learnables/learn2learn - Githubissues

learn2learn is a software library for meta-learning research.

learn2learn builds on top of PyTorch to accelerate two aspects of the meta-learning research cycle:

fast prototyping, essential in letting researchers quickly try new ideas, and
correct reproducibility, ensuring that these ideas are evaluated fairly.

learn2learn provides low-level utilities and unified interface to create new algorithms and domains, together with high-quality implementations of existing algorithms and standardized benchmarks. It retains compatibility with torchvision, torchaudio, torchtext, cherry, and any other PyTorch-based library you might be using.

To learn more, see our whitepaper: arXiv:2008.12284

Overview

learn2learn.data: Taskset and transforms to create few-shot tasks from any PyTorch dataset.
learn2learn.vision: Models, datasets, and benchmarks for computer vision and few-shot learning.
learn2learn.gym: Environment and utilities for meta-reinforcement learning.
learn2learn.algorithms: High-level wrappers for existing meta-learning algorithms.
learn2learn.optim: Utilities and algorithms for differentiable optimization and meta-descent.

Resources

Website: http://learn2learn.net/
Documentation: http://learn2learn.net/docs/learn2learn
Tutorials: http://learn2learn.net/tutorials/getting_started/
Examples: https://github.com/learnables/learn2learn/tree/master/examples
GitHub: https://github.com/learnables/learn2learn/
Slack: http://slack.learn2learn.net/

Installation

pip install learn2learn

Snippets & Examples

The following snippets provide a sneak peek at the functionalities of learn2learn.

High-level Wrappers

Few-Shot Learning with MAML

For more algorithms (ProtoNets, ANIL, Meta-SGD, Reptile, Meta-Curvature, KFO) refer to the examples folder. Most of them can be implemented with with the `GBML` wrapper. (documentation). ~~~python maml = l2l.algorithms.MAML(model, lr=0.1) opt = torch.optim.SGD(maml.parameters(), lr=0.001) for iteration in range(10): opt.zero_grad() task_model = maml.clone() # torch.clone() for nn.Modules adaptation_loss = compute_loss(task_model) task_model.adapt(adaptation_loss) # computes gradient, update task_model in-place evaluation_loss = compute_loss(task_model) evaluation_loss.backward() # gradients w.r.t. maml.parameters() opt.step() ~~~

Meta-Descent with Hypergradient

Learn any kind of optimization algorithm with the `LearnableOptimizer`. (example and documentation) ~~~python linear = nn.Linear(784, 10) transform = l2l.optim.ModuleTransform(l2l.nn.Scale) metaopt = l2l.optim.LearnableOptimizer(linear, transform, lr=0.01) # metaopt has .step() opt = torch.optim.SGD(metaopt.parameters(), lr=0.001) # metaopt also has .parameters() metaopt.zero_grad() opt.zero_grad() error = loss(linear(X), y) error.backward() opt.step() # update metaopt metaopt.step() # update linear ~~~

Learning Domains

Custom Few-Shot Dataset

Many standardized datasets (Omniglot, mini-/tiered-ImageNet, FC100, CIFAR-FS) are readily available in `learn2learn.vision.datasets`. (documentation) ~~~python dataset = l2l.data.MetaDataset(MyDataset()) # any PyTorch dataset transforms = [ # Easy to define your own transform l2l.data.transforms.NWays(dataset, n=5), l2l.data.transforms.KShots(dataset, k=1), l2l.data.transforms.LoadData(dataset), ] taskset = Taskset(dataset, transforms, num_tasks=20000) for task in taskset: X, y = task # Meta-train on the task ~~~

Environments and Utilities for Meta-RL

Parallelize your own meta-environments with `AsyncVectorEnv`, or use the standardized ones. (documentation) ~~~python def make_env(): env = l2l.gym.HalfCheetahForwardBackwardEnv() env = cherry.envs.ActionSpaceScaler(env) return env env = l2l.gym.AsyncVectorEnv([make_env for _ in range(16)]) # uses 16 threads for task_config in env.sample_tasks(20): env.set_task(task) # all threads receive the same task state = env.reset() # use standard Gym API action = my_policy(env) env.step(action) ~~~

Low-Level Utilities

Differentiable Optimization

Learn and differentiate through updates of PyTorch Modules. (documentation) ~~~python model = MyModel() transform = l2l.optim.KroneckerTransform(l2l.nn.KroneckerLinear) learned_update = l2l.optim.ParameterUpdate( # learnable update function model.parameters(), transform) clone = l2l.clone_module(model) # torch.clone() for nn.Modules error = loss(clone(X), y) updates = learned_update( # similar API as torch.autograd.grad error, clone.parameters(), create_graph=True, ) l2l.update_module(clone, updates=updates) loss(clone(X), y).backward() # Gradients w.r.t model.parameters() and learned_update.parameters() ~~~

Changelog

A human-readable changelog is available in the CHANGELOG.md file.

Citation

To cite the learn2learn repository in your academic publications, please use the following reference.

Arnold, Sebastien M. R., Praateek Mahajan, Debajyoti Datta, Ian Bunner, and Konstantinos Saitas Zarkias. 2020. “learn2learn: A Library for Meta-Learning Research.” arXiv [cs.LG]. http://arxiv.org/abs/2008.12284.

You can also use the following Bibtex entry.

@article{Arnold2020-ss,
  title         = "learn2learn: A Library for {Meta-Learning} Research",
  author        = "Arnold, S{\'e}bastien M R and Mahajan, Praateek and Datta,
                   Debajyoti and Bunner, Ian and Zarkias, Konstantinos Saitas",
  month         =  aug,
  year          =  2020,
  url           = "http://arxiv.org/abs/2008.12284",
  archivePrefix = "arXiv",
  primaryClass  = "cs.LG",
  eprint        = "2008.12284"
}

Acknowledgements & Friends

TorchMeta is similar library, with a focus on datasets for supervised meta-learning.
higher is a PyTorch library that enables differentiating through optimization inner-loops. While they monkey-patch nn.Module to be stateless, learn2learn retains the stateful PyTorch look-and-feel. For more information, refer to their ArXiv paper.
We are thankful to the following open-source implementations which helped guide the design of learn2learn:
- Tristan Deleu's pytorch-maml-rl
- Jonas Rothfuss' ProMP
- Kwonjoon Lee's MetaOptNet
- Han-Jia Ye's and Hexiang Hu's FEAT