1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
|
%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 is a neural network training interface based on TensorFlow.
[](http://tensorpack.readthedocs.io)
[](https://gitter.im/tensorpack/users)
[](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 is a neural network training interface based on TensorFlow.
[](http://tensorpack.readthedocs.io)
[](https://gitter.im/tensorpack/users)
[](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 is a neural network training interface based on TensorFlow.
[](http://tensorpack.readthedocs.io)
[](https://gitter.im/tensorpack/users)
[](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
|
