DynamicCustomOp.DynamicCustomOpsBuilder
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue
Constructor and Description |
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Conv3DDerivative() |
Conv3DDerivative(SameDiff sameDiff,
SDVariable[] inputFunctions,
Conv3DConfig conv3DConfig) |
Modifier and Type | Method and Description |
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List<DataType> |
calculateOutputDataTypes(List<DataType> inputDataTypes)
Calculate the data types for the output arrays.
|
List<SDVariable> |
doDiff(List<SDVariable> f1)
The actual implementation for automatic differentiation.
|
int |
getNumOutputs() |
String |
onnxName()
The opName of this function in onnx
|
String[] |
onnxNames()
The opName of this function in onnx
|
String |
opName()
This method returns op opName as string
|
String |
tensorflowName()
The opName of this function tensorflow
|
String[] |
tensorflowNames()
The opName of this function tensorflow
|
attributeAdaptersForFunction, configFieldName, getValue, iArgs, initFromTensorFlow, isConfigProperties, mappingsForFunction, propertiesForFunction
addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, dArgs, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, initFromOnnx, inputArguments, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numTArguments, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeTArgument, setInputArgument, setInputArguments, setOutputArgument, tArgs, toString, wrapFilterNull, wrapOrNull, wrapOrNull
arg, arg, argNames, args, diff, dup, equals, hashCode, larg, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
isInplaceCall
public Conv3DDerivative()
public Conv3DDerivative(SameDiff sameDiff, SDVariable[] inputFunctions, Conv3DConfig conv3DConfig)
public String opName()
DynamicCustomOp
public String tensorflowName()
DifferentialFunction
tensorflowName
in class Conv3D
public String[] tensorflowNames()
DifferentialFunction
tensorflowNames
in class DifferentialFunction
public String onnxName()
DifferentialFunction
public String[] onnxNames()
DifferentialFunction
onnxNames
in class DifferentialFunction
public List<SDVariable> doDiff(List<SDVariable> f1)
DifferentialFunction
public int getNumOutputs()
getNumOutputs
in class DifferentialFunction
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 Conv3D
inputDataTypes
- The data types of the inputsCopyright © 2021. All rights reserved.