org.nd4j.linalg.api.ops.impl.shape.DiagPart Maven / Gradle / Ivy
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package org.nd4j.linalg.api.ops.impl.shape;
import onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Collections;
import java.util.List;
import java.util.Map;
public class DiagPart extends DynamicCustomOp {
public DiagPart() {
}
public DiagPart(SameDiff sameDiff, SDVariable[] args, boolean inPlace) {
super(null, sameDiff, args, inPlace);
}
public DiagPart(SameDiff sameDiff, SDVariable in) {
this(sameDiff, new SDVariable[]{in}, false);
}
public DiagPart(INDArray in){
this(in, null);
}
public DiagPart(INDArray in, INDArray out){
super(null, in, out, null, null);
}
@Override
public List doDiff(List i_v) {
SDVariable grad = i_v.get(0);
SDVariable ret = sameDiff.math().diag(grad);
return Collections.singletonList(ret);
}
@Override
public String opName() {
return "diag_part";
}
@Override
public String tensorflowName() {
return "DiagPart";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
super.initFromTensorFlow(nodeDef, initWith, attributesForNode, graph);
}
@Override
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
super.initFromOnnx(node, initWith, attributesForNode, graph);
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes.size() == 1, "Expected list with exactly 1 datatype for %s, got %s", getClass(), dataTypes);
//Output type is same as input type
return dataTypes;
}
}