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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.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
package org.nd4j.linalg.api.ops.impl.shape;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
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.shape.LongShapeDescriptor;
import org.nd4j.shade.guava.base.Preconditions;
import java.util.Collections;
import java.util.List;
public class Eye extends DynamicCustomOp {
public static final DataType DEFAULT_DTYPE = DataType.FLOAT;
private int numRows;
private int numCols;
private int[] batchDimension = new int[] {};
private DataType dataType = DEFAULT_DTYPE;
public Eye() {
}
public Eye(@NonNull INDArray rows){
this(rows.getInt(0));
Preconditions.checkArgument(rows.isScalar(), "Rows INDArray must be a scalar");
}
public Eye(@NonNull INDArray rows, @NonNull INDArray columns){
this(rows.getInt(0), columns.getInt(0));
Preconditions.checkArgument(rows.isScalar(), "Rows INDArray must be a scalar");
Preconditions.checkArgument(columns.isScalar(), "Columns INDArray must be a scalar");
}
public Eye(int rows){
this.numRows = rows;
this.numCols = rows;
addArgs();
}
public Eye(SameDiff sameDiff, SDVariable numRows){
super(null, sameDiff, new SDVariable[] {numRows}, false);
}
public Eye(SameDiff sameDiff, SDVariable numRows, SDVariable numCols){
super(null, sameDiff, new SDVariable[] {numRows, numCols}, false);
}
public Eye(SameDiff sameDiff, SDVariable numRows, SDVariable numCols, SDVariable batch_shape){
super(null, sameDiff, new SDVariable[] {numRows, numCols, batch_shape}, false);
}
public Eye(SameDiff sameDiff, int numRows) {
this(sameDiff, numRows, numRows);
}
public Eye(SameDiff sameDiff, int numRows, int numCols) {
this(sameDiff, numRows, numCols, DEFAULT_DTYPE);
}
public Eye(SameDiff sameDiff, int numRows, int numCols, DataType dataType) {
this(sameDiff, numRows, numCols, dataType, null);
}
public Eye(int numRows, int numCols, DataType dataType, int[] batchDimension) {
this.numRows = numRows;
this.numCols = numCols;
this.batchDimension = batchDimension;
this.dataType = dataType;
addArgs();
}
public Eye(int numRows, int numCols) {
this(numRows, numCols, DEFAULT_DTYPE);
}
public Eye(int numRows, int numCols, DataType dataType) {
this(numRows, numCols, dataType, null);
}
public Eye(SameDiff sameDiff, int numRows, int numCols, DataType dataType, int[] batchDimension) {
super(null, sameDiff, new SDVariable[] {}, false);
this.numRows = numRows;
this.numCols = numCols;
this.batchDimension = batchDimension;
this.dataType = dataType;
addArgs();
}
public Eye(SameDiff sameDiff, SDVariable numRows, SDVariable numCols, DataType dataType, int[] batchDimension) {
super(null, sameDiff, new SDVariable[] {numRows, numCols}, false);
this.batchDimension = batchDimension;
this.dataType = dataType;
addArgs();
}
protected void addArgs() {
iArguments.clear();
tArguments.clear();
addIArgument(numRows);
addIArgument(numCols);
if(batchDimension != null) {
for (int dim : batchDimension) {
addIArgument(dim);
}
}
addTArgument((double) dataType.toInt());
}
@Override
public String opName() {
return "eye";
}
@Override
public List calculateOutputShape(){
List l = super.calculateOutputShape();
if(dataType != null && l != null && l.size() > 0){
l.set(0, l.get(0).asDataType(dataType));
}
return l;
}
@Override
public List doDiff(List outGrad){
if(arg() != null){
return Collections.singletonList(sameDiff.onesLike(arg()));
} else {
return Collections.emptyList();
}
}
@Override
public List calculateOutputDataTypes(List dataTypes){
return Collections.singletonList(dataType == null ? DEFAULT_DTYPE : dataType);
}
}