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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "BatchToSpaceNd.hpp"
#include "RefWorkloadUtils.hpp"
#include <armnn/Types.hpp>
#include <boost/assert.hpp>
using namespace armnnUtils;
namespace armnn
{
inline unsigned int Offset(const TensorShape& shape, unsigned int batch, unsigned int height, unsigned int width,
unsigned int channels, const DataLayoutIndexed& dataLayout)
{
if (dataLayout.GetDataLayout() == DataLayout::NHWC)
{
return ((batch * shape[dataLayout.GetHeightIndex()] + height) * shape[dataLayout.GetWidthIndex()] + width) *
shape[dataLayout.GetChannelsIndex()] + channels;
}
else
{
return ((batch * shape[dataLayout.GetChannelsIndex()] + channels) *
shape[dataLayout.GetHeightIndex()] + height) *
shape[dataLayout.GetWidthIndex()] + width;
}
}
void BatchToSpaceNd(const DataLayoutIndexed& dataLayout,
const TensorInfo& inputTensorInfo,
const TensorInfo& outputTensorInfo,
const std::vector<unsigned int>& blockShape,
const std::vector<std::pair<unsigned int, unsigned int>>& cropsData,
const float* inputData,
float* outputData)
{
TensorShape inputShape = inputTensorInfo.GetShape();
BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Expected Input with 4 Dimensions");
TensorShape outputShape = outputTensorInfo.GetShape();
BOOST_ASSERT_MSG(outputShape.GetNumDimensions() == 4, "Expected Output with 4 Dimensions");
const unsigned int inputBatchSize = inputShape[0];
const unsigned int channels = inputShape[dataLayout.GetChannelsIndex()];
const unsigned int outputBatchSize = outputShape[0];
const unsigned int outputHeight = outputShape[dataLayout.GetHeightIndex()];
const unsigned int outputWidth = outputShape[dataLayout.GetWidthIndex()];
BOOST_ASSERT_MSG(blockShape.size() > 0, "BlockShape must contain 1 or more entries");
const unsigned int blockShapeHeight = blockShape[0];
const unsigned int blockShapeWidth = blockShape[1];
BOOST_ASSERT_MSG(cropsData.size() > 0, "Crops must contain 1 or more entries");
const unsigned int cropsTop = cropsData[0].first;
const unsigned int cropsLeft = cropsData[1].first;
for (unsigned int inBatch = 0; inBatch < inputBatchSize; ++inBatch)
{
const unsigned int outBatch = inBatch % outputBatchSize;
const unsigned int spatialOffset = inBatch / outputBatchSize;
for (unsigned int inH = 0; inH < inputTensorInfo.GetShape()[dataLayout.GetHeightIndex()]; ++inH) {
const unsigned int outH = inH * blockShapeHeight + spatialOffset / blockShapeWidth - cropsTop;
if (outH >= outputHeight)
{
continue;
}
for (unsigned int inW = 0; inW < inputTensorInfo.GetShape()[dataLayout.GetWidthIndex()]; ++inW) {
const unsigned int outW = inW * blockShapeWidth + spatialOffset % blockShapeWidth - cropsLeft;
if (outW >= outputWidth)
{
continue;
}
for (unsigned int c = 0; c < channels; c++)
{
unsigned int outOffset = Offset(outputShape, outBatch, outH, outW, c, dataLayout);
unsigned int inOffset = Offset(inputShape, inBatch, inH, inW, c, dataLayout);
outputData[outOffset] = inputData[inOffset];
}
}
}
}
}
} //namespace armnn
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