| // |
| // Copyright © 2020 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include <Graph.hpp> |
| #include <Network.hpp> |
| |
| #include <neon/NeonTensorHandle.hpp> |
| #include <neon/NeonTensorHandleFactory.hpp> |
| |
| #include <armnn/utility/PolymorphicDowncast.hpp> |
| |
| #include <test/GraphUtils.hpp> |
| |
| #include <boost/test/unit_test.hpp> |
| |
| BOOST_AUTO_TEST_SUITE(NeonTensorHandleTests) |
| using namespace armnn; |
| |
| BOOST_AUTO_TEST_CASE(NeonTensorHandleGetCapabilitiesNoPadding) |
| { |
| std::shared_ptr<NeonMemoryManager> memoryManager = std::make_shared<NeonMemoryManager>(); |
| NeonTensorHandleFactory handleFactory(memoryManager); |
| |
| INetworkPtr network(INetwork::Create()); |
| |
| // Add the layers |
| IConnectableLayer* input = network->AddInputLayer(0); |
| SoftmaxDescriptor descriptor; |
| descriptor.m_Beta = 1.0f; |
| IConnectableLayer* softmax = network->AddSoftmaxLayer(descriptor); |
| IConnectableLayer* output = network->AddOutputLayer(2); |
| |
| // Establish connections |
| input->GetOutputSlot(0).Connect(softmax->GetInputSlot(0)); |
| softmax->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| // No padding required for input |
| std::vector<Capability> capabilities = handleFactory.GetCapabilities(input, |
| softmax, |
| CapabilityClass::PaddingRequired); |
| BOOST_TEST(capabilities.empty()); |
| |
| // No padding required for Softmax |
| capabilities = handleFactory.GetCapabilities(softmax, output, CapabilityClass::PaddingRequired); |
| BOOST_TEST(capabilities.empty()); |
| |
| // No padding required for output |
| capabilities = handleFactory.GetCapabilities(output, nullptr, CapabilityClass::PaddingRequired); |
| BOOST_TEST(capabilities.empty()); |
| } |
| |
| BOOST_AUTO_TEST_CASE(NeonTensorHandleGetCapabilitiesPadding) |
| { |
| std::shared_ptr<NeonMemoryManager> memoryManager = std::make_shared<NeonMemoryManager>(); |
| NeonTensorHandleFactory handleFactory(memoryManager); |
| |
| INetworkPtr network(INetwork::Create()); |
| |
| // Add the layers |
| IConnectableLayer* input = network->AddInputLayer(0); |
| Pooling2dDescriptor descriptor; |
| IConnectableLayer* pooling = network->AddPooling2dLayer(descriptor); |
| IConnectableLayer* output = network->AddOutputLayer(2); |
| |
| // Establish connections |
| input->GetOutputSlot(0).Connect(pooling->GetInputSlot(0)); |
| pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| |
| // No padding required for input |
| std::vector<Capability> capabilities = handleFactory.GetCapabilities(input, |
| pooling, |
| CapabilityClass::PaddingRequired); |
| BOOST_TEST(capabilities.empty()); |
| |
| // No padding required for output |
| capabilities = handleFactory.GetCapabilities(output, nullptr, CapabilityClass::PaddingRequired); |
| BOOST_TEST(capabilities.empty()); |
| |
| // Padding required for Pooling2d |
| capabilities = handleFactory.GetCapabilities(pooling, output, CapabilityClass::PaddingRequired); |
| BOOST_TEST(capabilities.size() == 1); |
| BOOST_TEST((capabilities[0].m_CapabilityClass == CapabilityClass::PaddingRequired)); |
| BOOST_TEST(capabilities[0].m_Value); |
| } |
| |
| BOOST_AUTO_TEST_CASE(ConcatOnXorYSubTensorsNoPaddinRequiredTest) |
| { |
| armnn::INetworkPtr net(armnn::INetwork::Create()); |
| |
| // Set up tensor infos |
| const armnn::TensorInfo inputInfo = armnn::TensorInfo({2, 3, 2, 2}, armnn::DataType::Float32); |
| const armnn::TensorInfo intermediateInfo = armnn::TensorInfo({2, 3, 2, 2}, armnn::DataType::Float32); |
| const armnn::TensorInfo outputInfo = armnn::TensorInfo({2, 3, 4, 2}, armnn::DataType::Float32); |
| |
| armnn::ElementwiseUnaryDescriptor descriptor(armnn::UnaryOperation::Abs); |
| |
| // Create the network |
| armnn::IConnectableLayer* const input0Layer = net->AddInputLayer(0, "input_0"); |
| input0Layer->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| armnn::IConnectableLayer* elementwiseUnaryLayer0 = net->AddElementwiseUnaryLayer(descriptor, "elementwiseUnary_0"); |
| elementwiseUnaryLayer0->GetOutputSlot(0).SetTensorInfo(intermediateInfo); |
| input0Layer->GetOutputSlot(0).Connect(elementwiseUnaryLayer0->GetInputSlot(0)); |
| |
| armnn::IConnectableLayer* const input1Layer = net->AddInputLayer(1, "input_1"); |
| input1Layer->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| armnn::IConnectableLayer* elementwiseUnaryLayer1 = net->AddElementwiseUnaryLayer(descriptor, "elementwiseUnary_1"); |
| elementwiseUnaryLayer1->GetOutputSlot(0).SetTensorInfo(intermediateInfo); |
| input1Layer->GetOutputSlot(0).Connect(elementwiseUnaryLayer1->GetInputSlot(0)); |
| |
| std::array<armnn::TensorShape, 2> concatInputShapes = { intermediateInfo.GetShape(), intermediateInfo.GetShape() }; |
| armnn::IConnectableLayer* const concatLayer = net->AddConcatLayer(armnn::CreateDescriptorForConcatenation( |
| concatInputShapes.begin(), concatInputShapes.end(), 2), "concatenation"); |
| concatLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| elementwiseUnaryLayer0->GetOutputSlot(0).Connect(concatLayer->GetInputSlot(0)); |
| elementwiseUnaryLayer1->GetOutputSlot(0).Connect(concatLayer->GetInputSlot(1)); |
| |
| armnn::IConnectableLayer* const outputLayer = net->AddOutputLayer(0, "output"); |
| concatLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| |
| armnn::IRuntime::CreationOptions options; |
| armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| |
| std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; |
| armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); |
| |
| const armnn::Graph& theGraph = static_cast<armnn::OptimizedNetwork*>(optimizedNet.get())->GetGraph(); |
| |
| // Load graph into runtime |
| armnn::NetworkId networkIdentifier; |
| runtime->LoadNetwork(networkIdentifier, std::move(optimizedNet)); |
| |
| // now check the concat how many sub-tensors it is using.. |
| auto TraceSubTensorHandleAncestry = [](armnn::ITensorHandle* const subTensorHandle) |
| { |
| if (subTensorHandle && subTensorHandle->GetParent()) |
| { |
| return true; |
| } |
| return false; |
| }; |
| |
| for (auto&& layer : theGraph) |
| { |
| if(layer->GetType() == armnn::LayerType::Concat) |
| { |
| unsigned int numberOfSubTensors = 0; |
| for (unsigned int i = 0; i < layer->GetNumInputSlots(); ++i) |
| { |
| const armnn::OutputSlot* slot = layer->GetInputSlot(i).GetConnectedOutputSlot(); |
| if (TraceSubTensorHandleAncestry(slot->GetOutputHandler().GetData())) |
| { |
| ++numberOfSubTensors; |
| } |
| } |
| // sub-tensors should be supported in this configuration |
| BOOST_CHECK(numberOfSubTensors > 0); |
| } |
| } |
| } |
| |
| BOOST_AUTO_TEST_SUITE_END() |