%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