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Diffstat (limited to 'src/backends/reference/test/RefCreateWorkloadTests.cpp')
| -rw-r--r-- | src/backends/reference/test/RefCreateWorkloadTests.cpp | 484 |
1 files changed, 484 insertions, 0 deletions
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() |