org.nd4j.linalg.api.ops.custom

Class FakeQuantWithMinMaxVarsPerChannel

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.DynamicCustomOp

org.nd4j.linalg.api.ops.custom.FakeQuantWithMinMaxVarsPerChannel

All Implemented Interfaces:

CustomOp

public class FakeQuantWithMinMaxVarsPerChannel
extends DynamicCustomOp

Nested Class Summary

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

DynamicCustomOp.DynamicCustomOpsBuilder

Field Summary

Fields

Modifier and Type	Field and Description
protected boolean	narrowRange
protected int	numBits

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
FakeQuantWithMinMaxVarsPerChannel()
FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max)
FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max, boolean narrow)
FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max, int num_bits)
FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max, int num_bits, boolean narrow)
FakeQuantWithMinMaxVarsPerChannel(SameDiff sameDiff, SDVariable x, SDVariable min, SDVariable max, int num_bits, boolean narrow)

Method Summary

Modifier and Type	Method and Description
List<DataType>	calculateOutputDataTypes(List<DataType> inputDataTypes) Calculate the data types for the output arrays.
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	tensorflowName() 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, doDiff, 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, 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, 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

narrowRange

protected boolean narrowRange

numBits

protected int numBits

Constructor Detail

FakeQuantWithMinMaxVarsPerChannel

public FakeQuantWithMinMaxVarsPerChannel()

FakeQuantWithMinMaxVarsPerChannel

public FakeQuantWithMinMaxVarsPerChannel(INDArray x,
                                         INDArray min,
                                         INDArray max,
                                         int num_bits,
                                         boolean narrow)

FakeQuantWithMinMaxVarsPerChannel

public FakeQuantWithMinMaxVarsPerChannel(INDArray x,
                                         INDArray min,
                                         INDArray max,
                                         int num_bits)

FakeQuantWithMinMaxVarsPerChannel

public FakeQuantWithMinMaxVarsPerChannel(INDArray x,
                                         INDArray min,
                                         INDArray max,
                                         boolean narrow)

FakeQuantWithMinMaxVarsPerChannel

public FakeQuantWithMinMaxVarsPerChannel(INDArray x,
                                         INDArray min,
                                         INDArray max)

FakeQuantWithMinMaxVarsPerChannel

public FakeQuantWithMinMaxVarsPerChannel(SameDiff sameDiff,
                                         SDVariable x,
                                         SDVariable min,
                                         SDVariable max,
                                         int num_bits,
                                         boolean narrow)

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:

tensorflowName

public String tensorflowName()

Description copied from class: DifferentialFunction

The opName of this function tensorflow

Overrides:

tensorflowName in class DynamicCustomOp

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

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