automatic import of python-torch-fidelityopeneuler20.03

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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