README.md

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# PyTorch Scatter

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<p align="center">
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**[Documentation](http://rusty1s.github.io/pytorch_scatter)**

This package consists of a small extension library of highly optimized sparse update (scatter) operations for the use in [PyTorch](http://pytorch.org/), which are missing in the main package.
Scatter operations can be roughly described as reduce operations based on a given "group-index" tensor.
The package consists of the following operations:

* [**Scatter Add**](https://rusty1s.github.io/pytorch_scatter/build/html/functions/add.html)
* [**Scatter Sub**](https://rusty1s.github.io/pytorch_scatter/build/html/functions/sub.html)
* [**Scatter Mul**](https://rusty1s.github.io/pytorch_scatter/build/html/functions/mul.html)
* [**Scatter Div**](https://rusty1s.github.io/pytorch_scatter/build/html/functions/div.html)
* [**Scatter Mean**](https://rusty1s.github.io/pytorch_scatter/build/html/functions/mean.html)
* [**Scatter Min**](https://rusty1s.github.io/pytorch_scatter/build/html/functions/min.html)
* [**Scatter Max**](https://rusty1s.github.io/pytorch_scatter/build/html/functions/max.html)

All included operations work on varying data types, are implemented both for CPU and GPU and include a backwards implementation.

## Installation

Check that `nvcc` is accessible from terminal, e.g. `nvcc --version`.
If not, add cuda (`/usr/local/cuda/bin`) to your `$PATH`.
Then run:

```
pip install cffi torch-scatter
```

## Example

```py
import torch
from torch_scatter import scatter_max

src = torch.tensor([[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]])
index = torch.tensor([[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]])

out, argmax = scatter_max(src, index)
```

```
print(out)
tensor([[ 0,  0,  4,  3,  2,  0],
        [ 2,  4,  3,  0,  0,  0]])

print(argmax)
tensor([[-1, -1,  3,  4,  0,  1]
        [ 1,  4,  3, -1, -1, -1]])
```

## Running tests

```
python setup.py test
```