public class SplitV extends DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilderaxis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, sArguments, tArgumentsdimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue| Constructor and Description |
|---|
SplitV() |
SplitV(INDArray input,
INDArray sizes,
int numSplit,
int splitDim) |
SplitV(SameDiff sd,
SDVariable input,
SDVariable sizes,
int numSplit,
int splitDim) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
calculateOutputDataTypes(List<DataType> dataTypes)
Calculate the data types for the output arrays.
|
void |
configureFromArguments()
This allows a custom op to configure relevant fields from its arguments.
|
void |
configureWithSameDiff(SameDiff sameDiff) |
int |
getNumOutputs() |
void |
initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph)
Initialize the function from the given
NodeDef |
Map<String,Map<String,PropertyMapping>> |
mappingsForFunction()
Returns the mappings for a given function (
for tensorflow and onnx import mapping properties
of this function).
|
String |
opName()
This method returns op opName as string
|
Map<String,Object> |
propertiesForFunction()
Returns the properties for a given function
|
void |
setPropertiesForFunction(Map<String,Object> properties) |
String |
tensorflowName()
The opName of this function tensorflow
|
addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addSArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, computeArrays, dArgs, doDiff, generateFake, generateFake, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getSArgument, getTArgument, getValue, iArgs, initFromOnnx, inputArguments, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numSArguments, numTArguments, onnxName, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeSArgument, removeTArgument, sArgs, setInputArgument, setInputArguments, setOutputArgument, setValueFor, tArgs, toString, wrapFilterNull, wrapOrNull, wrapOrNullarg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, equals, getBooleanFromProperty, getDoubleValueFromProperty, getIntValueFromProperty, getLongValueFromProperty, getStringFromProperty, hashCode, isConfigProperties, larg, onnxNames, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceId, tensorflowNamesclone, finalize, getClass, notify, notifyAll, wait, wait, waitisInplaceCallpublic SplitV()
public SplitV(SameDiff sd, SDVariable input, SDVariable sizes, int numSplit, int splitDim)
public String opName()
DynamicCustomOpopName in interface CustomOpopName in class DynamicCustomOppublic String tensorflowName()
DifferentialFunctiontensorflowName in class DynamicCustomOppublic void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph)
DifferentialFunctionNodeDefinitFromTensorFlow in class DynamicCustomOppublic Map<String,Object> propertiesForFunction()
DifferentialFunctionpropertiesForFunction in class DynamicCustomOppublic Map<String,Map<String,PropertyMapping>> mappingsForFunction()
DifferentialFunctionmappingsForFunction in class DynamicCustomOppublic void configureWithSameDiff(SameDiff sameDiff)
configureWithSameDiff in class DifferentialFunctionpublic void configureFromArguments()
CustomOpconfigureFromArguments in interface CustomOpconfigureFromArguments in class DynamicCustomOppublic void setPropertiesForFunction(Map<String,Object> properties)
setPropertiesForFunction in class DynamicCustomOppublic int getNumOutputs()
getNumOutputs in class DifferentialFunctionpublic List<DataType> calculateOutputDataTypes(List<DataType> dataTypes)
DifferentialFunctionDifferentialFunction.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 DifferentialFunctiondataTypes - The data types of the inputsCopyright © 2022. All rights reserved.