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Diffstat (limited to 'src/armnn/backends/RefWorkloads/ConvImpl.hpp')
| -rw-r--r-- | src/armnn/backends/RefWorkloads/ConvImpl.hpp | 187 |
1 files changed, 0 insertions, 187 deletions
diff --git a/src/armnn/backends/RefWorkloads/ConvImpl.hpp b/src/armnn/backends/RefWorkloads/ConvImpl.hpp deleted file mode 100644 index 4c9ab2a644..0000000000 --- a/src/armnn/backends/RefWorkloads/ConvImpl.hpp +++ /dev/null @@ -1,187 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "RefWorkloadUtils.hpp" - -#include <armnn/Tensor.hpp> - -#include <boost/assert.hpp> -#include <boost/numeric/conversion/cast.hpp> - -#include <cmath> -#include <limits> - -namespace armnn -{ - -/// Performs multiplication of an integer with a multiplier which is less than one, -/// using quantized integer arithmetic which is consistent with AndroidNN's CPU executor. -struct QuantizedMultiplierSmallerThanOne -{ -public: - /// Constructs a QuantizedMultiplierSmallerThanOne which will multiply by the given multiplier. - /// This stores the appropriate integer quantities (derived from the given multiplier) for later use. - /// The implementation of this function is adapted from Android NN's QuantizeMultiplierSmallerThanOne(). - QuantizedMultiplierSmallerThanOne(float multiplier); - - /// The implementation of this function is adapted from Android NN's MultiplyByQuantizedMultiplierSmallerThanOne(). - int32_t operator*(int32_t rhs) const; - -private: - /// The implementation of this function is adapted from gemmlowp's SaturatingRoundingDoublingHighMul(). - static int32_t SaturatingRoundingDoublingHighMul(int32_t a, int32_t b); - - /// The implementation of this function is adapted from gemmlowp's RoundingDivideByPOT(). - static int32_t RoundingDivideByPOT(int32_t x, int exponent); - - int32_t m_Multiplier; - int32_t m_RightShift; -}; - -/// An implementation shared by normal and depthwise convolution. -template<typename ConvData, typename InputType, typename BiasType, typename AccumulatorType> -static void ConvImpl(ConvData data, - const InputType* inputData, - float inputScale, - int32_t inputOffset, - const InputType* filterData, - float filterScale, - int32_t filterOffset, - const BiasType* biasData, - InputType* outputData, - float outputScale, - int32_t outputOffset, - const TensorInfo& filterInfo, - bool depthwise = false) -{ - if (data.m_Parameters.m_BiasEnabled && !biasData) - { - throw InvalidArgumentException("Bias is enabled but the bias data is invalid"); - } - - const TensorInfo& inputInfo0 = GetTensorInfo(data.m_Inputs[0]); - const TensorInfo& outputInfo0 = GetTensorInfo(data.m_Outputs[0]); - - unsigned int depthMult = depthwise ? filterInfo.GetShape()[0] : 1; - unsigned int channelsInput = filterInfo.GetShape()[1]; - unsigned int channelsOutput = depthwise ? channelsInput * depthMult : filterInfo.GetShape()[0]; - - unsigned int batchSize = outputInfo0.GetShape()[0]; - unsigned int heightOutput = outputInfo0.GetShape()[2]; - unsigned int widthOutput = outputInfo0.GetShape()[3]; - unsigned int heightInput = inputInfo0.GetShape()[2]; - unsigned int widthInput = inputInfo0.GetShape()[3]; - - unsigned int heightFilter = filterInfo.GetShape()[2]; - unsigned int widthFilter = filterInfo.GetShape()[3]; - - unsigned int paddingTop = data.m_Parameters.m_PadTop; - unsigned int paddingLeft = data.m_Parameters.m_PadLeft; - unsigned int hStride = data.m_Parameters.m_StrideY; - unsigned int xStride = data.m_Parameters.m_StrideX; - - // The world's least efficient convolution. - for (unsigned int batchIdx = 0; batchIdx < batchSize; batchIdx++) - { - for (unsigned int cOutput = 0; cOutput < channelsOutput; cOutput++) - { - for (unsigned int yOutput = 0; yOutput < heightOutput; yOutput++) - { - for (unsigned int xOutput = 0; xOutput < widthOutput; xOutput++) - { - // This loop goes over each output element. - AccumulatorType sum = AccumulatorType(); - - // For depthwise, each output channel corresponds to exactly one input channel. - // For normal, must loop over each input channel. - for (unsigned int cInput = 0; cInput < (depthwise ? 1 : channelsInput); cInput++) - { - unsigned int depthwiseMultiplierIdx = 0; - if (depthwise) - { - cInput = cOutput / depthMult; - depthwiseMultiplierIdx = cOutput % depthMult; - } - - for (unsigned int yFilter = 0; yFilter < heightFilter; yFilter++) - { - for (unsigned int xFilter = 0; xFilter < widthFilter; xFilter++) - { - // This loop goes over each input element for each output element. - - unsigned int filterIndex; - - // Since dimensionality of kernel depends on depthwiseness, so does index. - if (depthwise) - { - filterIndex = depthwiseMultiplierIdx * widthFilter * heightFilter * channelsInput + - cInput * widthFilter * heightFilter + - yFilter * widthFilter + - xFilter; - } - else - { - filterIndex = cOutput * widthFilter * heightFilter * channelsInput + - cInput * widthFilter * heightFilter + - yFilter * widthFilter + - xFilter; - } - AccumulatorType filterValue = filterData[filterIndex] - - boost::numeric_cast<AccumulatorType>(filterOffset); - - unsigned int yInput = yOutput * hStride + yFilter; - unsigned int xInput = xOutput * xStride + xFilter; - - AccumulatorType inputValue; - - // Check if we're in the padding. - if (yInput < paddingTop || yInput >= heightInput + paddingTop || - xInput < paddingLeft || xInput >= widthInput + paddingLeft ) - { - inputValue = AccumulatorType(); - } - else - { - inputValue = inputData[batchIdx * widthInput * heightInput * channelsInput + - widthInput * heightInput * cInput + - widthInput * (yInput - paddingTop) + - xInput - paddingLeft] - - boost::numeric_cast<AccumulatorType>(inputOffset); - } - sum += filterValue * inputValue; - } - } - } - - if (data.m_Parameters.m_BiasEnabled) - { - sum += biasData[cOutput]; - } - - if (outputScale != 0.0f) - { - float multiplier = (inputScale * filterScale) / outputScale; - // Apply the multiplier to sum, but do so using some quantized arithmetic which is consistent - // with the AndroidNN CPU implementation. This should be (roughly) equivalent to: - // sum = std::round(multiplier * sum + outputOffset); - sum = boost::numeric_cast<AccumulatorType>( - QuantizedMultiplierSmallerThanOne(multiplier) * boost::numeric_cast<int32_t>(sum)) - + boost::numeric_cast<AccumulatorType>(outputOffset); - sum = std::min<AccumulatorType>(std::max<AccumulatorType>(sum, 0), 255); - } - - outputData[batchIdx * widthOutput * heightOutput * channelsOutput + - widthOutput * heightOutput * cOutput + - widthOutput * yOutput + - xOutput] = boost::numeric_cast<InputType>(sum); - } - } - } - } -} - -} //namespace armnn |