Package org.nd4j.linalg.api.ops.impl.transforms.custom

Class InvertPermutation

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.DynamicCustomOp

org.nd4j.linalg.api.ops.impl.transforms.BaseDynamicTransformOp

org.nd4j.linalg.api.ops.impl.transforms.custom.InvertPermutation

All Implemented Interfaces:

CustomOp

public class InvertPermutation
extends BaseDynamicTransformOp

Inverse of index permutation.

Author:

Max Pumperla

Nested Class Summary

Nested classes/interfaces inherited from class org.nd4j.linalg.api.ops.DynamicCustomOp

DynamicCustomOp.DynamicCustomOpsBuilder

Field Summary

Fields inherited from class org.nd4j.linalg.api.ops.DynamicCustomOp

axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, sArguments, tArguments

Fields inherited from class org.nd4j.autodiff.functions.DifferentialFunction

dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue

Constructor Summary

Constructors

Constructor	Description
InvertPermutation(SameDiff sameDiff, SDVariable input)
InvertPermutation(SameDiff sameDiff, SDVariable[] args, boolean inPlace)
InvertPermutation(SameDiff sameDiff, SDVariable input, boolean inPlace)
InvertPermutation(INDArray input)

Method Summary

Modifier and Type	Method	Description
List<DataType>	calculateOutputDataTypes(List<DataType> dataTypes)	Calculate the data types for the output arrays.
List<SDVariable>	doDiff(List<SDVariable> grad)	The actual implementation for automatic differentiation.
INDArray	generateFake(long... shape)	Generate fake data for DynamicCustomOp.computeArrays() of the given shape with the data type Nd4j.defaultFloatingPointType()
INDArray	generateFake(DataType dataType, long... shape)	Generate fake data for DynamicCustomOp.computeArrays() of the the given shape with the given data type
String	onnxName()	The opName of this function in onnx
String	opName()	This method returns op opName as string
String	tensorflowName()	The opName of this function tensorflow

Methods inherited from class org.nd4j.linalg.api.ops.DynamicCustomOp

addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addOutputsToOp, addSArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, computeArrays, configureFromArguments, dArgs, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getSArgument, getTArgument, getValue, iArgs, initFromOnnx, initFromTensorFlow, inputArguments, mappingsForFunction, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numSArguments, numTArguments, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, propertiesForFunction, removeIArgument, removeInputArgument, removeOutputArgument, removeSArgument, removeTArgument, sArgs, setInputArgument, setInputArguments, setOutputArgument, setPropertiesForFunction, setValueFor, tArgs, toString, wrapFilterNull, wrapOrNull, wrapOrNull

Methods inherited from class org.nd4j.autodiff.functions.DifferentialFunction

arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, configureWithSameDiff, diff, dup, equals, getBooleanFromProperty, getDoubleValueFromProperty, getIntValueFromProperty, getLongValueFromProperty, getNumOutputs, getStringFromProperty, hashCode, isConfigProperties, larg, onnxNames, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceId, tensorflowNames

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.nd4j.linalg.api.ops.CustomOp

isInplaceCall

Constructor Detail

InvertPermutation

public InvertPermutation(SameDiff sameDiff,
                         SDVariable input)

InvertPermutation

public InvertPermutation(SameDiff sameDiff,
                         SDVariable input,
                         boolean inPlace)

InvertPermutation

public InvertPermutation(INDArray input)

InvertPermutation

public InvertPermutation(SameDiff sameDiff,
                         SDVariable[] args,
                         boolean inPlace)

Method Detail

opName

public String opName()

Description copied from class: DynamicCustomOp

This method returns op opName as string

Specified by:

opName in interface CustomOp

Overrides:

opName in class DynamicCustomOp

Returns:

onnxName

public String onnxName()

Description copied from class: DifferentialFunction

The opName of this function in onnx

Overrides:

onnxName in class DynamicCustomOp

Returns:

tensorflowName

public String tensorflowName()

Description copied from class: DifferentialFunction

The opName of this function tensorflow

Overrides:

tensorflowName in class DynamicCustomOp

Returns:

generateFake

public INDArray generateFake(long... shape)

Description copied from class: DynamicCustomOp

Generate fake data for DynamicCustomOp.computeArrays() of the given shape with the data type Nd4j.defaultFloatingPointType()

Overrides:

generateFake in class DynamicCustomOp

Parameters:

shape - the shape to use

Returns:

the generated data

generateFake

public INDArray generateFake(DataType dataType,
                             long... shape)

Description copied from class: DynamicCustomOp

Generate fake data for DynamicCustomOp.computeArrays() of the the given shape with the given data type

Overrides:

generateFake in class DynamicCustomOp

Parameters:

dataType - the data type of the output array

shape - the shape to use

Returns:

the generated data

doDiff

public List<SDVariable> doDiff(List<SDVariable> grad)

Description copied from class: DifferentialFunction

The actual implementation for automatic differentiation.

Overrides:

doDiff in class DynamicCustomOp

Returns:

calculateOutputDataTypes

public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes)

Description copied from class: DifferentialFunction

Calculate the data types for the output arrays. Though datatypes can also be inferred from DifferentialFunction.calculateOutputShape(), this method differs in that it does not require the input arrays to be populated. This is important as it allows us to do greedy datatype inference for the entire net - even if arrays are not available.

Overrides:

calculateOutputDataTypes in class BaseDynamicTransformOp

Parameters:

dataTypes - The data types of the inputs

Returns:

The data types of the outputs