public class Pad extends DynamicCustomOp
Modifier and Type | Class and Description |
---|---|
static class |
Pad.Mode |
DynamicCustomOp.DynamicCustomOpsBuilder
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue
Constructor and Description |
---|
Pad() |
Pad(@NonNull INDArray in,
@NonNull INDArray padding,
double padValue) |
Pad(@NonNull INDArray in,
@NonNull INDArray padding,
INDArray out,
@NonNull Pad.Mode mode,
double padValue) |
Pad(@NonNull INDArray in,
@NonNull INDArray padding,
INDArray out,
@NonNull PadMode mode,
double padValue) |
Pad(@NonNull INDArray in,
@NonNull INDArray padding,
@NonNull PadMode mode,
double padValue) |
Pad(SameDiff sd,
SDVariable in,
SDVariable padding,
double padValue) |
Pad(SameDiff sd,
SDVariable in,
SDVariable padding,
Pad.Mode mode,
double padValue) |
Pad(SameDiff sd,
SDVariable in,
SDVariable padding,
PadMode mode,
double padValue) |
Modifier and Type | Method and Description |
---|---|
List<DataType> |
calculateOutputDataTypes(List<DataType> inputDataTypes)
Calculate the data types for the output arrays.
|
List<SDVariable> |
doDiff(List<SDVariable> i_v)
The actual implementation for automatic differentiation.
|
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[] |
tensorflowNames()
The opName of this function tensorflow
|
addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, dArgs, 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, tensorflowName, toString, wrapFilterNull, wrapOrNull, wrapOrNull
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
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
isInplaceCall
public Pad()
public Pad(SameDiff sd, SDVariable in, SDVariable padding, PadMode mode, double padValue)
public Pad(SameDiff sd, SDVariable in, SDVariable padding, Pad.Mode mode, double padValue)
public Pad(SameDiff sd, SDVariable in, SDVariable padding, double padValue)
public Pad(@NonNull @NonNull INDArray in, @NonNull @NonNull INDArray padding, @NonNull @NonNull PadMode mode, double padValue)
public Pad(@NonNull @NonNull INDArray in, @NonNull @NonNull INDArray padding, INDArray out, @NonNull @NonNull Pad.Mode mode, double padValue)
public String opName()
DynamicCustomOp
opName
in interface CustomOp
opName
in class DynamicCustomOp
public String[] tensorflowNames()
DifferentialFunction
tensorflowNames
in class DifferentialFunction
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph)
DifferentialFunction
NodeDef
initFromTensorFlow
in class DynamicCustomOp
public List<SDVariable> doDiff(List<SDVariable> i_v)
DifferentialFunction
doDiff
in class DynamicCustomOp
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes)
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
inputDataTypes
- The data types of the inputsCopyright © 2021. All rights reserved.