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|
%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
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.16-1
- Package Spec generated
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