org.nd4j.linalg.api.ops.impl.shape.Diag Maven / Gradle / Ivy
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* Copyright (c) 2015-2018 Skymind, Inc.
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* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.shape;
import lombok.NonNull;
import onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.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.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;
/**
* Computes a diagonal matrix of shape (n, n) from a vector of length n.
* More generally puts a an n-dimensional tensor on the diagonal part
* of a tensor of 2*n dimensions.
*
* @author Max Pumperla
*/
public class Diag extends DynamicCustomOp {
public Diag() {
}
public Diag(@NonNull INDArray input) {
this(input, null);
}
public Diag(@NonNull INDArray input, @NonNull INDArray output){
super(null, new INDArray[]{input}, wrapOrNull(output));
}
public Diag(SameDiff sameDiff, SDVariable[] args, boolean inPlace) {
super(null, sameDiff, args, inPlace);
}
@Override
public List doDiff(List i_v) {
SDVariable grad = i_v.get(0);
SDVariable ret = sameDiff.math().diagPart(grad);
return Collections.singletonList(ret);
}
@Override
public String opName() {
return "diag";
}
@Override
public String tensorflowName() {
return "Diag";
}
@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;
}
}