setup.py

#!/usr/bin/env python
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

import glob
import os
import runpy
import warnings
from typing import List, Optional

import torch
from setuptools import find_packages, setup
from torch.utils.cpp_extension import CppExtension, CUDA_HOME, CUDAExtension


def get_existing_ccbin(nvcc_args: List[str]) -> Optional[str]:
    """
    Given a list of nvcc arguments, return the compiler if specified.

    Note from CUDA doc: Single value options and list options must have
    arguments, which must follow the name of the option itself by either
    one of more spaces or an equals character.
    """
    last_arg = None
    for arg in reversed(nvcc_args):
        if arg == "-ccbin":
            return last_arg
        if arg.startswith("-ccbin="):
            return arg[7:]
        last_arg = arg
    return None


def get_extensions():
    this_dir = os.path.dirname(os.path.abspath(__file__))
    extensions_dir = os.path.join(this_dir, "pytorch3d", "csrc")
    sources = glob.glob(os.path.join(extensions_dir, "**", "*.cpp"), recursive=True)
    source_cuda = glob.glob(os.path.join(extensions_dir, "**", "*.cu"), recursive=True)
    extension = CppExtension

    extra_compile_args = {"cxx": ["-std=c++14"]}
    define_macros = []
    include_dirs = [extensions_dir]

    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)]
        # Thrust is only used for its tuple objects.
        # With CUDA 11.0 we can't use the cudatoolkit's version of cub.
        # We take the risk that CUB and Thrust are incompatible, because
        # we aren't using parts of Thrust which actually use CUB.
        define_macros += [("THRUST_IGNORE_CUB_VERSION_CHECK", None)]
        cub_home = os.environ.get("CUB_HOME", None)
        nvcc_args = [
            "-DCUDA_HAS_FP16=1",
            "-D__CUDA_NO_HALF_OPERATORS__",
            "-D__CUDA_NO_HALF_CONVERSIONS__",
            "-D__CUDA_NO_HALF2_OPERATORS__",
        ]
        if os.name != "nt":
            nvcc_args.append("-std=c++14")
        if cub_home is None:
            prefix = os.environ.get("CONDA_PREFIX", None)
            if prefix is not None and os.path.isdir(prefix + "/include/cub"):
                cub_home = prefix + "/include"

        if cub_home is None:
            warnings.warn(
                "The environment variable `CUB_HOME` was not found. "
                "NVIDIA CUB is required for compilation and can be downloaded "
                "from `https://github.com/NVIDIA/cub/releases`. You can unpack "
                "it to a location of your choice and set the environment variable "
                "`CUB_HOME` to the folder containing the `CMakeListst.txt` file."
            )
        else:
            include_dirs.append(os.path.realpath(cub_home).replace("\\ ", " "))
        nvcc_flags_env = os.getenv("NVCC_FLAGS", "")
        if nvcc_flags_env != "":
            nvcc_args.extend(nvcc_flags_env.split(" "))

        # This is needed for pytorch 1.6 and earlier. See e.g.
        # https://github.com/facebookresearch/pytorch3d/issues/436
        # It is harmless after https://github.com/pytorch/pytorch/pull/47404 .
        # But it can be problematic in torch 1.7.0 and 1.7.1
        if torch.__version__[:4] != "1.7.":
            CC = os.environ.get("CC", None)
            if CC is not None:
                existing_CC = get_existing_ccbin(nvcc_args)
                if existing_CC is None:
                    CC_arg = "-ccbin={}".format(CC)
                    nvcc_args.append(CC_arg)
                elif existing_CC != CC:
                    msg = f"Inconsistent ccbins: {CC} and {existing_CC}"
                    raise ValueError(msg)

        extra_compile_args["nvcc"] = nvcc_args

    sources = [os.path.join(extensions_dir, s) for s in sources]

    ext_modules = [
        extension(
            "pytorch3d._C",
            sources,
            include_dirs=include_dirs,
            define_macros=define_macros,
            extra_compile_args=extra_compile_args,
        )
    ]

    return ext_modules


# Retrieve __version__ from the package.
__version__ = runpy.run_path("pytorch3d/__init__.py")["__version__"]


if os.getenv("PYTORCH3D_NO_NINJA", "0") == "1":

    class BuildExtension(torch.utils.cpp_extension.BuildExtension):
        def __init__(self, *args, **kwargs):
            super().__init__(use_ninja=False, *args, **kwargs)

else:
    BuildExtension = torch.utils.cpp_extension.BuildExtension

trainer = "projects.implicitron_trainer"

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", "tests.*", "docs.*", "projects.nerf.*")
    ),
    install_requires=["fvcore", "iopath"],
    extras_require={
        "all": ["matplotlib", "tqdm>4.29.0", "imageio", "ipywidgets"],
        "dev": ["flake8", "usort", "black==19.3b0"],
        "implicitron": ["hydra-core>=1.1", "visdom", "lpips", "matplotlib"],
    },
    entry_points={
        "console_scripts": [
            f"pytorch3d_implicitron_runner={trainer}.experiment:experiment",
            f"pytorch3d_implicitron_visualizer={trainer}.visualize_reconstruction:main",
        ]
    },
    ext_modules=get_extensions(),
    cmdclass={"build_ext": BuildExtension},
    package_data={
        "": ["*.json"],
    },
)