Continual Loader (CLLoader)

A library for PyTorch's loading of datasets in the field of Continual Learning

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

Example:

Install from and PyPi:

pip3 install clloader

And run!

from torch.utils.data import DataLoader

from clloader import CLLoader
from clloader.datasets import MNIST

clloader = CLLoader(
    MNIST("my/data/path", download=True),
    increment=1,
    initial_increment=5
)

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

for task_id, (train_dataset, test_dataset) in enumerate(clloader):
    train_loader = DataLoader(train_dataset)
    test_loader = DataLoader(test_dataset)

    # Do your cool stuff here

Supported Scenarios

Name	Acronym	Supported
New Instances	NI	❌
New Classes	NC	✅
New Instances & Classes	NIC	❌

Supported Datasets:

Note that the task sizes are fully customizable.

Name	Nb classes	Image Size	Automatic Download
MNIST	10	28x28x1	✅
Fashion MNIST	10	28x28x1	✅
KMNIST	10	28x28x1	✅
EMNIST	10	28x28x1	✅
QMNIST	10	28x28x1	✅
MNIST Fellowship	30	28x28x1	✅
CIFAR10	10	32x32x3	✅
CIFAR100	100	32x32x3	✅
CIFAR Fellowship	110	32x32x3	✅
ImageNet100	100	224x224x3	❌
ImageNet1000	1000	224x224x3	❌
Permuted MNIST	10	28x28x1	✅
Rotated MNIST	10	28x28x1	✅

Furthermore some "Meta"-datasets are available:

InMemoryDataset, for in-memory numpy array:

x_train, y_train = gen_numpy_array()
x_test, y_test = gen_numpy_array()

clloader = CLLoader(
    InMemoryDataset(x_train, y_train, x_test, y_test),
    increment=10,
)

PyTorchDataset,for any dataset defined in torchvision:

clloader = CLLoader(
    PyTorchDataset("/my/data/path", dataset_type=torchvision.datasets.CIFAR10),
    increment=10,
)

ImageFolderDataset, for datasets having a tree-like structure, with one folder per class:

clloader = CLLoader(
    ImageFolderDataset("/my/train/folder", "/my/test/folder"),
    increment=10,
)

Fellowship, to combine several continual datasets.:

clloader = CLLoader(
    Fellowship("/my/data/path", dataset_list=[CIFAR10, CIFAR100]),
    increment=10,
)

Some datasets cannot provide an automatic download of the data for miscealleneous reasons. For example for ImageNet, you'll need to download the data from the official page. Then load it likewise:

clloader = CLLoader(
    ImageNet1000("/my/train/folder", "/my/test/folder"),
    increment=10,
)

Some papers use a subset, called ImageNet100 or ImageNetSubset. You'll need to get the subset ids. It's either a file in the following format:

my/path/to/image0.JPEG target0
my/path/to/image1.JPEG target1

Or a list of tuple [("my/path/to/image0.JPEG", target0), ...]. Then loading the continual loader is very simple:

clloader = CLLoader(
    ImageNet100(
        "/my/train/folder",
        "/my/test/folder",
        train_subset=... # My subset ids
        test_subset=... # My subset ids
    ),
    increment=10,
)

Continual Loader

The Continual Loader CLLoader loads the data and batch it in several tasks. See there some example arguments:

clloader = CLLoader(
    my_continual_dataset,
    increment=10,
    initial_increment=2,
    train_transformations=[transforms.RandomHorizontalFlip()],
    common_transformations=[
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
    ],
    evaluate_on="seen"
)

Here the first task is made of 2 classes, then all following tasks of 10 classes. You can have a more finegrained increment by providing a list of ìncrement=[2, 10, 5, 10]`.

The train_transformations is applied only on the training data, while the common_transformations on both the training and testing data.

By default, we evaluate our model after each task on seen classes. But you can evalute only on current classes, or even on all classes.

Sample Images

MNIST:

Task 0	Task 1	Task 2	Task 3	Task 4

FashionMNIST:

Task 0	Task 1	Task 2	Task 3	Task 4

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