Pooling3D.Pooling3DTypeDynamicCustomOp.DynamicCustomOpsBuilderaxis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArgumentsdimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue| Constructor and Description |
|---|
AvgPooling3D(@NonNull INDArray input,
Pooling3DConfig pooling3DConfig) |
AvgPooling3D(SameDiff sameDiff,
SDVariable input,
Pooling3DConfig 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.
|
String |
getPoolingPrefix() |
void |
initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph)
Iniitialize the function from the given
Onnx.NodeProto |
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 |
tensorflowName()
The opName of this function tensorflow
|
addArgs, doDiff, iArgs, initFromTensorFlow, onnxNameaddBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, dArgs, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, inputArguments, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numTArguments, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeTArgument, setInputArgument, setInputArguments, setOutputArgument, tArgs, toString, wrapFilterNull, wrapOrNull, wrapOrNullarg, arg, argNames, args, attributeAdaptersForFunction, diff, dup, equals, getNumOutputs, getValue, hashCode, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNamesclone, finalize, getClass, notify, notifyAll, wait, wait, waitisInplaceCallpublic AvgPooling3D(SameDiff sameDiff, SDVariable input, Pooling3DConfig config)
public AvgPooling3D(@NonNull
@NonNull INDArray input,
Pooling3DConfig pooling3DConfig)
public boolean isConfigProperties()
DifferentialFunctionisConfigProperties in class Pooling3Dpublic String configFieldName()
DifferentialFunctionDifferentialFunction.isConfigProperties()
to facilitate mapping fields for model import.configFieldName in class Pooling3Dpublic Map<String,Object> propertiesForFunction()
DifferentialFunctionpropertiesForFunction in class Pooling3Dpublic String getPoolingPrefix()
getPoolingPrefix in class Pooling3Dpublic String opName()
DynamicCustomOpopName in interface CustomOpopName in class DynamicCustomOppublic void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map<String,Onnx.AttributeProto> attributesForNode, Onnx.GraphProto graph)
DifferentialFunctionOnnx.NodeProtoinitFromOnnx in class DynamicCustomOppublic String tensorflowName()
DifferentialFunctiontensorflowName in class Pooling3Dpublic 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 Pooling3DinputDataTypes - The data types of the inputsCopyright © 2021. All rights reserved.