[pypi-image]: https://badge.fury.io/py/torch-geometric.svg
[pypi-url]: https://pypi.python.org/pypi/torch-geometric
[build-image]: https://travis-ci.org/rusty1s/pytorch_geometric.svg?branch=master
[build-url]: https://travis-ci.org/rusty1s/pytorch_geometric
[docs-image]: https://readthedocs.org/projects/pytorch-geometric/badge/?version=latest
[docs-url]: https://pytorch-geometric.readthedocs.io/en/latest/?badge=latest
[coverage-image]: https://codecov.io/gh/rusty1s/pytorch_geometric/branch/master/graph/badge.svg
[coverage-url]: https://codecov.io/github/rusty1s/pytorch_geometric?branch=master
[contributing-image]: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat
[contributing-url]: https://github.com/rusty1s/pytorch_geometric/blob/master/CONTRIBUTING.md
<p align="center">
<img width="40%" src="https://raw.githubusercontent.com/rusty1s/pytorch_geometric/master/docs/source/_static/img/pyg_logo_text.svg?sanitize=true" />
</p>
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[![PyPI Version][pypi-image]][pypi-url]
[![Build Status][build-image]][build-url]
[![Docs Status][docs-image]][docs-url]
[![Code Coverage][coverage-image]][coverage-url]
[![Contributing][contributing-image]][contributing-url]
**[Documentation](https://pytorch-geometric.readthedocs.io)** | **[Paper](https://arxiv.org/abs/1903.02428)** | **[External Resources](https://pytorch-geometric.readthedocs.io/en/latest/notes/resources.html)**
*PyTorch Geometric* (PyG) is a geometric deep learning extension library for [PyTorch](https://pytorch.org/).
It consists of various methods for deep learning on graphs and other irregular structures, also known as *[geometric deep learning](http://geometricdeeplearning.com/)*, 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.
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PyTorch Geometric makes implementing Graph Neural Networks a breeze (see [here](https://pytorch-geometric.readthedocs.io/en/latest/notes/create_gnn.html) for the accompanying tutorial).
For example, this is all it takes to implement the [edge convolutional layer](https://arxiv.org/abs/1801.07829):
```python
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:
* **[SplineConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.SplineConv)** from Fey *et al.*: [SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels](https://arxiv.org/abs/1711.08920) (CVPR 2018)
* **[GCNConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.GCNConv)** from Kipf and Welling: [Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/abs/1609.02907) (ICLR 2017)
* **[ChebConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.ChebConv)** from Defferrard *et al.*: [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://arxiv.org/abs/1606.09375) (NIPS 2016)
* **[NNConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.NNConv)** from Gilmer *et al.*: [Neural Message Passing for Quantum Chemistry](https://arxiv.org/abs/1704.01212) (ICML 2017)
* **[ECConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.ECConv)** from Simonovsky and Komodakis: [Edge-Conditioned Convolution on Graphs](https://arxiv.org/abs/1704.02901) (CVPR 2017)
* **[GATConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.GATConv)** from Veličković *et al.*: [Graph Attention Networks](https://arxiv.org/abs/1710.10903) (ICLR 2018)
* **[SAGEConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.SAGEConv)** from Hamilton *et al.*: [Inductive Representation Learning on Large Graphs](https://arxiv.org/abs/1706.02216) (NIPS 2017)
* **[GraphConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.GraphConv)** from, *e.g.*, Morris *et al.*: [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://arxiv.org/abs/1810.02244) (AAAI 2019)
* **[GatedGraphConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.GatedGraphConv)** from Li *et al.*: [Gated Graph Sequence Neural Networks](https://arxiv.org/abs/1511.05493) (ICLR 2016)
* **[GINConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.GINConv)** from Xu *et al.*: [How Powerful are Graph Neural Networks?](https://arxiv.org/abs/1810.00826) (ICLR 2019)
* **[ARMAConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.ARMAConv)** from Bianchi *et al.*: [Graph Neural Networks with Convolutional ARMA Filters](https://arxiv.org/abs/1901.01343) (CoRR 2019)
* **[SGConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.SGConv)** from Wu *et al.*: [Simplifying Graph Convolutional Networks](https://arxiv.org/abs/1902.07153) (CoRR 2019)
* **[APPNP](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.APPNP)** from Klicpera *et al.*: [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://arxiv.org/abs/1810.05997) (ICLR 2019)
* **[AGNNConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.AGNNConv)** from Thekumparampil *et al.*: [Attention-based Graph Neural Network for Semi-Supervised Learning](https://arxiv.org/abs/1803.03735) (CoRR 2017)
* **[RGCNConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.RGCNConv)** from Schlichtkrull *et al.*: [Modeling Relational Data with Graph Convolutional Networks](https://arxiv.org/abs/1703.06103) (ESWC 2018)
* **[SignedConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.SignedConv)** from Derr *et al.*: [Signed Graph Convolutional Network](https://arxiv.org/abs/1808.06354) (ICDM 2018)
* **[DNAConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.DNAConv)** from Fey: [Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks](https://arxiv.org/abs/1904.04849) (ICLR-W 2019)
* **[EdgeConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.EdgeConv)** from Wang *et al.*: [Dynamic Graph CNN for Learning on Point Clouds](https://arxiv.org/abs/1801.07829) (CoRR, 2018)
* **[PointConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.PointConv)** (including **[Iterative Farthest Point Sampling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.pool.fps)**, dynamic graph generation based on
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Python-PyTorchGeometric用于PyTorch的几何深度学习扩展库
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Python-PyTorchGeometric用于PyTorch的几何深度学习扩展库 (393个子文件)
setup.cfg 107B
.coveragerc 259B
custom.css 141B
Dockerfile 5KB
.gitignore 124B
.gitignore 18B
index.html 163B
MANIFEST.in 46B
LICENSE 1KB
Makefile 196B
README.md 16KB
CONTRIBUTING.md 3KB
README.md 1KB
installation.md 983B
README.md 979B
README.md 790B
README.md 725B
README.md 673B
bug-report.md 643B
feature-request.md 587B
question-help.md 183B
README.md 160B
.nojekyll 0B
data.py 14KB
autoencoder.py 12KB
dna_conv.py 10KB
signed_gcn.py 9KB
re_net.py 8KB
sampler.py 7KB
ged_dataset.py 7KB
gin.py 7KB
shrec2016.py 6KB
in_memory_dataset.py 6KB
entities.py 6KB
spline_conv.py 6KB
hypergraph_conv.py 6KB
message_passing.py 6KB
dynamic_faust.py 6KB
shapenet.py 6KB
pcpnet_dataset.py 6KB
x_conv.py 5KB
meta.py 5KB
rgcn.py 5KB
train_eval.py 5KB
gat.py 5KB
geniepath.py 5KB
enzymes_diff_pool.py 5KB
arma_conv.py 5KB
dataset.py 5KB
ppi.py 4KB
metric.py 4KB
gat_conv.py 4KB
modelnet.py 4KB
loop.py 4KB
gcn_conv.py 4KB
icews.py 4KB
tu_dataset.py 4KB
qm9_nn_conv.py 4KB
tosca.py 4KB
pointnet2_segmentation.py 4KB
coma.py 4KB
signed_conv.py 4KB
s3dis.py 4KB
tu.py 4KB
ppf_conv.py 4KB
gmm_conv.py 4KB
bitcoin_otc.py 4KB
train_eval.py 4KB
pointnet2_classification.py 4KB
rgcn_conv.py 4KB
random.py 4KB
cheb_conv.py 4KB
mnist_superpixels.py 4KB
topk_pool.py 4KB
geometry.py 4KB
nn_conv.py 4KB
faust.py 3KB
dgcnn_segmentation.py 3KB
mutag_gin.py 3KB
deep_graph_infomax.py 3KB
line_graph.py 3KB
data_parallel.py 3KB
point_conv.py 3KB
dna.py 3KB
gdelt.py 3KB
sg_conv.py 3KB
convert.py 3KB
test_data.py 3KB
diff_pool.py 3KB
dataloader.py 3KB
batch.py 3KB
feast_conv.py 3KB
edge_conv.py 3KB
test_convert.py 3KB
mnist_nn_conv.py 3KB
planetoid.py 3KB
enzymes_topk_pool.py 3KB
max_pool.py 3KB
reddit.py 3KB
mnist_graclus.py 3KB
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