path: root/python-torch-spline-conv.spec

%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 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 2.0
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**   | ✅    | ✅      | ✅      |
| **Windows** | ✅    | ✅      | ✅      |
| **macOS**   | ✅    |         |         |
#### 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**   | ✅    | ✅      | ✅      |
| **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 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
<p align="center">
  <img width="50%" src="https://user-images.githubusercontent.com/6945922/38684093-36d9c52e-3e6f-11e8-9021-db054223c6b9.png" />
</p>
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.
<p align="center">
  <img width="45%" src="https://user-images.githubusercontent.com/6945922/38685443-3a2a0c68-3e72-11e8-8e13-9ce9ad8fe43e.png" />
  <img width="45%" src="https://user-images.githubusercontent.com/6945922/38685459-42b2bcae-3e72-11e8-88cc-4b61e41dbd93.png" />
</p>
### 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 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 2.0
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**   | ✅    | ✅      | ✅      |
| **Windows** | ✅    | ✅      | ✅      |
| **macOS**   | ✅    |         |         |
#### 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**   | ✅    | ✅      | ✅      |
| **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 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
<p align="center">
  <img width="50%" src="https://user-images.githubusercontent.com/6945922/38684093-36d9c52e-3e6f-11e8-9021-db054223c6b9.png" />
</p>
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.
<p align="center">
  <img width="45%" src="https://user-images.githubusercontent.com/6945922/38685443-3a2a0c68-3e72-11e8-8e13-9ce9ad8fe43e.png" />
  <img width="45%" src="https://user-images.githubusercontent.com/6945922/38685459-42b2bcae-3e72-11e8-88cc-4b61e41dbd93.png" />
</p>
### 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 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 2.0
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**   | ✅    | ✅      | ✅      |
| **Windows** | ✅    | ✅      | ✅      |
| **macOS**   | ✅    |         |         |
#### 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**   | ✅    | ✅      | ✅      |
| **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 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
<p align="center">
  <img width="50%" src="https://user-images.githubusercontent.com/6945922/38684093-36d9c52e-3e6f-11e8-9021-db054223c6b9.png" />
</p>
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.
<p align="center">
  <img width="45%" src="https://user-images.githubusercontent.com/6945922/38685443-3a2a0c68-3e72-11e8-8e13-9ce9ad8fe43e.png" />
  <img width="45%" src="https://user-images.githubusercontent.com/6945922/38685459-42b2bcae-3e72-11e8-88cc-4b61e41dbd93.png" />
</p>
### 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 <Python_Bot@openeuler.org> - 1.2.2-1
- Package Spec generated