RefL2NormalizationWorkload Class Reference

#include <RefL2NormalizationWorkload.hpp>

Inheritance diagram for RefL2NormalizationWorkload:

Public Member Functions
	RefL2NormalizationWorkload (const L2NormalizationQueueDescriptor &descriptor, const WorkloadInfo &info)
void	Execute () const override
Public Member Functions inherited from BaseWorkload< L2NormalizationQueueDescriptor >
	BaseWorkload (const L2NormalizationQueueDescriptor &descriptor, const WorkloadInfo &info)
void	PostAllocationConfigure () override
const L2NormalizationQueueDescriptor &	GetData () const
profiling::ProfilingGuid	GetGuid () const final
Public Member Functions inherited from IWorkload
virtual	~IWorkload ()
virtual void	RegisterDebugCallback (const DebugCallbackFunction &)

Additional Inherited Members
Protected Attributes inherited from BaseWorkload< L2NormalizationQueueDescriptor >
const L2NormalizationQueueDescriptor	m_Data
const profiling::ProfilingGuid	m_Guid

Detailed Description

Definition at line 14 of file RefL2NormalizationWorkload.hpp.

Constructor & Destructor Documentation

RefL2NormalizationWorkload()

RefL2NormalizationWorkload ( const L2NormalizationQueueDescriptor &  descriptor, const WorkloadInfo &  info  )

explicit

Definition at line 22 of file RefL2NormalizationWorkload.cpp.

    : BaseWorkload<L2NormalizationQueueDescriptor>(descriptor, info) {}

Member Function Documentation

Execute()

void Execute ( ) const

overridevirtual

Implements IWorkload.

Definition at line 27 of file RefL2NormalizationWorkload.cpp.

References ARMNN_SCOPED_PROFILING_EVENT, armnn::CpuRef, TensorShape::GetNumDimensions(), armnn::GetTensorInfo(), BaseWorkload< L2NormalizationQueueDescriptor >::m_Data, L2NormalizationDescriptor::m_DataLayout, L2NormalizationDescriptor::m_Eps, QueueDescriptor::m_Inputs, QueueDescriptor::m_Outputs, QueueDescriptorWithParameters< LayerDescriptor >::m_Parameters, and armnn::numeric_cast().

{
    ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefL2NormalizationWorkload_Execute");

    const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]);
    const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]);

    auto inputDecoder  = MakeDecoder<float>(inputInfo, m_Data.m_Inputs[0]->Map());
    auto outputEncoder = MakeEncoder<float>(outputInfo, m_Data.m_Outputs[0]->Map());

    DataLayoutIndexed dataLayout(m_Data.m_Parameters.m_DataLayout);

    const TensorShape& shape = inputInfo.GetShape();
    unsigned int paddedShapeArray[4];
    const int idxShift = 4 - armnn::numeric_cast<int>(shape.GetNumDimensions());

    const unsigned int batches = (idxShift == 0) ? shape[0] : 1;
    paddedShapeArray[0] = batches;

    const int channelsIdx = armnn::numeric_cast<int>(dataLayout.GetChannelsIndex());
    const unsigned int channels = (channelsIdx - idxShift >= 0)
                                  ? shape[armnn::numeric_cast<unsigned int>(channelsIdx - idxShift)]
                                  : 1;
    paddedShapeArray[channelsIdx] = channels;

    const int heightIdx = armnn::numeric_cast<int>(dataLayout.GetHeightIndex());
    const unsigned int height = (heightIdx - idxShift >= 0)
                                ? shape[armnn::numeric_cast<unsigned int>(heightIdx - idxShift)]
                                : 1;
    paddedShapeArray[heightIdx] = height;

    const int widthIdx = armnn::numeric_cast<int>(dataLayout.GetWidthIndex());
    const unsigned int width = (widthIdx - idxShift >= 0)
                               ? shape[armnn::numeric_cast<unsigned int>(widthIdx - idxShift)]
                               : 1;
    paddedShapeArray[widthIdx] = width;

    const TensorShape& paddedShape = TensorShape(4, paddedShapeArray);

    for (unsigned int n = 0; n < batches; ++n)
    {
        for (unsigned int c = 0; c < channels; ++c)
        {
            for (unsigned int h = 0; h < height; ++h)
            {
                for (unsigned int w = 0; w < width; ++w)
                {
                    float reduction = 0.0;
                    for (unsigned int d = 0; d < channels; ++d)
                    {
                        unsigned int inputIndex = dataLayout.GetIndex(paddedShape, n, d, h, w);

                        (*inputDecoder)[inputIndex];
                        const float value = inputDecoder->Get();
                        reduction += value * value;
                    }

                    unsigned int index = dataLayout.GetIndex(paddedShape, n, c, h, w);

                    float maximum = reduction < m_Data.m_Parameters.m_Eps ? m_Data.m_Parameters.m_Eps : reduction;

                    const float scale = 1.0f / sqrtf(maximum);

                    (*inputDecoder)[index];
                    (*outputEncoder)[index];
                    outputEncoder->Set(inputDecoder->Get() * scale);
                }
            }
        }
    }
}

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

• src/backends/reference/workloads/RefL2NormalizationWorkload.hpp
• src/backends/reference/workloads/RefL2NormalizationWorkload.cpp