| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include <armnn/ArmNN.hpp> |
| #include <Graph.hpp> |
| #include <Network.hpp> |
| |
| #include <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() |