%global _empty_manifest_terminate_build 0 Name: python-torch-spline-conv Version: 1.2.2 Release: 1 Summary: Implementation of the Spline-Based Convolution Operator of SplineCNN in PyTorch License: MIT License URL: https://github.com/rusty1s/pytorch_spline_conv Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a0/50/5f80c47bf09b561ded0981b088ab0064424843696aa35b3b83afde421e56/torch_spline_conv-1.2.2.tar.gz BuildArch: noarch %description 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 MΓΌller: [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 πŸ€— 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 2.0 To install the binaries for PyTorch 2.0.0, simply run ``` pip install torch-spline-conv -f https://data.pyg.org/whl/torch-2.0.0+${CUDA}.html ``` where `${CUDA}` should be replaced by either `cpu`, `cu117`, or `cu118` depending on your PyTorch installation. | | `cpu` | `cu117` | `cu118` | |-------------|-------|---------|---------| | **Linux** | βœ… | βœ… | βœ… | | **Windows** | βœ… | βœ… | βœ… | | **macOS** | βœ… | | | #### PyTorch 1.13 To install the binaries for PyTorch 1.13.0, simply run ``` pip install torch-spline-conv -f https://data.pyg.org/whl/torch-1.13.0+${CUDA}.html ``` where `${CUDA}` should be replaced by either `cpu`, `cu116`, or `cu117` depending on your PyTorch installation. | | `cpu` | `cu116` | `cu117` | |-------------|-------|---------|---------| | **Linux** | βœ… | βœ… | βœ… | | **Windows** | βœ… | βœ… | βœ… | | **macOS** | βœ… | | | **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 and PyTorch 1.12.0/1.12.1 (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 ``` %package -n python3-torch-spline-conv Summary: Implementation of the Spline-Based Convolution Operator of SplineCNN in PyTorch Provides: python-torch-spline-conv BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-torch-spline-conv 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 MΓΌller: [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 πŸ€— 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 2.0 To install the binaries for PyTorch 2.0.0, simply run ``` pip install torch-spline-conv -f https://data.pyg.org/whl/torch-2.0.0+${CUDA}.html ``` where `${CUDA}` should be replaced by either `cpu`, `cu117`, or `cu118` depending on your PyTorch installation. | | `cpu` | `cu117` | `cu118` | |-------------|-------|---------|---------| | **Linux** | βœ… | βœ… | βœ… | | **Windows** | βœ… | βœ… | βœ… | | **macOS** | βœ… | | | #### PyTorch 1.13 To install the binaries for PyTorch 1.13.0, simply run ``` pip install torch-spline-conv -f https://data.pyg.org/whl/torch-1.13.0+${CUDA}.html ``` where `${CUDA}` should be replaced by either `cpu`, `cu116`, or `cu117` depending on your PyTorch installation. | | `cpu` | `cu116` | `cu117` | |-------------|-------|---------|---------| | **Linux** | βœ… | βœ… | βœ… | | **Windows** | βœ… | βœ… | βœ… | | **macOS** | βœ… | | | **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 and PyTorch 1.12.0/1.12.1 (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 ``` %package help Summary: Development documents and examples for torch-spline-conv Provides: python3-torch-spline-conv-doc %description help 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 MΓΌller: [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 πŸ€— 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 2.0 To install the binaries for PyTorch 2.0.0, simply run ``` pip install torch-spline-conv -f https://data.pyg.org/whl/torch-2.0.0+${CUDA}.html ``` where `${CUDA}` should be replaced by either `cpu`, `cu117`, or `cu118` depending on your PyTorch installation. | | `cpu` | `cu117` | `cu118` | |-------------|-------|---------|---------| | **Linux** | βœ… | βœ… | βœ… | | **Windows** | βœ… | βœ… | βœ… | | **macOS** | βœ… | | | #### PyTorch 1.13 To install the binaries for PyTorch 1.13.0, simply run ``` pip install torch-spline-conv -f https://data.pyg.org/whl/torch-1.13.0+${CUDA}.html ``` where `${CUDA}` should be replaced by either `cpu`, `cu116`, or `cu117` depending on your PyTorch installation. | | `cpu` | `cu116` | `cu117` | |-------------|-------|---------|---------| | **Linux** | βœ… | βœ… | βœ… | | **Windows** | βœ… | βœ… | βœ… | | **macOS** | βœ… | | | **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 and PyTorch 1.12.0/1.12.1 (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 ``` %prep %autosetup -n torch-spline-conv-1.2.2 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-torch-spline-conv -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed May 10 2023 Python_Bot - 1.2.2-1 - Package Spec generated