setup.py 5.31 KB
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#!/usr/bin/env python
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# Copyright (c) Facebook, Inc. and its 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.
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import glob
import os
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import runpy
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import warnings
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from typing import List, Optional
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import torch
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from setuptools import find_packages, setup
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from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension


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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


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

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    extra_compile_args = {"cxx": ["-std=c++14"]}
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    define_macros = []
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    include_dirs = [extensions_dir]
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    force_cuda = os.getenv("FORCE_CUDA", "0") == "1"
    if (torch.cuda.is_available() and CUDA_HOME is not None) or force_cuda:
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        extension = CUDAExtension
        sources += source_cuda
        define_macros += [("WITH_CUDA", None)]
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        # 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)]
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        cub_home = os.environ.get("CUB_HOME", None)
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        nvcc_args = [
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            "-std=c++14",
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            "-DCUDA_HAS_FP16=1",
            "-D__CUDA_NO_HALF_OPERATORS__",
            "-D__CUDA_NO_HALF_CONVERSIONS__",
            "-D__CUDA_NO_HALF2_OPERATORS__",
        ]
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        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"

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        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:
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            include_dirs.append(os.path.realpath(cub_home).replace("\\ ", " "))
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        nvcc_flags_env = os.getenv("NVCC_FLAGS", "")
        if nvcc_flags_env != "":
            nvcc_args.extend(nvcc_flags_env.split(" "))
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        # This is needed for pytorch 1.6 and earlier. See e.g.
        # https://github.com/facebookresearch/pytorch3d/issues/436
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        # 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)
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        extra_compile_args["nvcc"] = nvcc_args
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    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


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


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setup(
    name="pytorch3d",
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    version=__version__,
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    author="FAIR",
    url="https://github.com/facebookresearch/pytorch3d",
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    description="PyTorch3D is FAIR's library of reusable components "
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    "for deep Learning with 3D data.",
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    packages=find_packages(
        exclude=("configs", "tests", "tests.*", "docs.*", "projects.*")
    ),
    install_requires=["fvcore", "iopath"],
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    extras_require={
        "all": ["matplotlib", "tqdm>4.29.0", "imageio", "ipywidgets"],
        "dev": ["flake8", "isort", "black==19.3b0"],
    },
    ext_modules=get_extensions(),
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    cmdclass={"build_ext": BuildExtension},
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    package_data={
        "": ["*.json"],
    },
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)