nixpkgs/pkgs/development/python-modules/pytorch3d/default.nix
2024-10-25 18:00:49 +00:00

72 lines
1.6 KiB
Nix

{
lib,
buildPythonPackage,
fetchFromGitHub,
setuptools,
wheel,
torch,
iopath,
cudaPackages,
config,
cudaSupport ? config.cudaSupport,
}:
assert cudaSupport -> torch.cudaSupport;
buildPythonPackage rec {
pname = "pytorch3d";
version = "0.7.8";
pyproject = true;
src = fetchFromGitHub {
owner = "facebookresearch";
repo = "pytorch3d";
rev = "V${version}";
hash = "sha256-DEEWWfjwjuXGc0WQInDTmtnWSIDUifyByxdg7hpdHlo=";
};
nativeBuildInputs = lib.optionals cudaSupport [ cudaPackages.cuda_nvcc ];
build-system = [
setuptools
wheel
];
dependencies = [
torch
iopath
];
buildInputs = [ (lib.getOutput "cxxdev" torch) ];
env =
{
FORCE_CUDA = cudaSupport;
}
// lib.optionalAttrs cudaSupport {
TORCH_CUDA_ARCH_LIST = "${lib.concatStringsSep ";" torch.cudaCapabilities}";
};
pythonImportsCheck = [ "pytorch3d" ];
passthru.tests.rotations-cuda =
cudaPackages.writeGpuTestPython { libraries = ps: [ ps.pytorch3d ]; }
''
import pytorch3d.transforms as p3dt
M = p3dt.random_rotations(n=10, device="cuda")
assert "cuda" in M.device.type
angles = p3dt.matrix_to_euler_angles(M, "XYZ")
assert "cuda" in angles.device.type
assert angles.shape == (10, 3), angles.shape
print(angles)
'';
meta = {
description = "FAIR's library of reusable components for deep learning with 3D data";
homepage = "https://github.com/facebookresearch/pytorch3d";
license = lib.licenses.bsd3;
maintainers = with lib.maintainers; [
pbsds
SomeoneSerge
];
};
}