| // |
| // Copyright © 2017, 2023-2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "NeonWorkloadFactoryHelper.hpp" |
| |
| #include <aclCommon/ArmComputeTensorUtils.hpp> |
| #include <armnn/utility/PolymorphicDowncast.hpp> |
| #include <armnn/backends/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> |
| |
| #include <doctest/doctest.h> |
| |
| TEST_SUITE("CreateWorkloadNeon") |
| { |
| namespace |
| { |
| |
| armnn::PredicateResult CompareIAclTensorHandleShape(IAclTensorHandle* tensorHandle, |
| std::initializer_list<unsigned int> expectedDimensions) |
| { |
| return CompareTensorHandleShape<IAclTensorHandle>(tensorHandle, expectedDimensions); |
| } |
| |
| 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 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| CHECK(TestNeonTensorHandleInfo(inputHandle, TensorInfo({1, 1}, DataType))); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, TensorInfo({1, 1}, DataType))); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(__APPLE__) |
| TEST_CASE("CreateActivationFloat16Workload") |
| { |
| NeonCreateActivationWorkloadTest<DataType::Float16>(); |
| } |
| #endif |
| |
| 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 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto inputHandle2 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[1]); |
| auto outputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| CHECK(TestNeonTensorHandleInfo(inputHandle1, TensorInfo({2, 3}, DataType))); |
| CHECK(TestNeonTensorHandleInfo(inputHandle2, TensorInfo({2, 3}, DataType))); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 3}, DataType))); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(__APPLE__) |
| TEST_CASE("CreateAdditionFloat16Workload") |
| { |
| NeonCreateElementwiseWorkloadTest<NeonAdditionWorkload, |
| AdditionQueueDescriptor, |
| AdditionLayer, |
| DataType::Float16>(); |
| } |
| #endif |
| |
| TEST_CASE("CreateAdditionFloatWorkload") |
| { |
| NeonCreateElementwiseWorkloadTest<NeonAdditionWorkload, |
| AdditionQueueDescriptor, |
| AdditionLayer, |
| DataType::Float32>(); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(__APPLE__) |
| TEST_CASE("CreateSubtractionFloat16Workload") |
| { |
| NeonCreateElementwiseWorkloadTest<NeonSubtractionWorkload, |
| SubtractionQueueDescriptor, |
| SubtractionLayer, |
| DataType::Float16>(); |
| } |
| #endif |
| |
| TEST_CASE("CreateSubtractionFloatWorkload") |
| { |
| NeonCreateElementwiseWorkloadTest<NeonSubtractionWorkload, |
| SubtractionQueueDescriptor, |
| SubtractionLayer, |
| DataType::Float32>(); |
| } |
| |
| TEST_CASE("CreateSubtractionUint8Workload") |
| { |
| NeonCreateElementwiseWorkloadTest<NeonSubtractionWorkload, |
| SubtractionQueueDescriptor, |
| SubtractionLayer, |
| DataType::QAsymmU8>(); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(__APPLE__) |
| TEST_CASE("CreateMultiplicationFloat16Workload") |
| { |
| NeonCreateElementwiseWorkloadTest<NeonMultiplicationWorkload, |
| MultiplicationQueueDescriptor, |
| MultiplicationLayer, |
| DataType::Float16>(); |
| } |
| #endif |
| |
| TEST_CASE("CreateMultiplicationFloatWorkload") |
| { |
| NeonCreateElementwiseWorkloadTest<NeonMultiplicationWorkload, |
| MultiplicationQueueDescriptor, |
| MultiplicationLayer, |
| DataType::Float32>(); |
| } |
| |
| TEST_CASE("CreateMultiplicationUint8Workload") |
| { |
| NeonCreateElementwiseWorkloadTest<NeonMultiplicationWorkload, |
| MultiplicationQueueDescriptor, |
| MultiplicationLayer, |
| DataType::QAsymmU8>(); |
| } |
| |
| TEST_CASE("CreateDivisionFloatWorkloadTest") |
| { |
| NeonCreateElementwiseWorkloadTest<NeonDivisionWorkload, |
| DivisionQueueDescriptor, |
| DivisionLayer, |
| armnn::DataType::Float32>(); |
| } |
| |
| 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 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = PolymorphicDowncast<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}; |
| |
| CHECK(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, DataType))); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType))); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(__APPLE__) |
| TEST_CASE("CreateBatchNormalizationFloat16NchwWorkload") |
| { |
| NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationWorkload, DataType::Float16>(DataLayout::NCHW); |
| } |
| |
| TEST_CASE("CreateBatchNormalizationFloat16NhwcWorkload") |
| { |
| NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationWorkload, DataType::Float16>(DataLayout::NHWC); |
| } |
| #endif |
| |
| TEST_CASE("CreateBatchNormalizationFloatNchwWorkload") |
| { |
| NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationWorkload, DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| 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 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| CHECK(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, DataType))); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType))); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(__APPLE__) |
| TEST_CASE("CreateConvolution2dFloat16NchwWorkload") |
| { |
| NeonCreateConvolution2dWorkloadTest<DataType::Float16>(); |
| } |
| |
| TEST_CASE("CreateConvolution2dFloat16NhwcWorkload") |
| { |
| NeonCreateConvolution2dWorkloadTest<DataType::Float16>(DataLayout::NHWC); |
| } |
| #endif |
| |
| TEST_CASE("CreateConvolution2dFloatNchwWorkload") |
| { |
| NeonCreateConvolution2dWorkloadTest<DataType::Float32>(); |
| } |
| |
| TEST_CASE("CreateConvolution2dFloatNhwcWorkload") |
| { |
| NeonCreateConvolution2dWorkloadTest<DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| TEST_CASE("CreateConvolution2dFastMathEnabledWorkload") |
| { |
| Graph graph; |
| using ModelOptions = std::vector<BackendOptions>; |
| ModelOptions modelOptions = {}; |
| BackendOptions cpuAcc("CpuAcc", { { "FastMathEnabled", true } }); |
| modelOptions.push_back(cpuAcc); |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager(), modelOptions); |
| |
| auto workload = CreateConvolution2dWorkloadFastMathTest<NeonConvolution2dWorkload, armnn::DataType::Float32>( |
| factory, graph, DataLayout::NCHW, modelOptions); |
| |
| CHECK(workload != nullptr); |
| auto conv2dWorkload = PolymorphicDowncast<NeonConvolution2dWorkload*>(workload.get()); |
| CHECK(conv2dWorkload != nullptr); |
| CHECK(conv2dWorkload->GetConvolutionMethod() == arm_compute::ConvolutionMethod::WINOGRAD); |
| } |
| |
| 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 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = PolymorphicDowncast<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 }); |
| |
| CHECK(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, DataType))); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType))); |
| } |
| |
| TEST_CASE("CreateDepthWiseConvolution2dFloat32NhwcWorkload") |
| { |
| NeonCreateDepthWiseConvolutionWorkloadTest<DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(__APPLE__) |
| 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 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateFullyConnectedWorkloadTest). |
| float inputsQScale = 1.0f; |
| float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 1.0; |
| CHECK(TestNeonTensorHandleInfo(inputHandle, TensorInfo({3, 1, 4, 5}, DataType, inputsQScale))); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, TensorInfo({3, 7}, DataType, outputQScale))); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(__APPLE__) |
| TEST_CASE("CreateFullyConnectedFloat16Workload") |
| { |
| NeonCreateFullyConnectedWorkloadTest<NeonFullyConnectedWorkload, DataType::Float16>(); |
| } |
| #endif |
| |
| TEST_CASE("CreateFullyConnectedFloatWorkload") |
| { |
| NeonCreateFullyConnectedWorkloadTest<NeonFullyConnectedWorkload, DataType::Float32>(); |
| } |
| |
| TEST_CASE("CreateFullyConnectedQAsymmU8Workload") |
| { |
| NeonCreateFullyConnectedWorkloadTest<NeonFullyConnectedWorkload, DataType::QAsymmU8>(); |
| } |
| |
| TEST_CASE("CreateFullyConnectedQAsymmS8Workload") |
| { |
| NeonCreateFullyConnectedWorkloadTest<NeonFullyConnectedWorkload, DataType::QAsymmS8>(); |
| } |
| |
| 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 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = PolymorphicDowncast<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}; |
| |
| CHECK(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, DataType))); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType))); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(__APPLE__) |
| TEST_CASE("CreateNormalizationFloat16NchwWorkload") |
| { |
| NeonCreateNormalizationWorkloadTest<NeonNormalizationFloatWorkload, DataType::Float16>(DataLayout::NCHW); |
| } |
| |
| TEST_CASE("CreateNormalizationFloat16NhwcWorkload") |
| { |
| NeonCreateNormalizationWorkloadTest<NeonNormalizationFloatWorkload, DataType::Float16>(DataLayout::NHWC); |
| } |
| #endif |
| |
| TEST_CASE("CreateNormalizationFloatNchwWorkload") |
| { |
| NeonCreateNormalizationWorkloadTest<NeonNormalizationFloatWorkload, DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| 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 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| CHECK(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, DataType))); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType))); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(__APPLE__) |
| TEST_CASE("CreatePooling2dFloat16Workload") |
| { |
| NeonCreatePooling2dWorkloadTest<DataType::Float16>(); |
| } |
| #endif |
| |
| TEST_CASE("CreatePooling2dFloatNchwWorkload") |
| { |
| NeonCreatePooling2dWorkloadTest<DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| TEST_CASE("CreatePooling2dFloatNhwcWorkload") |
| { |
| NeonCreatePooling2dWorkloadTest<DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| TEST_CASE("CreatePooling2dUint8NchwWorkload") |
| { |
| NeonCreatePooling2dWorkloadTest<DataType::QAsymmU8>(DataLayout::NCHW); |
| } |
| |
| TEST_CASE("CreatePooling2dUint8NhwcWorkload") |
| { |
| NeonCreatePooling2dWorkloadTest<DataType::QAsymmU8>(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 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto alphaHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[1]); |
| auto outputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| CHECK(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, dataType))); |
| CHECK(TestNeonTensorHandleInfo(alphaHandle, TensorInfo(alphaShape, dataType))); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, dataType))); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(__APPLE__) |
| TEST_CASE("CreatePreluFloat16Workload") |
| { |
| NeonCreatePreluWorkloadTest({ 1, 4, 1, 2 }, { 5, 4, 3, 1 }, { 5, 4, 3, 2 }, DataType::Float16); |
| } |
| #endif |
| |
| TEST_CASE("CreatePreluFloatWorkload") |
| { |
| NeonCreatePreluWorkloadTest({ 1, 4, 1, 2 }, { 5, 4, 3, 1 }, { 5, 4, 3, 2 }, DataType::Float32); |
| } |
| |
| TEST_CASE("CreatePreluUint8Workload") |
| { |
| NeonCreatePreluWorkloadTest({ 1, 4, 1, 2 }, { 5, 4, 3, 1 }, { 5, 4, 3, 2 }, DataType::QAsymmU8); |
| } |
| |
| 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 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| CHECK(TestNeonTensorHandleInfo(inputHandle, TensorInfo({4, 1}, DataType))); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, TensorInfo({1, 4}, DataType))); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(__APPLE__) |
| TEST_CASE("CreateReshapeFloat16Workload") |
| { |
| NeonCreateReshapeWorkloadTest<DataType::Float16>(); |
| } |
| #endif |
| |
| TEST_CASE("CreateReshapeFloatWorkload") |
| { |
| NeonCreateReshapeWorkloadTest<DataType::Float32>(); |
| } |
| |
| TEST_CASE("CreateReshapeUint8Workload") |
| { |
| NeonCreateReshapeWorkloadTest<DataType::QAsymmU8>(); |
| } |
| |
| template <typename ResizeWorkloadType, armnn::DataType DataType> |
| static void NeonCreateResizeWorkloadTest(DataLayout dataLayout) |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| auto workload = CreateResizeBilinearWorkloadTest<ResizeWorkloadType, DataType>(factory, graph, dataLayout); |
| |
| auto queueDescriptor = workload->GetData(); |
| |
| auto inputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| armnn::PredicateResult predResult(true); |
| switch (dataLayout) |
| { |
| case DataLayout::NHWC: |
| predResult = CompareIAclTensorHandleShape(inputHandle, { 2, 4, 4, 3 }); |
| CHECK_MESSAGE(predResult.m_Result, predResult.m_Message.str()); |
| predResult = CompareIAclTensorHandleShape(outputHandle, { 2, 2, 2, 3 }); |
| CHECK_MESSAGE(predResult.m_Result, predResult.m_Message.str()); |
| break; |
| default: // DataLayout::NCHW |
| predResult = CompareIAclTensorHandleShape(inputHandle, { 2, 3, 4, 4 }); |
| CHECK_MESSAGE(predResult.m_Result, predResult.m_Message.str()); |
| predResult = CompareIAclTensorHandleShape(outputHandle, { 2, 3, 2, 2 }); |
| CHECK_MESSAGE(predResult.m_Result, predResult.m_Message.str()); |
| } |
| } |
| |
| TEST_CASE("CreateResizeFloat32NchwWorkload") |
| { |
| NeonCreateResizeWorkloadTest<NeonResizeWorkload, armnn::DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| TEST_CASE("CreateResizeUint8NchwWorkload") |
| { |
| NeonCreateResizeWorkloadTest<NeonResizeWorkload, armnn::DataType::QAsymmU8>(DataLayout::NCHW); |
| } |
| |
| TEST_CASE("CreateResizeFloat32NhwcWorkload") |
| { |
| NeonCreateResizeWorkloadTest<NeonResizeWorkload, armnn::DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| TEST_CASE("CreateResizeUint8NhwcWorkload") |
| { |
| NeonCreateResizeWorkloadTest<NeonResizeWorkload, armnn::DataType::QAsymmU8>(DataLayout::NHWC); |
| } |
| |
| 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 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| armnn::TensorInfo tensorInfo({4, 1}, DataType); |
| if (DataType == armnn::DataType::QAsymmU8) |
| { |
| tensorInfo.SetQuantizationOffset(0); |
| tensorInfo.SetQuantizationScale(1.f / 256); |
| } |
| else if (DataType == armnn::DataType::QAsymmS8) |
| { |
| tensorInfo.SetQuantizationOffset(-128); |
| tensorInfo.SetQuantizationScale(1.f / 256); |
| } |
| CHECK(TestNeonTensorHandleInfo(inputHandle, tensorInfo)); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, tensorInfo)); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(__APPLE__) |
| TEST_CASE("CreateSoftmaxFloat16Workload") |
| { |
| NeonCreateSoftmaxWorkloadTest<NeonSoftmaxWorkload, DataType::Float16>(); |
| } |
| #endif |
| |
| TEST_CASE("CreateSoftmaxFloatWorkload") |
| { |
| NeonCreateSoftmaxWorkloadTest<NeonSoftmaxWorkload, DataType::Float32>(); |
| } |
| |
| TEST_CASE("CreateSoftmaxQAsymmU8Workload") |
| { |
| NeonCreateSoftmaxWorkloadTest<NeonSoftmaxWorkload, DataType::QAsymmU8>(); |
| } |
| |
| TEST_CASE("CreateSoftmaxQAsymmS8Workload") |
| { |
| NeonCreateSoftmaxWorkloadTest<NeonSoftmaxWorkload, DataType::QAsymmS8>(); |
| } |
| |
| 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 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| CHECK(TestNeonTensorHandleInfo(inputHandle, TensorInfo({ 1, 2, 2, 1 }, DataType))); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, TensorInfo({ 1, 1, 1, 4 }, DataType))); |
| } |
| |
| TEST_CASE("CreateSpaceToDepthFloat32Workload") |
| { |
| NeonSpaceToDepthWorkloadTest<NeonSpaceToDepthWorkload, armnn::DataType::Float32>(); |
| } |
| |
| TEST_CASE("CreateSpaceToDepthFloat16Workload") |
| { |
| NeonSpaceToDepthWorkloadTest<NeonSpaceToDepthWorkload, armnn::DataType::Float16>(); |
| } |
| |
| TEST_CASE("CreateSpaceToDepthQAsymm8Workload") |
| { |
| NeonSpaceToDepthWorkloadTest<NeonSpaceToDepthWorkload, armnn::DataType::QAsymmU8>(); |
| } |
| |
| TEST_CASE("CreateSpaceToDepthQSymm16Workload") |
| { |
| NeonSpaceToDepthWorkloadTest<NeonSpaceToDepthWorkload, armnn::DataType::QSymmS16>(); |
| } |
| |
| 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 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| CHECK(TestNeonTensorHandleInfo(inputHandle, TensorInfo({5, 7, 7}, DataType::Float32))); |
| |
| auto outputHandle0 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| CHECK(TestNeonTensorHandleInfo(outputHandle0, TensorInfo({1, 7, 7}, DataType::Float32))); |
| |
| auto outputHandle1 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[1]); |
| CHECK(TestNeonTensorHandleInfo(outputHandle1, TensorInfo({2, 7, 7}, DataType::Float32))); |
| |
| auto outputHandle2 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[2]); |
| CHECK(TestNeonTensorHandleInfo(outputHandle2, TensorInfo({2, 7, 7}, DataType::Float32))); |
| } |
| |
| 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]); |
| |
| CHECK(sOut0); |
| CHECK(sOut1); |
| CHECK(mIn0); |
| CHECK(mIn1); |
| |
| bool validDataPointers = (sOut0 == mIn1) && (sOut1 == mIn0); |
| |
| CHECK(validDataPointers); |
| } |
| |
| 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]); |
| |
| |
| CHECK(sOut0); |
| CHECK(sOut1); |
| CHECK(activ0_0Im); |
| CHECK(activ0_1Im); |
| CHECK(activ1_0Im); |
| CHECK(activ1_1Im); |
| |
| bool validDataPointers = (sOut0 == activ0_0Im) && (sOut0 == activ0_1Im) && |
| (sOut1 == activ1_0Im) && (sOut1 == activ1_1Im); |
| |
| CHECK(validDataPointers); |
| } |
| |
| #if defined(ARMNNREF_ENABLED) |
| |
| // This test unit needs the reference backend, it's not available if the reference backend is not built |
| |
| TEST_CASE("CreateMemCopyWorkloadsNeon") |
| { |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| CreateMemCopyWorkloads<IAclTensorHandle>(factory); |
| } |
| |
| #endif |
| |
| 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 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = PolymorphicDowncast<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 }; |
| |
| CHECK(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, DataType))); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType))); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(__APPLE__) |
| TEST_CASE("CreateL2NormalizationFloat16NchwWorkload") |
| { |
| NeonCreateL2NormalizationWorkloadTest<NeonL2NormalizationFloatWorkload, DataType::Float16>(DataLayout::NCHW); |
| } |
| |
| TEST_CASE("CreateL2NormalizationFloat16NhwcWorkload") |
| { |
| NeonCreateL2NormalizationWorkloadTest<NeonL2NormalizationFloatWorkload, DataType::Float16>(DataLayout::NHWC); |
| } |
| #endif |
| |
| TEST_CASE("CreateL2NormalizationNchwWorkload") |
| { |
| NeonCreateL2NormalizationWorkloadTest<NeonL2NormalizationFloatWorkload, DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| TEST_CASE("CreateL2NormalizationNhwcWorkload") |
| { |
| NeonCreateL2NormalizationWorkloadTest<NeonL2NormalizationFloatWorkload, DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| template <typename LogSoftmaxWorkloadType, typename armnn::DataType DataType> |
| static void NeonCreateLogSoftmaxWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateLogSoftmaxWorkloadTest<LogSoftmaxWorkloadType, DataType>(factory, graph); |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateLogSoftmaxWorkloadTest). |
| LogSoftmaxQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| armnn::TensorInfo tensorInfo({4, 1}, DataType); |
| |
| CHECK(TestNeonTensorHandleInfo(inputHandle, tensorInfo)); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, tensorInfo)); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(__APPLE__) |
| TEST_CASE("CreateLogSoftmaxFloat16Workload") |
| { |
| NeonCreateLogSoftmaxWorkloadTest<NeonLogSoftmaxWorkload, DataType::Float16>(); |
| } |
| #endif |
| |
| TEST_CASE("CreateLogSoftmaxFloatWorkload") |
| { |
| NeonCreateLogSoftmaxWorkloadTest<NeonLogSoftmaxWorkload, DataType::Float32>(); |
| } |
| |
| 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 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[1]); |
| |
| CHECK(TestNeonTensorHandleInfo(inputHandle, TensorInfo({ 2, 2 }, DataType::Float32))); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, TensorInfo({ 2, 4 }, DataType::Float32))); |
| } |
| |
| 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 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto inputHandle1 = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[1]); |
| auto outputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| CHECK(TestNeonTensorHandleInfo(inputHandle0, TensorInfo({ 2, 3, 2, 5 }, DataType))); |
| CHECK(TestNeonTensorHandleInfo(inputHandle1, TensorInfo({ 2, 3, 2, 5 }, DataType))); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType))); |
| } |
| |
| TEST_CASE("CreateConcatDim0Float32Workload") |
| { |
| NeonCreateConcatWorkloadTest<NeonConcatWorkload, armnn::DataType::Float32>({ 4, 3, 2, 5 }, 0); |
| } |
| |
| TEST_CASE("CreateConcatDim1Float32Workload") |
| { |
| NeonCreateConcatWorkloadTest<NeonConcatWorkload, armnn::DataType::Float32>({ 2, 6, 2, 5 }, 1); |
| } |
| |
| TEST_CASE("CreateConcatDim3Float32Workload") |
| { |
| NeonCreateConcatWorkloadTest<NeonConcatWorkload, armnn::DataType::Float32>({ 2, 3, 2, 10 }, 3); |
| } |
| |
| TEST_CASE("CreateConcatDim0Uint8Workload") |
| { |
| NeonCreateConcatWorkloadTest<NeonConcatWorkload, armnn::DataType::QAsymmU8>({ 4, 3, 2, 5 }, 0); |
| } |
| |
| TEST_CASE("CreateConcatDim1Uint8Workload") |
| { |
| NeonCreateConcatWorkloadTest<NeonConcatWorkload, armnn::DataType::QAsymmU8>({ 2, 6, 2, 5 }, 1); |
| } |
| |
| TEST_CASE("CreateConcatDim3Uint8Workload") |
| { |
| NeonCreateConcatWorkloadTest<NeonConcatWorkload, armnn::DataType::QAsymmU8>({ 2, 3, 2, 10 }, 3); |
| } |
| |
| template <armnn::DataType DataType> |
| static void NeonCreateStackWorkloadTest(const std::initializer_list<unsigned int>& inputShape, |
| const std::initializer_list<unsigned int>& outputShape, |
| unsigned int axis, |
| unsigned int numInputs) |
| { |
| armnn::Graph graph; |
| NeonWorkloadFactory factory = |
| NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateStackWorkloadTest<NeonStackWorkload, DataType>(factory, |
| graph, |
| TensorShape(inputShape), |
| TensorShape(outputShape), |
| axis, |
| numInputs); |
| |
| // Check inputs and output are as expected |
| StackQueueDescriptor queueDescriptor = workload->GetData(); |
| for (unsigned int i = 0; i < numInputs; ++i) |
| { |
| auto inputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[i]); |
| CHECK(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, DataType))); |
| } |
| auto outputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| CHECK(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType))); |
| } |
| |
| TEST_CASE("CreateStackFloat32Workload") |
| { |
| NeonCreateStackWorkloadTest<armnn::DataType::Float32>({ 3, 4, 5 }, { 3, 4, 2, 5 }, 2, 2); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(__APPLE__) |
| TEST_CASE("CreateStackFloat16Workload") |
| { |
| NeonCreateStackWorkloadTest<armnn::DataType::Float16>({ 3, 4, 5 }, { 3, 4, 2, 5 }, 2, 2); |
| } |
| #endif |
| |
| TEST_CASE("CreateStackUint8Workload") |
| { |
| NeonCreateStackWorkloadTest<armnn::DataType::QAsymmU8>({ 3, 4, 5 }, { 3, 4, 2, 5 }, 2, 2); |
| } |
| |
| template <typename QuantizedLstmWorkloadType> |
| static void NeonCreateQuantizedLstmWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateQuantizedLstmWorkloadTest<QuantizedLstmWorkloadType>(factory, graph); |
| |
| QuantizedLstmQueueDescriptor queueDescriptor = workload->GetData(); |
| |
| IAclTensorHandle* inputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| CHECK((inputHandle->GetShape() == TensorShape({2, 2}))); |
| CHECK((inputHandle->GetDataType() == arm_compute::DataType::QASYMM8)); |
| |
| IAclTensorHandle* cellStateInHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[1]); |
| CHECK((cellStateInHandle->GetShape() == TensorShape({2, 4}))); |
| CHECK((cellStateInHandle->GetDataType() == arm_compute::DataType::QSYMM16)); |
| |
| IAclTensorHandle* outputStateInHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[2]); |
| CHECK((outputStateInHandle->GetShape() == TensorShape({2, 4}))); |
| CHECK((outputStateInHandle->GetDataType() == arm_compute::DataType::QASYMM8)); |
| |
| IAclTensorHandle* cellStateOutHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| CHECK((cellStateOutHandle->GetShape() == TensorShape({2, 4}))); |
| CHECK((cellStateOutHandle->GetDataType() == arm_compute::DataType::QSYMM16)); |
| |
| IAclTensorHandle* outputStateOutHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[1]); |
| CHECK((outputStateOutHandle->GetShape() == TensorShape({2, 4}))); |
| CHECK((outputStateOutHandle->GetDataType() == arm_compute::DataType::QASYMM8)); |
| } |
| |
| TEST_CASE("CreateQuantizedLstmWorkload") |
| { |
| NeonCreateQuantizedLstmWorkloadTest<NeonQuantizedLstmWorkload>(); |
| } |
| |
| template <typename QLstmWorkloadType> |
| static void NeonCreateQLstmWorkloadTest() |
| { |
| Graph graph; |
| NeonWorkloadFactory factory = NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateQLstmWorkloadTest<QLstmWorkloadType>(factory, graph); |
| QLstmQueueDescriptor queueDescriptor = workload->GetData(); |
| |
| IAclTensorHandle* inputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| CHECK((inputHandle->GetShape() == TensorShape({2, 4}))); |
| CHECK((inputHandle->GetDataType() == arm_compute::DataType::QASYMM8_SIGNED)); |
| |
| IAclTensorHandle* cellStateOutHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[1]); |
| CHECK((cellStateOutHandle->GetShape() == TensorShape({2, 4}))); |
| CHECK((cellStateOutHandle->GetDataType() == arm_compute::DataType::QSYMM16)); |
| |
| IAclTensorHandle* outputHandle = PolymorphicDowncast<IAclTensorHandle*>(queueDescriptor.m_Outputs[2]); |
| CHECK((outputHandle->GetShape() == TensorShape({2, 4}))); |
| CHECK((outputHandle->GetDataType() == arm_compute::DataType::QASYMM8_SIGNED)); |
| } |
| |
| TEST_CASE("CreateQLstmWorkloadTest") |
| { |
| NeonCreateQLstmWorkloadTest<NeonQLstmWorkload>(); |
| } |
| |
| template <armnn::DataType DataType> |
| static void NeonCreateActivationWorkloadReplaceFunctionsTest() |
| { |
| shared_ptr<NeonMemoryManager> memoryManager = make_shared<NeonMemoryManager>(); |
| |
| Graph graph; |
| NeonWorkloadFactory factory = NeonWorkloadFactoryHelper::GetFactory(memoryManager); |
| // input and output are created as armnn::TensorInfo tensorInfo({1, 1}, DataType) |
| auto workloadPtr = CreateActivationWorkloadTest<NeonActivationWorkload, DataType>(factory, graph); |
| |
| // new input and output tensor handlers are created and then replace in the workload |
| const NeonTensorHandleFactory tensorHandleFactory(memoryManager); |
| TensorInfo inputInfo({2 , 2}, DataType::Float16); |
| TensorInfo outputInfo({2 , 2}, DataType::Float16); |
| unique_ptr<ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputInfo); |
| inputHandle->Allocate(); |
| unique_ptr<ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputInfo); |
| outputHandle->Allocate(); |
| |
| unsigned int slot = 0; |
| CHECK_THROWS_AS(workloadPtr->ReplaceInputTensorHandle(inputHandle.get(), slot), UnimplementedException); |
| CHECK_THROWS_AS(workloadPtr->ReplaceOutputTensorHandle(outputHandle.get(), slot), UnimplementedException); |
| } |
| |
| TEST_CASE("NeonReplaceFunctionsfromFloat32toFloat16ActivationWorkload") |
| { |
| NeonCreateActivationWorkloadReplaceFunctionsTest<armnn::DataType::Float32>(); |
| } |
| |
| TEST_CASE("NeonReplaceFunctionsfromUint8toFloat16ActivationWorkload") |
| { |
| NeonCreateActivationWorkloadReplaceFunctionsTest<armnn::DataType::QAsymmU8>(); |
| } |
| |
| } |