FuseBatchNorm< ConvLayer, ArmnnType, T > Class Template Reference

#include <FuseBatchNorm.hpp>

Public Member Functions
void	Run (Graph &graph, InputSlot &connection) const
	Run for every exclusive connection between any base Convolution layer and a child BatchNorm layer for not quantized layers.

Protected Member Functions
	FuseBatchNorm ()=default
	~FuseBatchNorm ()=default

Detailed Description

template<typename ConvLayer, armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
class armnn::optimizations::FuseBatchNorm< ConvLayer, ArmnnType, T >

Definition at line 19 of file FuseBatchNorm.hpp.

Constructor & Destructor Documentation

FuseBatchNorm()

template<typename ConvLayer, armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>

FuseBatchNorm ( )

protecteddefault

~FuseBatchNorm()

template<typename ConvLayer, armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>

~FuseBatchNorm ( )

protecteddefault

Member Function Documentation

Run()

template<typename ConvLayer, armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>

void Run ( Graph & graph, InputSlot & connection ) const

inline

Run for every exclusive connection between any base Convolution layer and a child BatchNorm layer for not quantized layers.

The child will be removed, the base will be removed if it's left unconnected. A new Convolution layer will be added, its weights and bias will be calculated using the weights and bias of the base Convolution layer combined with the parameters of the child BatchNorm layer.

Definition at line 27 of file FuseBatchNorm.hpp.

    {
        Layer& base = connection.GetConnectedOutputSlot()->GetOwningLayer();
        Layer& child = connection.GetOwningLayer();
 
        bool depthwise = (base.GetType() == LayerType::DepthwiseConvolution2d);
 
        ARMNN_ASSERT(base.GetType() == LayerType::Convolution2d || depthwise);
        ARMNN_ASSERT(child.GetType() == LayerType::BatchNormalization);
 
        if (base.GetDataType() == ArmnnType && child.GetDataType() == ArmnnType)
        {
            OutputSlot* parentOut = base.GetInputSlot(0).GetConnectedOutputSlot();
            auto convLayer = PolymorphicDowncast<ConvLayer*>(&base);
            auto batchNormLayer = PolymorphicDowncast<BatchNormalizationLayer*>(&child);
 
            // Read convolution and batch norm parameters
            BatchNormalizationDescriptor batchNormDescriptor = batchNormLayer->GetParameters();
            auto epsilon = batchNormDescriptor.m_Eps;
            IgnoreUnused(epsilon);
 
            ConstTensor betaTensor(batchNormLayer->m_Beta->GetTensorInfo(), batchNormLayer->m_Beta->Map(true));
            ConstTensor gammaTensor(batchNormLayer->m_Gamma->GetTensorInfo(), batchNormLayer->m_Gamma->Map(true));
            ConstTensor meanTensor(batchNormLayer->m_Mean->GetTensorInfo(), batchNormLayer->m_Mean->Map(true));
            ConstTensor varTensor(batchNormLayer->m_Variance->GetTensorInfo(), batchNormLayer->m_Variance->Map(true));
 
            auto convDescriptor = convLayer->GetParameters();
            ConstTensor weightsTensor;
            ARMNN_ASSERT_MSG(convLayer->GetInputSlots()[1].GetConnection() != nullptr,
                             "FuseBatchNorm: Weight data should not be null.");
 
            ConstantLayer* weightLayer = PolymorphicDowncast<ConstantLayer*>(
                                        &base.GetInputSlot(1).GetConnectedOutputSlot()->GetOwningLayer());
 
            weightsTensor = ConstTensor(weightLayer->m_LayerOutput->GetTensorInfo(),
                                        weightLayer->m_LayerOutput->Map(true));
 
            armnnUtils::DataLayoutIndexed dataLayout(convDescriptor.m_DataLayout);
            auto weightsShape = weightsTensor.GetInfo().GetShape();
            const unsigned int inputChannels   = parentOut->GetTensorInfo().GetShape()[dataLayout.GetChannelsIndex()];
            const unsigned int depthMultiplier = depthwise ? weightsShape[3] / inputChannels : 1;
            const unsigned int outputChannels  = depthwise ? weightsShape[3] : weightsShape[0];
            const unsigned int weightsHeight   = depthwise ? weightsShape[1] :
                                                 weightsShape[dataLayout.GetHeightIndex()];
            const unsigned int weightsWidth    = depthwise ? weightsShape[2] :
                                                 weightsShape[dataLayout.GetWidthIndex()];
 
            const auto* weightsBuffer = static_cast<const T*>(weightsTensor.GetMemoryArea());
            const auto* betaBuffer    = static_cast<const T*>(betaTensor.GetMemoryArea());
            const auto* gammaBuffer   = static_cast<const T*>(gammaTensor.GetMemoryArea());
            const auto* meanBuffer    = static_cast<const T*>(meanTensor.GetMemoryArea());
            const auto* varBuffer     = static_cast<const T*>(varTensor.GetMemoryArea());
 
            std::vector<T> weightsVector (weightsBuffer, weightsBuffer + weightsTensor.GetNumElements());
            std::vector<T> betaVector    (betaBuffer, betaBuffer + betaTensor.GetNumElements());
            std::vector<T> gammaVector   (gammaBuffer, gammaBuffer + gammaTensor.GetNumElements());
            std::vector<T> meanVector    (meanBuffer, meanBuffer + meanTensor.GetNumElements());
            std::vector<T> varianceVector(varBuffer, varBuffer + varTensor.GetNumElements());
 
            // fusedWeights = ( gamma * weights ) / ( std - epsilon);
            std::vector<T> fusedWeightsVector(weightsVector.size());
 
            for (unsigned int cInput = 0; cInput < inputChannels; ++cInput)
            {
                for (unsigned int cOut = 0; cOut < outputChannels; ++cOut)
                {
                    T mult = gammaVector[cOut] / static_cast<T>(sqrtf(varianceVector[cOut] + epsilon));
 
                    for (unsigned int h = 0; h < weightsHeight; ++h)
                    {
                        for (unsigned int w = 0; w < weightsWidth; ++w)
                        {
                            unsigned int weightsIdx = 0;
 
                            if (depthwise)
                            {
                                cInput = cOut / depthMultiplier;
                                weightsIdx = w * outputChannels + cOut +
                                             h * weightsWidth * outputChannels;
                            }
                            else if (convDescriptor.m_DataLayout == DataLayout::NHWC)
                            {
                                weightsIdx = cOut * weightsHeight * weightsWidth * inputChannels +
                                             h * weightsWidth * inputChannels +
                                             w * inputChannels +
                                             cInput;
                            }
                            else
                            {
                                weightsIdx = cOut * weightsWidth * weightsHeight * inputChannels +
                                             cInput * weightsWidth * weightsHeight +
                                             h * weightsWidth +
                                             w;
                            }
                            fusedWeightsVector[weightsIdx] = mult * weightsVector[weightsIdx];
                        }
                    }
                }
            }
            ConstTensor fusedWeightsTensor(weightsTensor.GetInfo(), fusedWeightsVector);
 
            //  fusedBias = (gamma * (bias - mean)) / (variance - epsilon) + beta;
            std::vector<T> fusedBiasVector(outputChannels);
            bool biasWasEnabledBeforeOpt = convDescriptor.m_BiasEnabled;
            if (biasWasEnabledBeforeOpt)
            {
                ConstTensor biasTensor;
                ARMNN_ASSERT_MSG(convLayer->GetInputSlots()[2].GetConnection() != nullptr,
                                 "FuseBatchNorm: Bias data should not be null if bias is enabled.");
 
                ConstantLayer* biasLayer = PolymorphicDowncast<ConstantLayer*>(
                                                &base.GetInputSlot(2).GetConnectedOutputSlot()->GetOwningLayer());
 
                biasTensor = ConstTensor(biasLayer->m_LayerOutput->GetTensorInfo(),
                                         biasLayer->m_LayerOutput->Map(true));
 
                const auto* biasBuffer = static_cast<const T*>(biasTensor.GetMemoryArea());
                std::vector<T> biasVector(biasBuffer, biasBuffer + biasTensor.GetNumElements());
 
                for (unsigned int cOut = 0; cOut < outputChannels; ++cOut)
                {
                    fusedBiasVector[cOut] = ((gammaVector[cOut] * (biasVector[cOut] - meanVector[cOut])) /
                                             sqrtf(varianceVector[cOut] + epsilon)) + betaVector[cOut];
                }
            }
            else
            {
                convDescriptor.m_BiasEnabled = true;
                std::vector<T> biasVector(outputChannels, T(0));
 
                for (unsigned int cOut = 0; cOut < outputChannels; ++cOut)
                {
                    fusedBiasVector[cOut] = ((gammaVector[cOut] * (biasVector[cOut] - meanVector[cOut])) /
                                             sqrtf(varianceVector[cOut] + epsilon)) + betaVector[cOut];
                }
            }
            ConstTensor fusedBiasTensor(TensorInfo({outputChannels}, ArmnnType, 0.0f, 0, true), fusedBiasVector);
 
            // Insert the new convolution layer that has batch norm parameters fused into
            const std::string name = std::string("fused-") + child.GetName() + std::string("-into-") + base.GetName();
            auto& newConv2dLayer = *graph.InsertNewLayer<ConvLayer>(base.GetInputSlot(0),
                                                                    convDescriptor,
                                                                    name.c_str());
 
            // Connect weights and bias from old to new Conv2d layer
            // This optimization will always have 3 input slots on the Conv2d base layer
            if (newConv2dLayer.GetNumInputSlots() > 1)
            {
                // Remove old connection and connect to new layer2d
                weightLayer->GetOutputSlot(0).Disconnect(base.GetInputSlot(1));
                weightLayer->GetOutputSlot(0).Connect(newConv2dLayer.GetInputSlot(1));
                weightLayer->m_LayerOutput = std::make_unique<ScopedTensorHandle>(fusedWeightsTensor);
 
                // Move bias const layers as normal if it was enabled before the optimisation
                ConstantLayer* biasLayer;
                if (biasWasEnabledBeforeOpt)
                {
                    biasLayer = PolymorphicDowncast<ConstantLayer*>(
                        &base.GetInputSlot(2).GetConnectedOutputSlot()->GetOwningLayer());
                    // Remove old connection and connect to new layer2d
                    biasLayer->GetOutputSlot(0).Disconnect(base.GetInputSlot(2));
                    biasLayer->GetOutputSlot(0).Connect(newConv2dLayer.GetInputSlot(2));
 
                }
                // Otherwise create a new bias layer and add to the new convolution2d
                else
                {
                    // Add in bias constant layer
                    biasLayer = graph.AddLayer<ConstantLayer>("Bias");
                    biasLayer->GetOutputSlot(0).SetTensorInfo(fusedBiasTensor.GetInfo());
                    biasLayer->GetOutputSlot(0).Connect(newConv2dLayer.GetInputSlot(2));
                }
                biasLayer->m_LayerOutput = std::make_unique<ScopedTensorHandle>(ConstTensor(fusedBiasTensor));
            }
 
 
            // Reconnects with original parent.
            newConv2dLayer.GetOutputSlot().MoveAllConnections(*parentOut);
            // Parent is now the new convolution2d layer.
            parentOut = &newConv2dLayer.GetOutputSlot();
 
            // Moves connections in child output to parent layer.
            // Child layer will be removed as it's left unconnected.
            // Base layer will be removed if left unconnected.
            child.GetOutputSlot().MoveAllConnections(*parentOut);
        }
    }

References Graph::AddLayer(), ARMNN_ASSERT, ARMNN_ASSERT_MSG, armnn::BatchNormalization, OutputSlot::Connect(), armnn::Convolution2d, armnn::DepthwiseConvolution2d, OutputSlot::Disconnect(), DataLayoutIndexed::GetChannelsIndex(), InputSlot::GetConnectedOutputSlot(), Layer::GetDataType(), DataLayoutIndexed::GetHeightIndex(), BaseTensor< MemoryType >::GetInfo(), Layer::GetInputSlot(), BaseTensor< MemoryType >::GetMemoryArea(), Layer::GetName(), BaseTensor< MemoryType >::GetNumElements(), Layer::GetOutputSlot(), InputSlot::GetOwningLayer(), OutputSlot::GetOwningLayer(), TensorInfo::GetShape(), OutputSlot::GetTensorInfo(), Layer::GetType(), DataLayoutIndexed::GetWidthIndex(), armnn::IgnoreUnused(), Graph::InsertNewLayer(), BatchNormalizationDescriptor::m_Eps, ConstantLayer::m_LayerOutput, OutputSlot::MoveAllConnections(), armnn::NHWC, armnn::PolymorphicDowncast(), and OutputSlot::SetTensorInfo().

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The documentation for this class was generated from the following file:

• src/armnn/optimizations/FuseBatchNorm.hpp