| // |
| // Copyright © 2020 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "LayersFwd.hpp" |
| |
| #include <Network.hpp> |
| #include <ResolveType.hpp> |
| #include <armnn/INetwork.hpp> |
| #include <test/TestUtils.hpp> |
| |
| #include <boost/test/unit_test.hpp> |
| |
| #include <QuantizeHelper.hpp> |
| #include <string> |
| |
| using namespace armnn; |
| |
| BOOST_AUTO_TEST_SUITE(Optimizer) |
| |
| namespace |
| { |
| const float g_qScale = 1.0f; |
| const int32_t g_qOffset = 0; |
| |
| template<typename T> |
| std::vector<T> GetVector(unsigned int size, float initial, float increment) |
| { |
| std::vector<float> typeVector(size, initial); |
| std::vector<T> vector(size); |
| |
| if (size > 1) |
| { |
| for (unsigned int i = 0; i < size; ++i) |
| { |
| vector[i] = T(initial + (increment * static_cast<float>(i))); |
| } |
| } |
| return vector; |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| struct Convolution2dTest |
| { |
| using LayerType = armnn::Convolution2dLayer; |
| static std::string GetReceiverLayerName() { return "Convolution2d"; }; |
| static const bool isElementWise = false; |
| |
| static TensorShape GetInputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCin |
| static TensorShape GetOutputShape() { return TensorShape( {1, 3, 3, 4}); } // NHWCout |
| static TensorShape GetWeightsShape() { return TensorShape( {4, 2, 2, 3}); } // CoutHWCin |
| |
| constexpr static const unsigned int inputSize = 48; // batchIn * heightIn * widthIn * channelIn |
| constexpr static const unsigned int outputSize = 36; // batchOut * heightOut * widthOut * channelOut |
| |
| static IConnectableLayer* AddReceiverLayer(INetwork* network, |
| const char* name) |
| { |
| Convolution2dDescriptor descriptor; |
| descriptor.m_BiasEnabled = false; |
| descriptor.m_DataLayout = DataLayout::NHWC; |
| descriptor.m_StrideX = 1; |
| descriptor.m_StrideY = 1; |
| |
| std::vector<float> weightsData = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, |
| 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, |
| 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, |
| 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42}; |
| std::vector<T> weightsVector = armnnUtils::QuantizedVector<T>(weightsData, g_qScale, g_qOffset); |
| TensorInfo weightsInfo(GetWeightsShape(), ArmnnType, g_qScale, g_qOffset); |
| ConstTensor weights(weightsInfo, weightsVector); |
| Optional<ConstTensor> optionalBias; |
| |
| return network->AddConvolution2dLayer(descriptor, weights, optionalBias, name); |
| } |
| }; |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| struct DepthwiseConvolution2dTest |
| { |
| public: |
| using LayerType = armnn::DepthwiseConvolution2dLayer; |
| static std::string GetReceiverLayerName() { return "DepthwiseConvolution2d"; }; |
| static const bool isElementWise = false; |
| |
| static TensorShape GetInputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCin |
| static TensorShape GetOutputShape() { return TensorShape( {1, 3, 3, 12}); } // NHWCout |
| static TensorShape GetWeightsShape() { return TensorShape( {4, 3, 2, 2}); } // MCinHW |
| |
| constexpr static const unsigned int inputSize = 48; //batchIn * heightIn * widthIn * channelIn; |
| constexpr static const unsigned int outputSize = 108; //batchOut * heightOut * widthOut * channelOut; |
| |
| static IConnectableLayer* AddReceiverLayer(INetwork* network, |
| const char* name) |
| { |
| DepthwiseConvolution2dDescriptor descriptor; |
| descriptor.m_BiasEnabled = false; |
| descriptor.m_DataLayout = DataLayout::NHWC; |
| descriptor.m_StrideX = 1; |
| descriptor.m_StrideY = 1; |
| |
| std::vector<float> weightsData = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, |
| 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, |
| 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, |
| 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42}; |
| std::vector<T> weightsVector = armnnUtils::QuantizedVector<T>(weightsData, g_qScale, g_qOffset); |
| TensorInfo weightsInfo(GetWeightsShape(), ArmnnType, g_qScale, g_qOffset); |
| ConstTensor weights(weightsInfo, weightsVector); |
| Optional<ConstTensor> optionalBias; |
| |
| return network->AddDepthwiseConvolution2dLayer(descriptor, weights, optionalBias, name); |
| } |
| }; |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| struct FullyConnectedTest |
| { |
| public: |
| using LayerType = armnn::FullyConnectedLayer; |
| static std::string GetReceiverLayerName() { return "FullyConnected"; }; |
| static const bool isElementWise = false; |
| |
| static TensorShape GetInputShape() { return TensorShape( {2, 5, 1, 1}); } // NCinHW |
| static TensorShape GetOutputShape() { return TensorShape( {2, 3}); } // NCout |
| static TensorShape GetWeightsShape() { return TensorShape( {5, 3}); } // CinCout |
| |
| constexpr static const unsigned int inputSize = 10; // batchIn * heightIn * widthIn * channelIn |
| constexpr static const unsigned int outputSize = 6; // batchOut * heightOut * widthOut * channelOut |
| |
| static IConnectableLayer* AddReceiverLayer(INetwork* network, |
| const char* name) |
| { |
| FullyConnectedDescriptor descriptor; |
| descriptor.m_BiasEnabled = false; |
| |
| std::vector<float> weightsData = { 1, 2, 3, 4, 5, |
| 6, 7, 8, 9, 10, |
| 11, 12, 13, 14, 15}; |
| std::vector<T> weightsVector = armnnUtils::QuantizedVector<T>(weightsData, g_qScale, g_qOffset); |
| TensorInfo weightsInfo(GetWeightsShape(), ArmnnType, g_qScale, g_qOffset); |
| ConstTensor weights(weightsInfo, weightsVector); |
| Optional<ConstTensor> optionalBias; |
| |
| return network->AddFullyConnectedLayer(descriptor, weights, optionalBias, name); |
| } |
| }; |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| struct BatchNormTest |
| { |
| public: |
| using LayerType = armnn::BatchNormalizationLayer; |
| static std::string GetReceiverLayerName() { return "BatchNorm"; }; |
| static const bool isElementWise = false; |
| |
| static TensorShape GetInputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCin |
| static TensorShape GetOutputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCout |
| |
| constexpr static const unsigned int inputSize = 48; // batchIn * heightIn * widthIn * channelIn |
| constexpr static const unsigned int outputSize = 48; // batchOut * heightOut * widthOut * channelOut |
| |
| static IConnectableLayer* AddReceiverLayer(INetwork* network, |
| const char* name) |
| { |
| BatchNormalizationDescriptor descriptor; |
| descriptor.m_DataLayout = DataLayout::NHWC; |
| |
| std::vector<T> betaVector = GetVector<T>(GetOutputShape()[3], 0.0f, 0.2f); |
| std::vector<T> gammaVector = GetVector<T>(GetOutputShape()[3], 0.5f, 0.1f); |
| std::vector<T> meanVector = GetVector<T>(GetOutputShape()[3], 0.1f, 0.1f); |
| std::vector<T> varianceVector = GetVector<T>(GetOutputShape()[3], 1.0f, 0.1f); |
| |
| const unsigned int outputChannelSize[] = { GetOutputShape()[3] }; |
| ConstTensor beta(TensorInfo(1, outputChannelSize, ArmnnType), betaVector); |
| ConstTensor gamma(TensorInfo(1, outputChannelSize, ArmnnType), gammaVector); |
| ConstTensor mean(TensorInfo(1, outputChannelSize, ArmnnType), meanVector); |
| ConstTensor variance(TensorInfo(1, outputChannelSize, ArmnnType), varianceVector); |
| |
| return network->AddBatchNormalizationLayer(descriptor, mean, variance, beta, gamma, name); |
| } |
| }; |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| struct MultiplicationTest |
| { |
| using LayerType = armnn::MultiplicationLayer; |
| static std::string GetReceiverLayerName() { return "Multiplication"; }; |
| static const bool isElementWise = true; |
| |
| static TensorShape GetInputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCin |
| static TensorShape GetOutputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCout |
| |
| constexpr static const unsigned int inputSize = 48; // batchIn * heightIn * widthIn * channelIn |
| constexpr static const unsigned int outputSize = 48; // batchOut * heightOut * widthOut * channelOut |
| |
| static IConnectableLayer* AddReceiverLayer(INetwork* network, |
| const char* name) |
| { |
| return network->AddMultiplicationLayer(name); |
| } |
| }; |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| struct AdditionTest |
| { |
| using LayerType = armnn::AdditionLayer; |
| static std::string GetReceiverLayerName() { return "Addition"; }; |
| static const bool isElementWise = true; |
| |
| static TensorShape GetInputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCin |
| static TensorShape GetOutputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCout |
| |
| constexpr static const unsigned int inputSize = 48; // batchIn * heightIn * widthIn * channelIn |
| constexpr static const unsigned int outputSize = 48; // batchOut * heightOut * widthOut * channelOut |
| |
| static IConnectableLayer* AddReceiverLayer(INetwork* network, |
| const char* name) |
| { |
| return network->AddAdditionLayer(name); |
| } |
| }; |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| struct SubtractionTest |
| { |
| using LayerType = armnn::SubtractionLayer; |
| static std::string GetReceiverLayerName() { return "Subtraction"; }; |
| static const bool isElementWise = true; |
| |
| static TensorShape GetInputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCin |
| static TensorShape GetOutputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCout |
| |
| constexpr static const unsigned int inputSize = 48; // batchIn * heightIn * widthIn * channelIn |
| constexpr static const unsigned int outputSize = 48; // batchOut * heightOut * widthOut * channelOut |
| |
| static IConnectableLayer* AddReceiverLayer(INetwork* network, |
| const char* name) |
| { |
| return network->AddSubtractionLayer(name); |
| } |
| }; |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| struct DivisionTest |
| { |
| using LayerType = armnn::DivisionLayer; |
| static std::string GetReceiverLayerName() { return "Division"; }; |
| static const bool isElementWise = true; |
| |
| static TensorShape GetInputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCin |
| static TensorShape GetOutputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCout |
| |
| constexpr static const unsigned int inputSize = 48; // batchIn * heightIn * widthIn * channelIn |
| constexpr static const unsigned int outputSize = 48; // batchOut * heightOut * widthOut * channelOut |
| |
| static IConnectableLayer* AddReceiverLayer(INetwork* network, |
| const char* name) |
| { |
| return network->AddDivisionLayer(name); |
| } |
| }; |
| |
| } // namespace |
| |
| template<typename LayerTest, |
| armnn::DataType ArmnnType> |
| INetworkPtr CreatNetwork(ActivationDescriptor activationDescriptor, bool preventFusing) |
| { |
| // Create a network |
| INetworkPtr network = INetwork::Create(); |
| |
| IConnectableLayer* inputLayer = network->AddInputLayer(0); |
| |
| IConnectableLayer* receiverLayer = LayerTest::AddReceiverLayer(network.get(), |
| "receiverLayer"); |
| |
| IConnectableLayer* activationLayer = network->AddActivationLayer(activationDescriptor, |
| "activation"); |
| |
| IConnectableLayer* outputLayer = network->AddOutputLayer(0); |
| IConnectableLayer* output2Layer = preventFusing?network->AddOutputLayer(1):nullptr; |
| |
| // Define layers information |
| TensorInfo inputInfo(LayerTest::GetInputShape(), ArmnnType, g_qScale, g_qOffset); |
| TensorInfo outputInfo(LayerTest::GetOutputShape(), ArmnnType, g_qScale, g_qOffset); |
| |
| // Set layer information |
| inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| receiverLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| activationLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| |
| // Connect layers |
| inputLayer->GetOutputSlot(0).Connect(receiverLayer->GetInputSlot(0)); |
| receiverLayer->GetOutputSlot(0).Connect(activationLayer->GetInputSlot(0)); |
| activationLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| |
| if (LayerTest::isElementWise) |
| { |
| inputLayer->GetOutputSlot(0).Connect(receiverLayer->GetInputSlot(1)); |
| } |
| if (preventFusing) |
| { |
| receiverLayer->GetOutputSlot(0).Connect(output2Layer->GetInputSlot(0)); |
| } |
| |
| return network; |
| } |
| |
| template<typename LayerTest, |
| armnn::DataType ArmnnType, |
| typename LayerType = typename LayerTest::LayerType, |
| typename T = armnn::ResolveType<ArmnnType>> |
| void FuseActivationIntoPreviousLayerTest(ActivationDescriptor activationDescriptor, float tolerance, armnn::Compute |
| backendId) |
| { |
| // FIRST NETWORK: Fused |
| // Construct ArmNN network |
| INetworkPtr networkFused = CreatNetwork<LayerTest, ArmnnType>(activationDescriptor, false); |
| |
| // Create ArmNN runtime |
| IRuntimePtr run = IRuntime::Create(IRuntime::CreationOptions()); // default options |
| |
| // Optimise ArmNN network |
| IOptimizedNetworkPtr optNetFused = Optimize(*networkFused, {backendId}, run->GetDeviceSpec()); |
| |
| Graph graphFused = PolymorphicDowncast<OptimizedNetwork*>(optNetFused.get())->GetGraph(); |
| |
| auto checkFusedConv2d = [](const armnn::Layer* const layer)->bool { |
| return IsLayerOfType<LayerType>(layer) && |
| (layer->GetNameStr() == "fused-activation-into-receiverLayer"); |
| }; |
| |
| BOOST_CHECK_MESSAGE(3 == graphFused.GetNumLayers(), LayerTest::GetReceiverLayerName()); |
| BOOST_TEST(CheckSequence(graphFused.cbegin(), |
| graphFused.cend(), |
| &IsLayerOfType<InputLayer>, |
| checkFusedConv2d, |
| &IsLayerOfType<OutputLayer>)); |
| |
| // Load network into runtime |
| NetworkId networkIdentifier; |
| BOOST_TEST(run->LoadNetwork(networkIdentifier, std::move(optNetFused)) == Status::Success); |
| |
| //Creates structures for inputs and outputs. |
| std::vector<float> data = GetVector<float>(LayerTest::inputSize, 1.0f, 0.1f); |
| std::vector<T> inputDataFused = armnnUtils::QuantizedVector<T>(data, g_qScale, g_qOffset); |
| std::vector<T> outputDataFused(LayerTest::outputSize); |
| |
| InputTensors inputTensorsFused{ |
| {0, ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), inputDataFused.data())}}; |
| OutputTensors outputTensorsFused{ |
| {0, Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputDataFused.data())}}; |
| |
| // Execute network |
| run->EnqueueWorkload(networkIdentifier, inputTensorsFused, outputTensorsFused); |
| |
| // SECOND NETWORK: NotFused |
| // Construct ArmNN network |
| INetworkPtr networkNotFused = CreatNetwork<LayerTest, ArmnnType>(activationDescriptor, true); |
| |
| // Create ArmNN runtime |
| IRuntimePtr runNotFused = IRuntime::Create(IRuntime::CreationOptions()); // default options |
| |
| // Optimise ArmNN network |
| IOptimizedNetworkPtr optNetNotFused = Optimize(*networkNotFused, {backendId}, runNotFused->GetDeviceSpec()); |
| |
| Graph graphNotFused = PolymorphicDowncast<OptimizedNetwork*>(optNetNotFused.get())->GetGraph(); |
| |
| BOOST_CHECK(5 == graphNotFused.GetNumLayers()); |
| BOOST_TEST(CheckSequence(graphNotFused.cbegin(), |
| graphNotFused.cend(), |
| &IsLayerOfType<armnn::InputLayer>, |
| &IsLayerOfType<LayerType>, |
| &IsLayerOfType<armnn::ActivationLayer>, |
| &IsLayerOfType<armnn::OutputLayer>, |
| &IsLayerOfType<armnn::OutputLayer>)); |
| |
| // Load network into runtime |
| NetworkId networkIdentifierNotFused; |
| BOOST_TEST(runNotFused->LoadNetwork(networkIdentifierNotFused, std::move(optNetNotFused)) == Status::Success); |
| |
| //Creates structures for inputs and outputs. |
| std::vector<T> inputDataNotFused = armnnUtils::QuantizedVector<T>(data, g_qScale, g_qOffset); |
| std::vector<T> outputDataNotFused(LayerTest::outputSize); |
| std::vector<T> outputData2NotFused(LayerTest::outputSize); |
| |
| InputTensors inputTensorsNotFused{ |
| {0, ConstTensor(runNotFused->GetInputTensorInfo(networkIdentifierNotFused, 0), inputDataNotFused.data())}}; |
| OutputTensors outputTensorsNotFused{ |
| {0, Tensor(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 0), outputDataNotFused.data())}, |
| {1, Tensor(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 1), outputData2NotFused.data())}}; |
| |
| // Execute network |
| runNotFused->EnqueueWorkload(networkIdentifierNotFused, inputTensorsNotFused, outputTensorsNotFused); |
| |
| // Check the output of the fused-activation matches with the output of the activation in the "NotFused" network |
| for (unsigned int n = 0; n < outputDataFused.size(); ++n) |
| { |
| BOOST_CHECK_CLOSE(static_cast<float>(outputDataFused[n]), static_cast<float>(outputDataNotFused[n]), |
| T(tolerance)); |
| } |
| } |
| |
| #if defined(ARMCOMPUTENEON_ENABLED) |
| // ReLu fused into Receiver Layers Float32 |
| BOOST_AUTO_TEST_CASE(FuseReLUIntoConvFloat32CpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::CpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseReLUIntoDWConvFloat32CpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<DepthwiseConvolution2dTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::CpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseReLUIntoFullyConnectedFloat32CpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<FullyConnectedTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::CpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseReLUIntoBatchNormFloat32CpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<BatchNormTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::CpuAcc); |
| } |
| |
| // BoundedReLu fused into Receiver Layers Float32 |
| BOOST_AUTO_TEST_CASE(FuseBoundedReLUIntoConvFloat32CpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::BoundedReLu; |
| activationDescriptor.m_A = 1.0f; |
| activationDescriptor.m_B = -1.0f; |
| |
| FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::CpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseBoundedReLUIntoDWConvFloat32CpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::BoundedReLu; |
| activationDescriptor.m_A = 1.0f; |
| activationDescriptor.m_B = -1.0f; |
| |
| FuseActivationIntoPreviousLayerTest < DepthwiseConvolution2dTest < DataType::Float32 > , DataType::Float32 > |
| (activationDescriptor, 0.0001f, armnn::Compute::CpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseBoundedReLUIntoFullyConnectedFloat32CpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::BoundedReLu; |
| activationDescriptor.m_A = 1.0f; |
| activationDescriptor.m_B = -1.0f; |
| |
| FuseActivationIntoPreviousLayerTest<FullyConnectedTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::CpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseBoundedReLUIntoBatchNormFloat32CpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::BoundedReLu; |
| activationDescriptor.m_A = 1.0f; |
| activationDescriptor.m_B = -1.0f; |
| |
| FuseActivationIntoPreviousLayerTest<BatchNormTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::CpuAcc); |
| } |
| |
| // ReLU fused into Receiver Layers QAsymmU8 |
| BOOST_AUTO_TEST_CASE(FuseReLUIntoConvQAsymmU8CpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::QAsymmU8>, DataType::QAsymmU8> |
| (activationDescriptor, 0.0001f, armnn::Compute::CpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseReLUIntoDWConvQAsymmU8CpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<DepthwiseConvolution2dTest<DataType::QAsymmU8>, DataType::QAsymmU8> |
| (activationDescriptor, 0.0001f, armnn::Compute::CpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseReLUIntoFullyConnectedQAsymmU8CpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<FullyConnectedTest<DataType::QAsymmU8>, DataType::QAsymmU8> |
| (activationDescriptor, 0.0001f, armnn::Compute::CpuAcc); |
| } |
| |
| // HardSwish fused into Receiver Layers Float32 |
| BOOST_AUTO_TEST_CASE(FuseHardSwishIntoConvFloat32CpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::HardSwish; |
| |
| FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::CpuAcc); |
| } |
| |
| // TanH fused into Receiver Layers Float32 |
| BOOST_AUTO_TEST_CASE(FuseTanHIntoConvFloat32CpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::TanH; |
| |
| FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::CpuAcc); |
| } |
| #endif |
| |
| #if defined(ARMCOMPUTECL_ENABLED) |
| // ReLu fused into Receiver Layers Float32 |
| BOOST_AUTO_TEST_CASE(FuseReLUIntoConvFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseReLUIntoDWConvFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<DepthwiseConvolution2dTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseReLUIntoFullyConnectedFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<FullyConnectedTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseReLUIntoBatchNormFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<BatchNormTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseReLUIntoMulFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<MultiplicationTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseReLUIntoAddFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<AdditionTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseReLUIntoSubFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<SubtractionTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseReLUIntoDivFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<DivisionTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| |
| // BoundedReLu fused into Receiver Layers Float32 |
| BOOST_AUTO_TEST_CASE(FuseBoundedReLUIntoConvFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::BoundedReLu; |
| activationDescriptor.m_A = 1.0f; |
| activationDescriptor.m_B = -1.0f; |
| |
| FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseBoundedReLUIntoDWConvFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::BoundedReLu; |
| activationDescriptor.m_A = 1.0f; |
| activationDescriptor.m_B = -1.0f; |
| |
| FuseActivationIntoPreviousLayerTest<DepthwiseConvolution2dTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseBoundedReLUIntoFullyConnectedFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::BoundedReLu; |
| activationDescriptor.m_A = 1.0f; |
| activationDescriptor.m_B = -1.0f; |
| |
| FuseActivationIntoPreviousLayerTest<FullyConnectedTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseBoundedReLUIntoBatchNormFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::BoundedReLu; |
| activationDescriptor.m_A = 1.0f; |
| activationDescriptor.m_B = -1.0f; |
| |
| FuseActivationIntoPreviousLayerTest<BatchNormTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseBoundedReLUIntoMulFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::BoundedReLu; |
| activationDescriptor.m_A = 1.0f; |
| activationDescriptor.m_B = -1.0f; |
| |
| FuseActivationIntoPreviousLayerTest<MultiplicationTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseBoundedReLUIntoAddFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::BoundedReLu; |
| activationDescriptor.m_A = 1.0f; |
| activationDescriptor.m_B = -1.0f; |
| |
| FuseActivationIntoPreviousLayerTest<AdditionTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseBoundedReLUIntoSubFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::BoundedReLu; |
| activationDescriptor.m_A = 1.0f; |
| activationDescriptor.m_B = -1.0f; |
| |
| FuseActivationIntoPreviousLayerTest<SubtractionTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseBoundedReLUIntoDivFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::BoundedReLu; |
| activationDescriptor.m_A = 1.0f; |
| activationDescriptor.m_B = -1.0f; |
| |
| FuseActivationIntoPreviousLayerTest<DivisionTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| |
| // ReLU fused into Receiver Layers QAsymmU8 |
| BOOST_AUTO_TEST_CASE(FuseReLUQIntoConvAsymmU8GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::QAsymmU8>, DataType::QAsymmU8> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseReLUQIntoDWConvAsymmU8GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<DepthwiseConvolution2dTest<DataType::QAsymmU8>, DataType::QAsymmU8> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseReLUQIntoFullyConnectedAsymmU8GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::ReLu; |
| |
| FuseActivationIntoPreviousLayerTest<FullyConnectedTest<DataType::QAsymmU8>, DataType::QAsymmU8> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| |
| // HardSwish fused into Receiver Layers Float32 |
| BOOST_AUTO_TEST_CASE(FuseHardSwishIntoConvFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::HardSwish; |
| |
| FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseHardSwishIntoMulFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::HardSwish; |
| |
| FuseActivationIntoPreviousLayerTest<MultiplicationTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseHardSwishIntoAddFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::HardSwish; |
| |
| FuseActivationIntoPreviousLayerTest<AdditionTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseHardSwishIntoSubFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::HardSwish; |
| |
| FuseActivationIntoPreviousLayerTest<SubtractionTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseHardSwishIntoDivFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::HardSwish; |
| |
| FuseActivationIntoPreviousLayerTest<DivisionTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| |
| // TanH fused into Receiver Layers Float32 |
| BOOST_AUTO_TEST_CASE(FuseTanHIntoConvFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::TanH; |
| |
| FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseTanHIntoMulFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::TanH; |
| |
| FuseActivationIntoPreviousLayerTest<MultiplicationTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseTanHIntoAddFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::TanH; |
| |
| FuseActivationIntoPreviousLayerTest<AdditionTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseTanHIntoSubFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::TanH; |
| |
| FuseActivationIntoPreviousLayerTest<SubtractionTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| BOOST_AUTO_TEST_CASE(FuseTanHIntoDivFloat32GpuAccTest) |
| { |
| ActivationDescriptor activationDescriptor; |
| activationDescriptor.m_Function = ActivationFunction::TanH; |
| |
| FuseActivationIntoPreviousLayerTest<DivisionTest<DataType::Float32>, DataType::Float32> |
| (activationDescriptor, 0.0001f, armnn::Compute::GpuAcc); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_SUITE_END() |
