Why use PyTorch Adapt?
PyTorch Adapt provides tools for domain adaptation, a type of machine learning algorithm that repurposes existing models to work in new domains. This library is:
1. Fully featured
Build a complete train/val domain adaptation pipeline in a few lines of code.
2. Modular
Use just the parts that suit your needs, whether it's the algorithms, loss functions, or validation methods.
3. Highly customizable
Customize and combine complex algorithms with ease.
4. Compatible with frameworks
Add additional functionality to your code by using one of the framework wrappers. Converting an algorithm into a PyTorch Lightning module is as simple as wrapping it with Lightning.
Documentation
Examples
See the examples folder for notebooks you can download or run on Google Colab.
How to...
Use in vanilla PyTorch
from pytorch_adapt.hooks import DANNHook
from pytorch_adapt.utils.common_functions import batch_to_device
# Assuming that models, optimizers, and dataloader are already created.
hook = DANNHook(optimizers)
for data in tqdm(dataloader):
data = batch_to_device(data, device)
# Optimization is done inside the hook.
# The returned loss is for logging.
_, loss = hook({**models, **data})
Build complex algorithms
Let's customize DANNHook with:
- minimum class confusion
- virtual adversarial training
from pytorch_adapt.hooks import MCCHook, VATHook
# G and C are the Generator and Classifier models
G, C = models["G"], models["C"]
misc = {"combined_model": torch.nn.Sequential(G, C)}
hook = DANNHook(optimizers, post_g=[MCCHook(), VATHook()])
for data in tqdm(dataloader):
data = batch_to_device(data, device)
_, loss = hook({**models, **data, **misc})
Wrap with your favorite PyTorch framework
First, set up the adapter and dataloaders:
from pytorch_adapt.adapters import DANN
from pytorch_adapt.containers import Models
from pytorch_adapt.datasets import DataloaderCreator
models_cont = Models(models)
adapter = DANN(models=models_cont)
dc = DataloaderCreator(num_workers=2)
dataloaders = dc(**datasets)
Then use a framework wrapper:
PyTorch Lightning
import pytorch_lightning as pl
from pytorch_adapt.frameworks.lightning import Lightning
L_adapter = Lightning(adapter)
trainer = pl.Trainer(gpus=1, max_epochs=1)
trainer.fit(L_adapter, dataloaders["train"])
PyTorch Ignite
trainer = Ignite(adapter)
trainer.run(datasets, dataloader_creator=dc)
Check your model's performance
You can do this in vanilla PyTorch:
from pytorch_adapt.validators import SNDValidator
# Assuming predictions have been collected
target_train = {"preds": preds}
validator = SNDValidator()
score = validator(target_train=target_train)
You can also do this during training with a framework wrapper:
PyTorch Lightning
from pytorch_adapt.frameworks.utils import filter_datasets
validator = SNDValidator()
dataloaders = dc(**filter_datasets(datasets, validator))
train_loader = dataloaders.pop("train")
L_adapter = Lightning(adapter, validator=validator)
trainer = pl.Trainer(gpus=1, max_epochs=1)
trainer.fit(L_adapter, train_loader, list(dataloaders.values()))
Pytorch Ignite
from pytorch_adapt.validators import ScoreHistory
validator = ScoreHistory(SNDValidator())
trainer = Ignite(adapter, validator=validator)
trainer.run(datasets, dataloader_creator=dc)
Run the above examples
See this notebook and the examples page for other notebooks.
Installation
Pip
pip install pytorch-adapt
To get the latest dev version:
pip install pytorch-adapt --pre
To use pytorch_adapt.frameworks.lightning:
pip install pytorch-adapt[lightning]
To use pytorch_adapt.frameworks.ignite:
pip install pytorch-adapt[ignite]
Conda
Coming soon...
Dependencies
See setup.py
Acknowledgements
Contributors
Thanks to the contributors who made pull requests!
| Contributor | Highlights |
|---|---|
| deepseek-eoghan | Improved the TargetDataset class |
Advisors
Thank you to Ser-Nam Lim, and my research advisor, Professor Serge Belongie.
Logo
Thanks to Jeff Musgrave for designing the logo.
Citing this library
If you'd like to cite pytorch-adapt in your paper, you can refer to this paper by copy-pasting this bibtex reference:
@article{Musgrave2022PyTorchA,
title={PyTorch Adapt},
author={Kevin Musgrave and Serge J. Belongie and Ser Nam Lim},
journal={ArXiv},
year={2022},
volume={abs/2211.15673}
}
Code references (in no particular order)
- https://github.com/wgchang/DSBN
- https://github.com/jihanyang/AFN
- https://github.com/thuml/Versatile-Domain-Adaptation
- https://github.com/tim-learn/ATDOC
- https://github.com/thuml/CDAN
- https://github.com/takerum/vat_chainer
- https://github.com/takerum/vat_tf
- https://github.com/RuiShu/dirt-t
- https://github.com/lyakaap/VAT-pytorch
- https://github.com/9310gaurav/virtual-adversarial-training
- https://github.com/thuml/Deep-Embedded-Validation
- https://github.com/lr94/abas
- https://github.com/thuml/Batch-Spectral-Penalization
- https://github.com/jvanvugt/pytorch-domain-adaptation
- https://github.com/ptrblck/pytorch_misc
1 maintainer
Metadata
1 maintainer
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