Function load

torch/serialization.py:866–1040

load(f, map_location=None, pickle_module=pickle, *, weights_only=False, mmap=None, **pickle_load_args) Loads an object saved with :func:`torch.save` from a file. :func:`torch.load` uses Python's unpickling facilities but treats storages, which underlie tensors, specially. They are firs

(
    f: FILE_LIKE,
    map_location: MAP_LOCATION = None,
    pickle_module: Any = None,
    *,
    weights_only: bool = False,
    mmap: Optional[bool] = None,
    **pickle_load_args: Any
)

def load(
    f: FILE_LIKE,
    map_location: MAP_LOCATION = None,
    pickle_module: Any = None,
    *,
    weights_only: bool = False,
    mmap: Optional[bool] = None,
    **pickle_load_args: Any
) -> Any:
    # Reference: https://github.com/pytorch/pytorch/issues/54354
    # The first line of this docstring overrides the one Sphinx generates for the
    # documentation. We need it so that Sphinx doesn't leak `pickle`s path from
    # the build environment (e.g. `<module 'pickle' from '/leaked/path').

    """load(f, map_location=None, pickle_module=pickle, *, weights_only=False, mmap=None, **pickle_load_args)

    Loads an object saved with :func:`torch.save` from a file.

    :func:`torch.load` uses Python&#x27;s unpickling facilities but treats storages,
    which underlie tensors, specially. They are first deserialized on the
    CPU and are then moved to the device they were saved from. If this fails
    (e.g. because the run time system doesn&#x27;t have certain devices), an exception
    is raised. However, storages can be dynamically remapped to an alternative
    set of devices using the :attr:`map_location` argument.

    If :attr:`map_location` is a callable, it will be called once for each serialized
    storage with two arguments: storage and location. The storage argument
    will be the initial deserialization of the storage, residing on the CPU.
    Each serialized storage has a location tag associated with it which
    identifies the device it was saved from, and this tag is the second
    argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'``
    for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors.
    :attr:`map_location` should return either ``None`` or a storage. If
    :attr:`map_location` returns a storage, it will be used as the final deserialized
    object, already moved to the right device. Otherwise, :func:`torch.load` will
    fall back to the default behavior, as if :attr:`map_location` wasn&#x27;t specified.

    If :attr:`map_location` is a :class:`torch.device` object or a string containing
    a device tag, it indicates the location where all tensors should be loaded.

    Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags
    appearing in the file (keys), to ones that specify where to put the
    storages (values).

    User extensions can register their own location tags and tagging and
    deserialization methods using :func:`torch.serialization.register_package`.

    Args:
        f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`),
            or a string or os.PathLike object containing a file name
        map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage
            locations
        pickle_module: module used for unpickling metadata and objects (has to
            match the :attr:`pickle_module` used to serialize file)
        weights_only: Indicates whether unpickler should be restricted to
            loading only tensors, primitive types and dictionaries
        mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory.
            Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they