diff options
Diffstat (limited to 'src/backends/neon/test/NeonTensorHandleTests.cpp')
| -rw-r--r-- | src/backends/neon/test/NeonTensorHandleTests.cpp | 83 |
1 files changed, 83 insertions, 0 deletions
diff --git a/src/backends/neon/test/NeonTensorHandleTests.cpp b/src/backends/neon/test/NeonTensorHandleTests.cpp index fe5e8f9fb3..8b3e3fdc99 100644 --- a/src/backends/neon/test/NeonTensorHandleTests.cpp +++ b/src/backends/neon/test/NeonTensorHandleTests.cpp @@ -2,9 +2,17 @@ // 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) @@ -77,4 +85,79 @@ BOOST_AUTO_TEST_CASE(NeonTensorHandleGetCapabilitiesPadding) 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() |