This package consists of a small extension library of highly optimized sparse update (scatter and segment) operations for the use in [PyTorch](http://pytorch.org/), which are missing in the main package.
#### 源码编译安装
Scatter and segment operations can be roughly described as reduce operations based on a given "group-index" tensor.
```shell
Segment operations require the "group-index" tensor to be sorted, whereas scatter operations are not subject to these requirements.
The package consists of the following operations with reduction types `"sum"|"mean"|"min"|"max"`:
python pymap_script.py /path/to/torch-scatter
python setup.py bdist_wheel
*[**scatter**](https://pytorch-scatter.readthedocs.io/en/latest/functions/scatter.html) based on arbitrary indices
pip install dist/*.whl
*[**segment_coo**](https://pytorch-scatter.readthedocs.io/en/latest/functions/segment_coo.html) based on sorted indices
*[**segment_csr**](https://pytorch-scatter.readthedocs.io/en/latest/functions/segment_csr.html) based on compressed indices via pointers
In addition, we provide the following **composite functions** which make use of `scatter_*` operations under the hood: `scatter_std`, `scatter_logsumexp`, `scatter_softmax` and `scatter_log_softmax`.
All included operations are broadcastable, work on varying data types, are implemented both for CPU and GPU with corresponding backward implementations, and are fully traceable.
## Installation
### Anaconda
**Update:** You can now install `pytorch-scatter` via [Anaconda](https://anaconda.org/pyg/pytorch-scatter) for all major OS/PyTorch/CUDA combinations 🤗
Given that you have [`pytorch >= 1.8.0` installed](https://pytorch.org/get-started/locally/), simply run
```
conda install pytorch-scatter -c pyg
```
### Binaries
We alternatively provide pip wheels for all major OS/PyTorch/CUDA combinations, see [here](https://data.pyg.org/whl).
#### PyTorch 1.9.0
To install the binaries for PyTorch 1.9.0, simply run
**Note:** Binaries of older versions are also provided for PyTorch 1.4.0, PyTorch 1.5.0, PyTorch 1.6.0 and PyTorch 1.7.0/1.7.1 (following the same procedure).
### From source
Ensure that at least PyTorch 1.4.0 is installed and verify that `cuda/bin` and `cuda/include` are in your `$PATH` and `$CPATH` respectively, *e.g.*:
