Examples with Pair ai.djl.util.Pair used on opensource projects

Example 1 with Pair

use of ai.djl.util.Pair in project djl by deepjavalibrary.

the class RandomAccessDataset method toArray.

/**
 * Returns the dataset contents as a Java array.
 *
 * <p>Each Number[] is a flattened dataset record and the Number[][] is the array of all
 * records.
 *
 * @return the dataset contents as a Java array
 * @throws IOException for various exceptions depending on the dataset
 * @throws TranslateException if there is an error while processing input
 */
public Pair<Number[][], Number[][]> toArray() throws IOException, TranslateException {
    try (NDManager manager = NDManager.newBaseManager()) {
        Sampler sampl = new BatchSampler(new SequenceSampler(), 1, false);
        int size = Math.toIntExact(size());
        Number[][] data = new Number[size][];
        Number[][] labels = new Number[size][];
        int index = 0;
        for (Batch batch : this.getData(manager, sampl)) {
            data[index] = flattenRecord(batch.getData());
            labels[index] = flattenRecord(batch.getLabels());
            batch.close();
            index++;
        }
        return new Pair<>(data, labels);
    }
}

Example 2 with Pair

use of ai.djl.util.Pair in project djl by deepjavalibrary.

the class SingleShotDetectionLoss method inputForComponent.

/**
 * Calculate loss between label and prediction.
 *
 * @param labels target labels. Must contain (offsetLabels, masks, classlabels). This is
 *     returned by MultiBoxTarget function
 * @param predictions predicted labels (class prediction, offset prediction)
 * @return loss value
 */
@Override
protected Pair<NDList, NDList> inputForComponent(int componentIndex, NDList labels, NDList predictions) {
    NDArray anchors = predictions.get(0);
    NDArray classPredictions = predictions.get(1);
    NDList targets = multiBoxTarget.target(new NDList(anchors, labels.head(), classPredictions.transpose(0, 2, 1)));
    switch(componentIndex) {
        case // ClassLoss
        0:
            NDArray classLabels = targets.get(2);
            return new Pair<>(new NDList(classLabels), new NDList(classPredictions));
        case // BoundingBoxLoss
        1:
            NDArray boundingBoxPredictions = predictions.get(2);
            NDArray boundingBoxLabels = targets.get(0);
            NDArray boundingBoxMasks = targets.get(1);
            return new Pair<>(new NDList(boundingBoxLabels.mul(boundingBoxMasks)), new NDList(boundingBoxPredictions.mul(boundingBoxMasks)));
        default:
            throw new IllegalArgumentException("Invalid component index");
    }
}

Example 3 with Pair

use of ai.djl.util.Pair in project djl by deepjavalibrary.

the class Accuracy method accuracyHelper.

/**
 * {@inheritDoc}
 */
@Override
protected Pair<Long, NDArray> accuracyHelper(NDList labels, NDList predictions) {
    NDArray label = labels.head();
    NDArray prediction = predictions.head();
    checkLabelShapes(label, prediction);
    NDArray predictionReduced;
    if (!label.getShape().equals(prediction.getShape())) {
        // Multi-class, sparse label
        predictionReduced = prediction.argMax(axis);
        predictionReduced = predictionReduced.reshape(label.getShape());
    } else {
        // Multi-class, one-hot label
        predictionReduced = prediction;
    }
    // result of sum is int64 now
    long total = label.size();
    try (NDArray nd = label.toType(DataType.INT64, true)) {
        NDArray correct = predictionReduced.toType(DataType.INT64, false).eq(nd).countNonzero();
        return new Pair<>(total, correct);
    }
}

Example 4 with Pair

use of ai.djl.util.Pair in project djl by deepjavalibrary.

the class BinaryAccuracy method accuracyHelper.

/**
 * {@inheritDoc}
 */
@Override
protected Pair<Long, NDArray> accuracyHelper(NDList labels, NDList predictions) {
    Preconditions.checkArgument(labels.size() == predictions.size(), "labels and prediction length does not match.");
    NDArray label = labels.head();
    NDArray prediction = predictions.head();
    checkLabelShapes(label, prediction, false);
    NDArray predictionReduced = prediction.gte(threshold);
    // result of sum is int64 now
    long total = label.size();
    NDArray correct = label.toType(DataType.INT64, false).eq(predictionReduced.toType(DataType.INT64, false)).countNonzero();
    return new Pair<>(total, correct);
}

Example 5 with Pair

use of ai.djl.util.Pair in project djl by deepjavalibrary.

the class TopKAccuracy method accuracyHelper.

/**
 * {@inheritDoc}
 */
@Override
protected Pair<Long, NDArray> accuracyHelper(NDList labels, NDList predictions) {
    NDArray label = labels.head();
    NDArray prediction = predictions.head();
    // number of labels and predictions should be the same
    checkLabelShapes(label, prediction);
    // ascending by default
    NDArray topKPrediction = prediction.argSort(axis).toType(DataType.INT64, false);
    int numDims = topKPrediction.getShape().dimension();
    NDArray numCorrect;
    if (numDims == 1) {
        numCorrect = topKPrediction.flatten().eq(label.flatten()).countNonzero();
    } else if (numDims == 2) {
        int numClasses = (int) topKPrediction.getShape().get(1);
        topK = Math.min(topK, numClasses);
        numCorrect = NDArrays.add(IntStream.range(0, topK).mapToObj(j -> {
            // get from last index as argSort is ascending
            NDArray jPrediction = topKPrediction.get(":, {}", numClasses - j - 1);
            return jPrediction.flatten().eq(label.flatten().toType(DataType.INT64, false)).countNonzero();
        }).toArray(NDArray[]::new));
    } else {
        throw new IllegalArgumentException("Prediction should be less than 2 dimensions");
    }
    long total = label.getShape().get(0);
    return new Pair<>(total, numCorrect);
}