| | |
- torch.nn.modules.module.Module(builtins.object)
-
- SubbandDSP
class SubbandDSP(torch.nn.modules.module.Module) |
| |
SubbandDSP(subband=2, window_size=2048, hop_size=441)
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv1 = nn.Conv2d(1, 20, 5)
self.conv2 = nn.Conv2d(20, 20, 5)
def forward(self, x):
x = F.relu(self.conv1(x))
return F.relu(self.conv2(x))
Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:`to`, etc.
:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool
|
| |
- Method resolution order:
- SubbandDSP
- torch.nn.modules.module.Module
- builtins.object
Methods defined here:
- __init__(self, subband=2, window_size=2048, hop_size=441)
- Args:
subband: int, [1,2,4,8]. The subband number you wanna divide. 'subbband==1' means do not need subband.
window_size: stft parameter
hop_size: stft parameter
- complex_sub_spec_to_wav(self, sps, length)
- The reverse function of wav_to_complex_subband_spectrogram. Convert complex spectrogram into waveform.
Args:
sps: tensor, complex as channel spectrogram, (batch_size, 2 * channels_num * subband_num, time_steps, freq_bins // subband_num),
length: int, expect sample length of the output tensor
Returns:
(batch_size, channels_num, samples)
- mag_phase_sub_spec_to_wav(self, sps, coss, sins, length)
- The reverse functino of wav_to_mag_phase_subband_spectrogram. Convert subband magnutde spectrogram and its subband phases into fullband waveform.
Args:
sps: tensor, magnitude spectrogram (batch_size, channels_num * subband_num, time_steps, freq_bins // subband_num),
coss: tensor, (batch_size, channels_num * subband_num, time_steps, freq_bins // subband_num)
sins: tensor, (batch_size, channels_num * subband_num, time_steps, freq_bins // subband_num)
length: int, expect sample length of the output tensor
Returns:
tensor, (batch_size, channels_num, samples)
- spectrogram_phase_to_wav(self, sps, coss, sins, length)
- The reverse function of wav_to_spectrogram_phase. Convert magnitude spectrogram and phase to waveform.
Args:
sps: tensor, magnitude spectrogram, (batch_size, channels_num, time_steps, freq_bins),
coss: tensor, phase angle, (batch_size, channels_num, time_steps, freq_bins)
sins: tensor, phase angle, (batch_size, channels_num, time_steps, freq_bins)
length: int, expect sample length of the output tensor
Returns:
output: tensor, (batch_size, channels_num, samples)
- sub_to_wav(self, subwav, length)
- The reverse function of wav_to_subband.
Args:
subwav: tensor, (batch_size, channels_num * subband_nums, ceil(samples / subbandnum))
length: int, expect sample length of the output tensor
Returns:
tensor, (batch_size, channels_num, samples)
- wav_to_complex_sub_spec(self, input)
- Convert waveform in each channel to several complex subband spectrogram. The real and imaginary parts are stored separately in different channels.
Args:
input: tensor, (batch_size, channels_num, samples)
Returns:
tensor, complex as channel spectrogram, (batch_size, 2 * channels_num * subband_num, time_steps, freq_bins // subband_num),
- wav_to_mag_phase_sub_spec(self, input)
- Convert the input waveform to its subband spectrograms, which are concatenated in the channel dimension.
Args:
input: (batch_size, channels_num, samples)
Returns:
magnitude spectrogram (batch_size, channels_num * subband_num, time_steps, freq_bins // subband_num),
coss: (batch_size, channels_num * subband_num, time_steps, freq_bins // subband_num)
sins: (batch_size, channels_num * subband_num, time_steps, freq_bins // subband_num)
- wav_to_spectrogram_phase(self, input)
- Convert input waveform to magnitude spectrogram and phases.
Args:
input: (batch_size, channels_num, samples)
Returns:
magnitude spectrogram (batch_size, channels_num, time_steps, freq_bins),
phase angle cos: (batch_size, channels_num, time_steps, freq_bins)
phase angle sin: (batch_size, channels_num, time_steps, freq_bins)
- wav_to_sub(self, input)
- Convert input waveform into several subband signals
Args:
input: tensor, (batch_size, channels_num, samples)
Returns:
tensor, (batch_size, channels_num * subbandnum, ceil(samples / subbandnum))
Methods inherited from torch.nn.modules.module.Module:
- __call__ = _call_impl(self, *input, **kwargs)
- __delattr__(self, name)
- Implement delattr(self, name).
- __dir__(self)
- Default dir() implementation.
- __getattr__(self, name: str) -> Union[torch.Tensor, ForwardRef('Module')]
- __repr__(self)
- Return repr(self).
- __setattr__(self, name: str, value: Union[torch.Tensor, ForwardRef('Module')]) -> None
- Implement setattr(self, name, value).
- __setstate__(self, state)
- add_module(self, name: str, module: Union[ForwardRef('Module'), NoneType]) -> None
- Adds a child module to the current module.
The module can be accessed as an attribute using the given name.
Args:
name (string): name of the child module. The child module can be
accessed from this module using the given name
module (Module): child module to be added to the module.
- apply(self: ~T, fn: Callable[[ForwardRef('Module')], NoneType]) -> ~T
- Applies ``fn`` recursively to every submodule (as returned by ``.children()``)
as well as self. Typical use includes initializing the parameters of a model
(see also :ref:`nn-init-doc`).
Args:
fn (:class:`Module` -> None): function to be applied to each submodule
Returns:
Module: self
Example::
>>> @torch.no_grad()
>>> def init_weights(m):
>>> print(m)
>>> if type(m) == nn.Linear:
>>> m.weight.fill_(1.0)
>>> print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1., 1.],
[ 1., 1.]])
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1., 1.],
[ 1., 1.]])
Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
- bfloat16(self: ~T) -> ~T
- Casts all floating point parameters and buffers to ``bfloat16`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
- buffers(self, recurse: bool = True) -> Iterator[torch.Tensor]
- Returns an iterator over module buffers.
Args:
recurse (bool): if True, then yields buffers of this module
and all submodules. Otherwise, yields only buffers that
are direct members of this module.
Yields:
torch.Tensor: module buffer
Example::
>>> for buf in model.buffers():
>>> print(type(buf), buf.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)
- children(self) -> Iterator[ForwardRef('Module')]
- Returns an iterator over immediate children modules.
Yields:
Module: a child module
- cpu(self: ~T) -> ~T
- Moves all model parameters and buffers to the CPU.
.. note::
This method modifies the module in-place.
Returns:
Module: self
- cuda(self: ~T, device: Union[int, torch.device, NoneType] = None) -> ~T
- Moves all model parameters and buffers to the GPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on GPU while being optimized.
.. 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
- double(self: ~T) -> ~T
- Casts all floating point parameters and buffers to ``double`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
- eval(self: ~T) -> ~T
- Sets the module in evaluation mode.
This has any effect only on certain modules. See documentations of
particular modules for details of their behaviors in training/evaluation
mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,
etc.
This is equivalent with :meth:`self.train(False) <torch.nn.Module.train>`.
See :ref:`locally-disable-grad-doc` for a comparison between
`.eval()` and several similar mechanisms that may be confused with it.
Returns:
Module: self
- 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.
- float(self: ~T) -> ~T
- Casts all floating point parameters and buffers to ``float`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
- forward = _forward_unimplemented(self, *input: Any) -> None
- Defines the computation performed at every call.
Should be overridden by all subclasses.
.. note::
Although the recipe for forward pass needs to be defined within
this function, one should call the :class:`Module` instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.
- get_buffer(self, target: str) -> 'Tensor'
- Returns the buffer given by ``target`` if it exists,
otherwise throws 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
- get_parameter(self, target: str) -> 'Parameter'
- Returns the parameter given by ``target`` if it exists,
otherwise throws 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``
- get_submodule(self, target: str) -> 'Module'
- Returns the submodule given by ``target`` if it exists,
otherwise throws 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``
- half(self: ~T) -> ~T
- Casts all floating point parameters and buffers to ``half`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
- load_state_dict(self, state_dict: 'OrderedDict[str, Tensor]', strict: bool = True)
- Copies parameters and buffers from :attr:`state_dict` into
this module and its descendants. If :attr:`strict` is ``True``, then
the keys of :attr:`state_dict` must exactly match the keys returned
by this module's :meth:`~torch.nn.Module.state_dict` function.
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``
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
- modules(self) -> Iterator[ForwardRef('Module')]
- Returns an iterator over all modules in the network.
Yields:
Module: a module in the network
Note:
Duplicate modules are returned only once. In the following
example, ``l`` will be returned only once.
Example::
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
print(idx, '->', m)
0 -> Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)
- named_buffers(self, prefix: str = '', recurse: bool = True) -> Iterator[Tuple[str, torch.Tensor]]
- Returns an iterator over module buffers, yielding both the
name of the buffer as well as the buffer itself.
Args:
prefix (str): prefix to prepend to all buffer names.
recurse (bool): if True, then yields buffers of this module
and all submodules. Otherwise, yields only buffers that
are direct members of this module.
Yields:
(string, torch.Tensor): Tuple containing the name and buffer
Example::
>>> for name, buf in self.named_buffers():
>>> if name in ['running_var']:
>>> print(buf.size())
- named_children(self) -> Iterator[Tuple[str, ForwardRef('Module')]]
- Returns an iterator over immediate children modules, yielding both
the name of the module as well as the module itself.
Yields:
(string, Module): Tuple containing a name and child module
Example::
>>> for name, module in model.named_children():
>>> if name in ['conv4', 'conv5']:
>>> print(module)
- named_modules(self, memo: Union[Set[ForwardRef('Module')], NoneType] = None, prefix: str = '', remove_duplicate: bool = True)
- Returns 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:
(string, Module): Tuple of name and module
Note:
Duplicate modules are returned only once. In the following
example, ``l`` will be returned only once.
Example::
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
print(idx, '->', m)
0 -> ('', Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
))
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))
- named_parameters(self, prefix: str = '', recurse: bool = True) -> Iterator[Tuple[str, torch.nn.parameter.Parameter]]
- Returns an iterator over module parameters, yielding both the
name of the parameter as well as the parameter itself.
Args:
prefix (str): prefix to prepend to all parameter names.
recurse (bool): if True, then yields parameters of this module
and all submodules. Otherwise, yields only parameters that
are direct members of this module.
Yields:
(string, Parameter): Tuple containing the name and parameter
Example::
>>> for name, param in self.named_parameters():
>>> if name in ['bias']:
>>> print(param.size())
- parameters(self, recurse: bool = True) -> Iterator[torch.nn.parameter.Parameter]
- Returns an iterator over module parameters.
This is typically passed to an optimizer.
Args:
recurse (bool): if True, then yields parameters of this module
and all submodules. Otherwise, yields only parameters that
are direct members of this module.
Yields:
Parameter: module parameter
Example::
>>> for param in model.parameters():
>>> print(type(param), param.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)
- register_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, torch.Tensor]]) -> torch.utils.hooks.RemovableHandle
- Registers a backward hook on the module.
This function is deprecated in favor of :meth:`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()``
- register_buffer(self, name: str, tensor: Union[torch.Tensor, NoneType], persistent: bool = True) -> None
- Adds 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 (string): name of the buffer. The buffer can be accessed
from this module using the given name
tensor (Tensor): buffer to be registered.
persistent (bool): whether the buffer is part of this module's
:attr:`state_dict`.
Example::
>>> self.register_buffer('running_mean', torch.zeros(num_features))
- register_forward_hook(self, hook: Callable[..., NoneType]) -> torch.utils.hooks.RemovableHandle
- Registers a forward hook on the module.
The hook will be called every time after :func:`forward` has computed an output.
It should have the following signature::
hook(module, input, output) -> None or modified output
The 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.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
- register_forward_pre_hook(self, hook: Callable[..., NoneType]) -> torch.utils.hooks.RemovableHandle
- Registers a forward pre-hook on the module.
The hook will be called every time before :func:`forward` is invoked.
It should have the following signature::
hook(module, input) -> None or modified input
The 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).
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
- register_full_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, torch.Tensor]]) -> torch.utils.hooks.RemovableHandle
- Registers a backward hook on the module.
The hook will be called every time the gradients with respect to module
inputs are computed. The hook should have the following signature::
hook(module, grad_input, grad_output) -> 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.
.. warning ::
Modifying inputs or outputs inplace is not allowed when using backward hooks and
will raise an error.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
- register_parameter(self, name: str, param: Union[torch.nn.parameter.Parameter, NoneType]) -> None
- Adds a parameter to the module.
The parameter can be accessed as an attribute using given name.
Args:
name (string): name of the parameter. The parameter can be accessed
from this module using the given name
param (Parameter): parameter to be added to the module.
- 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.
Args:
requires_grad (bool): whether autograd should record operations on
parameters in this module. Default: ``True``.
Returns:
Module: self
- share_memory(self: ~T) -> ~T
- See :meth:`torch.Tensor.share_memory_`
- state_dict(self, destination=None, prefix='', keep_vars=False)
- Returns a dictionary containing a whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.
Returns:
dict:
a dictionary containing a whole state of the module
Example::
>>> module.state_dict().keys()
['bias', 'weight']
- to(self, *args, **kwargs)
- Moves and/or casts the parameters and buffers.
This can be called as
.. function:: to(device=None, dtype=None, non_blocking=False)
.. function:: to(dtype, non_blocking=False)
.. function:: to(tensor, non_blocking=False)
.. function:: to(memory_format=torch.channels_last)
Its signature is similar to :meth:`torch.Tensor.to`, but only 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::
>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
[-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
[-0.5113, -0.2325]], dtype=torch.float64)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
[-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
[-0.5112, -0.2324]], dtype=torch.float16)
>>> 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)
- to_empty(self: ~T, *, device: Union[str, torch.device]) -> ~T
- Moves 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.
Returns:
Module: self
- train(self: ~T, mode: bool = True) -> ~T
- Sets the module in training mode.
This has any effect only on certain modules. See documentations of
particular modules for details of their behaviors in training/evaluation
mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,
etc.
Args:
mode (bool): whether to set training mode (``True``) or evaluation
mode (``False``). Default: ``True``.
Returns:
Module: self
- 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.
Args:
dst_type (type or string): the desired type
Returns:
Module: self
- xpu(self: ~T, device: Union[int, torch.device, NoneType] = None) -> ~T
- Moves 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
- zero_grad(self, set_to_none: bool = False) -> None
- Sets gradients of all model parameters to zero. 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.
Data descriptors inherited from torch.nn.modules.module.Module:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes inherited from torch.nn.modules.module.Module:
- T_destination = ~T_destination
- __annotations__ = {'__call__': typing.Callable[..., typing.Any], '_is_full_backward_hook': typing.Union[bool, NoneType], '_version': <class 'int'>, 'dump_patches': <class 'bool'>, 'forward': typing.Callable[..., typing.Any], 'training': <class 'bool'>}
- dump_patches = False
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