from collections import OrderedDict, namedtuple
import functools
import itertools
import torch
from ..parameter import Parameter
import torch.utils.hooks as hooks
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 stuff
if len(s) == 1:
return s_
first = s.pop(0)
s = [(numSpaces * ' ') + line for line in s]
s = '\n'.join(s)
s = first + '\n' + s
return s
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 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 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):
"""
Initializes internal Module state, shared by both nn.Module and ScriptModule.
"""
torch._C._log_api_usage_once("python.nn_module")
self.training = True
self._parameters = OrderedDict()
self._buffers = OrderedDict()
self._backward_hooks = OrderedDict()
self._forward_hooks = OrderedDict()
self._forward_pre_hooks = OrderedDict()
self._state_dict_hooks = OrderedDict()
self._load_state_dict_pre_hooks = OrderedDict()
self._modules = OrderedDict()
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
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 '_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] = tensor
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
param (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
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
module (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)
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 False
for 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_applied
else:
assert isinstance(param, Parameter)
assert param.is_leaf
self._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_applied
else:
assert param.grad.is_leaf
self._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 self
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:`nn-init-doc`).
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
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))
def cpu(self):
r"""Moves all model parameters and buffers to the CPU.
Returns:
Module: self
"""
return self._apply(lambda t: t.cpu())
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))
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)
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)
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)
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)
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()``
.. warning ::
The current implementation will not have the presented behavior
for complex :class:`Module` that perform many operations.
In some failure cases, :attr:`grad_input` and :attr:`grad_output` will only
contain 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.
"""
handle = hooks.RemovableHandle(self._backward_hooks)
self._backward_hooks[handle.id] = hook
return handle
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 or modified input
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).
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
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 or modified output
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.
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 _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_module_map is not None
if recording_scopes:
name = torch.jit._trace_module_map[self] if self in torch.jit._trace_module_map else None
if name:
cur_scope_name = tracing_state.current_scope()
tracing_state.push_scope(name)
else:
recording_scopes = False
try:
result = self.forward(*input, **kwargs)
finally:
if recording_scopes:
tracing_state.pop_scope()
return result
def __call__(self, *input, **kwargs):
for hook in self._forward_pre_hooks.values():
result = hook(self, input)
if result is not None:
if not isinstance(result, tuple):
result = (result,)
input = result
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:
result = hook_result
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)
# 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()
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)
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 are
guaranteed 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] = hook
return handle
def _save_to_state_dict(self, destination, prefix, keep_vars):
r"""Saves module state to `destination` dictionary, containing a state
of the module, but not its descendants. This is called on every
submodule in :meth:`~torch.nn.Module.state_dict`.
In rare cases, subclasses can achieve class-specific behavior by
overriding this method with custom logic.
Arguments:
destination (dict): a dict where state will be stored
prefix (str): the prefix for parameters and buffers used in this
module
"""
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
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]] = 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_result
return destination
def _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 arguments
are 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] = hook
return handle
def _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 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:`local_metadata`.
For state dicts without metadata, :attr:`local_metadata` is empty.
Subclasses can achieve class-specific backward compatible loading using
the 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`. 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
local_metadata (dict): a dict containing the metadata for this module.
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=True``, add missing keys to
this list
unexpected_keys (list of str): if ``strict=True``, 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`
"""
for hook in self._load_state_dict_pre_hooks.values():
hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
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 with shape {} from checkpoint, '
'the shape in current model is {}.'
.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 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/child
if input_name not in self._modules and input_name not in local_state:
unexpected_keys.append(key)
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``
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 = []
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, 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)
load = None # break load->load reference cycle
if 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:
continue
memo.add(v)
name = module_prefix + ('.' if module_prefix else '') + k
yield name, v
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
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
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
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
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
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
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(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 module
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(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, 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
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.
Args:
mode (bool): whether to set training mode (``True``) or evaluation
mode (``False``). Default: ``True``.
Returns:
Module: self
"""
self.training = mode
for module in self.children():
module.train(mode)
return self
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.
This is equivalent with :meth:`self.train(False) <torch.nn.Module.train>`.
Returns:
Module: self
"""
return self.train(False)
def requires_grad_(self, requires_grad=True):
r"""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).
Args:
requires_grad (bool): whether autograd should record operations on
parameters in this module. Default: ``True``.
Returns:
Module: self
"""
for p in self.parameters():
p.requires_grad_(requires_grad)
return self
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__
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)
def _replicate_for_data_parallel(self):
replica = self.__new__(type(self))
replica.__dict__ = self.__dict__.copy()
replica._parameters = replica._parameters.copy()
replica._buffers = replica._buffers.copy()
replica._modules = replica._modules.copy()
return replica