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%global _empty_manifest_terminate_build 0
Name: python-Omnis
Version: 0.0.10.42
Release: 1
Summary: Deep Learning for everyone
License: MIT License
URL: https://github.com/omnis-labs-company/omnis
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/01/9c/62b024b501914ff0c064bcc3045d50e434dc9f5e0a5f3eaf14ea4e853738/Omnis-0.0.10.42.tar.gz
BuildArch: noarch
%description
## Getting started: Implement a deep learning application with 4 lines of code!
The core data structure of Omnis is Application which is designed to be easy to use in each field.
Here is an `Image Classification` example with the [`Caltech 101`](http://www.vision.caltech.edu/Image_Datasets/Caltech101/) dataset:
```python
from omnis.application.image_processing.image_classification.image_classification import ImageClassification
```
Choose an application:
```python
image_classifier = ImageClassification()
```
Train:
```python
image_classifier.train(data_path='101_ObjectCategories', epochs=5, batch_size=32, model_type='densent121')
```
Now you can use the application to classify images:
```python
prediction_result = image_classifier.predict(data_path = '101_ObjectCategories/accordion')
print(prediction_result)
```
Save / Load:
```python
image_classifier.save(model_path="weights.h5")
```
```python
image_classifier = ImageClassification(model_path="weights.h5")
```
For a more in-depth tutorial about Omnis, you can check out:
%package -n python3-Omnis
Summary: Deep Learning for everyone
Provides: python-Omnis
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-Omnis
## Getting started: Implement a deep learning application with 4 lines of code!
The core data structure of Omnis is Application which is designed to be easy to use in each field.
Here is an `Image Classification` example with the [`Caltech 101`](http://www.vision.caltech.edu/Image_Datasets/Caltech101/) dataset:
```python
from omnis.application.image_processing.image_classification.image_classification import ImageClassification
```
Choose an application:
```python
image_classifier = ImageClassification()
```
Train:
```python
image_classifier.train(data_path='101_ObjectCategories', epochs=5, batch_size=32, model_type='densent121')
```
Now you can use the application to classify images:
```python
prediction_result = image_classifier.predict(data_path = '101_ObjectCategories/accordion')
print(prediction_result)
```
Save / Load:
```python
image_classifier.save(model_path="weights.h5")
```
```python
image_classifier = ImageClassification(model_path="weights.h5")
```
For a more in-depth tutorial about Omnis, you can check out:
%package help
Summary: Development documents and examples for Omnis
Provides: python3-Omnis-doc
%description help
## Getting started: Implement a deep learning application with 4 lines of code!
The core data structure of Omnis is Application which is designed to be easy to use in each field.
Here is an `Image Classification` example with the [`Caltech 101`](http://www.vision.caltech.edu/Image_Datasets/Caltech101/) dataset:
```python
from omnis.application.image_processing.image_classification.image_classification import ImageClassification
```
Choose an application:
```python
image_classifier = ImageClassification()
```
Train:
```python
image_classifier.train(data_path='101_ObjectCategories', epochs=5, batch_size=32, model_type='densent121')
```
Now you can use the application to classify images:
```python
prediction_result = image_classifier.predict(data_path = '101_ObjectCategories/accordion')
print(prediction_result)
```
Save / Load:
```python
image_classifier.save(model_path="weights.h5")
```
```python
image_classifier = ImageClassification(model_path="weights.h5")
```
For a more in-depth tutorial about Omnis, you can check out:
%prep
%autosetup -n Omnis-0.0.10.42
%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-Omnis -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.10.42-1
- Package Spec generated
|
