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https://github.com/aidos-lab/dect
Differentiable Euler Characteristic Transform
https://github.com/aidos-lab/dect
iclr2024 topological-data-analysis topological-deep-learning topological-machine-learning
Last synced: 3 days ago
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Differentiable Euler Characteristic Transform
- Host: GitHub
- URL: https://github.com/aidos-lab/dect
- Owner: aidos-lab
- License: bsd-3-clause
- Created: 2023-05-08T11:37:59.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-18T15:57:07.000Z (5 months ago)
- Last Synced: 2024-06-18T19:55:28.480Z (5 months ago)
- Topics: iclr2024, topological-data-analysis, topological-deep-learning, topological-machine-learning
- Language: Python
- Homepage: https://openreview.net/forum?id=MO632iPq3I
- Size: 21 MB
- Stars: 17
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# DECT - Differentiable Euler Characteristic Transform
[](https://arxiv.org/abs/2310.07630)   [](https://codeclimate.com/github/aidos-lab/dect/maintainability)
This is the official implementation for the **Differentiable Euler Characteristic
Transform**, a geometrical-topological method for shape classification. Our
implementation is fully optimized for hardware acceleration,
yielding a blazingly fast implementation for machine learning research with
[`PyTorch`](https://pytorch.org/).
If you find our work useful, please consider using the following citation:
```bibtex
@inproceedings{Roell24a,
title = {Differentiable Euler Characteristic Transforms for Shape Classification},
author = {Ernst R{\"o}ell and Bastian Rieck},
year = 2024,
booktitle = {International Conference on Learning Representations},
eprint = {2310.07630},
archiveprefix = {arXiv},
primaryclass = {cs.LG},
repository = {https://github.com/aidos-lab/dect-evaluation},
url = {https://openreview.net/forum?id=MO632iPq3I},
}
```
## Installation
For the installation we require an up-to-date installation of PyTorch, either
with or without CUDA support. The DECT implementation also relies on the
`torch-scatter` package, highly optimized for grouped operations such summing
of a vector given an index vector.
- First install `torch` and `torch-scatter`.
```python
pip install torch==2.2.0 --index-url https://download.pytorch.org/whl/cu117
pip install torch-scatter -f https://data.pyg.org/whl/torch-2.2.0+${CUDA}.html
```
- Then install our package in either one of two ways:
- As a git submodule
```sh
git submodule add https://github.com/aidos-lab/dect.git
```
- Or as a pip installable package.
```sh
pip install git+https://github.com/aidos-lab/dect.git
```
## Usage
For a demonstrastration of our new ECT computation, we provide the
`notebooks/01_example_usage.ipynb` file and the code therein provides an intuitive
example with explanation. The code is provided on an as-is basis; see
[LICENSE.md](https://github.com/aidos-lab/dect/blob/main/LICENSE.md) for more
information. You are cordially invited to both contribute and provide feedback.
Do not hesitate to contact us!
```python
import torch
from torch_geometric.data import Data, Batch
from dect.ect import ECTConfig, ECTLayer
from dect.directions import generate_uniform_2d_directions
v = generate_uniform_2d_directions(num_thetas=64)
layer = ECTLayer(ECTConfig(), v=v)
points_coordinates = torch.tensor(
[[0.5, 0.0], [-0.5, 0.0], [0.5, 0.5]], requires_grad=True
)
data = Data(x=points_coordinates)
batch = Batch.from_data_list([data])
ect = layer(batch)
```
## License
Our code is released under a BSD-3-Clause license. This license essentially
permits you to freely use our code as desired, integrate it into your projects,
and much more --- provided you acknowledge the original authors. Please refer to
[LICENSE.md](LICENSE.md) for more information.
## Contributing
We welcome contributions and suggestions for our DECT package! Here are some
basic guidelines for contributing:
### How to Submit an Issue
1. **Check Existing Issues**: Before submitting a new issue, please check if it
has already been reported.
2. **Open a New Issue**: If your issue is new, open a new issue in the
repository. Provide a clear and detailed description of the problem,
including steps to reproduce the issue if applicable.
3. **Include Relevant Information**: Include any relevant information, such as
system details, version numbers, and screenshots, to help us understand and
resolve the issue more efficiently.
### How to Contribute
If you're unfamiliar with contributing to open source repositories, here is a
basic roadmap:
1. **Fork the Repository**: Start by forking the repository to your own GitHub
account.
2. **Clone the Repository**: Clone the forked repository to your local machine.
```sh
git clone https://github.com/your-username/dect.git
```
3. **Create a Branch**: Create a new branch for your feature or bug fix.
```sh
git checkout -b feature/your-feature-name
```
4. **Make Changes**: Implement your changes in the new branch.
5. **Commit Changes**: Commit your changes with a descriptive commit message.
```sh
git commit -m "Description of your changes"
```
6. **Push Changes**: Push the changes to your forked repository.
```sh
git push origin feature/your-feature-name
```
7. **Submit a Pull Request**: Open a pull request to the main repository with a
clear description of your changes and the purpose of the contribution.
### Need Help?
If you need any help or have questions, feel free to reach out to the authors or
submit a pull request. We appreciate your contributions and are happy to assist!