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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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* * information regarding copyright ownership.
* * 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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* * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.transforms.clip;
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.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.shade.guava.primitives.Ints;
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 ClipByNorm extends DynamicCustomOp {
private double clipValue;
public ClipByNorm() {
}
public ClipByNorm(SameDiff sameDiff, SDVariable x, double clipValue, int... dimensions) {
super(null, sameDiff, new SDVariable[]{x});
this.clipValue = clipValue;
this.dimensions = dimensions;
addIArgument(dimensions);
addTArgument(clipValue);
}
public ClipByNorm(INDArray in, double clipValue, int... dimensions){
this(in, null, clipValue, dimensions);
}
public ClipByNorm(INDArray in, INDArray out, double clipValue, int... dimensions){
super(null, new INDArray[]{in}, wrapOrNull(out), Collections.singletonList(clipValue), dimensions);
}
public ClipByNorm(SameDiff sd, SDVariable x, SDVariable clipValue, SDVariable dimensions) {
super(null,sd,new SDVariable[]{x,clipValue,dimensions});
}
public ClipByNorm(INDArray x, INDArray clipValue, INDArray dimensions) {
super(null,new INDArray[]{x,clipValue,dimensions},null);
}
@Override
public String opName() {
return "clipbynorm";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
throw new UnsupportedOperationException("Not yet implemented");
}
@Override
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
throw new UnsupportedOperationException("Not yet implemented");
}
@Override
public void setPropertiesForFunction(Map properties) {
if(properties.containsKey("clipValue")) {
Double clip = getDoubleValueFromProperty("clipValue",properties);
this.clipValue = clip;
}
if(properties.containsKey("dimensions")) {
Long dimension = getLongValueFromProperty("dimensions",properties);
this.dimensions = new int[]{dimension.intValue()};
}
}
@Override
public void configureFromArguments() {
super.configureFromArguments();
if(!iArguments.isEmpty()) {
this.dimensions = Ints.toArray(iArguments);
}
if(!tArguments.isEmpty()) {
this.clipValue = tArguments.get(0);
}
}
@Override
public List doDiff(List grad) {
return new ClipByNormBp(sameDiff, arg(), grad.get(0), clipValue, dimensions).outputs();
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 1, "Expected exactly 1 input datatype for %s, got %s", getClass(), inputDataTypes);
return inputDataTypes;
}
}