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Diffstat (limited to 'src/backends/reference/test/RefOptimizedNetworkTests.cpp')
-rw-r--r-- | src/backends/reference/test/RefOptimizedNetworkTests.cpp | 212 |
1 files changed, 212 insertions, 0 deletions
diff --git a/src/backends/reference/test/RefOptimizedNetworkTests.cpp b/src/backends/reference/test/RefOptimizedNetworkTests.cpp new file mode 100644 index 0000000000..63615e6859 --- /dev/null +++ b/src/backends/reference/test/RefOptimizedNetworkTests.cpp @@ -0,0 +1,212 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <armnn/ArmNN.hpp> +#include <armnn/Graph.hpp> +#include <armnn/Network.hpp> + +#include <backends/reference/RefWorkloadFactory.hpp> + +#include <boost/test/unit_test.hpp> + +BOOST_AUTO_TEST_SUITE(RefOptimizedNetwork) + +BOOST_AUTO_TEST_CASE(OptimizeValidateCpuRefWorkloads) +{ + const armnn::TensorInfo desc({3, 5}, armnn::DataType::Float32); + + armnn::Network net; + + armnn::NormalizationDescriptor nmDesc; + armnn::ActivationDescriptor acDesc; + + // in + // | + // nm + // / | + // ac | + // \ | + // ml + // | + // sm + // | + // ot + armnn::IConnectableLayer* layer = net.AddInputLayer(0, "in"); + layer->GetOutputSlot(0).SetTensorInfo(desc); + + armnn::IConnectableLayer* const normLayer = net.AddNormalizationLayer(nmDesc, "nm"); + + layer->GetOutputSlot(0).Connect(normLayer->GetInputSlot(0)); + normLayer->GetOutputSlot(0).SetTensorInfo(desc); + + layer = net.AddActivationLayer(acDesc, "ac"); + + normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + layer->GetOutputSlot(0).SetTensorInfo(desc); + + armnn::IConnectableLayer* prevLayer = layer; + layer = net.AddMultiplicationLayer("ml"); + + prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1)); + layer->GetOutputSlot(0).SetTensorInfo(desc); + + prevLayer = layer; + armnn::SoftmaxDescriptor softmaxDescriptor; + layer = net.AddSoftmaxLayer(softmaxDescriptor, "sm"); + + prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + layer->GetOutputSlot(0).SetTensorInfo(desc); + + prevLayer = layer; + layer = net.AddOutputLayer(0, "ot"); + + prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; + armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec()); + static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph().AllocateDynamicBuffers(); + BOOST_CHECK(optNet); + + // Validates workloads. + armnn::RefWorkloadFactory fact; + for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph()) + { + BOOST_CHECK_NO_THROW( + layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact)); + } +} + +BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsCpuRefPermuteLayer) +{ + // Create runtime in which test will run + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + + // build up the structure of the network + armnn::INetworkPtr net(armnn::INetwork::Create()); + + armnn::IConnectableLayer* input = net->AddInputLayer(0); + + armnn::PermuteDescriptor descriptor({0, 2, 3, 1}); + armnn::IConnectableLayer* permute = net->AddPermuteLayer(descriptor); + + armnn::IConnectableLayer* output = net->AddOutputLayer(0); + + input->GetOutputSlot(0).Connect(permute->GetInputSlot(0)); + permute->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + permute->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 4, 1, 4 }, armnn::DataType::Float32)); + + // optimize the network + armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); + + for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph()) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); + } +} + +BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsCpuRefMeanLayer) +{ + // Create runtime in which test will run + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + + // build up the structure of the network + armnn::INetworkPtr net(armnn::INetwork::Create()); + + armnn::IConnectableLayer* input = net->AddInputLayer(0); + + armnn::MeanDescriptor descriptor({ 0, 1 }, false); + armnn::IConnectableLayer* meanLayer = net->AddMeanLayer(descriptor); + + armnn::IConnectableLayer* output = net->AddOutputLayer(0); + + input->GetOutputSlot(0).Connect(meanLayer->GetInputSlot(0)); + meanLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 4, 3, 2 }, armnn::DataType::Float32)); + meanLayer->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 2 }, armnn::DataType::Float32)); + + // optimize the network + armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); + + for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph()) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); + } +} + +BOOST_AUTO_TEST_CASE(FP16TurboModeTestOnCpuRef) +{ + // Test to check when FP16 Turbo mode set + // it converts the FP32 network to FP16 Network + // add FP32ToFP16 conversion layer after the InputLayer + // add FP16ToFP32 conversion layer after the OutputLayer + // checks the other layers if they are supported in FP16 + // if they are not put the conversion layers before and after + // if they are not supported in FP16 use FP32 instead + // if there are inverse conversion layers remove them with optimization + // at the moment FloorLayer is not supported in FP16 so it rolls back to FP32 + // and inverse conversion layers are removed by the optimizer + armnn::Network net; + + // Defines layers. + auto input = net.AddInputLayer(0); + auto floor = net.AddFloorLayer(); + auto output = net.AddOutputLayer(0); + + // Connects layers. + input->GetOutputSlot(0).Connect(floor->GetInputSlot(0)); + floor->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + armnn::TensorShape shape({4}); + armnn::TensorInfo info(shape, armnn::DataType::Float32); + input->GetOutputSlot(0).SetTensorInfo(info); + floor->GetOutputSlot(0).SetTensorInfo(info); + + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + + armnn::OptimizerOptions optimizerOptions; + optimizerOptions.m_ReduceFp32ToFp16 = true; + + armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec(), + optimizerOptions); + + std::ostringstream ss; + optimizedNet->SerializeToDot(ss); + + auto inputId = input->GetGuid(); + auto floorId = floor->GetGuid(); + auto outputId = output->GetGuid(); + + std::stringstream expected; + expected << + "digraph Optimized {\n" + " node [shape=\"record\"];\n" + " edge [fontsize=8 fontcolor=\"blue\" fontname=\"arial-bold\"];\n" + " " << inputId << " [label=\"{Input}\"];\n" + " " << floorId << " [label=\"{Floor}\"];\n" + " " << outputId << " [label=\"{Output}\"];\n" + " " << inputId << " -> " << floorId << " [label=< [4] >];\n" + " " << floorId << " -> " << outputId << " [label=< [4] >];\n" + "}\n"; + + BOOST_TEST(ss.str() == expected.str()); +} + +BOOST_AUTO_TEST_SUITE_END() |