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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));
TensorBufferArrayView<float> output(outputInfo.GetShape(), GetOutputTensorDataFloat(0, m_Data));
const unsigned int batchSize = inputInfo.GetShape()[0];
const unsigned int depth = inputInfo.GetShape()[1];
const unsigned int rows = inputInfo.GetShape()[2];
const unsigned int cols = inputInfo.GetShape()[3];
for (unsigned int n = 0; n < batchSize; ++n)
{
for (unsigned int d = 0; d < depth; ++d)
{
for (unsigned int h = 0; h < rows; ++h)
{
for (unsigned int w = 0; w < cols; ++w)
{
float reduction = 0.0;
for (unsigned int c = 0; c < depth; ++c)
{
const float value = input.Get(n, c, 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, d, h, w) = input.Get(n, d, h, w) * scale;
}
}
}
}
}
} //namespace armnn
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