public class TensorArraySplit extends BaseTensorOp
DynamicCustomOp.DynamicCustomOpsBuilder, DynamicCustomOp.SameDiffBuilder
axis, bArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, sameDiff, scalarValue
Constructor and Description |
---|
TensorArraySplit() |
TensorArraySplit(SameDiff sameDiff,
SDVariable[] args) |
TensorArraySplit(String name,
SameDiff sameDiff,
SDVariable[] args) |
Modifier and Type | Method and Description |
---|---|
List<DataType> |
calculateOutputDataTypes(List<DataType> inputDataType)
Calculate the data types for the output arrays.
|
void |
initFromOnnx(OnnxProto3.NodeProto node,
SameDiff initWith,
Map<String,OnnxProto3.AttributeProto> attributesForNode,
OnnxProto3.GraphProto graph)
Iniitialize the function from the given
OnnxProto3.NodeProto |
String |
opName()
This method returns op opName as string
|
String |
tensorflowName()
The opName of this function tensorflow
|
String |
toString() |
calculateOutputShape, doDiff, getNumOutputs, initFromTensorFlow, onnxName, opType
addBArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, iArgs, inputArguments, numBArguments, numIArguments, numInputArguments, numOutputArguments, numTArguments, opHash, opNum, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeTArgument, sameDiffBuilder, setInputArgument, setInputArguments, setOutputArgument, tArgs
arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, equals, f, getValue, hashCode, isConfigProperties, larg, mappingsForFunction, onnxNames, outputVariable, outputVariablesNames, propertiesForFunction, rarg, resolvePropertiesFromSameDiffBeforeExecution, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNames
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
isInplaceCall
public TensorArraySplit(String name, SameDiff sameDiff, SDVariable[] args)
public TensorArraySplit(SameDiff sameDiff, SDVariable[] args)
public TensorArraySplit()
public String tensorflowName()
DifferentialFunction
tensorflowName
in class DynamicCustomOp
public String toString()
toString
in class BaseTensorOp
public String opName()
DynamicCustomOp
opName
in interface CustomOp
opName
in class DynamicCustomOp
public void initFromOnnx(OnnxProto3.NodeProto node, SameDiff initWith, Map<String,OnnxProto3.AttributeProto> attributesForNode, OnnxProto3.GraphProto graph)
DifferentialFunction
OnnxProto3.NodeProto
initFromOnnx
in class DynamicCustomOp
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataType)
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
inputDataType
- The data types of the inputsCopyright © 2019. All rights reserved.