A clean and simple library for Continual Learning in PyTorch.
Project description
Continuum
A library for PyTorch's loading of datasets in the field of Continual Learning
Aka Continual Learning, Lifelong-Learning, Incremental Learning, etc.
Read the documentation.
Example:
Install from and PyPi:
pip3 install continuum
And run!
from torch.utils.data import DataLoader
from continuum import ClassIncremental, split_train_val
from continuum.datasets import MNIST
clloader = ClassIncremental(
MNIST("my/data/path", download=True, train=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 in enumerate(clloader):
train_dataset, val_dataset = split_train_val(train_dataset, val_split=0.1)
train_loader = DataLoader(train_dataset)
val_loader = DataLoader(val_dataset)
for x, y, t in train_loader:
# Do your cool stuff here
Supported Scenarios
| Name | Acronym | Supported | Scenario |
|---|---|---|---|
| New Instances | NI | :white_check_mark: | Instances Incremental |
| New Classes | NC | :white_check_mark: | Classes Incremental |
| New Instances & Classes | NIC | :white_check_mark: | Data Incremental |
Supported Datasets:
Note that the task sizes are fully customizable.
| Name | Nb classes | Image Size | Automatic Download | Type |
|---|---|---|---|---|
| MNIST | 10 | 28x28x1 | :white_check_mark: | :eyes: |
| Fashion MNIST | 10 | 28x28x1 | :white_check_mark: | :eyes: |
| KMNIST | 10 | 28x28x1 | :white_check_mark: | :eyes: |
| EMNIST | 10 | 28x28x1 | :white_check_mark: | :eyes: |
| QMNIST | 10 | 28x28x1 | :white_check_mark: | :eyes: |
| MNIST Fellowship | 30 | 28x28x1 | :white_check_mark: | :eyes: |
| CIFAR10 | 10 | 32x32x3 | :white_check_mark: | :eyes: |
| CIFAR100 | 100 | 32x32x3 | :white_check_mark: | :eyes: |
| CIFAR Fellowship | 110 | 32x32x3 | :white_check_mark: | :eyes: |
| ImageNet100 | 100 | 224x224x3 | :x: | :eyes: |
| ImageNet1000 | 1000 | 224x224x3 | :x: | :eyes: |
| Permuted MNIST | 10 | 28x28x1 | :white_check_mark: | :eyes: |
| Rotated MNIST | 10 | 28x28x1 | :white_check_mark: | :eyes: |
| CORe50 | 50 | 224x224x3 | :white_check_mark: | :eyes: |
| CORe50-v2-79 | 50 | 224x224x3 | :white_check_mark: | :eyes: |
| CORe50-v2-196 | 50 | 224x224x3 | :white_check_mark: | :eyes: |
| CORe50-v2-391 | 50 | 224x224x3 | :white_check_mark: | :eyes: |
| MultiNLI | 5 | :white_check_mark: | :book: |
Furthermore some "Meta"-datasets are available:
InMemoryDataset, for in-memory numpy array:
x_train, y_train = gen_numpy_array()
clloader = CLLoader(
InMemoryDataset(x_train, y_train),
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. They are automatically downloaded for you, but you can also provide your own.
Indexing
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].
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 |
RotatedMNIST:
 |
 |
 |
 |
 |
|---|---|---|---|---|
| Task 0 | Task 1 | Task 2 | Task 3 | Task 4 |
ImageNet100:
 |
 |
 |
 |
... |
|---|---|---|---|---|
| Task 0 | Task 1 | Task 2 | Task 3 | ... |
Citation
If you find this library useful in your work, please consider citing it:
@misc{douillardlesort2020continuum,
author={Douillard, Arthur and Lesort, Timothée},
title={Continuum, Data Loaders for Continual Learning},
howpublished={https://github.com/Continvvm/continuum},
year={2020},
doi={10.5281/zenodo.3759673}
}
Maintainers
This project was started by a joint effort from Arthur Douillard & Timothée Lesort.
Feel free to contribute! If you want to propose new features, please create an issue.
On PyPi
Our project is available on PyPi!
pip3 install continuum
Note that previously another project, a CI tool, was using that name. It is now there continuum_ci.
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