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Diffstat (limited to 'src/backends/reference/workloads/Reduce.cpp')
| -rw-r--r-- | src/backends/reference/workloads/Reduce.cpp | 151 |
1 files changed, 151 insertions, 0 deletions
diff --git a/src/backends/reference/workloads/Reduce.cpp b/src/backends/reference/workloads/Reduce.cpp new file mode 100644 index 0000000000..5375c7163a --- /dev/null +++ b/src/backends/reference/workloads/Reduce.cpp @@ -0,0 +1,151 @@ +// +// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "Reduce.hpp" + +#include <armnn/utility/NumericCast.hpp> + +#include <backendsCommon/WorkloadData.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); +} + +unsigned int ReducedOutputOffset(const unsigned int numDims, + const armnn::TensorShape& dims, + std::vector<unsigned int>& index, + const unsigned int numAxis, + const std::vector<unsigned int>& axis) +{ + unsigned int 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 * dims[idx] + index[idx]; + } + } + return offset; +} + + +void Reduce(const TensorInfo& inputInfo, + const TensorInfo& outputInfo, + Decoder<float>& input, + Encoder<float>& output, + const std::vector<uint32_t> axis, + const ReduceOperation reduceOperation) +{ + unsigned int inputNumDims = inputInfo.GetNumDimensions(); + unsigned int outputNumDims = outputInfo.GetNumDimensions(); + + armnn::TensorShape outputDims = outputInfo.GetShape(); + armnn::TensorShape inputDims = inputInfo.GetShape(); + + // Initialise output data. + unsigned int numOutputs = 1; + for (unsigned int idx = 0; idx < outputNumDims; ++idx) + { + numOutputs *= outputDims[idx]; + } + + std::vector<float> tempSum(numOutputs); + for (unsigned int idx = 0; idx < numOutputs; ++idx) + { + output[idx]; + output.Set(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); + } + } + auto numResolvedAxis = armnn::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)) + { + unsigned int inputOffset = ReducedOutputOffset(inputNumDims, inputDims, tempIndex, 0, {}); + unsigned int outputOffset = ReducedOutputOffset(inputNumDims, inputDims, tempIndex, + numResolvedAxis, resolvedAxis); + input[inputOffset]; + tempSum[outputOffset] += input.Get(); + } + + // Takes average by num of elements added to get mean. + size_t numElementsInAxis = 1; + for (unsigned int idx = 0; idx < numResolvedAxis; ++idx) + { + unsigned int current = inputDims[resolvedAxis[idx]]; + ARMNN_ASSERT(armnn::numeric_cast<float>(current) < + (std::numeric_limits<float>::max() / armnn::numeric_cast<float>(numElementsInAxis))); + numElementsInAxis *= current; + } + if (numElementsInAxis > 0) { + for (unsigned int idx = 0; idx < numOutputs; ++idx) + { + output[idx]; + if (reduceOperation == ReduceOperation::Sum) + { + output.Set(tempSum[idx]); + } + else if (reduceOperation == ReduceOperation::Mean) + { + output.Set(tempSum[idx] / armnn::numeric_cast<float>(numElementsInAxis)); + } + } + } +} + +} //namespace armnn
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