%global pypi_name nnunetv2
%global import_name nnunetv2

Name:           python-%{pypi_name}
Version:        2.8.1
Release:        2%{?dist}
Summary:        Self-configuring framework for medical image segmentation

License:        Apache-2.0
URL:            https://github.com/MIC-DKFZ/nnUNet
Source0:        %{pypi_source %{pypi_name}}

BuildArch:      noarch
BuildRequires:  python3-devel
BuildRequires:  pyproject-rpm-macros

%global _description %{expand:
nnU-Net is a self-configuring framework for biomedical image segmentation: from
a dataset fingerprint it derives the preprocessing, network topology, training
schedule and post-processing automatically, without manual architecture tuning.
It remains a standard baseline in medical segmentation and is the engine
underneath nnInteractive.

This package provides the library and the nnUNetv2_* command line tools.}

%description %_description

%package -n python3-%{pypi_name}
Summary:        %{summary}
%description -n python3-%{pypi_name} %_description

%prep
%autosetup -n %{pypi_name}-%{version}

# Fedora ships torch 2.9.1, which upstream excludes via `torch>=2.1.2,!=2.9.*`.
# That exclusion is a PERFORMANCE guard, not a correctness one: torch 2.9 shipped
# with a cuDNN bug that made the torch developers disable many fast convolution
# implementations, so 3D convolutions with AMP run several times slower
# (pytorch/pytorch#166122, now closed; MIC-DKFZ/nnUNet#3002, where upstream states
# "2.9 is just slow"). It is a CUDA/cuDNN code-path problem, and Fedora's torch is
# built without CUDA, so the regression does not apply to this package's users.
# Relax the exclusion so the dependency is satisfiable against Fedora's torch.
sed -i 's/"torch>=2\.1\.2,!=2\.9\.\*"/"torch>=2.1.2"/' pyproject.toml
grep -q '!=2.9' pyproject.toml && exit 1 || :

%generate_buildrequires
%pyproject_buildrequires

%build
%pyproject_wheel

%install
%pyproject_install
%pyproject_save_files %{import_name}

%check
# One module is excluded, and it is a real defect rather than a tidy-up:
# nnunetv2.training.nnUNetTrainer.primus.primus_trainers imports
# dynamic_network_architectures' primus architectures, which import timm.layers,
# which imports torchvision -- and on f45-aarch64 `import torchvision` itself
# raises (11028681):
#
#   torchvision/extension.py line 57, in _check_cuda_version
#       _version = torch.ops.torchvision._cuda_version()
#   AttributeError: '_OpNamespace' 'torchvision' object has no attribute
#                   '_cuda_version'
#
# That is Fedora's python3-torchvision 0.27.1 against python3-torch 2.12.0 on
# aarch64: the compiled extension has not registered its ops, so the version
# check at import time explodes. It is not ours to fix and not specific to this
# package -- anything importing torchvision on that chroot fails the same way.
# f45-x86_64 is unaffected, which is why this package is published there.
#
# So the Primus trainers genuinely do not work on aarch64 until torchvision is
# fixed. Everything else in nnunetv2 does, and the package is the segmentation
# engine under nnInteractive, so shipping it without Primus beats not shipping
# it. Remove this exclusion once torchvision imports on aarch64 -- see
# docs/COORDINATION.md.
%pyproject_check_import -e 'nnunetv2.training.nnUNetTrainer.primus*'

%files -n python3-%{pypi_name} -f %{pyproject_files}
%license LICENSE
%doc readme.md
%{_bindir}/nnUNetv2_*

%changelog
* Wed Sep 23 2026 Morgan Hough <morgan.hough@gmail.com> - 2.8.1-2
- Exclude the Primus trainers from %%pyproject_check_import on account of a
  torchvision defect, not one of ours: on f45-aarch64 `import torchvision`
  raises AttributeError from its own _check_cuda_version, so every consumer of
  timm.layers dies at import. Documented in the spec and on the board; the
  exclusion comes out when torchvision is fixed. Unblocks the package on
  f45-aarch64, where it was the only thing missing.

* Sat Jul 18 2026 Morgan Hough <morgan.hough@gmail.com> - 2.8.1-1
- Initial package. Segmentation engine underneath nnInteractive. Relaxes the
  upstream torch != 2.9.* exclusion, which is a CUDA/cuDNN performance guard
  that does not apply to Fedora's CPU-only torch (see the comment in %%prep).