narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include "Mean.hpp" |
| 7 | #include "backends/WorkloadData.hpp" |
| 8 | |
| 9 | #include <boost/numeric/conversion/cast.hpp> |
| 10 | |
| 11 | #include <cmath> |
| 12 | #include <cstddef> |
| 13 | #include <functional> |
| 14 | #include <limits> |
| 15 | |
| 16 | namespace armnn |
| 17 | { |
| 18 | bool NextIndex(const unsigned int numDims, const armnn::TensorShape& dims, std::vector<unsigned int>& current) |
| 19 | { |
| 20 | unsigned int carry = 1; |
| 21 | |
| 22 | for (unsigned int idx = numDims; idx-- > 0; ) |
| 23 | { |
| 24 | unsigned int current_val = current[idx] + carry; |
| 25 | if (dims[idx] == current_val) |
| 26 | { |
| 27 | current[idx] = 0; |
| 28 | } |
| 29 | else |
| 30 | { |
| 31 | current[idx] = current_val; |
| 32 | carry = 0; |
| 33 | break; |
| 34 | } |
| 35 | } |
| 36 | return (carry == 0); |
| 37 | } |
| 38 | |
| 39 | std::size_t ReducedOutputOffset(const unsigned int numDims, const armnn::TensorShape& dims, |
| 40 | std::vector<unsigned int>& index, const unsigned int numAxis, |
| 41 | const std::vector<unsigned int>& axis) { |
| 42 | std::size_t offset = 0; |
| 43 | for (unsigned int idx = 0; idx < numDims; ++idx) |
| 44 | { |
| 45 | bool isAxis = false; |
| 46 | if (!axis.empty()) |
| 47 | { |
| 48 | for (unsigned int axisIdx = 0; axisIdx < numAxis; ++axisIdx) |
| 49 | { |
| 50 | if (idx == axis[axisIdx]) |
| 51 | { |
| 52 | isAxis = true; |
| 53 | break; |
| 54 | } |
| 55 | } |
| 56 | } |
| 57 | if (!isAxis) |
| 58 | { |
| 59 | offset = offset * boost::numeric_cast<size_t>(dims[idx]) + boost::numeric_cast<size_t>(index[idx]); |
| 60 | } |
| 61 | } |
| 62 | return offset; |
| 63 | } |
| 64 | } // namespace |
| 65 | |
| 66 | namespace armnn |
| 67 | { |
| 68 | void Mean(const armnn::TensorInfo& inputInfo, |
| 69 | const armnn::TensorInfo& outputInfo, |
| 70 | const std::vector<unsigned int>& axis, |
| 71 | const float* inputData, |
| 72 | float* outputData) { |
| 73 | |
| 74 | unsigned int inputNumDims = inputInfo.GetNumDimensions(); |
| 75 | unsigned int outputNumDims = outputInfo.GetNumDimensions(); |
| 76 | |
| 77 | armnn::TensorShape outputDims = outputInfo.GetShape(); |
| 78 | armnn::TensorShape inputDims = inputInfo.GetShape(); |
| 79 | |
| 80 | // Initialise output data. |
| 81 | size_t numOutputs = 1; |
| 82 | for (unsigned int idx = 0; idx < outputNumDims; ++idx) |
| 83 | { |
| 84 | numOutputs *= boost::numeric_cast<size_t>(outputDims[idx]); |
| 85 | } |
| 86 | |
| 87 | std::vector<float> tempSum(numOutputs); |
| 88 | for (size_t idx = 0; idx < numOutputs; ++idx) |
| 89 | { |
| 90 | outputData[idx] = 0.0f; |
| 91 | tempSum[idx] = 0.0f; |
| 92 | } |
| 93 | |
| 94 | // Initialise temp index. |
| 95 | std::vector<unsigned int> tempIndex(inputNumDims); |
| 96 | for (unsigned int idx = 0; idx < inputNumDims; ++idx) |
| 97 | { |
| 98 | tempIndex[idx] = 0; |
| 99 | } |
| 100 | |
| 101 | std::vector<unsigned int> resolvedAxis = axis; |
| 102 | if (resolvedAxis.empty()) |
| 103 | { |
| 104 | for (unsigned int idx = 0; idx < inputNumDims; ++idx) |
| 105 | { |
| 106 | resolvedAxis.push_back(idx); |
| 107 | } |
| 108 | } |
| 109 | unsigned int numResolvedAxis = boost::numeric_cast<unsigned int>(resolvedAxis.size()); |
| 110 | |
| 111 | // Iterates through input_data and sum up the reduced axis. |
| 112 | for (bool hasNext = true; hasNext; hasNext = NextIndex(inputNumDims, inputDims, tempIndex)) |
| 113 | { |
| 114 | size_t inputOffset = ReducedOutputOffset(inputNumDims, inputDims, tempIndex, 0, {}); |
| 115 | size_t outputOffset = ReducedOutputOffset(inputNumDims, inputDims, tempIndex, |
| 116 | numResolvedAxis, resolvedAxis); |
| 117 | tempSum[outputOffset] += inputData[inputOffset]; |
| 118 | } |
| 119 | |
| 120 | // Takes average by num of elements added to get mean. |
| 121 | size_t numElementsInAxis = 1; |
| 122 | for (unsigned int idx = 0; idx < numResolvedAxis; ++idx) |
| 123 | { |
| 124 | size_t current = boost::numeric_cast<size_t>(inputDims[resolvedAxis[idx]]); |
| 125 | BOOST_ASSERT(boost::numeric_cast<float>(current) < |
| 126 | (std::numeric_limits<float>::max() / boost::numeric_cast<float>(numElementsInAxis))); |
| 127 | numElementsInAxis *= current; |
| 128 | } |
| 129 | if (numElementsInAxis > 0) { |
| 130 | for (size_t idx = 0; idx < numOutputs; ++idx) |
| 131 | { |
| 132 | outputData[idx] = tempSum[idx] / boost::numeric_cast<float>(numElementsInAxis); |
| 133 | } |
| 134 | } |
| 135 | } |
| 136 | } //namespace armnn |