diff options
Diffstat (limited to 'src')
21 files changed, 1245 insertions, 1052 deletions
diff --git a/src/armnn/test/EndToEndTest.cpp b/src/armnn/test/EndToEndTest.cpp index d34bf69548..4f202f174e 100644 --- a/src/armnn/test/EndToEndTest.cpp +++ b/src/armnn/test/EndToEndTest.cpp @@ -2,14 +2,15 @@ // Copyright © 2017 Arm Ltd. All rights reserved. // SPDX-License-Identifier: MIT // -#include <boost/test/unit_test.hpp> #include <armnn/Descriptors.hpp> #include <armnn/IRuntime.hpp> #include <armnn/INetwork.hpp> #include <backends/test/QuantizeHelper.hpp> + #include <boost/core/ignore_unused.hpp> +#include <boost/test/unit_test.hpp> #include <set> @@ -17,12 +18,14 @@ BOOST_AUTO_TEST_SUITE(EndToEnd) namespace { + template<typename T> bool IsFloatIterFunc(T iter) { boost::ignore_unused(iter); return IsFloatingPointIterator<T>::value; } + } //namespace BOOST_AUTO_TEST_CASE(QuantizedHelper) @@ -44,381 +47,6 @@ BOOST_AUTO_TEST_CASE(QuantizedHelper) BOOST_TEST(IsFloatIterFunc(&ints[0]) == false); } -BOOST_AUTO_TEST_CASE(Unsigned8) -{ - using namespace armnn; - - // Create runtime in which test will run - armnn::IRuntime::CreationOptions options; - armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); - - // Builds up the structure of the network. - armnn::INetworkPtr net(INetwork::Create()); - - IConnectableLayer* input = net->AddInputLayer(0, "input"); - IConnectableLayer* softmax = net->AddSoftmaxLayer(SoftmaxDescriptor(), "softmax"); - IConnectableLayer* output = net->AddOutputLayer(0, "output"); - - input->GetOutputSlot(0).Connect(softmax->GetInputSlot(0)); - softmax->GetOutputSlot(0).Connect(output->GetInputSlot(0)); - - // Sets the tensors in the network. - TensorInfo inputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8); - inputTensorInfo.SetQuantizationOffset(100); - inputTensorInfo.SetQuantizationScale(10000.0f); - input->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); - - TensorInfo outputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationOffset(0); - outputTensorInfo.SetQuantizationScale(1.0f/255.0f); - softmax->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); - - // optimize the network - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); - - // Loads it into the runtime. - NetworkId netId; - auto error = runtime->LoadNetwork(netId, std::move(optNet)); - BOOST_TEST(error == Status::Success); - - // Creates structures for input & output. - std::vector<uint8_t> inputData - { - 1, 10, 3, 200, 5 // Some inputs - one of which is sufficiently larger than the others to saturate softmax. - }; - std::vector<uint8_t> outputData(5); - - armnn::InputTensors inputTensors - { - {0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())} - }; - armnn::OutputTensors outputTensors - { - {0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} - }; - - // Does the inference. - runtime->EnqueueWorkload(netId, inputTensors, outputTensors); - - // Checks the results. - BOOST_TEST(outputData[0] == 0); - BOOST_TEST(outputData[1] == 0); - BOOST_TEST(outputData[2] == 0); - BOOST_TEST(outputData[3] == 255); // softmax has been saturated. - BOOST_TEST(outputData[4] == 0); -} - -template <typename T> -void ConstantUsageTest(const std::vector<armnn::BackendId>& computeDevice, - const armnn::TensorInfo& commonTensorInfo, - const std::vector<T>& inputData, - const std::vector<T>& constantData, - const std::vector<T>& expectedOutputData) -{ - using namespace armnn; - - // Create runtime in which test will run - armnn::IRuntime::CreationOptions options; - armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); - - // Builds up the structure of the network. - INetworkPtr net(INetwork::Create()); - - IConnectableLayer* input = net->AddInputLayer(0); - IConnectableLayer* constant = net->AddConstantLayer(ConstTensor(commonTensorInfo, constantData)); - IConnectableLayer* add = net->AddAdditionLayer(); - IConnectableLayer* output = net->AddOutputLayer(0); - - input->GetOutputSlot(0).Connect(add->GetInputSlot(0)); - constant->GetOutputSlot(0).Connect(add->GetInputSlot(1)); - add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); - - // Sets the tensors in the network. - input->GetOutputSlot(0).SetTensorInfo(commonTensorInfo); - constant->GetOutputSlot(0).SetTensorInfo(commonTensorInfo); - add->GetOutputSlot(0).SetTensorInfo(commonTensorInfo); - - // optimize the network - IOptimizedNetworkPtr optNet = Optimize(*net, computeDevice, runtime->GetDeviceSpec()); - - // Loads it into the runtime. - NetworkId netId; - runtime->LoadNetwork(netId, std::move(optNet)); - - // Creates structures for input & output. - std::vector<T> outputData(inputData.size()); - - InputTensors inputTensors - { - {0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())} - }; - OutputTensors outputTensors - { - {0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} - }; - - // Does the inference. - runtime->EnqueueWorkload(netId, inputTensors, outputTensors); - - // Checks the results. - BOOST_TEST(outputData == expectedOutputData); -} - -static void ConstantUsageFloat32Test(const std::vector<armnn::BackendId>& computeDevice) -{ - const armnn::TensorInfo commonTensorInfo({ 2, 3 }, armnn::DataType::Float32); - - ConstantUsageTest(computeDevice, - commonTensorInfo, - std::vector<float>{ 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }, // Input. - std::vector<float>{ 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }, // Const input. - std::vector<float>{ 7.f, 7.f, 7.f, 7.f, 7.f, 7.f } // Expected output. - ); -} - -static void ConstantUsageUint8Test(const std::vector<armnn::BackendId>& computeDevice) -{ - armnn::TensorInfo commonTensorInfo({ 2, 3 }, armnn::DataType::QuantisedAsymm8); - - const float scale = 0.023529f; - const int8_t offset = -43; - - commonTensorInfo.SetQuantizationScale(scale); - commonTensorInfo.SetQuantizationOffset(offset); - - ConstantUsageTest(computeDevice, - commonTensorInfo, - QuantizedVector<uint8_t>(scale, offset, { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }), // Input. - QuantizedVector<uint8_t>(scale, offset, { 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }), // Const input. - QuantizedVector<uint8_t>(scale, offset, { 7.f, 7.f, 7.f, 7.f, 7.f, 7.f }) // Expected output. - ); -} - -BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Float32) -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - ConstantUsageFloat32Test(backends); -} - -#if ARMCOMPUTENEON_ENABLED -BOOST_AUTO_TEST_CASE(ConstantUsage_Neon_Float32) -{ - ConstantUsageFloat32Test({armnn::Compute::CpuAcc}); -} -#endif - -#if ARMCOMPUTECL_ENABLED -BOOST_AUTO_TEST_CASE(ConstantUsage_Cl_Float32) -{ - ConstantUsageFloat32Test({armnn::Compute::GpuAcc}); -} -#endif - -BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Uint8) -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - ConstantUsageUint8Test(backends); -} - -BOOST_AUTO_TEST_CASE(TrivialAdd) -{ - // This test was designed to match "AddTwo" in android nn/runtime/test/TestTrivialModel.cpp. - - using namespace armnn; - - // Create runtime in which test will run - armnn::IRuntime::CreationOptions options; - armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); - - // Builds up the structure of the network. - armnn::INetworkPtr net(INetwork::Create()); - - IConnectableLayer* input1 = net->AddInputLayer(0); - IConnectableLayer* input2 = net->AddInputLayer(1); - IConnectableLayer* add = net->AddAdditionLayer(); - IConnectableLayer* output = net->AddOutputLayer(0); - - input1->GetOutputSlot(0).Connect(add->GetInputSlot(0)); - input2->GetOutputSlot(0).Connect(add->GetInputSlot(1)); - add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); - - // Sets the tensors in the network. - TensorInfo tensorInfo(TensorShape({3, 4}), DataType::Float32); - input1->GetOutputSlot(0).SetTensorInfo(tensorInfo); - input2->GetOutputSlot(0).SetTensorInfo(tensorInfo); - add->GetOutputSlot(0).SetTensorInfo(tensorInfo); - - // optimize the network - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); - - // Loads it into the runtime. - NetworkId netId; - runtime->LoadNetwork(netId, std::move(optNet)); - - // Creates structures for input & output - matching android nn test. - std::vector<float> input1Data - { - 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f - }; - std::vector<float> input2Data - { - 100.f, 200.f, 300.f, 400.f, 500.f, 600.f, 700.f, 800.f, 900.f, 1000.f, 1100.f, 1200.f - }; - std::vector<float> outputData(12); - - InputTensors inputTensors - { - {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input1Data.data())}, - {1,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input2Data.data())} - }; - OutputTensors outputTensors - { - {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} - }; - - // Does the inference. - runtime->EnqueueWorkload(netId, inputTensors, outputTensors); - - // Checks the results - BOOST_TEST(outputData[0] == 101); - BOOST_TEST(outputData[1] == 202); - BOOST_TEST(outputData[2] == 303); - BOOST_TEST(outputData[3] == 404); - BOOST_TEST(outputData[4] == 505); - BOOST_TEST(outputData[5] == 606); - BOOST_TEST(outputData[6] == 707); - BOOST_TEST(outputData[7] == 808); - BOOST_TEST(outputData[8] == 909); - BOOST_TEST(outputData[9] == 1010); - BOOST_TEST(outputData[10] == 1111); - BOOST_TEST(outputData[11] == 1212); -} - -BOOST_AUTO_TEST_CASE(MultipleOutputs) -{ - using namespace armnn; - - // Create runtime in which test will run - armnn::IRuntime::CreationOptions options; - armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); - - // Builds up the structure of the network. - INetworkPtr net(INetwork::Create()); - - IConnectableLayer* input = net->AddInputLayer(0); - - // ReLu1 - ActivationDescriptor activation1Descriptor; - activation1Descriptor.m_Function = ActivationFunction::BoundedReLu; - activation1Descriptor.m_A = 1.f; - activation1Descriptor.m_B = -1.f; - IConnectableLayer* activation1 = net->AddActivationLayer(activation1Descriptor); - - // ReLu6 - ActivationDescriptor activation2Descriptor; - activation2Descriptor.m_Function = ActivationFunction::BoundedReLu; - activation2Descriptor.m_A = 6.0f; - IConnectableLayer* activation2 = net->AddActivationLayer(activation2Descriptor); - - // BoundedReLu(min=2, max=5) - ActivationDescriptor activation3Descriptor; - activation3Descriptor.m_Function = ActivationFunction::BoundedReLu; - activation3Descriptor.m_A = 5.0f; - activation3Descriptor.m_B = 2.0f; - IConnectableLayer* activation3 = net->AddActivationLayer(activation3Descriptor); - - IConnectableLayer* output1 = net->AddOutputLayer(0); - IConnectableLayer* output2 = net->AddOutputLayer(1); - IConnectableLayer* output3 = net->AddOutputLayer(2); - - input->GetOutputSlot(0).Connect(activation1->GetInputSlot(0)); - input->GetOutputSlot(0).Connect(activation2->GetInputSlot(0)); - input->GetOutputSlot(0).Connect(activation3->GetInputSlot(0)); - - activation1->GetOutputSlot(0).Connect(output1->GetInputSlot(0)); - activation2->GetOutputSlot(0).Connect(output2->GetInputSlot(0)); - activation3->GetOutputSlot(0).Connect(output3->GetInputSlot(0)); - - // Sets the tensors in the network. - TensorInfo tensorInfo(TensorShape({ 10 }), DataType::Float32); - input->GetOutputSlot(0).SetTensorInfo(tensorInfo); - activation1->GetOutputSlot(0).SetTensorInfo(tensorInfo); - activation2->GetOutputSlot(0).SetTensorInfo(tensorInfo); - activation3->GetOutputSlot(0).SetTensorInfo(tensorInfo); - - // optimize the network - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); - - // Loads it into the runtime. - NetworkId netId; - runtime->LoadNetwork(netId, std::move(optNet)); - - // Creates structures for input & output. - const std::vector<float> inputData{ 3.f, 5.f, 2.f, 3.f, 7.f, 0.f, -2.f, -1.f, 3.f, 3.f }; - - std::vector<float> output1Data(inputData.size()); - std::vector<float> output2Data(inputData.size()); - std::vector<float> output3Data(inputData.size()); - - InputTensors inputTensors - { - {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())} - }; - OutputTensors outputTensors - { - {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), output1Data.data())}, - {1,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 1), output2Data.data())}, - {2,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 2), output3Data.data())} - }; - - // Does the inference. - runtime->EnqueueWorkload(netId, inputTensors, outputTensors); - - // Checks the results. - BOOST_TEST(output1Data == std::vector<float>({ 1.f, 1.f, 1.f, 1.f, 1.f, 0.f, -1.f, -1.f, 1.f, 1.f })); // ReLu1 - BOOST_TEST(output2Data == std::vector<float>({ 3.f, 5.f, 2.f, 3.f, 6.f, 0.f, 0.f, 0.f, 3.f, 3.f })); // ReLu6 - BOOST_TEST(output3Data == std::vector<float>({ 3.f, 5.f, 2.f, 3.f, 5.f, 2.f, 2.f, 2.f, 3.f, 3.f })); // [2, 5] -} - -#if ARMCOMPUTENEON_ENABLED -BOOST_AUTO_TEST_CASE(FallbackToCpuRef) -{ - using namespace armnn; - - // Create runtime in which test will run and allow fallback to CpuRef. - IRuntime::CreationOptions options; - IRuntimePtr runtime(IRuntime::Create(options)); - - // Builds up the structure of the network. - INetworkPtr net(INetwork::Create()); - - IConnectableLayer* input = net->AddInputLayer(0); - - // This layer configuration isn't supported by CpuAcc but we allow fallback to CpuRef so it shoud pass. - NormalizationDescriptor descriptor; - IConnectableLayer* pooling = net->AddNormalizationLayer(descriptor); - - IConnectableLayer* output = net->AddOutputLayer(0); - - input->GetOutputSlot(0).Connect(pooling->GetInputSlot(0)); - pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0)); - - input->GetOutputSlot(0).SetTensorInfo(TensorInfo({ 1, 1, 4, 4 }, DataType::Float32)); - pooling->GetOutputSlot(0).SetTensorInfo(TensorInfo({ 1, 1, 4, 4 }, DataType::Float32)); - - // optimize the network - std::vector<BackendId> backends = {Compute::CpuAcc, Compute::CpuRef}; - IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); - - // Load it into the runtime. It should pass. - NetworkId netId; - BOOST_TEST(runtime->LoadNetwork(netId, std::move(optNet)) == Status::Success); -} -#endif // ARMCOMPUTENEON_ENABLED - BOOST_AUTO_TEST_CASE(ErrorOnLoadNetwork) { using namespace armnn; diff --git a/src/armnn/test/GraphUtils.hpp b/src/armnn/test/GraphUtils.hpp index 4d385de92f..3325405eaf 100644 --- a/src/armnn/test/GraphUtils.hpp +++ b/src/armnn/test/GraphUtils.hpp @@ -4,7 +4,8 @@ // #pragma once -#include "Graph.hpp" +#include <armnn/Graph.hpp> + #include <string> namespace diff --git a/src/armnn/test/NetworkTests.cpp b/src/armnn/test/NetworkTests.cpp index 4f8dd7ea7b..91ff7fa983 100644 --- a/src/armnn/test/NetworkTests.cpp +++ b/src/armnn/test/NetworkTests.cpp @@ -2,16 +2,13 @@ // Copyright © 2017 Arm Ltd. All rights reserved. // SPDX-License-Identifier: MIT // -#include <boost/test/unit_test.hpp> + +#include "GraphUtils.hpp" #include <armnn/ArmNN.hpp> -#include <Network.hpp> -#include <Graph.hpp> -#include <backends/reference/RefWorkloadFactory.hpp> -#include <backends/neon/NeonWorkloadFactory.hpp> -#include <backends/cl/ClWorkloadFactory.hpp> +#include <armnn/Network.hpp> -#include "GraphUtils.hpp" +#include <boost/test/unit_test.hpp> namespace { @@ -43,54 +40,6 @@ BOOST_AUTO_TEST_CASE(LayerGuids) BOOST_TEST(inputId != outputId); } -BOOST_AUTO_TEST_CASE(SerializeToDot) -{ - armnn::Network net; - - //Defines layers. - auto input = net.AddInputLayer(0); - auto add = net.AddAdditionLayer(); - auto output = net.AddOutputLayer(0); - - // Connects layers. - input->GetOutputSlot(0).Connect(add->GetInputSlot(0)); - input->GetOutputSlot(0).Connect(add->GetInputSlot(1)); - add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); - - armnn::TensorShape shape({4}); - armnn::TensorInfo info(shape, armnn::DataType::Float32); - input->GetOutputSlot(0).SetTensorInfo(info); - add->GetOutputSlot(0).SetTensorInfo(info); - - armnn::IRuntime::CreationOptions options; - armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); - - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec()); - - std::ostringstream ss; - optimizedNet->SerializeToDot(ss); - - auto inputId = input->GetGuid(); - auto addId = add->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" - " " << addId << " [label=\"{Addition}\"];\n" - " " << outputId << " [label=\"{Output}\"];\n" - " " << inputId << " -> " << addId << " [label=< [4] >];\n" - " " << inputId << " -> " << addId << " [label=< [4] >];\n" - " " << addId << " -> " << outputId << " [label=< [4] >];\n" - "}\n"; - - BOOST_TEST(ss.str() == expected.str()); -} - BOOST_AUTO_TEST_CASE(NetworkBasic) { armnn::Network net; @@ -417,585 +366,4 @@ BOOST_AUTO_TEST_CASE(NetworkModification_SplitterMultiplication) prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); } -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)); - } -} - -#if ARMCOMPUTENEON_ENABLED -BOOST_AUTO_TEST_CASE(OptimizeValidateCpuAccDeviceSupportLayerNoFallback) -{ - // build up the structure of the network - armnn::INetworkPtr net(armnn::INetwork::Create()); - - armnn::IConnectableLayer* input = net->AddInputLayer(0); - - armnn::IConnectableLayer* output = net->AddOutputLayer(0); - - input->GetOutputSlot(0).Connect(output->GetInputSlot(0)); - - input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); - - armnn::IRuntime::CreationOptions options; - armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); - - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); - BOOST_CHECK(optNet); - // validate workloads - armnn::NeonWorkloadFactory fact; - for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph()) - { - BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuAcc); - BOOST_CHECK_NO_THROW( - layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact)); - } -} -#endif // ARMCOMPUTENEON_ENABLED - -#if ARMCOMPUTECL_ENABLED -BOOST_AUTO_TEST_CASE(OptimizeValidateGpuDeviceSupportLayerNoFallback) -{ - // build up the structure of the network - armnn::INetworkPtr net(armnn::INetwork::Create()); - - armnn::IConnectableLayer* input = net->AddInputLayer(0); - - armnn::IConnectableLayer* output = net->AddOutputLayer(0); - - input->GetOutputSlot(0).Connect(output->GetInputSlot(0)); - - input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); - - armnn::IRuntime::CreationOptions options; - armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); - - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); - BOOST_CHECK(optNet); - // validate workloads - armnn::ClWorkloadFactory fact; - for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph()) - { - BOOST_CHECK(layer->GetBackendId() == armnn::Compute::GpuAcc); - BOOST_CHECK_NO_THROW( - layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact)); - } -} -#endif // ARMCOMPUTECL_ENABLED - -BOOST_AUTO_TEST_CASE(OptimizeValidateDeviceNonSupportLayerNoFallback) -{ - // build up the structure of the network - armnn::INetworkPtr net(armnn::INetwork::Create()); - - armnn::IConnectableLayer* input = net->AddInputLayer(0); - - // This layer configuration isn't supported by CpuAcc and isn't allowed to fall back, so Optimize will return null. - armnn::NormalizationDescriptor descriptor; - armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor); - - armnn::IConnectableLayer* output = net->AddOutputLayer(0); - - input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0)); - normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0)); - - input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); - normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); - - armnn::IRuntime::CreationOptions options; - armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); - - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); - BOOST_CHECK(!optNet); -} - -BOOST_AUTO_TEST_CASE(OptimizeValidateDeviceNonSupportLayerWithFallback) -{ - // build up the structure of the network - armnn::INetworkPtr net(armnn::INetwork::Create()); - - armnn::IConnectableLayer* input = net->AddInputLayer(0); - - // This layer configuration isn't supported by CpuAcc but it allows to fallback to CpuRef. - armnn::NormalizationDescriptor descriptor; - armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor); - - armnn::IConnectableLayer* output = net->AddOutputLayer(0); - - input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0)); - normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0)); - - input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); - normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); - - armnn::IRuntime::CreationOptions options; - armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); - - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, armnn::Compute::CpuRef }; - armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); - BOOST_REQUIRE(optNet); - - for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph()) - { - // If NEON is enabled, Input and Output layers are supported by CpuAcc, - // the other layers are supported by CpuRef. - // If NEON is not enabled, all layers are supported by CpuRef. -#if ARMCOMPUTENEON_ENABLED - if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output) - { - BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuAcc); - } - else if (layer->GetType() == armnn::LayerType::Normalization) - { - BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); - } -#else - BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); -#endif - } -} - -BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsUndefinedComputeDevice) -{ - 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::Undefined }; - - armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec()); - BOOST_CHECK(!optNet); - -} - -BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsUndefinedComputeDeviceWithFallback) -{ - 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::Undefined, armnn::Compute::CpuRef }; - - armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec()); - BOOST_CHECK(optNet); - - // validate workloads - armnn::RefWorkloadFactory fact; - for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph()) - { - BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); - BOOST_CHECK_NO_THROW( - layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact)); - } -} -BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsDuplicateComputeDeviceWithFallback) -{ - // build up the structure of the network - armnn::INetworkPtr net(armnn::INetwork::Create()); - - armnn::IConnectableLayer* input = net->AddInputLayer(0); - - // This layer configuration isn't supported by CpuAcc but it allows to fallback to CpuRef. - armnn::NormalizationDescriptor descriptor; - armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor); - - armnn::IConnectableLayer* output = net->AddOutputLayer(0); - - input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0)); - normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0)); - - input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); - normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); - - armnn::IRuntime::CreationOptions options; - armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); - - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, - armnn::Compute::GpuAcc, - armnn::Compute::CpuRef }; - - armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); - BOOST_REQUIRE(optNet); - - for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph()) - { - // If NEON is enabled, Input and Output layers are supported by CpuAcc, - // the other layers are supported by CpuRef. - // If only CL is enabled, Input and Output layers are supported by GpuAcc, - // the other layers are supported by CpuRef. - // If neither NEON, nor CL is enabled, all layers are supported by CpuRef. -#if ARMCOMPUTENEON_ENABLED - if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output) - { - BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuAcc); - } - else if (layer->GetType() == armnn::LayerType::Normalization) - { - BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); - } -#elif ARMCOMPUTECL_ENABLED - if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output) - { - BOOST_CHECK(layer->GetBackendId() == armnn::Compute::GpuAcc); - } - else if (layer->GetType() == armnn::LayerType::Normalization) - { - BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); - } -#else - BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); -#endif - } -} - -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()); -} - -#if ARMCOMPUTECL_ENABLED -BOOST_AUTO_TEST_CASE(FP16TurboModeTestOnGpuAcc) -{ - // 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, "input layer"); - // ReLu1 - armnn::ActivationDescriptor activation1Descriptor; - activation1Descriptor.m_Function = armnn::ActivationFunction::BoundedReLu; - activation1Descriptor.m_A = 1.f; - activation1Descriptor.m_B = -1.f; - auto activation = net.AddActivationLayer(activation1Descriptor, "activation layer"); - auto output = net.AddOutputLayer(0, "output layer"); - - // Connects layers. - input->GetOutputSlot(0).Connect(activation->GetInputSlot(0)); - activation->GetOutputSlot(0).Connect(output->GetInputSlot(0)); - - armnn::TensorShape shape({4}); - armnn::TensorInfo info(shape, armnn::DataType::Float32); - input->GetOutputSlot(0).SetTensorInfo(info); - activation->GetOutputSlot(0).SetTensorInfo(info); - - armnn::IRuntime::CreationOptions options; - armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); - - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - - armnn::OptimizerOptions optimizerOptions; - optimizerOptions.m_ReduceFp32ToFp16 = true; - - armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize( - net, backends, runtime->GetDeviceSpec(), optimizerOptions); - - const armnn::Graph& graph = static_cast<armnn::OptimizedNetwork*>(optimizedNet.get())->GetGraph(); - - // Tests that all layers are present in the graph. - BOOST_TEST(graph.GetNumLayers() == 5); - - // Tests that the vertices exist and have correct names. - BOOST_TEST(GraphHasNamedLayer(graph, "input layer")); - BOOST_TEST(GraphHasNamedLayer(graph, "convert_fp32_to_fp16-0-input layer")); - BOOST_TEST(GraphHasNamedLayer(graph, "activation layer")); - BOOST_TEST(GraphHasNamedLayer(graph, "convert_fp16_to_fp32-0-output layer")); - BOOST_TEST(GraphHasNamedLayer(graph, "output layer")); -} -#endif - BOOST_AUTO_TEST_SUITE_END() diff --git a/src/backends/cl/backend.mk b/src/backends/cl/backend.mk index 1f89f3b0a4..996db3fbfd 100644 --- a/src/backends/cl/backend.mk +++ b/src/backends/cl/backend.mk @@ -44,9 +44,12 @@ BACKEND_SOURCES := \ BACKEND_TEST_SOURCES := \ test/ClCreateWorkloadTests.cpp \ + test/ClEndToEndTests.cpp \ + test/ClJsonPrinterTests.cpp \ test/ClLayerSupportTests.cpp \ test/ClLayerTests.cpp \ test/ClMemCopyTests.cpp \ + test/ClOptimizedNetworkTests.cpp \ test/ClRuntimeTests.cpp \ test/Fp16SupportTest.cpp \ test/OpenClTimerTest.cpp diff --git a/src/backends/cl/test/CMakeLists.txt b/src/backends/cl/test/CMakeLists.txt index 69aa08d42b..c017377768 100644 --- a/src/backends/cl/test/CMakeLists.txt +++ b/src/backends/cl/test/CMakeLists.txt @@ -6,9 +6,12 @@ list(APPEND armnnClBackendUnitTests_sources ClContextControlFixture.hpp ClCreateWorkloadTests.cpp + ClEndToEndTests.cpp + ClJsonPrinterTests.cpp ClLayerSupportTests.cpp ClLayerTests.cpp ClMemCopyTests.cpp + ClOptimizedNetworkTests.cpp ClRuntimeTests.cpp OpenClTimerTest.cpp ) diff --git a/src/backends/cl/test/ClEndToEndTests.cpp b/src/backends/cl/test/ClEndToEndTests.cpp new file mode 100644 index 0000000000..d6fd8875c4 --- /dev/null +++ b/src/backends/cl/test/ClEndToEndTests.cpp @@ -0,0 +1,18 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <backends/test/EndToEndTestImpl.hpp> + +#include <boost/test/unit_test.hpp> + +BOOST_AUTO_TEST_SUITE(ClEndToEnd) + +BOOST_AUTO_TEST_CASE(ConstantUsage_Cl_Float32) +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; + ConstantUsageFloat32Test(backends); +} + +BOOST_AUTO_TEST_SUITE_END() diff --git a/src/backends/cl/test/ClJsonPrinterTests.cpp b/src/backends/cl/test/ClJsonPrinterTests.cpp new file mode 100644 index 0000000000..f0b4b7acae --- /dev/null +++ b/src/backends/cl/test/ClJsonPrinterTests.cpp @@ -0,0 +1,23 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <armnn/BackendId.hpp> + +#include <backends/cl/test/ClContextControlFixture.hpp> +#include <backends/test/JsonPrinterTestImpl.hpp> + +#include <boost/test/unit_test.hpp> + +#include <vector> + +BOOST_FIXTURE_TEST_SUITE(ClJsonPrinter, ClProfilingContextControlFixture) + +BOOST_AUTO_TEST_CASE(SoftmaxProfilerJsonPrinterGpuAccTest) +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; + SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJsonPrinterResult(backends); +} + +BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file diff --git a/src/backends/cl/test/ClOptimizedNetworkTests.cpp b/src/backends/cl/test/ClOptimizedNetworkTests.cpp new file mode 100644 index 0000000000..b39a4b1304 --- /dev/null +++ b/src/backends/cl/test/ClOptimizedNetworkTests.cpp @@ -0,0 +1,101 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <armnn/ArmNN.hpp> +#include <armnn/Network.hpp> + +#include <armnn/test/GraphUtils.hpp> + +#include <backends/cl/ClWorkloadFactory.hpp> + +#include <boost/test/unit_test.hpp> + +BOOST_AUTO_TEST_SUITE(ClOptimizedNetwork) + +BOOST_AUTO_TEST_CASE(OptimizeValidateGpuDeviceSupportLayerNoFallback) +{ + // build up the structure of the network + armnn::INetworkPtr net(armnn::INetwork::Create()); + + armnn::IConnectableLayer* input = net->AddInputLayer(0); + armnn::IConnectableLayer* output = net->AddOutputLayer(0); + + input->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; + armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); + BOOST_CHECK(optNet); + // validate workloads + armnn::ClWorkloadFactory fact; + for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph()) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::GpuAcc); + BOOST_CHECK_NO_THROW( + layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact)); + } +} + +BOOST_AUTO_TEST_CASE(FP16TurboModeTestOnGpuAcc) +{ + // 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, "input layer"); + // ReLu1 + armnn::ActivationDescriptor activation1Descriptor; + activation1Descriptor.m_Function = armnn::ActivationFunction::BoundedReLu; + activation1Descriptor.m_A = 1.f; + activation1Descriptor.m_B = -1.f; + auto activation = net.AddActivationLayer(activation1Descriptor, "activation layer"); + auto output = net.AddOutputLayer(0, "output layer"); + + // Connects layers. + input->GetOutputSlot(0).Connect(activation->GetInputSlot(0)); + activation->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + armnn::TensorShape shape({4}); + armnn::TensorInfo info(shape, armnn::DataType::Float32); + input->GetOutputSlot(0).SetTensorInfo(info); + activation->GetOutputSlot(0).SetTensorInfo(info); + + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; + + armnn::OptimizerOptions optimizerOptions; + optimizerOptions.m_ReduceFp32ToFp16 = true; + + armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize( + net, backends, runtime->GetDeviceSpec(), optimizerOptions); + + const armnn::Graph& graph = static_cast<armnn::OptimizedNetwork*>(optimizedNet.get())->GetGraph(); + + // Tests that all layers are present in the graph. + BOOST_TEST(graph.GetNumLayers() == 5); + + // Tests that the vertices exist and have correct names. + BOOST_TEST(GraphHasNamedLayer(graph, "input layer")); + BOOST_TEST(GraphHasNamedLayer(graph, "convert_fp32_to_fp16-0-input layer")); + BOOST_TEST(GraphHasNamedLayer(graph, "activation layer")); + BOOST_TEST(GraphHasNamedLayer(graph, "convert_fp16_to_fp32-0-output layer")); + BOOST_TEST(GraphHasNamedLayer(graph, "output layer")); +} + +BOOST_AUTO_TEST_SUITE_END();
\ No newline at end of file diff --git a/src/backends/neon/backend.mk b/src/backends/neon/backend.mk index a4e6db9610..8f7e72b17c 100644 --- a/src/backends/neon/backend.mk +++ b/src/backends/neon/backend.mk @@ -41,8 +41,11 @@ BACKEND_SOURCES := \ BACKEND_TEST_SOURCES := \ test/NeonCreateWorkloadTests.cpp \ + test/NeonEndToEndTests.cpp \ + test/NeonJsonPrinterTests.cpp \ test/NeonLayerSupportTests.cpp \ test/NeonLayerTests.cpp \ test/NeonMemCopyTests.cpp \ + test/NeonOptimizedNetworkTests.cpp \ test/NeonRuntimeTests.cpp \ test/NeonTimerTest.cpp diff --git a/src/backends/neon/test/CMakeLists.txt b/src/backends/neon/test/CMakeLists.txt index e6a28590b5..999bd4f339 100644 --- a/src/backends/neon/test/CMakeLists.txt +++ b/src/backends/neon/test/CMakeLists.txt @@ -5,9 +5,12 @@ list(APPEND armnnNeonBackendUnitTests_sources NeonCreateWorkloadTests.cpp + NeonEndToEndTests.cpp + NeonJsonPrinterTests.cpp NeonLayerSupportTests.cpp NeonLayerTests.cpp NeonMemCopyTests.cpp + NeonOptimizedNetworkTests.cpp NeonRuntimeTests.cpp NeonTimerTest.cpp ) diff --git a/src/backends/neon/test/NeonEndToEndTests.cpp b/src/backends/neon/test/NeonEndToEndTests.cpp new file mode 100644 index 0000000000..f9aa8a5df5 --- /dev/null +++ b/src/backends/neon/test/NeonEndToEndTests.cpp @@ -0,0 +1,52 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <backends/test/EndToEndTestImpl.hpp> + +#include <boost/test/unit_test.hpp> + +BOOST_AUTO_TEST_SUITE(NeonEndToEnd) + +BOOST_AUTO_TEST_CASE(ConstantUsage_Neon_Float32) +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; + BOOST_TEST(ConstantUsageFloat32Test(backends)); +} + +BOOST_AUTO_TEST_CASE(FallbackToCpuRef) +{ + using namespace armnn; + + // Create runtime in which test will run and allow fallback to CpuRef. + IRuntime::CreationOptions options; + IRuntimePtr runtime(IRuntime::Create(options)); + + // Builds up the structure of the network. + INetworkPtr net(INetwork::Create()); + + IConnectableLayer* input = net->AddInputLayer(0); + + // This layer configuration isn't supported by CpuAcc but we allow fallback to CpuRef so it shoud pass. + NormalizationDescriptor descriptor; + IConnectableLayer* pooling = net->AddNormalizationLayer(descriptor); + + IConnectableLayer* output = net->AddOutputLayer(0); + + input->GetOutputSlot(0).Connect(pooling->GetInputSlot(0)); + pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + input->GetOutputSlot(0).SetTensorInfo(TensorInfo({ 1, 1, 4, 4 }, DataType::Float32)); + pooling->GetOutputSlot(0).SetTensorInfo(TensorInfo({ 1, 1, 4, 4 }, DataType::Float32)); + + // optimize the network + std::vector<BackendId> backends = {Compute::CpuAcc, Compute::CpuRef}; + IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); + + // Load it into the runtime. It should pass. + NetworkId netId; + BOOST_TEST(runtime->LoadNetwork(netId, std::move(optNet)) == Status::Success); +} + +BOOST_AUTO_TEST_SUITE_END() diff --git a/src/backends/neon/test/NeonJsonPrinterTests.cpp b/src/backends/neon/test/NeonJsonPrinterTests.cpp new file mode 100644 index 0000000000..6213c145ba --- /dev/null +++ b/src/backends/neon/test/NeonJsonPrinterTests.cpp @@ -0,0 +1,22 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <armnn/BackendId.hpp> + +#include <backends/test/JsonPrinterTestImpl.hpp> + +#include <boost/test/unit_test.hpp> + +#include <vector> + +BOOST_AUTO_TEST_SUITE(NeonJsonPrinter) + +BOOST_AUTO_TEST_CASE(SoftmaxProfilerJsonPrinterCpuAccTest) +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; + SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJsonPrinterResult(backends); +} + +BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file diff --git a/src/backends/neon/test/NeonOptimizedNetworkTests.cpp b/src/backends/neon/test/NeonOptimizedNetworkTests.cpp new file mode 100644 index 0000000000..ae657ba770 --- /dev/null +++ b/src/backends/neon/test/NeonOptimizedNetworkTests.cpp @@ -0,0 +1,70 @@ +// +// 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/neon/NeonWorkloadFactory.hpp> + +#include <boost/test/unit_test.hpp> + +BOOST_AUTO_TEST_SUITE(NeonOptimizedNetwork) + +BOOST_AUTO_TEST_CASE(OptimizeValidateCpuAccDeviceSupportLayerNoFallback) +{ + // build up the structure of the network + armnn::INetworkPtr net(armnn::INetwork::Create()); + + armnn::IConnectableLayer* input = net->AddInputLayer(0); + armnn::IConnectableLayer* output = net->AddOutputLayer(0); + + input->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; + armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); + BOOST_CHECK(optNet); + // validate workloads + armnn::NeonWorkloadFactory fact; + for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph()) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuAcc); + BOOST_CHECK_NO_THROW( + layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact)); + } +} + +BOOST_AUTO_TEST_CASE(OptimizeValidateDeviceNonSupportLayerNoFallback) +{ + // build up the structure of the network + armnn::INetworkPtr net(armnn::INetwork::Create()); + + armnn::IConnectableLayer* input = net->AddInputLayer(0); + + // This layer configuration isn't supported by CpuAcc and isn't allowed to fall back, so Optimize will return null. + armnn::NormalizationDescriptor descriptor; + armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor); + + armnn::IConnectableLayer* output = net->AddOutputLayer(0); + + input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0)); + normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; + armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); + BOOST_CHECK(!optNet); +} + +BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file diff --git a/src/backends/reference/backend.mk b/src/backends/reference/backend.mk index 455ab4618e..007efceb9b 100644 --- a/src/backends/reference/backend.mk +++ b/src/backends/reference/backend.mk @@ -65,6 +65,9 @@ BACKEND_SOURCES := \ BACKEND_TEST_SOURCES := \ test/RefCreateWorkloadTests.cpp \ + test/RefEndToEndTests.cpp \ + test/RefJsonPrinterTests.cpp \ test/RefLayerSupportTests.cpp \ test/RefLayerTests.cpp \ + test/RefOptimizedNetworkTests.cpp \ test/RefRuntimeTests.cpp diff --git a/src/backends/reference/test/CMakeLists.txt b/src/backends/reference/test/CMakeLists.txt index dea0ef6498..1eec594aa9 100644 --- a/src/backends/reference/test/CMakeLists.txt +++ b/src/backends/reference/test/CMakeLists.txt @@ -5,8 +5,11 @@ list(APPEND armnnRefBackendUnitTests_sources RefCreateWorkloadTests.cpp + RefEndToEndTests.cpp + RefJsonPrinterTests.cpp RefLayerSupportTests.cpp RefLayerTests.cpp + RefOptimizedNetworkTests.cpp RefRuntimeTests.cpp ) diff --git a/src/backends/reference/test/RefEndToEndTests.cpp b/src/backends/reference/test/RefEndToEndTests.cpp new file mode 100644 index 0000000000..8938d6f222 --- /dev/null +++ b/src/backends/reference/test/RefEndToEndTests.cpp @@ -0,0 +1,251 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <backends/test/EndToEndTestImpl.hpp> + +#include <boost/test/unit_test.hpp> + +BOOST_AUTO_TEST_SUITE(RefEndToEnd) + +BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Float32) +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + BOOST_TEST(ConstantUsageFloat32Test(backends)); +} + +BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Uint8) +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + BOOST_TEST(ConstantUsageUint8Test(backends)); +} + +BOOST_AUTO_TEST_CASE(Unsigned8) +{ + using namespace armnn; + + // Create runtime in which test will run + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + // Builds up the structure of the network. + armnn::INetworkPtr net(INetwork::Create()); + + IConnectableLayer* input = net->AddInputLayer(0, "input"); + IConnectableLayer* softmax = net->AddSoftmaxLayer(SoftmaxDescriptor(), "softmax"); + IConnectableLayer* output = net->AddOutputLayer(0, "output"); + + input->GetOutputSlot(0).Connect(softmax->GetInputSlot(0)); + softmax->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + // Sets the tensors in the network. + TensorInfo inputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8); + inputTensorInfo.SetQuantizationOffset(100); + inputTensorInfo.SetQuantizationScale(10000.0f); + input->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); + + TensorInfo outputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8); + outputTensorInfo.SetQuantizationOffset(0); + outputTensorInfo.SetQuantizationScale(1.0f/255.0f); + softmax->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); + + // optimize the network + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); + + // Loads it into the runtime. + NetworkId netId; + auto error = runtime->LoadNetwork(netId, std::move(optNet)); + BOOST_TEST(error == Status::Success); + + // Creates structures for input & output. + std::vector<uint8_t> inputData + { + 1, 10, 3, 200, 5 // Some inputs - one of which is sufficiently larger than the others to saturate softmax. + }; + std::vector<uint8_t> outputData(5); + + armnn::InputTensors inputTensors + { + {0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())} + }; + armnn::OutputTensors outputTensors + { + {0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} + }; + + // Does the inference. + runtime->EnqueueWorkload(netId, inputTensors, outputTensors); + + // Checks the results. + BOOST_TEST(outputData[0] == 0); + BOOST_TEST(outputData[1] == 0); + BOOST_TEST(outputData[2] == 0); + BOOST_TEST(outputData[3] == 255); // softmax has been saturated. + BOOST_TEST(outputData[4] == 0); +} + +BOOST_AUTO_TEST_CASE(TrivialAdd) +{ + // This test was designed to match "AddTwo" in android nn/runtime/test/TestTrivialModel.cpp. + + using namespace armnn; + + // Create runtime in which test will run + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + // Builds up the structure of the network. + armnn::INetworkPtr net(INetwork::Create()); + + IConnectableLayer* input1 = net->AddInputLayer(0); + IConnectableLayer* input2 = net->AddInputLayer(1); + IConnectableLayer* add = net->AddAdditionLayer(); + IConnectableLayer* output = net->AddOutputLayer(0); + + input1->GetOutputSlot(0).Connect(add->GetInputSlot(0)); + input2->GetOutputSlot(0).Connect(add->GetInputSlot(1)); + add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + // Sets the tensors in the network. + TensorInfo tensorInfo(TensorShape({3, 4}), DataType::Float32); + input1->GetOutputSlot(0).SetTensorInfo(tensorInfo); + input2->GetOutputSlot(0).SetTensorInfo(tensorInfo); + add->GetOutputSlot(0).SetTensorInfo(tensorInfo); + + // optimize the network + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); + + // Loads it into the runtime. + NetworkId netId; + runtime->LoadNetwork(netId, std::move(optNet)); + + // Creates structures for input & output - matching android nn test. + std::vector<float> input1Data + { + 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f + }; + std::vector<float> input2Data + { + 100.f, 200.f, 300.f, 400.f, 500.f, 600.f, 700.f, 800.f, 900.f, 1000.f, 1100.f, 1200.f + }; + std::vector<float> outputData(12); + + InputTensors inputTensors + { + {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input1Data.data())}, + {1,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input2Data.data())} + }; + OutputTensors outputTensors + { + {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} + }; + + // Does the inference. + runtime->EnqueueWorkload(netId, inputTensors, outputTensors); + + // Checks the results + BOOST_TEST(outputData[0] == 101); + BOOST_TEST(outputData[1] == 202); + BOOST_TEST(outputData[2] == 303); + BOOST_TEST(outputData[3] == 404); + BOOST_TEST(outputData[4] == 505); + BOOST_TEST(outputData[5] == 606); + BOOST_TEST(outputData[6] == 707); + BOOST_TEST(outputData[7] == 808); + BOOST_TEST(outputData[8] == 909); + BOOST_TEST(outputData[9] == 1010); + BOOST_TEST(outputData[10] == 1111); + BOOST_TEST(outputData[11] == 1212); +} + +BOOST_AUTO_TEST_CASE(MultipleOutputs) +{ + using namespace armnn; + + // Create runtime in which test will run + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + // Builds up the structure of the network. + INetworkPtr net(INetwork::Create()); + + IConnectableLayer* input = net->AddInputLayer(0); + + // ReLu1 + ActivationDescriptor activation1Descriptor; + activation1Descriptor.m_Function = ActivationFunction::BoundedReLu; + activation1Descriptor.m_A = 1.f; + activation1Descriptor.m_B = -1.f; + IConnectableLayer* activation1 = net->AddActivationLayer(activation1Descriptor); + + // ReLu6 + ActivationDescriptor activation2Descriptor; + activation2Descriptor.m_Function = ActivationFunction::BoundedReLu; + activation2Descriptor.m_A = 6.0f; + IConnectableLayer* activation2 = net->AddActivationLayer(activation2Descriptor); + + // BoundedReLu(min=2, max=5) + ActivationDescriptor activation3Descriptor; + activation3Descriptor.m_Function = ActivationFunction::BoundedReLu; + activation3Descriptor.m_A = 5.0f; + activation3Descriptor.m_B = 2.0f; + IConnectableLayer* activation3 = net->AddActivationLayer(activation3Descriptor); + + IConnectableLayer* output1 = net->AddOutputLayer(0); + IConnectableLayer* output2 = net->AddOutputLayer(1); + IConnectableLayer* output3 = net->AddOutputLayer(2); + + input->GetOutputSlot(0).Connect(activation1->GetInputSlot(0)); + input->GetOutputSlot(0).Connect(activation2->GetInputSlot(0)); + input->GetOutputSlot(0).Connect(activation3->GetInputSlot(0)); + + activation1->GetOutputSlot(0).Connect(output1->GetInputSlot(0)); + activation2->GetOutputSlot(0).Connect(output2->GetInputSlot(0)); + activation3->GetOutputSlot(0).Connect(output3->GetInputSlot(0)); + + // Sets the tensors in the network. + TensorInfo tensorInfo(TensorShape({ 10 }), DataType::Float32); + input->GetOutputSlot(0).SetTensorInfo(tensorInfo); + activation1->GetOutputSlot(0).SetTensorInfo(tensorInfo); + activation2->GetOutputSlot(0).SetTensorInfo(tensorInfo); + activation3->GetOutputSlot(0).SetTensorInfo(tensorInfo); + + // optimize the network + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); + + // Loads it into the runtime. + NetworkId netId; + runtime->LoadNetwork(netId, std::move(optNet)); + + // Creates structures for input & output. + const std::vector<float> inputData{ 3.f, 5.f, 2.f, 3.f, 7.f, 0.f, -2.f, -1.f, 3.f, 3.f }; + + std::vector<float> output1Data(inputData.size()); + std::vector<float> output2Data(inputData.size()); + std::vector<float> output3Data(inputData.size()); + + InputTensors inputTensors + { + {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())} + }; + OutputTensors outputTensors + { + {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), output1Data.data())}, + {1,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 1), output2Data.data())}, + {2,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 2), output3Data.data())} + }; + + // Does the inference. + runtime->EnqueueWorkload(netId, inputTensors, outputTensors); + + // Checks the results. + BOOST_TEST(output1Data == std::vector<float>({ 1.f, 1.f, 1.f, 1.f, 1.f, 0.f, -1.f, -1.f, 1.f, 1.f })); // ReLu1 + BOOST_TEST(output2Data == std::vector<float>({ 3.f, 5.f, 2.f, 3.f, 6.f, 0.f, 0.f, 0.f, 3.f, 3.f })); // ReLu6 + BOOST_TEST(output3Data == std::vector<float>({ 3.f, 5.f, 2.f, 3.f, 5.f, 2.f, 2.f, 2.f, 3.f, 3.f })); // [2, 5] +} + +BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file diff --git a/src/backends/reference/test/RefJsonPrinterTests.cpp b/src/backends/reference/test/RefJsonPrinterTests.cpp new file mode 100644 index 0000000000..ee668a2513 --- /dev/null +++ b/src/backends/reference/test/RefJsonPrinterTests.cpp @@ -0,0 +1,22 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <armnn/BackendId.hpp> + +#include <backends/test/JsonPrinterTestImpl.hpp> + +#include <boost/test/unit_test.hpp> + +#include <vector> + +BOOST_AUTO_TEST_SUITE(RefJsonPrinter) + +BOOST_AUTO_TEST_CASE(SoftmaxProfilerJsonPrinterCpuRefTest) +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJsonPrinterResult(backends); +} + +BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file 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() diff --git a/src/backends/test/EndToEndTestImpl.hpp b/src/backends/test/EndToEndTestImpl.hpp new file mode 100644 index 0000000000..5f17f782f3 --- /dev/null +++ b/src/backends/test/EndToEndTestImpl.hpp @@ -0,0 +1,102 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include <armnn/ArmNN.hpp> + +#include <backends/test/QuantizeHelper.hpp> + +#include <vector> + +namespace +{ + +using namespace armnn; + +template<typename T> +bool ConstantUsageTest(const std::vector<BackendId>& computeDevice, + const TensorInfo& commonTensorInfo, + const std::vector<T>& inputData, + const std::vector<T>& constantData, + const std::vector<T>& expectedOutputData) +{ + // Create runtime in which test will run + IRuntime::CreationOptions options; + IRuntimePtr runtime(IRuntime::Create(options)); + + // Builds up the structure of the network. + INetworkPtr net(INetwork::Create()); + + IConnectableLayer* input = net->AddInputLayer(0); + IConnectableLayer* constant = net->AddConstantLayer(ConstTensor(commonTensorInfo, constantData)); + IConnectableLayer* add = net->AddAdditionLayer(); + IConnectableLayer* output = net->AddOutputLayer(0); + + input->GetOutputSlot(0).Connect(add->GetInputSlot(0)); + constant->GetOutputSlot(0).Connect(add->GetInputSlot(1)); + add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + // Sets the tensors in the network. + input->GetOutputSlot(0).SetTensorInfo(commonTensorInfo); + constant->GetOutputSlot(0).SetTensorInfo(commonTensorInfo); + add->GetOutputSlot(0).SetTensorInfo(commonTensorInfo); + + // optimize the network + IOptimizedNetworkPtr optNet = Optimize(*net, computeDevice, runtime->GetDeviceSpec()); + + // Loads it into the runtime. + NetworkId netId; + runtime->LoadNetwork(netId, std::move(optNet)); + + // Creates structures for input & output. + std::vector<T> outputData(inputData.size()); + + InputTensors inputTensors + { + {0, ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())} + }; + OutputTensors outputTensors + { + {0, Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} + }; + + // Does the inference. + runtime->EnqueueWorkload(netId, inputTensors, outputTensors); + + // Checks the results. + return outputData == expectedOutputData; +} + +inline bool ConstantUsageFloat32Test(const std::vector<BackendId>& backends) +{ + const TensorInfo commonTensorInfo({ 2, 3 }, DataType::Float32); + + return ConstantUsageTest(backends, + commonTensorInfo, + std::vector<float>{ 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }, // Input. + std::vector<float>{ 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }, // Const input. + std::vector<float>{ 7.f, 7.f, 7.f, 7.f, 7.f, 7.f } // Expected output. + ); +} + +inline bool ConstantUsageUint8Test(const std::vector<BackendId>& backends) +{ + TensorInfo commonTensorInfo({ 2, 3 }, DataType::QuantisedAsymm8); + + const float scale = 0.023529f; + const int8_t offset = -43; + + commonTensorInfo.SetQuantizationScale(scale); + commonTensorInfo.SetQuantizationOffset(offset); + + return ConstantUsageTest(backends, + commonTensorInfo, + QuantizedVector<uint8_t>(scale, offset, { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }), // Input. + QuantizedVector<uint8_t>(scale, offset, { 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }), // Const input. + QuantizedVector<uint8_t>(scale, offset, { 7.f, 7.f, 7.f, 7.f, 7.f, 7.f }) // Expected output. + ); +} + +} // anonymous namespace
\ No newline at end of file diff --git a/src/armnn/test/JsonPrinterTests.cpp b/src/backends/test/JsonPrinterTestImpl.hpp index 93f32cc540..47e0ec761b 100644 --- a/src/armnn/test/JsonPrinterTests.cpp +++ b/src/backends/test/JsonPrinterTestImpl.hpp @@ -2,29 +2,27 @@ // Copyright © 2017 Arm Ltd. All rights reserved. // SPDX-License-Identifier: MIT // + +#include <armnn/Descriptors.hpp> +#include <armnn/IRuntime.hpp> +#include <armnn/INetwork.hpp> +#include <armnn/Profiling.hpp> + #include <boost/test/unit_test.hpp> #include <boost/algorithm/string.hpp> #include <boost/lexical_cast.hpp> + +#include <sstream> #include <stack> #include <string> #include <vector> -#include <sstream> - -#include <Profiling.hpp> -#include <armnn/Descriptors.hpp> -#include <armnn/IRuntime.hpp> -#include <armnn/INetwork.hpp> -#include <backends/cl/test/ClContextControlFixture.hpp> -#include <backends/cl/ClWorkloadFactory.hpp> -BOOST_FIXTURE_TEST_SUITE(JsonPrinterTests, ClProfilingContextControlFixture) - -bool AreMatchingPair(const char opening, const char closing) +inline bool AreMatchingPair(const char opening, const char closing) { return (opening == '{' && closing == '}') || (opening == '[' && closing == ']'); } -bool AreParenthesesMatching(const std::string& exp) +inline bool AreParenthesesMatching(const std::string& exp) { std::stack<char> expStack; for (size_t i = 0; i < exp.length(); ++i) @@ -48,7 +46,7 @@ bool AreParenthesesMatching(const std::string& exp) return expStack.empty(); } -std::vector<double> ExtractMeasurements(const std::string& exp) +inline std::vector<double> ExtractMeasurements(const std::string& exp) { std::vector<double> numbers; bool inArray = false; @@ -95,7 +93,7 @@ std::vector<double> ExtractMeasurements(const std::string& exp) return numbers; } -std::vector<std::string> ExtractSections(const std::string& exp) +inline std::vector<std::string> ExtractSections(const std::string& exp) { std::vector<std::string> sections; @@ -117,7 +115,7 @@ std::vector<std::string> ExtractSections(const std::string& exp) return sections; } -std::string SoftmaxProfilerTestSetupHelper(const std::vector<armnn::BackendId>& backends) +inline std::string SoftmaxProfilerTestSetupHelper(const std::vector<armnn::BackendId>& backends) { using namespace armnn; @@ -193,7 +191,7 @@ std::string SoftmaxProfilerTestSetupHelper(const std::vector<armnn::BackendId>& return ss.str(); } -void SoftmaxProfilerTestValidationHelper(std::string& result, const std::string& testData) +inline void SoftmaxProfilerTestValidationHelper(std::string& result, const std::string& testData) { // ensure all measurements are greater than zero std::vector<double> measurementsVector = ExtractMeasurements(result); @@ -238,7 +236,7 @@ void SoftmaxProfilerTestValidationHelper(std::string& result, const std::string& BOOST_CHECK(AreParenthesesMatching(result)); } -void SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJSONPrinterResult( +inline void SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJsonPrinterResult( const std::vector<armnn::BackendId>& backends) { // setup the test fixture and obtain JSON Printer result @@ -354,25 +352,3 @@ void SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJSONPrinterResult( // validate the JSON Printer result SoftmaxProfilerTestValidationHelper(result, testData); } - -BOOST_AUTO_TEST_CASE(SoftmaxProfilerJSONPrinterCpuRefTest) -{ - SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJSONPrinterResult({armnn::Compute::CpuRef}); -} - - -#if ARMCOMPUTENEON_ENABLED -BOOST_AUTO_TEST_CASE(SoftmaxProfilerJSONPrinterCpuAccTest) -{ - SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJSONPrinterResult({armnn::Compute::CpuAcc}); -} -#endif - -#if ARMCOMPUTECL_ENABLED -BOOST_AUTO_TEST_CASE(SoftmaxProfilerJSONPrinterGpuAccTest) -{ - SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJSONPrinterResult({armnn::Compute::GpuAcc}); -} -#endif - -BOOST_AUTO_TEST_SUITE_END() diff --git a/src/backends/test/OptimizedNetworkTests.cpp b/src/backends/test/OptimizedNetworkTests.cpp new file mode 100644 index 0000000000..72a35f99e0 --- /dev/null +++ b/src/backends/test/OptimizedNetworkTests.cpp @@ -0,0 +1,329 @@ +// +// 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(OptimizedNetwork) + +BOOST_AUTO_TEST_CASE(SerializeToDot) +{ + armnn::Network net; + + //Defines layers. + auto input = net.AddInputLayer(0); + auto add = net.AddAdditionLayer(); + auto output = net.AddOutputLayer(0); + + // Connects layers. + input->GetOutputSlot(0).Connect(add->GetInputSlot(0)); + input->GetOutputSlot(0).Connect(add->GetInputSlot(1)); + add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + armnn::TensorShape shape({4}); + armnn::TensorInfo info(shape, armnn::DataType::Float32); + input->GetOutputSlot(0).SetTensorInfo(info); + add->GetOutputSlot(0).SetTensorInfo(info); + + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec()); + + std::ostringstream ss; + optimizedNet->SerializeToDot(ss); + + auto inputId = input->GetGuid(); + auto addId = add->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" + " " << addId << " [label=\"{Addition}\"];\n" + " " << outputId << " [label=\"{Output}\"];\n" + " " << inputId << " -> " << addId << " [label=< [4] >];\n" + " " << inputId << " -> " << addId << " [label=< [4] >];\n" + " " << addId << " -> " << outputId << " [label=< [4] >];\n" + "}\n"; + + BOOST_TEST(ss.str() == expected.str()); +} + +BOOST_AUTO_TEST_CASE(OptimizeValidateDeviceNonSupportLayerNoFallback) +{ + // build up the structure of the network + armnn::INetworkPtr net(armnn::INetwork::Create()); + + armnn::IConnectableLayer* input = net->AddInputLayer(0); + + // This layer configuration isn't supported by CpuAcc and isn't allowed to fall back, so Optimize will return null. + armnn::NormalizationDescriptor descriptor; + armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor); + + armnn::IConnectableLayer* output = net->AddOutputLayer(0); + + input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0)); + normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; + armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); + BOOST_CHECK(!optNet); +} + +BOOST_AUTO_TEST_CASE(OptimizeValidateDeviceNonSupportLayerWithFallback) +{ + // build up the structure of the network + armnn::INetworkPtr net(armnn::INetwork::Create()); + + armnn::IConnectableLayer* input = net->AddInputLayer(0); + + // This layer configuration isn't supported by CpuAcc but it allows to fallback to CpuRef. + armnn::NormalizationDescriptor descriptor; + armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor); + + armnn::IConnectableLayer* output = net->AddOutputLayer(0); + + input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0)); + normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, armnn::Compute::CpuRef }; + armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); + BOOST_REQUIRE(optNet); + + for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph()) + { + // If NEON is enabled, Input and Output layers are supported by CpuAcc, + // the other layers are supported by CpuRef. + // If NEON is not enabled, all layers are supported by CpuRef. +#if ARMCOMPUTENEON_ENABLED + if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuAcc); + } + else if (layer->GetType() == armnn::LayerType::Normalization) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); + } +#else + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); +#endif + } +} + +BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsUndefinedComputeDevice) +{ + 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::Undefined }; + + armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec()); + BOOST_CHECK(!optNet); + +} + +BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsUndefinedComputeDeviceWithFallback) +{ + 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::Undefined, armnn::Compute::CpuRef }; + + armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec()); + BOOST_CHECK(optNet); + + // validate workloads + armnn::RefWorkloadFactory fact; + for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph()) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); + BOOST_CHECK_NO_THROW( + layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact)); + } +} + +BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsDuplicateComputeDeviceWithFallback) +{ + // build up the structure of the network + armnn::INetworkPtr net(armnn::INetwork::Create()); + + armnn::IConnectableLayer* input = net->AddInputLayer(0); + + // This layer configuration isn't supported by CpuAcc but it allows to fallback to CpuRef. + armnn::NormalizationDescriptor descriptor; + armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor); + + armnn::IConnectableLayer* output = net->AddOutputLayer(0); + + input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0)); + normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32)); + + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, + armnn::Compute::GpuAcc, + armnn::Compute::CpuRef }; + + armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec()); + BOOST_REQUIRE(optNet); + + for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph()) + { + // If NEON is enabled, Input and Output layers are supported by CpuAcc, + // the other layers are supported by CpuRef. + // If only CL is enabled, Input and Output layers are supported by GpuAcc, + // the other layers are supported by CpuRef. + // If neither NEON, nor CL is enabled, all layers are supported by CpuRef. +#if ARMCOMPUTENEON_ENABLED + if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuAcc); + } + else if (layer->GetType() == armnn::LayerType::Normalization) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); + } +#elif ARMCOMPUTECL_ENABLED + if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::GpuAcc); + } + else if (layer->GetType() == armnn::LayerType::Normalization) + { + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); + } +#else + BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef); +#endif + } +} + +BOOST_AUTO_TEST_SUITE_END() |