pytorch中nn.moudle模块

nn.moudle是所有卷积神经网络的基类，相信一定非常困扰大家的学习，特此出一期详细讲解它，如果大家觉得通透了，那笔者的存在就会有了意义

1.__init(self)__:

  def __init__(self):"""Initializes internal Module state, shared by both nn.Module and ScriptModule."""torch._C._log_api_usage_once("python.nn_module")self.training = Trueself._parameters = OrderedDict()self._buffers = OrderedDict()self._non_persistent_buffers_set = set()self._backward_hooks = OrderedDict()self._is_full_backward_hook = Noneself._forward_hooks = OrderedDict()self._forward_pre_hooks = OrderedDict()self._state_dict_hooks = OrderedDict()self._load_state_dict_pre_hooks = OrderedDict()self._modules = OrderedDict()forward: Callable[..., Any] = _forward_unimplemented

from collections import OrderedDict, namedtuple
import itertools
import warnings
import functoolsimport torch
from ..parameter import Parameter
import torch.utils.hooks as hooksfrom torch import Tensor, device, dtype
from typing import Union, Tuple, Any, Callable, Iterator, Set, Optional, overload, TypeVar, Mapping, Dict, List
from ...utils.hooks import RemovableHandle_grad_t = Union[Tuple[Tensor, ...], Tensor]
# See https://mypy.readthedocs.io/en/latest/generics.html#generic-methods-and-generic-self for the use
# of `T` to annotate `self`. Many methods of `Module` return `self` and we want those return values to be
# the type of the subclass, not the looser type of `Module`.
T = TypeVar('T', bound='Module')class _IncompatibleKeys(namedtuple('IncompatibleKeys', ['missing_keys', 'unexpected_keys'])):def __repr__(self):if not self.missing_keys and not self.unexpected_keys:return '<All keys matched successfully>'return super(_IncompatibleKeys, self).__repr__()__str__ = __repr__def _addindent(s_, numSpaces):s = s_.split('\n')# don't do anything for single-line stuffif len(s) == 1:return s_first = s.pop(0)s = [(numSpaces * ' ') + line for line in s]s = '\n'.join(s)s = first + '\n' + sreturn sr"""This tracks hooks common to all modules that are executed before/after
calling forward and backward. This is global state used for debugging/profiling
purposes"""
_global_backward_hooks: Dict[int, Callable] = OrderedDict()
_global_is_full_backward_hook: Optional[bool] = None
_global_forward_pre_hooks: Dict[int, Callable] = OrderedDict()
_global_forward_hooks: Dict[int, Callable] = OrderedDict()def register_module_forward_pre_hook(hook: Callable[..., None]) -> RemovableHandle:r"""Registers a forward pre-hook common to all modules... warning ::This adds global state to the `nn.module` moduleand it is only intended for debugging/profiling purposes.The hook will be called every time before :func:`forward` is invoked.It should have the following signature::hook(module, input) -> None or modified inputThe 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 asingle modified value in the hook. We will wrap the value into a tupleif a single value is returned(unless that value is already a tuple).This hook has precedence over the specific module hooks registered with``register_forward_pre_hook``.Returns::class:`torch.utils.hooks.RemovableHandle`:a handle that can be used to remove the added hook by calling``handle.remove()``"""handle = hooks.RemovableHandle(_global_forward_pre_hooks)_global_forward_pre_hooks[handle.id] = hookreturn handledef register_module_forward_hook(hook: Callable[..., None]) -> RemovableHandle:r"""Registers a global forward hook for all the modules.. warning ::This adds global state to the `nn.module` moduleand it is only intended for debugging/profiling purposes.The hook will be called every time after :func:`forward` has computed an output.It should have the following signature::hook(module, input, output) -> None or modified outputThe 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 butit will not have effect on forward since this is called after:func:`forward` is called.Returns::class:`torch.utils.hooks.RemovableHandle`:a handle that can be used to remove the added hook by calling``handle.remove()``This hook will be executed before specific module hooks registered with``register_forward_hook``."""handle = hooks.RemovableHandle(_global_forward_hooks)_global_forward_hooks[handle.id] = hookreturn handledef register_module_backward_hook(hook: Callable[['Module', _grad_t, _grad_t], Union[None, Tensor]]
) -> RemovableHandle:r"""Registers a backward hook common to all the modules.This function is deprecated in favor of :meth:`nn.module.register_module_full_backward_hook`and the behavior of this function will change in future versions.Returns::class:`torch.utils.hooks.RemovableHandle`:a handle that can be used to remove the added hook by calling``handle.remove()``"""global _global_is_full_backward_hookif _global_is_full_backward_hook is True:raise RuntimeError("Cannot use both regular backward hooks and full backward hooks as a ""global Module hook. Please use only one of them.")_global_is_full_backward_hook = Falsehandle = hooks.RemovableHandle(_global_backward_hooks)_global_backward_hooks[handle.id] = hookreturn handledef register_module_full_backward_hook(hook: Callable[['Module', _grad_t, _grad_t], Union[None, Tensor]]
) -> RemovableHandle:r"""Registers a backward hook common to all the modules... warning ::This adds global state to the `nn.module` moduleand it is only intended for debugging/profiling purposes.The current implementation will not have the presented behaviorfor complex :class:`Module` that perform many operations.In some failure cases, :attr:`grad_input` and :attr:`grad_output` will onlycontain the gradients for a subset of the inputs and outputs.For such :class:`Module`, you should use :func:`torch.Tensor.register_hook`directly on a specific input or output to get the required gradients.The hook will be called every time the gradients with respect to moduleinputs are computed. The hook should have the following signature::hook(module, grad_input, grad_output) -> Tensor or NoneThe :attr:`grad_input` and :attr:`grad_output` are tuples. The hook shouldnot modify its arguments, but it can optionally return a new gradient withrespect to the input that will be used in place of :attr:`grad_input` insubsequent computations. :attr:`grad_input` will only correspond to the inputs givenas positional arguments and all kwarg arguments will not appear in the hook. Entriesin :attr:`grad_input` and :attr:`grad_output` will be ``None`` for all non-Tensorarguments.Global hooks are called before hooks registered with `register_backward_hook`Returns::class:`torch.utils.hooks.RemovableHandle`:a handle that can be used to remove the added hook by calling``handle.remove()``"""global _global_is_full_backward_hookif _global_is_full_backward_hook is False:raise RuntimeError("Cannot use both regular backward hooks and full backward hooks as a ""global Module hook. Please use only one of them.")_global_is_full_backward_hook = Truehandle = hooks.RemovableHandle(_global_backward_hooks)_global_backward_hooks[handle.id] = hookreturn handle# Trick mypy into not applying contravariance rules to inputs by defining
# forward as a value, rather than a function.  See also
# https://github.com/python/mypy/issues/8795
def _forward_unimplemented(self, *input: Any) -> None:r"""Defines the computation performed at every call.Should be overridden by all subclasses... note::Although the recipe for forward pass needs to be defined withinthis function, one should call the :class:`Module` instance afterwardsinstead of this since the former takes care of running theregistered hooks while the latter silently ignores them."""raise NotImplementedErrorclass Module:r"""Base class for all neural network modules.Your models should also subclass this class.Modules can also contain other Modules, allowing to nest them ina tree structure. You can assign the submodules as regular attributes::import torch.nn as nnimport torch.nn.functional as Fclass Model(nn.Module):def __init__(self):super(Model, self).__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 have theirparameters converted too when you call :meth:`to`, etc.:ivar training: Boolean represents whether this module is in training orevaluation mode.:vartype training: bool"""dump_patches: bool = Falser"""This allows better BC support for :meth:`load_state_dict`. In:meth:`state_dict`, the version number will be saved as in the attribute`_metadata` of the returned state dict, and thus pickled. `_metadata` is adictionary with keys that follow the naming convention of state dict. See``_load_from_state_dict`` on how to use this information in loading.If new parameters/buffers are added/removed from a module, this number shallbe bumped, and the module's `_load_from_state_dict` method can compare theversion number and do appropriate changes if the state dict is from beforethe change."""_version: int = 1training: bool_is_full_backward_hook: Optional[bool]def __init__(self):"""Initializes internal Module state, shared by both nn.Module and ScriptModule."""torch._C._log_api_usage_once("python.nn_module")self.training = Trueself._parameters = OrderedDict()self._buffers = OrderedDict()self._non_persistent_buffers_set = set()self._backward_hooks = OrderedDict()self._is_full_backward_hook = Noneself._forward_hooks = OrderedDict()self._forward_pre_hooks = OrderedDict()self._state_dict_hooks = OrderedDict()self._load_state_dict_pre_hooks = OrderedDict()self._modules = OrderedDict()forward: Callable[..., Any] = _forward_unimplementeddef register_buffer(self, name: str, tensor: Optional[Tensor], persistent: bool = True) -> None:r"""Adds a buffer to the module.This is typically used to register a buffer that should not to beconsidered a model parameter. For example, BatchNorm's ``running_mean``is not a parameter, but is part of the module's state. Buffers, bydefault, are persistent and will be saved alongside parameters. Thisbehavior can be changed by setting :attr:`persistent` to ``False``. Theonly difference between a persistent buffer and a non-persistent bufferis that the latter will not be a part of this module's:attr:`state_dict`.Buffers can be accessed as attributes using given names.Args:name (string): name of the buffer. The buffer can be accessedfrom this module using the given nametensor (Tensor): buffer to be registered.persistent (bool): whether the buffer is part of this module's:attr:`state_dict`.Example::>>> self.register_buffer('running_mean', torch.zeros(num_features))"""if persistent is False and isinstance(self, torch.jit.ScriptModule):raise RuntimeError("ScriptModule does not support non-persistent buffers")if '_buffers' not in self.__dict__:raise AttributeError("cannot assign buffer before Module.__init__() call")elif not isinstance(name, torch._six.string_classes):raise TypeError("buffer name should be a string. ""Got {}".format(torch.typename(name)))elif '.' in name:raise KeyError("buffer name can't contain \".\"")elif name == '':raise KeyError("buffer name can't be empty string \"\"")elif hasattr(self, name) and name not in self._buffers:raise KeyError("attribute '{}' already exists".format(name))elif tensor is not None and not isinstance(tensor, torch.Tensor):raise TypeError("cannot assign '{}' object to buffer '{}' ""(torch Tensor or None required)".format(torch.typename(tensor), name))else:self._buffers[name] = tensorif persistent:self._non_persistent_buffers_set.discard(name)else:self._non_persistent_buffers_set.add(name)def register_parameter(self, name: str, param: Optional[Parameter]) -> None:r"""Adds a parameter to the module.The parameter can be accessed as an attribute using given name.Args:name (string): name of the parameter. The parameter can be accessedfrom this module using the given nameparam (Parameter): parameter to be added to the module."""if '_parameters' not in self.__dict__:raise AttributeError("cannot assign parameter before Module.__init__() call")elif not isinstance(name, torch._six.string_classes):raise TypeError("parameter name should be a string. ""Got {}".format(torch.typename(name)))elif '.' in name:raise KeyError("parameter name can't contain \".\"")elif name == '':raise KeyError("parameter name can't be empty string \"\"")elif hasattr(self, name) and name not in self._parameters:raise KeyError("attribute '{}' already exists".format(name))if param is None:self._parameters[name] = Noneelif not isinstance(param, Parameter):raise TypeError("cannot assign '{}' object to parameter '{}' ""(torch.nn.Parameter or None required)".format(torch.typename(param), name))elif param.grad_fn:raise ValueError("Cannot assign non-leaf Tensor to parameter '{0}'. Model ""parameters must be created explicitly. To express '{0}' ""as a function of another Tensor, compute the value in ""the forward() method.".format(name))else:self._parameters[name] = paramdef add_module(self, name: str, module: Optional['Module']) -> None:r"""Adds a child module to the current module.The module can be accessed as an attribute using the given name.Args:name (string): name of the child module. The child module can beaccessed from this module using the given namemodule (Module): child module to be added to the module."""if not isinstance(module, Module) and module is not None:raise TypeError("{} is not a Module subclass".format(torch.typename(module)))elif not isinstance(name, torch._six.string_classes):raise TypeError("module name should be a string. Got {}".format(torch.typename(name)))elif hasattr(self, name) and name not in self._modules:raise KeyError("attribute '{}' already exists".format(name))elif '.' in name:raise KeyError("module name can't contain \".\", got: {}".format(name))elif name == '':raise KeyError("module name can't be empty string \"\"")self._modules[name] = moduledef _apply(self, fn):for module in self.children():module._apply(fn)def compute_should_use_set_data(tensor, tensor_applied):if torch._has_compatible_shallow_copy_type(tensor, tensor_applied):# If the new tensor has compatible tensor type as the existing tensor,# the current behavior is to change the tensor in-place using `.data =`,# and the future behavior is to overwrite the existing tensor. However,# changing the current behavior is a BC-breaking change, and we want it# to happen in future releases. So for now we introduce the# `torch.__future__.get_overwrite_module_params_on_conversion()`# global flag to let the user control whether they want the future# behavior of overwriting the existing tensor or not.return not torch.__future__.get_overwrite_module_params_on_conversion()else:return Falsefor key, param in self._parameters.items():if param is not None:# Tensors stored in modules are graph leaves, and we don't want to# track autograd history of `param_applied`, so we have to use# `with torch.no_grad():`with torch.no_grad():param_applied = fn(param)should_use_set_data = compute_should_use_set_data(param, param_applied)if should_use_set_data:param.data = param_appliedelse:assert isinstance(param, Parameter)assert param.is_leafself._parameters[key] = Parameter(param_applied, param.requires_grad)if param.grad is not None:with torch.no_grad():grad_applied = fn(param.grad)should_use_set_data = compute_should_use_set_data(param.grad, grad_applied)if should_use_set_data:param.grad.data = grad_appliedelse:assert param.grad.is_leafself._parameters[key].grad = grad_applied.requires_grad_(param.grad.requires_grad)for key, buf in self._buffers.items():if buf is not None:self._buffers[key] = fn(buf)return selfdef apply(self: T, fn: Callable[['Module'], None]) -> T:r"""Applies ``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`).Args:fn (:class:`Module` -> None): function to be applied to each submoduleReturns:Module: selfExample::>>> @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.]])Linear(in_features=2, out_features=2, bias=True)Parameter containing:tensor([[ 1.,  1.],[ 1.,  1.]])Sequential((0): Linear(in_features=2, out_features=2, bias=True)(1): Linear(in_features=2, out_features=2, bias=True))Sequential((0): Linear(in_features=2, out_features=2, bias=True)(1): Linear(in_features=2, out_features=2, bias=True))"""for module in self.children():module.apply(fn)fn(self)return selfdef cuda(self: T, device: Optional[Union[int, device]] = None) -> T:r"""Moves all model parameters and buffers to the GPU.This also makes associated parameters and buffers different objects. Soit should be called before constructing optimizer if the module willlive on GPU while being optimized.Args:device (int, optional): if specified, all parameters will becopied to that deviceReturns:Module: self"""return self._apply(lambda t: t.cuda(device))def xpu(self: T, device: Optional[Union[int, device]] = None) -> T:r"""Moves all model parameters and buffers to the XPU.This also makes associated parameters and buffers different objects. Soit should be called before constructing optimizer if the module willlive on XPU while being optimized.Arguments:device (int, optional): if specified, all parameters will becopied to that deviceReturns:Module: self"""return self._apply(lambda t: t.xpu(device))def cpu(self: T) -> T:r"""Moves all model parameters and buffers to the CPU.Returns:Module: self"""return self._apply(lambda t: t.cpu())def type(self: T, dst_type: Union[dtype, str]) -> T:r"""Casts all parameters and buffers to :attr:`dst_type`.Args:dst_type (type or string): the desired typeReturns:Module: self"""return self._apply(lambda t: t.type(dst_type))def float(self: T) -> T:r"""Casts all floating point parameters and buffers to float datatype.Returns:Module: self"""return self._apply(lambda t: t.float() if t.is_floating_point() else t)def double(self: T) -> T:r"""Casts all floating point parameters and buffers to ``double`` datatype.Returns:Module: self"""return self._apply(lambda t: t.double() if t.is_floating_point() else t)def half(self: T) -> T:r"""Casts all floating point parameters and buffers to ``half`` datatype.Returns:Module: self"""return self._apply(lambda t: t.half() if t.is_floating_point() else t)def bfloat16(self: T) -> T:r"""Casts all floating point parameters and buffers to ``bfloat16`` datatype.Returns:Module: self"""return self._apply(lambda t: t.bfloat16() if t.is_floating_point() else t)@overloaddef to(self: T, device: Optional[Union[int, device]] = ..., dtype: Optional[Union[dtype, str]] = ...,non_blocking: bool = ...) -> T:...@overloaddef to(self: T, dtype: Union[dtype, str], non_blocking: bool = ...) -> T:...@overloaddef to(self: T, tensor: Tensor, non_blocking: bool = ...) -> T:...def to(self, *args, **kwargs):r"""Moves and/or casts the parameters and buffers.This can be called as.. function:: to(device=None, dtype=None, non_blocking=False).. function:: to(dtype, non_blocking=False).. function:: to(tensor, non_blocking=False).. function:: to(memory_format=torch.channels_last)Its signature is similar to :meth:`torch.Tensor.to`, but only acceptsfloating point or complex :attr:`dtype`s. In addition, this method willonly 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 asynchronouslywith respect to the host if possible, e.g., moving CPU Tensors withpinned memory to CUDA devices.See below for examples... note::This method modifies the module in-place.Args:device (:class:`torch.device`): the desired device of the parametersand buffers in this moduledtype (:class:`torch.dtype`): the desired floating point or complex dtype ofthe parameters and buffers in this moduletensor (torch.Tensor): Tensor whose dtype and device are the desireddtype and device for all parameters and buffers in this modulememory_format (:class:`torch.memory_format`): the desired memoryformat for 4D parameters and buffers in this module (keywordonly argument)Returns:Module: selfExamples::>>> linear = nn.Linear(2, 2)>>> linear.weightParameter containing:tensor([[ 0.1913, -0.3420],[-0.5113, -0.2325]])>>> linear.to(torch.double)Linear(in_features=2, out_features=2, bias=True)>>> linear.weightParameter containing:tensor([[ 0.1913, -0.3420],[-0.5113, -0.2325]], dtype=torch.float64)>>> gpu1 = torch.device("cuda:1")>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)Linear(in_features=2, out_features=2, bias=True)>>> linear.weightParameter 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.weightParameter containing:tensor([[ 0.1914, -0.3420],[-0.5112, -0.2324]], dtype=torch.float16)>>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble)>>> linear.weightParameter 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)"""device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(*args, **kwargs)if dtype is not None:if not (dtype.is_floating_point or dtype.is_complex):raise TypeError('nn.Module.to only accepts floating point or complex ''dtypes, but got desired dtype={}'.format(dtype))if dtype.is_complex:warnings.warn("Complex modules are a new feature under active development whose design may change, ""and some modules might not work as expected when using complex tensors as parameters or buffers. ""Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.md ""if a complex module does not work as expected.")def convert(t):if convert_to_format is not None and t.dim() == 4:return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None,non_blocking, memory_format=convert_to_format)return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)return self._apply(convert)def register_backward_hook(self, hook: Callable[['Module', _grad_t, _grad_t], Union[None, Tensor]]) -> RemovableHandle:r"""Registers a backward hook on the module.This function is deprecated in favor of :meth:`nn.Module.register_full_backward_hook` andthe behavior of this function will change in future versions.Returns::class:`torch.utils.hooks.RemovableHandle`:a handle that can be used to remove the added hook by calling``handle.remove()``"""if self._is_full_backward_hook is True:raise RuntimeError("Cannot use both regular backward hooks and full backward hooks on a ""single Module. Please use only one of them.")self._is_full_backward_hook = Falsehandle = hooks.RemovableHandle(self._backward_hooks)self._backward_hooks[handle.id] = hookreturn handledef register_full_backward_hook(self, hook: Callable[['Module', _grad_t, _grad_t], Union[None, Tensor]]) -> RemovableHandle:r"""Registers a backward hook on the module.The hook will be called every time the gradients with respect to moduleinputs are computed. The hook should have the following signature::hook(module, grad_input, grad_output) -> tuple(Tensor) or NoneThe :attr:`grad_input` and :attr:`grad_output` are tuples that contain the gradientswith respect to the inputs and outputs respectively. The hook shouldnot modify its arguments, but it can optionally return a new gradient withrespect to the input that will be used in place of :attr:`grad_input` insubsequent computations. :attr:`grad_input` will only correspond to the inputs givenas positional arguments and all kwarg arguments are ignored. Entriesin :attr:`grad_input` and :attr:`grad_output` will be ``None`` for all non-Tensorarguments... warning ::Modifying inputs or outputs inplace is not allowed when using backward hooks andwill raise an error.Returns::class:`torch.utils.hooks.RemovableHandle`:a handle that can be used to remove the added hook by calling``handle.remove()``"""if self._is_full_backward_hook is False:raise RuntimeError("Cannot use both regular backward hooks and full backward hooks on a ""single Module. Please use only one of them.")self._is_full_backward_hook = Truehandle = hooks.RemovableHandle(self._backward_hooks)self._backward_hooks[handle.id] = hookreturn handledef _get_backward_hooks(self):r"""Returns the backward hooks for use in the call function.It returns two lists, one with the full backward hooks and one with the non-fullbackward hooks."""full_backward_hooks: List[Callable] = []if (_global_is_full_backward_hook is True):full_backward_hooks += _global_backward_hooks.values()if (self._is_full_backward_hook is True):full_backward_hooks += self._backward_hooks.values()non_full_backward_hooks: List[Callable] = []if (_global_is_full_backward_hook is False):non_full_backward_hooks += _global_backward_hooks.values()if (self._is_full_backward_hook is False):non_full_backward_hooks += self._backward_hooks.values()return full_backward_hooks, non_full_backward_hooksdef _maybe_warn_non_full_backward_hook(self, inputs, result, grad_fn):if not isinstance(result, torch.Tensor):if not (isinstance(result, tuple) and all([isinstance(r, torch.Tensor) for r in result])):warnings.warn("Using non-full backward hooks on a Module that does not return a ""single Tensor or a tuple of Tensors is deprecated and will be removed ""in future versions. This hook will be missing some of the grad_output. ""Please use register_full_backward_hook to get the documented behavior.")returnelse:result = (result,)if not isinstance(inputs, torch.Tensor):if not (isinstance(inputs, tuple) and all([isinstance(i, torch.Tensor) for i in inputs])):warnings.warn("Using non-full backward hooks on a Module that does not take as input a ""single Tensor or a tuple of Tensors is deprecated and will be removed ""in future versions. This hook will be missing some of the grad_input. ""Please use register_full_backward_hook to get the documented behavior.")returnelse:inputs = (inputs,)# At this point we are sure that inputs and result are tuple of Tensorsout_grad_fn = set([r.grad_fn for r in result if r.grad_fn is not None])if len(out_grad_fn) == 0 or (len(out_grad_fn) == 1 and grad_fn not in out_grad_fn):warnings.warn("Using a non-full backward hook when outputs are nested in python data structure ""is deprecated and will be removed in future versions. This hook will be missing ""some grad_output.")elif len(out_grad_fn) > 1:warnings.warn("Using a non-full backward hook when outputs are generated by different autograd Nodes ""is deprecated and will be removed in future versions. This hook will be missing ""some grad_output. Please use register_full_backward_hook to get the documented behavior.")else:# At this point the grad_ouput part of the hook will most likely be correctinputs_grad_fn = set([i.grad_fn for i in inputs if i.grad_fn is not None])next_functions = set([n[0] for n in grad_fn.next_functions])if inputs_grad_fn != next_functions:warnings.warn("Using a non-full backward hook when the forward contains multiple autograd Nodes ""is deprecated and will be removed in future versions. This hook will be missing ""some grad_input. Please use register_full_backward_hook to get the documented ""behavior.")def register_forward_pre_hook(self, hook: Callable[..., None]) -> RemovableHandle:r"""Registers a forward pre-hook on the module.The hook will be called every time before :func:`forward` is invoked.It should have the following signature::hook(module, input) -> None or modified inputThe 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 asingle modified value in the hook. We will wrap the value into a tupleif a single value is returned(unless that value is already a tuple).Returns::class:`torch.utils.hooks.RemovableHandle`:a handle that can be used to remove the added hook by calling``handle.remove()``"""handle = hooks.RemovableHandle(self._forward_pre_hooks)self._forward_pre_hooks[handle.id] = hookreturn handledef register_forward_hook(self, hook: Callable[..., None]) -> RemovableHandle:r"""Registers a forward hook on the module.The hook will be called every time after :func:`forward` has computed an output.It should have the following signature::hook(module, input, output) -> None or modified outputThe 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 butit will not have effect on forward since this is called after:func:`forward` is called.Returns::class:`torch.utils.hooks.RemovableHandle`:a handle that can be used to remove the added hook by calling``handle.remove()``"""handle = hooks.RemovableHandle(self._forward_hooks)self._forward_hooks[handle.id] = hookreturn handledef _slow_forward(self, *input, **kwargs):tracing_state = torch._C._get_tracing_state()if not tracing_state or isinstance(self.forward, torch._C.ScriptMethod):return self.forward(*input, **kwargs)recording_scopes = torch.jit._trace._trace_module_map is not Noneif recording_scopes:# type ignore was added because at this point one knows that# torch.jit._trace._trace_module_map is not Optional and has type Dict[Any, Any] name = torch.jit._trace._trace_module_map[self] if self in torch.jit._trace._trace_module_map else None  # type: ignoreif name:tracing_state.push_scope(name)else:recording_scopes = Falsetry:result = self.forward(*input, **kwargs)finally:if recording_scopes:tracing_state.pop_scope()return resultdef _call_impl(self, *input, **kwargs):# Do not call functions when jit is usedfull_backward_hooks, non_full_backward_hooks = [], []if len(self._backward_hooks) > 0 or len(_global_backward_hooks) > 0:full_backward_hooks, non_full_backward_hooks = self._get_backward_hooks()for hook in itertools.chain(_global_forward_pre_hooks.values(),self._forward_pre_hooks.values()):result = hook(self, input)if result is not None:if not isinstance(result, tuple):result = (result,)input = resultbw_hook = Noneif len(full_backward_hooks) > 0:bw_hook = hooks.BackwardHook(self, full_backward_hooks)input = bw_hook.setup_input_hook(input)if torch._C._get_tracing_state():result = self._slow_forward(*input, **kwargs)else:result = self.forward(*input, **kwargs)for hook in itertools.chain(_global_forward_hooks.values(),self._forward_hooks.values()):hook_result = hook(self, input, result)if hook_result is not None:result = hook_resultif bw_hook:result = bw_hook.setup_output_hook(result)# Handle the non-full backward hooksif len(non_full_backward_hooks) > 0:var = resultwhile not isinstance(var, torch.Tensor):if isinstance(var, dict):var = next((v for v in var.values() if isinstance(v, torch.Tensor)))else:var = var[0]grad_fn = var.grad_fnif grad_fn is not None:for hook in non_full_backward_hooks:wrapper = functools.partial(hook, self)functools.update_wrapper(wrapper, hook)grad_fn.register_hook(wrapper)self._maybe_warn_non_full_backward_hook(input, result, grad_fn)return result__call__ : Callable[..., Any] = _call_impldef __setstate__(self, state):self.__dict__.update(state)# Support loading old checkpoints that don't have the following attrs:if '_forward_pre_hooks' not in self.__dict__:self._forward_pre_hooks = OrderedDict()if '_state_dict_hooks' not in self.__dict__:self._state_dict_hooks = OrderedDict()if '_load_state_dict_pre_hooks' not in self.__dict__:self._load_state_dict_pre_hooks = OrderedDict()if '_non_persistent_buffers_set' not in self.__dict__:self._non_persistent_buffers_set = set()if '_is_full_backward_hook' not in self.__dict__:self._is_full_backward_hook = Nonedef __getattr__(self, name: str) -> Union[Tensor, 'Module']:if '_parameters' in self.__dict__:_parameters = self.__dict__['_parameters']if name in _parameters:return _parameters[name]if '_buffers' in self.__dict__:_buffers = self.__dict__['_buffers']if name in _buffers:return _buffers[name]if '_modules' in self.__dict__:modules = self.__dict__['_modules']if name in modules:return modules[name]raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, name))def __setattr__(self, name: str, value: Union[Tensor, 'Module']) -> None:def remove_from(*dicts_or_sets):for d in dicts_or_sets:if name in d:if isinstance(d, dict):del d[name]else:d.discard(name)params = self.__dict__.get('_parameters')if isinstance(value, Parameter):if params is None:raise AttributeError("cannot assign parameters before Module.__init__() call")remove_from(self.__dict__, self._buffers, self._modules, self._non_persistent_buffers_set)self.register_parameter(name, value)elif params is not None and name in params:if value is not None:raise TypeError("cannot assign '{}' as parameter '{}' ""(torch.nn.Parameter or None expected)".format(torch.typename(value), name))self.register_parameter(name, value)else:modules = self.__dict__.get('_modules')if isinstance(value, Module):if modules is None:raise AttributeError("cannot assign module before Module.__init__() call")remove_from(self.__dict__, self._parameters, self._buffers, self._non_persistent_buffers_set)modules[name] = valueelif modules is not None and name in modules:if value is not None:raise TypeError("cannot assign '{}' as child module '{}' ""(torch.nn.Module or None expected)".format(torch.typename(value), name))modules[name] = valueelse:buffers = self.__dict__.get('_buffers')if buffers is not None and name in buffers:if value is not None and not isinstance(value, torch.Tensor):raise TypeError("cannot assign '{}' as buffer '{}' ""(torch.Tensor or None expected)".format(torch.typename(value), name))buffers[name] = valueelse:object.__setattr__(self, name, value)def __delattr__(self, name):if name in self._parameters:del self._parameters[name]elif name in self._buffers:del self._buffers[name]self._non_persistent_buffers_set.discard(name)elif name in self._modules:del self._modules[name]else:object.__delattr__(self, name)def _register_state_dict_hook(self, hook):r"""These hooks will be called with arguments: `self`, `state_dict`,`prefix`, `local_metadata`, after the `state_dict` of `self` is set.Note that only parameters and buffers of `self` or its children areguaranteed to exist in `state_dict`. The hooks may modify `state_dict`inplace or return a new one."""handle = hooks.RemovableHandle(self._state_dict_hooks)self._state_dict_hooks[handle.id] = hookreturn handledef _save_to_state_dict(self, destination, prefix, keep_vars):r"""Saves module state to `destination` dictionary, containing a stateof the module, but not its descendants. This is called on everysubmodule in :meth:`~torch.nn.Module.state_dict`.In rare cases, subclasses can achieve class-specific behavior byoverriding this method with custom logic.Args:destination (dict): a dict where state will be storedprefix (str): the prefix for parameters and buffers used in thismodule"""for name, param in self._parameters.items():if param is not None:destination[prefix + name] = param if keep_vars else param.detach()for name, buf in self._buffers.items():if buf is not None and name not in self._non_persistent_buffers_set:destination[prefix + name] = buf if keep_vars else buf.detach()# The user can pass an optional arbitrary mappable object to `state_dict`, in which case `state_dict` returns# back that same object. But if they pass nothing, an `OrederedDict` is created and returned.T_destination = TypeVar('T_destination', bound=Mapping[str, Tensor])@overloaddef state_dict(self, destination: T_destination, prefix: str = ..., keep_vars: bool = ...) -> T_destination:...# TODO: annotate with OrderedDict not Dict, but there is a problem:# https://docs.python.org/3/library/typing.html#typing.OrderedDict@overloaddef state_dict(self, prefix: str = ..., keep_vars: bool = ...) -> Dict[str, Tensor]:...def state_dict(self, destination=None, prefix='', keep_vars=False):r"""Returns a dictionary containing a whole state of the module.Both parameters and persistent buffers (e.g. running averages) areincluded. Keys are corresponding parameter and buffer names.Returns:dict:a dictionary containing a whole state of the moduleExample::>>> module.state_dict().keys()['bias', 'weight']"""if destination is None:destination = OrderedDict()destination._metadata = OrderedDict()destination._metadata[prefix[:-1]] = local_metadata = dict(version=self._version)self._save_to_state_dict(destination, prefix, keep_vars)for name, module in self._modules.items():if module is not None:module.state_dict(destination, prefix + name + '.', keep_vars=keep_vars)for hook in self._state_dict_hooks.values():hook_result = hook(self, destination, prefix, local_metadata)if hook_result is not None:destination = hook_resultreturn destinationdef _register_load_state_dict_pre_hook(self, hook):r"""These hooks will be called with arguments: `state_dict`, `prefix`,`local_metadata`, `strict`, `missing_keys`, `unexpected_keys`,`error_msgs`, before loading `state_dict` into `self`. These argumentsare exactly the same as those of `_load_from_state_dict`."""handle = hooks.RemovableHandle(self._load_state_dict_pre_hooks)self._load_state_dict_pre_hooks[handle.id] = hookreturn handledef _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,missing_keys, unexpected_keys, error_msgs):r"""Copies parameters and buffers from :attr:`state_dict` into onlythis module, but not its descendants. This is called on every submodulein :meth:`~torch.nn.Module.load_state_dict`. Metadata saved for thismodule in input :attr:`state_dict` is provided as :attr:`local_metadata`.For state dicts without metadata, :attr:`local_metadata` is empty.Subclasses can achieve class-specific backward compatible loading usingthe version number at `local_metadata.get("version", None)`... note:::attr:`state_dict` is not the same object as the input:attr:`state_dict` to :meth:`~torch.nn.Module.load_state_dict`. Soit can be modified.Args:state_dict (dict): a dict containing parameters andpersistent buffers.prefix (str): the prefix for parameters and buffers used in thismodulelocal_metadata (dict): a dict containing the metadata for this module.Seestrict (bool): whether to strictly enforce that the keys in:attr:`state_dict` with :attr:`prefix` match the names ofparameters and buffers in this modulemissing_keys (list of str): if ``strict=True``, add missing keys tothis listunexpected_keys (list of str): if ``strict=True``, add unexpectedkeys to this listerror_msgs (list of str): error messages should be added to thislist, and will be reported together in:meth:`~torch.nn.Module.load_state_dict`"""for hook in self._load_state_dict_pre_hooks.values():hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)persistent_buffers = {k: v for k, v in self._buffers.items() if k not in self._non_persistent_buffers_set}local_name_params = itertools.chain(self._parameters.items(), persistent_buffers.items())local_state = {k: v for k, v in local_name_params if v is not None}for name, param in local_state.items():key = prefix + nameif key in state_dict:input_param = state_dict[key]# This is used to avoid copying uninitialized parameters into# non-lazy modules, since they dont have the hook to do the checks# in such case, it will error when accessing the .shape attribute.is_param_lazy = isinstance(param, torch.nn.parameter.UninitializedParameter)# Backward compatibility: loading 1-dim tensor from 0.3.* to version 0.4+if not is_param_lazy and len(param.shape) == 0 and len(input_param.shape) == 1:input_param = input_param[0]if not is_param_lazy and input_param.shape != param.shape:# local shape should match the one in checkpointerror_msgs.append('size mismatch for {}: copying a param with shape {} from checkpoint, ''the shape in current model is {}.'.format(key, input_param.shape, param.shape))continuetry:with torch.no_grad():param.copy_(input_param)except Exception as ex:error_msgs.append('While copying the parameter named "{}", ''whose dimensions in the model are {} and ''whose dimensions in the checkpoint are {}, ''an exception occurred : {}.'.format(key, param.size(), input_param.size(), ex.args))elif strict:missing_keys.append(key)if strict:for key in state_dict.keys():if key.startswith(prefix):input_name = key[len(prefix):]input_name = input_name.split('.', 1)[0]  # get the name of param/buffer/childif input_name not in self._modules and input_name not in local_state:unexpected_keys.append(key)def load_state_dict(self, state_dict: 'OrderedDict[str, Tensor]',strict: bool = True):r"""Copies parameters and buffers from :attr:`state_dict` intothis module and its descendants. If :attr:`strict` is ``True``, thenthe keys of :attr:`state_dict` must exactly match the keys returnedby this module's :meth:`~torch.nn.Module.state_dict` function.Args:state_dict (dict): a dict containing parameters andpersistent buffers.strict (bool, optional): whether to strictly enforce that the keysin :attr:`state_dict` match the keys returned by this module's:meth:`~torch.nn.Module.state_dict` function. Default: ``True``Returns:``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:* **missing_keys** is a list of str containing the missing keys* **unexpected_keys** is a list of str containing the unexpected keys"""missing_keys: List[str] = []unexpected_keys: List[str] = []error_msgs: List[str] = []# copy state_dict so _load_from_state_dict can modify itmetadata = getattr(state_dict, '_metadata', None)state_dict = state_dict.copy()if metadata is not None:# mypy isn't aware that "_metadata" exists in state_dictstate_dict._metadata = metadata  # type: ignore[attr-defined]def load(module, prefix=''):local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})module._load_from_state_dict(state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)for name, child in module._modules.items():if child is not None:load(child, prefix + name + '.')load(self)del loadif strict:if len(unexpected_keys) > 0:error_msgs.insert(0, 'Unexpected key(s) in state_dict: {}. '.format(', '.join('"{}"'.format(k) for k in unexpected_keys)))if len(missing_keys) > 0:error_msgs.insert(0, 'Missing key(s) in state_dict: {}. '.format(', '.join('"{}"'.format(k) for k in missing_keys)))if len(error_msgs) > 0:raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(self.__class__.__name__, "\n\t".join(error_msgs)))return _IncompatibleKeys(missing_keys, unexpected_keys)def _named_members(self, get_members_fn, prefix='', recurse=True):r"""Helper method for yielding various names + members of modules."""memo = set()modules = self.named_modules(prefix=prefix) if recurse else [(prefix, self)]for module_prefix, module in modules:members = get_members_fn(module)for k, v in members:if v is None or v in memo:continuememo.add(v)name = module_prefix + ('.' if module_prefix else '') + kyield name, vdef parameters(self, recurse: bool = True) -> Iterator[Parameter]:r"""Returns an iterator over module parameters.This is typically passed to an optimizer.Args:recurse (bool): if True, then yields parameters of this moduleand all submodules. Otherwise, yields only parameters thatare direct members of this module.Yields:Parameter: module parameterExample::>>> for param in model.parameters():>>>     print(type(param), param.size())<class 'torch.Tensor'> (20L,)<class 'torch.Tensor'> (20L, 1L, 5L, 5L)"""for name, param in self.named_parameters(recurse=recurse):yield paramdef named_parameters(self, prefix: str = '', recurse: bool = True) -> Iterator[Tuple[str, Parameter]]:r"""Returns an iterator over module parameters, yielding both thename of the parameter as well as the parameter itself.Args:prefix (str): prefix to prepend to all parameter names.recurse (bool): if True, then yields parameters of this moduleand all submodules. Otherwise, yields only parameters thatare direct members of this module.Yields:(string, Parameter): Tuple containing the name and parameterExample::>>> for name, param in self.named_parameters():>>>    if name in ['bias']:>>>        print(param.size())"""gen = self._named_members(lambda module: module._parameters.items(),prefix=prefix, recurse=recurse)for elem in gen:yield elemdef buffers(self, recurse: bool = True) -> Iterator[Tensor]:r"""Returns an iterator over module buffers.Args:recurse (bool): if True, then yields buffers of this moduleand all submodules. Otherwise, yields only buffers thatare direct members of this module.Yields:torch.Tensor: module bufferExample::>>> for buf in model.buffers():>>>     print(type(buf), buf.size())<class 'torch.Tensor'> (20L,)<class 'torch.Tensor'> (20L, 1L, 5L, 5L)"""for name, buf in self.named_buffers(recurse=recurse):yield bufdef named_buffers(self, prefix: str = '', recurse: bool = True) -> Iterator[Tuple[str, Tensor]]:r"""Returns an iterator over module buffers, yielding both thename of the buffer as well as the buffer itself.Args:prefix (str): prefix to prepend to all buffer names.recurse (bool): if True, then yields buffers of this moduleand all submodules. Otherwise, yields only buffers thatare direct members of this module.Yields:(string, torch.Tensor): Tuple containing the name and bufferExample::>>> for name, buf in self.named_buffers():>>>    if name in ['running_var']:>>>        print(buf.size())"""gen = self._named_members(lambda module: module._buffers.items(),prefix=prefix, recurse=recurse)for elem in gen:yield elemdef children(self) -> Iterator['Module']:r"""Returns an iterator over immediate children modules.Yields:Module: a child module"""for name, module in self.named_children():yield moduledef named_children(self) -> Iterator[Tuple[str, 'Module']]:r"""Returns an iterator over immediate children modules, yielding boththe name of the module as well as the module itself.Yields:(string, Module): Tuple containing a name and child moduleExample::>>> for name, module in model.named_children():>>>     if name in ['conv4', 'conv5']:>>>         print(module)"""memo = set()for name, module in self._modules.items():if module is not None and module not in memo:memo.add(module)yield name, moduledef modules(self) -> Iterator['Module']:r"""Returns an iterator over all modules in the network.Yields:Module: a module in the networkNote:Duplicate modules are returned only once. In the followingexample, ``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)"""for name, module in self.named_modules():yield moduledef named_modules(self, memo: Optional[Set['Module']] = None, prefix: str = ''):r"""Returns an iterator over all modules in the network, yieldingboth the name of the module as well as the module itself.Yields:(string, Module): Tuple of name and moduleNote:Duplicate modules are returned only once. In the followingexample, ``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))"""if memo is None:memo = set()if self not in memo:memo.add(self)yield prefix, selffor name, module in self._modules.items():if module is None:continuesubmodule_prefix = prefix + ('.' if prefix else '') + namefor m in module.named_modules(memo, submodule_prefix):yield mdef train(self: T, mode: bool = True) -> T:r"""Sets the module in training mode.This has any effect only on certain modules. See documentations ofparticular modules for details of their behaviors in training/evaluationmode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,etc.Args:mode (bool): whether to set training mode (``True``) or evaluationmode (``False``). Default: ``True``.Returns:Module: self"""self.training = modefor module in self.children():module.train(mode)return selfdef eval(self: T) -> T:r"""Sets the module in evaluation mode.This has any effect only on certain modules. See documentations ofparticular modules for details of their behaviors in training/evaluationmode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,etc.This is equivalent with :meth:`self.train(False) <torch.nn.Module.train>`.Returns:Module: self"""return self.train(False)def requires_grad_(self: T, requires_grad: bool = True) -> T:r"""Change if autograd should record operations on parameters in thismodule.This method sets the parameters' :attr:`requires_grad` attributesin-place.This method is helpful for freezing part of the module for finetuningor training parts of a model individually (e.g., GAN training).Args:requires_grad (bool): whether autograd should record operations onparameters in this module. Default: ``True``.Returns:Module: self"""for p in self.parameters():p.requires_grad_(requires_grad)return selfdef zero_grad(self, set_to_none: bool = False) -> None:r"""Sets gradients of all model parameters to zero. See similar functionunder :class:`torch.optim.Optimizer` for more context.Args:set_to_none (bool): instead of setting to zero, set the grads to None.See :meth:`torch.optim.Optimizer.zero_grad` for details."""if getattr(self, '_is_replica', False):warnings.warn("Calling .zero_grad() from a module created with nn.DataParallel() has no effect. ""The parameters are copied (in a differentiable manner) from the original module. ""This means they are not leaf nodes in autograd and so don't accumulate gradients. ""If you need gradients in your forward method, consider using autograd.grad instead.")for p in self.parameters():if p.grad is not None:if set_to_none:p.grad = Noneelse:if p.grad.grad_fn is not None:p.grad.detach_()else:p.grad.requires_grad_(False)p.grad.zero_()def share_memory(self: T) -> T:return self._apply(lambda t: t.share_memory_())def _get_name(self):return self.__class__.__name__def extra_repr(self) -> str:r"""Set the extra representation of the moduleTo print customized extra information, you should re-implementthis method in your own modules. Both single-line and multi-linestrings are acceptable."""return ''def __repr__(self):# We treat the extra repr like the sub-module, one item per lineextra_lines = []extra_repr = self.extra_repr()# empty string will be split into list ['']if extra_repr:extra_lines = extra_repr.split('\n')child_lines = []for key, module in self._modules.items():mod_str = repr(module)mod_str = _addindent(mod_str, 2)child_lines.append('(' + key + '): ' + mod_str)lines = extra_lines + child_linesmain_str = self._get_name() + '('if lines:# simple one-liner info, which most builtin Modules will useif len(extra_lines) == 1 and not child_lines:main_str += extra_lines[0]else:main_str += '\n  ' + '\n  '.join(lines) + '\n'main_str += ')'return main_strdef __dir__(self):module_attrs = dir(self.__class__)attrs = list(self.__dict__.keys())parameters = list(self._parameters.keys())modules = list(self._modules.keys())buffers = list(self._buffers.keys())keys = module_attrs + attrs + parameters + modules + buffers# Eliminate attrs that are not legal Python variable nameskeys = [key for key in keys if not key[0].isdigit()]return sorted(keys)def _replicate_for_data_parallel(self):replica = self.__new__(type(self))replica.__dict__ = self.__dict__.copy()# replicas do not have parameters themselves, the replicas reference the original# module.replica._parameters = OrderedDict()replica._buffers = replica._buffers.copy()replica._modules = replica._modules.copy()replica._is_replica = Truereturn replica

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