Source code for torchvision

import warnings

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
        )
    if backend == "video_reader" and not io._HAS_VIDEO_OPT:
        warnings.warn("video_reader video backend is not available")
    else:
        _video_backend = backend


def get_video_backend():
    return _video_backend


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