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Source code for torch.nn.modules.module

from collections import OrderedDict
import functools
import itertools

import torch
from ..backends.thnn import backend as thnn_backend
from ..parameter import Parameter
import torch.utils.hooks as hooks


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[docs]class Module(object): 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 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): 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 their parameters converted too when you call :meth:`to`, etc. """ dump_patches = False r"""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 a dictionary with keys 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 shall be bumped, and the module's `_load_from_state_dict` method can compare the version number and do appropriate changes if the state dict is from before the change.""" _version = 1 def __init__(self): self._backend = thnn_backend self._parameters = OrderedDict() self._buffers = OrderedDict() self._backward_hooks = OrderedDict() self._forward_hooks = OrderedDict() self._forward_pre_hooks = OrderedDict() self._modules = OrderedDict() self.training = True
[docs] def forward(self, *input): 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 within this function, one should call the :class:`Module` instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them. """ raise NotImplementedError
[docs] def register_buffer(self, name, tensor): r"""Adds a persistent 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 persistent state. Buffers can be accessed as attributes using given names. Args: name (string): name of the buffer. The buffer can be accessed from this module using the given name tensor (Tensor): buffer to be registered. Example:: >>> self.register_buffer('running_mean', torch.zeros(num_features)) """ if 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] = tensor
[docs] def register_parameter(self, name, param): 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 accessed from this module using the given name parameter (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] = None elif 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] = param
[docs] def add_module(self, name, module): 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 be accessed from this module using the given name parameter (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 \".\"") elif name == '': raise KeyError("module name can't be empty string \"\"") self._modules[name] = module
def _apply(self, fn): for module in self.children(): module._apply(fn) for param in self._parameters.values(): if param is not None: # Tensors stored in modules are graph leaves, and we don't # want to create copy nodes, so we have to unpack the data. param.data = fn(param.data) if param._grad is not None: param._grad.data = fn(param._grad.data) for key, buf in self._buffers.items(): if buf is not None: self._buffers[key] = fn(buf) return self
[docs] def apply(self, fn): 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:`torch-nn-init`). Args: fn (:class:`Module` -> None): function to be applied to each submodule Returns: Module: self Example:: >>> def init_weights(m): print(m) if type(m) == nn.Linear: m.weight.data.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 self
[docs] def cuda(self, device=None): r"""Moves all model parameters and buffers to the GPU. This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized. Arguments: device (int, optional): if specified, all parameters will be copied to that device Returns: Module: self """ return self._apply(lambda t: t.cuda(device))
[docs] def cpu(self): r"""Moves all model parameters and buffers to the CPU. Returns: Module: self """ return self._apply(lambda t: t.cpu())
[docs] def type(self, dst_type): r"""Casts all parameters and buffers to :attr:`dst_type`. Arguments: dst_type (type or string): the desired type Returns: Module: self """ return self._apply(lambda t: t.type(dst_type))
[docs] def float(self): 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)
[docs] def double(self): 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)
[docs] def half(self): 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)
[docs] 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) Its signature is similar to :meth:`torch.Tensor.to`, but only accepts floating point desired :attr:`dtype` s. In addition, this method will only cast the floating point 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. Args: device (:class:`torch.device`): the desired device of the parameters and buffers in this module dtype (:class:`torch.dtype`): the desired floating point type of the floating point parameters and buffers in this module tensor (torch.Tensor): Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module Returns: Module: self Example:: >>> 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) >>> 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) """ device, dtype, non_blocking = torch._C._nn._parse_to(*args, **kwargs) if dtype is not None: if not dtype.is_floating_point: raise TypeError('nn.Module.to only accepts floating point ' 'dtypes, but got desired dtype={}'.format(dtype)) def convert(t): return t.to(device, dtype if t.is_floating_point() else None, non_blocking) return self._apply(convert)
[docs] def register_backward_hook(self, hook): r"""Registers a backward hook on the module. The hook will be called every time the gradients with respect to module inputs are computed. The hook should have the following signature:: hook(module, grad_input, grad_output) -> Tensor or None The :attr:`grad_input` and :attr:`grad_output` may be tuples if the module has multiple inputs or outputs. The hook should not modify its arguments, but it can optionally return a new gradient with respect to input that will be used in place of :attr:`grad_input` in subsequent computations. 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._backward_hooks) self._backward_hooks[handle.id] = hook return handle
[docs] def register_forward_pre_hook(self, hook): 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 The hook should not modify the input. 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] = hook return handle
[docs] def register_forward_hook(self, hook): 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 The hook should not modify the input or output. 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] = hook return handle
def _tracing_name(self, tracing_state): if not tracing_state._traced_module_stack: return None module = tracing_state._traced_module_stack[-1] for name, child in module.named_children(): if child is self: return name return None def _slow_forward(self, *input, **kwargs): input_vars = tuple(torch.autograd.function._iter_tensors(input)) tracing_state = torch._C._get_tracing_state() if not tracing_state: return self.forward(*input, **kwargs) if not hasattr(tracing_state, '_traced_module_stack'): tracing_state._traced_module_stack = [] name = self._tracing_name(tracing_state) if name: tracing_state.push_scope('%s[%s]' % (self._get_name(), name)) else: tracing_state.push_scope(self._get_name()) tracing_state._traced_module_stack.append(self) try: result = self.forward(*input, **kwargs) finally: tracing_state.pop_scope() tracing_state._traced_module_stack.pop() return result def __call__(self, *input, **kwargs): for hook in self._forward_pre_hooks.values(): hook(self, input) if torch._C._get_tracing_state(): result = self._slow_forward(*input, **kwargs) else: result = self.forward(*input, **kwargs) for hook in self._forward_hooks.values(): hook_result = hook(self, input, result) if hook_result is not None: raise RuntimeError( "forward hooks should never return any values, but '{}'" "didn't return None".format(hook)) if len(self._backward_hooks) > 0: var = result while 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_fn if grad_fn is not None: for hook in self._backward_hooks.values(): wrapper = functools.partial(hook, self) functools.update_wrapper(wrapper, hook) grad_fn.register_hook(wrapper) return result def __setstate__(self, state): self.__dict__.update(state) if '_forward_pre_hooks' not in self.__dict__: self._forward_pre_hooks = OrderedDict() def __getattr__(self, name): 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, value): def remove_from(*dicts): for d in dicts: if name in d: del d[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.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) modules[name] = value elif 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] = value else: 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] = value else: 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] elif name in self._modules: del self._modules[name] else: object.__delattr__(self, name)
[docs] 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) are included. Keys are corresponding parameter and buffer names. Returns: dict: a dictionary containing a whole state of the module Example:: >>> module.state_dict().keys() ['bias', 'weight'] """ if destination is None: destination = OrderedDict() destination._metadata = OrderedDict() destination._metadata[prefix[:-1]] = dict(version=self._version) for name, param in self._parameters.items(): if param is not None: destination[prefix + name] = param if keep_vars else param.data for name, buf in self._buffers.items(): if buf is not None: destination[prefix + name] = buf if keep_vars else buf.data for name, module in self._modules.items(): if module is not None: module.state_dict(destination, prefix + name + '.', keep_vars=keep_vars) return destination
def _load_from_state_dict(self, state_dict, prefix, metadata, strict, missing_keys, unexpected_keys, error_msgs): r"""Copies parameters and buffers from :attr:`state_dict` into only this module, but not its descendants. This is called on every submodule in :meth:`~torch.nn.Module.load_state_dict`. Metadata saved for this module in input :attr:`state_dict` is provided as :attr`metadata`. For state dicts without meta data, :attr`metadata` is empty. Subclasses can achieve class-specific backward compatible loading using the version number at `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`. So it can be modified. Arguments: state_dict (dict): a dict containing parameters and persistent buffers. prefix (str): the prefix for parameters and buffers used in this module metadata (dict): a dict containing the metadata for this moodule. See strict (bool): whether to strictly enforce that the keys in :attr:`state_dict` with :attr:`prefix` match the names of parameters and buffers in this module missing_keys (list of str): if ``strict=False``, add missing keys to this list unexpected_keys (list of str): if ``strict=False``, add unexpected keys to this list error_msgs (list of str): error messages should be added to this list, and will be reported together in :meth:`~torch.nn.Module.load_state_dict` """ local_name_params = itertools.chain(self._parameters.items(), self._buffers.items()) local_state = {k: v.data for k, v in local_name_params if v is not None} for name, param in local_state.items(): key = prefix + name if key in state_dict: input_param = state_dict[key] # Backward compatibility: loading 1-dim tensor from 0.3.* to version 0.4+ if len(param.shape) == 0 and len(input_param.shape) == 1: input_param = input_param[0] if input_param.shape != param.shape: # local shape should match the one in checkpoint error_msgs.append('size mismatch for {}: copying a param of {} from checkpoint, ' 'where the shape is {} in current model.' .format(key, input_param.shape, param.shape)) continue if isinstance(input_param, Parameter): # backwards compatibility for serialized parameters input_param = input_param.data try: param.copy_(input_param) except Exception: error_msgs.append('While copying the parameter named "{}", ' 'whose dimensions in the model are {} and ' 'whose dimensions in the checkpoint are {}.' .format(key, param.size(), input_param.size())) elif strict: missing_keys.append(key) if strict: for key, input_param in state_dict.items(): if key.startswith(prefix): input_name = key[len(prefix):] input_name = input_name.split('.', 1)[0] # get the name of param/buffer/child if input_name not in self._modules and input_name not in local_state: unexpected_keys.append(key)
[docs] def load_state_dict(self, state_dict, strict=True): r"""Copies 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. Arguments: state_dict (dict): a dict containing parameters and persistent buffers. strict (bool, optional): 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`` """ missing_keys = [] unexpected_keys = [] error_msgs = [] # copy state_dict so _load_from_state_dict can modify it metadata = getattr(state_dict, '_metadata', None) state_dict = state_dict.copy() if metadata is not None: state_dict._metadata = metadata 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, strict, missing_keys, unexpected_keys, error_msgs) for name, child in module._modules.items(): if child is not None: load(child, prefix + name + '.') load(self) if strict: error_msg = '' 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)))
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: continue memo.add(v) name = module_prefix + ('.' if module_prefix else '') + k yield name, v
[docs] def parameters(self, recurse=True): r"""Returns an iterator over module parameters. This is typically passed to an optimizer. Args: recurse (bool): if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. Yields: Parameter: module parameter Example:: >>> for param in model.parameters(): >>> print(type(param.data), param.size()) <class 'torch.FloatTensor'> (20L,) <class 'torch.FloatTensor'> (20L, 1L, 5L, 5L) """ for name, param in self.named_parameters(recurse=recurse): yield param
[docs] def named_parameters(self, prefix='', recurse=True): r"""Returns an iterator over module parameters, yielding both the name 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 module and all submodules. Otherwise, yields only parameters that are direct members of this module. Yields: (string, Parameter): Tuple containing the name and parameter Example:: >>> 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 elem
[docs] def buffers(self, recurse=True): r"""Returns an iterator over module buffers. Args: recurse (bool): if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Yields: torch.Tensor: module buffer Example:: >>> for buf in model.buffers(): >>> print(type(buf.data), buf.size()) <class 'torch.FloatTensor'> (20L,) <class 'torch.FloatTensor'> (20L, 1L, 5L, 5L) """ for name, buf in self.named_buffers(recurse=recurse): yield buf
[docs] def named_buffers(self, prefix='', recurse=True): r"""Returns an iterator over module buffers, yielding both the name 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 module and all submodules. Otherwise, yields only buffers that are direct members of this module. Yields: (string, torch.Tensor): Tuple containing the name and buffer Example:: >>> 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 elem
[docs] def children(self): r"""Returns an iterator over immediate children modules. Yields: Module: a child module """ for name, module in self.named_children(): yield module
[docs] def named_children(self): r"""Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself. Yields: (string, Module): Tuple containing a name and child module Example:: >>> 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, module
[docs] def modules(self): r"""Returns 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 (2 -> 2) (1): Linear (2 -> 2) ) 1 -> Linear (2 -> 2) """ for name, module in self.named_modules(): yield module
[docs] def named_modules(self, memo=None, prefix=''): r"""Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself. Yields: (string, 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 (2 -> 2) (1): Linear (2 -> 2) )) 1 -> ('0', Linear (2 -> 2)) """ if memo is None: memo = set() if self not in memo: memo.add(self) yield prefix, self for name, module in self._modules.items(): if module is None: continue submodule_prefix = prefix + ('.' if prefix else '') + name for m in module.named_modules(memo, submodule_prefix): yield m
[docs] def train(self, mode=True): r"""Sets the module in training mode. This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`, etc. Returns: Module: self """ self.training = mode for module in self.children(): module.train(mode) return self
[docs] def eval(self): r"""Sets the module in evaluation mode. This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`, etc. """ return self.train(False)
[docs] def zero_grad(self): r"""Sets gradients of all model parameters to zero.""" for p in self.parameters(): if p.grad is not None: p.grad.detach_() p.grad.zero_()
def share_memory(self): return self._apply(lambda t: t.share_memory_()) def _get_name(self): return self.__class__.__name__
[docs] def extra_repr(self): r"""Set the extra representation of the module To print customized extra information, you should reimplement this method in your own modules. Both single-line and multi-line strings are acceptable. """ return ''
def __repr__(self): # We treat the extra repr like the sub-module, one item per line extra_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_lines main_str = self._get_name() + '(' if lines: # simple one-liner info, which most builtin Modules will use if 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_str def __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 names keys = [key for key in keys if not key[0].isdigit()] return sorted(keys)

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