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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "RefWorkloadUtils.hpp"
#include "TensorBufferArrayView.hpp"
#include <armnn/Tensor.hpp>
#include <cmath>
namespace armnn
{
template<typename NormData>
static void BatchNormImpl(NormData data,
const float* varIn,
const float* meanIn,
const float* gammaIn,
const float* betaIn,
float* outputData,
const float* inputData)
{
const TensorInfo& inputInfo = GetTensorInfo(data.m_Inputs[0]);
const TensorInfo& outputInfo = GetTensorInfo(data.m_Outputs[0]);
TensorBufferArrayView<const float> input(inputInfo.GetShape(),
inputData,
data.m_Parameters.m_DataLayout);
TensorBufferArrayView<float> output(outputInfo.GetShape(),
outputData,
data.m_Parameters.m_DataLayout);
DataLayoutIndexed dataLayout(data.m_Parameters.m_DataLayout);
for (unsigned int c = 0; c < inputInfo.GetShape()[dataLayout.GetChannelsIndex()]; c++)
{
float var = varIn[c];
float mean = meanIn[c];
float gamma = gammaIn[c];
float beta = betaIn[c];
float mult = gamma / sqrtf(var + data.m_Parameters.m_Eps);
float add = beta - mult * mean;
for (unsigned int n = 0; n < inputInfo.GetShape()[0]; n++)
{
for (unsigned int h = 0; h < inputInfo.GetShape()[dataLayout.GetHeightIndex()]; h++)
{
for (unsigned int w = 0; w < inputInfo.GetShape()[dataLayout.GetWidthIndex()]; w++)
{
output.Get(n, c, h, w) = mult * input.Get(n, c, h, w) + add;
}
}
}
}
}
} //namespace armnn
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