mindspore.dataset.dataloader

This module provides iterators for loading datasets. It supports loading both Map Style and Iterable Style datasets, and offers multi-process concurrent loading.

Data Loader

mindspore.dataset.dataloader.DataLoader

Data loader provides an iterator over the given dataset.

Datasets

`mindspore.dataset.dataloader.Dataset`	Base class for map style datasets.
`mindspore.dataset.dataloader.IterableDataset`	Base class for iterable datasets.
`mindspore.dataset.dataloader.TensorDataset`	Each sample is retrieved by indexing the input tensors along their first dimension.

Samplers

`mindspore.dataset.dataloader.Sampler`	Base Class of the Sampler
`mindspore.dataset.dataloader.SequentialSampler`	Samples the dataset elements sequentially.
`mindspore.dataset.dataloader.RandomSampler`	Samples the dataset elements randomly.
`mindspore.dataset.dataloader.BatchSampler`	A sampler that generates mini-batch indices each time.
`mindspore.dataset.dataloader.DistributedSampler`	A sampler that partitioning datasets for distributed training.

Collate Functions

`mindspore.dataset.dataloader.default_collate`	Take in a batch of data and put the elements within the batch into a tensor with an additional outer dimension - batch size.
`mindspore.dataset.dataloader.default_convert`	Convert each NumPy array element into a `mindspore.Tensor`.
`mindspore.dataset.dataloader._utils.collate.collate`	General collate function that handles collection type of element within each batch.

Utilities

mindspore.dataset.dataloader.get_worker_info

Get the information about the current DataLoader worker process.