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@@ -0,0 +1 @@ +/torchetl-0.3.9.tar.gz diff --git a/python-torchetl.spec b/python-torchetl.spec new file mode 100644 index 0000000..71b1fdd --- /dev/null +++ b/python-torchetl.spec @@ -0,0 +1,192 @@ +%global _empty_manifest_terminate_build 0 +Name: python-torchetl +Version: 0.3.9 +Release: 1 +Summary: Efficiently Extract, Transform, and Load your dataset into PyTorch models +License: MIT License +URL: https://github.com/amajidsinar/torchetl +Source0: https://mirrors.aliyun.com/pypi/web/packages/8a/70/a96c6fbee3a56535cfb59954179a216361d07c268385af69c69a2920f3da/torchetl-0.3.9.tar.gz +BuildArch: noarch + + +%description +# TorchETL + +If you're working on classification problem, with dataset that is available in their native format (jpg, bmp, etc) and have PyTorch in your arsenal, you'll most likely feel that the **DatasetFolder** or **ImageFolder** is not good enough. So does vanilla **torch.utils.data.Dataset**. This library attempts to bridge that gap to effectively Extract, Transform, and Load your data by extending **torch.utils.data.Dataset**. + +### Main Features + +Extract class would partition your dataset into train, validation, and test csv + +TransformAndLoad class would Transform and consume your dataset efficiently + +### Prerequisites + +Python 3.7.2 (other versions might work if type checking is supported) + +torch + +torchvision + +numpy + +pandas + +opencv-python + +sklearn + + +Or simply download requirements.txt and fire 'pip3 install -r requirements.txt' + +### Installing + +pip3 install torchetl + + +### Tutorial + +See tutorial/Tutorial.ipynb + + + + + +%package -n python3-torchetl +Summary: Efficiently Extract, Transform, and Load your dataset into PyTorch models +Provides: python-torchetl +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-torchetl +# TorchETL + +If you're working on classification problem, with dataset that is available in their native format (jpg, bmp, etc) and have PyTorch in your arsenal, you'll most likely feel that the **DatasetFolder** or **ImageFolder** is not good enough. So does vanilla **torch.utils.data.Dataset**. This library attempts to bridge that gap to effectively Extract, Transform, and Load your data by extending **torch.utils.data.Dataset**. + +### Main Features + +Extract class would partition your dataset into train, validation, and test csv + +TransformAndLoad class would Transform and consume your dataset efficiently + +### Prerequisites + +Python 3.7.2 (other versions might work if type checking is supported) + +torch + +torchvision + +numpy + +pandas + +opencv-python + +sklearn + + +Or simply download requirements.txt and fire 'pip3 install -r requirements.txt' + +### Installing + +pip3 install torchetl + + +### Tutorial + +See tutorial/Tutorial.ipynb + + + + + +%package help +Summary: Development documents and examples for torchetl +Provides: python3-torchetl-doc +%description help +# TorchETL + +If you're working on classification problem, with dataset that is available in their native format (jpg, bmp, etc) and have PyTorch in your arsenal, you'll most likely feel that the **DatasetFolder** or **ImageFolder** is not good enough. So does vanilla **torch.utils.data.Dataset**. This library attempts to bridge that gap to effectively Extract, Transform, and Load your data by extending **torch.utils.data.Dataset**. + +### Main Features + +Extract class would partition your dataset into train, validation, and test csv + +TransformAndLoad class would Transform and consume your dataset efficiently + +### Prerequisites + +Python 3.7.2 (other versions might work if type checking is supported) + +torch + +torchvision + +numpy + +pandas + +opencv-python + +sklearn + + +Or simply download requirements.txt and fire 'pip3 install -r requirements.txt' + +### Installing + +pip3 install torchetl + + +### Tutorial + +See tutorial/Tutorial.ipynb + + + + + +%prep +%autosetup -n torchetl-0.3.9 + +%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-torchetl -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.9-1 +- Package Spec generated @@ -0,0 +1 @@ +a0484c176a7ff7362214fbc9b8edca1b torchetl-0.3.9.tar.gz |