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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
|
