Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/rusty1s/pytorch_spline_conv
Implementation of the Spline-Based Convolution Operator of SplineCNN in PyTorch
https://github.com/rusty1s/pytorch_spline_conv
geometric-deep-learning graph-neural-networks pytorch spline-cnn
Last synced: 15 days ago
JSON representation
Implementation of the Spline-Based Convolution Operator of SplineCNN in PyTorch
- Host: GitHub
- URL: https://github.com/rusty1s/pytorch_spline_conv
- Owner: rusty1s
- License: mit
- Created: 2017-12-27T15:47:09.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2024-02-01T15:46:45.000Z (10 months ago)
- Last Synced: 2024-04-03T00:11:25.764Z (8 months ago)
- Topics: geometric-deep-learning, graph-neural-networks, pytorch, spline-cnn
- Language: C++
- Homepage: https://arxiv.org/abs/1711.08920
- Size: 347 KB
- Stars: 165
- Watchers: 9
- Forks: 33
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[pypi-image]: https://badge.fury.io/py/torch-spline-conv.svg
[pypi-url]: https://pypi.python.org/pypi/torch-spline-conv
[testing-image]: https://github.com/rusty1s/pytorch_spline_conv/actions/workflows/testing.yml/badge.svg
[testing-url]: https://github.com/rusty1s/pytorch_spline_conv/actions/workflows/testing.yml
[linting-image]: https://github.com/rusty1s/pytorch_spline_conv/actions/workflows/linting.yml/badge.svg
[linting-url]: https://github.com/rusty1s/pytorch_spline_conv/actions/workflows/linting.yml
[coverage-image]: https://codecov.io/gh/rusty1s/pytorch_spline_conv/branch/master/graph/badge.svg
[coverage-url]: https://codecov.io/github/rusty1s/pytorch_spline_conv?branch=master
# Spline-Based Convolution Operator of SplineCNN
[![PyPI Version][pypi-image]][pypi-url]
[![Testing Status][testing-image]][testing-url]
[![Linting Status][linting-image]][linting-url]
[![Code Coverage][coverage-image]][coverage-url]
--------------------------------------------------------------------------------
This is a PyTorch implementation of the spline-based convolution operator of SplineCNN, as described in our paper:
Matthias Fey, Jan Eric Lenssen, Frank Weichert, Heinrich Müller: [SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels](https://arxiv.org/abs/1711.08920) (CVPR 2018)
The operator works on all floating point data types and is implemented both for CPU and GPU.
## Installation
### Anaconda
**Update:** You can now install `pytorch-spline-conv` via [Anaconda](https://anaconda.org/pyg/pytorch-spline-conv) 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-spline-conv -c pyg
```
### Binaries
We alternatively provide pip wheels for all major OS/PyTorch/CUDA combinations, see [here](https://data.pyg.org/whl).
#### PyTorch 2.4
To install the binaries for PyTorch 2.4.0, simply run
```
pip install torch-spline-conv -f https://data.pyg.org/whl/torch-2.4.0+${CUDA}.html
```
where `${CUDA}` should be replaced by either `cpu`, `cu118`, `cu121`, or `cu124` depending on your PyTorch installation.
| | `cpu` | `cu118` | `cu121` | `cu124` |
|-------------|-------|---------|---------|---------|
| **Linux** | ✅ | ✅ | ✅ | ✅ |
| **Windows** | ✅ | ✅ | ✅ | ✅ |
| **macOS** | ✅ | | | |
#### PyTorch 2.3
To install the binaries for PyTorch 2.3.0, simply run
```
pip install torch-spline-conv -f https://data.pyg.org/whl/torch-2.3.0+${CUDA}.html
```
where `${CUDA}` should be replaced by either `cpu`, `cu118`, or `cu121` depending on your PyTorch installation.
| | `cpu` | `cu118` | `cu121` |
|-------------|-------|---------|---------|
| **Linux** | ✅ | ✅ | ✅ |
| **Windows** | ✅ | ✅ | ✅ |
| **macOS** | ✅ | | |
**Note:** Binaries of older versions are also provided for PyTorch 1.4.0, PyTorch 1.5.0, PyTorch 1.6.0, PyTorch 1.7.0/1.7.1, PyTorch 1.8.0/1.8.1, PyTorch 1.9.0, PyTorch 1.10.0/1.10.1/1.10.2, PyTorch 1.11.0, PyTorch 1.12.0/1.12.1, PyTorch 1.13.0/1.13.1, PyTorch 2.0.0/2.0.1, PyTorch 2.1.0/2.1.1/2.1.2, and PyTorch 2.2.0/2.2.1/2.2.2 (following the same procedure).
For older versions, you need to explicitly specify the latest supported version number or install via `pip install --no-index` in order to prevent a manual installation from source.
You can look up the latest supported version number [here](https://data.pyg.org/whl).
### 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.*:
```
$ python -c "import torch; print(torch.__version__)"
>>> 1.4.0
$ echo $PATH
>>> /usr/local/cuda/bin:...
$ echo $CPATH
>>> /usr/local/cuda/include:...
```
Then run:
```
pip install torch-spline-conv
```
When running in a docker container without NVIDIA driver, PyTorch needs to evaluate the compute capabilities and may fail.
In this case, ensure that the compute capabilities are set via `TORCH_CUDA_ARCH_LIST`, *e.g.*:
```
export TORCH_CUDA_ARCH_LIST = "6.0 6.1 7.2+PTX 7.5+PTX"
```
## Usage
```python
from torch_spline_conv import spline_conv
out = spline_conv(x,
edge_index,
pseudo,
weight,
kernel_size,
is_open_spline,
degree=1,
norm=True,
root_weight=None,
bias=None)
```
Applies the spline-based convolution operator
over several node features of an input graph.
The kernel function is defined over the weighted B-spline tensor product basis, as shown below for different B-spline degrees.
### Parameters
* **x** *(Tensor)* - Input node features of shape `(number_of_nodes x in_channels)`.
* **edge_index** *(LongTensor)* - Graph edges, given by source and target indices, of shape `(2 x number_of_edges)`.
* **pseudo** *(Tensor)* - Edge attributes, ie. pseudo coordinates, of shape `(number_of_edges x number_of_edge_attributes)` in the fixed interval [0, 1].
* **weight** *(Tensor)* - Trainable weight parameters of shape `(kernel_size x in_channels x out_channels)`.
* **kernel_size** *(LongTensor)* - Number of trainable weight parameters in each edge dimension.
* **is_open_spline** *(ByteTensor)* - Whether to use open or closed B-spline bases for each dimension.
* **degree** *(int, optional)* - B-spline basis degree. (default: `1`)
* **norm** *(bool, optional)*: Whether to normalize output by node degree. (default: `True`)
* **root_weight** *(Tensor, optional)* - Additional shared trainable parameters for each feature of the root node of shape `(in_channels x out_channels)`. (default: `None`)
* **bias** *(Tensor, optional)* - Optional bias of shape `(out_channels)`. (default: `None`)
### Returns
* **out** *(Tensor)* - Out node features of shape `(number_of_nodes x out_channels)`.
### Example
```python
import torch
from torch_spline_conv import spline_conv
x = torch.rand((4, 2), dtype=torch.float) # 4 nodes with 2 features each
edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]]) # 6 edges
pseudo = torch.rand((6, 2), dtype=torch.float) # two-dimensional edge attributes
weight = torch.rand((25, 2, 4), dtype=torch.float) # 25 parameters for in_channels x out_channels
kernel_size = torch.tensor([5, 5]) # 5 parameters in each edge dimension
is_open_spline = torch.tensor([1, 1], dtype=torch.uint8) # only use open B-splines
degree = 1 # B-spline degree of 1
norm = True # Normalize output by node degree.
root_weight = torch.rand((2, 4), dtype=torch.float) # separately weight root nodes
bias = None # do not apply an additional bias
out = spline_conv(x, edge_index, pseudo, weight, kernel_size,
is_open_spline, degree, norm, root_weight, bias)
print(out.size())
torch.Size([4, 4]) # 4 nodes with 4 features each
```
## Cite
Please cite our paper if you use this code in your own work:
```
@inproceedings{Fey/etal/2018,
title={{SplineCNN}: Fast Geometric Deep Learning with Continuous {B}-Spline Kernels},
author={Fey, Matthias and Lenssen, Jan Eric and Weichert, Frank and M{\"u}ller, Heinrich},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2018},
}
```
## Running tests
```
pytest
```
## C++ API
`torch-spline-conv` also offers a C++ API that contains C++ equivalent of python models.
```
mkdir build
cd build
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
make
make install
```