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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "RefL2NormalizationFloat32Workload.hpp"
#include "RefWorkloadUtils.hpp"
#include "TensorBufferArrayView.hpp"
#include "Profiling.hpp"
#include <cmath>
namespace armnn
{
void RefL2NormalizationFloat32Workload::Execute() const
{
ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefL2NormalizationFloat32Workload_Execute");
const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]);
const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]);
TensorBufferArrayView<const float> input(inputInfo.GetShape(),
GetInputTensorDataFloat(0, m_Data),
m_Data.m_Parameters.m_DataLayout);
TensorBufferArrayView<float> output(outputInfo.GetShape(),
GetOutputTensorDataFloat(0, m_Data),
m_Data.m_Parameters.m_DataLayout);
DataLayoutIndexed dataLayout(m_Data.m_Parameters.m_DataLayout);
const unsigned int batches = inputInfo.GetShape()[0];
const unsigned int channels = inputInfo.GetShape()[dataLayout.GetChannelsIndex()];
const unsigned int height = inputInfo.GetShape()[dataLayout.GetHeightIndex()];
const unsigned int width = inputInfo.GetShape()[dataLayout.GetWidthIndex()];
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)
{
const float value = input.Get(n, d, h, w);
reduction += value * value;
}
// Using std::max(reduction, epsilon) below would prevent against division by 0.
// However, at the time of writing:
// - This is not supported by the ACL functions used to implement L2Normalization in the CL
// backend.
// - The reference semantics for this operator do not include this parameter.
const float scale = 1.0f / sqrtf(reduction);
output.Get(n, c, h, w) = input.Get(n, c, h, w) * scale;
}
}
}
}
}
} //namespace armnn
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