Chainer has a support of common interface of training and validation datasets. The dataset support consists of three components: datasets, iterators, and batch conversion functions.
Dataset represents a set of examples. The interface is only determined by combination with iterators you want to use on it. The built-in iterators of Chainer requires the dataset to support
__len__ method. In particular, the
__getitem__ method should support indexing by both an integer and a slice. We can easily support slice indexing by inheriting
DatasetMixin, in which case users only have to implement
get_example() method for indexing. Some iterators also restrict the type of each example. Basically, datasets are considered as stateless objects, so that we do not need to save the dataset as a checkpoint of the training procedure.
Iterator iterates over the dataset, and at each iteration, it yields a mini batch of examples as a list. Iterators should support the
Iterator interface, which includes the standard iterator protocol of Python. Iterators manage where to read next, which means they are stateful.
Batch conversion function converts the mini batch into arrays to feed to the neural nets. They are also responsible to send each array to an appropriate device. Chainer currently provides
concat_examples() as the only example of batch conversion functions.
These components are all customizable, and designed to have a minimum interface to restrict the types of datasets and ways to handle them. In most cases, though, implementations provided by Chainer itself are enough to cover the usages.
Chainer also has a light system to download, manage, and cache concrete examples of datasets. All datasets managed through the system are saved under the dataset root directory, which is determined by the
CHAINER_DATASET_ROOT environment variable, and can also be set by the
See Dataset examples for dataset implementations.
||Default implementation of dataset indexing.|
See Iterator examples for dataset iterator implementations.
||Base class of all dataset iterators.|
Batch conversion function¶
||Concatenates a list of examples into array(s).|
||Send an array to a given device.|
||Gets the path to the root directory to download and cache datasets.|
||Sets the root directory to download and cache datasets.|
||Downloads a file and caches it.|
||Caches a file if it does not exist, or loads it otherwise.|