public abstract class Pooling3D extends DynamicCustomOp
Modifier and Type | Class and Description |
---|---|
static class |
Pooling3D.Pooling3DType |
DynamicCustomOp.DynamicCustomOpsBuilder
Modifier and Type | Field and Description |
---|---|
protected Pooling3DConfig |
config |
axis, bArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, sameDiff, scalarValue
Constructor and Description |
---|
Pooling3D() |
Pooling3D(SameDiff sameDiff,
SDVariable[] inputs,
INDArray[] inputArrays,
INDArray[] outputs,
boolean inPlace,
Pooling3DConfig pooling3DConfig,
Pooling3D.Pooling3DType type) |
Modifier and Type | Method and Description |
---|---|
protected void |
addArgs() |
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.
|
String |
getPoolingPrefix() |
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 |
onnxName()
The opName of this function in onnx
|
Map<String,Object> |
propertiesForFunction()
Returns the properties for a given function
|
String |
tensorflowName()
The opName of this function tensorflow
|
addBArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getTArgument, initFromOnnx, inputArguments, numBArguments, numIArguments, numInputArguments, numOutputArguments, numTArguments, opHash, opName, opNum, opType, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeTArgument, setInputArgument, setInputArguments, setOutputArgument, tArgs, toString, wrapFilterNull, wrapOrNull
arg, arg, argNames, args, attributeAdaptersForFunction, diff, dup, equals, f, getNumOutputs, getValue, hashCode, larg, mappingsForFunction, onnxNames, outputVariable, outputVariablesNames, rarg, replaceArg, resolvePropertiesFromSameDiffBeforeExecution, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNames
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
isInplaceCall
protected Pooling3DConfig config
public Pooling3D()
public Pooling3D(SameDiff sameDiff, SDVariable[] inputs, INDArray[] inputArrays, INDArray[] outputs, boolean inPlace, Pooling3DConfig pooling3DConfig, Pooling3D.Pooling3DType type)
public long[] iArgs()
iArgs
in interface CustomOp
iArgs
in class DynamicCustomOp
public boolean isConfigProperties()
DifferentialFunction
isConfigProperties
in class DifferentialFunction
public String configFieldName()
DifferentialFunction
DifferentialFunction.isConfigProperties()
to facilitate mapping fields for model import.configFieldName
in class DifferentialFunction
public Map<String,Object> propertiesForFunction()
DifferentialFunction
propertiesForFunction
in class DifferentialFunction
protected void addArgs()
public List<SDVariable> doDiff(List<SDVariable> f1)
DifferentialFunction
doDiff
in class DynamicCustomOp
public String getPoolingPrefix()
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph)
DifferentialFunction
NodeDef
initFromTensorFlow
in class DynamicCustomOp
public String onnxName()
DifferentialFunction
onnxName
in class DynamicCustomOp
public String tensorflowName()
DifferentialFunction
tensorflowName
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 © 2019. All rights reserved.