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Diffstat (limited to 'src/backends/neon/test/NeonCreateWorkloadTests.cpp')
| -rw-r--r-- | src/backends/neon/test/NeonCreateWorkloadTests.cpp | 531 |
1 files changed, 531 insertions, 0 deletions
diff --git a/src/backends/neon/test/NeonCreateWorkloadTests.cpp b/src/backends/neon/test/NeonCreateWorkloadTests.cpp new file mode 100644 index 0000000000..d1a5b2a5f2 --- /dev/null +++ b/src/backends/neon/test/NeonCreateWorkloadTests.cpp @@ -0,0 +1,531 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <armnn/test/CreateWorkloadClNeon.hpp> + +#include <backends/MemCopyWorkload.hpp> +#include <backends/neon/NeonWorkloadFactory.hpp> +#include <backends/neon/NeonTensorHandle.hpp> +#include <backends/neon/workloads/NeonWorkloadUtils.hpp> +#include <backends/neon/workloads/NeonWorkloads.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() |