Providers#

Aicraft#

class archai.supergraph.datasets.providers.aircraft_provider.AircraftProvider(conf_dataset: Config)[source]#
get_datasets(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Dataset | None, Dataset | None][source]#
get_transforms(img_size: int | Tuple[int, int] | None) → tuple[source]#

CIFAR-100#

class archai.supergraph.datasets.providers.cifar100_provider.Cifar100Provider(conf_dataset: Config)[source]#
get_datasets(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Dataset | None, Dataset | None][source]#
get_transforms(img_size: int | Tuple[int, int] | None) → tuple[source]#

CIFAR-10#

class archai.supergraph.datasets.providers.cifar10_provider.Cifar10Provider(conf_dataset: Config)[source]#
get_datasets(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Dataset | None, Dataset | None][source]#
get_transforms(img_size: int | Tuple[int, int] | None) → tuple[source]#

Fashion-MNIST#

class archai.supergraph.datasets.providers.fashion_mnist_provider.FashionMnistProvider(conf_dataset: Config)[source]#
get_datasets(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Dataset | None, Dataset | None][source]#
get_transforms(img_size: int | Tuple[int, int] | None) → tuple[source]#

Flower-102#

class archai.supergraph.datasets.providers.flower102_provider.Flower102Provider(conf_dataset: Config)[source]#
get_datasets(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Dataset | None, Dataset | None][source]#
get_transforms(img_size: int | Tuple[int, int] | None) → tuple[source]#

Food-101#

class archai.supergraph.datasets.providers.food101_provider.Food101Provider(conf_dataset: Config)[source]#
get_datasets(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Dataset | None, Dataset | None][source]#
get_transforms(img_size: int | Tuple[int, int] | None) → tuple[source]#

ImageNet Folder#

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

ImageNetFolder 2012 Classification Dataset.

Parameters:

  • 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 – A function to load an image given its path.

download()[source]#
property meta_file#
property valid_splits#
property split_folder#
extra_repr()[source]#

ImageNet#

class archai.supergraph.datasets.providers.imagenet_provider.ImagenetProvider(conf_dataset: Config)[source]#
get_datasets(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Dataset | None, Dataset | None][source]#
get_transforms(img_size: int | Tuple[int, int] | None) → tuple[source]#

MIT-67#

class archai.supergraph.datasets.providers.mit67_provider.Mit67Provider(conf_dataset: Config)[source]#
get_datasets(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Dataset | None, Dataset | None][source]#
get_transforms(img_size: int | Tuple[int, int] | None) → tuple[source]#

MNIST#

class archai.supergraph.datasets.providers.mnist_provider.MnistProvider(conf_dataset: Config)[source]#
get_datasets(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Dataset | None, Dataset | None][source]#
get_transforms(img_size: int | Tuple[int, int] | None) → tuple[source]#

Person-COCO#

class archai.supergraph.datasets.providers.person_coco_provider.PersonCocoProvider(conf_dataset: Config)[source]#
get_datasets(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Dataset | None, Dataset | None][source]#
get_transforms(img_size: int | Tuple[int, int] | None) → tuple[source]#

Sport-8#

class archai.supergraph.datasets.providers.sport8_provider.Sport8Provider(conf_dataset: Config)[source]#
get_datasets(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Dataset | None, Dataset | None][source]#
get_transforms(img_size: int | Tuple[int, int] | None) → tuple[source]#

StanfordCars#

class archai.supergraph.datasets.providers.stanfordcars_provider.StanfordCarsProvider(conf_dataset: Config)[source]#
get_datasets(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Dataset | None, Dataset | None][source]#
get_transforms(img_size: int | Tuple[int, int] | None) → tuple[source]#

SVHN#

class archai.supergraph.datasets.providers.svhn_provider.SvhnProvider(conf_dataset: Config)[source]#
get_datasets(load_train: bool, load_test: bool, transform_train, transform_test) → Tuple[Dataset | None, Dataset | None][source]#
get_transforms(img_size: int | Tuple[int, int] | None) → tuple[source]#