SparseRCNN/detectron2/utils/collect_env.py

# Copyright (c) Facebook, Inc. and its affiliates.
import importlib
import numpy as np
import os
import re
import subprocess
import sys
from collections import defaultdict
import PIL
import torch
import torchvision
from tabulate import tabulate

__all__ = ["collect_env_info"]


def collect_torch_env():
    try:
        import torch.__config__

        return torch.__config__.show()
    except ImportError:
        # compatible with older versions of pytorch
        from torch.utils.collect_env import get_pretty_env_info

        return get_pretty_env_info()


def get_env_module():
    var_name = "DETECTRON2_ENV_MODULE"
    return var_name, os.environ.get(var_name, "<not set>")


def detect_compute_compatibility(CUDA_HOME, so_file):
    try:
        cuobjdump = os.path.join(CUDA_HOME, "bin", "cuobjdump")
        if os.path.isfile(cuobjdump):
            output = subprocess.check_output(
                "'{}' --list-elf '{}'".format(cuobjdump, so_file), shell=True
            )
            output = output.decode("utf-8").strip().split("\n")
            arch = []
            for line in output:
                line = re.findall(r"\.sm_([0-9]*)\.", line)[0]
                arch.append(".".join(line))
            arch = sorted(set(arch))
            return ", ".join(arch)
        else:
            return so_file + "; cannot find cuobjdump"
    except Exception:
        # unhandled failure
        return so_file


def collect_env_info():
    has_gpu = torch.cuda.is_available()  # true for both CUDA & ROCM
    torch_version = torch.__version__

    # NOTE: the use of CUDA_HOME and ROCM_HOME requires the CUDA/ROCM build deps, though in
    # theory detectron2 should be made runnable with only the corresponding runtimes
    from torch.utils.cpp_extension import CUDA_HOME

    has_rocm = False
    if tuple(map(int, torch_version.split(".")[:2])) >= (1, 5):
        from torch.utils.cpp_extension import ROCM_HOME

        if (getattr(torch.version, "hip", None) is not None) and (ROCM_HOME is not None):
            has_rocm = True
    has_cuda = has_gpu and (not has_rocm)

    data = []
    data.append(("sys.platform", sys.platform))
    data.append(("Python", sys.version.replace("\n", "")))
    data.append(("numpy", np.__version__))

    try:
        import detectron2  # noqa

        data.append(
            ("detectron2", detectron2.__version__ + " @" + os.path.dirname(detectron2.__file__))
        )
    except ImportError:
        data.append(("detectron2", "failed to import"))

    try:
        import detectron2._C as _C
    except ImportError as e:
        data.append(("detectron2._C", f"not built correctly: {e}"))

        # print system compilers when extension fails to build
        if sys.platform != "win32":  # don't know what to do for windows
            try:
                # this is how torch/utils/cpp_extensions.py choose compiler
                cxx = os.environ.get("CXX", "c++")
                cxx = subprocess.check_output("'{}' --version".format(cxx), shell=True)
                cxx = cxx.decode("utf-8").strip().split("\n")[0]
            except subprocess.SubprocessError:
                cxx = "Not found"
            data.append(("Compiler", cxx))

            if has_cuda and CUDA_HOME is not None:
                try:
                    nvcc = os.path.join(CUDA_HOME, "bin", "nvcc")
                    nvcc = subprocess.check_output("'{}' -V".format(nvcc), shell=True)
                    nvcc = nvcc.decode("utf-8").strip().split("\n")[-1]
                except subprocess.SubprocessError:
                    nvcc = "Not found"
                data.append(("CUDA compiler", nvcc))
    else:
        # print compilers that are used to build extension
        data.append(("Compiler", _C.get_compiler_version()))
        data.append(("CUDA compiler", _C.get_cuda_version()))  # cuda or hip
        if has_cuda:
            data.append(
                ("detectron2 arch flags", detect_compute_compatibility(CUDA_HOME, _C.__file__))
            )

    data.append(get_env_module())
    data.append(("PyTorch", torch_version + " @" + os.path.dirname(torch.__file__)))
    data.append(("PyTorch debug build", torch.version.debug))

    data.append(("GPU available", has_gpu))
    if has_gpu:
        devices = defaultdict(list)
        for k in range(torch.cuda.device_count()):
            cap = ".".join((str(x) for x in torch.cuda.get_device_capability(k)))
            name = torch.cuda.get_device_name(k) + f" (arch={cap})"
            devices[name].append(str(k))
        for name, devids in devices.items():
            data.append(("GPU " + ",".join(devids), name))

        if has_rocm:
            msg = " - invalid!" if not os.path.isdir(ROCM_HOME) else ""
            data.append(("ROCM_HOME", str(ROCM_HOME) + msg))
        else:
            msg = " - invalid!" if not os.path.isdir(CUDA_HOME) else ""
            data.append(("CUDA_HOME", str(CUDA_HOME) + msg))

            cuda_arch_list = os.environ.get("TORCH_CUDA_ARCH_LIST", None)
            if cuda_arch_list:
                data.append(("TORCH_CUDA_ARCH_LIST", cuda_arch_list))
    data.append(("Pillow", PIL.__version__))

    try:
        data.append(
            (
                "torchvision",
                str(torchvision.__version__) + " @" + os.path.dirname(torchvision.__file__),
            )
        )
        if has_cuda:
            try:
                torchvision_C = importlib.util.find_spec("torchvision._C").origin
                msg = detect_compute_compatibility(CUDA_HOME, torchvision_C)
                data.append(("torchvision arch flags", msg))
            except ImportError:
                data.append(("torchvision._C", "Not found"))
    except AttributeError:
        data.append(("torchvision", "unknown"))

    try:
        import fvcore

        data.append(("fvcore", fvcore.__version__))
    except ImportError:
        pass

    try:
        import cv2

        data.append(("cv2", cv2.__version__))
    except ImportError:
        data.append(("cv2", "Not found"))
    env_str = tabulate(data) + "\n"
    env_str += collect_torch_env()
    return env_str


if __name__ == "__main__":
    try:
        import detectron2  # noqa
    except ImportError:
        print(collect_env_info())
    else:
        from detectron2.utils.collect_env import collect_env_info

        print(collect_env_info())
    if torch.cuda.is_available():
        for k in range(torch.cuda.device_count()):
            device = f"cuda:{k}"
            try:
                x = torch.tensor([1, 2.0], dtype=torch.float32)
                x = x.to(device)
            except Exception:
                print(f"Unable to copy tensor to device={device}")