public class ExtractImagePatches extends DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilderaxis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArgumentsdimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue| Constructor and Description |
|---|
ExtractImagePatches() |
ExtractImagePatches(@NonNull INDArray input,
@NonNull int[] kSizes,
@NonNull int[] strides,
@NonNull int[] rates,
boolean sameMode) |
ExtractImagePatches(INDArray input,
int kH,
int kW,
int sH,
int sW,
int rH,
int rW,
boolean sameMode) |
ExtractImagePatches(@NonNull SameDiff samediff,
@NonNull SDVariable input,
@NonNull int[] kSizes,
@NonNull int[] strides,
@NonNull int[] rates,
boolean sameMode) |
ExtractImagePatches(@NonNull SameDiff samediff,
@NonNull SDVariable input,
int kH,
int kW,
int sH,
int sW,
int rH,
int rW,
boolean sameMode) |
| Modifier and Type | Method and Description |
|---|---|
protected void |
addArgs() |
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() |
void |
initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph)
Initialize the function from the given
NodeDef |
String |
opName()
This method returns op opName as string
|
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, iArgs, initFromOnnx, inputArguments, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numTArguments, onnxName, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, removeIArgument, removeInputArgument, removeOutputArgument, removeTArgument, setInputArgument, setInputArguments, setOutputArgument, tArgs, toString, wrapFilterNull, wrapOrNull, wrapOrNullarg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, equals, getValue, hashCode, isConfigProperties, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariablesNames, propertiesForFunction, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNamesclone, finalize, getClass, notify, notifyAll, wait, wait, waitisInplaceCallpublic ExtractImagePatches()
public ExtractImagePatches(@NonNull
@NonNull SameDiff samediff,
@NonNull
@NonNull SDVariable input,
int kH,
int kW,
int sH,
int sW,
int rH,
int rW,
boolean sameMode)
public ExtractImagePatches(@NonNull
@NonNull SameDiff samediff,
@NonNull
@NonNull SDVariable input,
@NonNull
@NonNull int[] kSizes,
@NonNull
@NonNull int[] strides,
@NonNull
@NonNull int[] rates,
boolean sameMode)
public ExtractImagePatches(@NonNull
@NonNull INDArray input,
@NonNull
@NonNull int[] kSizes,
@NonNull
@NonNull int[] strides,
@NonNull
@NonNull int[] rates,
boolean sameMode)
public ExtractImagePatches(INDArray input, int kH, int kW, int sH, int sW, int rH, int rW, boolean sameMode)
public String opName()
DynamicCustomOpopName in interface CustomOpopName in class DynamicCustomOppublic String tensorflowName()
DifferentialFunctiontensorflowName in class DynamicCustomOppublic void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph)
DifferentialFunctionNodeDefinitFromTensorFlow in class DynamicCustomOpprotected void addArgs()
public List<SDVariable> doDiff(List<SDVariable> f1)
DifferentialFunctiondoDiff in class DynamicCustomOppublic int getNumOutputs()
getNumOutputs in class DifferentialFunctionpublic 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.