IVGCVSW-1988: Refactor backend-specific unit tests
Change-Id: I8eca81d2e0780390eaa837c186ffe1c7d41fdebe
diff --git a/src/backends/reference/test/RefCreateWorkloadTests.cpp b/src/backends/reference/test/RefCreateWorkloadTests.cpp
new file mode 100644
index 0000000..e88fbed
--- /dev/null
+++ b/src/backends/reference/test/RefCreateWorkloadTests.cpp
@@ -0,0 +1,484 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <armnn/test/CreateWorkload.hpp>
+
+#include <backends/CpuTensorHandle.hpp>
+#include <backends/reference/RefWorkloadFactory.hpp>
+#include <backends/reference/workloads/RefWorkloads.hpp>
+
+namespace
+{
+
+template<typename Workload>
+void CheckInputOutput(std::unique_ptr<Workload> workload, const TensorInfo& inputInfo, const TensorInfo& outputInfo)
+{
+ auto queueDescriptor = workload->GetData();
+ auto inputHandle = boost::polymorphic_downcast<ConstCpuTensorHandle*>(queueDescriptor.m_Inputs[0]);
+ auto outputHandle = boost::polymorphic_downcast<CpuTensorHandle*>(queueDescriptor.m_Outputs[0]);
+ BOOST_TEST((inputHandle->GetTensorInfo() == inputInfo));
+ BOOST_TEST((outputHandle->GetTensorInfo() == outputInfo));
+}
+
+template <typename Workload>
+void CheckInputsOutput(std::unique_ptr<Workload> workload,
+ const TensorInfo& inputInfo0,
+ const TensorInfo& inputInfo1,
+ const TensorInfo& outputInfo)
+{
+ auto queueDescriptor = workload->GetData();
+ auto inputHandle0 = boost::polymorphic_downcast<ConstCpuTensorHandle*>(queueDescriptor.m_Inputs[0]);
+ auto inputHandle1 = boost::polymorphic_downcast<ConstCpuTensorHandle*>(queueDescriptor.m_Inputs[1]);
+ auto outputHandle = boost::polymorphic_downcast<CpuTensorHandle*>(queueDescriptor.m_Outputs[0]);
+ BOOST_TEST((inputHandle0->GetTensorInfo() == inputInfo0));
+ BOOST_TEST((inputHandle1->GetTensorInfo() == inputInfo1));
+ BOOST_TEST((outputHandle->GetTensorInfo() == outputInfo));
+}
+}
+
+BOOST_AUTO_TEST_SUITE(CreateWorkloadRef)
+
+template <typename ActivationWorkloadType, armnn::DataType DataType>
+static void RefCreateActivationWorkloadTest()
+{
+ Graph graph;
+ RefWorkloadFactory factory;
+ auto workload = CreateActivationWorkloadTest<ActivationWorkloadType, DataType>(factory, graph);
+
+ // Checks that outputs are as we expect them (see definition of CreateActivationWorkloadTest).
+ CheckInputOutput(std::move(workload),
+ TensorInfo({ 1, 1 }, DataType),
+ TensorInfo({ 1, 1 }, DataType));
+}
+
+BOOST_AUTO_TEST_CASE(CreateActivationFloat32Workload)
+{
+ RefCreateActivationWorkloadTest<RefActivationFloat32Workload, armnn::DataType::Float32>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateActivationUint8Workload)
+{
+ RefCreateActivationWorkloadTest<RefActivationUint8Workload, armnn::DataType::QuantisedAsymm8>();
+}
+
+template <typename WorkloadType,
+ typename DescriptorType,
+ typename LayerType,
+ armnn::DataType DataType>
+static void RefCreateArithmethicWorkloadTest()
+{
+ Graph graph;
+ RefWorkloadFactory factory;
+ auto workload = CreateArithmeticWorkloadTest<WorkloadType, DescriptorType, LayerType, DataType>(factory, graph);
+
+ CheckInputsOutput(std::move(workload),
+ TensorInfo({ 2, 3 }, DataType),
+ TensorInfo({ 2, 3 }, DataType),
+ TensorInfo({ 2, 3 }, DataType));
+}
+
+BOOST_AUTO_TEST_CASE(CreateAdditionFloatWorkload)
+{
+ RefCreateArithmethicWorkloadTest<RefAdditionFloat32Workload,
+ AdditionQueueDescriptor,
+ AdditionLayer,
+ armnn::DataType::Float32>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateAdditionUint8Workload)
+{
+ RefCreateArithmethicWorkloadTest<RefAdditionUint8Workload,
+ AdditionQueueDescriptor,
+ AdditionLayer,
+ armnn::DataType::QuantisedAsymm8>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateSubtractionFloatWorkload)
+{
+ RefCreateArithmethicWorkloadTest<RefSubtractionFloat32Workload,
+ SubtractionQueueDescriptor,
+ SubtractionLayer,
+ armnn::DataType::Float32>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateSubtractionUint8Workload)
+{
+ RefCreateArithmethicWorkloadTest<RefSubtractionUint8Workload,
+ SubtractionQueueDescriptor,
+ SubtractionLayer,
+ armnn::DataType::QuantisedAsymm8>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateMultiplicationFloatWorkload)
+{
+ RefCreateArithmethicWorkloadTest<RefMultiplicationFloat32Workload,
+ MultiplicationQueueDescriptor,
+ MultiplicationLayer,
+ armnn::DataType::Float32>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateMultiplicationUint8Workload)
+{
+ RefCreateArithmethicWorkloadTest<RefMultiplicationUint8Workload,
+ MultiplicationQueueDescriptor,
+ MultiplicationLayer,
+ armnn::DataType::QuantisedAsymm8>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateDivisionFloatWorkload)
+{
+ RefCreateArithmethicWorkloadTest<RefDivisionFloat32Workload,
+ DivisionQueueDescriptor,
+ DivisionLayer,
+ armnn::DataType::Float32>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateDivisionUint8Workload)
+{
+ RefCreateArithmethicWorkloadTest<RefDivisionUint8Workload,
+ DivisionQueueDescriptor,
+ DivisionLayer,
+ armnn::DataType::QuantisedAsymm8>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateBatchNormalizationWorkload)
+{
+ Graph graph;
+ RefWorkloadFactory factory;
+ auto workload = CreateBatchNormalizationWorkloadTest<RefBatchNormalizationFloat32Workload, armnn::DataType::Float32>
+ (factory, graph);
+
+ // Checks that outputs and inputs are as we expect them (see definition of CreateBatchNormalizationWorkloadTest).
+ CheckInputOutput(
+ std::move(workload), TensorInfo({2, 3, 1, 1}, DataType::Float32), TensorInfo({2, 3, 1, 1}, DataType::Float32));
+}
+
+BOOST_AUTO_TEST_CASE(CreateConvertFp16ToFp32Float32Workload)
+{
+ Graph graph;
+ RefWorkloadFactory factory;
+ auto workload = CreateConvertFp16ToFp32WorkloadTest<RefConvertFp16ToFp32Workload>(factory, graph);
+
+ // Checks that outputs and inputs are as we expect them
+ CheckInputOutput(
+ std::move(workload), TensorInfo({1, 3, 2, 3}, DataType::Float16), TensorInfo({1, 3, 2, 3}, DataType::Float32));
+}
+
+BOOST_AUTO_TEST_CASE(CreateConvertFp32ToFp16Float16Workload)
+{
+ Graph graph;
+ RefWorkloadFactory factory;
+ auto workload = CreateConvertFp32ToFp16WorkloadTest<RefConvertFp32ToFp16Workload>(factory, graph);
+
+ // Checks that outputs and inputs are as we expect them
+ CheckInputOutput(
+ std::move(workload), TensorInfo({1, 3, 2, 3}, DataType::Float32), TensorInfo({1, 3, 2, 3}, DataType::Float16));
+}
+
+BOOST_AUTO_TEST_CASE(CreateConvolution2dWorkload)
+{
+ Graph graph;
+ RefWorkloadFactory factory;
+ auto workload = CreateConvolution2dWorkloadTest<RefConvolution2dFloat32Workload,
+ DataType::Float32>(factory, graph);
+
+ // Checks that outputs and inputs are as we expect them (see definition of CreateConvolution2dWorkloadTest).
+ CheckInputOutput(std::move(workload),
+ TensorInfo({2, 3, 8, 16}, DataType::Float32),
+ TensorInfo({2, 2, 2, 10}, DataType::Float32));
+}
+
+BOOST_AUTO_TEST_CASE(CreateDepthwiseConvolution2dWorkload)
+{
+ Graph graph;
+ RefWorkloadFactory factory;
+ auto workload =
+ CreateDepthwiseConvolution2dWorkloadTest<RefDepthwiseConvolution2dFloat32Workload>(factory, graph);
+
+ // Checks that outputs and inputs are as we expect them (see definition of CreateConvolution2dWorkloadTest).
+ CheckInputOutput(std::move(workload),
+ TensorInfo({2, 3, 8, 16}, DataType::Float32),
+ TensorInfo({2, 9, 2, 10}, DataType::Float32));
+}
+
+template <typename FullyConnectedWorkloadType, armnn::DataType DataType>
+static void RefCreateFullyConnectedWorkloadTest()
+{
+ Graph graph;
+ RefWorkloadFactory factory;
+ auto workload = CreateFullyConnectedWorkloadTest<FullyConnectedWorkloadType, DataType>(factory, graph);
+
+ // Checks that outputs and inputs are as we expect them (see definition of CreateFullyConnectedWorkloadTest).
+ float inputsQScale = DataType == armnn::DataType::QuantisedAsymm8 ? 1.0f : 0.0;
+ float outputQScale = DataType == armnn::DataType::QuantisedAsymm8 ? 2.0f : 0.0;
+ CheckInputOutput(std::move(workload),
+ TensorInfo({ 3, 1, 4, 5 }, DataType, inputsQScale),
+ TensorInfo({ 3, 7 }, DataType, outputQScale));
+}
+
+BOOST_AUTO_TEST_CASE(CreateFullyConnectedFloat32Workload)
+{
+ RefCreateFullyConnectedWorkloadTest<RefFullyConnectedFloat32Workload, armnn::DataType::Float32>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateFullyConnectedUint8Workload)
+{
+ RefCreateFullyConnectedWorkloadTest<RefFullyConnectedUint8Workload, armnn::DataType::QuantisedAsymm8>();
+}
+
+template <typename NormalizationWorkloadType, armnn::DataType DataType>
+static void RefCreateNormalizationWorkloadTest()
+{
+ Graph graph;
+ RefWorkloadFactory factory;
+ auto workload = CreateNormalizationWorkloadTest<NormalizationWorkloadType, DataType>(factory, graph);
+
+ // Checks that outputs and inputs are as we expect them (see definition of CreateNormalizationWorkloadTest).
+ CheckInputOutput(std::move(workload),
+ TensorInfo({3, 5, 5, 1}, DataType),
+ TensorInfo({3, 5, 5, 1}, DataType));
+}
+
+BOOST_AUTO_TEST_CASE(CreateRefNormalizationNchwWorkload)
+{
+ RefCreateNormalizationWorkloadTest<RefNormalizationFloat32Workload, armnn::DataType::Float32>();
+}
+
+template <typename Pooling2dWorkloadType, armnn::DataType DataType>
+static void RefCreatePooling2dWorkloadTest()
+{
+ Graph graph;
+ RefWorkloadFactory factory;
+ auto workload = CreatePooling2dWorkloadTest<Pooling2dWorkloadType, DataType>(factory, graph);
+
+ // Checks that outputs and inputs are as we expect them (see definition of CreatePooling2dWorkloadTest).
+ CheckInputOutput(
+ std::move(workload),
+ TensorInfo({3, 2, 5, 5}, DataType),
+ TensorInfo({3, 2, 2, 4}, DataType));
+}
+
+BOOST_AUTO_TEST_CASE(CreatePooling2dFloat32Workload)
+{
+ RefCreatePooling2dWorkloadTest<RefPooling2dFloat32Workload, armnn::DataType::Float32>();
+}
+
+BOOST_AUTO_TEST_CASE(CreatePooling2dUint8Workload)
+{
+ RefCreatePooling2dWorkloadTest<RefPooling2dUint8Workload, armnn::DataType::QuantisedAsymm8>();
+}
+
+template <typename SoftmaxWorkloadType, armnn::DataType DataType>
+static void RefCreateSoftmaxWorkloadTest()
+{
+ Graph graph;
+ RefWorkloadFactory factory;
+ auto workload = CreateSoftmaxWorkloadTest<SoftmaxWorkloadType, DataType>(factory, graph);
+
+ // Checks that outputs and inputs are as we expect them (see definition of CreateSoftmaxWorkloadTest).
+ CheckInputOutput(
+ std::move(workload),
+ TensorInfo({4, 1}, DataType),
+ TensorInfo({4, 1}, DataType));
+}
+
+BOOST_AUTO_TEST_CASE(CreateSoftmaxFloat32Workload)
+{
+ RefCreateSoftmaxWorkloadTest<RefSoftmaxFloat32Workload, armnn::DataType::Float32>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateSoftmaxUint8Workload)
+{
+ RefCreateSoftmaxWorkloadTest<RefSoftmaxUint8Workload, armnn::DataType::QuantisedAsymm8>();
+}
+
+template <typename SplitterWorkloadType, armnn::DataType DataType>
+static void RefCreateSplitterWorkloadTest()
+{
+ Graph graph;
+ RefWorkloadFactory factory;
+ auto workload = CreateSplitterWorkloadTest<SplitterWorkloadType, DataType>(factory, graph);
+
+ // Checks that outputs are as we expect them (see definition of CreateSplitterWorkloadTest).
+ SplitterQueueDescriptor queueDescriptor = workload->GetData();
+ auto inputHandle = boost::polymorphic_downcast<ConstCpuTensorHandle*>(queueDescriptor.m_Inputs[0]);
+ BOOST_TEST((inputHandle->GetTensorInfo() == TensorInfo({ 5, 7, 7 }, DataType)));
+
+ auto outputHandle0 = boost::polymorphic_downcast<CpuTensorHandle*>(queueDescriptor.m_Outputs[0]);
+ BOOST_TEST((outputHandle0->GetTensorInfo() == TensorInfo({ 1, 7, 7 }, DataType)));
+
+ auto outputHandle1 = boost::polymorphic_downcast<CpuTensorHandle*>(queueDescriptor.m_Outputs[1]);
+ BOOST_TEST((outputHandle1->GetTensorInfo() == TensorInfo({ 2, 7, 7 }, DataType)));
+
+ auto outputHandle2 = boost::polymorphic_downcast<CpuTensorHandle*>(queueDescriptor.m_Outputs[2]);
+ BOOST_TEST((outputHandle2->GetTensorInfo() == TensorInfo({ 2, 7, 7 }, DataType)));
+}
+
+BOOST_AUTO_TEST_CASE(CreateSplitterFloat32Workload)
+{
+ RefCreateSplitterWorkloadTest<RefSplitterFloat32Workload, armnn::DataType::Float32>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateSplitterUint8Workload)
+{
+ RefCreateSplitterWorkloadTest<RefSplitterUint8Workload, armnn::DataType::QuantisedAsymm8>();
+}
+
+template <typename SplitterWorkloadType, typename MergerWorkloadType, armnn::DataType DataType>
+static void RefCreateSplitterMergerWorkloadTest()
+{
+ // Tests that it is possible to decide which output of the splitter layer
+ // should be lined to which input of the merger layer.
+ // We tested that is is possible to specify 0th output
+ // of the splitter to be the 1st input to the merger and the 1st output of the splitter to be 0th input
+ // of the merger.
+
+ Graph graph;
+ RefWorkloadFactory factory;
+ auto workloads = CreateSplitterMergerWorkloadTest<SplitterWorkloadType, MergerWorkloadType, DataType>
+ (factory, graph);
+
+ auto wlSplitter = std::move(workloads.first);
+ auto wlMerger = std::move(workloads.second);
+
+ //Checks that the index of inputs/outputs matches what we declared on InputDescriptor construction.
+ armnn::CpuTensorHandle* sOut0 = dynamic_cast<armnn::CpuTensorHandle*>(wlSplitter->GetData().m_Outputs[0]);
+ armnn::CpuTensorHandle* sOut1 = dynamic_cast<armnn::CpuTensorHandle*>(wlSplitter->GetData().m_Outputs[1]);
+ armnn::CpuTensorHandle* mIn0 = dynamic_cast<armnn::CpuTensorHandle*>(wlMerger->GetData().m_Inputs[0]);
+ armnn::CpuTensorHandle* mIn1 = dynamic_cast<armnn::CpuTensorHandle*>(wlMerger->GetData().m_Inputs[1]);
+
+ BOOST_TEST(sOut0);
+ BOOST_TEST(sOut1);
+ BOOST_TEST(mIn0);
+ BOOST_TEST(mIn1);
+
+ bool validDataPointers = (sOut0 == mIn1) && (sOut1 == mIn0);
+
+ BOOST_TEST(validDataPointers);
+}
+
+BOOST_AUTO_TEST_CASE(CreateSplitterMergerFloat32)
+{
+ RefCreateSplitterMergerWorkloadTest<RefSplitterFloat32Workload, RefMergerFloat32Workload, DataType::Float32>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateSplitterMergerUint8)
+{
+ RefCreateSplitterMergerWorkloadTest<RefSplitterUint8Workload, RefMergerUint8Workload, DataType::QuantisedAsymm8>();
+}
+
+template <typename SplitterWorkloadType, typename ActivationWorkloadType, armnn::DataType DataType>
+static void RefCreateSingleOutputMultipleInputsTest()
+{
+ // Tests that it is possible to assign multiple (two) different layers to each of the outputs of a splitter layer.
+ // We created a splitter with two outputs. That each of those outputs is used by two different activation layers.
+
+ Graph graph;
+ RefWorkloadFactory factory;
+ std::unique_ptr<SplitterWorkloadType> wlSplitter;
+ std::unique_ptr<ActivationWorkloadType> wlActiv0_0;
+ std::unique_ptr<ActivationWorkloadType> wlActiv0_1;
+ std::unique_ptr<ActivationWorkloadType> wlActiv1_0;
+ std::unique_ptr<ActivationWorkloadType> wlActiv1_1;
+
+ CreateSplitterMultipleInputsOneOutputWorkloadTest<SplitterWorkloadType,
+ ActivationWorkloadType, DataType>(factory, graph, wlSplitter, wlActiv0_0, wlActiv0_1, wlActiv1_0, wlActiv1_1);
+
+ armnn::CpuTensorHandle* sOut0 = dynamic_cast<armnn::CpuTensorHandle*>(wlSplitter->GetData().m_Outputs[0]);
+ armnn::CpuTensorHandle* sOut1 = dynamic_cast<armnn::CpuTensorHandle*>(wlSplitter->GetData().m_Outputs[1]);
+ armnn::CpuTensorHandle* activ0_0Im = dynamic_cast<armnn::CpuTensorHandle*>(wlActiv0_0->GetData().m_Inputs[0]);
+ armnn::CpuTensorHandle* activ0_1Im = dynamic_cast<armnn::CpuTensorHandle*>(wlActiv0_1->GetData().m_Inputs[0]);
+ armnn::CpuTensorHandle* activ1_0Im = dynamic_cast<armnn::CpuTensorHandle*>(wlActiv1_0->GetData().m_Inputs[0]);
+ armnn::CpuTensorHandle* activ1_1Im = dynamic_cast<armnn::CpuTensorHandle*>(wlActiv1_1->GetData().m_Inputs[0]);
+
+
+ BOOST_TEST(sOut0);
+ BOOST_TEST(sOut1);
+ BOOST_TEST(activ0_0Im);
+ BOOST_TEST(activ0_1Im);
+ BOOST_TEST(activ1_0Im);
+ BOOST_TEST(activ1_1Im);
+
+ bool validDataPointers = (sOut0 == activ0_0Im) && (sOut0 == activ0_1Im) &&
+ (sOut1 == activ1_0Im) && (sOut1 == activ1_1Im);
+
+ BOOST_TEST(validDataPointers);
+}
+
+BOOST_AUTO_TEST_CASE(CreateSingleOutputMultipleInputsFloat32)
+{
+ RefCreateSingleOutputMultipleInputsTest<RefSplitterFloat32Workload, RefActivationFloat32Workload,
+ armnn::DataType::Float32>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateSingleOutputMultipleInputsUint8)
+{
+ RefCreateSingleOutputMultipleInputsTest<RefSplitterUint8Workload, RefActivationUint8Workload,
+ armnn::DataType::QuantisedAsymm8>();
+}
+
+template <typename ResizeBilinearWorkloadType, armnn::DataType DataType>
+static void RefCreateResizeBilinearTest()
+{
+ Graph graph;
+ RefWorkloadFactory factory;
+ auto workload = CreateResizeBilinearWorkloadTest<ResizeBilinearWorkloadType, DataType>(factory, graph);
+
+ // Checks that outputs and inputs are as we expect them (see definition of CreateResizeBilinearWorkloadTest).
+ CheckInputOutput(
+ std::move(workload),
+ TensorInfo({ 2, 3, 4, 4 }, DataType),
+ TensorInfo({ 2, 3, 2, 2 }, DataType));
+}
+
+BOOST_AUTO_TEST_CASE(CreateResizeBilinearFloat32)
+{
+ RefCreateResizeBilinearTest<RefResizeBilinearFloat32Workload, armnn::DataType::Float32>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateResizeBilinearUint8)
+{
+ RefCreateResizeBilinearTest<RefResizeBilinearUint8Workload, armnn::DataType::QuantisedAsymm8>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateL2NormalizationFloat32)
+{
+ Graph graph;
+ RefWorkloadFactory factory;
+ auto workload = CreateL2NormalizationWorkloadTest<RefL2NormalizationFloat32Workload, armnn::DataType::Float32>
+ (factory, graph);
+
+ // Checks that outputs and inputs are as we expect them (see definition of CreateL2NormalizationWorkloadTest).
+ CheckInputOutput(
+ std::move(workload),
+ TensorInfo({ 5, 20, 50, 67 }, armnn::DataType::Float32),
+ TensorInfo({ 5, 20, 50, 67 }, armnn::DataType::Float32));
+}
+
+template <typename ReshapeWorkloadType, armnn::DataType DataType>
+static void RefCreateReshapeWorkloadTest()
+{
+ Graph graph;
+ RefWorkloadFactory factory;
+ auto workload = CreateReshapeWorkloadTest<ReshapeWorkloadType, DataType>(factory, graph);
+
+ // Checks that outputs and inputs are as we expect them (see definition of CreateReshapeWorkloadTest).
+ CheckInputOutput(
+ std::move(workload),
+ TensorInfo({ 4, 1 }, DataType),
+ TensorInfo({ 1, 4 }, DataType));
+}
+
+BOOST_AUTO_TEST_CASE(CreateReshapeFloat32Workload)
+{
+ RefCreateReshapeWorkloadTest<RefReshapeFloat32Workload, armnn::DataType::Float32>();
+}
+
+BOOST_AUTO_TEST_CASE(CreateReshapeUint8Workload)
+{
+ RefCreateReshapeWorkloadTest<RefReshapeUint8Workload, armnn::DataType::QuantisedAsymm8>();
+}
+
+BOOST_AUTO_TEST_SUITE_END()