/pytorch_geometric

Geometric Deep Learning Extension Library for PyTorch

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PyTorch Geometric (PyG) is a geometric deep learning extension library for PyTorch.

It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, multi gpu-support, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. Click here to join our Slack community!


OGB is hosting a large-scale graph machine learning challenge (OGB-LSC) at KDD Cup 2021 from March 15th to June 8th in order to discover innovative solutions for large-scale node classification, link prediction and graph regression. We are looking forward to your participation!


PyTorch Geometric makes implementing Graph Neural Networks a breeze (see here for the accompanying tutorial). For example, this is all it takes to implement the edge convolutional layer:

import torch
from torch.nn import Sequential as Seq, Linear as Lin, ReLU
from torch_geometric.nn import MessagePassing

class EdgeConv(MessagePassing):
    def __init__(self, F_in, F_out):
        super(EdgeConv, self).__init__(aggr='max')  # "Max" aggregation.
        self.mlp = Seq(Lin(2 * F_in, F_out), ReLU(), Lin(F_out, F_out))

    def forward(self, x, edge_index):
        # x has shape [N, F_in]
        # edge_index has shape [2, E]
        return self.propagate(edge_index, x=x)  # shape [N, F_out]

    def message(self, x_i, x_j):
        # x_i has shape [E, F_in]
        # x_j has shape [E, F_in]
        edge_features = torch.cat([x_i, x_j - x_i], dim=1)  # shape [E, 2 * F_in]
        return self.mlp(edge_features)  # shape [E, F_out]

In detail, the following methods are currently implemented:


Head over to our documentation to find out more about installation, data handling, creation of datasets and a full list of implemented methods, transforms, and datasets. For a quick start, check out our examples in the examples/ directory.

If you notice anything unexpected, please open an issue and let us know. If you have any questions or are missing a specific feature, feel free to discuss them with us. We are motivated to constantly make PyTorch Geometric even better.

Installation

We provide pip wheels for all major OS/PyTorch/CUDA combinations, see here.

PyTorch 1.8.0/1.8.1

To install the binaries for PyTorch 1.8.0 and 1.8.1, simply run

pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+${CUDA}.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.8.0+${CUDA}.html
pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.8.0+${CUDA}.html
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.8.0+${CUDA}.html
pip install torch-geometric

where ${CUDA} should be replaced by either cpu, cu101, cu102, or cu111 depending on your PyTorch installation. Binaries are provided for Python version <= 3.8.

cpu cu101 cu102 cu111
Linux
Windows
macOS

PyTorch 1.7.0/1.7.1

To install the binaries for PyTorch 1.7.0 and 1.7.1, simply run

pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+${CUDA}.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.7.0+${CUDA}.html
pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.7.0+${CUDA}.html
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.7.0+${CUDA}.html
pip install torch-geometric

where ${CUDA} should be replaced by either cpu, cu92, cu101, cu102, or cu110 depending on your PyTorch installation. Binaries are provided for Python version <= 3.8.

cpu cu92 cu101 cu102 cu110
Linux
Windows
macOS

Note: Binaries of older versions are also provided for PyTorch 1.4.0, PyTorch 1.5.0 and PyTorch 1.6.0 (following the same procedure).

From master

In case you want to experiment with the latest PyG features which are not fully released yet, you can install PyG from master via

pip install git+https://github.com/rusty1s/pytorch_geometric.git

Running examples

cd examples
python gcn.py

Cite

Please cite our paper (and the respective papers of the methods used) if you use this code in your own work:

@inproceedings{Fey/Lenssen/2019,
  title={Fast Graph Representation Learning with {PyTorch Geometric}},
  author={Fey, Matthias and Lenssen, Jan E.},
  booktitle={ICLR Workshop on Representation Learning on Graphs and Manifolds},
  year={2019},
}

Feel free to email us if you wish your work to be listed in the external resources.

Running tests

python setup.py test