Module wtracker.neural.mlp
View Source
from torch import Tensor, nn
from typing import Union, Sequence
from collections import defaultdict
from wtracker.neural.config import IOConfig
ACTIVATIONS = {
"relu": nn.ReLU,
"tanh": nn.Tanh,
"sigmoid": nn.Sigmoid,
"softmax": nn.Softmax,
"logsoftmax": nn.LogSoftmax,
"lrelu": nn.LeakyReLU,
"none": nn.Identity,
None: nn.Identity,
}
# Default keyword arguments to pass to activation class constructors, e.g.
# activation_cls(**ACTIVATION_DEFAULT_KWARGS[name])
ACTIVATION_DEFAULT_KWARGS = defaultdict(
dict,
{
###
"softmax": dict(dim=1),
"logsoftmax": dict(dim=1),
},
)
class WormPredictor(nn.Module):
"""
A class that represents neural network models that predict worm behavior. After a model is created from several layers or blocks, it is wrapped in this class
so that it can be distinguished from other models that don't predict worm behavior (for example the layers/blocks that make this model).
This class also holds the IOConfig object that is used to determine the input and output shapes of the model, and the specific frames it expects as input and output.
Attributes:
model: The neural network model that predicts worm behavior.
io_config: The IOConfig object of the model.
"""
def __init__(self, model: nn.Module, io_config: IOConfig):
super().__init__()
self.io_config: IOConfig = io_config
self.model: nn.Module = model
def forward(self, x: Tensor) -> Tensor:
return self.model(x)
class MLPLayer(nn.Module):
"""
A single layer perceptron, that can hold a bach-norm and activation layers as well.
"""
def __init__(
self,
in_dim: int,
out_dim: Sequence[int],
nonlin: Union[str, nn.Module],
batch_norm: bool = True,
) -> None:
super().__init__()
layers = []
layers.append(nn.Linear(in_dim, out_dim))
in_dim = out_dim
if batch_norm and nonlin not in ["none", None]:
layers.append(nn.BatchNorm1d(out_dim))
layers.append(self._make_activation(nonlin))
self.mlp_layer = nn.Sequential(*layers)
def _make_activation(self, act: Union[str, nn.Module]) -> nn.Module:
if isinstance(act, str):
return ACTIVATIONS[act](**ACTIVATION_DEFAULT_KWARGS[act])
return act
def forward(self, x: Tensor) -> Tensor:
"""
Args:
x: An input tensor, of shape (N, D) containing N samples with D features.
Returns:
An output tensor of shape (N, D_out) where D_out is the output dim.
"""
return self.mlp_layer.forward(x.reshape(x.size(0), -1))
class MlpBlock(nn.Module):
"""
A general-purpose MLP.
Args:
in_dim: Input dimension.
dims: Hidden dimensions, including output dimension.
nonlins: Non-linearities to apply after each one of the hidden
dimensions.
Can be either a sequence of strings which are keys in the ACTIVATIONS
dict, or instances of nn.Module (e.g. an instance of nn.ReLU()).
Length should match 'dims'.
"""
def __init__(
self,
in_dim: int,
dims: Sequence[int],
nonlins: Sequence[Union[str, nn.Module]],
batch_norm: bool = True,
):
assert len(nonlins) == len(dims)
self.in_dim = in_dim
self.out_dim = dims[-1]
self.dims = dims
self.nonlins = nonlins
super().__init__()
layers = []
for i, out_dim in enumerate(self.dims):
layers.append(MLPLayer(in_dim, out_dim, nonlins[i], batch_norm))
in_dim = out_dim
self.sequence = nn.Sequential(*layers)
def _make_activation(self, act: Union[str, nn.Module]) -> nn.Module:
if isinstance(act, str):
return ACTIVATIONS[act](**ACTIVATION_DEFAULT_KWARGS[act])
return act
def forward(self, x: Tensor) -> Tensor:
"""
Args:
x: An input tensor, of shape (N, D) containing N samples with D features.
Returns:
An output tensor of shape (N, D_out) where D_out is the output dim.
"""
return self.sequence.forward(x.reshape(x.size(0), -1))
class RMLP(nn.Module):
def __init__(
self,
block_in_dim: int,
block_dims: Sequence[int],
block_nonlins: Sequence[Union[str, nn.Module]],
n_blocks: int,
out_dim: int,
in_dim: int = None, # if in_dim is an int, then a first layer will be made
batch_norm: bool = True,
) -> None:
super().__init__()
# Create first layer if in_dim is not None
self.input = nn.Identity()
if in_dim is not None:
self.input = MLPLayer(in_dim, block_in_dim, block_nonlins[0], batch_norm)
# Create blocks
layers = []
for i in range(n_blocks):
layers.append(MlpBlock(block_in_dim, block_dims, block_nonlins, batch_norm))
self.blocks = nn.ModuleList(layers)
# Create output layer
self.output = nn.Linear(block_dims[-1], out_dim)
def _make_activation(self, act: Union[str, nn.Module]) -> nn.Module:
if isinstance(act, str):
return ACTIVATIONS[act](**ACTIVATION_DEFAULT_KWARGS[act])
return act
def forward(self, x: Tensor) -> Tensor:
"""
Args:
x: An input tensor, of shape (N, D) containing N samples with D features.
Returns:
An output tensor of shape (N, D_out) where D_out is the output dim.
"""
x = self.input(x)
for block in self.blocks:
out = block(x)
x = x + out
return self.output(x)
Variables
ACTIVATIONS
ACTIVATION_DEFAULT_KWARGS
Classes
MLPLayer
class MLPLayer(
in_dim: int,
out_dim: Sequence[int],
nonlin: Union[str, torch.nn.modules.module.Module],
batch_norm: bool = True
)
A single layer perceptron, that can hold a bach-norm and activation layers as well.
View Source
class MLPLayer(nn.Module):
"""
A single layer perceptron, that can hold a bach-norm and activation layers as well.
"""
def __init__(
self,
in_dim: int,
out_dim: Sequence[int],
nonlin: Union[str, nn.Module],
batch_norm: bool = True,
) -> None:
super().__init__()
layers = []
layers.append(nn.Linear(in_dim, out_dim))
in_dim = out_dim
if batch_norm and nonlin not in ["none", None]:
layers.append(nn.BatchNorm1d(out_dim))
layers.append(self._make_activation(nonlin))
self.mlp_layer = nn.Sequential(*layers)
def _make_activation(self, act: Union[str, nn.Module]) -> nn.Module:
if isinstance(act, str):
return ACTIVATIONS[act](**ACTIVATION_DEFAULT_KWARGS[act])
return act
def forward(self, x: Tensor) -> Tensor:
"""
Args:
x: An input tensor, of shape (N, D) containing N samples with D features.
Returns:
An output tensor of shape (N, D_out) where D_out is the output dim.
"""
return self.mlp_layer.forward(x.reshape(x.size(0), -1))
Ancestors (in MRO)
- torch.nn.modules.module.Module
Class variables
T_destination
call_super_init
dump_patches
Methods
add_module
def add_module(
self,
name: str,
module: Optional[ForwardRef('Module')]
) -> None
Add a child module to the current module.
The module can be accessed as an attribute using the given name.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| name | str | name of the child module. The child module can be accessed from this module using the given name |
None |
| module | Module | child module to be added to the module. | None |
View Source
def add_module(self, name: str, module: Optional['Module']) -> None:
r"""Add a child module to the current module.
The module can be accessed as an attribute using the given name.
Args:
name (str): 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(f"{torch.typename(module)} is not a Module subclass")
elif not isinstance(name, str):
raise TypeError(f"module name should be a string. Got {torch.typename(name)}")
elif hasattr(self, name) and name not in self._modules:
raise KeyError(f"attribute '{name}' already exists")
elif '.' in name:
raise KeyError(f"module name can't contain \".\", got: {name}")
elif name == '':
raise KeyError("module name can't be empty string \"\"")
for hook in _global_module_registration_hooks.values():
output = hook(self, name, module)
if output is not None:
module = output
self._modules[name] = module
apply
def apply(
self: ~T,
fn: Callable[[ForwardRef('Module')], NoneType]
) -> ~T
Apply fn recursively to every submodule (as returned by .children()) as well as self.
Typical use includes initializing the parameters of a model
(see also :ref:nn-init-doc).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| fn ( | None | class:Module -> None): function to be applied to each submodule |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def apply(self: T, fn: Callable[['Module'], None]) -> T:
r"""Apply ``fn`` recursively to every submodule (as returned by ``.children()``) as well as self.
Typical use includes initializing the parameters of a model
(see also :ref:`nn-init-doc`).
Args:
fn (:class:`Module` -> None): function to be applied to each submodule
Returns:
Module: self
Example::
>>> @torch.no_grad()
>>> def init_weights(m):
>>> print(m)
>>> if type(m) == nn.Linear:
>>> m.weight.fill_(1.0)
>>> print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[1., 1.],
[1., 1.]], requires_grad=True)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[1., 1.],
[1., 1.]], requires_grad=True)
Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
"""
for module in self.children():
module.apply(fn)
fn(self)
return self
bfloat16
def bfloat16(
self: ~T
) -> ~T
Casts all floating point parameters and buffers to bfloat16 datatype.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def bfloat16(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``bfloat16`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.bfloat16() if t.is_floating_point() else t)
buffers
def buffers(
self,
recurse: bool = True
) -> Iterator[torch.Tensor]
Return an iterator over module buffers.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| recurse | bool | if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. |
None |
Yields:
| Type | Description |
|---|---|
| torch.Tensor | module buffer |
View Source
def buffers(self, recurse: bool = True) -> Iterator[Tensor]:
r"""Return 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::
>>> # xdoctest: +SKIP("undefined vars")
>>> for buf in model.buffers():
>>> print(type(buf), buf.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)
"""
for _, buf in self.named_buffers(recurse=recurse):
yield buf
children
def children(
self
) -> Iterator[ForwardRef('Module')]
Return an iterator over immediate children modules.
Yields:
| Type | Description |
|---|---|
| Module | a child module |
View Source
def children(self) -> Iterator['Module']:
r"""Return an iterator over immediate children modules.
Yields:
Module: a child module
"""
for name, module in self.named_children():
yield module
compile
def compile(
self,
*args,
**kwargs
)
Compile this Module's forward using :func:torch.compile.
This Module's __call__ method is compiled and all arguments are passed as-is
to :func:torch.compile.
See :func:torch.compile for details on the arguments for this function.
View Source
def compile(self, *args, **kwargs):
"""
Compile this Module's forward using :func:`torch.compile`.
This Module's `__call__` method is compiled and all arguments are passed as-is
to :func:`torch.compile`.
See :func:`torch.compile` for details on the arguments for this function.
"""
self._compiled_call_impl = torch.compile(self._call_impl, *args, **kwargs)
cpu
def cpu(
self: ~T
) -> ~T
Move all model parameters and buffers to the CPU.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def cpu(self: T) -> T:
r"""Move all model parameters and buffers to the CPU.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.cpu())
cuda
def cuda(
self: ~T,
device: Union[int, torch.device, NoneType] = None
) -> ~T
Move all model parameters and buffers to the GPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device | int | if specified, all parameters will be copied to that device |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def cuda(self: T, device: Optional[Union[int, device]] = None) -> T:
r"""Move all model parameters and buffers to the GPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on GPU while being optimized.
.. note::
This method modifies the module in-place.
Args:
device (int, optional): if specified, all parameters will be
copied to that device
Returns:
Module: self
"""
return self._apply(lambda t: t.cuda(device))
double
def double(
self: ~T
) -> ~T
Casts all floating point parameters and buffers to double datatype.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def double(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``double`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.double() if t.is_floating_point() else t)
eval
def eval(
self: ~T
) -> ~T
Set 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>.
See :ref:locally-disable-grad-doc for a comparison between
.eval() and several similar mechanisms that may be confused with it.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def eval(self: T) -> T:
r"""Set 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>`.
See :ref:`locally-disable-grad-doc` for a comparison between
`.eval()` and several similar mechanisms that may be confused with it.
Returns:
Module: self
"""
return self.train(False)
extra_repr
def extra_repr(
self
) -> str
Set the extra representation of the module.
To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.
View Source
def extra_repr(self) -> str:
r"""Set the extra representation of the module.
To print customized extra information, you should re-implement
this method in your own modules. Both single-line and multi-line
strings are acceptable.
"""
return ''
float
def float(
self: ~T
) -> ~T
Casts all floating point parameters and buffers to float datatype.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def float(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``float`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.float() if t.is_floating_point() else t)
forward
def forward(
self,
x: torch.Tensor
) -> torch.Tensor
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| x | None | An input tensor, of shape (N, D) containing N samples with D features. | None |
Returns:
| Type | Description |
|---|---|
| None | An output tensor of shape (N, D_out) where D_out is the output dim. |
View Source
def forward(self, x: Tensor) -> Tensor:
"""
Args:
x: An input tensor, of shape (N, D) containing N samples with D features.
Returns:
An output tensor of shape (N, D_out) where D_out is the output dim.
"""
return self.mlp_layer.forward(x.reshape(x.size(0), -1))
get_buffer
def get_buffer(
self,
target: str
) -> 'Tensor'
Return the buffer given by target if it exists, otherwise throw an error.
See the docstring for get_submodule for a more detailed
explanation of this method's functionality as well as how to
correctly specify target.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| target | None | The fully-qualified string name of the buffer to look for. (See get_submodule for how to specify afully-qualified string.) |
None |
Returns:
| Type | Description |
|---|---|
| torch.Tensor | The buffer referenced by target |
Raises:
| Type | Description |
|---|---|
| AttributeError | If the target string references an invalid path or resolves to something that is not a buffer |
View Source
def get_buffer(self, target: str) -> "Tensor":
"""Return the buffer given by ``target`` if it exists, otherwise throw an error.
See the docstring for ``get_submodule`` for a more detailed
explanation of this method's functionality as well as how to
correctly specify ``target``.
Args:
target: The fully-qualified string name of the buffer
to look for. (See ``get_submodule`` for how to specify a
fully-qualified string.)
Returns:
torch.Tensor: The buffer referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not a
buffer
"""
module_path, _, buffer_name = target.rpartition(".")
mod: torch.nn.Module = self.get_submodule(module_path)
if not hasattr(mod, buffer_name):
raise AttributeError(mod._get_name() + " has no attribute `"
+ buffer_name + "`")
buffer: torch.Tensor = getattr(mod, buffer_name)
if buffer_name not in mod._buffers:
raise AttributeError("`" + buffer_name + "` is not a buffer")
return buffer
get_extra_state
def get_extra_state(
self
) -> Any
Return any extra state to include in the module's state_dict.
Implement this and a corresponding :func:set_extra_state for your module
if you need to store extra state. This function is called when building the
module's state_dict().
Note that extra state should be picklable to ensure working serialization of the state_dict. We only provide provide backwards compatibility guarantees for serializing Tensors; other objects may break backwards compatibility if their serialized pickled form changes.
Returns:
| Type | Description |
|---|---|
| object | Any extra state to store in the module's state_dict |
View Source
def get_extra_state(self) -> Any:
"""Return any extra state to include in the module's state_dict.
Implement this and a corresponding :func:`set_extra_state` for your module
if you need to store extra state. This function is called when building the
module's `state_dict()`.
Note that extra state should be picklable to ensure working serialization
of the state_dict. We only provide provide backwards compatibility guarantees
for serializing Tensors; other objects may break backwards compatibility if
their serialized pickled form changes.
Returns:
object: Any extra state to store in the module's state_dict
"""
raise RuntimeError(
"Reached a code path in Module.get_extra_state() that should never be called. "
"Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml "
"to report this bug.")
get_parameter
def get_parameter(
self,
target: str
) -> 'Parameter'
Return the parameter given by target if it exists, otherwise throw an error.
See the docstring for get_submodule for a more detailed
explanation of this method's functionality as well as how to
correctly specify target.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| target | None | The fully-qualified string name of the Parameter to look for. (See get_submodule for how to specify afully-qualified string.) |
None |
Returns:
| Type | Description |
|---|---|
| torch.nn.Parameter | The Parameter referenced by target |
Raises:
| Type | Description |
|---|---|
| AttributeError | If the target string references an invalid path or resolves to something that is not an nn.Parameter |
View Source
def get_parameter(self, target: str) -> "Parameter":
"""Return the parameter given by ``target`` if it exists, otherwise throw an error.
See the docstring for ``get_submodule`` for a more detailed
explanation of this method's functionality as well as how to
correctly specify ``target``.
Args:
target: The fully-qualified string name of the Parameter
to look for. (See ``get_submodule`` for how to specify a
fully-qualified string.)
Returns:
torch.nn.Parameter: The Parameter referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not an
``nn.Parameter``
"""
module_path, _, param_name = target.rpartition(".")
mod: torch.nn.Module = self.get_submodule(module_path)
if not hasattr(mod, param_name):
raise AttributeError(mod._get_name() + " has no attribute `"
+ param_name + "`")
param: torch.nn.Parameter = getattr(mod, param_name)
if not isinstance(param, torch.nn.Parameter):
raise AttributeError("`" + param_name + "` is not an "
"nn.Parameter")
return param
get_submodule
def get_submodule(
self,
target: str
) -> 'Module'
Return the submodule given by target if it exists, otherwise throw an error.
For example, let's say you have an nn.Module A that
looks like this:
.. code-block:: text
A(
(net_b): Module(
(net_c): Module(
(conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))
)
(linear): Linear(in_features=100, out_features=200, bias=True)
)
)
(The diagram shows an nn.Module A. A has a nested
submodule net_b, which itself has two submodules net_c
and linear. net_c then has a submodule conv.)
To check whether or not we have the linear submodule, we
would call get_submodule("net_b.linear"). To check whether
we have the conv submodule, we would call
get_submodule("net_b.net_c.conv").
The runtime of get_submodule is bounded by the degree
of module nesting in target. A query against
named_modules achieves the same result, but it is O(N) in
the number of transitive modules. So, for a simple check to see
if some submodule exists, get_submodule should always be
used.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| target | None | The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.) |
None |
Returns:
| Type | Description |
|---|---|
| torch.nn.Module | The submodule referenced by target |
Raises:
| Type | Description |
|---|---|
| AttributeError | If the target string references an invalid path or resolves to something that is not an nn.Module |
View Source
def get_submodule(self, target: str) -> "Module":
"""Return the submodule given by ``target`` if it exists, otherwise throw an error.
For example, let's say you have an ``nn.Module`` ``A`` that
looks like this:
.. code-block:: text
A(
(net_b): Module(
(net_c): Module(
(conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))
)
(linear): Linear(in_features=100, out_features=200, bias=True)
)
)
(The diagram shows an ``nn.Module`` ``A``. ``A`` has a nested
submodule ``net_b``, which itself has two submodules ``net_c``
and ``linear``. ``net_c`` then has a submodule ``conv``.)
To check whether or not we have the ``linear`` submodule, we
would call ``get_submodule("net_b.linear")``. To check whether
we have the ``conv`` submodule, we would call
``get_submodule("net_b.net_c.conv")``.
The runtime of ``get_submodule`` is bounded by the degree
of module nesting in ``target``. A query against
``named_modules`` achieves the same result, but it is O(N) in
the number of transitive modules. So, for a simple check to see
if some submodule exists, ``get_submodule`` should always be
used.
Args:
target: The fully-qualified string name of the submodule
to look for. (See above example for how to specify a
fully-qualified string.)
Returns:
torch.nn.Module: The submodule referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not an
``nn.Module``
"""
if target == "":
return self
atoms: List[str] = target.split(".")
mod: torch.nn.Module = self
for item in atoms:
if not hasattr(mod, item):
raise AttributeError(mod._get_name() + " has no "
"attribute `" + item + "`")
mod = getattr(mod, item)
if not isinstance(mod, torch.nn.Module):
raise AttributeError("`" + item + "` is not "
"an nn.Module")
return mod
half
def half(
self: ~T
) -> ~T
Casts all floating point parameters and buffers to half datatype.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def half(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``half`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.half() if t.is_floating_point() else t)
ipu
def ipu(
self: ~T,
device: Union[int, torch.device, NoneType] = None
) -> ~T
Move all model parameters and buffers to the IPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on IPU while being optimized.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device | int | if specified, all parameters will be copied to that device |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def ipu(self: T, device: Optional[Union[int, device]] = None) -> T:
r"""Move all model parameters and buffers to the IPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on IPU while being optimized.
.. note::
This method modifies the module in-place.
Arguments:
device (int, optional): if specified, all parameters will be
copied to that device
Returns:
Module: self
"""
return self._apply(lambda t: t.ipu(device))
load_state_dict
def load_state_dict(
self,
state_dict: Mapping[str, Any],
strict: bool = True,
assign: bool = False
)
Copy parameters and buffers from :attr:state_dict into this module and its descendants.
If :attr:strict is True, then
the keys of :attr:state_dict must exactly match the keys returned
by this module's :meth:~torch.nn.Module.state_dict function.
.. warning::
If :attr:assign is True the optimizer must be created after
the call to :attr:load_state_dict unless
:func:~torch.__future__.get_swap_module_params_on_conversion is True.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| state_dict | dict | a dict containing parameters and persistent buffers. |
None |
| strict | bool | 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 |
None |
| assign | bool | When False, the properties of the tensorsin the current module are preserved while when True, theproperties of the Tensors in the state dict are preserved. The only exception is the requires_grad field of :class:~torch.nn.Parametersfor which the value from the module is preserved. Default: False |
None |
Returns:
| Type | Description |
|---|---|
| None | 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 |
View Source
def load_state_dict(self, state_dict: Mapping[str, Any],
strict: bool = True, assign: bool = False):
r"""Copy parameters and buffers from :attr:`state_dict` into this module and its descendants.
If :attr:`strict` is ``True``, then
the keys of :attr:`state_dict` must exactly match the keys returned
by this module's :meth:`~torch.nn.Module.state_dict` function.
.. warning::
If :attr:`assign` is ``True`` the optimizer must be created after
the call to :attr:`load_state_dict` unless
:func:`~torch.__future__.get_swap_module_params_on_conversion` is ``True``.
Args:
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``
assign (bool, optional): When ``False``, the properties of the tensors
in the current module are preserved while when ``True``, the
properties of the Tensors in the state dict are preserved. The only
exception is the ``requires_grad`` field of :class:`~torch.nn.Parameter`s
for which the value from the module is preserved.
Default: ``False``
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
Note:
If a parameter or buffer is registered as ``None`` and its corresponding key
exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a
``RuntimeError``.
"""
if not isinstance(state_dict, Mapping):
raise TypeError(f"Expected state_dict to be dict-like, got {type(state_dict)}.")
missing_keys: List[str] = []
unexpected_keys: List[str] = []
error_msgs: List[str] = []
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, '_metadata', None)
state_dict = OrderedDict(state_dict)
if metadata is not None:
# mypy isn't aware that "_metadata" exists in state_dict
state_dict._metadata = metadata # type: ignore[attr-defined]
def load(module, local_state_dict, prefix=''):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
if assign:
local_metadata['assign_to_params_buffers'] = assign
module._load_from_state_dict(
local_state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
for name, child in module._modules.items():
if child is not None:
child_prefix = prefix + name + '.'
child_state_dict = {k: v for k, v in local_state_dict.items() if k.startswith(child_prefix)}
load(child, child_state_dict, child_prefix) # noqa: F821
# Note that the hook can modify missing_keys and unexpected_keys.
incompatible_keys = _IncompatibleKeys(missing_keys, unexpected_keys)
for hook in module._load_state_dict_post_hooks.values():
out = hook(module, incompatible_keys)
assert out is None, (
"Hooks registered with ``register_load_state_dict_post_hook`` are not"
"expected to return new values, if incompatible_keys need to be modified,"
"it should be done inplace."
)
load(self, state_dict)
del load
if strict:
if len(unexpected_keys) > 0:
error_msgs.insert(
0, 'Unexpected key(s) in state_dict: {}. '.format(
', '.join(f'"{k}"' for k in unexpected_keys)))
if len(missing_keys) > 0:
error_msgs.insert(
0, 'Missing key(s) in state_dict: {}. '.format(
', '.join(f'"{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)
modules
def modules(
self
) -> Iterator[ForwardRef('Module')]
Return an iterator over all modules in the network.
Yields:
| Type | Description |
|---|---|
| Module | a module in the network |
View Source
def modules(self) -> Iterator['Module']:
r"""Return an iterator over all modules in the network.
Yields:
Module: a module in the network
Note:
Duplicate modules are returned only once. In the following
example, ``l`` will be returned only once.
Example::
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
... print(idx, '->', m)
0 -> Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)
"""
for _, module in self.named_modules():
yield module
named_buffers
def named_buffers(
self,
prefix: str = '',
recurse: bool = True,
remove_duplicate: bool = True
) -> Iterator[Tuple[str, torch.Tensor]]
Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| prefix | str | prefix to prepend to all buffer names. | None |
| recurse | bool | if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True. |
None |
| remove_duplicate | bool | whether to remove the duplicated buffers in the result. Defaults to True. | True |
Yields:
| Type | Description |
|---|---|
| None | (str, torch.Tensor): Tuple containing the name and buffer |
View Source
def named_buffers(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> Iterator[Tuple[str, Tensor]]:
r"""Return 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, optional): if True, then yields buffers of this module
and all submodules. Otherwise, yields only buffers that
are direct members of this module. Defaults to True.
remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.
Yields:
(str, torch.Tensor): Tuple containing the name and buffer
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, buf in self.named_buffers():
>>> if name in ['running_var']:
>>> print(buf.size())
"""
gen = self._named_members(
lambda module: module._buffers.items(),
prefix=prefix, recurse=recurse, remove_duplicate=remove_duplicate)
yield from gen
named_children
def named_children(
self
) -> Iterator[Tuple[str, ForwardRef('Module')]]
Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
Yields:
| Type | Description |
|---|---|
| None | (str, Module): Tuple containing a name and child module |
View Source
def named_children(self) -> Iterator[Tuple[str, 'Module']]:
r"""Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
Yields:
(str, Module): Tuple containing a name and child module
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, module in model.named_children():
>>> if name in ['conv4', 'conv5']:
>>> print(module)
"""
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
named_modules
def named_modules(
self,
memo: Optional[Set[ForwardRef('Module')]] = None,
prefix: str = '',
remove_duplicate: bool = True
)
Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| memo | None | a memo to store the set of modules already added to the result | None |
| prefix | None | a prefix that will be added to the name of the module | None |
| remove_duplicate | None | whether to remove the duplicated module instances in the result or not |
None |
Yields:
| Type | Description |
|---|---|
| None | (str, Module): Tuple of name and module |
View Source
def named_modules(self, memo: Optional[Set['Module']] = None, prefix: str = '', remove_duplicate: bool = True):
r"""Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
Args:
memo: a memo to store the set of modules already added to the result
prefix: a prefix that will be added to the name of the module
remove_duplicate: whether to remove the duplicated module instances in the result
or not
Yields:
(str, Module): Tuple of name and module
Note:
Duplicate modules are returned only once. In the following
example, ``l`` will be returned only once.
Example::
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
... print(idx, '->', m)
0 -> ('', Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
))
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))
"""
if memo is None:
memo = set()
if self not in memo:
if remove_duplicate:
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
yield from module.named_modules(memo, submodule_prefix, remove_duplicate)
named_parameters
def named_parameters(
self,
prefix: str = '',
recurse: bool = True,
remove_duplicate: bool = True
) -> Iterator[Tuple[str, torch.nn.parameter.Parameter]]
Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| prefix | str | prefix to prepend to all parameter names. | None |
| recurse | bool | if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. |
None |
| remove_duplicate | bool | whether to remove the duplicated parameters in the result. Defaults to True. |
None |
Yields:
| Type | Description |
|---|---|
| None | (str, Parameter): Tuple containing the name and parameter |
View Source
def named_parameters(
self,
prefix: str = '',
recurse: bool = True,
remove_duplicate: bool = True
) -> Iterator[Tuple[str, Parameter]]:
r"""Return 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.
remove_duplicate (bool, optional): whether to remove the duplicated
parameters in the result. Defaults to True.
Yields:
(str, Parameter): Tuple containing the name and parameter
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, param in self.named_parameters():
>>> if name in ['bias']:
>>> print(param.size())
"""
gen = self._named_members(
lambda module: module._parameters.items(),
prefix=prefix, recurse=recurse, remove_duplicate=remove_duplicate)
yield from gen
parameters
def parameters(
self,
recurse: bool = True
) -> Iterator[torch.nn.parameter.Parameter]
Return an iterator over module parameters.
This is typically passed to an optimizer.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| recurse | bool | if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. |
None |
Yields:
| Type | Description |
|---|---|
| Parameter | module parameter |
View Source
def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
r"""Return 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::
>>> # xdoctest: +SKIP("undefined vars")
>>> 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 param
register_backward_hook
def register_backward_hook(
self,
hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]]
) -> torch.utils.hooks.RemovableHandle
Register a backward hook on the module.
This function is deprecated in favor of :meth:~torch.nn.Module.register_full_backward_hook and
the behavior of this function will change in future versions.
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_backward_hook(
self, hook: Callable[['Module', _grad_t, _grad_t], Union[None, _grad_t]]
) -> RemovableHandle:
r"""Register a backward hook on the module.
This function is deprecated in favor of :meth:`~torch.nn.Module.register_full_backward_hook` and
the behavior of this function will change in future versions.
Returns:
: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 = False
handle = hooks.RemovableHandle(self._backward_hooks)
self._backward_hooks[handle.id] = hook
return handle
register_buffer
def register_buffer(
self,
name: str,
tensor: Optional[torch.Tensor],
persistent: bool = True
) -> None
Add a buffer to the module.
This is typically used to register a buffer that should not to be
considered a model parameter. For example, BatchNorm's running_mean
is not a parameter, but is part of the module's state. Buffers, by
default, are persistent and will be saved alongside parameters. This
behavior can be changed by setting :attr:persistent to False. The
only difference between a persistent buffer and a non-persistent buffer
is that the latter will not be a part of this module's
:attr:state_dict.
Buffers can be accessed as attributes using given names.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| name | str | name of the buffer. The buffer can be accessed from this module using the given name |
None |
| tensor | Tensor or None | buffer to be registered. If None, then operationsthat run on buffers, such as :attr: cuda, are ignored. If None,the buffer is not included in the module's :attr: state_dict. |
None |
| persistent | bool | whether the buffer is part of this module's :attr: state_dict. |
None |
View Source
def register_buffer(self, name: str, tensor: Optional[Tensor], persistent: bool = True) -> None:
r"""Add a buffer to the module.
This is typically used to register a buffer that should not to be
considered a model parameter. For example, BatchNorm's ``running_mean``
is not a parameter, but is part of the module's state. Buffers, by
default, are persistent and will be saved alongside parameters. This
behavior can be changed by setting :attr:`persistent` to ``False``. The
only difference between a persistent buffer and a non-persistent buffer
is that the latter will not be a part of this module's
:attr:`state_dict`.
Buffers can be accessed as attributes using given names.
Args:
name (str): name of the buffer. The buffer can be accessed
from this module using the given name
tensor (Tensor or None): buffer to be registered. If ``None``, then operations
that run on buffers, such as :attr:`cuda`, are ignored. If ``None``,
the buffer is **not** included in the module's :attr:`state_dict`.
persistent (bool): whether the buffer is part of this module's
:attr:`state_dict`.
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> 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, str):
raise TypeError(f"buffer name should be a string. Got {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(f"attribute '{name}' already exists")
elif tensor is not None and not isinstance(tensor, torch.Tensor):
raise TypeError(f"cannot assign '{torch.typename(tensor)}' object to buffer '{name}' "
"(torch Tensor or None required)"
)
else:
for hook in _global_buffer_registration_hooks.values():
output = hook(self, name, tensor)
if output is not None:
tensor = output
self._buffers[name] = tensor
if persistent:
self._non_persistent_buffers_set.discard(name)
else:
self._non_persistent_buffers_set.add(name)
register_forward_hook
def register_forward_hook(
self,
hook: Union[Callable[[~T, Tuple[Any, ...], Any], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any], Any], Optional[Any]]],
*,
prepend: bool = False,
with_kwargs: bool = False,
always_call: bool = False
) -> torch.utils.hooks.RemovableHandle
Register a forward hook on the module.
The hook will be called every time after :func:forward has computed an output.
If with_kwargs is False or not specified, the input contains only
the positional arguments given to the module. Keyword arguments won't be
passed to the hooks and only to the forward. The hook can modify the
output. It can modify the input inplace but it will not have effect on
forward since this is called after :func:forward is called. The hook
should have the following signature::
hook(module, args, output) -> None or modified output
If with_kwargs is True, the forward hook will be passed the
kwargs given to the forward function and be expected to return the
output possibly modified. The hook should have the following signature::
hook(module, args, kwargs, output) -> None or modified output
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| hook | Callable | The user defined hook to be registered. | None |
| prepend | bool | If True, the provided hook will be firedbefore all existing forward hooks on this:class: torch.nn.modules.Module. Otherwise, the providedhook will be fired after all existing forward hooks onthis :class: torch.nn.modules.Module. Note that globalforward hooks registered with:func: register_module_forward_hook will fire before all hooksregistered by this method. Default: False |
None |
| with_kwargs | bool | If True, the hook will be passed thekwargs given to the forward function. Default: False |
None |
| always_call | bool | If True the hook will be run regardless ofwhether an exception is raised while calling the Module. Default: False |
None |
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_forward_hook(
self,
hook: Union[
Callable[[T, Tuple[Any, ...], Any], Optional[Any]],
Callable[[T, Tuple[Any, ...], Dict[str, Any], Any], Optional[Any]],
],
*,
prepend: bool = False,
with_kwargs: bool = False,
always_call: bool = False,
) -> RemovableHandle:
r"""Register a forward hook on the module.
The hook will be called every time after :func:`forward` has computed an output.
If ``with_kwargs`` is ``False`` or not specified, the input contains only
the positional arguments given to the module. Keyword arguments won't be
passed to the hooks and only to the ``forward``. The hook can modify the
output. It can modify the input inplace but it will not have effect on
forward since this is called after :func:`forward` is called. The hook
should have the following signature::
hook(module, args, output) -> None or modified output
If ``with_kwargs`` is ``True``, the forward hook will be passed the
``kwargs`` given to the forward function and be expected to return the
output possibly modified. The hook should have the following signature::
hook(module, args, kwargs, output) -> None or modified output
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If ``True``, the provided ``hook`` will be fired
before all existing ``forward`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``forward`` hooks on
this :class:`torch.nn.modules.Module`. Note that global
``forward`` hooks registered with
:func:`register_module_forward_hook` will fire before all hooks
registered by this method.
Default: ``False``
with_kwargs (bool): If ``True``, the ``hook`` will be passed the
kwargs given to the forward function.
Default: ``False``
always_call (bool): If ``True`` the ``hook`` will be run regardless of
whether an exception is raised while calling the Module.
Default: ``False``
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,
extra_dict=[self._forward_hooks_with_kwargs, self._forward_hooks_always_called],
)
self._forward_hooks[handle.id] = hook
if with_kwargs:
self._forward_hooks_with_kwargs[handle.id] = True
if always_call:
self._forward_hooks_always_called[handle.id] = True
if prepend:
self._forward_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
register_forward_pre_hook
def register_forward_pre_hook(
self,
hook: Union[Callable[[~T, Tuple[Any, ...]], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any]], Optional[Tuple[Any, Dict[str, Any]]]]],
*,
prepend: bool = False,
with_kwargs: bool = False
) -> torch.utils.hooks.RemovableHandle
Register a forward pre-hook on the module.
The hook will be called every time before :func:forward is invoked.
If with_kwargs is false or not specified, the input contains only
the positional arguments given to the module. Keyword arguments won't be
passed to the hooks and only to the forward. The hook can modify the
input. User can either return a tuple or a single modified value in the
hook. We will wrap the value into a tuple if a single value is returned
(unless that value is already a tuple). The hook should have the
following signature::
hook(module, args) -> None or modified input
If with_kwargs is true, the forward pre-hook will be passed the
kwargs given to the forward function. And if the hook modifies the
input, both the args and kwargs should be returned. The hook should have
the following signature::
hook(module, args, kwargs) -> None or a tuple of modified input and kwargs
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| hook | Callable | The user defined hook to be registered. | None |
| prepend | bool | If true, the provided hook will be fired beforeall existing forward_pre hooks on this:class: torch.nn.modules.Module. Otherwise, the providedhook will be fired after all existing forward_pre hookson this :class: torch.nn.modules.Module. Note that globalforward_pre hooks registered with:func: register_module_forward_pre_hook will fire before allhooks registered by this method. Default: False |
None |
| with_kwargs | bool | If true, the hook will be passed the kwargsgiven to the forward function. Default: False |
None |
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_forward_pre_hook(
self,
hook: Union[
Callable[[T, Tuple[Any, ...]], Optional[Any]],
Callable[[T, Tuple[Any, ...], Dict[str, Any]], Optional[Tuple[Any, Dict[str, Any]]]],
],
*,
prepend: bool = False,
with_kwargs: bool = False,
) -> RemovableHandle:
r"""Register a forward pre-hook on the module.
The hook will be called every time before :func:`forward` is invoked.
If ``with_kwargs`` is false or not specified, the input contains only
the positional arguments given to the module. Keyword arguments won't be
passed to the hooks and only to the ``forward``. The hook can modify the
input. User can either return a tuple or a single modified value in the
hook. We will wrap the value into a tuple if a single value is returned
(unless that value is already a tuple). The hook should have the
following signature::
hook(module, args) -> None or modified input
If ``with_kwargs`` is true, the forward pre-hook will be passed the
kwargs given to the forward function. And if the hook modifies the
input, both the args and kwargs should be returned. The hook should have
the following signature::
hook(module, args, kwargs) -> None or a tuple of modified input and kwargs
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``forward_pre`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``forward_pre`` hooks
on this :class:`torch.nn.modules.Module`. Note that global
``forward_pre`` hooks registered with
:func:`register_module_forward_pre_hook` will fire before all
hooks registered by this method.
Default: ``False``
with_kwargs (bool): If true, the ``hook`` will be passed the kwargs
given to the forward function.
Default: ``False``
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,
extra_dict=self._forward_pre_hooks_with_kwargs
)
self._forward_pre_hooks[handle.id] = hook
if with_kwargs:
self._forward_pre_hooks_with_kwargs[handle.id] = True
if prepend:
self._forward_pre_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
register_full_backward_hook
def register_full_backward_hook(
self,
hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]],
prepend: bool = False
) -> torch.utils.hooks.RemovableHandle
Register a backward hook on the module.
The hook will be called every time the gradients with respect to a module are computed, i.e. the hook will execute if and only if the gradients with respect to module outputs are computed. The hook should have the following signature::
hook(module, grad_input, grad_output) -> tuple(Tensor) or None
The :attr:grad_input and :attr:grad_output are tuples that contain the gradients
with respect to the inputs and outputs respectively. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the input that will be used in place of :attr:grad_input in
subsequent computations. :attr:grad_input will only correspond to the inputs given
as positional arguments and all kwarg arguments are ignored. Entries
in :attr:grad_input and :attr:grad_output will be None for all non-Tensor
arguments.
For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function.
.. warning :: Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| hook | Callable | The user-defined hook to be registered. | None |
| prepend | bool | If true, the provided hook will be fired beforeall existing backward hooks on this:class: torch.nn.modules.Module. Otherwise, the providedhook will be fired after all existing backward hooks onthis :class: torch.nn.modules.Module. Note that globalbackward hooks registered with:func: register_module_full_backward_hook will fire beforeall hooks registered by this method. |
None |
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_full_backward_hook(
self,
hook: Callable[["Module", _grad_t, _grad_t], Union[None, _grad_t]],
prepend: bool = False,
) -> RemovableHandle:
r"""Register a backward hook on the module.
The hook will be called every time the gradients with respect to a module
are computed, i.e. the hook will execute if and only if the gradients with
respect to module outputs are computed. The hook should have the following
signature::
hook(module, grad_input, grad_output) -> tuple(Tensor) or None
The :attr:`grad_input` and :attr:`grad_output` are tuples that contain the gradients
with respect to the inputs and outputs respectively. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the input that will be used in place of :attr:`grad_input` in
subsequent computations. :attr:`grad_input` will only correspond to the inputs given
as positional arguments and all kwarg arguments are ignored. Entries
in :attr:`grad_input` and :attr:`grad_output` will be ``None`` for all non-Tensor
arguments.
For technical reasons, when this hook is applied to a Module, its forward function will
receive a view of each Tensor passed to the Module. Similarly the caller will receive a view
of each Tensor returned by the Module's forward function.
.. warning ::
Modifying inputs or outputs inplace is not allowed when using backward hooks and
will raise an error.
Args:
hook (Callable): The user-defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``backward`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``backward`` hooks on
this :class:`torch.nn.modules.Module`. Note that global
``backward`` hooks registered with
:func:`register_module_full_backward_hook` will fire before
all hooks registered by this method.
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 = True
handle = hooks.RemovableHandle(self._backward_hooks)
self._backward_hooks[handle.id] = hook
if prepend:
self._backward_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
register_full_backward_pre_hook
def register_full_backward_pre_hook(
self,
hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]],
prepend: bool = False
) -> torch.utils.hooks.RemovableHandle
Register a backward pre-hook on the module.
The hook will be called every time the gradients for the module are computed. The hook should have the following signature::
hook(module, grad_output) -> tuple[Tensor] or None
The :attr:grad_output is a tuple. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the output that will be used in place of :attr:grad_output in
subsequent computations. Entries in :attr:grad_output will be None for
all non-Tensor arguments.
For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function.
.. warning :: Modifying inputs inplace is not allowed when using backward hooks and will raise an error.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| hook | Callable | The user-defined hook to be registered. | None |
| prepend | bool | If true, the provided hook will be fired beforeall existing backward_pre hooks on this:class: torch.nn.modules.Module. Otherwise, the providedhook will be fired after all existing backward_pre hookson this :class: torch.nn.modules.Module. Note that globalbackward_pre hooks registered with:func: register_module_full_backward_pre_hook will fire beforeall hooks registered by this method. |
None |
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_full_backward_pre_hook(
self,
hook: Callable[["Module", _grad_t], Union[None, _grad_t]],
prepend: bool = False,
) -> RemovableHandle:
r"""Register a backward pre-hook on the module.
The hook will be called every time the gradients for the module are computed.
The hook should have the following signature::
hook(module, grad_output) -> tuple[Tensor] or None
The :attr:`grad_output` is a tuple. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the output that will be used in place of :attr:`grad_output` in
subsequent computations. Entries in :attr:`grad_output` will be ``None`` for
all non-Tensor arguments.
For technical reasons, when this hook is applied to a Module, its forward function will
receive a view of each Tensor passed to the Module. Similarly the caller will receive a view
of each Tensor returned by the Module's forward function.
.. warning ::
Modifying inputs inplace is not allowed when using backward hooks and
will raise an error.
Args:
hook (Callable): The user-defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``backward_pre`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``backward_pre`` hooks
on this :class:`torch.nn.modules.Module`. Note that global
``backward_pre`` hooks registered with
:func:`register_module_full_backward_pre_hook` will fire before
all hooks registered by this method.
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_pre_hooks)
self._backward_pre_hooks[handle.id] = hook
if prepend:
self._backward_pre_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
register_load_state_dict_post_hook
def register_load_state_dict_post_hook(
self,
hook
)
Register a post hook to be run after module's load_state_dict is called.
It should have the following signature:: hook(module, incompatible_keys) -> None
The module argument is the current module that this hook is registered
on, and the incompatible_keys argument is a NamedTuple consisting
of attributes missing_keys and unexpected_keys. missing_keys
is a list of str containing the missing keys and
unexpected_keys is a list of str containing the unexpected keys.
The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling :func:load_state_dict with
strict=True are affected by modifications the hook makes to
missing_keys or unexpected_keys, as expected. Additions to either
set of keys will result in an error being thrown when strict=True, and
clearing out both missing and unexpected keys will avoid an error.
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_load_state_dict_post_hook(self, hook):
r"""Register a post hook to be run after module's ``load_state_dict`` is called.
It should have the following signature::
hook(module, incompatible_keys) -> None
The ``module`` argument is the current module that this hook is registered
on, and the ``incompatible_keys`` argument is a ``NamedTuple`` consisting
of attributes ``missing_keys`` and ``unexpected_keys``. ``missing_keys``
is a ``list`` of ``str`` containing the missing keys and
``unexpected_keys`` is a ``list`` of ``str`` containing the unexpected keys.
The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling :func:`load_state_dict` with
``strict=True`` are affected by modifications the hook makes to
``missing_keys`` or ``unexpected_keys``, as expected. Additions to either
set of keys will result in an error being thrown when ``strict=True``, and
clearing out both missing and unexpected keys will avoid an error.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
"""
handle = hooks.RemovableHandle(self._load_state_dict_post_hooks)
self._load_state_dict_post_hooks[handle.id] = hook
return handle
register_module
def register_module(
self,
name: str,
module: Optional[ForwardRef('Module')]
) -> None
Alias for :func:add_module.
View Source
def register_module(self, name: str, module: Optional['Module']) -> None:
r"""Alias for :func:`add_module`."""
self.add_module(name, module)
register_parameter
def register_parameter(
self,
name: str,
param: Optional[torch.nn.parameter.Parameter]
) -> None
Add a parameter to the module.
The parameter can be accessed as an attribute using given name.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| name | str | name of the parameter. The parameter can be accessed from this module using the given name |
None |
| param | Parameter or None | parameter to be added to the module. IfNone, then operations that run on parameters, such as :attr:cuda,are ignored. If None, the parameter is not included in themodule's :attr: state_dict. |
None |
View Source
def register_parameter(self, name: str, param: Optional[Parameter]) -> None:
r"""Add a parameter to the module.
The parameter can be accessed as an attribute using given name.
Args:
name (str): name of the parameter. The parameter can be accessed
from this module using the given name
param (Parameter or None): parameter to be added to the module. If
``None``, then operations that run on parameters, such as :attr:`cuda`,
are ignored. If ``None``, the parameter is **not** included in the
module's :attr:`state_dict`.
"""
if '_parameters' not in self.__dict__:
raise AttributeError(
"cannot assign parameter before Module.__init__() call")
elif not isinstance(name, str):
raise TypeError(f"parameter name should be a string. Got {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(f"attribute '{name}' already exists")
if param is None:
self._parameters[name] = None
elif not isinstance(param, Parameter):
raise TypeError(f"cannot assign '{torch.typename(param)}' object to parameter '{name}' "
"(torch.nn.Parameter or None required)"
)
elif param.grad_fn:
raise ValueError(
f"Cannot assign non-leaf Tensor to parameter '{name}'. Model "
f"parameters must be created explicitly. To express '{name}' "
"as a function of another Tensor, compute the value in "
"the forward() method.")
else:
for hook in _global_parameter_registration_hooks.values():
output = hook(self, name, param)
if output is not None:
param = output
self._parameters[name] = param
register_state_dict_pre_hook
def register_state_dict_pre_hook(
self,
hook
)
Register a pre-hook for the :meth:~torch.nn.Module.state_dict method.
These hooks will be called with arguments: self, prefix,
and keep_vars before calling state_dict on self. The registered
hooks can be used to perform pre-processing before the state_dict
call is made.
View Source
def register_state_dict_pre_hook(self, hook):
r"""Register a pre-hook for the :meth:`~torch.nn.Module.state_dict` method.
These hooks will be called with arguments: ``self``, ``prefix``,
and ``keep_vars`` before calling ``state_dict`` on ``self``. The registered
hooks can be used to perform pre-processing before the ``state_dict``
call is made.
"""
handle = hooks.RemovableHandle(self._state_dict_pre_hooks)
self._state_dict_pre_hooks[handle.id] = hook
return handle
requires_grad_
def requires_grad_(
self: ~T,
requires_grad: bool = True
) -> ~T
Change if autograd should record operations on parameters in this module.
This method sets the parameters' :attr:requires_grad attributes
in-place.
This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training).
See :ref:locally-disable-grad-doc for a comparison between
.requires_grad_() and several similar mechanisms that may be confused with it.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| requires_grad | bool | whether autograd should record operations on parameters in this module. Default: True. |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def requires_grad_(self: T, requires_grad: bool = True) -> T:
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).
See :ref:`locally-disable-grad-doc` for a comparison between
`.requires_grad_()` and several similar mechanisms that may be confused with it.
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
set_extra_state
def set_extra_state(
self,
state: Any
) -> None
Set extra state contained in the loaded state_dict.
This function is called from :func:load_state_dict to handle any extra state
found within the state_dict. Implement this function and a corresponding
View Source
def set_extra_state(self, state: Any) -> None:
"""Set extra state contained in the loaded `state_dict`.
This function is called from :func:`load_state_dict` to handle any extra state
found within the `state_dict`. Implement this function and a corresponding
:func:`get_extra_state` for your module if you need to store extra state within its
`state_dict`.
Args:
state (dict): Extra state from the `state_dict`
"""
raise RuntimeError(
"Reached a code path in Module.set_extra_state() that should never be called. "
"Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml "
"to report this bug.")
share_memory
def share_memory(
self: ~T
) -> ~T
See :meth:torch.Tensor.share_memory_.
View Source
def share_memory(self: T) -> T:
r"""See :meth:`torch.Tensor.share_memory_`."""
return self._apply(lambda t: t.share_memory_())
state_dict
def state_dict(
self,
*args,
destination=None,
prefix='',
keep_vars=False
)
Return a dictionary containing references to the whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.
Parameters and buffers set to None are not included.
.. note:: The returned object is a shallow copy. It contains references to the module's parameters and buffers.
.. warning::
Currently state_dict() also accepts positional arguments for
destination, prefix and keep_vars in order. However,
this is being deprecated and keyword arguments will be enforced in
future releases.
.. warning::
Please avoid the use of argument destination as it is not
designed for end-users.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| destination | dict | If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned.Default: None. |
None |
| prefix | str | a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''. |
None |
| keep_vars | bool | by default the :class:~torch.Tensor sreturned in the state dict are detached from autograd. If it's set to True, detaching will not be performed.Default: False. |
None |
Returns:
| Type | Description |
|---|---|
| dict | a dictionary containing a whole state of the module |
View Source
def state_dict(self, *args, destination=None, prefix='', keep_vars=False):
r"""Return a dictionary containing references to the whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.
Parameters and buffers set to ``None`` are not included.
.. note::
The returned object is a shallow copy. It contains references
to the module's parameters and buffers.
.. warning::
Currently ``state_dict()`` also accepts positional arguments for
``destination``, ``prefix`` and ``keep_vars`` in order. However,
this is being deprecated and keyword arguments will be enforced in
future releases.
.. warning::
Please avoid the use of argument ``destination`` as it is not
designed for end-users.
Args:
destination (dict, optional): If provided, the state of module will
be updated into the dict and the same object is returned.
Otherwise, an ``OrderedDict`` will be created and returned.
Default: ``None``.
prefix (str, optional): a prefix added to parameter and buffer
names to compose the keys in state_dict. Default: ``''``.
keep_vars (bool, optional): by default the :class:`~torch.Tensor` s
returned in the state dict are detached from autograd. If it's
set to ``True``, detaching will not be performed.
Default: ``False``.
Returns:
dict:
a dictionary containing a whole state of the module
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']
"""
# TODO: Remove `args` and the parsing logic when BC allows.
if len(args) > 0:
if destination is None:
destination = args[0]
if len(args) > 1 and prefix == '':
prefix = args[1]
if len(args) > 2 and keep_vars is False:
keep_vars = args[2]
# DeprecationWarning is ignored by default
warnings.warn(
"Positional args are being deprecated, use kwargs instead. Refer to "
"https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict"
" for details.")
if destination is None:
destination = OrderedDict()
destination._metadata = OrderedDict()
local_metadata = dict(version=self._version)
if hasattr(destination, "_metadata"):
destination._metadata[prefix[:-1]] = local_metadata
for hook in self._state_dict_pre_hooks.values():
hook(self, prefix, keep_vars)
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=destination, prefix=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
to
def to(
self,
*args,
**kwargs
)
Move and/or cast the parameters and buffers.
This can be called as
.. function:: to(device=None, dtype=None, non_blocking=False) :noindex:
.. function:: to(dtype, non_blocking=False) :noindex:
.. function:: to(tensor, non_blocking=False) :noindex:
.. function:: to(memory_format=torch.channels_last) :noindex:
Its signature is similar to :meth:torch.Tensor.to, but only accepts
floating point or complex :attr:dtype\ s. In addition, this method will
only cast the floating point or complex parameters and buffers to :attr:dtype
(if given). The integral parameters and buffers will be moved
:attr:device, if that is given, but with dtypes unchanged. When
:attr:non_blocking is set, it tries to convert/move asynchronously
with respect to the host if possible, e.g., moving CPU Tensors with
pinned memory to CUDA devices.
See below for examples.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device ( | None | class:torch.device): the desired device of the parametersand buffers in this module |
None |
| dtype ( | None | class:torch.dtype): the desired floating point or complex dtype ofthe parameters and buffers in this module |
None |
| tensor | torch.Tensor | Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module |
None |
| memory_format ( | None | class:torch.memory_format): the desired memoryformat for 4D parameters and buffers in this module (keyword only argument) |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def to(self, *args, **kwargs):
r"""Move and/or cast the parameters and buffers.
This can be called as
.. function:: to(device=None, dtype=None, non_blocking=False)
:noindex:
.. function:: to(dtype, non_blocking=False)
:noindex:
.. function:: to(tensor, non_blocking=False)
:noindex:
.. function:: to(memory_format=torch.channels_last)
:noindex:
Its signature is similar to :meth:`torch.Tensor.to`, but only accepts
floating point or complex :attr:`dtype`\ s. In addition, this method will
only cast the floating point or complex parameters and buffers to :attr:`dtype`
(if given). The integral parameters and buffers will be moved
:attr:`device`, if that is given, but with dtypes unchanged. When
:attr:`non_blocking` is set, it tries to convert/move asynchronously
with respect to the host if possible, e.g., moving CPU Tensors with
pinned memory to CUDA devices.
See below for examples.
.. note::
This method modifies the module in-place.
Args:
device (:class:`torch.device`): the desired device of the parameters
and buffers in this module
dtype (:class:`torch.dtype`): the desired floating point or complex dtype of
the 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
memory_format (:class:`torch.memory_format`): the desired memory
format for 4D parameters and buffers in this module (keyword
only argument)
Returns:
Module: self
Examples::
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
[-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
[-0.5113, -0.2325]], dtype=torch.float64)
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
[-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
[-0.5112, -0.2324]], dtype=torch.float16)
>>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble)
>>> linear.weight
Parameter containing:
tensor([[ 0.3741+0.j, 0.2382+0.j],
[ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)
>>> linear(torch.ones(3, 2, dtype=torch.cdouble))
tensor([[0.6122+0.j, 0.1150+0.j],
[0.6122+0.j, 0.1150+0.j],
[0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)
"""
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 '
f'dtypes, but got desired dtype={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.yml "
"if a complex module does not work as expected.")
def convert(t):
try:
if convert_to_format is not None and t.dim() in (4, 5):
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,
)
except NotImplementedError as e:
if str(e) == "Cannot copy out of meta tensor; no data!":
raise NotImplementedError(
f"{e} Please use torch.nn.Module.to_empty() instead of torch.nn.Module.to() "
f"when moving module from meta to a different device."
) from None
else:
raise
return self._apply(convert)
to_empty
def to_empty(
self: ~T,
*,
device: Union[int, str, torch.device, NoneType],
recurse: bool = True
) -> ~T
Move the parameters and buffers to the specified device without copying storage.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device ( | None | class:torch.device): The desired device of the parametersand buffers in this module. |
None |
| recurse | bool | Whether parameters and buffers of submodules should be recursively moved to the specified device. |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def to_empty(self: T, *, device: Optional[DeviceLikeType], recurse: bool = True) -> T:
r"""Move the parameters and buffers to the specified device without copying storage.
Args:
device (:class:`torch.device`): The desired device of the parameters
and buffers in this module.
recurse (bool): Whether parameters and buffers of submodules should
be recursively moved to the specified device.
Returns:
Module: self
"""
return self._apply(lambda t: torch.empty_like(t, device=device), recurse=recurse)
train
def train(
self: ~T,
mode: bool = True
) -> ~T
Set 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.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| mode | bool | whether to set training mode (True) or evaluationmode ( False). Default: True. |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def train(self: T, mode: bool = True) -> T:
r"""Set 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
"""
if not isinstance(mode, bool):
raise ValueError("training mode is expected to be boolean")
self.training = mode
for module in self.children():
module.train(mode)
return self
type
def type(
self: ~T,
dst_type: Union[torch.dtype, str]
) -> ~T
Casts all parameters and buffers to :attr:dst_type.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| dst_type | type or string | the desired type | None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def type(self: T, dst_type: Union[dtype, str]) -> T:
r"""Casts all parameters and buffers to :attr:`dst_type`.
.. note::
This method modifies the module in-place.
Args:
dst_type (type or string): the desired type
Returns:
Module: self
"""
return self._apply(lambda t: t.type(dst_type))
xpu
def xpu(
self: ~T,
device: Union[int, torch.device, NoneType] = None
) -> ~T
Move all model parameters and buffers to the XPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on XPU while being optimized.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device | int | if specified, all parameters will be copied to that device |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def xpu(self: T, device: Optional[Union[int, device]] = None) -> T:
r"""Move all model parameters and buffers to the XPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on XPU while being optimized.
.. note::
This method modifies the module in-place.
Arguments:
device (int, optional): if specified, all parameters will be
copied to that device
Returns:
Module: self
"""
return self._apply(lambda t: t.xpu(device))
zero_grad
def zero_grad(
self,
set_to_none: bool = True
) -> None
Reset gradients of all model parameters.
See similar function under :class:torch.optim.Optimizer for more context.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| set_to_none | bool | instead of setting to zero, set the grads to None. See :meth: torch.optim.Optimizer.zero_grad for details. |
None |
View Source
def zero_grad(self, set_to_none: bool = True) -> None:
r"""Reset gradients of all model parameters.
See similar function under :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 = None
else:
if p.grad.grad_fn is not None:
p.grad.detach_()
else:
p.grad.requires_grad_(False)
p.grad.zero_()
MlpBlock
class MlpBlock(
in_dim: int,
dims: Sequence[int],
nonlins: Sequence[Union[str, torch.nn.modules.module.Module]],
batch_norm: bool = True
)
A general-purpose MLP.
Attributes
| Name | Type | Description | Default |
|---|---|---|---|
| in_dim | None | Input dimension. | None |
| dims | None | Hidden dimensions, including output dimension. | None |
| nonlins | None | Non-linearities to apply after each one of the hidden dimensions. Can be either a sequence of strings which are keys in the ACTIVATIONS dict, or instances of nn.Module (e.g. an instance of nn.ReLU()). Length should match 'dims'. |
None |
View Source
class MlpBlock(nn.Module):
"""
A general-purpose MLP.
Args:
in_dim: Input dimension.
dims: Hidden dimensions, including output dimension.
nonlins: Non-linearities to apply after each one of the hidden
dimensions.
Can be either a sequence of strings which are keys in the ACTIVATIONS
dict, or instances of nn.Module (e.g. an instance of nn.ReLU()).
Length should match 'dims'.
"""
def __init__(
self,
in_dim: int,
dims: Sequence[int],
nonlins: Sequence[Union[str, nn.Module]],
batch_norm: bool = True,
):
assert len(nonlins) == len(dims)
self.in_dim = in_dim
self.out_dim = dims[-1]
self.dims = dims
self.nonlins = nonlins
super().__init__()
layers = []
for i, out_dim in enumerate(self.dims):
layers.append(MLPLayer(in_dim, out_dim, nonlins[i], batch_norm))
in_dim = out_dim
self.sequence = nn.Sequential(*layers)
def _make_activation(self, act: Union[str, nn.Module]) -> nn.Module:
if isinstance(act, str):
return ACTIVATIONS[act](**ACTIVATION_DEFAULT_KWARGS[act])
return act
def forward(self, x: Tensor) -> Tensor:
"""
Args:
x: An input tensor, of shape (N, D) containing N samples with D features.
Returns:
An output tensor of shape (N, D_out) where D_out is the output dim.
"""
return self.sequence.forward(x.reshape(x.size(0), -1))
Ancestors (in MRO)
- torch.nn.modules.module.Module
Class variables
T_destination
call_super_init
dump_patches
Methods
add_module
def add_module(
self,
name: str,
module: Optional[ForwardRef('Module')]
) -> None
Add a child module to the current module.
The module can be accessed as an attribute using the given name.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| name | str | name of the child module. The child module can be accessed from this module using the given name |
None |
| module | Module | child module to be added to the module. | None |
View Source
def add_module(self, name: str, module: Optional['Module']) -> None:
r"""Add a child module to the current module.
The module can be accessed as an attribute using the given name.
Args:
name (str): 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(f"{torch.typename(module)} is not a Module subclass")
elif not isinstance(name, str):
raise TypeError(f"module name should be a string. Got {torch.typename(name)}")
elif hasattr(self, name) and name not in self._modules:
raise KeyError(f"attribute '{name}' already exists")
elif '.' in name:
raise KeyError(f"module name can't contain \".\", got: {name}")
elif name == '':
raise KeyError("module name can't be empty string \"\"")
for hook in _global_module_registration_hooks.values():
output = hook(self, name, module)
if output is not None:
module = output
self._modules[name] = module
apply
def apply(
self: ~T,
fn: Callable[[ForwardRef('Module')], NoneType]
) -> ~T
Apply fn recursively to every submodule (as returned by .children()) as well as self.
Typical use includes initializing the parameters of a model
(see also :ref:nn-init-doc).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| fn ( | None | class:Module -> None): function to be applied to each submodule |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def apply(self: T, fn: Callable[['Module'], None]) -> T:
r"""Apply ``fn`` recursively to every submodule (as returned by ``.children()``) as well as self.
Typical use includes initializing the parameters of a model
(see also :ref:`nn-init-doc`).
Args:
fn (:class:`Module` -> None): function to be applied to each submodule
Returns:
Module: self
Example::
>>> @torch.no_grad()
>>> def init_weights(m):
>>> print(m)
>>> if type(m) == nn.Linear:
>>> m.weight.fill_(1.0)
>>> print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[1., 1.],
[1., 1.]], requires_grad=True)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[1., 1.],
[1., 1.]], requires_grad=True)
Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
"""
for module in self.children():
module.apply(fn)
fn(self)
return self
bfloat16
def bfloat16(
self: ~T
) -> ~T
Casts all floating point parameters and buffers to bfloat16 datatype.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def bfloat16(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``bfloat16`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.bfloat16() if t.is_floating_point() else t)
buffers
def buffers(
self,
recurse: bool = True
) -> Iterator[torch.Tensor]
Return an iterator over module buffers.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| recurse | bool | if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. |
None |
Yields:
| Type | Description |
|---|---|
| torch.Tensor | module buffer |
View Source
def buffers(self, recurse: bool = True) -> Iterator[Tensor]:
r"""Return 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::
>>> # xdoctest: +SKIP("undefined vars")
>>> for buf in model.buffers():
>>> print(type(buf), buf.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)
"""
for _, buf in self.named_buffers(recurse=recurse):
yield buf
children
def children(
self
) -> Iterator[ForwardRef('Module')]
Return an iterator over immediate children modules.
Yields:
| Type | Description |
|---|---|
| Module | a child module |
View Source
def children(self) -> Iterator['Module']:
r"""Return an iterator over immediate children modules.
Yields:
Module: a child module
"""
for name, module in self.named_children():
yield module
compile
def compile(
self,
*args,
**kwargs
)
Compile this Module's forward using :func:torch.compile.
This Module's __call__ method is compiled and all arguments are passed as-is
to :func:torch.compile.
See :func:torch.compile for details on the arguments for this function.
View Source
def compile(self, *args, **kwargs):
"""
Compile this Module's forward using :func:`torch.compile`.
This Module's `__call__` method is compiled and all arguments are passed as-is
to :func:`torch.compile`.
See :func:`torch.compile` for details on the arguments for this function.
"""
self._compiled_call_impl = torch.compile(self._call_impl, *args, **kwargs)
cpu
def cpu(
self: ~T
) -> ~T
Move all model parameters and buffers to the CPU.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def cpu(self: T) -> T:
r"""Move all model parameters and buffers to the CPU.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.cpu())
cuda
def cuda(
self: ~T,
device: Union[int, torch.device, NoneType] = None
) -> ~T
Move all model parameters and buffers to the GPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device | int | if specified, all parameters will be copied to that device |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def cuda(self: T, device: Optional[Union[int, device]] = None) -> T:
r"""Move all model parameters and buffers to the GPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on GPU while being optimized.
.. note::
This method modifies the module in-place.
Args:
device (int, optional): if specified, all parameters will be
copied to that device
Returns:
Module: self
"""
return self._apply(lambda t: t.cuda(device))
double
def double(
self: ~T
) -> ~T
Casts all floating point parameters and buffers to double datatype.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def double(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``double`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.double() if t.is_floating_point() else t)
eval
def eval(
self: ~T
) -> ~T
Set 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>.
See :ref:locally-disable-grad-doc for a comparison between
.eval() and several similar mechanisms that may be confused with it.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def eval(self: T) -> T:
r"""Set 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>`.
See :ref:`locally-disable-grad-doc` for a comparison between
`.eval()` and several similar mechanisms that may be confused with it.
Returns:
Module: self
"""
return self.train(False)
extra_repr
def extra_repr(
self
) -> str
Set the extra representation of the module.
To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.
View Source
def extra_repr(self) -> str:
r"""Set the extra representation of the module.
To print customized extra information, you should re-implement
this method in your own modules. Both single-line and multi-line
strings are acceptable.
"""
return ''
float
def float(
self: ~T
) -> ~T
Casts all floating point parameters and buffers to float datatype.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def float(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``float`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.float() if t.is_floating_point() else t)
forward
def forward(
self,
x: torch.Tensor
) -> torch.Tensor
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| x | None | An input tensor, of shape (N, D) containing N samples with D features. | None |
Returns:
| Type | Description |
|---|---|
| None | An output tensor of shape (N, D_out) where D_out is the output dim. |
View Source
def forward(self, x: Tensor) -> Tensor:
"""
Args:
x: An input tensor, of shape (N, D) containing N samples with D features.
Returns:
An output tensor of shape (N, D_out) where D_out is the output dim.
"""
return self.sequence.forward(x.reshape(x.size(0), -1))
get_buffer
def get_buffer(
self,
target: str
) -> 'Tensor'
Return the buffer given by target if it exists, otherwise throw an error.
See the docstring for get_submodule for a more detailed
explanation of this method's functionality as well as how to
correctly specify target.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| target | None | The fully-qualified string name of the buffer to look for. (See get_submodule for how to specify afully-qualified string.) |
None |
Returns:
| Type | Description |
|---|---|
| torch.Tensor | The buffer referenced by target |
Raises:
| Type | Description |
|---|---|
| AttributeError | If the target string references an invalid path or resolves to something that is not a buffer |
View Source
def get_buffer(self, target: str) -> "Tensor":
"""Return the buffer given by ``target`` if it exists, otherwise throw an error.
See the docstring for ``get_submodule`` for a more detailed
explanation of this method's functionality as well as how to
correctly specify ``target``.
Args:
target: The fully-qualified string name of the buffer
to look for. (See ``get_submodule`` for how to specify a
fully-qualified string.)
Returns:
torch.Tensor: The buffer referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not a
buffer
"""
module_path, _, buffer_name = target.rpartition(".")
mod: torch.nn.Module = self.get_submodule(module_path)
if not hasattr(mod, buffer_name):
raise AttributeError(mod._get_name() + " has no attribute `"
+ buffer_name + "`")
buffer: torch.Tensor = getattr(mod, buffer_name)
if buffer_name not in mod._buffers:
raise AttributeError("`" + buffer_name + "` is not a buffer")
return buffer
get_extra_state
def get_extra_state(
self
) -> Any
Return any extra state to include in the module's state_dict.
Implement this and a corresponding :func:set_extra_state for your module
if you need to store extra state. This function is called when building the
module's state_dict().
Note that extra state should be picklable to ensure working serialization of the state_dict. We only provide provide backwards compatibility guarantees for serializing Tensors; other objects may break backwards compatibility if their serialized pickled form changes.
Returns:
| Type | Description |
|---|---|
| object | Any extra state to store in the module's state_dict |
View Source
def get_extra_state(self) -> Any:
"""Return any extra state to include in the module's state_dict.
Implement this and a corresponding :func:`set_extra_state` for your module
if you need to store extra state. This function is called when building the
module's `state_dict()`.
Note that extra state should be picklable to ensure working serialization
of the state_dict. We only provide provide backwards compatibility guarantees
for serializing Tensors; other objects may break backwards compatibility if
their serialized pickled form changes.
Returns:
object: Any extra state to store in the module's state_dict
"""
raise RuntimeError(
"Reached a code path in Module.get_extra_state() that should never be called. "
"Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml "
"to report this bug.")
get_parameter
def get_parameter(
self,
target: str
) -> 'Parameter'
Return the parameter given by target if it exists, otherwise throw an error.
See the docstring for get_submodule for a more detailed
explanation of this method's functionality as well as how to
correctly specify target.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| target | None | The fully-qualified string name of the Parameter to look for. (See get_submodule for how to specify afully-qualified string.) |
None |
Returns:
| Type | Description |
|---|---|
| torch.nn.Parameter | The Parameter referenced by target |
Raises:
| Type | Description |
|---|---|
| AttributeError | If the target string references an invalid path or resolves to something that is not an nn.Parameter |
View Source
def get_parameter(self, target: str) -> "Parameter":
"""Return the parameter given by ``target`` if it exists, otherwise throw an error.
See the docstring for ``get_submodule`` for a more detailed
explanation of this method's functionality as well as how to
correctly specify ``target``.
Args:
target: The fully-qualified string name of the Parameter
to look for. (See ``get_submodule`` for how to specify a
fully-qualified string.)
Returns:
torch.nn.Parameter: The Parameter referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not an
``nn.Parameter``
"""
module_path, _, param_name = target.rpartition(".")
mod: torch.nn.Module = self.get_submodule(module_path)
if not hasattr(mod, param_name):
raise AttributeError(mod._get_name() + " has no attribute `"
+ param_name + "`")
param: torch.nn.Parameter = getattr(mod, param_name)
if not isinstance(param, torch.nn.Parameter):
raise AttributeError("`" + param_name + "` is not an "
"nn.Parameter")
return param
get_submodule
def get_submodule(
self,
target: str
) -> 'Module'
Return the submodule given by target if it exists, otherwise throw an error.
For example, let's say you have an nn.Module A that
looks like this:
.. code-block:: text
A(
(net_b): Module(
(net_c): Module(
(conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))
)
(linear): Linear(in_features=100, out_features=200, bias=True)
)
)
(The diagram shows an nn.Module A. A has a nested
submodule net_b, which itself has two submodules net_c
and linear. net_c then has a submodule conv.)
To check whether or not we have the linear submodule, we
would call get_submodule("net_b.linear"). To check whether
we have the conv submodule, we would call
get_submodule("net_b.net_c.conv").
The runtime of get_submodule is bounded by the degree
of module nesting in target. A query against
named_modules achieves the same result, but it is O(N) in
the number of transitive modules. So, for a simple check to see
if some submodule exists, get_submodule should always be
used.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| target | None | The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.) |
None |
Returns:
| Type | Description |
|---|---|
| torch.nn.Module | The submodule referenced by target |
Raises:
| Type | Description |
|---|---|
| AttributeError | If the target string references an invalid path or resolves to something that is not an nn.Module |
View Source
def get_submodule(self, target: str) -> "Module":
"""Return the submodule given by ``target`` if it exists, otherwise throw an error.
For example, let's say you have an ``nn.Module`` ``A`` that
looks like this:
.. code-block:: text
A(
(net_b): Module(
(net_c): Module(
(conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))
)
(linear): Linear(in_features=100, out_features=200, bias=True)
)
)
(The diagram shows an ``nn.Module`` ``A``. ``A`` has a nested
submodule ``net_b``, which itself has two submodules ``net_c``
and ``linear``. ``net_c`` then has a submodule ``conv``.)
To check whether or not we have the ``linear`` submodule, we
would call ``get_submodule("net_b.linear")``. To check whether
we have the ``conv`` submodule, we would call
``get_submodule("net_b.net_c.conv")``.
The runtime of ``get_submodule`` is bounded by the degree
of module nesting in ``target``. A query against
``named_modules`` achieves the same result, but it is O(N) in
the number of transitive modules. So, for a simple check to see
if some submodule exists, ``get_submodule`` should always be
used.
Args:
target: The fully-qualified string name of the submodule
to look for. (See above example for how to specify a
fully-qualified string.)
Returns:
torch.nn.Module: The submodule referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not an
``nn.Module``
"""
if target == "":
return self
atoms: List[str] = target.split(".")
mod: torch.nn.Module = self
for item in atoms:
if not hasattr(mod, item):
raise AttributeError(mod._get_name() + " has no "
"attribute `" + item + "`")
mod = getattr(mod, item)
if not isinstance(mod, torch.nn.Module):
raise AttributeError("`" + item + "` is not "
"an nn.Module")
return mod
half
def half(
self: ~T
) -> ~T
Casts all floating point parameters and buffers to half datatype.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def half(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``half`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.half() if t.is_floating_point() else t)
ipu
def ipu(
self: ~T,
device: Union[int, torch.device, NoneType] = None
) -> ~T
Move all model parameters and buffers to the IPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on IPU while being optimized.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device | int | if specified, all parameters will be copied to that device |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def ipu(self: T, device: Optional[Union[int, device]] = None) -> T:
r"""Move all model parameters and buffers to the IPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on IPU while being optimized.
.. note::
This method modifies the module in-place.
Arguments:
device (int, optional): if specified, all parameters will be
copied to that device
Returns:
Module: self
"""
return self._apply(lambda t: t.ipu(device))
load_state_dict
def load_state_dict(
self,
state_dict: Mapping[str, Any],
strict: bool = True,
assign: bool = False
)
Copy parameters and buffers from :attr:state_dict into this module and its descendants.
If :attr:strict is True, then
the keys of :attr:state_dict must exactly match the keys returned
by this module's :meth:~torch.nn.Module.state_dict function.
.. warning::
If :attr:assign is True the optimizer must be created after
the call to :attr:load_state_dict unless
:func:~torch.__future__.get_swap_module_params_on_conversion is True.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| state_dict | dict | a dict containing parameters and persistent buffers. |
None |
| strict | bool | 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 |
None |
| assign | bool | When False, the properties of the tensorsin the current module are preserved while when True, theproperties of the Tensors in the state dict are preserved. The only exception is the requires_grad field of :class:~torch.nn.Parametersfor which the value from the module is preserved. Default: False |
None |
Returns:
| Type | Description |
|---|---|
| None | 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 |
View Source
def load_state_dict(self, state_dict: Mapping[str, Any],
strict: bool = True, assign: bool = False):
r"""Copy parameters and buffers from :attr:`state_dict` into this module and its descendants.
If :attr:`strict` is ``True``, then
the keys of :attr:`state_dict` must exactly match the keys returned
by this module's :meth:`~torch.nn.Module.state_dict` function.
.. warning::
If :attr:`assign` is ``True`` the optimizer must be created after
the call to :attr:`load_state_dict` unless
:func:`~torch.__future__.get_swap_module_params_on_conversion` is ``True``.
Args:
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``
assign (bool, optional): When ``False``, the properties of the tensors
in the current module are preserved while when ``True``, the
properties of the Tensors in the state dict are preserved. The only
exception is the ``requires_grad`` field of :class:`~torch.nn.Parameter`s
for which the value from the module is preserved.
Default: ``False``
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
Note:
If a parameter or buffer is registered as ``None`` and its corresponding key
exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a
``RuntimeError``.
"""
if not isinstance(state_dict, Mapping):
raise TypeError(f"Expected state_dict to be dict-like, got {type(state_dict)}.")
missing_keys: List[str] = []
unexpected_keys: List[str] = []
error_msgs: List[str] = []
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, '_metadata', None)
state_dict = OrderedDict(state_dict)
if metadata is not None:
# mypy isn't aware that "_metadata" exists in state_dict
state_dict._metadata = metadata # type: ignore[attr-defined]
def load(module, local_state_dict, prefix=''):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
if assign:
local_metadata['assign_to_params_buffers'] = assign
module._load_from_state_dict(
local_state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
for name, child in module._modules.items():
if child is not None:
child_prefix = prefix + name + '.'
child_state_dict = {k: v for k, v in local_state_dict.items() if k.startswith(child_prefix)}
load(child, child_state_dict, child_prefix) # noqa: F821
# Note that the hook can modify missing_keys and unexpected_keys.
incompatible_keys = _IncompatibleKeys(missing_keys, unexpected_keys)
for hook in module._load_state_dict_post_hooks.values():
out = hook(module, incompatible_keys)
assert out is None, (
"Hooks registered with ``register_load_state_dict_post_hook`` are not"
"expected to return new values, if incompatible_keys need to be modified,"
"it should be done inplace."
)
load(self, state_dict)
del load
if strict:
if len(unexpected_keys) > 0:
error_msgs.insert(
0, 'Unexpected key(s) in state_dict: {}. '.format(
', '.join(f'"{k}"' for k in unexpected_keys)))
if len(missing_keys) > 0:
error_msgs.insert(
0, 'Missing key(s) in state_dict: {}. '.format(
', '.join(f'"{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)
modules
def modules(
self
) -> Iterator[ForwardRef('Module')]
Return an iterator over all modules in the network.
Yields:
| Type | Description |
|---|---|
| Module | a module in the network |
View Source
def modules(self) -> Iterator['Module']:
r"""Return an iterator over all modules in the network.
Yields:
Module: a module in the network
Note:
Duplicate modules are returned only once. In the following
example, ``l`` will be returned only once.
Example::
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
... print(idx, '->', m)
0 -> Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)
"""
for _, module in self.named_modules():
yield module
named_buffers
def named_buffers(
self,
prefix: str = '',
recurse: bool = True,
remove_duplicate: bool = True
) -> Iterator[Tuple[str, torch.Tensor]]
Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| prefix | str | prefix to prepend to all buffer names. | None |
| recurse | bool | if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True. |
None |
| remove_duplicate | bool | whether to remove the duplicated buffers in the result. Defaults to True. | True |
Yields:
| Type | Description |
|---|---|
| None | (str, torch.Tensor): Tuple containing the name and buffer |
View Source
def named_buffers(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> Iterator[Tuple[str, Tensor]]:
r"""Return 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, optional): if True, then yields buffers of this module
and all submodules. Otherwise, yields only buffers that
are direct members of this module. Defaults to True.
remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.
Yields:
(str, torch.Tensor): Tuple containing the name and buffer
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, buf in self.named_buffers():
>>> if name in ['running_var']:
>>> print(buf.size())
"""
gen = self._named_members(
lambda module: module._buffers.items(),
prefix=prefix, recurse=recurse, remove_duplicate=remove_duplicate)
yield from gen
named_children
def named_children(
self
) -> Iterator[Tuple[str, ForwardRef('Module')]]
Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
Yields:
| Type | Description |
|---|---|
| None | (str, Module): Tuple containing a name and child module |
View Source
def named_children(self) -> Iterator[Tuple[str, 'Module']]:
r"""Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
Yields:
(str, Module): Tuple containing a name and child module
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, module in model.named_children():
>>> if name in ['conv4', 'conv5']:
>>> print(module)
"""
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
named_modules
def named_modules(
self,
memo: Optional[Set[ForwardRef('Module')]] = None,
prefix: str = '',
remove_duplicate: bool = True
)
Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| memo | None | a memo to store the set of modules already added to the result | None |
| prefix | None | a prefix that will be added to the name of the module | None |
| remove_duplicate | None | whether to remove the duplicated module instances in the result or not |
None |
Yields:
| Type | Description |
|---|---|
| None | (str, Module): Tuple of name and module |
View Source
def named_modules(self, memo: Optional[Set['Module']] = None, prefix: str = '', remove_duplicate: bool = True):
r"""Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
Args:
memo: a memo to store the set of modules already added to the result
prefix: a prefix that will be added to the name of the module
remove_duplicate: whether to remove the duplicated module instances in the result
or not
Yields:
(str, Module): Tuple of name and module
Note:
Duplicate modules are returned only once. In the following
example, ``l`` will be returned only once.
Example::
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
... print(idx, '->', m)
0 -> ('', Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
))
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))
"""
if memo is None:
memo = set()
if self not in memo:
if remove_duplicate:
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
yield from module.named_modules(memo, submodule_prefix, remove_duplicate)
named_parameters
def named_parameters(
self,
prefix: str = '',
recurse: bool = True,
remove_duplicate: bool = True
) -> Iterator[Tuple[str, torch.nn.parameter.Parameter]]
Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| prefix | str | prefix to prepend to all parameter names. | None |
| recurse | bool | if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. |
None |
| remove_duplicate | bool | whether to remove the duplicated parameters in the result. Defaults to True. |
None |
Yields:
| Type | Description |
|---|---|
| None | (str, Parameter): Tuple containing the name and parameter |
View Source
def named_parameters(
self,
prefix: str = '',
recurse: bool = True,
remove_duplicate: bool = True
) -> Iterator[Tuple[str, Parameter]]:
r"""Return 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.
remove_duplicate (bool, optional): whether to remove the duplicated
parameters in the result. Defaults to True.
Yields:
(str, Parameter): Tuple containing the name and parameter
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, param in self.named_parameters():
>>> if name in ['bias']:
>>> print(param.size())
"""
gen = self._named_members(
lambda module: module._parameters.items(),
prefix=prefix, recurse=recurse, remove_duplicate=remove_duplicate)
yield from gen
parameters
def parameters(
self,
recurse: bool = True
) -> Iterator[torch.nn.parameter.Parameter]
Return an iterator over module parameters.
This is typically passed to an optimizer.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| recurse | bool | if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. |
None |
Yields:
| Type | Description |
|---|---|
| Parameter | module parameter |
View Source
def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
r"""Return 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::
>>> # xdoctest: +SKIP("undefined vars")
>>> 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 param
register_backward_hook
def register_backward_hook(
self,
hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]]
) -> torch.utils.hooks.RemovableHandle
Register a backward hook on the module.
This function is deprecated in favor of :meth:~torch.nn.Module.register_full_backward_hook and
the behavior of this function will change in future versions.
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_backward_hook(
self, hook: Callable[['Module', _grad_t, _grad_t], Union[None, _grad_t]]
) -> RemovableHandle:
r"""Register a backward hook on the module.
This function is deprecated in favor of :meth:`~torch.nn.Module.register_full_backward_hook` and
the behavior of this function will change in future versions.
Returns:
: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 = False
handle = hooks.RemovableHandle(self._backward_hooks)
self._backward_hooks[handle.id] = hook
return handle
register_buffer
def register_buffer(
self,
name: str,
tensor: Optional[torch.Tensor],
persistent: bool = True
) -> None
Add a buffer to the module.
This is typically used to register a buffer that should not to be
considered a model parameter. For example, BatchNorm's running_mean
is not a parameter, but is part of the module's state. Buffers, by
default, are persistent and will be saved alongside parameters. This
behavior can be changed by setting :attr:persistent to False. The
only difference between a persistent buffer and a non-persistent buffer
is that the latter will not be a part of this module's
:attr:state_dict.
Buffers can be accessed as attributes using given names.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| name | str | name of the buffer. The buffer can be accessed from this module using the given name |
None |
| tensor | Tensor or None | buffer to be registered. If None, then operationsthat run on buffers, such as :attr: cuda, are ignored. If None,the buffer is not included in the module's :attr: state_dict. |
None |
| persistent | bool | whether the buffer is part of this module's :attr: state_dict. |
None |
View Source
def register_buffer(self, name: str, tensor: Optional[Tensor], persistent: bool = True) -> None:
r"""Add a buffer to the module.
This is typically used to register a buffer that should not to be
considered a model parameter. For example, BatchNorm's ``running_mean``
is not a parameter, but is part of the module's state. Buffers, by
default, are persistent and will be saved alongside parameters. This
behavior can be changed by setting :attr:`persistent` to ``False``. The
only difference between a persistent buffer and a non-persistent buffer
is that the latter will not be a part of this module's
:attr:`state_dict`.
Buffers can be accessed as attributes using given names.
Args:
name (str): name of the buffer. The buffer can be accessed
from this module using the given name
tensor (Tensor or None): buffer to be registered. If ``None``, then operations
that run on buffers, such as :attr:`cuda`, are ignored. If ``None``,
the buffer is **not** included in the module's :attr:`state_dict`.
persistent (bool): whether the buffer is part of this module's
:attr:`state_dict`.
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> 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, str):
raise TypeError(f"buffer name should be a string. Got {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(f"attribute '{name}' already exists")
elif tensor is not None and not isinstance(tensor, torch.Tensor):
raise TypeError(f"cannot assign '{torch.typename(tensor)}' object to buffer '{name}' "
"(torch Tensor or None required)"
)
else:
for hook in _global_buffer_registration_hooks.values():
output = hook(self, name, tensor)
if output is not None:
tensor = output
self._buffers[name] = tensor
if persistent:
self._non_persistent_buffers_set.discard(name)
else:
self._non_persistent_buffers_set.add(name)
register_forward_hook
def register_forward_hook(
self,
hook: Union[Callable[[~T, Tuple[Any, ...], Any], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any], Any], Optional[Any]]],
*,
prepend: bool = False,
with_kwargs: bool = False,
always_call: bool = False
) -> torch.utils.hooks.RemovableHandle
Register a forward hook on the module.
The hook will be called every time after :func:forward has computed an output.
If with_kwargs is False or not specified, the input contains only
the positional arguments given to the module. Keyword arguments won't be
passed to the hooks and only to the forward. The hook can modify the
output. It can modify the input inplace but it will not have effect on
forward since this is called after :func:forward is called. The hook
should have the following signature::
hook(module, args, output) -> None or modified output
If with_kwargs is True, the forward hook will be passed the
kwargs given to the forward function and be expected to return the
output possibly modified. The hook should have the following signature::
hook(module, args, kwargs, output) -> None or modified output
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| hook | Callable | The user defined hook to be registered. | None |
| prepend | bool | If True, the provided hook will be firedbefore all existing forward hooks on this:class: torch.nn.modules.Module. Otherwise, the providedhook will be fired after all existing forward hooks onthis :class: torch.nn.modules.Module. Note that globalforward hooks registered with:func: register_module_forward_hook will fire before all hooksregistered by this method. Default: False |
None |
| with_kwargs | bool | If True, the hook will be passed thekwargs given to the forward function. Default: False |
None |
| always_call | bool | If True the hook will be run regardless ofwhether an exception is raised while calling the Module. Default: False |
None |
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_forward_hook(
self,
hook: Union[
Callable[[T, Tuple[Any, ...], Any], Optional[Any]],
Callable[[T, Tuple[Any, ...], Dict[str, Any], Any], Optional[Any]],
],
*,
prepend: bool = False,
with_kwargs: bool = False,
always_call: bool = False,
) -> RemovableHandle:
r"""Register a forward hook on the module.
The hook will be called every time after :func:`forward` has computed an output.
If ``with_kwargs`` is ``False`` or not specified, the input contains only
the positional arguments given to the module. Keyword arguments won't be
passed to the hooks and only to the ``forward``. The hook can modify the
output. It can modify the input inplace but it will not have effect on
forward since this is called after :func:`forward` is called. The hook
should have the following signature::
hook(module, args, output) -> None or modified output
If ``with_kwargs`` is ``True``, the forward hook will be passed the
``kwargs`` given to the forward function and be expected to return the
output possibly modified. The hook should have the following signature::
hook(module, args, kwargs, output) -> None or modified output
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If ``True``, the provided ``hook`` will be fired
before all existing ``forward`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``forward`` hooks on
this :class:`torch.nn.modules.Module`. Note that global
``forward`` hooks registered with
:func:`register_module_forward_hook` will fire before all hooks
registered by this method.
Default: ``False``
with_kwargs (bool): If ``True``, the ``hook`` will be passed the
kwargs given to the forward function.
Default: ``False``
always_call (bool): If ``True`` the ``hook`` will be run regardless of
whether an exception is raised while calling the Module.
Default: ``False``
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,
extra_dict=[self._forward_hooks_with_kwargs, self._forward_hooks_always_called],
)
self._forward_hooks[handle.id] = hook
if with_kwargs:
self._forward_hooks_with_kwargs[handle.id] = True
if always_call:
self._forward_hooks_always_called[handle.id] = True
if prepend:
self._forward_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
register_forward_pre_hook
def register_forward_pre_hook(
self,
hook: Union[Callable[[~T, Tuple[Any, ...]], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any]], Optional[Tuple[Any, Dict[str, Any]]]]],
*,
prepend: bool = False,
with_kwargs: bool = False
) -> torch.utils.hooks.RemovableHandle
Register a forward pre-hook on the module.
The hook will be called every time before :func:forward is invoked.
If with_kwargs is false or not specified, the input contains only
the positional arguments given to the module. Keyword arguments won't be
passed to the hooks and only to the forward. The hook can modify the
input. User can either return a tuple or a single modified value in the
hook. We will wrap the value into a tuple if a single value is returned
(unless that value is already a tuple). The hook should have the
following signature::
hook(module, args) -> None or modified input
If with_kwargs is true, the forward pre-hook will be passed the
kwargs given to the forward function. And if the hook modifies the
input, both the args and kwargs should be returned. The hook should have
the following signature::
hook(module, args, kwargs) -> None or a tuple of modified input and kwargs
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| hook | Callable | The user defined hook to be registered. | None |
| prepend | bool | If true, the provided hook will be fired beforeall existing forward_pre hooks on this:class: torch.nn.modules.Module. Otherwise, the providedhook will be fired after all existing forward_pre hookson this :class: torch.nn.modules.Module. Note that globalforward_pre hooks registered with:func: register_module_forward_pre_hook will fire before allhooks registered by this method. Default: False |
None |
| with_kwargs | bool | If true, the hook will be passed the kwargsgiven to the forward function. Default: False |
None |
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_forward_pre_hook(
self,
hook: Union[
Callable[[T, Tuple[Any, ...]], Optional[Any]],
Callable[[T, Tuple[Any, ...], Dict[str, Any]], Optional[Tuple[Any, Dict[str, Any]]]],
],
*,
prepend: bool = False,
with_kwargs: bool = False,
) -> RemovableHandle:
r"""Register a forward pre-hook on the module.
The hook will be called every time before :func:`forward` is invoked.
If ``with_kwargs`` is false or not specified, the input contains only
the positional arguments given to the module. Keyword arguments won't be
passed to the hooks and only to the ``forward``. The hook can modify the
input. User can either return a tuple or a single modified value in the
hook. We will wrap the value into a tuple if a single value is returned
(unless that value is already a tuple). The hook should have the
following signature::
hook(module, args) -> None or modified input
If ``with_kwargs`` is true, the forward pre-hook will be passed the
kwargs given to the forward function. And if the hook modifies the
input, both the args and kwargs should be returned. The hook should have
the following signature::
hook(module, args, kwargs) -> None or a tuple of modified input and kwargs
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``forward_pre`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``forward_pre`` hooks
on this :class:`torch.nn.modules.Module`. Note that global
``forward_pre`` hooks registered with
:func:`register_module_forward_pre_hook` will fire before all
hooks registered by this method.
Default: ``False``
with_kwargs (bool): If true, the ``hook`` will be passed the kwargs
given to the forward function.
Default: ``False``
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,
extra_dict=self._forward_pre_hooks_with_kwargs
)
self._forward_pre_hooks[handle.id] = hook
if with_kwargs:
self._forward_pre_hooks_with_kwargs[handle.id] = True
if prepend:
self._forward_pre_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
register_full_backward_hook
def register_full_backward_hook(
self,
hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]],
prepend: bool = False
) -> torch.utils.hooks.RemovableHandle
Register a backward hook on the module.
The hook will be called every time the gradients with respect to a module are computed, i.e. the hook will execute if and only if the gradients with respect to module outputs are computed. The hook should have the following signature::
hook(module, grad_input, grad_output) -> tuple(Tensor) or None
The :attr:grad_input and :attr:grad_output are tuples that contain the gradients
with respect to the inputs and outputs respectively. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the input that will be used in place of :attr:grad_input in
subsequent computations. :attr:grad_input will only correspond to the inputs given
as positional arguments and all kwarg arguments are ignored. Entries
in :attr:grad_input and :attr:grad_output will be None for all non-Tensor
arguments.
For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function.
.. warning :: Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| hook | Callable | The user-defined hook to be registered. | None |
| prepend | bool | If true, the provided hook will be fired beforeall existing backward hooks on this:class: torch.nn.modules.Module. Otherwise, the providedhook will be fired after all existing backward hooks onthis :class: torch.nn.modules.Module. Note that globalbackward hooks registered with:func: register_module_full_backward_hook will fire beforeall hooks registered by this method. |
None |
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_full_backward_hook(
self,
hook: Callable[["Module", _grad_t, _grad_t], Union[None, _grad_t]],
prepend: bool = False,
) -> RemovableHandle:
r"""Register a backward hook on the module.
The hook will be called every time the gradients with respect to a module
are computed, i.e. the hook will execute if and only if the gradients with
respect to module outputs are computed. The hook should have the following
signature::
hook(module, grad_input, grad_output) -> tuple(Tensor) or None
The :attr:`grad_input` and :attr:`grad_output` are tuples that contain the gradients
with respect to the inputs and outputs respectively. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the input that will be used in place of :attr:`grad_input` in
subsequent computations. :attr:`grad_input` will only correspond to the inputs given
as positional arguments and all kwarg arguments are ignored. Entries
in :attr:`grad_input` and :attr:`grad_output` will be ``None`` for all non-Tensor
arguments.
For technical reasons, when this hook is applied to a Module, its forward function will
receive a view of each Tensor passed to the Module. Similarly the caller will receive a view
of each Tensor returned by the Module's forward function.
.. warning ::
Modifying inputs or outputs inplace is not allowed when using backward hooks and
will raise an error.
Args:
hook (Callable): The user-defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``backward`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``backward`` hooks on
this :class:`torch.nn.modules.Module`. Note that global
``backward`` hooks registered with
:func:`register_module_full_backward_hook` will fire before
all hooks registered by this method.
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 = True
handle = hooks.RemovableHandle(self._backward_hooks)
self._backward_hooks[handle.id] = hook
if prepend:
self._backward_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
register_full_backward_pre_hook
def register_full_backward_pre_hook(
self,
hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]],
prepend: bool = False
) -> torch.utils.hooks.RemovableHandle
Register a backward pre-hook on the module.
The hook will be called every time the gradients for the module are computed. The hook should have the following signature::
hook(module, grad_output) -> tuple[Tensor] or None
The :attr:grad_output is a tuple. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the output that will be used in place of :attr:grad_output in
subsequent computations. Entries in :attr:grad_output will be None for
all non-Tensor arguments.
For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function.
.. warning :: Modifying inputs inplace is not allowed when using backward hooks and will raise an error.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| hook | Callable | The user-defined hook to be registered. | None |
| prepend | bool | If true, the provided hook will be fired beforeall existing backward_pre hooks on this:class: torch.nn.modules.Module. Otherwise, the providedhook will be fired after all existing backward_pre hookson this :class: torch.nn.modules.Module. Note that globalbackward_pre hooks registered with:func: register_module_full_backward_pre_hook will fire beforeall hooks registered by this method. |
None |
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_full_backward_pre_hook(
self,
hook: Callable[["Module", _grad_t], Union[None, _grad_t]],
prepend: bool = False,
) -> RemovableHandle:
r"""Register a backward pre-hook on the module.
The hook will be called every time the gradients for the module are computed.
The hook should have the following signature::
hook(module, grad_output) -> tuple[Tensor] or None
The :attr:`grad_output` is a tuple. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the output that will be used in place of :attr:`grad_output` in
subsequent computations. Entries in :attr:`grad_output` will be ``None`` for
all non-Tensor arguments.
For technical reasons, when this hook is applied to a Module, its forward function will
receive a view of each Tensor passed to the Module. Similarly the caller will receive a view
of each Tensor returned by the Module's forward function.
.. warning ::
Modifying inputs inplace is not allowed when using backward hooks and
will raise an error.
Args:
hook (Callable): The user-defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``backward_pre`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``backward_pre`` hooks
on this :class:`torch.nn.modules.Module`. Note that global
``backward_pre`` hooks registered with
:func:`register_module_full_backward_pre_hook` will fire before
all hooks registered by this method.
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_pre_hooks)
self._backward_pre_hooks[handle.id] = hook
if prepend:
self._backward_pre_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
register_load_state_dict_post_hook
def register_load_state_dict_post_hook(
self,
hook
)
Register a post hook to be run after module's load_state_dict is called.
It should have the following signature:: hook(module, incompatible_keys) -> None
The module argument is the current module that this hook is registered
on, and the incompatible_keys argument is a NamedTuple consisting
of attributes missing_keys and unexpected_keys. missing_keys
is a list of str containing the missing keys and
unexpected_keys is a list of str containing the unexpected keys.
The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling :func:load_state_dict with
strict=True are affected by modifications the hook makes to
missing_keys or unexpected_keys, as expected. Additions to either
set of keys will result in an error being thrown when strict=True, and
clearing out both missing and unexpected keys will avoid an error.
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_load_state_dict_post_hook(self, hook):
r"""Register a post hook to be run after module's ``load_state_dict`` is called.
It should have the following signature::
hook(module, incompatible_keys) -> None
The ``module`` argument is the current module that this hook is registered
on, and the ``incompatible_keys`` argument is a ``NamedTuple`` consisting
of attributes ``missing_keys`` and ``unexpected_keys``. ``missing_keys``
is a ``list`` of ``str`` containing the missing keys and
``unexpected_keys`` is a ``list`` of ``str`` containing the unexpected keys.
The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling :func:`load_state_dict` with
``strict=True`` are affected by modifications the hook makes to
``missing_keys`` or ``unexpected_keys``, as expected. Additions to either
set of keys will result in an error being thrown when ``strict=True``, and
clearing out both missing and unexpected keys will avoid an error.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
"""
handle = hooks.RemovableHandle(self._load_state_dict_post_hooks)
self._load_state_dict_post_hooks[handle.id] = hook
return handle
register_module
def register_module(
self,
name: str,
module: Optional[ForwardRef('Module')]
) -> None
Alias for :func:add_module.
View Source
def register_module(self, name: str, module: Optional['Module']) -> None:
r"""Alias for :func:`add_module`."""
self.add_module(name, module)
register_parameter
def register_parameter(
self,
name: str,
param: Optional[torch.nn.parameter.Parameter]
) -> None
Add a parameter to the module.
The parameter can be accessed as an attribute using given name.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| name | str | name of the parameter. The parameter can be accessed from this module using the given name |
None |
| param | Parameter or None | parameter to be added to the module. IfNone, then operations that run on parameters, such as :attr:cuda,are ignored. If None, the parameter is not included in themodule's :attr: state_dict. |
None |
View Source
def register_parameter(self, name: str, param: Optional[Parameter]) -> None:
r"""Add a parameter to the module.
The parameter can be accessed as an attribute using given name.
Args:
name (str): name of the parameter. The parameter can be accessed
from this module using the given name
param (Parameter or None): parameter to be added to the module. If
``None``, then operations that run on parameters, such as :attr:`cuda`,
are ignored. If ``None``, the parameter is **not** included in the
module's :attr:`state_dict`.
"""
if '_parameters' not in self.__dict__:
raise AttributeError(
"cannot assign parameter before Module.__init__() call")
elif not isinstance(name, str):
raise TypeError(f"parameter name should be a string. Got {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(f"attribute '{name}' already exists")
if param is None:
self._parameters[name] = None
elif not isinstance(param, Parameter):
raise TypeError(f"cannot assign '{torch.typename(param)}' object to parameter '{name}' "
"(torch.nn.Parameter or None required)"
)
elif param.grad_fn:
raise ValueError(
f"Cannot assign non-leaf Tensor to parameter '{name}'. Model "
f"parameters must be created explicitly. To express '{name}' "
"as a function of another Tensor, compute the value in "
"the forward() method.")
else:
for hook in _global_parameter_registration_hooks.values():
output = hook(self, name, param)
if output is not None:
param = output
self._parameters[name] = param
register_state_dict_pre_hook
def register_state_dict_pre_hook(
self,
hook
)
Register a pre-hook for the :meth:~torch.nn.Module.state_dict method.
These hooks will be called with arguments: self, prefix,
and keep_vars before calling state_dict on self. The registered
hooks can be used to perform pre-processing before the state_dict
call is made.
View Source
def register_state_dict_pre_hook(self, hook):
r"""Register a pre-hook for the :meth:`~torch.nn.Module.state_dict` method.
These hooks will be called with arguments: ``self``, ``prefix``,
and ``keep_vars`` before calling ``state_dict`` on ``self``. The registered
hooks can be used to perform pre-processing before the ``state_dict``
call is made.
"""
handle = hooks.RemovableHandle(self._state_dict_pre_hooks)
self._state_dict_pre_hooks[handle.id] = hook
return handle
requires_grad_
def requires_grad_(
self: ~T,
requires_grad: bool = True
) -> ~T
Change if autograd should record operations on parameters in this module.
This method sets the parameters' :attr:requires_grad attributes
in-place.
This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training).
See :ref:locally-disable-grad-doc for a comparison between
.requires_grad_() and several similar mechanisms that may be confused with it.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| requires_grad | bool | whether autograd should record operations on parameters in this module. Default: True. |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def requires_grad_(self: T, requires_grad: bool = True) -> T:
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).
See :ref:`locally-disable-grad-doc` for a comparison between
`.requires_grad_()` and several similar mechanisms that may be confused with it.
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
set_extra_state
def set_extra_state(
self,
state: Any
) -> None
Set extra state contained in the loaded state_dict.
This function is called from :func:load_state_dict to handle any extra state
found within the state_dict. Implement this function and a corresponding
View Source
def set_extra_state(self, state: Any) -> None:
"""Set extra state contained in the loaded `state_dict`.
This function is called from :func:`load_state_dict` to handle any extra state
found within the `state_dict`. Implement this function and a corresponding
:func:`get_extra_state` for your module if you need to store extra state within its
`state_dict`.
Args:
state (dict): Extra state from the `state_dict`
"""
raise RuntimeError(
"Reached a code path in Module.set_extra_state() that should never be called. "
"Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml "
"to report this bug.")
share_memory
def share_memory(
self: ~T
) -> ~T
See :meth:torch.Tensor.share_memory_.
View Source
def share_memory(self: T) -> T:
r"""See :meth:`torch.Tensor.share_memory_`."""
return self._apply(lambda t: t.share_memory_())
state_dict
def state_dict(
self,
*args,
destination=None,
prefix='',
keep_vars=False
)
Return a dictionary containing references to the whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.
Parameters and buffers set to None are not included.
.. note:: The returned object is a shallow copy. It contains references to the module's parameters and buffers.
.. warning::
Currently state_dict() also accepts positional arguments for
destination, prefix and keep_vars in order. However,
this is being deprecated and keyword arguments will be enforced in
future releases.
.. warning::
Please avoid the use of argument destination as it is not
designed for end-users.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| destination | dict | If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned.Default: None. |
None |
| prefix | str | a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''. |
None |
| keep_vars | bool | by default the :class:~torch.Tensor sreturned in the state dict are detached from autograd. If it's set to True, detaching will not be performed.Default: False. |
None |
Returns:
| Type | Description |
|---|---|
| dict | a dictionary containing a whole state of the module |
View Source
def state_dict(self, *args, destination=None, prefix='', keep_vars=False):
r"""Return a dictionary containing references to the whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.
Parameters and buffers set to ``None`` are not included.
.. note::
The returned object is a shallow copy. It contains references
to the module's parameters and buffers.
.. warning::
Currently ``state_dict()`` also accepts positional arguments for
``destination``, ``prefix`` and ``keep_vars`` in order. However,
this is being deprecated and keyword arguments will be enforced in
future releases.
.. warning::
Please avoid the use of argument ``destination`` as it is not
designed for end-users.
Args:
destination (dict, optional): If provided, the state of module will
be updated into the dict and the same object is returned.
Otherwise, an ``OrderedDict`` will be created and returned.
Default: ``None``.
prefix (str, optional): a prefix added to parameter and buffer
names to compose the keys in state_dict. Default: ``''``.
keep_vars (bool, optional): by default the :class:`~torch.Tensor` s
returned in the state dict are detached from autograd. If it's
set to ``True``, detaching will not be performed.
Default: ``False``.
Returns:
dict:
a dictionary containing a whole state of the module
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']
"""
# TODO: Remove `args` and the parsing logic when BC allows.
if len(args) > 0:
if destination is None:
destination = args[0]
if len(args) > 1 and prefix == '':
prefix = args[1]
if len(args) > 2 and keep_vars is False:
keep_vars = args[2]
# DeprecationWarning is ignored by default
warnings.warn(
"Positional args are being deprecated, use kwargs instead. Refer to "
"https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict"
" for details.")
if destination is None:
destination = OrderedDict()
destination._metadata = OrderedDict()
local_metadata = dict(version=self._version)
if hasattr(destination, "_metadata"):
destination._metadata[prefix[:-1]] = local_metadata
for hook in self._state_dict_pre_hooks.values():
hook(self, prefix, keep_vars)
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=destination, prefix=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
to
def to(
self,
*args,
**kwargs
)
Move and/or cast the parameters and buffers.
This can be called as
.. function:: to(device=None, dtype=None, non_blocking=False) :noindex:
.. function:: to(dtype, non_blocking=False) :noindex:
.. function:: to(tensor, non_blocking=False) :noindex:
.. function:: to(memory_format=torch.channels_last) :noindex:
Its signature is similar to :meth:torch.Tensor.to, but only accepts
floating point or complex :attr:dtype\ s. In addition, this method will
only cast the floating point or complex parameters and buffers to :attr:dtype
(if given). The integral parameters and buffers will be moved
:attr:device, if that is given, but with dtypes unchanged. When
:attr:non_blocking is set, it tries to convert/move asynchronously
with respect to the host if possible, e.g., moving CPU Tensors with
pinned memory to CUDA devices.
See below for examples.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device ( | None | class:torch.device): the desired device of the parametersand buffers in this module |
None |
| dtype ( | None | class:torch.dtype): the desired floating point or complex dtype ofthe parameters and buffers in this module |
None |
| tensor | torch.Tensor | Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module |
None |
| memory_format ( | None | class:torch.memory_format): the desired memoryformat for 4D parameters and buffers in this module (keyword only argument) |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def to(self, *args, **kwargs):
r"""Move and/or cast the parameters and buffers.
This can be called as
.. function:: to(device=None, dtype=None, non_blocking=False)
:noindex:
.. function:: to(dtype, non_blocking=False)
:noindex:
.. function:: to(tensor, non_blocking=False)
:noindex:
.. function:: to(memory_format=torch.channels_last)
:noindex:
Its signature is similar to :meth:`torch.Tensor.to`, but only accepts
floating point or complex :attr:`dtype`\ s. In addition, this method will
only cast the floating point or complex parameters and buffers to :attr:`dtype`
(if given). The integral parameters and buffers will be moved
:attr:`device`, if that is given, but with dtypes unchanged. When
:attr:`non_blocking` is set, it tries to convert/move asynchronously
with respect to the host if possible, e.g., moving CPU Tensors with
pinned memory to CUDA devices.
See below for examples.
.. note::
This method modifies the module in-place.
Args:
device (:class:`torch.device`): the desired device of the parameters
and buffers in this module
dtype (:class:`torch.dtype`): the desired floating point or complex dtype of
the 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
memory_format (:class:`torch.memory_format`): the desired memory
format for 4D parameters and buffers in this module (keyword
only argument)
Returns:
Module: self
Examples::
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
[-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
[-0.5113, -0.2325]], dtype=torch.float64)
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
[-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
[-0.5112, -0.2324]], dtype=torch.float16)
>>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble)
>>> linear.weight
Parameter containing:
tensor([[ 0.3741+0.j, 0.2382+0.j],
[ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)
>>> linear(torch.ones(3, 2, dtype=torch.cdouble))
tensor([[0.6122+0.j, 0.1150+0.j],
[0.6122+0.j, 0.1150+0.j],
[0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)
"""
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 '
f'dtypes, but got desired dtype={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.yml "
"if a complex module does not work as expected.")
def convert(t):
try:
if convert_to_format is not None and t.dim() in (4, 5):
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,
)
except NotImplementedError as e:
if str(e) == "Cannot copy out of meta tensor; no data!":
raise NotImplementedError(
f"{e} Please use torch.nn.Module.to_empty() instead of torch.nn.Module.to() "
f"when moving module from meta to a different device."
) from None
else:
raise
return self._apply(convert)
to_empty
def to_empty(
self: ~T,
*,
device: Union[int, str, torch.device, NoneType],
recurse: bool = True
) -> ~T
Move the parameters and buffers to the specified device without copying storage.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device ( | None | class:torch.device): The desired device of the parametersand buffers in this module. |
None |
| recurse | bool | Whether parameters and buffers of submodules should be recursively moved to the specified device. |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def to_empty(self: T, *, device: Optional[DeviceLikeType], recurse: bool = True) -> T:
r"""Move the parameters and buffers to the specified device without copying storage.
Args:
device (:class:`torch.device`): The desired device of the parameters
and buffers in this module.
recurse (bool): Whether parameters and buffers of submodules should
be recursively moved to the specified device.
Returns:
Module: self
"""
return self._apply(lambda t: torch.empty_like(t, device=device), recurse=recurse)
train
def train(
self: ~T,
mode: bool = True
) -> ~T
Set 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.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| mode | bool | whether to set training mode (True) or evaluationmode ( False). Default: True. |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def train(self: T, mode: bool = True) -> T:
r"""Set 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
"""
if not isinstance(mode, bool):
raise ValueError("training mode is expected to be boolean")
self.training = mode
for module in self.children():
module.train(mode)
return self
type
def type(
self: ~T,
dst_type: Union[torch.dtype, str]
) -> ~T
Casts all parameters and buffers to :attr:dst_type.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| dst_type | type or string | the desired type | None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def type(self: T, dst_type: Union[dtype, str]) -> T:
r"""Casts all parameters and buffers to :attr:`dst_type`.
.. note::
This method modifies the module in-place.
Args:
dst_type (type or string): the desired type
Returns:
Module: self
"""
return self._apply(lambda t: t.type(dst_type))
xpu
def xpu(
self: ~T,
device: Union[int, torch.device, NoneType] = None
) -> ~T
Move all model parameters and buffers to the XPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on XPU while being optimized.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device | int | if specified, all parameters will be copied to that device |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def xpu(self: T, device: Optional[Union[int, device]] = None) -> T:
r"""Move all model parameters and buffers to the XPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on XPU while being optimized.
.. note::
This method modifies the module in-place.
Arguments:
device (int, optional): if specified, all parameters will be
copied to that device
Returns:
Module: self
"""
return self._apply(lambda t: t.xpu(device))
zero_grad
def zero_grad(
self,
set_to_none: bool = True
) -> None
Reset gradients of all model parameters.
See similar function under :class:torch.optim.Optimizer for more context.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| set_to_none | bool | instead of setting to zero, set the grads to None. See :meth: torch.optim.Optimizer.zero_grad for details. |
None |
View Source
def zero_grad(self, set_to_none: bool = True) -> None:
r"""Reset gradients of all model parameters.
See similar function under :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 = None
else:
if p.grad.grad_fn is not None:
p.grad.detach_()
else:
p.grad.requires_grad_(False)
p.grad.zero_()
RMLP
class RMLP(
block_in_dim: int,
block_dims: Sequence[int],
block_nonlins: Sequence[Union[str, torch.nn.modules.module.Module]],
n_blocks: int,
out_dim: int,
in_dim: int = None,
batch_norm: bool = True
)
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().__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.
.. note::
As per the example above, an __init__() call to the parent class
must be made before assignment on the child.
View Source
class RMLP(nn.Module):
def __init__(
self,
block_in_dim: int,
block_dims: Sequence[int],
block_nonlins: Sequence[Union[str, nn.Module]],
n_blocks: int,
out_dim: int,
in_dim: int = None, # if in_dim is an int, then a first layer will be made
batch_norm: bool = True,
) -> None:
super().__init__()
# Create first layer if in_dim is not None
self.input = nn.Identity()
if in_dim is not None:
self.input = MLPLayer(in_dim, block_in_dim, block_nonlins[0], batch_norm)
# Create blocks
layers = []
for i in range(n_blocks):
layers.append(MlpBlock(block_in_dim, block_dims, block_nonlins, batch_norm))
self.blocks = nn.ModuleList(layers)
# Create output layer
self.output = nn.Linear(block_dims[-1], out_dim)
def _make_activation(self, act: Union[str, nn.Module]) -> nn.Module:
if isinstance(act, str):
return ACTIVATIONS[act](**ACTIVATION_DEFAULT_KWARGS[act])
return act
def forward(self, x: Tensor) -> Tensor:
"""
Args:
x: An input tensor, of shape (N, D) containing N samples with D features.
Returns:
An output tensor of shape (N, D_out) where D_out is the output dim.
"""
x = self.input(x)
for block in self.blocks:
out = block(x)
x = x + out
return self.output(x)
Ancestors (in MRO)
- torch.nn.modules.module.Module
Class variables
T_destination
call_super_init
dump_patches
Methods
add_module
def add_module(
self,
name: str,
module: Optional[ForwardRef('Module')]
) -> None
Add a child module to the current module.
The module can be accessed as an attribute using the given name.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| name | str | name of the child module. The child module can be accessed from this module using the given name |
None |
| module | Module | child module to be added to the module. | None |
View Source
def add_module(self, name: str, module: Optional['Module']) -> None:
r"""Add a child module to the current module.
The module can be accessed as an attribute using the given name.
Args:
name (str): 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(f"{torch.typename(module)} is not a Module subclass")
elif not isinstance(name, str):
raise TypeError(f"module name should be a string. Got {torch.typename(name)}")
elif hasattr(self, name) and name not in self._modules:
raise KeyError(f"attribute '{name}' already exists")
elif '.' in name:
raise KeyError(f"module name can't contain \".\", got: {name}")
elif name == '':
raise KeyError("module name can't be empty string \"\"")
for hook in _global_module_registration_hooks.values():
output = hook(self, name, module)
if output is not None:
module = output
self._modules[name] = module
apply
def apply(
self: ~T,
fn: Callable[[ForwardRef('Module')], NoneType]
) -> ~T
Apply fn recursively to every submodule (as returned by .children()) as well as self.
Typical use includes initializing the parameters of a model
(see also :ref:nn-init-doc).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| fn ( | None | class:Module -> None): function to be applied to each submodule |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def apply(self: T, fn: Callable[['Module'], None]) -> T:
r"""Apply ``fn`` recursively to every submodule (as returned by ``.children()``) as well as self.
Typical use includes initializing the parameters of a model
(see also :ref:`nn-init-doc`).
Args:
fn (:class:`Module` -> None): function to be applied to each submodule
Returns:
Module: self
Example::
>>> @torch.no_grad()
>>> def init_weights(m):
>>> print(m)
>>> if type(m) == nn.Linear:
>>> m.weight.fill_(1.0)
>>> print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[1., 1.],
[1., 1.]], requires_grad=True)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[1., 1.],
[1., 1.]], requires_grad=True)
Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
"""
for module in self.children():
module.apply(fn)
fn(self)
return self
bfloat16
def bfloat16(
self: ~T
) -> ~T
Casts all floating point parameters and buffers to bfloat16 datatype.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def bfloat16(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``bfloat16`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.bfloat16() if t.is_floating_point() else t)
buffers
def buffers(
self,
recurse: bool = True
) -> Iterator[torch.Tensor]
Return an iterator over module buffers.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| recurse | bool | if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. |
None |
Yields:
| Type | Description |
|---|---|
| torch.Tensor | module buffer |
View Source
def buffers(self, recurse: bool = True) -> Iterator[Tensor]:
r"""Return 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::
>>> # xdoctest: +SKIP("undefined vars")
>>> for buf in model.buffers():
>>> print(type(buf), buf.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)
"""
for _, buf in self.named_buffers(recurse=recurse):
yield buf
children
def children(
self
) -> Iterator[ForwardRef('Module')]
Return an iterator over immediate children modules.
Yields:
| Type | Description |
|---|---|
| Module | a child module |
View Source
def children(self) -> Iterator['Module']:
r"""Return an iterator over immediate children modules.
Yields:
Module: a child module
"""
for name, module in self.named_children():
yield module
compile
def compile(
self,
*args,
**kwargs
)
Compile this Module's forward using :func:torch.compile.
This Module's __call__ method is compiled and all arguments are passed as-is
to :func:torch.compile.
See :func:torch.compile for details on the arguments for this function.
View Source
def compile(self, *args, **kwargs):
"""
Compile this Module's forward using :func:`torch.compile`.
This Module's `__call__` method is compiled and all arguments are passed as-is
to :func:`torch.compile`.
See :func:`torch.compile` for details on the arguments for this function.
"""
self._compiled_call_impl = torch.compile(self._call_impl, *args, **kwargs)
cpu
def cpu(
self: ~T
) -> ~T
Move all model parameters and buffers to the CPU.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def cpu(self: T) -> T:
r"""Move all model parameters and buffers to the CPU.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.cpu())
cuda
def cuda(
self: ~T,
device: Union[int, torch.device, NoneType] = None
) -> ~T
Move all model parameters and buffers to the GPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device | int | if specified, all parameters will be copied to that device |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def cuda(self: T, device: Optional[Union[int, device]] = None) -> T:
r"""Move all model parameters and buffers to the GPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on GPU while being optimized.
.. note::
This method modifies the module in-place.
Args:
device (int, optional): if specified, all parameters will be
copied to that device
Returns:
Module: self
"""
return self._apply(lambda t: t.cuda(device))
double
def double(
self: ~T
) -> ~T
Casts all floating point parameters and buffers to double datatype.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def double(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``double`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.double() if t.is_floating_point() else t)
eval
def eval(
self: ~T
) -> ~T
Set 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>.
See :ref:locally-disable-grad-doc for a comparison between
.eval() and several similar mechanisms that may be confused with it.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def eval(self: T) -> T:
r"""Set 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>`.
See :ref:`locally-disable-grad-doc` for a comparison between
`.eval()` and several similar mechanisms that may be confused with it.
Returns:
Module: self
"""
return self.train(False)
extra_repr
def extra_repr(
self
) -> str
Set the extra representation of the module.
To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.
View Source
def extra_repr(self) -> str:
r"""Set the extra representation of the module.
To print customized extra information, you should re-implement
this method in your own modules. Both single-line and multi-line
strings are acceptable.
"""
return ''
float
def float(
self: ~T
) -> ~T
Casts all floating point parameters and buffers to float datatype.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def float(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``float`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.float() if t.is_floating_point() else t)
forward
def forward(
self,
x: torch.Tensor
) -> torch.Tensor
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| x | None | An input tensor, of shape (N, D) containing N samples with D features. | None |
Returns:
| Type | Description |
|---|---|
| None | An output tensor of shape (N, D_out) where D_out is the output dim. |
View Source
def forward(self, x: Tensor) -> Tensor:
"""
Args:
x: An input tensor, of shape (N, D) containing N samples with D features.
Returns:
An output tensor of shape (N, D_out) where D_out is the output dim.
"""
x = self.input(x)
for block in self.blocks:
out = block(x)
x = x + out
return self.output(x)
get_buffer
def get_buffer(
self,
target: str
) -> 'Tensor'
Return the buffer given by target if it exists, otherwise throw an error.
See the docstring for get_submodule for a more detailed
explanation of this method's functionality as well as how to
correctly specify target.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| target | None | The fully-qualified string name of the buffer to look for. (See get_submodule for how to specify afully-qualified string.) |
None |
Returns:
| Type | Description |
|---|---|
| torch.Tensor | The buffer referenced by target |
Raises:
| Type | Description |
|---|---|
| AttributeError | If the target string references an invalid path or resolves to something that is not a buffer |
View Source
def get_buffer(self, target: str) -> "Tensor":
"""Return the buffer given by ``target`` if it exists, otherwise throw an error.
See the docstring for ``get_submodule`` for a more detailed
explanation of this method's functionality as well as how to
correctly specify ``target``.
Args:
target: The fully-qualified string name of the buffer
to look for. (See ``get_submodule`` for how to specify a
fully-qualified string.)
Returns:
torch.Tensor: The buffer referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not a
buffer
"""
module_path, _, buffer_name = target.rpartition(".")
mod: torch.nn.Module = self.get_submodule(module_path)
if not hasattr(mod, buffer_name):
raise AttributeError(mod._get_name() + " has no attribute `"
+ buffer_name + "`")
buffer: torch.Tensor = getattr(mod, buffer_name)
if buffer_name not in mod._buffers:
raise AttributeError("`" + buffer_name + "` is not a buffer")
return buffer
get_extra_state
def get_extra_state(
self
) -> Any
Return any extra state to include in the module's state_dict.
Implement this and a corresponding :func:set_extra_state for your module
if you need to store extra state. This function is called when building the
module's state_dict().
Note that extra state should be picklable to ensure working serialization of the state_dict. We only provide provide backwards compatibility guarantees for serializing Tensors; other objects may break backwards compatibility if their serialized pickled form changes.
Returns:
| Type | Description |
|---|---|
| object | Any extra state to store in the module's state_dict |
View Source
def get_extra_state(self) -> Any:
"""Return any extra state to include in the module's state_dict.
Implement this and a corresponding :func:`set_extra_state` for your module
if you need to store extra state. This function is called when building the
module's `state_dict()`.
Note that extra state should be picklable to ensure working serialization
of the state_dict. We only provide provide backwards compatibility guarantees
for serializing Tensors; other objects may break backwards compatibility if
their serialized pickled form changes.
Returns:
object: Any extra state to store in the module's state_dict
"""
raise RuntimeError(
"Reached a code path in Module.get_extra_state() that should never be called. "
"Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml "
"to report this bug.")
get_parameter
def get_parameter(
self,
target: str
) -> 'Parameter'
Return the parameter given by target if it exists, otherwise throw an error.
See the docstring for get_submodule for a more detailed
explanation of this method's functionality as well as how to
correctly specify target.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| target | None | The fully-qualified string name of the Parameter to look for. (See get_submodule for how to specify afully-qualified string.) |
None |
Returns:
| Type | Description |
|---|---|
| torch.nn.Parameter | The Parameter referenced by target |
Raises:
| Type | Description |
|---|---|
| AttributeError | If the target string references an invalid path or resolves to something that is not an nn.Parameter |
View Source
def get_parameter(self, target: str) -> "Parameter":
"""Return the parameter given by ``target`` if it exists, otherwise throw an error.
See the docstring for ``get_submodule`` for a more detailed
explanation of this method's functionality as well as how to
correctly specify ``target``.
Args:
target: The fully-qualified string name of the Parameter
to look for. (See ``get_submodule`` for how to specify a
fully-qualified string.)
Returns:
torch.nn.Parameter: The Parameter referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not an
``nn.Parameter``
"""
module_path, _, param_name = target.rpartition(".")
mod: torch.nn.Module = self.get_submodule(module_path)
if not hasattr(mod, param_name):
raise AttributeError(mod._get_name() + " has no attribute `"
+ param_name + "`")
param: torch.nn.Parameter = getattr(mod, param_name)
if not isinstance(param, torch.nn.Parameter):
raise AttributeError("`" + param_name + "` is not an "
"nn.Parameter")
return param
get_submodule
def get_submodule(
self,
target: str
) -> 'Module'
Return the submodule given by target if it exists, otherwise throw an error.
For example, let's say you have an nn.Module A that
looks like this:
.. code-block:: text
A(
(net_b): Module(
(net_c): Module(
(conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))
)
(linear): Linear(in_features=100, out_features=200, bias=True)
)
)
(The diagram shows an nn.Module A. A has a nested
submodule net_b, which itself has two submodules net_c
and linear. net_c then has a submodule conv.)
To check whether or not we have the linear submodule, we
would call get_submodule("net_b.linear"). To check whether
we have the conv submodule, we would call
get_submodule("net_b.net_c.conv").
The runtime of get_submodule is bounded by the degree
of module nesting in target. A query against
named_modules achieves the same result, but it is O(N) in
the number of transitive modules. So, for a simple check to see
if some submodule exists, get_submodule should always be
used.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| target | None | The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.) |
None |
Returns:
| Type | Description |
|---|---|
| torch.nn.Module | The submodule referenced by target |
Raises:
| Type | Description |
|---|---|
| AttributeError | If the target string references an invalid path or resolves to something that is not an nn.Module |
View Source
def get_submodule(self, target: str) -> "Module":
"""Return the submodule given by ``target`` if it exists, otherwise throw an error.
For example, let's say you have an ``nn.Module`` ``A`` that
looks like this:
.. code-block:: text
A(
(net_b): Module(
(net_c): Module(
(conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))
)
(linear): Linear(in_features=100, out_features=200, bias=True)
)
)
(The diagram shows an ``nn.Module`` ``A``. ``A`` has a nested
submodule ``net_b``, which itself has two submodules ``net_c``
and ``linear``. ``net_c`` then has a submodule ``conv``.)
To check whether or not we have the ``linear`` submodule, we
would call ``get_submodule("net_b.linear")``. To check whether
we have the ``conv`` submodule, we would call
``get_submodule("net_b.net_c.conv")``.
The runtime of ``get_submodule`` is bounded by the degree
of module nesting in ``target``. A query against
``named_modules`` achieves the same result, but it is O(N) in
the number of transitive modules. So, for a simple check to see
if some submodule exists, ``get_submodule`` should always be
used.
Args:
target: The fully-qualified string name of the submodule
to look for. (See above example for how to specify a
fully-qualified string.)
Returns:
torch.nn.Module: The submodule referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not an
``nn.Module``
"""
if target == "":
return self
atoms: List[str] = target.split(".")
mod: torch.nn.Module = self
for item in atoms:
if not hasattr(mod, item):
raise AttributeError(mod._get_name() + " has no "
"attribute `" + item + "`")
mod = getattr(mod, item)
if not isinstance(mod, torch.nn.Module):
raise AttributeError("`" + item + "` is not "
"an nn.Module")
return mod
half
def half(
self: ~T
) -> ~T
Casts all floating point parameters and buffers to half datatype.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def half(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``half`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.half() if t.is_floating_point() else t)
ipu
def ipu(
self: ~T,
device: Union[int, torch.device, NoneType] = None
) -> ~T
Move all model parameters and buffers to the IPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on IPU while being optimized.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device | int | if specified, all parameters will be copied to that device |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def ipu(self: T, device: Optional[Union[int, device]] = None) -> T:
r"""Move all model parameters and buffers to the IPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on IPU while being optimized.
.. note::
This method modifies the module in-place.
Arguments:
device (int, optional): if specified, all parameters will be
copied to that device
Returns:
Module: self
"""
return self._apply(lambda t: t.ipu(device))
load_state_dict
def load_state_dict(
self,
state_dict: Mapping[str, Any],
strict: bool = True,
assign: bool = False
)
Copy parameters and buffers from :attr:state_dict into this module and its descendants.
If :attr:strict is True, then
the keys of :attr:state_dict must exactly match the keys returned
by this module's :meth:~torch.nn.Module.state_dict function.
.. warning::
If :attr:assign is True the optimizer must be created after
the call to :attr:load_state_dict unless
:func:~torch.__future__.get_swap_module_params_on_conversion is True.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| state_dict | dict | a dict containing parameters and persistent buffers. |
None |
| strict | bool | 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 |
None |
| assign | bool | When False, the properties of the tensorsin the current module are preserved while when True, theproperties of the Tensors in the state dict are preserved. The only exception is the requires_grad field of :class:~torch.nn.Parametersfor which the value from the module is preserved. Default: False |
None |
Returns:
| Type | Description |
|---|---|
| None | 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 |
View Source
def load_state_dict(self, state_dict: Mapping[str, Any],
strict: bool = True, assign: bool = False):
r"""Copy parameters and buffers from :attr:`state_dict` into this module and its descendants.
If :attr:`strict` is ``True``, then
the keys of :attr:`state_dict` must exactly match the keys returned
by this module's :meth:`~torch.nn.Module.state_dict` function.
.. warning::
If :attr:`assign` is ``True`` the optimizer must be created after
the call to :attr:`load_state_dict` unless
:func:`~torch.__future__.get_swap_module_params_on_conversion` is ``True``.
Args:
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``
assign (bool, optional): When ``False``, the properties of the tensors
in the current module are preserved while when ``True``, the
properties of the Tensors in the state dict are preserved. The only
exception is the ``requires_grad`` field of :class:`~torch.nn.Parameter`s
for which the value from the module is preserved.
Default: ``False``
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
Note:
If a parameter or buffer is registered as ``None`` and its corresponding key
exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a
``RuntimeError``.
"""
if not isinstance(state_dict, Mapping):
raise TypeError(f"Expected state_dict to be dict-like, got {type(state_dict)}.")
missing_keys: List[str] = []
unexpected_keys: List[str] = []
error_msgs: List[str] = []
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, '_metadata', None)
state_dict = OrderedDict(state_dict)
if metadata is not None:
# mypy isn't aware that "_metadata" exists in state_dict
state_dict._metadata = metadata # type: ignore[attr-defined]
def load(module, local_state_dict, prefix=''):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
if assign:
local_metadata['assign_to_params_buffers'] = assign
module._load_from_state_dict(
local_state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
for name, child in module._modules.items():
if child is not None:
child_prefix = prefix + name + '.'
child_state_dict = {k: v for k, v in local_state_dict.items() if k.startswith(child_prefix)}
load(child, child_state_dict, child_prefix) # noqa: F821
# Note that the hook can modify missing_keys and unexpected_keys.
incompatible_keys = _IncompatibleKeys(missing_keys, unexpected_keys)
for hook in module._load_state_dict_post_hooks.values():
out = hook(module, incompatible_keys)
assert out is None, (
"Hooks registered with ``register_load_state_dict_post_hook`` are not"
"expected to return new values, if incompatible_keys need to be modified,"
"it should be done inplace."
)
load(self, state_dict)
del load
if strict:
if len(unexpected_keys) > 0:
error_msgs.insert(
0, 'Unexpected key(s) in state_dict: {}. '.format(
', '.join(f'"{k}"' for k in unexpected_keys)))
if len(missing_keys) > 0:
error_msgs.insert(
0, 'Missing key(s) in state_dict: {}. '.format(
', '.join(f'"{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)
modules
def modules(
self
) -> Iterator[ForwardRef('Module')]
Return an iterator over all modules in the network.
Yields:
| Type | Description |
|---|---|
| Module | a module in the network |
View Source
def modules(self) -> Iterator['Module']:
r"""Return an iterator over all modules in the network.
Yields:
Module: a module in the network
Note:
Duplicate modules are returned only once. In the following
example, ``l`` will be returned only once.
Example::
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
... print(idx, '->', m)
0 -> Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)
"""
for _, module in self.named_modules():
yield module
named_buffers
def named_buffers(
self,
prefix: str = '',
recurse: bool = True,
remove_duplicate: bool = True
) -> Iterator[Tuple[str, torch.Tensor]]
Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| prefix | str | prefix to prepend to all buffer names. | None |
| recurse | bool | if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True. |
None |
| remove_duplicate | bool | whether to remove the duplicated buffers in the result. Defaults to True. | True |
Yields:
| Type | Description |
|---|---|
| None | (str, torch.Tensor): Tuple containing the name and buffer |
View Source
def named_buffers(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> Iterator[Tuple[str, Tensor]]:
r"""Return 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, optional): if True, then yields buffers of this module
and all submodules. Otherwise, yields only buffers that
are direct members of this module. Defaults to True.
remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.
Yields:
(str, torch.Tensor): Tuple containing the name and buffer
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, buf in self.named_buffers():
>>> if name in ['running_var']:
>>> print(buf.size())
"""
gen = self._named_members(
lambda module: module._buffers.items(),
prefix=prefix, recurse=recurse, remove_duplicate=remove_duplicate)
yield from gen
named_children
def named_children(
self
) -> Iterator[Tuple[str, ForwardRef('Module')]]
Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
Yields:
| Type | Description |
|---|---|
| None | (str, Module): Tuple containing a name and child module |
View Source
def named_children(self) -> Iterator[Tuple[str, 'Module']]:
r"""Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
Yields:
(str, Module): Tuple containing a name and child module
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, module in model.named_children():
>>> if name in ['conv4', 'conv5']:
>>> print(module)
"""
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
named_modules
def named_modules(
self,
memo: Optional[Set[ForwardRef('Module')]] = None,
prefix: str = '',
remove_duplicate: bool = True
)
Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| memo | None | a memo to store the set of modules already added to the result | None |
| prefix | None | a prefix that will be added to the name of the module | None |
| remove_duplicate | None | whether to remove the duplicated module instances in the result or not |
None |
Yields:
| Type | Description |
|---|---|
| None | (str, Module): Tuple of name and module |
View Source
def named_modules(self, memo: Optional[Set['Module']] = None, prefix: str = '', remove_duplicate: bool = True):
r"""Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
Args:
memo: a memo to store the set of modules already added to the result
prefix: a prefix that will be added to the name of the module
remove_duplicate: whether to remove the duplicated module instances in the result
or not
Yields:
(str, Module): Tuple of name and module
Note:
Duplicate modules are returned only once. In the following
example, ``l`` will be returned only once.
Example::
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
... print(idx, '->', m)
0 -> ('', Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
))
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))
"""
if memo is None:
memo = set()
if self not in memo:
if remove_duplicate:
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
yield from module.named_modules(memo, submodule_prefix, remove_duplicate)
named_parameters
def named_parameters(
self,
prefix: str = '',
recurse: bool = True,
remove_duplicate: bool = True
) -> Iterator[Tuple[str, torch.nn.parameter.Parameter]]
Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| prefix | str | prefix to prepend to all parameter names. | None |
| recurse | bool | if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. |
None |
| remove_duplicate | bool | whether to remove the duplicated parameters in the result. Defaults to True. |
None |
Yields:
| Type | Description |
|---|---|
| None | (str, Parameter): Tuple containing the name and parameter |
View Source
def named_parameters(
self,
prefix: str = '',
recurse: bool = True,
remove_duplicate: bool = True
) -> Iterator[Tuple[str, Parameter]]:
r"""Return 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.
remove_duplicate (bool, optional): whether to remove the duplicated
parameters in the result. Defaults to True.
Yields:
(str, Parameter): Tuple containing the name and parameter
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, param in self.named_parameters():
>>> if name in ['bias']:
>>> print(param.size())
"""
gen = self._named_members(
lambda module: module._parameters.items(),
prefix=prefix, recurse=recurse, remove_duplicate=remove_duplicate)
yield from gen
parameters
def parameters(
self,
recurse: bool = True
) -> Iterator[torch.nn.parameter.Parameter]
Return an iterator over module parameters.
This is typically passed to an optimizer.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| recurse | bool | if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. |
None |
Yields:
| Type | Description |
|---|---|
| Parameter | module parameter |
View Source
def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
r"""Return 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::
>>> # xdoctest: +SKIP("undefined vars")
>>> 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 param
register_backward_hook
def register_backward_hook(
self,
hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]]
) -> torch.utils.hooks.RemovableHandle
Register a backward hook on the module.
This function is deprecated in favor of :meth:~torch.nn.Module.register_full_backward_hook and
the behavior of this function will change in future versions.
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_backward_hook(
self, hook: Callable[['Module', _grad_t, _grad_t], Union[None, _grad_t]]
) -> RemovableHandle:
r"""Register a backward hook on the module.
This function is deprecated in favor of :meth:`~torch.nn.Module.register_full_backward_hook` and
the behavior of this function will change in future versions.
Returns:
: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 = False
handle = hooks.RemovableHandle(self._backward_hooks)
self._backward_hooks[handle.id] = hook
return handle
register_buffer
def register_buffer(
self,
name: str,
tensor: Optional[torch.Tensor],
persistent: bool = True
) -> None
Add a buffer to the module.
This is typically used to register a buffer that should not to be
considered a model parameter. For example, BatchNorm's running_mean
is not a parameter, but is part of the module's state. Buffers, by
default, are persistent and will be saved alongside parameters. This
behavior can be changed by setting :attr:persistent to False. The
only difference between a persistent buffer and a non-persistent buffer
is that the latter will not be a part of this module's
:attr:state_dict.
Buffers can be accessed as attributes using given names.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| name | str | name of the buffer. The buffer can be accessed from this module using the given name |
None |
| tensor | Tensor or None | buffer to be registered. If None, then operationsthat run on buffers, such as :attr: cuda, are ignored. If None,the buffer is not included in the module's :attr: state_dict. |
None |
| persistent | bool | whether the buffer is part of this module's :attr: state_dict. |
None |
View Source
def register_buffer(self, name: str, tensor: Optional[Tensor], persistent: bool = True) -> None:
r"""Add a buffer to the module.
This is typically used to register a buffer that should not to be
considered a model parameter. For example, BatchNorm's ``running_mean``
is not a parameter, but is part of the module's state. Buffers, by
default, are persistent and will be saved alongside parameters. This
behavior can be changed by setting :attr:`persistent` to ``False``. The
only difference between a persistent buffer and a non-persistent buffer
is that the latter will not be a part of this module's
:attr:`state_dict`.
Buffers can be accessed as attributes using given names.
Args:
name (str): name of the buffer. The buffer can be accessed
from this module using the given name
tensor (Tensor or None): buffer to be registered. If ``None``, then operations
that run on buffers, such as :attr:`cuda`, are ignored. If ``None``,
the buffer is **not** included in the module's :attr:`state_dict`.
persistent (bool): whether the buffer is part of this module's
:attr:`state_dict`.
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> 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, str):
raise TypeError(f"buffer name should be a string. Got {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(f"attribute '{name}' already exists")
elif tensor is not None and not isinstance(tensor, torch.Tensor):
raise TypeError(f"cannot assign '{torch.typename(tensor)}' object to buffer '{name}' "
"(torch Tensor or None required)"
)
else:
for hook in _global_buffer_registration_hooks.values():
output = hook(self, name, tensor)
if output is not None:
tensor = output
self._buffers[name] = tensor
if persistent:
self._non_persistent_buffers_set.discard(name)
else:
self._non_persistent_buffers_set.add(name)
register_forward_hook
def register_forward_hook(
self,
hook: Union[Callable[[~T, Tuple[Any, ...], Any], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any], Any], Optional[Any]]],
*,
prepend: bool = False,
with_kwargs: bool = False,
always_call: bool = False
) -> torch.utils.hooks.RemovableHandle
Register a forward hook on the module.
The hook will be called every time after :func:forward has computed an output.
If with_kwargs is False or not specified, the input contains only
the positional arguments given to the module. Keyword arguments won't be
passed to the hooks and only to the forward. The hook can modify the
output. It can modify the input inplace but it will not have effect on
forward since this is called after :func:forward is called. The hook
should have the following signature::
hook(module, args, output) -> None or modified output
If with_kwargs is True, the forward hook will be passed the
kwargs given to the forward function and be expected to return the
output possibly modified. The hook should have the following signature::
hook(module, args, kwargs, output) -> None or modified output
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| hook | Callable | The user defined hook to be registered. | None |
| prepend | bool | If True, the provided hook will be firedbefore all existing forward hooks on this:class: torch.nn.modules.Module. Otherwise, the providedhook will be fired after all existing forward hooks onthis :class: torch.nn.modules.Module. Note that globalforward hooks registered with:func: register_module_forward_hook will fire before all hooksregistered by this method. Default: False |
None |
| with_kwargs | bool | If True, the hook will be passed thekwargs given to the forward function. Default: False |
None |
| always_call | bool | If True the hook will be run regardless ofwhether an exception is raised while calling the Module. Default: False |
None |
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_forward_hook(
self,
hook: Union[
Callable[[T, Tuple[Any, ...], Any], Optional[Any]],
Callable[[T, Tuple[Any, ...], Dict[str, Any], Any], Optional[Any]],
],
*,
prepend: bool = False,
with_kwargs: bool = False,
always_call: bool = False,
) -> RemovableHandle:
r"""Register a forward hook on the module.
The hook will be called every time after :func:`forward` has computed an output.
If ``with_kwargs`` is ``False`` or not specified, the input contains only
the positional arguments given to the module. Keyword arguments won't be
passed to the hooks and only to the ``forward``. The hook can modify the
output. It can modify the input inplace but it will not have effect on
forward since this is called after :func:`forward` is called. The hook
should have the following signature::
hook(module, args, output) -> None or modified output
If ``with_kwargs`` is ``True``, the forward hook will be passed the
``kwargs`` given to the forward function and be expected to return the
output possibly modified. The hook should have the following signature::
hook(module, args, kwargs, output) -> None or modified output
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If ``True``, the provided ``hook`` will be fired
before all existing ``forward`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``forward`` hooks on
this :class:`torch.nn.modules.Module`. Note that global
``forward`` hooks registered with
:func:`register_module_forward_hook` will fire before all hooks
registered by this method.
Default: ``False``
with_kwargs (bool): If ``True``, the ``hook`` will be passed the
kwargs given to the forward function.
Default: ``False``
always_call (bool): If ``True`` the ``hook`` will be run regardless of
whether an exception is raised while calling the Module.
Default: ``False``
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,
extra_dict=[self._forward_hooks_with_kwargs, self._forward_hooks_always_called],
)
self._forward_hooks[handle.id] = hook
if with_kwargs:
self._forward_hooks_with_kwargs[handle.id] = True
if always_call:
self._forward_hooks_always_called[handle.id] = True
if prepend:
self._forward_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
register_forward_pre_hook
def register_forward_pre_hook(
self,
hook: Union[Callable[[~T, Tuple[Any, ...]], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any]], Optional[Tuple[Any, Dict[str, Any]]]]],
*,
prepend: bool = False,
with_kwargs: bool = False
) -> torch.utils.hooks.RemovableHandle
Register a forward pre-hook on the module.
The hook will be called every time before :func:forward is invoked.
If with_kwargs is false or not specified, the input contains only
the positional arguments given to the module. Keyword arguments won't be
passed to the hooks and only to the forward. The hook can modify the
input. User can either return a tuple or a single modified value in the
hook. We will wrap the value into a tuple if a single value is returned
(unless that value is already a tuple). The hook should have the
following signature::
hook(module, args) -> None or modified input
If with_kwargs is true, the forward pre-hook will be passed the
kwargs given to the forward function. And if the hook modifies the
input, both the args and kwargs should be returned. The hook should have
the following signature::
hook(module, args, kwargs) -> None or a tuple of modified input and kwargs
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| hook | Callable | The user defined hook to be registered. | None |
| prepend | bool | If true, the provided hook will be fired beforeall existing forward_pre hooks on this:class: torch.nn.modules.Module. Otherwise, the providedhook will be fired after all existing forward_pre hookson this :class: torch.nn.modules.Module. Note that globalforward_pre hooks registered with:func: register_module_forward_pre_hook will fire before allhooks registered by this method. Default: False |
None |
| with_kwargs | bool | If true, the hook will be passed the kwargsgiven to the forward function. Default: False |
None |
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_forward_pre_hook(
self,
hook: Union[
Callable[[T, Tuple[Any, ...]], Optional[Any]],
Callable[[T, Tuple[Any, ...], Dict[str, Any]], Optional[Tuple[Any, Dict[str, Any]]]],
],
*,
prepend: bool = False,
with_kwargs: bool = False,
) -> RemovableHandle:
r"""Register a forward pre-hook on the module.
The hook will be called every time before :func:`forward` is invoked.
If ``with_kwargs`` is false or not specified, the input contains only
the positional arguments given to the module. Keyword arguments won't be
passed to the hooks and only to the ``forward``. The hook can modify the
input. User can either return a tuple or a single modified value in the
hook. We will wrap the value into a tuple if a single value is returned
(unless that value is already a tuple). The hook should have the
following signature::
hook(module, args) -> None or modified input
If ``with_kwargs`` is true, the forward pre-hook will be passed the
kwargs given to the forward function. And if the hook modifies the
input, both the args and kwargs should be returned. The hook should have
the following signature::
hook(module, args, kwargs) -> None or a tuple of modified input and kwargs
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``forward_pre`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``forward_pre`` hooks
on this :class:`torch.nn.modules.Module`. Note that global
``forward_pre`` hooks registered with
:func:`register_module_forward_pre_hook` will fire before all
hooks registered by this method.
Default: ``False``
with_kwargs (bool): If true, the ``hook`` will be passed the kwargs
given to the forward function.
Default: ``False``
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,
extra_dict=self._forward_pre_hooks_with_kwargs
)
self._forward_pre_hooks[handle.id] = hook
if with_kwargs:
self._forward_pre_hooks_with_kwargs[handle.id] = True
if prepend:
self._forward_pre_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
register_full_backward_hook
def register_full_backward_hook(
self,
hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]],
prepend: bool = False
) -> torch.utils.hooks.RemovableHandle
Register a backward hook on the module.
The hook will be called every time the gradients with respect to a module are computed, i.e. the hook will execute if and only if the gradients with respect to module outputs are computed. The hook should have the following signature::
hook(module, grad_input, grad_output) -> tuple(Tensor) or None
The :attr:grad_input and :attr:grad_output are tuples that contain the gradients
with respect to the inputs and outputs respectively. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the input that will be used in place of :attr:grad_input in
subsequent computations. :attr:grad_input will only correspond to the inputs given
as positional arguments and all kwarg arguments are ignored. Entries
in :attr:grad_input and :attr:grad_output will be None for all non-Tensor
arguments.
For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function.
.. warning :: Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| hook | Callable | The user-defined hook to be registered. | None |
| prepend | bool | If true, the provided hook will be fired beforeall existing backward hooks on this:class: torch.nn.modules.Module. Otherwise, the providedhook will be fired after all existing backward hooks onthis :class: torch.nn.modules.Module. Note that globalbackward hooks registered with:func: register_module_full_backward_hook will fire beforeall hooks registered by this method. |
None |
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_full_backward_hook(
self,
hook: Callable[["Module", _grad_t, _grad_t], Union[None, _grad_t]],
prepend: bool = False,
) -> RemovableHandle:
r"""Register a backward hook on the module.
The hook will be called every time the gradients with respect to a module
are computed, i.e. the hook will execute if and only if the gradients with
respect to module outputs are computed. The hook should have the following
signature::
hook(module, grad_input, grad_output) -> tuple(Tensor) or None
The :attr:`grad_input` and :attr:`grad_output` are tuples that contain the gradients
with respect to the inputs and outputs respectively. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the input that will be used in place of :attr:`grad_input` in
subsequent computations. :attr:`grad_input` will only correspond to the inputs given
as positional arguments and all kwarg arguments are ignored. Entries
in :attr:`grad_input` and :attr:`grad_output` will be ``None`` for all non-Tensor
arguments.
For technical reasons, when this hook is applied to a Module, its forward function will
receive a view of each Tensor passed to the Module. Similarly the caller will receive a view
of each Tensor returned by the Module's forward function.
.. warning ::
Modifying inputs or outputs inplace is not allowed when using backward hooks and
will raise an error.
Args:
hook (Callable): The user-defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``backward`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``backward`` hooks on
this :class:`torch.nn.modules.Module`. Note that global
``backward`` hooks registered with
:func:`register_module_full_backward_hook` will fire before
all hooks registered by this method.
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 = True
handle = hooks.RemovableHandle(self._backward_hooks)
self._backward_hooks[handle.id] = hook
if prepend:
self._backward_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
register_full_backward_pre_hook
def register_full_backward_pre_hook(
self,
hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]],
prepend: bool = False
) -> torch.utils.hooks.RemovableHandle
Register a backward pre-hook on the module.
The hook will be called every time the gradients for the module are computed. The hook should have the following signature::
hook(module, grad_output) -> tuple[Tensor] or None
The :attr:grad_output is a tuple. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the output that will be used in place of :attr:grad_output in
subsequent computations. Entries in :attr:grad_output will be None for
all non-Tensor arguments.
For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function.
.. warning :: Modifying inputs inplace is not allowed when using backward hooks and will raise an error.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| hook | Callable | The user-defined hook to be registered. | None |
| prepend | bool | If true, the provided hook will be fired beforeall existing backward_pre hooks on this:class: torch.nn.modules.Module. Otherwise, the providedhook will be fired after all existing backward_pre hookson this :class: torch.nn.modules.Module. Note that globalbackward_pre hooks registered with:func: register_module_full_backward_pre_hook will fire beforeall hooks registered by this method. |
None |
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_full_backward_pre_hook(
self,
hook: Callable[["Module", _grad_t], Union[None, _grad_t]],
prepend: bool = False,
) -> RemovableHandle:
r"""Register a backward pre-hook on the module.
The hook will be called every time the gradients for the module are computed.
The hook should have the following signature::
hook(module, grad_output) -> tuple[Tensor] or None
The :attr:`grad_output` is a tuple. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the output that will be used in place of :attr:`grad_output` in
subsequent computations. Entries in :attr:`grad_output` will be ``None`` for
all non-Tensor arguments.
For technical reasons, when this hook is applied to a Module, its forward function will
receive a view of each Tensor passed to the Module. Similarly the caller will receive a view
of each Tensor returned by the Module's forward function.
.. warning ::
Modifying inputs inplace is not allowed when using backward hooks and
will raise an error.
Args:
hook (Callable): The user-defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``backward_pre`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``backward_pre`` hooks
on this :class:`torch.nn.modules.Module`. Note that global
``backward_pre`` hooks registered with
:func:`register_module_full_backward_pre_hook` will fire before
all hooks registered by this method.
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_pre_hooks)
self._backward_pre_hooks[handle.id] = hook
if prepend:
self._backward_pre_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
register_load_state_dict_post_hook
def register_load_state_dict_post_hook(
self,
hook
)
Register a post hook to be run after module's load_state_dict is called.
It should have the following signature:: hook(module, incompatible_keys) -> None
The module argument is the current module that this hook is registered
on, and the incompatible_keys argument is a NamedTuple consisting
of attributes missing_keys and unexpected_keys. missing_keys
is a list of str containing the missing keys and
unexpected_keys is a list of str containing the unexpected keys.
The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling :func:load_state_dict with
strict=True are affected by modifications the hook makes to
missing_keys or unexpected_keys, as expected. Additions to either
set of keys will result in an error being thrown when strict=True, and
clearing out both missing and unexpected keys will avoid an error.
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_load_state_dict_post_hook(self, hook):
r"""Register a post hook to be run after module's ``load_state_dict`` is called.
It should have the following signature::
hook(module, incompatible_keys) -> None
The ``module`` argument is the current module that this hook is registered
on, and the ``incompatible_keys`` argument is a ``NamedTuple`` consisting
of attributes ``missing_keys`` and ``unexpected_keys``. ``missing_keys``
is a ``list`` of ``str`` containing the missing keys and
``unexpected_keys`` is a ``list`` of ``str`` containing the unexpected keys.
The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling :func:`load_state_dict` with
``strict=True`` are affected by modifications the hook makes to
``missing_keys`` or ``unexpected_keys``, as expected. Additions to either
set of keys will result in an error being thrown when ``strict=True``, and
clearing out both missing and unexpected keys will avoid an error.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
"""
handle = hooks.RemovableHandle(self._load_state_dict_post_hooks)
self._load_state_dict_post_hooks[handle.id] = hook
return handle
register_module
def register_module(
self,
name: str,
module: Optional[ForwardRef('Module')]
) -> None
Alias for :func:add_module.
View Source
def register_module(self, name: str, module: Optional['Module']) -> None:
r"""Alias for :func:`add_module`."""
self.add_module(name, module)
register_parameter
def register_parameter(
self,
name: str,
param: Optional[torch.nn.parameter.Parameter]
) -> None
Add a parameter to the module.
The parameter can be accessed as an attribute using given name.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| name | str | name of the parameter. The parameter can be accessed from this module using the given name |
None |
| param | Parameter or None | parameter to be added to the module. IfNone, then operations that run on parameters, such as :attr:cuda,are ignored. If None, the parameter is not included in themodule's :attr: state_dict. |
None |
View Source
def register_parameter(self, name: str, param: Optional[Parameter]) -> None:
r"""Add a parameter to the module.
The parameter can be accessed as an attribute using given name.
Args:
name (str): name of the parameter. The parameter can be accessed
from this module using the given name
param (Parameter or None): parameter to be added to the module. If
``None``, then operations that run on parameters, such as :attr:`cuda`,
are ignored. If ``None``, the parameter is **not** included in the
module's :attr:`state_dict`.
"""
if '_parameters' not in self.__dict__:
raise AttributeError(
"cannot assign parameter before Module.__init__() call")
elif not isinstance(name, str):
raise TypeError(f"parameter name should be a string. Got {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(f"attribute '{name}' already exists")
if param is None:
self._parameters[name] = None
elif not isinstance(param, Parameter):
raise TypeError(f"cannot assign '{torch.typename(param)}' object to parameter '{name}' "
"(torch.nn.Parameter or None required)"
)
elif param.grad_fn:
raise ValueError(
f"Cannot assign non-leaf Tensor to parameter '{name}'. Model "
f"parameters must be created explicitly. To express '{name}' "
"as a function of another Tensor, compute the value in "
"the forward() method.")
else:
for hook in _global_parameter_registration_hooks.values():
output = hook(self, name, param)
if output is not None:
param = output
self._parameters[name] = param
register_state_dict_pre_hook
def register_state_dict_pre_hook(
self,
hook
)
Register a pre-hook for the :meth:~torch.nn.Module.state_dict method.
These hooks will be called with arguments: self, prefix,
and keep_vars before calling state_dict on self. The registered
hooks can be used to perform pre-processing before the state_dict
call is made.
View Source
def register_state_dict_pre_hook(self, hook):
r"""Register a pre-hook for the :meth:`~torch.nn.Module.state_dict` method.
These hooks will be called with arguments: ``self``, ``prefix``,
and ``keep_vars`` before calling ``state_dict`` on ``self``. The registered
hooks can be used to perform pre-processing before the ``state_dict``
call is made.
"""
handle = hooks.RemovableHandle(self._state_dict_pre_hooks)
self._state_dict_pre_hooks[handle.id] = hook
return handle
requires_grad_
def requires_grad_(
self: ~T,
requires_grad: bool = True
) -> ~T
Change if autograd should record operations on parameters in this module.
This method sets the parameters' :attr:requires_grad attributes
in-place.
This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training).
See :ref:locally-disable-grad-doc for a comparison between
.requires_grad_() and several similar mechanisms that may be confused with it.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| requires_grad | bool | whether autograd should record operations on parameters in this module. Default: True. |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def requires_grad_(self: T, requires_grad: bool = True) -> T:
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).
See :ref:`locally-disable-grad-doc` for a comparison between
`.requires_grad_()` and several similar mechanisms that may be confused with it.
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
set_extra_state
def set_extra_state(
self,
state: Any
) -> None
Set extra state contained in the loaded state_dict.
This function is called from :func:load_state_dict to handle any extra state
found within the state_dict. Implement this function and a corresponding
View Source
def set_extra_state(self, state: Any) -> None:
"""Set extra state contained in the loaded `state_dict`.
This function is called from :func:`load_state_dict` to handle any extra state
found within the `state_dict`. Implement this function and a corresponding
:func:`get_extra_state` for your module if you need to store extra state within its
`state_dict`.
Args:
state (dict): Extra state from the `state_dict`
"""
raise RuntimeError(
"Reached a code path in Module.set_extra_state() that should never be called. "
"Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml "
"to report this bug.")
share_memory
def share_memory(
self: ~T
) -> ~T
See :meth:torch.Tensor.share_memory_.
View Source
def share_memory(self: T) -> T:
r"""See :meth:`torch.Tensor.share_memory_`."""
return self._apply(lambda t: t.share_memory_())
state_dict
def state_dict(
self,
*args,
destination=None,
prefix='',
keep_vars=False
)
Return a dictionary containing references to the whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.
Parameters and buffers set to None are not included.
.. note:: The returned object is a shallow copy. It contains references to the module's parameters and buffers.
.. warning::
Currently state_dict() also accepts positional arguments for
destination, prefix and keep_vars in order. However,
this is being deprecated and keyword arguments will be enforced in
future releases.
.. warning::
Please avoid the use of argument destination as it is not
designed for end-users.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| destination | dict | If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned.Default: None. |
None |
| prefix | str | a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''. |
None |
| keep_vars | bool | by default the :class:~torch.Tensor sreturned in the state dict are detached from autograd. If it's set to True, detaching will not be performed.Default: False. |
None |
Returns:
| Type | Description |
|---|---|
| dict | a dictionary containing a whole state of the module |
View Source
def state_dict(self, *args, destination=None, prefix='', keep_vars=False):
r"""Return a dictionary containing references to the whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.
Parameters and buffers set to ``None`` are not included.
.. note::
The returned object is a shallow copy. It contains references
to the module's parameters and buffers.
.. warning::
Currently ``state_dict()`` also accepts positional arguments for
``destination``, ``prefix`` and ``keep_vars`` in order. However,
this is being deprecated and keyword arguments will be enforced in
future releases.
.. warning::
Please avoid the use of argument ``destination`` as it is not
designed for end-users.
Args:
destination (dict, optional): If provided, the state of module will
be updated into the dict and the same object is returned.
Otherwise, an ``OrderedDict`` will be created and returned.
Default: ``None``.
prefix (str, optional): a prefix added to parameter and buffer
names to compose the keys in state_dict. Default: ``''``.
keep_vars (bool, optional): by default the :class:`~torch.Tensor` s
returned in the state dict are detached from autograd. If it's
set to ``True``, detaching will not be performed.
Default: ``False``.
Returns:
dict:
a dictionary containing a whole state of the module
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']
"""
# TODO: Remove `args` and the parsing logic when BC allows.
if len(args) > 0:
if destination is None:
destination = args[0]
if len(args) > 1 and prefix == '':
prefix = args[1]
if len(args) > 2 and keep_vars is False:
keep_vars = args[2]
# DeprecationWarning is ignored by default
warnings.warn(
"Positional args are being deprecated, use kwargs instead. Refer to "
"https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict"
" for details.")
if destination is None:
destination = OrderedDict()
destination._metadata = OrderedDict()
local_metadata = dict(version=self._version)
if hasattr(destination, "_metadata"):
destination._metadata[prefix[:-1]] = local_metadata
for hook in self._state_dict_pre_hooks.values():
hook(self, prefix, keep_vars)
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=destination, prefix=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
to
def to(
self,
*args,
**kwargs
)
Move and/or cast the parameters and buffers.
This can be called as
.. function:: to(device=None, dtype=None, non_blocking=False) :noindex:
.. function:: to(dtype, non_blocking=False) :noindex:
.. function:: to(tensor, non_blocking=False) :noindex:
.. function:: to(memory_format=torch.channels_last) :noindex:
Its signature is similar to :meth:torch.Tensor.to, but only accepts
floating point or complex :attr:dtype\ s. In addition, this method will
only cast the floating point or complex parameters and buffers to :attr:dtype
(if given). The integral parameters and buffers will be moved
:attr:device, if that is given, but with dtypes unchanged. When
:attr:non_blocking is set, it tries to convert/move asynchronously
with respect to the host if possible, e.g., moving CPU Tensors with
pinned memory to CUDA devices.
See below for examples.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device ( | None | class:torch.device): the desired device of the parametersand buffers in this module |
None |
| dtype ( | None | class:torch.dtype): the desired floating point or complex dtype ofthe parameters and buffers in this module |
None |
| tensor | torch.Tensor | Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module |
None |
| memory_format ( | None | class:torch.memory_format): the desired memoryformat for 4D parameters and buffers in this module (keyword only argument) |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def to(self, *args, **kwargs):
r"""Move and/or cast the parameters and buffers.
This can be called as
.. function:: to(device=None, dtype=None, non_blocking=False)
:noindex:
.. function:: to(dtype, non_blocking=False)
:noindex:
.. function:: to(tensor, non_blocking=False)
:noindex:
.. function:: to(memory_format=torch.channels_last)
:noindex:
Its signature is similar to :meth:`torch.Tensor.to`, but only accepts
floating point or complex :attr:`dtype`\ s. In addition, this method will
only cast the floating point or complex parameters and buffers to :attr:`dtype`
(if given). The integral parameters and buffers will be moved
:attr:`device`, if that is given, but with dtypes unchanged. When
:attr:`non_blocking` is set, it tries to convert/move asynchronously
with respect to the host if possible, e.g., moving CPU Tensors with
pinned memory to CUDA devices.
See below for examples.
.. note::
This method modifies the module in-place.
Args:
device (:class:`torch.device`): the desired device of the parameters
and buffers in this module
dtype (:class:`torch.dtype`): the desired floating point or complex dtype of
the 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
memory_format (:class:`torch.memory_format`): the desired memory
format for 4D parameters and buffers in this module (keyword
only argument)
Returns:
Module: self
Examples::
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
[-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
[-0.5113, -0.2325]], dtype=torch.float64)
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
[-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
[-0.5112, -0.2324]], dtype=torch.float16)
>>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble)
>>> linear.weight
Parameter containing:
tensor([[ 0.3741+0.j, 0.2382+0.j],
[ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)
>>> linear(torch.ones(3, 2, dtype=torch.cdouble))
tensor([[0.6122+0.j, 0.1150+0.j],
[0.6122+0.j, 0.1150+0.j],
[0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)
"""
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 '
f'dtypes, but got desired dtype={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.yml "
"if a complex module does not work as expected.")
def convert(t):
try:
if convert_to_format is not None and t.dim() in (4, 5):
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,
)
except NotImplementedError as e:
if str(e) == "Cannot copy out of meta tensor; no data!":
raise NotImplementedError(
f"{e} Please use torch.nn.Module.to_empty() instead of torch.nn.Module.to() "
f"when moving module from meta to a different device."
) from None
else:
raise
return self._apply(convert)
to_empty
def to_empty(
self: ~T,
*,
device: Union[int, str, torch.device, NoneType],
recurse: bool = True
) -> ~T
Move the parameters and buffers to the specified device without copying storage.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device ( | None | class:torch.device): The desired device of the parametersand buffers in this module. |
None |
| recurse | bool | Whether parameters and buffers of submodules should be recursively moved to the specified device. |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def to_empty(self: T, *, device: Optional[DeviceLikeType], recurse: bool = True) -> T:
r"""Move the parameters and buffers to the specified device without copying storage.
Args:
device (:class:`torch.device`): The desired device of the parameters
and buffers in this module.
recurse (bool): Whether parameters and buffers of submodules should
be recursively moved to the specified device.
Returns:
Module: self
"""
return self._apply(lambda t: torch.empty_like(t, device=device), recurse=recurse)
train
def train(
self: ~T,
mode: bool = True
) -> ~T
Set 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.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| mode | bool | whether to set training mode (True) or evaluationmode ( False). Default: True. |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def train(self: T, mode: bool = True) -> T:
r"""Set 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
"""
if not isinstance(mode, bool):
raise ValueError("training mode is expected to be boolean")
self.training = mode
for module in self.children():
module.train(mode)
return self
type
def type(
self: ~T,
dst_type: Union[torch.dtype, str]
) -> ~T
Casts all parameters and buffers to :attr:dst_type.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| dst_type | type or string | the desired type | None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def type(self: T, dst_type: Union[dtype, str]) -> T:
r"""Casts all parameters and buffers to :attr:`dst_type`.
.. note::
This method modifies the module in-place.
Args:
dst_type (type or string): the desired type
Returns:
Module: self
"""
return self._apply(lambda t: t.type(dst_type))
xpu
def xpu(
self: ~T,
device: Union[int, torch.device, NoneType] = None
) -> ~T
Move all model parameters and buffers to the XPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on XPU while being optimized.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device | int | if specified, all parameters will be copied to that device |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def xpu(self: T, device: Optional[Union[int, device]] = None) -> T:
r"""Move all model parameters and buffers to the XPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on XPU while being optimized.
.. note::
This method modifies the module in-place.
Arguments:
device (int, optional): if specified, all parameters will be
copied to that device
Returns:
Module: self
"""
return self._apply(lambda t: t.xpu(device))
zero_grad
def zero_grad(
self,
set_to_none: bool = True
) -> None
Reset gradients of all model parameters.
See similar function under :class:torch.optim.Optimizer for more context.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| set_to_none | bool | instead of setting to zero, set the grads to None. See :meth: torch.optim.Optimizer.zero_grad for details. |
None |
View Source
def zero_grad(self, set_to_none: bool = True) -> None:
r"""Reset gradients of all model parameters.
See similar function under :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 = None
else:
if p.grad.grad_fn is not None:
p.grad.detach_()
else:
p.grad.requires_grad_(False)
p.grad.zero_()
WormPredictor
class WormPredictor(
model: torch.nn.modules.module.Module,
io_config: wtracker.neural.config.IOConfig
)
A class that represents neural network models that predict worm behavior. After a model is created from several layers or blocks, it is wrapped in this class
so that it can be distinguished from other models that don't predict worm behavior (for example the layers/blocks that make this model). This class also holds the IOConfig object that is used to determine the input and output shapes of the model, and the specific frames it expects as input and output.
Attributes
| Name | Type | Description | Default |
|---|---|---|---|
| model | None | The neural network model that predicts worm behavior. | None |
| io_config | None | The IOConfig object of the model. | None |
View Source
class WormPredictor(nn.Module):
"""
A class that represents neural network models that predict worm behavior. After a model is created from several layers or blocks, it is wrapped in this class
so that it can be distinguished from other models that don't predict worm behavior (for example the layers/blocks that make this model).
This class also holds the IOConfig object that is used to determine the input and output shapes of the model, and the specific frames it expects as input and output.
Attributes:
model: The neural network model that predicts worm behavior.
io_config: The IOConfig object of the model.
"""
def __init__(self, model: nn.Module, io_config: IOConfig):
super().__init__()
self.io_config: IOConfig = io_config
self.model: nn.Module = model
def forward(self, x: Tensor) -> Tensor:
return self.model(x)
Ancestors (in MRO)
- torch.nn.modules.module.Module
Class variables
T_destination
call_super_init
dump_patches
Methods
add_module
def add_module(
self,
name: str,
module: Optional[ForwardRef('Module')]
) -> None
Add a child module to the current module.
The module can be accessed as an attribute using the given name.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| name | str | name of the child module. The child module can be accessed from this module using the given name |
None |
| module | Module | child module to be added to the module. | None |
View Source
def add_module(self, name: str, module: Optional['Module']) -> None:
r"""Add a child module to the current module.
The module can be accessed as an attribute using the given name.
Args:
name (str): 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(f"{torch.typename(module)} is not a Module subclass")
elif not isinstance(name, str):
raise TypeError(f"module name should be a string. Got {torch.typename(name)}")
elif hasattr(self, name) and name not in self._modules:
raise KeyError(f"attribute '{name}' already exists")
elif '.' in name:
raise KeyError(f"module name can't contain \".\", got: {name}")
elif name == '':
raise KeyError("module name can't be empty string \"\"")
for hook in _global_module_registration_hooks.values():
output = hook(self, name, module)
if output is not None:
module = output
self._modules[name] = module
apply
def apply(
self: ~T,
fn: Callable[[ForwardRef('Module')], NoneType]
) -> ~T
Apply fn recursively to every submodule (as returned by .children()) as well as self.
Typical use includes initializing the parameters of a model
(see also :ref:nn-init-doc).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| fn ( | None | class:Module -> None): function to be applied to each submodule |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def apply(self: T, fn: Callable[['Module'], None]) -> T:
r"""Apply ``fn`` recursively to every submodule (as returned by ``.children()``) as well as self.
Typical use includes initializing the parameters of a model
(see also :ref:`nn-init-doc`).
Args:
fn (:class:`Module` -> None): function to be applied to each submodule
Returns:
Module: self
Example::
>>> @torch.no_grad()
>>> def init_weights(m):
>>> print(m)
>>> if type(m) == nn.Linear:
>>> m.weight.fill_(1.0)
>>> print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[1., 1.],
[1., 1.]], requires_grad=True)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[1., 1.],
[1., 1.]], requires_grad=True)
Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
"""
for module in self.children():
module.apply(fn)
fn(self)
return self
bfloat16
def bfloat16(
self: ~T
) -> ~T
Casts all floating point parameters and buffers to bfloat16 datatype.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def bfloat16(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``bfloat16`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.bfloat16() if t.is_floating_point() else t)
buffers
def buffers(
self,
recurse: bool = True
) -> Iterator[torch.Tensor]
Return an iterator over module buffers.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| recurse | bool | if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. |
None |
Yields:
| Type | Description |
|---|---|
| torch.Tensor | module buffer |
View Source
def buffers(self, recurse: bool = True) -> Iterator[Tensor]:
r"""Return 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::
>>> # xdoctest: +SKIP("undefined vars")
>>> for buf in model.buffers():
>>> print(type(buf), buf.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)
"""
for _, buf in self.named_buffers(recurse=recurse):
yield buf
children
def children(
self
) -> Iterator[ForwardRef('Module')]
Return an iterator over immediate children modules.
Yields:
| Type | Description |
|---|---|
| Module | a child module |
View Source
def children(self) -> Iterator['Module']:
r"""Return an iterator over immediate children modules.
Yields:
Module: a child module
"""
for name, module in self.named_children():
yield module
compile
def compile(
self,
*args,
**kwargs
)
Compile this Module's forward using :func:torch.compile.
This Module's __call__ method is compiled and all arguments are passed as-is
to :func:torch.compile.
See :func:torch.compile for details on the arguments for this function.
View Source
def compile(self, *args, **kwargs):
"""
Compile this Module's forward using :func:`torch.compile`.
This Module's `__call__` method is compiled and all arguments are passed as-is
to :func:`torch.compile`.
See :func:`torch.compile` for details on the arguments for this function.
"""
self._compiled_call_impl = torch.compile(self._call_impl, *args, **kwargs)
cpu
def cpu(
self: ~T
) -> ~T
Move all model parameters and buffers to the CPU.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def cpu(self: T) -> T:
r"""Move all model parameters and buffers to the CPU.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.cpu())
cuda
def cuda(
self: ~T,
device: Union[int, torch.device, NoneType] = None
) -> ~T
Move all model parameters and buffers to the GPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device | int | if specified, all parameters will be copied to that device |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def cuda(self: T, device: Optional[Union[int, device]] = None) -> T:
r"""Move all model parameters and buffers to the GPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on GPU while being optimized.
.. note::
This method modifies the module in-place.
Args:
device (int, optional): if specified, all parameters will be
copied to that device
Returns:
Module: self
"""
return self._apply(lambda t: t.cuda(device))
double
def double(
self: ~T
) -> ~T
Casts all floating point parameters and buffers to double datatype.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def double(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``double`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.double() if t.is_floating_point() else t)
eval
def eval(
self: ~T
) -> ~T
Set 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>.
See :ref:locally-disable-grad-doc for a comparison between
.eval() and several similar mechanisms that may be confused with it.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def eval(self: T) -> T:
r"""Set 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>`.
See :ref:`locally-disable-grad-doc` for a comparison between
`.eval()` and several similar mechanisms that may be confused with it.
Returns:
Module: self
"""
return self.train(False)
extra_repr
def extra_repr(
self
) -> str
Set the extra representation of the module.
To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.
View Source
def extra_repr(self) -> str:
r"""Set the extra representation of the module.
To print customized extra information, you should re-implement
this method in your own modules. Both single-line and multi-line
strings are acceptable.
"""
return ''
float
def float(
self: ~T
) -> ~T
Casts all floating point parameters and buffers to float datatype.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def float(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``float`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.float() if t.is_floating_point() else t)
forward
def forward(
self,
x: torch.Tensor
) -> torch.Tensor
Define 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.
View Source
def forward(self, x: Tensor) -> Tensor:
return self.model(x)
get_buffer
def get_buffer(
self,
target: str
) -> 'Tensor'
Return the buffer given by target if it exists, otherwise throw an error.
See the docstring for get_submodule for a more detailed
explanation of this method's functionality as well as how to
correctly specify target.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| target | None | The fully-qualified string name of the buffer to look for. (See get_submodule for how to specify afully-qualified string.) |
None |
Returns:
| Type | Description |
|---|---|
| torch.Tensor | The buffer referenced by target |
Raises:
| Type | Description |
|---|---|
| AttributeError | If the target string references an invalid path or resolves to something that is not a buffer |
View Source
def get_buffer(self, target: str) -> "Tensor":
"""Return the buffer given by ``target`` if it exists, otherwise throw an error.
See the docstring for ``get_submodule`` for a more detailed
explanation of this method's functionality as well as how to
correctly specify ``target``.
Args:
target: The fully-qualified string name of the buffer
to look for. (See ``get_submodule`` for how to specify a
fully-qualified string.)
Returns:
torch.Tensor: The buffer referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not a
buffer
"""
module_path, _, buffer_name = target.rpartition(".")
mod: torch.nn.Module = self.get_submodule(module_path)
if not hasattr(mod, buffer_name):
raise AttributeError(mod._get_name() + " has no attribute `"
+ buffer_name + "`")
buffer: torch.Tensor = getattr(mod, buffer_name)
if buffer_name not in mod._buffers:
raise AttributeError("`" + buffer_name + "` is not a buffer")
return buffer
get_extra_state
def get_extra_state(
self
) -> Any
Return any extra state to include in the module's state_dict.
Implement this and a corresponding :func:set_extra_state for your module
if you need to store extra state. This function is called when building the
module's state_dict().
Note that extra state should be picklable to ensure working serialization of the state_dict. We only provide provide backwards compatibility guarantees for serializing Tensors; other objects may break backwards compatibility if their serialized pickled form changes.
Returns:
| Type | Description |
|---|---|
| object | Any extra state to store in the module's state_dict |
View Source
def get_extra_state(self) -> Any:
"""Return any extra state to include in the module's state_dict.
Implement this and a corresponding :func:`set_extra_state` for your module
if you need to store extra state. This function is called when building the
module's `state_dict()`.
Note that extra state should be picklable to ensure working serialization
of the state_dict. We only provide provide backwards compatibility guarantees
for serializing Tensors; other objects may break backwards compatibility if
their serialized pickled form changes.
Returns:
object: Any extra state to store in the module's state_dict
"""
raise RuntimeError(
"Reached a code path in Module.get_extra_state() that should never be called. "
"Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml "
"to report this bug.")
get_parameter
def get_parameter(
self,
target: str
) -> 'Parameter'
Return the parameter given by target if it exists, otherwise throw an error.
See the docstring for get_submodule for a more detailed
explanation of this method's functionality as well as how to
correctly specify target.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| target | None | The fully-qualified string name of the Parameter to look for. (See get_submodule for how to specify afully-qualified string.) |
None |
Returns:
| Type | Description |
|---|---|
| torch.nn.Parameter | The Parameter referenced by target |
Raises:
| Type | Description |
|---|---|
| AttributeError | If the target string references an invalid path or resolves to something that is not an nn.Parameter |
View Source
def get_parameter(self, target: str) -> "Parameter":
"""Return the parameter given by ``target`` if it exists, otherwise throw an error.
See the docstring for ``get_submodule`` for a more detailed
explanation of this method's functionality as well as how to
correctly specify ``target``.
Args:
target: The fully-qualified string name of the Parameter
to look for. (See ``get_submodule`` for how to specify a
fully-qualified string.)
Returns:
torch.nn.Parameter: The Parameter referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not an
``nn.Parameter``
"""
module_path, _, param_name = target.rpartition(".")
mod: torch.nn.Module = self.get_submodule(module_path)
if not hasattr(mod, param_name):
raise AttributeError(mod._get_name() + " has no attribute `"
+ param_name + "`")
param: torch.nn.Parameter = getattr(mod, param_name)
if not isinstance(param, torch.nn.Parameter):
raise AttributeError("`" + param_name + "` is not an "
"nn.Parameter")
return param
get_submodule
def get_submodule(
self,
target: str
) -> 'Module'
Return the submodule given by target if it exists, otherwise throw an error.
For example, let's say you have an nn.Module A that
looks like this:
.. code-block:: text
A(
(net_b): Module(
(net_c): Module(
(conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))
)
(linear): Linear(in_features=100, out_features=200, bias=True)
)
)
(The diagram shows an nn.Module A. A has a nested
submodule net_b, which itself has two submodules net_c
and linear. net_c then has a submodule conv.)
To check whether or not we have the linear submodule, we
would call get_submodule("net_b.linear"). To check whether
we have the conv submodule, we would call
get_submodule("net_b.net_c.conv").
The runtime of get_submodule is bounded by the degree
of module nesting in target. A query against
named_modules achieves the same result, but it is O(N) in
the number of transitive modules. So, for a simple check to see
if some submodule exists, get_submodule should always be
used.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| target | None | The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.) |
None |
Returns:
| Type | Description |
|---|---|
| torch.nn.Module | The submodule referenced by target |
Raises:
| Type | Description |
|---|---|
| AttributeError | If the target string references an invalid path or resolves to something that is not an nn.Module |
View Source
def get_submodule(self, target: str) -> "Module":
"""Return the submodule given by ``target`` if it exists, otherwise throw an error.
For example, let's say you have an ``nn.Module`` ``A`` that
looks like this:
.. code-block:: text
A(
(net_b): Module(
(net_c): Module(
(conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))
)
(linear): Linear(in_features=100, out_features=200, bias=True)
)
)
(The diagram shows an ``nn.Module`` ``A``. ``A`` has a nested
submodule ``net_b``, which itself has two submodules ``net_c``
and ``linear``. ``net_c`` then has a submodule ``conv``.)
To check whether or not we have the ``linear`` submodule, we
would call ``get_submodule("net_b.linear")``. To check whether
we have the ``conv`` submodule, we would call
``get_submodule("net_b.net_c.conv")``.
The runtime of ``get_submodule`` is bounded by the degree
of module nesting in ``target``. A query against
``named_modules`` achieves the same result, but it is O(N) in
the number of transitive modules. So, for a simple check to see
if some submodule exists, ``get_submodule`` should always be
used.
Args:
target: The fully-qualified string name of the submodule
to look for. (See above example for how to specify a
fully-qualified string.)
Returns:
torch.nn.Module: The submodule referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not an
``nn.Module``
"""
if target == "":
return self
atoms: List[str] = target.split(".")
mod: torch.nn.Module = self
for item in atoms:
if not hasattr(mod, item):
raise AttributeError(mod._get_name() + " has no "
"attribute `" + item + "`")
mod = getattr(mod, item)
if not isinstance(mod, torch.nn.Module):
raise AttributeError("`" + item + "` is not "
"an nn.Module")
return mod
half
def half(
self: ~T
) -> ~T
Casts all floating point parameters and buffers to half datatype.
.. note:: This method modifies the module in-place.
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def half(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``half`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
return self._apply(lambda t: t.half() if t.is_floating_point() else t)
ipu
def ipu(
self: ~T,
device: Union[int, torch.device, NoneType] = None
) -> ~T
Move all model parameters and buffers to the IPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on IPU while being optimized.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device | int | if specified, all parameters will be copied to that device |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def ipu(self: T, device: Optional[Union[int, device]] = None) -> T:
r"""Move all model parameters and buffers to the IPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on IPU while being optimized.
.. note::
This method modifies the module in-place.
Arguments:
device (int, optional): if specified, all parameters will be
copied to that device
Returns:
Module: self
"""
return self._apply(lambda t: t.ipu(device))
load_state_dict
def load_state_dict(
self,
state_dict: Mapping[str, Any],
strict: bool = True,
assign: bool = False
)
Copy parameters and buffers from :attr:state_dict into this module and its descendants.
If :attr:strict is True, then
the keys of :attr:state_dict must exactly match the keys returned
by this module's :meth:~torch.nn.Module.state_dict function.
.. warning::
If :attr:assign is True the optimizer must be created after
the call to :attr:load_state_dict unless
:func:~torch.__future__.get_swap_module_params_on_conversion is True.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| state_dict | dict | a dict containing parameters and persistent buffers. |
None |
| strict | bool | 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 |
None |
| assign | bool | When False, the properties of the tensorsin the current module are preserved while when True, theproperties of the Tensors in the state dict are preserved. The only exception is the requires_grad field of :class:~torch.nn.Parametersfor which the value from the module is preserved. Default: False |
None |
Returns:
| Type | Description |
|---|---|
| None | 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 |
View Source
def load_state_dict(self, state_dict: Mapping[str, Any],
strict: bool = True, assign: bool = False):
r"""Copy parameters and buffers from :attr:`state_dict` into this module and its descendants.
If :attr:`strict` is ``True``, then
the keys of :attr:`state_dict` must exactly match the keys returned
by this module's :meth:`~torch.nn.Module.state_dict` function.
.. warning::
If :attr:`assign` is ``True`` the optimizer must be created after
the call to :attr:`load_state_dict` unless
:func:`~torch.__future__.get_swap_module_params_on_conversion` is ``True``.
Args:
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``
assign (bool, optional): When ``False``, the properties of the tensors
in the current module are preserved while when ``True``, the
properties of the Tensors in the state dict are preserved. The only
exception is the ``requires_grad`` field of :class:`~torch.nn.Parameter`s
for which the value from the module is preserved.
Default: ``False``
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
Note:
If a parameter or buffer is registered as ``None`` and its corresponding key
exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a
``RuntimeError``.
"""
if not isinstance(state_dict, Mapping):
raise TypeError(f"Expected state_dict to be dict-like, got {type(state_dict)}.")
missing_keys: List[str] = []
unexpected_keys: List[str] = []
error_msgs: List[str] = []
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, '_metadata', None)
state_dict = OrderedDict(state_dict)
if metadata is not None:
# mypy isn't aware that "_metadata" exists in state_dict
state_dict._metadata = metadata # type: ignore[attr-defined]
def load(module, local_state_dict, prefix=''):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
if assign:
local_metadata['assign_to_params_buffers'] = assign
module._load_from_state_dict(
local_state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
for name, child in module._modules.items():
if child is not None:
child_prefix = prefix + name + '.'
child_state_dict = {k: v for k, v in local_state_dict.items() if k.startswith(child_prefix)}
load(child, child_state_dict, child_prefix) # noqa: F821
# Note that the hook can modify missing_keys and unexpected_keys.
incompatible_keys = _IncompatibleKeys(missing_keys, unexpected_keys)
for hook in module._load_state_dict_post_hooks.values():
out = hook(module, incompatible_keys)
assert out is None, (
"Hooks registered with ``register_load_state_dict_post_hook`` are not"
"expected to return new values, if incompatible_keys need to be modified,"
"it should be done inplace."
)
load(self, state_dict)
del load
if strict:
if len(unexpected_keys) > 0:
error_msgs.insert(
0, 'Unexpected key(s) in state_dict: {}. '.format(
', '.join(f'"{k}"' for k in unexpected_keys)))
if len(missing_keys) > 0:
error_msgs.insert(
0, 'Missing key(s) in state_dict: {}. '.format(
', '.join(f'"{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)
modules
def modules(
self
) -> Iterator[ForwardRef('Module')]
Return an iterator over all modules in the network.
Yields:
| Type | Description |
|---|---|
| Module | a module in the network |
View Source
def modules(self) -> Iterator['Module']:
r"""Return an iterator over all modules in the network.
Yields:
Module: a module in the network
Note:
Duplicate modules are returned only once. In the following
example, ``l`` will be returned only once.
Example::
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
... print(idx, '->', m)
0 -> Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)
"""
for _, module in self.named_modules():
yield module
named_buffers
def named_buffers(
self,
prefix: str = '',
recurse: bool = True,
remove_duplicate: bool = True
) -> Iterator[Tuple[str, torch.Tensor]]
Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| prefix | str | prefix to prepend to all buffer names. | None |
| recurse | bool | if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True. |
None |
| remove_duplicate | bool | whether to remove the duplicated buffers in the result. Defaults to True. | True |
Yields:
| Type | Description |
|---|---|
| None | (str, torch.Tensor): Tuple containing the name and buffer |
View Source
def named_buffers(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> Iterator[Tuple[str, Tensor]]:
r"""Return 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, optional): if True, then yields buffers of this module
and all submodules. Otherwise, yields only buffers that
are direct members of this module. Defaults to True.
remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.
Yields:
(str, torch.Tensor): Tuple containing the name and buffer
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, buf in self.named_buffers():
>>> if name in ['running_var']:
>>> print(buf.size())
"""
gen = self._named_members(
lambda module: module._buffers.items(),
prefix=prefix, recurse=recurse, remove_duplicate=remove_duplicate)
yield from gen
named_children
def named_children(
self
) -> Iterator[Tuple[str, ForwardRef('Module')]]
Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
Yields:
| Type | Description |
|---|---|
| None | (str, Module): Tuple containing a name and child module |
View Source
def named_children(self) -> Iterator[Tuple[str, 'Module']]:
r"""Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
Yields:
(str, Module): Tuple containing a name and child module
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, module in model.named_children():
>>> if name in ['conv4', 'conv5']:
>>> print(module)
"""
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
named_modules
def named_modules(
self,
memo: Optional[Set[ForwardRef('Module')]] = None,
prefix: str = '',
remove_duplicate: bool = True
)
Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| memo | None | a memo to store the set of modules already added to the result | None |
| prefix | None | a prefix that will be added to the name of the module | None |
| remove_duplicate | None | whether to remove the duplicated module instances in the result or not |
None |
Yields:
| Type | Description |
|---|---|
| None | (str, Module): Tuple of name and module |
View Source
def named_modules(self, memo: Optional[Set['Module']] = None, prefix: str = '', remove_duplicate: bool = True):
r"""Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
Args:
memo: a memo to store the set of modules already added to the result
prefix: a prefix that will be added to the name of the module
remove_duplicate: whether to remove the duplicated module instances in the result
or not
Yields:
(str, Module): Tuple of name and module
Note:
Duplicate modules are returned only once. In the following
example, ``l`` will be returned only once.
Example::
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
... print(idx, '->', m)
0 -> ('', Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
))
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))
"""
if memo is None:
memo = set()
if self not in memo:
if remove_duplicate:
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
yield from module.named_modules(memo, submodule_prefix, remove_duplicate)
named_parameters
def named_parameters(
self,
prefix: str = '',
recurse: bool = True,
remove_duplicate: bool = True
) -> Iterator[Tuple[str, torch.nn.parameter.Parameter]]
Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| prefix | str | prefix to prepend to all parameter names. | None |
| recurse | bool | if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. |
None |
| remove_duplicate | bool | whether to remove the duplicated parameters in the result. Defaults to True. |
None |
Yields:
| Type | Description |
|---|---|
| None | (str, Parameter): Tuple containing the name and parameter |
View Source
def named_parameters(
self,
prefix: str = '',
recurse: bool = True,
remove_duplicate: bool = True
) -> Iterator[Tuple[str, Parameter]]:
r"""Return 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.
remove_duplicate (bool, optional): whether to remove the duplicated
parameters in the result. Defaults to True.
Yields:
(str, Parameter): Tuple containing the name and parameter
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, param in self.named_parameters():
>>> if name in ['bias']:
>>> print(param.size())
"""
gen = self._named_members(
lambda module: module._parameters.items(),
prefix=prefix, recurse=recurse, remove_duplicate=remove_duplicate)
yield from gen
parameters
def parameters(
self,
recurse: bool = True
) -> Iterator[torch.nn.parameter.Parameter]
Return an iterator over module parameters.
This is typically passed to an optimizer.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| recurse | bool | if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. |
None |
Yields:
| Type | Description |
|---|---|
| Parameter | module parameter |
View Source
def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
r"""Return 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::
>>> # xdoctest: +SKIP("undefined vars")
>>> 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 param
register_backward_hook
def register_backward_hook(
self,
hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]]
) -> torch.utils.hooks.RemovableHandle
Register a backward hook on the module.
This function is deprecated in favor of :meth:~torch.nn.Module.register_full_backward_hook and
the behavior of this function will change in future versions.
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_backward_hook(
self, hook: Callable[['Module', _grad_t, _grad_t], Union[None, _grad_t]]
) -> RemovableHandle:
r"""Register a backward hook on the module.
This function is deprecated in favor of :meth:`~torch.nn.Module.register_full_backward_hook` and
the behavior of this function will change in future versions.
Returns:
: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 = False
handle = hooks.RemovableHandle(self._backward_hooks)
self._backward_hooks[handle.id] = hook
return handle
register_buffer
def register_buffer(
self,
name: str,
tensor: Optional[torch.Tensor],
persistent: bool = True
) -> None
Add a buffer to the module.
This is typically used to register a buffer that should not to be
considered a model parameter. For example, BatchNorm's running_mean
is not a parameter, but is part of the module's state. Buffers, by
default, are persistent and will be saved alongside parameters. This
behavior can be changed by setting :attr:persistent to False. The
only difference between a persistent buffer and a non-persistent buffer
is that the latter will not be a part of this module's
:attr:state_dict.
Buffers can be accessed as attributes using given names.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| name | str | name of the buffer. The buffer can be accessed from this module using the given name |
None |
| tensor | Tensor or None | buffer to be registered. If None, then operationsthat run on buffers, such as :attr: cuda, are ignored. If None,the buffer is not included in the module's :attr: state_dict. |
None |
| persistent | bool | whether the buffer is part of this module's :attr: state_dict. |
None |
View Source
def register_buffer(self, name: str, tensor: Optional[Tensor], persistent: bool = True) -> None:
r"""Add a buffer to the module.
This is typically used to register a buffer that should not to be
considered a model parameter. For example, BatchNorm's ``running_mean``
is not a parameter, but is part of the module's state. Buffers, by
default, are persistent and will be saved alongside parameters. This
behavior can be changed by setting :attr:`persistent` to ``False``. The
only difference between a persistent buffer and a non-persistent buffer
is that the latter will not be a part of this module's
:attr:`state_dict`.
Buffers can be accessed as attributes using given names.
Args:
name (str): name of the buffer. The buffer can be accessed
from this module using the given name
tensor (Tensor or None): buffer to be registered. If ``None``, then operations
that run on buffers, such as :attr:`cuda`, are ignored. If ``None``,
the buffer is **not** included in the module's :attr:`state_dict`.
persistent (bool): whether the buffer is part of this module's
:attr:`state_dict`.
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> 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, str):
raise TypeError(f"buffer name should be a string. Got {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(f"attribute '{name}' already exists")
elif tensor is not None and not isinstance(tensor, torch.Tensor):
raise TypeError(f"cannot assign '{torch.typename(tensor)}' object to buffer '{name}' "
"(torch Tensor or None required)"
)
else:
for hook in _global_buffer_registration_hooks.values():
output = hook(self, name, tensor)
if output is not None:
tensor = output
self._buffers[name] = tensor
if persistent:
self._non_persistent_buffers_set.discard(name)
else:
self._non_persistent_buffers_set.add(name)
register_forward_hook
def register_forward_hook(
self,
hook: Union[Callable[[~T, Tuple[Any, ...], Any], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any], Any], Optional[Any]]],
*,
prepend: bool = False,
with_kwargs: bool = False,
always_call: bool = False
) -> torch.utils.hooks.RemovableHandle
Register a forward hook on the module.
The hook will be called every time after :func:forward has computed an output.
If with_kwargs is False or not specified, the input contains only
the positional arguments given to the module. Keyword arguments won't be
passed to the hooks and only to the forward. The hook can modify the
output. It can modify the input inplace but it will not have effect on
forward since this is called after :func:forward is called. The hook
should have the following signature::
hook(module, args, output) -> None or modified output
If with_kwargs is True, the forward hook will be passed the
kwargs given to the forward function and be expected to return the
output possibly modified. The hook should have the following signature::
hook(module, args, kwargs, output) -> None or modified output
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| hook | Callable | The user defined hook to be registered. | None |
| prepend | bool | If True, the provided hook will be firedbefore all existing forward hooks on this:class: torch.nn.modules.Module. Otherwise, the providedhook will be fired after all existing forward hooks onthis :class: torch.nn.modules.Module. Note that globalforward hooks registered with:func: register_module_forward_hook will fire before all hooksregistered by this method. Default: False |
None |
| with_kwargs | bool | If True, the hook will be passed thekwargs given to the forward function. Default: False |
None |
| always_call | bool | If True the hook will be run regardless ofwhether an exception is raised while calling the Module. Default: False |
None |
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_forward_hook(
self,
hook: Union[
Callable[[T, Tuple[Any, ...], Any], Optional[Any]],
Callable[[T, Tuple[Any, ...], Dict[str, Any], Any], Optional[Any]],
],
*,
prepend: bool = False,
with_kwargs: bool = False,
always_call: bool = False,
) -> RemovableHandle:
r"""Register a forward hook on the module.
The hook will be called every time after :func:`forward` has computed an output.
If ``with_kwargs`` is ``False`` or not specified, the input contains only
the positional arguments given to the module. Keyword arguments won't be
passed to the hooks and only to the ``forward``. The hook can modify the
output. It can modify the input inplace but it will not have effect on
forward since this is called after :func:`forward` is called. The hook
should have the following signature::
hook(module, args, output) -> None or modified output
If ``with_kwargs`` is ``True``, the forward hook will be passed the
``kwargs`` given to the forward function and be expected to return the
output possibly modified. The hook should have the following signature::
hook(module, args, kwargs, output) -> None or modified output
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If ``True``, the provided ``hook`` will be fired
before all existing ``forward`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``forward`` hooks on
this :class:`torch.nn.modules.Module`. Note that global
``forward`` hooks registered with
:func:`register_module_forward_hook` will fire before all hooks
registered by this method.
Default: ``False``
with_kwargs (bool): If ``True``, the ``hook`` will be passed the
kwargs given to the forward function.
Default: ``False``
always_call (bool): If ``True`` the ``hook`` will be run regardless of
whether an exception is raised while calling the Module.
Default: ``False``
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,
extra_dict=[self._forward_hooks_with_kwargs, self._forward_hooks_always_called],
)
self._forward_hooks[handle.id] = hook
if with_kwargs:
self._forward_hooks_with_kwargs[handle.id] = True
if always_call:
self._forward_hooks_always_called[handle.id] = True
if prepend:
self._forward_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
register_forward_pre_hook
def register_forward_pre_hook(
self,
hook: Union[Callable[[~T, Tuple[Any, ...]], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any]], Optional[Tuple[Any, Dict[str, Any]]]]],
*,
prepend: bool = False,
with_kwargs: bool = False
) -> torch.utils.hooks.RemovableHandle
Register a forward pre-hook on the module.
The hook will be called every time before :func:forward is invoked.
If with_kwargs is false or not specified, the input contains only
the positional arguments given to the module. Keyword arguments won't be
passed to the hooks and only to the forward. The hook can modify the
input. User can either return a tuple or a single modified value in the
hook. We will wrap the value into a tuple if a single value is returned
(unless that value is already a tuple). The hook should have the
following signature::
hook(module, args) -> None or modified input
If with_kwargs is true, the forward pre-hook will be passed the
kwargs given to the forward function. And if the hook modifies the
input, both the args and kwargs should be returned. The hook should have
the following signature::
hook(module, args, kwargs) -> None or a tuple of modified input and kwargs
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| hook | Callable | The user defined hook to be registered. | None |
| prepend | bool | If true, the provided hook will be fired beforeall existing forward_pre hooks on this:class: torch.nn.modules.Module. Otherwise, the providedhook will be fired after all existing forward_pre hookson this :class: torch.nn.modules.Module. Note that globalforward_pre hooks registered with:func: register_module_forward_pre_hook will fire before allhooks registered by this method. Default: False |
None |
| with_kwargs | bool | If true, the hook will be passed the kwargsgiven to the forward function. Default: False |
None |
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_forward_pre_hook(
self,
hook: Union[
Callable[[T, Tuple[Any, ...]], Optional[Any]],
Callable[[T, Tuple[Any, ...], Dict[str, Any]], Optional[Tuple[Any, Dict[str, Any]]]],
],
*,
prepend: bool = False,
with_kwargs: bool = False,
) -> RemovableHandle:
r"""Register a forward pre-hook on the module.
The hook will be called every time before :func:`forward` is invoked.
If ``with_kwargs`` is false or not specified, the input contains only
the positional arguments given to the module. Keyword arguments won't be
passed to the hooks and only to the ``forward``. The hook can modify the
input. User can either return a tuple or a single modified value in the
hook. We will wrap the value into a tuple if a single value is returned
(unless that value is already a tuple). The hook should have the
following signature::
hook(module, args) -> None or modified input
If ``with_kwargs`` is true, the forward pre-hook will be passed the
kwargs given to the forward function. And if the hook modifies the
input, both the args and kwargs should be returned. The hook should have
the following signature::
hook(module, args, kwargs) -> None or a tuple of modified input and kwargs
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``forward_pre`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``forward_pre`` hooks
on this :class:`torch.nn.modules.Module`. Note that global
``forward_pre`` hooks registered with
:func:`register_module_forward_pre_hook` will fire before all
hooks registered by this method.
Default: ``False``
with_kwargs (bool): If true, the ``hook`` will be passed the kwargs
given to the forward function.
Default: ``False``
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,
extra_dict=self._forward_pre_hooks_with_kwargs
)
self._forward_pre_hooks[handle.id] = hook
if with_kwargs:
self._forward_pre_hooks_with_kwargs[handle.id] = True
if prepend:
self._forward_pre_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
register_full_backward_hook
def register_full_backward_hook(
self,
hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]],
prepend: bool = False
) -> torch.utils.hooks.RemovableHandle
Register a backward hook on the module.
The hook will be called every time the gradients with respect to a module are computed, i.e. the hook will execute if and only if the gradients with respect to module outputs are computed. The hook should have the following signature::
hook(module, grad_input, grad_output) -> tuple(Tensor) or None
The :attr:grad_input and :attr:grad_output are tuples that contain the gradients
with respect to the inputs and outputs respectively. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the input that will be used in place of :attr:grad_input in
subsequent computations. :attr:grad_input will only correspond to the inputs given
as positional arguments and all kwarg arguments are ignored. Entries
in :attr:grad_input and :attr:grad_output will be None for all non-Tensor
arguments.
For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function.
.. warning :: Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| hook | Callable | The user-defined hook to be registered. | None |
| prepend | bool | If true, the provided hook will be fired beforeall existing backward hooks on this:class: torch.nn.modules.Module. Otherwise, the providedhook will be fired after all existing backward hooks onthis :class: torch.nn.modules.Module. Note that globalbackward hooks registered with:func: register_module_full_backward_hook will fire beforeall hooks registered by this method. |
None |
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_full_backward_hook(
self,
hook: Callable[["Module", _grad_t, _grad_t], Union[None, _grad_t]],
prepend: bool = False,
) -> RemovableHandle:
r"""Register a backward hook on the module.
The hook will be called every time the gradients with respect to a module
are computed, i.e. the hook will execute if and only if the gradients with
respect to module outputs are computed. The hook should have the following
signature::
hook(module, grad_input, grad_output) -> tuple(Tensor) or None
The :attr:`grad_input` and :attr:`grad_output` are tuples that contain the gradients
with respect to the inputs and outputs respectively. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the input that will be used in place of :attr:`grad_input` in
subsequent computations. :attr:`grad_input` will only correspond to the inputs given
as positional arguments and all kwarg arguments are ignored. Entries
in :attr:`grad_input` and :attr:`grad_output` will be ``None`` for all non-Tensor
arguments.
For technical reasons, when this hook is applied to a Module, its forward function will
receive a view of each Tensor passed to the Module. Similarly the caller will receive a view
of each Tensor returned by the Module's forward function.
.. warning ::
Modifying inputs or outputs inplace is not allowed when using backward hooks and
will raise an error.
Args:
hook (Callable): The user-defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``backward`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``backward`` hooks on
this :class:`torch.nn.modules.Module`. Note that global
``backward`` hooks registered with
:func:`register_module_full_backward_hook` will fire before
all hooks registered by this method.
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 = True
handle = hooks.RemovableHandle(self._backward_hooks)
self._backward_hooks[handle.id] = hook
if prepend:
self._backward_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
register_full_backward_pre_hook
def register_full_backward_pre_hook(
self,
hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]],
prepend: bool = False
) -> torch.utils.hooks.RemovableHandle
Register a backward pre-hook on the module.
The hook will be called every time the gradients for the module are computed. The hook should have the following signature::
hook(module, grad_output) -> tuple[Tensor] or None
The :attr:grad_output is a tuple. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the output that will be used in place of :attr:grad_output in
subsequent computations. Entries in :attr:grad_output will be None for
all non-Tensor arguments.
For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function.
.. warning :: Modifying inputs inplace is not allowed when using backward hooks and will raise an error.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| hook | Callable | The user-defined hook to be registered. | None |
| prepend | bool | If true, the provided hook will be fired beforeall existing backward_pre hooks on this:class: torch.nn.modules.Module. Otherwise, the providedhook will be fired after all existing backward_pre hookson this :class: torch.nn.modules.Module. Note that globalbackward_pre hooks registered with:func: register_module_full_backward_pre_hook will fire beforeall hooks registered by this method. |
None |
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_full_backward_pre_hook(
self,
hook: Callable[["Module", _grad_t], Union[None, _grad_t]],
prepend: bool = False,
) -> RemovableHandle:
r"""Register a backward pre-hook on the module.
The hook will be called every time the gradients for the module are computed.
The hook should have the following signature::
hook(module, grad_output) -> tuple[Tensor] or None
The :attr:`grad_output` is a tuple. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the output that will be used in place of :attr:`grad_output` in
subsequent computations. Entries in :attr:`grad_output` will be ``None`` for
all non-Tensor arguments.
For technical reasons, when this hook is applied to a Module, its forward function will
receive a view of each Tensor passed to the Module. Similarly the caller will receive a view
of each Tensor returned by the Module's forward function.
.. warning ::
Modifying inputs inplace is not allowed when using backward hooks and
will raise an error.
Args:
hook (Callable): The user-defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``backward_pre`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``backward_pre`` hooks
on this :class:`torch.nn.modules.Module`. Note that global
``backward_pre`` hooks registered with
:func:`register_module_full_backward_pre_hook` will fire before
all hooks registered by this method.
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_pre_hooks)
self._backward_pre_hooks[handle.id] = hook
if prepend:
self._backward_pre_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
register_load_state_dict_post_hook
def register_load_state_dict_post_hook(
self,
hook
)
Register a post hook to be run after module's load_state_dict is called.
It should have the following signature:: hook(module, incompatible_keys) -> None
The module argument is the current module that this hook is registered
on, and the incompatible_keys argument is a NamedTuple consisting
of attributes missing_keys and unexpected_keys. missing_keys
is a list of str containing the missing keys and
unexpected_keys is a list of str containing the unexpected keys.
The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling :func:load_state_dict with
strict=True are affected by modifications the hook makes to
missing_keys or unexpected_keys, as expected. Additions to either
set of keys will result in an error being thrown when strict=True, and
clearing out both missing and unexpected keys will avoid an error.
Returns:
| Type | Description |
|---|---|
| None | :class:torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling handle.remove() |
View Source
def register_load_state_dict_post_hook(self, hook):
r"""Register a post hook to be run after module's ``load_state_dict`` is called.
It should have the following signature::
hook(module, incompatible_keys) -> None
The ``module`` argument is the current module that this hook is registered
on, and the ``incompatible_keys`` argument is a ``NamedTuple`` consisting
of attributes ``missing_keys`` and ``unexpected_keys``. ``missing_keys``
is a ``list`` of ``str`` containing the missing keys and
``unexpected_keys`` is a ``list`` of ``str`` containing the unexpected keys.
The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling :func:`load_state_dict` with
``strict=True`` are affected by modifications the hook makes to
``missing_keys`` or ``unexpected_keys``, as expected. Additions to either
set of keys will result in an error being thrown when ``strict=True``, and
clearing out both missing and unexpected keys will avoid an error.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
"""
handle = hooks.RemovableHandle(self._load_state_dict_post_hooks)
self._load_state_dict_post_hooks[handle.id] = hook
return handle
register_module
def register_module(
self,
name: str,
module: Optional[ForwardRef('Module')]
) -> None
Alias for :func:add_module.
View Source
def register_module(self, name: str, module: Optional['Module']) -> None:
r"""Alias for :func:`add_module`."""
self.add_module(name, module)
register_parameter
def register_parameter(
self,
name: str,
param: Optional[torch.nn.parameter.Parameter]
) -> None
Add a parameter to the module.
The parameter can be accessed as an attribute using given name.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| name | str | name of the parameter. The parameter can be accessed from this module using the given name |
None |
| param | Parameter or None | parameter to be added to the module. IfNone, then operations that run on parameters, such as :attr:cuda,are ignored. If None, the parameter is not included in themodule's :attr: state_dict. |
None |
View Source
def register_parameter(self, name: str, param: Optional[Parameter]) -> None:
r"""Add a parameter to the module.
The parameter can be accessed as an attribute using given name.
Args:
name (str): name of the parameter. The parameter can be accessed
from this module using the given name
param (Parameter or None): parameter to be added to the module. If
``None``, then operations that run on parameters, such as :attr:`cuda`,
are ignored. If ``None``, the parameter is **not** included in the
module's :attr:`state_dict`.
"""
if '_parameters' not in self.__dict__:
raise AttributeError(
"cannot assign parameter before Module.__init__() call")
elif not isinstance(name, str):
raise TypeError(f"parameter name should be a string. Got {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(f"attribute '{name}' already exists")
if param is None:
self._parameters[name] = None
elif not isinstance(param, Parameter):
raise TypeError(f"cannot assign '{torch.typename(param)}' object to parameter '{name}' "
"(torch.nn.Parameter or None required)"
)
elif param.grad_fn:
raise ValueError(
f"Cannot assign non-leaf Tensor to parameter '{name}'. Model "
f"parameters must be created explicitly. To express '{name}' "
"as a function of another Tensor, compute the value in "
"the forward() method.")
else:
for hook in _global_parameter_registration_hooks.values():
output = hook(self, name, param)
if output is not None:
param = output
self._parameters[name] = param
register_state_dict_pre_hook
def register_state_dict_pre_hook(
self,
hook
)
Register a pre-hook for the :meth:~torch.nn.Module.state_dict method.
These hooks will be called with arguments: self, prefix,
and keep_vars before calling state_dict on self. The registered
hooks can be used to perform pre-processing before the state_dict
call is made.
View Source
def register_state_dict_pre_hook(self, hook):
r"""Register a pre-hook for the :meth:`~torch.nn.Module.state_dict` method.
These hooks will be called with arguments: ``self``, ``prefix``,
and ``keep_vars`` before calling ``state_dict`` on ``self``. The registered
hooks can be used to perform pre-processing before the ``state_dict``
call is made.
"""
handle = hooks.RemovableHandle(self._state_dict_pre_hooks)
self._state_dict_pre_hooks[handle.id] = hook
return handle
requires_grad_
def requires_grad_(
self: ~T,
requires_grad: bool = True
) -> ~T
Change if autograd should record operations on parameters in this module.
This method sets the parameters' :attr:requires_grad attributes
in-place.
This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training).
See :ref:locally-disable-grad-doc for a comparison between
.requires_grad_() and several similar mechanisms that may be confused with it.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| requires_grad | bool | whether autograd should record operations on parameters in this module. Default: True. |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def requires_grad_(self: T, requires_grad: bool = True) -> T:
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).
See :ref:`locally-disable-grad-doc` for a comparison between
`.requires_grad_()` and several similar mechanisms that may be confused with it.
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
set_extra_state
def set_extra_state(
self,
state: Any
) -> None
Set extra state contained in the loaded state_dict.
This function is called from :func:load_state_dict to handle any extra state
found within the state_dict. Implement this function and a corresponding
View Source
def set_extra_state(self, state: Any) -> None:
"""Set extra state contained in the loaded `state_dict`.
This function is called from :func:`load_state_dict` to handle any extra state
found within the `state_dict`. Implement this function and a corresponding
:func:`get_extra_state` for your module if you need to store extra state within its
`state_dict`.
Args:
state (dict): Extra state from the `state_dict`
"""
raise RuntimeError(
"Reached a code path in Module.set_extra_state() that should never be called. "
"Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml "
"to report this bug.")
share_memory
def share_memory(
self: ~T
) -> ~T
See :meth:torch.Tensor.share_memory_.
View Source
def share_memory(self: T) -> T:
r"""See :meth:`torch.Tensor.share_memory_`."""
return self._apply(lambda t: t.share_memory_())
state_dict
def state_dict(
self,
*args,
destination=None,
prefix='',
keep_vars=False
)
Return a dictionary containing references to the whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.
Parameters and buffers set to None are not included.
.. note:: The returned object is a shallow copy. It contains references to the module's parameters and buffers.
.. warning::
Currently state_dict() also accepts positional arguments for
destination, prefix and keep_vars in order. However,
this is being deprecated and keyword arguments will be enforced in
future releases.
.. warning::
Please avoid the use of argument destination as it is not
designed for end-users.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| destination | dict | If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned.Default: None. |
None |
| prefix | str | a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''. |
None |
| keep_vars | bool | by default the :class:~torch.Tensor sreturned in the state dict are detached from autograd. If it's set to True, detaching will not be performed.Default: False. |
None |
Returns:
| Type | Description |
|---|---|
| dict | a dictionary containing a whole state of the module |
View Source
def state_dict(self, *args, destination=None, prefix='', keep_vars=False):
r"""Return a dictionary containing references to the whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.
Parameters and buffers set to ``None`` are not included.
.. note::
The returned object is a shallow copy. It contains references
to the module's parameters and buffers.
.. warning::
Currently ``state_dict()`` also accepts positional arguments for
``destination``, ``prefix`` and ``keep_vars`` in order. However,
this is being deprecated and keyword arguments will be enforced in
future releases.
.. warning::
Please avoid the use of argument ``destination`` as it is not
designed for end-users.
Args:
destination (dict, optional): If provided, the state of module will
be updated into the dict and the same object is returned.
Otherwise, an ``OrderedDict`` will be created and returned.
Default: ``None``.
prefix (str, optional): a prefix added to parameter and buffer
names to compose the keys in state_dict. Default: ``''``.
keep_vars (bool, optional): by default the :class:`~torch.Tensor` s
returned in the state dict are detached from autograd. If it's
set to ``True``, detaching will not be performed.
Default: ``False``.
Returns:
dict:
a dictionary containing a whole state of the module
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']
"""
# TODO: Remove `args` and the parsing logic when BC allows.
if len(args) > 0:
if destination is None:
destination = args[0]
if len(args) > 1 and prefix == '':
prefix = args[1]
if len(args) > 2 and keep_vars is False:
keep_vars = args[2]
# DeprecationWarning is ignored by default
warnings.warn(
"Positional args are being deprecated, use kwargs instead. Refer to "
"https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict"
" for details.")
if destination is None:
destination = OrderedDict()
destination._metadata = OrderedDict()
local_metadata = dict(version=self._version)
if hasattr(destination, "_metadata"):
destination._metadata[prefix[:-1]] = local_metadata
for hook in self._state_dict_pre_hooks.values():
hook(self, prefix, keep_vars)
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=destination, prefix=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
to
def to(
self,
*args,
**kwargs
)
Move and/or cast the parameters and buffers.
This can be called as
.. function:: to(device=None, dtype=None, non_blocking=False) :noindex:
.. function:: to(dtype, non_blocking=False) :noindex:
.. function:: to(tensor, non_blocking=False) :noindex:
.. function:: to(memory_format=torch.channels_last) :noindex:
Its signature is similar to :meth:torch.Tensor.to, but only accepts
floating point or complex :attr:dtype\ s. In addition, this method will
only cast the floating point or complex parameters and buffers to :attr:dtype
(if given). The integral parameters and buffers will be moved
:attr:device, if that is given, but with dtypes unchanged. When
:attr:non_blocking is set, it tries to convert/move asynchronously
with respect to the host if possible, e.g., moving CPU Tensors with
pinned memory to CUDA devices.
See below for examples.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device ( | None | class:torch.device): the desired device of the parametersand buffers in this module |
None |
| dtype ( | None | class:torch.dtype): the desired floating point or complex dtype ofthe parameters and buffers in this module |
None |
| tensor | torch.Tensor | Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module |
None |
| memory_format ( | None | class:torch.memory_format): the desired memoryformat for 4D parameters and buffers in this module (keyword only argument) |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def to(self, *args, **kwargs):
r"""Move and/or cast the parameters and buffers.
This can be called as
.. function:: to(device=None, dtype=None, non_blocking=False)
:noindex:
.. function:: to(dtype, non_blocking=False)
:noindex:
.. function:: to(tensor, non_blocking=False)
:noindex:
.. function:: to(memory_format=torch.channels_last)
:noindex:
Its signature is similar to :meth:`torch.Tensor.to`, but only accepts
floating point or complex :attr:`dtype`\ s. In addition, this method will
only cast the floating point or complex parameters and buffers to :attr:`dtype`
(if given). The integral parameters and buffers will be moved
:attr:`device`, if that is given, but with dtypes unchanged. When
:attr:`non_blocking` is set, it tries to convert/move asynchronously
with respect to the host if possible, e.g., moving CPU Tensors with
pinned memory to CUDA devices.
See below for examples.
.. note::
This method modifies the module in-place.
Args:
device (:class:`torch.device`): the desired device of the parameters
and buffers in this module
dtype (:class:`torch.dtype`): the desired floating point or complex dtype of
the 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
memory_format (:class:`torch.memory_format`): the desired memory
format for 4D parameters and buffers in this module (keyword
only argument)
Returns:
Module: self
Examples::
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
[-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
[-0.5113, -0.2325]], dtype=torch.float64)
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
[-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
[-0.5112, -0.2324]], dtype=torch.float16)
>>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble)
>>> linear.weight
Parameter containing:
tensor([[ 0.3741+0.j, 0.2382+0.j],
[ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)
>>> linear(torch.ones(3, 2, dtype=torch.cdouble))
tensor([[0.6122+0.j, 0.1150+0.j],
[0.6122+0.j, 0.1150+0.j],
[0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)
"""
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 '
f'dtypes, but got desired dtype={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.yml "
"if a complex module does not work as expected.")
def convert(t):
try:
if convert_to_format is not None and t.dim() in (4, 5):
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,
)
except NotImplementedError as e:
if str(e) == "Cannot copy out of meta tensor; no data!":
raise NotImplementedError(
f"{e} Please use torch.nn.Module.to_empty() instead of torch.nn.Module.to() "
f"when moving module from meta to a different device."
) from None
else:
raise
return self._apply(convert)
to_empty
def to_empty(
self: ~T,
*,
device: Union[int, str, torch.device, NoneType],
recurse: bool = True
) -> ~T
Move the parameters and buffers to the specified device without copying storage.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device ( | None | class:torch.device): The desired device of the parametersand buffers in this module. |
None |
| recurse | bool | Whether parameters and buffers of submodules should be recursively moved to the specified device. |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def to_empty(self: T, *, device: Optional[DeviceLikeType], recurse: bool = True) -> T:
r"""Move the parameters and buffers to the specified device without copying storage.
Args:
device (:class:`torch.device`): The desired device of the parameters
and buffers in this module.
recurse (bool): Whether parameters and buffers of submodules should
be recursively moved to the specified device.
Returns:
Module: self
"""
return self._apply(lambda t: torch.empty_like(t, device=device), recurse=recurse)
train
def train(
self: ~T,
mode: bool = True
) -> ~T
Set 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.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| mode | bool | whether to set training mode (True) or evaluationmode ( False). Default: True. |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def train(self: T, mode: bool = True) -> T:
r"""Set 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
"""
if not isinstance(mode, bool):
raise ValueError("training mode is expected to be boolean")
self.training = mode
for module in self.children():
module.train(mode)
return self
type
def type(
self: ~T,
dst_type: Union[torch.dtype, str]
) -> ~T
Casts all parameters and buffers to :attr:dst_type.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| dst_type | type or string | the desired type | None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def type(self: T, dst_type: Union[dtype, str]) -> T:
r"""Casts all parameters and buffers to :attr:`dst_type`.
.. note::
This method modifies the module in-place.
Args:
dst_type (type or string): the desired type
Returns:
Module: self
"""
return self._apply(lambda t: t.type(dst_type))
xpu
def xpu(
self: ~T,
device: Union[int, torch.device, NoneType] = None
) -> ~T
Move all model parameters and buffers to the XPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on XPU while being optimized.
.. note:: This method modifies the module in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| device | int | if specified, all parameters will be copied to that device |
None |
Returns:
| Type | Description |
|---|---|
| Module | self |
View Source
def xpu(self: T, device: Optional[Union[int, device]] = None) -> T:
r"""Move all model parameters and buffers to the XPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on XPU while being optimized.
.. note::
This method modifies the module in-place.
Arguments:
device (int, optional): if specified, all parameters will be
copied to that device
Returns:
Module: self
"""
return self._apply(lambda t: t.xpu(device))
zero_grad
def zero_grad(
self,
set_to_none: bool = True
) -> None
Reset gradients of all model parameters.
See similar function under :class:torch.optim.Optimizer for more context.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| set_to_none | bool | instead of setting to zero, set the grads to None. See :meth: torch.optim.Optimizer.zero_grad for details. |
None |
View Source
def zero_grad(self, set_to_none: bool = True) -> None:
r"""Reset gradients of all model parameters.
See similar function under :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 = None
else:
if p.grad.grad_fn is not None:
p.grad.detach_()
else:
p.grad.requires_grad_(False)
p.grad.zero_()