org.nd4j.linalg.api.ops.custom.RandomCrop Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
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package org.nd4j.linalg.api.ops.custom;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import java.util.Collections;
import java.util.List;
public class RandomCrop extends DynamicCustomOp {
public RandomCrop() {}
public RandomCrop(@NonNull INDArray input, @NonNull INDArray shape) {
Preconditions.checkArgument(shape.isVector(),"RandomCrop:Shape tensor should be a vector");
Preconditions.checkArgument(input.rank() == shape.length(), "RandomCrop:The length of the shape vector is not match input rank");
addInputArgument(input, shape);
}
public RandomCrop(@NonNull SameDiff sameDiff, @NonNull SDVariable input, @NonNull SDVariable shape) {
super("", sameDiff, new SDVariable[]{input, shape});
}
@Override
public String opName() {
return "random_crop";
}
@Override
public String tensorflowName() {
return "RandomCrop";
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null /*&& inputDataTypes.size() == 4*/,
"Expected 4 input datatypes for %s, got %s", getClass(), inputDataTypes);
return Collections.singletonList(DataType.FLOAT); //TF import: always returns float32...
}
}