SEQ CIFAR100 224

Classes

class datasets.seq_cifar100_224.SequentialCIFAR100224(args, transform_type='weak')

Bases: ContinualDataset

The Sequential CIFAR100 dataset with 224x224 resolution with ViT-B/16.

Parameters:

• NAME (str) – name of the dataset.

• SETTING (str) – setting of the dataset.

• N_CLASSES_PER_TASK (int) – number of classes per task.

• N_TASKS (int) – number of tasks.

• N_CLASSES (int) – number of classes.

• SIZE (tuple) – size of the images.

• MEAN (tuple) – mean of the dataset.

• STD (tuple) – standard deviation of the dataset.

• TRANSFORM (torchvision.transforms) – transformation to apply to the data.

• TEST_TRANSFORM (torchvision.transforms) – transformation to apply to the test data.

MEAN = (0.485, 0.456, 0.406)

NAME: str = 'seq-cifar100-224'

N_CLASSES: int = 100

N_CLASSES_PER_TASK: int = 10

N_TASKS: int = 10

SETTING: str = 'class-il'

SIZE: Tuple[int] = (224, 224)

STD = (0.229, 0.224, 0.225)

TEST_TRANSFORM = Compose(     Resize(size=224, interpolation=bicubic, max_size=None, antialias=True)     ToTensor()     Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)) )

TRANSFORM = Compose(     RandomResizedCrop(size=(224, 224), scale=(0.08, 1.0), ratio=(0.75, 1.3333), interpolation=bicubic, antialias=True)     RandomHorizontalFlip(p=0.5)     ToTensor()     Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)) )

get_backbone()

get_batch_size()

get_class_names()

get_data_loaders()

Return type:

Tuple[DataLoader, DataLoader]

static get_denormalization_transform()

get_epochs()

static get_loss()

static get_normalization_transform()

static get_prompt_templates()

static get_transform()