IVGCVSW-1988: Refactor backend-specific unit tests
Change-Id: I8eca81d2e0780390eaa837c186ffe1c7d41fdebe
diff --git a/src/backends/neon/test/NeonCreateWorkloadTests.cpp b/src/backends/neon/test/NeonCreateWorkloadTests.cpp
new file mode 100644
index 0000000..d1a5b2a
--- /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()