org.nd4j.linalg.api.ops.impl.transforms.custom.Assign Maven / Gradle / Ivy
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* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* under the License.
*
* SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.transforms.custom;
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.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.Op;
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;
/**
* Assign op: x = y, with broadcast as required
*/
public class Assign extends DynamicCustomOp {
public Assign(){
}
public Assign(INDArray[] inputs, INDArray[] outputs) {
super(null,inputs, outputs);
}
@Override
public void addIArgument(int... arg) {
super.addIArgument(arg);
}
public Assign(SameDiff sameDiff, SDVariable x, SDVariable y){
super(null, sameDiff, new SDVariable[]{x,y});
}
@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 String opName() {
return "assign";
}
@Override
public String onnxName() {
return "GivenTensorFill";
}
@Override
public String tensorflowName() {
return "Assign";
}
@Override
public Op.Type opType() {
return Op.Type.CUSTOM;
}
@Override
public List doDiff(List f1){
//TODO replace with assign backprop op from libnd4j (that handles the broadcast case properly)
return Arrays.asList(f().zerosLike(larg()), f1.get(0));
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatypes for %s, got %s", getClass(), dataTypes);
return Collections.singletonList(dataTypes.get(0));
}
}