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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.shape;
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.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
/**
* Computes a batch of identity matrices of shape (numRows, numCols), returns a single tensor.
* This batch of identity matrices can be specified as list of integers.
*
* Example:
*
* batchShape: [3,3]
* numRows: 2
* numCols: 4
*
* returns a tensor of shape (3, 3, 2, 4) that consists of 3 * 3 batches of (2,4)-shaped identity matrices:
*
* 1 0 0 0
* 0 1 0 0
*
*
* @author Max Pumperla
*/
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(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) {
super(null, sameDiff, new SDVariable[] {}, false);
this.numRows = numRows;
this.numCols = numRows;
addArgs();
}
public Eye(SameDiff sameDiff, int numRows, int numCols) {
this(sameDiff, numRows, numCols, DEFAULT_DTYPE);
}
public Eye(SameDiff sameDiff, int numRows, int numCols, DataType dataType) {
super(null, sameDiff, new SDVariable[] {}, false);
this.numRows = numRows;
this.numCols = numCols;
this.dataType = dataType;
addArgs();
}
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();
}
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 onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
return "Eye";
}
@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);
}
}