archai.datasets.providers package

Submodules

archai.datasets.providers.cifar100_provider module

class archai.datasets.providers.cifar100_provider.Cifar100Provider(conf_dataset: archai.common.config.Config)[source]

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]][source]
get_transforms() → tuple[source]

archai.datasets.providers.cifar10_provider module

class archai.datasets.providers.cifar10_provider.Cifar10Provider(conf_dataset: archai.common.config.Config)[source]

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]][source]
get_transforms() → tuple[source]

archai.datasets.providers.fashion_mnist_provider module

class archai.datasets.providers.fashion_mnist_provider.FashionMnistProvider(conf_dataset: archai.common.config.Config)[source]

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]][source]
get_transforms() → tuple[source]

archai.datasets.providers.flower102_provider module

class archai.datasets.providers.flower102_provider.Flower102Provider(conf_dataset: archai.common.config.Config)[source]

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]][source]
get_transforms() → tuple[source]

archai.datasets.providers.food101_provider module

class archai.datasets.providers.food101_provider.Food101Provider(conf_dataset: archai.common.config.Config)[source]

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]][source]
get_transforms() → tuple[source]

archai.datasets.providers.imagenet_folder module

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

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()[source]
extra_repr()[source]
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)[source]

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]][source]
get_transforms() → tuple[source]

archai.datasets.providers.mit67_provider module

class archai.datasets.providers.mit67_provider.Mit67Provider(conf_dataset: archai.common.config.Config)[source]

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]][source]
get_transforms() → tuple[source]

archai.datasets.providers.mnist_provider module

class archai.datasets.providers.mnist_provider.MnistProvider(conf_dataset: archai.common.config.Config)[source]

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]][source]
get_transforms() → tuple[source]

archai.datasets.providers.sport8_provider module

class archai.datasets.providers.sport8_provider.Sport8Provider(conf_dataset: archai.common.config.Config)[source]

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]][source]
get_transforms() → tuple[source]

archai.datasets.providers.svhn_provider module

class archai.datasets.providers.svhn_provider.SvhnProvider(conf_dataset: archai.common.config.Config)[source]

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]][source]
get_transforms() → tuple[source]

Module contents