RefL2NormalizationWorkload.cpp

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//
// Copyright © 2019-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
 
#include "RefL2NormalizationWorkload.hpp"
#include "RefWorkloadUtils.hpp"
#include "Decoders.hpp"
#include "Encoders.hpp"
 
#include <Profiling.hpp>
 
#include <armnnUtils/DataLayoutIndexed.hpp>
#include <armnn/utility/NumericCast.hpp>
 
#include <cmath>
 
using namespace armnnUtils;
 
namespace armnn
{
RefL2NormalizationWorkload::RefL2NormalizationWorkload(
        const L2NormalizationQueueDescriptor& descriptor,
        const WorkloadInfo& info)
    : RefBaseWorkload<L2NormalizationQueueDescriptor>(descriptor, info) {}
 
void RefL2NormalizationWorkload::Execute() const
{
    Execute(m_Data.m_Inputs, m_Data.m_Outputs);
}
 
void RefL2NormalizationWorkload::ExecuteAsync(ExecutionData& executionData)
{
    WorkingMemDescriptor* workingMemDescriptor = static_cast<WorkingMemDescriptor*>(executionData.m_Data);
    Execute(workingMemDescriptor->m_Inputs, workingMemDescriptor->m_Outputs);
}
 
void RefL2NormalizationWorkload::Execute(std::vector<ITensorHandle*> inputs, std::vector<ITensorHandle*> outputs) const
{
    ARMNN_SCOPED_PROFILING_EVENT_REF_NAME_GUID("RefL2NormalizationWorkload_Execute");
 
    const TensorInfo& inputInfo = GetTensorInfo(inputs[0]);
    const TensorInfo& outputInfo = GetTensorInfo(outputs[0]);
 
    auto inputDecoder  = MakeDecoder<float>(inputInfo, inputs[0]->Map());
    auto outputEncoder = MakeEncoder<float>(outputInfo, 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);
                }
            }
        }
    }
}
 
} //namespace armnn