diff options
Diffstat (limited to 'src/backends/reference/test')
| -rw-r--r-- | src/backends/reference/test/CMakeLists.txt | 11 | ||||
| -rw-r--r-- | src/backends/reference/test/RefCreateWorkloadTests.cpp | 484 | ||||
| -rw-r--r-- | src/backends/reference/test/RefLayerSupportTests.cpp | 118 | ||||
| -rw-r--r-- | src/backends/reference/test/RefLayerTests.cpp | 273 |
4 files changed, 886 insertions, 0 deletions
diff --git a/src/backends/reference/test/CMakeLists.txt b/src/backends/reference/test/CMakeLists.txt index f41a074999..8f86f86d39 100644 --- a/src/backends/reference/test/CMakeLists.txt +++ b/src/backends/reference/test/CMakeLists.txt @@ -2,3 +2,14 @@ # Copyright © 2017 Arm Ltd. All rights reserved. # SPDX-License-Identifier: MIT # + +list(APPEND armnnRefBackendUnitTests_sources + RefCreateWorkloadTests.cpp + RefLayerSupportTests.cpp + RefLayerTests.cpp +) + +add_library(armnnRefBackendUnitTests STATIC ${armnnRefBackendUnitTests_sources}) +target_include_directories(armnnRefBackendUnitTests PRIVATE ${PROJECT_SOURCE_DIR}/src) +target_include_directories(armnnRefBackendUnitTests PRIVATE ${PROJECT_SOURCE_DIR}/src/armnn) +target_include_directories(armnnRefBackendUnitTests PRIVATE ${PROJECT_SOURCE_DIR}/src/armnnUtils)
\ No newline at end of file diff --git a/src/backends/reference/test/RefCreateWorkloadTests.cpp b/src/backends/reference/test/RefCreateWorkloadTests.cpp new file mode 100644 index 0000000000..e88fbed014 --- /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() diff --git a/src/backends/reference/test/RefLayerSupportTests.cpp b/src/backends/reference/test/RefLayerSupportTests.cpp new file mode 100644 index 0000000000..be3f3f8f97 --- /dev/null +++ b/src/backends/reference/test/RefLayerSupportTests.cpp @@ -0,0 +1,118 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <armnn/layers/ConvertFp16ToFp32Layer.hpp> +#include <armnn/layers/ConvertFp32ToFp16Layer.hpp> +#include <armnn/test/TensorHelpers.hpp> + +#include <backends/CpuTensorHandle.hpp> +#include <backends/reference/RefWorkloadFactory.hpp> +#include <backends/test/LayerTests.hpp> +#include <backends/test/IsLayerSupportedTestImpl.hpp> + +#include <boost/test/unit_test.hpp> + +#include <string> + +namespace +{ + +bool LayerTypeMatchesTest() +{ + return LayerTypeMatchesTestImpl<armnn::LayerType::FirstLayer>(Tag<armnn::LayerType::FirstLayer>()); +}; + +} // anonymous namespace + +BOOST_AUTO_TEST_SUITE(RefLayerSupported) + +BOOST_AUTO_TEST_CASE(IsLayerSupportedLayerTypeMatches) +{ + LayerTypeMatchesTest(); +} + +BOOST_AUTO_TEST_CASE(IsLayerSupportedFloat16Reference) +{ + armnn::RefWorkloadFactory factory; + IsLayerSupportedTests<armnn::RefWorkloadFactory, armnn::DataType::Float16>(&factory); +} + +BOOST_AUTO_TEST_CASE(IsLayerSupportedFloat32Reference) +{ + armnn::RefWorkloadFactory factory; + IsLayerSupportedTests<armnn::RefWorkloadFactory, armnn::DataType::Float32>(&factory); +} + +BOOST_AUTO_TEST_CASE(IsLayerSupportedUint8Reference) +{ + armnn::RefWorkloadFactory factory; + IsLayerSupportedTests<armnn::RefWorkloadFactory, armnn::DataType::QuantisedAsymm8>(&factory); +} + +BOOST_AUTO_TEST_CASE(IsConvertFp16ToFp32SupportedReference) +{ + std::string reasonIfUnsupported; + + bool result = IsConvertLayerSupportedTests<armnn::RefWorkloadFactory, armnn::ConvertFp16ToFp32Layer, + armnn::DataType::Float16, armnn::DataType::Float32>(reasonIfUnsupported); + + BOOST_CHECK(result); +} + +BOOST_AUTO_TEST_CASE(IsConvertFp16ToFp32SupportedFp32InputReference) +{ + std::string reasonIfUnsupported; + + bool result = IsConvertLayerSupportedTests<armnn::RefWorkloadFactory, armnn::ConvertFp16ToFp32Layer, + armnn::DataType::Float32, armnn::DataType::Float32>(reasonIfUnsupported); + + BOOST_CHECK(!result); + BOOST_CHECK_EQUAL(reasonIfUnsupported, "Layer is not supported with float32 data type input"); +} + +BOOST_AUTO_TEST_CASE(IsConvertFp16ToFp32SupportedFp16OutputReference) +{ + std::string reasonIfUnsupported; + + bool result = IsConvertLayerSupportedTests<armnn::RefWorkloadFactory, armnn::ConvertFp16ToFp32Layer, + armnn::DataType::Float16, armnn::DataType::Float16>(reasonIfUnsupported); + + BOOST_CHECK(!result); + BOOST_CHECK_EQUAL(reasonIfUnsupported, "Layer is not supported with float16 data type output"); +} + +BOOST_AUTO_TEST_CASE(IsConvertFp32ToFp16SupportedReference) +{ + std::string reasonIfUnsupported; + + bool result = IsConvertLayerSupportedTests<armnn::RefWorkloadFactory, armnn::ConvertFp32ToFp16Layer, + armnn::DataType::Float32, armnn::DataType::Float16>(reasonIfUnsupported); + + BOOST_CHECK(result); +} + +BOOST_AUTO_TEST_CASE(IsConvertFp32ToFp16SupportedFp16InputReference) +{ + std::string reasonIfUnsupported; + + bool result = IsConvertLayerSupportedTests<armnn::RefWorkloadFactory, armnn::ConvertFp32ToFp16Layer, + armnn::DataType::Float16, armnn::DataType::Float16>(reasonIfUnsupported); + + BOOST_CHECK(!result); + BOOST_CHECK_EQUAL(reasonIfUnsupported, "Layer is not supported with float16 data type input"); +} + +BOOST_AUTO_TEST_CASE(IsConvertFp32ToFp16SupportedFp32OutputReference) +{ + std::string reasonIfUnsupported; + + bool result = IsConvertLayerSupportedTests<armnn::RefWorkloadFactory, armnn::ConvertFp32ToFp16Layer, + armnn::DataType::Float32, armnn::DataType::Float32>(reasonIfUnsupported); + + BOOST_CHECK(!result); + BOOST_CHECK_EQUAL(reasonIfUnsupported, "Layer is not supported with float32 data type output"); +} + +BOOST_AUTO_TEST_SUITE_END() diff --git a/src/backends/reference/test/RefLayerTests.cpp b/src/backends/reference/test/RefLayerTests.cpp new file mode 100644 index 0000000000..de2c2fe332 --- /dev/null +++ b/src/backends/reference/test/RefLayerTests.cpp @@ -0,0 +1,273 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "test/TensorHelpers.hpp" +#include "test/UnitTests.hpp" + +#include <backends/reference/RefWorkloadFactory.hpp> +#include <backends/test/LayerTests.hpp> + +#include <boost/test/unit_test.hpp> + +BOOST_AUTO_TEST_SUITE(Compute_Reference) +using FactoryType = armnn::RefWorkloadFactory; + +// ============================================================================ +// UNIT tests + +// Convolution +ARMNN_AUTO_TEST_CASE(SimpleConvolution2d3x5, SimpleConvolution2d3x5Test, true) +ARMNN_AUTO_TEST_CASE(SimpleConvolution2d3x5Uint8, SimpleConvolution2d3x5Uint8Test, true) + +ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2d, SimpleConvolution2d3x5Test, false) +ARMNN_AUTO_TEST_CASE(UnbiasedConvolutionUint8, SimpleConvolution2d3x5Uint8Test, false) + +ARMNN_AUTO_TEST_CASE(SimpleConvolution1d, Convolution1dTest, true) +ARMNN_AUTO_TEST_CASE(SimpleConvolution1dUint8, Convolution1dUint8Test, true) + +ARMNN_AUTO_TEST_CASE(SimpleConvolution2d3x3, SimpleConvolution2d3x3Test, true) +ARMNN_AUTO_TEST_CASE(SimpleConvolution2d3x3Uint8, SimpleConvolution2d3x3Uint8Test, true) + +ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2dSquare, SimpleConvolution2d3x3Test, false) + +ARMNN_AUTO_TEST_CASE(SimpleConvolution2dAsymmetricPaddingLargerThanHalfKernelSize, + Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest) +ARMNN_AUTO_TEST_CASE(SimpleConvolution2dAsymmetricPadding, Convolution2dAsymmetricPaddingTest) + +// Depthwise Convolution +ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2d, DepthwiseConvolution2dTest, true) +ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dUint8, DepthwiseConvolution2dUint8Test, true) + +ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2d, DepthwiseConvolution2dTest, false) +ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2dUint8, DepthwiseConvolution2dUint8Test, false) + +ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dDepthMul1, DepthwiseConvolution2dDepthMul1Test, true) +ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dDepthMul1Uint8, DepthwiseConvolution2dDepthMul1Uint8Test, true) + +ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2dDepthMul1, DepthwiseConvolution2dDepthMul1Test, false) +ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2dDepthMul1Uint8, DepthwiseConvolution2dDepthMul1Uint8Test, false) + +ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dAsymmetric, DepthwiseConvolution2dAsymmetricTest, true) +ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2dAsymmetric, DepthwiseConvolution2dAsymmetricTest, false) + +// Pooling +ARMNN_AUTO_TEST_CASE(SimpleMaxPooling2dSize2x2Stride2x2, SimpleMaxPooling2dSize2x2Stride2x2Test, false) +ARMNN_AUTO_TEST_CASE(SimpleMaxPooling2dSize2x2Stride2x2Uint8, SimpleMaxPooling2dSize2x2Stride2x2Uint8Test, false) + +ARMNN_AUTO_TEST_CASE(SimpleMaxPooling2dSize3x3Stride2x4, SimpleMaxPooling2dSize3x3Stride2x4Test, false) +ARMNN_AUTO_TEST_CASE(SimpleMaxPooling2dSize3x3Stride2x4Uint8, SimpleMaxPooling2dSize3x3Stride2x4Uint8Test, false) + +ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleMaxPooling2d, IgnorePaddingSimpleMaxPooling2dTest) +ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleMaxPooling2dUint8, IgnorePaddingSimpleMaxPooling2dUint8Test) +ARMNN_AUTO_TEST_CASE(IgnorePaddingMaxPooling2dSize3, IgnorePaddingMaxPooling2dSize3Test) +ARMNN_AUTO_TEST_CASE(IgnorePaddingMaxPooling2dSize3Uint8, IgnorePaddingMaxPooling2dSize3Uint8Test) + +ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2d, IgnorePaddingSimpleAveragePooling2dTest) +ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2dUint8, IgnorePaddingSimpleAveragePooling2dUint8Test) +ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2dNoPadding, IgnorePaddingSimpleAveragePooling2dNoPaddingTest) +ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2dNoPaddingUint8, + IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test) +ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3, IgnorePaddingAveragePooling2dSize3Test) +ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3Uint8, IgnorePaddingAveragePooling2dSize3Uint8Test) + +ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleL2Pooling2d, IgnorePaddingSimpleL2Pooling2dTest) +ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleL2Pooling2dUint8, IgnorePaddingSimpleL2Pooling2dUint8Test) +ARMNN_AUTO_TEST_CASE(IgnorePaddingL2Pooling2dSize3, IgnorePaddingL2Pooling2dSize3Test) +ARMNN_AUTO_TEST_CASE(IgnorePaddingL2Pooling2dSize3Uint8, IgnorePaddingL2Pooling2dSize3Uint8Test) + +ARMNN_AUTO_TEST_CASE(SimpleAveragePooling2d, SimpleAveragePooling2dTest) +ARMNN_AUTO_TEST_CASE(SimpleAveragePooling2dUint8, SimpleAveragePooling2dUint8Test) +ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3x2Stride2x2, + IgnorePaddingAveragePooling2dSize3x2Stride2x2Test, false) +ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3x2Stride2x2NoPadding, + IgnorePaddingAveragePooling2dSize3x2Stride2x2Test, true) + +ARMNN_AUTO_TEST_CASE(LargeTensorsAveragePooling2d, LargeTensorsAveragePooling2dTest) +ARMNN_AUTO_TEST_CASE(LargeTensorsAveragePooling2dUint8, LargeTensorsAveragePooling2dUint8Test) + +ARMNN_AUTO_TEST_CASE(SimpleL2Pooling2d, SimpleL2Pooling2dTest) +ARMNN_AUTO_TEST_CASE(SimpleL2Pooling2dUint8, SimpleL2Pooling2dUint8Test) + +ARMNN_AUTO_TEST_CASE(L2Pooling2dSize7, L2Pooling2dSize7Test) +ARMNN_AUTO_TEST_CASE(L2Pooling2dSize7Uint8, L2Pooling2dSize7Uint8Test) + +ARMNN_AUTO_TEST_CASE(AsymmNonSquarePooling2d, AsymmetricNonSquarePooling2dTest) +ARMNN_AUTO_TEST_CASE(AsymmNonSquarePooling2dUint8, AsymmetricNonSquarePooling2dUint8Test) + +// Activation +ARMNN_AUTO_TEST_CASE(ConstantLinearActivation, ConstantLinearActivationTest) +ARMNN_AUTO_TEST_CASE(ConstantLinearActivationUint8, ConstantLinearActivationUint8Test) + +ARMNN_AUTO_TEST_CASE(SimpleNormalizationAcross, SimpleNormalizationAcrossTest) +ARMNN_AUTO_TEST_CASE(SimpleNormalizationWithin, SimpleNormalizationWithinTest) + +ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta1, SimpleSoftmaxTest, 1.0f) +ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta2, SimpleSoftmaxTest, 2.0f) +ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta1Uint8, SimpleSoftmaxUint8Test, 1.0f) +ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta2Uint8, SimpleSoftmaxUint8Test, 2.0f) + +ARMNN_AUTO_TEST_CASE(SimpleSigmoid, SimpleSigmoidTest) +ARMNN_AUTO_TEST_CASE(SimpleSigmoidUint8, SimpleSigmoidUint8Test) + +ARMNN_AUTO_TEST_CASE(ReLu1, BoundedReLuUpperAndLowerBoundTest) +ARMNN_AUTO_TEST_CASE(ReLu6, BoundedReLuUpperBoundOnlyTest) +ARMNN_AUTO_TEST_CASE(ReLu1Uint8, BoundedReLuUint8UpperAndLowerBoundTest) +ARMNN_AUTO_TEST_CASE(ReLu6Uint8, BoundedReLuUint8UpperBoundOnlyTest) + +// Fully Conected +ARMNN_AUTO_TEST_CASE(SimpleFullyConnected, FullyConnectedFloat32Test, false, false) +ARMNN_AUTO_TEST_CASE(FullyConnectedUint8, FullyConnectedUint8Test, false) +ARMNN_AUTO_TEST_CASE(SimpleFullyConnectedWithBias, FullyConnectedFloat32Test, true, false) +ARMNN_AUTO_TEST_CASE(FullyConnectedBiasedUint8, FullyConnectedUint8Test, true) +ARMNN_AUTO_TEST_CASE(SimpleFullyConnectedWithTranspose, FullyConnectedFloat32Test, false, true) + +ARMNN_AUTO_TEST_CASE(FullyConnectedLarge, FullyConnectedLargeTest, false) +ARMNN_AUTO_TEST_CASE(FullyConnectedLargeTransposed, FullyConnectedLargeTest, true) + +// Splitter +ARMNN_AUTO_TEST_CASE(SimpleSplitter, SplitterTest) +ARMNN_AUTO_TEST_CASE(SimpleSplitterUint8, SplitterUint8Test) + +ARMNN_AUTO_TEST_CASE(CopyViaSplitter, CopyViaSplitterTest) +ARMNN_AUTO_TEST_CASE(CopyViaSplitterUint8, CopyViaSplitterUint8Test) + +// Merger +ARMNN_AUTO_TEST_CASE(SimpleMerger, MergerTest) +ARMNN_AUTO_TEST_CASE(MergerUint8, MergerUint8Test) + +// Add +ARMNN_AUTO_TEST_CASE(SimpleAdd, AdditionTest) +ARMNN_AUTO_TEST_CASE(AddBroadcast1Element, AdditionBroadcast1ElementTest) +ARMNN_AUTO_TEST_CASE(AddBroadcast, AdditionBroadcastTest) + +ARMNN_AUTO_TEST_CASE(AdditionUint8, AdditionUint8Test) +ARMNN_AUTO_TEST_CASE(AddBroadcastUint8, AdditionBroadcastUint8Test) +ARMNN_AUTO_TEST_CASE(AddBroadcast1ElementUint8, AdditionBroadcast1ElementUint8Test) + +// Sub +ARMNN_AUTO_TEST_CASE(SimpleSub, SubtractionTest) +ARMNN_AUTO_TEST_CASE(SubBroadcast1Element, SubtractionBroadcast1ElementTest) +ARMNN_AUTO_TEST_CASE(SubBroadcast, SubtractionBroadcastTest) + +ARMNN_AUTO_TEST_CASE(SubtractionUint8, SubtractionUint8Test) +ARMNN_AUTO_TEST_CASE(SubBroadcastUint8, SubtractionBroadcastUint8Test) +ARMNN_AUTO_TEST_CASE(SubBroadcast1ElementUint8, SubtractionBroadcast1ElementUint8Test) + +// Div +ARMNN_AUTO_TEST_CASE(SimpleDivision, DivisionTest) +ARMNN_AUTO_TEST_CASE(DivisionByZero, DivisionByZeroTest) +ARMNN_AUTO_TEST_CASE(DivisionBroadcast1Element, DivisionBroadcast1ElementTest) +ARMNN_AUTO_TEST_CASE(DivisionBroadcast1DVector, DivisionBroadcast1DVectorTest) +// NOTE: division by zero for quantized div needs more attention +// see IVGCVSW-1849 +ARMNN_AUTO_TEST_CASE(DivisionUint8, DivisionUint8Test) +ARMNN_AUTO_TEST_CASE(DivisionUint8Broadcast1Element, DivisionBroadcast1ElementUint8Test) +ARMNN_AUTO_TEST_CASE(DivisionUint8Broadcast1DVector, DivisionBroadcast1DVectorUint8Test) + +// Mul +ARMNN_AUTO_TEST_CASE(SimpleMultiplication, MultiplicationTest) +ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1Element, MultiplicationBroadcast1ElementTest) +ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1DVector, MultiplicationBroadcast1DVectorTest) +ARMNN_AUTO_TEST_CASE(MultiplicationUint8, MultiplicationUint8Test) +ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1ElementUint8, MultiplicationBroadcast1ElementUint8Test) +ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1DVectorUint8, MultiplicationBroadcast1DVectorUint8Test) + +// Batch Norm +ARMNN_AUTO_TEST_CASE(BatchNorm, BatchNormTest) +ARMNN_AUTO_TEST_CASE(BatchNormUint8, BatchNormUint8Test) + +// Resize Bilinear +ARMNN_AUTO_TEST_CASE(SimpleResizeBilinear, SimpleResizeBilinearTest) +ARMNN_AUTO_TEST_CASE(SimpleResizeBilinearUint8, SimpleResizeBilinearUint8Test) +ARMNN_AUTO_TEST_CASE(ResizeBilinearNop, ResizeBilinearNopTest) +ARMNN_AUTO_TEST_CASE(ResizeBilinearNopUint8, ResizeBilinearNopUint8Test) +ARMNN_AUTO_TEST_CASE(ResizeBilinearSqMin, ResizeBilinearSqMinTest) +ARMNN_AUTO_TEST_CASE(ResizeBilinearSqMinUint8, ResizeBilinearSqMinUint8Test) +ARMNN_AUTO_TEST_CASE(ResizeBilinearMin, ResizeBilinearMinTest) +ARMNN_AUTO_TEST_CASE(ResizeBilinearMinUint8, ResizeBilinearMinUint8Test) +ARMNN_AUTO_TEST_CASE(ResizeBilinearMag, ResizeBilinearMagTest) +ARMNN_AUTO_TEST_CASE(ResizeBilinearMagUint8, ResizeBilinearMagUint8Test) + +// Fake Quantization +ARMNN_AUTO_TEST_CASE(FakeQuantization, FakeQuantizationTest) + +// L2 Normalization +ARMNN_AUTO_TEST_CASE(L2Normalization1d, L2Normalization1dTest) +ARMNN_AUTO_TEST_CASE(L2Normalization2d, L2Normalization2dTest) +ARMNN_AUTO_TEST_CASE(L2Normalization3d, L2Normalization3dTest) +ARMNN_AUTO_TEST_CASE(L2Normalization4d, L2Normalization4dTest) + +// NOTE: These tests are disabled until NHWC is supported by the reference L2Normalization implementation. +//ARMNN_AUTO_TEST_CASE(L2Normalization1dNhwc, L2Normalization1dNhwcTest); +//ARMNN_AUTO_TEST_CASE(L2Normalization2dNhwc, L2Normalization2dNhwcTest); +//ARMNN_AUTO_TEST_CASE(L2Normalization3dNhwc, L2Normalization3dNhwcTest); +//ARMNN_AUTO_TEST_CASE(L2Normalization4dNhwc, L2Normalization4dNhwcTest); + +// Constant +ARMNN_AUTO_TEST_CASE(Constant, ConstantTest) +ARMNN_AUTO_TEST_CASE(ConstantUint8, ConstantUint8Test) + +// Concat +ARMNN_AUTO_TEST_CASE(Concatenation1d, Concatenation1dTest) +ARMNN_AUTO_TEST_CASE(Concatenation1dUint8, Concatenation1dUint8Test) + +ARMNN_AUTO_TEST_CASE(Concatenation2dDim0, Concatenation2dDim0Test) +ARMNN_AUTO_TEST_CASE(Concatenation2dDim0Uint8, Concatenation2dDim0Uint8Test) +ARMNN_AUTO_TEST_CASE(Concatenation2dDim1, Concatenation2dDim1Test) +ARMNN_AUTO_TEST_CASE(Concatenation2dDim1Uint8, Concatenation2dDim1Uint8Test) + +ARMNN_AUTO_TEST_CASE(Concatenation2dDim0DiffInputDims, Concatenation2dDim0DiffInputDimsTest) +ARMNN_AUTO_TEST_CASE(Concatenation2dDim0DiffInputDimsUint8, Concatenation2dDim0DiffInputDimsUint8Test) +ARMNN_AUTO_TEST_CASE(Concatenation2dDim1DiffInputDims, Concatenation2dDim1DiffInputDimsTest) +ARMNN_AUTO_TEST_CASE(Concatenation2dDim1DiffInputDimsUint8, Concatenation2dDim1DiffInputDimsUint8Test) + +ARMNN_AUTO_TEST_CASE(Concatenation3dDim0, Concatenation3dDim0Test) +ARMNN_AUTO_TEST_CASE(Concatenation3dDim0Uint8, Concatenation3dDim0Uint8Test) +ARMNN_AUTO_TEST_CASE(Concatenation3dDim1, Concatenation3dDim1Test) +ARMNN_AUTO_TEST_CASE(Concatenation3dDim1Uint8, Concatenation3dDim1Uint8Test) +ARMNN_AUTO_TEST_CASE(Concatenation3dDim2, Concatenation3dDim2Test) +ARMNN_AUTO_TEST_CASE(Concatenation3dDim2Uint8, Concatenation3dDim2Uint8Test) + +ARMNN_AUTO_TEST_CASE(Concatenation3dDim0DiffInputDims, Concatenation3dDim0DiffInputDimsTest) +ARMNN_AUTO_TEST_CASE(Concatenation3dDim0DiffInputDimsUint8, Concatenation3dDim0DiffInputDimsUint8Test) +ARMNN_AUTO_TEST_CASE(Concatenation3dDim1DiffInputDims, Concatenation3dDim1DiffInputDimsTest) +ARMNN_AUTO_TEST_CASE(Concatenation3dDim1DiffInputDimsUint8, Concatenation3dDim1DiffInputDimsUint8Test) +ARMNN_AUTO_TEST_CASE(Concatenation3dDim2DiffInputDims, Concatenation3dDim2DiffInputDimsTest) +ARMNN_AUTO_TEST_CASE(Concatenation3dDim2DiffInputDimsUint8, Concatenation3dDim2DiffInputDimsUint8Test) + +// Floor +ARMNN_AUTO_TEST_CASE(SimpleFloor, SimpleFloorTest) + +// Reshape +ARMNN_AUTO_TEST_CASE(SimpleReshapeFloat32, SimpleReshapeFloat32Test) +ARMNN_AUTO_TEST_CASE(SimpleReshapeUint8, SimpleReshapeUint8Test) + +// Permute +ARMNN_AUTO_TEST_CASE(SimplePermuteFloat32, SimplePermuteFloat32Test) +ARMNN_AUTO_TEST_CASE(SimplePermuteUint8, SimplePermuteUint8Test) +ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet1, PermuteFloat32ValueSet1Test) +ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet2, PermuteFloat32ValueSet2Test) +ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet3, PermuteFloat32ValueSet3Test) + +// Convert from Float16 to Float32 +ARMNN_AUTO_TEST_CASE(SimpleConvertFp16ToFp32, SimpleConvertFp16ToFp32Test) +// Convert from Float32 to Float16 +ARMNN_AUTO_TEST_CASE(SimpleConvertFp32ToFp16, SimpleConvertFp32ToFp16Test) + +// Mean +ARMNN_AUTO_TEST_CASE(MeanUint8Simple, MeanUint8SimpleTest) +ARMNN_AUTO_TEST_CASE(MeanUint8SimpleAxis, MeanUint8SimpleAxisTest) +ARMNN_AUTO_TEST_CASE(MeanUint8KeepDims, MeanUint8KeepDimsTest) +ARMNN_AUTO_TEST_CASE(MeanUint8MultipleDims, MeanUint8MultipleDimsTest) +ARMNN_AUTO_TEST_CASE(MeanVtsUint8, MeanVtsUint8Test) + +ARMNN_AUTO_TEST_CASE(MeanFloatSimple, MeanFloatSimpleTest) +ARMNN_AUTO_TEST_CASE(MeanFloatSimpleAxis, MeanFloatSimpleAxisTest) +ARMNN_AUTO_TEST_CASE(MeanFloatKeepDims, MeanFloatKeepDimsTest) +ARMNN_AUTO_TEST_CASE(MeanFloatMultipleDims, MeanFloatMultipleDimsTest) +ARMNN_AUTO_TEST_CASE(MeanVtsFloat1, MeanVtsFloat1Test) +ARMNN_AUTO_TEST_CASE(MeanVtsFloat2, MeanVtsFloat2Test) + +BOOST_AUTO_TEST_SUITE_END() |