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Diffstat (limited to 'src/armnn/backends/test/CreateWorkloadNeon.cpp')
| -rw-r--r-- | src/armnn/backends/test/CreateWorkloadNeon.cpp | 302 |
1 files changed, 302 insertions, 0 deletions
diff --git a/src/armnn/backends/test/CreateWorkloadNeon.cpp b/src/armnn/backends/test/CreateWorkloadNeon.cpp new file mode 100644 index 0000000000..807937ba2b --- /dev/null +++ b/src/armnn/backends/test/CreateWorkloadNeon.cpp @@ -0,0 +1,302 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// See LICENSE file in the project root for full license information. +// +#include "backends/NeonWorkloadFactory.hpp" +#include "backends/NeonWorkloadUtils.hpp" +#include "backends/NeonWorkloads.hpp" +#include "backends/MemCopyWorkload.hpp" +#include "backends/NeonTensorHandle.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 + +BOOST_AUTO_TEST_CASE(CreateActivationWorkload) +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateActivationWorkloadTest<NeonActivationFloat32Workload>(factory, graph); + + // check 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::Float32))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({1, 1}, DataType::Float32))); +} + +BOOST_AUTO_TEST_CASE(CreateAdditionWorkload) +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateAdditionWorkloadTest<NeonAdditionFloat32Workload>(factory, graph); + + // check that inputs/outputs are as we expect them (see definition of CreateAdditionWorkloadTest) + AdditionQueueDescriptor 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::Float32))); + BOOST_TEST(TestNeonTensorHandleInfo(inputHandle2, TensorInfo({2, 3}, DataType::Float32))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 3}, DataType::Float32))); +} + +BOOST_AUTO_TEST_CASE(CreateBatchNormalizationWorkload) +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateBatchNormalizationWorkloadTest<NeonBatchNormalizationFloat32Workload>(factory, graph); + + // check 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::Float32))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 3, 1, 1}, DataType::Float32))); +} + +BOOST_AUTO_TEST_CASE(CreateConvolution2dWorkload) +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateConvolution2dWorkloadTest<NeonConvolution2dFloat32Workload>(factory, graph); + + // check 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({2, 3, 8, 16}, DataType::Float32))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 2, 2, 10}, DataType::Float32))); +} + +BOOST_AUTO_TEST_CASE(CreateFullyConnectedWorkload) +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateFullyConnectedWorkloadTest<NeonFullyConnectedFloat32Workload>(factory, graph); + + // check 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::Float32))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({3, 7}, DataType::Float32))); +} + +BOOST_AUTO_TEST_CASE(CreateMultiplicationWorkload) +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateMultiplicationWorkloadTest<NeonMultiplicationFloat32Workload>(factory, graph); + + // check that inputs/outputs are as we expect them (see definition of CreateMultiplicationWorkloadTest) + MultiplicationQueueDescriptor 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::Float32))); + BOOST_TEST(TestNeonTensorHandleInfo(inputHandle2, TensorInfo({2, 3}, DataType::Float32))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 3}, DataType::Float32))); +} + +BOOST_AUTO_TEST_CASE(CreateNormalizationWorkload) +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateNormalizationWorkloadTest<NeonNormalizationFloat32Workload>(factory, graph); + + // check 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::Float32))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({3, 5, 5, 1}, DataType::Float32))); +} + +BOOST_AUTO_TEST_CASE(CreatePooling2dWorkload) +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreatePooling2dWorkloadTest<NeonPooling2dFloat32Workload>(factory, graph); + + // check 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({3, 2, 5, 5}, DataType::Float32))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({3, 2, 2, 4}, DataType::Float32))); +} + +template <typename ReshapeWorkloadType> +static void NeonCreateReshapeWorkloadTest(DataType dataType) +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateReshapeWorkloadTest<ReshapeWorkloadType>(factory, graph); + + // check 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))); +} + +BOOST_AUTO_TEST_CASE(CreateReshapeFloat32Workload) +{ + NeonCreateReshapeWorkloadTest<NeonReshapeFloat32Workload>(DataType::Float32); +} + +BOOST_AUTO_TEST_CASE(CreateReshapeUint8Workload) +{ + NeonCreateReshapeWorkloadTest<NeonReshapeUint8Workload>(DataType::QuantisedAsymm8); +} + +BOOST_AUTO_TEST_CASE(CreateSoftmaxWorkload) +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateSoftmaxWorkloadTest<NeonSoftmaxFloat32Workload>(factory, graph); + + // check 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::Float32))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({4, 1}, DataType::Float32))); +} + +BOOST_AUTO_TEST_CASE(CreateSplitterWorkload) +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateSplitterWorkloadTest<NeonSplitterFloat32Workload>(factory, graph); + + // check 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({1, 7}, DataType::Float32))); + auto outputHandle0 = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle0, TensorInfo({1, 4}, DataType::Float32))); + auto outputHandle1 = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[1]); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle1, TensorInfo({1, 1}, DataType::Float32))); + auto outputHandle2 = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[2]); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle2, TensorInfo({1, 2}, DataType::Float32))); +} + +BOOST_AUTO_TEST_CASE(CreateSplitterMerger) +{ + // Test 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; + NeonWorkloadFactory factory; + + auto workloads = + CreateSplitterMergerWorkloadTest<NeonSplitterFloat32Workload, NeonMergerFloat32Workload>(factory, graph); + + auto wlSplitter = std::move(workloads.first); + auto wlMerger = std::move(workloads.second); + + //check 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) +{ + // 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; + NeonWorkloadFactory factory; + std::unique_ptr<NeonSplitterFloat32Workload> wlSplitter; + std::unique_ptr<NeonActivationFloat32Workload> wlActiv0_0; + std::unique_ptr<NeonActivationFloat32Workload> wlActiv0_1; + std::unique_ptr<NeonActivationFloat32Workload> wlActiv1_0; + std::unique_ptr<NeonActivationFloat32Workload> wlActiv1_1; + + CreateSplitterMultipleInputsOneOutputWorkloadTest<NeonSplitterFloat32Workload, + NeonActivationFloat32Workload>(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<CopyFromCpuToNeonWorkload,CopyFromNeonToCpuWorkload,INeonTensorHandle>(factory); +} + +BOOST_AUTO_TEST_SUITE_END() |