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

Class Pad

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.DynamicCustomOp

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

All Implemented Interfaces:

CustomOp

public class Pad
extends DynamicCustomOp

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	Pad.Mode

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, tArguments

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

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

Constructor Summary

Constructors

Constructor and Description
Pad()
Pad(@NonNull INDArray in, @NonNull INDArray padding, double padValue)
Pad(@NonNull INDArray in, @NonNull INDArray padding, INDArray out, @NonNull Pad.Mode mode, double padValue)
Pad(@NonNull INDArray in, @NonNull INDArray padding, INDArray out, @NonNull PadMode mode, double padValue)
Pad(@NonNull INDArray in, @NonNull INDArray padding, @NonNull PadMode mode, double padValue)
Pad(SameDiff sd, SDVariable in, SDVariable padding, double padValue)
Pad(SameDiff sd, SDVariable in, SDVariable padding, Pad.Mode mode, double padValue)
Pad(SameDiff sd, SDVariable in, SDVariable padding, PadMode mode, double padValue)

Method Summary

Modifier and Type	Method and Description
List<DataType>	calculateOutputDataTypes(List<DataType> inputDataTypes) Calculate the data types for the output arrays.
List<SDVariable>	doDiff(List<SDVariable> i_v) The actual implementation for automatic differentiation.
void	initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph) Initialize the function from the given NodeDef
String	opName() This method returns op opName as string
String[]	tensorflowNames() The opName of this function tensorflow

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

addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, dArgs, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, iArgs, initFromOnnx, inputArguments, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numTArguments, onnxName, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeTArgument, setInputArgument, setInputArguments, setOutputArgument, tArgs, tensorflowName, toString, wrapFilterNull, wrapOrNull, wrapOrNull

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

arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, equals, getNumOutputs, getValue, hashCode, isConfigProperties, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariablesNames, propertiesForFunction, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor

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

Pad

public Pad()

Pad

public Pad(SameDiff sd,
           SDVariable in,
           SDVariable padding,
           PadMode mode,
           double padValue)

Pad

public Pad(SameDiff sd,
           SDVariable in,
           SDVariable padding,
           Pad.Mode mode,
           double padValue)

Pad

public Pad(SameDiff sd,
           SDVariable in,
           SDVariable padding,
           double padValue)

Pad

public Pad(@NonNull
           @NonNull INDArray in,
           @NonNull
           @NonNull INDArray padding,
           double padValue)

Pad

public Pad(@NonNull
           @NonNull INDArray in,
           @NonNull
           @NonNull INDArray padding,
           @NonNull
           @NonNull PadMode mode,
           double padValue)

Pad

public Pad(@NonNull
           @NonNull INDArray in,
           @NonNull
           @NonNull INDArray padding,
           INDArray out,
           @NonNull
           @NonNull Pad.Mode mode,
           double padValue)

Pad

public Pad(@NonNull
           @NonNull INDArray in,
           @NonNull
           @NonNull INDArray padding,
           INDArray out,
           @NonNull
           @NonNull PadMode mode,
           double padValue)

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:

tensorflowNames

public String[] tensorflowNames()

Description copied from class: DifferentialFunction

The opName of this function tensorflow

Overrides:

tensorflowNames in class DifferentialFunction

Returns:

initFromTensorFlow

public void initFromTensorFlow(NodeDef nodeDef,
                               SameDiff initWith,
                               Map<String,AttrValue> attributesForNode,
                               GraphDef graph)

Description copied from class: DifferentialFunction

Initialize the function from the given NodeDef

Overrides:

initFromTensorFlow in class DynamicCustomOp

doDiff

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

Description copied from class: DifferentialFunction

The actual implementation for automatic differentiation.

Overrides:

doDiff in class DynamicCustomOp

Returns:

calculateOutputDataTypes

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

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 DifferentialFunction

Parameters:

inputDataTypes - The data types of the inputs

Returns:

The data types of the outputs