Method get_submodule

torch/nn/modules/module.py:626–690

Return the submodule given by ``target`` if it exists, otherwise throw an error. For example, let's say you have an ``nn.Module`` ``A`` that looks like this: .. code-block:: text A( (net_b): Module( (net_c): Module(

(self, target: str)

        self.add_module(name, module)

    def get_submodule(self, target: str) -> "Module":
        """Return the submodule given by ``target`` if it exists, otherwise throw an error.

        For example, let's say you have an ``nn.Module`` ``A`` that
        looks like this:

        .. code-block:: text

            A(
                (net_b): Module(
                    (net_c): Module(
                        (conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))
                    )
                    (linear): Linear(in_features=100, out_features=200, bias=True)
                )
            )

        (The diagram shows an ``nn.Module`` ``A``. ``A`` has a nested
        submodule ``net_b``, which itself has two submodules ``net_c``
        and ``linear``. ``net_c`` then has a submodule ``conv``.)

        To check whether or not we have the ``linear`` submodule, we
        would call ``get_submodule("net_b.linear")``. To check whether
        we have the ``conv`` submodule, we would call
        ``get_submodule("net_b.net_c.conv")``.

        The runtime of ``get_submodule`` is bounded by the degree
        of module nesting in ``target``. A query against
        ``named_modules`` achieves the same result, but it is O(N) in
        the number of transitive modules. So, for a simple check to see
        if some submodule exists, ``get_submodule`` should always be
        used.

        Args:
            target: The fully-qualified string name of the submodule
                to look for. (See above example for how to specify a
                fully-qualified string.)

        Returns:
            torch.nn.Module: The submodule referenced by ``target``

        Raises:
            AttributeError: If the target string references an invalid
                path or resolves to something that is not an
                ``nn.Module``
        """
        if target == "":
            return self

        atoms: List[str] = target.split(".")
        mod: torch.nn.Module = self

        for item in atoms:

            if not hasattr(mod, item):
                raise AttributeError(mod._get_name() + " has no "
                                     "attribute `" + item + "`")