Avalanche is an End-to-End Continual Learning Library based on PyTorch, born within ContinualAI with the goal of providing a shared and collaborative open-source (MIT licensed) codebase for fast prototyping, training and reproducibleevaluation of continual learning algorithms.
Looking for continual learning baselines? In the CL-Baseline sibling project based on Avalanche we reproduce seminal papers results you can directly use in your experiments!
Avalanche can help Continual Learning researchers and practitioners in several ways:
Write less code, prototype faster & reduce errors
Improve reproducibility, modularity and reusability
Augment impact and usability of your research products
The library is organized in five main modules:
Benchmarks: This module maintains a uniform API for data handling: mostly generating a stream of data from one or more datasets. It contains all the major CL benchmarks (similar to what has been done for torchvision).
Training: This module provides all the necessary utilities concerning model training. This includes simple and efficient ways of implement new continual learning strategies as well as a set pre-implemented CL baselines and state-of-the-art algorithms you will be able to use for comparison!
Evaluation: This modules provides all the utilities and metrics that can help evaluate a CL algorithm with respect to all the factors we believe to be important for a continually learning system.
Models: In this module you'll be able to find several model architectures and pre-trained models that can be used for your continual learning experiment (similar to what has been done in torchvision.models).
Logging: It includes advanced logging and plotting features, including native stdout, file and TensorBoard support (How cool it is to have a complete, interactive dashboard, tracking your experiment metrics in real-time with a single line of code?)
Avalanche the first experiment of a End-to-end Library for reproducible continual learning research & development where you can find benchmarks, algorithms,evaluation metrics and much more, in the same place.
Let's make it together 👫 a wonderful ride! 🎈
Check out how your code changes when you start using Avalanche! 👇
import torchimport torch.nn as nnfrom torch.nn import CrossEntropyLossfrom torch.optim import SGDfrom torchvision import transformsfrom torchvision.datasets import MNISTfrom torchvision.transforms import ToTensor, RandomCropfrom torch.utils.data import DataLoaderimport numpy as npfrom copy import copy# Configdevice = torch.device("cuda:0"if torch.cuda.is_available() else"cpu")# modelclassSimpleMLP(nn.Module):def__init__(self,num_classes=10,input_size=28*28):super(SimpleMLP, self).__init__() self.features = nn.Sequential( nn.Linear(input_size, 512), nn.ReLU(inplace=True), nn.Dropout(), ) self.classifier = nn.Linear(512, num_classes) self._input_size = input_sizedefforward(self,x): x = x.contiguous() x = x.view(x.size(0), self._input_size) x = self.features(x) x = self.classifier(x)return xmodel =SimpleMLP(num_classes=10)# CL Benchmark Creationlist_train_dataset = []list_test_dataset = []rng_permute = np.random.RandomState(0)train_transform = transforms.Compose([RandomCrop(28, padding=4),ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])test_transform = transforms.Compose([ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])# permutation transformationclassPixelsPermutation(object):def__init__(self,index_permutation): self.permutation = index_permutationdef__call__(self,x):return x.view(-1)[self.permutation].view(1, 28, 28)defget_permutation():return torch.from_numpy(rng_permute.permutation(784)).type(torch.int64)# for every incremental steppermutations = []for i inrange(3):# choose a random permutation of the pixels in the image idx_permute =get_permutation() current_perm =PixelsPermutation(idx_permute) permutations.append(idx_permute)# add the permutation to the default dataset transformation train_transform_list = train_transform.transforms.copy() train_transform_list.append(current_perm) new_train_transform = transforms.Compose(train_transform_list) test_transform_list = test_transform.transforms.copy() test_transform_list.append(current_perm) new_test_transform = transforms.Compose(test_transform_list)# get the datasets with the constructed transformation permuted_train =MNIST(root='./data/mnist', download=True, transform=new_train_transform) permuted_test =MNIST(root='./data/mnist', train=False, download=True, transform=new_test_transform) list_train_dataset.append(permuted_train) list_test_dataset.append(permuted_test)# Trainoptimizer =SGD(model.parameters(), lr=0.001, momentum=0.9)criterion =CrossEntropyLoss()for task_id, train_dataset inenumerate(list_train_dataset): train_data_loader =DataLoader( train_dataset, num_workers=4, batch_size=32)for ep inrange(2):for iteration, (train_mb_x, train_mb_y) inenumerate(train_data_loader): optimizer.zero_grad() train_mb_x = train_mb_x.to(device) train_mb_y = train_mb_y.to(device)# Forward logits =model(train_mb_x)# Loss loss =criterion(logits, train_mb_y)# Backward loss.backward()# Update optimizer.step()# Testacc_results = []for task_id, test_dataset inenumerate(list_test_dataset): test_data_loader =DataLoader( test_dataset, num_workers=4, batch_size=32) correct =0for iteration, (test_mb_x, test_mb_y) inenumerate(test_data_loader):# Move mini-batch data to device test_mb_x = test_mb_x.to(device) test_mb_y = test_mb_y.to(device)# Forward test_logits =model(test_mb_x)# Loss test_loss =criterion(test_logits, test_mb_y)# compute acc correct += test_mb_y.eq(test_logits.argmax(dim=1)).sum().item() acc_results.append(correct /len(test_dataset))
🚦 Getting Started
We know that learning a new tool may be tough at first. This is why we made Avalanche as easy as possible to learn with a set of resources that will help you along the way.
For example, you may start with our 5-minutesguide that will let you acquire the basics about Avalanche and how you can use it in your research project:
Having completed these two sections, you will already feel with superpowers ⚡, this is why we have also created an in-depth tutorial that will cover all the aspect of Avalanche in details and make you a true Continual Learner! 👨🎓️
If you use Avalanche in your research project, please remember to cite our JMLR-MLOSS paper https://jmlr.org/papers/v24/23-0130.html. This will help us make Avalanche better known in the machine learning community, ultimately making a better tool for everyone:
@article{JMLR:v24:23-0130,
author = {Antonio Carta and Lorenzo Pellegrini and Andrea Cossu and Hamed Hemati and Vincenzo Lomonaco},
title = {Avalanche: A PyTorch Library for Deep Continual Learning},
journal = {Journal of Machine Learning Research},
year = {2023},
volume = {24},
number = {363},
pages = {1--6},
url = {http://jmlr.org/papers/v24/23-0130.html}
}
@InProceedings{lomonaco2021avalanche,
title={Avalanche: an End-to-End Library for Continual Learning},
author={Vincenzo Lomonaco and Lorenzo Pellegrini and Andrea Cossu and Antonio Carta and Gabriele Graffieti and Tyler L. Hayes and Matthias De Lange and Marc Masana and Jary Pomponi and Gido van de Ven and Martin Mundt and Qi She and Keiland Cooper and Jeremy Forest and Eden Belouadah and Simone Calderara and German I. Parisi and Fabio Cuzzolin and Andreas Tolias and Simone Scardapane and Luca Antiga and Subutai Amhad and Adrian Popescu and Christopher Kanan and Joost van de Weijer and Tinne Tuytelaars and Davide Bacciu and Davide Maltoni},
booktitle={Proceedings of IEEE Conference on Computer Vision and Pattern Recognition},
series={2nd Continual Learning in Computer Vision Workshop},
year={2021}
}
🗂️ Maintained by ContinualAI Lab
Avalanche is the flagship open-source collaborative project of ContinualAI: a non profit research organization and the largest open community on Continual Learning for AI.
Do you have a question, do you want to report an issue or simply ask for a new feature? Check out the Questions & Issues center. Do you want to improve Avalanche yourself? Follow these simple rules on How to Contribute.
The Avalanche project is maintained by the collaborative research team ContinualAI Laband used extensively by the Units of the ContinualAI Research (CLAIR) consortium, a research network of the major continual learning stakeholders around the world.
We are always looking for new awesome members willing to join the ContinualAI Lab, so check out our official website if you want to learn more about us and our activities, or contact us.