public class DynamicPartitionBp extends DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilder
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, sameDiff, scalarValue
Constructor and Description |
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DynamicPartitionBp() |
DynamicPartitionBp(SameDiff sameDiff,
SDVariable input,
SDVariable partitions,
SDVariable[] gradsAtOutput,
int numPartitions) |
Modifier and Type | Method and Description |
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protected void |
addArgs() |
List<DataType> |
calculateOutputDataTypes(List<DataType> dataTypes)
Calculate the data types for the output arrays.
|
List<SDVariable> |
doDiff(List<SDVariable> i_v)
The actual implementation for automatic differentiation.
|
int |
getNumOutputs() |
String |
opName()
This method returns op opName as string
|
addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, dArgs, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, iArgs, initFromOnnx, initFromTensorFlow, 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
arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, equals, getValue, hashCode, isConfigProperties, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariablesNames, propertiesForFunction, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNames
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
isInplaceCall
public DynamicPartitionBp()
public DynamicPartitionBp(SameDiff sameDiff, SDVariable input, SDVariable partitions, SDVariable[] gradsAtOutput, int numPartitions)
public List<SDVariable> doDiff(List<SDVariable> i_v)
DifferentialFunction
doDiff
in class DynamicCustomOp
protected void addArgs()
public String opName()
DynamicCustomOp
opName
in interface CustomOp
opName
in class DynamicCustomOp
public int getNumOutputs()
getNumOutputs
in class DifferentialFunction
public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes)
DifferentialFunction
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.calculateOutputDataTypes
in class DifferentialFunction
dataTypes
- The data types of the inputsCopyright © 2020. All rights reserved.