org.nd4j.linalg.api.ops.impl.transforms.custom.segment.SegmentMean Maven / Gradle / Ivy
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package org.nd4j.linalg.api.ops.impl.transforms.custom.segment;
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 org.nd4j.linalg.api.ops.impl.transforms.segment.bp.SegmentMeanBp;
import java.util.Collections;
import java.util.List;
public class SegmentMean extends DynamicCustomOp {
public SegmentMean(SameDiff sameDiff, SDVariable data, SDVariable segmentIds) {
super(null, sameDiff, new SDVariable[] {data, segmentIds}, false);
}
public SegmentMean(){ }
public SegmentMean(INDArray data, INDArray segmentIds){
super(new INDArray[]{data, segmentIds}, null);
}
@Override
public String opName(){
return "segment_mean";
}
@Override
public String tensorflowName() {
return "SegmentMean";
}
@Override
public List doDiff(List gradients){
return new SegmentMeanBp(sameDiff, arg(0), arg(1), gradients.get(0)).outputs();
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 2, "Expected exactly 2 input datatypes for %s, got %s", getClass(), inputDataTypes);
Preconditions.checkState(inputDataTypes.get(1).isIntType(), "Datatype for input 1 (Segment IDs) must be an integer type, got %s", inputDataTypes.get(1));
//TODO TF output tensor has same type as input... but that doesn't make sense integer input types...
return Collections.singletonList(inputDataTypes.get(0));
}
}