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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "Mean.hpp"
#include "backends/WorkloadData.hpp"
#include <boost/numeric/conversion/cast.hpp>
#include <cmath>
#include <cstddef>
#include <functional>
#include <limits>
namespace armnn
{
bool NextIndex(const unsigned int numDims, const armnn::TensorShape& dims, std::vector<unsigned int>& current)
{
unsigned int carry = 1;
for (unsigned int idx = numDims; idx-- > 0; )
{
unsigned int current_val = current[idx] + carry;
if (dims[idx] == current_val)
{
current[idx] = 0;
}
else
{
current[idx] = current_val;
carry = 0;
break;
}
}
return (carry == 0);
}
std::size_t ReducedOutputOffset(const unsigned int numDims, const armnn::TensorShape& dims,
std::vector<unsigned int>& index, const unsigned int numAxis,
const std::vector<unsigned int>& axis) {
std::size_t offset = 0;
for (unsigned int idx = 0; idx < numDims; ++idx)
{
bool isAxis = false;
if (!axis.empty())
{
for (unsigned int axisIdx = 0; axisIdx < numAxis; ++axisIdx)
{
if (idx == axis[axisIdx])
{
isAxis = true;
break;
}
}
}
if (!isAxis)
{
offset = offset * boost::numeric_cast<size_t>(dims[idx]) + boost::numeric_cast<size_t>(index[idx]);
}
}
return offset;
}
} // namespace
namespace armnn
{
void Mean(const armnn::TensorInfo& inputInfo,
const armnn::TensorInfo& outputInfo,
const std::vector<unsigned int>& axis,
const float* inputData,
float* outputData) {
unsigned int inputNumDims = inputInfo.GetNumDimensions();
unsigned int outputNumDims = outputInfo.GetNumDimensions();
armnn::TensorShape outputDims = outputInfo.GetShape();
armnn::TensorShape inputDims = inputInfo.GetShape();
// Initialise output data.
size_t numOutputs = 1;
for (unsigned int idx = 0; idx < outputNumDims; ++idx)
{
numOutputs *= boost::numeric_cast<size_t>(outputDims[idx]);
}
std::vector<float> tempSum(numOutputs);
for (size_t idx = 0; idx < numOutputs; ++idx)
{
outputData[idx] = 0.0f;
tempSum[idx] = 0.0f;
}
// Initialise temp index.
std::vector<unsigned int> tempIndex(inputNumDims);
for (unsigned int idx = 0; idx < inputNumDims; ++idx)
{
tempIndex[idx] = 0;
}
std::vector<unsigned int> resolvedAxis = axis;
if (resolvedAxis.empty())
{
for (unsigned int idx = 0; idx < inputNumDims; ++idx)
{
resolvedAxis.push_back(idx);
}
}
unsigned int numResolvedAxis = boost::numeric_cast<unsigned int>(resolvedAxis.size());
// Iterates through input_data and sum up the reduced axis.
for (bool hasNext = true; hasNext; hasNext = NextIndex(inputNumDims, inputDims, tempIndex))
{
size_t inputOffset = ReducedOutputOffset(inputNumDims, inputDims, tempIndex, 0, {});
size_t outputOffset = ReducedOutputOffset(inputNumDims, inputDims, tempIndex,
numResolvedAxis, resolvedAxis);
tempSum[outputOffset] += inputData[inputOffset];
}
// Takes average by num of elements added to get mean.
size_t numElementsInAxis = 1;
for (unsigned int idx = 0; idx < numResolvedAxis; ++idx)
{
size_t current = boost::numeric_cast<size_t>(inputDims[resolvedAxis[idx]]);
BOOST_ASSERT(boost::numeric_cast<float>(current) <
(std::numeric_limits<float>::max() / boost::numeric_cast<float>(numElementsInAxis)));
numElementsInAxis *= current;
}
if (numElementsInAxis > 0) {
for (size_t idx = 0; idx < numOutputs; ++idx)
{
outputData[idx] = tempSum[idx] / boost::numeric_cast<float>(numElementsInAxis);
}
}
}
} //namespace armnn
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