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.impl.image;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException;
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.ops.Op;
import java.util.Collections;
import java.util.List;
public class NonMaxSuppression extends DynamicCustomOp {
public NonMaxSuppression() {}
public NonMaxSuppression(SameDiff sameDiff, @NonNull SDVariable boxes, @NonNull SDVariable scores, @NonNull SDVariable maxOutSize,
@NonNull SDVariable iouThreshold, @NonNull SDVariable scoreThreshold) {
super(null, sameDiff, new SDVariable[]{boxes, scores, maxOutSize, iouThreshold, scoreThreshold}, false);
}
public NonMaxSuppression(SameDiff sameDiff, SDVariable boxes, SDVariable scores, int maxOutSize,
double iouThreshold, double scoreThreshold) {
super(null, sameDiff, new SDVariable[]{boxes, scores}, false);
addIArgument(maxOutSize);
addTArgument(iouThreshold, scoreThreshold);
}
public NonMaxSuppression(INDArray boxes, INDArray scores, int maxOutSize, double iouThreshold, double scoreThreshold) {
addInputArgument(boxes,scores);
addIArgument(maxOutSize);
addTArgument(iouThreshold, scoreThreshold);
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx name found for shape " + opName());
}
@Override
public String tensorflowName() {
return "NonMaxSuppression";
}
@Override
public String[] tensorflowNames() {
return new String[]{"NonMaxSuppression", "NonMaxSuppressionV2"};
}
@Override
public String opName() {
return "non_max_suppression";
}
@Override
public Op.Type opType() {
return Op.Type.CUSTOM;
}
@Override
public List doDiff(List i_v) {
return Collections.singletonList(sameDiff.zerosLike(arg()));
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
//Always 1D integer tensor (indices)
return Collections.singletonList(DataType.INT);
}
}