https://github.com/tchaton/tsd
https://github.com/tchaton/tsd
Last synced: over 1 year ago
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- Host: GitHub
- URL: https://github.com/tchaton/tsd
- Owner: tchaton
- License: mit
- Created: 2019-11-10T20:05:45.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-11-27T23:15:34.000Z (over 6 years ago)
- Last Synced: 2025-01-21T03:24:49.689Z (over 1 year ago)
- Language: Python
- Size: 326 KB
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[pypi-image]: https://badge.fury.io/py/torch-scatter.svg
[pypi-url]: https://pypi.python.org/pypi/torch-scatter
[build-image]: https://travis-ci.org/rusty1s/pytorch_scatter.svg?branch=master
[build-url]: https://travis-ci.org/rusty1s/pytorch_scatter
[docs-image]: https://readthedocs.org/projects/pytorch-scatter/badge/?version=latest
[docs-url]: https://pytorch-scatter.readthedocs.io/en/latest/?badge=latest
[coverage-image]: https://codecov.io/gh/rusty1s/pytorch_scatter/branch/master/graph/badge.svg
[coverage-url]: https://codecov.io/github/rusty1s/pytorch_scatter?branch=master
# PyTorch Scatter
[![PyPI Version][pypi-image]][pypi-url]
[![Build Status][build-image]][build-url]
[![Docs Status][docs-image]][docs-url]
[![Code Coverage][coverage-image]][coverage-url]
--------------------------------------------------------------------------------
**[Documentation](https://pytorch-scatter.readthedocs.io)**
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://pytorch-scatter.readthedocs.io/en/latest/functions/add.html)
* [**Scatter Sub**](https://pytorch-scatter.readthedocs.io/en/latest/functions/sub.html)
* [**Scatter Mul**](https://pytorch-scatter.readthedocs.io/en/latest/functions/mul.html)
* [**Scatter Div**](https://pytorch-scatter.readthedocs.io/en/latest/functions/div.html)
* [**Scatter Mean**](https://pytorch-scatter.readthedocs.io/en/latest/functions/mean.html)
* [**Scatter Std**](https://pytorch-scatter.readthedocs.io/en/latest/functions/std.html)
* [**Scatter Min**](https://pytorch-scatter.readthedocs.io/en/latest/functions/min.html)
* [**Scatter Max**](https://pytorch-scatter.readthedocs.io/en/latest/functions/max.html)
All included operations are broadcastable, work on varying data types, and are implemented both for CPU and GPU with corresponding backward implementations.
## Installation
Ensure that at least PyTorch 1.1.0 is installed and verify that `cuda/bin` and `cuda/include` are in your `$PATH` and `$CPATH` respectively, *e.g.*:
```
$ python -c "import torch; print(torch.__version__)"
>>> 1.1.0
$ echo $PATH
>>> /usr/local/cuda/bin:...
$ echo $CPATH
>>> /usr/local/cuda/include:...
```
Then run:
```
pip install torch-scatter
```
If you are running into any installation problems, please create an [issue](https://github.com/rusty1s/pytorch_scatter/issues).
Be sure to import `torch` first before using this package to resolve symbols the dynamic linker must see.
## 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, fill_value=0)
```
```
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
```