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. More...

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()

FuseBatchNorm ( )

protecteddefault

Referenced by FuseBatchNorm< ConvLayer, ArmnnType, T >::Run().

~FuseBatchNorm()

~FuseBatchNorm ( )

protecteddefault

Referenced by FuseBatchNorm< ConvLayer, ArmnnType, T >::Run().

Member Function Documentation

Run()

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.

References ARMNN_ASSERT, ARMNN_ASSERT_MSG, armnn::BatchNormalization, armnn::Convolution2d, armnn::DepthwiseConvolution2d, FuseBatchNorm< ConvLayer, ArmnnType, T >::FuseBatchNorm(), InputSlot::GetConnectedOutputSlot(), Layer::GetDataType(), Layer::GetInputSlot(), Layer::GetName(), Layer::GetOutputSlot(), InputSlot::GetOwningLayer(), OutputSlot::GetOwningLayer(), Layer::GetType(), armnn::IgnoreUnused(), Graph::InsertNewLayer(), BatchNormalizationDescriptor::m_Eps, OutputSlot::MoveAllConnections(), armnn::NHWC, and FuseBatchNorm< ConvLayer, ArmnnType, T >::~FuseBatchNorm().

    {
        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();
            auto weightsInfo(convLayer->m_Weight->GetTensorInfo());
            ConstTensor weightsTensor(weightsInfo, convLayer->m_Weight->Map(true));

            armnnUtils::DataLayoutIndexed dataLayout(convDescriptor.m_DataLayout);
            auto weightsShape = weightsInfo.GetShape();
            const unsigned int depthMultiplier = depthwise ? weightsShape[0] : 1;
            const unsigned int inputChannels   = depthwise ? weightsShape[1] :
                                                             weightsShape[dataLayout.GetChannelsIndex()];
            const unsigned int outputChannels  = depthwise ? inputChannels * depthMultiplier : weightsShape[0];
            const unsigned int weightsHeight   = depthwise ? weightsShape[2] :
                                                             weightsShape[dataLayout.GetHeightIndex()];
            const unsigned int weightsWidth    = depthwise ? weightsShape[3] :
                                                             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());
            unsigned int depthwiseMultiplierIdx = 0;

            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));

                    if (depthwise)
                    {
                        cInput = cOut / depthMultiplier;
                        depthwiseMultiplierIdx = cOut % depthMultiplier;
                    }

                    for (unsigned int h = 0; h < weightsHeight; ++h)
                    {
                        for (unsigned int w = 0; w < weightsWidth; ++w)
                        {
                            unsigned int weightsIdx = 0;

                            if (depthwise)
                            {
                                weightsIdx = depthwiseMultiplierIdx * weightsWidth * weightsHeight * inputChannels +
                                             cInput * weightsWidth * weightsHeight +
                                             h * weightsWidth +
                                             w;
                            }
                            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(weightsInfo, fusedWeightsVector);

            //  fusedBias = (gamma * (bias - mean)) / (variance - epsilon) + beta;
            std::vector<T> fusedBiasVector(outputChannels);
            if (convDescriptor.m_BiasEnabled)
            {
                ARMNN_ASSERT_MSG(convLayer->m_Bias != nullptr,
                                 "FuseBatchNorm: Bias data should not be null if bias is enabled.");

                ConstTensor biasTensor(convLayer->m_Bias->GetTensorInfo(), convLayer->m_Bias->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), 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());
            newConv2dLayer.m_Weight = std::make_unique<ScopedCpuTensorHandle>(fusedWeightsTensor);
            newConv2dLayer.m_Bias = std::make_unique<ScopedCpuTensorHandle>(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);
        }
    }

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

• src/armnn/optimizations/FuseBatchNorm.hpp