| // |
| // 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 |