Aka Continual Learning, Lifelong-Learning, Incremental Learning, etc.

Read the documentation.

Install from and PyPi:

pip3 install continuum

And run!

from torch.utils.data import DataLoader

from continuum import ClassIncremental
from continuum.datasets import MNIST
from continuum.tasks import split_train_val


scenario = ClassIncremental(
    MNIST("my/data/path", download=True, train=True),
    increment=1,
    initial_increment=5
)

print(f"Number of classes: {scenario.nb_classes}.")
print(f"Number of tasks: {scenario.nb_tasks}.")

for task_id, train_taskset in enumerate(scenario):
    train_taskset, val_taskset = split_train_val(train_taskset, val_split=0.1)
    train_loader = DataLoader(train_taskset, batch_size=32, shuffle=True)
    val_loader = DataLoader(val_taskset, batch_size=32, shuffle=True)

    for x, y, t in train_loader:
        # Do your cool stuff here

Most dataset from torchvision.dasasets are supported, for the complete list, look at the documentation page on datasets here.

Furthermore some "Meta"-datasets are can be create or used from numpy array or any torchvision.datasets or from a folder for datasets having a tree-like structure or by combining several dataset and creating dataset fellowships!

All our continual loader are iterable (i.e. you can for loop on them), and are also indexable.

Meaning that clloader[2] returns the third task (index starts at 0). Likewise, if you want to evaluate after each task, on all seen tasks do clloader_test[:n].

CIFAR10:

Task 0	Task 1	Task 2	Task 3	Task 4

MNIST Fellowship (MNIST + FashionMNIST + KMNIST):

Task 0	Task 1	Task 2

PermutedMNIST:

Task 0	Task 1	Task 2	Task 3	Task 4

RotatedMNIST: