Class CropAndResize
- java.lang.Object
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- org.nd4j.autodiff.functions.DifferentialFunction
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- org.nd4j.linalg.api.ops.DynamicCustomOp
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- org.nd4j.linalg.api.ops.impl.image.CropAndResize
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- All Implemented Interfaces:
CustomOp
public class CropAndResize extends DynamicCustomOp
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Nested Class Summary
Nested Classes Modifier and Type Class Description static class
CropAndResize.Method
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Nested classes/interfaces inherited from class org.nd4j.linalg.api.ops.DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilder
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Field Summary
Fields Modifier and Type Field Description protected double
extrapolationValue
protected CropAndResize.Method
method
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Fields inherited from class org.nd4j.linalg.api.ops.DynamicCustomOp
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, sArguments, tArguments
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Fields inherited from class org.nd4j.autodiff.functions.DifferentialFunction
dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue
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Constructor Summary
Constructors Constructor Description 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)
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)
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method 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 givenNodeDef
String
opName()
This method returns op opName as stringString
tensorflowName()
The opName of this function tensorflow-
Methods inherited from class org.nd4j.linalg.api.ops.DynamicCustomOp
addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addOutputsToOp, addSArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, computeArrays, configureFromArguments, dArgs, generateFake, generateFake, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getSArgument, getTArgument, getValue, iArgs, initFromOnnx, inputArguments, mappingsForFunction, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numSArguments, numTArguments, onnxName, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, propertiesForFunction, removeIArgument, removeInputArgument, removeOutputArgument, removeSArgument, removeTArgument, sArgs, setInputArgument, setInputArguments, setOutputArgument, setPropertiesForFunction, setValueFor, tArgs, toString, wrapFilterNull, wrapOrNull, wrapOrNull
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Methods inherited from class org.nd4j.autodiff.functions.DifferentialFunction
arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, configureWithSameDiff, diff, dup, equals, getBooleanFromProperty, getDoubleValueFromProperty, getIntValueFromProperty, getLongValueFromProperty, getNumOutputs, getStringFromProperty, hashCode, isConfigProperties, larg, onnxNames, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceId, tensorflowNames
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Methods inherited from class java.lang.Object
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
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Methods inherited from interface org.nd4j.linalg.api.ops.CustomOp
isInplaceCall
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Field Detail
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method
protected CropAndResize.Method method
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extrapolationValue
protected double extrapolationValue
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Constructor Detail
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CropAndResize
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)
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CropAndResize
public CropAndResize(@NonNull @NonNull SameDiff sameDiff, SDVariable image, SDVariable cropBoxes, SDVariable boxIndices, SDVariable cropOutSize, double extrapolationValue)
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CropAndResize
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)
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Method Detail
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opName
public String opName()
Description copied from class:DynamicCustomOp
This method returns op opName as string- Specified by:
opName
in interfaceCustomOp
- Overrides:
opName
in classDynamicCustomOp
- Returns:
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tensorflowName
public String tensorflowName()
Description copied from class:DifferentialFunction
The opName of this function tensorflow- Overrides:
tensorflowName
in classDynamicCustomOp
- Returns:
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initFromTensorFlow
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph)
Description copied from class:DifferentialFunction
Initialize the function from the givenNodeDef
- Overrides:
initFromTensorFlow
in classDynamicCustomOp
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addArgs
protected void addArgs()
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doDiff
public List<SDVariable> doDiff(List<SDVariable> f1)
Description copied from class:DifferentialFunction
The actual implementation for automatic differentiation.- Overrides:
doDiff
in classDynamicCustomOp
- Returns:
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calculateOutputDataTypes
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes)
Description copied from class:DifferentialFunction
Calculate the data types for the output arrays. Though datatypes can also be inferred fromDifferentialFunction.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.- Overrides:
calculateOutputDataTypes
in classDifferentialFunction
- Parameters:
inputDataTypes
- The data types of the inputs- Returns:
- The data types of the outputs
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