| // |
| // 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> |
| |
| using namespace armnn; |
| |
| BOOST_AUTO_TEST_SUITE(Optimizer) |
| |
| namespace |
| { |
| |
| class Conv2dTest |
| { |
| public: |
| using ConvDescriptorType = armnn::Convolution2dDescriptor; |
| using ConvLayerType = armnn::Convolution2dLayer; |
| |
| static IConnectableLayer *AddConvolution(INetwork *network, |
| const Convolution2dDescriptor &descriptor, |
| const ConstTensor &weights, |
| const Optional<ConstTensor> &biases, |
| const char *name) |
| { |
| return network->AddConvolution2dLayer(descriptor, weights, biases, name); |
| } |
| }; |
| |
| class DepthwiseConv2dTest |
| { |
| public: |
| using ConvDescriptorType = armnn::DepthwiseConvolution2dDescriptor; |
| using ConvLayerType = armnn::DepthwiseConvolution2dLayer; |
| |
| static IConnectableLayer *AddConvolution(INetwork *network, |
| const DepthwiseConvolution2dDescriptor &descriptor, |
| const ConstTensor &weights, |
| const Optional<ConstTensor> &biases, |
| const char *name) |
| { |
| return network->AddDepthwiseConvolution2dLayer(descriptor, weights, biases, name); |
| } |
| }; |
| |
| 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; |
| } |
| |
| } // namespace |
| |
| template <typename Conv2dTest, |
| armnn::DataType ArmnnType, |
| typename ConvDescriptorType = typename Conv2dTest::ConvDescriptorType, |
| typename T = armnn::ResolveType<ArmnnType>> |
| INetworkPtr CreatNetwork(bool depthwise, bool preventFusing) |
| { |
| // Define layers information |
| ConvDescriptorType convolution2dDescriptor; |
| convolution2dDescriptor.m_BiasEnabled = false; |
| convolution2dDescriptor.m_DataLayout = DataLayout::NHWC; |
| convolution2dDescriptor.m_StrideX = 1; |
| convolution2dDescriptor.m_StrideY = 1; |
| BatchNormalizationDescriptor batchNormDescriptor; |
| batchNormDescriptor.m_DataLayout = DataLayout::NHWC; |
| |
| const unsigned int inputDimensionSizes[] = {1, 4, 4, 3}; // NHWCin |
| unsigned int weightsDimensionSizes[] = {4, 2, 2, 3}; // CoutHWCin |
| unsigned int outputDimensionSizes[] = {1, 3, 3, 4}; // NHWCout |
| |
| if (depthwise) |
| { |
| //M Cin H W |
| weightsDimensionSizes[0] = 4; |
| weightsDimensionSizes[1] = 3; |
| weightsDimensionSizes[2] = 2; |
| weightsDimensionSizes[3] = 2; |
| outputDimensionSizes[3] = weightsDimensionSizes[0] * weightsDimensionSizes[1]; |
| } |
| const unsigned int outputChannelSize[] = {outputDimensionSizes[3]}; // Cout |
| |
| TensorInfo inputInfo(4, inputDimensionSizes, ArmnnType); |
| TensorInfo outputInfo(4, outputDimensionSizes, ArmnnType); |
| |
| std::vector<int> weightsIntVector = { 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(begin(weightsIntVector), end(weightsIntVector)); |
| TensorInfo weightsInfo(4, weightsDimensionSizes, ArmnnType); |
| ConstTensor weights(weightsInfo, weightsVector); |
| |
| std::vector<T> biasVector = GetVector<T>(outputDimensionSizes[3], 3.3f, 0.1f); |
| TensorInfo biasInfo(1, outputChannelSize, ArmnnType); |
| ConstTensor bias(biasInfo, biasVector); |
| Optional<ConstTensor> optionalBias = Optional<ConstTensor>(bias); |
| |
| std::vector<T> betaVector = GetVector<T>(outputDimensionSizes[3], 0.0f, 0.2f); |
| std::vector<T> gammaVector = GetVector<T>(outputDimensionSizes[3], 0.5f, 0.1f); |
| std::vector<T> meanVector = GetVector<T>(outputDimensionSizes[3], 0.1f, 0.1f); |
| std::vector<T> varianceVector = GetVector<T>(outputDimensionSizes[3], 1.0f, 0.1f); |
| |
| 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); |
| |
| // Create a network |
| INetworkPtr network = INetwork::Create(); |
| |
| IConnectableLayer* inputLayer = network->AddInputLayer(0); |
| |
| IConnectableLayer* convLayer = Conv2dTest::AddConvolution(network.get(), |
| convolution2dDescriptor, |
| weights, |
| optionalBias, |
| "convolution"); |
| |
| IConnectableLayer* batchNormLayer = network->AddBatchNormalizationLayer(batchNormDescriptor, |
| mean, |
| variance, |
| beta, |
| gamma, |
| "batchNorm"); |
| |
| IConnectableLayer* outputLayer = network->AddOutputLayer(0); |
| IConnectableLayer* output2Layer = nullptr; |
| |
| if (preventFusing) |
| { |
| output2Layer = network->AddOutputLayer(1); |
| } |
| |
| // Set layer information |
| inputLayer ->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| convLayer ->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| batchNormLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| |
| // Connect layers |
| inputLayer ->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0)); |
| convLayer ->GetOutputSlot(0).Connect(batchNormLayer->GetInputSlot(0)); |
| batchNormLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| |
| if (preventFusing) |
| { |
| convLayer ->GetOutputSlot(0).Connect(output2Layer->GetInputSlot(0)); |
| } |
| |
| return network; |
| } |
| |
| template <typename Conv2dTest, |
| armnn::DataType ArmnnType, |
| typename ConvDescriptorType = typename Conv2dTest::ConvDescriptorType, |
| typename ConvLayerType = typename Conv2dTest::ConvLayerType, |
| typename T = armnn::ResolveType<ArmnnType>> |
| void FuseBatchNormIntoConvTest(bool depthwise, float tolerance, armnn::Compute backendId) |
| { |
| // FIRST NETWORK: Fused |
| // Construct ArmNN network |
| INetworkPtr networkFused = CreatNetwork<Conv2dTest, ArmnnType>(depthwise, 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<ConvLayerType>(layer) && |
| (layer->GetNameStr() == "fused-batchNorm-into-convolution"); |
| }; |
| |
| BOOST_CHECK(3 == graphFused.GetNumLayers()); |
| 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<T> inputDataFused = GetVector<T>(48, 1.0f, 0.1f); |
| |
| std::vector<T> outputDataFused(36); |
| |
| if (depthwise) |
| { |
| outputDataFused.resize(108); |
| } |
| |
| 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<Conv2dTest, ArmnnType>(depthwise, 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<ConvLayerType>, |
| &IsLayerOfType<armnn::BatchNormalizationLayer>, |
| &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 = GetVector<T>(48, 1.0f, 0.1f); |
| |
| std::vector<T> outputDataNotFused(36); |
| std::vector<T> outputData2NotFused(36); |
| |
| if (depthwise) |
| { |
| outputDataNotFused.resize(108); |
| outputData2NotFused.resize(108); |
| } |
| 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-convolution matches with the output of the batchNormm in the "NotFused" network |
| for (unsigned int n = 0; n < outputDataFused.size(); ++n) |
| { |
| BOOST_CHECK_CLOSE(outputDataFused[n], outputDataNotFused[n], T(tolerance)); |
| } |
| } |
| |
| // This unit test needs the reference backend, it's not available if the reference backend is not built |
| #if defined(ARMNNREF_ENABLED) |
| BOOST_AUTO_TEST_CASE(FuseBatchNormIntoConv2DFloat32Test) |
| { |
| FuseBatchNormIntoConvTest<Conv2dTest, DataType::Float32>(false, 0.0001f, armnn::Compute::CpuRef); |
| } |
| |
| BOOST_AUTO_TEST_CASE(FuseBatchNormIntoConv2DFloat16Test) |
| { |
| FuseBatchNormIntoConvTest<Conv2dTest, DataType::Float16>(false, 0.1f, armnn::Compute::CpuRef); |
| } |
| |
| BOOST_AUTO_TEST_CASE(FuseBatchNormIntoDepthwiseConv2DFloat32Test) |
| { |
| FuseBatchNormIntoConvTest<DepthwiseConv2dTest, DataType::Float32>(true, 0.0001f,armnn::Compute::CpuRef); |
| } |
| |
| BOOST_AUTO_TEST_CASE(FuseBatchNormIntoDepthwiseConv2DFloat16Test) |
| { |
| FuseBatchNormIntoConvTest<DepthwiseConv2dTest, DataType::Float16>(true, 0.1f,armnn::Compute::CpuRef); |
| } |
| #endif |
| |
| BOOST_AUTO_TEST_SUITE_END() |