loss.SiLogLoss ============== .. py:class:: loss.SiLogLoss(lambd=0.5) Bases: :py:obj:`torch.nn.Module` Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self) -> None: super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool .. py:method:: forward(pred, target, valid_mask) .. py:method:: register_buffer(name: str, tensor: Optional[torch.Tensor], persistent: bool = True) -> None Add a buffer to the module. This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm's ``running_mean`` is not a parameter, but is part of the module's state. Buffers, by default, are persistent and will be saved alongside parameters. This behavior can be changed by setting :attr:`persistent` to ``False``. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module's :attr:`state_dict`. Buffers can be accessed as attributes using given names. :param name: name of the buffer. The buffer can be accessed from this module using the given name :type name: str :param tensor: buffer to be registered. If ``None``, then operations that run on buffers, such as :attr:`cuda`, are ignored. If ``None``, the buffer is **not** included in the module's :attr:`state_dict`. :type tensor: Tensor or None :param persistent: whether the buffer is part of this module's :attr:`state_dict`. :type persistent: bool Example:: >>> # xdoctest: +SKIP("undefined vars") >>> self.register_buffer('running_mean', torch.zeros(num_features)) .. py:method:: register_parameter(name: str, param: Optional[torch.nn.parameter.Parameter]) -> None Add a parameter to the module. The parameter can be accessed as an attribute using given name. :param name: name of the parameter. The parameter can be accessed from this module using the given name :type name: str :param param: parameter to be added to the module. If ``None``, then operations that run on parameters, such as :attr:`cuda`, are ignored. If ``None``, the parameter is **not** included in the module's :attr:`state_dict`. :type param: Parameter or None .. py:method:: add_module(name: str, module: Optional[Module]) -> None Add a child module to the current module. The module can be accessed as an attribute using the given name. :param name: name of the child module. The child module can be accessed from this module using the given name :type name: str :param module: child module to be added to the module. :type module: Module .. py:method:: register_module(name: str, module: Optional[Module]) -> None Alias for :func:`add_module`. .. py:method:: get_submodule(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`` which 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. :param target: The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.) :returns: The submodule referenced by ``target`` :rtype: torch.nn.Module :raises AttributeError: If at any point along the path resulting from the target string the (sub)path resolves to a non-existent attribute name or an object that is not an instance of ``nn.Module``. .. py:method:: set_submodule(target: str, module: Module, strict: bool = False) -> None Set the submodule given by ``target`` if it exists, otherwise throw an error. .. note:: If ``strict`` is set to ``False`` (default), the method will replace an existing submodule or create a new submodule if the parent module exists. If ``strict`` is set to ``True``, the method will only attempt to replace an existing submodule and throw an error if the submodule does not exist. 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(3, 3, 3) ) (linear): Linear(3, 3) ) ) (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 override the ``Conv2d`` with a new submodule ``Linear``, you could call ``set_submodule("net_b.net_c.conv", nn.Linear(1, 1))`` where ``strict`` could be ``True`` or ``False`` To add a new submodule ``Conv2d`` to the existing ``net_b`` module, you would call ``set_submodule("net_b.conv", nn.Conv2d(1, 1, 1))``. In the above if you set ``strict=True`` and call ``set_submodule("net_b.conv", nn.Conv2d(1, 1, 1), strict=True)``, an AttributeError will be raised because ``net_b`` does not have a submodule named ``conv``. :param target: The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.) :param module: The module to set the submodule to. :param strict: If ``False``, the method will replace an existing submodule or create a new submodule if the parent module exists. If ``True``, the method will only attempt to replace an existing submodule and throw an error if the submodule doesn't already exist. :raises ValueError: If the ``target`` string is empty or if ``module`` is not an instance of ``nn.Module``. :raises AttributeError: If at any point along the path resulting from the ``target`` string the (sub)path resolves to a non-existent attribute name or an object that is not an instance of ``nn.Module``. .. py:method:: get_parameter(target: str) -> torch.nn.parameter.Parameter Return the parameter given by ``target`` if it exists, otherwise throw an error. See the docstring for ``get_submodule`` for a more detailed explanation of this method's functionality as well as how to correctly specify ``target``. :param target: The fully-qualified string name of the Parameter to look for. (See ``get_submodule`` for how to specify a fully-qualified string.) :returns: The Parameter referenced by ``target`` :rtype: torch.nn.Parameter :raises AttributeError: If the target string references an invalid path or resolves to something that is not an ``nn.Parameter`` .. py:method:: get_buffer(target: str) -> torch.Tensor Return the buffer given by ``target`` if it exists, otherwise throw an error. See the docstring for ``get_submodule`` for a more detailed explanation of this method's functionality as well as how to correctly specify ``target``. :param target: The fully-qualified string name of the buffer to look for. (See ``get_submodule`` for how to specify a fully-qualified string.) :returns: The buffer referenced by ``target`` :rtype: torch.Tensor :raises AttributeError: If the target string references an invalid path or resolves to something that is not a buffer .. py:method:: get_extra_state() -> Any Return any extra state to include in the module's state_dict. Implement this and a corresponding :func:`set_extra_state` for your module if you need to store extra state. This function is called when building the module's `state_dict()`. Note that extra state should be picklable to ensure working serialization of the state_dict. We only provide backwards compatibility guarantees for serializing Tensors; other objects may break backwards compatibility if their serialized pickled form changes. :returns: Any extra state to store in the module's state_dict :rtype: object .. py:method:: set_extra_state(state: Any) -> None Set extra state contained in the loaded `state_dict`. This function is called from :func:`load_state_dict` to handle any extra state found within the `state_dict`. Implement this function and a corresponding :func:`get_extra_state` for your module if you need to store extra state within its `state_dict`. :param state: Extra state from the `state_dict` :type state: dict .. py:method:: apply(fn: Callable[[Module], None]) -> T Apply ``fn`` recursively to every submodule (as returned by ``.children()``) as well as self. Typical use includes initializing the parameters of a model (see also :ref:`nn-init-doc`). :param fn: function to be applied to each submodule :type fn: :class:`Module` -> None :returns: self :rtype: Module Example:: >>> @torch.no_grad() >>> def init_weights(m): >>> print(m) >>> if type(m) == nn.Linear: >>> m.weight.fill_(1.0) >>> print(m.weight) >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2)) >>> net.apply(init_weights) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[1., 1.], [1., 1.]], requires_grad=True) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[1., 1.], [1., 1.]], requires_grad=True) Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) ) .. py:method:: cuda(device: Optional[Union[int, Module.cuda.device]] = None) -> T Move all model parameters and buffers to the GPU. This also makes associated parameters and buffers different objects. So it should be called before constructing the optimizer if the module will live on GPU while being optimized. .. note:: This method modifies the module in-place. :param device: if specified, all parameters will be copied to that device :type device: int, optional :returns: self :rtype: Module .. py:method:: ipu(device: Optional[Union[int, Module.ipu.device]] = None) -> T Move all model parameters and buffers to the IPU. This also makes associated parameters and buffers different objects. So it should be called before constructing the optimizer if the module will live on IPU while being optimized. .. note:: This method modifies the module in-place. :param device: if specified, all parameters will be copied to that device :type device: int, optional :returns: self :rtype: Module .. py:method:: xpu(device: Optional[Union[int, Module.xpu.device]] = None) -> T Move all model parameters and buffers to the XPU. This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on XPU while being optimized. .. note:: This method modifies the module in-place. :param device: if specified, all parameters will be copied to that device :type device: int, optional :returns: self :rtype: Module .. py:method:: mtia(device: Optional[Union[int, Module.mtia.device]] = None) -> T Move all model parameters and buffers to the MTIA. This also makes associated parameters and buffers different objects. So it should be called before constructing the optimizer if the module will live on MTIA while being optimized. .. note:: This method modifies the module in-place. :param device: if specified, all parameters will be copied to that device :type device: int, optional :returns: self :rtype: Module .. py:method:: cpu() -> T Move all model parameters and buffers to the CPU. .. note:: This method modifies the module in-place. :returns: self :rtype: Module .. py:method:: type(dst_type: Union[torch.dtype, str]) -> T Casts all parameters and buffers to :attr:`dst_type`. .. note:: This method modifies the module in-place. :param dst_type: the desired type :type dst_type: type or string :returns: self :rtype: Module .. py:method:: float() -> T Casts all floating point parameters and buffers to ``float`` datatype. .. note:: This method modifies the module in-place. :returns: self :rtype: Module .. py:method:: double() -> T Casts all floating point parameters and buffers to ``double`` datatype. .. note:: This method modifies the module in-place. :returns: self :rtype: Module .. py:method:: half() -> T Casts all floating point parameters and buffers to ``half`` datatype. .. note:: This method modifies the module in-place. :returns: self :rtype: Module .. py:method:: bfloat16() -> T Casts all floating point parameters and buffers to ``bfloat16`` datatype. .. note:: This method modifies the module in-place. :returns: self :rtype: Module .. py:method:: to_empty(*, device: Optional[torch._prims_common.DeviceLikeType], recurse: bool = True) -> T Move the parameters and buffers to the specified device without copying storage. :param device: The desired device of the parameters and buffers in this module. :type device: :class:`torch.device` :param recurse: Whether parameters and buffers of submodules should be recursively moved to the specified device. :type recurse: bool :returns: self :rtype: Module .. py:method:: to(device: Optional[torch._prims_common.DeviceLikeType] = ..., dtype: Optional[Module.to.dtype] = ..., non_blocking: bool = ...) -> typing_extensions.Self to(dtype: Module.to.dtype, non_blocking: bool = ...) -> typing_extensions.Self to(tensor: torch.Tensor, non_blocking: bool = ...) -> typing_extensions.Self Move and/or cast the parameters and buffers. This can be called as .. function:: to(device=None, dtype=None, non_blocking=False) :noindex: .. function:: to(dtype, non_blocking=False) :noindex: .. function:: to(tensor, non_blocking=False) :noindex: .. function:: to(memory_format=torch.channels_last) :noindex: Its signature is similar to :meth:`torch.Tensor.to`, but only accepts floating point or complex :attr:`dtype`\ s. In addition, this method will only cast the floating point or complex parameters and buffers to :attr:`dtype` (if given). The integral parameters and buffers will be moved :attr:`device`, if that is given, but with dtypes unchanged. When :attr:`non_blocking` is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices. See below for examples. .. note:: This method modifies the module in-place. :param device: the desired device of the parameters and buffers in this module :type device: :class:`torch.device` :param dtype: the desired floating point or complex dtype of the parameters and buffers in this module :type dtype: :class:`torch.dtype` :param tensor: Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module :type tensor: torch.Tensor :param memory_format: the desired memory format for 4D parameters and buffers in this module (keyword only argument) :type memory_format: :class:`torch.memory_format` :returns: self :rtype: Module Examples:: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> linear = nn.Linear(2, 2) >>> linear.weight Parameter containing: tensor([[ 0.1913, -0.3420], [-0.5113, -0.2325]]) >>> linear.to(torch.double) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1913, -0.3420], [-0.5113, -0.2325]], dtype=torch.float64) >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1) >>> gpu1 = torch.device("cuda:1") >>> linear.to(gpu1, dtype=torch.half, non_blocking=True) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1914, -0.3420], [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1') >>> cpu = torch.device("cpu") >>> linear.to(cpu) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1914, -0.3420], [-0.5112, -0.2324]], dtype=torch.float16) >>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble) >>> linear.weight Parameter containing: tensor([[ 0.3741+0.j, 0.2382+0.j], [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128) >>> linear(torch.ones(3, 2, dtype=torch.cdouble)) tensor([[0.6122+0.j, 0.1150+0.j], [0.6122+0.j, 0.1150+0.j], [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128) .. py:method:: register_full_backward_pre_hook(hook: Callable[[Module, _grad_t], Union[None, _grad_t]], prepend: bool = False) -> torch.utils.hooks.RemovableHandle Register a backward pre-hook on the module. The hook will be called every time the gradients for the module are computed. The hook should have the following signature:: hook(module, grad_output) -> tuple[Tensor] or None The :attr:`grad_output` is a tuple. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the output that will be used in place of :attr:`grad_output` in subsequent computations. Entries in :attr:`grad_output` will be ``None`` for all non-Tensor arguments. For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function. .. warning :: Modifying inputs inplace is not allowed when using backward hooks and will raise an error. :param hook: The user-defined hook to be registered. :type hook: Callable :param prepend: If true, the provided ``hook`` will be fired before all existing ``backward_pre`` hooks on this :class:`torch.nn.Module`. Otherwise, the provided ``hook`` will be fired after all existing ``backward_pre`` hooks on this :class:`torch.nn.Module`. Note that global ``backward_pre`` hooks registered with :func:`register_module_full_backward_pre_hook` will fire before all hooks registered by this method. :type prepend: bool :returns: a handle that can be used to remove the added hook by calling ``handle.remove()`` :rtype: :class:`torch.utils.hooks.RemovableHandle` .. py:method:: register_backward_hook(hook: Callable[[Module, _grad_t, _grad_t], Union[None, _grad_t]]) -> torch.utils.hooks.RemovableHandle Register a backward hook on the module. This function is deprecated in favor of :meth:`~torch.nn.Module.register_full_backward_hook` and the behavior of this function will change in future versions. :returns: a handle that can be used to remove the added hook by calling ``handle.remove()`` :rtype: :class:`torch.utils.hooks.RemovableHandle` .. py:method:: register_full_backward_hook(hook: Callable[[Module, _grad_t, _grad_t], Union[None, _grad_t]], prepend: bool = False) -> torch.utils.hooks.RemovableHandle Register a backward hook on the module. The hook will be called every time the gradients with respect to a module are computed, i.e. the hook will execute if and only if the gradients with respect to module outputs are computed. The hook should have the following signature:: hook(module, grad_input, grad_output) -> tuple(Tensor) or None The :attr:`grad_input` and :attr:`grad_output` are tuples that contain the gradients with respect to the inputs and outputs respectively. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the input that will be used in place of :attr:`grad_input` in subsequent computations. :attr:`grad_input` will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in :attr:`grad_input` and :attr:`grad_output` will be ``None`` for all non-Tensor arguments. For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function. .. warning :: Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error. :param hook: The user-defined hook to be registered. :type hook: Callable :param prepend: If true, the provided ``hook`` will be fired before all existing ``backward`` hooks on this :class:`torch.nn.Module`. Otherwise, the provided ``hook`` will be fired after all existing ``backward`` hooks on this :class:`torch.nn.Module`. Note that global ``backward`` hooks registered with :func:`register_module_full_backward_hook` will fire before all hooks registered by this method. :type prepend: bool :returns: a handle that can be used to remove the added hook by calling ``handle.remove()`` :rtype: :class:`torch.utils.hooks.RemovableHandle` .. py:method:: register_forward_pre_hook(hook: Union[Callable[[T, tuple[Any, Ellipsis]], Optional[Any]], Callable[[T, tuple[Any, Ellipsis], dict[str, Any]], Optional[tuple[Any, dict[str, Any]]]]], *, prepend: bool = False, with_kwargs: bool = False) -> torch.utils.hooks.RemovableHandle Register a forward pre-hook on the module. The hook will be called every time before :func:`forward` is invoked. If ``with_kwargs`` is false or not specified, the input contains only the positional arguments given to the module. Keyword arguments won't be passed to the hooks and only to the ``forward``. The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned (unless that value is already a tuple). The hook should have the following signature:: hook(module, args) -> None or modified input If ``with_kwargs`` is true, the forward pre-hook will be passed the kwargs given to the forward function. And if the hook modifies the input, both the args and kwargs should be returned. The hook should have the following signature:: hook(module, args, kwargs) -> None or a tuple of modified input and kwargs :param hook: The user defined hook to be registered. :type hook: Callable :param prepend: If true, the provided ``hook`` will be fired before all existing ``forward_pre`` hooks on this :class:`torch.nn.Module`. Otherwise, the provided ``hook`` will be fired after all existing ``forward_pre`` hooks on this :class:`torch.nn.Module`. Note that global ``forward_pre`` hooks registered with :func:`register_module_forward_pre_hook` will fire before all hooks registered by this method. Default: ``False`` :type prepend: bool :param with_kwargs: If true, the ``hook`` will be passed the kwargs given to the forward function. Default: ``False`` :type with_kwargs: bool :returns: a handle that can be used to remove the added hook by calling ``handle.remove()`` :rtype: :class:`torch.utils.hooks.RemovableHandle` .. py:method:: register_forward_hook(hook: Union[Callable[[T, tuple[Any, Ellipsis], Any], Optional[Any]], Callable[[T, tuple[Any, Ellipsis], dict[str, Any], Any], Optional[Any]]], *, prepend: bool = False, with_kwargs: bool = False, always_call: bool = False) -> torch.utils.hooks.RemovableHandle Register a forward hook on the module. The hook will be called every time after :func:`forward` has computed an output. If ``with_kwargs`` is ``False`` or not specified, the input contains only the positional arguments given to the module. Keyword arguments won't be passed to the hooks and only to the ``forward``. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after :func:`forward` is called. The hook should have the following signature:: hook(module, args, output) -> None or modified output If ``with_kwargs`` is ``True``, the forward hook will be passed the ``kwargs`` given to the forward function and be expected to return the output possibly modified. The hook should have the following signature:: hook(module, args, kwargs, output) -> None or modified output :param hook: The user defined hook to be registered. :type hook: Callable :param prepend: If ``True``, the provided ``hook`` will be fired before all existing ``forward`` hooks on this :class:`torch.nn.Module`. Otherwise, the provided ``hook`` will be fired after all existing ``forward`` hooks on this :class:`torch.nn.Module`. Note that global ``forward`` hooks registered with :func:`register_module_forward_hook` will fire before all hooks registered by this method. Default: ``False`` :type prepend: bool :param with_kwargs: If ``True``, the ``hook`` will be passed the kwargs given to the forward function. Default: ``False`` :type with_kwargs: bool :param always_call: If ``True`` the ``hook`` will be run regardless of whether an exception is raised while calling the Module. Default: ``False`` :type always_call: bool :returns: a handle that can be used to remove the added hook by calling ``handle.remove()`` :rtype: :class:`torch.utils.hooks.RemovableHandle` .. py:method:: __getstate__() .. py:method:: __setstate__(state) .. py:method:: __getattr__(name: str) -> Union[torch.Tensor, Module] .. py:method:: __setattr__(name: str, value: Union[torch.Tensor, Module]) -> None .. py:method:: __delattr__(name) .. py:method:: register_state_dict_post_hook(hook) Register a post-hook for the :meth:`~torch.nn.Module.state_dict` method. It should have the following signature:: hook(module, state_dict, prefix, local_metadata) -> None The registered hooks can modify the ``state_dict`` inplace. .. py:method:: register_state_dict_pre_hook(hook) Register a pre-hook for the :meth:`~torch.nn.Module.state_dict` method. It should have the following signature:: hook(module, prefix, keep_vars) -> None The registered hooks can be used to perform pre-processing before the ``state_dict`` call is made. .. py:method:: state_dict(*, destination: T_destination, prefix: str = ..., keep_vars: bool = ...) -> T_destination state_dict(*, prefix: str = ..., keep_vars: bool = ...) -> dict[str, Any] Return a dictionary containing references to the whole state of the module. Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to ``None`` are not included. .. note:: The returned object is a shallow copy. It contains references to the module's parameters and buffers. .. warning:: Currently ``state_dict()`` also accepts positional arguments for ``destination``, ``prefix`` and ``keep_vars`` in order. However, this is being deprecated and keyword arguments will be enforced in future releases. .. warning:: Please avoid the use of argument ``destination`` as it is not designed for end-users. :param destination: If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an ``OrderedDict`` will be created and returned. Default: ``None``. :type destination: dict, optional :param prefix: a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ``''``. :type prefix: str, optional :param keep_vars: by default the :class:`~torch.Tensor` s returned in the state dict are detached from autograd. If it's set to ``True``, detaching will not be performed. Default: ``False``. :type keep_vars: bool, optional :returns: a dictionary containing a whole state of the module :rtype: dict Example:: >>> # xdoctest: +SKIP("undefined vars") >>> module.state_dict().keys() ['bias', 'weight'] .. py:method:: register_load_state_dict_pre_hook(hook) Register a pre-hook to be run before module's :meth:`~nn.Module.load_state_dict` is called. It should have the following signature:: hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -> None # noqa: B950 :param hook: Callable hook that will be invoked before loading the state dict. :type hook: Callable .. py:method:: register_load_state_dict_post_hook(hook) Register a post-hook to be run after module's :meth:`~nn.Module.load_state_dict` is called. It should have the following signature:: hook(module, incompatible_keys) -> None The ``module`` argument is the current module that this hook is registered on, and the ``incompatible_keys`` argument is a ``NamedTuple`` consisting of attributes ``missing_keys`` and ``unexpected_keys``. ``missing_keys`` is a ``list`` of ``str`` containing the missing keys and ``unexpected_keys`` is a ``list`` of ``str`` containing the unexpected keys. The given incompatible_keys can be modified inplace if needed. Note that the checks performed when calling :func:`load_state_dict` with ``strict=True`` are affected by modifications the hook makes to ``missing_keys`` or ``unexpected_keys``, as expected. Additions to either set of keys will result in an error being thrown when ``strict=True``, and clearing out both missing and unexpected keys will avoid an error. :returns: a handle that can be used to remove the added hook by calling ``handle.remove()`` :rtype: :class:`torch.utils.hooks.RemovableHandle` .. py:method:: load_state_dict(state_dict: collections.abc.Mapping[str, Any], strict: bool = True, assign: bool = False) Copy parameters and buffers from :attr:`state_dict` into this module and its descendants. If :attr:`strict` is ``True``, then the keys of :attr:`state_dict` must exactly match the keys returned by this module's :meth:`~torch.nn.Module.state_dict` function. .. warning:: If :attr:`assign` is ``True`` the optimizer must be created after the call to :attr:`load_state_dict` unless :func:`~torch.__future__.get_swap_module_params_on_conversion` is ``True``. :param state_dict: a dict containing parameters and persistent buffers. :type state_dict: dict :param strict: whether to strictly enforce that the keys in :attr:`state_dict` match the keys returned by this module's :meth:`~torch.nn.Module.state_dict` function. Default: ``True`` :type strict: bool, optional :param assign: When set to ``False``, the properties of the tensors in the current module are preserved whereas setting it to ``True`` preserves properties of the Tensors in the state dict. The only exception is the ``requires_grad`` field of :class:`~torch.nn.Parameter`s for which the value from the module is preserved. Default: ``False`` :type assign: bool, optional :returns: * **missing_keys** is a list of str containing any keys that are expected by this module but missing from the provided ``state_dict``. * **unexpected_keys** is a list of str containing the keys that are not expected by this module but present in the provided ``state_dict``. :rtype: ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields .. note:: If a parameter or buffer is registered as ``None`` and its corresponding key exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a ``RuntimeError``. .. py:method:: parameters(recurse: bool = True) -> collections.abc.Iterator[torch.nn.parameter.Parameter] Return an iterator over module parameters. This is typically passed to an optimizer. :param recurse: if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. :type recurse: bool :Yields: *Parameter* -- module parameter Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for param in model.parameters(): >>> print(type(param), param.size()) (20L,) (20L, 1L, 5L, 5L) .. py:method:: named_parameters(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> collections.abc.Iterator[tuple[str, torch.nn.parameter.Parameter]] Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself. :param prefix: prefix to prepend to all parameter names. :type prefix: str :param recurse: if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. :type recurse: bool :param remove_duplicate: whether to remove the duplicated parameters in the result. Defaults to True. :type remove_duplicate: bool, optional :Yields: *(str, Parameter)* -- Tuple containing the name and parameter Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for name, param in self.named_parameters(): >>> if name in ['bias']: >>> print(param.size()) .. py:method:: buffers(recurse: bool = True) -> collections.abc.Iterator[torch.Tensor] Return an iterator over module buffers. :param recurse: if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. :type recurse: bool :Yields: *torch.Tensor* -- module buffer Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for buf in model.buffers(): >>> print(type(buf), buf.size()) (20L,) (20L, 1L, 5L, 5L) .. py:method:: named_buffers(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> collections.abc.Iterator[tuple[str, torch.Tensor]] Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself. :param prefix: prefix to prepend to all buffer names. :type prefix: str :param recurse: if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True. :type recurse: bool, optional :param remove_duplicate: whether to remove the duplicated buffers in the result. Defaults to True. :type remove_duplicate: bool, optional :Yields: *(str, torch.Tensor)* -- Tuple containing the name and buffer Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for name, buf in self.named_buffers(): >>> if name in ['running_var']: >>> print(buf.size()) .. py:method:: children() -> collections.abc.Iterator[Module] Return an iterator over immediate children modules. :Yields: *Module* -- a child module .. py:method:: named_children() -> collections.abc.Iterator[tuple[str, Module]] Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself. :Yields: *(str, Module)* -- Tuple containing a name and child module Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for name, module in model.named_children(): >>> if name in ['conv4', 'conv5']: >>> print(module) .. py:method:: modules() -> collections.abc.Iterator[Module] Return an iterator over all modules in the network. :Yields: *Module* -- a module in the network .. note:: Duplicate modules are returned only once. In the following example, ``l`` will be returned only once. Example:: >>> l = nn.Linear(2, 2) >>> net = nn.Sequential(l, l) >>> for idx, m in enumerate(net.modules()): ... print(idx, '->', m) 0 -> Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) ) 1 -> Linear(in_features=2, out_features=2, bias=True) .. py:method:: named_modules(memo: Optional[set[Module]] = None, prefix: str = '', remove_duplicate: bool = True) Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself. :param memo: a memo to store the set of modules already added to the result :param prefix: a prefix that will be added to the name of the module :param remove_duplicate: whether to remove the duplicated module instances in the result or not :Yields: *(str, Module)* -- Tuple of name and module .. note:: Duplicate modules are returned only once. In the following example, ``l`` will be returned only once. Example:: >>> l = nn.Linear(2, 2) >>> net = nn.Sequential(l, l) >>> for idx, m in enumerate(net.named_modules()): ... print(idx, '->', m) 0 -> ('', Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) )) 1 -> ('0', Linear(in_features=2, out_features=2, bias=True)) .. py:method:: train(mode: bool = True) -> T Set the module in training mode. This has an effect only on certain modules. See the documentation of particular modules for details of their behaviors in training/evaluation mode, i.e., whether they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`, etc. :param mode: whether to set training mode (``True``) or evaluation mode (``False``). Default: ``True``. :type mode: bool :returns: self :rtype: Module .. py:method:: eval() -> T Set the module in evaluation mode. This has an effect only on certain modules. See the documentation of particular modules for details of their behaviors in training/evaluation mode, i.e. whether they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`, etc. This is equivalent with :meth:`self.train(False) `. See :ref:`locally-disable-grad-doc` for a comparison between `.eval()` and several similar mechanisms that may be confused with it. :returns: self :rtype: Module .. py:method:: requires_grad_(requires_grad: bool = True) -> T Change if autograd should record operations on parameters in this module. This method sets the parameters' :attr:`requires_grad` attributes in-place. This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training). See :ref:`locally-disable-grad-doc` for a comparison between `.requires_grad_()` and several similar mechanisms that may be confused with it. :param requires_grad: whether autograd should record operations on parameters in this module. Default: ``True``. :type requires_grad: bool :returns: self :rtype: Module .. py:method:: zero_grad(set_to_none: bool = True) -> None Reset gradients of all model parameters. See similar function under :class:`torch.optim.Optimizer` for more context. :param set_to_none: instead of setting to zero, set the grads to None. See :meth:`torch.optim.Optimizer.zero_grad` for details. :type set_to_none: bool .. py:method:: share_memory() -> T See :meth:`torch.Tensor.share_memory_`. .. py:method:: extra_repr() -> str Return the extra representation of the module. To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable. .. py:method:: __repr__() .. py:method:: __dir__() .. py:method:: compile(*args, **kwargs) Compile this Module's forward using :func:`torch.compile`. This Module's `__call__` method is compiled and all arguments are passed as-is to :func:`torch.compile`. See :func:`torch.compile` for details on the arguments for this function.