public class DeConv3DTF extends DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilder| Modifier and Type | Field and Description |
|---|---|
protected DeConv3DConfig |
config |
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArgumentsdimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue| Constructor and Description |
|---|
DeConv3DTF(@NonNull SameDiff sameDiff,
@NonNull SDVariable shape,
@NonNull SDVariable weights,
@NonNull SDVariable input,
@NonNull DeConv3DConfig config) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
calculateOutputDataTypes(List<DataType> inputDataTypes)
Calculate the data types for the output arrays.
|
String |
configFieldName()
Returns the name of the field to be used for looking up field names.
|
List<SDVariable> |
doDiff(List<SDVariable> f1)
The actual implementation for automatic differentiation.
|
Object |
getValue(Field property)
Get the value for a given property
for this function
|
long[] |
iArgs() |
void |
initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph)
Initialize the function from the given
NodeDef |
boolean |
isConfigProperties()
Returns true if the fields for this class should be looked up from a configuration class.
|
String |
opName()
This method returns op opName as string
|
Map<String,Object> |
propertiesForFunction()
Returns the properties for a given function
|
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, 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, wrapOrNullarg, arg, argNames, args, attributeAdaptersForFunction, diff, dup, equals, getNumOutputs, hashCode, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueForclone, finalize, getClass, notify, notifyAll, wait, wait, waitisInplaceCallprotected DeConv3DConfig config
public DeConv3DTF(@NonNull
@NonNull SameDiff sameDiff,
@NonNull
@NonNull SDVariable shape,
@NonNull
@NonNull SDVariable weights,
@NonNull
@NonNull SDVariable input,
@NonNull
@NonNull DeConv3DConfig config)
public long[] iArgs()
iArgs in interface CustomOpiArgs in class DynamicCustomOppublic Map<String,Object> propertiesForFunction()
DifferentialFunctionpropertiesForFunction in class DifferentialFunctionpublic boolean isConfigProperties()
DifferentialFunctionisConfigProperties in class DifferentialFunctionpublic String configFieldName()
DifferentialFunctionDifferentialFunction.isConfigProperties()
to facilitate mapping fields for model import.configFieldName in class DifferentialFunctionpublic Object getValue(Field property)
DifferentialFunctiongetValue in class DifferentialFunctionproperty - the property to getpublic void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph)
DifferentialFunctionNodeDefinitFromTensorFlow in class DynamicCustomOppublic String opName()
DynamicCustomOpopName in interface CustomOpopName in class DynamicCustomOppublic String[] tensorflowNames()
DifferentialFunctiontensorflowNames in class DifferentialFunctionpublic List<SDVariable> doDiff(List<SDVariable> f1)
DifferentialFunctiondoDiff in class DynamicCustomOppublic List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes)
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 DifferentialFunctioninputDataTypes - The data types of the inputsCopyright © 2021. All rights reserved.