ai.djl.training.dataset.RandomAccessDataset Maven / Gradle / Ivy
/*
* Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
* with the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0/
*
* or in the "license" file accompanying this file. This file 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 ai.djl.training.dataset;
import ai.djl.Device;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDArrays;
import ai.djl.ndarray.NDList;
import ai.djl.ndarray.NDManager;
import ai.djl.translate.Batchifier;
import ai.djl.translate.Pipeline;
import ai.djl.translate.Transform;
import ai.djl.translate.TranslateException;
import ai.djl.util.Pair;
import ai.djl.util.Progress;
import ai.djl.util.RandomUtils;
import java.io.IOException;
import java.util.Arrays;
import java.util.Objects;
import java.util.concurrent.ExecutorService;
import java.util.stream.Collectors;
import java.util.stream.IntStream;
/**
* RandomAccessDataset represent the dataset that support random access reads. i.e. it could access
* a specific data item given the index.
*/
public abstract class RandomAccessDataset implements Dataset {
protected Sampler sampler;
protected Batchifier dataBatchifier;
protected Batchifier labelBatchifier;
protected Pipeline pipeline;
protected Pipeline targetPipeline;
protected int prefetchNumber;
protected long limit;
protected Device device;
RandomAccessDataset() {}
/**
* Creates a new instance of {@link RandomAccessDataset} with the given necessary
* configurations.
*
* @param builder a builder with the necessary configurations
*/
public RandomAccessDataset(BaseBuilder builder) {
this.sampler = builder.getSampler();
this.dataBatchifier = builder.dataBatchifier;
this.labelBatchifier = builder.labelBatchifier;
this.pipeline = builder.pipeline;
this.targetPipeline = builder.targetPipeline;
this.prefetchNumber = builder.prefetchNumber;
this.limit = builder.limit;
this.device = builder.device;
}
/**
* Gets the {@link Record} for the given index from the dataset.
*
* @param manager the manager used to create the arrays
* @param index the index of the requested data item
* @return a {@link Record} that contains the data and label of the requested data item
* @throws IOException if an I/O error occurs
*/
public abstract Record get(NDManager manager, long index) throws IOException;
/** {@inheritDoc} */
@Override
public Iterable getData(NDManager manager) throws IOException, TranslateException {
prepare();
return new DataIterable(
this,
manager,
sampler,
dataBatchifier,
labelBatchifier,
pipeline,
targetPipeline,
null,
prefetchNumber,
device);
}
/** {@inheritDoc} */
@Override
public Iterable getData(NDManager manager, ExecutorService executorService)
throws IOException, TranslateException {
prepare();
return new DataIterable(
this,
manager,
sampler,
dataBatchifier,
labelBatchifier,
pipeline,
targetPipeline,
executorService,
prefetchNumber,
device);
}
/**
* Fetches an iterator that can iterate through the {@link Dataset} with a custom sampler.
*
* @param manager the dataset to iterate through
* @param sampler the sampler to use to iterate through the dataset
* @return an {@link Iterable} of {@link Batch} that contains batches of data from the dataset
* @throws IOException for various exceptions depending on the dataset
* @throws TranslateException if there is an error while processing input
*/
public Iterable getData(NDManager manager, Sampler sampler)
throws IOException, TranslateException {
prepare();
return new DataIterable(
this,
manager,
sampler,
dataBatchifier,
labelBatchifier,
pipeline,
targetPipeline,
null,
prefetchNumber,
device);
}
/**
* Fetches an iterator that can iterate through the {@link Dataset} with a custom sampler
* multi-threaded.
*
* @param manager the dataset to iterate through
* @param sampler the sampler to use to iterate through the dataset
* @param executorService the executorService to multi-thread with
* @return an {@link Iterable} of {@link Batch} that contains batches of data from the dataset
* @throws IOException for various exceptions depending on the dataset
* @throws TranslateException if there is an error while processing input
*/
public Iterable getData(
NDManager manager, Sampler sampler, ExecutorService executorService)
throws IOException, TranslateException {
prepare();
return new DataIterable(
this,
manager,
sampler,
dataBatchifier,
labelBatchifier,
pipeline,
targetPipeline,
executorService,
prefetchNumber,
device);
}
/**
* Returns the size of this {@code Dataset}.
*
* @return the size of this {@code Dataset}
*/
public long size() {
return Math.min(limit, availableSize());
}
/**
* Returns the number of records available to be read in this {@code Dataset}.
*
* @return the number of records available to be read in this {@code Dataset}
*/
protected abstract long availableSize();
/**
* Splits the dataset set into multiple portions.
*
* @param ratio the ratio of each sub dataset
* @return an array of the sub dataset
* @throws IOException for various exceptions depending on the dataset
* @throws TranslateException if there is an error while processing input
*/
public RandomAccessDataset[] randomSplit(int... ratio) throws IOException, TranslateException {
prepare();
if (ratio.length < 2) {
throw new IllegalArgumentException("Requires at least two split portion.");
}
int size = Math.toIntExact(size());
int[] indices = IntStream.range(0, size).toArray();
for (int i = 0; i < size; ++i) {
swap(indices, i, RandomUtils.nextInt(size));
}
RandomAccessDataset[] ret = new RandomAccessDataset[ratio.length];
double sum = Arrays.stream(ratio).sum();
int from = 0;
for (int i = 0; i < ratio.length - 1; ++i) {
int to = from + (int) (ratio[i] / sum * size);
ret[i] = new SubDataset(this, indices, from, to);
from = to;
}
ret[ratio.length - 1] = new SubDataset(this, indices, from, size);
return ret;
}
/**
* Returns a view of the portion of this data between the specified {@code fromIndex},
* inclusive, and {@code toIndex}, exclusive.
*
* @param fromIndex low endpoint (inclusive) of the subDataset
* @param toIndex high endpoint (exclusive) of the subData
* @return a view of the specified range within this dataset
*/
public RandomAccessDataset subDataset(int fromIndex, int toIndex) {
int size = Math.toIntExact(size());
int[] indices = IntStream.range(0, size).toArray();
return new SubDataset(this, indices, fromIndex, toIndex);
}
/**
* Returns the dataset contents as a Java array.
*
* 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 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);
}
}
private Number[] flattenRecord(NDList data) {
NDList flattened =
new NDList(data.stream().map(NDArray::flatten).collect(Collectors.toList()));
if (flattened.size() == 0) {
return null;
}
if (flattened.size() == 1) {
return flattened.get(0).toArray();
}
return NDArrays.concat(flattened).toArray();
}
private static void swap(int[] arr, int i, int j) {
int tmp = arr[i];
arr[i] = arr[j];
arr[j] = tmp;
}
/** The Builder to construct a {@link RandomAccessDataset}. */
public abstract static class BaseBuilder> {
protected Sampler sampler;
protected Batchifier dataBatchifier = Batchifier.STACK;
protected Batchifier labelBatchifier = Batchifier.STACK;
protected Pipeline pipeline;
protected Pipeline targetPipeline;
protected int prefetchNumber = 2;
protected long limit = Long.MAX_VALUE;
protected Device device;
/**
* Gets the {@link Sampler} for the dataset.
*
* @return the {@code Sampler}
*/
public Sampler getSampler() {
Objects.requireNonNull(sampler, "The sampler must be set");
return sampler;
}
/**
* Sets the {@link Sampler} with the given batch size.
*
* @param batchSize the batch size
* @param random whether the sampling has to be random
* @return this {@code BaseBuilder}
*/
public T setSampling(int batchSize, boolean random) {
return setSampling(batchSize, random, false);
}
/**
* Sets the {@link Sampler} with the given batch size.
*
* @param batchSize the batch size
* @param random whether the sampling has to be random
* @param dropLast whether to drop the last incomplete batch
* @return this {@code BaseBuilder}
*/
public T setSampling(int batchSize, boolean random, boolean dropLast) {
if (random) {
sampler = new BatchSampler(new RandomSampler(), batchSize, dropLast);
} else {
sampler = new BatchSampler(new SequenceSampler(), batchSize, dropLast);
}
return self();
}
/**
* Sets the {@link Sampler} for the dataset.
*
* @param sampler the {@link Sampler} to be set
* @return this {@code BaseBuilder}
*/
public T setSampling(Sampler sampler) {
this.sampler = sampler;
return self();
}
/**
* Sets the {@link Batchifier} for the data.
*
* @param dataBatchifier the {@link Batchifier} to be set
* @return this {@code BaseBuilder}
*/
public T optDataBatchifier(Batchifier dataBatchifier) {
this.dataBatchifier = dataBatchifier;
return self();
}
/**
* Sets the {@link Batchifier} for the labels.
*
* @param labelBatchifier the {@link Batchifier} to be set
* @return this {@code BaseBuilder}
*/
public T optLabelBatchifier(Batchifier labelBatchifier) {
this.labelBatchifier = labelBatchifier;
return self();
}
/**
* Sets the {@link Pipeline} of {@link ai.djl.translate.Transform} to be applied on the
* data.
*
* @param pipeline the {@link Pipeline} of {@link ai.djl.translate.Transform} to be applied
* on the data
* @return this {@code BaseBuilder}
*/
public T optPipeline(Pipeline pipeline) {
this.pipeline = pipeline;
return self();
}
/**
* Adds the {@link Transform} to the {@link Pipeline} to be applied on the data.
*
* @param transform the {@link Transform} to be added
* @return this builder
*/
public T addTransform(Transform transform) {
if (pipeline == null) {
pipeline = new Pipeline();
}
pipeline.add(transform);
return self();
}
/**
* Sets the {@link Pipeline} of {@link ai.djl.translate.Transform} to be applied on the
* labels.
*
* @param targetPipeline the {@link Pipeline} of {@link ai.djl.translate.Transform} to be
* applied on the labels
* @return this {@code BaseBuilder}
*/
public T optTargetPipeline(Pipeline targetPipeline) {
this.targetPipeline = targetPipeline;
return self();
}
/**
* Adds the {@link Transform} to the target {@link Pipeline} to be applied on the labels.
*
* @param transform the {@link Transform} to be added
* @return this builder
*/
public T addTargetTransform(Transform transform) {
if (targetPipeline == null) {
targetPipeline = new Pipeline();
}
targetPipeline.add(transform);
return self();
}
/**
* Sets the number of batches to prefetch at once.
*
* @param prefetchNumber the number of batches to prefetch at once
* @return this {@code BaseBuilder}
*/
public T optPrefetchNumber(int prefetchNumber) {
this.prefetchNumber = prefetchNumber;
return self();
}
/**
* Sets the {@link Device}.
*
* @param device the device
* @return this {@code BaseBuilder}
*/
public T optDevice(Device device) {
this.device = device;
return self();
}
/**
* Sets this dataset's limit.
*
* The limit is usually used for testing purposes to test only with a subset of the
* dataset.
*
* @param limit the limit of this dataset's records
* @return this {@code BaseBuilder}
*/
public T optLimit(long limit) {
this.limit = limit;
return self();
}
/**
* Returns this {code Builder} object.
*
* @return this {@code BaseBuilder}
*/
protected abstract T self();
}
private static final class SubDataset extends RandomAccessDataset {
private RandomAccessDataset dataset;
private int[] indices;
private int from;
private int to;
public SubDataset(RandomAccessDataset dataset, int[] indices, int from, int to) {
this.dataset = dataset;
this.indices = indices;
this.from = from;
this.to = to;
this.sampler = dataset.sampler;
this.dataBatchifier = dataset.dataBatchifier;
this.labelBatchifier = dataset.labelBatchifier;
this.pipeline = dataset.pipeline;
this.targetPipeline = dataset.targetPipeline;
this.prefetchNumber = dataset.prefetchNumber;
this.device = dataset.device;
limit = Long.MAX_VALUE;
}
/** {@inheritDoc} */
@Override
public Record get(NDManager manager, long index) throws IOException {
if (index >= size()) {
throw new IndexOutOfBoundsException("index(" + index + ") > size(" + size() + ").");
}
return dataset.get(manager, indices[Math.toIntExact(index) + from]);
}
/** {@inheritDoc} */
@Override
protected long availableSize() {
return to - from;
}
/** {@inheritDoc} */
@Override
public void prepare(Progress progress) {}
}
}