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.transforms.custom;
import lombok.NonNull;
import org.apache.commons.lang3.ArrayUtils;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
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.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
public class DynamicStitch extends DynamicCustomOp {
private int numPartitions;
private SDVariable[] indices;
private String[] indexNames;
public DynamicStitch() {
}
public DynamicStitch(SameDiff sameDiff, SDVariable[] indices, SDVariable[] inputs) {
super(null, sameDiff, ArrayUtils.addAll(indices, inputs), false);
this.indices = indices;
this.numPartitions = inputs.length;
}
public DynamicStitch(@NonNull INDArray[] indices, @NonNull INDArray[] inputs) {
super(ArrayUtils.addAll(indices, inputs), null);
}
@Override
public List doDiff(List i_v) {
// DynamicPartition and DynamicStitch are mutually inverse
SDVariable gradient = i_v.get(0);
SDVariable[] partitionData = new SDVariable[indices.length];
for (int i = 0; i < indices.length; i++)
partitionData[i] = sameDiff.onesLike(indices[i]).mul(i);
SDVariable partitions = sameDiff.dynamicStitch(indices, partitionData);
SDVariable[] partition = sameDiff.dynamicPartition(gradient, partitions, numPartitions);
List ret = new ArrayList<>();
for (SDVariable i : indices)
ret.add(sameDiff.zerosLike(i));
Collections.addAll(ret, partition);
return ret;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
this.numPartitions = (int)attributesForNode.get("N").getI();
}
@Override
public void configureFromArguments() {
super.configureFromArguments();
}
@Override
public void setPropertiesForFunction(Map properties) {
if (properties.containsKey("indices")) {
//note we don't use the references here directly just the names
//we will use the names separately in configureWithSamediff to
//ensure the variables are from the proper samediff instance
if(properties.get("indices") instanceof String) {
indexNames = new String[1];
indexNames[0] = properties.get("indices").toString();
} else if(properties.get("indices") instanceof String[]) {
String[] indicesGet = (String[]) properties.get("indices");
indexNames = indicesGet;
}
}
if(properties.containsKey("numPartitions")) {
Integer numPartitions = (Integer) properties.get("numPartitions");
this.numPartitions = numPartitions;
}
}
@Override
public Map propertiesForFunction() {
Map base = super.propertiesForFunction();
base.put("numPartitions",numPartitions);
base.put("indices",indices);
return base;
}
@Override
public void configureWithSameDiff(SameDiff sameDiff) {
if(indexNames != null && indices == null) {
indices = new SDVariable[indexNames.length];
for(int i = 0; i < indices.length; i++) {
indices[i] = sameDiff.getVariable(indexNames[i]);
}
}
}
@Override
public String opName() {
return "dynamic_stitch";
}
@Override
public String[] tensorflowNames() {
return new String[]{"DynamicStitch", "ParallelDynamicStitch"};
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx name found for shape " + opName());
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && dataTypes.size() == 2*numPartitions, "Expected %s input datatypes for %s partitions for %s, got %s",
2 * numPartitions, numPartitions, getClass(), dataTypes);
//Output type: same as (data) input type... only 1 output, however
DataType inputType = dataTypes.get(dataTypes.size()-1);
return Collections.singletonList(inputType);
}
}