Package org.nd4j.linalg.api.ops.impl.layers.convolution

Class Upsampling2d

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.DynamicCustomOp

org.nd4j.linalg.api.ops.impl.layers.convolution.Upsampling2d

All Implemented Interfaces:

CustomOp

public class Upsampling2d
extends DynamicCustomOp

Upsampling operation

Nested Class Summary

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

DynamicCustomOp.DynamicCustomOpsBuilder

Field Summary

Fields

Modifier and Type	Field	Description
protected boolean	nchw
protected int	scaleH
protected int	scaleW

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
Upsampling2d(SameDiff sameDiff, SDVariable input, boolean nchw, int scaleH, int scaleW)
Upsampling2d(SameDiff sameDiff, SDVariable input, int scale)
Upsampling2d(SameDiff sameDiff, SDVariable input, int scaleH, int scaleW, boolean nchw)
Upsampling2d(INDArray input, int scale)
Upsampling2d(INDArray input, int scaleH, int scaleW, boolean nchw)

Method Summary

Modifier and Type	Method	Description
List<DataType>	calculateOutputDataTypes(List<DataType> inputDataTypes)	Calculate the data types for the output arrays.
void	configureFromArguments()	This allows a custom op to configure relevant fields from its arguments.
List<SDVariable>	doDiff(List<SDVariable> f1)	The actual implementation for automatic differentiation.
String	onnxName()	The opName of this function in onnx
String	opName()	This method returns op opName as string
void	setPropertiesForFunction(Map<String,Object> properties)
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, dArgs, generateFake, generateFake, 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, 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

Field Detail

nchw

protected boolean nchw

scaleH

protected int scaleH

scaleW

protected int scaleW

Constructor Detail

Upsampling2d

public Upsampling2d(SameDiff sameDiff,
                    SDVariable input,
                    boolean nchw,
                    int scaleH,
                    int scaleW)

Upsampling2d

public Upsampling2d(SameDiff sameDiff,
                    SDVariable input,
                    int scaleH,
                    int scaleW,
                    boolean nchw)

Upsampling2d

public Upsampling2d(SameDiff sameDiff,
                    SDVariable input,
                    int scale)

Upsampling2d

public Upsampling2d(INDArray input,
                    int scale)

Upsampling2d

public Upsampling2d(INDArray input,
                    int scaleH,
                    int scaleW,
                    boolean nchw)

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:

configureFromArguments

public void configureFromArguments()

Description copied from interface: CustomOp

This allows a custom op to configure relevant fields from its arguments. This is needed when ops are created via reflection for things like model import.

Specified by:

configureFromArguments in interface CustomOp

Overrides:

configureFromArguments in class DynamicCustomOp

setPropertiesForFunction

public void setPropertiesForFunction(Map<String,Object> properties)

Overrides:

setPropertiesForFunction in class DynamicCustomOp

doDiff

public List<SDVariable> doDiff(List<SDVariable> f1)

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