#!/usr/bin/env python # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. import glob import os import torch from setuptools import find_packages, setup from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension def get_extensions(): this_dir = os.path.dirname(os.path.abspath(__file__)) extensions_dir = os.path.join(this_dir, "pytorch3d", "csrc") main_source = os.path.join(extensions_dir, "ext.cpp") sources = glob.glob(os.path.join(extensions_dir, "**", "*.cpp")) source_cuda = glob.glob(os.path.join(extensions_dir, "**", "*.cu")) sources = [main_source] + sources extension = CppExtension extra_compile_args = {"cxx": ["-std=c++14"]} define_macros = [] force_cuda = os.getenv("FORCE_CUDA", "0") == "1" if (torch.cuda.is_available() and CUDA_HOME is not None) or force_cuda: extension = CUDAExtension sources += source_cuda define_macros += [("WITH_CUDA", None)] nvcc_args = [ "-DCUDA_HAS_FP16=1", "-D__CUDA_NO_HALF_OPERATORS__", "-D__CUDA_NO_HALF_CONVERSIONS__", "-D__CUDA_NO_HALF2_OPERATORS__", ] nvcc_flags_env = os.getenv("NVCC_FLAGS", "") if nvcc_flags_env != "": nvcc_args.extend(nvcc_flags_env.split(" ")) # It's better if pytorch can do this by default .. CC = os.environ.get("CC", None) if CC is not None: CC_arg = "-ccbin={}".format(CC) if CC_arg not in nvcc_args: if any(arg.startswith("-ccbin") for arg in nvcc_args): raise ValueError("Inconsistent ccbins") nvcc_args.append(CC_arg) extra_compile_args["nvcc"] = nvcc_args sources = [os.path.join(extensions_dir, s) for s in sources] include_dirs = [extensions_dir] ext_modules = [ extension( "pytorch3d._C", sources, include_dirs=include_dirs, define_macros=define_macros, extra_compile_args=extra_compile_args, ) ] return ext_modules __version__ = "" # Retrieve __version__ from the package. with open("pytorch3d/__init__.py", "r") as init: exec(init.read()) setup( name="pytorch3d", version=__version__, author="FAIR", url="https://github.com/facebookresearch/pytorch3d", description="PyTorch3D is FAIR's library of reusable components " "for deep Learning with 3D data.", packages=find_packages(exclude=("configs", "tests")), install_requires=["torchvision>=0.4", "fvcore"], extras_require={ "all": ["matplotlib", "tqdm>4.29.0", "imageio", "ipywidgets"], "dev": ["flake8", "isort", "black==19.3b0"], }, ext_modules=get_extensions(), cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, )