automatic import of python-torchetlopeneuler20.03

3 files changed, 194 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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+/torchetl-0.3.9.tar.gz
diff --git a/python-torchetl.spec b/python-torchetl.spec
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+%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
diff --git a/sources b/sources
new file mode 100644
index 0000000..3876128
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+a0484c176a7ff7362214fbc9b8edca1b  torchetl-0.3.9.tar.gz