Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* ******************************************************************************
* *
* *
* * 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.custom;
import lombok.NoArgsConstructor;
import lombok.NonNull;
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 java.util.Collections;
import java.util.List;
@NoArgsConstructor
public class MatrixBandPart extends DynamicCustomOp {
public MatrixBandPart(@NonNull INDArray input, int minLower, int maxUpper) {
Preconditions.checkArgument(input.rank() >= 2, "MatrixBandPart: Input rank should be 2 or higher");
long N = input.size(-2);
long M = input.size(-1);
Preconditions.checkArgument(minLower > -N && minLower < N, "MatrixBandPart: lower diagonal count %s should be less than %s",
minLower, N);
Preconditions.checkArgument(maxUpper > -M && maxUpper < M, "MatrixBandPart: upper diagonal count %s should be less than %s.",
maxUpper, M);
addInputArgument(input);
addIArgument(minLower, maxUpper);
}
public MatrixBandPart(@NonNull SameDiff sameDiff, @NonNull SDVariable input, SDVariable minLower, SDVariable maxUpper) {
super("", sameDiff, new SDVariable[]{input, minLower, maxUpper});
}
public MatrixBandPart(@NonNull SameDiff sameDiff, @NonNull SDVariable input, int minLower, int maxUpper) {
super("", sameDiff, new SDVariable[]{input});
addIArgument(minLower, maxUpper);
}
@Override
public String opName() {
return "matrix_band_part";
}
@Override
public String tensorflowName() {
return "MatrixBandPart";
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
int n = args().length;
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == n, "Expected %s input data types for %s, got %s", n, getClass(), inputDataTypes);
return Collections.singletonList(inputDataTypes.get(0));
}
}