org.apache.sysds.runtime.matrix.data

Class LibMatrixDNN

java.lang.Object

org.apache.sysds.runtime.matrix.data.LibMatrixDNN

public class LibMatrixDNN
extends Object

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	LibMatrixDNN.PoolingType

Constructor Summary

Constructors

Constructor and Description
LibMatrixDNN()

Method Summary

Modifier and Type	Method and Description
static void	addBias(double[] a, double[] bias, double biasMultiplier, int N, int K, int PQ)
static void	batchNorm2D(MatrixBlock image, MatrixBlock scale, MatrixBlock bias, MatrixBlock runningMean, MatrixBlock runningVar, String phase, double epsilon, double mu, MatrixBlock ret, MatrixBlock retRunningMean, MatrixBlock retRunningVar, MatrixBlock resultSaveMean, MatrixBlock resultSaveInvVariance)
static void	batchNorm2DBackward(MatrixBlock image, MatrixBlock dout, MatrixBlock scale, double epsilon, MatrixBlock resultSaveMean, MatrixBlock resultSaveInvVariance, MatrixBlock dX, MatrixBlock dScale, MatrixBlock dBias)
static void	biasAdd(MatrixBlock input, MatrixBlock bias, MatrixBlock outputBlock, int numThreads) Performs the operation corresponding to the DML script: ones = matrix(1, rows=1, cols=Hout*Wout) output = input + matrix(bias %*% ones, rows=1, cols=F*Hout*Wout) This operation is often followed by conv2d and hence we have introduced bias_add(input, bias) built-in function
static void	biasMultiply(MatrixBlock input, MatrixBlock bias, MatrixBlock outputBlock, int numThreads) Performs the operation corresponding to the DML script: ones = matrix(1, rows=1, cols=Hout*Wout) output = input * matrix(bias %*% ones, rows=1, cols=F*Hout*Wout) This operation is often followed by conv2d and hence we have introduced bias_multiply(input, bias) built-in function
static void	channelSums(MatrixBlock input, MatrixBlock outputBlock, int C, int HW) Perform channel sum operation
static void	conv2d(MatrixBlock input, MatrixBlock filter, MatrixBlock outputBlock, DnnParameters params) This method performs convolution (i.e.
static void	conv2dBackwardData(MatrixBlock filter, MatrixBlock dout, MatrixBlock outputBlock, DnnParameters params) This method computes the backpropogation errors for previous layer of convolution operation
static void	conv2dBackwardFilter(MatrixBlock input, MatrixBlock dout, MatrixBlock outputBlock, DnnParameters params) This method computes the backpropogation errors for filter of convolution operation
static void	multBias(double[] a, double[] bias, int N, int K, int PQ)
static void	pooling(MatrixBlock input, MatrixBlock output, DnnParameters params, LibMatrixDNN.PoolingType poolType)
static void	poolingBackward(MatrixBlock input, MatrixBlock dout, MatrixBlock outputBlock, DnnParameters params, boolean performReluBackward, LibMatrixDNN.PoolingType poolType) This method computes the backpropogation errors for previous layer of pooling operation
static void	reluBackward(MatrixBlock input, MatrixBlock dout, MatrixBlock outputBlock, int numThreads) This method computes the backpropagation errors for previous layer of relu operation
static void	resetStatistics()

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

LibMatrixDNN

public LibMatrixDNN()

Method Detail

resetStatistics

public static void resetStatistics()

conv2d

public static void conv2d(MatrixBlock input,
                          MatrixBlock filter,
                          MatrixBlock outputBlock,
                          DnnParameters params)

This method performs convolution (i.e. cross-correlation) operation on input

Parameters:

input - input batch

filter - filter

outputBlock - output of convolution

params - convolution parameters

conv2dBackwardData

public static void conv2dBackwardData(MatrixBlock filter,
                                      MatrixBlock dout,
                                      MatrixBlock outputBlock,
                                      DnnParameters params)

This method computes the backpropogation errors for previous layer of convolution operation

Parameters:

filter - filter used in conv2d

dout - errors from next layer

outputBlock - output errors

params - convolution parameters

conv2dBackwardFilter

public static void conv2dBackwardFilter(MatrixBlock input,
                                        MatrixBlock dout,
                                        MatrixBlock outputBlock,
                                        DnnParameters params)

This method computes the backpropogation errors for filter of convolution operation

Parameters:

input - input image

dout - errors from next layer

outputBlock - output errors

params - convolution parameters

pooling

public static void pooling(MatrixBlock input,
                           MatrixBlock output,
                           DnnParameters params,
                           LibMatrixDNN.PoolingType poolType)

poolingBackward

public static void poolingBackward(MatrixBlock input,
                                   MatrixBlock dout,
                                   MatrixBlock outputBlock,
                                   DnnParameters params,
                                   boolean performReluBackward,
                                   LibMatrixDNN.PoolingType poolType)

This method computes the backpropogation errors for previous layer of pooling operation

Parameters:

input - input matrix

dout - dout matrix

outputBlock - output matrix

params - convolution parameters

performReluBackward - perform ReLU backward

poolType - type of pooling

reluBackward

public static void reluBackward(MatrixBlock input,
                                MatrixBlock dout,
                                MatrixBlock outputBlock,
                                int numThreads)

This method computes the backpropagation errors for previous layer of relu operation

Parameters:

input - input matrix

dout - errors from next layer

outputBlock - output matrix

numThreads - number of threads

biasAdd

public static void biasAdd(MatrixBlock input,
                           MatrixBlock bias,
                           MatrixBlock outputBlock,
                           int numThreads)

Performs the operation corresponding to the DML script: ones = matrix(1, rows=1, cols=Hout*Wout) output = input + matrix(bias %*% ones, rows=1, cols=F*Hout*Wout) This operation is often followed by conv2d and hence we have introduced bias_add(input, bias) built-in function

Parameters:

input - input matrix

bias - bias matrix

outputBlock - output matrix

numThreads - number of threads

channelSums

public static void channelSums(MatrixBlock input,
                               MatrixBlock outputBlock,
                               int C,
                               int HW)

Perform channel sum operation

Parameters:

input - input matrix block

outputBlock - output matrix block

C - number of channels

HW - height X width

batchNorm2DBackward

public static void batchNorm2DBackward(MatrixBlock image,
                                       MatrixBlock dout,
                                       MatrixBlock scale,
                                       double epsilon,
                                       MatrixBlock resultSaveMean,
                                       MatrixBlock resultSaveInvVariance,
                                       MatrixBlock dX,
                                       MatrixBlock dScale,
                                       MatrixBlock dBias)

batchNorm2D

public static void batchNorm2D(MatrixBlock image,
                               MatrixBlock scale,
                               MatrixBlock bias,
                               MatrixBlock runningMean,
                               MatrixBlock runningVar,
                               String phase,
                               double epsilon,
                               double mu,
                               MatrixBlock ret,
                               MatrixBlock retRunningMean,
                               MatrixBlock retRunningVar,
                               MatrixBlock resultSaveMean,
                               MatrixBlock resultSaveInvVariance)

addBias

public static void addBias(double[] a,
                           double[] bias,
                           double biasMultiplier,
                           int N,
                           int K,
                           int PQ)

multBias

public static void multBias(double[] a,
                            double[] bias,
                            int N,
                            int K,
                            int PQ)

biasMultiply

public static void biasMultiply(MatrixBlock input,
                                MatrixBlock bias,
                                MatrixBlock outputBlock,
                                int numThreads)

Performs the operation corresponding to the DML script: ones = matrix(1, rows=1, cols=Hout*Wout) output = input * matrix(bias %*% ones, rows=1, cols=F*Hout*Wout) This operation is often followed by conv2d and hence we have introduced bias_multiply(input, bias) built-in function

Parameters:

input - input matrix

bias - bias matrix

outputBlock - output matrix

numThreads - number of threads