automatic import of python-essmc2openeuler20.03

3 files changed, 341 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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+/essmc2-0.1.4.tar.gz
diff --git a/python-essmc2.spec b/python-essmc2.spec
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--- /dev/null
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+%global _empty_manifest_terminate_build 0
+Name:		python-essmc2
+Version:	0.1.4
+Release:	1
+Summary:	EssentialMC2: A Video Understanding Algorithm Framework.
+License:	MIT License
+URL:		https://github.com/alibaba/EssentialMC2
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/32/b3/8d5502285344050e53d35cc42e33aa334429636652e4e556f204d9f816e8/essmc2-0.1.4.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-addict
+Requires:	python3-yapf
+Requires:	python3-numpy
+Requires:	python3-opencv-transforms
+Requires:	python3-packaging
+Requires:	python3-oss2
+Requires:	python3-opencv-python
+Requires:	python3-einops
+Requires:	python3-docstring-parser
+
+%description
+# EssentialMC2
+
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/essmc2)](https://pypi.org/project/essmc2/) [![PyPI](https://img.shields.io/pypi/v/essmc2)](https://pypi.org/project/essmc2) [![license](https://img.shields.io/github/license/alibaba/EssentialMC2.svg)](https://github.com/alibaba/EssentialMC2/blob/main/LICENSE)
+
+### Introduction
+
+EssentialMC2 is a complete system to solve video understanding tasks including MHRL(representation learning), MECR2(
+relation reasoning) and MOSL3(openset life-long learning) powered by [DAMO Academy](https://damo.alibaba.com/?lang=en)
+MinD(Machine IntelligenNce of Damo) Lab. This codebase provides a comprehensive solution for video classification, 
+temporal detection and noise learning.
+
+### Features
+
+- Simple and easy to use
+- High efficiency
+- Include SOTA papers presented by DAMO Academy
+- Include various pretrained models
+
+### Installation
+
+#### Install by pip
+
+Run `pip install essmc2`.
+
+#### Install from source
+
+##### Requirements
+
+* Python 3.6+
+* PytTorch 1.5+
+
+Run `python setup.py install`. For each specific task, please refer to task specific README.
+
+### Model Zoo
+
+Pretrained models can be found in the [MODEL_ZOO.md](MODEL_ZOO.md).
+
+### SOTA Tasks
+
+- TAda! Temporally-Adaptive Convolutions for Video Understanding <br>
+[[Project](https://github.com/alibaba-mmai-research/TAdaConv/blob/main/projects/tada/README.md)] [[Paper](https://arxiv.org/pdf/2110.06178.pdf)] [[Website](https://tadaconv-iclr2022.github.io)] **ICLR 2022**
+- NGC: A Unified Framework for Learning with Open-World Noisy Data <br>
+[[Project](papers/ICCV2021-NGC/README.md)] [[Paper](https://arxiv.org/abs/2108.11035)] **ICCV 2021**
+- Self-supervised Motion Learning from Static Images <br>
+[[Project](papers/CVPR2021-MOSI/README.md)] [[Paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Self-Supervised_Motion_Learning_From_Static_Images_CVPR_2021_paper)] **CVPR 2021**
+- A Stronger Baseline for Ego-Centric Action Detection <br>
+[[Project](https://github.com/alibaba-mmai-research/TAdaConv/blob/main/projects/epic-kitchen-tal/README.md)] [[Paper](https://arxiv.org/pdf/2106.06942)] 
+**First-place** submission to [EPIC-KITCHENS-100 Action Detection Challenge](https://competitions.codalab.org/competitions/25926#results)
+- Towards Training Stronger Video Vision Transformers for EPIC-KITCHENS-100 Action Recognition <br>
+[[Project](https://github.com/alibaba-mmai-research/TAdaConv/blob/main/projects/epic-kitchen-ar/README.md)] [[Paper](https://arxiv.org/pdf/2106.05058)] 
+**Second-place** submission to [EPIC-KITCHENS-100 Action Recognition challenge](https://competitions.codalab.org/competitions/25923#results)
+
+### License
+
+EssentialMC2 is released under [MIT license](https://github.com/alibaba/EssentialMC2/blob/main/LICENSE).
+
+```text
+MIT License
+
+Copyright (c) 2021 Alibaba
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+```
+
+### Acknowledgement
+
+EssentialMC2 is an open source project that is contributed by researchers from DAMO Academy. We appreciate users who
+give valuable feedbacks.
+
+
+
+
+%package -n python3-essmc2
+Summary:	EssentialMC2: A Video Understanding Algorithm Framework.
+Provides:	python-essmc2
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-essmc2
+# EssentialMC2
+
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/essmc2)](https://pypi.org/project/essmc2/) [![PyPI](https://img.shields.io/pypi/v/essmc2)](https://pypi.org/project/essmc2) [![license](https://img.shields.io/github/license/alibaba/EssentialMC2.svg)](https://github.com/alibaba/EssentialMC2/blob/main/LICENSE)
+
+### Introduction
+
+EssentialMC2 is a complete system to solve video understanding tasks including MHRL(representation learning), MECR2(
+relation reasoning) and MOSL3(openset life-long learning) powered by [DAMO Academy](https://damo.alibaba.com/?lang=en)
+MinD(Machine IntelligenNce of Damo) Lab. This codebase provides a comprehensive solution for video classification, 
+temporal detection and noise learning.
+
+### Features
+
+- Simple and easy to use
+- High efficiency
+- Include SOTA papers presented by DAMO Academy
+- Include various pretrained models
+
+### Installation
+
+#### Install by pip
+
+Run `pip install essmc2`.
+
+#### Install from source
+
+##### Requirements
+
+* Python 3.6+
+* PytTorch 1.5+
+
+Run `python setup.py install`. For each specific task, please refer to task specific README.
+
+### Model Zoo
+
+Pretrained models can be found in the [MODEL_ZOO.md](MODEL_ZOO.md).
+
+### SOTA Tasks
+
+- TAda! Temporally-Adaptive Convolutions for Video Understanding <br>
+[[Project](https://github.com/alibaba-mmai-research/TAdaConv/blob/main/projects/tada/README.md)] [[Paper](https://arxiv.org/pdf/2110.06178.pdf)] [[Website](https://tadaconv-iclr2022.github.io)] **ICLR 2022**
+- NGC: A Unified Framework for Learning with Open-World Noisy Data <br>
+[[Project](papers/ICCV2021-NGC/README.md)] [[Paper](https://arxiv.org/abs/2108.11035)] **ICCV 2021**
+- Self-supervised Motion Learning from Static Images <br>
+[[Project](papers/CVPR2021-MOSI/README.md)] [[Paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Self-Supervised_Motion_Learning_From_Static_Images_CVPR_2021_paper)] **CVPR 2021**
+- A Stronger Baseline for Ego-Centric Action Detection <br>
+[[Project](https://github.com/alibaba-mmai-research/TAdaConv/blob/main/projects/epic-kitchen-tal/README.md)] [[Paper](https://arxiv.org/pdf/2106.06942)] 
+**First-place** submission to [EPIC-KITCHENS-100 Action Detection Challenge](https://competitions.codalab.org/competitions/25926#results)
+- Towards Training Stronger Video Vision Transformers for EPIC-KITCHENS-100 Action Recognition <br>
+[[Project](https://github.com/alibaba-mmai-research/TAdaConv/blob/main/projects/epic-kitchen-ar/README.md)] [[Paper](https://arxiv.org/pdf/2106.05058)] 
+**Second-place** submission to [EPIC-KITCHENS-100 Action Recognition challenge](https://competitions.codalab.org/competitions/25923#results)
+
+### License
+
+EssentialMC2 is released under [MIT license](https://github.com/alibaba/EssentialMC2/blob/main/LICENSE).
+
+```text
+MIT License
+
+Copyright (c) 2021 Alibaba
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+```
+
+### Acknowledgement
+
+EssentialMC2 is an open source project that is contributed by researchers from DAMO Academy. We appreciate users who
+give valuable feedbacks.
+
+
+
+
+%package help
+Summary:	Development documents and examples for essmc2
+Provides:	python3-essmc2-doc
+%description help
+# EssentialMC2
+
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/essmc2)](https://pypi.org/project/essmc2/) [![PyPI](https://img.shields.io/pypi/v/essmc2)](https://pypi.org/project/essmc2) [![license](https://img.shields.io/github/license/alibaba/EssentialMC2.svg)](https://github.com/alibaba/EssentialMC2/blob/main/LICENSE)
+
+### Introduction
+
+EssentialMC2 is a complete system to solve video understanding tasks including MHRL(representation learning), MECR2(
+relation reasoning) and MOSL3(openset life-long learning) powered by [DAMO Academy](https://damo.alibaba.com/?lang=en)
+MinD(Machine IntelligenNce of Damo) Lab. This codebase provides a comprehensive solution for video classification, 
+temporal detection and noise learning.
+
+### Features
+
+- Simple and easy to use
+- High efficiency
+- Include SOTA papers presented by DAMO Academy
+- Include various pretrained models
+
+### Installation
+
+#### Install by pip
+
+Run `pip install essmc2`.
+
+#### Install from source
+
+##### Requirements
+
+* Python 3.6+
+* PytTorch 1.5+
+
+Run `python setup.py install`. For each specific task, please refer to task specific README.
+
+### Model Zoo
+
+Pretrained models can be found in the [MODEL_ZOO.md](MODEL_ZOO.md).
+
+### SOTA Tasks
+
+- TAda! Temporally-Adaptive Convolutions for Video Understanding <br>
+[[Project](https://github.com/alibaba-mmai-research/TAdaConv/blob/main/projects/tada/README.md)] [[Paper](https://arxiv.org/pdf/2110.06178.pdf)] [[Website](https://tadaconv-iclr2022.github.io)] **ICLR 2022**
+- NGC: A Unified Framework for Learning with Open-World Noisy Data <br>
+[[Project](papers/ICCV2021-NGC/README.md)] [[Paper](https://arxiv.org/abs/2108.11035)] **ICCV 2021**
+- Self-supervised Motion Learning from Static Images <br>
+[[Project](papers/CVPR2021-MOSI/README.md)] [[Paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Self-Supervised_Motion_Learning_From_Static_Images_CVPR_2021_paper)] **CVPR 2021**
+- A Stronger Baseline for Ego-Centric Action Detection <br>
+[[Project](https://github.com/alibaba-mmai-research/TAdaConv/blob/main/projects/epic-kitchen-tal/README.md)] [[Paper](https://arxiv.org/pdf/2106.06942)] 
+**First-place** submission to [EPIC-KITCHENS-100 Action Detection Challenge](https://competitions.codalab.org/competitions/25926#results)
+- Towards Training Stronger Video Vision Transformers for EPIC-KITCHENS-100 Action Recognition <br>
+[[Project](https://github.com/alibaba-mmai-research/TAdaConv/blob/main/projects/epic-kitchen-ar/README.md)] [[Paper](https://arxiv.org/pdf/2106.05058)] 
+**Second-place** submission to [EPIC-KITCHENS-100 Action Recognition challenge](https://competitions.codalab.org/competitions/25923#results)
+
+### License
+
+EssentialMC2 is released under [MIT license](https://github.com/alibaba/EssentialMC2/blob/main/LICENSE).
+
+```text
+MIT License
+
+Copyright (c) 2021 Alibaba
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+```
+
+### Acknowledgement
+
+EssentialMC2 is an open source project that is contributed by researchers from DAMO Academy. We appreciate users who
+give valuable feedbacks.
+
+
+
+
+%prep
+%autosetup -n essmc2-0.1.4
+
+%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-essmc2 -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.4-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..485f3d6
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+15cf7ff7d74388e054a9903f8f59d193  essmc2-0.1.4.tar.gz