chainer.dataset
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 __getitem__ and __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 ~DatasetMixin.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 set_dataset_root function.
See datasets for dataset implementations.
DatasetMixin
See iterators for dataset iterator implementations.
Iterator
concat_examples
to_device
get_dataset_root
set_dataset_root
cached_download
cache_or_load_file