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//
// 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]);
const DataLayoutIndexed dataLayoutIndexed(data.m_Parameters.m_DataLayout);
const unsigned int channelsIndex = dataLayoutIndexed.GetChannelsIndex();
const unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex();
const unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex();
unsigned int depthMult = depthwise ? filterInfo.GetShape()[0] : 1;
unsigned int channelsInput = filterInfo.GetShape()[channelsIndex];
unsigned int channelsOutput = depthwise ? channelsInput * depthMult : filterInfo.GetShape()[0];
unsigned int batchSize = outputInfo0.GetShape()[0];
unsigned int heightOutput = outputInfo0.GetShape()[heightIndex];
unsigned int widthOutput = outputInfo0.GetShape()[widthIndex];
unsigned int heightInput = inputInfo0.GetShape()[heightIndex];
unsigned int widthInput = inputInfo0.GetShape()[widthIndex];
unsigned int heightFilter = filterInfo.GetShape()[heightIndex];
unsigned int widthFilter = filterInfo.GetShape()[widthIndex];
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
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