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Diffstat (limited to 'src/backends/test/CreateWorkloadNeon.cpp')
| -rw-r--r-- | src/backends/test/CreateWorkloadNeon.cpp | 530 |
1 files changed, 0 insertions, 530 deletions
diff --git a/src/backends/test/CreateWorkloadNeon.cpp b/src/backends/test/CreateWorkloadNeon.cpp deleted file mode 100644 index b2ec563a69..0000000000 --- a/src/backends/test/CreateWorkloadNeon.cpp +++ /dev/null @@ -1,530 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include <backends/neon/NeonWorkloadFactory.hpp> -#include <backends/neon/NeonTensorHandle.hpp> -#include <backends/neon/workloads/NeonWorkloadUtils.hpp> -#include <backends/neon/workloads/NeonWorkloads.hpp> -#include <backends/MemCopyWorkload.hpp> - -#include "test/CreateWorkloadClNeon.hpp" - -BOOST_AUTO_TEST_SUITE(CreateWorkloadNeon) - -namespace -{ - -bool TestNeonTensorHandleInfo(armnn::INeonTensorHandle* handle, const armnn::TensorInfo& expectedInfo) -{ - using namespace armnn::armcomputetensorutils; - - const arm_compute::ITensorInfo* handleInfo = handle->GetTensor().info(); - const arm_compute::TensorInfo expectedAclInfo = BuildArmComputeTensorInfo(expectedInfo); - - if (handleInfo->data_type() != expectedAclInfo.data_type()) - { - return false; - } - - if (handleInfo->num_dimensions() != expectedAclInfo.num_dimensions()) - { - return false; - } - - if (handleInfo->quantization_info() != expectedAclInfo.quantization_info()) - { - return false; - } - - for (std::size_t d = 0; d < expectedAclInfo.num_dimensions(); ++d) - { - if (handleInfo->dimension(d) != expectedAclInfo.dimension(d)) - { - return false; - } - } - - return true; -} - -} // namespace - -template <typename ActivationWorkloadType, typename armnn::DataType DataType> -static void NeonCreateActivationWorkloadTest() -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateActivationWorkloadTest<ActivationWorkloadType, DataType> - (factory, graph); - - // Checks that inputs/outputs are as we expect them (see definition of CreateActivationWorkloadTest). - ActivationQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({1, 1}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({1, 1}, DataType))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateActivationFloat16Workload) -{ - NeonCreateActivationWorkloadTest<NeonActivationFloatWorkload, DataType::Float16>(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateActivationFloatWorkload) -{ - NeonCreateActivationWorkloadTest<NeonActivationFloatWorkload, DataType::Float32>(); -} - -template <typename WorkloadType, - typename DescriptorType, - typename LayerType, - armnn::DataType DataType> -static void NeonCreateArithmethicWorkloadTest() -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateArithmeticWorkloadTest<WorkloadType, DescriptorType, LayerType, DataType>(factory, graph); - - DescriptorType queueDescriptor = workload->GetData(); - auto inputHandle1 = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); - auto inputHandle2 = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[1]); - auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle1, TensorInfo({2, 3}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle2, TensorInfo({2, 3}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 3}, DataType))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateAdditionFloat16Workload) -{ - NeonCreateArithmethicWorkloadTest<NeonAdditionFloatWorkload, - AdditionQueueDescriptor, - AdditionLayer, - DataType::Float16>(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateAdditionFloatWorkload) -{ - NeonCreateArithmethicWorkloadTest<NeonAdditionFloatWorkload, - AdditionQueueDescriptor, - AdditionLayer, - DataType::Float32>(); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateSubtractionFloat16Workload) -{ - NeonCreateArithmethicWorkloadTest<NeonSubtractionFloatWorkload, - SubtractionQueueDescriptor, - SubtractionLayer, - DataType::Float16>(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateSubtractionFloatWorkload) -{ - NeonCreateArithmethicWorkloadTest<NeonSubtractionFloatWorkload, - SubtractionQueueDescriptor, - SubtractionLayer, - DataType::Float32>(); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateMultiplicationFloat16Workload) -{ - NeonCreateArithmethicWorkloadTest<NeonMultiplicationFloatWorkload, - MultiplicationQueueDescriptor, - MultiplicationLayer, - DataType::Float16>(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateMultiplicationFloatWorkload) -{ - NeonCreateArithmethicWorkloadTest<NeonMultiplicationFloatWorkload, - MultiplicationQueueDescriptor, - MultiplicationLayer, - DataType::Float32>(); -} - -template <typename BatchNormalizationWorkloadType, typename armnn::DataType DataType> -static void NeonCreateBatchNormalizationWorkloadTest() -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateBatchNormalizationWorkloadTest<BatchNormalizationWorkloadType, DataType>(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateBatchNormalizationWorkloadTest). - BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({2, 3, 1, 1}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 3, 1, 1}, DataType))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloat16Workload) -{ - NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationFloatWorkload, DataType::Float16>(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloatWorkload) -{ - NeonCreateBatchNormalizationWorkloadTest<NeonBatchNormalizationFloatWorkload, DataType::Float32>(); -} - -template <typename Convolution2dWorkloadType, typename armnn::DataType DataType> -static void NeonCreateConvolution2dWorkloadTest(DataLayout dataLayout = DataLayout::NCHW) -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateConvolution2dWorkloadTest<Convolution2dWorkloadType, - 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<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(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<NeonConvolution2dFloatWorkload, DataType::Float16>(); -} - -BOOST_AUTO_TEST_CASE(CreateConvolution2dFloat16NhwcWorkload) -{ - NeonCreateConvolution2dWorkloadTest<NeonConvolution2dFloatWorkload, DataType::Float16>(DataLayout::NHWC); -} - -#endif -BOOST_AUTO_TEST_CASE(CreateConvolution2dFloatNchwWorkload) -{ - NeonCreateConvolution2dWorkloadTest<NeonConvolution2dFloatWorkload, DataType::Float32>(); -} - -BOOST_AUTO_TEST_CASE(CreateConvolution2dFloatNhwcWorkload) -{ - NeonCreateConvolution2dWorkloadTest<NeonConvolution2dFloatWorkload, DataType::Float32>(DataLayout::NHWC); -} - -template <typename FullyConnectedWorkloadType, typename armnn::DataType DataType> -static void NeonCreateFullyConnectedWorkloadTest() -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateFullyConnectedWorkloadTest<FullyConnectedWorkloadType, - DataType>(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateFullyConnectedWorkloadTest). - FullyConnectedQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({3, 1, 4, 5}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({3, 7}, DataType))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateFullyConnectedFloat16Workload) -{ - NeonCreateFullyConnectedWorkloadTest<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; - 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<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({3, 5, 5, 1}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({3, 5, 5, 1}, DataType))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(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 Pooling2dWorkloadType, typename armnn::DataType DataType> -static void NeonCreatePooling2dWorkloadTest(DataLayout dataLayout = DataLayout::NCHW) -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreatePooling2dWorkloadTest<Pooling2dWorkloadType, 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<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(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<NeonPooling2dFloatWorkload, DataType::Float16>(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreatePooling2dFloatNchwWorkload) -{ - NeonCreatePooling2dWorkloadTest<NeonPooling2dFloatWorkload, DataType::Float32>(DataLayout::NCHW); -} - -BOOST_AUTO_TEST_CASE(CreatePooling2dFloatNhwcWorkload) -{ - NeonCreatePooling2dWorkloadTest<NeonPooling2dFloatWorkload, DataType::Float32>(DataLayout::NHWC); -} - -BOOST_AUTO_TEST_CASE(CreatePooling2dUint8NchwWorkload) -{ - NeonCreatePooling2dWorkloadTest<NeonPooling2dUint8Workload, DataType::QuantisedAsymm8>(DataLayout::NCHW); -} - -BOOST_AUTO_TEST_CASE(CreatePooling2dUint8NhwcWorkload) -{ - NeonCreatePooling2dWorkloadTest<NeonPooling2dUint8Workload, DataType::QuantisedAsymm8>(DataLayout::NHWC); -} - -template <typename ReshapeWorkloadType, typename armnn::DataType DataType> -static void NeonCreateReshapeWorkloadTest() -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateReshapeWorkloadTest<ReshapeWorkloadType, DataType>(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateReshapeWorkloadTest). - ReshapeQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({4, 1}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({1, 4}, DataType))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateReshapeFloat16Workload) -{ - NeonCreateReshapeWorkloadTest<NeonReshapeFloatWorkload, DataType::Float16>(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateReshapeFloatWorkload) -{ - NeonCreateReshapeWorkloadTest<NeonReshapeFloatWorkload, DataType::Float32>(); -} - -BOOST_AUTO_TEST_CASE(CreateReshapeUint8Workload) -{ - NeonCreateReshapeWorkloadTest<NeonReshapeUint8Workload, DataType::QuantisedAsymm8>(); -} - -template <typename SoftmaxWorkloadType, typename armnn::DataType DataType> -static void NeonCreateSoftmaxWorkloadTest() -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateSoftmaxWorkloadTest<SoftmaxWorkloadType, DataType>(factory, graph); - - // Checks that outputs and inputs are as we expect them (see definition of CreateSoftmaxWorkloadTest). - SoftmaxQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({4, 1}, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({4, 1}, DataType))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -BOOST_AUTO_TEST_CASE(CreateSoftmaxFloat16Workload) -{ - NeonCreateSoftmaxWorkloadTest<NeonSoftmaxFloatWorkload, DataType::Float16>(); -} -#endif - -BOOST_AUTO_TEST_CASE(CreateSoftmaxFloatWorkload) -{ - NeonCreateSoftmaxWorkloadTest<NeonSoftmaxFloatWorkload, DataType::Float32>(); -} - -BOOST_AUTO_TEST_CASE(CreateSplitterWorkload) -{ - Graph graph; - NeonWorkloadFactory factory; - auto workload = CreateSplitterWorkloadTest<NeonSplitterFloatWorkload, DataType::Float32>(factory, graph); - - // Checks that outputs are as we expect them (see definition of CreateSplitterWorkloadTest). - SplitterQueueDescriptor queueDescriptor = workload->GetData(); - auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({5, 7, 7}, DataType::Float32))); - - auto outputHandle0 = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle0, TensorInfo({1, 7, 7}, DataType::Float32))); - - auto outputHandle1 = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[1]); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle1, TensorInfo({2, 7, 7}, DataType::Float32))); - - auto outputHandle2 = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[2]); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle2, TensorInfo({2, 7, 7}, DataType::Float32))); -} - -BOOST_AUTO_TEST_CASE(CreateSplitterMerger) -{ - // Tests that it is possible to decide which output of the splitter layer - // should be lined to which input of the merger layer. - // We tested that is is possible to specify 0th output - // of the splitter to be the 1st input to the merger, and the 1st output of the splitter to be 0th input - // of the merger. - - Graph graph; - NeonWorkloadFactory factory; - - auto workloads = - CreateSplitterMergerWorkloadTest<NeonSplitterFloatWorkload, NeonMergerFloatWorkload, - DataType::Float32>(factory, graph); - - auto wlSplitter = std::move(workloads.first); - auto wlMerger = std::move(workloads.second); - - //Checks that the index of inputs/outputs matches what we declared on InputDescriptor construction. - armnn::INeonTensorHandle* sOut0 = dynamic_cast<armnn::INeonTensorHandle*>(wlSplitter->GetData().m_Outputs[0]); - armnn::INeonTensorHandle* sOut1 = dynamic_cast<armnn::INeonTensorHandle*>(wlSplitter->GetData().m_Outputs[1]); - armnn::INeonTensorHandle* mIn0 = dynamic_cast<armnn::INeonTensorHandle*>(wlMerger->GetData().m_Inputs[0]); - armnn::INeonTensorHandle* mIn1 = dynamic_cast<armnn::INeonTensorHandle*>(wlMerger->GetData().m_Inputs[1]); - - BOOST_TEST(sOut0); - BOOST_TEST(sOut1); - BOOST_TEST(mIn0); - BOOST_TEST(mIn1); - - bool validDataPointers = (sOut0 == mIn1) && (sOut1 == mIn0); - - BOOST_TEST(validDataPointers); -} - -BOOST_AUTO_TEST_CASE(CreateSingleOutputMultipleInputs) -{ - // Tests that it is possible to assign multiple (two) different layers to each of the outputs of a splitter layer. - // We created a splitter with two outputs. That each of those outputs is used by two different activation layers - - Graph graph; - NeonWorkloadFactory factory; - std::unique_ptr<NeonSplitterFloatWorkload> wlSplitter; - std::unique_ptr<NeonActivationFloatWorkload> wlActiv0_0; - std::unique_ptr<NeonActivationFloatWorkload> wlActiv0_1; - std::unique_ptr<NeonActivationFloatWorkload> wlActiv1_0; - std::unique_ptr<NeonActivationFloatWorkload> wlActiv1_1; - - CreateSplitterMultipleInputsOneOutputWorkloadTest<NeonSplitterFloatWorkload, - NeonActivationFloatWorkload, DataType::Float32>(factory, graph, wlSplitter, wlActiv0_0, wlActiv0_1, - wlActiv1_0, wlActiv1_1); - - armnn::INeonTensorHandle* sOut0 = dynamic_cast<armnn::INeonTensorHandle*>(wlSplitter->GetData().m_Outputs[0]); - armnn::INeonTensorHandle* sOut1 = dynamic_cast<armnn::INeonTensorHandle*>(wlSplitter->GetData().m_Outputs[1]); - armnn::INeonTensorHandle* activ0_0Im = dynamic_cast<armnn::INeonTensorHandle*>(wlActiv0_0->GetData().m_Inputs[0]); - armnn::INeonTensorHandle* activ0_1Im = dynamic_cast<armnn::INeonTensorHandle*>(wlActiv0_1->GetData().m_Inputs[0]); - armnn::INeonTensorHandle* activ1_0Im = dynamic_cast<armnn::INeonTensorHandle*>(wlActiv1_0->GetData().m_Inputs[0]); - armnn::INeonTensorHandle* activ1_1Im = dynamic_cast<armnn::INeonTensorHandle*>(wlActiv1_1->GetData().m_Inputs[0]); - - - BOOST_TEST(sOut0); - BOOST_TEST(sOut1); - BOOST_TEST(activ0_0Im); - BOOST_TEST(activ0_1Im); - BOOST_TEST(activ1_0Im); - BOOST_TEST(activ1_1Im); - - bool validDataPointers = (sOut0 == activ0_0Im) && (sOut0 == activ0_1Im) && - (sOut1 == activ1_0Im) && (sOut1 == activ1_1Im); - - BOOST_TEST(validDataPointers); -} - -BOOST_AUTO_TEST_CASE(CreateMemCopyWorkloadsNeon) -{ - NeonWorkloadFactory factory; - CreateMemCopyWorkloads<INeonTensorHandle>(factory); -} - -template <typename L2NormalizationWorkloadType, typename armnn::DataType DataType> -static void NeonCreateL2NormalizationWorkloadTest(DataLayout dataLayout) -{ - Graph graph; - NeonWorkloadFactory factory; - 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<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]); - auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); - BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({ 5, 20, 50, 67 }, DataType))); - BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({ 5, 20, 50, 67 }, 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); -} - -BOOST_AUTO_TEST_SUITE_END() |