SEQ CELEBA#

Classes#

class datasets.seq_celeba.CelebA(root, split='train', transform=None, target_transform=None, download=False, version=None)[source]#: Bases: BiasCelebA

class datasets.seq_celeba.SequentialCelebA(args, split_id=1)[source]#

Bases: ContinualDataset

MEAN = (0.4914, 0.4822, 0.4465)#

NAME: str = 'seq-celeba'#

N_CLASSES: int = 8#

N_CLASSES_PER_TASK: int = 1#

N_TASKS: int = 8#

SETTING: str = 'biased-class-il'#

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

STD = (0.247, 0.2435, 0.2615)#

TEST_TRANSFORM = Compose( Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=True) ToTensor() Normalize(mean=(0.4914, 0.4822, 0.4465), std=(0.247, 0.2435, 0.2615)) )#

TRANSFORM = Compose( RandomResizedCrop(size=(224, 224), scale=(0.08, 1.0), ratio=(0.75, 1.3333), interpolation=bilinear, antialias=True) RandomHorizontalFlip(p=0.5) ToTensor() Normalize(mean=(0.4914, 0.4822, 0.4465), std=(0.247, 0.2435, 0.2615)) )#

get_backbone(net_type=None)[source]#

get_batch_size()[source]#

get_data_loaders()[source]#

static get_denormalization_transform()[source]#

get_epochs()[source]#

static get_loss()[source]#

static get_normalization_transform()[source]#

get_scheduler_name()[source]#

static get_transform()[source]#