import os import glob import os.path as osp from itertools import product from setuptools import setup, find_packages import torch from torch.utils.cpp_extension import BuildExtension from torch.utils.cpp_extension import CppExtension, CUDAExtension, CUDA_HOME WITH_CUDA = torch.cuda.is_available() and CUDA_HOME is not None suffices = ['cpu', 'cuda'] if WITH_CUDA else ['cpu'] if os.getenv('FORCE_CUDA', '0') == '1': suffices = ['cuda'] if os.getenv('FORCE_CPU', '0') == '1': suffices = ['cpu'] BUILD_DOCS = os.getenv('BUILD_DOCS', '0') == '1' def get_extensions(): extensions = [] extensions_dir = osp.join(osp.dirname(osp.abspath(__file__)), 'csrc') main_files = glob.glob(osp.join(extensions_dir, '*.cpp')) for main, suffix in product(main_files, suffices): define_macros = [] extra_compile_args = {'cxx': ['-O2']} extra_link_args = ['-s'] if suffix == 'cuda': define_macros += [('WITH_CUDA', None)] nvcc_flags = os.getenv('NVCC_FLAGS', '') nvcc_flags = [] if nvcc_flags == '' else nvcc_flags.split(' ') nvcc_flags += ['-arch=sm_35', '--expt-relaxed-constexpr', '-O2'] extra_compile_args['nvcc'] = nvcc_flags name = main.split(os.sep)[-1][:-4] sources = [main] path = osp.join(extensions_dir, 'cpu', f'{name}_cpu.cpp') if osp.exists(path): sources += [path] path = osp.join(extensions_dir, 'cuda', f'{name}_cuda.cu') if suffix == 'cuda' and osp.exists(path): sources += [path] Extension = CppExtension if suffix == 'cpu' else CUDAExtension extension = Extension( f'torch_spline_conv._{name}_{suffix}', sources, include_dirs=[extensions_dir], define_macros=define_macros, extra_compile_args=extra_compile_args, extra_link_args=extra_link_args, ) extensions += [extension] return extensions install_requires = [] setup_requires = ['pytest-runner'] tests_require = ['pytest', 'pytest-cov'] setup( name='torch_spline_conv', version='1.2.0', author='Matthias Fey', author_email='matthias.fey@tu-dortmund.de', url='https://github.com/rusty1s/pytorch_spline_conv', description=('Implementation of the Spline-Based Convolution Operator of ' 'SplineCNN in PyTorch'), keywords=[ 'pytorch', 'geometric-deep-learning', 'graph-neural-networks', 'spline-cnn', ], license='MIT', python_requires='>=3.6', install_requires=install_requires, setup_requires=setup_requires, tests_require=tests_require, ext_modules=get_extensions() if not BUILD_DOCS else [], cmdclass={ 'build_ext': BuildExtension.with_options(no_python_abi_suffix=True) }, packages=find_packages(), )