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%global _empty_manifest_terminate_build 0
Name: python-torch-fidelity
Version: 0.3.0
Release: 1
Summary: High-fidelity performance metrics for generative models in PyTorch
License: Apache License 2.0
URL: https://www.github.com/toshas/torch-fidelity
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/dd/72/687a54bab9a11e351cff3859ece48fb58e13786a493eb13a5da32b42de32/torch_fidelity-0.3.0.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-Pillow
Requires: python3-scipy
Requires: python3-torch
Requires: python3-torchvision
Requires: python3-tqdm
%description
Evaluation of generative models such as GANs is an important part of the deep learning research.
In the domain of 2D image generation, three approaches became widely spread: Inception Score
(aka IS), Fréchet Inception Distance (aka FID), and Kernel Inception Distance (aka KID).
These metrics, despite having a clear mathematical and algorithmic description, were initially
implemented in TensorFlow, and inherited a few properties of the framework itself and the code
they relied upon. These design decisions were effectively baked into the evaluation protocol and
became an inherent part of the metrics specification. As a result, researchers wishing to
compare against state of the art in generative modeling are forced to perform evaluation using
codebases of the original metric authors. Reimplementations of metrics in PyTorch and other
frameworks exist, but they do not provide a proper level of fidelity, thus making them
unsuitable for reporting results and comparing to other methods.
This software aims to provide epsilon-exact implementations of the said metrics in PyTorch, and thus
remove inconveniences associated with generative models evaluation and development.
Find more details and the most up-to-date information on the project webpage:
https://www.github.com/toshas/torch-fidelity
%package -n python3-torch-fidelity
Summary: High-fidelity performance metrics for generative models in PyTorch
Provides: python-torch-fidelity
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-torch-fidelity
Evaluation of generative models such as GANs is an important part of the deep learning research.
In the domain of 2D image generation, three approaches became widely spread: Inception Score
(aka IS), Fréchet Inception Distance (aka FID), and Kernel Inception Distance (aka KID).
These metrics, despite having a clear mathematical and algorithmic description, were initially
implemented in TensorFlow, and inherited a few properties of the framework itself and the code
they relied upon. These design decisions were effectively baked into the evaluation protocol and
became an inherent part of the metrics specification. As a result, researchers wishing to
compare against state of the art in generative modeling are forced to perform evaluation using
codebases of the original metric authors. Reimplementations of metrics in PyTorch and other
frameworks exist, but they do not provide a proper level of fidelity, thus making them
unsuitable for reporting results and comparing to other methods.
This software aims to provide epsilon-exact implementations of the said metrics in PyTorch, and thus
remove inconveniences associated with generative models evaluation and development.
Find more details and the most up-to-date information on the project webpage:
https://www.github.com/toshas/torch-fidelity
%package help
Summary: Development documents and examples for torch-fidelity
Provides: python3-torch-fidelity-doc
%description help
Evaluation of generative models such as GANs is an important part of the deep learning research.
In the domain of 2D image generation, three approaches became widely spread: Inception Score
(aka IS), Fréchet Inception Distance (aka FID), and Kernel Inception Distance (aka KID).
These metrics, despite having a clear mathematical and algorithmic description, were initially
implemented in TensorFlow, and inherited a few properties of the framework itself and the code
they relied upon. These design decisions were effectively baked into the evaluation protocol and
became an inherent part of the metrics specification. As a result, researchers wishing to
compare against state of the art in generative modeling are forced to perform evaluation using
codebases of the original metric authors. Reimplementations of metrics in PyTorch and other
frameworks exist, but they do not provide a proper level of fidelity, thus making them
unsuitable for reporting results and comparing to other methods.
This software aims to provide epsilon-exact implementations of the said metrics in PyTorch, and thus
remove inconveniences associated with generative models evaluation and development.
Find more details and the most up-to-date information on the project webpage:
https://www.github.com/toshas/torch-fidelity
%prep
%autosetup -n torch-fidelity-0.3.0
%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-torch-fidelity -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.0-1
- Package Spec generated
|
