BertIterator sentence pair support for supervised training (#108)
* bert iterator sentence pair handling Signed-off-by: eraly <susan.eraly@gmail.com> * bert iterator sentence pair handling -seg Signed-off-by: eraly <susan.eraly@gmail.com> * bert iterator sentence pair handling tests Signed-off-by: eraly <susan.eraly@gmail.com> * test with pairs long done Signed-off-by: eraly <susan.eraly@gmail.com> * more tests with bert iter sent pairs done Signed-off-by: eraly <susan.eraly@gmail.com> * fixed copyright, formatting Signed-off-by: eraly <susan.eraly@gmail.com> * bert iterator - added featurizer for sentence pair inference Signed-off-by: eraly <susan.eraly@gmail.com> * bert iterator - finished tests Signed-off-by: eraly <susan.eraly@gmail.com> * bert iterator - finished tests, polish Signed-off-by: eraly <susan.eraly@gmail.com> * collection labeled sentence provider Signed-off-by: eraly <susan.eraly@gmail.com> * lombok fix for pojo class Signed-off-by: eraly <susan.eraly@gmail.com> * java doc misc clean up Signed-off-by: eraly <susan.eraly@gmail.com> * Private access modifiers Signed-off-by: AlexDBlack <blacka101@gmail.com>master
Alex Black
GitHub
parent
9cc8803b8d
commit
91de96588c
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@ -34,6 +34,7 @@ import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
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import org.nd4j.linalg.factory.Nd4j;
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import org.nd4j.linalg.indexing.NDArrayIndex;
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import org.nd4j.linalg.primitives.Pair;
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import org.nd4j.linalg.primitives.Triple;
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import java.util.ArrayList;
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import java.util.Arrays;
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@ -85,10 +86,20 @@ import java.util.Map;
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* <pre>
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* {@code
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* BertIterator b;
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* Pair<INDArray[],INDArray[]> featuresAndMask;
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* INDArray[] features;
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* INDArray[] featureMasks;
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*
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* //With sentences
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* List<String> forInference;
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* Pair<INDArray[],INDArray[]> featuresAndMask = b.featurizeSentences(forInference);
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* INDArray[] features = featuresAndMask.getFirst();
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* INDArray[] featureMasks = featuresAndMask.getSecond();
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* featuresAndMask = b.featurizeSentences(forInference);
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*
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* //OR with sentence pairs
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* List<Pair<String, String>> forInferencePair};
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* featuresAndMask = b.featurizeSentencePairs(forInference);
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*
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* features = featuresAndMask.getFirst();
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* featureMasks = featuresAndMask.getSecond();
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* }
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* </pre>
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* This iterator supports numerous ways of configuring the behaviour with respect to the sequence lengths and data layout.<br>
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@ -135,6 +146,7 @@ public class BertIterator implements MultiDataSetIterator {
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@Setter
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protected MultiDataSetPreProcessor preProcessor;
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protected LabeledSentenceProvider sentenceProvider = null;
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protected LabeledPairSentenceProvider sentencePairProvider = null;
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protected LengthHandling lengthHandling;
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protected FeatureArrays featureArrays;
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protected Map<String, Integer> vocabMap; //TODO maybe use Eclipse ObjectIntHashMap or similar for fewer objects?
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@ -142,6 +154,7 @@ public class BertIterator implements MultiDataSetIterator {
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protected UnsupervisedLabelFormat unsupervisedLabelFormat = null;
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protected String maskToken;
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protected String prependToken;
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protected String appendToken;
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protected List<String> vocabKeysAsList;
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@ -154,6 +167,7 @@ public class BertIterator implements MultiDataSetIterator {
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this.padMinibatches = b.padMinibatches;
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this.preProcessor = b.preProcessor;
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this.sentenceProvider = b.sentenceProvider;
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this.sentencePairProvider = b.sentencePairProvider;
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this.lengthHandling = b.lengthHandling;
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this.featureArrays = b.featureArrays;
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this.vocabMap = b.vocabMap;
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@ -161,11 +175,14 @@ public class BertIterator implements MultiDataSetIterator {
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this.unsupervisedLabelFormat = b.unsupervisedLabelFormat;
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this.maskToken = b.maskToken;
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this.prependToken = b.prependToken;
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this.appendToken = b.appendToken;
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}
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@Override
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public boolean hasNext() {
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if (sentenceProvider != null)
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return sentenceProvider.hasNext();
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return sentencePairProvider.hasNext();
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}
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@Override
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@ -181,29 +198,38 @@ public class BertIterator implements MultiDataSetIterator {
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@Override
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public MultiDataSet next(int num) {
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Preconditions.checkState(hasNext(), "No next element available");
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List<Pair<String, String>> list = new ArrayList<>(num);
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List<Pair<List<String>, String>> tokensAndLabelList;
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int mbSize = 0;
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int outLength;
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long[] segIdOnesFrom = null;
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if (sentenceProvider != null) {
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List<Pair<String, String>> list = new ArrayList<>(num);
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while (sentenceProvider.hasNext() && mbSize++ < num) {
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list.add(sentenceProvider.nextSentence());
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}
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SentenceListProcessed sentenceListProcessed = tokenizeMiniBatch(list);
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tokensAndLabelList = sentenceListProcessed.getTokensAndLabelList();
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outLength = sentenceListProcessed.getMaxL();
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} else if (sentencePairProvider != null) {
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List<Triple<String, String, String>> listPairs = new ArrayList<>(num);
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while (sentencePairProvider.hasNext() && mbSize++ < num) {
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listPairs.add(sentencePairProvider.nextSentencePair());
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}
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SentencePairListProcessed sentencePairListProcessed = tokenizePairsMiniBatch(listPairs);
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tokensAndLabelList = sentencePairListProcessed.getTokensAndLabelList();
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outLength = sentencePairListProcessed.getMaxL();
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segIdOnesFrom = sentencePairListProcessed.getSegIdOnesFrom();
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} else {
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//TODO - other types of iterators...
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throw new UnsupportedOperationException("Labelled sentence provider is null and no other iterator types have yet been implemented");
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}
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Pair<Integer, List<Pair<List<String>, String>>> outLTokenizedSentencesPair = tokenizeMiniBatch(list);
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List<Pair<List<String>, String>> tokenizedSentences = outLTokenizedSentencesPair.getRight();
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int outLength = outLTokenizedSentencesPair.getLeft();
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Pair<INDArray[], INDArray[]> featuresAndMaskArraysPair = convertMiniBatchFeatures(tokenizedSentences, outLength);
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Pair<INDArray[], INDArray[]> featuresAndMaskArraysPair = convertMiniBatchFeatures(tokensAndLabelList, outLength, segIdOnesFrom);
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INDArray[] featureArray = featuresAndMaskArraysPair.getFirst();
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INDArray[] featureMaskArray = featuresAndMaskArraysPair.getSecond();
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Pair<INDArray[], INDArray[]> labelsAndMaskArraysPair = convertMiniBatchLabels(tokenizedSentences, featureArray, outLength);
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Pair<INDArray[], INDArray[]> labelsAndMaskArraysPair = convertMiniBatchLabels(tokensAndLabelList, featureArray, outLength);
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INDArray[] labelArray = labelsAndMaskArraysPair.getFirst();
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INDArray[] labelMaskArray = labelsAndMaskArraysPair.getSecond();
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@ -224,32 +250,59 @@ public class BertIterator implements MultiDataSetIterator {
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public Pair<INDArray[], INDArray[]> featurizeSentences(List<String> listOnlySentences) {
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List<Pair<String, String>> sentencesWithNullLabel = addDummyLabel(listOnlySentences);
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SentenceListProcessed sentenceListProcessed = tokenizeMiniBatch(sentencesWithNullLabel);
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List<Pair<List<String>, String>> tokensAndLabelList = sentenceListProcessed.getTokensAndLabelList();
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int outLength = sentenceListProcessed.getMaxL();
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Pair<Integer, List<Pair<List<String>, String>>> outLTokenizedSentencesPair = tokenizeMiniBatch(sentencesWithNullLabel);
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List<Pair<List<String>, String>> tokenizedSentences = outLTokenizedSentencesPair.getRight();
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int outLength = outLTokenizedSentencesPair.getLeft();
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Pair<INDArray[], INDArray[]> featureFeatureMasks = convertMiniBatchFeatures(tokenizedSentences, outLength);
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if (preProcessor != null) {
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Pair<INDArray[], INDArray[]> featureFeatureMasks = convertMiniBatchFeatures(tokensAndLabelList, outLength, null);
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MultiDataSet dummyMDS = new org.nd4j.linalg.dataset.MultiDataSet(featureFeatureMasks.getFirst(), null, featureFeatureMasks.getSecond(), null);
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preProcessor.preProcess(dummyMDS);
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return new Pair<INDArray[],INDArray[]>(dummyMDS.getFeatures(), dummyMDS.getFeaturesMaskArrays());
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return new Pair<>(dummyMDS.getFeatures(), dummyMDS.getFeaturesMaskArrays());
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}
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return convertMiniBatchFeatures(tokenizedSentences, outLength);
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return convertMiniBatchFeatures(tokensAndLabelList, outLength, null);
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}
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private Pair<INDArray[], INDArray[]> convertMiniBatchFeatures(List<Pair<List<String>, String>> tokenizedSentences, int outLength) {
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int mbPadded = padMinibatches ? minibatchSize : tokenizedSentences.size();
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/**
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* For use during inference. Will convert a given pair of a list of sentences to features and feature masks as appropriate.
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*
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* @param listOnlySentencePairs
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* @return Pair of INDArrays[], first element is feature arrays and the second is the masks array
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*/
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public Pair<INDArray[], INDArray[]> featurizeSentencePairs(List<Pair<String, String>> listOnlySentencePairs) {
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Preconditions.checkState(sentencePairProvider != null, "The featurizeSentencePairs method is meant for inference with sentence pairs. Use only when the sentence pair provider is set (i.e not null).");
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List<Triple<String, String, String>> sentencePairsWithNullLabel = addDummyLabelForPairs(listOnlySentencePairs);
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SentencePairListProcessed sentencePairListProcessed = tokenizePairsMiniBatch(sentencePairsWithNullLabel);
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List<Pair<List<String>, String>> tokensAndLabelList = sentencePairListProcessed.getTokensAndLabelList();
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int outLength = sentencePairListProcessed.getMaxL();
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long[] segIdOnesFrom = sentencePairListProcessed.getSegIdOnesFrom();
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if (preProcessor != null) {
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Pair<INDArray[], INDArray[]> featuresAndMaskArraysPair = convertMiniBatchFeatures(tokensAndLabelList, outLength, segIdOnesFrom);
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MultiDataSet dummyMDS = new org.nd4j.linalg.dataset.MultiDataSet(featuresAndMaskArraysPair.getFirst(), null, featuresAndMaskArraysPair.getSecond(), null);
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preProcessor.preProcess(dummyMDS);
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return new Pair<>(dummyMDS.getFeatures(), dummyMDS.getFeaturesMaskArrays());
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}
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return convertMiniBatchFeatures(tokensAndLabelList, outLength, segIdOnesFrom);
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}
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private Pair<INDArray[], INDArray[]> convertMiniBatchFeatures(List<Pair<List<String>, String>> tokensAndLabelList, int outLength, long[] segIdOnesFrom) {
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int mbPadded = padMinibatches ? minibatchSize : tokensAndLabelList.size();
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int[][] outIdxs = new int[mbPadded][outLength];
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int[][] outMask = new int[mbPadded][outLength];
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for (int i = 0; i < tokenizedSentences.size(); i++) {
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Pair<List<String>, String> p = tokenizedSentences.get(i);
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int[][] outSegmentId = null;
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if (featureArrays == FeatureArrays.INDICES_MASK_SEGMENTID)
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outSegmentId = new int[mbPadded][outLength];
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for (int i = 0; i < tokensAndLabelList.size(); i++) {
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Pair<List<String>, String> p = tokensAndLabelList.get(i);
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List<String> t = p.getFirst();
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for (int j = 0; j < outLength && j < t.size(); j++) {
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Preconditions.checkState(vocabMap.containsKey(t.get(j)), "Unknown token encountered: token \"%s\" is not in vocabulary", t.get(j));
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int idx = vocabMap.get(t.get(j));
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outIdxs[i][j] = idx;
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outMask[i][j] = 1;
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if (segIdOnesFrom != null && j >= segIdOnesFrom[i])
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outSegmentId[i][j] = 1;
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}
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}
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@ -260,8 +313,7 @@ public class BertIterator implements MultiDataSetIterator {
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INDArray[] f;
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INDArray[] fm;
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if (featureArrays == FeatureArrays.INDICES_MASK_SEGMENTID) {
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//For now: always segment index 0 (only single s sequence input supported)
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outSegmentIdArr = Nd4j.zeros(DataType.INT, mbPadded, outLength);
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outSegmentIdArr = Nd4j.createFromArray(outSegmentId);
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f = new INDArray[]{outIdxsArr, outSegmentIdArr};
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fm = new INDArray[]{outMaskArr, null};
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} else {
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@ -271,16 +323,15 @@ public class BertIterator implements MultiDataSetIterator {
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return new Pair<>(f, fm);
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}
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private Pair<Integer, List<Pair<List<String>, String>>> tokenizeMiniBatch(List<Pair<String, String>> list) {
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private SentenceListProcessed tokenizeMiniBatch(List<Pair<String, String>> list) {
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//Get and tokenize the sentences for this minibatch
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List<Pair<List<String>, String>> tokenizedSentences = new ArrayList<>(list.size());
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SentenceListProcessed sentenceListProcessed = new SentenceListProcessed(list.size());
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int longestSeq = -1;
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for (Pair<String, String> p : list) {
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List<String> tokens = tokenizeSentence(p.getFirst());
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tokenizedSentences.add(new Pair<>(tokens, p.getSecond()));
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sentenceListProcessed.addProcessedToList(new Pair<>(tokens, p.getSecond()));
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longestSeq = Math.max(longestSeq, tokens.size());
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}
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//Determine output array length...
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int outLength;
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switch (lengthHandling) {
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@ -296,7 +347,52 @@ public class BertIterator implements MultiDataSetIterator {
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default:
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throw new RuntimeException("Not implemented length handling mode: " + lengthHandling);
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}
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return new Pair<>(outLength, tokenizedSentences);
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sentenceListProcessed.setMaxL(outLength);
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return sentenceListProcessed;
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}
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private SentencePairListProcessed tokenizePairsMiniBatch(List<Triple<String, String, String>> listPairs) {
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SentencePairListProcessed sentencePairListProcessed = new SentencePairListProcessed(listPairs.size());
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for (Triple<String, String, String> t : listPairs) {
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List<String> tokensL = tokenizeSentence(t.getFirst(), true);
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List<String> tokensR = tokenizeSentence(t.getSecond(), true);
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List<String> tokens = new ArrayList<>(maxTokens);
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int maxLength = maxTokens;
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if (prependToken != null)
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maxLength--;
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if (appendToken != null)
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maxLength -= 2;
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if (tokensL.size() + tokensR.size() > maxLength) {
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boolean shortOnL = tokensL.size() < tokensR.size();
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int shortSize = Math.min(tokensL.size(), tokensR.size());
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if (shortSize > maxLength / 2) {
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//both lists need to be sliced
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tokensL.subList(maxLength / 2, tokensL.size()).clear(); //if maxsize/2 is odd pop extra on L side to match implementation in TF
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tokensR.subList(maxLength - maxLength / 2, tokensR.size()).clear();
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} else {
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//slice longer list
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if (shortOnL) {
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//longer on R - slice R
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tokensR.subList(maxLength - tokensL.size(), tokensR.size()).clear();
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} else {
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//longer on L - slice L
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tokensL.subList(maxLength - tokensR.size(), tokensL.size()).clear();
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}
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}
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}
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if (prependToken != null)
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tokens.add(prependToken);
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tokens.addAll(tokensL);
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if (appendToken != null)
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tokens.add(appendToken);
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int segIdOnesFrom = tokens.size();
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tokens.addAll(tokensR);
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if (appendToken != null)
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tokens.add(appendToken);
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sentencePairListProcessed.addProcessedToList(segIdOnesFrom, new Pair<>(tokens, t.getThird()));
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}
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sentencePairListProcessed.setMaxL(maxTokens);
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return sentencePairListProcessed;
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}
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private Pair<INDArray[], INDArray[]> convertMiniBatchLabels(List<Pair<List<String>, String>> tokenizedSentences, INDArray[] featureArray, int outLength) {
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@ -316,6 +412,14 @@ public class BertIterator implements MultiDataSetIterator {
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classLabels[i] = labels.indexOf(lbl);
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Preconditions.checkState(classLabels[i] >= 0, "Provided label \"%s\" for sentence does not exist in set of classes/categories", lbl);
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}
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} else if (sentencePairProvider != null) {
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numClasses = sentencePairProvider.numLabelClasses();
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List<String> labels = sentencePairProvider.allLabels();
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for (int i = 0; i < mbSize; i++) {
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String lbl = tokenizedSentences.get(i).getRight();
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classLabels[i] = labels.indexOf(lbl);
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Preconditions.checkState(classLabels[i] >= 0, "Provided label \"%s\" for sentence does not exist in set of classes/categories", lbl);
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}
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} else {
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throw new RuntimeException();
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}
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@ -392,16 +496,22 @@ public class BertIterator implements MultiDataSetIterator {
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}
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private List<String> tokenizeSentence(String sentence) {
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return tokenizeSentence(sentence, false);
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}
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private List<String> tokenizeSentence(String sentence, boolean ignorePrependAppend) {
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Tokenizer t = tokenizerFactory.create(sentence);
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List<String> tokens = new ArrayList<>();
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if (prependToken != null)
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if (prependToken != null && !ignorePrependAppend)
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tokens.add(prependToken);
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while (t.hasMoreTokens()) {
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String token = t.nextToken();
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tokens.add(token);
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}
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if (appendToken != null && !ignorePrependAppend)
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tokens.add(appendToken);
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return tokens;
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}
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@ -414,6 +524,13 @@ public class BertIterator implements MultiDataSetIterator {
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return list;
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}
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private List<Triple<String, String, String>> addDummyLabelForPairs(List<Pair<String, String>> listOnlySentencePairs) {
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List<Triple<String, String, String>> list = new ArrayList<>(listOnlySentencePairs.size());
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for (Pair<String, String> p : listOnlySentencePairs) {
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list.add(new Triple<String, String, String>(p.getFirst(), p.getSecond(), null));
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}
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return list;
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}
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@Override
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public boolean resetSupported() {
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@ -446,12 +563,14 @@ public class BertIterator implements MultiDataSetIterator {
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protected boolean padMinibatches = false;
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protected MultiDataSetPreProcessor preProcessor;
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protected LabeledSentenceProvider sentenceProvider = null;
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protected LabeledPairSentenceProvider sentencePairProvider = null;
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protected FeatureArrays featureArrays = FeatureArrays.INDICES_MASK_SEGMENTID;
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protected Map<String, Integer> vocabMap; //TODO maybe use Eclipse ObjectIntHashMap for fewer objects?
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protected BertSequenceMasker masker = new BertMaskedLMMasker();
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protected UnsupervisedLabelFormat unsupervisedLabelFormat;
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protected String maskToken;
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protected String prependToken;
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protected String appendToken;
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/**
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* Specify the {@link Task} the iterator should be set up for. See {@link BertIterator} for more details.
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@ -519,14 +638,21 @@ public class BertIterator implements MultiDataSetIterator {
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}
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/**
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* Specify the source of the data for classification. Can also be used for unsupervised learning; in the unsupervised
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* use case, the labels will be ignored.
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* Specify the source of the data for classification.
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*/
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public Builder sentenceProvider(LabeledSentenceProvider sentenceProvider) {
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this.sentenceProvider = sentenceProvider;
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return this;
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}
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/**
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* Specify the source of the data for classification on sentence pairs.
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*/
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public Builder sentencePairProvider(LabeledPairSentenceProvider sentencePairProvider) {
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this.sentencePairProvider = sentencePairProvider;
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return this;
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}
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/**
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* Specify what arrays should be returned. See {@link BertIterator} for more details.
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*/
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|
|
@ -591,6 +717,19 @@ public class BertIterator implements MultiDataSetIterator {
|
|||
return this;
|
||||
}
|
||||
|
||||
/**
|
||||
* Append the specified token to the sequences, when doing training on sentence pairs.<br>
|
||||
* Generally "[SEP]" is used
|
||||
* No token in appended by default.
|
||||
*
|
||||
* @param appendToken Token at end of each sentence for pairs of sentences (null: no token will be appended)
|
||||
* @return
|
||||
*/
|
||||
public Builder appendToken(String appendToken) {
|
||||
this.appendToken = appendToken;
|
||||
return this;
|
||||
}
|
||||
|
||||
public BertIterator build() {
|
||||
Preconditions.checkState(task != null, "No task has been set. Use .task(BertIterator.Task.X) to set the task to be performed");
|
||||
Preconditions.checkState(tokenizerFactory != null, "No tokenizer factory has been set. A tokenizer factory (such as BertWordPieceTokenizerFactory) is required");
|
||||
|
|
@ -598,9 +737,69 @@ public class BertIterator implements MultiDataSetIterator {
|
|||
Preconditions.checkState(task != Task.UNSUPERVISED || masker != null, "If task is UNSUPERVISED training, a masker must be set via masker(BertSequenceMasker) method");
|
||||
Preconditions.checkState(task != Task.UNSUPERVISED || unsupervisedLabelFormat != null, "If task is UNSUPERVISED training, a label format must be set via masker(BertSequenceMasker) method");
|
||||
Preconditions.checkState(task != Task.UNSUPERVISED || maskToken != null, "If task is UNSUPERVISED training, the mask token in the vocab (such as \"[MASK]\" must be specified");
|
||||
|
||||
if (sentencePairProvider != null) {
|
||||
Preconditions.checkState(task == Task.SEQ_CLASSIFICATION, "Currently only supervised sequence classification is set up with sentence pairs. \".task(BertIterator.Task.SEQ_CLASSIFICATION)\" is required with a sentence pair provider");
|
||||
Preconditions.checkState(featureArrays == FeatureArrays.INDICES_MASK_SEGMENTID, "Currently only supervised sequence classification is set up with sentence pairs. \".featureArrays(FeatureArrays.INDICES_MASK_SEGMENTID)\" is required with a sentence pair provider");
|
||||
Preconditions.checkState(lengthHandling == LengthHandling.FIXED_LENGTH, "Currently only fixed length is supported for sentence pairs. \".lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, maxLength)\" is required with a sentence pair provider");
|
||||
Preconditions.checkState(sentencePairProvider != null, "Provide either a sentence provider or a sentence pair provider. Both cannot be non null");
|
||||
}
|
||||
if (appendToken != null) {
|
||||
Preconditions.checkState(sentencePairProvider != null, "Tokens are only appended with sentence pairs. Sentence pair provider is not set. Set sentence pair provider.");
|
||||
}
|
||||
return new BertIterator(this);
|
||||
}
|
||||
}
|
||||
|
||||
private static class SentencePairListProcessed {
|
||||
private int listLength = 0;
|
||||
|
||||
@Getter
|
||||
private long[] segIdOnesFrom;
|
||||
private int cursor = 0;
|
||||
private SentenceListProcessed sentenceListProcessed;
|
||||
|
||||
private SentencePairListProcessed(int listLength) {
|
||||
this.listLength = listLength;
|
||||
segIdOnesFrom = new long[listLength];
|
||||
sentenceListProcessed = new SentenceListProcessed(listLength);
|
||||
}
|
||||
|
||||
private void addProcessedToList(long segIdIdx, Pair<List<String>, String> tokenizedSentencePairAndLabel) {
|
||||
segIdOnesFrom[cursor] = segIdIdx;
|
||||
sentenceListProcessed.addProcessedToList(tokenizedSentencePairAndLabel);
|
||||
cursor++;
|
||||
}
|
||||
|
||||
private void setMaxL(int maxL) {
|
||||
sentenceListProcessed.setMaxL(maxL);
|
||||
}
|
||||
|
||||
private int getMaxL() {
|
||||
return sentenceListProcessed.getMaxL();
|
||||
}
|
||||
|
||||
private List<Pair<List<String>, String>> getTokensAndLabelList() {
|
||||
return sentenceListProcessed.getTokensAndLabelList();
|
||||
}
|
||||
}
|
||||
|
||||
private static class SentenceListProcessed {
|
||||
private int listLength;
|
||||
|
||||
@Getter
|
||||
@Setter
|
||||
private int maxL;
|
||||
|
||||
@Getter
|
||||
private List<Pair<List<String>, String>> tokensAndLabelList;
|
||||
|
||||
private SentenceListProcessed(int listLength) {
|
||||
this.listLength = listLength;
|
||||
tokensAndLabelList = new ArrayList<>(listLength);
|
||||
}
|
||||
|
||||
private void addProcessedToList(Pair<List<String>, String> tokenizedSentenceAndLabel) {
|
||||
tokensAndLabelList.add(tokenizedSentenceAndLabel);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,60 @@
|
|||
/*******************************************************************************
|
||||
* Copyright (c) 2019 Konduit K.K.
|
||||
*
|
||||
* 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.deeplearning4j.iterator;
|
||||
|
||||
import org.nd4j.linalg.primitives.Triple;
|
||||
|
||||
import java.util.List;
|
||||
|
||||
/**
|
||||
* LabeledPairSentenceProvider: a simple iterator interface over a pair of sentences/documents that have a label.<br>
|
||||
*/
|
||||
public interface LabeledPairSentenceProvider {
|
||||
|
||||
/**
|
||||
* Are there more sentences/documents available?
|
||||
*/
|
||||
boolean hasNext();
|
||||
|
||||
/**
|
||||
* @return Triple: two sentence/document texts and label
|
||||
*/
|
||||
Triple<String, String, String> nextSentencePair();
|
||||
|
||||
/**
|
||||
* Reset the iterator - including shuffling the order, if necessary/appropriate
|
||||
*/
|
||||
void reset();
|
||||
|
||||
/**
|
||||
* Return the total number of sentences, or -1 if not available
|
||||
*/
|
||||
int totalNumSentences();
|
||||
|
||||
/**
|
||||
* Return the list of labels - this also defines the class/integer label assignment order
|
||||
*/
|
||||
List<String> allLabels();
|
||||
|
||||
/**
|
||||
* Equivalent to allLabels().size()
|
||||
*/
|
||||
int numLabelClasses();
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,135 @@
|
|||
/*******************************************************************************
|
||||
* Copyright (c) 2019 Konduit K.K.
|
||||
*
|
||||
* 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.deeplearning4j.iterator.provider;
|
||||
|
||||
import lombok.NonNull;
|
||||
import org.deeplearning4j.iterator.LabeledPairSentenceProvider;
|
||||
import org.nd4j.base.Preconditions;
|
||||
import org.nd4j.linalg.primitives.Triple;
|
||||
import org.nd4j.linalg.util.MathUtils;
|
||||
|
||||
import java.util.*;
|
||||
|
||||
/**
|
||||
* Iterate over a pair of sentences/documents,
|
||||
* where the sentences and labels are provided in lists.
|
||||
*
|
||||
*/
|
||||
public class CollectionLabeledPairSentenceProvider implements LabeledPairSentenceProvider {
|
||||
|
||||
private final List<String> sentenceL;
|
||||
private final List<String> sentenceR;
|
||||
private final List<String> labels;
|
||||
private final Random rng;
|
||||
private final int[] order;
|
||||
private final List<String> allLabels;
|
||||
|
||||
private int cursor = 0;
|
||||
|
||||
/**
|
||||
* Lists containing sentences to iterate over with a third for labels
|
||||
* Sentences in the same position in the first two lists are considered a pair
|
||||
* @param sentenceL
|
||||
* @param sentenceR
|
||||
* @param labelsForSentences
|
||||
*/
|
||||
public CollectionLabeledPairSentenceProvider(@NonNull List<String> sentenceL, @NonNull List<String> sentenceR,
|
||||
@NonNull List<String> labelsForSentences) {
|
||||
this(sentenceL, sentenceR, labelsForSentences, new Random());
|
||||
}
|
||||
|
||||
/**
|
||||
* Lists containing sentences to iterate over with a third for labels
|
||||
* Sentences in the same position in the first two lists are considered a pair
|
||||
* @param sentenceL
|
||||
* @param sentenceR
|
||||
* @param labelsForSentences
|
||||
* @param rng If null, list order is not shuffled
|
||||
*/
|
||||
public CollectionLabeledPairSentenceProvider(@NonNull List<String> sentenceL, List<String> sentenceR, @NonNull List<String> labelsForSentences,
|
||||
Random rng) {
|
||||
if (sentenceR.size() != sentenceL.size()) {
|
||||
throw new IllegalArgumentException("Sentence lists must be same size (first list size: "
|
||||
+ sentenceL.size() + ", second list size: " + sentenceR.size() + ")");
|
||||
}
|
||||
if (sentenceR.size() != labelsForSentences.size()) {
|
||||
throw new IllegalArgumentException("Sentence pairs and labels must be same size (sentence pair size: "
|
||||
+ sentenceR.size() + ", labels size: " + labelsForSentences.size() + ")");
|
||||
}
|
||||
|
||||
this.sentenceL = sentenceL;
|
||||
this.sentenceR = sentenceR;
|
||||
this.labels = labelsForSentences;
|
||||
this.rng = rng;
|
||||
if (rng == null) {
|
||||
order = null;
|
||||
} else {
|
||||
order = new int[sentenceR.size()];
|
||||
for (int i = 0; i < sentenceR.size(); i++) {
|
||||
order[i] = i;
|
||||
}
|
||||
|
||||
MathUtils.shuffleArray(order, rng);
|
||||
}
|
||||
|
||||
//Collect set of unique labels for all sentences
|
||||
Set<String> uniqueLabels = new HashSet<>(labelsForSentences);
|
||||
allLabels = new ArrayList<>(uniqueLabels);
|
||||
Collections.sort(allLabels);
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean hasNext() {
|
||||
return cursor < sentenceR.size();
|
||||
}
|
||||
|
||||
@Override
|
||||
public Triple<String, String, String> nextSentencePair() {
|
||||
Preconditions.checkState(hasNext(),"No next element available");
|
||||
int idx;
|
||||
if (rng == null) {
|
||||
idx = cursor++;
|
||||
} else {
|
||||
idx = order[cursor++];
|
||||
}
|
||||
return new Triple<>(sentenceL.get(idx), sentenceR.get(idx), labels.get(idx));
|
||||
}
|
||||
|
||||
@Override
|
||||
public void reset() {
|
||||
cursor = 0;
|
||||
if (rng != null) {
|
||||
MathUtils.shuffleArray(order, rng);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public int totalNumSentences() {
|
||||
return sentenceR.size();
|
||||
}
|
||||
|
||||
@Override
|
||||
public List<String> allLabels() {
|
||||
return allLabels;
|
||||
}
|
||||
|
||||
@Override
|
||||
public int numLabelClasses() {
|
||||
return allLabels.size();
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -18,6 +18,7 @@ package org.deeplearning4j.iterator.provider;
|
|||
|
||||
import lombok.NonNull;
|
||||
import org.deeplearning4j.iterator.LabeledSentenceProvider;
|
||||
import org.nd4j.base.Preconditions;
|
||||
import org.nd4j.linalg.primitives.Pair;
|
||||
import org.nd4j.linalg.util.MathUtils;
|
||||
|
||||
|
|
@ -66,10 +67,7 @@ public class CollectionLabeledSentenceProvider implements LabeledSentenceProvide
|
|||
}
|
||||
|
||||
//Collect set of unique labels for all sentences
|
||||
Set<String> uniqueLabels = new HashSet<>();
|
||||
for (String s : labelsForSentences) {
|
||||
uniqueLabels.add(s);
|
||||
}
|
||||
Set<String> uniqueLabels = new HashSet<>(labelsForSentences);
|
||||
allLabels = new ArrayList<>(uniqueLabels);
|
||||
Collections.sort(allLabels);
|
||||
}
|
||||
|
|
@ -81,6 +79,7 @@ public class CollectionLabeledSentenceProvider implements LabeledSentenceProvide
|
|||
|
||||
@Override
|
||||
public Pair<String, String> nextSentence() {
|
||||
Preconditions.checkState(hasNext(), "No next element available");
|
||||
int idx;
|
||||
if (rng == null) {
|
||||
idx = cursor++;
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
/*******************************************************************************
|
||||
* Copyright (c) 2015-2019 Skymind, Inc.
|
||||
* Copyright (c) 2019 Konduit K.K.
|
||||
*
|
||||
* This program and the accompanying materials are made available under the
|
||||
* terms of the Apache License, Version 2.0 which is available at
|
||||
|
|
@ -27,6 +28,7 @@ import org.nd4j.linalg.dataset.api.MultiDataSet;
|
|||
import org.nd4j.linalg.factory.Nd4j;
|
||||
import org.nd4j.linalg.indexing.NDArrayIndex;
|
||||
import org.nd4j.linalg.primitives.Pair;
|
||||
import org.nd4j.linalg.primitives.Triple;
|
||||
import org.nd4j.resources.Resources;
|
||||
|
||||
import java.io.File;
|
||||
|
|
@ -43,7 +45,8 @@ public class TestBertIterator extends BaseDL4JTest {
|
|||
private File pathToVocab = Resources.asFile("other/vocab.txt");
|
||||
private static Charset c = StandardCharsets.UTF_8;
|
||||
|
||||
public TestBertIterator() throws IOException{ }
|
||||
public TestBertIterator() throws IOException {
|
||||
}
|
||||
|
||||
@Test(timeout = 20000L)
|
||||
public void testBertSequenceClassification() throws Exception {
|
||||
|
|
@ -102,10 +105,10 @@ public class TestBertIterator extends BaseDL4JTest {
|
|||
assertEquals(expF, b.featurizeSentences(forInference).getFirst()[0]);
|
||||
assertEquals(expM, b.featurizeSentences(forInference).getSecond()[0]);
|
||||
|
||||
b.next(); //pop the third element
|
||||
assertFalse(b.hasNext());
|
||||
b.reset();
|
||||
assertTrue(b.hasNext());
|
||||
MultiDataSet mds2 = b.next();
|
||||
|
||||
forInference.set(0, toTokenize2);
|
||||
//Same thing, but with segment ID also
|
||||
|
|
@ -120,6 +123,7 @@ public class TestBertIterator extends BaseDL4JTest {
|
|||
.build();
|
||||
mds = b.next();
|
||||
assertEquals(2, mds.getFeatures().length);
|
||||
//assertEquals(2, mds.getFeaturesMaskArrays().length); second element is null...
|
||||
assertEquals(2, b.featurizeSentences(forInference).getFirst().length);
|
||||
//Segment ID should be all 0s for single segment task
|
||||
INDArray segmentId = expM.like();
|
||||
|
|
@ -152,9 +156,10 @@ public class TestBertIterator extends BaseDL4JTest {
|
|||
System.out.println(mds.getLabels(0));
|
||||
System.out.println(mds.getLabelsMaskArray(0));
|
||||
|
||||
b.next(); //pop the third element
|
||||
assertFalse(b.hasNext());
|
||||
b.reset();
|
||||
mds = b.next();
|
||||
assertTrue(b.hasNext());
|
||||
}
|
||||
|
||||
@Test(timeout = 20000L)
|
||||
|
|
@ -168,6 +173,7 @@ public class TestBertIterator extends BaseDL4JTest {
|
|||
INDArray expEx0 = Nd4j.create(DataType.INT, 1, 16);
|
||||
INDArray expM0 = Nd4j.create(DataType.INT, 1, 16);
|
||||
List<String> tokens = t.create(toTokenize1).getTokens();
|
||||
System.out.println(tokens);
|
||||
Map<String, Integer> m = t.getVocab();
|
||||
for (int i = 0; i < tokens.size(); i++) {
|
||||
int idx = m.get(tokens.get(i));
|
||||
|
|
@ -178,6 +184,7 @@ public class TestBertIterator extends BaseDL4JTest {
|
|||
INDArray expEx1 = Nd4j.create(DataType.INT, 1, 16);
|
||||
INDArray expM1 = Nd4j.create(DataType.INT, 1, 16);
|
||||
List<String> tokens2 = t.create(toTokenize2).getTokens();
|
||||
System.out.println(tokens2);
|
||||
for (int i = 0; i < tokens2.size(); i++) {
|
||||
String token = tokens2.get(i);
|
||||
if (!m.containsKey(token)) {
|
||||
|
|
@ -236,9 +243,11 @@ public class TestBertIterator extends BaseDL4JTest {
|
|||
Nd4j.setDefaultDataTypes(DataType.FLOAT, DataType.FLOAT);
|
||||
String toTokenize1 = "I saw a girl with a telescope.";
|
||||
String toTokenize2 = "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum";
|
||||
String toTokenize3 = "Goodnight noises everywhere";
|
||||
List<String> forInference = new ArrayList<>();
|
||||
forInference.add(toTokenize1);
|
||||
forInference.add(toTokenize2);
|
||||
forInference.add(toTokenize3);
|
||||
BertWordPieceTokenizerFactory t = new BertWordPieceTokenizerFactory(pathToVocab, false, false, c);
|
||||
INDArray expEx0 = Nd4j.create(DataType.INT, 1, 16);
|
||||
INDArray expM0 = Nd4j.create(DataType.INT, 1, 16);
|
||||
|
|
@ -263,14 +272,27 @@ public class TestBertIterator extends BaseDL4JTest {
|
|||
expM1.putScalar(0, i, 1);
|
||||
}
|
||||
|
||||
INDArray zeros = Nd4j.create(DataType.INT, 2, 16);
|
||||
INDArray expEx3 = Nd4j.create(DataType.INT, 1, 16);
|
||||
INDArray expM3 = Nd4j.create(DataType.INT, 1, 16);
|
||||
List<String> tokens3 = t.create(toTokenize3).getTokens();
|
||||
for (int i = 0; i < tokens3.size(); i++) {
|
||||
String token = tokens3.get(i);
|
||||
if (!m.containsKey(token)) {
|
||||
throw new IllegalStateException("Unknown token: \"" + token + "\"");
|
||||
}
|
||||
int idx = m.get(token);
|
||||
expEx3.putScalar(0, i, idx);
|
||||
expM3.putScalar(0, i, 1);
|
||||
}
|
||||
|
||||
INDArray expF = Nd4j.vstack(expEx0, expEx1, zeros);
|
||||
INDArray expM = Nd4j.vstack(expM0, expM1, zeros);
|
||||
INDArray expL = Nd4j.createFromArray(new float[][]{{1, 0}, {0, 1}, {0, 0}, {0, 0}});
|
||||
INDArray zeros = Nd4j.create(DataType.INT, 1, 16);
|
||||
INDArray expF = Nd4j.vstack(expEx0, expEx1, expEx3, zeros);
|
||||
INDArray expM = Nd4j.vstack(expM0, expM1, expM3, zeros);
|
||||
INDArray expL = Nd4j.createFromArray(new float[][]{{1, 0}, {0, 1}, {1, 0}, {0, 0}});
|
||||
INDArray expLM = Nd4j.create(DataType.FLOAT, 4, 1);
|
||||
expLM.putScalar(0, 0, 1);
|
||||
expLM.putScalar(1, 0, 1);
|
||||
expLM.putScalar(2, 0, 1);
|
||||
|
||||
//--------------------------------------------------------------
|
||||
|
||||
|
|
@ -305,9 +327,234 @@ public class TestBertIterator extends BaseDL4JTest {
|
|||
assertEquals(expM, b.featurizeSentences(forInference).getSecond()[0]);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testSentencePairsSingle() throws IOException {
|
||||
String shortSent = "I saw a girl with a telescope.";
|
||||
String longSent = "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum";
|
||||
boolean prependAppend;
|
||||
BertWordPieceTokenizerFactory t = new BertWordPieceTokenizerFactory(pathToVocab, false, false, c);
|
||||
int shortL = t.create(shortSent).countTokens();
|
||||
int longL = t.create(longSent).countTokens();
|
||||
|
||||
Triple<MultiDataSet, MultiDataSet, MultiDataSet> multiDataSetTriple;
|
||||
MultiDataSet shortLongPair, shortSentence, longSentence;
|
||||
|
||||
// check for pair max length exactly equal to sum of lengths - pop neither no padding
|
||||
// should be the same as hstack with segment ids 1 for second sentence
|
||||
prependAppend = true;
|
||||
multiDataSetTriple = generateMultiDataSets(new Triple<>(shortL + longL, shortL, longL), prependAppend);
|
||||
shortLongPair = multiDataSetTriple.getFirst();
|
||||
shortSentence = multiDataSetTriple.getSecond();
|
||||
longSentence = multiDataSetTriple.getThird();
|
||||
assertEquals(shortLongPair.getFeatures(0), Nd4j.hstack(shortSentence.getFeatures(0), longSentence.getFeatures(0)));
|
||||
longSentence.getFeatures(1).addi(1);
|
||||
assertEquals(shortLongPair.getFeatures(1), Nd4j.hstack(shortSentence.getFeatures(1), longSentence.getFeatures(1)));
|
||||
assertEquals(shortLongPair.getFeaturesMaskArray(0), Nd4j.hstack(shortSentence.getFeaturesMaskArray(0), longSentence.getFeaturesMaskArray(0)));
|
||||
|
||||
//check for pair max length greater than sum of lengths - pop neither with padding
|
||||
// features should be the same as hstack of shorter and longer padded with prepend/append
|
||||
// segment id should 1 only in the longer for part of the length of the sentence
|
||||
prependAppend = true;
|
||||
multiDataSetTriple = generateMultiDataSets(new Triple<>(shortL + longL + 5, shortL, longL + 5), prependAppend);
|
||||
shortLongPair = multiDataSetTriple.getFirst();
|
||||
shortSentence = multiDataSetTriple.getSecond();
|
||||
longSentence = multiDataSetTriple.getThird();
|
||||
assertEquals(shortLongPair.getFeatures(0), Nd4j.hstack(shortSentence.getFeatures(0), longSentence.getFeatures(0)));
|
||||
longSentence.getFeatures(1).get(NDArrayIndex.all(), NDArrayIndex.interval(0, longL + 1)).addi(1); //segmentId stays 0 for the padded part
|
||||
assertEquals(shortLongPair.getFeatures(1), Nd4j.hstack(shortSentence.getFeatures(1), longSentence.getFeatures(1)));
|
||||
assertEquals(shortLongPair.getFeaturesMaskArray(0), Nd4j.hstack(shortSentence.getFeaturesMaskArray(0), longSentence.getFeaturesMaskArray(0)));
|
||||
|
||||
//check for pair max length less than shorter sentence - pop both
|
||||
//should be the same as hstack with segment ids 1 for second sentence if no prepend/append
|
||||
int maxL = shortL - 2;
|
||||
prependAppend = false;
|
||||
multiDataSetTriple = generateMultiDataSets(new Triple<>(maxL, maxL / 2, maxL - maxL / 2), prependAppend);
|
||||
shortLongPair = multiDataSetTriple.getFirst();
|
||||
shortSentence = multiDataSetTriple.getSecond();
|
||||
longSentence = multiDataSetTriple.getThird();
|
||||
assertEquals(shortLongPair.getFeatures(0), Nd4j.hstack(shortSentence.getFeatures(0), longSentence.getFeatures(0)));
|
||||
longSentence.getFeatures(1).addi(1);
|
||||
assertEquals(shortLongPair.getFeatures(1), Nd4j.hstack(shortSentence.getFeatures(1), longSentence.getFeatures(1)));
|
||||
assertEquals(shortLongPair.getFeaturesMaskArray(0), Nd4j.hstack(shortSentence.getFeaturesMaskArray(0), longSentence.getFeaturesMaskArray(0)));
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testSentencePairsUnequalLengths() throws IOException {
|
||||
//check for pop only longer (i.e between longer and longer + shorter), first row pop from second sentence, next row pop from first sentence, nothing to pop in the third row
|
||||
//should be identical to hstack if there is no append, prepend
|
||||
//batch size is 2
|
||||
int mbS = 4;
|
||||
String shortSent = "I saw a girl with a telescope.";
|
||||
String longSent = "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum";
|
||||
String sent1 = "Goodnight noises everywhere"; //shorter than shortSent - no popping
|
||||
String sent2 = "Goodnight moon"; //shorter than shortSent - no popping
|
||||
BertWordPieceTokenizerFactory t = new BertWordPieceTokenizerFactory(pathToVocab, false, false, c);
|
||||
int shortL = t.create(shortSent).countTokens();
|
||||
int longL = t.create(longSent).countTokens();
|
||||
int sent1L = t.create(sent1).countTokens();
|
||||
int sent2L = t.create(sent2).countTokens();
|
||||
//won't check 2*shortL + 1 because this will always pop on the left
|
||||
for (int maxL = longL + shortL - 1; maxL > 2 * shortL; maxL--) {
|
||||
MultiDataSet leftMDS = BertIterator.builder()
|
||||
.tokenizer(t)
|
||||
.minibatchSize(mbS)
|
||||
.featureArrays(BertIterator.FeatureArrays.INDICES_MASK_SEGMENTID)
|
||||
.vocabMap(t.getVocab())
|
||||
.task(BertIterator.Task.SEQ_CLASSIFICATION)
|
||||
.lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, longL + 10) //random big num guaranteed to be longer than either
|
||||
.sentenceProvider(new TestSentenceProvider())
|
||||
.padMinibatches(true)
|
||||
.build().next();
|
||||
|
||||
MultiDataSet rightMDS = BertIterator.builder()
|
||||
.tokenizer(t)
|
||||
.minibatchSize(mbS)
|
||||
.featureArrays(BertIterator.FeatureArrays.INDICES_MASK_SEGMENTID)
|
||||
.vocabMap(t.getVocab())
|
||||
.task(BertIterator.Task.SEQ_CLASSIFICATION)
|
||||
.lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, longL + 10) //random big num guaranteed to be longer than either
|
||||
.sentenceProvider(new TestSentenceProvider(true))
|
||||
.padMinibatches(true)
|
||||
.build().next();
|
||||
|
||||
MultiDataSet pairMDS = BertIterator.builder()
|
||||
.tokenizer(t)
|
||||
.minibatchSize(mbS)
|
||||
.featureArrays(BertIterator.FeatureArrays.INDICES_MASK_SEGMENTID)
|
||||
.vocabMap(t.getVocab())
|
||||
.task(BertIterator.Task.SEQ_CLASSIFICATION)
|
||||
.lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, maxL) //random big num guaranteed to be longer than either
|
||||
.sentencePairProvider(new TestSentencePairProvider())
|
||||
.padMinibatches(true)
|
||||
.build().next();
|
||||
|
||||
//Left sentences here are {{shortSent},
|
||||
// {longSent},
|
||||
// {Sent1}}
|
||||
//Right sentences here are {{longSent},
|
||||
// {shortSent},
|
||||
// {Sent2}}
|
||||
//The sentence pairs here are {{shortSent,longSent},
|
||||
// {longSent,shortSent}
|
||||
// {Sent1, Sent2}}
|
||||
|
||||
//CHECK FEATURES
|
||||
INDArray combinedFeat = Nd4j.create(DataType.INT,mbS,maxL);
|
||||
//left side
|
||||
INDArray leftFeatures = leftMDS.getFeatures(0);
|
||||
INDArray topLSentFeat = leftFeatures.getRow(0).get(NDArrayIndex.interval(0, shortL));
|
||||
INDArray midLSentFeat = leftFeatures.getRow(1).get(NDArrayIndex.interval(0, maxL - shortL));
|
||||
INDArray bottomLSentFeat = leftFeatures.getRow(2).get(NDArrayIndex.interval(0,sent1L));
|
||||
//right side
|
||||
INDArray rightFeatures = rightMDS.getFeatures(0);
|
||||
INDArray topRSentFeat = rightFeatures.getRow(0).get(NDArrayIndex.interval(0, maxL - shortL));
|
||||
INDArray midRSentFeat = rightFeatures.getRow(1).get(NDArrayIndex.interval(0, shortL));
|
||||
INDArray bottomRSentFeat = rightFeatures.getRow(2).get(NDArrayIndex.interval(0,sent2L));
|
||||
//expected pair
|
||||
combinedFeat.getRow(0).addi(Nd4j.hstack(topLSentFeat,topRSentFeat));
|
||||
combinedFeat.getRow(1).addi(Nd4j.hstack(midLSentFeat,midRSentFeat));
|
||||
combinedFeat.getRow(2).get(NDArrayIndex.interval(0,sent1L+sent2L)).addi(Nd4j.hstack(bottomLSentFeat,bottomRSentFeat));
|
||||
|
||||
assertEquals(maxL, pairMDS.getFeatures(0).shape()[1]);
|
||||
assertArrayEquals(combinedFeat.shape(), pairMDS.getFeatures(0).shape());
|
||||
assertEquals(combinedFeat, pairMDS.getFeatures(0));
|
||||
|
||||
//CHECK SEGMENT ID
|
||||
INDArray combinedFetSeg = Nd4j.create(DataType.INT, mbS, maxL);
|
||||
combinedFetSeg.get(NDArrayIndex.point(0), NDArrayIndex.interval(shortL, maxL)).addi(1);
|
||||
combinedFetSeg.get(NDArrayIndex.point(1), NDArrayIndex.interval(maxL - shortL, maxL)).addi(1);
|
||||
combinedFetSeg.get(NDArrayIndex.point(2), NDArrayIndex.interval(sent1L, sent1L+sent2L)).addi(1);
|
||||
assertArrayEquals(combinedFetSeg.shape(), pairMDS.getFeatures(1).shape());
|
||||
assertEquals(maxL, combinedFetSeg.shape()[1]);
|
||||
assertEquals(combinedFetSeg, pairMDS.getFeatures(1));
|
||||
}
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testSentencePairFeaturizer() throws IOException {
|
||||
String shortSent = "I saw a girl with a telescope.";
|
||||
String longSent = "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum";
|
||||
List<Pair<String, String>> listSentencePair = new ArrayList<>();
|
||||
listSentencePair.add(new Pair<>(shortSent, longSent));
|
||||
listSentencePair.add(new Pair<>(longSent, shortSent));
|
||||
BertWordPieceTokenizerFactory t = new BertWordPieceTokenizerFactory(pathToVocab, false, false, c);
|
||||
BertIterator b = BertIterator.builder()
|
||||
.tokenizer(t)
|
||||
.minibatchSize(2)
|
||||
.padMinibatches(true)
|
||||
.featureArrays(BertIterator.FeatureArrays.INDICES_MASK_SEGMENTID)
|
||||
.vocabMap(t.getVocab())
|
||||
.task(BertIterator.Task.SEQ_CLASSIFICATION)
|
||||
.lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, 128)
|
||||
.sentencePairProvider(new TestSentencePairProvider())
|
||||
.prependToken("[CLS]")
|
||||
.appendToken("[SEP]")
|
||||
.build();
|
||||
MultiDataSet mds = b.next();
|
||||
INDArray[] featuresArr = mds.getFeatures();
|
||||
INDArray[] featuresMaskArr = mds.getFeaturesMaskArrays();
|
||||
|
||||
Pair<INDArray[], INDArray[]> p = b.featurizeSentencePairs(listSentencePair);
|
||||
assertEquals(p.getFirst().length, 2);
|
||||
assertEquals(featuresArr[0], p.getFirst()[0]);
|
||||
assertEquals(featuresArr[1], p.getFirst()[1]);
|
||||
//assertEquals(p.getSecond().length, 2);
|
||||
assertEquals(featuresMaskArr[0], p.getSecond()[0]);
|
||||
//assertEquals(featuresMaskArr[1], p.getSecond()[1]);
|
||||
}
|
||||
|
||||
/**
|
||||
* Returns three multidatasets from bert iterator based on given max lengths and whether to prepend/append
|
||||
* Idea is the sentence pair dataset can be constructed from the single sentence datasets
|
||||
* First one is constructed from a sentence pair "I saw a girl with a telescope." & "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum"
|
||||
* Second one is constructed from the left of the sentence pair i.e "I saw a girl with a telescope."
|
||||
* Third one is constructed from the right of the sentence pair i.e "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum"
|
||||
*/
|
||||
private Triple<MultiDataSet, MultiDataSet, MultiDataSet> generateMultiDataSets(Triple<Integer, Integer, Integer> maxLengths, boolean prependAppend) throws IOException {
|
||||
BertWordPieceTokenizerFactory t = new BertWordPieceTokenizerFactory(pathToVocab, false, false, c);
|
||||
int maxforPair = maxLengths.getFirst();
|
||||
int maxPartOne = maxLengths.getSecond();
|
||||
int maxPartTwo = maxLengths.getThird();
|
||||
BertIterator.Builder commonBuilder;
|
||||
commonBuilder = BertIterator.builder()
|
||||
.tokenizer(t)
|
||||
.minibatchSize(1)
|
||||
.featureArrays(BertIterator.FeatureArrays.INDICES_MASK_SEGMENTID)
|
||||
.vocabMap(t.getVocab())
|
||||
.task(BertIterator.Task.SEQ_CLASSIFICATION);
|
||||
BertIterator shortLongPairFirstIter = commonBuilder
|
||||
.lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, prependAppend ? maxforPair + 3 : maxforPair)
|
||||
.sentencePairProvider(new TestSentencePairProvider())
|
||||
.prependToken(prependAppend ? "[CLS]" : null)
|
||||
.appendToken(prependAppend ? "[SEP]" : null)
|
||||
.build();
|
||||
BertIterator shortFirstIter = commonBuilder
|
||||
.lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, prependAppend ? maxPartOne + 2 : maxPartOne)
|
||||
.sentenceProvider(new TestSentenceProvider())
|
||||
.prependToken(prependAppend ? "[CLS]" : null)
|
||||
.appendToken(prependAppend ? "[SEP]" : null)
|
||||
.build();
|
||||
BertIterator longFirstIter = commonBuilder
|
||||
.lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, prependAppend ? maxPartTwo + 1 : maxPartTwo)
|
||||
.sentenceProvider(new TestSentenceProvider(true))
|
||||
.prependToken(null)
|
||||
.appendToken(prependAppend ? "[SEP]" : null)
|
||||
.build();
|
||||
return new Triple<>(shortLongPairFirstIter.next(), shortFirstIter.next(), longFirstIter.next());
|
||||
}
|
||||
|
||||
private static class TestSentenceProvider implements LabeledSentenceProvider {
|
||||
|
||||
private int pos = 0;
|
||||
private boolean invert;
|
||||
|
||||
private TestSentenceProvider() {
|
||||
this.invert = false;
|
||||
}
|
||||
|
||||
private TestSentenceProvider(boolean invert) {
|
||||
this.invert = invert;
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean hasNext() {
|
||||
|
|
@ -317,10 +564,20 @@ public class TestBertIterator extends BaseDL4JTest {
|
|||
@Override
|
||||
public Pair<String, String> nextSentence() {
|
||||
Preconditions.checkState(hasNext());
|
||||
if(pos++ == 0){
|
||||
return new Pair<>("I saw a girl with a telescope.", "positive");
|
||||
} else {
|
||||
if (pos == 0) {
|
||||
pos++;
|
||||
if (!invert) return new Pair<>("I saw a girl with a telescope.", "positive");
|
||||
return new Pair<>("Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum", "negative");
|
||||
} else {
|
||||
if (pos == 1) {
|
||||
pos++;
|
||||
if (!invert) return new Pair<>("Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum", "negative");
|
||||
return new Pair<>("I saw a girl with a telescope.", "positive");
|
||||
}
|
||||
pos++;
|
||||
if (!invert)
|
||||
return new Pair<>("Goodnight noises everywhere", "positive");
|
||||
return new Pair<>("Goodnight moon", "positive");
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -331,8 +588,54 @@ public class TestBertIterator extends BaseDL4JTest {
|
|||
|
||||
@Override
|
||||
public int totalNumSentences() {
|
||||
return 3;
|
||||
}
|
||||
|
||||
@Override
|
||||
public List<String> allLabels() {
|
||||
return Arrays.asList("positive", "negative");
|
||||
}
|
||||
|
||||
@Override
|
||||
public int numLabelClasses() {
|
||||
return 2;
|
||||
}
|
||||
}
|
||||
|
||||
private static class TestSentencePairProvider implements LabeledPairSentenceProvider {
|
||||
|
||||
private int pos = 0;
|
||||
|
||||
@Override
|
||||
public boolean hasNext() {
|
||||
return pos < totalNumSentences();
|
||||
}
|
||||
|
||||
@Override
|
||||
public Triple<String, String, String> nextSentencePair() {
|
||||
Preconditions.checkState(hasNext());
|
||||
if (pos == 0) {
|
||||
pos++;
|
||||
return new Triple<>("I saw a girl with a telescope.", "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum", "positive");
|
||||
} else {
|
||||
if (pos == 1) {
|
||||
pos++;
|
||||
return new Triple<>("Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum", "I saw a girl with a telescope.", "negative");
|
||||
}
|
||||
pos++;
|
||||
return new Triple<>("Goodnight noises everywhere", "Goodnight moon", "positive");
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public void reset() {
|
||||
pos = 0;
|
||||
}
|
||||
|
||||
@Override
|
||||
public int totalNumSentences() {
|
||||
return 3;
|
||||
}
|
||||
|
||||
@Override
|
||||
public List<String> allLabels() {
|
||||
|
|
|
|||
Loading…
Reference in New Issue