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https://github.com/paganpasta/eqxvision

A Python package of computer vision models for the Equinox ecosystem.
https://github.com/paganpasta/eqxvision

equinox python pytorch vision

Last synced: 3 months ago
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A Python package of computer vision models for the Equinox ecosystem.

Host: GitHub
URL: https://github.com/paganpasta/eqxvision
Owner: paganpasta
License: mit
Created: 2022-07-24T22:02:02.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2024-01-05T14:08:30.000Z (6 months ago)
Last Synced: 2024-03-20T09:14:35.362Z (3 months ago)
Topics: equinox, python, pytorch, vision
Language: Python
Homepage: https://eqxvision.readthedocs.io
Size: 15.7 MB
Stars: 92
Watchers: 4
Forks: 9
Open Issues: 8
Metadata Files:
- Readme: README.md
- License: LICENSE.md

Lists

awesome-jax - Eqxvision - Equinox version of Torchvision. <img src="https://img.shields.io/github/stars/paganpasta/eqxvision?style=social" align="center"> (Libraries / New Libraries)
awesome-equinox - eqxvision - A Python package of computer vision models for the Equinox ecosystem. <img src="https://img.shields.io/github/stars/paganpasta/eqxvision?style=social" align="center"> (Models and Projects)

README

        # Eqxvision

[![PyPI](https://img.shields.io/pypi/v/eqxvision?style=flat-square)](https://pypi.org/project/eqxvision/) 

[![Github](https://img.shields.io/badge/Documentation-link-yellowgreen?style=flat-square)](https://eqxvision.readthedocs.io/en/latest/)

[![GitHub Release Date](https://img.shields.io/github/release-date/paganpasta/eqxvision?style=flat-square)](https://github.com/paganpasta/eqxvision/releases) 

[![GitHub](https://img.shields.io/github/license/paganpasta/eqxvision?style=flat-square)](https://github.com/paganpasta/eqxvision/blob/main/LICENSE.md)

Eqxvision is a package of popular computer vision model architectures built using [Equinox](https://docs.kidger.site/equinox/).

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install eqxvision.

```bash

pip install eqxvision

```

*requires:* `python>=3.7`

*optional:* `torch`, only if `pretrained` models are required. 

## Documentation

Available at [https://eqxvision.readthedocs.io/en/latest/](https://eqxvision.readthedocs.io/en/latest/).

## Usage

Picking a model and doing a forward pass is as simple as ...

```python

    import jax

    import jax.random as jr

    import equinox as eqx

    from eqxvision.models import alexnet

    from eqxvision.utils import CLASSIFICATION_URLS

    

    

    @eqx.filter_jit

    def forward(net, images, key):

        keys = jax.random.split(key, images.shape[0])

        output = jax.vmap(net, axis_name=('batch'))(images, key=keys)

        ...

        

    net = alexnet(torch_weights=CLASSIFICATION_URLS['alexnet'])

    

    images = jr.uniform(jr.PRNGKey(0), shape=(1,3,224,224))

    output = forward(net, images, jr.PRNGKey(0))

```

## What's New?

- `FCN`, `DeepLabV3` and `LRASPP` added as new image segmentation models.

- Backward incompatible changes to `v0.2.0` for loading a `pretrained` model.

- Almost all image classification models are ported from `torchvision`.

- New tutorial for generating `adversarial examples` and others coming soon.

## Get Started!

Start with any one of these easy to follow [tutorials](https://eqxvision.readthedocs.io/en/latest/getting_started/Transfer_Learning/). 

       

       

## Tips

- Better to use `@equinox.filter_jit` instead of `@jax.jit`.

- Use `jax.{v,p}map` with `axis_name='batch'` when using models that use batch normalisation.

- Don't forget to switch to `inference` mode for evaluations. (`model = eqx.tree_inference(model)`)

- Initialise Optax optimisers as `optim.init(eqx.filter(net, eqx.is_array))`. ([See here.](https://docs.kidger.site/equinox/faq/#optax-is-throwing-an-error))

## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

### Development Process

If you plan to modify the code or documentation, please follow the steps below:

1. Fork the repository and create your branch from `dev`.

2. If you have modified the code (new feature or bug-fix), please add unit tests.

3. If you have changed APIs, update the documentation. Make sure the documentation builds. `mkdocs serve`

4. Ensure the test suite passes. `pytest tests -vvv`

5. Make sure your code passes the formatting checks. Automatically checked with a `pre-commit` hook. 

## Acknowledgements

- [Equinox](https://github.com/patrick-kidger/equinox)

- [Patrick Kidger](https://github.com/patrick-kidger)

- [Torchvision](https://pytorch.org/vision/stable/index.html)

## License

[MIT](https://choosealicense.com/licenses/mit/)