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Diffstat (limited to 'src/backends/reference/workloads/Pooling3d.cpp')
| -rw-r--r-- | src/backends/reference/workloads/Pooling3d.cpp | 328 |
1 files changed, 328 insertions, 0 deletions
diff --git a/src/backends/reference/workloads/Pooling3d.cpp b/src/backends/reference/workloads/Pooling3d.cpp new file mode 100644 index 0000000000..3cae2a94b9 --- /dev/null +++ b/src/backends/reference/workloads/Pooling3d.cpp @@ -0,0 +1,328 @@ +// +// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "Pooling3d.hpp" + +#include <armnn/Exceptions.hpp> +#include <armnn/Types.hpp> + +#include <armnnUtils/DataLayoutIndexed.hpp> +#include <armnn/utility/NumericCast.hpp> + +#include <limits> +#include <algorithm> +#include <functional> + +namespace +{ + using PoolingAlgorithm = armnn::PoolingAlgorithm; + + float DefaultInitializer(PoolingAlgorithm algorithm) + { + switch (algorithm) + { + case PoolingAlgorithm::Max: + { + return std::numeric_limits<float>::lowest(); + } + case PoolingAlgorithm::Average: + case PoolingAlgorithm::L2: + { + return 0.0f; + } + default: + { + throw armnn::InvalidArgumentException("Unsupported pooling algorithm"); + } + } + } + + using Accumulator = std::function<void(float & accu, float value)>; + + Accumulator GetAccumulator(PoolingAlgorithm algorithm) + { + switch (algorithm) + { + case PoolingAlgorithm::Max: + { + return [](float & accu, float value) { + if (value > accu) { + accu = value; + } + }; + } + + case PoolingAlgorithm::Average: + { + return [](float & accu, float value) { + accu += value; + }; + } + + case PoolingAlgorithm::L2: + { + return [](float & accu, float value) { + accu += (value*value); + }; + } + + default: + { + throw armnn::InvalidArgumentException("Unsupported pooling algorithm"); + } + } + } + + using Executor = std::function<void(float & accumulated, float kernelSize)>; + + Executor GetExecutor(PoolingAlgorithm algorithm) + { + switch (algorithm) + { + case PoolingAlgorithm::Max: + { + return [](float & /*accumulated*/, float /*kernelSize*/) {}; + } + + case PoolingAlgorithm::Average: + { + return [](float & accumulated, float kernelSize) { + accumulated /= kernelSize; + }; + } + + case PoolingAlgorithm::L2: + { + return [](float & accumulated, float kernelSize) { + accumulated = sqrtf(accumulated / kernelSize); + }; + } + + default: + { + throw armnn::InvalidArgumentException("Unsupported pooling algorithm"); + } + } + } + + bool OnPaddingOnly(int start, int end, int maxRange) + { + if (end <= 0 || start > maxRange) + { + return true; + } + else + { + return false; + } + } + + + bool ClampRange(int & start, int & end, int maxRange) + { + if (start < 0 || end > maxRange) + { + start = std::min(std::max(start, 0), maxRange); + end = std::min(std::max(end, 0), maxRange); + return true; + } + else + { + return false; + } + } + + int CalculateIndex(int channels, int depth, int height, int width, + int n, int c, int z, int y, int x, + armnnUtils::DataLayoutIndexed dataLayout) { + switch (dataLayout.GetDataLayout()) + { + case armnn::DataLayout::NDHWC: + { + int outputIndex = n * depth * height * width * channels + + z * height * width * channels + + y * width * channels + + x * channels + + c; + return outputIndex; + } + case armnn::DataLayout::NCDHW: + { + int outputIndex = n * channels * depth * height * width + + c * depth * height * width + + z * height * width + + y * width + + x; + return outputIndex; + } + default: + { + throw armnn::InvalidArgumentException("Unsupported data layout."); + } + } + } +} + +using namespace armnnUtils; + +namespace armnn +{ +void Pooling3d(Decoder<float>& rInputDecoder, + Encoder<float>& rOutputEncoder, + const TensorInfo& inputInfo, + const TensorInfo& outputInfo, + const Pooling3dDescriptor& params) +{ + const DataLayoutIndexed dataLayout(params.m_DataLayout); + + auto channelsIndex = dataLayout.GetChannelsIndex(); + + auto depthIndex = dataLayout.GetDepthIndex(); + auto heightIndex = dataLayout.GetHeightIndex(); + auto widthIndex = dataLayout.GetWidthIndex(); + + const int batchSize = armnn::numeric_cast<int>(outputInfo.GetShape()[0]); + const int channels = armnn::numeric_cast<int>(outputInfo.GetShape()[channelsIndex]); + + const int depthOutput = armnn::numeric_cast<int>(outputInfo.GetShape()[depthIndex]); + const int heightOutput = armnn::numeric_cast<int>(outputInfo.GetShape()[heightIndex]); + const int widthOutput = armnn::numeric_cast<int>(outputInfo.GetShape()[widthIndex]); + + const int depthInput = armnn::numeric_cast<int>(inputInfo.GetShape()[depthIndex]); + const int heightInput = armnn::numeric_cast<int>(inputInfo.GetShape()[heightIndex]); + const int widthInput = armnn::numeric_cast<int>(inputInfo.GetShape()[widthIndex]); + + const int padLeft = armnn::numeric_cast<int>(params.m_PadLeft); + const int padRight = armnn::numeric_cast<int>(params.m_PadRight); + const int padTop = armnn::numeric_cast<int>(params.m_PadTop); + const int padBottom = armnn::numeric_cast<int>(params.m_PadBottom); + const int padFront = armnn::numeric_cast<int>(params.m_PadFront); + const int padBack = armnn::numeric_cast<int>(params.m_PadBack); + + const int strideX = armnn::numeric_cast<int>(params.m_StrideX); + const int strideY = armnn::numeric_cast<int>(params.m_StrideY); + const int strideZ = armnn::numeric_cast<int>(params.m_StrideZ); + + const int poolHeight = armnn::numeric_cast<int>(params.m_PoolHeight); + const int poolWidth = armnn::numeric_cast<int>(params.m_PoolWidth); + const int poolDepth = armnn::numeric_cast<int>(params.m_PoolDepth); + + float defaultInitializer = DefaultInitializer(params.m_PoolType); + Accumulator accumulate = GetAccumulator(params.m_PoolType); + Executor execute = GetExecutor(params.m_PoolType); + + // Check supported padding methods outside the loop to simplify + // the inner loop. + if (params.m_PaddingMethod != PaddingMethod::Exclude && + params.m_PaddingMethod != PaddingMethod::IgnoreValue) + { + throw armnn::InvalidArgumentException("Unsupported padding type"); + } + + const std::vector<float> decodedInputVec = rInputDecoder.DecodeTensor(inputInfo.GetShape()); + + for (int n = 0; n < batchSize; n++) + { + for (int c = 0; c < channels; c++) + { + for (int zOutput = 0; zOutput < depthOutput; zOutput++) + { + // Calculate values independent of the x and y axis + int dstart = (zOutput * strideZ) - padFront; + int dend = dstart + poolDepth; + // Clamp the pooling region inside the valid input area (which includes the padding). + // This is necessary because the final pooling in a row may overlap beyond the padding. + dend = std::min(dend, depthInput + padBack); + + int depth = dend - dstart; + bool dclamped = ClampRange(dstart, dend, depthInput); + int depthClamped = dend - dstart; + + for (int yOutput = 0; yOutput < heightOutput; yOutput++) + { + int hstart = (yOutput * strideY) - padTop; + int hend = hstart + poolHeight; + // Clamp the pooling region inside the valid input area (which includes the padding). + // This is necessary because the final pooling in a row may overlap beyond the padding. + hend = std::min(hend, heightInput + padBottom); + + int height = hend - hstart; + bool hclamped = ClampRange(hstart, hend, heightInput); + int heightClamped = hend - hstart; + + for (int xOutput = 0; xOutput < widthOutput; xOutput++) + { + int wstart = (xOutput * strideX) - padLeft; + int wend = wstart + poolWidth; + // Clamp the pooling region inside the valid input area (which includes the padding). + // This is necessary because the final pooling in a row may overlap beyond the padding. + wend = std::min(wend, widthInput + padRight); + + int width = wend - wstart; + bool wclamped = ClampRange(wstart, wend, widthInput); + int widthClamped = wend - wstart; + + float result = defaultInitializer; + float poolAreaSize = armnn::numeric_cast<float>(depth * height * width); + + // Special case: when the pooling kernel is over a padding region and the padding + // size is larger or equal to the kernel and the kernel only covers + // padding and no real values, then we initialize the result as zero + // by convention. This is because we need to choose a value here and + // all values we have are padding, which we ignore. + if (OnPaddingOnly(dstart, dend, depthInput) || + OnPaddingOnly(hstart, hend, heightInput) || + OnPaddingOnly(wstart, wend, widthInput)) + { + result = 0.0f; + + int outputIndex = CalculateIndex(channels, depthOutput, heightOutput, widthOutput, + n, c, zOutput, yOutput, xOutput, dataLayout); + + rOutputEncoder[static_cast<unsigned int>(outputIndex)]; + rOutputEncoder.Set(result); + + continue; + } + + bool clamped = (dclamped | hclamped | wclamped); + + if (clamped && params.m_PaddingMethod == PaddingMethod::Exclude) + { + // When we exclude the padding, it means we calculate with a smaller + // kernel size, so I changed the divisor here. + poolAreaSize = armnn::numeric_cast<float>(depthClamped * heightClamped * widthClamped); + } + + for (auto zInput = dstart; zInput < dend; zInput++) + { + for (auto yInput = hstart; yInput < hend; yInput++) + { + for (auto xInput = wstart; xInput < wend; xInput++) + { + + int inputIndex = CalculateIndex(channels, depthInput, heightInput, widthInput, + n, c, zInput, yInput, xInput, dataLayout); + + accumulate(result, decodedInputVec[static_cast<unsigned int>(inputIndex)]); + } + } + } + + execute(result, poolAreaSize); + + int outputIndex = CalculateIndex(channels, depthOutput, heightOutput, widthOutput, + n, c, zOutput, yOutput, xOutput, dataLayout); + + rOutputEncoder[static_cast<unsigned int>(outputIndex)]; + rOutputEncoder.Set(result); + } + } + } + } + } +} + +} //namespace armnn |