| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "ClContextControlFixture.hpp" |
| #include "ClWorkloadFactoryHelper.hpp" |
| |
| #include <backendsCommon/MemCopyWorkload.hpp> |
| |
| #include <aclCommon/test/CreateWorkloadClNeon.hpp> |
| |
| #include <cl/ClTensorHandle.hpp> |
| #include <cl/ClWorkloadFactory.hpp> |
| #include <cl/workloads/ClWorkloads.hpp> |
| #include <cl/workloads/ClWorkloadUtils.hpp> |
| |
| boost::test_tools::predicate_result CompareIClTensorHandleShape(IClTensorHandle* tensorHandle, |
| std::initializer_list<unsigned int> expectedDimensions) |
| { |
| return CompareTensorHandleShape<IClTensorHandle>(tensorHandle, expectedDimensions); |
| } |
| |
| BOOST_FIXTURE_TEST_SUITE(CreateWorkloadCl, ClContextControlFixture) |
| |
| template <armnn::DataType DataType> |
| static void ClCreateActivationWorkloadTest() |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateActivationWorkloadTest<ClActivationWorkload, 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<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {1, 1})); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {1, 1})); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateActivationFloatWorkload) |
| { |
| ClCreateActivationWorkloadTest<armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateActivationFloat16Workload) |
| { |
| ClCreateActivationWorkloadTest<armnn::DataType::Float16>(); |
| } |
| |
| template <typename WorkloadType, |
| typename DescriptorType, |
| typename LayerType, |
| armnn::DataType DataType> |
| static void ClCreateElementwiseWorkloadTest() |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateElementwiseWorkloadTest<WorkloadType, DescriptorType, LayerType, DataType>(factory, graph); |
| |
| // Checks that inputs/outputs are as we expect them (see definition of CreateElementwiseWorkloadTest). |
| DescriptorType queueDescriptor = workload->GetData(); |
| auto inputHandle1 = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto inputHandle2 = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[1]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle1, {2, 3})); |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle2, {2, 3})); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {2, 3})); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateAdditionFloatWorkload) |
| { |
| ClCreateElementwiseWorkloadTest<ClAdditionWorkload, |
| AdditionQueueDescriptor, |
| AdditionLayer, |
| armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateAdditionFloat16Workload) |
| { |
| ClCreateElementwiseWorkloadTest<ClAdditionWorkload, |
| AdditionQueueDescriptor, |
| AdditionLayer, |
| armnn::DataType::Float16>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSubtractionFloatWorkload) |
| { |
| ClCreateElementwiseWorkloadTest<ClSubtractionWorkload, |
| SubtractionQueueDescriptor, |
| SubtractionLayer, |
| armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSubtractionFloat16Workload) |
| { |
| ClCreateElementwiseWorkloadTest<ClSubtractionWorkload, |
| SubtractionQueueDescriptor, |
| SubtractionLayer, |
| armnn::DataType::Float16>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMultiplicationFloatWorkloadTest) |
| { |
| ClCreateElementwiseWorkloadTest<ClMultiplicationWorkload, |
| MultiplicationQueueDescriptor, |
| MultiplicationLayer, |
| armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMultiplicationFloat16WorkloadTest) |
| { |
| ClCreateElementwiseWorkloadTest<ClMultiplicationWorkload, |
| MultiplicationQueueDescriptor, |
| MultiplicationLayer, |
| armnn::DataType::Float16>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMultiplicationUint8WorkloadTest) |
| { |
| ClCreateElementwiseWorkloadTest<ClMultiplicationWorkload, |
| MultiplicationQueueDescriptor, |
| MultiplicationLayer, |
| armnn::DataType::QuantisedAsymm8>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateDivisionFloatWorkloadTest) |
| { |
| ClCreateElementwiseWorkloadTest<ClDivisionFloatWorkload, |
| DivisionQueueDescriptor, |
| DivisionLayer, |
| armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateDivisionFloat16WorkloadTest) |
| { |
| ClCreateElementwiseWorkloadTest<ClDivisionFloatWorkload, |
| DivisionQueueDescriptor, |
| DivisionLayer, |
| armnn::DataType::Float16>(); |
| } |
| |
| template <typename BatchNormalizationWorkloadType, armnn::DataType DataType> |
| static void ClCreateBatchNormalizationWorkloadTest(DataLayout dataLayout) |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateBatchNormalizationWorkloadTest<BatchNormalizationWorkloadType, DataType> |
| (factory, graph, dataLayout); |
| |
| // Checks that inputs/outputs are as we expect them (see definition of CreateBatchNormalizationWorkloadTest). |
| BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| switch (dataLayout) |
| { |
| case DataLayout::NHWC: |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, { 2, 4, 4, 3 })); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, { 2, 4, 4, 3 })); |
| break; |
| default: // NCHW |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, { 2, 3, 4, 4 })); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, { 2, 3, 4, 4 })); |
| } |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloatNchwWorkload) |
| { |
| ClCreateBatchNormalizationWorkloadTest<ClBatchNormalizationFloatWorkload, |
| armnn::DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloat16NchwWorkload) |
| { |
| ClCreateBatchNormalizationWorkloadTest<ClBatchNormalizationFloatWorkload, |
| armnn::DataType::Float16>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloatNhwcWorkload) |
| { |
| ClCreateBatchNormalizationWorkloadTest<ClBatchNormalizationFloatWorkload, |
| armnn::DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateBatchNormalizationNhwcFloat16NhwcWorkload) |
| { |
| ClCreateBatchNormalizationWorkloadTest<ClBatchNormalizationFloatWorkload, |
| armnn::DataType::Float16>(DataLayout::NHWC); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateConvertFp16ToFp32Workload) |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateConvertFp16ToFp32WorkloadTest<ClConvertFp16ToFp32Workload>(factory, graph); |
| |
| ConvertFp16ToFp32QueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {1, 3, 2, 3})); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {1, 3, 2, 3})); |
| BOOST_TEST((inputHandle->GetTensor().info()->data_type() == arm_compute::DataType::F16)); |
| BOOST_TEST((outputHandle->GetTensor().info()->data_type() == arm_compute::DataType::F32)); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateConvertFp32ToFp16Workload) |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateConvertFp32ToFp16WorkloadTest<ClConvertFp32ToFp16Workload>(factory, graph); |
| |
| ConvertFp32ToFp16QueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {1, 3, 2, 3})); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {1, 3, 2, 3})); |
| BOOST_TEST((inputHandle->GetTensor().info()->data_type() == arm_compute::DataType::F32)); |
| BOOST_TEST((outputHandle->GetTensor().info()->data_type() == arm_compute::DataType::F16)); |
| } |
| |
| template <typename Convolution2dWorkloadType, typename armnn::DataType DataType> |
| static void ClConvolution2dWorkloadTest(DataLayout dataLayout) |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateConvolution2dWorkloadTest<ClConvolution2dWorkload, DataType>(factory, |
| graph, |
| dataLayout); |
| |
| std::initializer_list<unsigned int> inputShape = (dataLayout == DataLayout::NCHW) ? |
| std::initializer_list<unsigned int>({2, 3, 8, 16}) : std::initializer_list<unsigned int>({2, 8, 16, 3}); |
| std::initializer_list<unsigned int> outputShape = (dataLayout == DataLayout::NCHW) ? |
| std::initializer_list<unsigned int>({2, 2, 2, 10}) : std::initializer_list<unsigned int>({2, 2, 10, 2}); |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateConvolution2dWorkloadTest). |
| Convolution2dQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, inputShape)); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, outputShape)); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateConvolution2dFloatNchwWorkload) |
| { |
| ClConvolution2dWorkloadTest<ClConvolution2dWorkload, armnn::DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateConvolution2dFloatNhwcWorkload) |
| { |
| ClConvolution2dWorkloadTest<ClConvolution2dWorkload, armnn::DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateConvolution2dFloat16NchwWorkload) |
| { |
| ClConvolution2dWorkloadTest<ClConvolution2dWorkload, armnn::DataType::Float16>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateConvolution2dFloat16NhwcWorkload) |
| { |
| ClConvolution2dWorkloadTest<ClConvolution2dWorkload, armnn::DataType::Float16>(DataLayout::NHWC); |
| } |
| |
| template <typename DepthwiseConvolutionWorkloadType, typename armnn::DataType DataType> |
| static void ClDepthwiseConvolutionWorkloadTest(DataLayout dataLayout) |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateDepthwiseConvolution2dWorkloadTest<DepthwiseConvolutionWorkloadType, DataType> |
| (factory, graph, dataLayout); |
| |
| // Checks that inputs/outputs are as we expect them (see definition of CreateDepthwiseConvolution2dWorkloadTest). |
| DepthwiseConvolution2dQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| std::initializer_list<unsigned int> inputShape = (dataLayout == DataLayout::NCHW) |
| ? std::initializer_list<unsigned int>({ 2, 2, 5, 5 }) |
| : std::initializer_list<unsigned int>({ 2, 5, 5, 2 }); |
| std::initializer_list<unsigned int> outputShape = (dataLayout == DataLayout::NCHW) |
| ? std::initializer_list<unsigned int>({ 2, 2, 5, 5 }) |
| : std::initializer_list<unsigned int>({ 2, 5, 5, 2 }); |
| |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, inputShape)); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, outputShape)); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateDepthwiseConvolutionFloat32NhwcWorkload) |
| { |
| ClDepthwiseConvolutionWorkloadTest<ClDepthwiseConvolutionWorkload, DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| template <typename Convolution2dWorkloadType, typename armnn::DataType DataType> |
| static void ClDirectConvolution2dWorkloadTest() |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateDirectConvolution2dWorkloadTest<ClConvolution2dWorkload, DataType>(factory, graph); |
| |
| // Checks that outputs and inputs are as we expect them (see definition of CreateDirectConvolution2dWorkloadTest). |
| Convolution2dQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {2, 3, 6, 6})); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {2, 2, 6, 6})); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateDirectConvolution2dFloatWorkload) |
| { |
| ClDirectConvolution2dWorkloadTest<ClConvolution2dWorkload, armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateDirectConvolution2dFloat16Workload) |
| { |
| ClDirectConvolution2dWorkloadTest<ClConvolution2dWorkload, armnn::DataType::Float16>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateDirectConvolution2dUint8Workload) |
| { |
| ClDirectConvolution2dWorkloadTest<ClConvolution2dWorkload, armnn::DataType::QuantisedAsymm8>(); |
| } |
| |
| template <typename FullyConnectedWorkloadType, typename armnn::DataType DataType> |
| static void ClCreateFullyConnectedWorkloadTest() |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::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<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {3, 1, 4, 5})); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {3, 7})); |
| } |
| |
| |
| BOOST_AUTO_TEST_CASE(CreateFullyConnectedFloatWorkloadTest) |
| { |
| ClCreateFullyConnectedWorkloadTest<ClFullyConnectedWorkload, armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateFullyConnectedFloat16WorkloadTest) |
| { |
| ClCreateFullyConnectedWorkloadTest<ClFullyConnectedWorkload, armnn::DataType::Float16>(); |
| } |
| |
| template <typename NormalizationWorkloadType, typename armnn::DataType DataType> |
| static void ClNormalizationWorkloadTest(DataLayout dataLayout) |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateNormalizationWorkloadTest<NormalizationWorkloadType, DataType>(factory, graph, dataLayout); |
| |
| // Checks that inputs/outputs are as we expect them (see definition of CreateNormalizationWorkloadTest). |
| NormalizationQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| std::initializer_list<unsigned int> inputShape = (dataLayout == DataLayout::NCHW) ? |
| std::initializer_list<unsigned int>({3, 5, 5, 1}) : std::initializer_list<unsigned int>({3, 1, 5, 5}); |
| std::initializer_list<unsigned int> outputShape = (dataLayout == DataLayout::NCHW) ? |
| std::initializer_list<unsigned int>({3, 5, 5, 1}) : std::initializer_list<unsigned int>({3, 1, 5, 5}); |
| |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, inputShape)); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, outputShape)); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateNormalizationFloat32NchwWorkload) |
| { |
| ClNormalizationWorkloadTest<ClNormalizationFloatWorkload, armnn::DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateNormalizationFloat16NchwWorkload) |
| { |
| ClNormalizationWorkloadTest<ClNormalizationFloatWorkload, armnn::DataType::Float16>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateNormalizationFloat32NhwcWorkload) |
| { |
| ClNormalizationWorkloadTest<ClNormalizationFloatWorkload, armnn::DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateNormalizationFloat16NhwcWorkload) |
| { |
| ClNormalizationWorkloadTest<ClNormalizationFloatWorkload, armnn::DataType::Float16>(DataLayout::NHWC); |
| } |
| |
| template <typename armnn::DataType DataType> |
| static void ClPooling2dWorkloadTest(DataLayout dataLayout) |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreatePooling2dWorkloadTest<ClPooling2dWorkload, DataType>(factory, graph, dataLayout); |
| |
| std::initializer_list<unsigned int> inputShape = (dataLayout == DataLayout::NCHW) ? |
| std::initializer_list<unsigned int>({3, 2, 5, 5}) : std::initializer_list<unsigned int>({3, 5, 5, 2}); |
| std::initializer_list<unsigned int> outputShape = (dataLayout == DataLayout::NCHW) ? |
| std::initializer_list<unsigned int>({3, 2, 2, 4}) : std::initializer_list<unsigned int>({3, 2, 4, 2}); |
| |
| // Check that inputs/outputs are as we expect them (see definition of CreatePooling2dWorkloadTest). |
| Pooling2dQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, inputShape)); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, outputShape)); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreatePooling2dFloatNchwWorkload) |
| { |
| ClPooling2dWorkloadTest<armnn::DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreatePooling2dFloatNhwcWorkload) |
| { |
| ClPooling2dWorkloadTest<armnn::DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreatePooling2dFloat16NchwWorkload) |
| { |
| ClPooling2dWorkloadTest<armnn::DataType::Float16>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreatePooling2dFloat16NhwcWorkload) |
| { |
| ClPooling2dWorkloadTest<armnn::DataType::Float16>(DataLayout::NHWC); |
| } |
| |
| template <typename armnn::DataType DataType> |
| static void ClCreateReshapeWorkloadTest() |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateReshapeWorkloadTest<ClReshapeWorkload, 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<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {4, 1})); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {1, 4})); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateReshapeFloatWorkload) |
| { |
| ClCreateReshapeWorkloadTest<armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateReshapeFloat16Workload) |
| { |
| ClCreateReshapeWorkloadTest<armnn::DataType::Float16>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateReshapeUint8Workload) |
| { |
| ClCreateReshapeWorkloadTest<armnn::DataType::QuantisedAsymm8>(); |
| } |
| |
| template <typename SoftmaxWorkloadType, typename armnn::DataType DataType> |
| static void ClSoftmaxWorkloadTest() |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateSoftmaxWorkloadTest<SoftmaxWorkloadType, DataType>(factory, graph); |
| |
| // Checks that inputs/outputs are as we expect them (see definition of ClSoftmaxFloatWorkload). |
| SoftmaxQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {4, 1})); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {4, 1})); |
| } |
| |
| |
| BOOST_AUTO_TEST_CASE(CreateSoftmaxFloatWorkloadTest) |
| { |
| ClSoftmaxWorkloadTest<ClSoftmaxFloatWorkload, armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSoftmaxFloat16WorkloadTest) |
| { |
| ClSoftmaxWorkloadTest<ClSoftmaxFloatWorkload, armnn::DataType::Float16>(); |
| } |
| |
| template <typename armnn::DataType DataType> |
| static void ClSplitterWorkloadTest() |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateSplitterWorkloadTest<ClSplitterWorkload, DataType>(factory, graph); |
| |
| // Checks that outputs are as we expect them (see definition of CreateSplitterWorkloadTest). |
| SplitterQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {5, 7, 7})); |
| |
| auto outputHandle1 = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[1]); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle1, {2, 7, 7})); |
| |
| auto outputHandle2 = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[2]); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle2, {2, 7, 7})); |
| |
| auto outputHandle0 = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle0, {1, 7, 7})); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSplitterFloatWorkload) |
| { |
| ClSplitterWorkloadTest<armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSplitterFloat16Workload) |
| { |
| ClSplitterWorkloadTest<armnn::DataType::Float16>(); |
| } |
| |
| template <typename armnn::DataType DataType> |
| static void ClSplitterMergerTest() |
| { |
| // Tests that it is possible to decide which output of the splitter layer |
| // should be lined to which input of the merger layer. |
| // We test 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; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workloads = |
| CreateSplitterMergerWorkloadTest<ClSplitterWorkload, ClMergerWorkload, DataType> |
| (factory, graph); |
| |
| auto wlSplitter = std::move(workloads.first); |
| auto wlMerger = std::move(workloads.second); |
| |
| //Checks that the index of inputs/outputs matches what we declared on InputDescriptor construction. |
| armnn::ClSubTensorHandle* sOut0 = dynamic_cast<armnn::ClSubTensorHandle*>(wlSplitter->GetData().m_Outputs[0]); |
| armnn::ClSubTensorHandle* sOut1 = dynamic_cast<armnn::ClSubTensorHandle*>(wlSplitter->GetData().m_Outputs[1]); |
| armnn::ClSubTensorHandle* mIn0 = dynamic_cast<armnn::ClSubTensorHandle*>(wlMerger->GetData().m_Inputs[0]); |
| armnn::ClSubTensorHandle* mIn1 = dynamic_cast<armnn::ClSubTensorHandle*>(wlMerger->GetData().m_Inputs[1]); |
| |
| BOOST_TEST(sOut0); |
| BOOST_TEST(sOut1); |
| BOOST_TEST(mIn0); |
| BOOST_TEST(mIn1); |
| |
| //Fliped order of inputs/outputs. |
| bool validDataPointers = (sOut0 == mIn1) && (sOut1 == mIn0); |
| BOOST_TEST(validDataPointers); |
| |
| |
| //Also make sure that the inputs are subtensors of one tensor and outputs are sub tensors of another tensor. |
| bool validSubTensorParents = (mIn0->GetTensor().parent() == mIn1->GetTensor().parent()) |
| && (sOut0->GetTensor().parent() == sOut1->GetTensor().parent()); |
| |
| BOOST_TEST(validSubTensorParents); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSplitterMergerFloatWorkload) |
| { |
| ClSplitterMergerTest<armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateSplitterMergerFloat16Workload) |
| { |
| ClSplitterMergerTest<armnn::DataType::Float16>(); |
| } |
| |
| |
| BOOST_AUTO_TEST_CASE(CreateSingleOutputMultipleInputs) |
| { |
| // Test that it is possible to assign multiple (two) different layers to each of the outputs of a splitter layer. |
| // We create a splitter with two outputs. That each of those outputs is used by two different activation layers. |
| |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| std::unique_ptr<ClSplitterWorkload> wlSplitter; |
| std::unique_ptr<ClActivationWorkload> wlActiv0_0; |
| std::unique_ptr<ClActivationWorkload> wlActiv0_1; |
| std::unique_ptr<ClActivationWorkload> wlActiv1_0; |
| std::unique_ptr<ClActivationWorkload> wlActiv1_1; |
| |
| CreateSplitterMultipleInputsOneOutputWorkloadTest<ClSplitterWorkload, |
| ClActivationWorkload, armnn::DataType::Float32>(factory, graph, wlSplitter, wlActiv0_0, wlActiv0_1, |
| wlActiv1_0, wlActiv1_1); |
| |
| //Checks that the index of inputs/outputs matches what we declared on InputDescriptor construction. |
| armnn::ClSubTensorHandle* sOut0 = dynamic_cast<armnn::ClSubTensorHandle*>(wlSplitter->GetData().m_Outputs[0]); |
| armnn::ClSubTensorHandle* sOut1 = dynamic_cast<armnn::ClSubTensorHandle*>(wlSplitter->GetData().m_Outputs[1]); |
| armnn::ClSubTensorHandle* activ0_0Im = dynamic_cast<armnn::ClSubTensorHandle*>(wlActiv0_0->GetData().m_Inputs[0]); |
| armnn::ClSubTensorHandle* activ0_1Im = dynamic_cast<armnn::ClSubTensorHandle*>(wlActiv0_1->GetData().m_Inputs[0]); |
| armnn::ClSubTensorHandle* activ1_0Im = dynamic_cast<armnn::ClSubTensorHandle*>(wlActiv1_0->GetData().m_Inputs[0]); |
| armnn::ClSubTensorHandle* activ1_1Im = dynamic_cast<armnn::ClSubTensorHandle*>(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(CreateMemCopyWorkloadsCl) |
| { |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| CreateMemCopyWorkloads<IClTensorHandle>(factory); |
| } |
| |
| template <typename L2NormalizationWorkloadType, typename armnn::DataType DataType> |
| static void ClL2NormalizationWorkloadTest(DataLayout dataLayout) |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::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<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| std::initializer_list<unsigned int> inputShape = (dataLayout == DataLayout::NCHW) |
| ? std::initializer_list<unsigned int>({ 5, 20, 50, 67 }) |
| : std::initializer_list<unsigned int>({ 5, 50, 67, 20 }); |
| std::initializer_list<unsigned int> outputShape = (dataLayout == DataLayout::NCHW) |
| ? std::initializer_list<unsigned int>({ 5, 20, 50, 67 }) |
| : std::initializer_list<unsigned int>({ 5, 50, 67, 20 }); |
| |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, inputShape)); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, outputShape)); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateL2NormalizationFloatNchwWorkload) |
| { |
| ClL2NormalizationWorkloadTest<ClL2NormalizationFloatWorkload, armnn::DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateL2NormalizationFloatNhwcWorkload) |
| { |
| ClL2NormalizationWorkloadTest<ClL2NormalizationFloatWorkload, armnn::DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateL2NormalizationFloat16NchwWorkload) |
| { |
| ClL2NormalizationWorkloadTest<ClL2NormalizationFloatWorkload, armnn::DataType::Float16>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateL2NormalizationFloat16NhwcWorkload) |
| { |
| ClL2NormalizationWorkloadTest<ClL2NormalizationFloatWorkload, armnn::DataType::Float16>(DataLayout::NHWC); |
| } |
| |
| template <typename LstmWorkloadType> |
| static void ClCreateLstmWorkloadTest() |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateLstmWorkloadTest<LstmWorkloadType>(factory, graph); |
| |
| LstmQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[1]); |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, { 2, 2 })); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, { 2, 4 })); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateLSTMWorkloadFloatWorkload) |
| { |
| ClCreateLstmWorkloadTest<ClLstmFloatWorkload>(); |
| } |
| |
| template <typename ResizeBilinearWorkloadType, typename armnn::DataType DataType> |
| static void ClResizeBilinearWorkloadTest(DataLayout dataLayout) |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateResizeBilinearWorkloadTest<ResizeBilinearWorkloadType, DataType>(factory, graph, dataLayout); |
| |
| // Checks that inputs/outputs are as we expect them (see definition of CreateResizeBilinearWorkloadTest). |
| ResizeBilinearQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| switch (dataLayout) |
| { |
| case DataLayout::NHWC: |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, { 2, 4, 4, 3 })); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, { 2, 2, 2, 3 })); |
| break; |
| case DataLayout::NCHW: |
| default: |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, { 2, 3, 4, 4 })); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, { 2, 3, 2, 2 })); |
| } |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateResizeBilinearFloat32NchwWorkload) |
| { |
| ClResizeBilinearWorkloadTest<ClResizeBilinearFloatWorkload, armnn::DataType::Float32>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateResizeBilinearFloat16NchwWorkload) |
| { |
| ClResizeBilinearWorkloadTest<ClResizeBilinearFloatWorkload, armnn::DataType::Float16>(DataLayout::NCHW); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateResizeBilinearFloat32NhwcWorkload) |
| { |
| ClResizeBilinearWorkloadTest<ClResizeBilinearFloatWorkload, armnn::DataType::Float32>(DataLayout::NHWC); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateResizeBilinearFloat16NhwcWorkload) |
| { |
| ClResizeBilinearWorkloadTest<ClResizeBilinearFloatWorkload, armnn::DataType::Float16>(DataLayout::NHWC); |
| } |
| |
| template <typename MeanWorkloadType, typename armnn::DataType DataType> |
| static void ClMeanWorkloadTest() |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateMeanWorkloadTest<MeanWorkloadType, DataType>(factory, graph); |
| |
| // Checks that inputs/outputs are as we expect them (see definition of CreateMeanWorkloadTest). |
| MeanQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| // The first dimension (batch size) in both input and output is singular thus it has been reduced by ACL. |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle, { 1, 3, 7, 4 })); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, { 1, 4 })); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMeanFloat32Workload) |
| { |
| ClMeanWorkloadTest<ClMeanWorkload, armnn::DataType::Float32>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMeanFloat16Workload) |
| { |
| ClMeanWorkloadTest<ClMeanWorkload, armnn::DataType::Float16>(); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMeanUint8Workload) |
| { |
| ClMeanWorkloadTest<ClMeanWorkload, armnn::DataType::QuantisedAsymm8>(); |
| } |
| |
| template <typename MergerWorkloadType, armnn::DataType DataType> |
| static void ClCreateMergerWorkloadTest(std::initializer_list<unsigned int> outputShape, |
| unsigned int concatAxis) |
| { |
| Graph graph; |
| ClWorkloadFactory factory = |
| ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager()); |
| |
| auto workload = CreateMergerWorkloadTest<MergerWorkloadType, DataType>(factory, graph, outputShape, concatAxis); |
| |
| MergerQueueDescriptor queueDescriptor = workload->GetData(); |
| auto inputHandle0 = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]); |
| auto inputHandle1 = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[1]); |
| auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]); |
| |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle0, { 2, 3, 2, 5 })); |
| BOOST_TEST(CompareIClTensorHandleShape(inputHandle1, { 2, 3, 2, 5 })); |
| BOOST_TEST(CompareIClTensorHandleShape(outputHandle, outputShape)); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMergerDim0Float32Workload) |
| { |
| ClCreateMergerWorkloadTest<ClMergerWorkload, armnn::DataType::Float32>({ 4, 3, 2, 5 }, 0); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMergerDim1Float32Workload) |
| { |
| ClCreateMergerWorkloadTest<ClMergerWorkload, armnn::DataType::Float32>({ 2, 6, 2, 5 }, 1); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMergerDim3Float32Workload) |
| { |
| ClCreateMergerWorkloadTest<ClMergerWorkload, armnn::DataType::Float32>({ 2, 3, 2, 10 }, 3); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMergerDim0Uint8Workload) |
| { |
| ClCreateMergerWorkloadTest<ClMergerWorkload, armnn::DataType::QuantisedAsymm8>({ 4, 3, 2, 5 }, 0); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMergerDim1Uint8Workload) |
| { |
| ClCreateMergerWorkloadTest<ClMergerWorkload, armnn::DataType::QuantisedAsymm8>({ 2, 6, 2, 5 }, 1); |
| } |
| |
| BOOST_AUTO_TEST_CASE(CreateMergerDim3Uint8Workload) |
| { |
| ClCreateMergerWorkloadTest<ClMergerWorkload, armnn::DataType::QuantisedAsymm8>({ 2, 3, 2, 10 }, 3); |
| } |
| |
| BOOST_AUTO_TEST_SUITE_END() |
