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
Name | Acronym | Supported | Scenario |
---|---|---|---|
New Instances | NI | ✅ | Instances Incremental |
New Classes | NC | ✅ | Classes Incremental |
New Instances & Classes | NIC | ✅ | Data Incremental |
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:
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---|---|---|---|---|
Task 0 | Task 1 | Task 2 | Task 3 | Task 4 |
MNIST Fellowship (MNIST + FashionMNIST + KMNIST):
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---|---|---|
Task 0 | Task 1 | Task 2 |
PermutedMNIST:
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---|---|---|---|---|
Task 0 | Task 1 | Task 2 | Task 3 | Task 4 |
RotatedMNIST:
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---|---|---|---|---|
Task 0 | Task 1 | Task 2 | Task 3 | Task 4 |
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},
publisher={Github},
journal={Github repository},
howpublished={\url{https://github.com/Continvvm/continuum}},
year={2020}
}
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.
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.