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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "Pad.hpp"
#include "backends/WorkloadData.hpp"
#include <boost/numeric/conversion/cast.hpp>
#include "TensorBufferArrayView.hpp"
#include <cmath>
#include <cstddef>
#include <functional>
#include <limits>
#include <cassert>
namespace armnn
{
void Pad(const TensorInfo& inputInfo,
const TensorInfo& outputInfo,
std::vector<std::pair<unsigned int, unsigned int>> m_PadList,
const float* inputData,
float* outData)
{
unsigned int numOutputElements = outputInfo.GetNumElements();
TensorShape outputShape = outputInfo.GetShape();
TensorShape inputShape = inputInfo.GetShape();
unsigned int numInputDimensions = inputShape.GetNumDimensions();
#ifndef NDEBUG
unsigned int numOutputDimensions = outputShape.GetNumDimensions();
assert(numInputDimensions == numOutputDimensions);
#endif
unsigned int inputBatches = 0;
unsigned int inputChannels = 0;
unsigned int inputHeight = 0;
unsigned int inputWidth = 0;
unsigned int outputChannels = 0;
unsigned int outputHeight = 0;
unsigned int outputWidth = 0;
for (unsigned int i = 0; i < numOutputElements; ++i)
{
outData[i] = 0;
}
switch(numInputDimensions) {
case 1:
inputWidth = inputShape[0];
for (unsigned int w = 0; w < inputWidth ; w++)
{
outData[w+std::get<0>(m_PadList[0])] = inputData[w];
}
break;
case 2 :
inputHeight = inputShape[0];
inputWidth = inputShape[1];
outputHeight = outputShape[0];
outputWidth = outputShape[1];
for (unsigned int h = 0; h < inputHeight; h++)
{
for (unsigned int w = 0; w < inputWidth ; w++)
{
outData[(h+std::get<0>(m_PadList[0]))*outputWidth
+ (w+std::get<0>(m_PadList[1]))] = inputData[h * inputWidth + w];
}
}
break;
case 3 :
inputChannels = inputShape[0];
inputHeight = inputShape[1];
inputWidth = inputShape[2];
outputChannels = outputShape[0];
outputHeight = outputShape[1];
outputWidth = outputShape[2];
for (unsigned int c = 0; c < inputChannels; c++)
{
for (unsigned int h = 0; h < inputHeight; h++)
{
for (unsigned int w = 0; w < inputWidth ; w++)
{
outData[(c+std::get<0>(m_PadList[0]))*outputHeight*outputWidth
+ (h+std::get<0>(m_PadList[1]))*outputWidth
+ (w+std::get<0>(m_PadList[2]))] = inputData[c * inputHeight * inputWidth
+ h * inputWidth
+ w];
}
}
}
break;
case 4 :
inputBatches = inputShape[0];
inputChannels = inputShape[1];
inputHeight = inputShape[2];
inputWidth = inputShape[3];
outputChannels = outputShape[1];
outputHeight = outputShape[2];
outputWidth = outputShape[3];
for (unsigned int b = 0; b < inputBatches; b++)
{
for (unsigned int c = 0; c < inputChannels; c++)
{
for (unsigned int h = 0; h < inputHeight; h++)
{
for (unsigned int w = 0; w < inputWidth ; w++)
{
outData[(b+std::get<0>(m_PadList[0])) * outputChannels * outputHeight * outputWidth
+ (c+std::get<0>(m_PadList[1])) * outputHeight * outputWidth
+ (h+std::get<0>(m_PadList[2])) * outputWidth
+ (w+std::get<0>(m_PadList[3]))] = inputData[b * inputChannels * inputHeight
* inputWidth
+ c * inputHeight * inputWidth
+ h * inputWidth
+ w];
}
}
}
}
break;
default :
break;
}
}
} //namespace armnn
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