SEQ ISIC

Classes

class datasets.seq_isic.Isic(root, train=True, transform=None, target_transform=None, download=False)

Bases: Dataset

LABELS = ['melanoma', 'basal cell carcinoma', 'actinic keratosis or intraepithelial carcinoma', 'benign keratosis', 'dermatofibroma', 'vascular skin lesion']

Overrides the ChestX dataset to change the getitem function.

N_CLASSES = 6

class datasets.seq_isic.SequentialIsic(args)

Bases: ContinualDataset

MEAN = [0.485, 0.456, 0.406]

NAME: str = 'seq-isic'

N_CLASSES: int = 6

N_CLASSES_PER_TASK: int = 2

N_TASKS: int = 3

SETTING: str = 'class-il'

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

STD = [0.229, 0.224, 0.225]

TEST_TRANSFORM = Compose(     Resize(size=(256, 256), interpolation=bicubic, max_size=None, antialias=True)     CenterCrop(size=(224, 224))     ToTensor()     Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) )

TRANSFORM = Compose(     Resize(size=256, interpolation=bicubic, max_size=None, antialias=True)     RandomCrop(size=(224, 224), padding=None)     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()

static get_denormalization_transform()

get_epochs()

static get_loss()

static get_normalization_transform()

static get_prompt_templates()

static get_transform()