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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "OutputShapeUtils.hpp"
#include <algorithm>
#include <vector>
namespace armnn_driver
{
using namespace armnn;
bool IsDynamicOutput(const TensorInfo& outputInfo)
{
return outputInfo.GetNumElements() == 0u;
}
TensorShape InferPadOutputShape(const TensorShape& inputShape,
const std::vector<std::pair<unsigned int, unsigned int>>& padList)
{
const unsigned int numDims = inputShape.GetNumDimensions();
std::vector<unsigned int> outputDims;
TensorShape outputShape = TensorShape(numDims);
for (unsigned int dim = 0; dim < numDims; ++dim)
{
unsigned int dimSize = inputShape[dim];
const std::pair<unsigned int, unsigned int>& dimPadding = padList[dim];
dimSize += dimPadding.first;
dimSize += dimPadding.second;
outputShape[dim] = dimSize;
}
return outputShape;
}
TensorShape InferPreluOutputShape(const TensorShape& inputShape, const TensorShape& alphaShape)
{
// NOTE: The inferred PReLU output size will be the maximum size along each dimension
// of input and alpha, starting with the trailing dimensions, and working its way forward.
//
// Example: inputShape={4, 1, 2}, alphaShape={5, 4, 3, 1} => outputShape={5, 4, 3, 2}
const unsigned int numInputDims = inputShape.GetNumDimensions();
const unsigned int numAlphaDims = alphaShape.GetNumDimensions();
const unsigned int maxNumDims = std::max(numInputDims, numAlphaDims);
TensorShape outputShape = TensorShape(maxNumDims);
for (unsigned int reverseIdx = 1u; reverseIdx <= maxNumDims; ++reverseIdx)
{
const int inputIdx = numInputDims - reverseIdx;
const int alphaIdx = numAlphaDims - reverseIdx;
const unsigned int inputDimSize = inputIdx >= 0 ? inputShape[inputIdx] : 0u;
const unsigned int alphaDimSize = alphaIdx >= 0 ? alphaShape[alphaIdx] : 0u;
const unsigned int outputIdx = maxNumDims - reverseIdx;
outputShape[outputIdx] = std::max(inputDimSize, alphaDimSize);
}
return outputShape;
}
} // namespace armnn_driver
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