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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "RefNormalizationFloat32Workload.hpp"
#include "RefWorkloadUtils.hpp"
#include "TensorBufferArrayView.hpp"
#include "Profiling.hpp"
#include <armnn/Tensor.hpp>
#include <boost/log/trivial.hpp>
#include <boost/numeric/conversion/cast.hpp>
namespace armnn
{
// Helper function to compute "Within" normalization using Krichevsky 2012: Local Brightness Normalization.
static void NormalizeWithinUingLbr(const float* inputData,
float* outputData,
const TensorShape& tensorShape,
uint32_t norm_size,
float alpha,
float beta,
float kappa)
{
const unsigned int batchSize = tensorShape[0];
const unsigned int depth = tensorShape[1];
const unsigned int rows = tensorShape[2];
const unsigned int cols = tensorShape[3];
int radius = boost::numeric_cast<int>(norm_size / 2u); /* Strong Assumption on rounding Mode */
for (unsigned int n = 0; n < batchSize; n++)
{
for (unsigned int c = 0; c < depth; c++)
{
for (unsigned int h = 0; h < rows; h++)
{
for (unsigned int w = 0; w < cols; w++)
{
float accumulated_scale = 0.0;
for (int y = -radius; y <= radius; y++)
{
for (int x = -radius; x <= radius; x++)
{
int i = boost::numeric_cast<int>(w) + x;
int j = boost::numeric_cast<int>(h) + y;
if ((i < 0) || (i >= boost::numeric_cast<int>(cols)))
{
continue;
}
if ((j < 0) || (j >= boost::numeric_cast<int>(rows)))
{
continue;
}
float inval = inputData[n * cols * rows * depth +
c * cols * rows +
boost::numeric_cast<unsigned int>(j) * cols +
boost::numeric_cast<unsigned int>(i)];
accumulated_scale += inval*inval;
}
}
outputData[n * cols * rows * depth +
c * cols * rows +
h * cols +
w] = inputData[n * cols * rows * depth +
c * cols * rows +
h * cols +
w] / (powf((kappa + (accumulated_scale * alpha)), beta));
}
}
}
}
}
// Helper function to compute "Across" normalization using Krichevsky 2012: Local Brightness Normalization.
void NormalizeAcrossUingLbr(const float* inputData,
float* outputData,
const TensorShape& tensorShape,
uint32_t norm_size,
float alpha,
float beta,
float kappa,
DataLayout dataLayout)
{
TensorBufferArrayView<const float> input(tensorShape,
inputData,
dataLayout);
TensorBufferArrayView<float> output(tensorShape,
outputData,
dataLayout);
DataLayoutIndexed dataLayoutIndexed(dataLayout);
const unsigned int batchSize = tensorShape[0];
const unsigned int depth = tensorShape[dataLayoutIndexed.GetChannelsIndex()];
const unsigned int rows = tensorShape[dataLayoutIndexed.GetHeightIndex()];
const unsigned int cols = tensorShape[dataLayoutIndexed.GetWidthIndex()];
int radius = boost::numeric_cast<int>(norm_size / 2u); /* Strong Assumption on rounding Mode */
for (unsigned int n = 0; n < batchSize; n++)
{
for (unsigned int c = 0; c < depth; c++)
{
for (unsigned int h = 0; h < rows; h++)
{
for (unsigned int w = 0; w < cols; w++)
{
float accumulated_scale = 0.0;
for (int z = -radius; z <= radius; z++)
{
int k = boost::numeric_cast<int>(c) + z;
if ((k < 0) || (k >= boost::numeric_cast<int>(depth)))
{
continue;
}
float inval = input.Get(n, boost::numeric_cast<unsigned int>(k), h, w);
accumulated_scale += inval * inval;
}
float scale = kappa + (accumulated_scale * alpha);
scale = powf(scale, -beta);
output.Get(n, c, h, w) = scale * input.Get(n, c, h, w);
}
}
}
}
}
void RefNormalizationFloat32Workload::Execute() const
{
ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefNormalizationFloat32Workload_Execute");
const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]);
float* outputData = GetOutputTensorDataFloat(0, m_Data);
const float* inputData = GetInputTensorDataFloat(0, m_Data);
if (NormalizationAlgorithmMethod::LocalBrightness == m_Data.m_Parameters.m_NormMethodType)
{
if (NormalizationAlgorithmChannel::Within == m_Data.m_Parameters.m_NormChannelType)
{
NormalizeWithinUingLbr(inputData,
outputData,
inputInfo.GetShape(),
m_Data.m_Parameters.m_NormSize,
m_Data.m_Parameters.m_Alpha,
m_Data.m_Parameters.m_Beta,
m_Data.m_Parameters.m_K);
}
else if (NormalizationAlgorithmChannel::Across == m_Data.m_Parameters.m_NormChannelType)
{
NormalizeAcrossUingLbr(inputData,
outputData,
inputInfo.GetShape(),
m_Data.m_Parameters.m_NormSize,
m_Data.m_Parameters.m_Alpha,
m_Data.m_Parameters.m_Beta,
m_Data.m_Parameters.m_K,
m_Data.m_Parameters.m_DataLayout);
}
else
{
BOOST_LOG_TRIVIAL(warning) << "Illegal NORMALIZATION mode in normalization_f32";
return;
}
}
else
{
BOOST_LOG_TRIVIAL(warning) << "Lcr method (Jarret 2009: Local Contrast Normalization) not supported yet.";
return;
}
}
} //namespace armnn
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