From 9fdbd4dc22ce9ac31eee94f37ee036d87999fb8a Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Fri, 5 May 2023 10:42:08 +0000 Subject: automatic import of python-torch-fidelity --- .gitignore | 1 + python-torch-fidelity.spec | 138 +++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 140 insertions(+) create mode 100644 python-torch-fidelity.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..e5f0f75 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/torch_fidelity-0.3.0.tar.gz diff --git a/python-torch-fidelity.spec b/python-torch-fidelity.spec new file mode 100644 index 0000000..827a5e2 --- /dev/null +++ b/python-torch-fidelity.spec @@ -0,0 +1,138 @@ +%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 - 0.3.0-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..7eba3a2 --- /dev/null +++ b/sources @@ -0,0 +1 @@ +8a76b251039103c8c9fc762f8b281771 torch_fidelity-0.3.0.tar.gz -- cgit v1.2.3