| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #include "backends/NeonWorkloadFactory.hpp" |
| #include "backends/NeonWorkloadUtils.hpp" |
| #include "backends/NeonWorkloads.hpp" |
| #include "backends/MemCopyWorkload.hpp" |
| #include "backends/NeonTensorHandle.hpp" |
| |
| #include "test/CreateWorkloadClNeon.hpp" |
| |
| BOOST_AUTO_TEST_SUITE(CreateWorkloadNeon) |
| |
| namespace |
| { |
| |
| bool TestNeonTensorHandleInfo(armnn::INeonTensorHandle* handle, const armnn::TensorInfo& expectedInfo) |
| { |
| using namespace armnn::armcomputetensorutils; |
| |
| const arm_compute::ITensorInfo* handleInfo = handle->GetTensor().info(); |
| const arm_compute::TensorInfo expectedAclInfo = BuildArmComputeTensorInfo(expectedInfo); |
| |
| if (handleInfo->data_type() != expectedAclInfo.data_type()) |
| { |
| return false; |
| } |
| |
| if (handleInfo->num_dimensions() != expectedAclInfo.num_dimensions()) |
| { |
| return false; |
| } |
| |
| if (handleInfo->quantization_info() != expectedAclInfo.quantization_info()) |
| { |
| return false; |
| } |
| |
| for (std::size_t d = 0; d < expectedAclInfo.num_dimensions(); ++d) |
| { |
| if (handleInfo->dimension(d) != expectedAclInfo.dimension(d)) |
| { |
| return false; |
| } |
| } |
| |
| return true; |
| } |
| |
| } // namespace |
| |
| template <typename ActivationWorkloadType, typename armnn::DataType DataType> |
| static void NeonCreateActivationWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory; |
| auto workload = CreateActivationWorkloadTest<ActivationWorkloadType, DataType> |
| (factory, graph); |
| |
| // Checks that inputs/outputs are as we expect them (see definition of CreateActivationWorkloadTest). |
| ActivationQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({1, 1}, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({1, 1}, DataType))); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateActivationFloat16Workload) |
| { |
| NeonCreateActivationWorkloadTest<NeonActivationFloatWorkload, DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateActivationFloatWorkload) |
| { |
| NeonCreateActivationWorkloadTest<NeonActivationFloatWorkload, DataType::Float32>(); |
| } |
| |
| template <typename WorkloadType, |
| typename DescriptorType, |
| typename LayerType, |
| armnn::DataType DataType> |
| static void NeonCreateArithmethicWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory; |
| auto workload = CreateArithmeticWorkloadTest<WorkloadType, DescriptorType, LayerType, DataType>(factory, graph); |
| |
| DescriptorType queueDescriptor = workload->GetData(); |
| auto inputHandle1 = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto inputHandle2 = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[1]); |
| auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle1, TensorInfo({2, 3}, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle2, TensorInfo({2, 3}, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 3}, DataType))); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateAdditionFloat16Workload) |
| { |
| NeonCreateArithmethicWorkloadTest<NeonAdditionFloatWorkload, |
| AdditionQueueDescriptor, |
| AdditionLayer, |
| DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateAdditionFloatWorkload) |
| { |
| NeonCreateArithmethicWorkloadTest<NeonAdditionFloatWorkload, |
| AdditionQueueDescriptor, |
| AdditionLayer, |
| DataType::Float32>(); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateSubtractionFloat16Workload) |
| { |
| NeonCreateArithmethicWorkloadTest<NeonSubtractionFloatWorkload, |
| SubtractionQueueDescriptor, |
| SubtractionLayer, |
| DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateSubtractionFloatWorkload) |
| { |
| NeonCreateArithmethicWorkloadTest<NeonSubtractionFloatWorkload, |
| SubtractionQueueDescriptor, |
| SubtractionLayer, |
| DataType::Float32>(); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateMultiplicationFloat16Workload) |
| { |
| NeonCreateArithmethicWorkloadTest<NeonMultiplicationFloatWorkload, |
| MultiplicationQueueDescriptor, |
| MultiplicationLayer, |
| DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateMultiplicationFloatWorkload) |
| { |
| NeonCreateArithmethicWorkloadTest<NeonMultiplicationFloatWorkload, |
| MultiplicationQueueDescriptor, |
| MultiplicationLayer, |
| DataType::Float32>(); |
| } |
| |
| template <typename BatchNormalizationWorkloadType, typename armnn::DataType DataType> |
| static void NeonCreateBatchNormalizationWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory; |
| auto workload = CreateBatchNormalizationWorkloadTest<BatchNormalizationWorkloadType, DataType>(factory, graph); |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateBatchNormalizationWorkloadTest). |
| BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({2, 3, 1, 1}, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 3, 1, 1}, DataType))); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloat16Workload) |
| { |
| NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationFloatWorkload, DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloatWorkload) |
| { |
| NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationFloatWorkload, DataType::Float32>(); |
| } |
| |
| template <typename Convolution2dWorkloadType, typename armnn::DataType DataType> |
| static void NeonCreateConvolution2dWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory; |
| auto workload = CreateConvolution2dWorkloadTest<Convolution2dWorkloadType, |
| DataType>(factory, graph); |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateConvolution2dWorkloadTest). |
| Convolution2dQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({2, 3, 8, 16}, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 2, 2, 10}, DataType))); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateConvolution2dFloat16Workload) |
| { |
| NeonCreateConvolution2dWorkloadTest<NeonConvolution2dFloatWorkload, DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateConvolution2dFloatWorkload) |
| { |
| NeonCreateConvolution2dWorkloadTest<NeonConvolution2dFloatWorkload, DataType::Float32>(); |
| } |
| |
| template <typename FullyConnectedWorkloadType, typename armnn::DataType DataType> |
| static void NeonCreateFullyConnectedWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory; |
| auto workload = CreateFullyConnectedWorkloadTest<FullyConnectedWorkloadType, |
| DataType>(factory, graph); |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateFullyConnectedWorkloadTest). |
| FullyConnectedQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({3, 1, 4, 5}, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({3, 7}, DataType))); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateFullyConnectedFloat16Workload) |
| { |
| NeonCreateFullyConnectedWorkloadTest<NeonFullyConnectedFloatWorkload, DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateFullyConnectedFloatWorkload) |
| { |
| NeonCreateFullyConnectedWorkloadTest<NeonFullyConnectedFloatWorkload, DataType::Float32>(); |
| } |
| |
| template <typename NormalizationWorkloadType, typename armnn::DataType DataType> |
| static void NeonCreateNormalizationWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory; |
| auto workload = CreateNormalizationWorkloadTest<NormalizationWorkloadType, DataType>(factory, graph); |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateNormalizationWorkloadTest). |
| NormalizationQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({3, 5, 5, 1}, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({3, 5, 5, 1}, DataType))); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateNormalizationFloat16Workload) |
| { |
| NeonCreateNormalizationWorkloadTest<NeonNormalizationFloatWorkload, DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateNormalizationFloatWorkload) |
| { |
| NeonCreateNormalizationWorkloadTest<NeonNormalizationFloatWorkload, DataType::Float32>(); |
| } |
| |
| template <typename Pooling2dWorkloadType, typename armnn::DataType DataType> |
| static void NeonCreatePooling2dWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory; |
| auto workload = CreatePooling2dWorkloadTest<Pooling2dWorkloadType, DataType> |
| (factory, graph); |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreatePooling2dWorkloadTest). |
| Pooling2dQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({3, 2, 5, 5}, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({3, 2, 2, 4}, DataType))); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreatePooling2dFloat16Workload) |
| { |
| NeonCreatePooling2dWorkloadTest<NeonPooling2dFloatWorkload, DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreatePooling2dFloatWorkload) |
| { |
| NeonCreatePooling2dWorkloadTest<NeonPooling2dFloatWorkload, DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreatePooling2dUint8Workload) |
| { |
| NeonCreatePooling2dWorkloadTest<NeonPooling2dUint8Workload, DataType::QuantisedAsymm8>(); |
| } |
| |
| template <typename ReshapeWorkloadType, typename armnn::DataType DataType> |
| static void NeonCreateReshapeWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory; |
| auto workload = CreateReshapeWorkloadTest<ReshapeWorkloadType, DataType>(factory, graph); |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateReshapeWorkloadTest). |
| ReshapeQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({4, 1}, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({1, 4}, DataType))); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateReshapeFloat16Workload) |
| { |
| NeonCreateReshapeWorkloadTest<NeonReshapeFloatWorkload, DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateReshapeFloatWorkload) |
| { |
| NeonCreateReshapeWorkloadTest<NeonReshapeFloatWorkload, DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateReshapeUint8Workload) |
| { |
| NeonCreateReshapeWorkloadTest<NeonReshapeUint8Workload, DataType::QuantisedAsymm8>(); |
| } |
| |
| template <typename SoftmaxWorkloadType, typename armnn::DataType DataType> |
| static void NeonCreateSoftmaxWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory; |
| auto workload = CreateSoftmaxWorkloadTest<SoftmaxWorkloadType, DataType>(factory, graph); |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateSoftmaxWorkloadTest). |
| SoftmaxQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({4, 1}, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({4, 1}, DataType))); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateSoftmaxFloat16Workload) |
| { |
| NeonCreateSoftmaxWorkloadTest<NeonSoftmaxFloatWorkload, DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateSoftmaxFloatWorkload) |
| { |
| NeonCreateSoftmaxWorkloadTest<NeonSoftmaxFloatWorkload, DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSplitterWorkload) |
| { |
| Graph graph; |
| NeonWorkloadFactory factory; |
| auto workload = CreateSplitterWorkloadTest<NeonSplitterFloatWorkload, DataType::Float32>(factory, graph); |
| |
| // Checks that outputs are as we expect them (see definition of CreateSplitterWorkloadTest). |
| SplitterQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({5, 7, 7}, DataType::Float32))); |
| |
| auto outputHandle0 = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle0, TensorInfo({1, 7, 7}, DataType::Float32))); |
| |
| auto outputHandle1 = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[1]); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle1, TensorInfo({2, 7, 7}, DataType::Float32))); |
| |
| auto outputHandle2 = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[2]); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle2, TensorInfo({2, 7, 7}, DataType::Float32))); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSplitterMerger) |
| { |
| // 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; |
| NeonWorkloadFactory factory; |
| |
| auto workloads = |
| CreateSplitterMergerWorkloadTest<NeonSplitterFloatWorkload, NeonMergerFloatWorkload, |
| DataType::Float32>(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::INeonTensorHandle* sOut0 = dynamic_cast<armnn::INeonTensorHandle*>(wlSplitter->GetData().m_Outputs[0]); |
| armnn::INeonTensorHandle* sOut1 = dynamic_cast<armnn::INeonTensorHandle*>(wlSplitter->GetData().m_Outputs[1]); |
| armnn::INeonTensorHandle* mIn0 = dynamic_cast<armnn::INeonTensorHandle*>(wlMerger->GetData().m_Inputs[0]); |
| armnn::INeonTensorHandle* mIn1 = dynamic_cast<armnn::INeonTensorHandle*>(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(CreateSingleOutputMultipleInputs) |
| { |
| // 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; |
| NeonWorkloadFactory factory; |
| std::unique_ptr<NeonSplitterFloatWorkload> wlSplitter; |
| std::unique_ptr<NeonActivationFloatWorkload> wlActiv0_0; |
| std::unique_ptr<NeonActivationFloatWorkload> wlActiv0_1; |
| std::unique_ptr<NeonActivationFloatWorkload> wlActiv1_0; |
| std::unique_ptr<NeonActivationFloatWorkload> wlActiv1_1; |
| |
| CreateSplitterMultipleInputsOneOutputWorkloadTest<NeonSplitterFloatWorkload, |
| NeonActivationFloatWorkload, DataType::Float32>(factory, graph, wlSplitter, wlActiv0_0, wlActiv0_1, |
| wlActiv1_0, wlActiv1_1); |
| |
| armnn::INeonTensorHandle* sOut0 = dynamic_cast<armnn::INeonTensorHandle*>(wlSplitter->GetData().m_Outputs[0]); |
| armnn::INeonTensorHandle* sOut1 = dynamic_cast<armnn::INeonTensorHandle*>(wlSplitter->GetData().m_Outputs[1]); |
| armnn::INeonTensorHandle* activ0_0Im = dynamic_cast<armnn::INeonTensorHandle*>(wlActiv0_0->GetData().m_Inputs[0]); |
| armnn::INeonTensorHandle* activ0_1Im = dynamic_cast<armnn::INeonTensorHandle*>(wlActiv0_1->GetData().m_Inputs[0]); |
| armnn::INeonTensorHandle* activ1_0Im = dynamic_cast<armnn::INeonTensorHandle*>(wlActiv1_0->GetData().m_Inputs[0]); |
| armnn::INeonTensorHandle* activ1_1Im = dynamic_cast<armnn::INeonTensorHandle*>(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(CreateMemCopyWorkloadsNeon) |
| { |
| NeonWorkloadFactory factory; |
| CreateMemCopyWorkloads<INeonTensorHandle>(factory); |
| } |
| |
| BOOST_AUTO_TEST_SUITE_END() |