__init__.py

from torchvision import models
from torchvision import datasets
from torchvision import ops
from torchvision import transforms
from torchvision import utils
from torchvision import io

from .extension import _HAS_OPS

try:
    from .version import __version__  # noqa: F401
except ImportError:
    pass

_image_backend = 'PIL'

_video_backend = "pyav"


def set_image_backend(backend):
    """
    Specifies the package used to load images.

    Args:
        backend (string): Name of the image backend. one of {'PIL', 'accimage'}.
            The :mod:`accimage` package uses the Intel IPP library. It is
            generally faster than PIL, but does not support as many operations.
    """
    global _image_backend
    if backend not in ['PIL', 'accimage']:
        raise ValueError("Invalid backend '{}'. Options are 'PIL' and 'accimage'"
                         .format(backend))
    _image_backend = backend


def get_image_backend():
    """
    Gets the name of the package used to load images
    """
    return _image_backend


def set_video_backend(backend):
    """
    Specifies the package used to decode videos.

    Args:
        backend (string): Name of the video backend. one of {'pyav', 'video_reader'}.
            The :mod:`pyav` package uses the 3rd party PyAv library. It is a Pythonic
                binding for the FFmpeg libraries.
            The :mod:`video_reader` package includes a native c++ implementation on
                top of FFMPEG libraries, and a python API of TorchScript custom operator.
                It is generally decoding faster than pyav, but perhaps is less robust.
    """
    global _video_backend
    if backend not in ["pyav", "video_reader"]:
        raise ValueError(
            "Invalid video backend '%s'. Options are 'pyav' and 'video_reader'" % backend
        )
    _video_backend = backend


def get_video_backend():
    return _video_backend


def _is_tracing():
    import torch
    return torch._C._get_tracing_state()