From c9d461a3a29bb1ac788fa5f202693436634a6f3a Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Mon, 15 May 2023 04:11:56 +0000 Subject: automatic import of python-kindle --- .gitignore | 1 + python-kindle.spec | 319 +++++++++++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 321 insertions(+) create mode 100644 python-kindle.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..2f7d82e 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/kindle-0.4.16.tar.gz diff --git a/python-kindle.spec b/python-kindle.spec new file mode 100644 index 0000000..c4e1446 --- /dev/null +++ b/python-kindle.spec @@ -0,0 +1,319 @@ +%global _empty_manifest_terminate_build 0 +Name: python-kindle +Version: 0.4.16 +Release: 1 +Summary: Kindle - Making a PyTorch model easier than ever! +License: MIT License Copyright (c) 2021 Jongkuk Lim 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. +URL: https://github.com/JeiKeiLim/kindle +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b4/fb/16afb261e17f822952c64d021d4cb35d0f2ba97e0ebf55191b98ad5ffb5a/kindle-0.4.16.tar.gz +BuildArch: noarch + +Requires: python3-tqdm +Requires: python3-PyYAML +Requires: python3-torch +Requires: python3-ptflops +Requires: python3-timm +Requires: python3-tabulate +Requires: python3-einops + +%description + 0 | -1 | 1 | 616 | Conv | [6, 5, 1, 0], activation: LeakyReLU | 3 | 8 | [3, 32, 32] | [8, 32, 32] | + 1 | -1 | 1 | 0 | MaxPool | [2] | 8 | 8 | [8 32 32] | [8, 16, 16] | + 2 | -1 | 1 | 3,200 | nn.Conv2d | [16, 5, 1, 2], bias: False | 8 | 16 | [8 16 16] | [16, 16, 16] | + 3 | -1 | 1 | 32 | nn.BatchNorm2d | [] | 16 | 16 | [16 16 16] | [16, 16, 16] | + 4 | -1 | 1 | 0 | nn.ReLU | [] | 16 | 16 | [16 16 16] | [16, 16, 16] | + 5 | -1 | 1 | 0 | MaxPool | [2] | 16 | 16 | [16 16 16] | [16, 8, 8] | + 6 | -1 | 1 | 0 | Flatten | [] | -1 | 1024 | [16 8 8] | [1024] | + 7 | -1 | 1 | 123,000 | Linear | [120, 'ReLU'] | 1024 | 120 | [1024] | [120] | + 8 | -1 | 1 | 10,164 | Linear | [84, 'ReLU'] | 120 | 84 | [120] | [84] | + 9 | -1 | 1 | 850 | Linear | [10] | 84 | 10 | [84] | [10] | +Model Summary: 20 layers, 137,862 parameters, 137,862 gradients +``` +## AutoML with Kindle +* [Kindle](https://github.com/JeiKeiLim/kindle) offers the easiest way to build your own deep learning architecture. Beyond building a model, AutoML became easier with [Kindle](https://github.com/JeiKeiLim/kindle) and [Optuna](https://optuna.org) or other optimization frameworks. +* For further information, please refer to [https://limjk.ai/kindle/usages/#automl-with-optuna](https://limjk.ai/kindle/usages/#automl-with-optuna) +# Supported modules +* Detailed documents can be found [https://limjk.ai/kindle/modules/](https://limjk.ai/kindle/modules/) +|Module|Components|Arguments| +|------|----------|---------| +|Conv|Conv -> BatchNorm -> Activation|[out_channels, kernel_size, stride, padding, groups, activation]| +|DWConv|DWConv -> BatchNorm -> Activation|[out_channels, kernel_size, stride, padding, activation]| +|Focus|Reshape x -> Conv -> Concat|[out_channels, kernel_size, stride, padding, activation]| +|Bottleneck|Expansion ConvBNAct -> ConvBNAct|[out_channels, shortcut, groups, expansion, activation]| +|BottleneckCSP|CSP Bottleneck|[out_channels, shortcut, groups, expansion, activation] +|C3|CSP Bottleneck with 3 Conv|[out_channels, shortcut, groups, expansion, activation]| +|MV2Block|MobileNet v2 block|[out_channels, stride, expand_ratio, activation]| +|AvgPool|Average pooling|[kernel_size, stride, padding]| +|MaxPool|Max pooling|[kernel_size, stride, padding]| +|GlobalAvgPool|Global Average Pooling|[]| +|SPP|Spatial Pyramid Pooling|[out_channels, [kernel_size1, kernel_size2, ...], activation]| +|SPPF|Spatial Pyramid Pooling - Fast|[out_channels, kernel_size, activation]| +|Flatten|Flatten|[]| +|Concat|Concatenation|[dimension]| +|Linear|Linear|[out_channels, activation]| +|Add|Add|[]| +|UpSample|UpSample|[]| +|Identity|Identity|[]| +|YamlModule|Custom module from yaml file|['yaml/file/path', arg0, arg1, ...]| +|nn.{module_name}|PyTorch torch.nn.* module|Please refer to [https://pytorch.org/docs/stable/nn.html](https://pytorch.org/docs/stable/nn.html)| +|Pretrained|timm.create_model|[model_name, use_feature_maps, features_only, pretrained]| +|PreTrainedFeatureMap|Bypass feature layer map from `Pretrained`|[feature_idx]| +|YOLOHead|YOLOv5 head module|[n_classes, anchors, out_xyxy]| +|MobileViTBlock|MobileVit Block(experimental)|[conv_channels, mlp_channels, depth, kernel_size, patch_size, dropout, activation] +* **nn.{module_name}** is currently experimental. This might change in the future release. Use with caution. +* For the supported model of **Pretrained** module, please refer to [https://rwightman.github.io/pytorch-image-models/results](https://rwightman.github.io/pytorch-image-models/results) +# Custom module support +## Custom module with yaml +* You can make your own custom module with yaml file. Please refer to [https://limjk.ai/kindle/tutorial/#2-design-custom-module-with-yaml](https://limjk.ai/kindle/tutorial/#2-design-custom-module-with-yaml) for further detail. +## Custom module from source code +* You can also make your own custom module from the source code. Please refer to https://limjk.ai/kindle/tutorial/#3-design-custom-module-from-source for further detail. +# Pretrained model support +* Pre-trained model from [timm](https://github.com/rwightman/pytorch-image-models) can be loaded in kindle yaml config file. Please refer to [https://limjk.ai/kindle/tutorial/#4-utilize-pretrained-model](https://limjk.ai/kindle/tutorial/#4-utilize-pretrained-model) for further detail. +# Model profiler +* Kindle provides model profiling option for each layers and calculating MACs. +* Please refer to https://limjk.ai/kindle/functionality/#1-model-profiling for further detail. +# Test Time Augmentation +* Kindle model supports TTA with easy usability. Just pass the model input and augmentation function. +* Please refer to https://limjk.ai/kindle/functionality/#3-test-time-augmentation for further detail. +# Recent changes +|Version|Description|Date| +|-------|-----------|----| +|0.4.16|Fix decomposed conv fuse and add kindle version variable.|2021. 10. 25| +|0.4.14|Add MobileViTBlock module|2021. 10. 18| +|0.4.12|Add MV2Block module|2021. 10. 14| +|0.4.11|Add SPPF module in yolov5 v6.0|2021. 10. 13| +|0.4.10|Fix ONNX export padding issue.|2021. 10. 13| +|0.4.6|Add YOLOHead to choose coordinates format.|2021. 10. 09| +|0.4.5|Add C3 Module|2021. 10. 08| +|0.4.4|Fix YOLOHead module issue with anchor scaling|2021. 10. 08| +|0.4.2|Add YOLOModel, and ConvBN fusion, and Fix activation apply issue|2021. 09. 19| +|0.4.1|Add YOLOHead, SPP, BottleneckCSP, and Focus modules|2021. 09. 13| +|0.3.2|Fix PreTrained to work without PreTrainedFeatureMap|2021. 06. 03| +|0.3.1|Calculating MACs in profiler|2021. 05. 02| +|0.3.0|Add PreTrained support|2021. 04. 20| +# Planned features +* ~~Custom module support~~ +* ~~Custom module with yaml support~~ +* ~~Use pre-trained model~~ +* Graphical model file generator +* Ensemble model +* More modules! + +%package -n python3-kindle +Summary: Kindle - Making a PyTorch model easier than ever! +Provides: python-kindle +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-kindle + 0 | -1 | 1 | 616 | Conv | [6, 5, 1, 0], activation: LeakyReLU | 3 | 8 | [3, 32, 32] | [8, 32, 32] | + 1 | -1 | 1 | 0 | MaxPool | [2] | 8 | 8 | [8 32 32] | [8, 16, 16] | + 2 | -1 | 1 | 3,200 | nn.Conv2d | [16, 5, 1, 2], bias: False | 8 | 16 | [8 16 16] | [16, 16, 16] | + 3 | -1 | 1 | 32 | nn.BatchNorm2d | [] | 16 | 16 | [16 16 16] | [16, 16, 16] | + 4 | -1 | 1 | 0 | nn.ReLU | [] | 16 | 16 | [16 16 16] | [16, 16, 16] | + 5 | -1 | 1 | 0 | MaxPool | [2] | 16 | 16 | [16 16 16] | [16, 8, 8] | + 6 | -1 | 1 | 0 | Flatten | [] | -1 | 1024 | [16 8 8] | [1024] | + 7 | -1 | 1 | 123,000 | Linear | [120, 'ReLU'] | 1024 | 120 | [1024] | [120] | + 8 | -1 | 1 | 10,164 | Linear | [84, 'ReLU'] | 120 | 84 | [120] | [84] | + 9 | -1 | 1 | 850 | Linear | [10] | 84 | 10 | [84] | [10] | +Model Summary: 20 layers, 137,862 parameters, 137,862 gradients +``` +## AutoML with Kindle +* [Kindle](https://github.com/JeiKeiLim/kindle) offers the easiest way to build your own deep learning architecture. Beyond building a model, AutoML became easier with [Kindle](https://github.com/JeiKeiLim/kindle) and [Optuna](https://optuna.org) or other optimization frameworks. +* For further information, please refer to [https://limjk.ai/kindle/usages/#automl-with-optuna](https://limjk.ai/kindle/usages/#automl-with-optuna) +# Supported modules +* Detailed documents can be found [https://limjk.ai/kindle/modules/](https://limjk.ai/kindle/modules/) +|Module|Components|Arguments| +|------|----------|---------| +|Conv|Conv -> BatchNorm -> Activation|[out_channels, kernel_size, stride, padding, groups, activation]| +|DWConv|DWConv -> BatchNorm -> Activation|[out_channels, kernel_size, stride, padding, activation]| +|Focus|Reshape x -> Conv -> Concat|[out_channels, kernel_size, stride, padding, activation]| +|Bottleneck|Expansion ConvBNAct -> ConvBNAct|[out_channels, shortcut, groups, expansion, activation]| +|BottleneckCSP|CSP Bottleneck|[out_channels, shortcut, groups, expansion, activation] +|C3|CSP Bottleneck with 3 Conv|[out_channels, shortcut, groups, expansion, activation]| +|MV2Block|MobileNet v2 block|[out_channels, stride, expand_ratio, activation]| +|AvgPool|Average pooling|[kernel_size, stride, padding]| +|MaxPool|Max pooling|[kernel_size, stride, padding]| +|GlobalAvgPool|Global Average Pooling|[]| +|SPP|Spatial Pyramid Pooling|[out_channels, [kernel_size1, kernel_size2, ...], activation]| +|SPPF|Spatial Pyramid Pooling - Fast|[out_channels, kernel_size, activation]| +|Flatten|Flatten|[]| +|Concat|Concatenation|[dimension]| +|Linear|Linear|[out_channels, activation]| +|Add|Add|[]| +|UpSample|UpSample|[]| +|Identity|Identity|[]| +|YamlModule|Custom module from yaml file|['yaml/file/path', arg0, arg1, ...]| +|nn.{module_name}|PyTorch torch.nn.* module|Please refer to [https://pytorch.org/docs/stable/nn.html](https://pytorch.org/docs/stable/nn.html)| +|Pretrained|timm.create_model|[model_name, use_feature_maps, features_only, pretrained]| +|PreTrainedFeatureMap|Bypass feature layer map from `Pretrained`|[feature_idx]| +|YOLOHead|YOLOv5 head module|[n_classes, anchors, out_xyxy]| +|MobileViTBlock|MobileVit Block(experimental)|[conv_channels, mlp_channels, depth, kernel_size, patch_size, dropout, activation] +* **nn.{module_name}** is currently experimental. This might change in the future release. Use with caution. +* For the supported model of **Pretrained** module, please refer to [https://rwightman.github.io/pytorch-image-models/results](https://rwightman.github.io/pytorch-image-models/results) +# Custom module support +## Custom module with yaml +* You can make your own custom module with yaml file. Please refer to [https://limjk.ai/kindle/tutorial/#2-design-custom-module-with-yaml](https://limjk.ai/kindle/tutorial/#2-design-custom-module-with-yaml) for further detail. +## Custom module from source code +* You can also make your own custom module from the source code. Please refer to https://limjk.ai/kindle/tutorial/#3-design-custom-module-from-source for further detail. +# Pretrained model support +* Pre-trained model from [timm](https://github.com/rwightman/pytorch-image-models) can be loaded in kindle yaml config file. Please refer to [https://limjk.ai/kindle/tutorial/#4-utilize-pretrained-model](https://limjk.ai/kindle/tutorial/#4-utilize-pretrained-model) for further detail. +# Model profiler +* Kindle provides model profiling option for each layers and calculating MACs. +* Please refer to https://limjk.ai/kindle/functionality/#1-model-profiling for further detail. +# Test Time Augmentation +* Kindle model supports TTA with easy usability. Just pass the model input and augmentation function. +* Please refer to https://limjk.ai/kindle/functionality/#3-test-time-augmentation for further detail. +# Recent changes +|Version|Description|Date| +|-------|-----------|----| +|0.4.16|Fix decomposed conv fuse and add kindle version variable.|2021. 10. 25| +|0.4.14|Add MobileViTBlock module|2021. 10. 18| +|0.4.12|Add MV2Block module|2021. 10. 14| +|0.4.11|Add SPPF module in yolov5 v6.0|2021. 10. 13| +|0.4.10|Fix ONNX export padding issue.|2021. 10. 13| +|0.4.6|Add YOLOHead to choose coordinates format.|2021. 10. 09| +|0.4.5|Add C3 Module|2021. 10. 08| +|0.4.4|Fix YOLOHead module issue with anchor scaling|2021. 10. 08| +|0.4.2|Add YOLOModel, and ConvBN fusion, and Fix activation apply issue|2021. 09. 19| +|0.4.1|Add YOLOHead, SPP, BottleneckCSP, and Focus modules|2021. 09. 13| +|0.3.2|Fix PreTrained to work without PreTrainedFeatureMap|2021. 06. 03| +|0.3.1|Calculating MACs in profiler|2021. 05. 02| +|0.3.0|Add PreTrained support|2021. 04. 20| +# Planned features +* ~~Custom module support~~ +* ~~Custom module with yaml support~~ +* ~~Use pre-trained model~~ +* Graphical model file generator +* Ensemble model +* More modules! + +%package help +Summary: Development documents and examples for kindle +Provides: python3-kindle-doc +%description help + 0 | -1 | 1 | 616 | Conv | [6, 5, 1, 0], activation: LeakyReLU | 3 | 8 | [3, 32, 32] | [8, 32, 32] | + 1 | -1 | 1 | 0 | MaxPool | [2] | 8 | 8 | [8 32 32] | [8, 16, 16] | + 2 | -1 | 1 | 3,200 | nn.Conv2d | [16, 5, 1, 2], bias: False | 8 | 16 | [8 16 16] | [16, 16, 16] | + 3 | -1 | 1 | 32 | nn.BatchNorm2d | [] | 16 | 16 | [16 16 16] | [16, 16, 16] | + 4 | -1 | 1 | 0 | nn.ReLU | [] | 16 | 16 | [16 16 16] | [16, 16, 16] | + 5 | -1 | 1 | 0 | MaxPool | [2] | 16 | 16 | [16 16 16] | [16, 8, 8] | + 6 | -1 | 1 | 0 | Flatten | [] | -1 | 1024 | [16 8 8] | [1024] | + 7 | -1 | 1 | 123,000 | Linear | [120, 'ReLU'] | 1024 | 120 | [1024] | [120] | + 8 | -1 | 1 | 10,164 | Linear | [84, 'ReLU'] | 120 | 84 | [120] | [84] | + 9 | -1 | 1 | 850 | Linear | [10] | 84 | 10 | [84] | [10] | +Model Summary: 20 layers, 137,862 parameters, 137,862 gradients +``` +## AutoML with Kindle +* [Kindle](https://github.com/JeiKeiLim/kindle) offers the easiest way to build your own deep learning architecture. Beyond building a model, AutoML became easier with [Kindle](https://github.com/JeiKeiLim/kindle) and [Optuna](https://optuna.org) or other optimization frameworks. +* For further information, please refer to [https://limjk.ai/kindle/usages/#automl-with-optuna](https://limjk.ai/kindle/usages/#automl-with-optuna) +# Supported modules +* Detailed documents can be found [https://limjk.ai/kindle/modules/](https://limjk.ai/kindle/modules/) +|Module|Components|Arguments| +|------|----------|---------| +|Conv|Conv -> BatchNorm -> Activation|[out_channels, kernel_size, stride, padding, groups, activation]| +|DWConv|DWConv -> BatchNorm -> Activation|[out_channels, kernel_size, stride, padding, activation]| +|Focus|Reshape x -> Conv -> Concat|[out_channels, kernel_size, stride, padding, activation]| +|Bottleneck|Expansion ConvBNAct -> ConvBNAct|[out_channels, shortcut, groups, expansion, activation]| +|BottleneckCSP|CSP Bottleneck|[out_channels, shortcut, groups, expansion, activation] +|C3|CSP Bottleneck with 3 Conv|[out_channels, shortcut, groups, expansion, activation]| +|MV2Block|MobileNet v2 block|[out_channels, stride, expand_ratio, activation]| +|AvgPool|Average pooling|[kernel_size, stride, padding]| +|MaxPool|Max pooling|[kernel_size, stride, padding]| +|GlobalAvgPool|Global Average Pooling|[]| +|SPP|Spatial Pyramid Pooling|[out_channels, [kernel_size1, kernel_size2, ...], activation]| +|SPPF|Spatial Pyramid Pooling - Fast|[out_channels, kernel_size, activation]| +|Flatten|Flatten|[]| +|Concat|Concatenation|[dimension]| +|Linear|Linear|[out_channels, activation]| +|Add|Add|[]| +|UpSample|UpSample|[]| +|Identity|Identity|[]| +|YamlModule|Custom module from yaml file|['yaml/file/path', arg0, arg1, ...]| +|nn.{module_name}|PyTorch torch.nn.* module|Please refer to [https://pytorch.org/docs/stable/nn.html](https://pytorch.org/docs/stable/nn.html)| +|Pretrained|timm.create_model|[model_name, use_feature_maps, features_only, pretrained]| +|PreTrainedFeatureMap|Bypass feature layer map from `Pretrained`|[feature_idx]| +|YOLOHead|YOLOv5 head module|[n_classes, anchors, out_xyxy]| +|MobileViTBlock|MobileVit Block(experimental)|[conv_channels, mlp_channels, depth, kernel_size, patch_size, dropout, activation] +* **nn.{module_name}** is currently experimental. This might change in the future release. Use with caution. +* For the supported model of **Pretrained** module, please refer to [https://rwightman.github.io/pytorch-image-models/results](https://rwightman.github.io/pytorch-image-models/results) +# Custom module support +## Custom module with yaml +* You can make your own custom module with yaml file. Please refer to [https://limjk.ai/kindle/tutorial/#2-design-custom-module-with-yaml](https://limjk.ai/kindle/tutorial/#2-design-custom-module-with-yaml) for further detail. +## Custom module from source code +* You can also make your own custom module from the source code. Please refer to https://limjk.ai/kindle/tutorial/#3-design-custom-module-from-source for further detail. +# Pretrained model support +* Pre-trained model from [timm](https://github.com/rwightman/pytorch-image-models) can be loaded in kindle yaml config file. Please refer to [https://limjk.ai/kindle/tutorial/#4-utilize-pretrained-model](https://limjk.ai/kindle/tutorial/#4-utilize-pretrained-model) for further detail. +# Model profiler +* Kindle provides model profiling option for each layers and calculating MACs. +* Please refer to https://limjk.ai/kindle/functionality/#1-model-profiling for further detail. +# Test Time Augmentation +* Kindle model supports TTA with easy usability. Just pass the model input and augmentation function. +* Please refer to https://limjk.ai/kindle/functionality/#3-test-time-augmentation for further detail. +# Recent changes +|Version|Description|Date| +|-------|-----------|----| +|0.4.16|Fix decomposed conv fuse and add kindle version variable.|2021. 10. 25| +|0.4.14|Add MobileViTBlock module|2021. 10. 18| +|0.4.12|Add MV2Block module|2021. 10. 14| +|0.4.11|Add SPPF module in yolov5 v6.0|2021. 10. 13| +|0.4.10|Fix ONNX export padding issue.|2021. 10. 13| +|0.4.6|Add YOLOHead to choose coordinates format.|2021. 10. 09| +|0.4.5|Add C3 Module|2021. 10. 08| +|0.4.4|Fix YOLOHead module issue with anchor scaling|2021. 10. 08| +|0.4.2|Add YOLOModel, and ConvBN fusion, and Fix activation apply issue|2021. 09. 19| +|0.4.1|Add YOLOHead, SPP, BottleneckCSP, and Focus modules|2021. 09. 13| +|0.3.2|Fix PreTrained to work without PreTrainedFeatureMap|2021. 06. 03| +|0.3.1|Calculating MACs in profiler|2021. 05. 02| +|0.3.0|Add PreTrained support|2021. 04. 20| +# Planned features +* ~~Custom module support~~ +* ~~Custom module with yaml support~~ +* ~~Use pre-trained model~~ +* Graphical model file generator +* Ensemble model +* More modules! + +%prep +%autosetup -n kindle-0.4.16 + +%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-kindle -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 15 2023 Python_Bot - 0.4.16-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..0b01df2 --- /dev/null +++ b/sources @@ -0,0 +1 @@ +f98c36f51f698428f297f55bdf1783db kindle-0.4.16.tar.gz -- cgit v1.2.3