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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
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.api.ops.impl.scatter;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.buffer.DataType;
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.*;
/**
* @author [email protected]
* @author Alex Black
*/
public class ScatterMin extends DynamicCustomOp {
public ScatterMin(SameDiff sameDiff, SDVariable ref, SDVariable indices, SDVariable updates) {
super(null, sameDiff, new SDVariable[]{ref, indices, updates}, false);
}
public ScatterMin() {}
@Override
public String opName() {
return "scatter_min";
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
return "ScatterMin";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
if (nodeDef.containsAttr("use_locking")) {
if (nodeDef.getAttrOrThrow("use_locking").getB() == true) {
bArguments.add(true);
} else {
bArguments.add(false);
}
} else
bArguments.add(false);
}
@Override
public List doDiff(List gradOut) {
//3 args: ref, indices, updates
//For non-modified indices, input gradient (reference) is same as output gradient
//For modified indices, dL/dref = dL/dOut if(ref[index[i],j] == min) or 0 otherwise
//And for updates, dL/du = dL/dOut if(update[i,j]==min) or 0 otherwise
List ret = new ArrayList<>(3);
SDVariable notModified = arg(0).eq(outputVariable()).castTo(arg(0).dataType()); //0 if modified, 1 otherwise
SDVariable refGrad = gradOut.get(0).mul(notModified);
SDVariable gatherOut = f().gather(outputVariable(), arg(1), 0);
SDVariable gatherGrad = f().gather(gradOut.get(0), arg(1), 0);
SDVariable outIsUpdate = gatherOut.eq(arg(2)).castTo(arg(2).dataType());
SDVariable updateGrad = gatherGrad.mul(outIsUpdate);
return Arrays.asList(refGrad, f().zerosLike(arg(1)), updateGrad);
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 3, "Expected exactly 3 input datatypes for %s, got %s", getClass(), inputDataTypes);
Preconditions.checkState(inputDataTypes.get(0) == inputDataTypes.get(2), "Reference (input 0) and updates (input 2) must have exactly same data types, got %s and %s",
inputDataTypes.get(0), inputDataTypes.get(2));
return Collections.singletonList(inputDataTypes.get(0));
}
}
