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%global _empty_manifest_terminate_build 0
Name: pytorch3d
Version: 0.7.5
Release: 1
Summary: PyTorch3D is FAIR's library of reusable components for deep learning with 3D data
License: BSD License
URL: https://pytorch3d.org/
Source0: https://github.com/facebookresearch/pytorch3d/archive/refs/tags/v%{version}.zip
BuildRequires: g++
Requires: python3-fvcore
Requires: python3-iopath
%description
PyTorch3D provides efficient, reusable components for 3D Computer Vision research with PyTorch.
Key features include:
- Data structure for storing and manipulating triangle meshes
- Efficient operations on triangle meshes (projective transformations, graph convolution, sampling, loss functions)
- A differentiable mesh renderer
- Implicitron, see its README, a framework for new-view synthesis via implicit representations.
%package -n python3-pytorch3d
Summary: PyTorch3D is FAIR's library of reusable components for deep learning with 3D data
Provides: python-pytorch3d
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-setuptools_scm
BuildRequires: python3-pbr
BuildRequires: python3-pip
BuildRequires: python3-wheel
BuildRequires: python3-hatchling
BuildRequires: python3-pytorch
%description -n python3-pytorch3d
PyTorch3D provides efficient, reusable components for 3D Computer Vision research with PyTorch.
Key features include:
- Data structure for storing and manipulating triangle meshes
- Efficient operations on triangle meshes (projective transformations, graph convolution, sampling, loss functions)
- A differentiable mesh renderer
- Implicitron, see its README, a framework for new-view synthesis via implicit representations.
%package help
Summary: Development documents and examples for pytorch3d
Provides: python3-pytorch3d-doc
%description help
PyTorch3D provides efficient, reusable components for 3D Computer Vision research with PyTorch.
Key features include:
- Data structure for storing and manipulating triangle meshes
- Efficient operations on triangle meshes (projective transformations, graph convolution, sampling, loss functions)
- A differentiable mesh renderer
- Implicitron, see its README, a framework for new-view synthesis via implicit representations.
%prep
%autosetup -p1 -n %{name}-%{version}
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
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}/doclist.lst .
%files -n python3-pytorch3d
%doc *.md
%license LICENSE
%{_bindir}/pytorch3d_implicitron_runner
%{_bindir}/pytorch3d_implicitron_visualizer
%{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Jan 28 2024 Binshuo Zu <274620705z@gmail.com> - 0.7.5-1
- Package init
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