Arulkumar03/Fox_Sheep_Detector_Computer_Vision_model

1# Copyright (c) Facebook, Inc. and its affiliates.2import importlib3import numpy as np4import os5import re6import subprocess7import sys8from collections import defaultdict9import PIL10import torch11import torchvision12from tabulate import tabulate13 14__all__ = ["collect_env_info"]15 16 17def collect_torch_env():18    try:19        import torch.__config__20 21        return torch.__config__.show()22    except ImportError:23        # compatible with older versions of pytorch24        from torch.utils.collect_env import get_pretty_env_info25 26        return get_pretty_env_info()27 28 29def get_env_module():30    var_name = "DETECTRON2_ENV_MODULE"31    return var_name, os.environ.get(var_name, "<not set>")32 33 34def detect_compute_compatibility(CUDA_HOME, so_file):35    try:36        cuobjdump = os.path.join(CUDA_HOME, "bin", "cuobjdump")37        if os.path.isfile(cuobjdump):38            output = subprocess.check_output(39                "'{}' --list-elf '{}'".format(cuobjdump, so_file), shell=True40            )41            output = output.decode("utf-8").strip().split("\n")42            arch = []43            for line in output:44                line = re.findall(r"\.sm_([0-9]*)\.", line)[0]45                arch.append(".".join(line))46            arch = sorted(set(arch))47            return ", ".join(arch)48        else:49            return so_file + "; cannot find cuobjdump"50    except Exception:51        # unhandled failure52        return so_file53 54 55def collect_env_info():56    has_gpu = torch.cuda.is_available()  # true for both CUDA & ROCM57    torch_version = torch.__version__58 59    # NOTE that CUDA_HOME/ROCM_HOME could be None even when CUDA runtime libs are functional60    from torch.utils.cpp_extension import CUDA_HOME, ROCM_HOME61 62    has_rocm = False63    if (getattr(torch.version, "hip", None) is not None) and (ROCM_HOME is not None):64        has_rocm = True65    has_cuda = has_gpu and (not has_rocm)66 67    data = []68    data.append(("sys.platform", sys.platform))  # check-template.yml depends on it69    data.append(("Python", sys.version.replace("\n", "")))70    data.append(("numpy", np.__version__))71 72    try:73        import detectron2  # noqa74 75        data.append(76            ("detectron2", detectron2.__version__ + " @" + os.path.dirname(detectron2.__file__))77        )78    except ImportError:79        data.append(("detectron2", "failed to import"))80    except AttributeError:81        data.append(("detectron2", "imported a wrong installation"))82 83    try:84        import detectron2._C as _C85    except ImportError as e:86        data.append(("detectron2._C", f"not built correctly: {e}"))87 88        # print system compilers when extension fails to build89        if sys.platform != "win32":  # don't know what to do for windows90            try:91                # this is how torch/utils/cpp_extensions.py choose compiler92                cxx = os.environ.get("CXX", "c++")93                cxx = subprocess.check_output("'{}' --version".format(cxx), shell=True)94                cxx = cxx.decode("utf-8").strip().split("\n")[0]95            except subprocess.SubprocessError:96                cxx = "Not found"97            data.append(("Compiler ($CXX)", cxx))98 99            if has_cuda and CUDA_HOME is not None:100                try:101                    nvcc = os.path.join(CUDA_HOME, "bin", "nvcc")102                    nvcc = subprocess.check_output("'{}' -V".format(nvcc), shell=True)103                    nvcc = nvcc.decode("utf-8").strip().split("\n")[-1]104                except subprocess.SubprocessError:105                    nvcc = "Not found"106                data.append(("CUDA compiler", nvcc))107        if has_cuda and sys.platform != "win32":108            try:109                so_file = importlib.util.find_spec("detectron2._C").origin110            except (ImportError, AttributeError):111                pass112            else:113                data.append(114                    ("detectron2 arch flags", detect_compute_compatibility(CUDA_HOME, so_file))115                )116    else:117        # print compilers that are used to build extension118        data.append(("Compiler", _C.get_compiler_version()))119        data.append(("CUDA compiler", _C.get_cuda_version()))  # cuda or hip120        if has_cuda and getattr(_C, "has_cuda", lambda: True)():121            data.append(122                ("detectron2 arch flags", detect_compute_compatibility(CUDA_HOME, _C.__file__))123            )124 125    data.append(get_env_module())126    data.append(("PyTorch", torch_version + " @" + os.path.dirname(torch.__file__)))127    data.append(("PyTorch debug build", torch.version.debug))128    try:129        data.append(("torch._C._GLIBCXX_USE_CXX11_ABI", torch._C._GLIBCXX_USE_CXX11_ABI))130    except Exception:131        pass132 133    if not has_gpu:134        has_gpu_text = "No: torch.cuda.is_available() == False"135    else:136        has_gpu_text = "Yes"137    data.append(("GPU available", has_gpu_text))138    if has_gpu:139        devices = defaultdict(list)140        for k in range(torch.cuda.device_count()):141            cap = ".".join((str(x) for x in torch.cuda.get_device_capability(k)))142            name = torch.cuda.get_device_name(k) + f" (arch={cap})"143            devices[name].append(str(k))144        for name, devids in devices.items():145            data.append(("GPU " + ",".join(devids), name))146 147        if has_rocm:148            msg = " - invalid!" if not (ROCM_HOME and os.path.isdir(ROCM_HOME)) else ""149            data.append(("ROCM_HOME", str(ROCM_HOME) + msg))150        else:151            try:152                from torch.utils.collect_env import get_nvidia_driver_version, run as _run153 154                data.append(("Driver version", get_nvidia_driver_version(_run)))155            except Exception:156                pass157            msg = " - invalid!" if not (CUDA_HOME and os.path.isdir(CUDA_HOME)) else ""158            data.append(("CUDA_HOME", str(CUDA_HOME) + msg))159 160            cuda_arch_list = os.environ.get("TORCH_CUDA_ARCH_LIST", None)161            if cuda_arch_list:162                data.append(("TORCH_CUDA_ARCH_LIST", cuda_arch_list))163    data.append(("Pillow", PIL.__version__))164 165    try:166        data.append(167            (168                "torchvision",169                str(torchvision.__version__) + " @" + os.path.dirname(torchvision.__file__),170            )171        )172        if has_cuda:173            try:174                torchvision_C = importlib.util.find_spec("torchvision._C").origin175                msg = detect_compute_compatibility(CUDA_HOME, torchvision_C)176                data.append(("torchvision arch flags", msg))177            except (ImportError, AttributeError):178                data.append(("torchvision._C", "Not found"))179    except AttributeError:180        data.append(("torchvision", "unknown"))181 182    try:183        import fvcore184 185        data.append(("fvcore", fvcore.__version__))186    except (ImportError, AttributeError):187        pass188 189    try:190        import iopath191 192        data.append(("iopath", iopath.__version__))193    except (ImportError, AttributeError):194        pass195 196    try:197        import cv2198 199        data.append(("cv2", cv2.__version__))200    except (ImportError, AttributeError):201        data.append(("cv2", "Not found"))202    env_str = tabulate(data) + "\n"203    env_str += collect_torch_env()204    return env_str205 206 207def test_nccl_ops():208    num_gpu = torch.cuda.device_count()209    if os.access("/tmp", os.W_OK):210        import torch.multiprocessing as mp211 212        dist_url = "file:///tmp/nccl_tmp_file"213        print("Testing NCCL connectivity ... this should not hang.")214        mp.spawn(_test_nccl_worker, nprocs=num_gpu, args=(num_gpu, dist_url), daemon=False)215        print("NCCL succeeded.")216 217 218def _test_nccl_worker(rank, num_gpu, dist_url):219    import torch.distributed as dist220 221    dist.init_process_group(backend="NCCL", init_method=dist_url, rank=rank, world_size=num_gpu)222    dist.barrier(device_ids=[rank])223 224 225if __name__ == "__main__":226    try:227        from detectron2.utils.collect_env import collect_env_info as f228 229        print(f())230    except ImportError:231        print(collect_env_info())232 233    if torch.cuda.is_available():234        num_gpu = torch.cuda.device_count()235        for k in range(num_gpu):236            device = f"cuda:{k}"237            try:238                x = torch.tensor([1, 2.0], dtype=torch.float32)239                x = x.to(device)240            except Exception as e:241                print(242                    f"Unable to copy tensor to device={device}: {e}. "243                    "Your CUDA environment is broken."244                )245        if num_gpu > 1:246            test_nccl_ops()247