/*-
* * Copyright 2016 Skymind,Inc.
* *
* * Licensed under the Apache License, Version 2.0 (the "License");
* * you may not use this file except in compliance with the License.
* * You may obtain a copy of the License at
* *
* * http://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.
*/
package org.deeplearning4j.spark.data;
import org.datavec.spark.functions.FlatMapFunctionAdapter;
import org.datavec.spark.transform.BaseFlatMapFunctionAdaptee;
import org.nd4j.linalg.dataset.DataSet;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
/**
* Function used to batch DataSet objects together. Typically used to combine singe-example DataSet objects out of
* something like {@link org.deeplearning4j.spark.datavec.DataVecDataSetFunction} together into minibatches.<br>
*
* Usage:
* <pre>
* {@code
* RDD<DataSet> mySingleExampleDataSets = ...;
* RDD<DataSet> batchData = mySingleExampleDataSets.mapPartitions(new BatchDataSetsFunction(batchSize));
* }
* </pre>
*
* @author Alex Black
*/
public class BatchDataSetsFunction extends BaseFlatMapFunctionAdaptee<Iterator<DataSet>, DataSet> {
public BatchDataSetsFunction(int minibatchSize) {
super(new BatchDataSetsFunctionAdapter(minibatchSize));
}
}
/**
* Function used to batch DataSet objects together. Typically used to combine singe-example DataSet objects out of
* something like {@link org.deeplearning4j.spark.datavec.DataVecDataSetFunction} together into minibatches.<br>
*
* Usage:
* <pre>
* {@code
* RDD<DataSet> mySingleExampleDataSets = ...;
* RDD<DataSet> batchData = mySingleExampleDataSets.mapPartitions(new BatchDataSetsFunction(batchSize));
* }
* </pre>
*
* @author Alex Black
*/
class BatchDataSetsFunctionAdapter implements FlatMapFunctionAdapter<Iterator<DataSet>, DataSet> {
private final int minibatchSize;
public BatchDataSetsFunctionAdapter(int minibatchSize) {
this.minibatchSize = minibatchSize;
}
@Override
public Iterable<DataSet> call(Iterator<DataSet> iter) throws Exception {
List<DataSet> out = new ArrayList<>();
while (iter.hasNext()) {
List<DataSet> list = new ArrayList<>();
int count = 0;
while (count < minibatchSize && iter.hasNext()) {
DataSet ds = iter.next();
count += ds.getFeatureMatrix().size(0);
list.add(ds);
}
DataSet next;
if (list.size() == 0)
next = list.get(0);
else
next = DataSet.merge(list);
out.add(next);
}
return out;
}
}