public class Conv3D extends DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilder
Modifier and Type | Field and Description |
---|---|
protected Conv3DConfig |
config |
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue
Constructor and Description |
---|
Conv3D() |
Conv3D(INDArray[] inputs,
INDArray[] outputs,
Conv3DConfig config) |
Conv3D(INDArray input,
INDArray weights,
Conv3DConfig config) |
Conv3D(INDArray input,
INDArray weights,
INDArray bias,
Conv3DConfig config) |
Conv3D(@NonNull INDArray input,
@NonNull INDArray weights,
INDArray bias,
INDArray output,
@NonNull Conv3DConfig config) |
Conv3D(SameDiff sameDiff,
SDVariable[] inputFunctions,
Conv3DConfig config) |
Conv3D(@NonNull SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable weights,
SDVariable bias,
@NonNull Conv3DConfig config) |
Modifier and Type | Method and Description |
---|---|
Map<String,Map<String,AttributeAdapter>> |
attributeAdaptersForFunction()
Returns the
AttributeAdapter s for each of the
possible ops for import (typically tensorflow and onnx)
See AttributeAdapter for more information on what the
adapter does. |
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.
|
Map<String,Map<String,PropertyMapping>> |
mappingsForFunction()
Returns the mappings for a given function (
for tensorflow and onnx import mapping properties
of this function).
|
String |
onnxName()
The opName of this function in onnx
|
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
|
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, getNumOutputs, hashCode, larg, onnxNames, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNames
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
isInplaceCall
protected Conv3DConfig config
public Conv3D()
public Conv3D(@NonNull @NonNull SameDiff sameDiff, @NonNull @NonNull SDVariable input, @NonNull @NonNull SDVariable weights, SDVariable bias, @NonNull @NonNull Conv3DConfig config)
public Conv3D(SameDiff sameDiff, SDVariable[] inputFunctions, Conv3DConfig config)
public Conv3D(INDArray[] inputs, INDArray[] outputs, Conv3DConfig config)
public Conv3D(@NonNull @NonNull INDArray input, @NonNull @NonNull INDArray weights, INDArray bias, INDArray output, @NonNull @NonNull Conv3DConfig config)
public Conv3D(INDArray input, INDArray weights, INDArray bias, Conv3DConfig config)
public Conv3D(INDArray input, INDArray weights, Conv3DConfig config)
public Object getValue(Field property)
DifferentialFunction
getValue
in class DifferentialFunction
property
- the property to getpublic long[] iArgs()
iArgs
in interface CustomOp
iArgs
in class DynamicCustomOp
public Map<String,Map<String,AttributeAdapter>> attributeAdaptersForFunction()
DifferentialFunction
AttributeAdapter
s for each of the
possible ops for import (typically tensorflow and onnx)
See AttributeAdapter
for more information on what the
adapter does.
Similar to DifferentialFunction.mappingsForFunction()
, the returned map
contains a AttributeAdapter
for each field name
when one is present. (It is optional for one to exist)_attributeAdaptersForFunction
in class DifferentialFunction
public Map<String,Object> propertiesForFunction()
DifferentialFunction
propertiesForFunction
in class DifferentialFunction
public String opName()
DynamicCustomOp
opName
in interface CustomOp
opName
in class DynamicCustomOp
public Map<String,Map<String,PropertyMapping>> mappingsForFunction()
DifferentialFunction
mappingsForFunction
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> f1)
DifferentialFunction
doDiff
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 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 © 2021. All rights reserved.