# archai.datasets.providers package

## Submodules

## archai.datasets.providers.cifar100_provider module

class `archai.datasets.providers.cifar100_provider.``Cifar100Provider`(conf_dataset: archai.common.config.Config)

  Bases: `archai.datasets.dataset_provider.DatasetProvider`

  `get_datasets`(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Optional[torch.utils.data.dataset.Dataset], Optional[torch.utils.data.dataset.Dataset]]
  `get_transforms`() → tuple

## archai.datasets.providers.cifar10_provider module

class `archai.datasets.providers.cifar10_provider.``Cifar10Provider`(conf_dataset: archai.common.config.Config)

  Bases: `archai.datasets.dataset_provider.DatasetProvider`

  `get_datasets`(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Optional[torch.utils.data.dataset.Dataset], Optional[torch.utils.data.dataset.Dataset]]
  `get_transforms`() → tuple

## archai.datasets.providers.fashion_mnist_provider module

class `archai.datasets.providers.fashion_mnist_provider.``FashionMnistProvider`(conf_dataset: archai.common.config.Config)

  Bases: `archai.datasets.dataset_provider.DatasetProvider`

  `get_datasets`(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Optional[torch.utils.data.dataset.Dataset], Optional[torch.utils.data.dataset.Dataset]]
  `get_transforms`() → tuple

## archai.datasets.providers.flower102_provider module

class `archai.datasets.providers.flower102_provider.``Flower102Provider`(conf_dataset: archai.common.config.Config)

  Bases: `archai.datasets.dataset_provider.DatasetProvider`

  `get_datasets`(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Optional[torch.utils.data.dataset.Dataset], Optional[torch.utils.data.dataset.Dataset]]
  `get_transforms`() → tuple

## archai.datasets.providers.food101_provider module

class `archai.datasets.providers.food101_provider.``Food101Provider`(conf_dataset: archai.common.config.Config)

  Bases: `archai.datasets.dataset_provider.DatasetProvider`

  `get_datasets`(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Optional[torch.utils.data.dataset.Dataset], Optional[torch.utils.data.dataset.Dataset]]
  `get_transforms`() → tuple

## archai.datasets.providers.imagenet_folder module

class `archai.datasets.providers.imagenet_folder.``ImageNetFolder`(root, split='train', download=False, **kwargs)

  Bases: `torchvision.datasets.folder.ImageFolder`

  ImageNetFolder 2012 Classification Dataset.

  Args:

    > root (string): Root directory of the ImageNet Dataset. split (string, optional): The dataset split, supports `train`, or `val`. download (bool, optional): If true, downloads the dataset from the internet and

    > > puts it in root directory. If dataset is already downloaded, it is not downloaded again.

    > transform (callable, optional): A function/transform that takes in an PIL image

    >   and returns a transformed version. E.g, `transforms.RandomCrop`

    > target_transform (callable, optional): A function/transform that takes in the

    >   target and transforms it.

    > loader (callable, optional): A function to load an image given its path.

    Attributes:

      classes (list): List of the class names. class_to_idx (dict): Dict with items (class_name, class_index). wnids (list): List of the WordNet IDs. wnid_to_idx (dict): Dict with items (wordnet_id, class_index). imgs (list): List of (image path, class_index) tuples targets (list): The class_index value for each image in the dataset

  `download`()
  `extra_repr`()
  property `meta_file`
  property `split_folder`
  property `valid_splits`

## archai.datasets.providers.imagenet_provider module

class `archai.datasets.providers.imagenet_provider.``ImagenetProvider`(conf_dataset: archai.common.config.Config)

  Bases: `archai.datasets.dataset_provider.DatasetProvider`

  `get_datasets`(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Optional[torch.utils.data.dataset.Dataset], Optional[torch.utils.data.dataset.Dataset]]
  `get_transforms`() → tuple

## archai.datasets.providers.mit67_provider module

class `archai.datasets.providers.mit67_provider.``Mit67Provider`(conf_dataset: archai.common.config.Config)

  Bases: `archai.datasets.dataset_provider.DatasetProvider`

  `get_datasets`(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Optional[torch.utils.data.dataset.Dataset], Optional[torch.utils.data.dataset.Dataset]]
  `get_transforms`() → tuple

## archai.datasets.providers.mnist_provider module

class `archai.datasets.providers.mnist_provider.``MnistProvider`(conf_dataset: archai.common.config.Config)

  Bases: `archai.datasets.dataset_provider.DatasetProvider`

  `get_datasets`(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Optional[torch.utils.data.dataset.Dataset], Optional[torch.utils.data.dataset.Dataset]]
  `get_transforms`() → tuple

## archai.datasets.providers.sport8_provider module

class `archai.datasets.providers.sport8_provider.``Sport8Provider`(conf_dataset: archai.common.config.Config)

  Bases: `archai.datasets.dataset_provider.DatasetProvider`

  `get_datasets`(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Optional[torch.utils.data.dataset.Dataset], Optional[torch.utils.data.dataset.Dataset]]
  `get_transforms`() → tuple

## archai.datasets.providers.svhn_provider module

class `archai.datasets.providers.svhn_provider.``SvhnProvider`(conf_dataset: archai.common.config.Config)

  Bases: `archai.datasets.dataset_provider.DatasetProvider`

  `get_datasets`(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Optional[torch.utils.data.dataset.Dataset], Optional[torch.utils.data.dataset.Dataset]]
  `get_transforms`() → tuple

## Module contents