Source code for torch.nn.modules.container
import warnings
from collections import OrderedDict, abc as container_abcs
from itertools import chain, islice
import operator
import torch
from .module import Module
from torch._jit_internal import _copy_to_script_wrapper
from typing import Any, Dict, Iterable, Iterator, Mapping, Optional, TYPE_CHECKING, overload, Tuple, TypeVar, Union
if TYPE_CHECKING:
from torch.nn import Parameter
T = TypeVar('T', bound=Module)
class Container(Module):
def __init__(self, **kwargs: Any) -> None:
super(Container, self).__init__()
# DeprecationWarning is ignored by default <sigh>
warnings.warn("nn.Container is deprecated. All of it's functionality "
"is now implemented in nn.Module. Subclass that instead.")
for key, value in kwargs.items():
self.add_module(key, value)
[docs]class Sequential(Module):
r"""A sequential container.
Modules will be added to it in the order they are passed in the
constructor. Alternatively, an ``OrderedDict`` of modules can be
passed in. The ``forward()`` method of ``Sequential`` accepts any
input and forwards it to the first module it contains. It then
"chains" outputs to inputs sequentially for each subsequent module,
finally returning the output of the last module.
The value a ``Sequential`` provides over manually calling a sequence
of modules is that it allows treating the whole container as a
single module, such that performing a transformation on the
``Sequential`` applies to each of the modules it stores (which are
each a registered submodule of the ``Sequential``).
What's the difference between a ``Sequential`` and a
:class:`torch.nn.ModuleList`? A ``ModuleList`` is exactly what it
sounds like--a list for storing ``Module`` s! On the other hand,
the layers in a ``Sequential`` are connected in a cascading way.
Example::
# Using Sequential to create a small model. When `model` is run,
# input will first be passed to `Conv2d(1,20,5)`. The output of
# `Conv2d(1,20,5)` will be used as the input to the first
# `ReLU`; the output of the first `ReLU` will become the input
# for `Conv2d(20,64,5)`. Finally, the output of
# `Conv2d(20,64,5)` will be used as input to the second `ReLU`
model = nn.Sequential(
nn.Conv2d(1,20,5),
nn.ReLU(),
nn.Conv2d(20,64,5),
nn.ReLU()
)
# Using Sequential with OrderedDict. This is functionally the
# same as the above code
model = nn.Sequential(OrderedDict([
('conv1', nn.Conv2d(1,20,5)),
('relu1', nn.ReLU()),
('conv2', nn.Conv2d(20,64,5)),
('relu2', nn.ReLU())
]))
"""
_modules: Dict[str, Module] # type: ignore[assignment]
@overload
def __init__(self, *args: Module) -> None:
...
@overload
def __init__(self, arg: 'OrderedDict[str, Module]') -> None:
...
def __init__(self, *args):
super(Sequential, self).__init__()
if len(args) == 1 and isinstance(args[0], OrderedDict):
for key, module in args[0].items():
self.add_module(key, module)
else:
for idx, module in enumerate(args):
self.add_module(str(idx), module)
def _get_item_by_idx(self, iterator, idx) -> T:
"""Get the idx-th item of the iterator"""
size = len(self)
idx = operator.index(idx)
if not -size <= idx < size:
raise IndexError('index {} is out of range'.format(idx))
idx %= size
return next(islice(iterator, idx, None))
@_copy_to_script_wrapper
def __getitem__(self, idx) -> Union['Sequential', T]:
if isinstance(idx, slice):
return self.__class__(OrderedDict(list(self._modules.items())[idx]))
else:
return self._get_item_by_idx(self._modules.values(), idx)
def __setitem__(self, idx: int, module: Module) -> None:
key: str = self._get_item_by_idx(self._modules.keys(), idx)
return setattr(self, key, module)
def __delitem__(self, idx: Union[slice, int]) -> None:
if isinstance(idx, slice):
for key in list(self._modules.keys())[idx]:
delattr(self, key)
else:
key = self._get_item_by_idx(self._modules.keys(), idx)
delattr(self, key)
@_copy_to_script_wrapper
def __len__(self) -> int:
return len(self._modules)
@_copy_to_script_wrapper
def __dir__(self):
keys = super(Sequential, self).__dir__()
keys = [key for key in keys if not key.isdigit()]
return keys
@_copy_to_script_wrapper
def __iter__(self) -> Iterator[Module]:
return iter(self._modules.values())
# NB: We can't really type check this function as the type of input
# may change dynamically (as is tested in
# TestScript.test_sequential_intermediary_types). Cannot annotate
# with Any as TorchScript expects a more precise type
def forward(self, input):
for module in self:
input = module(input)
return input
[docs] def append(self, module: Module) -> 'Sequential':
r"""Appends a given module to the end.
Args:
module (nn.Module): module to append
"""
self.add_module(str(len(self)), module)
return self
[docs]class ModuleList(Module):
r"""Holds submodules in a list.
:class:`~torch.nn.ModuleList` can be indexed like a regular Python list, but
modules it contains are properly registered, and will be visible by all
:class:`~torch.nn.Module` methods.
Args:
modules (iterable, optional): an iterable of modules to add
Example::
class MyModule(nn.Module):
def __init__(self):
super(MyModule, self).__init__()
self.linears = nn.ModuleList([nn.Linear(10, 10) for i in range(10)])
def forward(self, x):
# ModuleList can act as an iterable, or be indexed using ints
for i, l in enumerate(self.linears):
x = self.linears[i // 2](x) + l(x)
return x
"""
_modules: Dict[str, Module] # type: ignore[assignment]
def __init__(self, modules: Optional[Iterable[Module]] = None) -> None:
super(ModuleList, self).__init__()
if modules is not None:
self += modules
def _get_abs_string_index(self, idx):
"""Get the absolute index for the list of modules"""
idx = operator.index(idx)
if not (-len(self) <= idx < len(self)):
raise IndexError('index {} is out of range'.format(idx))
if idx < 0:
idx += len(self)
return str(idx)
@_copy_to_script_wrapper
def __getitem__(self, idx: int) -> Union[Module, 'ModuleList']:
if isinstance(idx, slice):
return self.__class__(list(self._modules.values())[idx])
else:
return self._modules[self._get_abs_string_index(idx)]
def __setitem__(self, idx: int, module: Module) -> None:
idx = self._get_abs_string_index(idx)
return setattr(self, str(idx), module)
def __delitem__(self, idx: Union[int, slice]) -> None:
if isinstance(idx, slice):
for k in range(len(self._modules))[idx]:
delattr(self, str(k))
else:
delattr(self, self._get_abs_string_index(idx))
# To preserve numbering, self._modules is being reconstructed with modules after deletion
str_indices = [str(i) for i in range(len(self._modules))]
self._modules = OrderedDict(list(zip(str_indices, self._modules.values())))
@_copy_to_script_wrapper
def __len__(self) -> int:
return len(self._modules)
@_copy_to_script_wrapper
def __iter__(self) -> Iterator[Module]:
return iter(self._modules.values())
def __iadd__(self, modules: Iterable[Module]) -> 'ModuleList':
return self.extend(modules)
def __add__(self, other: Iterable[Module]) -> 'ModuleList':
combined = ModuleList()
for i, module in enumerate(chain(self, other)):
combined.add_module(str(i), module)
return combined
@_copy_to_script_wrapper
def __dir__(self):
keys = super(ModuleList, self).__dir__()
keys = [key for key in keys if not key.isdigit()]
return keys
[docs] def insert(self, index: int, module: Module) -> None:
r"""Insert a given module before a given index in the list.
Args:
index (int): index to insert.
module (nn.Module): module to insert
"""
for i in range(len(self._modules), index, -1):
self._modules[str(i)] = self._modules[str(i - 1)]
self._modules[str(index)] = module
[docs] def append(self, module: Module) -> 'ModuleList':
r"""Appends a given module to the end of the list.
Args:
module (nn.Module): module to append
"""
self.add_module(str(len(self)), module)
return self
[docs] def extend(self, modules: Iterable[Module]) -> 'ModuleList':
r"""Appends modules from a Python iterable to the end of the list.
Args:
modules (iterable): iterable of modules to append
"""
if not isinstance(modules, container_abcs.Iterable):
raise TypeError("ModuleList.extend should be called with an "
"iterable, but got " + type(modules).__name__)
offset = len(self)
for i, module in enumerate(modules):
self.add_module(str(offset + i), module)
return self
# remove forward alltogether to fallback on Module's _forward_unimplemented
[docs]class ModuleDict(Module):
r"""Holds submodules in a dictionary.
:class:`~torch.nn.ModuleDict` can be indexed like a regular Python dictionary,
but modules it contains are properly registered, and will be visible by all
:class:`~torch.nn.Module` methods.
:class:`~torch.nn.ModuleDict` is an **ordered** dictionary that respects
* the order of insertion, and
* in :meth:`~torch.nn.ModuleDict.update`, the order of the merged
``OrderedDict``, ``dict`` (started from Python 3.6) or another
:class:`~torch.nn.ModuleDict` (the argument to
:meth:`~torch.nn.ModuleDict.update`).
Note that :meth:`~torch.nn.ModuleDict.update` with other unordered mapping
types (e.g., Python's plain ``dict`` before Python version 3.6) does not
preserve the order of the merged mapping.
Args:
modules (iterable, optional): a mapping (dictionary) of (string: module)
or an iterable of key-value pairs of type (string, module)
Example::
class MyModule(nn.Module):
def __init__(self):
super(MyModule, self).__init__()
self.choices = nn.ModuleDict({
'conv': nn.Conv2d(10, 10, 3),
'pool': nn.MaxPool2d(3)
})
self.activations = nn.ModuleDict([
['lrelu', nn.LeakyReLU()],
['prelu', nn.PReLU()]
])
def forward(self, x, choice, act):
x = self.choices[choice](x)
x = self.activations[act](x)
return x
"""
_modules: Dict[str, Module] # type: ignore[assignment]
def __init__(self, modules: Optional[Mapping[str, Module]] = None) -> None:
super(ModuleDict, self).__init__()
if modules is not None:
self.update(modules)
@_copy_to_script_wrapper
def __getitem__(self, key: str) -> Module:
return self._modules[key]
def __setitem__(self, key: str, module: Module) -> None:
self.add_module(key, module)
def __delitem__(self, key: str) -> None:
del self._modules[key]
@_copy_to_script_wrapper
def __len__(self) -> int:
return len(self._modules)
@_copy_to_script_wrapper
def __iter__(self) -> Iterator[str]:
return iter(self._modules)
@_copy_to_script_wrapper
def __contains__(self, key: str) -> bool:
return key in self._modules
[docs] def pop(self, key: str) -> Module:
r"""Remove key from the ModuleDict and return its module.
Args:
key (string): key to pop from the ModuleDict
"""
v = self[key]
del self[key]
return v
[docs] @_copy_to_script_wrapper
def keys(self) -> Iterable[str]:
r"""Return an iterable of the ModuleDict keys.
"""
return self._modules.keys()
[docs] @_copy_to_script_wrapper
def items(self) -> Iterable[Tuple[str, Module]]:
r"""Return an iterable of the ModuleDict key/value pairs.
"""
return self._modules.items()
[docs] @_copy_to_script_wrapper
def values(self) -> Iterable[Module]:
r"""Return an iterable of the ModuleDict values.
"""
return self._modules.values()
[docs] def update(self, modules: Mapping[str, Module]) -> None:
r"""Update the :class:`~torch.nn.ModuleDict` with the key-value pairs from a
mapping or an iterable, overwriting existing keys.
.. note::
If :attr:`modules` is an ``OrderedDict``, a :class:`~torch.nn.ModuleDict`, or
an iterable of key-value pairs, the order of new elements in it is preserved.
Args:
modules (iterable): a mapping (dictionary) from string to :class:`~torch.nn.Module`,
or an iterable of key-value pairs of type (string, :class:`~torch.nn.Module`)
"""
if not isinstance(modules, container_abcs.Iterable):
raise TypeError("ModuleDict.update should be called with an "
"iterable of key/value pairs, but got " +
type(modules).__name__)
if isinstance(modules, (OrderedDict, ModuleDict, container_abcs.Mapping)):
for key, module in modules.items():
self[key] = module
else:
# modules here can be a list with two items
for j, m in enumerate(modules):
if not isinstance(m, container_abcs.Iterable):
raise TypeError("ModuleDict update sequence element "
"#" + str(j) + " should be Iterable; is" +
type(m).__name__)
if not len(m) == 2:
raise ValueError("ModuleDict update sequence element "
"#" + str(j) + " has length " + str(len(m)) +
"; 2 is required")
# modules can be Mapping (what it's typed at), or a list: [(name1, module1), (name2, module2)]
# that's too cumbersome to type correctly with overloads, so we add an ignore here
self[m[0]] = m[1] # type: ignore[assignment]
# remove forward alltogether to fallback on Module's _forward_unimplemented
[docs]class ParameterList(Module):
r"""Holds parameters in a list.
:class:`~torch.nn.ParameterList` can be indexed like a regular Python
list, but parameters it contains are properly registered, and will be
visible by all :class:`~torch.nn.Module` methods.
Args:
parameters (iterable, optional): an iterable of :class:`~torch.nn.Parameter` to add
Example::
class MyModule(nn.Module):
def __init__(self):
super(MyModule, self).__init__()
self.params = nn.ParameterList([nn.Parameter(torch.randn(10, 10)) for i in range(10)])
def forward(self, x):
# ParameterList can act as an iterable, or be indexed using ints
for i, p in enumerate(self.params):
x = self.params[i // 2].mm(x) + p.mm(x)
return x
"""
_parameters: Dict[str, 'Parameter'] # type: ignore[assignment]
def __init__(self, parameters: Optional[Iterable['Parameter']] = None) -> None:
super(ParameterList, self).__init__()
self._initialized = True
if parameters is not None:
self += parameters
def __setstate__(self, state):
state['_initialized'] = False
super(ParameterList, self).__setstate__(state)
self._initialized = True
def _get_abs_string_index(self, idx):
"""Get the absolute index for the list of modules"""
idx = operator.index(idx)
if not (-len(self) <= idx < len(self)):
raise IndexError('index {} is out of range'.format(idx))
if idx < 0:
idx += len(self)
return str(idx)
@overload
def __getitem__(self, idx: int) -> 'Parameter':
...
@overload
def __getitem__(self: T, idx: slice) -> T:
...
def __getitem__(self, idx):
if isinstance(idx, slice):
return self.__class__(list(self._parameters.values())[idx])
else:
idx = self._get_abs_string_index(idx)
return self._parameters[str(idx)]
def __setitem__(self, idx: int, param: 'Parameter') -> None:
idx = self._get_abs_string_index(idx)
return self.register_parameter(str(idx), param)
def __setattr__(self, key: Any, value: Any) -> None:
if getattr(self, "_initialized", False):
if not hasattr(self, key) and not isinstance(value, torch.nn.Parameter):
warnings.warn("Setting attributes on ParameterList is not supported.")
super(ParameterList, self).__setattr__(key, value)
def __len__(self) -> int:
return len(self._parameters)
def __iter__(self) -> Iterator['Parameter']:
return iter(self._parameters.values())
def __iadd__(self, parameters: Iterable['Parameter']) -> 'ParameterList':
return self.extend(parameters)
def __dir__(self):
keys = super(ParameterList, self).__dir__()
keys = [key for key in keys if not key.isdigit()]
return keys
[docs] def append(self, parameter: 'Parameter') -> 'ParameterList':
"""Appends a given parameter at the end of the list.
Args:
parameter (nn.Parameter): parameter to append
"""
self.register_parameter(str(len(self)), parameter)
return self
[docs] def extend(self, parameters: Iterable['Parameter']) -> 'ParameterList':
"""Appends parameters from a Python iterable to the end of the list.
Args:
parameters (iterable): iterable of parameters to append
"""
if not isinstance(parameters, container_abcs.Iterable):
raise TypeError("ParameterList.extend should be called with an "
"iterable, but got " + type(parameters).__name__)
offset = len(self)
for i, param in enumerate(parameters):
self.register_parameter(str(offset + i), param)
return self
def extra_repr(self) -> str:
child_lines = []
for k, p in self._parameters.items():
size_str = 'x'.join(str(size) for size in p.size())
device_str = '' if not p.is_cuda else ' (GPU {})'.format(p.get_device())
parastr = 'Parameter containing: [{} of size {}{}]'.format(
torch.typename(p), size_str, device_str)
child_lines.append(' (' + str(k) + '): ' + parastr)
tmpstr = '\n'.join(child_lines)
return tmpstr
def __call__(self, input):
raise RuntimeError('ParameterList should not be called.')
def _replicate_for_data_parallel(self):
warnings.warn("nn.ParameterList is being used with DataParallel but this is not "
"supported. This list will appear empty for the models replicated "
"on each GPU except the original one.")
return super(ParameterList, self)._replicate_for_data_parallel()
[docs]class ParameterDict(Module):
r"""Holds parameters in a dictionary.
ParameterDict can be indexed like a regular Python dictionary, but parameters it
contains are properly registered, and will be visible by all Module methods.
:class:`~torch.nn.ParameterDict` is an **ordered** dictionary that respects
* the order of insertion, and
* in :meth:`~torch.nn.ParameterDict.update`, the order of the merged ``OrderedDict``
or another :class:`~torch.nn.ParameterDict` (the argument to
:meth:`~torch.nn.ParameterDict.update`).
Note that :meth:`~torch.nn.ParameterDict.update` with other unordered mapping
types (e.g., Python's plain ``dict``) does not preserve the order of the
merged mapping.
Args:
parameters (iterable, optional): a mapping (dictionary) of
(string : :class:`~torch.nn.Parameter`) or an iterable of key-value pairs
of type (string, :class:`~torch.nn.Parameter`)
Example::
class MyModule(nn.Module):
def __init__(self):
super(MyModule, self).__init__()
self.params = nn.ParameterDict({
'left': nn.Parameter(torch.randn(5, 10)),
'right': nn.Parameter(torch.randn(5, 10))
})
def forward(self, x, choice):
x = self.params[choice].mm(x)
return x
"""
_parameters: Dict[str, 'Parameter'] # type: ignore[assignment]
def __init__(self, parameters: Optional[Mapping[str, 'Parameter']] = None) -> None:
super(ParameterDict, self).__init__()
self._initialized = True
if parameters is not None:
self.update(parameters)
def __setstate__(self, state):
state['_initialized'] = False
super(ParameterDict, self).__setstate__(state)
self._initialized = True
def __getitem__(self, key: str) -> 'Parameter':
return self._parameters[key]
def __setitem__(self, key: str, parameter: 'Parameter') -> None:
self.register_parameter(key, parameter)
def __delitem__(self, key: str) -> None:
del self._parameters[key]
def __setattr__(self, key: Any, value: Any) -> None:
if getattr(self, "_initialized", False):
if not hasattr(self, key) and not isinstance(value, torch.nn.Parameter):
warnings.warn("Setting attributes on ParameterDict is not supported.")
super(ParameterDict, self).__setattr__(key, value)
def __len__(self) -> int:
return len(self._parameters)
def __iter__(self) -> Iterator[str]:
return iter(self._parameters.keys())
def __reversed__(self) -> Iterator[str]:
return reversed(list(self._parameters.keys()))
[docs] def copy(self) -> 'ParameterDict':
"""Returns a copy of this :class:`~torch.nn.ParameterDict` instance.
"""
return ParameterDict(self._parameters.copy())
def __contains__(self, key: str) -> bool:
return key in self._parameters
[docs] def setdefault(self, key: str, default: Optional['Parameter'] = None) -> 'Parameter':
"""If key is in the ParameterDict, return its parameter.
If not, insert `key` with a parameter `default` and return `default`.
`default` defaults to `None`.
Args:
key (string): key to set default for
default (:class:`~torch.nn.Parameter`): the parameter set to the key
"""
if key in self._parameters:
return self._parameters[key]
self[key] = default # type: ignore[assignment]
return self._parameters[key]
[docs] def clear(self) -> None:
"""Remove all items from the ParameterDict.
"""
self._parameters.clear()
[docs] def pop(self, key: str) -> 'Parameter':
r"""Remove key from the ParameterDict and return its parameter.
Args:
key (string): key to pop from the ParameterDict
"""
v = self[key]
del self[key]
return v
[docs] def popitem(self) -> Tuple[str, 'Parameter']:
"""Remove and return the last inserted `(key, parameter)` pair
from the ParameterDict
"""
return self._parameters.popitem()
[docs] def get(self, key: str, default: Optional['Parameter'] = None) -> 'Parameter | None':
r"""Return the parameter associated with key if present.
Otherwise return default if provided, None if not.
Args:
key (string): key to get from the ParameterDict
default (Parameter, optional): value to return if key not present
"""
return self._parameters.get(key, default)
[docs] def fromkeys(self, keys: Iterable['str'], default: Optional['Parameter'] = None) -> 'ParameterDict':
r"""Return a new ParameterDict with the keys provided
Args:
keys (iterable, string): keys to make the new ParameterDict from
default (Parameter, optional): value to set for all keys
"""
return ParameterDict(self._parameters.fromkeys(keys, default)) # type: ignore[arg-type]
[docs] def keys(self) -> Iterable[str]:
r"""Return an iterable of the ParameterDict keys.
"""
return self._parameters.keys()
[docs] def items(self) -> Iterable[Tuple[str, 'Parameter']]:
r"""Return an iterable of the ParameterDict key/value pairs.
"""
return self._parameters.items()
[docs] def values(self) -> Iterable['Parameter']:
r"""Return an iterable of the ParameterDict values.
"""
return self._parameters.values()
[docs] def update(self, parameters: Mapping[str, 'Parameter']) -> None:
r"""Update the :class:`~torch.nn.ParameterDict` with the key-value pairs from a
mapping or an iterable, overwriting existing keys.
.. note::
If :attr:`parameters` is an ``OrderedDict``, a :class:`~torch.nn.ParameterDict`, or
an iterable of key-value pairs, the order of new elements in it is preserved.
Args:
parameters (iterable): a mapping (dictionary) from string to
:class:`~torch.nn.Parameter`, or an iterable of
key-value pairs of type (string, :class:`~torch.nn.Parameter`)
"""
if not isinstance(parameters, container_abcs.Iterable):
raise TypeError("ParametersDict.update should be called with an "
"iterable of key/value pairs, but got " +
type(parameters).__name__)
if isinstance(parameters, (OrderedDict, ParameterDict)):
for key, parameter in parameters.items():
self[key] = parameter
elif isinstance(parameters, container_abcs.Mapping):
for key, parameter in sorted(parameters.items()):
self[key] = parameter
else:
for j, p in enumerate(parameters):
if not isinstance(p, container_abcs.Iterable):
raise TypeError("ParameterDict update sequence element "
"#" + str(j) + " should be Iterable; is" +
type(p).__name__)
if not len(p) == 2:
raise ValueError("ParameterDict update sequence element "
"#" + str(j) + " has length " + str(len(p)) +
"; 2 is required")
# parameters as length-2 list too cumbersome to type, see ModuleDict.update comment
self[p[0]] = p[1] # type: ignore[assignment]
def extra_repr(self) -> str:
child_lines = []
for k, p in self._parameters.items():
size_str = 'x'.join(str(size) for size in p.size())
device_str = '' if not p.is_cuda else ' (GPU {})'.format(p.get_device())
parastr = 'Parameter containing: [{} of size {}{}]'.format(
torch.typename(p), size_str, device_str)
child_lines.append(' (' + k + '): ' + parastr)
tmpstr = '\n'.join(child_lines)
return tmpstr
def __call__(self, input):
raise RuntimeError('ParameterDict should not be called.')
def _replicate_for_data_parallel(self):
warnings.warn("nn.ParameterDict is being used with DataParallel but this is not "
"supported. This dict will appear empty for the models replicated "
"on each GPU except the original one.")
return super(ParameterDict, self)._replicate_for_data_parallel()
def __or__(self, other: 'ParameterDict') -> 'ParameterDict':
copy = self.copy()
copy.update(other._parameters)
return copy
def __ror__(self, other: 'ParameterDict') -> 'ParameterDict':
copy = other.copy()
copy.update(self._parameters)
return copy
def __ior__(self, other : 'ParameterDict') -> 'ParameterDict':
self.update(other._parameters)
return self
