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diff --git a/python-tf-explain.spec b/python-tf-explain.spec new file mode 100644 index 0000000..ef1b4f3 --- /dev/null +++ b/python-tf-explain.spec @@ -0,0 +1,971 @@ +%global _empty_manifest_terminate_build 0 +Name: python-tf-explain +Version: 0.3.1 +Release: 1 +Summary: Interpretability Callbacks for Tensorflow 2.0 +License: MIT +URL: https://github.com/sicara/tf-explain +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/1d/ad/d9467ccd256ff5764f08bdd76da859539b35f01a9059fc8c2e412e6b912a/tf-explain-0.3.1.tar.gz +BuildArch: noarch + +Requires: python3-sphinx +Requires: python3-sphinx-rtd-theme +Requires: python3-bumpversion +Requires: python3-twine +Requires: python3-black +Requires: python3-pylint +Requires: python3-pytest +Requires: python3-pytest-timeout +Requires: python3-pytest-mock +Requires: python3-pytest-cov +Requires: python3-tox + +%description +# tf-explain + +[](https://pypi.org/project/tf-explain/) +[](https://github.com/sicara/tf-explain/actions) +[](https://tf-explain.readthedocs.io/en/latest/?badge=latest) + + +[](https://github.com/python/black) + +__tf-explain__ implements interpretability methods as Tensorflow 2.x callbacks to __ease neural network's understanding__. +See [Introducing tf-explain, Interpretability for Tensorflow 2.0](https://blog.sicara.com/tf-explain-interpretability-tensorflow-2-9438b5846e35) + +__Documentation__: https://tf-explain.readthedocs.io + +## Installation + +__tf-explain__ is available on PyPi as an alpha release. To install it: + +```bash +virtualenv venv -p python3.8 +pip install tf-explain +``` + +tf-explain is compatible with Tensorflow 2.x. It is not declared as a dependency +to let you choose between full and standalone-CPU versions. Additionally to the previous install, run: + +```bash +# For CPU or GPU +pip install tensorflow==2.6.0 +``` +Opencv is also a dependency. To install it, run: +```bash +# For CPU or GPU +pip install opencv-python +``` + +## Quickstart + +tf-explain offers 2 ways to apply interpretability methods. The full list of methods is the [Available Methods](#available-methods) section. + +### On trained model + +The best option is probably to load a trained model and apply the methods on it. + +```python +# Load pretrained model or your own +model = tf.keras.applications.vgg16.VGG16(weights="imagenet", include_top=True) + +# Load a sample image (or multiple ones) +img = tf.keras.preprocessing.image.load_img(IMAGE_PATH, target_size=(224, 224)) +img = tf.keras.preprocessing.image.img_to_array(img) +data = ([img], None) + +# Start explainer +explainer = GradCAM() +grid = explainer.explain(data, model, class_index=281) # 281 is the tabby cat index in ImageNet + +explainer.save(grid, ".", "grad_cam.png") +``` + +### During training + +If you want to follow your model during the training, you can also use it as a Keras Callback, +and see the results directly in [TensorBoard](https://www.tensorflow.org/tensorboard/). + +```python +from tf_explain.callbacks.grad_cam import GradCAMCallback + +model = [...] + +callbacks = [ + GradCAMCallback( + validation_data=(x_val, y_val), + class_index=0, + output_dir=output_dir, + ) +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + + +## Available Methods + +1. [Activations Visualization](#activations-visualization) +1. [Vanilla Gradients](#vanilla-gradients) +1. [Gradients*Inputs](#gradients-inputs) +1. [Occlusion Sensitivity](#occlusion-sensitivity) +1. [Grad CAM (Class Activation Maps)](#grad-cam) +1. [SmoothGrad](#smoothgrad) +1. [Integrated Gradients](#integrated-gradients) + +### Activations Visualization + +> Visualize how a given input comes out of a specific activation layer + +```python +from tf_explain.callbacks.activations_visualization import ActivationsVisualizationCallback + +model = [...] + +callbacks = [ + ActivationsVisualizationCallback( + validation_data=(x_val, y_val), + layers_name=["activation_1"], + output_dir=output_dir, + ), +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<p align="center"> + <img src="./docs/assets/activations_visualisation.png" width="400" /> +</p> + + +### Vanilla Gradients + +> Visualize gradients importance on input image + +```python +from tf_explain.callbacks.vanilla_gradients import VanillaGradientsCallback + +model = [...] + +callbacks = [ + VanillaGradientsCallback( + validation_data=(x_val, y_val), + class_index=0, + output_dir=output_dir, + ), +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<p align="center"> + <img src="./docs/assets/vanilla_gradients.png" width="200" /> +</p> + + +### Gradients*Inputs + +> Variant of [Vanilla Gradients](#vanilla-gradients) ponderating gradients with input values + +```python +from tf_explain.callbacks.gradients_inputs import GradientsInputsCallback + +model = [...] + +callbacks = [ + GradientsInputsCallback( + validation_data=(x_val, y_val), + class_index=0, + output_dir=output_dir, + ), +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<p align="center"> + <img src="./docs/assets/gradients_inputs.png" width="200" /> +</p> + + +### Occlusion Sensitivity + +> Visualize how parts of the image affects neural network's confidence by occluding parts iteratively + +```python +from tf_explain.callbacks.occlusion_sensitivity import OcclusionSensitivityCallback + +model = [...] + +callbacks = [ + OcclusionSensitivityCallback( + validation_data=(x_val, y_val), + class_index=0, + patch_size=4, + output_dir=output_dir, + ), +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<div align="center"> + <img src="./docs/assets/occlusion_sensitivity.png" width="200" /> + <p style="color: grey; font-size:small; width:350px;">Occlusion Sensitivity for Tabby class (stripes differentiate tabby cat from other ImageNet cat classes)</p> +</div> + +### Grad CAM + +> Visualize how parts of the image affects neural network's output by looking into the activation maps + +From [Grad-CAM: Visual Explanations from Deep Networks +via Gradient-based Localization](https://arxiv.org/abs/1610.02391) + +```python +from tf_explain.callbacks.grad_cam import GradCAMCallback + +model = [...] + +callbacks = [ + GradCAMCallback( + validation_data=(x_val, y_val), + class_index=0, + output_dir=output_dir, + ) +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + + +<p align="center"> + <img src="./docs/assets/grad_cam.png" width="200" /> +</p> + +### SmoothGrad + +> Visualize stabilized gradients on the inputs towards the decision + +From [SmoothGrad: removing noise by adding noise](https://arxiv.org/abs/1706.03825) + +```python +from tf_explain.callbacks.smoothgrad import SmoothGradCallback + +model = [...] + +callbacks = [ + SmoothGradCallback( + validation_data=(x_val, y_val), + class_index=0, + num_samples=20, + noise=1., + output_dir=output_dir, + ) +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<p align="center"> + <img src="./docs/assets/smoothgrad.png" width="200" /> +</p> + +### Integrated Gradients + +> Visualize an average of the gradients along the construction of the input towards the decision + +From [Axiomatic Attribution for Deep Networks](https://arxiv.org/pdf/1703.01365.pdf) + +```python +from tf_explain.callbacks.integrated_gradients import IntegratedGradientsCallback + +model = [...] + +callbacks = [ + IntegratedGradientsCallback( + validation_data=(x_val, y_val), + class_index=0, + n_steps=20, + output_dir=output_dir, + ) +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<p align="center"> + <img src="./docs/assets/integrated_gradients.png" width="200" /> +</p> + + +## Roadmap + +- [ ] Subclassing API Support +- [ ] Additional Methods + - [ ] [GradCAM++](https://arxiv.org/abs/1710.11063) + - [x] [Integrated Gradients](https://arxiv.org/abs/1703.01365) + - [x] [Guided SmoothGrad](https://arxiv.org/abs/1706.03825) + - [ ] [LRP](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130140) +- [ ] Auto-generated API Documentation & Documentation Testing + +## Contributing + +To contribute to the project, please read the [dedicated section](./CONTRIBUTING.md). + +## Citation + +A [citation file](./CITATION.cff) is available for citing this work. + + + + +%package -n python3-tf-explain +Summary: Interpretability Callbacks for Tensorflow 2.0 +Provides: python-tf-explain +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-tf-explain +# tf-explain + +[](https://pypi.org/project/tf-explain/) +[](https://github.com/sicara/tf-explain/actions) +[](https://tf-explain.readthedocs.io/en/latest/?badge=latest) + + +[](https://github.com/python/black) + +__tf-explain__ implements interpretability methods as Tensorflow 2.x callbacks to __ease neural network's understanding__. +See [Introducing tf-explain, Interpretability for Tensorflow 2.0](https://blog.sicara.com/tf-explain-interpretability-tensorflow-2-9438b5846e35) + +__Documentation__: https://tf-explain.readthedocs.io + +## Installation + +__tf-explain__ is available on PyPi as an alpha release. To install it: + +```bash +virtualenv venv -p python3.8 +pip install tf-explain +``` + +tf-explain is compatible with Tensorflow 2.x. It is not declared as a dependency +to let you choose between full and standalone-CPU versions. Additionally to the previous install, run: + +```bash +# For CPU or GPU +pip install tensorflow==2.6.0 +``` +Opencv is also a dependency. To install it, run: +```bash +# For CPU or GPU +pip install opencv-python +``` + +## Quickstart + +tf-explain offers 2 ways to apply interpretability methods. The full list of methods is the [Available Methods](#available-methods) section. + +### On trained model + +The best option is probably to load a trained model and apply the methods on it. + +```python +# Load pretrained model or your own +model = tf.keras.applications.vgg16.VGG16(weights="imagenet", include_top=True) + +# Load a sample image (or multiple ones) +img = tf.keras.preprocessing.image.load_img(IMAGE_PATH, target_size=(224, 224)) +img = tf.keras.preprocessing.image.img_to_array(img) +data = ([img], None) + +# Start explainer +explainer = GradCAM() +grid = explainer.explain(data, model, class_index=281) # 281 is the tabby cat index in ImageNet + +explainer.save(grid, ".", "grad_cam.png") +``` + +### During training + +If you want to follow your model during the training, you can also use it as a Keras Callback, +and see the results directly in [TensorBoard](https://www.tensorflow.org/tensorboard/). + +```python +from tf_explain.callbacks.grad_cam import GradCAMCallback + +model = [...] + +callbacks = [ + GradCAMCallback( + validation_data=(x_val, y_val), + class_index=0, + output_dir=output_dir, + ) +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + + +## Available Methods + +1. [Activations Visualization](#activations-visualization) +1. [Vanilla Gradients](#vanilla-gradients) +1. [Gradients*Inputs](#gradients-inputs) +1. [Occlusion Sensitivity](#occlusion-sensitivity) +1. [Grad CAM (Class Activation Maps)](#grad-cam) +1. [SmoothGrad](#smoothgrad) +1. [Integrated Gradients](#integrated-gradients) + +### Activations Visualization + +> Visualize how a given input comes out of a specific activation layer + +```python +from tf_explain.callbacks.activations_visualization import ActivationsVisualizationCallback + +model = [...] + +callbacks = [ + ActivationsVisualizationCallback( + validation_data=(x_val, y_val), + layers_name=["activation_1"], + output_dir=output_dir, + ), +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<p align="center"> + <img src="./docs/assets/activations_visualisation.png" width="400" /> +</p> + + +### Vanilla Gradients + +> Visualize gradients importance on input image + +```python +from tf_explain.callbacks.vanilla_gradients import VanillaGradientsCallback + +model = [...] + +callbacks = [ + VanillaGradientsCallback( + validation_data=(x_val, y_val), + class_index=0, + output_dir=output_dir, + ), +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<p align="center"> + <img src="./docs/assets/vanilla_gradients.png" width="200" /> +</p> + + +### Gradients*Inputs + +> Variant of [Vanilla Gradients](#vanilla-gradients) ponderating gradients with input values + +```python +from tf_explain.callbacks.gradients_inputs import GradientsInputsCallback + +model = [...] + +callbacks = [ + GradientsInputsCallback( + validation_data=(x_val, y_val), + class_index=0, + output_dir=output_dir, + ), +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<p align="center"> + <img src="./docs/assets/gradients_inputs.png" width="200" /> +</p> + + +### Occlusion Sensitivity + +> Visualize how parts of the image affects neural network's confidence by occluding parts iteratively + +```python +from tf_explain.callbacks.occlusion_sensitivity import OcclusionSensitivityCallback + +model = [...] + +callbacks = [ + OcclusionSensitivityCallback( + validation_data=(x_val, y_val), + class_index=0, + patch_size=4, + output_dir=output_dir, + ), +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<div align="center"> + <img src="./docs/assets/occlusion_sensitivity.png" width="200" /> + <p style="color: grey; font-size:small; width:350px;">Occlusion Sensitivity for Tabby class (stripes differentiate tabby cat from other ImageNet cat classes)</p> +</div> + +### Grad CAM + +> Visualize how parts of the image affects neural network's output by looking into the activation maps + +From [Grad-CAM: Visual Explanations from Deep Networks +via Gradient-based Localization](https://arxiv.org/abs/1610.02391) + +```python +from tf_explain.callbacks.grad_cam import GradCAMCallback + +model = [...] + +callbacks = [ + GradCAMCallback( + validation_data=(x_val, y_val), + class_index=0, + output_dir=output_dir, + ) +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + + +<p align="center"> + <img src="./docs/assets/grad_cam.png" width="200" /> +</p> + +### SmoothGrad + +> Visualize stabilized gradients on the inputs towards the decision + +From [SmoothGrad: removing noise by adding noise](https://arxiv.org/abs/1706.03825) + +```python +from tf_explain.callbacks.smoothgrad import SmoothGradCallback + +model = [...] + +callbacks = [ + SmoothGradCallback( + validation_data=(x_val, y_val), + class_index=0, + num_samples=20, + noise=1., + output_dir=output_dir, + ) +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<p align="center"> + <img src="./docs/assets/smoothgrad.png" width="200" /> +</p> + +### Integrated Gradients + +> Visualize an average of the gradients along the construction of the input towards the decision + +From [Axiomatic Attribution for Deep Networks](https://arxiv.org/pdf/1703.01365.pdf) + +```python +from tf_explain.callbacks.integrated_gradients import IntegratedGradientsCallback + +model = [...] + +callbacks = [ + IntegratedGradientsCallback( + validation_data=(x_val, y_val), + class_index=0, + n_steps=20, + output_dir=output_dir, + ) +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<p align="center"> + <img src="./docs/assets/integrated_gradients.png" width="200" /> +</p> + + +## Roadmap + +- [ ] Subclassing API Support +- [ ] Additional Methods + - [ ] [GradCAM++](https://arxiv.org/abs/1710.11063) + - [x] [Integrated Gradients](https://arxiv.org/abs/1703.01365) + - [x] [Guided SmoothGrad](https://arxiv.org/abs/1706.03825) + - [ ] [LRP](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130140) +- [ ] Auto-generated API Documentation & Documentation Testing + +## Contributing + +To contribute to the project, please read the [dedicated section](./CONTRIBUTING.md). + +## Citation + +A [citation file](./CITATION.cff) is available for citing this work. + + + + +%package help +Summary: Development documents and examples for tf-explain +Provides: python3-tf-explain-doc +%description help +# tf-explain + +[](https://pypi.org/project/tf-explain/) +[](https://github.com/sicara/tf-explain/actions) +[](https://tf-explain.readthedocs.io/en/latest/?badge=latest) + + +[](https://github.com/python/black) + +__tf-explain__ implements interpretability methods as Tensorflow 2.x callbacks to __ease neural network's understanding__. +See [Introducing tf-explain, Interpretability for Tensorflow 2.0](https://blog.sicara.com/tf-explain-interpretability-tensorflow-2-9438b5846e35) + +__Documentation__: https://tf-explain.readthedocs.io + +## Installation + +__tf-explain__ is available on PyPi as an alpha release. To install it: + +```bash +virtualenv venv -p python3.8 +pip install tf-explain +``` + +tf-explain is compatible with Tensorflow 2.x. It is not declared as a dependency +to let you choose between full and standalone-CPU versions. Additionally to the previous install, run: + +```bash +# For CPU or GPU +pip install tensorflow==2.6.0 +``` +Opencv is also a dependency. To install it, run: +```bash +# For CPU or GPU +pip install opencv-python +``` + +## Quickstart + +tf-explain offers 2 ways to apply interpretability methods. The full list of methods is the [Available Methods](#available-methods) section. + +### On trained model + +The best option is probably to load a trained model and apply the methods on it. + +```python +# Load pretrained model or your own +model = tf.keras.applications.vgg16.VGG16(weights="imagenet", include_top=True) + +# Load a sample image (or multiple ones) +img = tf.keras.preprocessing.image.load_img(IMAGE_PATH, target_size=(224, 224)) +img = tf.keras.preprocessing.image.img_to_array(img) +data = ([img], None) + +# Start explainer +explainer = GradCAM() +grid = explainer.explain(data, model, class_index=281) # 281 is the tabby cat index in ImageNet + +explainer.save(grid, ".", "grad_cam.png") +``` + +### During training + +If you want to follow your model during the training, you can also use it as a Keras Callback, +and see the results directly in [TensorBoard](https://www.tensorflow.org/tensorboard/). + +```python +from tf_explain.callbacks.grad_cam import GradCAMCallback + +model = [...] + +callbacks = [ + GradCAMCallback( + validation_data=(x_val, y_val), + class_index=0, + output_dir=output_dir, + ) +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + + +## Available Methods + +1. [Activations Visualization](#activations-visualization) +1. [Vanilla Gradients](#vanilla-gradients) +1. [Gradients*Inputs](#gradients-inputs) +1. [Occlusion Sensitivity](#occlusion-sensitivity) +1. [Grad CAM (Class Activation Maps)](#grad-cam) +1. [SmoothGrad](#smoothgrad) +1. [Integrated Gradients](#integrated-gradients) + +### Activations Visualization + +> Visualize how a given input comes out of a specific activation layer + +```python +from tf_explain.callbacks.activations_visualization import ActivationsVisualizationCallback + +model = [...] + +callbacks = [ + ActivationsVisualizationCallback( + validation_data=(x_val, y_val), + layers_name=["activation_1"], + output_dir=output_dir, + ), +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<p align="center"> + <img src="./docs/assets/activations_visualisation.png" width="400" /> +</p> + + +### Vanilla Gradients + +> Visualize gradients importance on input image + +```python +from tf_explain.callbacks.vanilla_gradients import VanillaGradientsCallback + +model = [...] + +callbacks = [ + VanillaGradientsCallback( + validation_data=(x_val, y_val), + class_index=0, + output_dir=output_dir, + ), +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<p align="center"> + <img src="./docs/assets/vanilla_gradients.png" width="200" /> +</p> + + +### Gradients*Inputs + +> Variant of [Vanilla Gradients](#vanilla-gradients) ponderating gradients with input values + +```python +from tf_explain.callbacks.gradients_inputs import GradientsInputsCallback + +model = [...] + +callbacks = [ + GradientsInputsCallback( + validation_data=(x_val, y_val), + class_index=0, + output_dir=output_dir, + ), +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<p align="center"> + <img src="./docs/assets/gradients_inputs.png" width="200" /> +</p> + + +### Occlusion Sensitivity + +> Visualize how parts of the image affects neural network's confidence by occluding parts iteratively + +```python +from tf_explain.callbacks.occlusion_sensitivity import OcclusionSensitivityCallback + +model = [...] + +callbacks = [ + OcclusionSensitivityCallback( + validation_data=(x_val, y_val), + class_index=0, + patch_size=4, + output_dir=output_dir, + ), +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<div align="center"> + <img src="./docs/assets/occlusion_sensitivity.png" width="200" /> + <p style="color: grey; font-size:small; width:350px;">Occlusion Sensitivity for Tabby class (stripes differentiate tabby cat from other ImageNet cat classes)</p> +</div> + +### Grad CAM + +> Visualize how parts of the image affects neural network's output by looking into the activation maps + +From [Grad-CAM: Visual Explanations from Deep Networks +via Gradient-based Localization](https://arxiv.org/abs/1610.02391) + +```python +from tf_explain.callbacks.grad_cam import GradCAMCallback + +model = [...] + +callbacks = [ + GradCAMCallback( + validation_data=(x_val, y_val), + class_index=0, + output_dir=output_dir, + ) +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + + +<p align="center"> + <img src="./docs/assets/grad_cam.png" width="200" /> +</p> + +### SmoothGrad + +> Visualize stabilized gradients on the inputs towards the decision + +From [SmoothGrad: removing noise by adding noise](https://arxiv.org/abs/1706.03825) + +```python +from tf_explain.callbacks.smoothgrad import SmoothGradCallback + +model = [...] + +callbacks = [ + SmoothGradCallback( + validation_data=(x_val, y_val), + class_index=0, + num_samples=20, + noise=1., + output_dir=output_dir, + ) +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<p align="center"> + <img src="./docs/assets/smoothgrad.png" width="200" /> +</p> + +### Integrated Gradients + +> Visualize an average of the gradients along the construction of the input towards the decision + +From [Axiomatic Attribution for Deep Networks](https://arxiv.org/pdf/1703.01365.pdf) + +```python +from tf_explain.callbacks.integrated_gradients import IntegratedGradientsCallback + +model = [...] + +callbacks = [ + IntegratedGradientsCallback( + validation_data=(x_val, y_val), + class_index=0, + n_steps=20, + output_dir=output_dir, + ) +] + +model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks) +``` + +<p align="center"> + <img src="./docs/assets/integrated_gradients.png" width="200" /> +</p> + + +## Roadmap + +- [ ] Subclassing API Support +- [ ] Additional Methods + - [ ] [GradCAM++](https://arxiv.org/abs/1710.11063) + - [x] [Integrated Gradients](https://arxiv.org/abs/1703.01365) + - [x] [Guided SmoothGrad](https://arxiv.org/abs/1706.03825) + - [ ] [LRP](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130140) +- [ ] Auto-generated API Documentation & Documentation Testing + +## Contributing + +To contribute to the project, please read the [dedicated section](./CONTRIBUTING.md). + +## Citation + +A [citation file](./CITATION.cff) is available for citing this work. + + + + +%prep +%autosetup -n tf-explain-0.3.1 + +%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-tf-explain -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.1-1 +- Package Spec generated |