10.0f, 1.0f, 11.0f,
- 0.0f, 10.0f, 1.0f, 11.0f,
- 0.0f, 0.0f, 0.0f, 0.0f
- });
- std::vector<float> expectedDetectionScores({ 0.95f, 0.93f, 0.0f });
- std::vector<float> expectedDetectionClasses({ 1.0f, 0.0f, 0.0f });
- std::vector<float> expectedNumDetections({ 2.0f });
-
- return DetectionPostProcessImpl<FactoryType, armnn::DataType::Float32>(boxEncodingsInfo,
- scoresInfo,
- anchorsInfo,
- boxEncodingsData,
- scoresData,
- anchorsData,
- expectedDetectionBoxes,
- expectedDetectionClasses,
- expectedDetectionScores,
- expectedNumDetections,
- true);
+ return DetectionPostProcessImpl<FactoryType, armnn::DataType::Float32>(
+ armnn::TensorInfo(TestData::s_BoxEncodingsShape, armnn::DataType::Float32),
+ armnn::TensorInfo(TestData::s_ScoresShape, armnn::DataType::Float32),
+ armnn::TensorInfo(TestData::s_AnchorsShape, armnn::DataType::Float32),
+ TestData::s_BoxEncodings,
+ TestData::s_Scores,
+ TestData::s_Anchors,
+ RegularNmsExpectedResults::s_DetectionBoxes,
+ RegularNmsExpectedResults::s_DetectionClasses,
+ RegularNmsExpectedResults::s_DetectionScores,
+ RegularNmsExpectedResults::s_NumDetections,
+ true);
}
-template <typename FactoryType>
-void DetectionPostProcessRegularNmsUint8Test()
+template<typename FactoryType,
+ armnn::DataType QuantizedType,
+ typename RawType = armnn::ResolveType<QuantizedType>>
+void DetectionPostProcessRegularNmsQuantizedTest()
{
- armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::QuantisedAsymm8);
- armnn::TensorInfo scoresInfo({ 1, 6, 3 }, armnn::DataType::QuantisedAsymm8);
- armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::QuantisedAsymm8);
+ armnn::TensorInfo boxEncodingsInfo(TestData::s_BoxEncodingsShape, QuantizedType);
+ armnn::TensorInfo scoresInfo(TestData::s_ScoresShape, QuantizedType);
+ armnn::TensorInfo anchorsInfo(TestData::s_AnchorsShape, QuantizedType);
- boxEncodingsInfo.SetQuantizationScale(1.0f);
- boxEncodingsInfo.SetQuantizationOffset(1);
- scoresInfo.SetQuantizationScale(0.01f);
- scoresInfo.SetQuantizationOffset(0);
- anchorsInfo.SetQuantizationScale(0.5f);
- anchorsInfo.SetQuantizationOffset(0);
+ boxEncodingsInfo.SetQuantizationScale(TestData::s_BoxEncodingsQuantData.first);
+ boxEncodingsInfo.SetQuantizationOffset(TestData::s_BoxEncodingsQuantData.second);
- std::vector<float> boxEncodings({
- 0.0f, 0.0f, 0.0f, 0.0f,
- 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, 1.0f, 0.0f, 0.0f,
- 0.0f, 0.0f, 0.0f, 0.0f
- });
- std::vector<float> scores({
- 0.0f, 0.9f, 0.8f,
- 0.0f, 0.75f, 0.72f,
- 0.0f, 0.6f, 0.5f,
- 0.0f, 0.93f, 0.95f,
- 0.0f, 0.5f, 0.4f,
- 0.0f, 0.3f, 0.2f
- });
- std::vector<float> anchors({
- 0.5f, 0.5f, 1.0f, 1.0f,
- 0.5f, 0.5f, 1.0f, 1.0f,
- 0.5f, 0.5f, 1.0f, 1.0f,
- 0.5f, 10.5f, 1.0f, 1.0f,
- 0.5f, 10.5f, 1.0f, 1.0f,
- 0.5f, 100.5f, 1.0f, 1.0f
- });
+ scoresInfo.SetQuantizationScale(TestData::s_ScoresQuantData.first);
+ scoresInfo.SetQuantizationOffset(TestData::s_ScoresQuantData.second);
- std::vector<uint8_t> boxEncodingsData(boxEncodings.size(), 0);
- std::vector<uint8_t> scoresData(scores.size(), 0);
- std::vector<uint8_t> anchorsData(anchors.size(), 0);
- QuantizeData(boxEncodingsData.data(), boxEncodings.data(), boxEncodingsInfo);
- QuantizeData(scoresData.data(), scores.data(), scoresInfo);
- QuantizeData(anchorsData.data(), anchors.data(), anchorsInfo);
+ anchorsInfo.SetQuantizationScale(TestData::s_AnchorsQuantData.first);
+ anchorsInfo.SetQuantizationOffset(TestData::s_BoxEncodingsQuantData.second);
- std::vector<float> expectedDetectionBoxes({
- 0.0f, 10.0f, 1.0f, 11.0f,
- 0.0f, 10.0f, 1.0f, 11.0f,
- 0.0f, 0.0f, 0.0f, 0.0f
- });
- std::vector<float> expectedDetectionScores({ 0.95f, 0.93f, 0.0f });
- std::vector<float> expectedDetectionClasses({ 1.0f, 0.0f, 0.0f });
- std::vector<float> expectedNumDetections({ 2.0f });
+ std::vector<RawType> boxEncodingsData(TestData::s_BoxEncodingsShape.GetNumElements());
+ QuantizeData<QuantizedType>(boxEncodingsData.data(),
+ TestData::s_BoxEncodings.data(),
+ boxEncodingsInfo);
- return DetectionPostProcessImpl<FactoryType, armnn::DataType::QuantisedAsymm8>(boxEncodingsInfo,
- scoresInfo,
- anchorsInfo,
- boxEncodingsData,
- scoresData,
- anchorsData,
- expectedDetectionBoxes,
- expectedDetectionClasses,
- expectedDetectionScores,
- expectedNumDetections,
- true);
+ std::vector<RawType> scoresData(TestData::s_ScoresShape.GetNumElements());
+ QuantizeData<QuantizedType>(scoresData.data(),
+ TestData::s_Scores.data(),
+ scoresInfo);
+
+ std::vector<RawType> anchorsData(TestData::s_AnchorsShape.GetNumElements());
+ QuantizeData<QuantizedType>(anchorsData.data(),
+ TestData::s_Anchors.data(),
+ anchorsInfo);
+
+ return DetectionPostProcessImpl<FactoryType, QuantizedType>(
+ boxEncodingsInfo,
+ scoresInfo,
+ anchorsInfo,
+ boxEncodingsData,
+ scoresData,
+ anchorsData,
+ RegularNmsExpectedResults::s_DetectionBoxes,
+ RegularNmsExpectedResults::s_DetectionClasses,
+ RegularNmsExpectedResults::s_DetectionScores,
+ RegularNmsExpectedResults::s_NumDetections,
+ true);
}
-template <typename FactoryType>
+template<typename FactoryType>
void DetectionPostProcessFastNmsFloatTest()
{
- armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::Float32);
- armnn::TensorInfo scoresInfo({ 1, 6, 3}, armnn::DataType::Float32);
- armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32);
-
- std::vector<float> boxEncodingsData({
- 0.0f, 0.0f, 0.0f, 0.0f,
- 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, 1.0f, 0.0f, 0.0f,
- 0.0f, 0.0f, 0.0f, 0.0f
- });
- std::vector<float> scoresData({
- 0.0f, 0.9f, 0.8f,
- 0.0f, 0.75f, 0.72f,
- 0.0f, 0.6f, 0.5f,
- 0.0f, 0.93f, 0.95f,
- 0.0f, 0.5f, 0.4f,
- 0.0f, 0.3f, 0.2f
- });
- std::vector<float> anchorsData({
- 0.5f, 0.5f, 1.0f, 1.0f,
- 0.5f, 0.5f, 1.0f, 1.0f,
- 0.5f, 0.5f, 1.0f, 1.0f,
- 0.5f, 10.5f, 1.0f, 1.0f,
- 0.5f, 10.5f, 1.0f, 1.0f,
- 0.5f, 100.5f, 1.0f, 1.0f
- });
-
- std::vector<float> expectedDetectionBoxes({
- 0.0f, 10.0f, 1.0f, 11.0f,
- 0.0f, 0.0f, 1.0f, 1.0f,
- 0.0f, 100.0f, 1.0f, 101.0f
- });
- std::vector<float> expectedDetectionScores({ 0.95f, 0.9f, 0.3f });
- std::vector<float> expectedDetectionClasses({ 1.0f, 0.0f, 0.0f });
- std::vector<float> expectedNumDetections({ 3.0f });
-
- return DetectionPostProcessImpl<FactoryType, armnn::DataType::Float32>(boxEncodingsInfo,
- scoresInfo,
- anchorsInfo,
- boxEncodingsData,
- scoresData,
- anchorsData,
- expectedDetectionBoxes,
- expectedDetectionClasses,
- expectedDetectionScores,
- expectedNumDetections,
- false);
+ return DetectionPostProcessImpl<FactoryType, armnn::DataType::Float32>(
+ armnn::TensorInfo(TestData::s_BoxEncodingsShape, armnn::DataType::Float32),
+ armnn::TensorInfo(TestData::s_ScoresShape, armnn::DataType::Float32),
+ armnn::TensorInfo(TestData::s_AnchorsShape, armnn::DataType::Float32),
+ TestData::s_BoxEncodings,
+ TestData::s_Scores,
+ TestData::s_Anchors,
+ FastNmsExpectedResults::s_DetectionBoxes,
+ FastNmsExpectedResults::s_DetectionClasses,
+ FastNmsExpectedResults::s_DetectionScores,
+ FastNmsExpectedResults::s_NumDetections,
+ false);
}
-template <typename FactoryType>
-void DetectionPostProcessFastNmsUint8Test()
+template<typename FactoryType,
+ armnn::DataType QuantizedType,
+ typename RawType = armnn::ResolveType<QuantizedType>>
+void DetectionPostProcessFastNmsQuantizedTest()
{
- armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::QuantisedAsymm8);
- armnn::TensorInfo scoresInfo({ 1, 6, 3 }, armnn::DataType::QuantisedAsymm8);
- armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::QuantisedAsymm8);
+ armnn::TensorInfo boxEncodingsInfo(TestData::s_BoxEncodingsShape, QuantizedType);
+ armnn::TensorInfo scoresInfo(TestData::s_ScoresShape, QuantizedType);
+ armnn::TensorInfo anchorsInfo(TestData::s_AnchorsShape, QuantizedType);
- boxEncodingsInfo.SetQuantizationScale(1.0f);
- boxEncodingsInfo.SetQuantizationOffset(1);
- scoresInfo.SetQuantizationScale(0.01f);
- scoresInfo.SetQuantizationOffset(0);
- anchorsInfo.SetQuantizationScale(0.5f);
- anchorsInfo.SetQuantizationOffset(0);
+ boxEncodingsInfo.SetQuantizationScale(TestData::s_BoxEncodingsQuantData.first);
+ boxEncodingsInfo.SetQuantizationOffset(TestData::s_BoxEncodingsQuantData.second);
- std::vector<float> boxEncodings({
- 0.0f, 0.0f, 0.0f, 0.0f,
- 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, 1.0f, 0.0f, 0.0f,
- 0.0f, 0.0f, 0.0f, 0.0f
- });
- std::vector<float> scores({
- 0.0f, 0.9f, 0.8f,
- 0.0f, 0.75f, 0.72f,
- 0.0f, 0.6f, 0.5f,
- 0.0f, 0.93f, 0.95f,
- 0.0f, 0.5f, 0.4f,
- 0.0f, 0.3f, 0.2f
- });
- std::vector<float> anchors({
- 0.5f, 0.5f, 1.0f, 1.0f,
- 0.5f, 0.5f, 1.0f, 1.0f,
- 0.5f, 0.5f, 1.0f, 1.0f,
- 0.5f, 10.5f, 1.0f, 1.0f,
- 0.5f, 10.5f, 1.0f, 1.0f,
- 0.5f, 100.5f, 1.0f, 1.0f
- });
+ scoresInfo.SetQuantizationScale(TestData::s_ScoresQuantData.first);
+ scoresInfo.SetQuantizationOffset(TestData::s_ScoresQuantData.second);
- std::vector<uint8_t> boxEncodingsData(boxEncodings.size(), 0);
- std::vector<uint8_t> scoresData(scores.size(), 0);
- std::vector<uint8_t> anchorsData(anchors.size(), 0);
- QuantizeData(boxEncodingsData.data(), boxEncodings.data(), boxEncodingsInfo);
- QuantizeData(scoresData.data(), scores.data(), scoresInfo);
- QuantizeData(anchorsData.data(), anchors.data(), anchorsInfo);
+ anchorsInfo.SetQuantizationScale(TestData::s_AnchorsQuantData.first);
+ anchorsInfo.SetQuantizationOffset(TestData::s_BoxEncodingsQuantData.second);
- std::vector<float> expectedDetectionBoxes({
- 0.0f, 10.0f, 1.0f, 11.0f,
- 0.0f, 0.0f, 1.0f, 1.0f,
- 0.0f, 100.0f, 1.0f, 101.0f
- });
- std::vector<float> expectedDetectionScores({ 0.95f, 0.9f, 0.3f });
- std::vector<float> expectedDetectionClasses({ 1.0f, 0.0f, 0.0f });
- std::vector<float> expectedNumDetections({ 3.0f });
+ std::vector<RawType> boxEncodingsData(TestData::s_BoxEncodingsShape.GetNumElements());
+ QuantizeData<QuantizedType>(boxEncodingsData.data(),
+ TestData::s_BoxEncodings.data(),
+ boxEncodingsInfo);
- return DetectionPostProcessImpl<FactoryType, armnn::DataType::QuantisedAsymm8>(boxEncodingsInfo,
- scoresInfo,
- anchorsInfo,
- boxEncodingsData,
- scoresData,
- anchorsData,
- expectedDetectionBoxes,
- expectedDetectionClasses,
- expectedDetectionScores,
- expectedNumDetections,
- false);
-}
+ std::vector<RawType> scoresData(TestData::s_ScoresShape.GetNumElements());
+ QuantizeData<QuantizedType>(scoresData.data(),
+ TestData::s_Scores.data(),
+ scoresInfo);
+
+ std::vector<RawType> anchorsData(TestData::s_AnchorsShape.GetNumElements());
+ QuantizeData<QuantizedType>(anchorsData.data(),
+ TestData::s_Anchors.data(),
+ anchorsInfo);
+
+ return DetectionPostProcessImpl<FactoryType, QuantizedType>(
+ boxEncodingsInfo,
+ scoresInfo,
+ anchorsInfo,
+ boxEncodingsData,
+ scoresData,
+ anchorsData,
+ FastNmsExpectedResults::s_DetectionBoxes,
+ FastNmsExpectedResults::s_DetectionClasses,
+ FastNmsExpectedResults::s_DetectionScores,
+ FastNmsExpectedResults::s_NumDetections,
+ false);
+}
\ No newline at end of file