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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "BatchNormImpl.hpp"
#include "RefWorkloadUtils.hpp"
#include <armnn/Tensor.hpp>
#include <armnnUtils/DataLayoutIndexed.hpp>
#include <cmath>
namespace armnn
{
void BatchNormImpl(const BatchNormalizationQueueDescriptor& data,
Decoder<float>& meanDecoder,
Decoder<float>& varianceDecoder,
Decoder<float>& betaDecoder,
Decoder<float>& gammaDecoder,
Decoder<float>& inputDecoder,
Encoder<float>& outputEncoder)
{
const TensorInfo& inputInfo = GetTensorInfo(data.m_Inputs[0]);
const TensorShape inputShape = inputInfo.GetShape();
armnnUtils::DataLayoutIndexed dataLayout(data.m_Parameters.m_DataLayout);
unsigned int inputBatches = inputShape[0];
unsigned int inputHeight = inputShape[dataLayout.GetHeightIndex()];
unsigned int inputWidth = inputShape[dataLayout.GetWidthIndex()];
unsigned int inputChannels = inputShape[dataLayout.GetChannelsIndex()];
for (unsigned int c = 0; c < inputChannels; c++)
{
meanDecoder[c];
varianceDecoder[c];
betaDecoder[c];
gammaDecoder[c];
float mean = meanDecoder.Get();
float var = varianceDecoder.Get();
float beta = betaDecoder.Get();
float gamma = gammaDecoder.Get();
float mult = gamma / sqrtf(var + data.m_Parameters.m_Eps);
float add = beta - mult * mean;
for (unsigned int n = 0; n < inputBatches; n++)
{
for (unsigned int h = 0; h < inputHeight; h++)
{
for (unsigned int w = 0; w < inputWidth; w++)
{
unsigned int index = dataLayout.GetIndex(inputShape, n, c, h, w);
inputDecoder[index];
outputEncoder[index];
outputEncoder.Set(mult * inputDecoder.Get() + add);
}
}
}
}
}
} // namespace armnn
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