E.g., in theĬommon case with stochastic gradient decent (SGD), a They represent iterable objects over the indices to datasets. Ĭlasses are used to specify the sequence of indices/keys used in data loading. The rest of this section concerns the case with Implementations of chunk-reading and dynamic batch size (e.g., by yielding a
Is entirely controlled by the user-defined iterable. Data Loading Order and Sampler ¶įor iterable-style datasets, data loading order IterableDataset documentations for how toĪchieve this. Replicas must be configured differently to avoid duplicated data. Stream of data reading from a database, a remote server, or even logs generatedĭataset object is replicated on each worker process, and thus the Random reads are expensive or even improbable, and where the batch size dependsįor example, such a dataset, when called iter(dataset), could return a This type of datasets is particularly suitable for cases where That implements the _iter_() protocol, and represents an iterable overĭata samples. Iterable-style datasets ¶Īn iterable-style dataset is an instance of a subclass of IterableDataset
The idx-th image and its corresponding label from a folder on the disk. _len_() protocols, and represents a map from (possibly non-integral)įor example, such a dataset, when accessed with dataset, could read PyTorch supports two different types of datasets:Ī map-style dataset is one that implements the _getitem_() and The most important argument of DataLoaderĬonstructor is dataset, which indicates a dataset object to load dataįrom. The sections below describe in details the effects and usages of these options. Extending torch.func with autograd.FunctionĭataLoader ( dataset, batch_size = 1, shuffle = False, sampler = None, batch_sampler = None, num_workers = 0, collate_fn = None, pin_memory = False, drop_last = False, timeout = 0, worker_init_fn = None, *, prefetch_factor = 2, persistent_workers = False ).CPU threading and TorchScript inference.CUDA Automatic Mixed Precision examples.
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