public class CropAndResize extends DynamicCustomOp
Modifier and Type | Class and Description |
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static class |
CropAndResize.Method |
DynamicCustomOp.DynamicCustomOpsBuilder
Modifier and Type | Field and Description |
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protected double |
extrapolationValue |
protected CropAndResize.Method |
method |
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, tArguments
dimensions, extraArgs, inPlace, sameDiff, scalarValue
Constructor and Description |
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CropAndResize(@NonNull INDArray image,
@NonNull INDArray cropBoxes,
@NonNull INDArray boxIndices,
@NonNull INDArray cropOutSize,
@NonNull CropAndResize.Method method,
double extrapolationValue,
INDArray output) |
CropAndResize(INDArray image,
INDArray cropBoxes,
INDArray boxIndices,
INDArray cropOutSize,
double extrapolationValue) |
CropAndResize(@NonNull SameDiff sameDiff,
@NonNull SDVariable image,
@NonNull SDVariable cropBoxes,
@NonNull SDVariable boxIndices,
@NonNull SDVariable cropOutSize,
@NonNull CropAndResize.Method method,
double extrapolationValue) |
CropAndResize(@NonNull SameDiff sameDiff,
SDVariable image,
SDVariable cropBoxes,
SDVariable boxIndices,
SDVariable cropOutSize,
double extrapolationValue) |
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.
|
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, wrapOrNull
arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, equals, getNumOutputs, getValue, hashCode, isConfigProperties, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariablesNames, propertiesForFunction, rarg, replaceArg, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNames
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
isInplaceCall
protected CropAndResize.Method method
protected double extrapolationValue
public CropAndResize(@NonNull @NonNull SameDiff sameDiff, @NonNull @NonNull SDVariable image, @NonNull @NonNull SDVariable cropBoxes, @NonNull @NonNull SDVariable boxIndices, @NonNull @NonNull SDVariable cropOutSize, @NonNull @NonNull CropAndResize.Method method, double extrapolationValue)
public CropAndResize(@NonNull @NonNull SameDiff sameDiff, SDVariable image, SDVariable cropBoxes, SDVariable boxIndices, SDVariable cropOutSize, double extrapolationValue)
public CropAndResize(@NonNull @NonNull INDArray image, @NonNull @NonNull INDArray cropBoxes, @NonNull @NonNull INDArray boxIndices, @NonNull @NonNull INDArray cropOutSize, @NonNull @NonNull CropAndResize.Method method, double extrapolationValue, INDArray output)
public String opName()
DynamicCustomOp
opName
in interface CustomOp
opName
in class DynamicCustomOp
public String tensorflowName()
DifferentialFunction
tensorflowName
in class DynamicCustomOp
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph)
DifferentialFunction
NodeDef
initFromTensorFlow
in class DynamicCustomOp
protected void addArgs()
public List<SDVariable> doDiff(List<SDVariable> f1)
DifferentialFunction
doDiff
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 © 2020. All rights reserved.