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diffsort 0.2.0
Differentiable Sorting Networks.
Requires Python: >=3.6

# diffsort - Differentiable Sorting Networks

![diffsort_logo](diffsort_logo.png)

Official implementation for our [ICLR 2022 Paper "Monotonic Differentiable Sorting Networks"](https://arxiv.org/abs/2203.09630) and our [ICML 2021 Paper "Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision"](https://arxiv.org/pdf/2105.04019.pdf).
In this work, we leverage classic sorting networks and relax them to propose a new differentiable sorting function: diffsort.
This allows propagating gradients through (an approximation of) the sorting / ranking function / operation.
Herein, diffsort outperforms existing differentiable sorting functions on the four-digit MNIST and the SVHN sorting tasks.
In this repo, we present the PyTorch implementation of differentiable sorting networks.

Video @ [Youtube](https://www.youtube.com/watch?v=Rl-sFaE1z4M).

[![video](https://www.petersen.ai/images/monotonic_diffsort_thumbnail_2_tiny.jpg)](https://www.youtube.com/watch?v=Rl-sFaE1z4M)


## 💻 Installation

`diffsort` can be installed via pip from PyPI with
```shell
pip install diffsort
```

Or from source, e.g., in a virtual environment like
```shell
virtualenv -p python3 .env1
. .env1/bin/activate
pip install .
```

## 👩‍💻 Usage

```python
import torch
from diffsort import DiffSortNet

vector_length = 2**4
vectors = torch.randperm(vector_length, dtype=torch.float32, device='cpu', requires_grad=True).view(1, -1)
vectors = vectors - 5.

# sort using a bitonic-sorting-network
sorter = DiffSortNet('bitonic', vector_length, steepness=5)
sorted_vectors, permutation_matrices = sorter(vectors)
print(sorted_vectors)
```

## 🧪 Experiments 

You can find the main experiment in this [Colab notebook](https://colab.research.google.com/drive/1q0TZFFYB9FlOJYWKt0_7ZaXQT190anhm?usp=sharing).

You can run the four-digit MNIST experiment as
```shell
python experiments/main.py -n 5 -m odd_even -s 10 -d mnist
```
or for the bitonic network
```shell
python experiments/main.py -n 16 -m bitonic -s 20 -d mnist
```
or for SVHN
```shell
python experiments/main.py -n 5 -m odd_even -s 10 -d svhn
```

## 📖 Citing

```bibtex
@inproceedings{petersen2022monotonic,
  title={Monotonic Differentiable Sorting Networks},
  author={Petersen, Felix and Borgelt, Christian and Kuehne, Hilde and Deussen, Oliver},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2022}
}

@inproceedings{petersen2021diffsort,
  title={Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision},
  author={Petersen, Felix and Borgelt, Christian and Kuehne, Hilde and Deussen, Oliver},
  booktitle={International Conference on Machine Learning (ICML)},
  year={2021}
}
```

## License

`diffsort` is released under the MIT license. See [LICENSE](LICENSE) for additional details about it.