blob: b6d1ce17ee04edbf2382c4f433652475465ef532 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
|
%global _empty_manifest_terminate_build 0
Name: python-unet
Version: 0.7.7
Release: 1
Summary: PyTorch implementation of 2D and 3D U-Net
License: MIT license
URL: https://github.com/fepegar/unet
Source0: https://mirrors.aliyun.com/pypi/web/packages/70/ba/08283475e35353237ea4b19159e3917dcc6fb8497482adb9ef5798bbdbf2/unet-0.7.7.tar.gz
BuildArch: noarch
Requires: python3-torch
%description
PyTorch implementation of 2D and 3D U-Net.
The U-Net architecture was first described in
`Ronneberger et al. 2015, U-Net: Convolutional Networks for Biomedical Image
Segmentation <https://arxiv.org/abs/1505.04597>`_.
The 3D version was described in
`Çiçek et al. 2016, 3D U-Net: Learning Dense Volumetric Segmentation from
Sparse Annotation <https://arxiv.org/abs/1606.06650>`_.
%package -n python3-unet
Summary: PyTorch implementation of 2D and 3D U-Net
Provides: python-unet
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-unet
PyTorch implementation of 2D and 3D U-Net.
The U-Net architecture was first described in
`Ronneberger et al. 2015, U-Net: Convolutional Networks for Biomedical Image
Segmentation <https://arxiv.org/abs/1505.04597>`_.
The 3D version was described in
`Çiçek et al. 2016, 3D U-Net: Learning Dense Volumetric Segmentation from
Sparse Annotation <https://arxiv.org/abs/1606.06650>`_.
%package help
Summary: Development documents and examples for unet
Provides: python3-unet-doc
%description help
PyTorch implementation of 2D and 3D U-Net.
The U-Net architecture was first described in
`Ronneberger et al. 2015, U-Net: Convolutional Networks for Biomedical Image
Segmentation <https://arxiv.org/abs/1505.04597>`_.
The 3D version was described in
`Çiçek et al. 2016, 3D U-Net: Learning Dense Volumetric Segmentation from
Sparse Annotation <https://arxiv.org/abs/1606.06650>`_.
%prep
%autosetup -n unet-0.7.7
%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-unet -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.7-1
- Package Spec generated
|