| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "NeonWorkloadFactoryHelper.hpp" |
| |
| #include <backendsCommon/MemCopyWorkload.hpp> |
| |
| #include <aclCommon/test/CreateWorkloadClNeon.hpp> |
| |
| #include <neon/NeonWorkloadFactory.hpp> |
| #include <neon/NeonTensorHandle.hpp> |
| #include <neon/workloads/NeonWorkloadUtils.hpp> |
| #include <neon/workloads/NeonWorkloads.hpp> |
| |
| BOOST_AUTO_TEST_SUITE(CreateWorkloadNeon) |
| |
| namespace |
| { |
| |
| bool TestNeonTensorHandleInfo(armnn::IAclTensorHandle* 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 armnn::DataType DataType> |
| static void NeonCreateActivationWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateActivationWorkloadTest<NeonActivationWorkload, 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<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(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<DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateActivationFloatWorkload) |
| { |
| NeonCreateActivationWorkloadTest<DataType::Float32>(); |
| } |
| |
| template <typename WorkloadType, |
| typename DescriptorType, |
| typename LayerType, |
| armnn::DataType DataType> |
| static void NeonCreateElementwiseWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateElementwiseWorkloadTest<WorkloadType, DescriptorType, LayerType, DataType>(factory, graph); |
| |
| DescriptorType queueDescriptor = workload->GetData(); |
| auto inputHandle1 = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto inputHandle2 = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Inputs[1]); |
| auto outputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(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) |
| { |
| NeonCreateElementwiseWorkloadTest<NeonAdditionWorkload, |
| AdditionQueueDescriptor, |
| AdditionLayer, |
| DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateAdditionFloatWorkload) |
| { |
| NeonCreateElementwiseWorkloadTest<NeonAdditionWorkload, |
| AdditionQueueDescriptor, |
| AdditionLayer, |
| DataType::Float32>(); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateSubtractionFloat16Workload) |
| { |
| NeonCreateElementwiseWorkloadTest<NeonSubtractionWorkload, |
| SubtractionQueueDescriptor, |
| SubtractionLayer, |
| DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateSubtractionFloatWorkload) |
| { |
| NeonCreateElementwiseWorkloadTest<NeonSubtractionWorkload, |
| SubtractionQueueDescriptor, |
| SubtractionLayer, |
| DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSubtractionUint8Workload) |
| { |
| NeonCreateElementwiseWorkloadTest<NeonSubtractionWorkload, |
| SubtractionQueueDescriptor, |
| SubtractionLayer, |
| DataType::QuantisedAsymm8>(); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateMultiplicationFloat16Workload) |
| { |
| NeonCreateElementwiseWorkloadTest<NeonMultiplicationWorkload, |
| MultiplicationQueueDescriptor, |
| MultiplicationLayer, |
| DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateMultiplicationFloatWorkload) |
| { |
| NeonCreateElementwiseWorkloadTest<NeonMultiplicationWorkload, |
| MultiplicationQueueDescriptor, |
| MultiplicationLayer, |
| DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMultiplicationUint8Workload) |
| { |
| NeonCreateElementwiseWorkloadTest<NeonMultiplicationWorkload, |
| MultiplicationQueueDescriptor, |
| MultiplicationLayer, |
| DataType::QuantisedAsymm8>(); |
| } |
| |
| template <typename BatchNormalizationWorkloadType, typename armnn::DataType DataType> |
| static void NeonCreateBatchNormalizationWorkloadTest(DataLayout dataLayout) |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateBatchNormalizationWorkloadTest<BatchNormalizationWorkloadType, DataType> |
| (factory, graph, dataLayout); |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateBatchNormalizationWorkloadTest). |
| BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 4, 4} : TensorShape{2, 4, 4, 3}; |
| TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 4, 4} : TensorShape{2, 4, 4, 3}; |
| |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType))); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloat16NchwWorkload) |
| { |
| NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationWorkload, DataType::Float16>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloat16NhwcWorkload) |
| { |
| NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationWorkload, DataType::Float16>(DataLayout::NHWC); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloatNchwWorkload) |
| { |
| NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationWorkload, DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloatNhwcWorkload) |
| { |
| NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationWorkload, DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| template <typename armnn::DataType DataType> |
| static void NeonCreateConvolution2dWorkloadTest(DataLayout dataLayout = DataLayout::NCHW) |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateConvolution2dWorkloadTest<NeonConvolution2dWorkload, DataType>(factory, graph, dataLayout); |
| |
| TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 8, 16} : TensorShape{2, 8, 16, 3}; |
| TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 2, 2, 10} : TensorShape{2, 2, 10, 2}; |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateConvolution2dWorkloadTest). |
| Convolution2dQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType))); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateConvolution2dFloat16NchwWorkload) |
| { |
| NeonCreateConvolution2dWorkloadTest<DataType::Float16>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateConvolution2dFloat16NhwcWorkload) |
| { |
| NeonCreateConvolution2dWorkloadTest<DataType::Float16>(DataLayout::NHWC); |
| } |
| |
| #endif |
| BOOST_AUTO_TEST_CASE(CreateConvolution2dFloatNchwWorkload) |
| { |
| NeonCreateConvolution2dWorkloadTest<DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateConvolution2dFloatNhwcWorkload) |
| { |
| NeonCreateConvolution2dWorkloadTest<DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| template <typename armnn::DataType DataType> |
| static void NeonCreateDepthWiseConvolutionWorkloadTest(DataLayout dataLayout) |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateDepthwiseConvolution2dWorkloadTest<NeonDepthwiseConvolutionWorkload, |
| DataType>(factory, graph, dataLayout); |
| |
| // Checks that inputs/outputs are as we expect them (see definition of CreateNormalizationWorkloadTest). |
| DepthwiseConvolution2dQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? std::initializer_list<unsigned int>({ 2, 2, 5, 5 }) |
| : std::initializer_list<unsigned int>({ 2, 5, 5, 2 }); |
| TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? std::initializer_list<unsigned int>({ 2, 2, 5, 5 }) |
| : std::initializer_list<unsigned int>({ 2, 5, 5, 2 }); |
| |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType))); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateDepthWiseConvolution2dFloat32NhwcWorkload) |
| { |
| NeonCreateDepthWiseConvolutionWorkloadTest<DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateDepthWiseConvolution2dFloat16NhwcWorkload) |
| { |
| NeonCreateDepthWiseConvolutionWorkloadTest<DataType::Float16>(DataLayout::NHWC); |
| } |
| #endif |
| |
| template <typename FullyConnectedWorkloadType, typename armnn::DataType DataType> |
| static void NeonCreateFullyConnectedWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| 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<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(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<NeonFullyConnectedWorkload, DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateFullyConnectedFloatWorkload) |
| { |
| NeonCreateFullyConnectedWorkloadTest<NeonFullyConnectedWorkload, DataType::Float32>(); |
| } |
| |
| template <typename NormalizationWorkloadType, typename armnn::DataType DataType> |
| static void NeonCreateNormalizationWorkloadTest(DataLayout dataLayout) |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateNormalizationWorkloadTest<NormalizationWorkloadType, DataType>(factory, graph, dataLayout); |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateNormalizationWorkloadTest). |
| NormalizationQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{3, 5, 5, 1} : TensorShape{3, 1, 5, 5}; |
| TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{3, 5, 5, 1} : TensorShape{3, 1, 5, 5}; |
| |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType))); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateNormalizationFloat16NchwWorkload) |
| { |
| NeonCreateNormalizationWorkloadTest<NeonNormalizationFloatWorkload, DataType::Float16>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateNormalizationFloat16NhwcWorkload) |
| { |
| NeonCreateNormalizationWorkloadTest<NeonNormalizationFloatWorkload, DataType::Float16>(DataLayout::NHWC); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateNormalizationFloatNchwWorkload) |
| { |
| NeonCreateNormalizationWorkloadTest<NeonNormalizationFloatWorkload, DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateNormalizationFloatNhwcWorkload) |
| { |
| NeonCreateNormalizationWorkloadTest<NeonNormalizationFloatWorkload, DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| |
| template <typename armnn::DataType DataType> |
| static void NeonCreatePooling2dWorkloadTest(DataLayout dataLayout = DataLayout::NCHW) |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreatePooling2dWorkloadTest<NeonPooling2dWorkload, DataType>(factory, graph, dataLayout); |
| |
| TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{3, 2, 5, 5} : TensorShape{3, 5, 5, 2}; |
| TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{3, 2, 2, 4} : TensorShape{3, 2, 4, 2}; |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreatePooling2dWorkloadTest). |
| Pooling2dQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType))); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreatePooling2dFloat16Workload) |
| { |
| NeonCreatePooling2dWorkloadTest<DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreatePooling2dFloatNchwWorkload) |
| { |
| NeonCreatePooling2dWorkloadTest<DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreatePooling2dFloatNhwcWorkload) |
| { |
| NeonCreatePooling2dWorkloadTest<DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreatePooling2dUint8NchwWorkload) |
| { |
| NeonCreatePooling2dWorkloadTest<DataType::QuantisedAsymm8>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreatePooling2dUint8NhwcWorkload) |
| { |
| NeonCreatePooling2dWorkloadTest<DataType::QuantisedAsymm8>(DataLayout::NHWC); |
| } |
| |
| static void NeonCreatePreluWorkloadTest(const armnn::TensorShape& inputShape, |
| const armnn::TensorShape& alphaShape, |
| const armnn::TensorShape& outputShape, |
| armnn::DataType dataType) |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreatePreluWorkloadTest<NeonPreluWorkload>(factory, |
| graph, |
| inputShape, |
| alphaShape, |
| outputShape, |
| dataType); |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateReshapeWorkloadTest). |
| PreluQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto alphaHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Inputs[1]); |
| auto outputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, dataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(alphaHandle, TensorInfo(alphaShape, dataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, dataType))); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreatePreluFloat16Workload) |
| { |
| NeonCreatePreluWorkloadTest({ 1, 4, 1, 2 }, { 5, 4, 3, 1 }, { 5, 4, 3, 2 }, DataType::Float16); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreatePreluFloatWorkload) |
| { |
| NeonCreatePreluWorkloadTest({ 1, 4, 1, 2 }, { 5, 4, 3, 1 }, { 5, 4, 3, 2 }, DataType::Float32); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreatePreluUint8Workload) |
| { |
| NeonCreatePreluWorkloadTest({ 1, 4, 1, 2 }, { 5, 4, 3, 1 }, { 5, 4, 3, 2 }, DataType::QuantisedAsymm8); |
| } |
| |
| template <typename armnn::DataType DataType> |
| static void NeonCreateReshapeWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateReshapeWorkloadTest<NeonReshapeWorkload, 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<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(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<DataType::Float16>(); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateReshapeFloatWorkload) |
| { |
| NeonCreateReshapeWorkloadTest<DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateReshapeUint8Workload) |
| { |
| NeonCreateReshapeWorkloadTest<DataType::QuantisedAsymm8>(); |
| } |
| |
| template <typename SoftmaxWorkloadType, typename armnn::DataType DataType> |
| static void NeonCreateSoftmaxWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| 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<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(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>(); |
| } |
| |
| template <typename SpaceToDepthWorkloadType, typename armnn::DataType DataType> |
| static void NeonSpaceToDepthWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateSpaceToDepthWorkloadTest<SpaceToDepthWorkloadType, DataType>(factory, graph); |
| |
| SpaceToDepthQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({ 1, 2, 2, 1 }, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({ 1, 1, 1, 4 }, DataType))); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSpaceToDepthFloat32Workload) |
| { |
| NeonSpaceToDepthWorkloadTest<NeonSpaceToDepthWorkload, armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSpaceToDepthFloat16Workload) |
| { |
| NeonSpaceToDepthWorkloadTest<NeonSpaceToDepthWorkload, armnn::DataType::Float16>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSpaceToDepthQAsymm8Workload) |
| { |
| NeonSpaceToDepthWorkloadTest<NeonSpaceToDepthWorkload, armnn::DataType::QuantisedAsymm8>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSpaceToDepthQSymm16Workload) |
| { |
| NeonSpaceToDepthWorkloadTest<NeonSpaceToDepthWorkload, armnn::DataType::QuantisedSymm16>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSplitterWorkload) |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateSplitterWorkloadTest<NeonSplitterWorkload, 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<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({5, 7, 7}, DataType::Float32))); |
| |
| auto outputHandle0 = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle0, TensorInfo({1, 7, 7}, DataType::Float32))); |
| |
| auto outputHandle1 = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Outputs[1]); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle1, TensorInfo({2, 7, 7}, DataType::Float32))); |
| |
| auto outputHandle2 = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Outputs[2]); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle2, TensorInfo({2, 7, 7}, DataType::Float32))); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSplitterConcat) |
| { |
| // Tests that it is possible to decide which output of the splitter layer |
| // should be lined to which input of the concat layer. |
| // We tested that is is possible to specify 0th output |
| // of the splitter to be the 1st input to the concat, and the 1st output of the splitter to be 0th input |
| // of the concat. |
| |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workloads = |
| CreateSplitterConcatWorkloadTest<NeonSplitterWorkload, NeonConcatWorkload, |
| DataType::Float32>(factory, graph); |
| |
| auto wlSplitter = std::move(workloads.first); |
| auto wlConcat = std::move(workloads.second); |
| |
| //Checks that the index of inputs/outputs matches what we declared on InputDescriptor construction. |
| armnn::IAclTensorHandle* sOut0 = dynamic_cast<armnn::IAclTensorHandle*>(wlSplitter->GetData().m_Outputs[0]); |
| armnn::IAclTensorHandle* sOut1 = dynamic_cast<armnn::IAclTensorHandle*>(wlSplitter->GetData().m_Outputs[1]); |
| armnn::IAclTensorHandle* mIn0 = dynamic_cast<armnn::IAclTensorHandle*>(wlConcat->GetData().m_Inputs[0]); |
| armnn::IAclTensorHandle* mIn1 = dynamic_cast<armnn::IAclTensorHandle*>(wlConcat->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 = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| std::unique_ptr<NeonSplitterWorkload> wlSplitter; |
| std::unique_ptr<NeonActivationWorkload> wlActiv0_0; |
| std::unique_ptr<NeonActivationWorkload> wlActiv0_1; |
| std::unique_ptr<NeonActivationWorkload> wlActiv1_0; |
| std::unique_ptr<NeonActivationWorkload> wlActiv1_1; |
| |
| CreateSplitterMultipleInputsOneOutputWorkloadTest<NeonSplitterWorkload, |
| NeonActivationWorkload, DataType::Float32>(factory, graph, wlSplitter, wlActiv0_0, wlActiv0_1, |
| wlActiv1_0, wlActiv1_1); |
| |
| armnn::IAclTensorHandle* sOut0 = dynamic_cast<armnn::IAclTensorHandle*>(wlSplitter->GetData().m_Outputs[0]); |
| armnn::IAclTensorHandle* sOut1 = dynamic_cast<armnn::IAclTensorHandle*>(wlSplitter->GetData().m_Outputs[1]); |
| armnn::IAclTensorHandle* activ0_0Im = dynamic_cast<armnn::IAclTensorHandle*>(wlActiv0_0->GetData().m_Inputs[0]); |
| armnn::IAclTensorHandle* activ0_1Im = dynamic_cast<armnn::IAclTensorHandle*>(wlActiv0_1->GetData().m_Inputs[0]); |
| armnn::IAclTensorHandle* activ1_0Im = dynamic_cast<armnn::IAclTensorHandle*>(wlActiv1_0->GetData().m_Inputs[0]); |
| armnn::IAclTensorHandle* activ1_1Im = dynamic_cast<armnn::IAclTensorHandle*>(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 = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| CreateMemCopyWorkloads<IAclTensorHandle>(factory); |
| } |
| |
| template <typename L2NormalizationWorkloadType, typename armnn::DataType DataType> |
| static void NeonCreateL2NormalizationWorkloadTest(DataLayout dataLayout) |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = |
| CreateL2NormalizationWorkloadTest<L2NormalizationWorkloadType, DataType>(factory, graph, dataLayout); |
| |
| // Checks that inputs/outputs are as we expect them (see definition of CreateNormalizationWorkloadTest). |
| L2NormalizationQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? |
| TensorShape{ 5, 20, 50, 67 } : TensorShape{ 5, 50, 67, 20 }; |
| TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? |
| TensorShape{ 5, 20, 50, 67 } : TensorShape{ 5, 50, 67, 20 }; |
| |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType))); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| BOOST_AUTO_TEST_CASE(CreateL2NormalizationFloat16NchwWorkload) |
| { |
| NeonCreateL2NormalizationWorkloadTest<NeonL2NormalizationFloatWorkload, DataType::Float16>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateL2NormalizationFloat16NhwcWorkload) |
| { |
| NeonCreateL2NormalizationWorkloadTest<NeonL2NormalizationFloatWorkload, DataType::Float16>(DataLayout::NHWC); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_CASE(CreateL2NormalizationNchwWorkload) |
| { |
| NeonCreateL2NormalizationWorkloadTest<NeonL2NormalizationFloatWorkload, DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateL2NormalizationNhwcWorkload) |
| { |
| NeonCreateL2NormalizationWorkloadTest<NeonL2NormalizationFloatWorkload, DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| template <typename LstmWorkloadType> |
| static void NeonCreateLstmWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateLstmWorkloadTest<LstmWorkloadType>(factory, graph); |
| |
| LstmQueueDescriptor queueDescriptor = workload->GetData(); |
| |
| auto inputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Outputs[1]); |
| |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({ 2, 2 }, DataType::Float32))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({ 2, 4 }, DataType::Float32))); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateLSTMWorkloadFloatWorkload) |
| { |
| NeonCreateLstmWorkloadTest<NeonLstmFloatWorkload>(); |
| } |
| |
| template <typename ConcatWorkloadType, armnn::DataType DataType> |
| static void NeonCreateConcatWorkloadTest(std::initializer_list<unsigned int> outputShape, |
| unsigned int concatAxis) |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateConcatWorkloadTest<ConcatWorkloadType, DataType>(factory, graph, outputShape, concatAxis); |
| |
| ConcatQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle0 = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto inputHandle1 = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Inputs[1]); |
| auto outputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle0, TensorInfo({ 2, 3, 2, 5 }, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(inputHandle1, TensorInfo({ 2, 3, 2, 5 }, DataType))); |
| BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType))); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateConcatDim0Float32Workload) |
| { |
| NeonCreateConcatWorkloadTest<NeonConcatWorkload, armnn::DataType::Float32>({ 4, 3, 2, 5 }, 0); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateConcatDim1Float32Workload) |
| { |
| NeonCreateConcatWorkloadTest<NeonConcatWorkload, armnn::DataType::Float32>({ 2, 6, 2, 5 }, 1); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateConcatDim3Float32Workload) |
| { |
| NeonCreateConcatWorkloadTest<NeonConcatWorkload, armnn::DataType::Float32>({ 2, 3, 2, 10 }, 3); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateConcatDim0Uint8Workload) |
| { |
| NeonCreateConcatWorkloadTest<NeonConcatWorkload, armnn::DataType::QuantisedAsymm8>({ 4, 3, 2, 5 }, 0); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateConcatDim1Uint8Workload) |
| { |
| NeonCreateConcatWorkloadTest<NeonConcatWorkload, armnn::DataType::QuantisedAsymm8>({ 2, 6, 2, 5 }, 1); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateConcatDim3Uint8Workload) |
| { |
| NeonCreateConcatWorkloadTest<NeonConcatWorkload, armnn::DataType::QuantisedAsymm8>({ 2, 3, 2, 10 }, 3); |
| } |
| |
| BOOST_AUTO_TEST_SUITE_END() |
