automatic import of python-pygmtools

1 files changed, 464 insertions, 0 deletions

diff --git a/python-pygmtools.spec b/python-pygmtools.spec
new file mode 100644
index 0000000..aa4a26c
--- /dev/null
+++ b/python-pygmtools.spec
@@ -0,0 +1,464 @@
+%global _empty_manifest_terminate_build 0
+Name:		python-pygmtools
+Version:	0.3.8
+Release:	1
+Summary:	pygmtools provides graph matching solvers in Python API and supports numpy and pytorch backends. pygmtools also provides dataset API for standard graph matching benchmarks.
+License:	Mulan PSL v2
+URL:		https://pygmtools.readthedocs.io/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/3f/50/1afc01420fa4262d42c1d89ebaa283673a4992b9c1a6ddd0134b41376c7a/pygmtools-0.3.8.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-requests
+Requires:	python3-scipy
+Requires:	python3-Pillow
+Requires:	python3-numpy
+Requires:	python3-easydict
+Requires:	python3-appdirs
+Requires:	python3-tqdm
+Requires:	python3-wget
+
+%description
+``pygmtools`` (Python Graph Matching Tools) provides graph matching solvers in Python and is easily accessible via:
+```bash
+$ pip install pygmtools
+```
+Official documentation: https://pygmtools.readthedocs.io
+Source code: https://github.com/Thinklab-SJTU/pygmtools
+Graph matching is a fundamental yet challenging problem in pattern recognition, data mining, and others.
+Graph matching aims to find node-to-node correspondence among multiple graphs, by solving an NP-hard combinatorial
+optimization problem.
+Doing graph matching in Python used to be difficult, and this library wants to make researchers' lives easier. 
+To highlight, ``pygmtools`` has the following features:
+* *Support various solvers*, including traditional combinatorial solvers (including linear, quadratic, and multi-graph) 
+  and novel deep learning-based solvers;
+* *Support various backends*, including ``numpy`` which is universally accessible, and some state-of-the-art deep 
+  learning architectures with GPU support: 
+  ``pytorch``, ``paddle``, ``jittor``, ``Tensorflow``; 
+* *Deep learning friendly*, the operations are designed to best preserve the gradient during computation and batched 
+  operations support for the best performance.
+## Installation
+You can install the stable release on PyPI:
+```bash
+$ pip install pygmtools
+```
+or get the latest version by running:
+```bash
+$ pip install -U https://github.com/Thinklab-SJTU/pygmtools/archive/master.zip # with --user for user install (no root)
+```
+Now the pygmtools is available with the ``numpy`` backend.
+The following packages are required, and shall be automatically installed by ``pip``:
+```
+Python >= 3.7
+requests >= 2.25.1
+scipy >= 1.4.1
+Pillow >= 7.2.0
+numpy >= 1.18.5
+easydict >= 1.7
+appdirs >= 1.4.4
+tqdm >= 4.64.1
+wget >= 3.2
+```
+## Available Graph Matching Solvers
+This library offers user-friendly API for the following solvers:
+* [Two-Graph Matching Solvers](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.classic_solvers.html)
+    * Linear assignment solvers including the differentiable soft 
+      [Sinkhorn algorithm](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.linear_solvers.sinkhorn.html) [1], 
+      and the exact solver [Hungarian](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.linear_solvers.hungarian.html) [2].
+    * Soft and differentiable quadratic assignment solvers, including [spectral graph matching](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.classic_solvers.sm.html) [3] 
+      and [random-walk-based graph matching](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.classic_solvers.rrwm.html) [4].
+    * Discrete (non-differentiable) quadratic assignment solver 
+      [integer projected fixed point method](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.classic_solvers.ipfp.html) [5]. 
+* [Multi-Graph Matching Solvers](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.multi_graph_solvers.html)
+    * [Composition based Affinity Optimization (CAO) solver](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.multi_graph_solvers.cao.html) [6] 
+      by optimizing the affinity score, meanwhile gradually infusing the consistency.
+    * Multi-Graph Matching based on 
+      [Floyd shortest path algorithm](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.multi_graph_solvers.mgm_floyd.html) [7].
+    * [Graduated-assignment based multi-graph matching solver](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.multi_graph_solvers.gamgm.html) [8][9]
+      by graduated annealing of Sinkhorn’s temperature.
+* [Neural Graph Matching Solvers](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.neural_solvers.html)
+    * Intra-graph and cross-graph embedding based neural graph matching solvers 
+      [PCA-GM](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.neural_solvers.pca_gm.html) 
+      and [IPCA-GM](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.neural_solvers.ipca_gm.html) [10]
+      for matching individual graphs.
+    * [Channel independent embedding (CIE)](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.neural_solvers.cie.html) [11]
+      based neural graph matching solver for matching individual graphs.
+    * [Neural graph matching solver (NGM)](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.neural_solvers.ngm.html) [12]
+      for the general quadratic assignment formulation.
+## Available Backends
+This library is designed to support multiple backends with the same set of API. 
+Please follow the official instructions to install your backend.
+The following backends are available:
+* [Numpy](https://numpy.org/) (**default** backend, CPU only)
+<img src="https://pygmtools.readthedocs.io/en/latest/_images/numpy_logo.png" alt="numpy logo" width="200"/>
+* [PyTorch](https://pytorch.org/) (GPU friendly, deep learning friendly)
+<img src="https://pygmtools.readthedocs.io/en/latest/_images/pytorch_logo.png" alt="pytorch logo" width="200"/>
+* [Jittor](https://github.com/Jittor/Jittor) (GPU friendly, JIT support, deep learning friendly)
+<img src="https://pygmtools.readthedocs.io/en/latest/_images/jittor_logo.png" alt="jittor logo" width="200"/>
+* [PaddlePaddle](https://www.paddlepaddle.org.cn/en) (GPU friendly, deep learning friendly)
+<img src="https://pygmtools.readthedocs.io/en/latest/_images/paddle_logo.png" alt="paddle logo" width="200"/>
+* [Tensorflow](https://tensorflow.google.cn/) (GPU friendly, deep learning friendly)
+<img src="https://pygmtools.readthedocs.io/en/latest/_images/tensorflow_logo.png" alt="tensorflow logo" width="200"/>
+### Development status (0.3.8)
+|                     | Numpy | PyTorch | Jittor | PaddlePaddle | Tensorflow | MindSpore |
+| ------------------- | ----- | ------- | ------ | ------------ | ---------- | --------- |
+| Linear Solvers      | ✔     | ✔       | ✔      | ✔            | ✔         | ✔        |
+| Classic Solvers     | ✔     | ✔       | ✔      | ✔            | ✔         | ✔        |
+| Multi-Graph Solvers | ✔    | ✔       | ✔      | ✔            | 📆         | 📆        |
+| Neural Solvers      | ✔    | ✔       | ✔      | ✔           | 📆         | 📆        |
+| Examples Gallery    | ✔    | ✔       | ✔      | ✔           | 📆         | 📆        |
+✔: Supported; 📆: Planned for future versions (contributions welcomed!).
+For more details, please [read the documentation](https://pygmtools.readthedocs.io/en/latest/guide/get_started.html#install-other-backends).
+## Pretrained Models
+The library includes several neural network solvers. The pretrained models shall be automatically downloaded upon 
+needed from Google Drive. If you are experiencing issues accessing Google Drive, please download the pretrained models
+manually and put them at ``~/.cache/pygmtools`` (for Linux).
+Available at:
+[[google drive]](https://drive.google.com/drive/folders/1O7vkIW8QXBJsNsHUIRiSw91HJ_0FAzu_?usp=sharing)
+[[baidu drive]](https://pan.baidu.com/s/1MvzfM52NJeLWx2JXbbc6HA?pwd=x8bv)
+## The Deep Graph Matching Benchmark
+``pygmtools`` is also featured with a standard data interface of several graph matching benchmarks. Please read 
+[the corresponding documentation](https://pygmtools.readthedocs.io/en/latest/guide/benchmark.html) for details.
+We also maintain a repository containing non-trivial implementation of deep graph matching models, please check out
+[ThinkMatch](https://thinkmatch.readthedocs.io/) if you are interested!
+## Chat with the Community
+If you have any questions, or if you are experiencing any issues, feel free to raise an issue on GitHub. 
+We also offer the following chat rooms if you are more comfortable with them:
+* Discord (for English speakers): 
+  [![discord](https://discordapp.com/api/guilds/1028701206526304317/widget.png?style=banner2)](https://discord.gg/8m6n7rRz9T)
+* QQ Group (for Chinese speakers)/QQ群(中文用户): 696401889
+  [![ThinkMatch/pygmtools交流群](http://pub.idqqimg.com/wpa/images/group.png)](https://qm.qq.com/cgi-bin/qm/qr?k=NlPuwwvaFaHzEWD8w7jSOTzoqSLIM80V&jump_from=webapi&authKey=chI2htrWDujQed6VtVid3V1NXEoJvwz3MVwruax6x5lQIvLsC8BmpmzBJOCzhtQd)
+## Contributing
+Any contributions/ideas/suggestions from the community is welcomed! Before starting your contribution, please read the
+[Contributing Guide](https://github.com/Thinklab-SJTU/pygmtools/blob/main/CONTRIBUTING.md).
+## Developers and Maintainers
+``pygmtools`` is currently developed and maintained by members from [ThinkLab](http://thinklab.sjtu.edu.cn) at 
+Shanghai Jiao Tong University. 
+## References
+<!--MLA style references-->
+[1] Sinkhorn, Richard, and Paul Knopp. "Concerning nonnegative matrices and doubly stochastic matrices." Pacific Journal of Mathematics 21.2 (1967): 343-348.
+[2] Munkres, James. "Algorithms for the assignment and transportation problems." Journal of the society for industrial and applied mathematics 5.1 (1957): 32-38.
+[3] Leordeanu, Marius, and Martial Hebert. "A spectral technique for correspondence problems using pairwise constraints." International Conference on Computer Vision (2005).
+[4] Cho, Minsu, Jungmin Lee, and Kyoung Mu Lee. "Reweighted random walks for graph matching." European conference on Computer vision. Springer, Berlin, Heidelberg, 2010.
+[5] Leordeanu, Marius, Martial Hebert, and Rahul Sukthankar. "An integer projected fixed point method for graph matching and map inference." Advances in neural information processing systems 22 (2009).
+[6] Yan, Junchi, et al. "Multi-graph matching via affinity optimization with graduated consistency regularization." IEEE transactions on pattern analysis and machine intelligence 38.6 (2015): 1228-1242.
+[7] Jiang, Zetian, Tianzhe Wang, and Junchi Yan. "Unifying offline and online multi-graph matching via finding shortest paths on supergraph." IEEE transactions on pattern analysis and machine intelligence 43.10 (2020): 3648-3663.
+[8] Solé-Ribalta, Albert, and Francesc Serratosa. "Graduated assignment algorithm for multiple graph matching based on a common labeling." International Journal of Pattern Recognition and Artificial Intelligence 27.01 (2013): 1350001.
+[9] Wang, Runzhong, Junchi Yan, and Xiaokang Yang. "Graduated assignment for joint multi-graph matching and clustering with application to unsupervised graph matching network learning." Advances in Neural Information Processing Systems 33 (2020): 19908-19919.
+[10] Wang, Runzhong, Junchi Yan, and Xiaokang Yang. "Combinatorial learning of robust deep graph matching: an embedding based approach." IEEE Transactions on Pattern Analysis and Machine Intelligence (2020).
+[11] Yu, Tianshu, et al. "Learning deep graph matching with channel-independent embedding and hungarian attention." International conference on learning representations. 2019.
+[12] Wang, Runzhong, Junchi Yan, and Xiaokang Yang. "Neural graph matching network: Learning lawler’s quadratic assignment problem with extension to hypergraph and multiple-graph matching." IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).
+
+%package -n python3-pygmtools
+Summary:	pygmtools provides graph matching solvers in Python API and supports numpy and pytorch backends. pygmtools also provides dataset API for standard graph matching benchmarks.
+Provides:	python-pygmtools
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pygmtools
+``pygmtools`` (Python Graph Matching Tools) provides graph matching solvers in Python and is easily accessible via:
+```bash
+$ pip install pygmtools
+```
+Official documentation: https://pygmtools.readthedocs.io
+Source code: https://github.com/Thinklab-SJTU/pygmtools
+Graph matching is a fundamental yet challenging problem in pattern recognition, data mining, and others.
+Graph matching aims to find node-to-node correspondence among multiple graphs, by solving an NP-hard combinatorial
+optimization problem.
+Doing graph matching in Python used to be difficult, and this library wants to make researchers' lives easier. 
+To highlight, ``pygmtools`` has the following features:
+* *Support various solvers*, including traditional combinatorial solvers (including linear, quadratic, and multi-graph) 
+  and novel deep learning-based solvers;
+* *Support various backends*, including ``numpy`` which is universally accessible, and some state-of-the-art deep 
+  learning architectures with GPU support: 
+  ``pytorch``, ``paddle``, ``jittor``, ``Tensorflow``; 
+* *Deep learning friendly*, the operations are designed to best preserve the gradient during computation and batched 
+  operations support for the best performance.
+## Installation
+You can install the stable release on PyPI:
+```bash
+$ pip install pygmtools
+```
+or get the latest version by running:
+```bash
+$ pip install -U https://github.com/Thinklab-SJTU/pygmtools/archive/master.zip # with --user for user install (no root)
+```
+Now the pygmtools is available with the ``numpy`` backend.
+The following packages are required, and shall be automatically installed by ``pip``:
+```
+Python >= 3.7
+requests >= 2.25.1
+scipy >= 1.4.1
+Pillow >= 7.2.0
+numpy >= 1.18.5
+easydict >= 1.7
+appdirs >= 1.4.4
+tqdm >= 4.64.1
+wget >= 3.2
+```
+## Available Graph Matching Solvers
+This library offers user-friendly API for the following solvers:
+* [Two-Graph Matching Solvers](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.classic_solvers.html)
+    * Linear assignment solvers including the differentiable soft 
+      [Sinkhorn algorithm](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.linear_solvers.sinkhorn.html) [1], 
+      and the exact solver [Hungarian](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.linear_solvers.hungarian.html) [2].
+    * Soft and differentiable quadratic assignment solvers, including [spectral graph matching](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.classic_solvers.sm.html) [3] 
+      and [random-walk-based graph matching](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.classic_solvers.rrwm.html) [4].
+    * Discrete (non-differentiable) quadratic assignment solver 
+      [integer projected fixed point method](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.classic_solvers.ipfp.html) [5]. 
+* [Multi-Graph Matching Solvers](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.multi_graph_solvers.html)
+    * [Composition based Affinity Optimization (CAO) solver](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.multi_graph_solvers.cao.html) [6] 
+      by optimizing the affinity score, meanwhile gradually infusing the consistency.
+    * Multi-Graph Matching based on 
+      [Floyd shortest path algorithm](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.multi_graph_solvers.mgm_floyd.html) [7].
+    * [Graduated-assignment based multi-graph matching solver](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.multi_graph_solvers.gamgm.html) [8][9]
+      by graduated annealing of Sinkhorn’s temperature.
+* [Neural Graph Matching Solvers](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.neural_solvers.html)
+    * Intra-graph and cross-graph embedding based neural graph matching solvers 
+      [PCA-GM](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.neural_solvers.pca_gm.html) 
+      and [IPCA-GM](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.neural_solvers.ipca_gm.html) [10]
+      for matching individual graphs.
+    * [Channel independent embedding (CIE)](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.neural_solvers.cie.html) [11]
+      based neural graph matching solver for matching individual graphs.
+    * [Neural graph matching solver (NGM)](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.neural_solvers.ngm.html) [12]
+      for the general quadratic assignment formulation.
+## Available Backends
+This library is designed to support multiple backends with the same set of API. 
+Please follow the official instructions to install your backend.
+The following backends are available:
+* [Numpy](https://numpy.org/) (**default** backend, CPU only)
+<img src="https://pygmtools.readthedocs.io/en/latest/_images/numpy_logo.png" alt="numpy logo" width="200"/>
+* [PyTorch](https://pytorch.org/) (GPU friendly, deep learning friendly)
+<img src="https://pygmtools.readthedocs.io/en/latest/_images/pytorch_logo.png" alt="pytorch logo" width="200"/>
+* [Jittor](https://github.com/Jittor/Jittor) (GPU friendly, JIT support, deep learning friendly)
+<img src="https://pygmtools.readthedocs.io/en/latest/_images/jittor_logo.png" alt="jittor logo" width="200"/>
+* [PaddlePaddle](https://www.paddlepaddle.org.cn/en) (GPU friendly, deep learning friendly)
+<img src="https://pygmtools.readthedocs.io/en/latest/_images/paddle_logo.png" alt="paddle logo" width="200"/>
+* [Tensorflow](https://tensorflow.google.cn/) (GPU friendly, deep learning friendly)
+<img src="https://pygmtools.readthedocs.io/en/latest/_images/tensorflow_logo.png" alt="tensorflow logo" width="200"/>
+### Development status (0.3.8)
+|                     | Numpy | PyTorch | Jittor | PaddlePaddle | Tensorflow | MindSpore |
+| ------------------- | ----- | ------- | ------ | ------------ | ---------- | --------- |
+| Linear Solvers      | ✔     | ✔       | ✔      | ✔            | ✔         | ✔        |
+| Classic Solvers     | ✔     | ✔       | ✔      | ✔            | ✔         | ✔        |
+| Multi-Graph Solvers | ✔    | ✔       | ✔      | ✔            | 📆         | 📆        |
+| Neural Solvers      | ✔    | ✔       | ✔      | ✔           | 📆         | 📆        |
+| Examples Gallery    | ✔    | ✔       | ✔      | ✔           | 📆         | 📆        |
+✔: Supported; 📆: Planned for future versions (contributions welcomed!).
+For more details, please [read the documentation](https://pygmtools.readthedocs.io/en/latest/guide/get_started.html#install-other-backends).
+## Pretrained Models
+The library includes several neural network solvers. The pretrained models shall be automatically downloaded upon 
+needed from Google Drive. If you are experiencing issues accessing Google Drive, please download the pretrained models
+manually and put them at ``~/.cache/pygmtools`` (for Linux).
+Available at:
+[[google drive]](https://drive.google.com/drive/folders/1O7vkIW8QXBJsNsHUIRiSw91HJ_0FAzu_?usp=sharing)
+[[baidu drive]](https://pan.baidu.com/s/1MvzfM52NJeLWx2JXbbc6HA?pwd=x8bv)
+## The Deep Graph Matching Benchmark
+``pygmtools`` is also featured with a standard data interface of several graph matching benchmarks. Please read 
+[the corresponding documentation](https://pygmtools.readthedocs.io/en/latest/guide/benchmark.html) for details.
+We also maintain a repository containing non-trivial implementation of deep graph matching models, please check out
+[ThinkMatch](https://thinkmatch.readthedocs.io/) if you are interested!
+## Chat with the Community
+If you have any questions, or if you are experiencing any issues, feel free to raise an issue on GitHub. 
+We also offer the following chat rooms if you are more comfortable with them:
+* Discord (for English speakers): 
+  [![discord](https://discordapp.com/api/guilds/1028701206526304317/widget.png?style=banner2)](https://discord.gg/8m6n7rRz9T)
+* QQ Group (for Chinese speakers)/QQ群(中文用户): 696401889
+  [![ThinkMatch/pygmtools交流群](http://pub.idqqimg.com/wpa/images/group.png)](https://qm.qq.com/cgi-bin/qm/qr?k=NlPuwwvaFaHzEWD8w7jSOTzoqSLIM80V&jump_from=webapi&authKey=chI2htrWDujQed6VtVid3V1NXEoJvwz3MVwruax6x5lQIvLsC8BmpmzBJOCzhtQd)
+## Contributing
+Any contributions/ideas/suggestions from the community is welcomed! Before starting your contribution, please read the
+[Contributing Guide](https://github.com/Thinklab-SJTU/pygmtools/blob/main/CONTRIBUTING.md).
+## Developers and Maintainers
+``pygmtools`` is currently developed and maintained by members from [ThinkLab](http://thinklab.sjtu.edu.cn) at 
+Shanghai Jiao Tong University. 
+## References
+<!--MLA style references-->
+[1] Sinkhorn, Richard, and Paul Knopp. "Concerning nonnegative matrices and doubly stochastic matrices." Pacific Journal of Mathematics 21.2 (1967): 343-348.
+[2] Munkres, James. "Algorithms for the assignment and transportation problems." Journal of the society for industrial and applied mathematics 5.1 (1957): 32-38.
+[3] Leordeanu, Marius, and Martial Hebert. "A spectral technique for correspondence problems using pairwise constraints." International Conference on Computer Vision (2005).
+[4] Cho, Minsu, Jungmin Lee, and Kyoung Mu Lee. "Reweighted random walks for graph matching." European conference on Computer vision. Springer, Berlin, Heidelberg, 2010.
+[5] Leordeanu, Marius, Martial Hebert, and Rahul Sukthankar. "An integer projected fixed point method for graph matching and map inference." Advances in neural information processing systems 22 (2009).
+[6] Yan, Junchi, et al. "Multi-graph matching via affinity optimization with graduated consistency regularization." IEEE transactions on pattern analysis and machine intelligence 38.6 (2015): 1228-1242.
+[7] Jiang, Zetian, Tianzhe Wang, and Junchi Yan. "Unifying offline and online multi-graph matching via finding shortest paths on supergraph." IEEE transactions on pattern analysis and machine intelligence 43.10 (2020): 3648-3663.
+[8] Solé-Ribalta, Albert, and Francesc Serratosa. "Graduated assignment algorithm for multiple graph matching based on a common labeling." International Journal of Pattern Recognition and Artificial Intelligence 27.01 (2013): 1350001.
+[9] Wang, Runzhong, Junchi Yan, and Xiaokang Yang. "Graduated assignment for joint multi-graph matching and clustering with application to unsupervised graph matching network learning." Advances in Neural Information Processing Systems 33 (2020): 19908-19919.
+[10] Wang, Runzhong, Junchi Yan, and Xiaokang Yang. "Combinatorial learning of robust deep graph matching: an embedding based approach." IEEE Transactions on Pattern Analysis and Machine Intelligence (2020).
+[11] Yu, Tianshu, et al. "Learning deep graph matching with channel-independent embedding and hungarian attention." International conference on learning representations. 2019.
+[12] Wang, Runzhong, Junchi Yan, and Xiaokang Yang. "Neural graph matching network: Learning lawler’s quadratic assignment problem with extension to hypergraph and multiple-graph matching." IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).
+
+%package help
+Summary:	Development documents and examples for pygmtools
+Provides:	python3-pygmtools-doc
+%description help
+``pygmtools`` (Python Graph Matching Tools) provides graph matching solvers in Python and is easily accessible via:
+```bash
+$ pip install pygmtools
+```
+Official documentation: https://pygmtools.readthedocs.io
+Source code: https://github.com/Thinklab-SJTU/pygmtools
+Graph matching is a fundamental yet challenging problem in pattern recognition, data mining, and others.
+Graph matching aims to find node-to-node correspondence among multiple graphs, by solving an NP-hard combinatorial
+optimization problem.
+Doing graph matching in Python used to be difficult, and this library wants to make researchers' lives easier. 
+To highlight, ``pygmtools`` has the following features:
+* *Support various solvers*, including traditional combinatorial solvers (including linear, quadratic, and multi-graph) 
+  and novel deep learning-based solvers;
+* *Support various backends*, including ``numpy`` which is universally accessible, and some state-of-the-art deep 
+  learning architectures with GPU support: 
+  ``pytorch``, ``paddle``, ``jittor``, ``Tensorflow``; 
+* *Deep learning friendly*, the operations are designed to best preserve the gradient during computation and batched 
+  operations support for the best performance.
+## Installation
+You can install the stable release on PyPI:
+```bash
+$ pip install pygmtools
+```
+or get the latest version by running:
+```bash
+$ pip install -U https://github.com/Thinklab-SJTU/pygmtools/archive/master.zip # with --user for user install (no root)
+```
+Now the pygmtools is available with the ``numpy`` backend.
+The following packages are required, and shall be automatically installed by ``pip``:
+```
+Python >= 3.7
+requests >= 2.25.1
+scipy >= 1.4.1
+Pillow >= 7.2.0
+numpy >= 1.18.5
+easydict >= 1.7
+appdirs >= 1.4.4
+tqdm >= 4.64.1
+wget >= 3.2
+```
+## Available Graph Matching Solvers
+This library offers user-friendly API for the following solvers:
+* [Two-Graph Matching Solvers](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.classic_solvers.html)
+    * Linear assignment solvers including the differentiable soft 
+      [Sinkhorn algorithm](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.linear_solvers.sinkhorn.html) [1], 
+      and the exact solver [Hungarian](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.linear_solvers.hungarian.html) [2].
+    * Soft and differentiable quadratic assignment solvers, including [spectral graph matching](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.classic_solvers.sm.html) [3] 
+      and [random-walk-based graph matching](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.classic_solvers.rrwm.html) [4].
+    * Discrete (non-differentiable) quadratic assignment solver 
+      [integer projected fixed point method](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.classic_solvers.ipfp.html) [5]. 
+* [Multi-Graph Matching Solvers](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.multi_graph_solvers.html)
+    * [Composition based Affinity Optimization (CAO) solver](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.multi_graph_solvers.cao.html) [6] 
+      by optimizing the affinity score, meanwhile gradually infusing the consistency.
+    * Multi-Graph Matching based on 
+      [Floyd shortest path algorithm](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.multi_graph_solvers.mgm_floyd.html) [7].
+    * [Graduated-assignment based multi-graph matching solver](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.multi_graph_solvers.gamgm.html) [8][9]
+      by graduated annealing of Sinkhorn’s temperature.
+* [Neural Graph Matching Solvers](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.neural_solvers.html)
+    * Intra-graph and cross-graph embedding based neural graph matching solvers 
+      [PCA-GM](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.neural_solvers.pca_gm.html) 
+      and [IPCA-GM](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.neural_solvers.ipca_gm.html) [10]
+      for matching individual graphs.
+    * [Channel independent embedding (CIE)](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.neural_solvers.cie.html) [11]
+      based neural graph matching solver for matching individual graphs.
+    * [Neural graph matching solver (NGM)](https://pygmtools.readthedocs.io/en/latest/api/_autosummary/pygmtools.neural_solvers.ngm.html) [12]
+      for the general quadratic assignment formulation.
+## Available Backends
+This library is designed to support multiple backends with the same set of API. 
+Please follow the official instructions to install your backend.
+The following backends are available:
+* [Numpy](https://numpy.org/) (**default** backend, CPU only)
+<img src="https://pygmtools.readthedocs.io/en/latest/_images/numpy_logo.png" alt="numpy logo" width="200"/>
+* [PyTorch](https://pytorch.org/) (GPU friendly, deep learning friendly)
+<img src="https://pygmtools.readthedocs.io/en/latest/_images/pytorch_logo.png" alt="pytorch logo" width="200"/>
+* [Jittor](https://github.com/Jittor/Jittor) (GPU friendly, JIT support, deep learning friendly)
+<img src="https://pygmtools.readthedocs.io/en/latest/_images/jittor_logo.png" alt="jittor logo" width="200"/>
+* [PaddlePaddle](https://www.paddlepaddle.org.cn/en) (GPU friendly, deep learning friendly)
+<img src="https://pygmtools.readthedocs.io/en/latest/_images/paddle_logo.png" alt="paddle logo" width="200"/>
+* [Tensorflow](https://tensorflow.google.cn/) (GPU friendly, deep learning friendly)
+<img src="https://pygmtools.readthedocs.io/en/latest/_images/tensorflow_logo.png" alt="tensorflow logo" width="200"/>
+### Development status (0.3.8)
+|                     | Numpy | PyTorch | Jittor | PaddlePaddle | Tensorflow | MindSpore |
+| ------------------- | ----- | ------- | ------ | ------------ | ---------- | --------- |
+| Linear Solvers      | ✔     | ✔       | ✔      | ✔            | ✔         | ✔        |
+| Classic Solvers     | ✔     | ✔       | ✔      | ✔            | ✔         | ✔        |
+| Multi-Graph Solvers | ✔    | ✔       | ✔      | ✔            | 📆         | 📆        |
+| Neural Solvers      | ✔    | ✔       | ✔      | ✔           | 📆         | 📆        |
+| Examples Gallery    | ✔    | ✔       | ✔      | ✔           | 📆         | 📆        |
+✔: Supported; 📆: Planned for future versions (contributions welcomed!).
+For more details, please [read the documentation](https://pygmtools.readthedocs.io/en/latest/guide/get_started.html#install-other-backends).
+## Pretrained Models
+The library includes several neural network solvers. The pretrained models shall be automatically downloaded upon 
+needed from Google Drive. If you are experiencing issues accessing Google Drive, please download the pretrained models
+manually and put them at ``~/.cache/pygmtools`` (for Linux).
+Available at:
+[[google drive]](https://drive.google.com/drive/folders/1O7vkIW8QXBJsNsHUIRiSw91HJ_0FAzu_?usp=sharing)
+[[baidu drive]](https://pan.baidu.com/s/1MvzfM52NJeLWx2JXbbc6HA?pwd=x8bv)
+## The Deep Graph Matching Benchmark
+``pygmtools`` is also featured with a standard data interface of several graph matching benchmarks. Please read 
+[the corresponding documentation](https://pygmtools.readthedocs.io/en/latest/guide/benchmark.html) for details.
+We also maintain a repository containing non-trivial implementation of deep graph matching models, please check out
+[ThinkMatch](https://thinkmatch.readthedocs.io/) if you are interested!
+## Chat with the Community
+If you have any questions, or if you are experiencing any issues, feel free to raise an issue on GitHub. 
+We also offer the following chat rooms if you are more comfortable with them:
+* Discord (for English speakers): 
+  [![discord](https://discordapp.com/api/guilds/1028701206526304317/widget.png?style=banner2)](https://discord.gg/8m6n7rRz9T)
+* QQ Group (for Chinese speakers)/QQ群(中文用户): 696401889
+  [![ThinkMatch/pygmtools交流群](http://pub.idqqimg.com/wpa/images/group.png)](https://qm.qq.com/cgi-bin/qm/qr?k=NlPuwwvaFaHzEWD8w7jSOTzoqSLIM80V&jump_from=webapi&authKey=chI2htrWDujQed6VtVid3V1NXEoJvwz3MVwruax6x5lQIvLsC8BmpmzBJOCzhtQd)
+## Contributing
+Any contributions/ideas/suggestions from the community is welcomed! Before starting your contribution, please read the
+[Contributing Guide](https://github.com/Thinklab-SJTU/pygmtools/blob/main/CONTRIBUTING.md).
+## Developers and Maintainers
+``pygmtools`` is currently developed and maintained by members from [ThinkLab](http://thinklab.sjtu.edu.cn) at 
+Shanghai Jiao Tong University. 
+## References
+<!--MLA style references-->
+[1] Sinkhorn, Richard, and Paul Knopp. "Concerning nonnegative matrices and doubly stochastic matrices." Pacific Journal of Mathematics 21.2 (1967): 343-348.
+[2] Munkres, James. "Algorithms for the assignment and transportation problems." Journal of the society for industrial and applied mathematics 5.1 (1957): 32-38.
+[3] Leordeanu, Marius, and Martial Hebert. "A spectral technique for correspondence problems using pairwise constraints." International Conference on Computer Vision (2005).
+[4] Cho, Minsu, Jungmin Lee, and Kyoung Mu Lee. "Reweighted random walks for graph matching." European conference on Computer vision. Springer, Berlin, Heidelberg, 2010.
+[5] Leordeanu, Marius, Martial Hebert, and Rahul Sukthankar. "An integer projected fixed point method for graph matching and map inference." Advances in neural information processing systems 22 (2009).
+[6] Yan, Junchi, et al. "Multi-graph matching via affinity optimization with graduated consistency regularization." IEEE transactions on pattern analysis and machine intelligence 38.6 (2015): 1228-1242.
+[7] Jiang, Zetian, Tianzhe Wang, and Junchi Yan. "Unifying offline and online multi-graph matching via finding shortest paths on supergraph." IEEE transactions on pattern analysis and machine intelligence 43.10 (2020): 3648-3663.
+[8] Solé-Ribalta, Albert, and Francesc Serratosa. "Graduated assignment algorithm for multiple graph matching based on a common labeling." International Journal of Pattern Recognition and Artificial Intelligence 27.01 (2013): 1350001.
+[9] Wang, Runzhong, Junchi Yan, and Xiaokang Yang. "Graduated assignment for joint multi-graph matching and clustering with application to unsupervised graph matching network learning." Advances in Neural Information Processing Systems 33 (2020): 19908-19919.
+[10] Wang, Runzhong, Junchi Yan, and Xiaokang Yang. "Combinatorial learning of robust deep graph matching: an embedding based approach." IEEE Transactions on Pattern Analysis and Machine Intelligence (2020).
+[11] Yu, Tianshu, et al. "Learning deep graph matching with channel-independent embedding and hungarian attention." International conference on learning representations. 2019.
+[12] Wang, Runzhong, Junchi Yan, and Xiaokang Yang. "Neural graph matching network: Learning lawler’s quadratic assignment problem with extension to hypergraph and multiple-graph matching." IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).
+
+%prep
+%autosetup -n pygmtools-0.3.8
+
+%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-pygmtools -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.8-1
+- Package Spec generated