| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include <test/CreateWorkload.hpp> |
| |
| #include <backendsCommon/CpuTensorHandle.hpp> |
| #include <reference/RefWorkloadFactory.hpp> |
| #include <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 RefCreateElementwiseWorkloadTest() |
| { |
| Graph graph; |
| RefWorkloadFactory factory; |
| auto workload = CreateElementwiseWorkloadTest<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) |
| { |
| RefCreateElementwiseWorkloadTest<RefAdditionFloat32Workload, |
| AdditionQueueDescriptor, |
| AdditionLayer, |
| armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateAdditionUint8Workload) |
| { |
| RefCreateElementwiseWorkloadTest<RefAdditionUint8Workload, |
| AdditionQueueDescriptor, |
| AdditionLayer, |
| armnn::DataType::QuantisedAsymm8>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSubtractionFloatWorkload) |
| { |
| RefCreateElementwiseWorkloadTest<RefSubtractionFloat32Workload, |
| SubtractionQueueDescriptor, |
| SubtractionLayer, |
| armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSubtractionUint8Workload) |
| { |
| RefCreateElementwiseWorkloadTest<RefSubtractionUint8Workload, |
| SubtractionQueueDescriptor, |
| SubtractionLayer, |
| armnn::DataType::QuantisedAsymm8>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMultiplicationFloatWorkload) |
| { |
| RefCreateElementwiseWorkloadTest<RefMultiplicationFloat32Workload, |
| MultiplicationQueueDescriptor, |
| MultiplicationLayer, |
| armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMultiplicationUint8Workload) |
| { |
| RefCreateElementwiseWorkloadTest<RefMultiplicationUint8Workload, |
| MultiplicationQueueDescriptor, |
| MultiplicationLayer, |
| armnn::DataType::QuantisedAsymm8>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateDivisionFloatWorkload) |
| { |
| RefCreateElementwiseWorkloadTest<RefDivisionFloat32Workload, |
| DivisionQueueDescriptor, |
| DivisionLayer, |
| armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateDivisionUint8Workload) |
| { |
| RefCreateElementwiseWorkloadTest<RefDivisionUint8Workload, |
| DivisionQueueDescriptor, |
| DivisionLayer, |
| armnn::DataType::QuantisedAsymm8>(); |
| } |
| |
| template <typename BatchNormalizationWorkloadType, armnn::DataType DataType> |
| static void RefCreateBatchNormalizationWorkloadTest(DataLayout dataLayout) |
| { |
| Graph graph; |
| RefWorkloadFactory factory; |
| auto workload = |
| CreateBatchNormalizationWorkloadTest<BatchNormalizationWorkloadType, DataType>(factory, graph, dataLayout); |
| |
| TensorShape inputShape; |
| TensorShape outputShape; |
| |
| switch (dataLayout) |
| { |
| case DataLayout::NHWC: |
| inputShape = { 2, 4, 4, 3 }; |
| outputShape = { 2, 4, 4, 3 }; |
| break; |
| case DataLayout::NCHW: |
| default: |
| inputShape = { 2, 3, 4, 4 }; |
| outputShape = { 2, 3, 4, 4 }; |
| break; |
| } |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateBatchNormalizationWorkloadTest). |
| CheckInputOutput(std::move(workload), TensorInfo(inputShape, DataType), TensorInfo(outputShape, DataType)); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloat32Workload) |
| { |
| RefCreateBatchNormalizationWorkloadTest<RefBatchNormalizationFloat32Workload,armnn::DataType::Float32> |
| (DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloat32WorkloadNhwc) |
| { |
| RefCreateBatchNormalizationWorkloadTest<RefBatchNormalizationFloat32Workload, armnn::DataType::Float32> |
| (DataLayout::NHWC); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateBatchNormalizationUint8Workload) |
| { |
| RefCreateBatchNormalizationWorkloadTest<RefBatchNormalizationUint8Workload, armnn::DataType::QuantisedAsymm8> |
| (DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateBatchNormalizationUint8WorkloadNhwc) |
| { |
| RefCreateBatchNormalizationWorkloadTest<RefBatchNormalizationUint8Workload, armnn::DataType::QuantisedAsymm8> |
| (DataLayout::NHWC); |
| } |
| |
| 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)); |
| } |
| |
| static void RefCreateConvolution2dWorkloadTest(DataLayout dataLayout = DataLayout::NCHW) |
| { |
| Graph graph; |
| RefWorkloadFactory factory; |
| auto workload = CreateConvolution2dWorkloadTest<RefConvolution2dFloat32Workload, DataType::Float32> |
| (factory, graph, dataLayout); |
| |
| std::initializer_list<unsigned int> inputShape = (dataLayout == DataLayout::NCHW) ? |
| std::initializer_list<unsigned int>({2, 3, 8, 16}) : std::initializer_list<unsigned int>({2, 8, 16, 3}); |
| std::initializer_list<unsigned int> outputShape = (dataLayout == DataLayout::NCHW) ? |
| std::initializer_list<unsigned int>({2, 2, 2, 10}) : std::initializer_list<unsigned int>({2, 2, 10, 2}); |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateConvolution2dWorkloadTest). |
| CheckInputOutput(std::move(workload), |
| TensorInfo(inputShape, DataType::Float32), |
| TensorInfo(outputShape, DataType::Float32)); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateConvolution2dFloatNchwWorkload) |
| { |
| RefCreateConvolution2dWorkloadTest(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateConvolution2dFloatNhwcWorkload) |
| { |
| RefCreateConvolution2dWorkloadTest(DataLayout::NHWC); |
| } |
| |
| 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(DataLayout dataLayout) |
| { |
| Graph graph; |
| RefWorkloadFactory factory; |
| auto workload = CreateNormalizationWorkloadTest<NormalizationWorkloadType, DataType>(factory, graph, dataLayout); |
| |
| TensorShape inputShape; |
| TensorShape outputShape; |
| |
| switch (dataLayout) |
| { |
| case DataLayout::NHWC: |
| inputShape = { 3, 1, 5, 5 }; |
| outputShape = { 3, 1, 5, 5 }; |
| break; |
| case DataLayout::NCHW: |
| default: |
| inputShape = { 3, 5, 5, 1 }; |
| outputShape = { 3, 5, 5, 1 }; |
| break; |
| } |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateNormalizationWorkloadTest). |
| CheckInputOutput(std::move(workload), TensorInfo(inputShape, DataType), TensorInfo(outputShape, DataType)); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateRefNormalizationNchwWorkload) |
| { |
| RefCreateNormalizationWorkloadTest<RefNormalizationFloat32Workload, armnn::DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateRefNormalizationNhwcWorkload) |
| { |
| RefCreateNormalizationWorkloadTest<RefNormalizationFloat32Workload, armnn::DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| template <typename Pooling2dWorkloadType, armnn::DataType DataType> |
| static void RefCreatePooling2dWorkloadTest(DataLayout dataLayout) |
| { |
| Graph graph; |
| RefWorkloadFactory factory; |
| auto workload = CreatePooling2dWorkloadTest<Pooling2dWorkloadType, DataType>(factory, graph, dataLayout); |
| |
| TensorShape inputShape; |
| TensorShape outputShape; |
| |
| switch (dataLayout) |
| { |
| case DataLayout::NHWC: |
| inputShape = { 3, 5, 5, 2 }; |
| outputShape = { 3, 2, 4, 2 }; |
| break; |
| case DataLayout::NCHW: |
| default: |
| inputShape = { 3, 2, 5, 5 }; |
| outputShape = { 3, 2, 2, 4 }; |
| } |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreatePooling2dWorkloadTest). |
| CheckInputOutput(std::move(workload), |
| TensorInfo(inputShape, DataType), |
| TensorInfo(outputShape, DataType)); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreatePooling2dFloat32Workload) |
| { |
| RefCreatePooling2dWorkloadTest<RefPooling2dFloat32Workload, armnn::DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreatePooling2dFloat32NhwcWorkload) |
| { |
| RefCreatePooling2dWorkloadTest<RefPooling2dFloat32Workload, armnn::DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreatePooling2dUint8Workload) |
| { |
| RefCreatePooling2dWorkloadTest<RefPooling2dUint8Workload, armnn::DataType::QuantisedAsymm8>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreatePooling2dUint8NhwcWorkload) |
| { |
| RefCreatePooling2dWorkloadTest<RefPooling2dUint8Workload, armnn::DataType::QuantisedAsymm8>(DataLayout::NHWC); |
| } |
| |
| 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(DataLayout dataLayout) |
| { |
| Graph graph; |
| RefWorkloadFactory factory; |
| auto workload = CreateResizeBilinearWorkloadTest<ResizeBilinearWorkloadType, DataType>(factory, graph, dataLayout); |
| |
| TensorShape inputShape; |
| TensorShape outputShape; |
| |
| switch (dataLayout) |
| { |
| case DataLayout::NHWC: |
| inputShape = { 2, 4, 4, 3 }; |
| outputShape = { 2, 2, 2, 3 }; |
| break; |
| case DataLayout::NCHW: |
| default: |
| inputShape = { 2, 3, 4, 4 }; |
| outputShape = { 2, 3, 2, 2 }; |
| } |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateResizeBilinearWorkloadTest). |
| CheckInputOutput(std::move(workload), |
| TensorInfo(inputShape, DataType), |
| TensorInfo(outputShape, DataType)); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateResizeBilinearFloat32) |
| { |
| RefCreateResizeBilinearTest<RefResizeBilinearFloat32Workload, armnn::DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateResizeBilinearUint8) |
| { |
| RefCreateResizeBilinearTest<RefResizeBilinearUint8Workload, armnn::DataType::QuantisedAsymm8>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateResizeBilinearFloat32Nhwc) |
| { |
| RefCreateResizeBilinearTest<RefResizeBilinearFloat32Workload, armnn::DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| template <typename L2NormalizationWorkloadType, armnn::DataType DataType> |
| static void RefCreateL2NormalizationTest(DataLayout dataLayout) |
| { |
| Graph graph; |
| RefWorkloadFactory factory; |
| auto workload = |
| CreateL2NormalizationWorkloadTest<L2NormalizationWorkloadType, DataType>(factory, graph, dataLayout); |
| |
| TensorShape inputShape; |
| TensorShape outputShape; |
| |
| switch (dataLayout) |
| { |
| case DataLayout::NHWC: |
| inputShape = { 5, 50, 67, 20 }; |
| outputShape = { 5, 50, 67, 20 }; |
| break; |
| case DataLayout::NCHW: |
| default: |
| inputShape = { 5, 20, 50, 67 }; |
| outputShape = { 5, 20, 50, 67 }; |
| break; |
| } |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateL2NormalizationWorkloadTest). |
| CheckInputOutput(std::move(workload), TensorInfo(inputShape, DataType), TensorInfo(outputShape, DataType)); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateL2NormalizationFloat32) |
| { |
| RefCreateL2NormalizationTest<RefL2NormalizationFloat32Workload, armnn::DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateL2NormalizationFloat32Nhwc) |
| { |
| RefCreateL2NormalizationTest<RefL2NormalizationFloat32Workload, armnn::DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| 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>(); |
| } |
| |
| template <typename MergerWorkloadType, armnn::DataType DataType> |
| static void RefCreateMergerWorkloadTest(const armnn::TensorShape& outputShape, |
| unsigned int concatAxis) |
| { |
| Graph graph; |
| RefWorkloadFactory factory; |
| auto workload = CreateMergerWorkloadTest<MergerWorkloadType, DataType>(factory, graph, outputShape, concatAxis); |
| |
| CheckInputsOutput(std::move(workload), |
| TensorInfo({ 2, 3, 2, 5 }, DataType), |
| TensorInfo({ 2, 3, 2, 5 }, DataType), |
| TensorInfo(outputShape, DataType)); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMergerDim0Float32Workload) |
| { |
| RefCreateMergerWorkloadTest<RefMergerFloat32Workload, armnn::DataType::Float32>({ 4, 3, 2, 5 }, 0); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMergerDim0Uint8Workload) |
| { |
| RefCreateMergerWorkloadTest<RefMergerUint8Workload, armnn::DataType::QuantisedAsymm8>({ 4, 3, 2, 5 }, 0); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMergerDim1Float32Workload) |
| { |
| RefCreateMergerWorkloadTest<RefMergerFloat32Workload, armnn::DataType::Float32>({ 2, 6, 2, 5 }, 1); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMergerDim1Uint8Workload) |
| { |
| RefCreateMergerWorkloadTest<RefMergerUint8Workload, armnn::DataType::QuantisedAsymm8>({ 2, 6, 2, 5 }, 1); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMergerDim2Float32Workload) |
| { |
| RefCreateMergerWorkloadTest<RefMergerFloat32Workload, armnn::DataType::Float32>({ 2, 3, 4, 5 }, 2); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMergerDim2Uint8Workload) |
| { |
| RefCreateMergerWorkloadTest<RefMergerUint8Workload, armnn::DataType::QuantisedAsymm8>({ 2, 3, 4, 5 }, 2); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMergerDim3Float32Workload) |
| { |
| RefCreateMergerWorkloadTest<RefMergerFloat32Workload, armnn::DataType::Float32>({ 2, 3, 2, 10 }, 3); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMergerDim3Uint8Workload) |
| { |
| RefCreateMergerWorkloadTest<RefMergerUint8Workload, armnn::DataType::QuantisedAsymm8>({ 2, 3, 2, 10 }, 3); |
| } |
| |
| BOOST_AUTO_TEST_SUITE_END() |