%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