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/*
* ******************************************************************************
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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.
* *
* * See the NOTICE file distributed with this work for additional
* * 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
* * License for the specific language governing permissions and limitations
* * under the License.
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* * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.transforms;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDIndex;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.enums.Mode;
import org.nd4j.enums.PadMode;
import org.nd4j.linalg.api.buffer.DataType;
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;
public class Pad extends DynamicCustomOp {
public Pad(SameDiff sd, SDVariable input, SDVariable padding, org.nd4j.enums.Mode mode, double padValue) {
this(sd,input,padding,adaptCodeGenMode(mode),padValue);
}
public Pad(INDArray input, INDArray padding, org.nd4j.enums.Mode mode, double padValue) {
this(input,padding,null,adaptCodeGenMode(mode),padValue);
}
public enum Mode {CONSTANT, REFLECT, SYMMETRIC}
private Mode mode;
private double constant;
public Pad(){ }
private static Mode adaptCodeGenMode(org.nd4j.enums.Mode mode) {
switch(mode) {
case REFLECT:
return Mode.REFLECT;
case CONSTANT:
return Mode.CONSTANT;
case SYMMETRIC:
return Mode.SYMMETRIC;
default:throw new IllegalArgumentException("Invalid mode specified " + mode);
}
}
private static Mode adaptMode(PadMode mode) {
Mode legacyMode = Mode.CONSTANT;
if (mode == PadMode.CONSTANT) {
legacyMode = Mode.CONSTANT;
}
else if (mode == PadMode.REFLECT) {
legacyMode = Mode.REFLECT;
}
else if (mode == PadMode.SYMMETRIC) {
legacyMode = Mode.SYMMETRIC;
}
return legacyMode;
}
public Pad(SameDiff sd, SDVariable in, SDVariable padding, PadMode mode, double padValue) {
this(sd, in, padding, adaptMode(mode), padValue);
}
public Pad(SameDiff sd, SDVariable in, SDVariable padding, Mode mode, double padValue) {
super(sd, new SDVariable[]{in, padding}, false);
Preconditions.checkState(padding.dataType().isIntType(), "Padding array must be an integer datatype, got %s", padding.dataType());
this.mode = mode;
addIArgument(mode.ordinal());
addTArgument(padValue);
}
public Pad(SameDiff sd, SDVariable in, SDVariable padding, double padValue) {
this(sd, in, padding, Mode.CONSTANT, padValue);
}
public Pad(@NonNull INDArray in, @NonNull INDArray padding, double padValue){
this(in, padding, null, Mode.CONSTANT, padValue);
}
public Pad(@NonNull INDArray in, @NonNull INDArray padding, @NonNull PadMode mode, double padValue) {
this(in, padding, null, adaptMode(mode), padValue);
}
public Pad(@NonNull INDArray in, @NonNull INDArray padding, INDArray out, @NonNull Mode mode, double padValue){
super(null, new INDArray[]{in, padding}, out == null ? null : new INDArray[]{out});
Preconditions.checkState(padding.dataType().isIntType(), "Padding array must be an integer datatype, got %s", padding.dataType());
this.mode = mode;
addIArgument(mode.ordinal());
addTArgument(padValue);
}
public Pad(@NonNull INDArray in, @NonNull INDArray padding, INDArray out, @NonNull PadMode mode, double padValue) {
this(in, padding, out, adaptMode(mode), padValue);
}
@Override
public String opName(){
return "pad";
}
@Override
public String[] tensorflowNames() {
return new String[]{"Pad", "PadV2"};
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
//Based on TF codebase: gen_array_ops.mirror_pad is osed for BOTH REFLECT and SYMMETRIC mode. Hence only constant being imported here
this.mode = Mode.CONSTANT;
addIArgument(mode.ordinal());
//Constant value is resolved just before execution
}
@Override
public List doDiff(List i_v) {
//Pad backprop: it's basically slice op...
//Inputs to pad: input array (rank N), and padding array (rank 2, shape [N,2])
//Begin values for slice: given by column 0 of padding array; size is given by input array
SDVariable shape = arg().shape();
SDVariable begin = arg(1).get(SDIndex.all(), SDIndex.point(0));
SDVariable gradAtIn = sameDiff.slice(i_v.get(0), begin, shape);
SDVariable zeros = sameDiff.zerosLike(arg(1));
return Arrays.asList(gradAtIn, zeros);
}
@Override
public List calculateOutputDataTypes(List inputDataTypes) {
Preconditions.checkState(inputDataTypes != null && (inputDataTypes.size() >= 1 && inputDataTypes.size() <= 3),
"Expected 1-3 input datatypes for %s, got %s", getClass(), inputDataTypes); //input, padding, pad value
return Collections.singletonList(inputDataTypes.get(0));
}
}