automatic import of python-tensorpack

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diff --git a/.gitignore b/.gitignore
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--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/tensorpack-0.11.tar.gz
diff --git a/python-tensorpack.spec b/python-tensorpack.spec
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+++ b/python-tensorpack.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-tensorpack
+Version:	0.11
+Release:	1
+Summary:	A Neural Network Training Interface on TensorFlow
+License:	Apache
+URL:		https://github.com/tensorpack/tensorpack
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/d2/f0/edfda47ca6cc9ece30a893362c336b9121b691529e4cdf3b8732565be790/tensorpack-0.11.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-six
+Requires:	python3-termcolor
+Requires:	python3-tabulate
+Requires:	python3-tqdm
+Requires:	python3-msgpack
+Requires:	python3-msgpack-numpy
+Requires:	python3-pyzmq
+Requires:	python3-psutil
+Requires:	python3-scipy
+Requires:	python3-h5py
+Requires:	python3-lmdb
+Requires:	python3-matplotlib
+Requires:	python3-scikit-learn
+Requires:	python3-prctl
+
+%description
+![Tensorpack](https://github.com/tensorpack/tensorpack/raw/master/.github/tensorpack.png)
+
+Tensorpack is a neural network training interface based on TensorFlow.
+
+[![ReadTheDoc](https://readthedocs.org/projects/tensorpack/badge/?version=latest)](http://tensorpack.readthedocs.io)
+[![Gitter chat](https://img.shields.io/badge/chat-on%20gitter-46bc99.svg)](https://gitter.im/tensorpack/users)
+[![model-zoo](https://img.shields.io/badge/model-zoo-brightgreen.svg)](http://models.tensorpack.com)
+## Features:
+
+It's Yet Another TF high-level API, with __speed__, and __flexibility__ built together.
+
+1. Focus on __training speed__.
+	+ Speed comes for free with Tensorpack -- it uses TensorFlow in the __efficient way__ with no extra overhead.
+	  On common CNNs, it runs training [1.2~5x faster](https://github.com/tensorpack/benchmarks/tree/master/other-wrappers) than the equivalent Keras code.
+		Your training can probably gets faster if written with Tensorpack.
+
+	+ Data-parallel multi-GPU/distributed training strategy is off-the-shelf to use.
+    It scales as well as Google's [official benchmark](https://www.tensorflow.org/performance/benchmarks).
+
+	+ See [tensorpack/benchmarks](https://github.com/tensorpack/benchmarks) for
+    some benchmark scripts.
+
+2. Focus on __large datasets__.
+	+ [You don't usually need `tf.data`](https://tensorpack.readthedocs.io/tutorial/philosophy/dataflow.html#alternative-data-loading-solutions).
+    Symbolic programming often makes data processing harder.
+	  Tensorpack helps you efficiently process large datasets (e.g. ImageNet) in __pure Python__ with autoparallelization.
+
+3. It's not a model wrapper.
+	+ There are too many symbolic function wrappers in the world. Tensorpack includes only a few common models.
+	  But you can use any symbolic function library inside Tensorpack, including tf.layers/Keras/slim/tflearn/tensorlayer/....
+
+See [tutorials and documentations](http://tensorpack.readthedocs.io/tutorial/index.html#user-tutorials) to know more about these features.
+
+## Examples:
+
+We refuse toy examples.
+Instead of showing tiny CNNs trained on MNIST/Cifar10,
+we provide training scripts that reproduce well-known papers.
+
+We refuse low-quality implementations.
+Unlike most open source repos which only __implement__ papers,
+[Tensorpack examples](examples) faithfully __reproduce__ papers,
+demonstrating its __flexibility__ for actual research.
+
+### Vision:
++ [Train ResNet](examples/ResNet) and [other models](examples/ImageNetModels) on ImageNet
++ [Train Mask/Faster R-CNN on COCO object detection](examples/FasterRCNN)
++ [Unsupervised learning with Momentum Contrast](https://github.com/ppwwyyxx/moco.tensorflow) (MoCo)
++ [Generative Adversarial Network(GAN) variants](examples/GAN), including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN
++ [DoReFa-Net: train binary / low-bitwidth CNN on ImageNet](examples/DoReFa-Net)
++ [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](examples/HED)
++ [Spatial Transformer Networks on MNIST addition](examples/SpatialTransformer)
++ [Visualize CNN saliency maps](examples/Saliency)
++ [Similarity learning on MNIST](examples/SimilarityLearning)
+
+### Reinforcement Learning:
++ [Deep Q-Network(DQN) variants on Atari games](examples/DeepQNetwork), including DQN, DoubleDQN, DuelingDQN.
++ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](examples/A3C-Gym)
+
+### Speech / NLP:
++ [LSTM-CTC for speech recognition](examples/CTC-TIMIT)
++ [char-rnn for fun](examples/Char-RNN)
++ [LSTM language model on PennTreebank](examples/PennTreebank)
+
+## Install:
+
+Dependencies:
+
++ Python 3.3+.
++ Python bindings for OpenCV. (Optional, but required by a lot of features)
++ TensorFlow ≥ 1.5, < 2
+  * TF is not not required if you only want to use `tensorpack.dataflow` alone as a data processing library
+  * TF2 is supported if used in graph mode (and use `tf.compat.v1` when needed)
+```
+pip install --upgrade git+https://github.com/tensorpack/tensorpack.git
+# or add `--user` to install to user's local directories
+```
+
+Please note that tensorpack is not yet stable.
+If you use tensorpack in your code, remember to mark the exact version of tensorpack you use as your dependencies.
+
+## Citing Tensorpack:
+
+If you use Tensorpack in your research or wish to refer to the examples, please cite with:
+```
+@misc{wu2016tensorpack,
+  title={Tensorpack},
+  author={Wu, Yuxin and others},
+  howpublished={\url{https://github.com/tensorpack/}},
+  year={2016}
+}
+```
+
+
+
+
+%package -n python3-tensorpack
+Summary:	A Neural Network Training Interface on TensorFlow
+Provides:	python-tensorpack
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-tensorpack
+![Tensorpack](https://github.com/tensorpack/tensorpack/raw/master/.github/tensorpack.png)
+
+Tensorpack is a neural network training interface based on TensorFlow.
+
+[![ReadTheDoc](https://readthedocs.org/projects/tensorpack/badge/?version=latest)](http://tensorpack.readthedocs.io)
+[![Gitter chat](https://img.shields.io/badge/chat-on%20gitter-46bc99.svg)](https://gitter.im/tensorpack/users)
+[![model-zoo](https://img.shields.io/badge/model-zoo-brightgreen.svg)](http://models.tensorpack.com)
+## Features:
+
+It's Yet Another TF high-level API, with __speed__, and __flexibility__ built together.
+
+1. Focus on __training speed__.
+	+ Speed comes for free with Tensorpack -- it uses TensorFlow in the __efficient way__ with no extra overhead.
+	  On common CNNs, it runs training [1.2~5x faster](https://github.com/tensorpack/benchmarks/tree/master/other-wrappers) than the equivalent Keras code.
+		Your training can probably gets faster if written with Tensorpack.
+
+	+ Data-parallel multi-GPU/distributed training strategy is off-the-shelf to use.
+    It scales as well as Google's [official benchmark](https://www.tensorflow.org/performance/benchmarks).
+
+	+ See [tensorpack/benchmarks](https://github.com/tensorpack/benchmarks) for
+    some benchmark scripts.
+
+2. Focus on __large datasets__.
+	+ [You don't usually need `tf.data`](https://tensorpack.readthedocs.io/tutorial/philosophy/dataflow.html#alternative-data-loading-solutions).
+    Symbolic programming often makes data processing harder.
+	  Tensorpack helps you efficiently process large datasets (e.g. ImageNet) in __pure Python__ with autoparallelization.
+
+3. It's not a model wrapper.
+	+ There are too many symbolic function wrappers in the world. Tensorpack includes only a few common models.
+	  But you can use any symbolic function library inside Tensorpack, including tf.layers/Keras/slim/tflearn/tensorlayer/....
+
+See [tutorials and documentations](http://tensorpack.readthedocs.io/tutorial/index.html#user-tutorials) to know more about these features.
+
+## Examples:
+
+We refuse toy examples.
+Instead of showing tiny CNNs trained on MNIST/Cifar10,
+we provide training scripts that reproduce well-known papers.
+
+We refuse low-quality implementations.
+Unlike most open source repos which only __implement__ papers,
+[Tensorpack examples](examples) faithfully __reproduce__ papers,
+demonstrating its __flexibility__ for actual research.
+
+### Vision:
++ [Train ResNet](examples/ResNet) and [other models](examples/ImageNetModels) on ImageNet
++ [Train Mask/Faster R-CNN on COCO object detection](examples/FasterRCNN)
++ [Unsupervised learning with Momentum Contrast](https://github.com/ppwwyyxx/moco.tensorflow) (MoCo)
++ [Generative Adversarial Network(GAN) variants](examples/GAN), including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN
++ [DoReFa-Net: train binary / low-bitwidth CNN on ImageNet](examples/DoReFa-Net)
++ [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](examples/HED)
++ [Spatial Transformer Networks on MNIST addition](examples/SpatialTransformer)
++ [Visualize CNN saliency maps](examples/Saliency)
++ [Similarity learning on MNIST](examples/SimilarityLearning)
+
+### Reinforcement Learning:
++ [Deep Q-Network(DQN) variants on Atari games](examples/DeepQNetwork), including DQN, DoubleDQN, DuelingDQN.
++ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](examples/A3C-Gym)
+
+### Speech / NLP:
++ [LSTM-CTC for speech recognition](examples/CTC-TIMIT)
++ [char-rnn for fun](examples/Char-RNN)
++ [LSTM language model on PennTreebank](examples/PennTreebank)
+
+## Install:
+
+Dependencies:
+
++ Python 3.3+.
++ Python bindings for OpenCV. (Optional, but required by a lot of features)
++ TensorFlow ≥ 1.5, < 2
+  * TF is not not required if you only want to use `tensorpack.dataflow` alone as a data processing library
+  * TF2 is supported if used in graph mode (and use `tf.compat.v1` when needed)
+```
+pip install --upgrade git+https://github.com/tensorpack/tensorpack.git
+# or add `--user` to install to user's local directories
+```
+
+Please note that tensorpack is not yet stable.
+If you use tensorpack in your code, remember to mark the exact version of tensorpack you use as your dependencies.
+
+## Citing Tensorpack:
+
+If you use Tensorpack in your research or wish to refer to the examples, please cite with:
+```
+@misc{wu2016tensorpack,
+  title={Tensorpack},
+  author={Wu, Yuxin and others},
+  howpublished={\url{https://github.com/tensorpack/}},
+  year={2016}
+}
+```
+
+
+
+
+%package help
+Summary:	Development documents and examples for tensorpack
+Provides:	python3-tensorpack-doc
+%description help
+![Tensorpack](https://github.com/tensorpack/tensorpack/raw/master/.github/tensorpack.png)
+
+Tensorpack is a neural network training interface based on TensorFlow.
+
+[![ReadTheDoc](https://readthedocs.org/projects/tensorpack/badge/?version=latest)](http://tensorpack.readthedocs.io)
+[![Gitter chat](https://img.shields.io/badge/chat-on%20gitter-46bc99.svg)](https://gitter.im/tensorpack/users)
+[![model-zoo](https://img.shields.io/badge/model-zoo-brightgreen.svg)](http://models.tensorpack.com)
+## Features:
+
+It's Yet Another TF high-level API, with __speed__, and __flexibility__ built together.
+
+1. Focus on __training speed__.
+	+ Speed comes for free with Tensorpack -- it uses TensorFlow in the __efficient way__ with no extra overhead.
+	  On common CNNs, it runs training [1.2~5x faster](https://github.com/tensorpack/benchmarks/tree/master/other-wrappers) than the equivalent Keras code.
+		Your training can probably gets faster if written with Tensorpack.
+
+	+ Data-parallel multi-GPU/distributed training strategy is off-the-shelf to use.
+    It scales as well as Google's [official benchmark](https://www.tensorflow.org/performance/benchmarks).
+
+	+ See [tensorpack/benchmarks](https://github.com/tensorpack/benchmarks) for
+    some benchmark scripts.
+
+2. Focus on __large datasets__.
+	+ [You don't usually need `tf.data`](https://tensorpack.readthedocs.io/tutorial/philosophy/dataflow.html#alternative-data-loading-solutions).
+    Symbolic programming often makes data processing harder.
+	  Tensorpack helps you efficiently process large datasets (e.g. ImageNet) in __pure Python__ with autoparallelization.
+
+3. It's not a model wrapper.
+	+ There are too many symbolic function wrappers in the world. Tensorpack includes only a few common models.
+	  But you can use any symbolic function library inside Tensorpack, including tf.layers/Keras/slim/tflearn/tensorlayer/....
+
+See [tutorials and documentations](http://tensorpack.readthedocs.io/tutorial/index.html#user-tutorials) to know more about these features.
+
+## Examples:
+
+We refuse toy examples.
+Instead of showing tiny CNNs trained on MNIST/Cifar10,
+we provide training scripts that reproduce well-known papers.
+
+We refuse low-quality implementations.
+Unlike most open source repos which only __implement__ papers,
+[Tensorpack examples](examples) faithfully __reproduce__ papers,
+demonstrating its __flexibility__ for actual research.
+
+### Vision:
++ [Train ResNet](examples/ResNet) and [other models](examples/ImageNetModels) on ImageNet
++ [Train Mask/Faster R-CNN on COCO object detection](examples/FasterRCNN)
++ [Unsupervised learning with Momentum Contrast](https://github.com/ppwwyyxx/moco.tensorflow) (MoCo)
++ [Generative Adversarial Network(GAN) variants](examples/GAN), including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN
++ [DoReFa-Net: train binary / low-bitwidth CNN on ImageNet](examples/DoReFa-Net)
++ [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](examples/HED)
++ [Spatial Transformer Networks on MNIST addition](examples/SpatialTransformer)
++ [Visualize CNN saliency maps](examples/Saliency)
++ [Similarity learning on MNIST](examples/SimilarityLearning)
+
+### Reinforcement Learning:
++ [Deep Q-Network(DQN) variants on Atari games](examples/DeepQNetwork), including DQN, DoubleDQN, DuelingDQN.
++ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](examples/A3C-Gym)
+
+### Speech / NLP:
++ [LSTM-CTC for speech recognition](examples/CTC-TIMIT)
++ [char-rnn for fun](examples/Char-RNN)
++ [LSTM language model on PennTreebank](examples/PennTreebank)
+
+## Install:
+
+Dependencies:
+
++ Python 3.3+.
++ Python bindings for OpenCV. (Optional, but required by a lot of features)
++ TensorFlow ≥ 1.5, < 2
+  * TF is not not required if you only want to use `tensorpack.dataflow` alone as a data processing library
+  * TF2 is supported if used in graph mode (and use `tf.compat.v1` when needed)
+```
+pip install --upgrade git+https://github.com/tensorpack/tensorpack.git
+# or add `--user` to install to user's local directories
+```
+
+Please note that tensorpack is not yet stable.
+If you use tensorpack in your code, remember to mark the exact version of tensorpack you use as your dependencies.
+
+## Citing Tensorpack:
+
+If you use Tensorpack in your research or wish to refer to the examples, please cite with:
+```
+@misc{wu2016tensorpack,
+  title={Tensorpack},
+  author={Wu, Yuxin and others},
+  howpublished={\url{https://github.com/tensorpack/}},
+  year={2016}
+}
+```
+
+
+
+
+%prep
+%autosetup -n tensorpack-0.11
+
+%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-tensorpack -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.11-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..02d07e1
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+e79663b664f8371097b7f925d4bf1d08  tensorpack-0.11.tar.gz