diff options
Diffstat (limited to 'src/backends/neon/test/NeonTensorHandleTests.cpp')
| -rw-r--r-- | src/backends/neon/test/NeonTensorHandleTests.cpp | 283 |
1 files changed, 1 insertions, 282 deletions
diff --git a/src/backends/neon/test/NeonTensorHandleTests.cpp b/src/backends/neon/test/NeonTensorHandleTests.cpp index c8e781b71d..a94e4dd187 100644 --- a/src/backends/neon/test/NeonTensorHandleTests.cpp +++ b/src/backends/neon/test/NeonTensorHandleTests.cpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020-2021,2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #include <Graph.hpp> @@ -89,81 +89,6 @@ TEST_CASE("NeonTensorHandleGetCapabilitiesPadding") CHECK(capabilities[0].m_Value); } -TEST_CASE("ConcatOnXorYSubTensorsNoPaddingRequiredTest") -{ - 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 = GetGraphForTesting(optimizedNet.get()); - - // 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 - ARMNN_ASSERT(numberOfSubTensors > 0); - } - } -} - TEST_CASE("ConcatonXorYPaddingRequiredTest") { armnn::INetworkPtr net(armnn::INetwork::Create()); @@ -247,212 +172,6 @@ TEST_CASE("ConcatonXorYPaddingRequiredTest") ARMNN_ASSERT(numberOfSubTensors == 0); } -TEST_CASE("SplitteronXorYNoPaddingRequiredTest") -{ - using namespace armnn; - - unsigned int splitAxis = 2; - unsigned int numSplit = 2; - - const TensorShape& inputShape = { 2, 3, 4, 2 }; - const armnn::TensorInfo intermediateInfo = armnn::TensorInfo({ 2, 3, 2, 2 }, armnn::DataType::Float32); - const std::vector<TensorShape> outputShapes{{ 2, 3, 2, 2 }, - { 2, 3, 2, 2 }}; - const float qScale = 1.0f; - const int32_t qOffset = 0; - - // Creates structures for input & output. - std::vector<float> inputData{ - 1, 2, - 3, 4, - 5, 6, - 7, 8, - 9, 10, - 11, 12, - 13, 14, - 15, 16, - 17, 18, - 19, 20, - 21, 22, - 23, 24, - 25, 26, - 27, 28, - 29, 30, - 31, 32, - 33, 34, - 35, 36, - 37, 38, - 39, 40, - 41, 42, - 43, 44, - 45, 46, - 47, 48 - }; - - std::vector<float> expectedOutput0{ - 1, 2, - 3, 4, - 9, 10, - 11, 12, - 17, 18, - 19, 20, - 25, 26, - 27, 28, - 33, 34, - 35, 36, - 41, 42, - 43, 44 - }; - - std::vector<float> expectedOutput1{ - 5, 6, - 7, 8, - 13, 14, - 15, 16, - 21, 22, - 23, 24, - 29, 30, - 31, 32, - 37, 38, - 39, 40, - 45, 46, - 47, 48 - }; - - // Builds up the structure of the network. - INetworkPtr net(INetwork::Create()); - - TensorInfo inputTensorInfo(inputShape, armnn::DataType::Float32, qScale, qOffset); - - armnn::ElementwiseUnaryDescriptor descriptor(armnn::UnaryOperation::Abs); - - // Splitter - std::vector<unsigned int> splitterDimSizes(inputShape.GetNumDimensions()); - - // Add current input shape to splitterDimSizes - for (unsigned int i = 0; i < inputShape.GetNumDimensions(); ++i) - { - splitterDimSizes[i] = inputTensorInfo.GetShape()[i]; - } - - if (splitterDimSizes[splitAxis] % numSplit != 0) - { - throw ParseException("Number of splits must evenly divide the dimension"); - } - - splitterDimSizes[splitAxis] /= numSplit; - - SplitterDescriptor splitDesc(numSplit, inputShape.GetNumDimensions()); - - for (unsigned int g = 0; g < numSplit; ++g) - { - // Set the size of the views. - for (unsigned int dimIdx = 0; dimIdx < splitterDimSizes.size(); ++dimIdx) - { - splitDesc.SetViewSize(g, dimIdx, splitterDimSizes[dimIdx]); - } - splitDesc.SetViewOriginCoord(g, splitAxis, splitterDimSizes[splitAxis] * g); - } - IConnectableLayer* input = net->AddInputLayer(0, "input"); - IConnectableLayer* elementWiseUnary0 = net->AddElementwiseUnaryLayer(descriptor, "elementwiseunary_0"); - IConnectableLayer* elementWiseUnary1 = net->AddElementwiseUnaryLayer(descriptor, "elementwiseunary_0"); - IConnectableLayer* splitter = net->AddSplitterLayer(splitDesc, "splitter"); - - // Connections - Connect(input, splitter, inputTensorInfo, 0, 0); - Connect(splitter, elementWiseUnary0, intermediateInfo, 0, 0); - Connect(splitter, elementWiseUnary1, intermediateInfo, 1, 0); - - std::vector<IConnectableLayer*> pooling2dLayers{elementWiseUnary0, elementWiseUnary1}; - - for (unsigned int i = 0; i < outputShapes.size(); ++i) - { - TensorInfo outputTensorInfo(outputShapes[i], armnn::DataType::Float32, qScale, qOffset); - IConnectableLayer* output = net->AddOutputLayer(armnn::numeric_cast<LayerBindingId>(i)); - Connect(pooling2dLayers[i], output, outputTensorInfo, 0, 0); - } - - std::map<int, std::vector<float>> inputTensorData = {{ 0,inputData }}; - std::map<int, std::vector<float>> expectedOutputData = {{ 0, expectedOutput0 }, { 1, expectedOutput1 }}; - - 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 = GetGraphForTesting(optimizedNet.get()); - - // 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::ElementwiseUnary) - { - 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 - ARMNN_ASSERT(numberOfSubTensors > 0); - } - } - - InputTensors inputTensors; - inputTensors.reserve(inputTensorData.size()); - for (auto&& it : inputTensorData) - { - TensorInfo inputTensorInfo = runtime->GetInputTensorInfo(networkIdentifier, it.first); - inputTensorInfo.SetConstant(true); - inputTensors.push_back({it.first, - ConstTensor(inputTensorInfo, it.second.data())}); - } - OutputTensors outputTensors; - outputTensors.reserve(expectedOutputData.size()); - std::map<int, std::vector<float>> outputStorage; - for (auto&& it : expectedOutputData) - { - std::vector<float> out(it.second.size()); - outputStorage.emplace(it.first, out); - outputTensors.push_back({it.first, - Tensor(runtime->GetOutputTensorInfo(networkIdentifier, it.first), - outputStorage.at(it.first).data())}); - } - - // Does the inference. - runtime->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors); - - // Checks the results. - float tolerance = 0.000001f; - for (auto&& it : expectedOutputData) - { - std::vector<float> out = outputStorage.at(it.first); - for (unsigned int i = 0; i < out.size(); ++i) - { - CHECK_MESSAGE(Compare<armnn::DataType::Float32>(it.second[i], out[i], tolerance) == true, - "Actual output: " << out[i] << ". Expected output:" << it.second[i]); - - } - } -} - TEST_CASE("SplitteronXorYPaddingRequiredTest") { using namespace armnn; |